Search Results (46,418)

Rapid urbanisation is threatening agriculture in major cities worldwide. In the Greater Bandung Area (GBA), large-scale conversion of agricultural land into built-up areas has occurred over recent decades. Therefore, this study aimed to understand the rural–urban transition and its control in the agricultural context over the last 20 years. The methods adopted were multitemporal analysis of land cover change (2003–2023), calculation of the sub-district development index (SDI) (2005–2014–2021), spatial clustering analysis, and assessment of the level of agricultural land control. The results showed a transformation of GBA’s spatial structure from a monocentric growth pattern to a polycentric configuration, with the peri-urban zone within a 10–20 km radius evolving as a high-performance area. This shift has diminished the dominance of the traditional city centre and produced a pronounced “donut effect”. An integrated analysis of SDI and spatial clustering identified three interrelated functional zones, namely urban, peri-urban, and rural, forming a continuous spatial gradient. The peri-urban area functioned as a dynamic interface where agricultural activities coexisted and competed with urban expansion pressures. These results outlined the need for context-specific and differentiated planning methods, supported by selective spatial control to guide metropolitan transition toward balanced and sustainable development. Full article

We propose a new nonparametric estimator of the conditional mode in a regression framework where the covariates are functional in nature. The estimator is constructed through a quantile regression approach, which provides a robust alternative to classical density-based procedures. It is well documented that employing the $L^1$-structure in quantile regression, the estimation procedure improves robustness properties, particularly resistance to outliers and heavy-tailed error distributions. This feature makes the $L^1$estimation of the conditional mode more stable and reliable in complex and high-variability functional data settings. The main objective of this paper is to establish strong consistency, with explicit convergence rates, for the associated kernel estimators, uniformly over a range of bandwidth parameters. The latter is developed under general regularity conditions involving the concentration distribution of the functional regressor, smoothness assumptions on the structural components of the model, and entropy conditions ensuring adequate control of the functional class complexity. Uniformity in bandwidth is essential both from a theoretical and practical issues, as it guarantees stability of the estimator under data-driven smoothing parameter selection. Beyond its theoretical contribution, this paper has direct implications for applied statistics. Specifically, it provides mathematical support for the automatic bandwidth selection procedures in the high-dimensional data context. Furthermore, the main theoretical novelty is highlighted through simulation experiments and applications to real data. Full article

Cyberworthiness extends the concept of cybersecurity by evaluating whether systems and networks can perform their intended functions securely while maintaining protection against cyber threats. In corporate environments, cyberworthiness aims to ensure security, operational resilience, and trustworthiness across interconnected business processes and digital infrastructures. Modern organisations increasingly rely on complex cyber–physical and information systems, where vulnerabilities in software, networks, and devices can introduce significant operational and security risks. Cyberworthiness, therefore, encompasses security controls, risk management practices, and compliance with recognised cybersecurity standards and governance frameworks. It supports the assessment of information technology components and their exposure to both known and emerging cyber attacks, enabling organisations to evaluate system robustness and operational continuity. While cyberworthiness has historical foundations in system assurance and dependability, it also provides a conceptual basis for contemporary cyber resilience strategies. This paper discusses the concept of cyberworthiness in corporate organisations and identifies potential pathways for its practical implementation. It analyses existing cybersecurity standards and governance frameworks to support structured cyberworthiness assessment. This study presents a structured comparative review of fifteen cyberworthiness-relevant standards, supported by a Source Quality Appraisal Framework, a Framework Selection Guide specifying when each standard should be preferred and where conflicts arise, and a five-dimensional Cyberworthiness Assessment Readiness Model (CARM), a directional self-assessment instrument. The Efficient Automatic Safety and Security Assurance (EASSA) concept is proposed as a direction for future research, not a validated deployed system. Ensuring cyberworthiness remains challenging due to automation limitations in all reviewed standards, evolving threat landscapes, and governance complexity, requiring organisations to adopt integrated and measurable approaches to safeguard their digital assets and operational systems. Full article

This study investigates the effect of the gas mixture composition ratio (Ar:N₂:O₂) during magnetron sputtering on the morphology, phase composition, and visual characteristics of TiN_xO_y thin films. Five different modes were used with a variable N₂:O₂ ratio ranging from 0.5 to 3. The resulting coatings exhibited noticeable differences in color—from golden to dark blue—which correlates with changes in chemical composition and phase state. The morphology of the films, examined by scanning electron microscopy (SEM), varied from a dense to a columnar structure. These results demonstrate that the properties of TiON coatings can be controlled by adjusting the N₂:O₂ ratio: nitrogen-rich conditions promote denser coatings with higher hardness and improved wear resistance, whereas a balanced N₂:O₂ ratio enhances coating adhesion to the substrate. Full article

