Search Results (3,895)

This paper presents an experimental evaluation of the connectivity reliability of a LoRaWAN (Long Range Wide Area Network), deployed in a rural area of Mozambique, focusing on the influence of distance and relative altitude between end nodes and the gateway. The absence of telecommunications and power infrastructure in the study region provided a realistic and challenging scenario to assess LoRaWAN’s feasibility as a low-cost, low-power solution for remote sensing in disconnected environments. Field trials were conducted using an Arduino-based node (with 2 dBi antenna) powered by a 2200 mAh power bank, with no GPS or cellular support. Data were collected at four georeferenced points along a 1 km path, capturing Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Packet Delivery Rate (PDR). Results confirmed that both distance and terrain elevation strongly affect performance, with significantly degraded metrics when the end nodes were located at lower altitudes relative to the gateway. Despite operational constraints, such as the need for manual firmware resets and lack of real-time monitoring, the network consistently achieved PDR above 89% and remained operational autonomously for over 24 h. The study highlights the effectiveness of installing gateways on natural elevations to improve coverage and demonstrates that even with basic hardware, LoRaWAN (Low Power Wide Area Network), is a viable and scalable option for rural connectivity. These findings offer valuable empirical evidence to promote national digital inclusion policies and future LPWAN deployments. Full article

This study focuses on the challenges of resident mobility in low-density areas. Amid China’s rapid urbanization, rural landscapes and travel patterns are undergoing significant transformation. Using Lanzhou’s rural areas as a representative case study, this research employs questionnaire surveys to collect data. It applies a multi-nominal logit (MNL) model to examine factors influencing travel mode choices and utilizes structural equation modeling (SEM) to assess travel satisfaction—a composite metric derived from residents’ subjective evaluations of convenience, cost, time, and comfort. Findings indicate that private cars and public transportation are the primary travel modes. The MNL model reveals that age and destination accessibility significantly influence travel choices. SEM path analysis further shows that annual household income has a direct positive effect on satisfaction, while age exerts an indirect negative influence through mediating variables. Female satisfaction levels were significantly lower than those of males. Both road density and perceived infrastructure quality significantly enhanced satisfaction, while destination accessibility may exert a slight negative indirect effect by increasing travel expectations. The study theoretically enriches research on rural travel patterns and provides practical insights into rural transportation planning and infrastructure development. Full article

To improve the adaptability of 4WID electric vehicles under various operating conditions, this study introduces a model predictive control approach utilizing a neural network for adaptive weight parameter prediction, which integrates four-wheel steering and longitudinal driving force control. To address the difficulty in adjusting the MPC weight parameters, the neural network undergoes offline training, and the Snake Optimization method is used to iteratively optimize the controller parameters under diverse driving conditions. To further enhance vehicle stability, the real-time stability state of the vehicle is assessed using the $\beta -\dot{\beta }$ phase plane method. The influence of vehicle speed and road adhesion on the instability boundary of the phase plane is comprehensively considered to design a stability controller based on different instability degree zones. This includes an integral sliding mode controller that accounts for both vehicle tracking capability and stability, as well as a PID controller, which calculates the additional yaw moment based on the degree of instability. Finally, an optimal distribution control algorithm coordinates the longitudinal driving torque and direct yaw moment while also considering the vehicle’s understeering characteristics in determining the torque distribution for each wheel. The simulation results show that under various operating conditions, the proposed control strategy achieves smaller tracking errors and more concentrated phase trajectories compared to traditional controllers, thereby improving path tracking precision, vehicle stability, and adaptability to varying conditions. Full article

