Search Results (7,068)

China is a major producer, consumer, and exporter of automobiles. As an important component of the national industrial system, the automotive industry is associated with strong economic support functions, notable industrial spillover effects, and significant technological externalities, and its core values constitute a powerful driving force in achieving the global Sustainable Development Goals. Accordingly, this paper establishes a complex network evolutionary game model that involves two types of automobile manufacturers (established and latecomer automakers) in a strategic interaction within an exogenous environment jointly shaped by the government and the consumer community. We conduct a numerical simulation analysis to explore the organic relationships between the core elements within the system and the long-term performance of the automotive industry. The main findings are as follows. First, in adopting sustainable business models (SBMs), latecomer automakers exhibit a “high-start, low-end” evolutionary trajectory, whereas established automakers follow a “low-start, high-end” convergence path. Second, regarding the characteristics of game rules, the proportion of automakers that adopt SBMs is positively correlated with a larger proportion of ESG consumer groups, stronger comprehensive production and consumption subsidy standards, a more favorable expected payoff, and stronger market advantages on the part of established automakers. Finally, regarding network-structure characteristics, the proportion of automakers that adopt SBMs is positively correlated with a moderate total number of automakers, a reasonable proportion of established automakers, and a higher edge-addition probability. Moreover, this proportion is nearly independent of the noise interference coefficient, thus indicating that the mathematical model constructed as part of this study exhibits strong anti-interference capability. Full article

Rapid progress in quantum computing threatens the long-term security of classical cryptographic primitives, and with them the integrity of contemporary blockchain systems that rely fundamentally on computational hardness assumptions. Hence, quantum-native blockchain architectures have emerged as a conceptual pathway toward information-theoretic disturbance detectability. Two influential approaches have emerged in the literature. The temporal GHZ-state blockchain provides disturbance-detectable tamper sensitivity through entanglement in time, whereas the weighted quantum-hypergraph blockchain achieves high encoding efficiency through phase-based quantum representations of classical information. However, each addresses only part of the problem. In this work, we introduce a hybrid quantum blockchain framework whose primary novelty is the integration of phase-encoded classical data representation with recursively generated temporal GHZ entanglement within a single blockchain architecture. Rather than proposing a new encoding scheme or a new temporal-entanglement construction, the framework combines both mechanisms found in the literature and introduces a corresponding verification procedure for validating phase-encoded temporally entangled blocks. This architecture preserves the physics-based measurement-disturbance detectability of temporal entanglement while enabling more efficient classical-to-quantum data encoding inspired by hypergraph-based phase weighting. The result is a conceptual blockchain model that simultaneously enhances tamper sensitivity and encoding efficiency, providing a coherent foundation for future research on secure and practical quantum-era ledger systems. Full article

This paper presents the design, construction, and experimental evaluation of a low-cost, portable solar tracking system for monitoring ultraviolet solar radiation in real time. It integrates a Raspberry Pi Zero 2W as the embedded control unit, an AS7331 spectral sensor to measure UVA, UVB, and UVC irradiance, two 270° servomotors to position the system toward the sun, an NEO-6M GPS module to geolocate the system, and a DS3231 real-time clock to synchronize the time. To enable autonomous outdoor operation, a multistage power supply architecture based on a solar panel, a rechargeable battery, and LM2596 and MP1584EN DC-DC regulators was implemented. The tracking algorithm uses astronomical equations to estimate the solar azimuth and elevation and updates the sensor orientation during daylight hours. This allows the UV sensor to remain approximately normal to the incoming solar radiation. Experimental tests were conducted in Arequipa, Peru. The recorded data included UVA, UVB, and UVC irradiance; sensor temperature; geographic coordinates; time; and solar angles. The measured UV profiles exhibited the anticipated diurnal behavior: maximum values around solar noon, higher UVA levels than UVB levels, and minimal UVC levels due to atmospheric absorption. We compared the radiometric response with reference information from EarthKit, PVGIS 5.3, SAMPA, and a Davis Vantage Pro 2 weather station. We evaluated the solar positioning performance against Stellarium, NOAA, and the NREL Solar Position Algorithm. Across the complete five-day validation at three daily evaluation times, the maximum percentage errors were 0.0584% for azimuth and 0.5059% for elevation relative to the NREL SPA, NOAA, and Stellarium reference calculations. The results demonstrate that the proposed system constitutes an embedded, portable, autonomous, and low-cost platform for in situ monitoring of solar ultraviolet radiation. Due to its modular architecture, georeferencing capability, time synchronization, and independent power supply, the prototype can be used as a mobile measurement unit or as part of a distributed network of UV stations at various locations in Arequipa. In this regard, the system enables multipoint measurement campaigns, complements fixed weather stations, validates solar models, and generates local experimental data for the spatial and temporal assessment of the solar UV resource under real-world field conditions. Full article

