Non-woody forest products play a vital role in the economic uplifts of the local communities living in forest areas. This study was conducted in the Shishikoh Valley, district Chital to identify the economic importance of the Chilghoza nuts. The study was based on interviews and questionnaires, and a total of 50 questionnaires were collected from the whole valley. The results showed that before 2003, the people were unaware of the high economic value of the Chilghoza nuts, they used to sell the Chilghoza nuts at a very low price (US$ 1.7 to 2.4 per bag: approximately 5 Kg) and harvest it for fuelwood purposes. After the IPRP interventions, training and awareness, the local people started conservation of Chilghoza trees and sustainable cone harvesting. The total collection and sell out of Chilghoza nuts in the Shishikoh valley jumped to 87.9 tons and US$ 7.64/kg in 2005 from 32.7 tons and US$ 0.34-0.48/kg, respectively. This study concludes that IPRP interventions improved the economic values of Chilghoza nuts as well as the living standards of the local people.

The world is facing an unprecedented socio-economic crisis caused by the novel coronavirus infection (COVID-19). It is also spreading through the import and export food supply chains. The Chinese authorities have discovered the COVID-19 virus in various imported frozen meat packages. Traceability plays a vital role in food quality and food safety. The Internet of Things (IoT) provides solutions to keep an eye on environmental conditions, product quality, and product traceability. These solutions are traditionally based on the centralized architecture, which does not guarantee tamper-proof data sharing. The blockchain is an emerging technology that provides tamper-proof data sharing in real-time. This article presents Hyperledger Fabric-based blockchain use case and a quick reference guide to develop the blockchain network for tracking and tracing the supply chain to minimize the risk of COVID-19 in the frozen meat supply chain.

Cotton (Gossypium L.) is a major source of natural fibre for the world's textile industry and is also an important oil plant. Cotton fibre is the main source of textiles, the seeds are used for oil and the rest of the bagasse is a high-protein animal feed. In addition, more than 50 industries use cotton's so-called short fibre. Breeding studies in cotton are generally based on crosses between the best genotypes in terms of yield and fibre quality. However, the narrow genetic diversity of cultivars has a negative impact on cotton breeding programmes. For this reason, the identification of genetic resources and the disclosure of genetic diversity are important. Herein, high-resolution capillary gel electrophoresis was used to determine the genetic diversity of G. hirsutum and G. barbadense genotypes. Using 19 EST-SSR markers, a total of 47 genotypes were screened. The PIC values of the markers used ranged from 0.268 to 0.889. The mean PIC value was calculated to be 0.603. Concerning the clustering, PCoA and population structure analyses gave similar results, and the genotypes could be divided into 3 main groups. Genetic admixture with G. hirsutum was found in some genotypes of G. barbadense species. We can conclude that i) the EST-SSR markers used in this study are effective in the determination of genetic diversity, ii) The genetic diversity should be increased through the collection of genetic resources iii) genic EST-SSR markers in this study should be considered in breeding programmes by using them in QTL studies.

Blockchain and Machine Learning gives the best solutions together in performing various tasks in the Smart Health care system. With these two new emerging technologies, that have materialized in the last decade. In this paper, we proposed secure, transparent and intelligent methods in the Internate of medical things industry using Machine learning models and blockchain technology to enhance security level and train our models to improve diagnostic, prevention, treatment of the patient, patient rights, patient autonomy and equality in the health care system.

Rice is the grain feeds more than half of the world population. It is an important staple food provides twenty percent of the calories consumed worldwide. Food safety affects the health of consumers. Establishing an effective traceability system is one of the primary measures to protect rice quality and food safety. Blockchain-based traceability system has the characteristics of data tamper-proofing, decentralization, and co-governance. This paper proposed a rice traceability scheme based on blockchain technology that selects the critical nodes in the rice industry chain to construct a blockchain network to achieve transparent data sharing. It is meaningful for both enterprises and consumers. It can let enterprises get the real quality information of rice and related raw materials. Consumers can get the true information of rice and rice products to buy safe and healthy food. The scheme is proved feasible by simulating the traceability of the rice supply chain.

Artificial Intelligence (AI) in healthcare has ushered in a revolutionary era by transforming the way we provide care and reduce adverse consequences. This paper explores the field of AI applications in healthcare, including side effect management, streamlined administrative procedures, and customized treatment programs.

Artificial Intelligence has transformed the cyber security industry by enabling organizations to systematize and enlarge outdated safety procedures. AI can provide more effective threat detection and response capabilities, enhance vulnerability management, and improve compliance and governance. AI technologies such as machine learning, natural language processing, behavioral analytics, and deep learning can enhance cyber security defenses and protect against a wide range of cyber threats, including malware, phishing attacks, and insider threats.Theoretical underpinnings of AI in cyber security, such as machine learning, natural language processing, behavioral analytics, and deep learning, are discussed. The advantages of using AI in cyber security are discussed including speed and accuracy, continuous learning and adaptation, and efficiency and scalability. It's important to note that AI is not a silver bullet for cyber security and should be used in conjunction with other security measures to provide a comprehensive defense strategy.AI has transformed the way cyber security operates in today's digital age. By analyzing vast amounts of data quickly and accurately it has become a valuable tool for organizations looking to protect their assets from cyber threats.

Background: Artificial intelligence can help improve the quality of healthcare by analyzing vast amounts of data and providing more effective and personalized treatment plans. Researchers are working on developing AI-powered solutions that can help improve the outcomes of patients. Objective: To explore the potential of AI in improving healthcare outcomes and patient experience. Results: The study suggests that AI can improve healthcare efficiency and patient outcomes but cannot fully replace human healthcare professionals. AI can assist healthcare professionals in their work, leading to better resource utilization and improved patient care. However, there is still a need for human healthcare professionals to oversee AI systems and provide empathy and personalized care to patients. Conclusion: While there is immense potential for AI in healthcare, it is not yet feasible to replace human healthcare workers. Instead, it should be viewed as a tool that can help improve the efficiency and effectiveness of human healthcare.

Background: Cyber security is used to save the important data from getting hacked, took by some unknown access. It makes our environment a safe place for us to work out or share our information and since privacy is almost everyone’s top priority, there must be the surety of saving the private information. Methods: In today’s world cyber security is really important as there are now many devices, websites, new technology which makes it a lot easier for hackers to get into anyone’s files which stores crucial data. However, many steps can be reserved to protect the data such as; educating your employees with how to prevent your files or computers from getting hacked, avoid clicking on websites that don’t seem safe, use firewalls, antimalware system and most importantly make sure to keep passwords that are hard to guess and try to go for a face recognition instead of pins and passcodes. Results and Conclusion: This all does not only makes using technology safe but also favors your business for the safety of employees working within the organization. The customers being assured about their data being safe, improves productivity since viruses can slow down computers which may trigger the focus of staff during the working hours. It could save your system from adware; the links of websites are also prevented from slow internet speeds. There is no denying at the fact that cyber security is a major requirement for everyone who is indulged with technology today.

Background: The outbreak of the Covid-19 pandemic has significantly affected the operations of higher education institutions. Due to the limited use of video conferencing and cloud computing in these facilities, distance learning became the only option available to them. Objective: The study focused on identifying the most common types of attacks that can affect e-learning assets. Results: There was a lack of clear cybersecurity policies for educational institutes and universities in 2020, according to a report by Microsoft Security Intelligence. The report showed that the education industry was the most targeted sector for malware attacks in the last 30 days. Conclusion: The recommendations for improving the security of e-learning systems. Some of these include implementing policies that restrict access to the resources and applications, updating security patches, and using cryptographic protocols.

Background: The rise of cloud computing has led to the increasing number of organizations that rely on it for various tasks and services, such as education, healthcare, and e-commerce. Unfortunately, many security threats can be caused by the sudden use of cloud platforms. Objective: This paper aims to provide a comprehensive overview of these threats and how they can be mitigated. Many companies are moving toward cloud computing to sustain their business growth and provide their employees with the best possible work environment. Results: Due to the rise of cyber security threats and the unprecedented number of breaches of data, small and medium-sized enterprises are also starting to take a huge leap. The outbreak of COVID-19 has affected the lives of people all around the world. Conclusion: Due to the seriousness of the situation, the WHO has declared the COVID-19 pandemic a public health emergency. To minimize the spread of the virus, the entire world has started adopting social distancing

Background: Smartphones have become an indispensable part of the daily lives of adolescents in the 21st century, which is characterized by a highly digitized modern world. Besides their many advantages, smartphones might pave the way to compulsive usage and addictive experiences. To remedy this problem, this study proposes an authentic approach which integrates consumer behavior theories and techniques such as unhook and gamification. An education program has been designed based on these approaches to decrease the problematic smartphone use. Method: The participants of the education program consisted of 305 students (48.2% girls and 51.8% boys) with a mean age of 14.57 (SD = 0.74). The Demographic Form and Smartphone Addiction Scale for Adolescents (SASA) were conducted before the education program and three weeks after the education. Results: The results of the paired sample t-test analysis before and after the education program revealed that the SASA total scores decreased significantly (p < 0.01). There are significant differences in terms of gender, mothers’ education and class levels. Conclusion: This research emphasizes the role of an interdisciplinary approach to the addiction problem. The content used in the education program includes strategies that originally aimed at increasing consumption. The effectiveness of the program can be enhanced further in the future along with self-regulatory additions.

No single evolutionary event has been identified as the cause for the final emergence of our species. I propose that a mutation on CD155 receptor gene occurred to establish a symbiosis with poliovirus, which exerted its beneficial impact via RNA dependent non-genetic transgenerational inheritance, which caused a qualitative enhancement of cognitive functions. I posit that this mutation occurred in what we call, Anatomically Modern Humans, our immediate ancestor species and that the disruption of this symbiosis causes autism spectrum disorder. Positive selection of CD155 to the extent of becoming a species defining characteristic, the chronology of autism spectrum disorder prevalence increase and continued increase, the multigenerational nature of RNA inheritance, the universal infection of humans by poliovirus and a very low associated mortality rate, and several other factors support this hypothesis. Specific genetic, epidemiological and sperm miRNA content studies are suggested to test this hypothesis.

In this paper, we investigate a new subclass of univalent functions defined by a generalized differential operator, and obtain some interesting properties of functions belonging to the class R^m λµ ( α, β, γ, υ ).

This study investigated the potential of electrospun collagen/Hyaluronic Acid nanofibers in biomedicine and skincare. Needleless electrospinning technology employed in the production process resulted in nanofibers with consistent fiber diameters and improved mechanical properties. The composite nanofibers demonstrated impressive tensile strength and elongation capabilities, indicating a favorable interaction between collagen and HA. Cellular assays revealed that the collagen/HA scaffolds facilitated superior cell adhesion and proliferation compared to the control scaffolds, indicating their biocompatibility and potential for tissue engineering. This biomaterial offers an environment-friendly alternative to conventional skincare products by eliminating the need for preservatives and reducing waste. Although the initial costs are high, the long-term benefits of material durability and active ingredient efficacy provide a compelling case for industrial adoption. Our findings suggest that collagen/HA nanofibers have the potential to be next-generation materials for regenerative medicine and skincare, and further research is needed to optimize their production and assess the economic feasibility of skincare and tissue engineering applications.

Coronary no-reflow (CNR) is a frequent phenomenon that develops in patients with ST-segment elevation myocardial infarction (STEMI) following reperfusion therapy. CNR is highly dynamic and develops gradually (over hours) and persists for days to weeks after reperfusion. Microvascular obstruction (MVO) developing as a consequence of myocardial ischemia, distal embolization and reperfusion-related injury is the main pathophysiological mechanism of CNR. The frequency of CNR or MVO after primary PCI differs widely depending on the sensitivity of the tools used for diagnosis and timing of examination. Coronary angiography is readily available and most convenient to diagnose CNR but it is highly conservative and underestimates the true frequency of CNR. Cardiac magnetic resonance (CMR) imaging is the most sensitive method to diagnose MVO and CNR that provides information on the presence, localization and extent of MVO. CMR imaging detects intramyocardial hemorrhage and accurately estimates the infract size. MVO and CNR markedly negate the benefits of reperfusion therapy and contribute to poor clinical outcomes including adverse remodeling of left ventricle, worsening or new congestive heart failure and reduced survival. Despite extensive research and the use of therapies that target almost all known pathophysiological mechanisms of CNR, no therapy has been found that prevents or reverses CNR and provides consistent clinical benefit in patients with STEMI undergoing reperfusion. Currently the prevention or alleviation of MVO and CNR remain unmet goals in the therapy of STEMI that continue to be under intense research.

Background: This research examines how market knowledge and artificial intelligence (AI) interact in different market designs such as business-to-business (B2B) settings while taking emerging technologies and the changing digitalization landscape into account. Objective: The main goal is to understand how AI affects market knowledge in different market designs such as business-to-business (B2B) contexts, taking into account language barriers, practical difficulties, and the revolutionary effects on decision-making and customer interactions. Result: They underscore the transformative potential of artificial intelligence (AI) by highlighting how it shapes market knowledge, encourages customized approaches, and improves marketing efficacy in the business-to-business (B2B) space. Conclusion: In order to create a path for responsible AI integration in B2B marketing, the study concludes with recommendations for standardized terminology related to AI, practical insights into implementation challenges, and ethical issues.