Landslide runout distance is governed not only by collapsed magnitude but also by site-specific geoenvironmental conditions. While remote sensing techniques has advanced landslide susceptibility mapping, its application to runout modeling remains limited. This study examined the role of collecting data on various site conditions through remote sensing and field surveys datasets in predicting the landslide travel distance from the 2022 disaster in Petrópolis, Rio de Janeiro. A total of 218 multivariate linear regression models were developed using morphometric, remote sensing, and field survey variables collected across collapse, transport, and deposition zones. Results show that predictive accuracy was limited when based solely on landslide scale (R² = 0.06–0.10) but improved substantially with the inclusion of site condition data across collapse, transport, and deposition zones (R² = 0.49–0.51). Additionally, model performance was strongly influenced by runout path typology, with channelized flows producing the most stable and accurate predictions (R² = 0.73–0.90), while obstructed and open-slope paths performed worse (R² = 0.39–0.61). These findings demonstrate that empirical models integrating multizonal site-condition data and runout path typology offer a scalable, reproducible framework for landslide hazard mapping in data-scarce, complex mountainous urban environments. Full article

Improving students’ course satisfaction is conducive to the development of sustainable education. Based on the stimulus–organism–response (SOR) theory and the perceived value theory, this paper constructs an analytical framework of “teacher teaching–students’ psychological transformation–students’ course satisfaction”; puts forward hypotheses; builds models; collects 270 valid questionnaires from current students in universities majoring in economics and management in mainland China; and uses correlation analysis, regression analysis, and mediation test methods to study the influence of the economics and management major teachers’ teaching on students’ course satisfaction. This research shows that (1) teachers’ teaching has a differentiated driving effect on students’ psychology, with the usefulness of teaching content being the key path to enhancing students’ perceived value, teachers’ teaching attitudes and methods being the core means to building trust in a course, and the moderate difficulty of examination driving students’ examination preparation intention, and (2) students’ psychology is the core means for the transformation of course satisfaction, with perceived value directly driving course satisfaction; course trust being the cornerstone of course satisfaction and playing a significant mediating role between teachers’ teaching attitudes and methods and students’ course satisfaction, and students’ examination preparation intention positively promoting course satisfaction and playing a significant mediating role between moderate difficulty of examination and course satisfaction. This study provides some scientific basis for improving course satisfaction and teaching efficiency, enhancing the teaching quality of higher education, and promoting the development of sustainable education. Full article

This study presents a prism-based self-referencing digital holographic microscopy (PSDHM) system that utilizes a wedge prism. The front and rear surfaces of the prism have a wedge angle of 2°, which can reflect the parallel incident light, respectively, to generate a lateral displacement that varies with the propagation distance of the optical path. Focusing on the quantitative analysis of droplets, this innovative system effectively images water droplets and their dynamic evaporation processes. Results show that the evaporation process of water droplets undergoes three stages, each stage corresponding to a theoretical model. These are the constant contact radius (CCR) mode, the stick-slip (SS) mode, and the stick-jump (SJ) mode. Furthermore, by comprehensively analyzing the contact angle and the specific morphology of the droplet’s contact area, we revealed that the hydrophilicity of the cover glass influences the droplet morphology, contact area, and the evaporation process. Full article

This study examines the impact of physicochemical and geological factors on radon concentrations in groundwater throughout the Mexican Altiplano. Geological diversity, uranium deposits, seismic zones, and geothermal areas with high heat flow are all potential factors contributing to the presence of radon in groundwater. To move beyond local-scale assessments, this research employs spatial prediction methodologies that incorporate geological and geochemical variables recognized for their role in radon transport and geogenic potential. Certain properties of radon enable it to serve as an ideal tracer, viz., short half-life, inertness, and higher incidence in groundwater than surface water. Twenty-five variables were analyzed in samples from 135 water wells. Geostatistical techniques, including inverse distance weighted interpolation and kriging, were used in conjunction with multivariate statistical analyses. Salinity and geothermal heat flow are key indicators for determining groundwater origin, revealing a dynamic interplay between geothermal activity and hydrogeochemical evolution, where high temperatures do not necessarily correlate with increased solute concentrations. The occurrence of toxic trace elements such as Cd, Cr, and Pb is primarily governed by lithogenic sources and proximity to mineralized zones. Radon levels in groundwater are mainly influenced by geological and structural features, notably rhyolitic formations and deep hydrothermal systems. These findings underscore the importance of site-specific groundwater examination, combined with spatiotemporal models, to account for uranium–radium dynamics and flow paths, thereby enhancing radiological risk assessment. Full article

