Search Results (6,659)

Background: Blockchain is increasingly proposed to strengthen pharmaceutical traceability, anti-counterfeiting, and chain of custody in multi-actor supply chains, but the evidence base remains heterogeneous in technical rigor and operational clarity. Methods: We conducted a mapping review of Scopus and Web of Science to map publication patterns, identify dominant thematic configurations, and compare citation-salient studies across recurring solution profiles and operational design dimensions. The final corpus comprised 103 records. Results: The literature expanded rapidly from 2019 to 2025, with notable geographic concentration and dissemination mainly through technically focused outlets. Keyword analysis identified a core traceability theme, an implementation stream centered on smart contracts, Ethereum, and security, and additional streams involving vaccines and regulatory or credentialing concerns. Citation-salient studies clustered into implemented systems and prototypes, architecture or framework proposals, and contextual maturity or decision-layer evidence. Across these profiles, transferability depended less on platform choice than on governance and access-control assumptions, modular smart contract roles, and verifiable on-chain/off-chain data placement. Conclusions: Chain-of-custody semantics and evaluation methods remain inconsistently formalized, limiting cross-study comparability and the interpretability of operational claims. Benchmark-oriented assessments and minimal reporting standards specifying governance parameters, logistics scope and checkpoints, workload, measurement conditions, and concrete evidence artifacts are needed. Full article

This paper formulates the “Minimum Vertex Cut with Reachable Set” (MVCRS) problem as an optimization framework to suppress botnet propagation in networked systems, and clarifies its computational complexity and algorithmic solutions. Building a firewall to minimize damage is essential for addressing botnet propagation in Internet of Things (IoT) networks. We define the basic MVCRS problem as minimizing the sum of the weight of the deployed resources and the resulting propagation scope. While we demonstrate that the constrained version of the problem is NP-complete, we show that the fundamental trade-off optimization model can be solved in polynomial time by reducing it to the maximum flow–minimum cut problem. This provides a theoretical baseline for optimal resource allocation in cybersecurity. Experimental evaluations reveal the limitations of conventional heuristics. In community-structured networks, the degree-based greedy algorithm overlooks critical bridge nodes, yielding an optimality gap of up to 72.6% above the theoretical minimum cost. Conversely, our exact algorithm consistently guarantees the optimal minimum cost (a 0% gap) with high statistical stability across diverse topologies. Furthermore, it scales efficiently to solve 100,000-node IoT networks within practical time limits, proving to be a reliable and efficient foundation for botnet suppression in complex real-world systems. Full article

As one of the core technologies in modern national defense and security fields, infrared stealth technology aims to realize the controllable regulation of the radiation characteristics of targets in the infrared band. This paper focuses on a novel electrochromic device with a structure of WO₃/nickel mesh/Al³⁺-Zn²⁺gel electrolyte/zinc foil. The structural composition and working mechanism are systematically analyzed, and the infrared stealth regulation performance is emphatically studied. The WO₃ thin film and device structure were characterized by scanning electron microscopy (SEM). The infrared emissivity modulation and optical response properties of the device were measured using an infrared thermal imager and a UV-Vis-NIR spectrophotometer. The prepared WO₃ film exhibits a dense spherical morphology, indicating excellent uniformity and compactness. After 1000 cycles, the areal capacitance of the device remains 83.7% of its initial value, demonstrating good cycling stability. Under the voltage regulation of −0.1 V to 1.1 V, the emissivity ε of the device at the typical mid-wave infrared wavelength of 4.0 μm decreases from 0.89 (−0.1 V) to 0.67 (1.1 V), with an absolute modulation amplitude Δε of 0.22. At the typical long-wave infrared wavelength of 8.7 μm, ε decreases from 0.96 (−0.1 V) to 0.69 (1.1 V), with an absolute modulation amplitude Δε of 0.29. The electrochromic switching times for coloring and bleaching are 10.1 s and 2.44 s, respectively. According to infrared thermal imaging tests, in the temperature range of 30–40 °C, the surface temperature difference ΔT between the colored state and bleached state increases from 4.3 °C to 4.6 °C. The maximum regulation amplitude reaches 4.6 °C at 40 °C. The device achieves efficient regulation of infrared emissivity through the electrochromic effect, providing a new device design strategy for infrared stealth technology. Full article