Artificial intelligence-based management systems are becoming increasingly embedded in labor processes, particularly in platform-mediated work. While existing research has shown that algorithmic management intensifies data-driven control, opacity, and performance monitoring, less attention has been paid to how algorithmic decision-making reshapes the institutional conditions of representation, negotiation, and accountability in employment relations. This article examines how AI-based management may reconfigure workplace conflict by translating managerial decisions into “system outputs” and narrowing the extent to which disputes remain institutionally addressable and negotiable. Drawing on a qualitative case study of platform-based motorcycle couriers in Türkiye, the analysis is based on semi-structured, decision-moment-focused interviews with 19 couriers and 5 representation actors. Rather than testing a full causal model or advancing a universal claim about algorithmic management, the article traces recurring processual linkages among the technicalization of decision-making, epistemic opacity, weakened addressability, and the thinning of representational intervention. The findings suggest that, in the Turkish platform courier context examined here, representationless governance appears as an empirically observable pattern where consequential algorithmic decisions intersect with limited transparency, fragmented appeal channels, income-sensitive sanctions, and constrained collective representation. In this configuration, decision-making remains procedurally dense yet substantively difficult to contest through identifiable, accountable, and negotiable channels. The article argues that the social sustainability of labor governance depends not only on efficiency, flexibility, or access to work, but also on whether decisions affecting workers’ livelihoods remain intelligible, contestable, attributable, and open to institutional negotiation. Full article

Background: Transitioning from hospital to home presents substantial challenges for patients with chronic obstructive pulmonary disease (COPD), often leading to difficulties maintaining self-management, functional independence, and psychological well-being after discharge. Although transitional care programs are increasingly implemented, their effects on multidimensional patient-centered outcomes remain insufficiently examined. This study aimed to evaluate the effectiveness of a nurse-coordinated transitional care service for patients with COPD during the transition from hospital to home and to examine its broader implications for improving continuity of care and patient-centered outcomes within the healthcare system. Methods: This randomized controlled trial was conducted in three university hospitals in South Korea between November 2022 and December 2024. A total of 465 patients were randomly assigned to either a nurse-coordinated transitional care intervention group or a usual care group. The intervention included structured self-management education during hospitalization, post-discharge home visits, and follow-up telephone consultations during the first month after discharge. Outcomes were assessed at baseline, 1 month, and 3 months. Statistical analyses included linear mixed-effects models for continuous outcomes and chi-square tests and independent t-tests for group comparisons. Results: Patients in the Transitional Care Group (TCG) showed marked improvements: disease awareness increased from 27.9% to 94.3% (vs. 35.7% in the Usual Care Group [UCG], RR = 2.64, 95% CI: 2.19–3.18, p < 0.001) and exercise adherence to 76.3% (vs. 43.0%, RR = 1.78, 95% CI: 1.49–2.11, p < 0.001). After adjusting for age, cognitive function declined in both groups but showed significantly smaller decreases in the TCG than in the UCG at 3 months (mean difference = −0.92, p < 0.001), and IADL demonstrated significantly better preservation in the TCG (mean difference = −1.77, p < 0.001). Self-efficacy declined in both groups but remained significantly higher in the TCG (mean difference = 2.65, p < 0.001). Anxiety and depression were significantly reduced in the TCG compared with the UCG (anxiety: −1.45, p < 0.001; depression: −2.72, p < 0.001). After adjusting for age, discharge preparedness and post-discharge management capacity were significantly higher in the TCG than in the UCG (adjusted mean differences = 3.25 and 4.93, respectively; both p < 0.001). Conclusions: These findings indicate that nurse-coordinated transitional care enhances patients’ self-management capacity and improves patient-centered outcomes during the transition from hospital to home. Full article

