Search Results (236)

Microplastics (particles ≤ 5 mm) are ubiquitous and persistent, posing threats to ecosystems and human health. Thus, the development of technologies for evaluating their dynamics is crucial. Settling velocity is a critical parameter for predicting the fate of microplastics in aquatic environments. Current methods for computing this metric are highly subjective and lack a standard. The goal of this research is to develop an objective, automated technique employing the technological advances in computer vision. In the laboratory, a camera recorded the trajectories of microplastics as they sank through a water column. The settling velocity of each microplastic was calculated using a YOLOv12n-based object detection model. The system was tested with three classes of spherical microplastics and three types of water. Ground truth settling times, recorded manually with a stopwatch, allowed for quantification of the system’s accuracy. When comparing the velocities calculated using the computer vision system to the stopwatch ground truth, the average error across all water types was 5.97% for the 3 mm microplastics, 7.14% for the 4 mm microplastics, and 6.15% for the 5 mm microplastics. This new method will enable the research community to predict microplastic distribution and transport patterns, as well as implement more timely strategies for mitigating pollution. Full article

Insider threats remain a persistent challenge in cybersecurity, as malicious or negligent insiders exploit legitimate access to compromise systems and data. This study presents a bibliometric review of 325 peer-reviewed publications from 2015 to 2025 to examine how machine learning (ML) and deep learning (DL) techniques for insider threat detection have evolved. The analysis investigates temporal publication trends, influential authors, international collaboration networks, thematic shifts, and algorithmic preferences. Results show a steady rise in research output and a transition from traditional ML models, such as decision trees and random forests, toward advanced DL methods, including long short-term memory (LSTM) networks, autoencoders, and hybrid ML–DL frameworks. Co-authorship mapping highlights China, India, and the United States as leading contributors, while keyword analysis underscores the increasing focus on behavior-based and eXplainable AI models. Symmetry emerges as a central theme, reflected in balancing detection accuracy with computational efficiency, and minimizing false positives while avoiding false negatives. The study recommends adaptive hybrid architectures, particularly Bidirectional LSTM–Variational Auto-Encoder (BiLSTM-VAE) models with eXplainable AI, as promising solutions that restore symmetry between detection accuracy and transparency, strengthening both technical performance and organizational trust. Full article

Antimicrobial resistance (AMR) is a growing global threat, exacerbated by the adaptive mechanisms of Gram-negative ESKAPE pathogens, which include biofilm formation and outer membrane vesicle (OMV) production. Biofilms create robust protective barriers that shield bacterial communities from immune responses and antibiotic treatments, while OMVs contribute to both defense and offense by carrying antibiotic-degrading enzymes and delivering virulence factors to host cells. These mechanisms not only enhance bacterial survival but also increase the virulence and persistence of infections, making them a significant concern in clinical settings. This review explores the molecular processes that drive biofilm and OMV formation, emphasizing their critical roles in the development of AMR. By understanding these mechanisms, new therapeutic strategies can be developed to disrupt these defenses, potentially improving the efficacy of existing antibiotics and slowing the spread of resistance. Additionally, the use of OMVs in vaccine development and drug delivery offers promising avenues for future research. Addressing these challenges requires a comprehensive approach, combining advanced research with innovative therapies to combat the escalating threat of AMR and improve patient outcomes. Full article

Per- and polyfluoroalkyl substances (PFASs) are highly chemically stable synthetic compounds. They are widely used in industrial and commercial sectors due to their ability to repel water and oil, thermal stability, and surfactant properties. However, this stability results in environmental persistence and bioaccumulation, posing significant health risks as PFASs eventually find their way into environmental media. Key PFAS compounds, including PerFluoroOctanoic Acid (PFOA), PerFluoroOctane Sulfonic acid (PFOS), and PerFluoroHexane Sulfonic acid (PFHxS), have been linked to hepatotoxicity, immunotoxicity, neurotoxicity, and endocrine disruption. In response to the health threats these substances pose, global regulatory measures, such as the Stockholm Convention restrictions and national drinking water standards, have been implemented to reduce PFAS exposure. Despite these efforts, a lack of universally accepted definitions or comprehensive inventories of PFAS compounds hampers the effective management of these substances. As definitions differ across regulatory bodies, research and policy integration have become complicated. PFASs are broadly categorized as either perfluoroalkyl acids (PFAAs), precursors, or other fluorinated substances; however, PFASs encompass over 5000 distinct compounds, many of which are poorly characterized. PFAS contamination arises from direct industrial emissions and indirect environmental formation, these substances have been detected in water, soil, and even air samples from all over the globe, including from remote regions like Antarctica. Analytical methods, such as primarily liquid and gas chromatography coupled with tandem mass spectrometry, have advanced PFAS detection. However, standardized monitoring protocols remain inadequate. Future management requires unified definitions, expanded monitoring efforts, and standardized methodologies to address the persistent environmental and health impacts of PFAS. This review underscores the need for improved regulatory frameworks and further research. Full article

