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

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

Decentralized Finance (DeFi) is an emerging and revolutionizing field with notable uncertainties of reliability to be used on a mass scale. On the other hand, Artificial Intelligence (AI) has proved to be a crucial helping tool in numerous domains. In this study, we present a systematic review of the utility of AI in Defi in terms of impact, reliability, and security and conduct exhaustive analysis. We further conclude from our extensive literature review that we can identify possible new research opportunities in AI to bridge the gap of trust between peers and make the integration of DeFi more agile in the near future.

Kei-apple (Dovyalis caffra L) is an indigenous fruit tree of southern Africa. It is currently found in the dry, wooded grassland and forest edges of the arid and semi-arid regions of the world. Existing literature have mainly focused on the medicinal and nutritional as well as phytochemical characterization of Kei-apple. Thus, this review highlights beneficial economic prospects of Dovyalis caffra and its contribution to the economic prosperity. It is a drought tolerant plant with diverse uses such as income from the sale of the fruit, cultivated as a fence or to form an impenetrable hedge while the leaves are used for nutrient leaching prevention, fodder and compost making. The trunk of Dovyalis caffra serves as a good source of hard wood for fuel, house building and furniture making. It is also a good apicultural fruit tree and an excellent habitat and fertilizer derivation agent for farming activities. Considering the multidimensional effect of poverty and the food insecurity on many rural communities globally, there is a need to explore the potentials by encouraging the cultivation of Dovyalis caffra to assist with reducing unemployment, food insecurity and income-poverty problems in the arid and semi-arid areas of the world.

The emergence of pathogenic fungi is a major and rapidly growing problem (7% increase) that affects human and animal health, ecosystems, food security and the economy worldwide. The Dermocystida group in particular has emerged relatively recently and includes species that affect both humans and animals. Within this group, one species in particular, Sphareothecum destruens, also known as the Rosette agent, represents a major risk to global aquatic biodiversity and aquaculture and has caused severe declines in wild fish populations in Europe and large losses in salmon farms in the USA. It is a species that has been associated with a healthy carrier for millions of years but the host has managed in recent decades to invade parts of Southeast Asia, Central Asia, Europe and North Africa. In order to better understand the emergence of this new disease, we have for the first time synthesised current knowledge on the distribution, detection, prevalence of S. destruens as well as the associated mortality curves, and the potential economic impact in countries where the healthy carrier has been introduced. Finally, we propose solutions and perspectives to manage and mitigate the emergence of this fungus in countries where it has been introduced.

Predictive maintenance (PdM) represents a transformative approach to ensuring the security and reliability of critical infrastructure systems. This proactive maintenance strategy leverages advanced data analytics, machine learning, and sensor technology to anticipate and address potential failures before they occur. By continuously monitoring the health of infrastructure components and analyzing performance data, predictive maintenance enables timely interventions, minimizes unplanned downtimes, and enhances overall system resilience. This paper explores the application of predictive maintenance in safeguarding critical infrastructure, focusing on its impact on security, operational efficiency, and cost-effectiveness. Case studies demonstrate how PdM has been successfully implemented across various sectors, including transportation, energy, and utilities, highlighting its role in mitigating risks and extending the lifespan of essential assets. The findings underscore the importance of integrating predictive maintenance into security strategies to protect vital infrastructure from emerging threats and ensure uninterrupted service delivery.

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

Internet of Things (IoT) provides a huge business value for customers, organizations, and governments due to the developments of so many applications in different sectors like energy and healthcare. Nevertheless, as a new emerging technology, IoT faces several security concerns that are more challenging than conventional Internet because of its limited resources as well as its complex ecosystem. Toward this end, we first highlight IoT security challenges and briefly discuss its security goals like confidentiality and integrity. Second, we discuss the most common attacks against IoT, along with their violated security goals. We also review the existing frameworks of security and privacy guidelines for IoT and illustrate their shortcomings. Third, we propose a novel framework for securing IoT objects, the key objective of which is to assign different Security Level Certificates (SLCs) for IoT objects based on their hardware capabilities and protection measures. Objects with SLCs, therefore, will be able to communicate with each other or with the Internet in a secure manner. The proposed framework is composed of five main phases. In phase 1, we classify IoT assets into four components: (i) physical objects, (ii) protocols, (iii) data at rest, and (iv) software, which includes operating systems, middlewares, and applications. We also classify IoT objects into five categories based on their hardware capabilities. In phase 2, we propose security and privacy guidelines for previously mentioned IoT assets, along with their protection measures. In phase 3, we classify protection measures into five SLCs, and then we assign different SLCs for IoT objects. In phase 4: we develop a communication plan between objects based on their SLCs. In phase 5, we propose a four-step method to seamlessly integrate our objects with legacy objects ( objects are not developed according to our framework). Fourth, the feasibility and application of this framework are illustrated using smart homes as a case study. Finally, we investigate how our framework would lessen several attacks and threats against IoT like routing attacks and physical damage. We also provide qualitative arguments to show that this framework could be utilized to solve some of IoT security challenges such as tight resource constrains. Moreover, we discuss the shortcomings of our suggested framework.

In this paper, we first investigate secure communications for a two-hop interference relay system in the wireless Internet of Things (IoT), where K source-destination pairs communicate in the presence of an eavesdropper. Explicitly, we jointly design source, relay and destination matrices upon minimizing total mean-squared error (MSE) of all legitimate destinations while keeping the MSE at eavesdropper above a certain threshold. We illuminate that the joint design of the source, relay and destination matrices subject to both secrecy and transmit power constraints. More specifically, we proposed an efficient iterative distributed algorithm to simplify the process of the joint design for optimal source, relay and destination matrices. Furthermore, the convergence of the iterative distributed algorithm is described. Additionally, the performances of the proposed algorithm, including both its secrecy rate and MSE, are characterized with the aid of simulation results. We demonstrate that our proposed algorithm outperforms the traditional approach. As a benefit, secure communications can be guaranteed by using the proposed algorithm for the multiple input multiple output (MIMO) interference relay IoT network in the presence of an eavesdropper.

The ignorance of or lack of knowledge about cybersecurity aspects causes a critical problem regarding confidentiality and privacy. This security problem will continue to exist even if the user possesses less expertise in information security. The modern IT technologies are well developed, and almost everyone uses the features of IT technologies and services within the Internet. However, people are being affected due to cybersecurity threats. People can adhere to the recommended cybersecurity guidelines, rules, adopted standards, and cybercrime preventive measures. However, it is not possible to entirely avoid cybercrimes. Cybercrimes often lead to sufficient business losses and spread forbidden themes (hatred, terrorism, child porn, etc.). Therefore, to reduce the risk of cybercrimes, a web-based Blockchain-enabled cybersecurity awareness program (WBCA) process is introduced in this paper. The proposed web-based cybersecurity awareness program trains users to improve their security skills. The proposed program helps with understanding the common behaviors of cybercriminals and improves user knowledge of cybersecurity hygiene, best cybersecurity practices, modern cybersecurity vulnerabilities, and trends. Furthermore, the proposed WBCA uses the Blockchain technology to protect the model from the potential threats. The proposed model is validated and tested using real-world cybersecurity topics with real users and cybersecurity experts. We anticipate that the proposed program can be extended to other domains, such as national or corporate courses, to increase the cybersecurity awareness level of users.

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

Cyber-threat is now a global concern to all internet users, as the rate of crimes has tremendously changed from physical to virtual. Implementing awareness programs at various organization levels is required, Also there is a need to know the methodologies used in measuring cybersecurity awareness program effectiveness. The paper aims to review and explore the previous methodologies applied, target organizations, and application of Machine Learning techniques in the assessment of awareness programs. Systematic literature review techniques were used to conduct the search using pre-defined keywords and published papers from 2006 to 2019, the information was used in providing an answer to the research questions. The finding shows questionnaire was widely used as a method of evaluating cybersecurity awareness and also organizations implement more awareness programs, the gap found is the lack of using ML in assessing the effectiveness of design, and implemented cybersecurity awareness programs

The purpose of this paper is to develop WebSecuDMiner algorithm to discover unusual web access patterns based on analysing the potential rules hidden in web server log and user navigation history. Design/methodology/approach: WebSecuDMiner uses equivalence class transformation (ECLAT) algorithm to extract user access patterns from the web log data, which will be used to identify the user access behaviours pattern and detect unusual one. Data extracted from the web serve log and user browsing behaviour is exploited to retrieve the web access pattern that is produced by the same user. Findings: WebSecuDMiner is used to detect whether any unauthorized access have been posed and take appropriate decisions regarding the review of the original rights of suspicious user. Research limitations/implications: The present work uses the database which is extracted from web serve log file and user browsing behaviour. Although the page is viewed by the user, the visit is not recorded in the server log file, since it can be access from the browser's cache.

