This study focuses on land cover and land management changes in relation to food security and environmental services in a semi-arid area of East Nusa Tenggara (ENT) – Indonesia. The study was conducted in Central Sumba District of ENT Province. Classification and regression tree (CART) for land cover classification has been analyzed using machine learning techniques using Google Earth Engine. A survey with Focus Group Discussion (FGD) and followed with in-depth interviews were conducted for primary data collection involving a total of 871 respondents. The Socio-economic data analyzed statistics descriptively and non-parametric tests. The study showed that: 1). There has been a substantial land use change during the devo-lution era that has both positive and negative implications for food security and envi-ronmental services. 2). There has been population pressure in the fertile or agricultur-al land as the direct impact of the development of city infrastructures; and 3). Nation-al intervention through the Food Estate program has fostered and shaped land use change and land management in Central Sumba District. The study highlights the importance that the devolution spirit should help to well manage the limited ara-ble/agricultural land in dominated semi-arid to ensure food security and environment services.

Generation of unique identifiers extracted from the physical characteristics of the underlying hardware ensures the protection of electronic devices against counterfeiting and provides security to the data they store and process. This work describes the design of an efficient Physical Unclonable Function (PUF) based on the differences in the frequency of Ring Oscillators (ROs) with identical layout due to variations in technological processes involved in the manufacture of the integrated circuit. The logic resources available in the Xilinx Series-7 programmable devices are exploited in the design to make it more compact and achieve an optimal bit-per-area rate. On the other hand, the design parameters can also be adjusted to provide a high bit-per-time rate for a particular target device. The PUF has been encapsulated as a configurable Intellectual Property (IP) module, providing it with an AXI4-Lite interface to ease its incorporation into embedded systems in combination with soft- or hard-core implementations of general-purpose processors. The capability of the proposed RO-PUF to generate implementation-dependent identifiers has been extensively tested, using a series of metrics to evaluate its reliability and robustness for different configuration options. Finally, in order to demonstrate its utility to improve system security, the identifiers provided by RO-PUFs implemented on different devices have been used in a Helper Data Algorithm (HDA) to obfuscate and retrieve a secret key.

The Metabolic Food Waste [MFW (kg of food)], first time developed in 2016 as a new indicator by Serafini and Toti, indicates the amount of food consumed over the nutritional requirements and the impact of this overconsumption on the environment. It is necessary to identify the causes and to develop potential methods to prevent and reduce MFW, at the same time with increasing consumer awareness about unsustainable diets and changing diet habits toward more environmentally conscious consumption patterns. By analysing and corroborating external data available for food waste, nutritional requirements, environmental impact of food waste and consumer behaviour, we identified as primary causes for MFW the lack of nutritional education and little understanding of the nutritional requirements amongst all categories of consumers, no access to appropriate food resources or reduced availability of fresh produced food. We conclude that for the quantification of the negative impact of MFW on both the environment and human health, we need a decisive action to raise consumer awareness for healthy and sustainable diets, together with an uniform worldwide distribution of the nutritious food.

Plukenetia volubilis is an underutilized oilseed crop native to the Amazon basin, where it has been utilised by humans since Incan times. The large seeds contain approx. 45–50 % lipid, of which approx. 35.2–50.8 % is α-linolenic acid (C18:3 n-3, ω-3) and approx. 33.4–41.0 % is linoleic acid (C18:2 n-6, ω-6), the two essential fatty acids required by humans. The seeds also contain 22–30 % protein and have antioxidant properties. Due to its excellent nutritional composition and good agronomic properties, it has attracted increasing attention in recent years, and cultivation is expanding. When considering current global challenges, a reformation of our food systems is imperative in order to ensure food security, mitigation of climate change, and alleviation of malnutrition. For this purpose, underutilized crops may be essential tools, which can provide agricultural hardiness and reduced need for external inputs, climate resilience, diet diversification, and improved income opportunities for smallholders. Plukenetia volubilis is a promising up and coming crop in this regard and has considerable potential for further domestication; it has an exceptional oil composition, good sensory acceptability, is well suited for cultivation, and has numerous potential applications in, e.g. gastronomy, medicine, and cosmetics.

State legitimacy and effectiveness could be seen by the way to deliver welfare to citizens to mitigate social grievances, that could eventually lead to conflicts (Kivimäki, 2021). Social security systems in Maghreb countries are quite similar in their architecture and aims to provide social insurance to all the workers in the labor market. However, they suffer from the same main problem: the low rate of enrollment of workers. Many workers (employees and self-employed) work informally without any social security coverage. The issue of whether informal jobs are chosen voluntarily by workers or as a strategy of last resort is controversial. Many authors recognize that the informal sector is heterogeneous and it is made up of workers who voluntary choose it and others who are pushed inside because of entry barriers to the formal sector (Günther & Launov, 2012). Using the SAHWA survey and discrete choice models, this article confirms the heterogeneity of the informal labor market in three Maghreb countries: Algeria, Morocco, and Tunisia. Furthermore, this article highlights the profiles of workers who voluntarily choose informality, which is missing from previous studies. Finally, this article proposes policy recommendations in order to extend social security to informal workers and to include them in the formal labour market.

To define and evaluate the areas of consideration concerning in identifying the critical factors that top universities in Nueva Ecija, Philippines can be used for triangulating the courses of actions that can be applied to improve the current practices of universities towards its combat to the COVID–19 disease is the primary objective of this study. The researchers used a descriptive design of methodology by using questionnaire–checklist to scientifically describe the situation, problems, phenomenon, or program, or provide information about certain issues related to the virus outbreak. The respondents of the study were faculty and staff of five established universities in Nueva Ecija, Philippines wherein the researchers employed a non–probability sampling technique to be logically assumed as the representative of the entire population. The results of the study shown that the top universities in Nueva Ecija have made efforts to ensure the safety of university workers by complying with the Inter–Agency Task Force (IATF) protocols. It can be inferred, in reality, that there are some areas that must be improved especially when it comes to ensuring the welfare of the personnel who are still reporting to work even in this time of the pandemic. The researchers suggested an enhancement plan that can be adapted by these universities to resolve the concerns of the faculty and staff especially in reducing the spread of the virus without sacrificing the day–to–day transactions of the academic institutions.

Crop plants should be resilient to climatic factors in order to feed ever-increasing populations. Plants have developed stress-responsive mechanisms by changing their metabolic pathways and switching the stress-responsive genes. The discovery of plant transcriptional factors (TFs) as key regulators of different biotic and abiotic stresses have opened up new horizons for plant scientists. TFs perceive the signal and switch certain stress-responsive genes on and off by binding to different cis-regulatory elements. The above 50 species of plant TFs have been reported in nature. DREB, bZIP, MYB, NAC, Zinc-finger, HSF, Dof, WRKY, and NF-Y are important with respect to biotic and abiotic stresses whereas the role of many TFs is yet to explore. In this review, we summarize the role of different stress-responsive TFs with respect to biotic and abiotic stresses. Further, challenges and future opportunities linked with TFs for developing climate-resilient crops are also elaborated.

Thanks to rapid advances in the Internet of Things (IoT) and Edge-Fog-Cloud Computing technologies, Smart Public Safety (SPS) system has become feasible by integrating heterogeneous computing devices and different types of networks to collaboratively provide seamless public safety services. While SPS facilitates convenient exchanges of surveillance data streams among device owners and third-party applications, the existing monolithic service oriented architecture (SOA) architecture is unable to provide scalable and extensible services in a large-scale heterogeneous network environment. Moreover, traditional security solutions rely on centralized trusted third-party authority, which not only can be a performance bottleneck or the single point of failure, but it also incurs privacy concerns on improperly use of private information. Inspired by blockchain and microservices technologies, this paper proposed a BLockchain-ENabled Decentralized Smart Public Safety (BlendSPS) system. Leveraging hybrid blockchain fabrics, a microservices based security mechanism is implemented to enable decentralized security architecture, and it supports immutability, auditability and traceability for secured data sharing and operations among participants of the SPS system. An extensive experimental study verified the feasibility of the proposed Blend-SPS that possesses security and privacy proprieties with limited overhead on IoT based edge networks.

COVID-19 is now a major global health crisis, can lead to severe food crisis unless proper measures are not taken. Though a number of scientific studies have addressed the possible impacts of COVID-19 in Bangladesh on variety of issues, problems and food crises associated with aquatic resources and communities are missing. Therefore, this study aimed at bridging the gap in the existing situation and challenges of COVID-19 by linking its impact on aquatic food sector and small-scale fisheries with dependent population. The study was conducted based on secondary data analysis and primary fieldwork. Secondary data focused on COVID-19 overview and number of confirmed, recovered and death cases in Bangladesh; at the same time its connection with small-scale fisheries, aquatic food production, demand and supply was analyzed. Community perceptions were elicited to present how the changes felt and how they affected aquatic food system and small-scale fisheries and found devastating impact. Sudden illness, reduced income, complication to start production and input collection, labor crisis, transportation abstraction, complexity in food supply, weak value chain, low consumer demand, rising commodity prices, creditor’s pressure were identified as the primary affecting drivers. Dependent people felt the measures taken by the Government should be based on protecting the health and food security, although it could be detrimental to economic growth in the short term. The study provides insight into policies adopted by the policy makers to mitigate the effects of the pandemic on aquatic food sector and small-scale fisheries.

This paper investigates the contribution of business model innovations in improvement of food supply chains. Through a systematic literature review, the notable business model innovations in the food industry are identified, surveyed, and evaluated. Findings reveal that the innovations in value proposition, value creation processes, and value delivery processes of business models are the successful strategies proposed in food industry. It is further disclosed that rural female entrepreneurs, social movements, and also urban conditions are the most important driving forces inducing the farmers to reconsider their business models. In addition, the new technologies and environmental factors are the secondary contributors in business model innovation for the food processors. It is concluded that digitalization has disruptively changed the food distributors models. E-commerce models and internet of things are reported as the essential factors imposing the retailers to innovate their business models. Furthermore, the consumption demand and the product quality are two main factors affecting the business models of all the firms operating in the food supply chain regardless of their positions in the chain. The findings of the current study provide an insight into the food industry to design a sustainable business model to bridge the gap between food supply and food demand.