Background: The research is conducted against the backdrop of the rising incidence of diabetes and the demand for novel approaches to improve patient outcomes. With the goal of revolutionizing conventional methods and addressing the changing healthcare landscape, the study concentrated on the application of AI technology in diabetes treatment. Objective: Examining the various ways AI can be used in diabetes care and assessing how it can enhance automated screenings, tailored treatment recommendations, predictive modeling, and patient self-management were the main goals of the study. Result: The findings demonstrated how well AI works to anticipate a patient's unique reaction to a therapy, assist medical personnel in making decisions, and give patients more control over their own care. Artificial intelligence (AI)-powered solutions showed promise in improving real-time monitoring and encouraging patient adherence, supporting a comprehensive strategy for managing diabetes. Conclusion: The research indicates that artificial intelligence (AI) has great potential to change the way diabetes is treated. However, it also highlights the need for more research, ethical issues, and validation of AI. The results advocate for joint efforts to ensure responsible and patient-centered technology innovation in diabetes care and encourage the integration of AI with new technologies.

ackground: The swift assimilation of Artificial Intelligence (AI) across multiple industries is altering the worldwide economic terrain. Unprecedented opportunities and difficulties are brought about by this revolutionary technology, which has an impact on innovation, trade, labor markets, and security. Objective: The goal of this study is to thoroughly investigate the economic effects of AI by exploring how it may affect labor markets, innovation, trade internationally, and security. The goal is to offer insights that support strategic decision-making and policy formation by examining current trends, opportunities, and difficulties. Results: The research highlights the dual character of artificial intelligence's influence, highlighting both its potential to spur economic expansion and its drawbacks, including the loss of jobs, a lack of standards, and security issues. The study highlights how crucial cooperation is to overcoming these obstacles and realizing AI's full potential. Conclusion: The balancing opportunities and risks becomes critical as AI continues to change the economic landscape. The report promotes proactive steps including ethical AI design guidelines, ongoing worker retraining, and flexible regulatory strategies. As we shape an AI future that puts inclusion, creativity, and responsible governance first, collaboration emerges as a key theme.

Intracortical microelectrodes that can be implanted have the ability to capture fast-changing neuron action potentials in the living brain. Despite this potential, many obstacles must still be overcome for reliable, long-term, high-quality recordings and accurate analysis of brain activity. These challenges include improving recording quality, enhancing recording stability, increasing recording capacity, and adding multi-functional features. One potential approach to enhance implantable microelectrodes involves the advancement of materials, refinement of implantation methodologies, and augmentation of the number of sites for recording. However, these challenges are still being actively researched, and advancements in microelectrode technology are underway. The difficulties with implantable microelectrodes are evaluated, and solutions are presented from different perspectives. The latest advancements in microelectrode technology are reviewed and condensed, and future possibilities are explored.

This paper is a review of the current state of IoT-based technologies in the healthcare field for the elderly population. The increasing population of elderly people has led to a greater demand for healthcare services to meet their unique needs. IoT-based technologies have emerged as a promising solution to address this demand. The paper analyses the current trends, challenges, and future opportunities for research in this area. It highlights how IoT-based technologies can be utilized to improve healthcare for older adults, from remote monitoring and telemedicine to fall detection and medication management. The paper also acknowledges that there are still challenges to be addressed, such as data privacy and security, but the potential benefits for older adults outweigh the challenges. The paper also examines the impact of the industrial revolution on the aging population and the corresponding increase in demand for geriatric healthcare. In summary, this paper delves into the current status of IoT-based technologies in geriatric healthcare and its future prospects, with a focus on addressing the needs of the aging population and improving their quality of life.

This review paper delves into using micro-credentials in higher education ecosystems as a digital enablers. Micro-credentials, which are digital credentials that attest to a learner’s mastery of a specific skill or knowledge area, are becoming more popular in higher education. The paper examines the successful implementation of micro-credential frameworks in higher education, using case studies to demonstrate the advantages of micro-credentials. The review emphasizes the agility and flexibility of microcredentials, which enable learners to acquire new skills quickly and respond to changes in the job market. In addition, the paper discusses the digital nature of micro-credentials and how they allow institutionsto provide targeted, skills-based training that isrelevant to employers. It also explores how micro-credentials are delivered through online platforms, making them convenient and easily accessible for learners. The review underscores the significance of digital infrastructure, connectivity, and public utility for promoting micro-credentials. The paper argues that micro-credentials function as a digital enabler for higher edu- cation ecosystems, allowing learners to acquire targeted training and enabling institutions to expand their offerings and reach more students. The paper concludes by highlighting the potential for micro-credentials to help bridge the skills gap and equip learners with the skills necessary to succeed in today’s digital economy.

The concept of a metaverse, a shared virtual space that brings together the physical and digital worlds, has been a topic of interest for decades. With recent advancements in technology, particularly in virtual reality (VR), augmented reality (AR), and blockchain, the metaverse has become a rapidly evolving and promising ecosystem. This paper provides a review of the metaverse, exploring its history, evolution, and current state of development. The paper delves into the different types of the metaverse, including fully immersive VR metaverses, hybrid metaverses that blend the physical and virtual worlds, and decentralized metaverses that are powered by blockchain. Additionally, the paper examines the impact of web 3.0 on the metaverse, as well as the various technologies used to create and sustain the metaverse, including VR, AR, and blockchain. Furthermore, the paper explores the concept of digital ownership and the potential for marketing and commerce within the metaverse. This paper serves as a review of the metaverse and its various components, providing insights into its history, development, and prospects, and is intended to provide a foundation for further research into this exciting and rapidly evolving ecosystem.

Artificial intelligence (AI) is a versatile term that is a conclusive remedy to solve the problem using past rational data after deep contemplation using these terms i-e basic statistics, carving data, familiarity with common AI algorithms. Seafood especially tiger prawn export as a busi-ness will provide enormous foreign exchange to any country if the farmers overcome the corre-lated vulnerabilities in prawn farming. This research is elucidating lacking in Tiger prawn (TP) farming like curbing of Oxygen, pH, water temperature, and nutrients, etc. Moreover, hatchery statistics in terms of juveniles will depict this study's clear picture of curbed aquaculture. For normative decisions, the Analytical Hierarchical Process (AHP) is used. The problem which has been faced by local prawn farmers that there is a stagnant TP growth in ponds, the reason is the predominant sensitivity factor in TP. For this reason, they need indemnification of thirteen fac-tors with natural resources to get the plausible results to get calmness in their lives. This study will solely focus on the TP growth model, and the monitoring effect will be established by the Artificial Intelligence algorithm. This study will employ the AHP, 0-1 scaling method, data cura-tion techniques, and ecological statistics. The life of Tiger Prawn (TP) depends upon these factors mainly, a) Physical and b) Chemical parameters. Physical parameters contain environment (E) provided to TP like season (S) and temperature (T) etc. whereas the quality of Ammonia NH3 (N) from fish waste, Oxygen level (O), and water quality hard & soft (W) lies in chemicals do-main. This research will Elucidate the factors which cause conceptual muddles in the aquamarine life of TP, for this reason, Statistical tools will assess the current result, forecast the gap. AHP will analyze the domain inputs, circumspect ramification which will depict visceral factors, later results depict which pond suits the TP. In curtail, these factors will be curbed to improve the growth of TP in a control conditioned environment.

The observed data of COVID-19 progression in Pakistan for first 50 days from the first patient been reported has shown quite an unusual trend which is in opposition to clear exponential spread pattern of any infectious disease. The data of positive cases of 50 days of disease progression has been collected from COVID-19 dashboard of Pakistan and analyzed to see the graphical trend and to forecast the behaviour of disease progression for next 30 days. Mathematical equations regarding exponential growth are used to analyse the disease progression and different possible trajectories are plotted to understand the approximate trend pattern. The possible projections estimated 20k-456k positive cases within 80 days of disease spread in Pakistan. Although, the disease progression pattern is not perfectly exponential, it is still threatening a major fraction of susceptible population and demands effective strategic planning and control.

Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.

This study investigates the failure of a roof with steel truss construction of a factory building in Tekirdag in North-western part of Turkey. The failure occurred under hefty weather conditions including thunderbolt, lightning strikes, heavy rain and fierce winds. In order to interpret the reason for the failure, the effects of different combinations of factors on the design and dimensioning of the roof are checked. Therefore, finite element analysis is performed several times under different assumptions and considering different factors aiming to determine the dominant ones that are responsible for the failure using the commercial software Abaqus (Dassault Systèmes, Vélizy-Villacoublay, France). Each loading condition gives out a characteristic form of failure. The scenario with the most similar form of failure to the real collapse is considered as the most likely scenario of failure. Also, the factors included in this scenario are expected to be the responsible factors for the partial collapse of the steel truss structure.

To close the nutrient-related yield gaps in maize, balanced nutrition using primary and secondary macronutrients as well as micronutrients is recommended. Multi-nutrient diagnostic field trials were conducted in Guinea Savanna of Nigeria to assess the interactive effects of macronutrients and micronutrients on maize grain yields, nutrient uptake as well as N, P and K use efficiencies. The treatments consisted of a control (zero fertilizer applied), an NPK treatment and 10 other treatments in which macronutrient (Mg, S) and/or micronutrients (B, and Zn) were added to the NPK. The experiment was laid out in Randomized Complete Block Design with 3 replications. Data collected were subjected to mixed model with nutrient management strategy as fixed effects while replication nested in location and interaction between location and nutrient management strategy as random effects. The results revealed significant effects (P < 0.05) of nutrient management strategy on maize yield, nutrient uptake and nutrient use efficiencies of N, P and K. The study revealed that yield advantage over the recommended NPK fertilizer as a result of application macronutrients and micronutrients were highest with Mg in Lere (2.4 t ha⁻¹), S + B + Zn in Faskari (2.8 t ha⁻¹), S + B in Doguwa (1.5 t ha⁻¹) and S + Zn in Toro (2.4 t ha⁻¹). Addition of Mg, S and B significantly increases macronutrient uptakes over the recommended NPK only. Agronomic use efficiency, internal utilization efficiency, apparent recovery efficiency and partial factor productivity were significantly increased with the addition of S, Mg, and B but were not improved with Zn application. It was concluded that nutrient limitations to maize in the Guinea Savanna go beyond N, P and K. This study recommends that S, Mg, and B are needed to improve maize productivity and engender improve the use efficiency of NPK fertilizers.

A cognitive radio sensor network (CRSN) based Smart Grid (SG) is a new paradigm for a modern SG. It is totally different from the traditional power grid and also different from the conventional SG that uses a static resource allocation technique to allocate resources to sensor nodes and communication devices in the SG network. Due to the challenges associated with competitive sensor nodes and communication devices in accessing and utilizing radio resources, the need for dynamic radio resource allocation (RRA) has been proposed as a solution for allocating radio resources to sensor nodes in a CRSN based smart grid ecosystem (network). These challenges include energy/power constraints, poor quality of service (QoS), interference, delay, spectrum efficiency issues, and excessive spectrum hand-offs. Hence, the optimization of resource allocation criteria, such as energy efficiency, throughput maximization, QoS guarantee, fairness, priority, interference mitigation/avoidance, etc., will go a long way in addressing the problems of RRA in a CRSN based SG. Consequently, this work explores RRA in CRSNs for SGs. Various resource allocation schemes, as well as its architecture in a CRSN for SG environment, are presented. The work reported in this paper introduces a model called the “guaranteed network connectivity channel allocation” for throughput maximization (GNC-TM) and optimal spectrum band determination in RRA for improved throughput criteria in CRSNs for SGs. The results show that the model outperforms the existing protocol in terms of throughput and error probability. Finally, the contribution to knowledge and future research direction, such as energy efficiency and hybrid energy harvesting schemes are highlighted.

With the rapid surge in technological advancements, an equal amount of investment in technology-embedded teaching has become vital to pace up with the ongoing educational needs. Distance education has evolved from the era of postal services to the use of ICT tools in current times. With the aid of globally updated content across the board, technology usage ensures all students receive equal attention without any discrimination. Importantly, web-based teaching allows all kind of students to learn at their own pace, without the fear of being judged, including professionals who can learn remotely without disturbing their job schedules. Having web-based content allows low-cost and robust implementation of the content upgradation. An improved, yet effective, version of the education using such tools is Hybrid Learning (HL). This learning mode aims to provide luxurious reinforcement to its legitimate candidates while maintaining the quality standards of various elements. Incorporated with both traditional and distance learning methods, along with exploiting social media tools for increased comfort level and peer-to-peer collaboration, HL ultimately facilitates the end user and educational setup. The structure of such a hybrid model is realized by delivering the study material via a learning management system (LMS) designed in compliance with quality standards, which is one of the fundamental tackling techniques for controlling quality constraints. In this paper, we present the recently piloted project by COMSATS University Islamabad (previously known as COMSATS Institute of Information Technology) which is driven by technology-embedded teaching model. This model is an amalgam of the traditional class room model with the aid of state-of-the-art online learning technologies. The students are enrolled as full-time students, with all the courses in traditional classroom mode, except one course offered as hybrid course. This globally adapted model helps the students to benefit from both face-to-face learning as well as gaining hands-on experience on technology-enriched education model providing flexibility of timings, learning pace, and boundaries. Our HL model is equipped with two major synchronous and asynchronous blocks. The synchronous block delivers real-time live interaction scenarios using discussion boards, thereby providing a face-to-face environment. Interactions via social network has witnessed equally surging improvement in the output performance. The asynchronous block refers to the lecture videos, slides and handouts, prepared by imminent professors, available 24/7 for students. To ensure quality output, our HL model follows the course learning outcomes (CLOs), and program learning outcomes (PLOs) as per international standards. As a proof of concept, we have deployed a mechanism at the end of each semester to verify the effectiveness of our model. This mechanism fundamentally surveys the satisfaction levels of all the students enrolled in the HL courses. With the surveys already conducted, a significant level of satisfaction has been noted. Extensive results from these surveys are presented in the paper to further validate the efficiency and robustness of our proposed HL model.

