Search Results (5,492)

Nakaseomyces glabrata (Candida glabrata), the second most prevalent Candida pathogen globally, has emerged as a major clinical threat due to its ability to develop high-level azole resistance. In this study, two new 5,6-dihydrotetrazolo[1,5-c]quinazoline derivatives (c11 and c12) were synthesized and characterized using IR, LC-MS, ¹H, and ¹³C NMR spectra. Along with 13 previously reported analogues, these compounds underwent in vitro antifungal testing against clinical N. glabrata isolates using a serial dilution method (0.125–64 mg/L). Remarkably, compounds c5 and c1 exhibited potent antifungal activity, with minimum inhibitory concentrations of 0.37 μM and 0.47 μM, respectively—about a 20-fold improvement in μM concentration over standard drugs like amphotericin B, caspofungin, and micafungin. A detailed structure–activity relationship analysis revealed crucial molecular features enhancing antifungal potency. Extensive molecular docking studies across 18 protein targets explored potential binding pockets and affinities of the lead compounds. A robust 3D-QSAR model, incorporating molecular descriptors Mor26m and Mor29e, displayed good predictive ability for antifungal activity. In silico predictions indicated an absence of herbicidal effect, negligible environmental toxicity (to honeybees, avian species, and aquatic organisms), and mild human toxicity concerns for these compounds. This comprehensive approach aims to develop novel and effective antifungal compounds against the clinically relevant pathogen N. glabrata. Full article

The rise in abusive language on social media is a significant threat to mental health and social cohesion. For Bengali speakers, the need for effective detection is critical. However, current methods fall short in addressing the massive volume of content. Improved techniques are urgently needed to combat online hate speech in Bengali. Traditional machine learning techniques, while useful, often require large, linguistically diverse datasets to train models effectively. This paper addresses the urgent need for improved hate speech detection methods in Bengali, aiming to fill the existing research gap. Contextual understanding is crucial in differentiating between harmful speech and benign expressions. Large language models (LLMs) have shown state-of-the-art performance in various natural language tasks due to their extensive training on vast amounts of data. We explore the application of LLMs, specifically GPT-3.5 Turbo and Gemini 1.5 Pro, for Bengali hate speech detection using Zero-Shot and Few-Shot Learning approaches. Unlike conventional methods, Zero-Shot Learning identifies hate speech without task-specific training data, making it highly adaptable to new datasets and languages. Few-Shot Learning, on the other hand, requires minimal labeled examples, allowing for efficient model training with limited resources. Our experimental results show that LLMs outperform traditional approaches. In this study, we evaluate GPT-3.5 Turbo and Gemini 1.5 Pro on multiple datasets. To further enhance our study, we consider the distribution of comments in different datasets and the challenge of class imbalance, which can affect model performance. The BD-SHS dataset consists of 35,197 comments in the training set, 7542 in the validation set, and 7542 in the test set. The Bengali Hate Speech Dataset v1.0 and v2.0 include comments distributed across various hate categories: personal hate (629), political hate (1771), religious hate (502), geopolitical hate (1179), and gender abusive hate (316). The Bengali Hate Dataset comprises 7500 non-hate and 7500 hate comments. GPT-3.5 Turbo achieved impressive results with 97.33%, 98.42%, and 98.53% accuracy. In contrast, Gemini 1.5 Pro showed lower performance across all datasets. Specifically, GPT-3.5 Turbo excelled with significantly higher accuracy compared to Gemini 1.5 Pro. These outcomes highlight a 6.28% increase in accuracy compared to traditional methods, which achieved 92.25%. Our research contributes to the growing body of literature on LLM applications in natural language processing, particularly in the context of low-resource languages. Full article