The ongoing electrification of the heat and transport sectors is expected to lead to a substantial increase in peak electricity demand over the coming decades, which may drive significant investment in network reinforcement in order to maintain a secure supply of electricity to consumers. The traditional way of security provision has been based on conventional investments such as the upgrade of the capacity of electricity transmission or distribution lines. However, the energy storage technology can also provide security of supply, thereby constituting a cost-efficient alternative to expensive conventional reinforcements. In this context, the current paper presents a methodology for the economic quantification of the security contribution of energy storage. This methodology makes use of mathematical optimization for the calculation of the F-factor metric, which reflects the optimal amount of peak demand reduction as compared to the power capability of the energy storage asset. In this context, a case study is presented in which the security contribution of energy storage is analyzed as a function of its power capability, energy capacity and efficiency as well as of characteristics of load patterns.

Over the past few years, we have experienced great technological advancements in the information and communication field, which has significantly contributed to reshaping the Intelligent Transportation System (ITS) concept. Evolving from the platform of a collection of sensors aiming to collect data, the data exchanged paradigm among vehicles is shifted from the local network to the cloud. With the introduction of cloud and edge computing along with ubiquitous 5G mobile network, it is expected to see the role of Artificial Intelligence (AI) in data processing and smart decision imminent. So as to fully understand the future automobile scenario in this verge of industrial revolution 4.0, it is necessary first of all to get a clear understanding of the cutting-edge technologies that going to take place in the automotive ecosystem so that the cyber-physical impact on transportation system can be measured. CIoV, which is abbreviated from Cognitive Internet of Vehicle, is one of the recently proposed architectures of the technological evolution in transportation, and it has amassed great attention. It introduces cloud-based artificial intelligence and machine learning into transportation system. What are the future expectations of CIoV? To fully contemplate this architecture’s future potentials, and milestones set to achieve, it is crucial to understand all the technologies that leaned into it. Also, the security issues to meet the security requirements of its practical implementation. Aiming to that, this paper presents the evolution of CIoV along with the layer abstractions to outline the distinctive functional parts of the proposed architecture. It also gives an investigation of the prime security and privacy issues associated with technological evolution to take measures.

Meteorological drought indicators are commonly used for agricultural drought contingency planning in Ethiopia. Agricultural droughts arise due to soil moisture deficits. While these deficits may be caused by meteorological droughts, the timing and duration of agricultural droughts need not coincide with the onset of meteorological droughts due to soil moisture buffering. Similarly, agricultural droughts can persist even after the cessation of meteorological droughts due to delayed hydrologic processes. Understanding the relationship between meteorological and agricultural droughts is therefore crucial. An evaluation framework was developed to compare meteorological and agricultural droughts using a suite of exploratory and confirmatory tools. Receiver operator characteristics (ROC) was used to understand the covariation of meteorological and agricultural droughts. Comparisons were carried out between SPI-2, SPEI-2 and Palmer Z-index to assess intra-seasonal droughts and between SPI-6, SPEI-6 and PDSI for full-season evaluations. SPI was seen to correlate well with selected agricultural drought indicators but did not explain all the variability noted in agricultural droughts. The relationships between meteorological and agricultural droughts exhibited spatial variability which varied across indicators. SPI is better suited to predict non-agricultural drought states more so than agricultural drought states. Differences between agricultural and meteorological droughts must be accounted for better drought-preparedness planning.

The violation of privacy, others people or personal, is a very current problem, which concerns not only on the web but also in private life. In the years 1990 it was expected that nowadays, that any routine operation was carried out "manually", and it would be performed through mobile phones or personal computers. The problem pertains the distribution network that allows to share and bring together information and as result the network becomes unsafe, if subjected to attacks. Nowaday we put personal information on web because otherwise we are seen as “weak”. This work aims to measure and analyze how much information are shared by users of a pre-established social network and it is carried out through a set of algorithms techniques of machine learning.

SerIoT is a Research and Innovation Project funded by the European Commission to demonstrate secure networks for the Internet of Things (IoT). This paper presents an overview of the design for smart and secure network infrastructures for the IoT, based on Software Defined Networks (SDN), named the SerCPN system, whose main component is a smart SDN-Controller with online cognitive security surveillance and reporting, which establishes and dynamically modifies paths that enhance security for IoT devices and end users, and offers a high quality of service (QoS) with required security constraints. This online cognitive surveillance and path management is based on the Cognitive Packet Network (CPN) principle and uses Random Neural Networks (RNN) for decision making.

Rice production is important for global food security but given its large water footprint, efficient irrigation management strategies need to be developed. Expansion of rice growing area is larger than any other crop in Africa due to increasing demand for rice. Three rice irrigation management alternatives with the system of rice intensification (SRI) were field-evaluated against the conventional continuously flooded system (CF) in Tanzania. Production systems included: (1) CF (50 mm ponding depth for the entire season); (2) SRI (40 mm ponding for 3 days and no irrigation for next 5 days); (3) 80% SRI (80% of the SRI ponding); and (4) 50% SRI (50% of the SRI ponding). Experimental evaluation of the four systems was conducted for both wet and dry seasons. For the dry season, the SRI and 80% SRI produced higher yields of 9.68 tons/ha and 11.45 tons/ha and saved 26% and 35% of water, respectively compared to the CF (8.69 tons/ha). The yield advantage of the 80% SRI and SRI over the CF was less during the wet season with 6.01 tons/ha and 5.99 tons/ha of production, and water savings of 30% and 14%, respectively compared to the CF (5.64 tons/ha). The 50% SRI had lowest yield of all for both seasons, 7.48 tons/ha and 4.99 tons/ha for the dry and wet seasons, respectively. Statistically, the 80% SRI treatment outperformed all other treatments over the two seasons with an additional yield of 1.57 tons/ha and 33% (345 mm) water savings compared to the CF. Economic productivity of water (US$/ha-cm) over two seasons was highest for the 80% SRI ($20.27/ha-cm), while it was lowest for the CF ($12.89/ha-cm). Water saved by converting from the CF to the 80% SRI (1.98 million ha-cm) can support a 50% expansion in the current rice irrigated area in Tanzania. Even without irrigation expansion, the 80% SRI can increase rice production by 1.5 million tons annually while enhancing water availability for industrial and environmental uses (e.g., ecological preserves) and help achieve food security in Tanzania and the greater sub-Saharan Africa.

In recent years, online social media information has been subject of study in several data science fields due to its impact on users as a communication and expression channel. Data~gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users opinions and make predictions about real events. Cyber attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ1 regularization.

Elevated carbon dioxide (eCO₂) stimulates wheat grain yield, but simultaneously reduces protein (N) concentration. Also other essential nutrients are subject to change. This study is a comprehensive synthesis of wheat experiments with eCO₂, estimating effects on N, minerals (B, Ca, Cd, Fe, K, Mg, Mn, Na, P, S, Zn), and starch. Analysis was made by i) deriving response functions for the relative effect on element concentration in relation to CO₂ concentration, ii) meta-analysis to test the magnitude and significance of observed effects, and iii) relating CO₂ effects on minerals to effects on N and grain yield. Responses range from zero to strong negative effects of eCO₂ on mineral concentration, with largest reductions for the nutritionally important elements N, Fe, S, Zn and Mg. Together with the positive but small and non-significant effect on starch concentration, the large variation in effects suggests that CO₂-induced responses cannot be explained by a simple dilution model. To explain the observed pattern, uptake and transport mechanisms may have to be considered, along with the link of different elements to N uptake. Our study shows that eCO₂ has a significant effect on wheat grain stoichiometry, with implications for human nutrition in a world of rising CO₂.

Maize is the staple cereal in Malawi, with a daily per capita consumption of 383g (dry matter basis), primarily consumed as dehulled maize flour used to cook nsima, a thick porridge. However, the extent of mass and nutrient loss during dehulling remains unclear in this food-insecure country, where the household maize harvest typically lasts only 6–7 months. In the current study, a milling experiment to determine yield and micronutrient losses was conducted. Milling batches (30 kg) of four maize grain varieties were dehulled at three abrasive disk dehullers under controlled conditions. Mass losses ranged from 18.8% to 42.7%, with a mean of 28.2±5.7%. Maize variety and dehuller significantly affected mass loss (p<0.05). Initial protein, iron, and zinc concentrations were 8.8 ±1.0 g/100g, 20.5±2.0 µg/g, and 18.2 ±2.6 µg/g, respectively. Post-dehulling nutrient true retention varied for protein (49.7% to 75.6%, mean=64.7±7.0%), iron (18.7% to 71.6%, mean=37.6±12.0 %), and zinc (11.0% to 46.2%, mean=27.9±8.7 %). Maize variety significantly influenced mass and protein loss (p < 0.05), while dehuller influenced mass, iron, and zinc losses. However, no significant difference (p > 0.05) was found in the interaction between dehullers and variety on mass and nutrient losses, respectively. Given Malawi's precarious food insecurity situation, transitioning from dehulled maize flour nsima to whole maize flour or less refined nsima, is imperative. Our study findings can have food and nutritional savings for other southern Africa countries where the dehulling practice is rampant.

As practiced in Africa, urban agriculture (UA) has been examined in the literature and by architecture and urban planning experts, revealing both advantages and disadvantages for health. While the recognized disadvantages are not negligible, the literature focuses mainly on the positive outcomes about food security and improved socio-economic conditions for farmers. However, little research has focused on farmers' perceptions of the effect of this practice on their psychological well-being. This study seeks to delve into this aspect by surveying 733 farmers in Dar es Salaam and Greater Lomé to map UA's distribution, assess its benefits and challenges, study its psychosocial associations, and demonstrate how architectural design and urban planning can enhance its spatial distribution and overall benefits. The standardized Mental Health Continuum-Short Form (MHC-SF) and non-standardized questionnaires are used to evaluate their responses. Using logistic regression and spatial analysis techniques with Python and QGIS 3.32.2-Lima, the analysis uncovers that farmers attribute improved psychological well-being to their involvement in UA. The study found that farmers' happiness due to UA varies by location within a city, with those on the outskirts generally more satisfied than those in urban centers. Additionally, women farmers reported greater happiness than men farmers, highlighting UA's role in empowering women. The research underscores UA's positive association with practitioners' psychological health in Africa and advocates for its integration into urban planning to promote equitable and beneficial urban spaces.

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 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.

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.