The aim of this study was to investigate in-vitro antioxidant properties and in-vivo protective effects of different concentrations of Hypericum triquetrifolium Turra. (HT) seed methanol extracts against acute hepatotoxicity, myelotoxicity and hematotoxicity in rats exposed to overdose of cyclophosphamide (CP). HT seed methanol extracts were tested in view of its in-vitro antioxidant activities as total phenolic contents and DPPH free radical-scavenging activity. To investigate in-vivo protective effects of HT seed methanol extracts on rat tissues; tested animals were divided into nine groups. Three groups only were treated with HT extracts (25, 50 and 100 mg/kg HT) for 6 days. Three groups were pre-treated with the extract of HT (25, 50 and 100 mg/kg HT) for 6 days and on the last day they were injected with single dose of CP (150-mg/kg body weight). Two groups were used as control groups and one group was only treated with CP (150 mg/kg) on the 6th day. The toxic effects of CP and protective effects of HT extracts on the nucleated cells which were produced by bone marrow and serum alanine transaminase (ALT), alkaline phosphatase (ALP), lactate dehydrogenase (LDH), oxidative stress index (OSI) levels were investigated biochemically. Additionally, liver tissue samples were examined histopathologically. Our results show that HT seed methanol extract has high total phenolic content and antioxidant activity. Over dose CP administration caused hepatotoxicity, myelotoxicity and hematotoxicity on rat. Whereas, 25, 50 and 100 mg/kg HT plus CP administered groups showed significant protective effects on nucleated cells. And 25, 50, 100 mg/kg HT plus CP treated groups showed an important decrease on serum ALT, ALP, LDH and OSI levels when compared with CP treated group. Our results showed that the administration of different HT doses with high doses of CP significantly reduced hepatotoxicity, myelotoxicity and hematoxicity on rats.

Sorghum has become increasingly popular for diverse applications, including food, feed, and bioenergy. The present study aimed to perform a bibliometric and content analysis on sorghum research in the industrial-agricultural sector to evaluate global research patterns, emerging topics, and future trends. The bibliometric analysis of the Scopus bibliographic metadata was done using Bibliometrix and Vosviewer. The results of this investigation show the descriptive analysis underscored fluctuation in research output and citation impact over the years. The identified four patterns through co-occurrence analysis encompassed central themes of sorghum research, including genetic expression and plant stress cluster, cereal crop and yield cluster, biomass and microbial bioprocessing, and metabolism and antioxidant cluster. The thematic map portrays a comprehensive view of cereal crops and yields, biomass and microbial bioprocessing, metabolism and antioxidant research, environmental remediation and sustainable agriculture practices, and the declining theme of starch. The findings provide valuable insights into the global pattern of sorghum research and emerging trends, thus directing future multidisciplinary research efforts for enhanced knowledge and sustainable practices in the industrial-agricultural sector.

ADHD, a neurodevelopmental condition, is distinguished by a triad of symptoms including inattention, hyperactivity, and impulsivity. It is frequently accompanied by comorbidities such as anxiety, depression, and learning disabilities. As a result, clinicians often face challenges in accurately diagnosing ADHD and differentiating it from other conditions. As a prospective remedy for ADHD, scientists have investigated Transcranial Direct Current Stimulation (tDCS), a non-invasive technique of stimulating the brain. It involves applying low-intensity electrical currents to specific regions of the brain to modulate neural activity. This review paper aims to provide an overview of the comorbidities associated with ADHD and the differential diagnosis of the condition. It also explores the potential of tDCS as a treatment option for ADHD, including its mechanisms of action and efficacy in improving ADHD symptoms. The comorbidities discussed in this review include anxiety disorders, mood disorders, and substance use disorders. These comorbidities are frequently seen in individuals with ADHD and can complicate the diagnosis and treatment of the condition. The paper also highlights the importance of considering comorbidities when assessing ADHD, as well as the potential impact of these conditions on treatment outcomes. The differential diagnosis section of the paper explores conditions that can present with symptoms similar to ADHD, such as anxiety disorders, mood disorders, and learning disabilities. It emphasizes the need for a thorough assessment and differential diagnosis to identify ADHD and differentiate it from other conditions accurately. The final section of the paper discusses the potential of tDCS as a treatment option for ADHD. It examines the mechanisms of action of tDCS and its efficacy in improving ADHD symptoms, including attention, hyperactivity, and impulsivity. It also explores the potential for tDCS to improve comorbid conditions associated with ADHD. Overall, this review provides a comprehensive overview of comorbidities and differential diagnoses in ADHD and the potential of tDCS as a treatment option. The paper highlights the importance of a thorough assessment and personalized treatment plan for individuals with ADHD, particularly those with comorbidities.

This review paper explores auditory tactile (AT) synesthesia, a rare neurological condition where sounds evoke tactile sensations. The paper provides a historical overview of the condition and discusses its epidemiology, with a prevalence of less than 1% of the general population. The neurological basis of AT synesthesia is explored, including the role of cross-modal processing and hyperconnectivity within the brain. The paper also describes the phenomenology of the condition, including the range of tactile sensations that can be experienced in response to different sounds. The occurrence of AT synesthesia in the present-day world is discussed, including its relationship to music and art. Various hypotheses surrounding the development and maintenance of AT synesthesia are reviewed, focusing on genetic and environmental factors. The implications for clinical practice are explored, including potential benefits for individuals with sensory processing disorders. Finally, the paper concludes with a discussion of future directions for research in this field, including the need to explore further the underlying neural mechanisms of AT synesthesia and potential therapeutic interventions.

Oil spillage over a sea or ocean’s surface is a threat to marine and coastal ecosystems. Spaceborne synthetic aperture radar (SAR) data has been used efficiently for the detection of oil spills due to its operational capability in all-day all-weather conditions. The problem is often modeled as a semantic segmentation task. The images need to be segmented into multiple regions of interest such as sea surface, oil spill, look-alikes, ships and land. Training of a classifier for this task is particularly challenging since there is an inherent class imbalance. In this work, we train a convolutional neural network (CNN) with multiple feature extractors for pixel-wise classification; and introduce to use a new loss function, namely ‘gradient profile’ (GP) loss, which is in fact the constituent of the more generic Spatial Profile loss proposed for image translation problems. For the purpose of training, testing and performance evaluation, we use a publicly available dataset with selected oil spill events verified by the European Maritime Safety Agency (EMSA). The results obtained show that the proposed CNN trained with a combination of GP, Jaccard and focal loss functions can detect oil spills with an intersection over union (IoU) value of 63.95%. The IoU value for sea surface, look-alikes, ships and land class is 96.00%, 60.87%, 74.61% and 96.80%, respectively. The mean intersection over union (mIoU) value for all the classes is 78.45%, which accounts for a 13% improvement over the state of the art for this dataset. Moreover, we provide extensive ablation on different Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) based hybrid models to demonstrate the effectiveness of adding GP loss as an additional loss function for training. Results show that GP loss significantly improves the mIoU and F1 scores for CNNs as well as ViTs based hybrid models. GP loss turns out to be a promising loss function in the context of deep learning with SAR images.

Extensive genomic analyses of Enterococcus cecorum isolates from sepsis outbreaks in broilers suggests a polyphyletic origin likely arising from core genome mutations rather than gene acquisition. This species is normal intestinal flora in avian species with particular isolates associated with osteomyelitis. More recently, the species has been associated with sepsis outbreaks affecting broilers during the first 3 weeks post hatch. Understanding the genetic and management basis of this new phenotype is critical for developing strategies to mitigate this emerging problem. Phylogenomic analyses of 227 genomes suggest that sepsis isolates are polyphyletic and closely related to both commensal and osteomyelitis isolate genomes. Pangenome analyses detect no gene acquisitions that distinguish all sepsis isolates. Core genome SNP analyses identify a number of mutations, affecting protein coding sequence, that are enriched in sepsis isolates. Analysis of the protein substitutions supports mutational origins of sepsis isolates.

The ozonation reaction in a bubble column reactor (BCR) has been widely used in the removal of phenol from wastewater, but the phenol removal efficiency in this type of reactor is limited because of low ozone solubility and reactivity in the system. In the present study, the phenol degradation was enhanced using α-Al2O3 as a packing material and a ZnO nanocatalyst in the BCR. The reactor diameter and height were 8 cm and 180 cm, respectively. The gas distributor was designed to include 52 holes of 0.5-mm diameter. Also, the gas holdup, pressure drop, and bubble size were measured as a function of the superficial gas velocity (i.e., 0.5, 1, 1.5, 2, 2.5, and 3 cm/s). The evaluation of the hydrodynamic parameters provided a deep understanding of the ozonation process to select the optimal operating parameters in the reactor. It was found that the best superficial gas velocity was 2.5 cm/s. A complete (100%) phenol removal was achieved for phenol concentrations of 15, 20, and 25 ppm at reaction times of 80, 90, and 100 min, respectively, using α-Al2O3 packing material and a ZnO nanocatalyst in the BCR. Additionally, the results provided a clear reaction mechanism describing the ozonized BCR. Finally, the applied treatment method can be used efficiently to remove phenol from wastewater at a low cost, with little consumed energy, and a simple operation.

This study investigates the elastic buckling behaviour of skew Functionally Graded Material (FGM) thin plates featuring a circular opening. FGM materials, known for their unique property gradients, have gained prominence in structural engineering due to their mechanical performance and durability. Including a circular opening introduces a critical geometric consideration, influencing the structural stability and load-carrying capacity of FGM plates. The study examines the effects of the skew angle, plate’s aspect ratio, opening position, and size on the critical buckling load, normalized buckling load, and various buckling failure modes through computer modelling and finite element analysis. The results offer valuable insights into the interplay between material heterogeneity, geometric configuration, and structural stability. For instance, the critical buckling load increases by 29%, 82%, and 194% with the increment of skew angle from 0° to 30°, 45°, and 60°, respectively. Moreover, as the opening shifts from the plate’s edge closer to the center, the critical buckling load decreases by 26%. This research contributes to the advancement of understanding FGM thin plates’ behaviour under skew loading conditions, with implications for the design and optimization of innovative structures. The findings presented provide a foundation for further exploration of advanced composite materials and their applications in structural engineering.

This study presents an exploration into identifying the interactions between ocean waves and the continental margin in the origination of double-frequency (DF, 0.1-0.5 Hz) microseisms recorded at 33 stations across East Coast of USA (ECUSA) during a ten-day period of ordinary ocean wave climate. Daily primary vibration directions are calculated in three frequency bands and projected as great circles passing through each station. In each band, the great circles from all stations exhibit largest spatial density primarily near the continental slope in the western North Atlantic Ocean. Generation mechanisms of three DF microseism events are explored by comparing temporal and spatial variations of the DF microseisms with the migration patterns of ocean wave fronts in Wavewatch III hindcasts. Correlation analyses are conducted by comparing the frequency compositions of and calculating the correlation coefficients between the DF microseisms and the ocean waves recorded at selected buoys. The observations and analyses lead to a hypothesis that the continental slope causes wave reflection, generating low frequency DF energy and that the continental shelf is where high frequency DF energy is mainly generated in ECUSA. The hypothesis is supported by the primary vibration directions being mainly perpendicular to the strike of the continental slope.

In this paper, new N-way Wilkinson power dividers (WPDs) are represented by using slow-wave structures such as narrow slits loaded and meandered transmission lines. The slit loaded and meandered lines are used to represent the quarter wavelength transmission lines of a conventional WPD for size reduction. Based on the proposed approaches, 2-, 4- and 8-way power dividers are designed, simulated and fabricated. The fabricated -, 4- and 8-way circuits have been measured at the center frequencies of 2.03, 1.77, and 1.73 GHz in an excellent agreement with the predicted ones. The meandered transmission lines are also used to design new types of WPDs having novel input/output port arrangements. For this purpose, two 3-way WPDs are located at both sides of the same board so as to have different power splitting ratios at different inputs and outputs to provide alternative solutions for antenna arrays. Furthermore, a 5-way dual layer WPD is introduced by locating the meandered transmission lines to two layers. The proposed 3- and 5-way WPDs allow halving the input power at the next output port in the order of P/2, P/4, P/8, P/16, and P/16. All of the designed power halving WPDs have been simulated, fabricated and successfully tested.

Social media platforms are not serving only as tools of communication rather they are acting like intricate systems managing our interactions. A major setback to our interactions occur when the flow of information in these networks gets distorted through polarization. It resembles a society where every debate turns into divide. Our research focuses exclusively on quantifying polarization as well as solving this issue head-on. In order to accurately gauge polarization, we’ve developed a new method that takes into consideration majority of its influencing factors. This helps us in correctly quantifying the polarization value of a given network and then by proposing it as an optimization problem we are attempting to maximally decrease polarization while staying within the set budget. We test our method on synthetic and real-world data sets, and find out that polarization is declining, and diversity is increasing. This proves that our recommendation engine has a penchant for finding the ideal connections between the nodes to start a dialogue. Our research presents a successful tactic to quantifying and reducing polarization in social media networks. The results indicate that our novel metric and intervention technique are effective in dismantling echo chambers, thereby promoting diversity in these networks.

In this paper a low profile antenna is presented for wideband communication applications. The presented design is consists of an I-shape driven strip and a rectangular ground strip with an open slot in the middle and steeped lower portion. Measured result demonstrates that the achieved operating band of the proposed antenna has the potential to cover Global Star Satellite Phones (1.61-1.63 GHz, uplink), Advanced Wireless Systems (1.71-1.76 GHz, 2.11-2.17 GHz), DCS (1710-1880 MHz), GSM (1800MHz), DCP (1.88-1.90 GHz), DCS-1900/PCS/PHS (1850-1990MHz), WCDMA/IMT-2000 (1920-2170MHz), UMTS (1920-2170 MHz), LTE bands (FDD LTE bands 1-4, 9-10, 15-16, 23-25, 33-37, 39). The designed antenna possessed a very small size of 0.35λ0 × 0.027λ0 at the lowest frequency (S11 ≤ -10dB) and achieved good gain, exhibits stable radiation patterns which make it suitable for handheld communication devices.

