Search Results (7,156)

Additive manufacturing (AM) has emerged as a transformative manufacturing technology for producing complex components with unprecedented design flexibility. However, the widespread application of AM parts in tribological environments is often limited by inherent defects such as high surface roughness, porosity, residual stresses, anisotropy, and weak interlayer bonding, which adversely affect friction, wear resistance, and tribocorrosion performance. This review critically examines the tribological behavior of AM materials and components, emphasizing the influence of processing routes, material selection, secondary reinforcing phases, and microstructural evolution on tribological performance. Particular attention is given to surface engineering strategies, including thermal spray coatings, laser surface treatments, plasma electrolytic oxidation, vapor deposition technologies, and mechanical surface modification techniques for mitigating AM-induced defects and improving surface durability. Recent advances in machine learning (ML) and artificial intelligence (AI) for wear prediction, process optimization, and intelligent tribological monitoring are also discussed. The review highlights the relationships among manufacturing parameters, surface integrity, and wear mechanisms, while identifying key challenges associated with process variability, long-term reliability, and industrial implementation. Future research should focus on multifunctional surface systems, smart coatings, real-time condition monitoring, and data-driven design approaches to accelerate the deployment of tribologically optimized AM components in aerospace, biomedical, automotive, and energy applications. Full article

Uncontrolled hemorrhage remains a major challenge in trauma care and surgical interventions, where rapid hemostasis and wound sealing are essential for improving patient survival. Natural polymer-based hydrogels have emerged as promising hemostatic materials owing to their excellent biocompatibility, biodegradability, and biomimetic properties. However, their clinical translation remains limited by insufficient mechanical robustness, wet adhesion, and functional responsiveness. To address these challenges, considerable progress has been achieved through rational material design and structural engineering strategies. Representative natural polymers, particularly polysaccharides and proteins, exhibit distinct physicochemical and biological characteristics that determine their hemostatic mechanisms and design strategies. Based on these material platforms, molecular modification strategies, including charge regulation, hydrophobic modification, and bioactive functionalization, have been widely employed to modulate interfacial interactions, platelet adhesion, coagulation activation, and tissue adhesion. In parallel, advanced structural architectures, such as porous, particulate, fibrous, multicrosslinked/multinetwork, and nanocomposite systems, have significantly enhanced fluid absorption, mechanical resilience, stress dissipation, and hemorrhage sealing efficiency. Beyond conventional hemostasis, increasing efforts have focused on integrating multifunctional properties, including antibacterial activity, inflammatory regulation, oxidative stress modulation, tissue regeneration, dynamic monitoring, and stimuli-responsive behaviors. This review systematically summarizes recent advances in natural polymer-based hemostatic hydrogels from the perspectives of advanced material modification strategies, structural engineering approaches, and functional integration, with particular emphasis on the relationships among material characteristics, interfacial behavior, structural organization, and hemostatic performance. Finally, current challenges and future perspectives for clinical translation are discussed, aiming to provide valuable insights for the rational design and clinical implementation of next-generation hemostatic biomaterials. Full article

The growing reliance on cyber deception as a defensive mechanism has revealed persistent limitations in existing deception infrastructures, particularly in their ability to scale, adapt, and provide continuous observability under realistic adversarial workloads. Conventional honeypot deployments are predominantly monolithic and statically configured, which constrains their responsiveness to dynamic attack conditions and limits their applicability in contemporary distributed environments. This work presents a microservice-oriented cyber deception platform that reconceptualizes deception infrastructure as a composition of loosely coupled, independently deployable services. The platform integrates containerized honeypots, a lightweight API-driven orchestration layer, and a centralized telemetry pipeline to enable rapid instantiation, dynamic reconfiguration, and high-resolution monitoring of attacker interactions. Unlike prior approaches that treat deployment, orchestration, and monitoring as separate concerns, the proposed design explicitly unifies these components within a single, measurable system architecture. To support principled reasoning about system behaviour, the paper introduces first-order analytical models that characterize deployment latency, resource utilisation, telemetry throughput, and operational cost as functions of attacker concurrency. These models are not intended as exact predictors, but as tractable abstractions that enable interpretation of system performance and guide capacity planning. Model parameters are empirically derived and validated through controlled experimentation. Evaluation is conducted within a reproducible cyber-range environment using scripted adversarial workloads that emulate reconnaissance, authentication attempts, and sustained interactive sessions. The results indicate that containerised deployment reduces instantiation latency to approximately 1.2 s under warm-start conditions, compared to tens of seconds for virtual machine-based baselines. Resource utilisation exhibits approximately linear scaling under moderate concurrency, while the telemetry pipeline sustains ingestion rates exceeding 18,000 events per minute without observable loss. Stress testing further reveals that telemetry processing, rather than orchestration, constitutes the primary scalability bottleneck. These findings suggest that microservice-based architectures can provide a viable and extensible infrastructure substrate for cyber deception, supporting both operational deployment and integration with higher-level adaptive and learning-based defence mechanisms. The contribution of this work lies not in introducing new deception strategies, but in enabling their practical realisation through a scalable and observable system design. Full article