The activities of contemporary financial institutions require significant geographic expansion. Even the increased level of industry digitalisation does not minimise the importance of the physical assets of financial institutions. The environmental factors specific to each geographic region can significantly influence the efficiency of operations of financial institutions. The goal of the article is to determine the impact of the geographic location of physical assets via environmental risks affecting the other risks of fintech as a representative of financial institutions. The impact is determined by the employment of the PLS-SEM model implemented in SmartPLS 4.0 software. The model determines the impact of environmental risks on governance risks, operational risks, human resources and safety risks, ICT risks, compliance risks, and strategic risks. These groups of risks form the latent variables, which comprise the experts’ estimation of threats and vulnerabilities impacts and their likelihoods. After testing five hypotheses, two of them were supported—environmental risks impact human resources safety risks and operational risks. Full article

Chinese pine has been extensively planted in the Loess Plateau, but it faces significant threats from Gansu zokor. Traditional methods for monitoring rodent damage rely on manual surveys to assess damage rates but are time-consuming and often underestimate the actual degree of damage, particularly in mildly affected pines. This study proposes a remote sensing monitoring method that integrates hyperspectral analysis with physiological and biochemical parameter models to enhance the accuracy of rodent damage detection. Using ASD Field Spec 4, we analyzed spectral data from 125 Chinese pine needles, measuring chlorophyll (CHC), carotenoid (CAC), and water content (WAC). Through correlation analysis, we identified sensitive vegetation indices (VIs) and red-edge parameters (REPs) linked to different levels of damage. We report several key results. The 680 nm spectral band is instrumental in monitoring damage, with significant decreases in CHC, CAC, and WAC corresponding to increased damage severity. We identified six VIs and five REPs, which were later predicted using stepwise regression (SR), support vector machine (SVM), and random forest (RF) models. Among all models, the vegetation index-based RF model exhibited the best predictive performance, achieving coefficient of determination (R²) values of 0.988, 0.949, and 0.999 for CHC, CAC, and WAC, with root mean square errors (RMSEs) of 0.115 mg/g, 0.042 mg/g, and 0.007 mg/g, and mean relative errors (MREs) of 8.413%, 9.169%, and 1.678%. This study demonstrates the potential of hyperspectral remote sensing technology for monitoring rodent infestations in Chinese pines, providing a reliable basis for large-scale assessments and effective management strategies for pest control. Full article

Trajectory data are embedded within driving paths, GPS positioning systems, and mobile signaling information. A vast amount of trajectory data play a crucial role in the development of smart cities. However, these trajectory data contain a significant amount of sensitive user information, which poses a substantial threat to personal privacy. In this work, we have constructed an internal secure information granule model based on differential privacy to ensure the secure sharing and analysis of trajectory data. This model deeply integrates granular computing with differential privacy, addressing the issue of privacy leakage during the sharing of trajectory data. We introduce the Laplace mechanism during the granulation of information granules to ensure data security, and the flexibility at the granularity level provides a solid foundation for subsequent data analysis. Meanwhile, this work demonstrates the practical applications of the solution for the secure sharing of trajectory data. It integrates trajectory data with economic data using the Takagi–Sugeno fuzzy rule model to fit and predict regional economies, thereby verifying the feasibility of the granular computing model based on differential privacy and ensuring the privacy and security of users’ trajectory information. The experimental results show that the information granule model based on differential privacy can more effectively enable data analysis. Full article

Storm waves and rising sea levels pose significant threats to low-lying coastal areas, particularly sandy beaches, which are especially vulnerable. The research on the long-time-scale changes in sandy coasts, especially the identification of tipping points in the shoreline-retreat rate, is limited. Vung Tau beach, characterized by its low terrain and rapid tourism-driven economic growth, was selected as a typical study area to quantify the shoreline retreat throughout the 21st century under various sea-level rise (SLR) scenarios, and to identify the existence of tipping points by investigating the projected annual change in shoreline retreat (m/yr). This study employs the Probabilistic Coastline Recession (PCR) model, a physics-based tool specifically designed for long-term coastline change assessments. The results indicate that shoreline retreat accelerates over time, particularly after a tipping point is reached around 2050 in the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Under the SSP5-8.5 scenario, the median retreat distance is projected to increase from 19 m in 2050 to 89 m by 2100, nearly a fourfold rise. In comparison, the retreat distances are smaller under the SSP1-2.6 and SSP2-4.5 scenarios, but the same accelerating trend is observed beyond 2050. These findings highlight the growing risks associated with sea-level rise, especially the rapid increase in exceedance probabilities for retreat distances by the end of the century. By 2100, the probability of losing the entire beach at Vung Tau is projected to be 22% under SSP5-8.5. The approach of identifying tipping points based on the PCR model presented here can be applied to other sandy coastal regions, providing critical references for timely planning and the implementation of adaptation measures. Full article