This article presents the potential for maliciously influencing a control system by interfering with the program code of an industrial controller, using a temperature control system for a heating process based on resistive electric heating as an example. The presented attack scenarios are crucial for the energy efficiency of electric heating systems, which is related to the issue of cybersecurity in the area of energy security. The aim of this research was to demonstrate that a cyberattack involving the malicious manipulation of the setpoint can be carried out in a manner invisible to the heating process operator and be difficult to detect using classical time-domain control quality indicators (time-response specifications). The first involves incorporating proportional elements with mutually inverted gains into the input and output of a closed-loop system. The second method is based on adding an additional transfer function G_m(s) in parallel to the control system. The difference between the correct and manipulated setpoints is introduced into the input, and the output signal is added to the actual (hidden) value of the controlled variable. In the first method, at the moment of starting the control system, there is a difference between the apparent (falsified) value and the ambient temperature. In the second method, the inclusion of an additional G_m(s) ensures that the apparent (falsified) value of the controlled variable matches the temperature at the moment of starting the system. PID control enables achieving satisfactory control quality in heating processes, which are characterized by high inertia and time delays. Compared to classical PID regulation, advanced control methods can, under certain conditions, provide better performance in terms of quality indicators. However, due to their high computational complexity and sensitivity to model uncertainty—particularly in methods relying on accurate system identification—PID controllers continue to be widely used in industrial practice. For this reason, the present study focuses on a control system based on a PID controller as a practical solution. Based on the results, it was found that the most effective manipulation occurred within the range from 0.9 to 1.1 of the actual setpoint value for both the first and second method, using a model with $T_m$ between 5 s and 30 s. In these cases, the quality indicators referenced to the nominal values, determined for the falsified control system responses to a step change in the setpoint, were as follows: overshoot—0.97 and 1.30 (method 1), and 0.90 and 1.10 (method 2 for 5 s), 0.75 and 1.30 (method 2 for 30 s); settling time—1.06 (method 1), and 0.98 and 1.17 (method 2 for 5 s), 0.85 and 1.14 (method 2 for 30 s). The settling times determined for the system’s response to a disturbance were: 1.00 and 1.15 (method 1), and 1.13 and 1.16 (method 2 for 5 s), 1.12 and 1.02 (method 2 for 30 s). Based on the conducted analysis, it was demonstrated that the relatively simple setpoint manipulation methods presented can effectively mask the impact of malicious interference on the temperature value in the control system of a heating process. Full article

Soil salinity poses a major challenge to agricultural productivity, especially threatening food security in arid and semi-arid areas. Traditional soil reclamation methods, such as leaching, chemical amendments, and drainage engineering, usually need large amounts of water, involve high costs, and can lead to environmental problems. This review compiles existing knowledge on innovative strategies for managing saline soils, focusing on buried interlayer systems that use materials like straw, sand, gravel–sand mixtures, and biochar. These interlayers improve soil hydraulic properties by preventing capillary rise, encouraging salt leaching, and reducing surface salt buildup. Biochar stands out as a particularly useful material because of its stability, large surface area, porosity, and high cation exchange capacity. These features help improve soil structure, increase water retention, and effectively retain sodium. Evidence from lab and field tests shows that buried biochar layers can stop salt from moving upward, aid in desalinating the root zone, and boost crop yields. While straw and sand interlayers show potential in reducing salinity, biochar is noted for its multifunctionality and long-term effectiveness in addressing salinity problems. The success of buried biochar systems depends on several factors, including the properties of the biochar, how much is used, how deep it is buried, and the specific soil and climate conditions. This review highlights how these systems work, compares their performance, and points out research gaps, advocating for their potential as a sustainable, resource-efficient way to manage salinity and improve soil health over the long term. A substantial proportion of the existing evidence is derived from controlled laboratory studies, and the buried biochar layer approach remains an emerging technique that requires further validation under field conditions. Still, significant knowledge gaps persist regarding long-term performance and water-salt dynamics, while site-specific soil variability and scalability challenges may limit the effective implementation of biochar interlayer systems under field conditions. Full article