In ground-based space surveillance, space target attitude estimation is critical for space situational awareness, yet existing methods based on inverse synthetic aperture radar (ISAR) images suffer from three core limitations: phase information is discarded in amplitude-only processing, convolutional neural networks have a restricted global receptive field, and the physical topology of satellite components is not explicitly modeled. To address these issues, we propose a complex-domain Transformer–graph neural network (CD-TrGNN) that unifies global context modeling and adaptive topological reasoning in an end-to-end framework. Specifically, a complex-domain Transformer module (CD-Transformer) with tailored attention captures long-range dependencies among image patches while preserving both amplitude and phase information; a complex-domain graph convolution module (CD-GC) with learnable adjacency matrices and a dual-path update mechanism explicitly encodes the structural relationships among satellite parts. On a self-built ISAR complex image dataset, CD-TrGNN achieves a three-axis mean absolute error of only 1.70°, substantially outperforming six representative baselines. Ablation experiments confirm the effectiveness of complex-domain processing, global attention, and topological reasoning. At a 5 dB signal-to-noise ratio, the error remains at 2.81°, and the accuracy stays below 2° for two different satellite structures. These results demonstrate that CD-TrGNN can fully exploit the information in ISAR complex images, enabling high-accuracy and highly robust attitude estimation. Full article

Farmers’ income growth is a central issue in consolidating the achievements of poverty alleviation in China and advancing common prosperity. It is also crucial for addressing the imbalance between urban and rural development and promoting agricultural and rural modernization. Based on theoretical analysis and mathematical modeling, this study develops a series of research hypotheses. A two-way fixed-effects model is employed to examine the impact of the land trusteeship interest linkage mechanism on farmers’ income growth. Furthermore, heterogeneity analyses are conducted from the perspectives of farmer characteristics, income levels, and crop types, followed by a series of robustness tests. The main findings are as follows: (1) The land trusteeship interest linkage mechanism has a significant positive effect on farmers’ income, and this conclusion remains robust after a series of robustness and endogeneity tests. Among the three core components, namely value cocreation, profit sharing, and risk division, all are found to effectively increase farmers’ income, with their marginal effects decreasing in sequence. (2) In terms of heterogeneity analysis, from the perspective of farmer types, the interest linkage mechanism has the most significant income-enhancing effect on large-scale specialized farmers, followed by small-scale full-time farmers and part-time farmers. From the perspective of income structure, the effect is mainly reflected in the increase in agricultural operating income, followed by wage income and transfer income. Regarding crop types, the income growth effect is more pronounced for vegetable producers than for wheat and maize producers. Based on these findings, efforts should focus on strengthening the interest linkage mechanism through enhanced value cocreation while implementing targeted and differentiated support measures for small-scale full-time farming households and vegetable growers. Particular attention should be paid to improving their operating income in order to maximize the income growth effects for farmers. This study provides important insights for improving mechanisms through which agricultural enterprises link with and benefit farmers, thereby contributing to the realization of China’s strategic goal of common prosperity. Full article