Asthma is a prevalent, long-term inflammatory airway condition that is difficult to diagnose and treat because there is no single reliable diagnostic test. Misdiagnosis is therefore common, with rates as high as 73% in juvenile groups and up to 35% in adult populations. This ultimately exacerbates their illness by postponing therapy for some people and administering needless medication to others. Although well-known biomarkers such as blood eosinophils and fractional exhaled nitric oxide, as well as conventional diagnostic techniques such as spirometry, have improved clinical assessment, they are nevertheless constrained in many healthcare settings by limited availability, high cost, and inconsistent use. Furthermore, these indicators primarily reflect type-2 inflammation and are less useful for non-type-2 asthma, highlighting the need for more comprehensive, readily accessible diagnostic techniques. Identifying novel biomarkers of oxidative stress, metabolic alterations, and airway inflammation, including volatile organic compounds and redox-related chemicals, has been the focus of recent studies. These biomarkers offer opportunities for improved disease phenotyping and non-invasive detection. Simultaneously, advances in biosensor technology have enabled highly sensitive platforms to rapidly detect these biomarkers at low concentrations. In particular, optical biosensors are becoming more and more popular due to their ability to do real-time detection without the need for labels and their ease of miniaturization for point-of-care devices. This work summarizes traditional diagnostic tools alongside existing information on asthma phenotypes and clinically important biomarkers, and discusses advanced biosensors ranging from electrochemical to optical systems, including recent developments in nanomaterial-enhanced optical biosensing techniques. The importance of artificial intelligence and smartphone-integrated hardware is also covered, along with the main challenges that need to be overcome for these technologies to become useful clinical tools for asthma diagnosis and monitoring. Full article

Drug repurposing represents a rational and resource-efficient strategy to expand the oncological armamentarium by leveraging the established pharmacology, clinical experience, and safety-monitoring frameworks of approved non-oncological agents. Clozapine (CZP), an atypical antipsychotic characterized by broad receptor pharmacology, complex biotransformation, and clinically relevant toxicological liabilities, has emerged as a candidate of interest following preclinical evidence of context-dependent anticancer activity across multiple tumor types. As such, CZP provides an informative case study at the interface between therapeutic drug action and xenobiotic behavior. This review provides a critical and integrated synthesis of the current evidence supporting the repurposing of CZP in oncology, with particular emphasis on the relationship between its molecular mechanisms, dose–exposure requirements, pharmacological complexity, and potential toxicity. Analysis of in vitro and in vivo studies across glioblastoma, non-small cell lung cancer, breast cancer, and melanoma brain metastasis models indicates that CZP can impair tumor cell proliferation and survival through a form of mechanistic plasticity. Rather than acting through a single conserved pathway, CZP appears to disrupt shared upstream processes related to pro-survival signaling, cellular stress tolerance, and metabolic homeostasis, while engaging tumor-specific downstream responses, including autophagic cell death, mitochondria-dependent apoptosis, oxidative stress, and coordinated modulation of survival and angiogenic pathways. Despite this mechanistic rationale, translation remains substantially constrained, most notably by the order of magnitude gap between anticancer-effective concentrations in vitro and clinically achievable plasma exposures, requiring careful distinction between potentially useful anticancer pharmacology and nonspecific xenobiotic-induced cellular stress and clinically unacceptable toxicity. Key limitations include the discrepancy between anticancer-effective concentrations observed in vitro and exposures achievable during standard psychiatric dosing, the limited understanding of how CZP metabolism and metabolite formation may influence efficacy and toxicity, the absence of integrated pharmacokinetic–pharmacodynamic and toxicokinetic modeling, and the lack of dedicated clinical trial evidence. To address these challenges, this review examines complementary translational strategies, including patient-derived organoids, co-culture systems, microphysiological platforms, pharmacokinetic and toxicological modeling, and computational digital twin frameworks. Together, these approaches may support a biologically informed and risk-aware evaluation of CZP, helping to identify responsive tumor contexts, anticipate exposure-related liabilities, and prioritize rational combination strategies. By integrating therapeutic potential with xenobiotic pharmacology and toxicology, this review positions CZP within the evolving landscape of precision oncology and evidence-driven drug repurposing. Full article