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

Short Message Service (SMS) is becoming the secure medium of communication due to large-scale global coverage, reliability, and power efficiency. As person--to--person (P2P) messaging is less secure than application-to-person (A2P) messaging, anyone can send a message, leading to the attack. Attackers mistreat this opportunity to spread malicious content, perform harmful activities, and abuse other people, commonly known as spam. Moreover, such messages can waste a lot of time, and important messages are sometimes overlooked. As a result, accurate spam detection in SMS and its computational time are burning issues. In this paper, we conduct six different experiments to detect SMS spam from the dataset of 5574 messages using machine learning classifiers such as Multinomial Naïve Bayes (MNB) and Support Vector Machine (SVM), considering variations of \textit{Term Frequency-- Inverse Document Frequency (TF--IDF)} features for exploring the trade-off among accuracy, F1-score and computational time. The experiments achieve the best result of the accuracy of 98.50\%, F1--score of 98\%, and area under roc curve (AUC) of 0.97 for multinomial naïve bayes classifier with TF--IDF after stemming.

This article titled "AI and IoT in Automation and Security: A Vision for Next Generation Applications," explored the implications of marrying IoT with AI in the realms of automation and security; this piece goes further into what that entails. In this paper, we present innovative application descriptions arising from this technology convergence and its future-generation applications. We found that AI brings strong cyber security, predictive maintenance, and decision-making capabilities to the table due to its advanced algorithms and machine learning models. IoT has the potential to allow businesses to extract and generate big data, automate processes, and monitor in real-time thousands and even millions of businesses at once. For now, we believe some of the possible main conclusions will be that the smart city, stand-alone systems, and user experience will be quite consumer-friendly. Importantly, the study also assessed facets such as data protection, system interoperability, and a few others. Our vision underscores the essential of a strong structure to connect the full latent of AI and IoT, finally aiming to create more capable, protected, and approachable environments. Significant results include achieving 95.8% accuracy in anomaly detection and 93.4% accuracy in predictive maintenance. The showcasing results efficacy of AI models to enhance system reliability as well as operational efficiency. Vigorous encryption protocols performance ensures data security hence addressing issues of privacy. These culminations underscore the transformative potential of AI-IoT integration in next-generation automation and security applications.

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

Nowadays one of the issues interesting for researchers of political, social, and economic sciences is to investigate immigration from different angles. Accordingly, they use a diverse range of instruments, different types of graphs, descriptive indices and complex mathematical models. Immigration has always been one of the paths helping humans in their own efforts for getting compatible with the environment and coping with difficulties. Nevertheless, if this issue has been considered as an instrument regulating resources and species automatically, in the present era, its negative aspects have been evident and socioeconomic consequences of immigration in the framework of development economy and security has attracted scholars’ attentions. One of the countries attracting for immigrants particularly in the continent of Africa is South Africa. Statistics indicates that this country is the most attractive one for refugees. Somalis are one of the most important and largest groups seeking refuge in South Africa because they are usually merchants who earn money in the poor places. However, business in the poor places endangers their lives and properties because of high rate of crimes and xenophobia. As a result, the target countries face a lot of socioeconomic and security problems. The present study tries to investigate the issue of Somali immigration using network models.

Entomopathogenic fungi perform important functions in the ecosystem as natural antagonists of insects, which can be used in agriculture. Interestingly, recent studies showed a significant promotion of tested plants growth in the presence of fungi. We hypothesize that some of various compounds produced by entomopathogenic fungi can positively affect plant development. To test this hypothesis, fungal extracts of entomopathogenic fungus Beauveria bassiana were prepared at different conditions of temperature and pH. In addition to determination of the ammonium nitrogen content, the composition of extracts was analyzed by elemental ICP-OES. Then, their effect on the wheat germ growth was studied using various extract concentrations. After experiments, tested plants were measured, weighed, and the chlorophyll content was determined. Finally, the impact of extracts on the selected G+ and G- bacteria growth was examined to exclude the possibility of interference with soil microorganisms. The highest length of the wheat shoot was obtained for the use of 10-times diluted extract (10%) at pH 10 obtained at 20°C. In contrast, addition of 10% extract (pH 10) obtained at 75°C resulted in the shortest shoot. Generally, the extracts obtained at 75°C showed phytotoxic properties leading to lower values of shoot length and fresh weight in comparison to the control group. Our preliminary results are the first confirming the potential of fungal water extracts as factors promoting plant growth. Further detailed study should be carried out to confirm the effects in real environment conditions. Also, the consistency of the plant growth stimulation across different entomopathogenic fungi, and agriculturally used plant species should be tested.

The goal of this study is to categorize stakeholders in Internet of Things (IoT) security based on their roles in order to address the challenges associated with managing the large amount of data generated by IoT devices. With the increasing number of connected devices, the security risks also escalate, necessitating skilled stakeholders to mitigate these risks and prevent potential attacks. This study proposes a two-part approach that involves clustering stakeholders according to their responsibilities and determining relevant features to enhance decision-making. By grouping stakeholders into shared categories, the study aims to provide a more efficient and effective method for managing IoT security. The insights gained from this research will not only benefit stakeholders involved in IoT security but also assist policymakers and regulators in developing effective strategies.

Data security is a fundamental aspect to be considered in Internet of Things (IoT) information gathering systems, as IoT is a network of interconnected devices that collect and share real-time data, becoming increasingly prevalent in our lives. However, data security in IoT systems presents unique challenges due to the large number of devices and access points involved. This study aims to conduct a literature review on IoT security to analyze the performance of security mechanisms on current development platforms, specifically on a Raspberry Pi 3. Some functions from the OpenSSL library were used, including popular hash functions and cipher algorithms. Additionally, a bash code was developed to obtain the time spent in seconds and the memory consumption in kilobytes. In addition to time and memory calculations, statistical values such as variance and standard deviation were also obtained and compared with results obtained on a personal computer. The tests conducted in this study demonstrated that it is possible to implement these algorithms on platforms with more limited resources, with AES and RSA algorithms being the most suitable for IoT scenarios.

The tourism sector in general and the hotelier in particular face the challenge of managing appropriate measures to deal with the covid-19 pandemic. In this sense it is useful to know what the measures are most demanded by the clientele. This research, by analyzing the variance (ANOVA) of a factor concluded that women are more demanding than men in terms of the level of demand in relation to the measures to be taken in hotels. This, as a practical application, allows to know more accurately the expectations, demands and demands of the client segmented by sex, in order to satisfy and improve the tourist experiences.

The aimed of this study was to explore the current state of knowledge of agrobiodiversity to improve nutrition and health. The study focused on wild mushrooms commonly consumed in North-Eastern of Thailand in term of ecology, cooking and preservation method and folk remedies. The monosaccharide, sugar alcohol, glucans and carbohydrate polymer content and the glycemic index of these wild mushrooms was determined using Enzymatic method. The mushrooms collected were belong to three biological groups and mostly saprotrophic and symbiotic. The most abundant mushrooms are Amanita, Boletus and Russula and followed by Calostoma sp., Astraeus asiaticus C. Phosri and Astraeus odoratus C. Phosri. Wild edible mushrooms can be used include food and medicinal purpose. Cooking methods utilized in the area are steaming, boiling and grilling. The carbohydrate profiles results showed that glucose is the major monosaccharide detected in mushroom samples. Xylitol and inositol was found in all mushroom sample while mannitol and arabitol was detected in some samples. Glucan was presented in all mushroom samples ranged from 8.03-31.1 mg/g DW. All mushrooms are classified as low GI food. Therefore, this finding provide important information that may be adopted to enhance and promote the utilization of wild mushroom to improve nutrition security and sustainability.

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

The tourism sector in general and the hotelier in particular face the challenge of managing appropriate measures to deal with the covid-19 pandemic. In this sense it is useful to know what the measures are most demanded by the clientele. This research, by analyzing the variance (ANOVA) of a factor concluded that women are more demanding than men in terms of the level of demand in relation to the measures to be taken in hotels. This, as a practical application, allows to know more accurately the expectations, demands and demands of the client segmented by sex, in order to satisfy and improve the tourist experiences.