Understanding how desert riparian vegetation responds to managed flow releases is essential for ecological restoration in arid inland river basins. This study examines vegetation dynamics and hydrological responses in the desert reach of the Hotan River, a seasonal river crossing the Taklimakan Desert. To avoid temporal inconsistency, two data windows were explicitly separated: Landsat-derived vegetation information was used to describe long-term vegetation changes from 1985 to 2020, while environmental flow release, river-section water consumption, and groundwater-depth analyses were limited to the period with available hydrological observations, 2006–2020. NDVI and vegetation-cover classes were derived from cloud-screened and atmospherically corrected Landsat imagery, and the response of vegetation indicators to cumulative environmental flow release and groundwater depth was evaluated using transparent regression models with diagnostic statistics. Results indicate that vegetation cover improved overall during the study period, although the response was spatially heterogeneous. Vegetation conditions were generally better near the upper and terminal parts of the desert reach, whereas a relatively vulnerable zone occurred approximately 15–115 km downstream of the river confluence. During 2006–2020, NDVI and grassland area generally increased with cumulative environmental flow release, whereas annual grassland-area change showed large interannual fluctuations and was not significantly explained by cumulative release alone. The revised analysis clarifies that the study contributes a reach-scale synthesis linking long-term vegetation mapping with monitored environmental flow releases and groundwater response in the Hotan River desert reach, rather than a full 40-year ecohydrological attribution. These findings provide a basis for improving environmental flow scheduling and monitoring design in arid desert rivers. Full article

This article describes the process of designing and developing an Android application to support students in a university environment by automating study schedule management, facilitating access to study materials, providing navigation to educational buildings, and sending notifications about upcoming classes. The main problem that current development addresses is the lack of a centralized system for timely notifications and difficulties in navigating university campuses and obtaining summaries of study material files. Using two multilingual machine learning (ML) models, the solution integrates an automated notification system using Firebase Cloud Messaging (FCM), which operates in real time and provides geospatial navigation to educational buildings. Two ML models for natural language processing are used to automatically generate short and meaningful text summaries, and they accept long articles or documents in different languages and create abstract summaries, which makes them suitable for easy absorption of academic/educational materials. The technology stack includes the Django REST Framework 3.10 for the server part, PostgreSQL 18 for database management, and Java SE 21 for the mobile application, with security guaranteed through JWT (JSON Web Token) authentication and TLS encryption 1.2. The result is a comprehensive application that provides students with personalized access to weekly schedules, information about classes and assigned classroom numbers, and access to learning materials that are trained with a model optimized to create short, informative summaries. This contributes to better organization, reducing absences and increasing the efficiency of the educational process. Full article

In-situ alloying during laser powder bed fusion (LPBF) offers great compositional flexibility but is prone to process-induced defects. To address this problem, we developed a machine learning framework to predict and minimize major defects such as porosity (inclusive of lack of fusion, gas pores, and keyhole-induced porosity) and unmelted Nb particles (partially and completely unmelted particles) in LPBF-fabricated in-situ alloyed Ti–45Nb alloy. For this purpose, two independent least-squares boosting (LSBoost) ensemble regressors were trained using five process parameters (part shape, laser power, scan speed, hatch spacing, and scan rotation), along with their polynomial and interaction terms, to capture nonlinear relationships. Under a restricted 4-fold cross-validation, these models achieved pooled out-of-fold R² values of 0.672 for porosity and 0.702 for unmelted Nb, despite being trained on a small dataset. The grouped permutation importance analysis revealed that porosity is primarily governed by hatch spacing and laser power, whereas unmelted Nb particles are primarily governed by laser power and scan speed. The models were implemented in two graphical interfaces: a forward predictor for real-time defect estimation and an inverse optimizer for identifying low-defect parameter sets. Together, they establish a unified, data-driven approach for defect-aware process detection, prediction, and optimization in in-situ alloyed systems, offering a pathway towards reproducible, low-defect additive manufacturing. Full article