Marine pollution incidents pose significant threats to marine ecosystems and coastal communities across Pacific Island nations, necessitating advanced predictive capabilities for effective environmental management. This study analyzes 8133 marine pollution incidents from 2001–2014 across 25 Pacific Island nations to develop predictive models for pollution type classification, hotspot identification, and seasonal pattern forecasting. Our analysis reveals Papua New Guinea as the dominant pollution hotspot, experiencing 51.9% of all regional incidents, with plastic waste dumping comprising 78.8% of pollution events and exhibiting pronounced seasonal peaks during June (coinciding with critical fish breeding periods). Machine learning classification achieved 99.1% accuracy in predicting pollution types, with material composition emerging as the strongest predictor, followed by seasonal timing and geographic location. Temporal analysis identified distinct seasonal dependencies, with June representing peak pollution activity (755 average incidents), coinciding with vulnerable marine ecological periods. The predictive framework successfully distinguishes between persistent geographic hotspots and episodic pollution events, enabling targeted conservation interventions during high-risk periods. These findings demonstrate that pollution type and location are highly predictable from environmental and temporal variables, providing marine conservationists with tools to anticipate when and where pollution will most likely impact fish populations and ecosystem health. The study establishes the first comprehensive baseline for Pacific Island marine pollution patterns and validates machine learning approaches for proactive pollution monitoring, offering scalable solutions for protecting ocean ecosystems and supporting evidence-based policy formulation across the region. Full article

Emerging complex multi-step attacks such as Advanced Persistent Threats (APTs) pose significant risks to national economic development, security, and social stability. Effectively detecting these sophisticated threats is a critical challenge. While deep learning methods show promise in identifying unknown malicious behaviors, they often struggle with fragmented modal information, limited feature representation, and generalization. To address these limitations, we propose GATransformer, a new dual-modal detection method that integrates topological structure analysis with temporal sequence modeling. Its core lies in a cross-attention semantic fusion mechanism, which deeply integrates heterogeneous features and effectively mitigates the constraints of unimodal representations. GATransformer reconstructs network behavior representation via a parallel processing framework in which graph attention captures intricate spatial dependencies, and self-attention focuses on modeling long-range temporal correlations. Experimental results on the CIDDS-001 and CIDDS-002 datasets demonstrate the superior performance of our method compared to baseline methods with detection accuracies of 99.74% (nodes) and 88.28% (edges) on CIDDS-001 and 99.99% and 99.98% on CIDDS-002, respectively. Full article

Cyberattacks, especially Advanced Persistent Threats (APTs), have become more complex. These evolving threats challenge traditional defense systems, which struggle to counter long-lasting and covert attacks. Cybersecurity Knowledge Graphs (CKGs), enabled through the integration of multi-source CTI, introduce novel approaches for proactive defense. However, building CKGs faces challenges such as unclear terminology, overlapping entity relationships in attack chains, and differences in CTI across sources. To tackle these challenges, we propose the CyberKG framework, which improves entity recognition and relation extraction using a SecureBERT_Plus-BiLSTM-Attention-CRF joint architecture. Semantic features are captured using a domain-adapted SecureBERT_Plus model, while temporal dependencies are modeled through BiLSTM. Attention mechanisms highlight key cross-sentence relationships, while CRF incorporates ATT&CK rule constraints. Hierarchical clustering (HAC), based on contextual embeddings, facilitates dynamic entity disambiguation and semantic fusion. Experimental evaluations on the DNRTI and MalwareDB datasets demonstrate strong performance in extraction accuracy, entity normalization, and the resolution of overlapping relations. The constructed knowledge graph supports APT tracking, attack-chain provenance, proactive defense prediction. Full article