The problem of marine noise pollution has a long history. Strong noise (>120 dB re 1 µPa) will affects the growth, development, physiological responses, and behaviors of fish, and also can induce the stress response, posing a mortal threat. Although many studies have reported that underwater noise may affect the survival of fish by disturbing their nervous system and endocrine system, the underlying causes of death due to noise stimulation remain unknown. Therefore, in this study, we used the underwater noise stress models to conduct underwater strong noise (50–125 dB re 1 µPa, 10–22,000 Hz) stress experiments on small yellow croaker for 10 min (short-term noise stress) and 6 days (long-term noise stress). A total of 150 fishes (body weight: 40–60 g; body length: 12–14 cm) were used in this study. Omics (metabolomics and transcriptomics) studies and quantitative analyses of important genes (HPA (hypothalamic–pituitary–adrenal)-axis functional genes) were performed to reveal genetic and metabolic changes in the important tissues associated with the HPA axis (brain, heart, and adrenal gland). Finally, we found that the strong noise pollution can significantly interfere with the expression of HPA-axis functional genes (including corticotropin releasing hormone (CRH), corticotropin releasing hormone receptor 2 (CRHR2), and arginine vasotocin (AVT)), and long-term stimulation can further induce metabolic disorders of the functional tissues (brain, heart, and adrenal gland), posing a lethal threat. Meanwhile, we also found that there were two kinds of death processes, direct death and chronic death, and both were closely related to the duration of stimulation and the regulation of the HPA axis. Full article

The Web of Things (WoT) represents a significant advancement on the Internet of Things (IoT), where web technologies are integrated to enhance device interoperability and accessibility. While this integration offers numerous benefits, it also introduces new and complex security challenges. This paper presents a critical analysis of WoT security, examining the ecosystem’s vulnerabilities and associated threats. Our contributions include an in-depth analysis of existing threat enumeration methodologies, highlighting misconceptions and inefficiencies that may weaken security measures. We further conduct a comprehensive survey of critical threats within the WoT environment, detailing potential attack vectors and misuses linked to these threats. To address these security gaps, we propose a set of defenses tailored to each identified threat, providing a holistic view of the WoT’s security landscape. We also develop abstract architectural models of the WoT using UML, serving as foundational tools for understanding the interactions and risks within WoT systems. Finally, we model a specific attack scenario, demonstrating how attacks unfold in real-world WoT environments and the importance of defense strategies. These findings aim to guide the development of secure WoT systems, ensuring robust defenses against evolving security threats. Full article

Modern workplaces increasingly use algorithmic management practices (AMPs), which shape task assignment, monitoring, and evaluation. Despite the potential benefits these practices offer, like increased efficiency and objectivity, their impact on workforce well-being (WFW) has raised concerns. Drawing on self-determination theory (SDT) and conservation of resources theory (COR), this study examines the relationship between algorithmic management practices and workforce well-being, incorporating job burnout (JBO) and perceived threat (PT) as parallel mediators and person–job fit (PJF) as a moderator. The research employed a cross-sectional survey design targeting 2450 KOSGEB-registered manufacturing SMEs in Istanbul, Turkey. A sample of 666 respondents participated, and the data were analyzed using Smart PLS 4, employing structural equation modeling to test the proposed model. The results indicated that algorithmic management practices significantly increased job burnout and perceived threat, both of which negatively impacted workforce well-being. However, the direct effect of algorithmic management practices on workforce well-being was non-significant. Person–job fit moderated the relationships between algorithmic management practices and both job burnout and perceived threat, further influencing workforce well-being. The findings underscore the critical need for organizations to balance algorithmic efficiency with human-centric practices. Prioritizing person–job fit and fostering transparency in algorithmic processes can mitigate negative impacts, enhance employee well-being, and drive sustainable organizational success in the digital age. Full article