Certain Trichoderma isolates provide biofertilizer, biocontrol, and other plant-beneficial activities while inhabiting the soil or internal plant issues; and their use in agricultural systems could contribute to sustainable food production. It is thought that this colonization of soil and internal plant tissues is fundamental for biocontrol and biofertilizer applications. Our collective analyses of prior surveys, where the tef1 sequence was almost exclusively used to identify Trichoderma species, showed that isolates from the Harzianum Complex Clade, the T. asperellum/T. asperelloides group, T. virens, T. hamatum, and T. atroviride were prevalent in soil and/or as endophytes. Population structure and genetic diversity of these species were investigated, and new lineages with geo-graphic significance within T. atroviride, T. asperellum, T. hamatum, and T. virens populations were found. The nearest relatives of some species were also revealed. Choosing isolates from among more than 500 known Trichoderma species for use in non-targeted evaluation screens for biocontrol or biofertilizer applications is time-consuming and expensive. Preferentially selecting species from T. atroviride, T. asperellum/T. asperelloides, T. hamatum, species from the T. harzianum Complex Clade, T. virens, and possibly nearest relatives, may speed the identification of candidates for commercialization due to the demonstrated ability of these species to successfully inhabit the soil and endorhizosphere. To our knowledge, this is the first report where dominant soil- and en-dophyte-inhabiting Trichoderma species were identified from past survey data and population structure and genetic diversity analyses conducted.

Dubermatinib (DMB, TP-0903), a benzenesulfonamide, is an inhibitor of the tyrosine kinase AXL, which is a member of the TAM family, and can prevent GAS6-mediated activation of AXL in cancer cells. Patients with previously treated chronic lymphocytic leukemia are being studied in phase I/II clinical trials to determine its antineoplastic potential (CLL). In the current work, the reactive intermediates of DMB were studied using liquid chromatography-mass spectrometry (LC-MS). Human liver microsomes (HLMs) were exposed to dimethylbenzene in a laboratory setting, and the resulting metabolites were collected through protein precipitation. Intense reactivity toward nucleophilic macromolecules was seen in the metabolites of the piperazine and pyrimidine rings in DMB, iminium and 2,5-quinone-imine, respectively. To assess the toxicities of the possibly reactive metabolites, DMB was incubated with HLMs in the presence of 1.0 mM KCN and 1.0 mM glutathione. The DMB metabolites found by LC-MS/MS were seven in vitro phase I metabolites, three cyano adducts, and two GSH conjugates. Phase I in vitro metabolic reactions included N-demethylation, hydroxylation, and dechlorination. The in vitro metabolism of DMB and the metabolites it produces have not been studied before.

As the number of ships for marine transportation increases with the advancement of global trade, encountering multiple ships in marine traffic becomes common. This situation raises the risk of collision of the ships; hence this paper proposes a novel Fuzzy-logic based intelligent conflict detection and resolution algorithm, where the collision courses and possible avoiding actions are analyzed by considering ship motion dynamics and the input and output fuzzy membership functions are derived. As a conflict detection module, the Collision Risk (CR) is measured for each ship by using a scaled nondimensional Distance to the Closest Point of Approach (DCPA) and Time to the Closest Point of Approach (TCPA) as inputs. Afterwards, the decisions for collision avoidance are made based on the calculated CR, encountering angle and relative angle of each ship measured from others. In this regard, the rules for the Fuzzy interface system are defined in accordance with the COLREGs, and the whole system is implemented on the MATLAB Simulink platform. In addition, to deal with the multiple ship encounters, the paper proposes a unique maximum-course and minimum-speed change approach for decision making, which has been found to be efficient to solve Imazu problems, and other complicated multiple-ship encounters.

Offshore vessels (OVs) often requires precise station-keeping and some vessels, for example, vessel involves in geotechnical drilling generally use Spread Mooring (SM) or Dynamic Positioning (DP) systems. Most of these vessels are equipped with both systems to cover all ranges of water depths. However, determining which systems to use for a particular operational scenario depends on many factors and requires significant balancing in terms of cost-benefit. Therefore, this research aims to develop a platform that will determine the cost factors for both the SM and DP station keeping systems. Operational information and cost data are collected for several field operations, and Artificial Neural Networks (ANN) is trained using those data samples. After that, the trained ANN is used to predict the components of cost for any given environmental situation, fieldwork duration and water depth. Later, the total cost is investigated against water depth for both DP and SM systems to determine the most cost-effective option. The results are validated using two operational scenarios for a specific geotechnical vessel. This decision-making algorithm can be further developed by adding up more operational data for various vessels and can be applied in the development of sustainable decision-making business models for OVs operators.

Over the years, sustainability, impact on the environment, as well as the operation expenditure have been a major concern to the deployment of mobile cellular base stations worldwide. This is because the mobile cellular base stations are known to consume a high percentage of power within the mobile cellular network. Such energy consumption contributes to the emission of Greenhouse Gases (GHG) through the use of conventional diesel generating a set. As a result, the mobile cellular operators are faced with the dilemma of minimising the power consumption, GHG emission, and the operation cost, while improving the Quality of Service of the networks. In attempting to find a solution, this study presents the feasibility and simulation of a solar photovoltaic (PV) with battery hybrid power system (HPS) as a predominant source of power for a specific mobile cellular base station site situated in Soshanguve area of the city of Pretoria, South Africa. It also presents the technical development, showed the environmental advantage and cost benefits of using a solar PV-battery HPS to power a base station site of a 24 hrs daily load of 241.10 kWh/d and the peak load of 20.31 kW as compared to using the HPS of solar PV-diesel generating set-battery. The solar resource pattern for the city of Pretoria was collected from The National Aeronautics and Space Administration and modelled statistically. Thus, the statistical modelling done using solar radiation resource exposure characteristic patterns of Pretoria, South Africa, revealed an average annual daily solar radiation of 5.4645 Wh/m2/d and 0.605 clearness index. The simulation and the design were done using the Hybrid Optimization Model for Electric Renewables and Matlab/Simulink software. The simulation finding shows that the HPS of solar PV-battery combination has about 59.62 % saving on Net Present Cost, Levelized Cost of Energy, and 80.87% saving on Operating cost as against conventional BS powered with Gen Set-Battery.

The manuscript presents the use of three novel technologies for the implementation of wireless green battery-less sensors that can be used in agriculture. The three technologies, namely additive manufacturing, energy harvesting, and wireless power transfer from airborne transmitters carried from UAVs, are considered for smart agriculture applications. Additive manufacturing is exploited for the implementation of both RFID based sensors and passive sensors based on humidity sensitive materials. A number of energy-harvesting systems at UHF and ISM frequencies are presented which are in position to power platforms of wireless sensors, including humidity and temperature IC sensors used as agriculture sensors. Finally, in order to provide the wireless energy to the soil-based sensors with energy harvesting features, wireless power transfer (WPT) from UAV carried transmitters is utilized. The use of these novel technologies can facilitate the extensive use and exploitation of battery-less wireless sensors which are environmentally friendly and thus “green”. Additionally, it can potentially drive precision agriculture in the next era, through the implementation of a vast network of wireless green sensors which can collect and communicate data, to airborne readers so as to support with data the Artificial Intelligence and Machine Learning based, decision making platforms.

The COVID-19 pandemic creates anxiety among hospitalised SARS-CoV-2 patients. Therefore, this study aims to determine the prevalence of anxiety and its associated factors among stable inpatient COVID-19 patients in Malaysia. A cross-sectional study was conducted using a web-based online survey involving 401 patients from Malaysia's leading COVID-19 hospitals from 15th April until 30th June 2020 who were chosen using quota sampling. General Anxiety Disorders 7 items (GAD-7), Coping Orientation to Problems Experienced Inventory (Brief-COPE) and Socio-demographic profile questionnaire were used. Descriptive analysis and multiple logistic regression were performed using SPSS v23 to determine the prevalence of anxiety and its associated factors. The results showed that prevalence of anxiety was 7.0%. Multiple logistic regression analysis revealed that female (p < 0.05), fear of infection (p < 0.05), lack of information (p < 0.05), maladaptive coping mechanism of behavioural disengagement (p < 0.001) and self-blame (p < 0.001) were significantly associated with anxiety. Whereas adaptive coping mechanisms via instrumental support (p < 0.001) was a significant protective predictor of anxiety. COVID-19 infection has had a significant influence on the mental health of patients. Findings in our study provides baseline findings on prevalence of anxiety among stabilized COVID-19 inpatient in Malaysia. Despite the relative low prevalence, the data has the potential to improve the present mental health monitoring system and the deployment of suitable treatments in dealing with similar circumstances

This paper is about to estimate the suspended sediment transport amount in the streams flowing into the Sapanca Lake Basin. There are 12 subsidiary streams flowing into the Sapanca Lake Basin. With the aim of estimating the suspended sediment transport in 2012-204 in these subsidiary streams, measurements belonging the parameters such as level, cross sectioning, flow rate, temperature and suspended sediment were made monthly. Along the measurement period, weather conditions were above seasonal normal and precipitations decreased. In order to estimate the suspended sediment amount by using results of the measurement obtained, Artificial Neuron Network (ANN), Sediment Rating Curve (SRC) and Multiple Linear Regression (MLR) models were used for different scenarios. It was seen that artificial neuron networks yielded the most accurate results among the models.

Hansenula polymorpha is a non-conventional, methylotrophic, and thermo-tolerant yeast well known for the industrial production of recombinant proteins. However, the research to evaluate this yeast potential for sugar fermentation to produce alcohols for biofuel applications is at an infant stage. For research advancement, the present work investigated the wild type H. polymorpha strain (DSM 70277) for the co-production of methanol, ethanol, and butanol using a batch fermentation reactor and performing experiments at an ideal temperature of 40°C with a pH of 5.5 using different sugars (fructose, glucose, and xylose) as substrates. The maximum specific growth rates (h-1) of H. polymorpha yeast were 0.59–3.02 for fructose, 0.08–2.60 for glucose, and 0.06–0.08 for xylose. The overall yields (g/g) of methanol were higher from fructose (0.132–0.226) than glucose (0.020–0.021) and xylose (0.009–0.030). Ethanol overall yield (g/g) from fructose (0.130–0.325) was more than glucose (0.054–0.167), and xylose (0.042–0.068). The overall butanol yield (g/g) ranged from 0.018–0.031 (fructose), 0.017 (glucose), and 0.009–0.010 (xylose). Due to the methylotrophic nature of H. polymorpha, a shorter time (preferably 2 h) was found suitable for co-production of methanol, ethanol, and butanol.

DNA extraction for downstream molecular diagnostic applications can be an expensive, time-consuming process. We devised a method to quickly extract genomic DNA from environmental samples based on sodium hydroxide lysis of cells with or without capture by magnetic beads, for subsequent PCR or quantitative PCR. The final DNA extraction method using NaOH is extremely low-cost and can be completed in 10 minutes at room temperature. NaOH extraction was effective for Gram-negative and Gram-positive bacteria in samples from air, soil, sewage, food, laboratory surfaces, and chicken cloacal swabs. The NaOH extraction method was comparable to commercial kits for extraction of DNA from pig fecal samples for 16S amplicon sequencing analyses. We demonstrate that an impinger and portable pump can efficiently capture bacteria in poultry facilities for rapid DNA extraction for quantification of total bacteria and for detection of specific species using qPCR. The air sampling and NaOH extraction procedures are well-suited for routine, high throughput screening, and for metagenomic analyses for specific pathogens, even in resource-limited situations.

This article discusses the fabrication of pullulan/polyvinyl alcohol (PVA) and PVA blends as carrier polymers for drug delivery. This study utilized coaxial electrospinning to encapsulate doxorubicin (DOX), a common anticancer drug, within nanofibers, which helped mitigate the adverse effects of DOX on normal cells. The study also demonstrated that the coaxial electrospinning technique using PVA/pullulan cores was effective in inhibiting the attachment and proliferation of HeLa cells, a type of cervical cancer cell. The use of pullulan/PVA and PVA blends as carrier polymers in conjunction with coaxial electrospinning could provide a safe and effective drug delivery system for the treatment of cervical and other solid malignancies. The study concluded that nanofibers showed promising results in inhibiting the growth of HeLa cells, indicating their potential as safe and effective drug delivery systems for treating cervical cancer and other solid malignant tumors.

The safe and reliable operation of autonomous vehicles in complex and dynamic environments is heavily dependent upon exteroceptive and proprioceptive sensors. A LiDAR system generates accurate 3D point clouds, which are crucial for the detection, classification, and tracking of multiple targets. LiDAR data, however, presents significant challenges due to its density, noise, and varying sampling rates. In this study, various clustering and MTT techniques for LiDAR point clouds will be identified and classified within the context of autonomous driving to assess the state-of-the-art methods’ key challenges and their performances. We have categorized clustering and MTT methods used in AV applications, identified research gaps and challenges and analyzed existing algorithms and their challenges in detail. Researchers and practitioners in the field of autonomous driving will find this review to be a valuable resource.

This paper presents a theoretical framework aimed at improving IoT network cybersecurity by AI-driven anomaly detection in the context of rapid urbanization and the development of smart cities. The framework draws upon the Complex Adaptive Systems Theory, Theory of Planned Behavior, the Technology Acceptance Model, and the Socio-Technical Systems Theory, and intri-cately examines how AI might be integrated into the intricate ecosystem of smart cities to detect and mitigate cyber threats. It emphasizes AI-driven anomaly detection methods as pivotal in-struments that impact cybersecurity enhancement in smart city infrastructures. The proposed framework also highlights the crucial role that human-related factors, including user behavior and adoption, play in determining how effective AI applications are in enhancing cybersecurity in smart cities. It also recognizes the important moderating roles played by outside factors like legislative frameworks, environmental contexts, and technological breakthroughs in the relationship between AI-enabled anomaly detection and enhanced cybersecurity. The goal of this research is to shed light on the interactions between technology and socio-technical dynamics by offering a thorough theoretical understanding of AI's potential in smart city cybersecurity. It highlights the significance of taking into account a variety of contextual aspects when employing AI to strengthen the digital security of urban areas, and provides a strategic direction for future research and practical implementation.