Background and Objectives: Cardiovascular disease (CVD) is the leading cause of death globally. The World Health Organization has called for investigations into traditional systems of medicine for CVD prevention. Ayurveda includes a classical herbal formulation called Maharishi Amrit Kalash (MAK) traditionally used for disease prevention, health promotion and healthy aging. The study objective was to evaluate MAK effects on biomarkers of vascular function and structure compared to vitamin C and E supplementation in a high CVD risk population. Materials and Methods: In this double-blind randomized controlled trial, 138 Black men and women (mean age 65 ± 7 years) with established CVD or high CVD risk were assigned to either MAK (n = 46), vitamin C/E (n = 46), or placebo (n = 46) for 12 months. The primary outcomes were change in brachial artery reactivity testing (BART) with flow-mediated dilation (FMD, endothelium-dependent) and nitroglycerin-mediated dilation (NMD, endothelium-independent). Other outcomes included carotid intima-media thickness (cIMT), blood pressure, and serum lipids. ANCOVA and pairwise comparisons were performed. Results: After 12 months of intervention, the MAK group demonstrated significant improvement in BART-NMD compared to placebo (mean change + 4.18% vs. +2.95%, p = 0.018) and numerical but non-significant improvement compared to the +3.32% mean change for the Vitamin C/E group (p = NS). There were no significant group differences for BART-FMD, cIMT, blood pressure, and lipids. Intervention compliance ranged from 70–80%. Conclusions: In this randomized controlled trial, 12 months of MAK supplementation improved endothelium-independent vascular smooth muscle function (BART-NMD) in Black adults at high CVD risk. The MAK group achieved a mean BART-NMD of approximately 15.6%, reaching the established threshold for normal vascular smooth muscle function. This selective improvement in smooth muscle responsiveness without changes in endothelial function, vascular structure, or conventional risk factors suggests MAK may influence specific pathways relevant to vascular aging. Larger studies with clinical outcomes are needed to further evaluate this effect on cardiovascular health in aging and high-risk populations. Full article

Combined functional training (FT), high-intensity interval training (HIIT) and aquatic exercise may improve health-related fitness in aging populations; however, the influence of baseline adiposity on training responses remains unclear. This study evaluated the effects of a 12-week multicomponent training program on aerobic capacity, body composition, metabolic health, and physical performance in middle-aged and older women and explored whether baseline body fat percentage modulated these responses. Thirty-four women (50–72 years) were assigned to a control group (Ctrl, n = 10) or an exercise group, stratified into normal fat (NF%, n = 10) and high fat (HF%, n = 14). The intervention included three weekly 60 min sessions consisting of HIIT, FT, and aquatic-based interval and resistance exercises, while controls maintained their habitual lifestyle without structured exercise. Significant improvements were observed in VO₂max, skeletal muscle mass, fasting insulin, triglycerides, total cholesterol, HDL cholesterol, and functional performance. Baseline adiposity influenced metabolic adaptations, with greater improvements in the HF% group. These findings suggest that multicomponent training may improve cardiometabolic health and physical performance; however, the results should be interpreted cautiously due to the quasi-experimental design and small sample size. Full article

Beyond gas transport, red blood cells (RBCs) have emerging roles regarding innate immunity, regulating blood flow, and participating in nutrient transport, which can be engineered as drug delivery systems since they contribute to maintaining water homeostasis. Following extensive thermoanalytical studies of human blood plasma, our working group investigated the properties of RBCs, examining their role in healthy and in different disease states by using differential scanning calorimetry (DSC) and the deconvolution of the resulting thermal curve. In the first study, guinea pigs were treated with intraperitoneal chemotherapy. Cyclophosphamide treatment showed a dose-dependent difference between the thermal parameters of control and treated samples, indicating that DSC can be used in this area. Following deconvolution of the DSC studies, the changes can be attributed to the damaged compounds. In the second part of our study, a method for the thermal analysis and deconvolution of RBCs in patients with lower limb ischemia during a three-month cilostazol treatment was developed. The control DSC curve showed 5-6 distinct thermal domains, and in contrast to other drug treatments, this remained stable throughout the entire study period. No effects of stiffness or compact were caused by the anticancer drug cyclophosphamide were observed in the structure of RBCs. These preliminary results highlight the uniqueness of thermodynamic studies of RBCs and provide a fingerprint-like identification of a given individual or disease state. Full article