Telemedicine can address access barriers in childhood obesity management by supporting continuity of care and caregiver engagement. This randomized controlled trial compared a telemedicine-based program with guideline-based usual care among 70 children with obesity (aged 5–15 years) and their caregivers, randomized to telemedicine (n = 35) or usual care (n = 35) for 6 months. The telemedicine program included online consultations, digital caregiver education, remote monitoring, and secure messaging via the SUTH application integrated with the hospital information system. The control group received standard outpatient care with routine counseling and printed materials; baseline characteristics were similar between groups. Baseline demographic and clinical characteristics were comparable between groups. After 6 months, both groups showed modest reductions in BMI; however, ANCOVA-adjusted analyses indicated no significant between-group difference in post-intervention BMI. Weight-for-height decreased in both groups, with a slightly greater percentage reduction in the telemedicine group. Caregiver satisfaction and knowledge were significantly higher in the telemedicine group at follow-up (all p < 0.01; knowledge p < 0.001). These findings suggest that telemedicine-based care may contribute to modest improvements in anthropometric outcomes while substantially enhancing caregiver knowledge and healthcare service satisfaction, supporting its role as a scalable adjunct in pediatric obesity management. Full article

Nuclear energy has emerged as a crucial technological solution for ensuring energy security and achieving carbon neutrality goals, given its ultra-high energy density and near-zero carbon emissions against the backdrop of rapid socioeconomic development, increasing energy demands, and accelerated global transition toward low-carbon energy structures. As the core component for energy conversion in nuclear reactors, fuel elements critically determine reactor efficiency and safety performance, with the fission product retention capability of silicon carbide layers in multilayer-coated fuel particles having been thoroughly validated through high-temperature gas-cooled reactor irradiation tests. The precise sphericity control of large-sized UO₂ fuel kernels represents a fundamental requirement for enhancing tristructural isotropic (TRISO) fuel particle performance and advancing Generation IV nuclear power plant development. This study presents a sphericity control strategy based on sol–gel processing that synergistically integrates physicochemical regulation of gelling media with multi-field washing flow field optimization. By implementing silicone oil-mediated interfacial tension gradient control, we effectively suppressed gel sphere destabilization while developing an innovative three-phase sequential washing technique involving kerosene washing, anhydrous ethanol interfacial transition, and ammonia solution replacement, which significantly enhanced mass transfer diffusion in stagnant liquid films and revolutionized fuel microsphere washing technology with improved efficiency and quality. Experimental results demonstrate that this integrated approach increases kernel sphericity qualification to 99.8%, reduces washing solution consumption by 79%, and achieves an average sphericity of 1.03. The research establishes a coupling mechanism between gelling media and multi-field washing processes, elucidating the synergistic effect between interfacial tension regulation and washing optimization, thereby providing both theoretical foundations and engineering application basis for the precision manufacturing of high-performance nuclear fuels. Full article