Hybrid inorganic-polymer nanocomposites can be employed in diverse applications due to the potential combination of desired properties from both the organic and inorganic components. The use of novel bottom-up in situ synthesis methods for the fabrication of these nanocomposites is advantageous compared to top-down ex situ mixing methods, as it offers increased control over the structure and properties of the material. In this review, the focus will be on the application of the sol-gel process for the synthesis of inorganic oxide nanoparticles in epoxy and polysiloxane matrices. The effect of the synthesis conditions and the reactants used on the inorganic structures formed, the interactions between the polymer chains and the inorganic nanoparticles, and the resulting properties of the nanocomposites are appraised from several studies over the last two decades. Lastly, alternative in situ techniques and the applications of various polymer-inorganic oxide nanocomposites are briefly discussed.

Rice grassy stunt virus (RGSV) a member of Tenuivirus family, is very potent and destructive which effects rice crop in many countries, particularly China. Non coding RNAs have important functions in development and epigenetic regulation of gene expression in numerous organsisms. There is three type of small non coding RNAs have been found in eukaryotes, which are small interferring RNAs (siRNAs), microRNAs (miRNAs) and piwi interacting RNAs (piRNAs). Small RNAs (sRNAs) origination is from the infecting virus which is known as virus-derived small interfering RNAs (vsiRNAs), has responsibility for RNA silencing in plants. Virus-induced gene silencing (VIGS) is mainly dependent on RNA silencing (RNAi). Interestingly, RNA silencing happens in plants during viral infections. RNAi technique showed significant results in Nephotettix cincticeps. RNAi technique demonstrated the gene silencing of planthopper Nilaparvata lugens. The proteins P5, pcf4, Dnj, psn5, and pn6 act as potential movement proteins and serve as silencing suppressors for RGSV. VsiRNAs originate from dsRNA molecules which require Dicer-like (DCL) proteins, RNA dependent RNA polymerase (RdRP) proteins, and Argonaute (AGO) proteins. RdRP uses ssRNA for perfect RNA amplification process and can also be used for DCL dependent secondary vsiRNA formation. VSRs interfere with the movement of signals during silencing mechanism. Moreover, intercellular movement of viruses is facilitated by virus-encoded movement protein. RNAi is found in many eukaryotes which are related to transcriptional or post-transcriptional regulation by gene suppression. Transcription is bidirectional in ssDNA viruses which are originated from dsRNA molecules. In this review, we highlighted the biology of Rice grassy stunt virus and its insect vector and its silencing suppressors. This work will be helpful for plant virologists to understand the whole biogenesis mechanism for rice viruses especially RGSV.

Underwater wireless sensor networks (UWSN) have recently been proposed as a way to monitor and explore the water depths' environments. Efficiently delivering the data is still a challenging problem in these networks because of the weaknesses in the acoustic transmission. To tackle such a problem, we propose a novel algorithm provides controlling mechanisms for critical long-term data forwarding underwater sensor networks, called Hop by Hop Power-Efficient Routing Protocol (Hⁿ-PERP). The proposed Hⁿ-PERP is a centralized full-control model that enhances the network's throughput and energy efficiency by a set of solutions depend on power monitoring in UWSN nodes. In particular, the model provides a guaranteed mechanism for scheduling and processing data transmission based on number of nodes, hops between the nodes, energy level and congestion within each node to minimize energy levels or power consumption by avoiding disconnected probability for any node, which in turn maximizing the network lifetime. Simulation results show that our proposed model is consistent with energy level and congestion, and is more accurate for enabling routing and data transmission. Therefore, the data packet delivery ratio and overall throughput also achieves robust scenarios of very sparse or/and weak networks, to keep on Performance stability in UWSN via adjusting hop-by-hop delay and energy consumption during packages delivery.

For bounded unit interval, we propose a new Kumaraswamy generalized (G) family of distributions from a new generator which could be an alternate to the Kumaraswamy-G family proposed earlier by Cordeiro and de-Castro in 2011. This new generator can also be used to develop alternate G-classes such as beta-G, McDonald-G, Topp-Leone-G, Marshall-Olkin-G and Transmuted-G for bounded unit interval. Some mathematical properties of this new family are obtained and maximum likelihood method is used for estimating the family parameters. We investigate the properties of one special model called a new Kumaraswamy-Weibull (NKwW) distribution. Parameter estimation is dealt and maximum likelihood estimators are assessed through simulation study. Two real life data sets are analyzed to illustrate the importance and flexibility of this distribution. In fact, this model outperforms some generalized Weibull models such as the Kumaraswamy-Weibull, McDonald-Weibull, beta-Weibull, exponentiated-generalized Weibull, gamma-Weibull, odd log-logistic-Weibull, Marshall-Olkin-Weibull, transmuted-Weibull, exponentiated-Weibull and Weibull distributions when applied to these data sets. The bivariate extension of the family is proposed and the estimation of parameters is given. The usefulness of the bivariate NKwW model is illustrated empirically by means of a real-life data set.

This paper focuses on the combination of the method of moments and the wire-grid approximation as an effective computational technique for modeling symmetrical antennas with low computational cost and quite accurate results. The criteria and conditions for the use of wire-grid surface approximation from various sources are presented together with new recommendations for modeling symmetrical antenna structures using the wire-grid approximation. These recommendations are used to calculate the characteristics of biconical and horn antennas at different frequencies. The results obtained using different grid and mesh settings are compared to those obtained analytically. Moreover, the results are compared to those obtained using the finite difference time domain numerical method, as well as the measured ones. All results are shown to be in a good agreement. The used recommendations for building a symmetrical wire-grid of those symmetrical antenna elements provided the most advantageous parameters of the grid and mesh settings and the wire radius, which are able to give a quite accurate results with low computational cost. Additionally, the known equal area rule was modified for a rectangular grid form. The obtained radiation patterns of a conductive plate using both the original rule and the modified one are compared with the electrodynamic analysis results. It is shown that the use of the modified rule is more accurate when using a rectangle grid form.

Previous studies reported the physical, transcriptomics and metabolomics changes in in-vitro acute heat stressed bovine granulosa cells. Granulosa cells exhibited transient proliferation senescence, oxidative stress, increased rate of apoptosis, and decline in steroidogenic activity. This study performs joint integration and network analysis of metabolomics and transcriptomics data to further narrow down and elucidate the role of differentially expressed genes, important metab-olites and relevant cellular and metabolic pathways in acute heat-stressed granulosa cells. Among significant (Raw P-value <0.05) metabolic pathways where metabolites and genes did converge, this study found Vitamin B6 metabolism, Glycine, serine and threonine metabolism, Phenylalanine metabo-lism, Arginine biosynthesis, Tryptophan metabolism, Arginine and proline metabolism, Histidine metabolism, and Glyoxylate and dicarboxylate metabolism. Important significant convergent bio-logical pathways included, ABC transporters and Protein digestion and absorption, while func-tional signaling pathways included cAMP, mTOR, and AMPK signaling pathways together with Ovarian steroidogenesis pathway. Among caner pathways, the most important pathway was Central carbon metabolism in cancer. Through multiple analysis query, Progesterone, Serotonin, citric acid, Pyridoxal, L-Lysine, Succinic acid, L-Glutamine, L-Leucine, L-Threonine, L-Tyrosine, Vitamin B6, Choline, and CYP1B1, MAOB, VEGFA, WNT11, AOX1, ADCY2, ICAM1, PYGM, SLC2A4, SLC16A3, HSD11B2 and NOS2 appeared to be important enriched metabolites and genes, respectively. These genes, metabolites, metabolic, cellular and cell signaling pathways com-prehensively elucidate the mechanisms underlying the intricate fight between death and survival in acute heat-stressed bovine granulosa cells, and essentially help further our understanding and future quest of research in this direction.

In the study of QSAR/QSPR, due to high degree of predictability of pharmaceutical properties, the eccentric-connectivity index has very important place among the other topological descriptors, In this paper, we compute the exact formulas of eccentric-connectivity index and its corresponding polynomial, total eccentric-connectivity index and its corresponding polynomial, first Zagreb eccentricity index, augmented eccentric-connectivity index, modified eccentric-connectivity index and its corresponding polynomial for a class of phosphorus containing dendrimers.

Cadmium(II) contamination in the environment is an emerging problem due to its acute toxicity and mobility, so it is very urgent to remove this species from industrial wastewater before it is discharged into the environment. Thus, a starch-based activated carbon (AC) with a specific surface area of 1600 m2g-1 is used as an adsorbent for the capturing of toxic cadmium(II) ions from synthetic solutions. The sorbent is characterized by BET, SEM, TEM, XRD, FT-IR, TGA, and zeta potential. The maximum uptake (284 mg g-1) of Cadmium(II) ion is obtained at pH 6. The thermodynamic parameters like ∆G, ∆H, ΔS are found to be -17.42 kJmol-1, 6.49 kJ mol-1, and 55.66 Jmol-1K-1 respectively, revealing that the adsorption mechanism is endothermic, spontaneous, and feasible. The experimental data follows the D-R and Langmuir models well. The mass transfer is controlled by pseudo 2nd order kinetics. Furthermore, the density functional theory simulations demonstrate that the activated carbon strongly interacted with the Cd(II) ion through its various active sites. The adsorption energy noted for all interactive sites is highly negative (-0.45 eV to -10.03 eV), which shows that the adsorption process is spontaneous and stable which is in agreement with the experimental thermodynamics analysis.

Coronavirus that is also known as COVID-19 disease is produced by SARSCoV-2. This causative agent is highly contagious and can cause potentially fatal pneumonia worldwide with serious public health concerns. In the beginning among infected individuals, most of them were those who were mainly shown to the wet animal market in a big city of China known as Wuhan. So, it was suggested that this was almost certainly the zoonotic source of COVID-19illness. The transitional source of origin and their mode of transmission to humans were not known obviously. Conversely, from human to human rapidly transformation have been confirmed generally. Currently, there is no availability of FDA approved clinically antiviral drugs and/or vaccines to be used against the COVID-19. Afterward, SARS-CoV and MERS-CoV, the occurrence of SARS-CoV-2 has been manifested as the third sketch of an enormously pathogenic coronavirus into human population globally. In this review, we provide a brief overview of the history of COVID-19 in Pakistan up-to 18^th weeks after beginning, current situation, epidemiology, and its impact on the human population. Moreover, we focused on physiological variation during the incubation period, genome analysis of SARS-CoV2, supportive treatment approaches, and safety measures in the Pakistani population, which may be supportive for combating the risk of COVID-19 epidemic. We also reviewed the future approaches for the development of therapeutic interventions and vaccines to cope with the COVID-19 epidemic.

Agriculture is one of the indispensable fields for the survival of mankind. Potatoes also have a significant role in the field of agriculture. The quality and quantity of potatoes are significantly impacted by several diseases, such as early blight and late blight, and manual interpretation of these leaf diseases is time-consuming and inconvenient. Fortunately, leaf appearance is used to detect diseases in potato plants. Productivity will considerably rise if the infections are detected early. Different types of image processing methods and machine learning methods are used for early recognition of those diseases from leaf images so that the product losses are decreased significantly. For the incredible and marvelous performance of CNN, it is the most popular deep learning method that is used immensely for the recognition of leaf diseases from images. Several pretrained deep learning models, such as VGG16, ResNet50, InceptionV3, MobileNetV2, Xception, and a deep learning model developed using CNN, are employed for potato leaf disease classification and recognition on the same dataset. The transfer learning technique is applied to the pretrained model and the data augmentation technique is applied to the Proposed CNN model for potato disease classification from leaf samples. Compared to pretrained models, the proposed CNN model offers the lowest loss and highest accuracy for potato leaf disease detection while using fewer parameters and layers. It achieves the best performance with a test accuracy of 99.33% compared to other Pretrained models used in the diagnosis of potato leaf disease.

As time continues to advance, the need for robust security threat mitigation has become increasingly vital in software. However, ensuring early effective security threat mitigation requires optimal test data and consistent test case design. It is a constant struggle to maximize test coverage through test data optimization. We conducted explanatory research to maximize test coverage of security requirements as modeled in Structured Misuse Case Description (SMCD) i.e., structured specification of misuse case, so as to improve consistency in optimal test data generation. We specified constraints upon Mal activity in Object Constraint Language (OCL) in order to minimize human dependency and improve consistency in optimal test data generation. It was evident through results that MC/DC generated optimal test data of security threats through SMCD in comparison to the Decision Coverage method thus resulting in designing a significantly lower number of test cases and yet maximizing test coverage of security threats. MC/DC generated test data with n+1, while Decision Coverage generated test data with〖 2〗^n, we, therefore, conclude that MC/DC maximizes test coverage through optimal test data from SMCD in comparison to Decision Coverage.

Alzheimer’s disease has become a major concern in the healthcare domain as it is growing rapidly. Many researches have been conducted to detect it from MRI images through various deep-learning approaches. However, the problem of the availability of medical data and the privacy of the patients still exists. To mitigate this issue in Alzheimer’s disease detection, we have implemented the federated approach which is found to be more efficient, robust, and consistent compared to the conventional approach. For this, we needed deep excavation on various orientations of MRI images and transfer learning architectures. Then, we utilized the publicly available two datasets (OASIS, and ADNI), and designed various cases to evaluate the performance of the federated approach. In almost all of them, the federated approach achieved better accuracy and sensitivity. In distinguishing between two classes equally, the capability of the models was better in the found model when trained with federated approach compared to the conventional approach. In this approach. MobileNet, which is a low-cost transfer learning architecture, achieved the highest 95.24%, 81.94%, and 83.97% accuracy in OASIS, ADNI, and merged (ADNI+OASIS) test set, which was way higher than the achieved performance in Conventional approach. Moreover, in this approach, only the weights of the model were shared which keeps the original MRI images in their respective hospital or institutions, mitigating the privacy concern in the healthcare domain.