Neuropeptide Y (NPY) is a stress-modulating neuropeptide and a promising biomarker for non-invasive assessment. Herein, a sensitive electrochemical aptasensor was developed on reduced graphene oxide/gold nanoparticle (rGO/AuNP)-modified screen-printed electrodes for selective NPY detection. A methylene blue (MB)-labeled NPY-specific aptamer was immobilized on the electrode surface through Au–S chemistry, and square-wave voltammetry (SWV) was used for signal readout. The rGO/AuNP-modified interface provided high conductivity and a large effective surface area, facilitating electron transfer and probe immobilization. Under optimized conditions, the aptasensor exhibited a linear detection range of 10–10,000 pg mL⁻¹ in PBS with a low detection limit of 1.17 pg mL⁻¹ and good linearity (R² = 0.991). In addition, the sensor showed satisfactory selectivity, reproducibility, and mechanical stability. Recovery tests in artificial sweat yielded recoveries of 91.8–107.8% with relative standard deviations below 5%, demonstrating good analytical accuracy in complex matrices. Combined with an agarose-hydrogel-assisted sampling interface and a reverse-iontophoresis-compatible wearable platform, this low-cost and facile sensing strategy provides a portable proof-of-concept approach for NPY analysis in artificial sweat and shows potential for future wearable-oriented biofluid monitoring. Full article

Mature thin oil reservoirs remain pivotal to maintaining reserves, sustaining production, and enhancing profitability due to their substantial annual output and untapped recovery potential. However, prolonged development leads to compromised fracturing efficacy, manifesting as severe formation-energy depletion, rapid production decline, and short effective periods of stimulation measures. Energy-storage fracturing technology addresses these challenges through fluid-injection energization and imbibition displacement, thereby replenishing formation energy and mobilizing residual oil. Leveraging a geo-engineering integrated platform, this study establishes an inverted seven-spot well-pattern energization model to systematically investigate pore pressure–stress field evolution and dynamic responses under varying energization parameters, including energy-storage injection rate, energy-storage volume, and energy-storage sequence. Key findings include: (1) increasing the energy-storage injection rate from 1.5 m³/min to 3.5 m³/min elevates average pore pressure by 7.8 MPa, with minimum and maximum horizontal principal stresses increasing by 1.4 MPa and 1.7 MPa, respectively; (2) raising the energy-storage volume from 2800 m³ to 4200 m³ enhances pore pressure by 5.5 MPa, accompanied by 2.5 MPa and 2.6 MPa increments in minimum and maximum horizontal principal stresses; (3) simultaneous energizing of all injection wells (1–6) is identified as the optimal injection sequence, yielding the highest average pore pressure of 40.3 MPa at equivalent monitoring positions within the well group, with corresponding average minimum and maximum horizontal principal stresses of 55.3 MPa and 60.3 MPa, respectively. The results provide theoretical and technical support for optimizing energy-storage fracturing strategies in mature thin oil reservoirs. Full article