Artificial Intelligence (AI) models are expected to have a great impact in the manufacturing industry, optimizing time and resource cost by enabling applications such as predictive maintenance (PM) of production machines. A necessary condition for this is the availability of high quality data collected as close as possible to the process in question. With the advent of 5G equipped multi sensor platforms (MSPs), high sampling rate data can be collected and transmitted for processing in real time. This poses a data security challenge, since this data may give valuable insight into confidential business information of companies. Federated learning (FL) enables the training of AI models with data from multiple sources without it leaving the shop floor, by utilizing distributed computing resources available on premise. This paper introduces an architecture of FL based on data collected from 5G MSPs for enabling PM in industrial environments and discusses its potential benefits and challenges.

Dataspaces are decentralized and open ecosystems that guarantee trustworthy and secure sharing of data among their participants. In recent years, dataspaces have gained popularity due to their design for managing and sharing heterogeneous data from various sources and domains; and their capability to incrementally solve data integration issues. Leveraging dataspace and advanced technologies plays a vital role in solving many real-world applications effectively and efficiently in real-time. Drone technology is one type of technology that can be deployed to gather data from different resources in harsh or smart environments. Beyond fifth-generation (B5G) communication networks significantly contribute to drones’ development and widespread use by providing low latency and high throughput. Therefore, data sharing among drones in B5G networks offer significant potential to enhance commercial and civilian applications. However, several security issues for collaboration and data sharing, such as data privacy leakage, because of sensitive data and the lack of trustworthy centralized monitoring. Furthermore, sharing data is one of the essential requirements for drone collaboration to achieve their tasks effectively and efficiently in real-time. This conceptual framework presents a novel dataspace in the sky, focusing on securing drone data sharing in B5G for Industry 4.0 toward Industry 5.0. We present how Federated Learning (FL) assists drones in collaboration effectively and efficiently, sharing models instead of raw data. However, because of the fragility of the central curator, the reliability of contribution recording, and the poor quality of shared local models, there are still significant security and privacy issues for drone-assisted smart environments in B5G. Therefore, we present the conceptual framework for leveraging blockchain and FL to secure and manage data sharing of collaborative drones’ dataspace in space in a decentralized fashion. The decentralisation of dataspaces would significantly expand the drive and market for the development of citizen-friendly mobility services.

Locust is a pest that attacks human and animal food and endangers national security by threatening food security. Spraying is the easiest and fastest way to deal with them. But the easiest way is not always the best way. With chemical resistance, not only will permanent immunity from locust attacks not arise, but they will also become resistant to poisons. While we have to pay billions of dollars annually for this strategy, we will also cause water and soil pollution. Therefore, the research results of other researchers were used and modeled to measure the possibility of hunting invasive locusts to investigate the possibility and benefits of this action. So, this study was conducted in the field method. The obtained results indicate that it is possible to hunt invasive locusts. If traps are placed on time and in the right place, the possibility of preserving crops after the pest attack increases by 2.3 times more in protected fields than in unprotected fields. By hunting locusts, fields and even pastures are preserved, poisons are prevented from entering the food chain and ecosystem, part of the economic loss of farmers affected by the invasion of locusts will be compensated, and by providing animal feed through hunting locusts, water consumption will be reduced, soil erosion and energy consumption are saved.

After the great success of Mobile wallet, the Internet of Things (IoT) leaves the door wide open for consumers to use their connected devices to access their bank accounts and perform routine banking activities from anywhere, anytime and with any device. However, consumers need to feel safe when interacting with IoT-based payment systems, and their personal information should be protected as much as possible. Unlike as usually done in the literature, in this paper, we introduce two lightweight and secure IoT-based payment protocols based on an identity-based signature scheme. We adopt a server-aided verification technique to construct the first scheme. This technique allows to outsource the heavy computation overhead on the sensor node to a cloud server while maintaining the user's privacy. The second scheme is built upon a pairing-free ECC-based security protocol to avoid the heavy computational complexity of bilinear pairing operations. The security reduction results of both schemes are held in the Random Oracle Model (ROM) under the discrete logarithm and computational Diffie-Hellman assumptions. Finally, we experimentally compare the proposed schemes against each other and against the original scheme on the most commonly used IoT devices: a smartphone, a smartwatch and the embedded device Raspberry Pi. Compared with existing schemes, our proposed schemes achieve significant efficiency in the term of communication and computational overheads

Social safety, security, and comfort of school-going children during the travel time to school becomes a subject of anxiety to the parents and is a crucial issue in recent times. In this regard, community-based transport can be a significant way to address social security issues in travel at a reasonable cost and reduce the burden on private mode. In Dhaka city, school van service already exists but due to some sort of problems the service has not been proved an efficient and formal mode of transport for solving mobility problems. This study seeks to identify the existing problems and prospects of the school van service and provide a unique, healthy, safe, and reliable transport mode for children. Applying different tools of the Participatory Rural Appraisal (PRA) method, the problems and solutions have been drawn from the community. The recommendations of this study will help the school van services (a community-managed para-transit system) to be more functional in playing a vital role in solving the problems of short-distance travel. This service has great potentialities to be adopted in other trips such as trips to and from offices which will lessen the road congestion at the peak periods.

The Urban Eco-Sustainable Index for Upscaling Water Security at Catchment Level in Langat River, Malaysia has developed by using the Modified Watershed Sustainability Index or MicroWSI (MWSI), which was based on the Participation, Design and Management Components. The study has successfully applied spatial and social dimensions on ecohydrology of the selected Langat River reach for stormwater management, natural ecosystems health and quality of life. The planning and public participation aspects of the study have evaluated the surrounding neighborhood area of Langat. The conceptual design of rehabilitation works implementation related to Urban Stormwater Management Manual for Malaysia (MSMA) Stormwater Management Ecohydrology (SME) within the study area has been developed with four components of MSMA-SME to be implemented in the study area i.e Bioretention System, Porous Pavement System, Constructed Wetland and Slope Protection Works. These components were proposed to be applied in the development of Langat Riverfront Community Park (LRCP) which has taken into account the components of Design, Management and Participation of Community and Stakeholders in Langat River Basin, Malaysia. This study analyzed the MWSI for the Upscaling of MSMA Ecohydrology at Catchment Level of Langat River and has found the medium level of sustainability for the level of participation, proposed design, and management. Thus, there is a need to increase the level of readiness in the community and stakeholder participation in the Langat River towards sustainability of river conservation and rehabilitation programmes in this basin.

Despite the ongoing land administration reforms being implemented across sub-Saharan Africa (SSA), including Ghana as viable pathway to achieve tenure security and greater efficiency in land administration, the subject of land dispute resolution has received relatively less attention. Whereas customary tenure institutions play a central role in land administration (controlling ~80% of all land in Ghana), they remain at the fringes of the formal land dispute adjudicatory process. Recognizing the pivotal role traditional institutions as development agents and potential vehicles for promoting good land governance, recent discourse on land tenure have geared towards mainstreaming traditional land disputes institutions into the architecture of formal judicial process via alternative dispute resolution pathways. Yet little is known at least empirically as to the operations of traditional dispute resolution institutions in the contemporary context. This study therefore explores the importance of traditional dispute resolution institutions in the management of land-related disputes in southcentral and western Ghana. Drawing on data collated from 380 farming households operating 746 plots. The results show that contrary to the conventional thinking that traditional institutions are anachronistic and not fit for purpose, they remain strong and preferred forum for land dispute resolution (proving resilient and adaptable) given the changing socio-economic and tenurial conditions. Yet these forums have differing implications for different actors within the customary spheres accessing them. The results highlight practical ways for incorporating traditional dispute resolution in the overall land governance setup in Ghana and elsewhere in sub-Saharan Africa. This has implications for redesigning context-specific and appropriate land-use policy interventions that address local land dispute resolution.

We applied evolutionary game theory to extend a resource constrained security game model for confidentiality attacks in an Advanced Metering Infrastructure (AMI), which is a component of IoT-enabled Smart Grids. The AMI is modelled as a tree structure where each node aggregates the information of its children before encrypting it and passing it on to its parent. As a part of the model, we developed a discretization scheme for solving the replicator equations. The aim of this work is to explore the space of possible behaviours of attackers and to develop a framework where the AMI nodes adaptively select the most profitable strategies. Using this model, we simulated the evolution of a population of attackers and defenders on various cases resembling the real life implementation of AMI. We discuss in depth how to enhance security in AMI using evolutionary game theory either by a priori analysis or as a tool to run dynamic and adaptive infrastructure defence.

In the context of the economic and political uncertainty associated with the 2019-nCoV pandemic, it is necessary to determine the socio-psychological factors involved in the transformation of the behavior of insurance consumers under the influence of a biogenic threat. This study measures financial anxiety and its impact on the insurance behavior of Russian citizens. The correlation, comparative, and regression analyses of the financial anxiety of Russian citizens cover three stages of observation: before the start of the 2019 nCoV pandemic (“FA up to 19 nCoV; N = 766), during the period of quarantine measures announced in Russia in March 2020 (“FA 19-nCoV-1”; N = 856), and after the relaxation of quarantine measures at the end of April 2020 (“FA 19-nCoV-2”; N = 963).Psychological analysis data were obtained from the online survey “Financial anxiety (in the context of insurance)”. The questionnaire is psychometrically reliable and easy to use. It includes five measurement scales: MR1—Physical manifestations of financial incentive anxiety, MR2—With money shortages and financial uncertainty, MR3—The value of insurance coverage, MP4—Financial Confidence, and MR5—Perception of insurance and investment risks. It was found that Russian citizens consider it important to have insurance coverage for a “rainy day”, and they showed confidence in the insurance market during the biogenic crisis. However, unfortunately, during the 19-nCoV-1 and 19-nCoV-2 periods, Russian citizens did not feel financially secure, unlike in the period before 19-nCoV. Women showed high scores for physical manifestations of financial anxiety and low financial confidence in the future, in contrast to men, regardless of the observation period.