Over the last 15 years, cyberattacks have moved from attacking IT systems to targeted attacks on Operational Technology (OT) systems, also known as Cyber–Physical Systems (CPS). The first targeted OT cyberattack was Stuxnet in 2010, at which time the term Advanced Persistent Threat (APT) appeared. An APT often refers to a sophisticated two-stage cyberattack requiring an extensive reconnaissance period before executing the actual attack. Following Stuxnet, a sizable number of APTs have been discovered and documented. APTs are difficult to detect due to the many steps involved, the large number of attacker capabilities that are in use, and the timeline. Such attacks are carried out over an extended time period, sometimes spanning several years, which means that they cannot be recognized using signatures, anomalies, or similar patterns. APTs require detection capabilities beyond what current detection paradigms are capable of, such as behavior-based, signature-based, protocol-based, or other types of Intrusion Detection and Prevention Systems (IDS/IPS). This paper describes steps towards improving the detection of APTs by means of APT group digital fingerprints. An APT group fingerprint is a digital representation of the attacker’s capabilities, their relations and dependencies, and their technical implementation for an APT group. The fingerprint is represented as a directed graph, which models the relationships between the relevant capabilities. This paper describes part of the analysis behind establishing the APT group digital fingerprint for the Russian Cyberspace Operations Group - Sandworm. Full article

Malaria persists as a paradigmatic model of co-evolutionary complexity, emerging from the dynamic interplay among a human host, Anopheles vectors, and Plasmodium falciparum parasites. In human populations, centuries of selective pressures have sculpted an intricate and heterogeneous immunogenetic landscape. Classical adaptations, such as hemoglobinopathies, are complemented by a diverse array of genetic polymorphisms that modulate innate and adaptive immune responses. These genetic traits, along with the acquisition of functional immunity following repeated exposures, mitigate disease severity but are continually challenged by the parasite’s highly evolved mechanisms of antigenic variation and immunomodulation. Such host adaptations underscore an evolutionary arms race that perpetually shapes the clinical and epidemiological outcomes. Intermediaries in malaria transmission have evolved robust responses to both natural and anthropogenic pressures. Their vector competence is governed by complex polygenic traits that affect physiological barriers and immune responses during parasite development. Recent studies reveal that these mosquitoes exhibit rapid behavioral and biochemical adaptations, including shifts in host-seeking behavior and the evolution of insecticide resistance. Mechanisms such as enhanced metabolic detoxification and target site insensitivity have emerged in response to the widespread use of insecticides, thereby eroding the efficacy of conventional interventions like insecticide-treated bed nets and indoor residual spraying. These adaptations not only sustain transmission dynamics in intervention saturated landscapes but also challenge current vector control paradigms, necessitating the development of innovative, integrated management strategies. At the molecular level, P. falciparum exemplifies evolutionary ingenuity through extensive genomic streamlining and metabolic reconfiguration. Its compact genome, a result of strategic gene loss and pruning, is optimized for an obligate parasitic lifestyle. The repurposing of the apicoplast for critical anabolic functions including fatty acid, isoprenoid, and haem biosynthesis highlights the parasite’s ability to exploit host derived nutrients efficiently. Moreover, the rapid accumulation of mutations, coupled with an elaborate repertoire for antigenic switching and epigenetic regulation, not only facilitates immune escape but also accelerates the emergence of antimalarial drug resistance. Advanced high throughput sequencing and functional genomics have begun to elucidate the metabolic epigenetic nexus that governs virulence gene expression and antigenic diversity in P. falciparum. By integrating insights from molecular biology, genomics, and evolutionary ecology, this study delineates the multifaceted co-adaptive dynamics that render malaria a recalcitrant global health threat. Our findings provide critical insights into the molecular arms race at the heart of host–pathogen vector interactions and underscore promising avenues for the development of next generation therapeutic and vector management strategies aimed at sustainable malaria elimination. Full article