The loop and knot securities of 2 braided and 2 monofilament sutures of four diameters (3.0, 2.0, 0, 1) were evaluated with a tensiometer for four 4-throw knots known to be secure with a 2.0 polyfilament suture. Loop security of monocryl 1 is low, being 14.7 + 3.0 Newton (N) for a 3-throw half-knot (H3) and 15.4 + 2.4 N and 28.3+ 10 N for 2 (SSs) and 4-throw (SSsSsSs) symmetrical sliding half-hitches. This is lower than 18, 24, and 46 N for similar knots with vicryl. Polyfilament sutures have excellent knot security for all diameters. Occasionally, some slide to open with slightly lower knot security, especially for larger diameters, although not clinically problematic. Knot se-curity of monofilament sutures was unpredictable for all 4 knots, especially for larger diameters, resulting in many clinically insecure knots. A secure monofilament knot requires a 6-throw knot with 2 symmetrical sliding half-hitches or 2 symmetrical half-knots secured with 4 asymmetric blocking half-hitches. In conclusion, with polyfilament sutures, 4 or 5-throw half-knot or half-hitch sequences result in secure knots. For monofilament sutures, loop and knot security is much less, half-knot combinations should be avoided, and secure knots require 6-throw knots with 4 asym-metric half hitches. This conclusion is valid for gynecologic laparoscopic and open surgery, and for surgery in general.

Reconfigurable computing is becoming ubiquitous in the form of consumer-based Internet of Things (IoT) devices. Reconfigurable computing architectures have found their place in safety-critical infrastructures such as the automotive industry. As the target architecture evolves, it also needs to be updated remotely on the target platform. This process is susceptible to remote hijacking, where the attacker can maliciously update the reconfigurable hardware target with tainted hardware configuration. This paper proposes an architecture of establishing Root of Trust at the hardware level using cryptographic co-processors and Trusted Platform Modules (TPMs) and enable over the air updates. The proposed framework implements secure boot protocol on Xilinx based FPGAs. The project demonstrates the configuration of the bitstream, boot process integration with TPM and secure over-the-air updates for the hardware reconfiguration.

The impact that new technologies have on all aspects of our lives, work, and businesses is significant and growing over time. Blockchain and artificial intelligence, among other innovative technologies, are having a profound effect on practically all business functions, including most human resources (HR) tasks. The HR sector is currently facing a variety of challenges as HR departments invest many hours in vetting candidates' applications and authenticating records to decrease the chances of imperfect recruiting. Nowadays, more technologically based processes are utilised for talent search, selection and management, as well as for accumulating, retaining, and integrating new talents into a company. These HR practices are far more effective compared to traditional methods but can also be more expensive if the transactional costs are considered. This paper consists of two parts. Following an introduction, the first part discusses the trust and confidentiality that can be attained, while the second part examines security, fraud prevention and the productivity gained from using blockchain technology in HR activities. Finally, there is a concluding section, discussing the implications of blockchain technology for firms who choose to employ it.

The novel coronavirus SARS-CoV-2, the causative agent of COVID-19, emerged in the human population in December 2019 and spread worldwide within a few short months. Much of the public health focus for preventing and mitigating the spread of COVID-19 has been on individual and collective behaviors, such as social distancing, mask-wearing, and hygiene. Yet it is equally important to recognize that these behaviors and health outcomes occur within broader social and environmental contexts. Factors within local communities, regional policy, race, history, personal beliefs, and natural- and built environmental characteristics affect underlying population health and the spread of disease. For example, COVID-19 has renewed attention to secure water and sanitation services and their importance in protecting human health. Many remote Alaskan communities are particularly vulnerable because of inadequate water and sanitation systems. In this paper, we describe how inadequate water and sewer services may place the inhabitants of remote Alaskan communities at higher risk of COVID spread. We argue that insufficient water security and inadequate sewer systems, along with household overcrowding, multigenerational residences, limited transportation options, limited medical facilities, and higher prevalence of chronic diseases could lead to a greater potential of COVID-19 transmission and to more severe disease outcomes in these communities.

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

Recent reports have it that over 85\% of data breaches are still caused by the human element, of which healthcare is one of the suitable organizations mostly targeted by cybercriminals. The work of healthcare staff is often associated with high workloads, high emergency cases, and a broad range of psychological, social, and cultural factors. The significance of these factors could undermine conscious care information security (IS) practice leading to serious violations. This study comprehensively examined the correlation between the psycho-social-cultural factors, work factors with IS and privacy behaviour in a hospital that has fully adopted electronic health records (EHR) management system. The findings are to facilitate the decision-making process towards improving the cyber-security practice in healthcare. A quantitative approach was adopted where we collected responses from 212 healthcare staff through an online questionnaire survey. A broad range of constructs was selected from psychological, social, cultural perception and work factors based on earlier review work. These were therefore related to some security practices, to assess the IS knowledge, attitude and behaviour gaps among healthcare staff in a comprehensive way. From the study, IS self-reported conscious care behaviour (ISCCB) risk was relatively higher as compared to information security knowledge (ISK) risks and information security attitude (ISA) risk. Furthermore, the study revealed that work emergency has a positive correlation with ISCCB (r=1.95, p-value =0.001) risk. Conscientiousness also had positive correlation with ISCCB risk (r=0.157, p-value=0.05) however agreeableness negatively correlated with ISK risk (r=-0.166, p-value =0.05), and ISA risk (r=-0.140, p-value =0.05). Based on these findings, intrinsic and extrinsic motivation methods combined with cutting-edge technologies can be explored to discourage IS risks behaviours while enhancing conscious care security practice.

This study examines the balancing between urban transportation security and supply chain efficiency amid global disruptions like the COVID-19 pandemic, attacks on maritime lanes, and geopolitical conflicts. The authors combine bibliometric network analysis and natural language processing (NLP) to review scholarly literature from 2017 to 2023. This approach identifies and visualizes evolving thematic trends. Findings reveal a shift towards integrating advanced technologies such as blockchain, AI, and IoT to enhance security without compromising efficiency. This study highlights the significance of integrating urban planning principles and sustainable practices to enhance transportation security and supply chain efficiency within urban environments. Actionable insights for policymakers and supply chain managers emphasize the adoption of integrated, technology-driven security solutions to foster resilient and sustainable supply chains. This research contributes to academic discourse by refining the understanding of evolving priorities in transportation security and supply chain management, offering both theoretical and practical implications for secure and efficient global supply chains.

As organizations are vulnerable to cyber attacks, their protection becomes a significant issue. Capability Maturity Models can enable organizations to benchmark current maturity levels against best practices. Although many maturity models have been already proposed in the literature, a need for models that integrate several regulations exists. This article presents a light web-based model that can be used as a cybersecurity assessment tool for Higher Education Institutes (HEIs) of the UK. The novel Holistic Cybersecurity Maturity Assessment Framework incorporates all security and privacy regulations and best practices that HEIs must be compliant to and can be used as a self-assessment or a cybersecurity audit tool.

Typical modern information systems are required to process copious data. Conventional manual approaches can no longer effectively analyze such massive amounts of data, and thus humans resort to smart techniques and tools to complement human effort. Currently, network security events occur frequently, and generate abundant log and alert files. Processing such vast quantities of data particularly requires smart techniques. This study reviewed several crucial developments of existent data mining algorithms, including those that compile alerts generated by heterogeneous IDSs into scenarios and employ various HMMs to detect complex network attacks. Moreover, sequential pattern mining algorithms were examined to develop multi-step intrusion detection. These studies can focus on applying these algorithms in practical settings to effectively reduce the occurrence of false alerts. This article researched the application of data mining algorithms in network security. The academic community has recently generated numerous studies on this topic.

This paper explores decision analysis on product integration of family security services and residential fire insurance in the London and Taiwan markets by using the proposed mathematical models for counting sustainable value. This paper shows the five main different results between London and Taiwan markets with ten different parameters of the family security market, to find out the optimal number of family security integrated services for each security company in London. The improvement of the risk aversion effect based on risk and financial management will enhance the market share of the private security industries in the London and Taiwan markets. The results of this research can serve as a reference for the decision-making of private security industries on product integration under sustainable value consideration. The research findings highlight the potential benefits for both the private security industry and the insurance industry in their design and negotiation for product integration to improve both of business operation and achieve corporate social responsibility goals to match the sustainability in the future.