Epigenetic aging has been found associated with a number of phenotypes and diseases. Few studies investigated its effect on lung function in relatively older people. However, this effect has not been explored in younger population. This study examines whether lung function at adolescent can be predicted with epigenetic age accelerations (AAs) using machine learning techniques. DNA methylation based AAs were estimated in 326 matched samples at two time points (at 10 years and 18 years) from the Isle of Wight Birth Cohort. Five machine learning regression models (linear, lasso, ridge, elastic net, and Bayesian ridge) were used to predict FEV1 (Forced Expiratory Volume in one second) and FVC (Forced Vital Capacity) at 18 years from feature selected predictor variables (based on mutual information) and AA changes between the two time points. The best models were ridge regression (R2 = 75.21% ± 7.42%; RMSE = 0.3768 ± 0.0653) and elastic net regression (R2 = 75.38% ± 6.98%; RMSE = 0.445 ± 0.069) for FEV1 and FVC, respectively. This study suggests that the application of machine learning in conjunction with tracking changes in AA over life span can be beneficial to assess the lung health in adolescence.

Background: Dengue incidence has grown dramatically around the world in recent years. It transmitted by Aedes mosquitoes. Many factors contributed to the vector densities such as environmental and sociological factors. Objective: This study is aimed at determining the environmental and sociological factors contributing to dengue cases. Methods: The study used questionnaire survey involving 379 respondent with dengue history. Result: The study showed that there is significant association between the time departs to work and mobility of respondents (95%CI = 2.779 and 5.594, p < 0.0001). Similarly, there is significant association between the time of arrival to work and mobility of respondents (95%CI = 1.617 and 2.155, p < 0.0001). Moreover, the type of housing and the surrounding vegetation were the environmental factors that showed significant values; p = 0.023, and p = 0.017. Conclusion: The study indicated the factors contributed are patient who lived in independent houses and the time of mobility patient.

The increasing growth in road users who are unfamiliar with traffic rules and regulations provides a serious safety problem for Pakistani traffic conditions. This study has two main objectives. First, it seeks to validate the reliability and applicability of the Dula Dangerous Driving Index (DDDI) in Pakistan. Second, it intends to investigate the effects of DDDI, sociodemographic characteristics, seat belt use, and driving training on the occurrence of traffic accidents. A total of 623 Pakistani individuals completed a questionnaire as part of this study. The questionnaire consists of items related to dangerous driving behaviors, sociodemographic characteristics, seat belt use, driving instruction, and accident involvement. The DDDI's accuracy was evaluated by comparing it to self-reported traffic incidents and seat belt compliance. To discover predictors within the DDDI dimensions, generalized linear models were used. A binary logistic regression analysis was also performed to evaluate the factors that influence traffic accidents. DDDI dimensions, demographic characteristics, seat belt usage, and driving training were all considered in this study. The use of seat belts while driving reduced the likelihood of an accident by 25%. On the other hand, aggressive driving and risky driving increased the chances of getting involved in traffic accidents by 36%% and 20%. Furthermore, the chances of being involved in a traffic accident decreased by 21% as the drivers age increased. The results also illustrate that Pakistani drivers are more aggressive and risk taker compared to Chinese drivers who are more disciplined. This study suggests that lack of comprehensive and standardized driving training and education programs, in consistent enforcement of traffic laws, the quality of road infrastructure and lack of public awareness campaigns can be attributed to adverse road safety situations in country.

The goal of the current study is to analyse several nonlinear two-dimensional time-fractional Rosenau Hymanequations. The two-dimensional fractional Rosenau-Hyman equation has extensive use in engineering and applied sciences. The fractional view analysis of two-dimensional time-fractional Rosenau-Hyman equations is discussed using the homotopy perturbation approach, adomian decomposition method, and Yang transformation. Some examples involving two-dimensional time-fractional Rosenau-Hyman equations are provided in order to better understand the suggested approaches. The solutions appear as infinite series. We offer a comparison between the accurate solutions and those that are generated employing the proposed approaches in order to demonstrate the effectiveness and applicability of the proposed techniques. The results are graphically illustrated using 2D and 3D graphs. It has been noted that the obtained results and the targeted problems real solutions are quite similar. Calculated solutions at various fractional levels describe some of the problems useful dynamics. Other fractional problems that arise in other fields of science and engineering can be solved using a modified version of the current techniques.

Background: Polycystic ovary syndrome is a common reproductive and endocrinological disease characterized by ovulatory dysfunction, polycystic ovaries, and hyperandrogenism. Adipokines are believed to contribute to developing polycystic ovary syndrome and its accompanying metabolic complications such as dyslipidemia, insulin resistance, and cardiovascular diseases. Oncostatin M, a novel adipokine, plays a role in oogenesis, lipogenesis, and inflammation and may contribute to polycystic ovary syndrome pathogenesis and its related metabolic problems. Methods: In this case-control study, the patients were grouped in a 1:1 ratio into either the polycystic ovary syndrome (n = 32) or the control group (n = 32). Blood samples were obtained between the third and fifth days of the menstrual cycle. Serum levels of fasting glucose, insulin, C-reactive protein, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglyceride, white blood cell count, thyroid-stimulating hormone, luteinizing hormone, follicle-stimulating hormone, total testosterone, prolactin, estradiol, homeostasis model assessment of insulin resistance and oncostatin M were analyzed. Results: Oncostatin M levels were significantly lower, but C-reactive protein levels were substantially higher in the polycystic ovary syndrome group than in the control group (p = 0.002, p = 0.001, respectively). Oncostatin M was inversely correlated with total cholesterol, non-high-density lipoprotein cholesterol, fasting glucose, and the luteinizing hormone/follicle-stimulating hormone ratio (ρ = -0.329, p =0 .017; ρ = -0.386, p = 0.005; ρ = -0.440, p = 0.001; ρ = -0.316, p = 0.023, respectively). Conversely, there was no correlation between oncostatin M and total testosterone level (ρ = 0.220; p = 0.118). In the context of inflammation and metabolic parameters, oncostatin M was inversely correlated with C-reactive protein, homeostatic model assessment for insulin resistance score, and low-density lipoprotein cholesterol (ρ = -0.353, p = 0.019; ρ = -0.275, p = 0.048; ρ = -0.470, p &lt; 0.001, respectively). The diagnostic performance of oncostatin M, C-reactive protein, homeostasis model assessment of insulin resistance, and non-high-density lipoprotein cholesterol for polycystic ovary syndrome is as follows: Oncostatin M, sensitivity: 50%, specificity: 75%, Accuracy: 63%, p = 0.009; C-reactive protein, sensitivity: 84%, specificity: 61%, Accuracy: 74%, p = 0.004; homeostasis model assessment of insulin resistance, sensitivity: 72%, specificity: 81%, Accuracy: 77%, p &lt; 0.001; and non-high-density lipoprotein cholesterol; sensitivity: 72%, specificity: 75%, accuracy: 73%, p &lt; 0.001. Conclusions: Plasma oncostatin M levels were considerably lower in patients with polycystic ovary syndrome than in the control group, and this was inversely correlated with the hormonal and metabolic parameters of polycystic ovary syndrome. Thus, oncostatin M may be a novel therapeutic target for polycystic ovary syndrome and its metabolic complications.

Background: Polycystic ovary syndrome (PCOS) is a common reproductive and endocrinological disease characterized by ovulatory dysfunction, polycystic ovaries, and hyperandrogenism. Adipokines are believed to contribute to developing PCOS and its accompanying metabolic complications such as dyslipidemia, insulin resistance, and cardiovascular diseases. Oncostatin M (OSM), a novel adipokine, plays a role in oogenesis, lipogenesis, and inflammation and may contribute to PCOS pathogenesis and its related metabolic problems. Methods: In this case-control study, the patients were grouped in a 1:1 ratio into either the PCOS (n = 32) or the control group (n = 32). Blood samples were obtained between the 3rd and 5th days of the menstrual cycle. Serum levels of fasting glucose, insulin, C-reactive protein (CRP), low-density lipoprotein, high-density lipoprotein, triglyceride, white blood cell count, thyroid-stimulating hormone, luteinizing hormone, follicle-stimulating hormone, total testosterone, prolactin estradiol, and OSM were analyzed. Results: OSM levels were significantly lower, but CRP levels were substantially higher in the PCOS group than in the control group (p = .002, p = .001, respectively). OSM was inversely correlated with total cholesterol, non-high-density lipoprotein cholesterol, fasting glucose, and the luteinizing hormone/follicle-stimulating hormone ratio (ρ = .329, p = .017; ρ = .386, p = .005; ρ = .440, p = .001; ρ = .316, p = .023, respectively). Conversely, there was no correlation between OSM and total testosterone level (ρ = .220; p = .118). In the context of inflammation and metabolic parameters, OSM was inversely correlated with CRP, Homeostatic Model Assessment for Insulin Resistance score (HOMA-IR), and low-density lipoprotein cholesterol (LDL) (ρ = .353, p = .019; ρ = .275, p = .048; ρ = .470, p &lt; .001, respectively). The performance of Oncostatin M for diagnosing PCOS showed 50% sensitivity, 75% specificity, and 63% accuracy (p = .009) in ROC curve analysis. Conclusions: Plasma OSM levels were considerably lower in patients with PCOS than in the control group, and this was inversely correlated with the hormonal and metabolic parameters of PCOS. Thus, OSM may be a novel therapeutic target for PCOS and metabolic complications.

Cadmium (II) contamination in the environment is an emerging problem due to its acute toxicity and mobility, so it is very urgent to remove this species from industrial wastewater before it is discharged into the environment. Thus, a starch-based activated carbon (AC) with a specific surface area of 1600 m2g-1 is used as an adsorbent for the capturing of toxic Cadmium (II) ions from synthetic solution. The sorbent is characterized by BET, SEM, TEM, XRD, FT-IR, TGA, and zeta potential. The maximum uptake (284 mg g-1) of Cadmium (II) ion is obtained at pH 6. The thermodynamics parameters like ∆G, ∆H, ΔS are found to be -17.42 kJmol-1, 6.49 kJ mol-1, and 55.66 Jmol-1K-1 respectively, revealing that the adsorption mechanism is endothermic, spontaneous, and feasible. The experimental data follows the D-R and Langmuir models well. The mass transfer is controlled by pseudo 2nd order kinetics. Furthermore, the density functional theory simulations demonstrate that the activated carbon strongly interacted with the Cd (II) ion through its various active sites. The adsorption energy noted for all interactive sites is highly negative (-0.45 eV to -10.03 eV), which shows that the adsorption process is spontaneous and stable which is in agreement with the experimental thermodynamics analysis.

The dynamic process of plants’ growth is primarily influenced by interconnected biochemical and physiological activities that operate throughout different growing phases. These processes respond to environmental conditions, affecting plant development and productivity. The present studies were carried out to investigate greenhouse conditions' impact on winter-grown cherry tomatoes (Cheramy F1) in terms of growth, yield, and nutritional composition.Data was collected on various dates during the period extending from December 2022 to March 2023. The greenhouse temperature varies from 22°C to 25.47°C (avg. 21.20°C) during plant growth and from 20°C to 23.85°C during fruit-bearing. Average CO2 concentration was 349.19 ppm; average light was 240.93 w/m2. Measured growth parameters include plant height, leaf growth, stem diameter, leaf spacing, leaf count, leaf area, and inflorescence count per plant. The key results of this study revealed the progressive growth report (PGR), which predicted the daily potential growth rate of plants: plant height, 2.86 to 3.81 cm/day; growth rate of mature old leaf: 0.003988 m2/day; middle younger leaf: 0.008733 m2/day; top Nascent leaf: 0.010722 m2/day; three to five leaves per week; and one inflorescence per week. In our accidental observation, we observed that plants with different orientations and growing spots within the greenhouse responded differently in view of their growth parameters. A exceedingly significant interaction among the inflorescence was found in view of length of inflorescence, number of fruits inflorescence, total fruit mass inflorescence, average fruit mass inflorescence, fruit dry matter inflorescence, fruit height inflorescence, and fruit diameter inflorescence. A non-significant interaction was found between the fruit keeping quality (Shelf days), fruit size, and inflorescence number. Inflorescence with a high, lower leaf area appeared to have high productivity. The nutritional composition of fruit was also different among the inflorescences.. The study's goal is to investigate and gather data to test the hypothesis that changes in greenhouse conditions and the adoption of green management practices will directly influence plant growth attributes, productivity, and fruit quality. The findings of this research could be valuable for optimizing greenhouse operations to enhance crop yields and quality while also promoting environmental sustainability.

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

In the search of potent α-amylase inhibitors, we have synthesized seventeen derivatives of 2-mercaptobenzimidazole bearing sulfonamide (1-17) and evaluated for their α-amylase inhibitory potential. All compounds display a variable degree of α-amylase activity having IC50 values ranging between 0.90 ± 0.05 to 11.20 ± 0.30 µM when compared with the standard drug acarbose having IC50 value 1.70 ± 0.10 µM. Compound 1, 2, 11, 12 and 14 having IC50 values 1.40 ± 0.10, 1.30 ± 0.05, 0.90 ± 0.05, 1.60 ± 0.05 and 1.60 ± 0.10 µM respectively were found many folds better than the standard drug acarbose. The remaining analogs showed good inhibitory potentials. All the synthesized compounds were characterized by HREI-MS, 1H and 13C-NMR. Structure activity relationship (SAR) has been recognized for all newly synthesized analogs. Through molecular docking study, binding mode of active analogs with α-amylase enzyme was confirmed.