Healthcare-associated infections (HAIs) represent one of the most persistent challenges in modern healthcare delivery, affecting millions of patients worldwide and imposing substantial clinical and economic burdens on healthcare systems. The emergence of antimicrobial resistance (AMR) has further complicated infection management, creating an urgent need for innovative therapeutic and preventive strategies. Current strategies for combating AMR in hospital settings encompass comprehensive infection prevention and control measures, antimicrobial stewardship programs, enhanced environmental cleaning protocols and innovative surface modification technologies. Nanotechnology has emerged as a valuable approach to address the limitations of conventional antimicrobial strategies. Various nanomaterial categories offer innovative platforms for developing novel treatment strategies and for providing advantages including reduced toxicity through lower dosage requirements, diminished resistance development potential, and enhanced antibacterial effects through combined action mechanisms. Particularly, metal-based nanoparticles and their oxides demonstrate exceptional antimicrobial properties through multiple mechanisms including membrane damage, protein binding and reactive oxygen species generation. This comprehensive review examines the current landscape of hospital-acquired infections, the growing threat of antimicrobial resistance, and the promising role of nanotechnology-based solutions, with particular emphasis on silver nanoparticles as innovative tool for HAI control in clinical settings. Recent advances in nanotechnology-enabled antimicrobial coatings are assessed along with their clinical translation in hospital settings, identifying key barriers concerning material durability, safety profiles, and regulatory pathways. Full article

Artificial intelligence (AI) is rapidly redefining both computer science and cybersecurity by enabling more intelligent, scalable, and privacy-conscious systems. While most prior surveys treat these fields in isolation, this paper provides a unified review of 256 peer-reviewed publications to bridge that gap. We examine how emerging AI paradigms, such as explainable AI (XAI), AI-augmented software development, and federated learning, are shaping technological progress across both domains. In computer science, AI is increasingly embedded throughout the software development lifecycle to boost productivity, improve testing reliability, and automate decision making. In cybersecurity, AI drives advances in real-time threat detection and adaptive defense. Our synthesis highlights powerful cross-cutting findings, including shared challenges such as algorithmic bias, interpretability gaps, and high computational costs, as well as empirical evidence that AI-enabled defenses can reduce successful breaches by up to 30%. Explainability is identified as a cornerstone for trust and bias mitigation, while privacy-preserving techniques, including federated learning and local differential privacy, emerge as essential safeguards in decentralized environments such as the Internet of Things (IoT) and healthcare. Despite transformative progress, we emphasize persistent limitations in fairness, adversarial robustness, and the sustainability of large-scale model training. By integrating perspectives from two traditionally siloed disciplines, this review delivers a unified framework that not only maps current advances and limitations but also provides a foundation for building more resilient, ethical, and trustworthy AI systems. Full article

As digital infrastructures become increasingly dynamic and complex, traditional static access control mechanisms are no longer sufficient to counter advanced and persistent cyber threats. In response, Zero Trust Architecture ($ZTA$) emphasizes continuous verification and context-aware access decisions. To realize these principles in practice, this study introduces a Trust Score ($TS$)-based access control model as a systematic alternative to legacy, rule-driven approaches that lack adaptability in real-time environments. The proposed $TS$ model quantifies the trustworthiness of users or devices based on four core factors—User Behavior (B), Network Environment (N), Device Status (D), and Threat History (T)—each derived from measurable operational attributes. These factors were carefully structured to reflect real-world Zero Trust environments, and a total of 20 detailed sub-metrics were developed to support their evaluation. This design enables accurate and granular trust assessment using live operational data, allowing for fine-tuned access control decisions aligned with Zero Trust principles. A comprehensive sensitivity analysis was conducted to evaluate the relative impact of each factor under different weight configurations and operational conditions. The results revealed that B and N are most influential in real-time evaluation scenarios, while B and T play a decisive role in triggering adaptive policy responses. This analysis provides a practical basis for designing and optimizing context-aware access control strategies. Empirical evaluations using the UNSW-NB15 dataset confirmed the $TS$ model’s computational efficiency and scalability. Compared to legacy access control approaches, the $TS$ model achieved significantly lower latency and higher throughput with minimal memory usage, validating its suitability for deployment in real-time, resource-constrained Zero Trust environments. Full article

