As digital communication becomes increasingly integral to personal and corporate activities, phishing attacks have emerged as a prevalent threat, ingeniously mimicking legitimate sources to illicitly acquire sensitive information. This research paper details the development of a sophisticated phishing detection application utilizing the DistilBERT-based model, finetuned on a diverse array of email datasets. The application significantly enhances the precision of phishing detection mechanisms, adeptly reducing the incidence of successful phishing attacks. Initial tests have demonstrated a precision rate of over 95% in detecting phishing emails, outperforming traditional rule-based filters substantially. The application exhibits robust defences against zero-day phishing attacks through its advanced machine learning framework, which dynamically adapts to emerging phishing strategies. This paper explores the methodology of developing the DistilBERT model, evaluates its efficacy against existing solutions, and discusses its implications for future cybersecurity practices. The study’s findings underscore the potential of AI-driven tools in transforming cybersecurity measures, offering a proactive approach to thwarting phishing attempts and safeguarding sensitive data.

In the age of the digital economy, the security and privacy for multimedia systems issues are increasingly manifested in the form of data. The level of data protection governance capability determines whether the security and privacy of multimedia systems can be protected. In recent years, China has introduced laws and regulations on the protection of personal information, personal privacy, data security, and cyber-security. Although data protection is still to be improved, China has been refining the provisions on data security management through practice and the adoption of supporting regulations.

Balancing the efficiency and robustness of food systems is a well-known trade-off process. Over-optimization of efficiency may lead to excessive use of resources. On the other hand, under-optimization of efficiency may lead to excessive waste of resources. Using non-parametric methods can help in developing actionable frameworks through which decision-makers and stakeholders can effectively balance and improve food systems' efficiency and robustness. Under this context, this paper presents a new structured approach integrating two well-suited non-parametric methods for analyzing and balancing the interconnection between the efficiency and robustness of food systems. This approach consists of three main steps. First, the slacks-based data envelopment analysis (SBM-DEA) is utilized to analyze the efficiency of five food robustness dimensions. Second, the weighted efficiency of these dimensions is computed and analyzed to provide an insight into their influence on the food system efficiency. Finally, two search methods were developed to identify opportunities for improving efficiency and robustness. The outcomes of these methods have been analyzed and validated to ensure their effectiveness and viability. The operational and mathematical properties of the proposed approach were evaluated using data from thirty-seven food-producing countries. The weights of the robustness dimensions in this study were determined using the analytic hierarchy process (AHP) method. The input matrix of the AHP was constructed using expert judgment and consultations with stakeholders. The results of this study have shown an appropriate level of applicability and effectiveness of the proposed approach. While the first search method provided valuable insights, the second method proved to be more effective in identifying the sources of inefficacy of robustness dimensions. Several conclusions have been drawn and reported with some recommendations for future research works.

Background: This study addresses the burgeoning impact of Large Language Models (LLMs) such as ChatGPT on the integrity of online examinations and seeks to demonstrate latent and advanced LLM reasoning capabilities. The research devises an iterative self-reflective strategy for invoking critical thinking and higher-order reasoning in LLMs based on multimodal exam questions using visuals and text. Methods: The proposed strategy was demonstrated and evaluated on real exam questions by subject experts and the performance of ChatGPT (GPT-4) with vision was estimated on an additional dataset of 600 text descriptions of multimodal exam questions, focusing on its response to complex, visual, and textual prompts. Results: The results indicate that latent multi-hop reasoning capabilities can be invoked using the proposed self-reflective strategy, which can effectively steer ChatGPT towards correct answers by integrating critical thinking from each modality into the final response. Meanwhile, ChatGPT demonstrated considerable proficiency in being able to answer multimodal exam questions across 12 subjects. Conclusions: This work challenges findings from recent studies which claim that existing LLMs are unable to reason on complex multimodal tasks, and therefore this study underscores the need for enhanced online exam security measures like proctoring software, that can mitigate the potential misuse of AI technologies in educational settings.

This literature review paper delves into the synergies between Climate-Smart Agriculture (CSA) and the effective management of food losses. With the escalating challenges posed by climate change and the pressing need to address food security concerns, the intersection of these two critical domains becomes paramount for sustainable agricultural practices. The review explores existing research on the implementation of climate-smart agricultural techniques and their impact on mitigating food losses throughout the agricultural value chain. The paper synthesizes findings related to climate-resilient crop varieties, precision farming, water-use efficiency, and sustainable soil management, highlighting their contributions to reducing post-harvest losses and enhancing overall food security. In addition to summarizing current knowledge, this paper identifies gaps in the literature and proposes avenues for future research. This review serves as a foundation for researchers, policymakers, and practitioners seeking to contribute to the advancement of climate-smart agriculture and the reduction of food losses, paving the way for a more sustainable and resilient global food system.

The lack of application of management models that allow strategies to be converted into action and then measure their results in order to apply improvement actions to meet the objectives in small and medium-sized companies (SMEs); they generate problems in their management causing the closure of their operations unexpectedly; To mitigate the risks of closure and bankruptcy with the support of ICT, an alternative was presented considering that risk management is important in decision making at a strategic, tactical and operational level. The objective is to design a prototype to mitigate risks and prevent threats to avoid the closure of this type of company. Through the analysis of related articles, applying sequences of methodological steps; It was determined that the proposed results are aimed at how to identify and mitigate the risks and threats that cause their bankruptcy and closure. Prototypes resulted that allow mitigating risks, preventing threats and minimizing vulnerabilities in the management of SMEs. It was concluded that the proposed prototype to detect threats and vulnerabilities provides an optimal security model for small and medium-sized companies; As a result of the simulations, a security percentage of 77.92% was established, which means that the greater the number of risks and threats, the greater the probability that a small and medium-sized company will close.

Given the landscape of intricate and constantly evolving cyber threats, organizations demand refined strategies to deploy a Security Information and Event Management to support the management of a Cyber Security Operations Center. The dynamic nature of cyber threats complicates the efficient allocation of the location of network intrusion detection sensors, a critical component of a robust cybersecurity framework. Our research introduces an approach that integrates the precision of biomimetic optimization algorithms with the adaptability of Deep Q–Learning. By employing different biomimetic algorithms enhanced with deep learning, we aim to refine the deployment of sensors in network infrastructures, balancing the network security imperative against deployment costs. The results of computational tests demonstrate that the improved iterations leveraging Deep Q–Learning have outperformed their native counterparts. These findings underscore the importance of reinforcement learning, specifically through Deep Q–Learning, as a powerful tool to enhance the effectiveness of metaheuristics in addressing optimization challenges.

With the fintech industry growing at an unprecedented rate, it is critical to protect cybersecurity and ensure data privacy. The research presented here offers a thorough examination of the challenges faced by financial technology companies, highlighting the growing risks posed by malware, phishing, and network vulnerabilities. As essential components of a proactive defense plan, the report promotes strong cybersecurity measures like frequent security assessments, encryption, and stringent access controls. The findings highlight how important it is for fintech companies to give cybersecurity equal importance with open and honest data privacy policies in order to win over customers. In order to protect the industry from cybersecurity risks regulatory compliance, intrusion detection systems, and collaborative information sharing are considered essential components. The article advises businesses to include cybersecurity and data privacy into their core business operations and customer relations, highlighting the critical role these factors play in sustaining success in the rapidly evolving fintech industry.

This paper addresses the conceptual exploration of various issues that form an integral part of the ethical dimensions surrounding the use of artificial intelligence (AI) in the field of marketing. We critically review some of the main ethical challenges AI poses to humanity: privacy, data security, bias, transparency, and accountability. Therefore, we will discuss the delicate balance between exploiting the transformation potential of AI for personally designed marketing strategies and ethical imperatives to safeguard the rights of consumers to retain trust. Then, the present regulatory landscape responding to these ethical challenges will be assessed by building on effective regulation, like the GDPR, existing proposed legislative frameworks, and industry guidelines. To underscore the role of an ongoing dialogue between marketers, technologists, ethicists, and regulators for developing a responsible AI ecosystem in marketing. Further, strategic recommendations for implementing company-based ethical AI marketing practices. The further strong guidelines provide enhancement transparency to consumers and investment in research to remove biases from algorithms. It also underscores principal scopes of further research and development, focusing on the need for new-age solutions to guide the ethical quagmires that AI-led marketing throws up. We suggest that integration should be done proactively and collaboratively. Thus, if attention is paid more to ethics and multidisciplinary dialogue, only such hyperbolic promises can be realized by AI-driven marketing. There are such promises through which AI-driven marketing will meet business goals and be huge at societies' values.

Global public health systems have continuously faced challenges from the advent of novel infectious illnesses, necessitating quick and efficient actions to stop their spread.First discovered in Saudi Arabia in 2012, Middle East respiratory syndrome (MERS) is a viral respiratory disorder caused by the Middle East respiratory syndrome coronavirus (MERS-CoV). Since then, the nation has reported most cases and fatalities related to this sickness, with occasional outbreaks happening in other nations and regions. Severe sickness and high mortality are caused by MERS-CoV, especially in older persons and those with underlying chronic conditions. Through intimate contact, the virus can spread from people to dromedary camels and vice versa. It has sparked multiple epidemics both domestically and abroad in the Middle East, raising the possibility of fatalities and widespread transmission, especially in susceptible groups. The Riyadh region was the main target of the most current MERS-CoV outbreak in Saudi Arabia, which began in 2023 and ended on September 1, 2023, with 312 confirmed cases and ninety-seven fatalities. With the use of published studies and their contributions to the field, we hope to present a thorough overview of the MERS-CoV outbreak in Saudi Arabia in this review of the literature, including its epidemiological investigation, public health response, impact, and lessons learned. In addition to highlighting the importance and continued relevance of this problem, our work suggests ways to lower the risk of MERS-CoV transmission and avert further outbreaks.

A novel concept of ion separation Anisotropic Diffusion Mobility Analyser (ADMA) with Individual Ion Counter (IIC) was shown by numerical modelling to be able to separate ions with high resolution and ultra-high sensitivity. An ADMA-IIC device has been developed and tested at sheath flow rates below 40 l/min with a number of chemicals relevant to airport security and military applications. The resolving power of the ADMA was observed to be in the range of 40 to 80. These data were close to the resolution obtained in numerical modelling predictions. It is greater than or comparable with the RP of currently available on-the-market devices for airport security and other applications. It was also confirmed that the ADMA technology enables very fast detection of analytes, at least 100 times faster than ion mobility spectrometers. It was shown that Brownian diffusion can be controlled and even overpowered by generating highly anisotropic diffusion in the ADMA device. The ultimate sensitivity of single-ion detection below the ppq range has been demonstrated.

The growing shift toward Internet of Things (IoT)-based solutions expands the attack surface of systems by connecting an extensive network of heterogeneous devices and technologies. The heterogeneity of IoT devices and the scale of the network often make conventional security measures impractical. Therefore, recent research efforts have focused on machine learning (ML)-based device-agnostic IoT security solutions. However, most proposed solutions have focused solely on detecting malicious traffic in IoT networks. While this is important, further information about the attack is needed to provide a comprehensive defense before, during, and after the incident. To address this gap, we proposed an anomaly-based intrusion detection model that detects malicious traffic and identifies the attack category and subcategory with high accuracy, recall, precision, and F1-score. The proposed intrusion detection model is an ensemble model that integrates multiple ML models to produce more robust and reliable detections. For evaluation, we performed three sets of experiments: (1) a binary classification to detect malicious traffic; (2) a multi-class classification to detect the attack category; and (3) a multi-class classification to detect the attack subcategory. In the experiments, our model achieved an accuracy, recall, precision, and F1-score of 100% for the binary detection; a precision, recall, and F1-score of 99% and an accuracy of 100% for the multi-class category classification; and an accuracy, recall, precision, and F1-score of 88% for the multi-class subcategory classification. These results suggest that the proposed model can reliably detect anomalies in IoT devices.

Given the financial demands of attending college, the transition to new living situations, abrupt changes in social support, and overall lifestyle adjustments, college students are at an increased risk of food insecurity (FI) compared to the general population. Collegiate athletes experience an even greater risk of FI as a result of greater time commitments and energy demands associated with their sports. This heightened vulnerability poses a tremendous threat to student-athletes’ academic and athletic achievements. This study aims to address the prevalence and primary determinants of FI among collegiate athletes while providing potential solutions to navigate and alleviate the effects of diminished food security among this demographic. To address these aims, a total of 18 articles were selected from both peer-reviewed and gray literature. The U.S. Household Food Security Survey Module (US-HFSSM) survey tools were predominantly utilized across universities throughout the United States to gather data on FI. Student-athletes reported experiencing FI across various regions of the United States, including universities in northeastern states (n=5), the southwest region (n=3), the southeast region (n=3), the northwest (n=1), and the Midwest (n=1). Overall, FI prevalence rates ranged from 9.9% to 65% and the most significant contributors included limited financial resources, time management, meal plans, and housing location/amenities. These findings highlight a need for screening, education, and intervention to address FI among collegiate athletes.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

The Vu Gia - Thu Bon River Basin (VG-TB) is confronting numerous challenges related to water security (WS), particularly in the face of the impacts of climate change. These challenges manifest in salinity intrusion, altered rainfall patterns, and reduced water supply in downstream areas. To assess the current state of water security in the basin, the study employed statistical analysis methods, Process Analysis Method (PAM), SMART, and Analytic Hierarchy Process (AHP) to develop a comprehensive Assessment Framework for Water Security (AFWS) in the VG-TB. The AFWS encompasses five key dimensions, 17 indicators, and 28 variables, aligning with the UN-Water definition of water security and addressing the Sustainable Development Goal 6 (SDG6) criteria for global water sustainability. It also adheres to the Asian Development Bank's (ADB) approach to water security assessment as outlined in the Asian Water Development Outlook (AWDO) reports. This allows for the computation of a comprehensive Water Security Index (WSI) for specific regions, serving as a foundation for decision-making and policy formulation aimed at enhancing water security in the context of climate change and facilitating sustainable basin development in the future.