Livestock represent a key asset in the livelihood of smallholder farmers and play a critical role in the social dynamics and nutritional security of resource-poor communities. However, within these resource-poor communities, livestock productivity remains low. This is often due to seasonal changes in the quantity and quality of available feed from the natural veld, which in turn also contributes to methane production. This study aimed to evaluate the effects of supplementing Eragrostis curvula hay with Searsia lancea leaf or silage meal on in vitro fermentation efficiency and gas and methane production. Therefore, an in vitro study using a semi-automated pressure transducer technique was conducted on grass hay alone (control) and grass hay supplemented with 15% or 30% of either S. lancea leaf or silage meal. The dietary treatments were arranged in a complete randomized design, with each treatment replicated four times. Total gas and methane production was recorded at 3, 6, 12, 24 and 48 h using a pressure transducer attached to a data logger. After incubation, samples were collected to determine volatile fatty acids. Supplementing grass hay with 15% S. lancea leaf meal increased gas production by 76%, 52%, 32% and 12% in the first 24 h of incubation. Similarly, increasing the supplementation level to 30% increased gas production by 75%, 63%, 45% and 14%. However, supplementing grass hay with silage meal at 15% significantly reduced gas production by 37% during the first 3 h of incubation, whereas supplementation at 30% had no effect. Supplementing grass hay with S. lancea meals effectively reduced methane production at 24 and 48 h. Grass hay supplemented with 15% or 30% silage meal reduced methane by 46% and 39% at 24 h, while at 48 h, methane was reduced by 39% and 49%, respectively. Supplementing grass hay with S. lancea meals, however, did not affect volatile fatty acids. In conclusion, S. lancea can be strategically used as a supplementary feed source to modulate the rumen ecosystem by attenuating enteric methane production. Further studies are required to determine the effect of S. lancea on rumen microbial composition and its metabolic function. Full article

The fall armyworm (Spodoptera frugiperda) is a major global migratory pest. Its increasing insecticide resistance poses a severe threat to food security. Developing biopesticides such as SfMNPV is critical for sustainable control. Nevertheless, the early molecular mechanisms underlying the S. frugiperda midgut response to oral SfMNPV challenge remain poorly understood. This study utilized high-throughput transcriptome sequencing to systematically characterize the dynamic transcriptional profiles of the larval midgut at 1, 12, and 24 h after oral SfMNPV inoculation. Results showed that the midgut transcriptional response to SfMNPV is time and stage-specific. During this period, the physical midgut barrier underwent remodeling, with core components of the peritrophic matrix downregulated at 1 h, followed by the basal lamina at 12 h, alongside the activation of cytoskeleton genes during 12–24 h. Concurrently, sustained endoplasmic reticulum stress, autophagy, and ubiquitin system responses occurred from 12 to 24 h. At the metabolic level, the defense system exhibited a functional succession, shifting from ABC transporters and UDP-glycosyltransferases at 1 h to glutathione S-transferases and superoxide dismutase at 12–24 h. Additionally, the midgut tissue exhibited a cascade transition from pro-apoptotic signaling at 1 h to compensatory regenerative repair mediated by the Wnt, mTOR, and Hippo pathways at 12–24 h. This study elucidates the molecular process of barrier damage, homeostatic imbalance, and tissue remodeling during early oral SfMNPV challenge. These findings provide a global perspective on baculovirus-host interactions and establish a theoretical foundation for designing novel biopesticides targeting the midgut interaction. Full article

Considerations of gender have long been overlooked in legal discourses and public debates on asylum. In more recent years, the right-wing narrative has taken a strategic U-turn, instead misappropriating gendered concerns including gender-based violence for the purposes of promoting racialised border controls on the grounds of cultural incompatibilities, and by painting refugees as a threat to British values, economy and security. This paper calls out the hypocrisy of such femonationalist framings for overlooking the ways in which Western institutions sustain refugee women’s ongoing vulnerabilities. Drawing from qualitative interviews and focus groups with refugee women survivors of female genital mutilation/cutting (FGM/C), this paper examines the continuities of harm in the lives of women who have fled gender-based persecution to Britain. The paper critically maps the way prolonged state control during the asylum process perpetuates a sense of violence as ongoing, and its damaging implications on survivors striving to navigate life after flight. In doing so, the findings contribute new insights into established scholarship on asylum harms by illuminating the gendered consequences of violence of waiting, and refugee women’s subtle individual and collective strategies to struggle against violent continuums. Full article