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

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

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

Wireless sensor networks are usually applied in hostile areas where nodes are easy to be moni-tored and captured by adversary. Key distribution is an essential primitive to provide most of security mechanism. However, the characteristic of limited resources of sensors restricts the direct use of conventional key distribution schemes. In this paper, a complete security key distribution scheme based on asymmetric cryptography technology is proposed in both static and mobile scenarios. Mutual authentication is guaranteed using challenge-response mechanism. The per-formance evaluation and security analysis show that the proposed scheme with low complexity not only provides better security for wireless sensor networks, but also reduces storage overhead and key exposure risks.

The recent COVID-19 pandemic has showcased the implications of using virtual private networks (VPNs) as home-working is now common. Establishing the current state of knowledge on VPNs and their processes is vital. This corroborates an up to date understanding on the fundamentals of VPNs and their usage in today’s society; it informs on how to better use VPNs too. Insight into the security issues VPNs face and possible solutions to these allows for the identification of gaps for potential future research. Addressing these gaps would then indicate how to further improve VPNs, making sure VPNs are indeed beneficial to users.

Data could be in many forms like text, image, audio, video and many others. Amongst these data, processing image is of important concern even with sophisticated technologies. Generally, images consume more storage space and processing time. This work aims in attaining a novel image encryption strategy that consumes very less computational time with maximum security. The idea is to split the image into blocks and rearrange the pixels in diagonal fashion to achieve confusion. Next, the blocks are combined to form sub images and one block of the sub image is subjected to Secure Hash Algorithm (SHA 512 bits). The SHA bits are XORed with the remaining blocks of the sub image. Finally, the entire image is encrypted using Advanced Encryption Standard (AES). Decryption process is the exact reverse of encryption process. The computational overhead is very low, and security is efficient. The results are compared with existing encryption techniques.

Poor hygiene in the food services industry is a critical public health concern in the Maldives, with an average of 35 public complaints per month regarding the poor hygienic conditions of the restaurants in Male’, the capital of Maldives. Thus, this study aimed to identify the level of knowledge, attitude, and the gap of self-reported with the observed practice (KAP) of food handlers as well as the potential association between KAP. 290 food handlers from restaurants were surveyed, demonstrating moderate knowledge (55.5%), moderate attitude (3.62±0.51) and good practice (4.18±0.54). However, it is critical the result shows the food handlers in Male’ have poor practice on the time and temperature control where especially on thawing and storage resulting from a lack of training in the temperature danger zone. The work experience impacting the level of knowledge, education impacts the level of attitude and age, and education and work experience impacting practice. Male’ restaurants mostly employed the foreigners mostly from Bangladesh and the food handlers were identified to have low level of education. Association analysis identified significant relationships between knowledge, attitude, and practice, while the principle component analysis shows categories that are related to each of KAP. This study is fundamental for policymakers to review and improve law enforcement to address the current food safety situation in the Maldives.

Abstract Introduction. Burnout syndrome develops as a consequence of chronic stress among employees. The study objectives were to examine burnout syndrome and to evaluate the quality of life among security employees of the professional private security sector in Central Serbia. Methods. A multicenter cross-sectional questionnaire-based study was performed. The questionnaire for staff employed at institutions who are in direct contact with people (Human Services Survey, MBI-HSS) and Short Form 36 Health Survey-(SF-36). Results. A total of 353 respondents participated in the study. The response rate was 80%. Approximately one-third (32.6%) had symptoms of total burnout, and a much higher number of responders had a moderate or high level of burnout on individual subscales:- depersonalization (high 82.4%, moderate 16.2%) and emotional exhaustion (high 66.3%, moderate 19.8%) and, slightly more than one-third (34.5%) had low personal achievement, while about one-third (32.9%) had moderate personal achievement. Female sex and older age were associated with a higher risk of total burnout and the development of emotional exhaustion. Male sex, higher education, and managerial position were associated with higher personal achievement and lower risk of total burnout. Male sex, marital union, two or more children, and direct contact with clients were significantly associated with a lower quality of life of employees. Conclusion. Female sex and older age were associated with a higher risk of total burnout and the development of emotional exhaustion. Male sex, university education and managerial position were associated with greater personal achievement and were protective factors in relation to the development of burnout. Male sex, marital union, two or more children, and direct contact with clients were significantly associated with lower quality of life of employees. Keywords: burnout syndrome; security employees; professional private sector; quality of life

This study explores the impact of mobile technologies on consumer behavior in retail marketing, examining how advancements in mobile platforms have transformed shopping habits and interactions with brands. Mobile technologies have revolutionized the retail experience by providing unprecedented convenience, allowing consumers to shop at any time and from any location. This shift has led to a marked preference for mobile shopping over traditional methods, fundamentally altering consumer behavior. Key findings reveal that personalization plays a crucial role in enhancing consumer engagement and satisfaction, with mobile technologies enabling tailored recommendations and offers based on individual preferences. Access to real-time information, such as product details and price comparisons, empowers consumers to make more informed purchasing decisions, reflecting a shift towards deliberate and confident buying choices. Social media and social commerce have also become significant factors, with consumers discovering new products and brands through these platforms and benefiting from integrated shopping features. Impulse buying behavior has increased due to the convenience of mobile shopping, with promotional alerts and simplified payment processes contributing to spontaneous purchases. The integration of mobile technologies with physical store experiences has created a more seamless shopping journey, bridging the gap between online and offline interactions. However, challenges such as privacy concerns, data security, and information overload remain significant. Consumers are increasingly aware of data collection risks and may experience fatigue from excessive marketing messages. To navigate this evolving landscape, retailers must balance the advantages of mobile technologies with addressing these challenges to effectively meet consumer needs and expectations.

With the emerging technology connected with the internet, there is one constant issue related to that is data security. The only solution with which this issue can be resolved at a limit and can be used to protect the data is various algorithms for encryption. Though different approaches were used for the same, Cryptography seems to be efficiently protecting the data while transmitting in network from sender to receiver. Firstly the data is encrypted before sending to receiver using the most secure and reliable encryption algorithm. Secondly, at the receiver end it can be decrypted using the same decryption algorithm. Only receiver will have the key with which the data can be decrypted. In this paper, AES, DSS and RSA algorithms were implemented. These algorithms are encryption algorithms which perform encoding and decoding of data, to be sent from sender to receiver, using the keys. Each have different criteria for encryption and are then compared based on different parameters viz. delay, throughput, PDR is an acronym for packet delivery ratio, PLR represents packet loss ratio and RPC denotes Received Packet Count. The results in the form of graphs are given to analyze the security provided by each algorithm.

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.

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

We are already more than year away from the pandemic period, followed by a year within a global economic crisis and a war in Ukraine, with the marks and the changes caused in all parameters of life becoming clear now including food choice motives of citizens worldwide. In this study we investigate the changes in food choice motives caused by the pandemic to college students in Greece in the 10 key food motives namely health, convenience, sensory, appeal, nutritional quality, moral concerns, weight control, mood and stress, familiarity, price, and shopping frequency and behavior. A self-response questionnaire survey was carried out in January to February 2023 on a sample of 1017 college students’ participants through the Google platform. Basic statistical tools, combined with cross and Chi-square tests were used in order to analyze the collected data. The results show that students exhibit quiet and very important preferences on health, convenience, weight control, and mood and stress. Food choices related to sensory appeal, nutritional quality, and familiarity is of less importance for the students, similarly to ethical concerns expect the environmental impact of the food which is high to their concern. The motive which continues to have the highest concern for students before and after the pandemic is price looking for value for money food (88.8%), not to be expensive (80.7%), and be cheap (78.7%). The shopping frequency and behavior motives, which were changed during the pandemic, have now returned to the motives of the pre pandemic period, with purchase of foods from supermarket (29%), local grocery (37.6%), and only 12.3% via online, and 20.4% by delivery, weekly or every two weeks. They prefer to cook at home full meals is now very high reaching 74.4%, avoiding eating at a restaurant or fast food (only 27% positives answers). Our findings indicate that students have already returned to their food choice motives of the period before COVID-19 except the home cooking food which is now high in their preference.