This research aims to introduce a model, SectraBank, designed to mitigate cyber fraud attacks, specifically targeting SIM Swapping and Fake App schemes in mobile banking users. A quantitative methodology with an experimental design was meticulously employed to examine the rise of cyber fraud within mobile banking. The study involved a sample of 100 professionals with advanced knowledge in Information and Communication Technologies (ICTs). Surveys were utilized to measure awareness of threats and perceptions of security before and after the implementation of the proposed model. SectraBank leverages unique user identifiers such as fingerprint authentication and the International Mobile Equipment Identity (IMEI) number, in addition to geolocation and One Time Passwords (OTP) for validating electronic transactions in mobile banking. The effectiveness of the proposed model was notable, achieving a 96.1% efficacy rate in mitigating Fake App attacks and a 90.2% success rate in countering SIM Swapping attacks. These findings underscore the potential of SectraBank as a robust solution to enhance cybersecurity measures in the realm of mobile banking.

Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 can directly affect the security of the upper-level robotic systems. Therefore, it is a worthwhile research topic to detect and analyze the security of ROS2. In this paper, we adopt a formal approach to analyze the security of the communication mechanism of ROS2. First, we use a state transition system to model the potential vulnerabilities of ROS2 based on the ROS2 communication mechanism and the basic process of penetration testing. Secondly, we introduce the CIA model on the basis of the established vulnerability model and use LTL to define its security properties. Then, we design and implement a vulnerability detection tool for ROS2 applications based on the vulnerability model and security properties. Finally, we experimentally test some ROS2-based applications, and the results show that ROS2 has vulnerabilities without additional protection safeguards.

In this paper, we dive into the exciting world of wireless communication, focusing on how millimeter-wave technology and Multiple-Input Multiple-Output phased array antennas are shaping the future of 5G and the upcoming 6G technologies. We cover the latest advancements in millimeter-wave and beam-forming technologies, emphasizing their role in enhancing network security and efficiency in automotive vehicles through dual radar communication. Our discussion spans the benefits, applications, challenges, and solutions of these technologies individually from millimeter-wave to beam-forming technology and joint radar communications, alongside a look at their theoretical and practical implementations. We emphasize the integration of beam-forming technology in joint radar communications for future automotive vehicles and its impact on automotive systems, smart cities, and the Internet of Things (IoT). Looking ahead, we discuss the potential of these technologies to transform future tech landscapes, while also addressing the security implications of merging communication and radar capabilities. This paper aims to provide a clear view of the advancements and prospects of millimeter-wave, beam-forming, and dual radar communication technologies.

Nowadays, the transactions carried out with digital currencies are increasing. Citizens are asked to respond to growing challenges related to the sustainable management of digital currencies in their daily lives. However, due to the lack of digital skills of users, the sustainable management of digital currencies hides risks. The methodology of this study was based on the Digital Competence Framework for Citizens (DigComp) and is applied through a questionnaire completed by 443 respondents. The main objective was to evaluate their digital skills from the perspective of digital currency use. The analysis of the responses was carried out by using Structural Equation Modeling (SEM). The most important result from this research reveals that users of digital currencies are significantly capable of developing sustainable communication to solve everyday problems. At the same time, users of digital currencies mostly detect digital threats and effectively manage fake news without being affected by them. However, users of digital currencies consider that security issues are important, but only for transactions and not for their supporting functions. The study concludes with suggestions for improving the experience of digital currency users through individual actions, thus having a positive impact on the state and banking institutions.

: In the realm of data management, data preservation stands as a critical undertaking aimed at preserving and upholding the integrity of data. Regardless of whether it concerns personal or enterprise data, the detrimental effects of malicious alterations implemented by attackers cannot be overlooked. Particularly in conventional industrial control environments, the prevalent practice involves the transmission of data from sensors to databases for storage purposes. However, it is essential to recognize that this process exposes the data to various vulnerabilities. Thus, to ensure the long-term security and reliability of the data, it becomes imperative to implement robust data preservation strategies within these industrial control systems. However, the reliance of these databases on physical hard disks introduces inherent vulnerabilities, including the potential for data loss due to disk damage or targeted malicious attacks. Consequently, it becomes imperative to prioritize the implementation of robust data preservation measures. These measures are crucial in mitigating the risk of disruptions and protecting critical data from compromise. By establishing effective data backup systems, employing advanced security protocols, and implementing proactive monitoring mechanisms, organizations can bolster their data preservation capabilities and safeguard against potential threats to data integrity and availability. As a result, many enterprises opt to store their data with third-party providers to ensure data integrity. However, this approach carries inherent risks. If the third-party service experiences an attack or if the data is tampered with, it becomes challenging to verify the integrity of the data. To address these concerns and ensure data preservation within the context of the Internet of Things (IoT), a growing number of individuals are integrating IoT with Distributed Ledger Technology (DLT). By leveraging DLT, the integrity of data can be ensured, reducing reliance on centralized third-party storage and enhancing security in the IoT ecosystem. In this article, IOTA is the DLT, which employs Directed Acyclic Graph (DAG) to store transaction information. Compared to Ethereum or other blockchain technologies, IOTA offers notable advantages in terms of transaction verification speed, making it highly suitable for real-time IoT environments. However, the conventional transmission path from sensors to IOTA nodes entails a complex route, involving multiple hardware devices before reaching the intended destination. This complexity poses challenges in ensuring data integrity during transmission and introduces vulnerabilities such as man-in-the-middle attacks or SQL injection attacks. To address these issues, we propose a method to streamline the transmission path between sensors and IOTA, specifically tailored for industrial fields with numerous IoT devices. Our approach involves preprocessing the data stored on the server using our method before uploading, ensuring data confidentiality, and leveraging IOTA to guarantee data integrity. To achieve the shortest path between IoT and DLT nodes, it becomes necessary to establish IOTA nodes on lower-level devices, such as Raspberry Pi or IoT controllers. By simplifying the transmission path, we can reduce the potential for tampering and enhance overall data security. Implementing our proposed method enables the assurance of data confidentiality and integrity during both transmission and storage on the server, strengthening the trustworthiness of the IoT, and IOTA integration.

Grid modernization has been ushered in by rising electricity consumption, deteriorating infrastructure, and increasing reliability concerns for electric utilities. New advances, collectively referred to as the smart grid, include modern electronics, technology, telecommunications, and computing capabilities. Smart grid telecommunication frameworks provide two-way communication to support grid operations. Software-defined networking (SDN) has been proposed as a method of monitoring and controlling telecommunication networks, enabling increased smart grid visibility, control, and security. However, being connected to telecommunications infrastructure means that smart grid networks are exposed to cyber-attacks. Attackers may use unauthorized access to intercept messages, inject false data into system measurements, flood communication channels with false data packets, or target centralized controllers to cripple network control. Defense and security techniques against these threats are constantly evolving, necessitating an up-to-date, comprehensive study analyzing cyber attacks and defense methods for smart grid networks. Previous smart grid security surveys do not contain recent techniques and to our knowledge most, if not all, address only one type of attack type and/or one type of defense. This survey considers the latest security techniques, simultaneous multi-pronged cyber attacks and defensutilityes to meet the challenges of the next-generation SDN smart grid research with the goal of identifying future research needs, describing the open security challenges, and exposing both emerging threats and their potential impact on SD-SG deployment.

In a hyperconnected society, computer networks play a pivotal role in all activities that permeate our daily lives. In the context of smart cities, theses networks play a pivotal role in enhancing urban efficiency and sustainability. The continuous flow of packets in these networks poses security challenges, from personal data leakage to service unavailability. Anomaly-based methods are commonly used to detect network attacks. In this context, Software-Defined Networking (SDN) emerges as a solution for anomaly detection in network traffic, facilitating comprehensive, real-time analyses and dynamic adaptation to changes. This study aims to present a systematic review of feature selection techniques and evaluate the effectiveness of attribute selection with a multi-class classifier in network anomaly detection. The objective of this paper is to inspire future research and identify trade-offs among these techniques for detecting Denial of Service (DoS) attacks. Based on the findings, the analysis of the entire dataset yields superior results in terms of precision (99%), but using the output of OneR, although resulting in a slight loss of precision compared to the complete dataset, presents the highest precision among other studied techniques, indicating a trade-off between precision and processing time efficiency.

The digital transformation of critical infrastructures, such as energy or water distribution systems, is essential for their smart management. Faster issue identification and smoother services enable better adaptation to consumers' evolving demands. However, these large-scale infrastructures are often outdated. Their digital transformation is crucial to enable them supporting societies. This process must be carefully planned, providing guidance that ensures that the data they rely on is dependable and that the system remains fully operational during the transition. This paper presents a formal model that supports reliable data acquisition in legacy critical infrastructures, facilitating their evolution towards a data-driven smart system. Our model provides the foundation for a flexible transformation process while generating dependable data for system management. We demonstrate the model's applicability in a use case within the water distribution domain and discuss its benefits.

Supply chain disruptions, a pandemic, and war in Ukraine have exposed faultlines in a globalised food system that depends on a few staple crops grown in a few exporting regions and transported to consumers around the world. In the UK, just three crops, (wheat, barley, and oilseed rape), account for 75 per cent of the UK’s 4.5 million hectares of arable land whilst the country imports around half its food - nearly 40 per cent from just four EU countries (the Netherlands, Ireland, Germany, and France). Poor diets contribute to one in seven deaths in the UK, 63 per cent of the population is overweight or obese and health inequality is increasing between the poorest and most affluent regions. The food security and health of the UK population is therefore dependent on a small number of locally grown crops, vulnerable supply chains, and an unhealthy, obesogenic diet. The UK food system must diversify if it is to become food and nutritionally secure, meet its climate and biodiversity goals and have a healthy and active population. Climate resilient and nutritious underutilised crops can help diversify the UK agrifood system but, research and investment in them is sporadic, piecemeal, and unfocused. In this paper we compare two approaches to identifying potentially suitable underutilised crops for the UK. The first, based on Defra Project CH0224 was delivered through literature and database searches and expertise of growers, advisers, breeders, seed suppliers, processors, traders, and researchers. The second, used the CropBASE digital knowledge base for underutilised crops described in Jahanshiri et. al. (2023). The two approaches produced no single crop that was common to both shortlists. We propose that the analytical and predictive tools derived from CropBASE could be combined with local knowledge and expertise from the Defra project to provide a common framework for the identification of underutilised crops that are best suited to local UK circumstances now and in climates of the future.

In politics and society, organic farming is widely seen as the ideal way to address the many challenges facing agriculture and food security. In order to examine the extent to which this view is realistic, the impact of organic and conventional farming is compared on the basis of relevant criteria such as land requirements, greenhouse gas emissions, biodiversity, food quality, production and environmental costs, reduced meat consumption, the need for agricultural imports and the global expansion of arable land. Land is considered to be a central factor. Such a comprehensive analysis is necessary to assess the impact of different influencing factors and to provide important suggestions for targeted decision making. Although organic farming has a positive impact on land use in many ways, it quickly reaches its limits. The low yields and high production costs resulting from the limited choice of means of production make organic farming an unsuitable model for the future in a world with limited arable land, a high and growing demand for food, and low incomes for large sections of the population. It can, however, provide significant impetus and contribute important technological knowledge for the further development of agriculture as a whole to meet the complex objectives of sustainability, greenhouse gas mitigation, biodiversity and global food security by incorporating all available technologies. Based on this, a model for responsible, sustainable intensive agriculture is outlined and proposals for an eco-social framework to support this model are put forward.

This article delves into the enduring relevance of Liberal International Relations (IR) Theory amidst persistent criticisms regarding power dynamics and its applicability in emerging states. Despite its detractors, Liberal IR Theory offers a nuanced understanding of state behavior, blending social contexts with power dynamics. It recognizes the intricacies of economic interdependence and advocates for a balanced approach in advancing democracy and human rights. While nationalism poses challenges to international institutions, their adaptability endures. Leaders, albeit constrained, retain the capacity to foster peace through visionary leadership and global cooperation. By acknowledging its limitations and fostering open dialogue, Liberal IR Theory continues to evolve, poised to contribute to a just and peaceful global order.

As technology and multimedia production have advanced, there has been a significant rise in attacks on digital media, resulting in duplicated, fraudulent, and altered data and the infringement of copyright laws. This paper presents a robust and secure digital image watermarking technique that has been implemented in the spatial domain and exploits the erratic and chaotic behaviour of the powerful elementary cellular automata rule-30. The crucial characteristics of the watermarking system, i.e., imperceptibility, capacity, and robustness, have been perfectly balanced by the suggested blind watermarking technique. In this approach, prior to embedding, the grayscale watermark image is downsized to its two Most Significant Bits (MSBs). Then, the 2-MSBs watermark is encrypted using an ECA rule-30 so as to level up the security attribute of the system. Then, the host image is scrambled using ECA rule-30 to distribute the watermark pixels throughout the host image and thus achieve the highest robustness against geometrical attacks. Finally, the encrypted watermark data is embedded into the scrambled host image using the ECA rule-30-based embedding key. The proposed method performs better in terms of imperceptibility, capacity, and robustness when compared to several systems with similar competencies. The simulation's findings demonstrate strong imperceptibility as evaluated by the Peak Signal-to-Noise Ratio (PSNR), which has an average value of 58.3735 dB and a high payload. The experimental outcomes, observed across a diverse range of standardized attack scenarios, unequivocally establish the ascendancy of the proposed algorithm over competing methodologies in the realm of image watermarking.