Background: The resurgence of Mpox (formerly known as monkeypox) since the 2022 global outbreak has exposed weaknesses in surveillance, diagnosis, and public risk communication systems. Despite increased clinical understanding, limitations in knowledge, attitudes, and practices (KAP) among both healthcare workers (HCWs) and the general population continue to challenge prevention and control measures. Numerous systematic reviews have been published on KAP toward Mpox, yet their findings remain fragmented. This review aimed to consolidate the existing evidence from published systematic reviews to provide a unified understanding of global KAP levels related to Mpox. Methods: We followed the PRISMA guidelines for this systematic review of systematic reviews. The article search was conducted in PubMed, Embase, and the Cochrane Library for systematic reviews published between January 2010 and October 2025. Data was extracted on study design, population, and reported quantitative outcomes. Results: Five studies met the inclusion criteria: three focused on HCWs, while two focused on the general population. Among HCWs, knowledge ranged from 26.0% to 46.7%, and attitudes from 28.2% to 62.2%. In the general population, knowledge ranged from 33.0% to 46.6%, attitudes from 40.0% to 71.9%, and perceptions averaged around 40.0%. Across both groups, Mpox knowledge was limited, attitudes were moderately positive, and preventive behaviors remained consistently low, revealing a persistent gap between awareness and practice. Conclusions: This review highlights persistent gaps in knowledge, attitudes, and practices among HCWs and the general population. Although global attention increased substantially following the 2022 outbreak, important weaknesses remain in translating knowledge into consistent preventive behaviors. Addressing these gaps requires structured and context-specific interventions. Integrating Mpox-focused modules into mandatory Continuing Medical Education credits for HCWs could ensure sustained competency in diagnosis, infection prevention, and outbreak response beyond peak epidemic periods. For the general population, strategic risk communication campaigns should leverage trusted community leaders and social media influencers in high-risk regions to counter misinformation, reduce stigma, and promote evidence-based preventive behaviors. Embedding these targeted strategies within broader pandemic preparedness and global health security frameworks will be essential to strengthening early detection, public trust, and coordinated outbreak response in future Mpox or other emerging infectious disease events. Full article

Asset discovery is a fundamental but inherently flawed capability in cybersecurity, as current methodologies frequently confuse preliminary discovery observations with definitive asset inventories, thereby obscuring uncertainty, restricting auditability, and eroding trust in security-critical decision-making. This work addresses the issue of inconsistent asset identification in dynamic IT settings by presenting an evidence-based architectural paradigm that clearly distinguishes observation, identity resolution, and inventory representation. The principal research aim is to develop and authenticate an architecture that maintains discovery evidence, facilitates deterministic, verifiable identity resolution, and supports interpretable inventory derivation. In contrast to state-centric and model-driven methodologies, the proposed architecture enhances (i) traceability through the preservation of time-scoped, method-attributed observations, (ii) identity continuity amidst dynamic conditions such as IP reassignment and infrastructure modifications, and (iii) auditability by facilitating the reconstruction of inventory claims from foundational evidence. An examined proof-of-concept implementation in a controlled yet realistic network environment shows superior identity stability, greater discovery traceability, and retention of historical context relative to traditional inventory models. The results validate the practicality and architectural benefits of the strategy; nevertheless, the evaluation is constrained by a lack of formalised performance indicators and adversarial robustness, which are recognised as priorities for further investigation. Full article