Turbulent wind conditions pose significant challenges to the blade structural reliability of small wind turbines. Different from the authors’ previous work, which mainly focused on the output characteristics of the same 5 kW prototype under variable inflow conditions, this study combines field-test observations with numerical simulations to further investigate the blade structural dynamic responses of a 5 kW variable-pitch wind turbine under both uniform inflow and extreme wind conditions. Owing to the unique pitch-regulation mechanism of the proposed turbine, two pitch-control modes, namely conventional power-limited pitch control and active stall pitch control, are comparatively analyzed to clarify their effects on blade load, stress, and displacement responses. The results indicate that, under uniform inflow conditions, stresses are concentrated near the leading edge of the blade mid-span, while the maximum displacement occurs at the blade tip. Both stress and displacement decrease with increasing conventional pitch angle. Under extreme wind conditions, increasing gust intensity causes a nonlinear growth in blade loads and aggravates blade structural response. During active stall pitch control, the load distribution pattern is generally consistent with that under conventional pitch control, whereas the blade structural response first decreases and then increases as the pitch angle is adjusted toward negative values. Under uniform inflow at the rated wind speed of 11 m/s, the blade-tip maximum displacement decreased from 56.51 mm under the +6° power-limited/reference pitch condition to 48.42 mm under the −6° active-stall-related pitch condition, corresponding to a reduction of approximately 14.3%. These results provide a useful reference for the blade structural design and control optimization of distributed small wind turbines under complex inflow conditions. Full article

The decoupling of physical loading configurations from dynamic temperature control in cold-chain logistics exposes supply chains to severe thermal compliance risks and exponential cost penalties. To address this structural gap, this study formulated the Cold Chain Unitization Loading Optimization Problem (CCULP). We propose a mixed-integer linear programming (MILP) model that integrates continuous-time heat-transfer dynamics—including door-opening impulse disturbances—and Q10-driven quality-decay kinetics as endogenous constraints within the hierarchical assignment of perishable goods to insulated containers, pallets, and vehicles. By treating container thermal resistance as a core decision variable, the model operationalizes a “prevention-first” economic strategy. To solve this NP-hard problem, we developed a Temperature-Aware Heuristic Algorithm (TAHA) that embeds a forward-Euler temperature simulation loop directly into the combinatorial search. Computational experiments on instances up to 100 SKU types demonstrate that TAHA achieves near-optimal solutions (within 0.7% of the MILP proven optimum) while converging 63 times faster than a genetic algorithm benchmark. Moreover, compared with traditional geometry-centric heuristics, TAHA’s proactive container-polarization strategy effectively eliminates the “penalty cliff,” yielding up to a 25.9% reduction in total system cost on Large-scale instances, almost entirely attributable to the elimination of temperature-violation penalties. Sensitivity analyses further confirm TAHA’s robustness under extreme environmental stress (e.g., 40 °C ambient temperatures) and frequent logistical disturbances, offering an integrated framework for proactive risk mitigation and for reducing food loss in sustainable temperature-controlled distribution. Full article

The agricultural industry is at a critical juncture, experiencing global pressures in the form of climate volatility, a shortage of labor, and an increase in production costs. Although artificial intelligence (AI) has the potential for revolution due to its predictive analytics and self-controlled machinery, it has not achieved widespread and even distribution for use, especially among small-to-medium-sized farms in the Midwestern United States. This study formulates and empirically examines a comprehensive socio-technical model to determine the drivers and barriers to the adoption of AI in this agricultural region. Based on a synthesized framework of the “Unified Theory of Acceptance and Use of Technology” (UTAUT) and “Task–Technology Fit” (TTF), the study incorporates agriculture-specific contextual factors such as “environmental risk, access to broadband, economic constraints, and policy support”. The analyses of the 489 farmers in the U.S. Midwest were conducted through the “partial least squares structural equation modeling” (PLS-SEM) “SmartPLS v.3.9”. The findings provide full empirical evidence of the proposed model, which supports 11 hypothesized relationships. The key results show that the strongest positive predictors of adoption intention are “performance expectancy, effort expectancy, and trust”. On the other hand, data security concerns and financial restrictions are strong deterrents. The paper also outlines the significant facilitating functions of the broadband infrastructure and policy support in building farmer perceptions of technology’s ease-of-use and facilitating conditions. These lessons can provide policymakers, ag-tech developers, and extension agencies with a roadmap on how to create more equitable and contextual interventions that overcome the rural digital divide and create resilient data-driven farming systems. Full article