The modern supply chain (SC) is growing in terms of data, devices, users, and stakeholders, which introduced new security challenges and threats, especially with the reliance on centralized servers or cloud platforms. In addition, increased trust among system participants exposes the SC to a higher risk of vulnerabilities which require strong security measures. This article proposes a hybrid security framework for SC systems, BC-DRLzSC, that integrates Blockchain (BC) and Deep Reinforcement Learning (DRL) designed to operate in a zero trust (ZT) environment. In particular, we propose a decentralized BC-based approach integrated with smart contracts to manage system participant registration and authentication and to control access to system resources. BC-DRLzSC adopts a ZT architecture to reinforce SC security, which can be achieved with an advocate to verify each entity’s trustworthiness before granting or retaining access to system resources. Incorporating the ZT architecture, with BC and DRL, can potentially and significantly bolster SC system security. DRL is employed to develop a proactive attack detection model that continuously monitors the incoming traffic from authenticated nodes within the network and predicts any malicious actions. Finally, we evaluate the performance of our proposed DRL solution using the NSL-KDD dataset.

Climate change poses a significant threat to global food security, necessitating a thorough examination across multiple dimensions. Establishing appropriate food security evaluation indicators that align with the evolving concept of food security is imperative. This study enhances food security evaluation by refining a multi-dimensional framework and analyzing the impact of climate change across various regions from 2002 to 2021. By constructing an Food Security Index system composed of a three-tier indicator system and employing the entropy method for weighting, we assess the impacts of climate change on food security using a climate-economic model. Regression analysis reveals a negative linear relationship between mean temperatures and food security (p<0.01), while precipitation impacts food security non-linearly, particularly exhibiting inverse U-shaped patterns in major grain-producing and grain-consuming areas. Extreme high temperatures consistently reduce food security, whereas extreme precipitation displays a complex, statistically significant inverse U-shaped association. In regions where production and consumption are balanced, mean temperatures have a negative effect on food security, while precipitation exhibits a positive correlation, but excessive precipitation can have adverse effects. These findings highlight the intricate interplay between climate change, regional disparities, and food security in China, emphasizing the need to consider multi-dimensional factors and regional variations in addressing food security challenges. These insights are invaluable for policy-making and planning aimed at enhancing food security in China.

In the era of mounting water scarcity, particularly in arid regions, the agricultural sector is striving to optimize the water resource management practices. Treated wastewater (TWW) has emerged as a viable alternative to freshwater for irrigation, offering a sustainable solution to water conservation. However, ensuring the safety and quality of TWW is paramount to safeguard for both crop health and environmental integrity. Electrical conductivity (EC), a critical factor in assessing TWW suitability for irrigation, can significantly impact crop yields and soil health since it is directly linked to the concentration of dissolved salts and ions. Therefore, this study delves into the development of advanced predictive models for EC assessment by employing supervised machine learning (ML) techniques like the Support Vector Regression (SVR), Adaptive Network-based Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR). The TWW samples collected from Al-Qatif area, Saudi Arabia were assessed in the lab through a sensor-based real-time monitoring system integrated with Internet of Things (IoT) network and a multiprobe device. The key water quality parameters assessed involved, temperature (T), pH, Oxidation Reduction Potential (ORP), Resistivity (RES), Total Dissolved Solids (TDS), Salinity and Turbidity which served as the foundation for these models, given their profound influence on and their anticipated impact on EC levels. Following feature selection, two distinct model combinations were identified for the SVR, ANFIS and MLR models. Furthermore, to evaluate prediction performance, four statistical criteria, including coefficient of determination (R²), correlation coefficient (R), mean square error (MSE), and root mean square error (RMSE), were employed. Overall, the comparative performance results proved that ANFIS-M2 indicated the strongest predictive capability with the R² value of 0.99572 and 0.75145 and the RMSE values of 0.00965 and 0.01134, respectively in both calibration and verification stages.

Over the years, the need for autonomous systems such as drones, also known as unmanned aerial vehicles (UAVs), has significantly increased in popularity across several industries, including the military. The Internet of Battlefield Things (IoBT) represents a modern technological advancement that significantly enhances the operational efficacy of defense systems. It establishes an integrated military force by connecting individuals to intelligent technology in drones, radios, equipment, antennas and ground stations. This is achieved through the utilization of cloud and edge computing, sensors, mobile devices, embedded systems, and IoT-based systems. However, IoBT security is significantly compromised by enemies that exploit the vulnerabilities and malicious intrusions that specifically target the external environment or to obtain sensitive data. In addition, a comprehensive understanding of cyberattacks and countermeasures is essential for assuring the security of IoBT. On the other hand, as cyber threats get more complicated, trust becomes increasingly important for the security of digital systems. Within this context, the zero trust architecture contends that the security of a complex network is inherently vulnerable to internal and external threats. In defense, UAV security system applications, for example, the primary purpose of the zero trust architecture is to limit the vulnerability of military UAV systems to cyberattacks, hence reducing the possibility of data breaches and illegal entry. This study conducts a systematic review aimed at identifying different aspects of cybersecurity strategies for protecting UAV systems within the IoBT domain. Furthermore, zero trust architecture is presented in the study as an effective prospective solution to the security issues that develop in defense UAV systems.

The emergence of Internet of Things (IoT) devices is revolutionizing healthcare. This dynamic healthcare environment relies on Internet of Healthcare Things (IoHT) and Internet of Medical Things (IoMT) technologies. Economic, social, and technological opportunities from the IoHT are transforming healthcare systems. This article covers the history, present condition, and future prospects of IoHT, as well as its topologies, platforms, services, applications, market trends, and solution types. It examines security issues, requirements, and a threat taxonomy, studying smart healthcare privacy and security. It explores future research and the challenges IoHT-based solutions face. International health rules and regulations based on the IoT and their social and economic effects on long-term development are also examined. Integrating IoMT into healthcare infrastructures enhances scalability, efficiency, reliability, and accuracy. Through communication networks and data-processing algorithms, IoMT creates smart hardware and software platforms that enhance decision-making. IoMT devices' low resources raise privacy and security concerns. Security and privacy issues are organised by healthcare IoMT adoption in this study. We give an extensive attack taxonomy against the IoMT infrastructure, encompassing devices, security mechanisms, and their pros and cons. Furthermore, it describes the privacy and security requirements for any unique security solutions against IoMT's multiple attack vectors. This article explains the complicated difficulties, security concerns, and future research pathways needed to safeguard and ensure the long-term expansion of these game-changing technologies in the healthcare ecosystem.

The rapid evolution of technology has given rise to a connected world, where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While IoT offers incredible convenience and efficiency, it presents a significant challenge in cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques are employed within Intrusion Detection Systems (IDS) to enhance their capabilities in identifying and responding to security threats. The key features selected to improve IDS efficiency and reduce dataset size, thereby decreasing the time required for attack detection, are drawn from the extensive network dataset. This paper introduces an enhanced feature selection method designed to reduce the computational overhead on IoT resources while simultaneously strengthening intrusion detection capabilities within the IoT environment. Experimental results based on the InSDN dataset demonstrate that our proposed methodology achieves the highest accuracy with the fewest number of features and low computational cost. Specifically, we attain a 99.99% accuracy with 11 features and a computational time of 0.8599 seconds.

Text mining is a technological trend often highlighted in the continuous exchange of information through interconnected media. Its applicability goes beyond private organizations, as the public sector also requires it to treat textual information regarding services offered. Within this scenario, public security emerges as a prominent user of text mining that seeks to ensure the construction of data and knowledgebases to support decision-making about law enforcement actions to ensure citizen welfare. The primary objectives of this article are: (i) to develop a survey to identify text mining applications, techniques, opportunities, and challenges in public security, and (ii) to outline research directions concerning these topics and provide insights so that interested researchers can develop new studies. The literature was searched within four databases: Scopus, IEEE Xplore, ACM Digital Library, and Web of Science. A filtering process was applied to extract the works most aligned with the target theme, resulting in the selection of 194 of the most relevant works for a literature review. There were identified nineteen key applications of text mining related to public security and the most recurrent techniques and technologies reported between 2014 to 2021, supporting outlining three axes for future directions: one with possible expansion of objectives for new research; another on changes and adaptations in scopes for the methodological context; and the last one on expansions and changes in application scenarios based on the literature.

Nature-Based Solutions (NBSs) can help to achieve multiple Water-Ecosystem-Food Nexus Do-main Objectives & Nexus Resilience Qualities, facilitating the transition to more resilient agrifood systems and providing an evidence base for a broader Nexus policy dialogue. This paper de-scribes the stepwise methodology developed in the EU-funded LENSES project to carry out a comprehensive analysis of NBSs in 6 pilot areas in 5 Mediterranean countries, and presents the results of NBSs implementation in 4 pilot areas, highlighting obstacles and opportunities. The methodology includes the development of an analytical evaluation framework and of a com-prehensive catalogue of Nexus-related NBSs, whose suitability needs to be assessed at the local level to achieve better use of protected/natural ecosystems, increase the sustainability and mul-tifunctionality of managed ecosystems and design and manage novel ecosystems. Subsequently, in a collective learning process which supported the operationalisation of the WEF Nexus, NBSs were identified that address specific contextual vulnerabilities, improve water distribution and enhance food security, while preserving ecosystems and supporting adaptation to climate change. The added value of the proposed methodology lies in the multi-stakeholder participatory approach to gain in-depth knowledge of local agri-food systems, including their main WEF-related challenges, and to facilitate overcoming barriers to NBS implementation.

This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected was merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port's activities.

Climate change, recognized as a global crisis, is not only altering our environment but also significantly impacting human health. Particularly, its influence on non-communicable diseases (NCDs), a leading cause of global mortality, is becoming increasingly evident yet underexplored. This review aims to synthesize current research on the impact of climate change on NCDs, addressing a critical gap in our understanding of how environmental changes affect human health. We examine the complex interplay between climate change and NCDs, drawing insights from recent studies that highlight the adverse effects of climate variables on chronic diseases, such as cardiovascular and kidney diseases, and the role of food and nutrition security in this nexus. Furthermore, the review explores the disproportionate impact on vulnerable regions and populations, particularly in Pacific Island countries and territories, underscoring the urgent need for context-specific solutions and a cross-sectoral approach. By integrating findings from various disciplines, this review aims to inform policy and public health interventions, highlighting the necessity of collaborative efforts to mitigate the burgeoning burden of NCDs in a rapidly changing climate.

The purpose of this review is to elucidate the actual threat of the most prevalent mycotoxins in agricultural commodities and human/animal food/feed for appearance of foodborne ailments and diseases. The underestimated hazard of joint mycotoxin uptake of animals or humans is critically discussed with regards to synergistic or additive interaction between some target mycotoxins. The real toxicity of target mycotoxin combinations as it happens in practice is evaluated and possible lower limit values or control measures are suggested in such cases. Some critical points in adequate risk assessment, hygiene control and regulation of mycotoxns are discussed. The efficiency of current mycotoxin regulations and control measures is evaluated in regards to human/animal health hazard. The risk assessment in the case of multiple mycotoxin exposure of humans/animals via the food/feed or agricultural commodities is evaluated and some suggestions are proposed in such cases. Appropriate control measures and food safety issues throughout the food supply chain are proposed in order to prevent target foodborne diseases. Some preventive measures and possible veterinary hygiene control or risk assessment are proposed in some practical cases of foodborne diseases for preventing mycotoxin contamination of animal products designed for human consumption and to avoid possible public health issues.

In recent years, the manufacturing sector has benefited from a variety of technological innovations regarding automation and utilization of artificial intelligence on the shopfloor. During the manufacturing processes of a product, a variety of meta data can be derived from each working step and considered during subsequent stages of its liefycle like shipment, utilization or recycling. With upcoming regulations decided by the European Union, manufacturers must begin to react to the demand of a comprehensive circular economies by introducing technical solutions like Digital Product Passports (DPP). By associating the specific conditions of all stages in the life of a product within a digital identity, e.g., the DPP, transparency and efficiency can be increased. The Gaia-X service landscape aims at realizing federated data infrastructures, enabling robust and transparent services while considering data sovereignty. In this work, a modular and generic multi-tenant framework for utilizing DPPs within cross-company ecosystems is presented with respect to the manufacturing sector. In applying the Secret Network-blockchain for implementing the automation of DPP management processes, premises for Gaia-X compliance and therefore strengthened trust-anchors are realized. By considering details of ecosystems, as well as most interesting security threats to the proposed architecture, a holistic view of applying DPPs in the context of smart production supply chains is shown. In presenting a simple but vivid use case of the production and compilation of car wheels, the fundamental benefits of the work on hand are underlined, once more.