Agricultural production in arid regions is strongly constrained by water stress, making timely evaluation of crop water conditions increasingly important. However, conventional measurements of plant moisture content (PMC) primarily rely on destructive oven-drying methods, which are not only labor-intensive and time-consuming but also constrained by limited sample size and spatial coverage. These shortcomings make it difficult to capture the spatial heterogeneity of crop water status across large agricultural regions, thereby restricting regional-scale water diagnosis and precision irrigation decision-making. Focusing on silage maize cultivated in the arid region of Gansu Province, China, this work develops a regional PMC estimation approach by combining multi-source remote sensing data. High-resolution unmanned aerial vehicle (UAV) observations were integrated with Sentinel-2 and Sentinel-3 imagery, while radiometric and temperature corrections were applied to improve data consistency. A set of spectral, textural, and thermal features was derived from multispectral, visible, and thermal infrared datasets. Feature selection based on Pearson correlation was then carried out, followed by the construction of three models, namely Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). Among them, the RF model performed more reliably, achieving a validation R² of 0.92 with relatively low prediction error. In addition, calibration using UAV data led to a clear improvement in satellite-based estimates, with R² increasing from 0.52–0.62 to 0.71–0.74. The generated PMC maps captured both the temporal decline during the growing season and the spatial variability across the study area. Overall, the proposed approach offers a practical option for large-scale monitoring of crop water status and can support irrigation management in water-limited environments. Full article

Ginseng Alternaria leaf and stem blight (GALSB), caused by Alternaria alternata, poses a severe threat to ginseng cultivation. Although azoxystrobin is a cornerstone fungicide for GALSB management, the emergence of widespread adaptive resistance has severely curtailed its field efficacy. This study integrated molecular, transcriptomic, and genetic approaches to unravel the underlying resistance mechanisms. Targeted gene sequencing and molecular docking revealed that resistant strains harbor a conserved G143A point mutation in the AaCyt b protein. This mutation weakens the azoxystrobin–AaCyt b protein binding affinity by elevating the binding energy from −8.31 to −7.08 kcal/mol. Additionally, comparative transcriptomics and RT-qPCR demonstrated pronounced upregulation of the alternative oxidase gene (AaAOX) and core energy metabolism pathways in resistant strain TYC8-2, with AaAOX expression increasing 4.45–6.91-fold. Fungicidal inhibition of AOX via salicylhydroxamic acid (SHAM) restored fungal sensitivity, increasing azoxystrobin sensitivity by 11.66-fold. Crucially, genetic knockout of AaAOX enhanced sensitivity by approximately 2.7 × 10⁴-fold. Phenotypic assays further established AaAOX as a multifunctional regulator; the AaAOX mutant exhibited attenuated virulence on ginseng leaves and increased sensitivity to oxidative and osmotic stresses (NaCl, H₂O₂, NaAc). The G143A mutation in AaCyt b and the transcriptional overexpression of AaAOX contribute independently to drive azoxystrobin resistance in A. alternata. These findings provide comprehensive mechanistic insights to guide resistance surveillance, rational fungicide application, and precision prevention of GALSB in ginseng cultivation. We conclude that the G143A mutation in AaCyt b and the transcriptional overexpression of AaAOX act independently to drive azoxystrobin resistance in A. alternata. These findings provide comprehensive mechanistic insights to guide resistance monitoring, optimize fungicide applications, and develop precision strategies for GALSB management. Full article

Student mental health, stress, and success are interconnected, yet the mechanisms linking them remain insufficiently understood. Drawing on Stress Optimization and Self-Regulated Learning (SRL) theories, this study examined how stress appraisals and learning practices jointly contribute to student mental health and academic functioning in post-secondary students, supporting a view of student success that comprises both feeling well psychosocially and functioning well academically. Using a sample of 226 university students, the study replicated prior work on the predictive roles of coping self-efficacy (CSE) and stress mindset (SM) across indicators of student success, including flourishing mental health, motivation-related challenges, social-emotional challenges, and GPA. It extended this work by testing whether metacognitive monitoring and adaptation, and academic social engagement, mediated these relationships. Results showed that neither CSE nor SM significantly predicted GPA, suggesting that stress appraisals alone may be insufficient to explain academic achievement. However, both CSE and SM significantly predicted flourishing mental health, and CSE was additionally associated with fewer motivation-related and social-emotional challenges. Mediation analyses indicated that metacognitive monitoring partially explained the relationship between CSE and reduced motivation challenges, while academic social engagement mediated relationships between stress appraisals and social-emotional challenges and mental health. Findings underscore the value of integrating psychosocial and educational perspectives in promoting student success. Full article