Under the combined influence of global climate change and intensified human activities, ecosystem services (ESs) are undergoing substantial transformations. Identifying their nonlinear driving mechanisms is crucial for promoting regional sustainable development. Taking Anhui Province as a case study, this research evaluates the spatial patterns and temporal dynamics of six key ecosystem services from 2000 to 2020—namely, biodiversity maintenance (BM), carbon fixation (CF), crop production (CP), net primary productivity (NPP), soil retention (SR), and water yield (WY). The InVEST and CASA models were employed to quantify service values, and the XGBoost–SHAP framework was used to reveal the nonlinear response paths and threshold effects of dominant drivers. Results show a distinct “high in the south, low in the north” spatial gradient of ES across Anhui. Regulatory services such as BM, NPP, and WY are concentrated in the southern mountainous areas (high-value zones > 0.7), while CP is prominent in the northern and central agricultural zones (>0.8), indicating a clear spatial complementarity of service types. Over the two-decade period, areas with significant increases in NPP and CP accounted for 50% and 64%, respectively, suggesting notable achievements in ecological restoration and agricultural modernization. CF remained stable across 98.3% of the region, while SR and WY exhibited strong sensitivity to topography and precipitation. Temporal trend analysis indicated that NPP rose from 395.83 in 2000 to 537.59 in 2020; SR increased from 150.02 to 243.28; and CP rose from 203.18 to 283.78, reflecting an overall enhancement in ecosystem productivity and regulatory functions. Driver analysis identified precipitation (PRE) as the most influential factor for most services, while elevation (DEM) was particularly important for CF and NPP. Temperature (TEM) and potential evapotranspiration (PET) affected biomass formation and hydrothermal balance. SHAP analysis revealed key threshold effects, such as the peak positive contribution of PRE to NPP occurring near 1247 mm, and the optimal temperature for BM at approximately 15.5 °C. The human footprint index (HFI) exerted negative impacts on both BM and NPP, highlighting the suppressive effect of intensive anthropogenic disturbances on ecosystem functioning. Anhui’s ES exhibit a trend of multifunctional synergy, governed by the nonlinear coupling of climatic, hydrological, topographic, and anthropogenic drivers. This study provides both a modeling toolkit and quantitative evidence to support ecosystem restoration and service optimization in similar transitional regions. Full article

Morchella spp. is a type of valuable and rare edible fungi cultivated in soil. Optimization of the cultivation medium for Morchella spp. is key to obtaining high-efficiency production in an ecologically friendly manner. Recently, the sustainable resource utilization of agricultural waste has gathered attention. Specifically, reusing tomato substrate, mushroom residues, and coconut shells can lower the production costs and reduce environmental pollution, demonstrating remarkable ecological and economic benefits. To determine the soil microbial communities of Morchella spp. using different culture medias and influencing factors, this study analysed the relative abundance of bacterial and fungal communities in natural soil, soil with 5% tomato substrate, soil with 5% mushroom residues, and soil with 5% coconut shells using Illumina NovaSeq high-throughput sequencing. In addition, intergroup differences, soil physiochemical properties, and product quality were also determined. Results demonstrated that agricultural waste consisting of mushroom residues, waste tomato substrate, and coconut shells can improve the efficiency of Morchella spp. cultivation. When considering yield and quality, mushroom residue achieved the highest yield (soil nutrient enrichment), followed by tomato substrate (water holding + grass carbon nutrient). All three types of agricultural waste promoted early fruiting, significantly increased polysaccharide, crude protein, and potassium content, and lowered crude fat and fibre. In regard to soil improvement, the addition of different materials optimized the soil’s physical structure (reducing volume weight and increasing water holding capacity) and chemical properties (enrichment of nitrogen, phosphorus, and potassium, regulating nitrogen and medium trace elements). For microbial regulation, the added materials significantly increased the abundance of beneficial bacteria (e.g., Actinomycetota, Gemmatimonadota and Devosia) and strengthened nitrogen’s fixation/nitration/decomposition functions. In the mushroom residue group, the abundance of Bacillaceae was positively related to yield. Moreover, it inhibited pathogenic fungi like Mortierella and Trichoderma, and lowered fungal diversity to decrease ecological competition. In summary, mushroom residues have nutrient releasing and microbial regulation advantages, while tomato substrate and coconut shells are new high-efficiency resources. These increase yield through the “physiochemical–microorganism” collaborative path. Future applications may include regulating the function of microorganisms and optimizing waste preprocessing technologies to achieve sustainability. Full article