The industrial metaverse paradigm is generally a virtual environment that integrates various technologies such as augmented reality (AR) and mixed reality (MR) to enhance business operations and processes. It aims to streamline workflows, reduce error rates, improve efficiency, and provide a more engaging experience for employees. The promise of the industrial Metaverse to drive sustainability and resource efficiency is compelling. Utilizing advanced technologies like the Industrial Metaverse is essential for staying competitive in today’s rapidly evolving business environment. However, the environmental impact of the technologies underpinning the industrial metaverse, like data centres and network infrastructure, should not be overlooked. The ecological footprint of these technologies must be considered in the sustainability equation. Researchers have warned that by 2025, without sustainable AI practices, AI will consume more energy than the human workforce, significantly offsetting carbon-zero gains. As the metaverse persists in evolving and gaining momentum, it will be necessary for companies to prioritize sustainability and explore new ways to balance technological advancements with environmental stewardship. However, recent studies have conjectured that the Metaverse holds the potential to reduce carbon emissions, as digital replacements for physical goods become more prevalent and physical activities like mobility and construction are reduced. However, the specific extent to which this substitution can alleviate environmental concerns remains understudied, presenting a knowledge gap in understanding the real-world impact of digital replacements. Thus, the objective of this paper is to provide a comprehensive review of the industrial metaverse as well as explore the environmental impact of the industrial metaverse. The methodological approach is that of integrative literature review design wherein multiple sources from web science and databases such as ACM library, IEEE Library, and Google Scholar, were analysed to provide a comprehensive understanding of the developments in the industrial metaverse. Specifically, firstly by considering the Industrial metaverse architecture we elucidate the concept of the "industrial metaverse" and the associated enabling technologies. Secondly, an exploration through discussion, of the prevalent use cases and deployment of the emerging industrial metaverse. Thirdly, exploration of the impact of the industrial metaverse on the environment. In the fourth instance, we address novel security and privacy risks as well as upcoming research challenges, keeping in mind that the industrial metaverse is based on a strong data fabric. Results point to the Industrial Metaverse as having both positive and negative environmental effects by energy consumption, e-waste, and pollution. Overall research, however, indicates that most industrial metaverse applications have a positive environmental impact and subsequently trend toward sustainability. Ultimately for sustainability in the industrial metaverse, enterprises may consider utilizing renewable energy sources and cloud services. Furthermore, examining the effects of products on the environment as well as creating a circular economy.

Micro nano bubbles (MNBs) can generate ·OH in situ, which provides a new idea for the safe and efficient removal of pollutants in water supply systems. However, the difficulty in obtaining stable MNBs causes the low efficiency of ·OH generation, which makes the inability to guarantee the removal efficiency of pollutants. This paper reviews the application research of MNBs technology in water security from three aspects: the generation process of MNBs in water, the generation rule of ·OH during MNBs collapse, and the control mechanism of MNBs on pollutants and biofilms. We found that MNBs generation methods are divided into chemical and mechanical (about 10 kinds) categories, and the instability of bubble size restricts the application of MNBs technology. The generation of ·OH by MNBs is affected by pH, gas source, bubble size, temperature, external stimulation. And pH and external stimulus have more influence on ·OH generation in situ than other factors. Adjusting pH to alkaline or acidic conditions and selecting ozone or oxygen as the gas source can promote the ·OH generation. MNBs’ collapse also releases a large amount of energy, which the temperature and pressure can reach 3000K and 5Gpa respectively, making it efficient to remove ≈90 % of pollutants (i.e., trichloroethylene, benzene, and chlorobenzene). The biofilm can also be removed by physical, chemical, and thermal effect. MNBs technology also has great application potential in drinking water, which can be applied to improve water quality, optimize household water purifiers, and enhance the taste of bottled water. Under the premise of safety, let people of different ages taste, finding that compared with ordinary drinking water, 85.7 % of people think MNBs water is softer, 73.3 % of people think MNBs water is sweeter. This further proves that MNBs water has a great prospect in the drinking water application. This review provides innovative theoretical support for solving the problem of drinking water safety.

With the development of Internet, big data and artificial intelligence technology, personal information protection has become the focus of social attention. The legal boundary of data protection has become increasingly prominent, involving the collection, storage, use, transmission and disclosure of personal information. This paper aims to explore the legal boundary of data protection, analyze the legal basis, legal principles and legal provisions of personal information protection, and put forward corresponding legal suggestions, in order to provide reference for the improvement of China's data protection legal system.

The Internet of Things (IoT) has a growing presence in society's daily lives. These lightweight devices can be easily deployed and maintained, enabling extensive adoption in different environments. Furthermore, one of the most promising areas for using IoT devices is healthcare, comprising devices referred to as the Internet of Medical Things (IoMT). Several examples of healthcare services are supported by IoMT devices, e.g., continuous health monitoring. Conversely, there is an increasing concern with the cybersecurity aspects of these devices, and several attacks against IoT infrastructures have been launched in the past few years. These cybersecurity concerns also apply to healthcare applications, where the tradeoff between the benefits and security of IoMT devices must be observed. Given the complexity and amount of data IoMT network traffic generates, advanced methods become especially useful in these environments. Although Machine Learning (ML) brings various techniques and solutions to improve cyberattack detection, prevention, and mitigation, essential features are not addressed in the current state-of-the-art benchmark dataset contributions. Thereupon, the main goal of this research is to propose a realistic benchmark dataset to enable the development and evaluation of IoMT security solutions. In order to accomplish this, 18 attacks were executed against an IoMT testbed composed of 40 IoMT devices (25 real devices and 15 simulated devices), considering the plurality of protocols used in healthcare (e.g., Wi-Fi, MQTT, and Bluetooth). These attacks are categorized into five classes: DDoS, DoS, Recon, MQTT, and spoofing. This effort aims to establish a baseline complementary to the state-of-the-art contributions. The outcome supports researchers in investigating and developing new solutions to make healthcare systems more secure using different mechanisms (e.g., machine learning - ML). This research goes beyond merely conducting attacks on IoMT devices. We also attempt to capture the lifecycle of these devices in different vital phases, from the moment they join the network until they leave, which is called profiling. Profiling allows the different classifiers to identify anomalies of each device individually in the healthcare network. The CICIoMT2024 dataset has been published on CIC's dataset page, making it available for other researchers to use.

Infections with multi-drug-resistant (MDR) pathogens in food-animals threaten public health and food security. However, the epidemiology and factors associated with MDR Escherichia coli on Ugandan farms are not well known. This study investigated the prevalence, resistance patterns and factors associated with MDR E. coli carriage on chicken farms. A total of 158 E. coli strains were isolated from 158 chicken farms. The disc diffusion method for susceptibility testing was performed. Clinical breakpoints were interpreted according to Clinical and Laboratory Standards Institute guidelines. MDR was defined as resistance to three or more classes of antimicrobials. MDR E. coli prevalence on chicken farms was 62.7% (95% CI: 55.0 – 70.3). High resistance was observed against ampicillin 79.8% (95% CI: 72.7–85.4), tetracycline 72.8% (95% CI: 65.2–79.2), cotrimoxazole 55.7% (95% CI: 47.8–63.3), and ciprofloxacin 38% (95% CI: 30.7–45.9). Farmers who were males (APR = 0.72, CI: 0.55, 0.93), and attained at least secondary education (APR=0.64, CI: 0.46, 0.88) and administration of recommended antibiotic doses (APR = 0.76, CI: 0.59–0.96) posed a lower risk of MDR E. coli carriage. Farms without footbaths posed a heighten risk (APR=1.48, CI: 1.16–1.88). MDR E. coli were highly prevalent on chicken farms. This study underscores the need for antimicrobial stewardship and improved infection prevention strategies on chicken farms.

The next-generation "Industrial Internet of Things" (IIoT) will support "Machine-to-Machine" (M2M) communications for smart Cyber-Physical-Systems and Industry 4.0, and require guaranteed cyber-security. This paper explores hardware-enforced cyber-security for critical infrastructures. It examines a Quantum-Safe "Software-Defined Deterministic IIoT" (SDD-IIoT), with a new forwarding-plane (sub-layer-3a) for deterministic M2M traffic flows. A "Software-Defined-Networking" (SDN) control-plane controls many "SDD Wide Area Networks" (SDD-WANs), realized with FPGAs. The SDN control-plane provides an "Admission-Control/Access-Control" system for network-bandwidth, using collaborating Artificial Intelligence (AI) rule-based "Zero Trust Architectures" (ZTAs). Hardware-enforced access-control eliminates all congestion, BufferBloat, and DoS/DDoS attacks in the forwarding-plane, reduces buffer-sizes by 100,000+ times, and supports ultra-reliable and uItra-low-latency communications in the SDD-WANs. The SDD-WANs can: (i) Encrypt/Authenticate M2M flows using Quantum-Safe ciphers, to withstand attacks by Quantum Computers; (ii) Implement "Guaranteed Intrusion Detection Systems" in FPGAs, to detect cyber-attacks embedded within billions of IIoT packets/second; (iii) Provide guaranteed immunity to external cyber-attacks against critical infrastructure, and exceptionally-strong immunity to internal cyber-attacks; (iv) Save $US100s of billions annually by exploiting FPGAs; and (v) Enable "Quantum Key Distribution" (QKD) Networks by providing a programmable forwarding-plane with "authenticated classical channels" and full-immunity to DoS/DDoS attacks. Extensive experimental results for an SDD-WAN over the European Union are reported.

The management of large medical files is a critical challenge in the health sector. Conventional systems have security, scalability, and efficiency deficiencies. This project proposes a hybrid architecture between blockchain and cloud computing to address these challenges. Blockchain ensures the immutability and traceability of medical records, while the cloud allows scalable and efficient storage. Together, these technologies can transform data management in electronic health record applications. The essential objective of this research is to thoroughly look at the existing strategies, devices, and conventions that encourage a secure interaction between blockchain applications and the cloud. Special emphasis is set on maintaining the integrity and security of the blockchain while tackling the potential and efficiency of cloud infrastructures. Through a meticulous investigation of these approaches, along with their successful usage and encountered challenges, this paper offers a comprehensive overview of the present state of this integration. Altogether, this paper does not propose new concepts or developments. Its central focus is to provide a comprehensive and insightful examination of the modern landscape concerning the integration of blockchain and cloud advances. By shedding light on the existing approaches and assessing their viability, this work aims to highlight the current challenges and build up a solid establishment for future development. This, in turn, will cultivate a consistent integration of blockchain security with the dynamic potential of cloud computing while guaranteeing information integrity and security remain uncompromised. In conclusion, this paper serves as a important resource for analysts, specialists, and partners looking for to dig into and development the integration of blockchain and cloud innovations, subsequently contributing to a more secure and productive cloud environment.

In the past few decades, the transmission of data over an unsecure channel resulted in an increased rate of hacking. Therefore, the requirement to make multimedia data more secure is increasing day by day. Numerous algorithms are developed to improve efficiency and robustness. In this article, a novel and secure image encryption algorithm is presented. It is based on a modified chaotic logistic map (CLM) that provides the advantage of having less computational time to encrypt an input image. The encryption algorithm is based on Shannon’s idea of using substitution-permutation and one-time-pad network to achieve ideal secrecy. The CLM is used for substitution and permutation to improve randomness and increase dependency on the encryption key. Various statistical tests are conducted, such as Key Space Analysis, Complexity Analysis, Sensitivity Analysis, Strict Avalanche Criteria (SAC), Histogram Analysis, Entropy Analysis, Mean of Absolute Deviation (MAD) Analysis, Correlation Analysis, Contrast Analysis, and Homogeneity to give a comparative analysis of the proposed algorithm and verify its security. As a result of various statistical tests, it is evident that the proposed algorithm is more efficient and robust as compared to previous ones.

The rapid and sensitive detection of pathogenic bacteria is becoming increasingly important for the timely prevention of contamination and treatment of infections. Biosensors based on nucleic acid aptamers integrat-ed with optical, electrochemical, and mass-sensitive analytical techniques have garnered intense interest for their versatility, cost-efficiency, and ability to exhibit high affinity and specificity in binding bacterial bi-omarkers, toxins, and whole cells. This review emphasizes the development of aptamers, their structural op-timization, and their use in bacterial sensing applications. Moreover, we explore barriers and develop per-spectives in applying aptamer-based bacterial detection.

Feedback from the earthquake zone in Kahramanmaraş, Turkey has established that the emerging field of Crisis Translation and Interpreting (CTInt) requires a free smartphone application, to enable Crisis Translators and Interpreters (CTIs) to access multilingual terminology resources on-/offline when assisting Search and Rescue (SAR) teams responding to man-made and/or natural disasters.The aim is to design not only a multilingual compilation of Crisis Terminology (CTerm), downloadable to smartphones enabling access when there is a failure in the internet connection, but also a unified search tool which will browse and redirect the user to existing termbanks when the internet is accessible. Nevertheless, terminological records available in the domain of CTerm are inadequate and dispersed, with different interfaces and language combinations, requiring enhancement and harmonisation. Furthermore, automated translation and interpreting software/systems supported by the multilingual compilation of the crisis terminology portal (CTermPort) ideally need to be developed or, alternatively, existing ones integrated into the crisis termbank (CTermBank) collection for those who would lack the support of CTIs.The design of the CTermPort will significantly impact civil societies by providing a lifesaving tool for CTIs who need a reliable terminological reference point and automated translation options, and those unable to be assisted. In this respect, the project also promises strong prospects of sustainability past the project duration, as the tool to be developed will serve international SAR teams working closely with CInts during response times, but also medical staff in field hospitals collaborating with CTIs in rehabilitation and recovery phases, and beyond.