Reservoir water level fluctuations can reactivate landslides and cause severe losses. This study examines the Niulanjiang landslide, reactivated by the impoundment of the Xiluodu Hydropower Station in Southwest China, using field investigations, in situ displacement monitoring, and direct shear tests on soil–rock mixtures. The results show that the land-slide experienced a progressive failure process, evolving from long-term shear creep in the sliding zone to localized abrupt creep and finally to overall fracture sliding. The loose soil–rock mixture provided the structural basis for instability, whereas reservoir water level fluctuation was the dominant trigger. Rising water levels increased shear stress and promoted seepage-induced weakening, causing local failure of the sliding surface and gradual formation of a shear outlet. Laboratory tests indicate that rock block content and moisture content strongly affect mechanical behavior: higher rock block content enhances shear dilatancy and strain softening, while higher moisture content promotes shear contraction, plastic deformation, and linear reductions in cohesion and internal friction angle. The failure mechanism involves coupled strength degradation and increased seepage force. Initial instability occurred in the middle slope under hydrostatic–hydrodynamic pressure, then propagated rearward and forward, reducing front resistance and driving overall sliding toward the Niulanjiang River. These findings support early warning and mitigation of similar reservoir-induced landslides. Full article

Standardized drought indices such as the Standardized Precipitation Index (SPI) are widely used in both monitoring and climate-change impact assessments. However, SPI values are not uniquely defined unless the reference climatology used for standardization is explicitly stated and justified−a methodological issue that becomes critical under non-stationary climate conditions. Here, we present a methodological assessment of how reference-climatology strategy affects SPI-based drought projections under climate change, using Türkiye’s 26 major basins as a hydroclimatically diverse testbed. These assessments inform sustainable water resources planning, agricultural adaptation, and climate-resilient infrastructure design under non-stationary climate. Daily precipitation projections from 56 GCM-RCM pairs (EURO-CORDEX EUR-11, 0.11° (approximately 12 km at the mid-latitudes of the study domain); CMIP5 RCP8.5) were bias-corrected against ERA5-Land and aggregated to basin means. We computed SPI-9 and compared two commonly used reference strategies: (i) a fixed historical baseline (1970–2005), applied consistently to both historical and future periods (fixed-baseline SPI); and (ii) a period-specific baseline (period-specific SPI; future SPI values are standardized to the climatology of the future evaluation period itself). Using the same climate simulations, the two strategies yield markedly different drought projections. At the country scale, end-of-century drought time reaches 458 months under the fixed-baseline strategy, whereas the period-specific strategy indicates 393 drought months. Corresponding severity summaries are likewise stronger under fixed-baseline standardization. The contrast is even stronger in several Mediterranean basins, where fixed-baseline standardization produces persistently severe drought conditions. These results show that SPI-based drought projections are substantially sensitive to the choice of reference-climatology strategy, and that the same climate ensemble can support materially different drought narratives depending on how anomalies are standardized. Because the two strategies differ in both reference-timing and calibration-window length (36 versus 95 years), the headline contrast should be interpreted as a combined effect rather than as a pure baseline-timing result. In the present implementation, the period-specific strategy uses a single future calibration period (2006–2100), so the comparison should be interpreted as a stress test of reference framing under non-stationary climate rather than as an equal-length baseline experiment. An equal-length late-baseline sensitivity check (1970–2005 versus 2065–2100; both spanning 36 years) shows that the fixed-to-late-baseline contrast is larger than the fixed-to-period-specific contrast in 25 of 27 spatial units, including a 3.0-fold amplification at the national scale, indicating that the reference-timing effect persists when calibration-window length is held constant. Because the analysis is based on a CMIP5-driven RCP8.5 ensemble, the numerical projections should be interpreted as a high-end stress-test envelope rather than as the most likely outcome. We therefore recommend that drought projection studies explicitly report the reference-climatology strategy, justify the calibration window, and distinguish between analyses designed to quantify change relative to a historical climate and analyses designed to describe anomalies relative to an evolving future climate. These methodological choices have direct implications for sustainable water resources management and drought-risk preparedness in water-stressed Mediterranean systems, and contribute to broader sustainability targets such as Sustainable Development Goal 6 (Clean Water and Sanitation), SDG 13 (Climate Action), and SDG 15 (Life on Land). Full article