In the context of economic and political uncertainty associated with the 2019-nCoV pandemic, it is necessary to determine the socio-psychological factors in the transformation of the behavior of insurance consumers under the influence of the biogenic threat. This study is devoted to measuring financial anxiety and its impact on the insurance behavior of Russian citizens. Correlation, comparative and regression analyzes of financial anxiety of Russian citizens cover three stages of observation: before the start of the 2019 nCoV pandemic (“FA up to 19 nCoV; N = 766), during the period of quarantine measures announced in Russia in March 2020 (“ FA 19- nCoV-1 "; N = 856) and after the relaxation of quarantine measures at the end of April 2020 (" FA 19-nCoV-2 "; N = 963). Psychological analysis data were obtained from the online survey "Financial anxiety (in the context of insurance)". The questionnaire is psychometrically reliable and easy to use, including 5 measurement scales: MR1. Physical manifestations of financial incentive anxiety, MR2. With money shortages and financial uncertainty, MR3. The value of insurance coverage, MP4. Financial Confidence, MR5. Perception of insurance and investment risks. Russian citizens considered it important to have insurance coverage on a “rainy day” and showed confidence in the insurance market during the biogenic crisis. But, unfortunately, Russian citizens during the 19-nCoV-1 and 19-nCoV-2 periods did not feel financially secure in the future, unlike the period before 19-nCoV. ”Women showed high scores for physical manifestations of financial anxiety and low financial confidence in the future, in contrast to men, regardless of the observation period.

This paper examines dynamic identity, as it pertains to the IoT; and explores the practical implementation of a mitigation to some of the key weaknesses of a conventional dynamic identity model. This paper explores human-centric and machine-based observer approaches for confirming device identity, permitting automated identity confirmation for deployed systems. It also assesses the advantages of dynamic identity in the context of identity revocation permitting secure change of ownership for IoT devices. The paper explores use-cases for human and machine-based observation for authentication of device identity when devices join a C2 network, and considers the relative merits for these two approaches for different types of system.

Optical networks are prone to power jamming attacks intending service disruption. This paper presents a Machine Learning (ML) framework for detection and prevention of jamming attacks in optical networks. We evaluate various ML classifiers for detecting out-of-band jamming attacks with varying intensities. Numerical results show that artificial neural network is the fastest ($10^6$ detection per second) for inference and most accurate ($\approx 100 \%$) in detecting power jamming attacks as well as identifying the optical channels attacked. We also discuss and study a novel prevention mechanism when the system is under active jamming attacks. For this scenario, we propose a novel resource reallocation scheme that utilizes the statistical information of attack detection accuracy to lower the probability of successful jamming of lightpaths while minimizing lightpaths' reallocations. Simulation results show that the likelihood of jamming a lightpath reduces with increasing detection accuracy, and localization reduces the number of reallocations required.

Background: New open-source electric-grid planning models have the potential to improve power system planning and bring a wider range of stakeholders into the planning process for next-generation, high-renewable power systems. However, it has not yet been established whether open-source models perform similarly to the more established commercial models for power system analysis. This reduces their credibility and attractiveness to stakeholders, postponing the benefits they could offer. In this paper, we report the first model intercomparison between an open-source power system model and an established commercial production cost model. Results: We compare the open-source Switch 2.0 to GE Energy Consulting’s Multi Area Production Simulation (MAPS) for production-cost modeling, considering hourly operation under 17 scenarios of renewable energy adoption in Hawaii. We find that after configuring Switch with similar inputs to MAPS, the two models agree closely on hourly and annual production from all power sources. Comparing production gave a coefficient of determination of 0.996 across all energy sources and scenarios, indicating that the two models agree on 99.6% of the variation. For individual energy sources, the coefficient of determination was 69–100. Conclusions: Although some disagreement remains between the two models, this work indicates that Switch is a viable choice for renewable integration modeling, at least for the small power systems considered here. Although some disagreement remains between the two models, this work indicates that Switch is a viable choice for renewable integration modeling, at least for the small power systems considered here.

Vehicle to vehicle (V2V) and Vehicle to infrastructure (V2I) or briefly V2X communications are the one of hot topics in automotive industry. Therefore, this situation is providing many advantages of connected vehicles and infrastructures which bring to human life. For instance, vehicles and road infrastructures which shares information with each other, provides a neat flow regulation, more ordered traffic flow and therefore jammed traffic dependent accident’s percentage will be decreased. On the other hand, security is the most important issue for these systems because the operation of V2X networks is completely dependent on uninterrupted and accurate information sharing. In the light of these information, in this paper we review security issues and current solution architectures. We also propose some open problems in this lively field.

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.

This paper intends to discuss SSLs, how they work, their advantages and drawbacks. It will end with the author’s thoughts on the efficacy of the system and whether it is a viable solution for the majority of network security problems or if those drawbacks prevent that.

For the purpose of improve the sustainability of products sold in the EU, Digital Product Passports is a mandatory solution. These passports would systematically gather information about products, such as detailing the materials involved to aid recycling and remanufacturing efforts, or recording the emissions produced throughout the manufacturing process. The overall goal is the establishment of a circular economy in the EU. Current research on Digital Product Passports is focused on collating content for sector-specific applications. However, achieving sustainability goals through Digital Product Passports will require new methods and technical solutions for digital collaboration between supply chain participants, especially in the manufacturing sector. Collaborative work on Digital Product Passports requires trust and traceability in data and access management to ensure privacy over the data contained. One way to enable this securely and confidentially is through the use of blockchain technology, in which stored data cannot be concealed, which is key to the traceability of digital product data documentation. This paper therefore presents a concept for the implementation of generically applicable cross-industry blockchain-based digital product passports. To this end, generic supply chain processes have been abstracted and condensed into the architecture of smart contracts. Great attention is paid to the establishment of traceability as well as privacy through the selection of the blockchain technology Secret Network in combination with Gaia-X functionalities. An illustrative use case for digital product passports in manufacturing is used to demonstrate the applicability of the presented concept. In this use case information about the digital nameplate and carbon footprint is exchanged securely and confidentially in a multi-tenant supply chain network based on the Industry 4.0 infomation model of the Asset Administration Shell.

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.

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.

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.

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.

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.

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.

Initially introduced almost thirty years ago for the express purpose of providing electronic warfare systems the capabilities to detect, characterize, and identify radar emitters, Specific Emitter Identification (SEI) has recently received a lot of attention within the research community as a physical layer technique for securing Internet of Things (IoT) deployments. This attention is due in large part to SEI’s demonstrated success in passively and uniquely identifying wireless emitters using traditional machine learning and the success of Deep Learning (DL) within the natural language processing and computer vision areas. SEI exploits distinct and unintentional features present within an emitter’s transmitted signals. The existence of these distinctive and unintentional features is attributed to slight manufacturing and assembly variations that exist within and between the components, sub-systems, and systems that comprise an emitter’s Radio Frequency (RF) front end. Although sufficient to facilitate SEI, these features do not hinder normal operations such as detection, channel estimation, timing, and demodulation. However, despite the plethora of SEI publications, it has remained largely a focus of academic endeavors that primarily focus on proof-of-concept demonstration and little to no use in operational networks for various reasons. The focus of this survey is a review of SEI publications from the perspective of its use as a practical, effective, and usable IoT security mechanism, thus we use IoT requirements and constraints (e.g., wireless standard, nature of their deployment) as a lens through which each reviewed paper is analyzed. Previous surveys have not taken such an approach and have only used IoT as motivation, a setting, or a context. In this survey, we consider operating conditions, SEI threats, SEI at scale, publicly available data sets, and SEI considerations that are dictated by the fact that it is to be employed by IoT devices or IoT infrastructure.

Water hyacinth (Eichhornia crassipes) is a potential accumulator of water pollutants in aquatic ecosystems, and its presence in water systems can affect water quality. This study used different field measurements and laboratory tests of Lake Water to determine the impact of water hyacinth phytoremediation capacity. A total of eight sampling stations used for the two lakes; Lake Koka and Lake Ziway. Sampling stations were selected from sites infested with water hyacinth (low, medium and high) and non-water hyacinth aquatic plants during wet and dry seasons to compare the effects of plants on water quality in the two lakes. All the sampled stations had various human interventions. The water samples were tested for the selected physico-chemical properties namely phosphate, nitrate, pH, electrical conductivity (EC), Biological oxygen demand (BOD5), temperature, and heavy metals (Chromium (Cr), Lead (Pb), Cadmium (Cd), Zinc (Zn), and Copper (Cu). These water quality variables were compared by means of ANOVA. Despite the COD of Lake Ziway, this study found no significant (p > 0.05) variation in the concentrations of Cu, EC, pH and temperature between wet and dry seasons in either lake. Variations in Zn concentration and other physico-chemical parameters (EC, BOD, COD, nitrate, phosphate) between low, medium and high levels of water hyacinth were significant in both lakes (p<0.05). Water hyacinth has shown significant phytoremediation nature during wet and dry seasons. The lowest average heavy metal, phosphate, and nitrate concentrations; and significant pH and temperature variations were observed in Lakes Koka and Lake Ziway, among water hyacinth and other grass-infested sites.

A cross-sectional study was applied to investigate the influence of food systems and food environments on food choices and nutrition transition of households in Limpopo province, South Africa. A sample of 429 households were systematically selected using a paper selection draw from three districts. Food systems, food environments, food choices, and nutritional measurements of the households and informants were assessed, using a validated questionnaire. Most of the household informants responsible for food procurement were females (80.4%). There was a significant association (p < 0.001) between proximity to food stores and dietary diversity of the households. Staple foods were available in almost all surveyed households (95.6%). More than half of the households (59.8%) had home gardens in their yards. Almost half of the households (48.4%) had a low Dietary Diversity Score. The study revealed that 46.0% of households were food secure. Over a third (36.2%) of households’ informants were obese and 32.5% were diabetic. The mean total blood cholesterol was 3.69 ± 0.74 mmol/L. A high percentage of both females (89.6%) and males (91.5%) had normal haemoglobin levels. Most of the informants had normal systolic blood pressure levels (63%), with just over half who had high diastolic levels (57%). The 25 coping strategies were applied during food shortages periods. Even though the food environments provided both obesogenic and protective foods, the consumption of unhealthy foods was high.