The world has changed. New technologies, new trends, new protocols, new efficient algorithms, and systems have been used widely by civilized countries to have better and more convenient living. In this research, there have been introduced many new terms, technologies, and mechanisms. Moreover, threats, attacks, and vulnerabilities have been studied and a novel scenario has been proposed in order to overcome all these violations that have been presented in this paper. The scenario is based on a security model that has been proposed for critical sectors, such as hospitals, industries, etc., which need efficient solutions to go one step further and improve the secure and efficient living and treatment of people. The aim of this research has been to understand through research the IoT security issues and propose the appropriate solutions based on the most suitable security algorithms. The results, which have been based on the complexity, the throughput, the power consumption, the strength, and the memory usage of the algorithms studied, have been hopeful and helpful for future research and experimentation.

Abstract:The digital landscape is rapidly reshaping under the influence of AI-powered cloud-based e-commerce platforms (PCEPs). These platforms leverage the transformative power of AI to personalize customer experiences, optimize pricing strategies, enhance security, and streamline operations, offering businesses a significant competitive edge. However, challenges surrounding data privacy, algorithmic bias, and potential job displacement raise crucial ethical questions regarding widespread AI adoption in e-commerce.This study delves into the untapped potential and ethical considerations of PCEPs, distinguishing itself by proposing practical strategies for responsible development and ethical implementation.Objectives: Investigate the transformative potential of PCEPs: Personalized product recommendations:Analyze effectiveness and potential bias. Dynamic pricing strategies:Explore impact on consumer fairness and market stability. Enhanced fraud detection:Evaluate accuracy and privacy implications. Optimized supply chain management:Assess efficiency gains and job displacement risks. Critically analyze the ethical considerations of AI in e-commerce: Data privacy:Assess current practices and develop mitigation strategies. Algorithmic bias:Identify potential sources and propose solutions for fairer decision-making. Job displacement:Evaluate impacts and propose reskilling initiatives. Propose comprehensive and practical strategies for responsible PCEP development and implementation: Transparency:Enhance user awareness and control over data usage. Fairness:Design AI systems to mitigate bias and uphold consumer rights. Accountability:Establish clear guidelines and oversight mechanisms for ethical AI development.Conclusion: While PCEPs offer a plethora of transformative benefits, their ethical implementation is paramount. By adhering to principles of transparency, fairness, and accountability, businesses can unlock the full potential of PCEPs while minimizing potential risks and shaping the future of online commerce in a sustainable and equitable manner

The Internet of Medical Things(IoMT) has powerful cloud computing ability and efficient data collection ability, which can improve the accuracy and convenience of medical work. As most communications are over open networks, data should encrypted before uploading to ensure the confidentiality of sensitive user data. Searching for encrypted data has become a challenging problem. Public key Encryption with Keyword Search (PEKS) supports the search of encrypted data and provides data privacy protection. PEKS has become a trendy technology, but PEKS still has the following problems: 1. The cloud server, as a semi-honest but curious third party, is likely to return some wrong search results to save computing and bandwidth resources. 2. The single keyword search inevitably produces irrelevant results, wasting computing and bandwidth resources. 3. Most PEKS schemes use bilinear pairing, so the computational efficiency is relatively low. 4. PEKS schemes based on Public Key Infrastructure (PKI) or identity-based cryptography inevitably face problems with certificate management or key escrow. 5. Most PEKS schemes face serious security threats such as Keyword Guessing Attacks (KGA) and File Injection Attacks (FIA). To solve the above problems, we propose a new certificateless verifiable bilinear pair-free conjunctive keyword search encryption scheme(CLVPFCKS) for IoMT using multi-signature.

In May of 2022, millions of U.S. parents faced uncertainty in safely feeding their infants due to the infant formula shortage. Methods: An anonymous, electronic, cross-sectional, retrospective survey was used. Results: U.S. parents (n =187) whose infants were 10.2 weeks old during the short-age completed the survey. Eighty-one percent of parents switched formulas during the shortage, 87% switched because they could not find the formula they typically used, 34% switched 3-5 times, 29% of parents visited ≥ 4 stores/24 hours and 26% of parents traveled >20 miles/24 hours to purchase formula. Use of infant formula increased (p< 0.01); in infants requiring specialty for-mula, use of intact cow’s milk formula increased (p < 0.05) and use of premature infant formulas decreased (p < 0.05). Infants relying on specialty formulas experienced at least one undesirable outcome compared with non-specialty users. Parents used social media, relatives/friends, and healthcare providers for support during the shortage, but their helpfulness scores were suboptimal. Parents reported needs for greater infant formula availability, free prenatal lactation education and postpartum lactation support. Conclusions: Government, regulatory and healthcare policy oversight are needed to protect the infant feeding system, including more commercially available products, access to banked donor milk and lactation support.

Spiritual security should be a priority in our modern globalized society. Therefore, the study provides convincing evidence that it is important to develop a theoretical understanding of spiritual security. Several theoretical concerns regarding the establishment and maintenance of spiritual security are addressed in the article. He explores many ideas that are interrelated, including spiritual security, worldview, and spirituality. To ensure spiritual security, a practical analysis is carried out of the need to carry out various measures that strengthen the spiritual health of society.The article explains the methodology used in spiritual Security Investigations. According to the author, different levels of spiritual security can be analyzed using a structural-logical model and a dialectical method of cognition. Comparative analysis is also seen as a valuable tool. Spiritual intelligence is essential to overcome barriers, manage change, and ensure personal security and its impact on relationships. The process begins with introspection and evaluation, concluding with the ability to assess and understand the environment. By analyzing and understanding natural phenomena, it is possible to develop a sense of reality and a deeper appreciation of the nature of human existence. A person can feel spiritually safe if he adheres to his moral and spiritual beliefs and complies with the norms of society. It contains such things as worldview, social behavior, culture, research and social development. By expanding the spiritual point of view, the danger to the spiritual security of a person can be reduced.

This paper examines hybrid threats to maritime transportation systems, and their governance re-sponses; focusing on the congested Straits of Malacca and Singapore (SOMS) as an illustrative case study. The methodology combines secondary sources with primary data from 42 expert in-terviews, a 30-respondent survey, and two maritime security roundtables. Key findings were that ships’ critical systems are increasingly interconnected, yet aging IT infrastructure and minimal cybersecurity awareness among crews heighten risks. Meanwhile, regional terrorist groups have previously targeted shipping and shown considerable skill in exploiting online tools, aligning with broader calls for jihadist violence. Further, opportunistic piracy persists in the SOMS with the po-tential to disrupt shipping. Experts confirmed maritime cybersecurity lags other critical infra-structure sectors and needs updated governance. Initial International Maritime Organization (IMO) guidelines lack specificity but revisions and updated IMO guidance are in process, while Port state implementation of maritime cybersecurity standards varies. Crucially, information sharing remains inadequate, even as recorded attacks increase. Findings underscore that although major hybrid incidents have not occurred, simulations and threat actors’ capabilities demonstrate potential for catastrophic collisions or cascading disruption in congested waterways. Mitigating factors like redundancy and crew training are deficient currently. Some alignment between SOMS states on maritime security cooperation exists, but not cyber threats specifically. Key recommen-dations include an anonymous cyber-attack reporting system, reinforced training and shipboard systems, and consolidated regional frameworks. Until these priorities are addressed, the analysis concludes that hybrid vulnerabilities in this vital global chokepoint remain a serious concern.

Background: School meals represent a significant supply of nutrients for children in Brazil, especially those in conditions of social vulnerability. Objectives: This study aimed to assess the levels of arsenic (As), cadmium (Cd), and lead (Pb) in meals served in public elementary schools in four municipalities in the state of Bahia, Brazil, and assess the risk posed to children's health. Methods: Ninety-six samples were collected from 16 schools, freeze-dried, and subjected to microwave-assisted digestion. The As, Cd, and Pb levels were determined by graphite furnace atomic absorption spectrometry. The risk assessment was based on calculating each element's hazard quotient (HQ). Results: None of the samples reached or exceeded the tolerable levels for the elements analyzed. Pb was the metal that obtained the most significant result, reaching maximum levels of 39-157 µg.kg-¹. Conclusions: No element exceeded the PTWI proposed by JECFA; thus, the toxic metal content in school meals poses a negligible risk to children´s health.

The necessity for effective intrusion detection systems (IDS) in big data environments has grown critical due to the rising prevalence of big data systems and the rising amount of security threats. Applying conventional intrusion detection methods to the enormous and intricate data produced by big data ecosystems presents difficulties. Deep learning, has proven to be exceptionally adept at deciphering complex, large-scale data. This study proposes a Deep Learning-based Intrusion Detection System (IDS) created specifically for the Big Data environment. Traditional intrusion detection systems struggle to efficiently identify and prevent cyber threats due to the ever-increasing volume and complexity of data in current networks. We suggest an IDS that makes use of Deep Learning (CNN, LSTM, GAN, etc.), to address this problem. These cutting-edge neural network topologies give the system the ability to process and analyze massive amounts of data for precise intrusion detection. Data collection, data preprocessing, feature engineering, DL model training, intrusion detection, warning generation, and reaction are some of the crucial parts of the suggested IDS. The data collection module mines the Big Data environment for network traffic, system logs, and security event information. To make sure the data is suitable for analysis, it is preprocessed. To improve the detection abilities of the DL models, important features are extracted from the data using feature engineering approaches.

Data security in the Internet of things (IoT) is often implemented by means of encryption, which can be burdensome for some entities. We propose in this paper a solution based on routing, by which data are forwarded only to entities that are intended to receive them. An IoT network can be seen as a partial order of equivalence classes of entities, and each entity can be labeled according to the position of its equivalence class in the partial order. The partial order can be constructed according to requirements of secrecy (or confidentiality), integrity and conflicts. Routing tables among entities can be compiled by using the labels. The method is demonstrated in this paper for Software defined networking (SDN) routers and controllers. We propose a centralized IoT architecture with a cloud structure using SDN as networking infrastructure, where storage entities (i.e. cloud servers) are associated with application entities. A small ‘hospital’ example is shown for illustration. Procedures for network reconfigurations are discussed. We also demonstrate the method for the normal case where different partial orders coexist among a set of entities. The method proposed does not impose an overhead on the normal functioning of an SDN network, since it requires calculations only when the network must be reconfigured, because of administrative intervention or policies.

: Digital communication is becoming an integral component of contemporary society and the use of mobile devices is increasing faster than ever. The function of message communication is becoming more popular as for mobile phone users, a short messaging service (SMS) requires an extremely high level of security. Any SMS that enters a person's smartphone must be considered a private affair, with privacy and security safeguarded. This research study found a novel Deoxyribonucleic acid (DNA) Cryptography method that uses a dynamic DNA sequence table to increase security. Cryptography is a popular approach for ensuring the confidentiality of a message. This paper proposed an Efficient Cryptographic Scheme for SMS (ECSS) algorithm to protect texts received from an Android smartphone. ECSS-based encryption has been identified as a novel method of securing information in the form of DNA molecules that employ DNA strands to conceal the information. This Paper provides the DNA cryptography approach for encrypting and decrypting plain texts in this work. The primary goal of ECSS cryptography is to guarantee secrecy when people transfer data across a network. This paper examines DNA Crypto, the distinction between classical cryptography and DNA Data encryption. An Android-based app is created to perform the proposed technique and the results show that the algorithms produce a consistent encryption result in which the length of the message before encryption is always the same as the length of the message after decryption. The suggested security solution has the potential to greatly improve the security of mobile communication. This Paper also provides experimental results for our chat application performance, such as received text message correctness, cipher text, average encryption time and average decryption time. According to the findings/results of this study, the speed of average encryption and decryption of the description is roughly 0.011 and 0.037 sec and prevents plaintext and dictionary attacks.

The rapid evolution of information and communication technologies, notably the Internet, has yielded substantial benefits while posing challenges to information system security. With an increasing frequency of cyber threats—from unauthorized access to data breaches—the digital landscape's vulnerability is evident. Addressing the financial impact of cybercrime, this study delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cybersecurity. Analyzing advancements and outcomes, the research explores practical techniques for anomaly detection, threat prediction, and automated response. By investigating prior research and real-world implementations, the study provides valuable insights into the potential of AI/ML, uncovering current trends, challenges, and prospects in enhancing cybersecurity tactics amid a dynamically changing threat landscape.