In the development of power systems it is indicated very often, that transformation of power systems should be carried out in accordance with the idea of energy democracy. This will develop energy communities, that are trying to meet energy needs by using local renewable generation sources. This may result with a temporary low load on the MV lines connecting the community grid and the power system. Such state may cause incorrect operation of power protection systems. This can cause an extended protection operation time, due to decision algorithms inactivity at low values of measurement currents. Therefore, the detailed MV lines overcurrent digital protection model and a dynamic model of the power network were developed. The simulation results are showing that the settings of the parameters activating the protection decision algorithms affect their operation time in dynamic conditions. The conclusion is that the development of the power protection automatics must be carried out in the same time (preferably in advance) with the change of the power system operation model. This is very important for future power systems with high penetration energy communities and renewable generation sources.

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

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

Integrated Water Resources Management (IWRM) is one of the system thinking approaches emerged in the 1990s. Since then it has been applied in various countries and contexts. However, the implementation of the IWRM is contested. There are paucity of literature and guidelines as to how the concept can be operationalized. In Ethiopia, there is no evidence that IWRM is successfully instituted. Particularly, IWRM has never been implemented in the Awash River Basin. The study generated data from household and institutional surveys, in-depth interviews, focused group discussions, workshops, and secondary sources. Multiple sources of data were triangulated and thematically summarized. We found that pragmatic water resources management through system approach helps to recognize river basin as a bigger system in which the natural and human systems function. This resolves the problem of fragmentations among among various actors, sectors, interest and priorities. That it facilitates the coordination of various subsystems. The operationalization of IWRM as a system to secure water resources require the establishment and/or strengthening of the interactions of various systems, subsystems, and the elements within the entire basin system. Finally, enabling institutional environments should be considered as a medium of realizing IWRM.

The 2030 Agenda for Sustainable Development, the SDGs, are high on the agenda for most countries of the world. In its publication of the SDGs, the UN has provided the goals and target descriptions that, if implemented at a country level, would lead towards a sustainable future. The IAEG (InterAgency Expert Group of the SDGs) was tasked with disseminating indicators and methods to countries that can be used to gather data describing the global progress towards sustainability. However 2030 Agenda leaves it to countries to adopt the targets with each government setting its own national targets guided by the global level of ambition but taking into account national circumstances. At present, guidance on how to go about this is scant, but it is clear that the responsibility is with countries to implement and that it is actions at a country level that will determine the success of the SDGs. SDG reporting by countries takes on two forms 1) global reporting using prescribed indicator methods and data; 2) National Voluntary Reviews where a country reports on its own progress in more detail but is also able to present data that are more appropriate for the country. For the latter, countries need to be able to adapt the global indicators to fit national priorities and context, thus the global description of an indicator could be reduced to describe only what is relevant to the country. Countries may also, for the National Voluntary Review, use indicators that are unique to the country but nevertheless contribute to measurement of progress towards the global SDG target. Importantly, for those indicators that relate to the security of natural resources security (e.g. water) indicators, there are no prescribed numerical targets/standards or benchmarks. Rather countries will need to set their own benchmarks or standards against which performance can be evaluated. This paper presents a procedure that would enable a country to describe national targets with associated benchmarks that are appropriate for the country. The procedure focusses on those SDG targets that are natural resource-security focussed e.g. extent of water-related ecosystems (6.6), desertification (15.3) etc., because the selection of indicator methods and benchmarks is based on the location of natural resources, their use and present state and how they fit into national strategies.

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

Water scarcity is significantly increasing water stress in Africa and some parts of the world. This is due to human induced factors such as climate change, increase of human population that raises demand which outstrips food availability, and put great changes of land use which results in changes of hydrological mechanics and water availability as whole. The investigation was through literature review and it tries to examine the criticality of water scarcity in Africa regions, and the major factors that mastermind this menace. Also possible strategies that can be promptly used to manage water scarcity at domestic level and in agriculture are described in this paper, not with standing the fact that agriculture sectors in Africa and the rest of the world remain the utmost vulnerable enterprise to water scarcity and withdrawal on the planet earth.

Antimicrobial resistance (AMR) threatens to reverse the essential benefits of antibiotics not only in humans, where decades of advancements in healthcare outcomes are endangered but also in the food production industry. The emergence of AMR in the pre- and post-harvest systems presents a serious risk of contamination or infection directly by antibiotic-resistant bacteria (ARB) and genes (ARGs) for farmers, agricultural practitioners, abattoir workers, food handlers and their associated contacts as well as consumers at the end of the food chain. Any breach in the food safety barrier leading to the emergence and spread of ARB and ARGs has severe multi-sectorial implications and threatens to reverse decades of human and animal health improvements globally. As the world moves towards Sustainable Development Goals (SDGs), food safety is a critical element to improve and strengthen global health, security and ensure sustainable development. This paper presents the challenge of AMR through the lens of food safety, by highlighting its multi-sectoral and multi-dimensional implications not only the SDG on food safety but also on food security, public health, animal health and welfare, the environment and climate and socio-economic development.

The quickest and easiest way to avoid greenhouse gas (GHG) emissions is to purchase renewable electricity and offset the remaining emissions. However, the industrial sector’s electricity needs already exceed renewable electricity generation. Moreover, electricity accounts for only one third of the industry’s energy needs. Simultaneously, the advance of sectoral coupling and the decarbonisation of industrial processes, as well as the desire to rapidly decrease dependence on fossil fuels, are creating significant additional demand for renewable energy. Neither existing nor planned generation and transmission infrastructure will suffice to meet the expected short-term demand. Based on survey data from the German Industry Energy Efficiency Index, this article therefore examines the share of GHG savings that companies intend to achieve on- and off-site. Understanding how much additional generation and transmission capacity is needed by the industry to decarbonise and by when is crucial to identify and address the extent of excess demand. On average, companies plan to avoid 22 % of their 2019 emissions by 2025 and 27 % by 2030, primarily through on-site measures. In combination with the extrapolation of the entire industry’s needs for off-site capacity, the data calls for a rapid expansion of planning authority and green generation capacities.

Plant breeding is central to agriculture, and shifts in plant breeding practices (e.g., hybrid development) and selection goals (e.g., response to synthetic fertilizer) have catalyzed monumental and persistent changes in agricultural production systems of all scales with social, political, economic, and environmental repercussions. While plant breeders are largely trained in the sciences of biology, genetics, and statistics, we posit an ethical imperative to examine the degree of equity with which the benefits of new research and plant varieties are distributed. In the United States, the history of plant breeding parallels the colonial history of agriculture, which compels reflection by current plant breeders about their role in shaping our agricultural system. In this perspective essay, we examine longstanding ideas about equitable food systems through the lens of public plant breeding in the United States. We propose a framework for equitable public plant breeding with respect to both its process and outcomes, and we intend for the ideas presented herein to catalyze reflection, discussions, and actions as the plant breeding community seeks greater equity in the food and seed systems our work supports.

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

In the age of next-generation computer, the role of the cloud, the internet and smart devices will become stronger. These days we all know the word smart well. This word is often used in our daily lives. The Internet of Things (IoT) will generate a variety of information from a variety of resources. It can store big data in the cloud. Fog computing acts as a signal between cloud and IoT. Fog extensions in this framework apply to material under IoT. IoT devices are called Fog nodes, which can be accessed anywhere within the network range. A blockchain is a novel way of recording in a secure sequence. Creating a new framework in the development of Internet of Things is one of the critical problems of wireless communication where solving such a problem can lead to continued growth in the use and popularity of IoT. Proposed research creates a framework for providing a framework for middleware on the internet of smart devices network for the internet of things using blockchains technology. Our great offering connects new research that integrates blockchains into the Internet of Things and provides secure Internet connection for smart devices. Blockchain (BC) Internet of Things (IoT) is a new technology that works with low-level, distributed, public and real-time leaders to maintain transactions between IoT sites. A blockchain is a series of blocks, each block being linked to its previous blocks. All blocks have cryptographic hash code, previous block hash, and its data. Transactions in BC are the basic components used to transfer data between IoT nodes. IoT nodes are a variety of portable but smart devices with embedded sensors, actuators, systems and the ability to communicate with other IoT nodes. The role of BC in IoT is to provide a process for processing secure data records using IoT nodes. BC is a protected technology that can be used publicly and openly. IoT requires this type of technology to allow secure communication between IoT nodes in different environments. Events in BC can be tracked and monitored by anyone who is certified to communicate within IoT.