The proliferation of devices for the Internet of Things (IoT) and their implication in many activities of our lives have led to a considerable increase in concern about the security of these devices, posing a double challenge for designers and developers of products. On the one hand, the design of new security primitives, suitable for resource-limited devices, that can facilitate the inclusion of mechanisms and protocols to ensure the integrity and privacy of the data exchanged over the Internet. On the other hand, the development of techniques and tools to evaluate the quality of the proposed solutions as a step prior to their deployment, as well as to monitor their behavior once in operation against possible changes in operating conditions arising naturally or as a consequence of a stress situation forced by an attacker. To address these challenges, this paper first describes the design of a security primitive that plays an important role as a component of a hardware-based root of trust, as it can act as a source of entropy for true random number generation (TRNG) or as a physical unclonable function (PUF) to facilitate the generation of identifiers linked to the device on which it is implemented. The work also illustrates different software components that allow carrying out a self-assessment strategy to characterize and validate the performance of this primitive in its dual functionality, as well as to monitor possible changes in security levels that may occur during operation as a result of device aging and variations in power supply or operating temperature. The designed PUF/TRNG is provided as a configurable IP module, which takes advantage of the internal architecture of the Xilinx Series-7 and Zynq-7000 programmable devices and incorporates an AXI4-based standard interface to facilitate its interaction with soft- and hard-core processing systems. Several test systems that contain different instances of the IP have been implemented and subjected to an exhaustive set of on-line tests to obtain the metrics that determine its quality in terms of uniqueness, reliability, and entropy characteristics. The results obtained prove that the proposed module is a suitable candidate for various security applications. As an example, an implementation that uses less than 5% of the resources of a low-cost programmable device is capable of obfuscating and recovering 512-bit cryptographic keys with virtually zero error rate.

Background: Cyber Security is to protect online data and software from cyber threats. These cyberattacks are typically intended to gain access to, change, or delete sensitive information; extort money from users; or disrupt regular corporate activities. It is difficult to keep up a regular follow up with new technologies so it is necessary to keep the important data safe from cyber threats. There are many types of cyber threats; malware, ransom-ware, social engineering, phishing etc. To prevent cyber-attacks one can use password manager tools like LastPass and others. People also use two factor authentication for double security on their accounts. Methods: Boards such as the National Institute of Standards and Technology (NIST) are developing frameworks to assist firms in understanding their security risks, improving cybersecurity procedures, and preventing cyber assaults. The fight against cybercrimes and attack, rganisations needed a strong base there are 5 types of cyber securities; Critical Infrastructure Security, application security, network security, cloud security and (IoT) Security. In the modern time US is highly based on computers and on different software so it is really important for US to be more conscious about the security as they get many threats almost everyday for hacking their data and accounts.Results and Conclusion: Nowadays, even small businesses rarely recover their loss from the cyber-attacks and many back-off from continuing their businesses after being target of hackers. The first cybercrime attack was recorded on 1988 by a graduate student. Now that large companies and even small businesses are aware of cyber-attacks so they try their best to take every precaution to prevent the hacking with double security and password manager tools.

Satellite Crop Detection technologies are focused on detection of different types of crops on the field in the early stage before harvesting. Crop detection is usually done on a time series of satellite data by classification of the desired fields. Currently, data obtained from Remote Sensing (RS) are used to solve tasks related to the identification of the type of agricultural crops, also modern technologies using AI methods are desired in the postprocessing part. In this challenge Sentinel-1 and Sentinel-2 time series data were used due to their periodic availability. Our focus was to develop methodology for classification of time series of Sentinel 2 and Sentinel 1 data and compare how accuracy of classification can be increased, but also how to guarantee availability of data. We analyse phenology of single crops and on the basis of this analysis we started to provide crop classification. Original crop classifications were made from Enhanced Vegetation Index (EVI) layers made from Sentinel-2 time-series data and then we added also . To increase accuracy we also integrate into the process parcel borders and provide classification of fields..

Artificial Intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (Industry 4.0), which can be used for the protection of Internet-connected systems from cyber-threats, attacks, damage, or unauthorized access. To intelligently solve today's various cybersecurity issues, popular AI techniques involving Machine Learning (ML) and Deep Learning (DL) methods, the concept of Natural Language Processing (NLP), Knowledge Representation and Reasoning (KRR), as well as the concept of knowledge or rule-based Expert Systems (ES) modeling can be used. Based on these AI methods, in this paper, we present a comprehensive view on "AI-driven Cybersecurity" that can play an important role for intelligent cybersecurity services and management. The security intelligence modeling based on such AI methods can make the cybersecurity computing process automated and intelligent than the conventional security systems. We also highlight several research directions within the scope of our study, which can help researchers do future research in the area. Overall, this paper's ultimate objective is to serve as a reference point and guidelines for cybersecurity researchers as well as industry professionals in the area, especially from an AI-based technical point of view.

Global economy, financial markets and technology are on the verge of moving to a new stage of development – information society. Three major world countries the US, Russia and China will play a leading role in this process. However, the specific development path will depend on many factors, including the stability of legal systems, security of computer and financial technologies, the effectiveness of legislation. The focus of this article is to assess the current condition of the economy the major countries, to identify trends and methods of stabilization of legal systems on the basis of balance of rights and obligations of the business, suggest some methods of objective evaluation of the impact factors of the total taxes, fixed costs and the legal system dynamics on financial indicators.

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning-based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

We developed a honey bee RNA-virus vector based on the genome of a picorna-like Deformed wing virus (DWV), the main viral pathogen of the honey bee (Apis mellifera). To test the potential of DWV to be utilized as a vector, the 717 nt sequence coding for the enhanced green fluorescent protein (eGFP), flanked by the peptides targeted by viral protease, was inserted into an infectious cDNA clone of DWV in-frame between the leader protein and the virus structural protein VP2 genes. The in vitro RNA transcripts from egfp-tagged DWV cDNA clones were infectious when injected into honey bee pupae. Stable DWV particles containing genomic RNA of the recovered DWV with egfp inserts were produced, as evidenced by cesium chloride density gradient centrifugation. These particles were infectious to honey bee pupae when injected intra-abdominally. Fluorescent microscopy showed GFP expression in the infected cells and Western blot analysis demonstrated accumulation of free eGFP rather than its fusions with DWV LP and/or VP2 proteins. Analysis of the progeny egfp-tagged DWV showed gradual accumulation of genome deletions for egfp, providing estimates for the rate of loss of a non-essential gene an insect RNA virus genome during natural infection.

The Internet of Things (IoT) is an emerging technology that aims to enable the interconnection of a large number of smart devices and heterogeneous networks. Ad hoc networks play an important role in the designing of IoT-enabled platforms due to their efficient, flexible, low-cost, and dynamic infrastructures. These networks utilize the available resources efficiently to maintain the Quality of Service (QoS) in a multi-hop communication. However, in a multi-hop communication, the relay nodes can be malicious, thus requiring a secured and reliable data transmission. In this paper, we propose a QoS-aware secured communication scheme for IoT-based networks (QoS-IoT). In QoS-IoT, a Sybil attack detection mechanism is used for the identification of Sybil nodes and their forged identities in multi-hop communication. %by high-power and mobile nodes. After Sybil nodes detection, an optimal contention window (CW) is selected for QoS provisioning, i.e., to achieve per-flow fairness and efficient utilization of the available bandwidth. In a multi-hop communication, the MAC layer protocols do not perform well in terms of fairness and throughput, especially when the nodes generate a large amount of data. It is because the MAC layer has no capability of providing QoS to prioritized or forwarding flows. We evaluate the performance of QoS-IoT in terms of Sybil attack detection, fairness, throughput, and buffer utilization. The simulation results show that the proposed scheme outperforms the existing schemes and significantly enhances the performance of the network with a large volume of data. Moreover, the proposed scheme is resilient against Sybil attack.

This paper builds upon a theory of emerging-state actors using ISIS as a case study. This paper seeks to apply the theory in analyzing intervention & containment policies to use against emerging-state actors, using ISIS as the case study. Two baseline scenarios are used for evaluation – one replicating the historical foreign intervention against ISIS and a counter-factual where no foreign intervention occurred. Eleven contemporary military policies were tested against these baseline in isolation, combination, at different timing windows and under hypothetical “best case” conditions as well as operationally constrained. Insights of these tests include the influence of ethnographic envelopes, timing windows. Finally, a policy based on emerging-state actor theory is tested performing substantially better across primary measures than other policies or the historical baseline. This is compared against a falsified-policy designed to disprove that emerging-state actor theory contributed to the benefits. This paper’s contributions are a practical application of system dynamics simulations and systems-thinking to current problems, generate insights into the dynamics of emerging-state actors and intervention strategies, and demonstrate utility for future application of the underlying simulation in other scenarios involving non-state actor irregular conflict including terrorism, insurgents, or emerging-state actors.

The chemical industry is an integral part of the world economy and a substantial income source for developing countries. However, existing regulations or the enforcement of these regulations, on controlling atmospheric pollutants sometimes may be insufficient, leading to the deterioration of surrounding ecosystems and to a quality decrease of the atmospheric environment. Previous works in this domain fail to generate executable solutions for inspection agencies due to practical challenges. In addressing these challenges, we introduce a so-called Chemical Plant Environment Protection Game (CPEP) to generate reasonable schedules of high-accuracy air quality monitoring stations for inspection agencies. First, Stackelberg Security Games (SSGs) are incorporated together with source estimation methods into this research. Second, high-accuracy air quality monitoring stations as well as gas sensors are modeled into the CPEP. Third, simplified data analysis on the regularly discharging of chemical plants is utilized to construct the CPEP. Finally, an illustrative case study is used to investigate the effectiveness of the CPEP Game, and a realistic case study is conducted to illustrate how the models and algorithms being proposed in this paper, work. Results show that playing a CPEP Game can reduce operational costs of high-accuracy air quality monitoring stations; moreover, playing the game leads to more compliance from the chemical plants towards the inspection agencies.

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.

Environmental monitoring systems play a crucial role in assessing environmental quality, detecting limits exceedances, and predicting potential ecological episodes. These systems rely on the measurement of various variables at specific locations and time intervals over an extended period. The concept of environmental monitoring encompasses the assessment of health and safety issues for public and environmental health purposes. Pollution of the atmosphere and water, climate change, and natural disasters are among the consequences of continuous industrial and municipal development and human interference in natural ecosystems. To address these challenges and to protect human lives and the environment, with a special concern on mitigating the ecological effects of industrial development, advanced technical solutions, including the technologies associated with artificial intelligence (artificial neural networks ANNs, machine learning ML) have been developed. These technologies offer powerful tools for analysing the vast amount of data collected by monitoring systems and extracting valuable insights. By applying ANNs and machine learning algorithms, environmental monitoring systems can effectively process and interpret the measured variables to assess environmental quality. Despite challenges and limitations, such as data quality and interpretability of AI models, ongoing research and interdisciplinary collaboration are paving the way for the successful implementation of AI in environmental monitoring, ultimately supporting informed decision-making and sustainable resource management.While several review papers have explored the theory of artificial intelligence (AI), here I aim to review the application of ANNs and ML, in environmental aspects, specifically in automotive and industrial emissions toxicity measurements, as well as atmospheric pollution prevention. By examining the potential of AI in these domains, the paper contributes to understanding the role of advanced technologies in environmental monitoring and protection.

