Search Results (297)

Rock pillars in deep underground mines are subjected to complex stress environments. The combined effects of in situ stress and cyclic disturbances from mining activities lead to a redistribution of the surrounding rock mass stress field, which readily triggers instability and failure, posing severe threats to mining engineering safety. To investigate the damage mechanism of cyclic loading on rock and its weakening effect on the bearing capacity of mine pillars, this study takes limestone as the research object. A series of uniaxial compression tests were conducted on limestone specimens subjected to triaxial cyclic pre-damage, complemented by numerical simulations to further characterize the energy and deformation evolution of the damaged limestone under cyclic loading conditions. The findings are as follows: (i) Triaxial cyclic tests on limestone show that both the input energy and dissipated energy follow similar trends, decreasing rapidly in the initial stage before stabilizing. The elastic strain energy remains largely constant, with most of the input energy being stored as elastic strain energy. Under constant stress levels and cycle numbers, increases in confining pressure and frequency reduce the rock’s input energy, elastic strain energy, and dissipated energy. (ii) The peak stress of damaged limestone exhibits a positive correlation with the pre-damage confining pressure and cyclic frequency, while it decreases with an increasing number of cycles. Higher confining pressure and frequency raise the input energy, elastic potential energy, and dissipated energy at the peak stress point. (iii) Deformation and failure in damaged limestone originate from the development and propagation of localized deformation zones. Increased lateral displacement within these zones promotes the formation of macroscopic fractures. Due to significant structural heterogeneity inside the localized areas, the evolution of deformation energy is influenced by regional characteristics. (iv) Simulation results indicate that the uniaxial compressive failure of limestone involves the accumulation and propagation of micro-scale tensile cracks, which ultimately coalesce into macro-scale shear fracture surfaces. During uniaxial loading of pre-damaged limestone, newly generated cracks predominantly initiate around pre-existing cracks, with only a limited number distributed randomly. Their peak intensity shows a positive correlation with the pre-damage confining pressure. Full article

Under climate change, the increasing typhoon intensity poses a severe threat to fishery harbor safety through storm surges and extreme waves. Traditional empirical management approaches fail to capture the complex wave-surge coupling inside harbors, leading to risk blind spots in berth allocation. This study enhances the fishery harbor disaster resilience by employing high-resolution coupled wave-storm surge modeling, taking Xinying Central Fishing Harbor (Hainan, China) during Super Typhoon Yagi (September 2024) as a case study. A Holland typhoon model integrated with ERA5 reanalysis data was used to reconstruct the wind field, which subsequently drove a one-way coupled MIKE 21 FM–SW model to simulate regional tides and deep-water waves. A Boussinesq wave model was then applied to resolve nearshore shallow-water wave transformations inside the harbor. Model validation showed strong agreement with observations: correlation coefficients of 0.97 for tides in Xinying station and 0.95, 0.97, 0.93 for significant wave heights in three buoys around Hainan island, with root-mean-square errors of 0.19 m and 0.67, 0.69, 0.31 m, respectively. The Boussinesq wave simulations revealed detailed spatial distributions of wave heights inside the harbor during the typhoon. Based on these simulations, a dynamic berth zoning strategy was developed, mapping safety zones for different vessel sizes according to wave-height tolerance (e.g., ≤0.6 m for medium-sized trawlers). This framework can provide potential support for decision-making regarding fishing vessel refuge during typhoons, maximizing safe capacity while minimizing capsizing risks. Overall, this study demonstrates a feasible pathway from advanced numerical modeling to practical engineering management, supporting a transition from experience-based to data- and model-driven disaster prevention for coastal fishery harbors. Full article