Security patterns encompass security-related issues in secure software system development and operations that often appear in certain contexts. Since the late 1990s about 500 security patterns have been proposed. Although the technical components are well investigated, the direction, overall picture, and barriers to implementation are not. Here, a systematic literature review of 240 papers is used to devise a taxonomy for security pattern research. Our taxonomy and the survey results should improve communications among practitioners and researchers, standardize the terminology, and increase the effectiveness of security patterns.

Having access to a unique quantum circuit that one wants to protect against use by others is a very likely scenario. However currently users rely on classical computer security schemes, which have known shortcomings. In this chapter we introduce a novel protection scheme along with a survey of other known methods to protect quantum information. In particular we review physically unclonable functions (PUFs), obfuscation, and introduce quantum logic locking.

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

Objective: Food security during public health emergencies relies on situational awareness of needs and resources. Artificial intelligence (AI) has revolutionized situational awareness during crises, allowing the allocation of resources to needs through machine learning algorithms. Limited research exists monitoring Twitter for changes in the food security-related public discourse during the COVID-19 pandemic. We aim to address that gap with AI by classifying food security topics on Twitter and showing topic frequency per day. Methods: Tweets were scraped from Twitter from January 2020 through December 2021 using food security keywords. Latent Dirichlet Allocation (LDA) topic modeling was performed, followed by time-series analyses on topic frequency per day.Results: 237,107 tweets were scraped and classified into topics, including food needs and resources, emergency preparedness and response, and mental/physical health. After the WHO’s pandemic declaration, there were relative increases in topic density per day regarding food pantries, food banks, economic and food security crises, essential services, and emergency preparedness advice. Threats to food security in Tigray emerged in 2021.Conclusions: AI is a powerful yet underused tool to monitor food insecurity on social media. Machine learning tools to improve emergency response should be prioritized, along with measurement of impact. Further food insecurity word patterns testing, as generated by this research, with supervised machine learning models can accelerate the uptake of these tools by policymakers and aid organizations.

The rapid evolution of digital payment systems has significantly transformed the retail industry, reshaping how transactions are processed and enhancing the overall shopping experience. This research explores the adoption of digital payment technologies in retail settings, focusing on the motivations, challenges, benefits, and impacts associated with their implementation. Retailers are increasingly driven by the need to meet consumer demands for convenience and speed, as well as to gain a competitive edge in a saturated market. Digital payment systems, including mobile wallets, contactless payments, and online payment gateways, have become essential tools for retailers seeking to streamline operations and improve customer satisfaction. The study highlights several key findings, including the challenges related to technical integration, security concerns, and cost implications. Technical difficulties in integrating new payment systems with existing point-of-sale infrastructure, coupled with the need for robust security measures, present significant obstacles. Despite these challenges, the long-term benefits of digital payments, such as increased transaction volumes, higher average transaction values, and valuable data insights, often outweigh the initial investment and ongoing costs. The research also underscores the positive impact of digital payments on customer behavior, noting improvements in transaction speed, convenience, and overall satisfaction. Furthermore, the findings reveal variations in the impact of digital payment systems across different retail settings, including large department stores, independent retailers, and e-commerce businesses. The ability to tailor digital payment solutions to the specific needs of each retail environment is crucial for maximizing benefits and ensuring successful implementation. Overall, the study provides a comprehensive understanding of the role of digital payment systems in modern retail and offers insights into the future trajectory of these technologies.

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

Tourism activities generally have a ∩-type lock on the level of tourism ecological security in an area, but when applied to the border areas of China, there are certain specificities in the spatial evolution of tourism ecological security (TES) compared to traditional findings. This paper measures tourism ecological security in China’s border areas from 2009 to 2020 by using the DPSIR model with the superefficient SBM-DEA and analyzes the spatial differences, evolutionary characteristics, and driving factors of tourism ecological security in border areas by using Pearson’s correlation coefficients, center of gravity models, and geographic probes: (1) The overall tourism ecological security index of China’s border provinces is relatively good. Tourism activities do not completely affect the traditional “∩ lock” of the border provinces. The tourism ecological security level of the border provinces presents three spatial-temporal changes (“∩” type, “U” type, “\” type) and four evolution trends (“high–high–high”, “middle–middle–medium”, “medium–low–low”, and “low–low–low”). (2) The overall tourism ecological security level in border areas is polarized between high and low levels, and the ecological security efficiency of the three large areas is spatially characterized as “Southwest Area > Northeast Area > Northwest Area”, and the center of gravity of ecological security is mostly concentrated in Xinjiang, Tibet, and Neimenggu, where the ecological security level is higher. (3) Social and environmental factors are the main factors that influence tourism ecological security in border areas, while economic factors account for a smaller proportion. Accordingly, this thesis also proposes the driving mechanism of the ecological security of tourism sites in border areas in China with a view to providing theoretical support for policy formulation.

This research work focused on the risks we are faced with on our day to day activities, either directly or indirectly, which can gradually accumulates and results to communal crisis, religious crisis, socio-economic agitations and tribal crises. Nasarawa Eggon Local Government Area of Nasarawa State was used as case study and a well structure close–ended questionnaire was used for data collection as it relates to Controlled Self Risk Assessment (CSRA) and linear programming technique was used to analyze. The results proved that many of this processes were neglected in these communities, making them to be prone to incessant communal crisis. The study recommend that Government should strongly, as a matter of urgency address the unemployment rate, distribution of political position, schools, primary health care and equip security personnel with the modern technology facilities, which happen to record the highest risk level.

Disasters such as the 2015-2018 drought in South Africa usually negatively impact agricultural water, especially in smallholder farming systems. This study assessed the availability of irrigation water, performance of irrigation infrastructure, and water governance systems in Genadendal, Western Cape, with a focus on smallholder farmers. Data for streamflow, dam levels, and rainfall were acquired from water institutions and analysed using Microsoft Excel. The performance of the infrastructure and water governance were assessed based on the perceptions of smallholder farmers and key informants. A questionnaire was administered to eight smallholder farmers, followed by a focus group discussion with 15 smallholder farmers. Interviews were conducted with eight key informants who worked in water-related institutions. Qualitative data from the interviews were analysed using thematic content analysis. The study showed adequate agricultural water resources during the 2015-2018 drought period for the smallholder farmers who relied on water from dams. However, smallholder farmers who relied on the Riviersonderend River experienced severe water shortages during the same period. The findings also showed that there was poor performance of irrigation water infrastructure due to inadequate maintenance. Lack of coordination among institutions, insufficient funding, political interference, fragmentation of roles and responsibilities, lack of human resources, and farmers’ lack of participation and commitment in water resources management contributed to the dysfunctionality of irrigation water governance systems, leading to water insecurity. This study recommends adequate funding for the operation and maintenance of infrastructure. Furthermore, water institutions need to support smallholder farmers with training skills in agricultural water management and infrastructure maintenance. Further studies are recommended to quantify the water lost due to the poor performance of water infrastructure and to develop effective water governance in Genadendal.

The study of values at European and global level has gained momentum along with the theory of values launched by Schwartz. Values are dormant, hidden, unobservable and impossible to measure directly. They exist somewhere within the human being, manifested by different attitudes, depending on their degree of generality. The paradigm of basic human values implies shared universal values that are encountered in cultures all over the world. The theory of values designed by Schwartz has refined the pool of 19 basic individual values representing principles in individuals and group, further describing the dynamic relationships among them. This study presents practical evidence of the dynamics between individual values, the dynamics theorized by Schwartz in 2012. Following the development of an online questionnaire made up of 46 items with online responses options on a Lickert scale from 1 to 6, where 1 represents less important and 6 represent very important, 220 young people from Western Romania voluntarily responded. By testing the hypothesis assuming the dynamic relationship between the two values through multiple regression analysis, the results demonstrate that in Model 1, which involves a linear relationship, health explains 42% of the variance in security with a F =161,215 significant at p <.01. In Model 2, which involves a curvilinear relationship, health explains 50% of the variance in security version with a F = 35,336 significant at p <.01. The incremental prediction optimization of 8% added by including the squared security accounts for the curve in the regression line, indicating the existence of a curvilinear relationship between security and health. The dynamic relationship demonstrates that upper limit and lower limit aspects of security, significantly influence the health value in a negative way. Normal levels of security trigger a high level of prioritization of health value. The implications of this type of relationship are discussed in explaining the value phenomenon at individual level. The study shows limits due to the selected sample, focusing exclusively on young people with higher education from the Western region of Romania. This sample was the target group of "The National Identity of Romanian Youth" project which designed this research.