Message Queuing Telemetry Transport (MQTT) is a lightweight publish–subscribe protocol widely deployed in Internet of Things (IoT) systems. Although MQTT defines authentication and authorization mechanisms, their enforcement accuracy, configuration sensitivity, and operational cost under controlled misconfiguration conditions remain insufficiently quantified. This study experimentally quantifies authentication enforcement behavior and Access Control List (ACL) misconfiguration impact within a standards-compliant MQTT deployment under controlled laboratory conditions. Rather than benchmarking a specific software product, the work measures protocol-defined security behavior—including authentication success rate, false acceptance rate (FAR), false rejection rate (FRR), privilege-boundary preservation, authentication latency, and broker CPU utilization—across systematically constructed operational and failure scenarios. Username/password and mutual TLS authentication were evaluated under valid and stress-induced connection conditions, alongside structured ACL policies incorporating wildcard over-permission. Across repeated trials, username/password authentication achieved higher observed connection reliability (≈0.95), while TLS-based authentication provided stronger cryptographic identity assurance at the cost of increased authentication latency (≈42.6 ms vs. 14.8 ms) and higher CPU utilization (≈23.7% vs. 9.4%). No false acceptances were observed within 100 unauthorized trials per configuration, corresponding to a 95% confidence upper bound of <3% for FAR under a binomial model. Under controlled ACL misconfiguration, 22 of 100 evaluated authorization operations accessed topics beyond the originally intended least-privilege scope, yielding a reproducible privilege expansion rate of 0.22. This expansion resulted from wildcard policy semantics rather than an enforcement malfunction. The results provide controlled empirical quantification of reliability–security trade-offs and configuration-driven privilege-boundary behavior within a standards-compliant MQTT deployment. While the findings reflect enforcement behavior as realized in the evaluated implementation and laboratory environment, the proposed measurement framework establishes reproducible criteria for assessing MQTT security enforcement accuracy under controlled conditions. Full article

In recent years, Large Language Models (LLMs) have demonstrated strong capabilities in contextual reasoning and knowledge retrieval. However, their application in industrial domains is limited by concerns regarding data security, reliance on cloud infrastructure, and high operational costs. To address these challenges, this study proposes the use of the Model Context Protocol (MCP) as a middleware framework that enables the deployment of offline-operable Small Language Models (SLMs) for industrial data processing. MCP facilitates structured interaction between SLMs and external resources (e.g., databases, APIs, and processors), allowing secure and controlled data access without exposing proprietary systems. As illustrated in the proposed framework, user input is first processed by the SLM (Qwen-7B) for intent determination. When external data is required, MCP coordinates the invocation of relevant resources and integrates the returned results into the model. The SLM then generates the final response. This approach enables SLMs to perform local computation for contextual analysis and decision support while maintaining low computational requirements and full data locality. The proposed system eliminates dependence on cloud-based LLM services and enhances security and cost efficiency. Experimental results demonstrate that the MCP-based architecture provides a practical and effective solution for deploying intelligent assistants in industrial environments without relying on large-scale external AI services. Full article

The increasing deployment of Internet of Things (IoT) devices introduces significant security challenges, while privacy concerns limit centralized data aggregation for intrusion detection. Federated learning (FL) offers a decentralized alternative, yet the interaction between feature representation, model architecture, and data heterogeneity remains insufficiently understood in IoT malware detection. This study provides a controlled comparative analysis of centralized and federated learning, optionally using amino acid encoding, under IID and Non-IID conditions using a 10,000-sample subset of the CTU–IoT–Malware–Capture dataset. First, we evaluate raw tabular features versus amino acid-based feature encoding, followed by a lightweight multi-layer perceptron (2882 parameters) versus a deeper residual network (70,532 parameters), across binary and multi-class classification tasks. In the binary setting, centralized training achieved up to 98.6% accuracy, while federated IID training reached 98.6%, with differences within statistical variance. Under Non-IID conditions, performance decreased modestly (0.1–0.5 percentage points), and accuracy was consistently lower when using encoded features compared with raw features. The degradation is smaller in deeper architectures and may offer improved stability under highly skewed federated conditions. In the four-class setting, the complex network achieved up to 97.8% accuracy with raw features, while amino acid encoding achieves up to 93.3%. The results show that federated learning can achieve performance comparable to centralized training under moderate heterogeneity, that lightweight architectures are sufficient for low-dimensional IoT traffic features, and that feature compression via amino acid encoding does not inherently mitigate Non-IID effects. These findings clarify the relative impact of representation, heterogeneity, and architectural capacity in practical FL-based IoT intrusion detection systems. Full article