Search Results (38,156)

Power cyber–physical systems such as multi-area power systems (MAPSs) have gained considerable attention due to their integration of power electronics with wireless communications technologies. Incorporating a communication setup enhances the sustainability, reliability, and efficiency of these systems. Amidst these exceptional benefits, such systems’ distributed nature invites various cyber-attacks. This work focuses on the equal power sharing of MAPSs in the event of false data injection (FDI) attacks. The proposed work uses a sliding mode control (SMC) mechanism to ensure timely detection of challenges such as FDI attacks and load change, making MAPSs reliable and secure. First, a SMC-based strategy is deployed to enable the detection and isolation of compromised participants in MAPS operations to achieve equal power sharing. Second, time-varying FDI attacks on MAPSs are formulated and demonstrate their impact on equal power sharing. Third, a robust adaptive sliding mode observer is used to accurately assess the state of the MAPS to handle state errors robustly and automatically adjust parameters for identifying FDI attacks and load changes. Lastly, simulation results are presented to explain the useful ability of the suggested method. Full article

Bolted connections are a common feature of connection in mechanical structures, employed to secure connected parts by tightening nuts and providing preload. The preload is susceptible to various factors leading to potential bolt loosening. The acoustoelastic theory is the most common measure of a bolt structure’s stress. The present study investigates the relationship between the inherent properties of a structure and its acousticelastic properties. The modal response of the bolted structure under different preload forces is studied by translating the acoustoelastic relationship of the structure into an analysis of its intrinsic properties. The modal analysis reflects the relative change in wave velocity to be determined implicitly based on the eigenfrequencies of the structure. A frequency formulation of classical bolted structures based on acoustoelastic theory is presented in this paper to conduct the intrinsic characteristic analysis of bolted structures. The COMSOL5.4 simulation results are under the acoustic elasticity coefficients for ultrasonic wave propagation in bolt structures, as predicted by the acoustic elasticity theory, and the present solutions are compared with those available in the literature to confirm their validity. A systematic parameter study for bolted structures under the varying preloads with different material parameters, Lame elastic constants, Murnaghan third-order elastic constants, and structural parameters are presented. These results may serve as a benchmark for researchers in this field. Full article

Aimed at understanding how pastoralist livelihoods are affected within the Northwest Region of Cameroon, this article explores the nexus of social justice, indigenous know-how, livelihoods, social security, and sustainability through a political ecology lens. Through a qualitative case study based on in-depth interviews with 59 key informants, this study departs from existing literature by exploring the linkages that exacerbate risks and vulnerabilities for pastoralist livelihoods. We situate the contending issues through emerging data and analysis, which highlight indigenous elements that sustain pastoralist livelihoods (coping strategies and sustenance) and identify diversified barriers that impede pastoralists’ sense of social justice and community-mindedness. Other intersecting pointers identified relate to environmental interactions, social security, sustainability, and decision-making within local and national governance mechanisms that either enhance or impede sustainable development. We proposed a social justice ecosystem framework (SJEF) that uncovers the enmeshments of social justice, social security, indigenous know-how, and livelihoods, with implications for sustainable development. The framework makes a compelling case for co-produced policies; implementing symbiotic social justice-based policies is mandatory, encapsulating thriving aspects of pastoralists’ unique traditions, which are often missed by governments and agencies in social community development planning and sustainable development initiatives. Full article

In contemporary wildlife conservation, drones have become essential for the non-invasive monitoring of animal populations and habitats. However, the sensitive data captured by drones, including images and videos, require robust encryption to prevent unauthorized access and exploitation. This paper presents a novel encryption algorithm designed specifically for safeguarding wildlife data. The proposed approach integrates one-dimensional and two-dimensional memory cellular automata (1D MCA and 2D MCA) with a bitwise XOR operation as an intermediate confusion layer. The 2D MCA, guided by chaotic rules from the sine-exponential (SE) map, utilizes varying neighbor configurations to enhance both diffusion and confusion, making the encryption more resilient to attacks. A final layer of 1D MCA, controlled by pseudo-random number generators, ensures comprehensive diffusion and confusion across the image. The SHA-256 hash of the input image is used to derive encryption parameters, providing resistance against plaintext attacks. Extensive performance evaluations demonstrate the effectiveness of the proposed scheme, which balances security and complexity while outperforming existing algorithms. Full article