Private cloud object stores provide infrastructure isolation but leave application-layer data exposed to insider threats and compromised credentials. This paper presents an engineering integration of an Add-Rotate-XOR (ARX) block cipher and multi-bit Least Significant Bit (LSB) steganography into an end-to-end pipeline for private MinIO object storage. The cipher, KREA v2, is a SPECK-64/128 derived ARX construction with three application-driven choices: CRC32 key whitening, byte-aligned rotations ($\alpha =7$, $\beta =2$), and deterministic CTR-mode nonces. Mixed Integer Linear Programming (MILP) trail analysis matches SPECK-64/128’s minimum-trail weights through rounds 1–4. KREA v2 ciphertext meets standard keystream-quality preconditions (NIST SP 800-22 battery, 49.98% mean avalanche, Shannon entropy 7.9992–7.9998 bits/byte across realistic XML, JSON, video, and HTTP/2 payloads). Modified LSB (MLSB) embeds 3 bits per RGB channel with an XOR watermark at 37–38 dB Peak Signal-to-Noise Ratio (PSNR), providing $3\times$ standard-LSB capacity. Steganalysis uses chi-square and RS detectors plus a Convolutional Neural Network (CNN) detector (Yedroudj-Net) trained on 8000 BOSSBase-1.01 cover/stego pairs; CNN area under the ROC curve is ≥0.999 against the watermarked variant. The MinIO pipeline runs at 355.1 ms (68.6% network I/O) with 100% message fidelity. The XOR watermark increases RS detectability above 75% capacity; a 200-image ablation cuts median RS detection (0.289 to 0.000) and mean (0.342 to 0.130) in a sparse-keystream variant, prioritised for follow-on full-scale evaluation. The architecture is offered as a documented engineering integration with explicit security caveats and threat-model boundaries, not as a production-hardened cryptographic primitive. Full article

Identifying insider threats in modern enterprise environments presents a unique cybersecurity challenge. Although malicious activity may often appear to be legitimate user activity, it is difficult to recognize the distinction. This study presents an innovative approach to insider threat detection by analyzing enterprise activity logs for session-level behavioural risk monitoring with behavioural biometrics. Behavioural patterns are modelled as temporal sequences across consecutive monitoring windows to capture both short-term behavioural intensity and long-term behavioural drift. The proposed system utilizes a hybrid deep learning architecture that includes a Long Short-Term Memory (LSTM) network and an autoencoder model to model temporal dependence of a user’s behaviour and to identify anomalies through reconstruction error analysis. The LSTM network captures user’s sequential activity and autoencoder determines variance from the user’s typical behavioural profile. The outputs of both models are aggregated using a unified behavioural risk scoring mechanism for session-level risk monitoring and ongoing insider threat assessment. The experimental results from Insider Threat Dataset for Corporate Environments demonstrate that proposed approach is effective in classifying normal versus malicious behaviours of users. The proposed framework achieves an accuracy of 97.65%, a precision of 96.35%, a recall of 99.05%, an F1-score of 97.68%, and a ROC-AUC of 99.20% on a near-balanced benchmark split. Under realistic class imbalance conditions, the framework achieves a PR-AUC of 0.842 and MCC of 0.781, representing the more operationally conservative performance estimate. These findings confirm that the proposed framework constitutes a viable solution for integrating behavioural modelling and anomaly detection within continuous enterprise authentication systems. Full article

This study investigates product abuse, reconciliation challenges, and cybersecurity risks in warehouse management systems (WMS) within increasingly digitized supply chain environments. As warehouses evolve into data-driven operational hubs, vulnerabilities such as data manipulation, insider threats, and fraudulent activities pose significant risks to financial accountability and system integrity. To address these challenges, this research proposes a security-centric WMS framework that integrates blockchain-based immutable logging, Internet of Things (IoT)-enabled tracking, and artificial intelligence (AI)-driven anomaly detection. The methodology follows a hybrid iterative–incremental development approach, supported by real-world deployment of a prototype WMS implemented using a scalable microservices architecture. Over a five-year operational period, the system processed more than 10 million transactions with no recorded successful cybersecurity incidents leading to data breaches, operational compromise, or unauthorized system access, while achieving improvements in reconciliation accuracy, operational efficiency, and fraud detection capabilities. Results demonstrate reductions in manual reconciliation efforts, mispricing incidents, and operational losses, while maintaining high system availability and low latency. In addition, the reported 18–22% improvement associated with AI-assisted anomaly detection is presented as a simulation-based projection rather than a production-validated measurement. The findings indicate that combining secure software engineering practices with automation, auditability, and advanced analytics can significantly enhance transparency and resilience in warehouse operations. The study concludes that integrating decentralized and intelligent technologies provides a viable pathway toward secure, privacy-preserving, and abuse-resistant warehouse ecosystems. Full article