A systematic study was conducted on the fatigue performance of Q500qENH weathering steel welded joints under low-temperature conditions of −40 °C in this paper. Low-temperature fatigue tests were conducted on V-groove butt joints and cross-shaped welded joints and S-N curves with a 95% reliability level were obtained. A comparative analysis with the Eurocode 3 reveals that low-temperature conditions lead to a regular increase in the design fatigue strength for both types of welded joints. Fracture surface morphology was examined using scanning electron microscopy, and combined with fracture characteristic analysis, the fatigue fracture mechanisms of welded joints under low-temperature conditions were elucidated. Based on linear elastic fracture mechanics theory, a numerical simulation approach was employed to investigate the fatigue crack propagation behavior of welded joints. The results indicate that introducing an elliptical surface initial crack with a semi-major axis length of 0.4 mm in the model effectively predicts the fatigue life and crack growth patterns of both joint types. A parametric analysis was conducted on key influencing factors, including the initial crack size, initial crack location, and initial crack angle. The results reveal that these factors exert varying degrees of influence on the fatigue life and crack propagation paths of welded joints. Among them, the position of the initial crack along the length direction of the fillet weld has the most significant impact on the fatigue life of cross-shaped welded joints. Full article

The integration of artificial intelligence (AI) in the manufacturing industry is increasingly prevalent, presenting both ongoing opportunities and challenges for organizations while also significantly impacting worker behavior and psychology. Drawing on data from 405 workers in China, this study employs hierarchical regression analysis and fuzzy-set qualitative comparative analysis (fsQCA) to investigate the influence mechanism of AI-enabled job characteristics on work-related flow. Key findings reveal that: AI-enabled job characteristics positively predict work-related flow by increasing perceived challenge stress, yet simultaneously exert a negative influence by exacerbating perceived hindrance stress; techno-efficacy significantly alleviates the relationship between AI-enabled job characteristics and perceived hindrance stress but does not moderate the path via perceived challenge stress; fsQCA identifies four distinct causal configurations of antecedents leading to high work-related flow. This research elucidates the complexities of AI-enabled job characteristics and their dual-faceted impact on work-related flow. By integrating AI into the study of worker psychology and behavior, it extends the contextual scope of job characteristics research. Furthermore, the application of fsQCA provides novel insights into the antecedent conditions and configurational pathways for achieving work-related flow, offering significant theoretical and practical implications. Full article

As the European Union’s energy systems are transforming towards achieving climate goals, this article examines the energy balances of EU member states. This analysis covers, among other things, the dynamics of energy dependence and strategies for decoupling economic growth from the level of emissions in the European Union (EU), with particular emphasis on Poland, which is strongly influenced by its historical reliance on coal in the energy balance. Using panel data from 1990 to 2022, the article investigates differences in energy dependence between individual countries, shaped by economic structures and national energy policies. The study results confirm significant heterogeneity between member states and emphasize that the stability and direction of decoupling economic growth from greenhouse gas (GHG) emissions are strongly dependent on the composition of the energy mix and vulnerability to external conditions. Based on scenario analysis, potential paths for Poland’s energy transition are assessed. We demonstrate that a high share of renewable energy sources (RES) significantly reduces CO₂ emissions, provided it is accompanied by infrastructure modernization and the development of energy storage. Furthermore, integrating nuclear energy as a stabilizing element of the energy mix offers an additional path to deep decarbonization while ensuring supply reliability. Finally, we demonstrate that improving energy efficiency and demand management can effectively increase energy security and reduce emissions, even in a scenario with a stable coal share. The study addresses a research gap by integrating decoupling analysis with scenario-based stochastic modeling for Poland, a country for which few comprehensive transition assessments exist. The results provide practical guidance for developing resilient, low-emission energy policies in Poland and the EU. Results are reported for 2025–2050 (with 2040 as an interim milestone). Full article