Background: Cardiovascular diseases (CVDs) are among the leading causes of death globally. In order to treat CVDs successfully in the early stages, it is crucial to diagnose them in time. The ECG is one of the most common and non-invasive methods to detect heart rhythms and to diagnose arrhythmias. However, the analysis of ECG recordings manually requires a lot of time and experience because the morphology of ECG signals and the characteristics of their waveforms are very complex and show large overlaps between different types of arrhythmias. So far, various approaches for automated analysis of ECG signals have been developed, mostly based on deep learning (DL). In general, these methods are able to analyze ECG signals automatically and to detect different types of arrhythmias. Most approaches, however, are based on a purely data-driven feature learning and do not pay attention to the morphology-sensitive temporal structure of ECG signals, which is important for a discriminative diagnosis of arrhythmias. Methods: In this paper, we propose an Attention-Based Morphology-Aware (ABMA) framework to leverage multilead ECG signals in conjunction with automatically computed physiological features using a hybrid deep learning architecture. ABMA leverages multi-scale convolutional neural networks to learn local morphology features, and bidirectional long short-term memory (BiLSTM) networks to model temporal rhythms in ECG signals. We designed an ABMA module that incorporates a morphology scoring network (MSN) in order to (1) estimate the morphology-aware importance of different ECG segments and (2) learn the temporal importance of ECG features. The learned attention weights enable learning to focus on key sections of ECG signals without predefined boundaries or manual annotation of fiducial points. To understand the contribution of each individual component of the framework, we performed an extensive ablation study, where we removed the handcrafted feature branch, the ABMA module, the MSN, and the multi-head attention mechanism, one at a time, and compared the results against a fixed set of experimental configurations. Results: To assess the performance of the proposed framework in three-class classification between sinus rhythm (SA), atrial fibrillation (AFIB), and ventricular tachycardia (VT), we employed a stratified 10-fold cross-validation protocol. Our approach achieved a mean accuracy of 95.18 ± 1.18%, followed by a corresponding weighted F1-score of 95.19 ± 1.18% and a macro F1-score of 94.66 ± 1.35%. Notably, the performance of the proposed complete ABMA framework considerably outperformed the baseline CNN–BiLSTM architecture. Furthermore, in the primary evaluation metrics (i.e., accuracy, F1-score), the complete framework showed statistically significant improvements against the baseline through paired two-sided t-tests (p < 0.001). The ablation study indicated that each architectural component contributed positively to the overall classification performance, with the complete ABMA framework outperforming all reduced variants. Conclusions: The framework was evaluated by stratified cross-validation on a publicly available dataset. Our framework outperformed the baseline CNN–BiLSTM model as well as the respective ablation models in terms of classification performance. The findings from the current study are based on a retrospective analysis and therefore future studies using an independent external dataset, from multiple centers, or as part of a prospective clinical study are necessary in order to establish the generalizability and clinical utility of the proposed framework. The ABMA framework is currently viewed as a very promising research framework for intelligent ECG analysis, but it is not yet a clinically validated diagnostic tool. Full article

Tendon profile geometry is a structural design variable whose effects on prestressed concrete beams have, until now, been studied separately for shear, ductility, and stiffness response. This paper unifies those threads through a single nonlinear finite element investigation of eight tendon profiles (B0–B7) across two beam depths (300 × 600 mm, L/d = 13.33; and 300 × 900 mm, L/d = 8.48), comprising 16 validation beam models plus an 82-simulation parametric sweep of tendon inclination angle (0–20°). Part A characterizes cracking, yield, and ultimate loads, three-stage stiffness (Ki → Kpc → Ku), ductility index, and deflection serviceability. Ductility is quantified as the peak-displacement-based index μΔ = Δu/Δy; post-peak plateau behavior is additionally quantified through a failure-displacement ductility index (μΔ,f) and an absorbed-energy index (μE), recovered from the full descending load–deflection branch of each model. Key findings: the trapezoidal beveled profile (B6) achieves the highest ductility overall (μΔ = 3.83 in deeper beams, +99.5% over straight); the hybrid parabolic–straight profile (B5) leads ductility in shallow beams (μΔ = 2.16, +14.9%); the five-row distributed trapezoidal profile (B7) achieves the highest post-cracking stiffness in deeper beams (Kpc = 61.93 kN/mm, +7.6%) and highest yield load, but at a ductility cost; and all 16 models satisfy ECP 203-2020 and ACI 318-19 deflection limits at service load. Part B develops and validates a dimensionless shear-inclination efficiency index ηv, calibrated by nonlinear regression on the 82-simulation database: ηv = 1 + 0.14·μps·λps·(d/h)^0.6·θ^0.9, achieving R² = 0.93 and RMSE < 5%, with a mean conservative safety margin of 6% across the 16 validation configurations. Sensitivity analysis identifies inclination angle θ as the dominant variable, ahead of depth ratio d/h and distribution index λps. A step-by-step design procedure with a profile-specific compliance table allows ηv to be applied directly to ACI 318-19 or ECP 203-2020 shear predictions: at a sub-minimum stirrup ratio of 0.14%, ηv enables five of eight profiles in shallow beams and three in deeper beams to achieve full code compliance, numerically indicating potential stirrup savings of up to 40%, pending experimental verification and reliability-based calibration before design use. Read together, the two parts show that tendon geometry simultaneously governs shear efficiency, post-cracking stiffness, and ductility—three previously disconnected performance axes—and that the optimal profile choice is depth-dependent and objective-dependent, a distinction current codes do not address. The finite element procedure is validated against six post-tensioned specimens for the global load–deflection response and peak-load agreement (R² = 0.97 and 0.95, respectively); cracking load, yield point, post-peak plateau behavior, and the shear-governed failure mode of the deeper beams were not independently observed in the same experimental dataset, so the ductility, stiffness-degradation, and shear-governed-failure findings reported here should be read as numerically-derived results requiring further experimental confirmation, distinct from the peak-load response that is directly validated. Full article