By integrating advanced technologies and data-driven systems in smart grids, there has been a significant revolution in the energy distribution sector, bringing a new era of efficiency and sustainability. Nevertheless, with this advancement comes vulnerability, particularly in the form of cyber threats, which have the potential to damage critical infrastructure. False data injection attacks are among the threats to the cyber–physical layer of smart grids. False data injection attacks pose a significant risk, manipulating the data in the control system layer to compromise the grid’s integrity. An early detection and mitigation of such cyberattacks are crucial to ensuring the smart grid operates securely and reliably. In this research paper, we demonstrate different machine learning classification models for detecting false data injection attacks, including the Extra Tree, Random Forest, Extreme Gradient Boosting, Logistic Regression, Decision Tree, and Bagging Classifiers, to secure the integrity of smart grids. A comprehensive dataset of various attack scenarios provides insights to explore and develop effective detection models. Results show that the Extra Tree, Random Forest, and Extreme Gradient Boosting models’ accuracy in detecting the attack outperformed the existing literature, an achieving accuracy of 98%, 97%, and 97%, respectively. Full article

During the process of natural gas development, sustained casing pressure (SCP) frequently occurs within the annulus of the gas wells; we specifically referred to the “A” annular space located between the tubing and the production casing in this paper. SCP in an annulus poses a paramount safety challenge, universally acknowledged as a significant threat to gas field development and production, jeopardizing well integrity, personnel safety, and environmental protection. There are multiple factors that contribute to this issue. Due to the multitude of factors contributing to SCP in an annulus and the unclear mechanisms underlying the pressure buildup in wells, an early assessment of downhole leakage risks remains challenging. Hence, this study focused on a comprehensive analysis of the SCP in the annulus of gas wells. A detailed experimental study on the pressure buildup in an annulus due to tubing leakage below the liquid level was conducted, and the variation patterns of the annulus pressure under various leakage conditions were explored. The findings indicated that the equilibrium attainment time of annulus pressure at the wellhead subsequent to tubing leakage decreases with the increase in the pressure difference between the tubing and the casing, the liquid level height, the leakage orifice diameter, and the quantity, while it increases with the increase in the leakage position and gas temperature. According to the theory of gas fluid dynamics, a predictive model of the annulus pressure buildup with sustained gas leakage below the liquid level was proposed, which was well-validated against experimental results, achieving a model accuracy of over 95%. This study provided a theoretical framework for diagnosing SCP in the annulus of gas wells and developing mitigation strategies, thereby contributing to the advancement of the research field and ensuring the safety of industrial operations. Full article

Eutrophication, a global threat that leads to degradation of freshwater and seawater aquatic ecosystems, is driven by excessive nutrient loading. This study explores the sustainable management of eutrophic water bodies with the application of natural and modified clay-based materials as a practical solution to mitigate eutrophication by removing ammonium and orthophosphate ions. Comparative analyses of six materials: natural zeolite, bentonite, and perlite, along with their modification with calcium and iron, were assessed after kinetic analysis of each material. Batch adsorption experiments were performed to evaluate the material’s performance in fresh and seawater. Fitting experimental data assessed adsorption kinetics to pseudo-second-order models. Furthermore, Langmuir isotherm models were employed to determine each material’s maximum adsorption capacity for ammonium and orthophosphate ion uptake. The results revealed that freshwater applications of modified zeolite or natural bentonite achieved better orthophosphate ion removal efficiency from seawater, whereas employing natural zeolite maximized the ammonium ion removal efficiency in freshwater bodies. Finally, orthophosphate and ammonium ion removal efficiency results for almost all materials were diminished in seawater. This research contributes valuable insights to the development of efficient and sustainable nutrient removal methodologies to remediate natural eutrophic water bodies and protect aquatic ecosystems. Full article