This work presents a theoretical investigation of ionic conductivity in cubic zirconia (c-ZrO₂) doped with magnesium, using density functional theory (DFT) with the hybrid B3LYP functional as implemented in the CRYSTAL23 software package. It was found that the spatial arrangement of magnesium atoms and oxygen vacancies significantly affects the energy barriers for oxygen ion migration. Configurations with magnesium located along and outside the migration path were analyzed. The results show that when Mg²⁺ is positioned along the migration trajectory and near an oxygen vacancy, stable defect complexes are formed with minimal migration barriers ranging from 0.96 to 1.32 eV. An increased number of Mg atoms can lead to a further reduction in the barrier, although in certain configurations the barriers increase up to 3.0–4.6 eV. When doping occurs outside the migration path, the energy profile remains symmetric and moderate (0.9–1.1 eV), indicating only a weak background influence. These findings highlight the critical role of coordinated distribution of Mg atoms and oxygen vacancies along the migration pathway in forming efficient ion-conducting channels. This insight offers potential for designing high-performance zirconia-based electrolytes for solid oxide fuel cells and sensor applications. Full article

We investigate the Optimal Obstacle Placement (OOP) problem under uncertainty, framed as the dual of the Optimal Traversal Path problem in the Stochastic Obstacle Scene paradigm. We consider both continuous domains, discretized for analysis, and already discrete spatial grids that form weighted geospatial networks using 8-adjacency lattices. Our unified framework integrates OOP with stochastic geometry, modeling obstacle placement via Strauss (regular) and Matérn (clustered) processes, and evaluates traversal using the Reset Disambiguation algorithm. Through extensive Monte Carlo experiments, we show that traversal cost increases by up to 40% under strongly regular placements, while clustered configurations can decrease traversal costs by as much as 25% by leaving navigable corridors compared to uniform random layouts. In mixed (with both true and false obstacles) scenarios, increasing the proportion of true obstacles from 30% to 70% nearly doubles the traversal cost. These findings are further supported by statistical analysis and stochastic ordering, providing rigorous insights into how spatial patterns and obstacle compositions influence navigation under uncertainty. Full article

The response of the microscopic structure and macroscopic mechanical parameters of SRM under F–T cycles is a key factor affecting the safety and stability of engineering projects in cold regions. In this study, F–T tests, EIS, and uniaxial compression tests were conducted on SRM. The construct equivalent model of different conductive paths based on EIS was constructed. A peak strength prediction model was developed using characteristic parameters derived from the equivalent models, thereby revealing the mechanism by which F–T cycles influenced both microscopic structure and macroscopic strength. The results showed that with increasing cycles, both $R_{CP}$ and $R_{CPP}$ exhibited an exponential decreasing trend, whereas C_DSRP and D_f increased exponentially. Peak strength and peak secant modulus decreased exponentially, but peak strain increased exponentially. The expansion and interconnection of pores with different radii within $CPP$ and $CP$ caused smaller pores to evolve into larger ones while generating new pores, which led to a decline in $R_{CPP}$ and $R_{CP}$. Moreover, this expansion enlarged the soil–rock contact area by connecting adjacent gas-phase pores and promoted the transformation of $CSRPP$ into $DSRPP$, enhancing the parallel-plate capacitance effect and resulting in an increase in $C_{DSRP}$. Moreover, the interconnection increased the roughness of soil–soil and soil–rock contact surfaces, leading to a rising trend in $D_f$. The combined influence of $C_{DSRP}$ and $D_f$ yielded a strength prediction model with higher correlation than a single factor, providing more accurate predictions of UCS. However, the increases in $C_{DSRP}$ and $D_f$ induced by F–T cycles also contributed to microscopic structure damage and strength deterioration, reducing the load-bearing capacity and ultimately causing a decline in UCS. Full article