Pick-up points (PUPs) have become a very attractive alternative for conventional home deliveries due to the growth of e-commerce. This paper investigates the level of satisfaction of the students (Gen Z) as well as the research, teaching, and administrative staff of the Aristotle University of Thessaloniki (AUTH), Greece, and the University of Enna “Kore”, Italy, implemented in November 2023. Optimizing the PUP users’ satisfaction is contingent upon various aspects, including but not limited to location accessibility, expedient pick-up procedures, unambiguous communication, and ensured item availability. The research recorded information about the users’ knowledge about the specific service, their level of satisfaction, their preferences on when and how they use the service, and information about the types of goods they order using the PUPs as their point of collection. The analysis of the collected data revealed very interesting findings that could be useful to the providers of this service, especially when taking into consideration that the majority of the poll’s participants are familiar with the existence of the PUPs in the Municipality of Thessaloniki, that they use this service mainly occasionally, and that the majority are quite pleased with the level of the provided services (accessibility, availability, safety, and security). For the case of Enna in Sicily, similar trends are shown: a high percentage of respondents are familiar with PUPs, and they use pick-up points occasionally and are pleased with the provided level of service. The comparative statistical analysis makes it possible to compare two contexts located in areas of the Mediterranean, i.e., two urban areas with different population sizes but with similar habits on the part of the university student cluster. Full article

Land use conflict is an inevitable and objective phenomenon during regional development, with significant impacts on both regional economic growth and ecological security. Scientifically assessing the spatiotemporal evolution of these conflicts is essential to optimize land use structures and promote sustainable resource utilization. This study employs multi-period land use/land cover remote sensing data from China to develop a model for the measurement of land use conflict from the perspective of the landscape ecological risk. By applying the optimal landscape scale method to determine the most appropriate analysis scale, this research investigates the spatiotemporal evolution characteristics of land use conflicts in the Central Yunnan Urban Agglomeration from 2000 to 2020. Furthermore, by integrating the Patch-Generating Land Use Simulation (PLUS) model with the Multi-Objective Programming (MOP) algorithm, this study simulates the spatial patterns of land use conflict in 2030 under four scenarios: Natural Development (ID), Economic Development (ED), Ecological Conservation (PD), and Sustainable Development (SD). The findings reveal that, from 2000 to 2020, the proportion of areas with strong and moderately strong conflict levels in the Central Yunnan Urban Agglomeration increased by 2.19%, while the proportion of areas with weak and moderately weak conflict levels decreased by 1.45%, underscoring the growing severity of land use conflict. The predictions for 2030 suggest that the spatial pattern of conflict under various scenarios will largely reflect the trends observed in 2020. Under the ID scenario, areas with weak and moderately weak conflict levels constitute 57.5% of the region; this increases by 0.85% under the SD scenario. Conversely, areas experiencing strong and moderately strong conflict levels, which stand at 33.02% under the ID scenario, decrease by 1.04% under the SD scenario. These projections indicate that the SD scenario, which aims to balance ecological conservation with economic development, effectively mitigates land use conflict, making it the most viable strategy for future regional development. Full article

In recent years, the rapid development of the Taishan tea industry has become a business card of local specialty agriculture. However, as consumers’ demands for Taishan tea product quality and safety continue to improve, the centralized database traceability system that the traditional Taishan tea industry relies on shows insufficient information credibility and core data security risks, making it difficult to match the diversified expectations of the market and consumers. In order to solve this problem, this paper proposes a trusted traceability model for Taishan tea based on blockchain technology, which utilizes blockchain technology and data hierarchical uploading mechanism to ensure data accuracy and transparency, and, at the same time, improves data uploading efficiency. The optimized SM2 encryption algorithm is introduced, and the execution efficiency of the encryption algorithm is improved by the concurrent processing framework, which guarantees the security and transmission speed of the data. The experimental results show that the blockchain-based trusted traceability model for Taishan tea significantly improves the data security, query, and writing speed, and greatly optimizes the problems of traditional traceability methods. With this research, the results in this paper not only help to improve the quality and safety of Taishan tea products but also provide technical support for the production enterprises to enhance their brand competitiveness. Full article