In the current socio-economic context, pension systems have become a crucial topic in the agenda of governments and international bodies. In this paper, an empirical study regarding the economic and social sustainability of pension systems in Central and Eastern European Countries was performed. The goal of the research is to establish the sustainability of pension systems with econometric modeling techniques on three dimensions as the research hypotheses are revealed: first, by strengthening the financial soundness of pension systems; second, consolidating the economic environment of pension systems; third, increasing the degree of social security systems with a direct effect of reducing poverty. The results led to relevant insights emphasizing the need for more inclusive economic, financial, and social reforms. From a policy perspective, these findings could be a starting point for the enhancement of the financial adequacy and economic and social sustainability of pension systems in Central and Eastern European Countries. The econometric techniques in this study highlight those modern pensions systems that need to be addressed in a more complex and empirical context that must encompass the main technical aspects. The conclusions suggest that in the future it needs judicial, economic, and social adjustments for the enhancement of the financial soundness of the pension systems.

The Internet of Medical Things (IoMT) is playing a pivotal role in the healthcare sector by allowing faster and more informed hospital care, personalized treatment, and medical solutions. A very effective and trustworthy solution for resource-constrained medical devices is provided by Physical Unclonable Functions (PUF) - based identity and authentication systems, however they are not yet entirely reliable. This paper proposes VXorPUF, a Vedic Principles - Based Hybrid XOR Arbiter PUF. Modeling attacks were performed on the proposed architecture and an accuracy of 49.80 % was achieved. Uniqueness, Reliability and Randomness were the figures of merit used to evaluate PUF.

The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which is often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage, and sharing. Thanks to the characteristics of decentralization and security nature, Blockchain has a great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind Software Defined Network (SDN) technology, this paper envisions SAUSA, a blockchain-based authentication network that is capable of recording, tracking, and auditing the access, usage, and storage of 3D point cloud data sets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-enabled point cloud service architecture which allows for efficient data processing and delivery to satisfy diverse Quality-of-Service (QoS) requirements. A blockchain-based authentication framework is proposed to ensure security and privacy preservation in point cloud data acquisition, storage, and analytics. Leveraging smart contracts for digitizing access control policies and point cloud data on the blockchain, data owners have full control of their 3D sensors and point clouds. In addition, anyone can verify the authenticity and integrity of point clouds in use without relying on a third party. Moreover, SAUSA integrates a decentralized storage platform to store encrypted point clouds while recording references of raw data on the distributed ledger. Such a hybrid on-chain and off-chain storage strategy not only improves robustness and availability but also ensures privacy preservation for sensitive information in point cloud applications. A proof-of-concept prototype is implemented and tested on a physical network. The experimental evaluation validates the feasibility and effectiveness of the proposed SAUSA solution.

Computers have evolved over the years and as the evolution continues, we have been ushered into an era where high-speed internet has made it possible for devices in our homes, hospital, energy and industry to communicate with each other. This era is what is known as the Internet of Things (IoT). IoT has several benefits in the health, energy, transportation and agriculture sectors of a country’s economy. These enormous benefits coupled with the computational constraint of IoT devices which makes it difficult to deploy enhanced security protocols on them make IoT devices a target of cyber-attacks. One approach that has been used in traditional computing over the years to fight cyber-attacks is Intrusion Detection System (IDS). However, it is practically impossible to deploy IDS meant for traditional computers in IoT environments because of the computational constraint of these devices. In this regard, this study proposes a lightweight IDS for IoT devices using an incremental ensemble learning technique. We used Gaussian Naive Bayes and Hoeffding tree to build our incremental ensemble model. The model was then evaluated on the TON IoT dataset. Our proposed model was compared with other state-of-the-art methods proposed and evaluated using the same dataset. The experimental results show that the proposed model achieved an average accuracy of 99.98\%. We also evaluated the memory consumption of our model which showed that our model achieved a lightweight model status of 650.11KB as the highest memory consumption and 122.38KB as the lowest memory consumption.

The rapid advancement in artificial intelligence (AI) and wide deployment of Internet of Video Things (IoVT) enable situation awareness (SAW). Robustness and security of the IoVT systems are essential to a sustainable urban environment. While blockchain technology has shown great potentials to enable trust-free and decentralized security mechanisms, directly embedding crypto-currency oriented blockchain schemes into resource-constrained Internet of Video Things (IoVT) networks at the edge is not feasible. Leveraging Electrical Network Frequency (ENF) signals extracted from multimedia recordings as region-of-recording proofs, this paper proposes EconLedger, an ENF-based consensus mechanism that enables secure and lightweight distributed ledgers for small scale IoVT edge networks. The proposed consensus mechanism relies on a novel Proof-of-ENF (PoENF) algorithm where a validator is qualified to generate a new block if and only if a proper ENF-containing multimedia signal proof is produced within the current round. Decentralized database (DDB) is adopted to guarantee efficiency and resilience of raw ENF proofs on the off-chain storage. A proof-of-concept prototype is developed and tested in a physical IoVT network environment. The experimental results validated the feasibility of the proposed EconLedger to provide a trust-free and partially decentralized security infrastructure for IoVT edge networks.

Transport Layer Security (TLS) is a cryptographic protocol that provides communications security between two peers and it is widely used in many applications. To reduce the latency in TLS handshake session resumption using pre-shared key (PSK) had been used. But current methods in PSK mode handshake uses a fixed session key multiple times for the lifetime of session ticket. Reuse of fixed session key should be very careful in the point of communications security. It is vulnerable to replay attacks and there is a possibility of tracking users. Paired token (PT) is a new secondary credential scheme that provides pre-shared key in stateless way in client-server environment. Server issues paired token (public token and secret token) to authenticated client. Public token represents signed identity of client and secret token is a kind of shared secret between client and server. Once client is equipped with PT, it can be used for many symmetric key based cryptographic applications such as authentication, authorization, key establishment, etc. It was also shown that it can be used for one-time authenticated key establishment using the time-based one-time password (TOTP) approach. In this paper we apply the PT and TOTP approach to TLS to achieve stateless one-time authenticated session resumption. Server executes full handshake of TLS 1.3 and issues PT to authenticated client. Then client and server can execute one-time authenticated session resumption using PT in stateless way in server side. In every runs of session resumption distinct session keys are established that the same PT can be used safely for longer lifetime. If anonymous PT is used with renewal issuing, user privacy, untraceability and forward security can be achieved easily. It will provide a huge performance gain in large-scale distributed services.

The Smartphone industry has expanded significantly over the last few years. According to the available data, each year, a marked increase in the number of devices in use is observed. Most consumers opt for Smartphones due to the extensive number of software applications that can be downloaded on their devices, thus increasing their functionality. However, this growing trend of application installation brings an issue of user protection, as most applications seek permission to access data on a user’s device. The risks this poses to sensitive data is real to both corporate and individual users. While Android has grown in popularity, this trend has not been followed by the efforts to increase the security of its users. This is a well-known set of problems, and prior solutions have approached it from the ground up; that is, they have focused on implementing reasonable security policies within the Android’s open-source kernel. While these solutions have achieved the goals of improving Android with such security policies, they are severely hampered by the way in which they have implemented them. In this work, a framework referred to as CenterYou is proposed to overcome these issues. It applies a pseudo data technique and a cloud-based decision-making system to scan and protect Smartphone devices from unnecessarily requested permissions by installed applications and identifies potential privacy leakages. The current paper demonstrated all aspects of the CenterYou application technical design. The work presented here provides a significant contribution to the field, as the technique based on pseudo data is used in the actual permissions administration of Android applications. Moreover, this system is user and cloud-driven, rather than being governed by over-privileged applications.

Communication between devices has transitioned from wired to unwired. Wireless networks have been in use widely around the globe since the advent of smartphones, IoT devices and other technologies that are compatible with wireless mode of communication. At the same time security issues have also increased in such communication methods. The aim of this paper is to propose security and privacy issues of the wireless networks and present them through comprehensive surveys. In context of security issues, there are 2 typical DDoS attacks - HTTP flood and SYN flood. Other than DDoS attacks, there are several other threats to wireless networks. One of the most prevalent include security issues in Internet of Things. In terms of privacy issues in a wireless network, location-based applications, individual data, cellular network and V2G (Vehicle to Grid) network are surveyed. The survey is hosted using questionnaire and responses of 70 participants is recorded. It is observed from the survey results that many groups of people lack the knowledge of security and privacy of wireless technologies and networks despite their increased use, however, students are relatively more aware and have strong knowledge of those issues. It is concluded from the results that an effective solution to these problems can be hosting campaigns for spreading the security and privacy laws to help the groups of people who are lagging behind in this domain of knowledge become more aware. A unique solution is also presented to overcome the security issues which include implementation of detection and mitigation techniques, implementing Blockchain in the IoT devices and implementing fog computing solutions. The unique solutions to overcome the privacy issues are proposed in the form of a privacy approach from the LBS server between pairs of users to increase the implementation of DSPM and blockchain as a solution.

Can investing in women’s agriculture increase productivity? This paper argues that it can. We assess climate impacts and gender bias on women’s production in the global South and North and challenge the male model of agricultural development to argue further that women’s farming approaches can be more sustainable. Level-based analysis (global, regional, local) draws on literature review, including authors’ published longitudinal field research in Ghana and the United States. Women farmers are shown to be undervalued and to work harder, with fewer resources, for less compensation; gender bias challenges are shared globally while economic disparities differentiate; breaches of distributive, gender, and intergenerational justices as well as compromise of food sovereignty affect women everywhere. We conclude that investing in women’s agriculture needs more than standard approaches of capital and technology investment. Effective ‘investment’ would include systemic interventions into agricultural policy, governance, education, and industry; be directed at men as well as women; and use gender metrics, e.g. quotas, budgets, vulnerability and impacts assessments, to generate assessment reports and track gender parity in agriculture. Increasing women’s access, capacity, and productivity cannot succeed without men’s awareness and proactivity. Systemic change can increase productivity and sustainability.