Irrigation water management components evaluation is mandatory for sustainable irrigated agriculture production in the era of water scarcity. In this research spatio-temporal distribution of irrigation water components were evaluated at canal command area in Indus Basin Irrigation System (IBIS) using remote sensing based geo-informatics approach. Satellite derived MODIS product-based Surface Energy Balance Algorithm for Land (SEBAL) was used for the estimation of the Actual Evapotranspiration (ETa). Satellite derived SEBAL based ETa was calibrated and validated using the ground data-based advection aridity method (AA). Statistical analysis of the SEBAL based ETa and AA shows the mean 87.1 mm and 47.9 mm and, 100 mm and 77 mm, Standard deviation of 27.7 mm and 15.9 mm and, 34.9 mm and 16.1 mm, R of 0.93 and 0.94, NSE of 0.72 and 0.85, PBIASE -12.9 and -4.4, RMSE 34.9 and 5.76 for the Kharif and Rabi season, respectively. Rainfall data was acquired from the Tropical Rainfall Measuring Mission (TRMM). TRMM based rainfall was calibrated with the point observatory data of the Pakistan Metrological Department Stations. Canal water data was collected from the Punjab Irrigation department for the assessment of canal water availability. Water The water balance approach was applied in the unsaturated zone for the quantification of the gross and net Groundwater irrigation. Mmonthly variation of ETa with the minimum average value of 63.3 mm in January and the maximum average value of 110.6 mm in August was found. While, the average annual of four cropping years (2011-12 to 2014-15) ETa was found 899 mm. Average of the sum of Net Canal Water Use (NCWU) and Rainfall during the study period of four years was only 548 mm (36% of ETa) and this resulted the 739.6 mm of groundwater extraction. While the annual based variation in groundwater extraction of 632 mm and 780 mm was found. Seasonal analysis revealed 39% and 61% of groundwater extraction proportion during Rabi and Kharif season, respectively. The variation in four cropping year’s monthly groundwater extraction was found 28.7 mm to 120.3 mm. This variation was high in the 2011-12 to 2012-13 cropping year (0 mm to 148.7 mm), dependent upon the occurrence of rainfall and crop phenology. Net groundwater irrigation, estimated after incorporating the efficiencies was 503 mm year-1 on average for the four cropping years.

7β-(3-ethyl-cis-crotonoyloxy)-1α-(2-methylbutyryloxy)-3,14-dehydro-Z-notonipetranone (ECN), a sesquiterpenoid obtained from a natural origin (Tussilago farfara)has proved to be effective in minimizing various side effects associated with opioids and nonsteroidal anti-inflammatory drugs. The current study focused on investigating the effects of ECN on neuropathic pain induced by partial sciatic nerve ligation (PSNL) by mainly focusing on oxidative stress, inflammatory and apoptotic proteins expression in mice. Neuropathic pain was induced in mice by PSNL surgery performed on day 1 and ECN (1 and 10 mg/kg, i.p.), was administered once daily for 11 days, starting from the third day after surgery. ECN post-treatment was found to reduce hyperalgesia and allodynia in a dose dependent manner. ECN significantly reversed the severity of neuropathic pain by improving distress symptoms and survival rate. ECN remarkably reversed the histopathological abnormalities associated with oxidative stress, apoptosis and inflammation. Furthermore, ECN prevented the suppression of antioxidants (glutathione, glutathione-S-transferase, catalase, superoxide dismutase, NF-E2-related factor-2 (Nrf2), hemeoxygenase-1 and NAD(P)H: quinone oxidoreductase) by PSNL. Moreover, pro-inflammatory cytokines (tumor necrotic factor alpha, interleukin 1 beta, interleukin 6, cyclooxygenase-2 and inducible nitric oxide synthase) expression was reduced by ECN administration. Treatment with ECN was successful in reducing caspase-3 level consistent with the observed modulation of pro-apoptotic proteins. Additionally, ECN showed protective effect on the lipid content of myelin sheath as evident from FTIR spectroscopy which showed the shift of lipid component bands to higher values. Thus, anti-neuropathic potential of ECN might be due to inhibition of oxidative stress, inflammatory mediators and pro-apoptotic proteins.

The aim of this study is to emphasize on the clinical uses of amniotic membrane (AM) in multiple ocular disorders. AM possesses many properties including promotion of epithelialization, anti-fibrotic, anti-apoptotic, anti-angiogenic properties. Epithelial wound healing on the eye surface is promoted due to several epidermal and keratocyte growth factors as well as by its anti-inflammatory and anti-scarring effects due to interleukin-10, interleukin-1 receptor antagonists and inhibition of transforming growth factor- beta (TGF-β) signal transduction by AM in the corneal and conjunctival fibroblasts respectively. It plays a crucial role by functioning as a substitute of basement membrane (BM) and as a temporary graft material. It is not only being used as an eye graft but also its extract can be used as an eye drop for corneal and external eye diseases. So its proper preparation, preservation and clinical application can bring a revolutionary change in the treatment of different ocular disorders.

Probiotics are namely yeast and bacteria which are regarded as dietary supplements and food. Many probiotics are being commonly used now, the most frequently used, lactic acid bacteria are called Lactobacillus and Bifidobacterium. Many of the diseases associated with GIT are related to microbiota found in GIT, an imbalance of which causes gastrointestinal diseases. Probiotics, in light of scientific literature, are thought to play an important role in relieving symptoms of many diseases associated with GIT, i.e. beneficially regulating the microbiota composition. In the present review, we aimed to highlight the main considerations for main probiotic formulations to date. This study reviews the role of different probiotic formulations introduced so far in treating GI diseases in cohort ageing ≤18 years. We searched PubMed and Clinicaltrials.gov without any restrictions. This study comprises the descriptive and comparative analysis between Single-organism and Composite probiotics. These GI diseases include NEC, FAP, AGE, Acute Diarrhea, Ulcerative Colitis and etc. The results have been categorized according to title and outcomes. The positive outcomes emphasize the drug’s effectiveness in improving health and the negative outcome elaborates on the adverse effects the drug may have shown. Conclusively, discussed practices will assist in reducing GI disorders and strengthening the gut. Further insight into the various gut microbes and microbiomes with specific demographic is recommended.

An increasing global aridification due to climate change has made the health monitoring of vegetation indispensable to maintaining the food supply chain. Cost-effective and smart irrigation systems are required not only to ensure the efficient distribution of water, but also to track the moisture of plant leaves, which is an important marker of the overall health of the plant. This paper presents a novel electromagnetic method to monitor the water content (WC) and characterization in plant leaves utilizing the absorption spectra of water molecules in the terahertz (THz) frequency for four consecutive days. We extracted the material properties of leaves of eight types of pot herbs from the scattering parameters, measured using a material characterization kit in the frequency range of 0.75 to 1.1 THz. From the computed permittivity, it is deduced that the leaf specimens increasingly become transparent to the THz waves as they dry out with the passage of days. Moreover, the loss in weight and thickness of leaves were observed due to the natural evaporation of leaf moisture cells and change occurred in the morphology of fresh and water-stressed leaves. It is also illustrated that loss observed in WC on day 1 was in the range of 5\% to 22\%, and increased from 83.12\% to 99.33\% on day 4. Furthermore, we observed an exponential decaying trend in the peaks of the real part of the permittivity from day 1 to 4, which was reminiscent of the trend observed in the weight of all leaves. Thus, results in paper demonstrated that timely detection of water stress in leaves can help to take proactive action in relation to plants health monitoring, and for precision agriculture applications, which is of high importance to improve the overall productivity.

Abstract: The aim of this study is to emphasize on the clinical uses of amniotic membrane (AM) in multiple ocular disorders. AM possesses many properties including promotion of epithelialization, anti-fibrotic, anti-apoptotic, anti-angiogenic properties. Epithelial wound healing on the eye surface is promoted due to several epidermal and keratocyte growth factors as well as by its anti-inflammatory and anti-scarring effects due to interleukin-10, interleukin-1 receptor antagonists and inhibition of transforming growth factor- beta (TGF-β) signal transduction by AM in the corneal and conjunctival fibroblasts respectively. It plays a crucial role by functioning as a substitute of basement membrane (BM) and as a temporary graft material. It is not only being used as an eye graft but also its extract can be used as an eye drop for corneal and external eye diseases. So its proper preparation, preservation and clinical application can bring a revolutionary change in the treatment of different ocular disorders.

In this study, by employing the think-aloud model, schema theory and the perceptual cycle model (PCM) from the perspective of vulnerable road traffic participants, the current traffic imperfections of a target section in Wudaokou were analyzed to explore potential improvement measures to enhance safety. Specifically, the present study recruited 30 local volunteers, which were equally divided into a pedestrian group, bicyclist group and E-cyclist group. The research procedure was mainly divided into three steps. By using the think-aloud method, we analyzed and recorded the contents of oral reports of the experimental road section. Then, through translation, segmentation, coding, analysis and other techniques, we sorted and studied the report data and drew a conclusion that was universal in improving the current road traffic conditions. It is hoped that through this study, on the one hand, the applicability of the oral report method, schema theory and perceptual cycle model in road traffic safety can be verified, and, on the other hand, some better improvement measures can be extracted for the road traffic state of the Wudaokou section.

Background: Our knowledge of prostate cancer (PCa) genomics mainly reflects European (EUR) and Asian (ASN) populations. Our understanding of the influence of Middle Eastern (ME) and African (AFR) ancestry on the mutational profiles of prostate cancer is limited. Design, setting and participants: To characterize genomic differences between ME, EUR, ASN and AFR ancestry, fluorescent in situ hybridization (FISH) studies for NKX3-1 deletions and MYC amplification was carried out on 42 tumors arising in individuals of ME ancestry. These were supplemented by analysis of copy number profiles of 401 tumors of all ancestries. Outcome measurement and statistical analysis: FISH results of NKX3-1 and MYC were assessed in the ME cohort and compared to other ancestries. Gene level copy number aberrations (CNAs) for each sample were statistically compared between ancestry groups. Results and Limitation: NKX3-1 deletions by FISH were observed in 17/42 (17.5%) prostate tumors arising in men of ME ancestry, while MYC amplifications were only observed in 1/42 (0.23%). Using CNAs called from arrays, the incidence of NKX3-1 deletions was significantly lower in ME vs. other ancestries (20% vs. 52%; p = 2.3 x 10-3). No differences in MYC amplifications was noted between the two cohorts using array-based CNAs. Across the genome, tumors arising in men of ME ancestry had fewer CNAs than those of other ancestries (p = 0.014). Additionally, the somatic amplification of 21 specific genes was more frequent in tumors arising in men of ME vs. EUR ancestry (Proportions test; Q < 0.05). Those included amplifications in the glutathione S-transferase family on chromosome 1 (GSTM1, GSTM2, GSTM5) and the IQ Motif Containing family on chromosome 3 (IQCF1, IQCF2, IQCF13, IQCF4, IQCF5, IQCF6). Conclusion: Significant differences in genetic background exists between different ancestries. The lower number of somatic CNAs observed in prostate cancer arising in men of ME ancestry may explain the relatively good prognosis in this population. Larger studies are warranted.

Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO2, ITO), one Metal-Organic Frame as Cu(BPDC), and 7 different polymer films including PVA, PEDOT: PSS, PFO, SB, SW, SG, PB were used for functionalizing of QCM sensors. The purpose was to discriminate six economically relevant Mint species (Mentha x piperita, Mentha spicata, Mentha spicata ssp. crispa, Mentha longifolia, Agastache rugosa, and Nepeta cataria). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models including Principal Component Analy-sis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner.

Background: Patients with severe chronic kidney disease (CKD G4-G5) often have chronically elevated high-sensitivity cardiac troponin T (hs-cTnT) values above the 99th percentile of the upper reference limit. In these patients, optimal cutoff levels for diagnosing non-ST-elevation acute cor-onary syndrome (NSTE-ACS) requiring revascularization remain undefined. Methods: Of 11,912 patients undergoing coronary angiography from 2012 to 2017 for suspected NSTE-ACS, 325 (3%) had severe CKD. Of these, 290 with available serial hs-cTnT measurements were included and 300 matched patients with normal renal function were selected as a control cohort. Results: Diagnostic performance for patients with severe CKD was high at presentation and similar to that of the control population (AUC, 95% CI: 0.81, 0.75-0.87 versus 0.85, 0.80-0.89, p=0.68) and the ROC-derived cutoff value at presentation was 4 times higher compared to the conventional 99th percentile. Combining the ROC-derived cutoff levels for hs-cTnT at presentation and absolute 3-hour changes, sensitivity increased to 98%, PPV and NPV improved up to 93% and 86%, re-spectively. (4) Conclusions: In patients with severe CKD and suspected ACS the diagnostic accu-racy of hs-cTnT for the diagnosis of NSTE-ACS requiring revascularization is improved by using higher assay specific cutoff levels combined with early absolute changes.

Background: Pneumococcal carriage surveillance study took place in urban and rural areas for Jordanian children in the period 2015-2019. Objectives: Determine urban and rural differences in pneumococcal carriage rate, resistance, and serotypes from healthy Jordanian children of Amman (urban) and eastern Madaba (rural). Methods: Nasopharyngeal swabs (NP) were taken from 682 children aged 1 to 163 months. Pneumococcal identification, serotyping and resistance were done according to standard method. Results: Number of cases tested for Amman 267 and for eastern Madaba 415. Carriage rate for eastern Madaba was 39.5% and for Amman 31.1%. Predominant serotypes for eastern Madaba and Amman were 19F (21.3%; 15.7%), 23F (12.2%; 9.6%), 14 (6.7%; 2.4%), 19A (4.9%; 2.4%), 6A (5.5%; 3.6%). Resistance rates for eastern Madaba and Amman were: penicillin (95.8%; 81.9%), clarithromycin (68.9%; 59.0%), clindamycin (40.8%; 31.3%), and trimethoprim-sulfamethoxazole (73.2%; 61.4%). Coverage of PCV7, PCV13, and the future PCV20 for Amman were 42.2%, 48.2%, and 60.2%; and for eastern Madaba were 50.0%, 62.2%, and 73.2%, respectively. In Amman 25.8% have received 1-3 PCV7 injections compared to 1.9% in eastern Madaba. Conclusions: There was significant differences in carriage, resistance and coverage in both regions. The potential inclusion of PCV vaccination program for rural areas is essential.