Today, there are many security problems at the technological level, especially in telecommunications. Cybercriminals invade and steal data from any system using vector attacks such as phishing through scam mail, fake websites and phone calls. This latter form of phishing is called vishing (phishing using voice). Through vishing and using social engineering techniques, attackers can impersonate family members or friends of potential victims and obtain information or money or a specific target objective. Traditionally, to carry out vishing attacks, attackers imitated the vocabulary, voice and tone of a person known to the victim. However, with current artificial intelligence (AI) tools, obtaining synthetic voices similar or identical to the person to be impersonated is more straightforward and precise. In this regard, this paper, using ChatGPT and three AI-enabled applications for voice synthesis presents a practical approach for deploying vishing attacks in an academic environment to identify the limitations, implications and possible countermeasures to mitigate the effects on Internet users. Results demonstrate the effectiveness of vishing attacks, and the maturity level of the employed AI tools. Full article

Pandemics of infectious disease and growing anti-microbial resistance (AMR) pose major threats to global health, trade, and security. Conflict and climate change compound and accelerate these threats. The One Health approach recognizes the interconnectedness of human, animal, and environmental health, but is grounded in the biomedical model, which reduces health to the absence of disease. Biomedical responses are insufficient to meet the challenges. The COVID-19 pandemic is the most recent example of the failure of this biomedical model to address global threats, the limitations of laboratory-based surveillance, and the exclusive focus on vaccination for disease control. This paper examines the current paradigm through the lens of polio and the global campaign to eradicate it, as well as other infectious threats including mpox and drug-resistant tuberculosis, particularly in the context of armed conflict. Decades before vaccines became widely available, public health measures—ventilation, chlorination, nutrition and sanitation— led to longer, healthier, and even taller lives. Chlorine, our primary tool of public health, conquered cholera and transformed infection control in hospitals. The World Health Organization (WHO), part of the One Health alliance, focuses mainly on antibiotics and vaccines to reduce deaths due to superbugs and largely ignores the critical role of chlorine to control water-borne diseases (including polio) and other infections. Moreover, the One Health approach ignores armed conflict. Contemporary wars are characterized by indiscriminate bombing of civilians, attacks targeting healthcare, mass displacement and lack of humanitarian access, conditions which drive polio outbreaks and incubate superbugs. We discuss the growing trend of attacks on healthcare and differentiate between types: community-driven attacks targeting vaccinators in regions like Pakistan, and state-sponsored attacks by governments such as those of Syria and Russia that weaponize healthcare to deliberately harm whole populations. Both fuel outbreaks of disease. These distinct motivations necessitate tailored responses, yet the WHO aggregates these attacks in a manner that hampers effective intervention. While antimicrobial resistance is predictable, the escalating pandemic is the consequence of our reliance on antibiotics and commitment to a biomedical model that now borders on pathological. Our analysis reveals the international indenture to the biomedical model as the basis of disease control is the root driver of AMR and vaccine-derived polio. The unique power of vaccines is reduced by vaccination-only strategy, and in fact breeds vaccine-derived polio. The non-specific effects of vaccines must be leveraged, and universal vaccination must be supplement by international investment in water chlorination will reduce health costs and strengthen global health security. While vaccines are an important weapon to combat pandemics and AMR,, they must be accompanied by the entire arsenal of public health interventions. Full article

This study aims to develop a Secured Fiscal Credits Model to address the challenges of managing Italy’s “Superbonus 110%” tax credit. Using a decentralised governance approach, our research objective is to provide a feasible system to track and control the entire tax credit process, from generation to redemption. The method integrates Artificial Intelligence and blockchain technology within a Decentralised Autonomous Organisation architecture, combined with a Multi-agent System to establish a tokenomics model. The system is structured to prevent accidental errors, such as double spending or overspending, and detect fraudulent behaviours, like false claims of completed work. Our main findings indicate that deploying two Decentralised Autonomous Organisations on the Algorand blockchain significantly enhances trust and security, supporting effective oversight of the Superbonus process and facilitating transparent value exchange among stakeholders. This decentralised governance model introduces substantial automation, reduces biases, and offers a viable solution to strengthen tax credit management. This work proposes an innovative, technology-driven framework that can be generalised to similar fiscal and governance contexts, enhancing transparency and control. Full article