A Smart Home is characterized by the presence of a huge number of small, low power devices, along with more classical devices. According to the Internet of Things (IoT) paradigm, all of them are expected to be always connected to the Internet in order to provide enhanced services. In this scenario, an attacker can undermine both the network security and the user’s security/privacy. Traditional security measures are not sufficient, because they are too difficult to setup and are either too weak to effectively protect the user or too limiting for the new services effectiveness. The paper suggests to dynamically adapt the security level of the smart home network according to the user perceived risk level what we have called network sentiment analysis. The security level is not fixed, established by a central system (usually by the Internet Service Provider) but can be changed with the users cooperation. The security of the smart home network is improved by a distributed firewalling and Intrusion Detection Systems both to the smart home side as to the Internet Service Provider side. These two parts must cooperate and integrate their actions for reacting dynamically to new and ongoing threats. Moreover, the level of network sentiment detected can be propagate to nearby home networks (e.g. the smart home networks of the apartments inside a building) to increase/decrease their level of security, thus creating a true in-line Intrusion Prevention System (IPS). The paper also presents a test bed for Smart Home to detect and counteract to different attacks against the IoT devices,,Wi-Fi and Ethernet connections .

Recently, the world has experienced severe food insecurity problems with most countries having inadequate access to affordable, nutritious and safe foods. Consequently, many food innovations and technologies have been approved to secure sustainable access to food for millions of people. This study investigated the implementation of two technologies to address food insecurity, namely genetically modified foods (GMOs) and the use of antibiotics in crops and animal production. In particular, the study explored how their implementation can be governed responsibly through approved legislation. Therefore, the knowledge, attitudes and practices as well as the governance of antibiotic resistance risks and GMO foods were assessed. In-depth key interviews were conducted for the qualitative survey with triangulation with quantitative data sources. The findings showed that 46% of the population have little knowledge about GMOs with about 79% indicating that food with GM ingredients were being consumed in the country. The main concerns reported on GM foods by most respondents were impact on environment, human health and adverse effects on traditional farming practices with 36% indicating that it intensifies contamination and 32% indicating that it contributes to loss of biodiversity. Notably, 64%) reported that GMOs are a solution to food security and that they are safe. On the use of antimicrobials mainly meant to prevent diseases and access better markets, respondents perceived their use to be associated with antimicrobial resistance a “large level of risk” (score 2 in a scale of 1-3) (M = 1.85, SD = 1.06). Overall, the study found that efforts towards promoting awareness on antibiotic resistance risks and response in human health is relatively limited as reported by 56% of the respondents. Findings show that most of the respondents have only seen minimal or small-scale awareness campaigns. On governance of the two technologies, 71% and 50% of the respondents reported that scientists and elected officials respectively have the greatest role in governance of GMOs with small scale farmers playing a negligible role. Further, it was noted that all the respondents were knowledgeable in AMR and GMO technologies and these findings are crucial to the advancement of food innovations that are geared towards achieving food security in Kenya. This study highlights the risks associated with the poor governance and implementation of technologies and the need for a framework for technological risk governance that is sensitive to local values and socio-economic circumstances that will benefit the achievement.

Background: Machine learning has revolutionized situational awareness during disaster management by classifying, clustering, and predicting impacted locations and people. Despite its importance, no review has been conducted on machine learning tools for food assistance efforts during emergency or non-emergency situations. The purpose of this scoping review is to address that gap. Methods: Keywords were defined within the concepts of food assistance and machine learning. After the database searches, PRISMA guidelines were followed to perform a partnered, two-round scoping literature review. Text mining and Latent Dirichlet Allocation topic modeling algorithms were used to determine trends. Results: 28 articles met criteria and were included in the analysis. The types of study designs included: model development (42.9%), non-study (i.e., text and opinion) (28.6%), qualitative research (14.3%), case study (10.7%), and meta-analysis (3.6%). There were no quantitative studies. The machine learning tools’ main functions were improving SNAP programs (32.1%), detecting needs and resources (25%), predicting food insecurity (21.4%), and situational awareness of current food insecurity issues (21.4%). None of these studies took place during a disaster or explicitly addressed emergency mitigation, preparedness, or recovery. All of the studies were in early phases of development and implementation. Conclusion: Machine learning tools for improving situational awareness, resource allocation, policymaking, and prediction have the potential to improve food assistance, but there is a lack of implementation and evaluation during all disaster phases. Also needed is more formative work on generating food-related queries and defining variables and features of food security.

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

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

Countries in West Africa are adversely affected by climate change (erratic rainfall and rising temperature) resulting in floods, desertification, drought and sea level rise. These events are anticipated to have negative impacts on agricultural development on the continent, ultimately, contributing to food insecurity and environmental degradation. This implies that the production capacity of agrarian communities is unable to meet the food demand of the growing urban population. Can sustainable and innovative urban farming technology such as aquaponics achieve food security as well as sustainable development in countries vulnerable to climate change? This study uses inferential statistic to examine the plant growth performance in micro-scale aquaponics and specific growth rate per day (SGR) for the fish growth performance vis-à-vis conventional urban farming production. A quantitative analysis use to examine the barriers to adoption based on survey of (five) urban aquaculture practitioners in Lagos, Nigeria. Literature review was use to assess the economic feasibility of a small-scale aquaponics system in developing countries based on Net-Discounted Benefit-Cost Rate (DBCR). The results suggest that aquaponics can improve food security through fish and vegetable production and it is likely that urban farming practitioners will adopt the technology if support mechanism are in place. Aquaponics systems present a novel opportunity to promote environmental conservation as well as sustainable food production and consumption in urban areas in Western Africa if adequate financial credit and knowledge transfer is provided.

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

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

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

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

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

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

The application of organic fertilizers plays a crucial role in achieving carbon sequestration in the agricultural sector. This paper discusses how farmers can more smoothly promote organic fertilizer extension services. An evolutionary game model is developed to describe the conflicting interests of Chinese farmers and local governments in organic fertilizer extension services, and the dynamic evolution of the game players and the influence of parameter adjustment on the strategic choices of both parties is presented. In this paper, the game model and the main results are validated with the help of simulation tools, and a sensitivity analysis of the selected parameters is performed. The results show that (1) the implementation of subsidy policy is less helpful for organic fertilizer extension services; (2) The ideal event probability of the game was found to be positively related to the cost of applying inorganic fertilizers, additional benefits to farmers, political returns to local governments, and penalties for not using organic fertilizers; (3) This is important for improving the performance of local governments, reducing government regulatory costs, improving policy support for organic fertilizer extension services, and reducing the cost of implementing organic fertilizer extension services.

Bangladesh is located in the alluvial floodplain of the Ganges–Brahmaputra–Meghna (GMB) river system and its numerous tributaries. In this country, the frequency, as well as severity, of riverbank erosion is high. To examine the impacts of riverbank erosion on rural food security, a baseline survey was conducted in Zajira, Bangladesh. The findings show that most people in the study area are affected by riverbank erosion. Moreover, agricultural production is gradually decreasing as a result of loss of farmland due to the catastrophe and its impacts. In some cases, people lose their entire homestead and are left with few income opportunities and low purchasing power. Consequently, they face the constant threat of a food crisis. The affected people follow some coping mechanisms to face these stressful situations, such as relying on less expensive or less preferred food items. While the local-government authorities have implemented some programmes to assist them, such as food aid and social-safety-net schemes, these are insufficient, due to the government's limited institutional resources and capacities. These findings suggest that for formulating effective rural-development plans, assessing the impact of riverbank erosion on the food security of people living in the affected areas is a prerequisite.