In fixed-view interaction scenarios of unmanned restaurants, face detection models face two core bottlenecks: the mismatch between training data distribution and real deployment scenarios, and the misalignment between model feature capacity allocation and business priority. To address these problems, this paper takes YOLOv8n (You Only Look Once version 8n) as the baseline, proposes a unified Scale-Aligned Capacity Allocation (SACA) theoretical framework, and constructs an end-to-end Scale Distribution Reconstruction Network (SDRNet) for lightweight face detection. First, we define the SACA loss with KL (Kullback-Leibler) divergence as the core optimization objective, which mathematically characterizes the matching degree between model capacity allocation and real scene face scale distribution. Second, a two-stage scene-aware scale distribution reconstruction strategy is designed based on the SACA framework, which derives the core face scale interval of the unmanned restaurant scene through a monocular imaging model, and constructs a scene-adaptive training dataset based on the public WIDER FACE benchmark, which is highly consistent with the real scale distribution of unmanned restaurant scenarios. Third, three scale-aligned lightweight modules, including LFEM (Lightweight Feature Extraction Module), LDown (Feature Segmentation and Sparse Optimization Module), and MSCH (Multi-Feature Shared Convolution Module), are proposed to realize the priority allocation of model capacity to core interaction scales, achieving collaborative optimization of data distribution and model structure. Fourth, a 2 × 2 controlled experiment is designed to separate the independent contributions of the data strategy and architectural improvements, and the robustness of the proposed model is verified on the standard WIDER FACE benchmark. Finally, a scale-specific validation mechanism is established to conduct fine-grained evaluation of the model’s detection performance on faces of different scales, avoiding the overall indicator masking the accuracy fluctuation of core scenarios. Experimental results show that the parameters of the proposed model are reduced to 1.76 M (a decrease of 41%), and the computational complexity is reduced to 5.5 GFLOPs (Giga Floating-point Operations Per Second) (a decrease of 32%). The mAP@0.5 (mean Average Precision) of the core medium-scale face reaches 0.684, with the performance loss controlled within 2% compared with the baseline. On the standard WIDER FACE benchmark, the model maintains competitive detection accuracy under extreme lightweight compression, which fully verifies its robustness. On the NVIDIA Jetson Orin NX embedded platform, the inference frame rate of TensorRT-FP16 reaches 79.9 FPS (Frames Per Second), which fully meets the real-time deployment requirements of resource-constrained unmanned restaurant scenarios. Full article

Stiffened plate structures widely used in military aircraft are frequently subjected to severe shock environments, such as those generated by gunfire or explosive blasts, which can significantly compromise the integrity and reliability of onboard equipment and devices. Accurate characterization and prediction of the shock response, especially the damping behavior of such structures, remains a critical yet challenging problem in aeronautical engineering. This study presents an integrated experimental–numerical framework for analyzing the shock response and damping characteristics of representative stiffened plates under shock wave excitation. Controlled shock loading is applied using a shock tube, with real-time acceleration responses measured at multiple locations on both plain and rib-reinforced plates. A high-fidelity finite element model is developed, and three commonly used damping models—Rayleigh Damping, wave attenuation Model, and Maximum Loss Factor Model—are systematically evaluated. Damping parameters are identified through a Particle Swarm Optimization (PSO) algorithm, using the shock response spectrum (SRS) as the performance metric. Experimental results reveal that the incorporation of reinforcing ribs can reduce peak acceleration responses and significantly enhance the damping performance, particularly in the mid-to-high frequency range. The identified damping parameters further show that the maximum loss factor model provides superior agreement with experimental SRS data compared to traditional approaches. The proposed methodology offers a robust method for modeling damping behavior in stiffened plates, providing practical insights for the design of aircraft structures exposed to shock environments. Full article

We present a modular microfluidic platform for constructing miniaturized, compartmentalized cell culture systems that support monoculture, co-culture, and organ-on-a-chip models of human tissues. The devices provide architecturally defined three-dimensional microenvironments in which heterogeneous cell populations can be cultured in close proximity while maintaining precise spatial organization and independent access to each compartment. In vivo-like perfusion into, from, and between adjacent chambers is achieved via micro-engineered porous barriers that act as perfusion microchannels, enabling controlled convective and diffusive transport and recapitulating paracrine signaling between tissue units. As a proof of concept, we implement an adipose–immune co-culture model that reproduces key features of inflamed, insulin-resistant adipose tissue, including altered cytokine secretion and glucose uptake. Together, these features establish a versatile platform for the biofabrication of customizable single-organ and multi-organ in vitro models that more faithfully recapitulate human tissue structure and function for applications in disease modeling, immunometabolic studies, and preclinical drug testing. Full article