Differentiated thyroid cancer (DTC), comprising papillary and follicular thyroid cancers, is the most prevalent type of thyroid malignancy. Accurate prediction of DTC is crucial for improving patient outcomes. Machine learning (ML) offers a promising approach to analyze risk factors and predict cancer recurrence. In this study, we aimed to develop predictive models to identify patients at an elevated risk of DTC recurrence based on 16 risk factors. We developed six ML models and applied them to a DTC dataset. We evaluated the ML models using Synthetic Minority Over-Sampling Technique (SMOTE) and with hyperparameter tuning. We measured the models’ performance using precision, recall, F1 score, and accuracy. Results showed that Random Forest consistently outperformed the other investigated models (KNN, SVM, Decision Tree, AdaBoost, and XGBoost) across all scenarios, demonstrating high accuracy and balanced precision and recall. The application of SMOTE improved model performance, and hyperparameter tuning enhanced overall model effectiveness. Full article

In underwater clustering and benchmark networks, nodes need to reduce the rate and energy consumption of acoustic communication while ensuring synchronization accuracy. In large-scale networks, the improvement in the efficiency of existing network time synchronization often relies on the optimization of topological structures, and the improvement in efficiency within local areas is limited. This paper proposes a method to synchronize underwater time using the probability graph model. The method utilizes the positional and motion status information of sensor networks to construct a factor graph model for distributed network synchronization. By simplifying the marginal probability density function of the system clock difference, it can quickly calculate the clock difference parameters of nodes, thereby effectively improve the synchronization efficiency. The experimental results show that the method can complete global time synchronization within a cycle while achieving a clock difference correction accuracy higher than seconds, which significantly optimized the synchronization cycle and efficiency, and reduced the energy consumption of the acoustic communication. Full article

Due to the widespread acceptance of ChatGPT, implementing large language models (LLMs) in real-world applications has become an important research area. Such productisation of technologies allows the public to use AI without technical knowledge. LLMs can revolutionise and automate various healthcare processes, but security is critical. If implemented in critical sectors such as healthcare, adversaries can manipulate the vulnerabilities present in such systems to perform malicious activities such as data exfiltration and manipulation, and the results can be devastating. While LLM implementation in healthcare has been discussed in numerous studies, threats and vulnerabilities identification in LLMs and their safe implementation in healthcare remain largely unexplored. Based on a comprehensive review, this study provides new findings which do not exist in the current literature. This research has proposed a taxonomy to explore LLM applications in healthcare, a threat model considering the vulnerabilities of LLMs which may affect their implementation in healthcare, and a security framework for the implementation of LLMs in healthcare and has identified future avenues of research in LLMs, cybersecurity, and healthcare. Full article

In actual production processes, analysis and prediction tasks commonly rely on large amounts of time-series data. However, real-world scenarios often face issues such as insufficient or imbalanced data, severely impacting the accuracy of analysis and predictions. To address this challenge, this paper proposes a dual-layer transfer model based on Generative Adversarial Networks (GANs) aiming to enhance the training speed and generation quality of time-series data augmentation under small-sample conditions while reducing the reliance on large training datasets. This method introduces a module transfer strategy based on the traditional GAN framework which balances the training between the discriminator and the generator, thereby improving the model’s performance and convergence speed. By employing a dual-layer network structure to transfer the features of time-series signals, the model effectively reduces the generation of noise and other irrelevant features, improving the similarity of the generated signals’ characteristics. This paper uses speech signals as a case study, addressing scenarios where speech data are difficult to collect and the limited number of speech samples available for effective feature extraction and analysis. Simulated speech timbre generation is conducted, and the experimental results on the CMU-ARCTIC database show that, compared to traditional methods, this approach achieves significant improvements in enhancing the consistency of generated signal features and the model’s convergence speed. Full article