Background: Very-low-birth-weight (VLBW) infants require ongoing medical follow-up and coordinated family support after discharge due to their immature physiological development and a high risk of complications. Fragmented transitional care and caregiver burden may compromise follow-up adherence and infant health outcomes. This study aimed to describe the implementation, feasibility, and service-level outcomes of a multidisciplinary transitional home care program designed to support continuity of care and family-centered transitional support for high-risk infants through a retrospective descriptive program evaluation. Methods: Since 2022, our hospital has implemented a government-supported transitional home care program for low and VLBW infants. A multidisciplinary team provided individualized discharge planning, risk stratification, home-based follow-up, telehealth consultations, developmental monitoring, caregiver education, and psychosocial support. Program outcomes were evaluated using enrollment coverage, follow-up completion, developmental assessment attendance, caregiver stress scores, and service utilization. Results: From 2022 to September 2025, enrollment coverage reached 97.7–100% for infants ≤ 1500 g and 100% for preterm infants > 1500 g. A total of 949 video consultations and 2168 telephone or in-person follow-ups were conducted, totaling 3117 service encounters. Developmental assessment attendance rates reached 95%, 93%, and 88% at scheduled corrected-age intervals. Mean caregiver stress scores showed favorable observational trends, decreasing from 14.64 to 10.81. Fifty-two referrals to social resources enhanced service accessibility and family support. Conclusions: This multidisciplinary transitional home care program demonstrated high enrollment coverage and sustained follow-up engagement within a tertiary medical center setting. The findings support the feasibility and potential applicability of integrated and family-centered transitional care models in supporting continuity of care and caregiver support for high-risk infants after discharge. Due to the descriptive retrospective design and absence of a control group, causal relationships cannot be established. Full article

Hydrogen peroxide (H₂O₂) is a key reactive oxygen species involved in both cellular signaling and oxidative stress, making its reliable detection essential in biological and environmental systems. Electrochemical sensing has emerged as a promising approach for H₂O₂ monitoring due to its high sensitivity, rapid response, and suitability for in situ analysis. This review provides a comprehensive overview of nanostructured metal oxide electrodes for non-enzymatic electrochemical detection of H₂O₂. The effects of material composition, nanostructure morphology, and synthesis strategies (particularly hydrothermal methods) on sensor performance are critically discussed. Special attention is given to our previously reported studies, enabling a consistent comparison of structure–property relationships under similar experimental conditions. Furthermore, the application of these sensors in plant stress analysis is examined, including both the monitoring of oxidative stress and the evaluation of stress mitigation strategies using metal oxide nanoparticles. The role of nanoparticles as reactive oxygen species scavengers and enhancers of plant antioxidant systems is highlighted, demonstrating their ability to reduce H₂O₂ levels and improve plant physiological status under adverse environmental conditions. Overall, this work emphasizes the dual functionality of nanostructured materials as both sensing platforms and active agents for stress mitigation, highlighting their potential in agricultural and environmental applications. Full article

As the pace of modern life continues to accelerate, the pressure participants face is growing, and mental health issues are becoming increasingly prominent. Against this backdrop, meditation, as a proven method for stress relief and relaxation, has garnered widespread attention. However, many people face challenges during meditation, such as difficulty entering a meditative state quickly or achieving sub-optimal outcomes. This is particularly true for beginners, who often struggle to accurately gauge the rhythm of meditation and thus fail to fully harness its regulatory effects on both body and mind. To address these issues, this study proposes a handheld meditation device. By making contact with the body via sensors, the device can measure multiple physiological metrics in real time, including skin conductance, electromyography, and heart rate. Based on these measurements, the device can monitor the user’s emotional fluctuations in real time. When emotional changes are detected, it uses the data to play music, release specific scents, or adjust lighting ambiance, thereby dynamically regulating the user’s psychological state. This helps users better immerse themselves in a meditative state and effectively enhances the benefits of meditation. This paper provides an in-depth analysis of the device’s design principles, detailing its hardware components—including various sensors and emotional regulation modules—and explaining the operational logic of its software algorithms. The effectiveness and reliability of the device were verified through rigorous experiments. The study also thoroughly examines the application prospects and potential value of this handheld meditation device, exploring new approaches and methods for the development of meditation technology and related equipment. Full article