The steep decline in the price of wind and solar photovoltaics provides a possibility to decarbonize electricity deeply and affordably. This study uses HOMER Pro energy modeling tool to model an optimized grid-connected renewable energy system for a community in southern Alberta, Canada. The study's goal is to identify the best renewable energy technology combinations that can provide electricity at the lowest levelized cost of energy (LCOE) and has lesser greenhouse gas emission as compared to the electricity produced by traditional fossil fuel. Gleichen, is a small town in southern Alberta and is near to numerous commercial wind and solar projects given the region’s high quality renewable resources. “Tri-brid” systems consisting of wind turbines, solar photovoltaics, and battery energy storage systems (BESS) are considered and compared based on electricity prices, net present cost, and greenhouse gas emissions savings. This tri-brid system is connected to the grid to sell excess electricity generated or buying electricity when there is less or no availability of solar and wind. The tri-brid energy system has an estimated LCOE of 0.0705 CAD/kWh which is competitive with the price of electricity generated by natural gas and coal which is 0.127 CAD/kWh.

This paper provides a comprehensive review of the current understanding of non-alcoholic fatty liver disease (NAFLD) and its progression to non-alcoholic steatohepatitis (NASH), focusing on key factors influencing its pathogenesis and emerging therapeutic strategies. The background highlights the growing prevalence of NAFLD and NASH, emphasizing their multifactorial nature. The methods section outlines the various contributors to NAFLD development, including genetic, dietary, and environmental factors. The results highlight the intricate interplay between these factors and their impact on hepatic lipid metabolism, inflammation, and insulin resistance. Genetic predisposition, dietary fat intake, and excessive fructose consumption are discussed as significant contributors to NAFLD progression. The conclusions emphasize the lack of a single therapeutic approach and underscore the need for combination strategies. Lifestyle interventions, particularly weight loss through diet and exercise, remain crucial, while pharmacological options like GLP-1 receptor agonists, obeticholic acid, lanifibranor, and resmetirom show promise but require further validation. Bariatric surgery and emerging endoscopic procedures offer potential in eligible patients. In sum, this review underscores the complexity of NAFLD and NASH, advocating for a multifaceted approach to address this increasingly prevalent and clinically relevant condition.

Sulfotransferases (SULTs) family plays a significant role in the biotransformation of a variety of xenobiotics and endogenous compounds by which carcinogenesis and mutagenicity of different malignancies are increasingly affected. Recent data identified various genetically polymorphic SULTs enzymes with significant variations in the enzyme activity. This study aimed to investigate the impact of SULT1A1 gene polymorphism and and its potential risk on females with breast cancer in Jordan using a PCR-RFLP and Sanger Sequencing methods. The analysis showed that 24.7% of the patients and 25.3% of the controls were homozygous for the SULT1A1*1 allele (SULT1A1*1/SULT1A1*1) compared to 8.8% and 5.7% homozygous for the SULT1A1*2 allele (SULT1A1*2/SULT1A1*2) for patients and controls respectively. Most of the patients and controls were heterozygous for SULT1A1*1 allele (SULT1A1*1/SULT1A1*2) with rates of 66.5% and 69.0% in patients and controls respectively. In addition, the frequencies of the mutant SULT1A1*2 allele were 0.42 and 0.4 in the patient and control groups respectively. No significant difference in genotype and allele distribution was noted between the breast cancer and control groups. The risk of breast cancer in individuals carrying the SULT1A1*2 allele was determined by combining the SULT1A1*1/SULT1A1*2 and ULT1A1*2/SULT1A1*2 genotypes. No association was observed between SULT1A1 polymorphism and breast cancer incidence (P = 0.63; OR, 0.93; 95% CI, 0.68–1.26). However, SULT1A1*2 allele was found to increase the risk of breast cancer by 1.26-fold.

The methyltransferase, KMT5A has been proposed as an oncogene in prostate cancer and therefore represents a putative therapeutic target. To confirm this hypothesis we have performed a microarray study in a prostate cancer cell line model of androgen independence following KMT5A knockdown in the presence of transcriptionally active androgen receptor (AR) to understand which genes and cellular processes are regulated by KMT5A in the presence of an active AR. We observed that 301 genes were down-regulated whilst 408 were up-regulated when KMT5A expression was reduced. KEGG pathway and Gene Ontology analysis revealed apoptosis and DNA damage signaling are up-regulated in response to KMT5A knockdown whilst protein folding and RNA splicing were down-regulated. Under these conditions, the top non-AR regulated gene was found to be CDC20, a key regulator of the spindle assembly checkpoint with an oncogenic role in several cancer types. Further investigation revealed that KMT5A regulates CDC20 in a methyltransferase dependent manner to modulate both histone H4K20 methylation within its promoter region and indirectly via the p53 signalling pathway. A positive correlation between KMT5A and CDC20 expression was also observed in clinical prostate cancer samples further supporting this association. Therefore, we conclude that KMT5A is a valid therapeutic target for the treatment of prostate cancer and CDC20 could potentially be utilized as a biomarker for effective therapeutic targeting.

The Lamiaceae belong to the species-richest families of flowering plants and harbor many species used as herbs or for medicinal applications, such as Basils or Mints. Evolution of this group has been driven by chemical speciation, mainly of Volatile Organic Compounds (VOCs). The commercial use of these plants is characterized by a large extent of adulteration and surrogation. To authenticate and discern the species, is, thus, relevant for consumer safety, but usually requires cumbersome analytics, such as Gas Chromatography, often to be coupled with Mass Spectroscopy. We demon-strate here that quartz-crystal microbalance (QCM)-based electronic noses provide a very cost-efficient alternative, allowing for a fast, automated discrimination of scents emitted from leaves of different plants. To explore the range of this strategy, we used leaf material from four genera of Lamiaceae along with Lemongrass as similarly scented, but non-related outgroup. In order to unambiguously differentiate the scents from the different plants, the output of the 6 different SURMOF/QCM sensors was analyzed using machine learning (ML) methods, together with a thorough statistical analysis. The exposure and purging datasets (4 cycles) obtained from a QCM-based, low-cost homemade portable e-Nose were analyzed with Linear Discriminant Analysis (LDA) classification model. Prediction accuracies with repeating test measurements reached values of up to 90%. We show that it is not only possible to discern and identify plants on the genus level, but even to discriminate closely related sister clades within a genus (Basil), demonstrating that e-Noses are a powerful technology to safeguard consumer safety against the challenges of globalized trade.

A new series of 1,3,4-oxadiazoles derivatives was synthesized, characterized and evaluated for their in vitro and in vivo anti-thrombotic activity. Compounds (3a-3i) exhibited significant clot lysis with respect to negative control and reference drug streptokinase (30,000 IU) while enhanced clotting time (CT) values were observed (130-342 sec) for these tested compounds than the standard drug heparin (110 sec.). High affinity towards 1NFY with greater docking score was observed for the compounds (3a, 3i, 3e, 3d and 3h) than the control ligand RPR200095. In addition, very good inhibitory potential against factor Xa (F-Xa) was observed with higher docking scores (5612-6270) with ACE values (–189.68 to –352.28 kcal/mol) than the control ligand RPR200095 (Docking score 5192; ACE –197.81 kcal/mol. In vitro, in vivo and in silico results proposed that these newly synthesized compounds can be used as anti-coagulant agents.

We have synthesized quinoxaline analogs (1-25), characterized by 1HNMR and HREI-MS and evaluated for thymidine phosphorylase inhibition. Among the series, nineteen analogs showed better inhibition when compared with the standard inhibitor 7-Deazaxanthine (IC50 = 38.68 ± 4.42 µM). The most potent analog among the series is analog 25 with IC50 value 3.20 ± 0.10 µM. Sixteen analogs 1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 15, 16, 17, 18, 21and 24 showed outstanding inhibition which is many folds better than the standard 7-Deazaxanthine. Two analogs 8 and 9 showed moderate inhibition. A structure- activity relationship has been established mainly based upon the substitution pattern on the phenyl ring. The binding interactions of the active compounds were confirmed through molecular docking studies.

Background: Flow-through dot-blot assay (FT-DBA) for SARS-CoV-2 specific IgG detection will provide a reliable and affordable immunoassay for the rapid serosurveillance against COVID-19. Method: SARS-CoV-2 antigens were immobilized on nitrocellulose membrane to capture IgG immunoglobulins, which were then detected with AuNP anti-human IgG. A total of 181 samples were characterized with in-house and commercial immunoassay. The positive panel consisted of RT-PCR positive samples from patients with both <14 days and >14 days from the onset of symptoms, while the negative panel contained samples collected either from the pre-pandemic era dengue patients from healthy donors during the pandemic period. Results: In-house ELISA selected a total of 79 true seropositive and 100 seronegative samples. The sensitivity of samples with <14 days using FT-DBA was 94.7% which increased to 100% for samples >14 days. The overall detection sensitivity and specificity were 98.8% and 98%, respectively, whereas the overall PPV and NPV were 97.6% and 99%. Moreover, comparative analysis between ELISA and FT-DBA revealed clinical agreement of Cohen’s Kappa value of 0.944. Conclusion: The assay can confirm past SARS-CoV-2 infection with high accuracy within 2 minutes compared to ELISA. It can help track SARS-CoV-2 disease progression, population screening, and vaccination response.

Background: Worldwide, microbes are becoming more dangerous by acquiring virulent skills to adapt and develop antimicrobial resistance (AMR). This is a concern as this increases morbidity, mortality, and costs. Consequently, physicians need to be trained inappropriate prescribing, starting with medical students. Objective: Evaluate medical students' confidence in antimicrobial agent prescribing and drug resistance Methods: Cross-sectional study assessing medical students' knowledge, perception, and confidence in prescribing antimicrobial agents and drug resistance in a Malaysian University. A universal sampling method was used. Results: Most respondents believe that educational input regarding overall prescribing was sufficient. Regarding the principle of appropriate and accurate prescriptions, female medical students had less knowledge [Odds Ratio (OR)=0.51; 95% Confidence Interval (CI) 0.25-0.99; p=0.050]. Year-IV and Year-V students had more excellent knowledge than Year-III students regarding confidence in antibiotic prescribing. Year-V students also showed appreciably higher confidence in the broad principles of prescribing, including infectious diseases, compared to those in other years. Conclusion: Overall, medical students, gain more excellent knowledge and confidence regarding prescribing, including antimicrobials, as their academic careers progress.

Abstract Successful treatment against infectious agents depends on rapid and accurate detection of the causative organisms. Misdiagnosis can hamper such success while leading to improper advising of antibiotics. In Bangladesh, the majority of the diagnostic centers detect and identify pathogens through culture and biochemical test-based methods and suggest antibiotics based solely on the results of disk-diffusion methods. This pilot study tried to validate the identity of the isolates characterized by diagnostic facilities near Dhaka. One hundred and twenty pre-characterized clinical isolates were analyzed biochemically and genotypically. Random Amplification of Polymorphic DNA-PCR, rcsA, and phoA genes-based PCR, and Loop-Mediated Isothermal Amplification (LAMP)-based identification of Klebsiella pneumoniae and Escherichia coli, respectively, followed by 16S rRNA sequencing confirmed misidentification of some clinical pathogens of other genera as Klebsiella spp. and E. coli. According to the antibiotic susceptibility testing guidelines, antibiotic choice, sensitivity pattern, and breakpoint measurement are different for each group of organisms. The lack of adherence to proper standards results in misdiagnosis and may facilitate the development of antibiotic resistance. The pilot study observers misidentification of clinical pathogens identified by the diagnostic centers. Well-characterized rapid molecular techniques like LAMP are suggested in clinical diagnosis to avoid misdiagnosis and subsequently circumvent antibiotic resistance development.

In this review, we covered the recent advances in synthesis of gold nanoparticles and their uses in degradation of dyes. This study provides framework to develop a low cost, ecofriendly and highly efficient synthesis of gold nanoparticles. These synthetic methods do not produce any toxic byproduct. The present study is focus on removal of dyes by Au-NPs because gold nanoparticles act as good absorbent for dyes in short time. Synthesis of Au-NPs from plant extract e.g. marine alga, Scutellarin Barbata, A.nigra, Fruit peels, Bacillus marisflavi from raw silk cocoons, amylopectin and poly acrylic acid, L.asparagine, Grephene oxide, IPEI coated Au-NPs. The synthesizes Au-NPs were used further for removal of dyes like methylene blue (MB), Rhodamine B (RB) Degradation, methylene orange, acid red degradation, Congo red.

Background: Health policy is a set of comprehensive principles and legislations that guide how healthcare should be effectively delivered in the community. Medical schools should prepare students to undertake managerial responsibilities by incorporating health policy in the curriculum to deal with the intricacies of healthcare systems and their clinical roles in their future professional careers. Objective: To examine medical students' perception at a Public University in Malaysia regarding teaching health policy and their participation in health policy roles. Material and Methods: A cross-sectional study using universal sampling was carried out among the medical students using a paper-based questionnaire to collect the data. Results: Most respondents opined their willingness to learn health policy (80.9%) and that teaching health policy (83.6%) should be compulsory for medical students. The respondents thought health policy should be introduced earlier in Year 1 or 2. The student scores on their knowledge regarding health policy and year of study were significantly associated with their involvement in the health policy roles in both the simple and multiple logistic regression. Both statistical tests reported higher participation in health policy roles with the higher year of study, though only Year 4 and 5 were significant in the simple logistic regression and only Year 5 in the multiple logistic regression compared to Year 1. On the other hand, age and type of admission show significant results only in the simple logistic regression, while the race was only significant at the multivariate level. Conclusion: This study demonstrated that most respondents showed their willingness to learn health policy, participate in the health policy programs, and recommend that health policy be considered an essential topic in the medical curriculum, which should be taught right from the first year of medical school. We recommend encouraging students’ participation in health policy activities.