Power devices in series are low-voltage power devices used in medium- and high-voltage applications in a more direct program. However, when power devices in series are used, because of their electrical performance parameters or external circuit conditions, there are unique short-circuit voltage imbalances, a serious threat to the safety of the device. The article first summarizes the research status and characteristics of the four models of SiC MOSFETs based on the domestic and international research on the models of SiC MOSFETs in recent years; second, the voltage balancing technology of series-connected SiC MOSFETs is sorted out and summarized, and then the driving circuits of SiC MOSFETs are sorted out and summarized. Again, several voltage balancing techniques reviewed are compared in six different aspects: cost, modularity, complexity, speed of voltage balancing, losses, and effectiveness of voltage balancing. Finally, an outlook of voltage balancing techniques for series SiC MOSFETs is provided. Full article

Background: The emergence and prevalence of antibiotic-resistant bacteria (ARBs) have become a serious global threat, as the morbidity and mortality associated with ARB infections are continuously rising. The activation of quorum sensing (QS) genes can promote biofilm formation, which contributes to the acquisition of drug resistance and increases virulence. Therefore, there is an urgent need to develop new antimicrobial agents to control ARB and prevent further development. Antimicrobial peptides (AMPs) are naturally occurring defense molecules in organisms known to suppress pathogens through a broad range of antimicrobial mechanisms. Methods: In this study, we utilized a previously developed deep-learning model to identify AMP candidates from the venom gland transcriptome of the spider Pardosa astrigera, followed by experimental validation. Results: PA-Win2 was among the top-scoring predicted peptides and was selected based on physiochemical features. Subsequent experimental validation demonstrated that PA-Win2 inhibits the growth of Bacillus subtilis, Escherichia coli, Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, and multidrug-resistant P. aeruginosa (MRPA) strain CCARM 2095. The peptide exhibited strong bactericidal activity against P. aeruginosa, and MRPA CCARM 2095 through the depolarization of bacterial cytoplasmic membranes and alteration of gene expression associated with bacterial survival. In addition, PA-Win2 effectively inhibited biofilm formation and degraded pre-formed biofilms of P. aeruginosa. The gene expression study showed that the peptide treatment led to the downregulation of QS genes in the Las, Pqs, and Rhl systems. Conclusions: These findings suggest PA-Win2 as a promising drug candidate against ARB and demonstrate the potential of in silico methods in discovering functional peptides from biological data. Full article

The integrity of global elections is increasingly under threat from artificial intelligence (AI) technologies. As AI continues to permeate various aspects of society, its influence on political processes and elections has become a critical area of concern. This is because AI language models are far from neutral or objective; they inherit biases from their training data and the individuals who design and utilize them, which can sway voter decisions and affect global elections and democracy. In this research paper, we explore how AI can directly impact election outcomes through various techniques. These include the use of generative AI for disseminating false political information, favoring certain parties over others, and creating fake narratives, content, images, videos, and voice clones to undermine opposition. We highlight how AI threats can influence voter behavior and election outcomes, focusing on critical areas, including political polarization, deepfakes, disinformation, propaganda, and biased campaigns. In response to these challenges, we propose a Blockchain-based Deepfake Authenticity Verification Framework (B-DAVF) designed to detect and authenticate deepfake content in real time. It leverages the transparency of blockchain technology to reinforce electoral integrity. Finally, we also propose comprehensive countermeasures, including enhanced legislation, technological solutions, and public education initiatives, to mitigate the risks associated with AI in electoral contexts, proactively safeguard democracy, and promote fair elections. Full article