Evaluation of agronomic practices, genetic diversity and population structure of Moringa oleifera (Lam.) is crucial to its sustainable utilization to ensure food and nutritional security. Four agronomic practices of field preparation and soil analysis, regeneration, weeding and pruning, as well as harvesting, were adopted. Genetic diversity and population structure of 20 populations were evaluated using amplified fragment length polymorphism (AFLP) primer pairs (E-ACC/M-CAC and E-ACA/M-CAG). The effects of agronomic practices on leaf quantity production were evaluated using standard procedures. The physico-chemical and morphological data were analyzed using descriptive statistics, while genetic diversity, population structure, dendrogram reconstruction, and Principal Component Analysis (PCA) were analyzed from the AFLP genetic data. Significant effects (P ≤ 0.05) of agronomic practices on the quantity of leaf production were recorded across the accessions. The two primer pairs generated a total of 80 alleles with a mean major allele frequency of 0.0250. Gene diversity and polymorphic information content (PIC) values were high with a mean of 0.98 and 0.974, respectively. The Gst value of 0.0490 indicated that 5 % of the total genetic divergence was among the population while 95 % within the population. Dendrogram reconstruction with genetic distance ranging from 0.87 to 0.96 segregated the accessions into eleven clusters while PCA generated six cluster groups. Field preparation and physico-chemical soil properties, regeneration, weeding and pruning, and stand development, as well as harvesting, influenced the leaf yield and genetic diversity observed in this study. The genetic data revealed that some accessions were clustered along eco-geographical lines while others grouped disparately. Identified potential parent genotypes with valuable and desirable genetic traits can be exploited for commercial, breeding and conservation purposes to ensure sustainable utilization of the species in Nigeria.

The need for the most accurate and flexible system of revenue collection from internal sources has become a matter of extreme urgency and importance in e-governance. This need underscores the eagerness on the part of the Government to look for a new principle and policy of revenue collection or to become aggressive and innovative in the mode of collecting revenue from existing sources using the present system. The Boards of some Governments in Africa, even up to the moment are facing a lot of setbacks in performing their tasks due to the manual system of revenue collection from the public. This can be improved through an effective collection of revenue using the most accurate and flexible system. Tax is usually collected in the form of specific sales tax, general sales tax, corporate income tax, individual income tax, property tax and inheritance tax. Problems such as high cost of collection, fraud, underpayment, leakage in revenue, poor access to information, poor tracking of defaulters is at the increase. As a result of this, there is need to computerize the revenue collection system. Computerized systems have proven to introduce massive efficiencies and quick collection of revenue from the public. This research work demonstrates how to design and implement an automated system of revenue collection and how to maintain a secured database for collected tax information. This research delves into the study of how machine learning algorithms and Software-defined Networks improve the security of such automated systems.

Ethiopian agriculture is not only affected by precipitation declines (meteorological droughts) but also soil dryness caused by temperature increases and associated long-term hydrological changes. Meteorological drought indicators (e.g., SPI), do not fully capture the water deficits in agricultural systems (i.e., agricultural droughts). An Ethiopia-wide assessment of meteorological and agricultural drought trends was carried out to characterize century-scale (1902 – 2016) changes in droughts. SPI and SPEI calculated using two-month accumulation and the Palmer Z-index were used for assessing intra-season drought trends. SPI and SPEI at six-month accumulations and PDSI were used to define full season droughts. Detrended variance corrected Mann-Kendall test was used for trend analysis during Bega (dry), Belg (short-rainy) and Meher (long-rainy) seasons. The SPEI-2 and PDSI were most aggressive in characterizing intra-season and seasonal-drought trends. There is on average 1% - 6% annual increase in dryness with the lower estimate based on precipitation declines and the upper end accounting for seasonal soil moisture dynamics. The area between 37.5° E – 42.5° E denotes a climate hot-spot. Precipitation declines in Belg along the Ethiopia-South-Sudan/Sudan border during Belg and along Eretria-Ethiopia border during Meher have the potential to exacerbate transboundary water conflicts and further threaten the food security of the region.

Physical Unclonable Functions (PUFs) based on the retention times of the cells of a Dynamic Random Access Memory (DRAM) can be utilised for the implementation of cost-efficient and lightweight cryptographic protocols. However, as recent work has demonstrated, the times needed in order to generate their responses may prohibit their widespread usage. In order to address this issue, the Row Hammer PUF has been proposed by Schaller et al. [1], which leverages the row hammer effect in DRAM modules to reduce the retention times of their cells and, therefore, significantly speed up the generation times for the responses of PUFs based on these retention times. In this work, we extend the work of Schaller et al. by presenting a run-time accessible implementation of this PUF and further reducing the time required for the generation of its responses. Additionally, we also provide a more thorough investigation of the effects of temperature variations on the the Row Hammer PUF and briefly discuss potential statistical relationships between the cells used to implement it. As our results prove, the Row Hammer PUF could potentially provide an adequate level of security for Commercial Off-The-Shelf (COTS) devices, if its dependency on temperature is mitigated, and, may therefore, be commercially adopted in the near future.

This paper considers a two-way relay network, where two source nodes exchange messages through several relays in the presence of an eavesdropper, and the channel state information (CSI) of the eavesdropper is imperfectly known. The amplify-and-forward relay protocol is used and the relay beamforming weights are designed. The model is built up to minimize the total relay transmit power while guaranteeing the quality of service at users and preventing the eavesdropper from decoding the signals. Due to the imperfect CSI, a semi-infinite programming problem is obtained. An algorithm is proposed to solve the problem, and the iterative points are updated through the linesearch technique, where the feasibility are preserved during iterations. The optimality property is analyzed. The obtained subproblems are quadratic constrained quadratic programming problems, either with less than $4$ constraints or with only one variable, which are solved optimally. Simulation results demonstrate the importance of the proposed model, and imply that the proposed algorithm is efficient and converges very fast, where more than 85% of the problems are solved optimally.

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.

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.

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.

The Internet of Things (IoT) continues to grow at a rapid pace, becoming integrated into the daily operations of individuals and organisations. IoT systems automate crucial services within daily life that users may rely on, which makes the assurance of security towards entities such as devices and information even more significant. In this paper, we present a comprehensive survey of papers that model cyber risk management processes within the context of IoT, and provide recommendations for further work. Using 39 collected papers, we studied IoT cyber risk management frameworks against four research questions that delve into cyber risk management concepts and human-orientated vulnerabilities. The importance of this work being human-driven is to better understand how individuals can affect risk and the ways that humans can be impacted by attacks within different IoT domains. Through the analysis, we identified open areas for future research and ideas that researchers should consider.

The Industrial Internet of Things (IIoT) is bringing evolution with remote monitoring, intelligent analytics, and control of industrial processes. A reference architecture provides the general layout information for the flexible integration of IIoT systems; however, as the industrial world is currently in its initial stage of adopting the full-stack development solutions with IIoT, some challenges need to be addressed. To cope with the rising challenges and provide the blueprint guidelines to develop and implement IIoT in real-time, researchers around the globe have proposed IIoT architectures based on different architectural layers and emerging technologies. In this paper, we first review and compare some widely accepted IIoT reference architectures and present a state-of-the-art review of conceptual and experimental IIoT architectures in literature. We highlight scalability, interoperability, security, privacy, reliability, and low latency as the main IIoT architectural requirements and compare how the current architectures address these challenges. We also highlight the role of emerging technologies in current IIoT architectures to address these requirements and present the literature gap for future research work to address the challenges.

Technological advances such as blockchain, artificial intelligence, big data, social media, geographic information systems represent a building block of the digital transformation that supports the resilience of the food supply chain (FSC) and increases its efficiency. This paper reviews the literature surrounding digitalization in FSCs. A bibliometric and key-route main path analysis was carried out to objectively and analytically uncover the knowledge development in digitalization within the context of sustainable FSCs. The research began with the selection of 2140 articles published nearly over five decades. Then, the articles were examined according to several bibliometric metrics such as year of publication, countries, institutions, sources, authors, and keywords frequency. A keyword co-occurrence network was generated to cluster the relevant literature. Findings of the review and bibliometric analysis indicate that research at the intersection of technology and the FSC has gained substantial interest from scholars. On the basis of keyword co-occurrence network, the literature is focused on the role of information communication technology for agriculture and food security, food waste and circular economy, and the merge of the Internet of Things and blockchain in the FSC. The analysis of the key-route main path uncovers three critical periods marking the development of technology-enabled FSCs. The study offers scholars a better understanding of digitalization within the agri-food industry and the current knowledge gaps for future research. Practitioners may find the review useful to remain ahead of the latest discussions of technology-enabled FSCs. To the authors’ best knowledge, the current study is one of the few endeavors to explore technology-enabled FSCs using a comprehensive sample of journals articles published during the past five decades.

To safeguard against meat supply shortages during pandemics or other catastrophes, this study analyzed the potential to provide the average household’s entire protein consumption using either soybean production or distributed meat production at the household level in the U.S. with: 1) pasture-fed rabbits, 2) pellet and hay-fed rabbits, or 3) pellet-fed chickens. Only using the average backyard resources, soybean cultivation can provide 80%-160% of household protein and 0%-50% of a household’s protein needs can be provided by pasture-fed rabbits using only the yard grass as feed. If external supplementation of feed is available, raising 52 chickens while also harvesting the concomitant eggs or alternately 107 grain-fed rabbits can meet 100% of an average household’s protein requirements. These results show that resilience to future pandemics and challenges associated with growing meat demands can be incrementally addressed through backyard distributed protein production. Backyard production of chicken meat, eggs, and rabbit meat reduces environmental costs of protein due to savings in production, transportation, and refrigeration of meat products and even more so with soybeans. Generally, distributed production of protein was found to be economically competitive with centralized production of meat if distributed labor costs were ignored.

Gas and power networks are tightly coupled and interact with each other due to physically interconnected facilities. In an integrated gas and power network, a contingency observed in one system may cause iterative cascading failures, resulting in network wide disruptions. Therefore, understanding the impacts of the interactions in both systems is crucial for governments, system operators, regulators and operational planners, particularly, to ensure security of supply for the overall energy system. Although simulation has been widely used in the assessment of gas systems as well as power systems, there is a significant gap in simulation models that are able to address the coupling of both systems. In this paper, a simulation framework that models and simulates the gas and power network in an integrated manner is proposed. The framework consist of a transient model for the gas system and a steady state model for the power system based on AC-Optimal Power Flow. The gas and power system model are coupled through an interface which uses the coupling equations to establish the data exchange and coordination between the individual models. The bidirectional interlink between both systems considered in this studies are the fuel gas offtake of gas fired power plants for power generation and the power supply to LNG terminals and electric drivers installed in gas compressor stations and underground gas storage facilities. The simulation framework is implemented into an innovative simulation tool named SAInt (Scenario Analysis Interface for Energy Systems) and the capabilities of the tool are demonstrated by performing a contingency analysis for a real world example. Results indicate how a disruption triggered in one system propagates to the other system and affects the operation of critical facilities. In addition, the studies show the importance of using transient gas models for security of supply studies instead of successions of steady state models, where the time evolution of the line pack is not captured correctly.