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

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

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

The advent of 5G technology represents a transformative leap in the realms of retail marketing and supply chain operations, offering unprecedented enhancements and posing unique challenges. This study explores the multifaceted impacts of 5G, focusing on its influence on customer experiences, operational efficiency, and supply chain management. The integration of 5G enables advanced technologies such as augmented reality (AR) and virtual reality (VR), significantly enhancing the interactivity and personalization of the shopping experience. Real-time data collection and analysis, facilitated by 5G, allow for more precise inventory management and optimized store operations, leading to improved customer satisfaction and reduced operational costs. In the supply chain domain, 5G technology offers enhanced visibility and control through real-time tracking and environmental monitoring, which improves logistics efficiency and product quality management. Despite these advancements, the adoption of 5G technology involves considerable challenges, including high infrastructure costs, data security concerns, and global disparities in technology deployment. Businesses must navigate these obstacles while leveraging the benefits of 5G to achieve competitive advantage. The study also highlights the role of 5G in integrating with other advanced technologies, such as artificial intelligence (AI) and blockchain, to drive further innovation. Additionally, 5G supports sustainability efforts by optimizing energy use and reducing waste. Overall, the successful implementation of 5G requires strategic investment and adaptability, but it holds the potential to revolutionize retail and supply chain operations, enhancing efficiency, customer engagement, and sustainability in a rapidly evolving digital landscape.

In the intricate landscape of software development, the distinction between average and exceptional outcomes often hinges on the approach taken by the engineers involved. This paper explores the core aspects that characterize responsible and professional software engineering, emphasizing the importance of stakeholder engagement, innovative problem-solving, and adherence to standards and best practices. Through detailed real-life examples, we illustrate how professional engineers ensure the creation of robust, efficient, and adaptable software solutions. Additionally, the paper delves into advanced strategies for enhancing database security, including the implementation of Sealed Secrets in Kubernetes environments, continuous improvement, and future enhancements. The goal is to provide a comprehensive understanding of professional software engineering practices and their impact on delivering superior value to projects and organizations.

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.

Even if the field of medicine has made great strides in recent years, infectious diseases caused by novel viruses that damage the respiratory system continue to plague people all over the world. This type of virus is very dangerous, especially for people who deal with serious long-term breathing problems, like triggering asthma, pneumonia, or bronchitis infections. Thus, this paper demonstrates a new Secure Machine Learning Monitoring System for a model for virus detection. Our proposed model makes use of 4 basic emerging technologies, Internet of Things (IoT), Wireless Sensor Networks (WSN), Cloud Computing (CC), and Machine Learning (ML), to detect dangerous types of viruses that infect people or animals causing panic worldwide and deregulating human daily life. The proposed system is a robust system that could be established in various buildings, like hospitals, entertainment halls, universities, etc., and will provide accuracy, speed, and privacy for data collected in the detection of viruses.

Ransomware, a combination of "ransom" and "malware," is a type of malicious software designed to encrypt or block access to a victim's data or system, demanding a ransom for its release Initially targeting individuals, ransomware has evolved to attack businesses for greater financial gain. It mainly exists in two forms: encrypting ransomware, which holds data hostage, and non-encrypting ransomware, which blocks system access and displays a ransom note. The energy sector has been notably targeted by ransomware, exemplified by the 2021 attack on Colonial Pipeline by the DarkSide group, which led to a temporary shutdown and a significant fuel shortage on the US East Coast. Similarly, in 2023, Russian hackers using LockBit ransomware disrupted Royal Mail, halting international deliveries and incurring millions in recovery costs despite no ransom payment. These incidents underscore the need for comprehensive cybersecurity strategies that combine human vigilance with advanced technologies like AI and machine learning. By adopting a multi-layered protection approach, organizations can better prepare for and mitigate the risks posed by ransomware attacks safeguarding sensitive data and ensuring business continuity.

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

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

Food insecurity remains a critical challenge in developing countries like Ethiopia. Majority of its population derive their livelihood from agriculture characterized by mixed farming systems. Smallholder farmers the principal actors of agriculture are also facing a rapidly increasing population and urbanization had been unable to be self-sufficient in food. This paper examines the role of commercialization on food security and its determinants in rural settings. We used cross-sectional data from 295 households randomly selected in northwest Ethiopia. Results from bivariate Tobit regression revealed both crop and livestock commercialization improves household food security but in quite different ways. The income from crop sales was instrumental in allowing households to buy additional foods while livestock sales foster crop diversity for own consumption by allowing them to purchase nonfood items. Thus, agriculture-food security nexus may not require the production systems to be subsistent. Improving commercialization with better access to off-farm employment and institutional services (credit and market infrastructure), and enhanced awareness of the extension towards promoting commercialization and diversified food consumption seem to be more promising. The research paper concludes commercialization is vital not just for economic growth and food security, but also for the smallholder agriculture in providing additional nutrition.

Botnet Attacks are one of the many types of ways to attack users in their day to day browsing, often of different trends and techniques to take your information without your control. Most firewalls can amply protect you against these trends and techniques, but scarily enough, recent articles and research have shown that they are starting to bypass said firewalls through layers of attacks, confusing your best line of defense. In this Report, we will be looking into exactly what are Botnets and the different subsets, what we can do to combat Botnet Attacks, where do they come from, their origin, the analysis of their kit and what we can put together from information collected. Through this we will be able to reach a conclusion of what we can use to possibly mitigate the effect they have on the internet and stop them from enacting their purpose.

Questionnaires are useful instruments for gathering responses to specific factual questions. However, the problems of questionnaire responses impede the effective use of the questionnaire. Some of these problems including non-responses, non-completion, issues of judgement, and social desirability such as information that the respondent is not willing to disclose need to be dealt with. The situation is more compounded in a scenario where an information security practice study in a typical hospital consists of broad categories of respondents and the survey findings are to be relied on to actually address information security compliance issues. This study, therefore, shares "pitfalls" to watch when preparing a questionnaire to measure the information security practice level in a hospital which is characterized by different respondents with varying domain knowledge such as knowledge in information security, information communication technology, and that of the domain knowledge of healthcare. A synergy of a conventional pretesting method and behaviour coding were therefore used to pretest the questionnaire. Questionnaire problems including a lack of understanding of the healthcare information systems’ structure of all hospitals, unclear questions, the insignificant difference between questions, problematic questions, inadequate questions, and complex terms were among the identified pitfalls to watch. Out of a total of 118 questionnaire items that were used in the pretesting, a total of 50 questionnaire items (representing 42%) were identified to have problems after the pretesting was conducted with a total of 36 respondents in behavior coding and 21 respondents in conventional pretesting.

When we talk about the Internet of Things, we are referring to the connecting of things to a1 physical network that is embedded with software, sensors, and other devices that allow information2 to be exchanged between devices. It is possible that the interconnection of devices will present3 issues in terms of security, trustworthiness, reliability, and confidentiality, among other things.4 The proposed approach is effective at detecting intrusions into the Internet of Things network.5 Initially, the privacy-preserving technology was deployed utilising a Blockchain-based methodology6 to ensure that personal information was protected. Patients’ health records (PHR) security is the7 most crucial component of encryption over the Internet because of the value and importance of these8 records, particularly in the context of the Internet of Medical Things (IoMT). The search terms access9 mechanism is one of the most common approaches used to access personal health records from a10 database, but it is vulnerable to a number of security flaws. However, while blockchain-enabled11 healthcare systems provide increased security, they may also introduce weaknesses into the current12 state of the art. Blockchain-enabled frameworks have been proposed in the literature as a means13 of resolving those challenges. These solutions, on the other hand, are primarily concerned with14 data storage, with Blockchain serving as a database. To enable secure search and keyword-based15 access to a distributed database, this study proposes the use of blockchain technology as a distributed16 database, together with a homomorphic encryption mechanism. Aside from that, the suggested17 system includes a secure key revocation mechanism that can be used to automatically update various18 policies.As a result, our proposed approach provides greater security, efficiency, and transparency19 while also being more cost-effective. We have compared the findings of our proposed models with20 those of the benchmark models, if appropriate. Our comparison research demonstrates that our21 suggested framework provides a more secure and searchable mechanism for the healthcare system22 than the current state of the art.

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.

Product counterfeiting is an on-going problem in supply chains and retail environments, Recently an anti-counterfeiting protocol to address this issue via cost-effective use of auto-identification technologies such as radio-frequency identification (RFID) was proposed by researchers.Yet the use case of re-selling the same product was not been fully addressed which might cause serious problem for the exciting and proposed schemes and transactions. This paper proposes an extended RFID-based anti-counterfeiting to address the use case of the original buyer reselling the same item to a second buyer. The extended scheme will be followed by a formal security analysis to show that the proposed protocol satisfies the requirements of security correctness and is resistant to compromise through security attacks.