In today's digital landscape, cybersecurity has become a priority, with attacks becoming increasingly sophisticated. Traditional security approaches are no longer enough, requiring a more dynamic and advanced response. In this context, integrating Artificial Intelligence (AI) and Machine Learning (ML) appears key to addressing this growing threat. However, despite their high effectiveness, there is a developed problem: the integration of various data sources and technologies for comprehensive protection. This article presents an in-depth review of integrating Artificial Intelligence and Machine Learning in cybersecurity, focusing particularly on Open Extended Detection and Response (Open XDR) technology. The literature review in this article was meticulously conducted with specific criteria in mind. It focused on sourcing peer-reviewed journals, authoritative cybersecurity publications, and recent conferences that primarily concentrate on the advancements in AI and ML within the cybersecurity domain, particularly from 2018 to 2023. This timeframe was chosen to ensure the inclusion of the most current and relevant developments in Open XDR technology, Intrusion Detection Systems, Endpoint Detection and Response, and Security Information and Event Management systems. Additionally, the review gave special attention to studies and reports highlighting practical implementations and real-world applications of these technologies, thereby ensuring a comprehensive understanding of their impact and effectiveness in enhancing cybersecurity resilience. The methodology used is a detailed literature review, examining how various cybersecurity components interact and function. These components include Intrusion Detection Systems (IDS), which monitor networks for malicious activities; Endpoint Detection and Response (EDR), which focuses on detecting and investigating security incidents on endpoints; and Security Information and Event Management (SIEM), systems that provide real-time analysis of security alerts. The review also considers the role of Active Directory, a directory service for Windows domain networks, and the process of log forwarding, where log files are transmitted to a central server for analysis, in the context of AI and ML. The paper delves into the development of AI and ML, underscoring their roles in cybersecurity for advanced data processing, pattern recognition, and predicting threats. It explores both supervised (where the model is trained on labeled data) and unsupervised learning (where the model learns from unlabeled data) in ML, and how these techniques bolster cybersecurity measures. The article highlights the significance of Open XDR as a critical innovation that integrates data from multiple sources for comprehensive security analysis. Further, the review discusses how the integration of AI and ML into various cybersecurity tools, such as IDS, EDR, and SIEM, augments capabilities in threat detection and response. It addresses the challenges and opportunities that AI and ML present in the cybersecurity domain, focusing on ethical issues, data privacy concerns, and the necessity for ongoing professional development in this rapidly advancing field. The paper concludes by affirming the effectiveness of merging AI and ML with these cybersecurity tools within the Open XDR framework.

Ensuring the security of vertical applications in fifth-generation (5G) mobile communication systems and previous generations is crucial. These systems must prioritize maintaining the confidentiality, integrity, and availability of services and data. Examples of vertical applications include smart cities, smart transportation, public services, Industry 4.0, smart grids, smart health, and smart agriculture. Each vertical application has specific security requirements and faces unique threats within the mobile network environment. Thus, it is essential to implement comprehensive and robust security measures. This approach helps minimize the attack surface and effectively manage risks. This survey thoroughly examines mobile networks and their security challenges in vertical applications, shedding light on associated threats and potential solutions. Our study considers the interplay between security considerations in 5G, legacy networks, and vertical applications. We emphasize the challenges, opportunities, and promising directions for future research in this field and the importance of securing vertical applications in the evolving landscape of mobile technology.

WBAN is a blessing to mankind because it can monitor people's health and activities regardless of place or time. It brings feasibility in patients’ treatment. The main problems with WBAN, however, are related to the security and privacy of health data. To prevent data misuse, health information should be safeguarded. On the other hand, the medical professional needs to receive the patient's health information promptly. As security of health data impact human life greatly so secure an efficient method is required. Efficient cryptographic technique for data protection of wireless body area network is proposed. This research aims to provide data protection in WBAN to increase data security and efficient transmission. The suggested approach offers effective security with quick key generation, quick encryption, and secure data transmission across the network. The proposed methodology is considered to be secure and will consume less time. The proposed method further uses a less complex encryption algorithm. The less complex the approach is the higher performance it will provide in wireless network. The proposed methodology will prevent many attacks, which makes the proposed approach highly suitable for wireless body area network’s security.

: Cybersecurity has become a critical area in the digital field in recent years. The expansion of networks has revolutionised the way network structures are organised and managed. However, with increased connectivity and the growing complexity of modern networks, the threat of cyber-attacks has become more intense. As technology continues to advance, it brings both opportunities and challenges. One of the major challenges is the need to secure networks and sensitive data from various malicious activities. Traditional networks have evolved to include Software Defined Network (SDN), which offers a more flexible and programmable framework. Researchers should focus on detecting attacks in SDN because SDN networks are becoming more popular and attractive targets for clients due to their programmability and dynamic nature. The centralised controller, known as the backbone of an SDN, becomes a single point of failure and a potential target for attackers if not properly secured. Researchers need to emphasise the detection of attacks in SDN in order to mitigate these risks. By understanding the potential vulnerabilities and attack methods specific to SDN, researchers can develop effective detection approaches and propose countermeasures. This methodology helps protect the network from potential threats and minimises the impact of successful attacks. Therefore, this survey paper provides a review of Intrusion Detection Systems (IDSs) in Software-Defined Networks (SDN) to provide a thorough understanding of SDN security issues.

In contemporary times, the landscape of malware analysis has advanced into an era of sophisticated threat detection. Today's malware sandboxes not only conduct rudimentary analyses but have evolved to incorporate cutting-edge artificial intelligence and machine learning capabilities. These advancements empower them to discern subtle anomalies and recognize emerging threats with a heightened level of accuracy. Moreover, malware sandboxes have adeptly adapted to counteract evasion tactics, creating a more realistic and challenging environment for malicious entities attempting to detect and evade analysis. This paper delves into the maturation of malware sandbox technology, tracing its progression from basic analysis to the intricate realm of advanced threat hunting. At the core of this evolution is the instrumental role played by malware sandboxes in providing a secure and dynamic environment for the in-depth examination of malicious code, contributing significantly to the ongoing battle against evolving cyber threats. In addressing the ongoing challenges of evasive malware detection, the focus lies on advancing detection mechanisms, leveraging machine learning models, and evolving malware sandboxes to create adaptive environments. Future efforts should prioritize the creation of comprehensive datasets, distinguish between legitimate and malicious evasion techniques, enhance detection of unknown tactics, optimize execution environments, and enable adaptability to zero-day malware through efficient learning mechanisms, thereby fortifying cybersecurity defences against emerging threats.

The ability of deep learning to process vast data and uncover concealed malicious patterns has spurred the adoption of deep learning methods within the cybersecurity domain. Nonetheless, a notable hurdle confronting cybersecurity researchers today is the acquisition of a sufficiently large dataset to train deep learning models effectively. Privacy and security concerns associated with using real-world organization data have made cybersecurity researchers seek alternative strategies, notably focusing on generating synthetic data. Generative Adversarial Networks (GANs) have emerged as a prominent solution, lauded for their capacity to generate synthetic data spanning diverse domains. Despite their widespread use, the efficacy of GANs in generating realistic cyber attack data remains a subject requiring thorough investigation. Moreover, the proficiency of deep learning models trained on such synthetic data to accurately discern real-world attacks and anomalies poses an additional challenge that demands exploration. This paper delves into essential aspects of generative learning, scrutinizing their data generation capabilities, and conducts a comprehensive review to address the above questions. Through this exploration, we aim to shed light on the potential of synthetic data in fortifying deep learning models for robust cybersecurity applications.

Security in the Internet of Things (IoT) is a critical concern due to the growing number of connected devices and the limited resources to implement robust security mechanisms in most of them. Adopting standard assessment frameworks, such as CVSS (Common Vulnerability Score System), allows for systematic vulnerability assessment. At the same time, CVE (Common Vulnerabilities and Exposures) records provide unique identifiers for known security issues, making it easier to share information. However, comparing security solutions is still challenging due to the diverse nature of IoT devices and the ever-evolving threat landscape, requiring continual innovation and collaboration between stakeholders. Furthermore, there still needs to be a simple way to compare technologies and solutions, taking into account the security provided by them. In this sense, in this article, we propose a tool for calculating a security score based on the CVSS index of each existing vulnerability in a given technology. We explain how the tool performs the calculation, discuss some challenges, and propose improvements for future work.

This paper proposes to combat active eavesdropping by intelligent reflecting surface (IRS) backscatter techniques. To be specific, the source (Alice) sends the confidential information to the intended user (Bob), while the eavesdropper (Willie) transmits jamming signal to interrupt the transmission for more data interception. To enhance the secrecy, an IRS is deployed and connected with Alice through fiber to transform the jamming signal into the confidential signal by employing backscatter techniques. Based on the considered model, an optimization problem is formulated to maximize signal-to-interference-plus-noise ratio (SINR) at Bob under the constraints of the transmit power at Alice, the reflection vector at the IRS and the allowable maximum SINR at Willie. To address the optimization problem, an alternate optimization algorithm is developed. The simulation results verify the achievable secrecy gain of the proposed scheme.

Lime application with other complementary sustainable management practices increases crop yield, but liming is modestly applied in Serbia. This study investigated the influence of liming (1000 kg/ha) combined with the common application of mineral fertilizers on maize yield and the chemical properties of Pseudogley soil. The experiment was conducted near Kraljevo, Western Serbia, on maize hybrid ZP 606, sown in a two-year monoculture. The experiment had three treatments: fertilizer, fertilizer+lime, and control treatment. The soil is acid, poor in humus, and contains an increased content of mobile aluminum. There was a significant increase in yield under the fertilizer and lime+fertilizer treatments, compared with the control. The yield of maize in the limed treatment was 4.4–9% higher than in the fertilizer treatment. The positive effects of liming on soil are related to an increase in pH, base saturation, and available phosphorus and a decrease in available aluminum. In the fertilizer treatment, there was a small decrease in pH and base saturation, whereas the amount of aluminum remained high, indicating that further application of fertilizers without lime can increase the aluminum content and foster its toxicity. The long-term sustainability of maize production in Serbia should include liming as a regular management practice on Pseudogley soil, with the utilization of smaller doses of lime because of the potential CO2 effects. To improve soil health, food, and environmental security, and incorporate new crops, developing a framework promoting liming as a sustainable management practice is of high importance.

As artificial intelligence continues to advance, researchers are increasingly using machine learning algorithms to study the factors that make people more susceptible to phishing scams. Most studies in this area have taken one of two approaches: either they explore statistical associations between various factors and susceptibility, or they use complex models such as deep neural networks to predict phishing behavior. However, these approaches have limitations in terms of providing practical insights for individuals to avoid future phishing attacks and delivering personalized explanations regarding their susceptibility to phishing. In this paper, we propose a machine learning approach that leverages explainable artificial intelligence techniques to examine the influence of human and demographic factors on susceptibility to phishing attacks. Our analysis reveals that psychological factors such as impulsivity and conscientiousness, as well as appropriate online security habits, significantly affect an individual's susceptibility to phishing attacks. Furthermore, our individualized case-by-case approach offers personalized recommendations on mitigating the risk of falling prey to phishing exploits, considering the specific circumstances of each individual.

The importance of the national security of supply has been emphasized in recent years due to the COVID-19 pandemic and changes in operating environments. Thanks to autonomy and resiliency in fuel procurement, organizations with decentralized energy plants may be able to quickly adapt to the changing needs of society. The main aim of this study was to increase the sustainability of a Finnish CHP plant's wood procurement by determining the company's profitable wood procurement region while keeping the forests renewable. The income received from the sale of energy and the costs incurred from the procurement of wood were collected from the ERP system as input data for the developed DSS. The environmental sustainability of wood procurement was assessed by considering the additional costs caused through the EEAP mechanism if the carbon sink of the forest worsened due to wood procurement in the area. The supply chains were compared so that the CHP plant managers could adopt the best energy wood value chains. The results showed that updating the procurement strategy would be necessary. The CHP plant managers should purchase energy wood from a larger procurement area than the current one and focus on roadside purchases of wood. The Techno-Economic Wood Payment Ability Model was a useful part of the DSS to guide wood buyers toward sustainable wood procurement by increasing the share of renewable forests.

One of the fastest growing customer service delivery platforms is online banking. However, the increasing number of attacks on online banking platforms is driving the banks to constantly review their security. This study developed an enhanced Multi-factor authentication scheme with a track and trace capability to reduce these threats. Five modalities of authentication were incorporated in the proposed scheme: The traditional username, password, personal identification number (PIN) and one-time PIN (OTP), augmented with fingerprints or facial scans, registered smart devices, and a time locked user's location. One of the scheme's most impressive accomplishments is its capacity to seamlessly detect undesired activities and send alerts in the form of secretly obtained photographs and location triangulation.

The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things – IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where IoT, edge and cloud converge to form a computing continuum, is also known as IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems architectural approach, including: i) design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; ii) design of efficient task offloading algorithms aimed at optimizing services execution; iii) support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; iv) scalability, flexibility and reliability guarantees to face the expected mobility for IoT systems, and; v) design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges become even more significant towards the new era of sixth generation (6G) networks, which will be based on the integration of various cutting edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented, from an IEC continuum perspective.

In today's world, wherein more than 80% of world trade is carried out by maritime routes, the safety and security of the seas where this trade takes place is of vast importance for nations and the in-ternational community. For this reason, ensuring the sustainable safety and security of the seas has become an integral part of the mission of all maritime-related entities. The utilization of big data extracted from the seas and maritime activities into meaningful information with artificial intelli-gence applications and developing applications that can be used in maritime surveillance will be of great importance for augmenting maritime safety and security. In this article, data sources which can be used by a maritime surveillance system based on big data and artificial intelligence tech-nologies and which can be established around sensitive sea areas and critical coastal facilities, are identified and a model proposal using this maritime big data is put forward.

Consumer IoT (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem like a smart home. Due to security and privacy concerns, Blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their Machine Learning (ML) model using end-user data. FL uses privacy-preserving ML techniques to forecast customers' needs and consumption habits, and blockchain replaces the centralised aggregator to safeguard the ecosystem. However, Blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research contributes a novel architecture emphasising Gateway Peer (GWP) in blockchain network to resolve scalability, ledger optimisation and secure model transmission issues. In the architecture we replace the centralised aggregator by the blockchain network, while GWP restricts the number of local transactions to execute in BCN. Considering the security and privacy of FL processes, we have added differential privacy and advanced normalisation techniques to ML processes. The approaches strengthen end-users' cyber security and encourage the adoption of technological innovation standards by service providers. The proposed approach has been tested extensively using a well-respected Stanford Cars dataset. We experimentally demonstrate that the proposed architecture makes the network scalable and optimises the ledger significantly. In addition, the normalisation technique outperforms batch normalisation when features are under DP protection.