Highland pastures in Montenegro, known as katuns, are seasonal settlements without grid power or network coverage and which are located where conventional monitoring is unfeasible. This study presents a solar-powered, off-grid system for livestock and environmental monitoring. It integrates, into a single portable unit, a solar power station, an edge-AI computer, a camera, environmental sensors, a LoRaWAN gateway, and a cellular router for backhaul. All parts are pre-wired in a modular enclosure, deployable by one operator in under 30 min. Data are fed to the agroNET farm-management platform and a purpose-built mobile web application; livestock detection runs on-device using a model from our earlier work. The system was evaluated at three sites, including a highland katun near Žabljak (~1450 m), under a two-phase energy-measurement protocol. During field logging it drew ~75 W on average against ~125 W solar input—a measured surplus that is used to recharge the battery—with a daily monitoring load of ~1560 Wh. The four-panel array’s nameplate potential in summer is an estimated ~3700 Wh/day, indicating substantial headroom relative to the measured load. At 80% depth of discharge the battery gives ~20 h autonomy, and the detection pipeline ran continuously, processing ~10,000 frames at under 3 s latency. The results demonstrate the feasibility of off-grid precision livestock farming, reaching TRL 6. Full article

Accurate underwater acoustic localization is a key enabling capability for autonomous underwater vehicles and underwater drones, which cannot rely on satellite positioning while submerged and therefore depend on acoustic methods to determine their position. Localization based on time-of-arrival (TOA) measurements requires precise time synchronization between the source and the receivers, which is difficult to maintain in practical deployments. The time-difference-of-arrival (TDOA) representation removes this requirement but discards part of the absolute timing information, reducing localization accuracy. This study investigates a physics-based feedforward multilayer perceptron (FF-MLP) framework for two-dimensional range–depth underwater localization that learns directly from the arrival-time structure induced by sound-speed variability and multipath, with the receiver-array geometry treated as a central design variable for improving synchronization-free TDOA localization. Using multi-receiver arrival times generated with the BELLHOP beam-tracing model under a representative Mediterranean underwater environment, synchronous TOA, biased TOA, and TDOA measurement representations are compared on a common footing, and the effects of the receiver depth distribution, the number of receivers, and the reference-receiver position are systematically examined through Monte Carlo evaluation. The results show that the receiver-array geometry, rather than the measurement representation alone, is decisive for TDOA-based localization: with an appropriately designed geometry, synchronization-free TDOA localization achieves a median two-dimensional RMSE of 11.28 m, approaching the accuracy attainable with synchronous TOA, which requires precise time synchronization. These findings indicate that careful receiver-geometry design can make synchronization-free TDOA a practical alternative to synchronous TOA for the acoustic localization of autonomous underwater vehicles. Full article