In this paper, we present an agent-based model of a controlled detonation system for dynamic sandbox analysis of suspicious software. Instead of treating the sandbox as a passive observer, the model places an AI operator inside the analysis loop and allows it to perform adaptive GUI interactions in a plausible, isolated execution environment. The controlled detonation process is formulated as a partially observable Markov decision process (POMDP), while the proposed proof-of-concept architecture combines initial profiling, VM preparation, multi-layer telemetry, and an RL policy with visual perception and temporal memory. Evaluation in a controlled emulation setting on 180 malware samples from three threat classes shows higher Activity Rates and Coverage, and shorter Time-to-Reveal than passive and fixed scripted baselines. These results support the feasibility of adaptive interactions as a promising direction for sandbox analysis, while broader external validation, matched comparisons with prior systems, and component-wise ablation remain future work. Full article

The contamination of aquatic environments, including treated and drinking water, by microplastics poses a significant threat to ecosystems and human health. Current detection methods often rely on slow laboratory-based tests and offline analysis, which do not support real-time monitoring. This paper presents a novel focusing and concentrating device designed to enhance the real-time detection of microplastics in flowing water. The device utilizes an electrokinetic manipulation mechanism to focus microplastics toward the center of the water flow inside a pipe or fluid channel. A set of 3D rectangular electrodes, with dimensions of 5 mm × 2.5 mm × 1 mm, are arranged circumferentially and longitudinally along the inner perimeter of the fluid channel to generate an intense, non-uniform electric field. Simulation results indicate that microplastics near the channel wall experience a repulsive force on the order of ${10}^{-16}$ to ${10}^{-10}$ N toward the channel center. The applied signal amplitude and the physical properties of the microplastics strongly influence this repulsive force. The trajectories and output concentration of microplastics are investigated under varied conditions. A Voltage of approximately 25 V and a flow rate of 0.05 m/s are found to be ideal for concentrating microplastics into a narrow particle stream, enabling more efficient downstream detection and analysis. Pre-concentrating microplastics in fluid channels prior to sensing is expected to increase sensor sensitivity and improve selectivity. Full article

The Model Context Protocol (MCP) has become the de facto standard for connecting large language models (LLMs) to external tools, and its remote deployment mode lets users add third-party servers with a single URL—shifting a substantial portion of the host’s attack surface to infrastructure operated by anonymous parties. Existing MCP security work has concentrated on tool-description poisoning and studied individual techniques in isolation, leaving it unclear what a malicious remote server can accomplish across its full surface. In this paper, we explore the malicious-server threat space along the axis of whether the host LLM participates in producing the harmful outcome, yielding two categories: LLM-passive attacks, which complete inside the server, and LLM-active attacks, which require the LLM to deliver the malicious content. We implement five scenarios spanning both categories—realizing each LLM-active scenario with both description-based and response-based variants against the same goal—and evaluate all configurations on ChatGPT, Claude Desktop, and Gemini CLI. We find that host-side filtering of MCP-bound data varies sharply across platforms (95% vs. 50% ASR on the same email request), that the description and response channels succeed on disjoint scenarios, and that successful attacks are almost never disclosed to the user. These findings suggest that defending remote MCP deployment requires a multi-layer approach combining host-side filtering, LLM-level response auditing, and user-visible output transparency. Full article

As organizational competition intensifies, employees have become increasingly responsive to evaluative cues from their work environment. Among these, underdog expectations—employees’ perceptions that others view them as unlikely to succeed—can trigger strong psychological reactions that shape interpersonal behavior. Drawing on self-determination theory, this study examines how underdog expectations influence employees’ interpersonal counterproductive work behavior (CWB-I). Using a three-wave time-lagged survey design with 221 employees, we found that underdog expectations positively predict CWB-I through two parallel psychological mechanisms: increased moral disengagement and reduced perceived insider status. In addition, organization-based self-esteem (OBSE) strengthens these indirect effects, such that the mediating relationships are stronger among employees with high OBSE. These findings extend research on underdog expectations by revealing both relational and cognitive pathways linking negative evaluative expectations to interpersonal deviance, while also highlighting the complex role of self-evaluative organizational identity in shaping employees’ behavioral responses to status-based threats. Full article

The intense low-frequency magnetic field generated by the Electromagnetic Aircraft Launch System (EMALS) during operation poses a serious EMI threat to electronic equipment within carrier-based aircraft nacelles. To address this, a three-dimensional transient finite element model of a long-primary double-sided linear induction motor is established. Using a quasi-static equivalent method, the 118 Hz magnetic field distribution inside and outside a typical engine nacelle is characterized. Results indicate that due to the skin depth significantly exceeding material thickness, the eddy-current shielding of the aluminum alloy nacelle is inadequate, producing internal field intensities that far exceed standard limits and directly threaten sensitive onboard electronics. Based on the magnetic shunting principle, a composite shielding strategy is proposed: applying a flexible high-permeability coating on the nacelle surface to attenuate the overall field, supplemented by local permalloy shields for core equipment. Simulation verification demonstrates that this approach reduces the internal field to safe levels. It achieves effective shielding performance while balancing engineering feasibility with lightweight requirements, providing a viable pathway for ensuring the reliable protection of carrier-based aircraft in intense electromagnetic environments. Full article