Rice provides ~20% of human dietary energy and, for many people, a similar share of their protein. Rice cultivation, however, produces significant greenhouse gas (GHG) emissions, comparable to those from the aviation sector. The main GHG from rice production is methane, mostly a result of conventional rice cultivation (CRC) keeping rice fields continuously flooded during the crop cycle. There is extensive evidence that alternate wetting and drying (AWD) of rice fields substantially reduces methane emissions. AWD is one component of the System of Rice Intensification (SRI), an agroecological approach to the management of plants, water, soil and nutrients practiced by millions of farmers in both lowland irrigated rice and upland rainfed cultivation. Thirteen countries have included SRI in their Nationally Determined Contributions to GHG reduction or climate change mitigation. This article reviews 16 field studies of the net reduction in GHG emissions from the adoption of AWD, eight from the adoption of SRI, and two that compared SRI and AWD. Where available, the review includes data on yield and therefore on carbon dioxide-equivalent GHG emissions per kilogram of rice produced. The evidence indicates that AWD and SRI offer a similar and substantial reduction (~35–41%) in GHG emissions per hectare compared with conventional rice cultivation. However, SRI offers ~66% greater yield than CRC, and therefore greater reduction in emissions per kilogram of rice, ~54% more than AWD. The limited data directly comparing SRI and AWD support this finding. SRI also appears to have greater potential to sequester carbon in the soil. SRI lowers rice farmers’ costs of production, adds to their income and can make climate-friendly methods more attractive. Both AWD and SRI are greatly preferable to current conventional practices, but SRI offers opportunities to contribute to food security while directly addressing the drivers of climate change.

The Global Hunger Index (GHI) is a vital tool for assessing and understanding the state of hunger and food security across the world. In this review article, we examine the GHI scores of various countries in the years 2000, 2008, 2015, and 2023. We aim to explore the changes in GHI scores over this period, identify trends, and discuss the implications for global food security. The GHI provides a comprehensive snapshot of the world's progress in reducing hunger, and this analysis sheds light on the successes and challenges faced by different countries.

The Cantareira Water Producer System (CWPS), which supplies water to the most populous Brazilian region, Metropolitan Region of São Paulo (MRSP), faces significant challenges due to climate change, threatening its water security. Climate change affects the hydrological cycle affecting water availability. The CWPS is vulnerable to periods of scarcity and instability due to increased climatic variability and increasing water consumption. Current water resource management systems do not consider the impacts of climate change, making it difficult to ensure water security in the future. This study analyzes the impact of climate change on the availability of CWPS using hydrological modeling approach with forecast precipitation data by CMIP6 and assessment of existing systems. The finds will provide a comprehensive understanding of the challenges faced by CWPS in the context of climate change, as well as support strategies and adaptive measures to ensure water security for MRSP.

Widely used estimates of food-insecure populations are likely to be biased upwards, lacking adjustment for global increases in sedentary behavior in recent decades. We first construct a household model to account for sedentary choices during leisure and work time decisions. The model rationalizes increasing sedentary behavior from the household by accounting for increasing returns to cognitive human capital vs physical capital, alongside increased productivity of more sedentary activities both at work and at home. The household model then informs an empirical model that makes use of our unique international pseudo-panel data on sitting time, which serves as a proxy for sedentarism. We econometrically estimate a transfer function linking sedentarism to widely available covariates, which then are used to make out-of-sample predictions and can be applied to a large set of countries. The estimated sedentary time can be used to adjust the physical activity level reflected in the minimum dietary energy requirement used to determine a cutoff for food insecurity.

This paper aims to analyze the intellectual structure and research fronts in application information security in smart cities to identify research boundaries, trends, and new opportunities in the area. It applies bibliometric analyses to identify the main authors and their influences on the information security and smart city area. Moreover, this analysis focuses on journals indexed in Scopus databases. The results indicate that there is an opportunity for further advances in the adoption of information security policies in government institutions. Moreover, the production indicators presented herein are useful for the planning and implementation of information security policies, and the knowledge of the scientific community about smart cities. The bibliometric analysis provides support for the visualization of the leading research technical collaboration networks among authors, co-authors, countries, and research areas. The methodology offers a broader view of the application information security in smarty city areas and makes it possible to assist new research that may contribute to further advances. The smart city topic has been receiving much attention in recent years, but to the best of our knowledge, there is no research on reporting new possibilities for advances. Therefore, this article may contribute to an emerging body of literature that explores the nature of application information security and smart city research productivity to assist researchers in better understanding the current emerging of the area.

To proactively manage information security, enterprises often employ information security risk assessment techniques. Asset value - which is used to calculate the financial impact of possible threats - is one of the key parameters of information security risk. However, assets in a system are rarely independent, and their values are typically interdependent. Asset owners and IT teams may hold different views as regards these values, and there is thus the need to reduce subjectivity in a qualitative risk assessment. The research entails the development of a conceptual framework derived from the literature to minimize subjectivity, and the design of a system based on those concepts. The study uses the Unified Modeling Language as a design tool and puts forward an object-oriented model for defining asset values, in which the relationships between assets, vulnerabilities and related threats are identified. A “segregation of duties” approach is integrated into the risk management system to mitigate against subjectivity and better determine asset values. Survey responses from 16 practitioners working in the private and public sectors confirm the validity of the approach, but suggest it may be more workable in larger organisations where resources allow dedicated risk professionals to operate.

in our increasingly interconnected world, sensor networks are critical in gathering and sending data for various applications, from environmental monitoring and industrial automation to healthcare and smart cities. However, as sensor networks expand in importance, so does the need to solve the multidimensional concerns of security, privacy, and forensics. This article explores the complex world of sensor network security, the delicate balance between data privacy and utility, and the emerging area of sensor network forensics. This article focuses on risk assessment of a network.

Rapid urbanization has led to many ecological problems such as landscape fragmentation, decreased landscape connectivity, and decreased biodiversity. Building an ecological security pattern is an important way to ensure ecological security and maintain sustainable urban development. Harbin is an important large-scale city in northeastern China, and the urban central district of Harbin is used as the study area. Ecological sensitivity assessment, MSPA, gravity model, landscape connectivity assessment and MCR model were used to extract ecological source areas, ecological corridors and ecological nodes in the study area to construct and optimize the ecological security pattern. Research shows that: A total of 23 ecological source areas were identified, mainly located along the Songhua River in the central part of the study area and in the mountain forest areas within the A-cheng District in the southeastern part of the study area. 48 ecological corridors were extracted, mainly in Daowai District, A-cheng District and the eastern part of Xiangfang District. There are 8 important ecological corridors and 10 important connecting corridors. 61 ecological nodes of the first level and 65 ecological nodes of the second level were extracted. High, higher, medium, lower and low security areas were classified based on the minimum cumulative resistance surface, with the highest proportion of high ecological security areas accounting for 45.75% of the study area. 65 ecological breakpoints were extracted and restoration measures were proposed. Finally, The structure of the ecological security pattern optimization of "two axes, two belts and four areas" is proposed, and corresponding ecological management measures are put forward. The experimental system of this study can provide new ideas for the construction of ecological security patterns in other regions and provide scientific support for the ecological sustainable development of similar urban areas.

China is striving to leverage the power of science and technology to green its agriculture while increasing grain productivity. In this context, this study presents the green growth rate of agriculture from the DEA-based Green Total Factor Productivity (GTFP) indicator, together with the growth rate of grain yields, and applies it to the case of Jiangsu, a major grain-producing province with a well-developed economy. It is found that Jiangsu's agriculture has generally performed well in the implementation of the two major national strategies of green development and grain security, especially in the northern Jiangsu, which is a major grain-producing area. In contrast, the phased fluctuations of green growth in agriculture in Southern Jiangsu are more pronounced, with even negative green growth occurring during the green agriculture movement. Much of the volatility in agricultural green growth at the provincial, subregional, municipal level comes from the effects of the movement of the green technology frontier led by best practitioners. Accordingly, the possibility of improving the weak state of the catch-up effect on green growth is explored from the perspective of the Chinese government-led agricultural science and innovation system; it also traces the green agricultural initiatives in the main grain-marketing areas that have failed to deliver the expected green growth, and recommends a review of such policies and refinement of the GTFP Index tool for assessing sustainable green growth.

Simple Network Management Protocol (SNMP) is extensively utilized for monitoring and managing computer networks. This paper synthesizes and reviews recent research on harnessing SNMP data for developing effective network security solutions driven by artificial intelligence and machine learning techniques. The comprehensive device-level visibility and timely data provided by SNMP offers significant potential to enable high-performance intrusion detection systems, security analytics, and real-time monitoring capabilities to protect against evolving cyber threats. However, inherent vulnerabilities in versions like SNMP v1/v2c persist, especially due to default community strings allowing uncontrolled access. A number of techniques like newer SNMPv3 standard have been proposed to improve security through encryption, authentication, and fine-grained access control. Additionally, general best practices around firewalling, traffic monitoring, and compliance audits are recommended for robust SNMP deployment. The literature reviewed demonstrates SNMP’s capabilities in enabling anomaly detection with over 99% accuracy by applying supervised learning methods like random forests and neural networks to SNMP Management Information Base (MIB) data. The network-centric visibility offered by SNMP's extensive statistics on traffic configurations and behaviors provides an invaluable advantage over packetlevel data for developing intelligent detection models. Beyond intrusion detection systems, SNMP data has also shown significant utility for security analytics like attack pattern discovery, bandwidth modeling, and large-scale threat intelligence by aggregating data from distributed network devices. Further research opportunities exist in areas like feature engineering, model optimization, and continued evaluation of evolving attacks patterns. In conclusion, with appropriate secure configurations, SNMP provides the comprehensive and real-time data needed by modern artificial intelligence security applications in a scalable manner due to its device-centric focus, standardized schema, and ubiquitous deployment

This article explores the intersection of innovative leadership and climate change, aiming to provide outstanding contributions to the existing body of knowledge in this field. The article identifies the critical role of innovative leadership in driving sustainable solutions and strategies for addressing climate change. It examines the defining characteristics and behaviors of innovative leaders and explores how their transformative leadership approaches can create a positive impact on climate change mitigation and adaptation efforts. The article also highlights the importance of fostering innovation within organizations and societies to tackle the complex challenges posed by climate change. Through a comprehensive review of relevant literature and case studies, this article presents novel insights, theoretical frameworks, and practical implications for policymakers, organizations, and individuals involved in climate change leadership.

Image encryption based on chaotic maps is an important way to ensure the secure communication of digital multimedia on the Internet. To improve the encryption performance and security of image encryption systems, a new image encryption algorithm is proposed by employing a compound chaotic map and random cyclic shift. Firstly, an improved Hybrid chaotic system is designed by coupling Logistic, ICMIC, Tent, and Chebyshev maps (HLITC). Comparison tests with previous chaotic maps in terms of chaotic trajectory, Lyapunov exponent, and Kolmogorov entropy illustrate the new hybrid chaotic map has better chaotic performance. Then the proposed HLITC chaotic system is used to develop a new chaotic image encryption scheme with the double cyclic shift. The improved HLITC chaotic system is performed to generate key sequences used in the image scrambling and diffusion stage. The spiral transformation controlled by chaotic sequence is used to scramble the pixels of the plaintext image, while the XOR operation based on a chaotic map is used for pixel diffusion. Extensive experiments of statistical analysis, key sensitivity, and keyspace analysis are conducted. Experimental results show the proposed encryption scheme has good robustness against brute force attacks, statistical attacks, and differential attacks, and is more effective than many existing chaotic image encryption algorithms.

Limnospira maxima is widely cultivated due to its abundance of proteins, carbohydrates, vitamins, minerals, essential fatty acids, and phycobiliproteins. However, limited information, high production costs, and nitrogen source limitations have hindered the optimal utilization of this cyanobacteria. The main goal of this study was to address these limitations by identifying ideal cultivation parameters to maximize yields, reduce costs, and contribute to global food security. It was demonstrated that the white and yellow light spectra are the most suitable for cultivating L. maxima, and the use of KNO3 as a nitrogen source proved to be the most cost-effective on a large scale. Nitrogen deficiency stimulates the production of dry biomass but also affects the concentration of key pigments such as chlorophyll and phycocyanin. A notable characteristic of L. maxima is its adaptability and survival capability in various environmental conditions, including nitrogen-deficient environments. This resilience is achieved through the degradation of phycobilisomes and the storage of glycogen. By identifying the optimal growth conditions and understanding the physiological responses of L. maxima, cultivation practices can be improved to be more efficient and sustainable.

This study provides an in-depth exploration and analysis of the complex aspects of Public Key Cryptography, focusing on the vulnerabilities, potential attack vectors, and the consequential impacts of key compromises. The discussion extends beyond the conventional technical paradigms, offering a holistic perspective that intertwines theoretical insights with practical implications, thereby contributing to a more comprehensive understanding of cryptographic security. The paper delves into the intricacies of brute-force attacks, elucidating the significance of key sizes, encryption algorithms, and the inherent resilience of cryptographic systems against such attacks. It further examines the ramifications of key compromises in Public Key Cryptography, highlighting the repercussions on confidentiality, integrity, trust, and legal and reputational standing. The study underscores the importance of maintaining the security and integrity of cryptographic keys and provides practical recommendations to mitigate the risks associated with key compromises. These include enhanced awareness and education, stringent protection of sensitive information, proactive risk management, strict compliance with regulations and standards, and the promotion of a security-centric culture. The insights gleaned from this study are pivotal in informing future research, policy development, cryptographic implementations, and the cultivation of secure practices, fostering a more secure, resilient, and ethical digital environment in the face of an evolving threat landscape. The contributions of this study are instrumental in advancing the field of cryptography and enhancing the overall understanding of the challenges and considerations inherent in implementing robust cryptographic practices.