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.

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.

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.

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.

Patient safety is a top priority for all healthcare systems globally. Promoting the adoption of policies for reporting and learning from errors is an important strategy for improving care safety. Therefore, the aim of our study was to detect how much patient safety culture influences the reporting of adverse events and the use of the incident reporting tool. The study protocol was developed according to PRISMA guidelines. Articles were searched electronically in PubMed/MEDLINE, the COCHRANE library, and Google Scholar by two independent reviewers, and those that met the eligibility criteria were included. Synthesis of qualitative data from included studies was performed by graphical descriptive statistical analysis. The results of the systematic review showed that health care organizations' increasing focus on staff development of a safety culture has led to a significant increase in incident reporting rates over the years. Moreover, in situations where safety culture is placed at the top of the nations, there is a higher frequency of incident reporting. An efficient incident reporting system should be an essential foundation for healthcare organizations, as it allows them to collect experiences and data and provide feedback to healthcare providers and staff involved in care.

The development of wireless sensor networks (WSNs) technology comes with inherent limitations and vulnerabilities that make it exploitable by intruders. The fundamental goal of this article is to solve security problems related to black hole attacks, which interfere with the proper performance of the network and can lead to data leakage and loss. The proposed solution in this article is to apply IDS to a WSN for the first time. We also used the NS2.35 simulator to compare the three routing protocols, AODV, AODV under Hacker Node (HNAODV), and the proposed solution (IDSHNAODV) in the WSN model to reduce the effects of black hole attacks.

Blockchain technology has been gaining great interest from a variety of sectors including healthcare, supply chain, and cryptocurrencies. However, Blockchain suffers from its limited ability to scale (i.e. low throughput and high latency). Several solutions have been proposed to tackle this. In particular, sharding proved that it is one of the most promising solutions to Blockchain’s scalability issue. Sharding can be divided into two major categories: (1) Sharding-based Proof-of-Work (PoW) Blockchain protocols, and (2) Sharding-based Proof-of-Stake (PoS) Blockchain protocols. The two categories achieve good performances (i.e. good throughput with a reasonable latency), but raise security issues. This article focuses on the second category. In this paper, we start by introducing the key components of sharding-based PoS Blockchain protocols. Then, we briefly introduce two consensus mechanisms, namely PoS and practical Byzantine Fault Tolerance (pBFT), and discuss their use and limitations in the context of sharding-based Blockchain protocols. Next, we provide a probabilistic model to analyze the security of these protocols. More specifically, we compute the probability of committing a faulty block and measure the security by computing the number of years to fail. Finally, we evaluate the effectiveness of the proposed model via numerical analysis.

Fall prediction using machine learning has become one of the most fruitful and socially relevant applications of computer vision in gerontological research. Since its inception in the early 2000s, this subfield has proliferated into a robust body of research underpinned by various machine learning algorithms (including neural networks, support vector machines, and decision trees) as well as statistical modeling approaches (Markov chains, Gaussian mixture models, and hidden Markov models). Furthermore, some advancements have been translated into commercial and clinical practice, with companies in various stages of development capitalizing on the aging population to develop new commercially available products. Yet despite the marvel of modern machine learning-enabled fall prediction, little research has been conducted to shed light on the security and privacy concerns that such systems pose for older adults. The present study employs an interdisciplinary lens in examining privacy issues associated with machine learning fall prediction and exploring the implications of these models in elderly care and the Internet of Medical Things (IoMT). Ultimately, a justice-informed set of best practices rooted in social geroscience is suggested to help fall prediction researchers and companies continue to advance the field while preserving elderly privacy and autonomy.

The Internet of Things (IoT) is one of the most widely used technologies today, and it has a significant effect on our lives in a variety of ways, including social, commercial, and economic aspects. In terms of automation, productivity, and comfort for consumers across a wide range of application areas, from education to smart cities, the present and future IoT technologies hold great promise for improving the overall quality of human life. However, cyber-attacks and threats greatly affect smart applications in the environment of IoT. The traditional IoT security techniques are insufficient with the recent security challenges considering the advanced booming of different kinds of attacks and threats. Utilizing artificial intelligence (AI) expertise, especially machine and deep learning solutions, is the key to delivering a dynamically enhanced and up-to-date security system for the next-generation IoT system. Throughout this article, we present a comprehensive picture on IoT security intelligence, which is built on machine and deep learning technologies that extract insights from raw data to intelligently protect IoT devices against a variety of cyber-attacks. Finally, based on our study, we highlight the associated research issues and future directions within the scope of our study. Overall, this article aspires to serve as a reference point and guide, particularly from a technical standpoint, for cybersecurity experts and researchers working in the context of IoT.

The COVID-19 pandemic affects not only physical health but also deteriorates mental health resulting in sleep problems, depression, and traumatic stress. Our research investigates the level of posttraumatic stress, perceived social support, opinions on positive and negative consequences of the pandemic, sense of security, and sense of meaning among nurses in the face of new and not fully understood global epidemiological phenomena. For this purpose, computer-assisted web interviewing methods were conducted between May 1 and May 15, 2020. Participating nurses completed the following research tools: The Impact Event Scale-Revised (IES-R), The Multidimensional Scale of Perceived Social Support (MSPSS), The Changes in Outlook Questionnaire (CIOQ), The Safety Experience Questionnaire (SEQ) and The Meaning in Life Questionnaire (MLQ). Three hundred and twenty-five nurses working throughout Poland joined the study of an average age of 39.18 ± 11.16 years. The average overall IES-R score in the study group was 1.78 ± 0.65. Among the dimensions of traumatic stress, the highest score was obtained in the dimension Avoidance 1.86 ± 0.73, then in the dimensions Hyperarousal 1.8 ± 0.78 and Intrusion 1.74 ± 0.83. Amongst studied nurses, the highest support rates were provided by significant others (22.58 ± 5.22), then from friends (21.91 ± 5) and family (21.45 ± 4.4). Among the surveyed nurses, higher average scores were noted in the subscale measuring positive psychological changes (18.56 ± 4.04). In contrast, a higher average level of assessment was noted on the Reflection on the safety dimension (4.21 ± 0.49). The mean MLQ score was 5.33 ± 0.87. A slightly higher result was observed in the subscale Presence (5.35 ± 1.14). The results of the research implemented during the period of severe psychological pressure associated with the COVID-19 pandemic provided information on symptoms of traumatic stress in the examined group of nurses. Their sense of security has been lowered and accompanied by an intensified reflection on issues concerning security. However, the sense of meaning (currently felt) in life remains higher than the tendency to searching for it. The surveyed nurses perceive individual support from the so-called significant others (apart from family and friends). They see positive changes resulting from painful experiences related to the COVID-19 pandemic, which can be characterized by adaptation in the form of posttraumatic growth.

African locust bean (Parkia biglobosa (Jacq.) is a multi-purpose economic tree with genetic potentials in sub-Saharan Africa. Its cultivation and production is declining with increased aging and genetically threatened throughout its natural ranges. Research efforts are needed to change the present scenario to sustainable cultivation and utilization, hence this present study. This study was aimed at evaluating genetic diversity and geographical spread relationships of twenty landraces collected from different ecological zones of Nigeria using simple sequence repeat (SSR) markers. Ten SSR markers were screened and five primers (PbL02, PbL03, PbL04, PbL05 and PbL09) were selected based on clear amplification products and reproducible scorable bands. The SSR primers detected a total of 55 alleles ranged from 10 to 14 alleles with a mean of 11. The percentage polymorphisms were high and ranged from 68.75 % in PbL04 to 84.21 % in PbL05 with a mean of 74.16 %. The polymorphic information content (PIC) was in the range of 0.31 in PbL02 to 0.37 in PbL09. The genetic diversity and heterozygosity values ranged from 0.39 to 0.50 and 0.00 to 0.68 while the average genetic distance for all pair wise comparisons was 0.31.The first five Principal Component (PC) accounted for 70.20 % of the total variation out of which PC1 (31.50%) and PC2 (19.20%) extracted 49.70% molecular similarity. The dendrogram resulted in separation of the 19 landraces into three major clusters based on unweighted pair group method with arithmetic average. Cluster I comprised of five landraces: ABNo130 and BENo023; OYNo11, KANo125 and NiNo262 while cluster II had only one (BANo116). Cluster III was diverse comprising 13 landraces: ZANo188, KNNo162, KENo220, GMNo076 and EbNo260, ADNo64, EdNo164, KANo137, KENo217, KwNo270, NiNo241, OsNo206 and PLNo120. The homogeneity of alleles among the studied landraces suggested suspicion of loss of genetic intra-specific variation among the landraces of P. biglobosa which calls for concerted efforts toward better cultivation, conservation, management, utilization and genetic improvement of the species in Nigeria.

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.

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.

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.

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.

Elliptic curve cryptography (ECC) over prime fields relies on scalar point multiplication realized by point addition and point doubling. Point addition and point doubling operations consist of many modular multiplications of large operands (256 bits for example), especially in projective and Jacobian coordinates which eliminate the modular inversion required in affine coordinates for every point addition or point doubling operation. Accelerating modular multiplication is therefore important for high-performance ECC. This paper presents the hardware implementations of modular multiplication algorithms, including 1) Interleaved modular multiplication (IMM), 2) Montgomery modular multiplication (MMM), 3) Shift-sub modular multiplication (SSMM), 4) SSMM with advance preparation (SSMMPRE), and 5) SSMM with CSAs and sign detection (SSMMCSA) algorithms, and evaluates their execution time (the number of clock cycles and clock frequency) and required hardware resources (ALMs and registers). Experimental results show that SSMM is 1.76 times faster than IMM, and SSMMCSA is 3.21 times faster than IMM. We also present the ECC hardware implementations based on the Secp256k1 protocol in affine, projective, and Jacobian coordinates using the IMM, SSMM, SSMMPRE, and SSMMCSA algorithms, and investigate their cost and performance. Our ECC implementations can be applied to the design of hardware security module systems.