In response to the increasing complexity of insider threats, this study proposes an explainable AI-driven framework designed to emulate real-world analyst workflows in security operations centers (SOCs). The framework integrates ensemble learning models—Random Forest, XGBoost, and Stacking—with behavioral feature engineering across multiple temporal granularities (session, daily, and weekly), enabling both fine-grained detection and long-term behavioral analysis. The framework follows a structured pipeline in which LLM-driven filtering is first applied to refine behavioral data using dataset metadata and MITRE ATT&CK-aligned logic, followed by ensemble learning for detection, explainability through SHAP and LIME, and LLM-based interpretation for analyst-oriented insights. A key contribution of this work is a dual-layer explainability architecture, where SHAP values capture global feature importance and LIME values provide instance-level explanations, enhanced by LLM-generated interpretations aligned with the MITRE ATT&CK framework. Due to computational constraints, modeling, full SHAP/LIME explainability, and LLM-guided filtering are applied at the weekly level. This design enables stable and interpretable behavioral analysis, while finer-grained analysis at daily and session levels remains part of future work. The filtering logic simulates SOC playbook-based automation using dataset metadata and MITRE-aligned patterns, reflecting how large-scale behavioral data are handled in practice. Despite the absence of contextual telemetry such as Security Information and Event Management (SIEM), Data Loss Prevention (DLP), or network logs, the proposed pipeline produces transparent and prioritized alerts that reduce false positives and improve analyst trust. Future work will extend the framework to finer temporal granularities—particularly daily and session levels—by applying the same pipeline to ensure consistency across analysis levels, in addition to exploring semi-supervised learning to adapt to evolving insider threat tactics. Full article

The secure management of classified documents containing sensitive information is critical for governments, military organizations, and the industry. Traditional data loss prevention (DLP) systems lack robustness against insider threats, particularly regarding access log integrity and tamper-proof auditing. To address log security, the previous literature has proposed multiple solutions, including private and hybrid blockchain models (e.g., Ethereum + MultiChain) to ensure audit trail integrity. However, hybrid architectures often face challenges such as unpredictable transaction costs (gas fees) and potential privacy risks when scaled for enterprise DLP logs. Conversely, private architectures may require higher resources, potentially causing bottlenecks on endpoints. In this paper, we propose an optimized Blockchain-Based Classified Document Management System (B2CDMS) utilizing a permissioned architecture. Our work demonstrates the challenges, advantages, and weak points of current solutions. We optimized a permissioned blockchain (BC) (Hyperledger Fabric v2.5) with an External Chaincode Builder using the Chaincode-as-a-Service (CCaaS) pattern. We compared our proposed private architecture with a hybrid architecture (Ethereum + MultiChain) and a public solution (Ethereum). We conducted a comprehensive analysis using pseudo Trellix ePolicy Orchestrator (ePO) Data Loss Prevention (DLP) logs. Experimental results on an Apple Silicon M4 (Apple Inc., Cupertino, CA, USA) testbed show that the proposed architecture achieves a throughput of 845.8 Transactions Per Second (TPS) with a sub-second latency of 55 ms, aiming to eliminate the bottlenecks of public blockchains. Furthermore, the system introduces a privacy-preserving hashing mechanism (i.e., committing only deterministic Secure Hash Algorithm 256-bit (SHA-256) digests to the immutable ledger while keeping the actual sensitive Personally Identifiable Information (PII) strictly in off-chain databases) compliant with General Data Protection Regulation (GDPR). It ensures that classified document metadata remains immutable and secure against rogue access benefiting from admin privileges. This study concludes that permissioned blockchain architectures offer a scalable and resource-efficient solution for forensic evidence preservation throughout the classified document lifecycle. Full article