Background/Objectives: Alzheimer’s Disease (AD) and vascular dementia contribute up to ~75% of dementia cases, as determined via autopsy. AD arises in part due to the buildup of aberrant proteins (amyloid beta (Aβ) and Tau); vascular dementia is caused by reduced cerebral blood flow. Each dementia mechanisms damages brain. Bobola et al. found that their low-intensity focused ultrasound (FUS) protocol applied to the brains of the 5XFAD mouse model of AD reduced Aβ by 50% through activation of microglia. Eguchi et al. found that their own FUS protocol applied to the brains of the same mouse model reduced Aβ by 15% and increased cerebral blood flow by 50% through an increase in endothelial nitric oxide synthase (eNOS). Here, we sought to test a combined version of those two FUS protocols, expecting both a decrease in Aβ burden and an increase in eNOS. Methods: Using a diagnostic ultrasound probe, we applied our combined FUS protocol primarily to the left hippocampus of anesthetized 5XFAD mice, for an hour a day, for three days for younger mice and for five days for older mice. On day three or five, respectively, we harvested their brains and performed histological analysis to assess Aβ burden, microglial activation and their co-localization with Aβ, as well as the burden of eNOS within neuronal nuclei (here called intra-neuronal eNOS) and outside of neurons. Results: Relative to untreated mice, the treated younger mice had more activated microglia co-localized with Aβ and reduced Aβ burden for large plaques, as well as no change in each measure of eNOS. In contrast, the treated older AD mice had no change in activated microglia co-localized with Aβ, and no change in Aβ burden. However, relative to untreated older AD mice, FUS decreased total and extra-neuronal eNOS and increased intra-neuronal eNOS. Conclusions: The ability of our FUS protocol to reduce Aβ burden and alter the eNOS distribution depends critically upon the age of the AD mice (more Aβ plaques for a comparable number of microglia for older mice relative to younger mice) and duration of the treatment. The observed decrease in extra-neuronal eNOS distribution in older AD mice caused by FUS raises the concern that our protocol may increase ischemia, while the increase in intra-neuronal eNOS may counteract that effect via protection of synaptic function. These findings also identify two candidate therapeutic windows for our FUS treatment protocol, each requiring more research before translation to humans. One window is early intervention to maximize Aβ plaque removal via activation of microglia. The second is later intervention to protect synaptic function if it is possible to mitigate the potential ischemic risk caused by the differential effects of FUS on eNOS. Full article

Digital technologies are an integral part of young children’s everyday lives, yet their pedagogical integration in early childhood education and care (ECEC) remains limited and highly variable across ECEC centers. Drawing on the extended Structure–Process (SP-E) framework, this study examines how structural characteristics of ECEC centers, particularly the range of available ICT equipment, and educators’ ICT-related professional competencies, especially beliefs and motivational orientations, predict ICT implementation in pedagogical practice. Data from the DIGIPaed project comprised 266 educators across 97 German ECEC centers. Hierarchical regression models with cluster-adjusted standard errors were estimated for two outcomes: ICT use with children and pedagogical ICT activities. Results revealed distinct predictor patterns for the two outcomes. The range of available ICT equipment was positively associated with pedagogical ICT activities but not with ICT use with children. ICT-related self-efficacy was the only professional competence variable to predict both outcomes, explaining additional variance beyond structural conditions and beliefs. Centers with higher proportions of children with a migration background showed lower levels of ICT use with children. These findings suggest that ICT implementation in ECEC is not a unitary construct and that different dimensions of implementation may depend on different conditions. Self-efficacy emerged as a particularly promising target for professional development aimed at supporting meaningful ICT integration in ECEC centers. Full article

Cellular manufacturing (CM) has been extensively studied and is widely recognized as a resilient and effective production system. With respect to labor-intensive cells, recent studies have primarily concentrated on mixed-integer programming (MIP) models, often embedded within multi-phase solution frameworks. However, the computational complexity of such models remains a significant challenge. To address this issue, several studies have adopted hierarchical, multi-phase approaches that decompose the problem into more tractable subproblems, thereby significantly reducing computation time and enhancing their applicability in real-world environments, albeit at the cost of potential optimality. Considering the improvement in computer processing power in recent years, a new integrated mixed-integer programming model without phases is proposed and compared with the two-phase model via numerical experiments in this paper. In addition, considering unique multi-objective optimization models using integer and fractional parts without Pareto solutions, the newly proposed model is subjected to a comprehensive comparison with the two-phase model. As a result, it is demonstrated that, in the reported experimental setting and under the tested configuration of the two-phase procedure, the proposed model can lead to more feasible solutions that require fewer operators than the two-phase model under variable demand across 100 weeks. These findings pertain to the tested setting rather than representing a general property of the method. In addition, in the same setting, the proposed model can also lead to a smaller cumulative value of the secondary assignment-count metric than the two-phase model. Full article