Abstract: The Internet of Things (IoT) has witnessed rapid and widespread adoption across various domains, including transportation, healthcare, education, agriculture, urban planning, smart homes, and more. Despite its transformative potential, this pervasive deployment of IoT devices has introduced new challenges, particularly concerning security and privacy threats such as unauthorized data access and device breaches. The excessive usage of these technological devices, coupled with the absence of robust security and privacy systems for user data, calls for a comprehensive approach to address these issues. In this study, we propose a novel framework designed to analyze, audit, test, and detect potential vulnerabilities within IoT environments and applications. The central components of the proposed framework include a machine learning algorithm for data classification and attack detection, along with the integration of Blockchain technology to enhance security measures. Specifically, the framework performs an in-depth analysis of user data to identify potential security or privacy vulnerabilities. Additionally, it conducts rigorous testing of smart services and automated data-collecting devices. To evaluate the effectiveness of our classification algorithm, we conducted a comprehensive implementation on a real-world IoT dataset. The results showcased the efficiency and accuracy of our approach in detecting and mitigating potential threats. Furthermore, based on our research findings, we provide valuable recommendations for enhancing security and privacy in IoT ecosystems. We also highlight emerging trends in the security and privacy domains, which can serve as valuable insights for researchers and practitioners. In conclusion, our proposed framework offers a robust and proactive approach to address the security and privacy challenges, such as unauthorized data access and device breaches posed by the widespread adoption of IoT devices. By combining machine learning algorithms and Blockchain technology, we contribute to safeguarding user data and fostering a secure environment for IoT applications. This study lays the groundwork for further advancements in the realm of IoT security and privacy, ensuring a safer and more resilient IoT landscape for the future.

This study aims to investigate how the adoption of an online to offline (O2O) transition strategy impacts the sales volume of Kampoeng Merdeka Clothing. The O2O strategy includes various components, such as establishing O2O platforms, delivering personalized services, ensuring secure transactions, and engaging in offline business activities. The research employs a quantitative approach, focusing on consumers who have made clothing purchases from Kampoeng Merdeka Clothing in Macassar. The sample size comprises 83 respondents, deliberately selected using the Slovin approach. The data analysis and methodology involve the application of Partial Least Squares (PLS) structural equation modeling to assess both the external and internal models. The findings of this study emphasize the significant and positive influence of the variables "Build O2O Network Platform," "Provide Personalized Experience," "Safeguard Transactions Security," and "Offline Business" on driving the sales growth of Kampoeng Merdeka Clothing. These results indicate that the O2O transition strategy has a substantial impact on the brand's sales performance, with the establishment of O2O platforms and personalized services playing crucial roles. Additionally, ensuring secure transactions and engaging in offline business activities further contribute to the overall success of Kampoeng Merdeka Clothing in the market. This research sheds light on the effectiveness of the O2O approach in boosting sales for clothing brands and underscores the importance of integrating online and offline elements to maximize business outcomes.

Smart grid capabilities have grown significantly in recent years. The smart grid provides advanced real-time handling of faults, advanced automatic control for efficient electricity transmission, monitoring and collection of the electrical system's capacity, and communication for information sharing. Unfortunately, its exposure to public networks makes it increasingly vulnerable to privacy breaches, vulnerabilities, and cyber-attacks. Cyber security threats and vulnerabilities in smart grid networks have become a primary concern that needs to be addressed before deploying a smart grid. Furthermore, the wide range of protocols increases the attack surface of a smart grid. This study focuses on the vulnerability of Modbus, which is regarded as one of the most prevalent protocols in smart grid communication networks. This paper presents preliminary findings of analyzing cyber-attacks against the Modbus protocol using a virtual testbed to investigate its effects on the smart grid network protocol. The concept incorporates an emulated Modbus/TCP network environment built from open-source software components that imitate fundamental industrial control features of the smart grid. Finally, we analyze the cycle of a cyber-attack leading through Reconnaissance to a DoS attack on the Modbus/TCP protocol and propose improvements to the test bed for protocol attack detection and mitigation.

Evidence shows how food system activities from production to consumption underpin food security. However, studies exploring climate impacts on food security in northern Ghana have overly focused on production systems, neglecting post-production activities that loom large in food security. This paper addresses the research need to comprehensively analyze how climate change affects post-production activities and exacerbates food insecurity risks in northern Ghana. The study collects and analyzes data on climate hazards, impacts, and food system vulnerabilities using questionnaires and participatory engagement with farming households in northern Ghana. Results show that climate-induced food insecurity risks in northern Ghana are not just products of persistent climate impacts on food production in the region. Instead, risks are inextricably connected to the vulnerability contexts within which food is harvested, processed, stored, and marketed. Specifically, the results reveal that climate hazard events such as floods, extreme temperatures, and droughts damage stored grain, disrupt food supply to the market, and cause seasonal volatilities in food prices. However, these impacts are not solely externally generated circumstances. The food system is highly vulnerable; most households lack access to threshing and grinding machines, warehouse storage, post-harvest management information, and transportation services. These underlying characteristics of the post-food-production system of northern Ghana, which is ultimately quite remote from climate change, exacerbate household-level food insecurity risks.

Good health and well-being is one of the essential SDGs that ensure healthy lives and promote well-being for all ages and further entails providing substantial medical services to the public at low cost and with minimal adverse effects on the environment. Information and communication technologies (ICTs) have taken on an increasingly important function as significant facilitators of healthcare reform, with the goals of enhancing access to health services, the quality of treatment provided, and the overall productivity of the healthcare system. However, the integration of ever-increasing ICT technologies into the healthcare systems, also referred to as digital transformation, is not a straightforward process, but it comes with different types of challenges from integration level to application design level and security level. Although several studies have been proposed to address the integration of ICT technologies into healthcare systems, there is still a need for a comprehensive research study on the integration and design challenges, security and privacy challenges, application areas, and possible positive and negative impacts. Therefore, this paper contributes as the research literature study covering an important SDG, "Good health and well-being," and its digital transformation, along with summarising our research findings in a detailed and taxonomical way. To start with, firstly, we present a detailed comparison of existing studies on healthcare and well-being, mainly focusing on integrating ICT technologies in healthcare in terms of sustainable aspects, security and privacy challenges, design and integration challenges, E-health-related applications, and future directions. We also present an overview and the need for digital transformation in healthcare, discuss its significant components, highlight E-health's importance and benefits, explore its integration and design challenges, and categorise the security and privacy challenges. Next, we present an in-depth discussion on the role of Blockchain technology as today's leading technology in E-health, discussing Blockchain technology and its characteristics, highlighting its benefits, and describing the possible types of Blockchain-based E-health use cases. Furthermore, we discuss the positive and negative impact of ICT integration along with identifying open issues and challenges of integrating ICT technologies into the healthcare systems and discuss future research directions, which provide the strength for researchers to address the issues in future solutions.

Egypt faces numerous challenges in achieving food and nutritional security, due largely to its unique geography, water stress, and population growth. However, nontraditional crops and pseudo cereals such as buckwheat, quinoa, sorghum, teff, and millet offer promising avenues for sustainable agricultural development and improved nutrition. These crops exhibit high tolerance to adverse environmental conditions such as soil salinity and drought and are rich in essential nutrients like protein, amino acids, and vitamins. They also present an economic advantage due to their short growing seasons and adaptability to marginal lands. Recent advancements in remote sensing and Geographic Information Systems (GIS) technology, along with crop simulation models, provide further opportunities for optimizing land and water resources for these nontraditional crops. Their exceptional nutritional values, including low glycemic indices and gluten-free properties, make them well-suited to address current health challenges, such as malnutrition and the increasing prevalence of non-communicable diseases. The study emphasizes the need for further research to investigate land suitability, water management, and economic viability for the cultivation and commercialization of these crops. By leveraging these opportunities, Egypt has the potential to not only close its food gap but also improve overall nutritional well-being, contributing to both food security and sustainable agriculture.

Static and dynamic analysis of website security risks is generally used for website security meas-urements. The website security measurements method recently proposed includes similarity hash-based website security risk analysis and machine learning-based website security risk anal-ysis. In this study, a method that can be performed through information disclosed on the Internet was proposed to measure the risk of a website. DNS information, IP information, and website history information are required to measure the risk of a website. In addition, global traffic rank-ing, malicious code distribution history, and HTTP access status can be checked in the website history information. In this study, information related to the security risk of websites in a total of 2,000 domains was collected and analyzed in 1,000 normal and malicious domains. Through the experiment results, 11 open information collection items were selected to measure the security risk of the website. This study presented the possibility of using public information collection to measure website security risk.

In recent years web applications and on-line business transactions have grown many folds. Consequently, also cyberattacks have increased and represent a serious threat to the pervasive digital services upon which our society relies. To mitigate cyberattacks, many countermeasures are deployed on computing nodes (e.g., anti-malware software) as well as on network devices to detect and possibly block malicious packets in transit; these monitoring devices broadly go under the name of firewalls. Firewalls are designed according to two main architectural approaches: software running on a standard or embedded computer, or purposedly designed hardware, e.g., ASICs. Software-based solutions have the advantage of high flexibility and can be ported on easily upgradable hardware. However, hardware implementation represents the only viable solution for high data rates. On the market, very fast devices of the latter kind are available, but their cost is typically very high, especially considering that their ultra-optimized design makes updating them very difficult, with the consequence of a rather short lifespan. As a more balanced alternative, we wanted to investigate the use of an FPGA architecture, , which is significantly easier to update than custom-built chips, and features low-latency and high-throughput characteristics concurrently, making it preferrable to other programmable systems based on GPUs or microcontrollers. In this paper a packet sniffer that has been designed on FPGA with a 1 Gbit/s data transfer rate is presented. The system is implemented on the FPGA development board KC705 by Xilinx, can analyze Ethernet frames, checking the frame fields against a set of rules defined by the user and calculates statistics of the received Ethernet frames over time. The designed packet sniffer has been successfully tested both with Ethernet frames ad hoc generated using a packets generator, and with real web traffic by connecting the packet sniffer to the internet.

The study aims to determine the trust and presence of police officers in schools in Serbia, as well as the perception of the principals and secretaries, teachers and staff, parents and students on how successful the certain police units dedicated to schools were in fulfilling their tasks. The ex–post analysis was conducted through PEST/SWAT analysis, mapping of the key actors and using batteries of online questionnaires. Besides interviews with the MOI representatives, there were conducted: Survey with personal interviewing, Computer–aided surveying and Desk analysis and content analysis. The survey was conducted in the period from September 2021 to June 2022. The research methods were implemented in 1140 schools in Serbia and 8,617 people were included in surveys: police officers (308); principals and secretaries (1085); the team for protection against discrimination (982); teachers and staff (2988); parents (938) and students (2316). The relationships between the covariates and perception were investigated using the t–test, one–way ANOVA, multivariate linear regression, and binary regression. The results showed that a project of school police officers was not fully recognized as one of the strategically important instruments for safe schools, trust is low but presence is high. Besides that, the results suggest that the entire public believe that a police is needed in schools and that it has a very positive effect on school safety. Regarding school safety policy, it is necessary to undertake three types of measures for sustainable development of trust and presence of police in school: regulatory, informa-tive-educational and institutional-organizational.

We investigate if anyone is being left behind in accessing safely managed drinking water (SM) in achieving the 2030 agenda. We use 23 countries grouped globally as critically insecure water (CIWC). The results indicate none of the CIWC had over 50% of the population accessing SM, N. America and Europe had highest percentages while Sub Saharan Africa had the least. People left behind are found in rural areas, in countries vulnerable to climate change because of poor governance, low-income, and women’s low education. Institutional quality, equity and equality should be improved in using resources critical to leaving no one behind.

Vehicle-to-Vehicle (V2V) communication plays a pivotal role in modern intelligent transportation systems, enabling seamless information exchange among vehicles to enhance road safety, traffic efficiency, and overall driving experience. However, the secure transmission of sensitive data between vehicles remains a critical concern due to potential security threats and vulnerabilities. This research paper focuses on investigating the security protocols employed in Vehicle-to-Vehicle communication systems. A comprehensive review and analysis of relevant literature and research papers is conducted to gather information on existing V2V communication security protocols and techniques. The analysis encompasses key areas, including authentication mechanisms, encryption algorithms, key management protocols, and intrusion detection systems specifically applicable to V2V communication networks. Within the context of real-world V2V environments, this study delves into the challenges and limitations associated with implementing these protocols. Moreover, to foster a deeper understanding, the paper investigates present communication protocols in the field of Internet of Things (IoT) that are tailored for V2V. Various parameters, such as band-width consumption, energy consumption, latency, and message size, are considered during the evaluation of these protocols to gauge their effectiveness. The research outcomes aim to provide a comprehensive understanding of the strengths and weaknesses of the current V2V communication security protocols. Furthermore, based on the findings, this paper will propose improvements and recommendations to enhance the security measures of the V2V communication protocol. Ultimately, this research will contribute to the development of more secure and reliable V2V communication systems, propelling the advancement of intelligent transportation technology.