Both plants and animals have developed a sophisticated two-tiered innate immune system. This involves an initial recognition of microbial patterns conserved on the cell surface (PAMP-triggered immunity) and a subsequent more specific intracellular recognition of pathogenic effectors or their activities (effector-triggered immunity). A common fundamental feature is the use of NLR-like intracellular receptors to detect insider threats. Both plant NLRs (receptors containing nucleotide-binding domains and leucine-rich repeats) and animal NLRs (NOD-like receptors) share a modular tripartite architecture, typically featuring a central nucleotide-binding domain (NBD/NOD) and C-terminal leucine-rich repeats (LRRs). The NBD/NOD is crucial for facilitating the exchange of ADP/ATP, acting as a molecular switch to promote oligomerization and activation of NLRs in both kingdoms. In this review, we summarize the similarities and differences between plant and animal molecular perception and immunity mechanisms. Additionally, we highlight the fact that some human pathogens can infect plants, and crucially, some plant pathogens are capable of causing disease in humans. This suggests conserved molecular strategies to invade and manipulate host cells belonging to different biological kingdoms, uncovering that plant and human pathology may benefit from future investigations in their respective fields. Full article

Mobile data collectors (MDCs) play a very important role in Internet of Things (IoT) sensing networks. However, ensuring their trustworthiness against insider threats, such as on–off attacks and spatiotemporal fabrication, remains a critical challenge. Existing trust evaluation methods frequently struggle with these threats due to insufficient evidence dimensions and the inability to quantify behavioral stability. To address these limitations, this paper proposes an enhanced proactive trust evaluation system based on stability sequence extraction (E-PTES-S). E-PTES-S improves the evaluation accuracy by integrating five factors of evidence, stability-computation mechanisms, and an adaptive weight allocation scheme to maintain robustness even when proactive verification data is scarce. In addition to the usual interaction and proactive verification indicators, regional consistency (${\mathcal{T}}_{RC}$) and task timeliness (${\mathcal{T}}_{TT}$) are introduced to mitigate location falsification and transmit-time deviations more rigorously. Then, a sliding window technique is used to obtain an integrated evidence sequence, which includes a new continuous stability sequence (${\mathcal{F}}_{CSS}$) and traditional credible, untrustworthy, and uncertain sequences. This continuous stability sequence adds a variance-based incentive scheme to measure behavioral stability. Finally, the normalized trust value is derived from multiple indicators including multidimensional spatiotemporal evidence and stability metrics. Experimental results show that the proposed E-PTES-S achieves a normal node detection rate of 98.7% under complex dynamic conditions, outperforming the baseline PTES and Trust-SIoT algorithms by approximately 9% and 1%, respectively, while also improving the cumulative data collection profit by 4.8%. Furthermore, robustness analysis demonstrates that E-PTES-S exhibits excellent robustness against physical-layer uncertainties, successfully sustaining an 84.4% detection rate even under severe environmental shadowing. Full article

Cross-kingdom pathogenesis—human and animal pathogens colonizing and persisting in plants—is transforming our understanding of microbial ecology, food safety, and public health. This review translates incoming research that demonstrates plants as more than mute carriers to dynamic ecological interfaces where human and zoonotic pathogens, such as Salmonella enterica, Escherichia coli O157:H7, and Listeria monocytogenes, will adhere, internalize, and, in some cases, potentially evade host defenses. Such pathogens exploit evolutionarily conserved molecular processes like Type III secretion system 1 (TTSS), biofilm formation, quorum sensing, and small RNA-mediated immune sabotage that have allowed them to cross biological kingdom boundaries. To provide an entry point for pathogens, environmental conditions (e.g., contaminated irrigation water, manure application, wildlife access, and mechanical wounding) promote pathogen transfer to and penetration into plant tissues through stomata hydathodes above ground or roots below ground. Once inside, pathogens confront a range of plant immune responses, indigenous microbiota, and abiotic stresses such as UV radiation exposure, nutrient starvation, and osmotic fluctuations. Nonetheless, biofilm production, metabolic versatility, and virulence gene expression contribute to their persistence. Interactions with plant pathogens and microbiomes additionally shape colonization dynamics, for example, through co-survival and niche manipulation. With the acceleration of these processes due to climate change, urbanization, and intensified agriculture, cross-kingdom pathogenesis becomes a rising concern for One Health. Critical knowledge gaps, including seedborne transmission, microbiome engineering, and predictive modeling, are pointed out in the review along with emerging mitigation strategies, including point-of-care diagnostics and microbial biocontrol. In conclusion, this review advocates for interdisciplinary collaboration from microbiology, plant science, and One Health perspectives to predict and mitigate cross-kingdom threats to global food production. Full article