Malicious actors often exploit persistence mechanisms, such as unauthorized modifications to Windows startup directories or registry keys, to achieve privilege escalation and maintain access on compromised systems. While information technology (IT) teams legitimately use these AutoStart Extension Points (ASEPs), adversaries frequently deploy malicious binaries with non-standard naming conventions or execute files from transient directories (e.g., Temp or Public folders). This study proposes a threat-hunting framework using a custom Elasticsearch Security Information and Event Management (SIEM) system to detect such persistence tactics. Two hypothesis-driven investigations were conducted: the first focused on identifying unauthorized ASEP registry key modifications during user logon events, while the second targeted malicious Dynamic Link Library (DLL) injections within temporary directories. By correlating Sysmon event logs (e.g., registry key creation/modification and process creation events), the researchers identified attack chains involving sequential registry edits and malicious file executions. Analysis confirmed that Sysmon Event ID 12 (registry object creation) and Event ID 7 (DLL loading) provided critical forensic evidence for detecting these tactics. The findings underscore the efficacy of real-time event correlation in SIEM systems in disrupting adversarial workflows, enabling rapid mitigation through the removal of malicious entries. This approach advances proactive defense strategies against privilege escalation and persistence, emphasizing the need for granular monitoring of registry and filesystem activities in enterprise environments. Full article

Cancer remains a leading global health challenge, with conventional diagnostic and treatment methods often lacking precision and adaptability. This review explores transformative advancements that are reshaping oncology by addressing these limitations. It begins with an overview of cancer’s complexity, emphasizing the shortcomings of conventional tools such as imaging and chemotherapy, which frequently fail to deliver targeted care. The discussion then shifts to biomarkers, which represent a groundbreaking frontier in early detection, enabling the identification of unique biological signatures that signal the presence of cancer with heightened sensitivity. Building on this foundation, the review examines personalized molecular therapies, which target the specific genetic and molecular vulnerabilities of tumors. These therapies not only enhance treatment efficacy but also minimize adverse effects, offering patients improved outcomes and quality of life. By integrating biomarker-driven diagnostics with tailored therapeutic strategies, a new paradigm of precision oncology emerges, bridging the gap between early detection and effective intervention. Real-world case studies highlight both successes, such as significantly improved survival rates, and persistent challenges, including accessibility and cost barriers. Looking ahead, the review outlines pathways by which to scale these innovations, emphasizing the critical need for robust infrastructure, sustained research investment, and equitable healthcare policies. It concludes by envisioning a future where biomarkers and personalized therapies converge to redefine cancer care, offering earlier detection, precise interventions, and better patient experiences. This work underscores the urgency of adopting cutting-edge approaches to overcome cancer’s persistent threats, paving the way for a more effective and humane era in oncology. Full article

Vector-borne parasitic diseases (VBPDs), including malaria, schistosomiasis, leishmaniasis, Chagas disease, African trypanosomiasis, lymphatic filariasis, and onchocerciasis, impose a significant global health burden. This study analyzes the global disease burden of VBPDs from 1990 to 2021 using Global Burden of Disease (GBD) 2021 data and projects trends to 2036. Metrics include prevalence, deaths, disability-adjusted life years (DALYs), and age-standardized rates (ASRs) across regions, sexes, age groups, and Socio-demographic Index (SDI) levels. Key findings reveal persistent disparities: malaria dominated the burden (42% of cases, 96.5% of deaths), disproportionately affecting sub-Saharan Africa. Schistosomiasis ranked second in prevalence (36.5%). While African trypanosomiasis, Chagas disease, lymphatic filariasis, and onchocerciasis declined significantly, leishmaniasis showed rising prevalence (EAPC = 0.713). Low-SDI regions bore the highest burden, linked to environmental, socioeconomic, and healthcare access challenges. Males exhibited greater DALY burdens than females, attributed to occupational exposure. Age disparities were evident: children under five faced high malaria mortality and leishmaniasis DALY peaks, while older adults experienced complications from diseases like Chagas and schistosomiasis. ARIMA modeling forecasts divergent trends: lymphatic filariasis prevalence nears elimination by 2029, but leishmaniasis burden rises across all metrics. Despite overall progress, VBPDs remain critical public health threats, exacerbated by climate change, drug resistance, and uneven resource distribution. Targeted interventions are urgently needed, prioritizing vector control in endemic areas, enhanced surveillance for leishmaniasis, gender- and age-specific strategies, and optimized resource allocation in low-SDI regions. This analysis provides a foundation for evidence-based policy and precision public health efforts to achieve elimination targets and advance global health equity. Full article