Search Results (14,915)

This study examines the extent to which smart cities are expressions of nationally homogeneous development trends by way of an analysis of their structural characteristics from a multivariate viewpoint. Drawing on data from the International Institute for Management Development IMD Smart City Index 2024, we find a sample of 102 cities across the world clustering along six key dimensions of smartness: mobility, environment, government, economy, people, and living. The aim is to examine if cities within a country have similar profiles and, if so, to what degree such similarity translates to other macro-level institutional, political, and cultural conditions. Our results verify a tight correspondence between city profiles and national contexts, implying that macro-level governance arrangements, policy coordination, and institutional capacity are pivotal in influencing local smart city development. Planned centralised countries possess more uniform city characteristics, while decentralised nations possess more variant urban policies. This study contributes to international debate regarding smart cities by empirically identifying national directions of urban innovation. It offers pragmatic inputs for policymakers that aim to align local efforts with overall sustainable development agendas. Moreover, this study introduces a novel application of Linear Discriminant Analysis (LDA) to classify smart city profiles based on national models. While the analysis yields high classification accuracy, it is important to note that the sample is skewed toward cities from the Global North, potentially limiting the generalisability of the results. Full article

This study aimed to validate the 9-point body condition score (BCS) system in sled dogs by assessing its reliability and by comparing it with objective measures including real-time ultrasonography, plicometry, and anthropometry. Twenty-seven Siberian Huskies (11 females, 16 males) from three sled dog teams were assessed for BCS by three trained veterinarians and their respective mushers. Intra-observer reliability was substantial (Krippendorff’s α = 0.734), while agreement between expert raters (Kα = 0.580) and between the expert rater and mushers (Kα = 0.691) was moderate, with mushers tending to overestimate the BCS of their own dogs (median difference = −0.5). BCS showed positive correlations with body mass index (BMI) and subcutaneous fat at the chest and flank via plicometry (for all: p < 0.05). Ultrasonography showed weak correlations with BCS, likely due to the different anatomical layers evaluated and the distinctively high muscle-to-fat ratio typical of sled dogs. Both univariate and multivariate analyses revealed sex- and neutering-related differences in body composition, with males generally presenting larger skeletal dimensions and neutering influencing patterns of fat distribution. These findings support the reliability and field applicability of the BCS system when used by trained evaluators, highlighting the importance of considering sex and anatomical site when assessing body condition in athletic dogs. The 9-point BCS, combined with accessible objective tools, represents a consistent, cost-effective method for monitoring body condition in long-distance performance sled dogs. Full article

Background/Objectives: The advancing technological developments of recent decades have also changed the stress profile of pilots of high-performance aircraft (HPA) immensely. Pilots are exposed to different gravitational (G)-forces and are only able to fly with anti-G suits that compensate for the physiological loss of cerebral perfusion by applying external pressure to the body, and positive pressure breathing during G [PBG]. The present study therefore aims to investigate long-term effects of PBG on the lung capacity of fighter pilots. Methods: In a retrospective data analysis (1972–2024), the clinical findings of all German military pilots were analyzed. In total, 1838 subjects were included in the analysis, divided into three groups: HPA with PBG, HPA without PBG, and fixed-wing aircraft. Results: Lung function analysis showed that no significant decrease in FVC was found in the HPA group with PBG, but a decrease was found in the HPA group without PBG. FEV1 and FEV1/FVC decreased significantly in all groups. Multiple regression analyses indicated that the variables age and aircraft type were significant predictors of the changes in FVC and FEV1, but not for the Tiffeneau index. Conclusions: Our study showed that the lung function of HPA pilots who were exposed to both PBG and repeated increased G-forces did not deteriorate to a significantly greater extent compared with other pilots without these conditions; in some cases, it even deteriorated to a lesser extent. Overall, age has primarily been shown to be the predisposing factor for a deterioration in lung function parameters over time. Full article

Lubricating oil degradation directly impacts friction coefficient, wear rate, and lubrication regime transitions, making precise health quantification essential for predictive tribological maintenance. However, conventional evaluation methods fail to capture subtle tribological changes preceding lubrication failure, often oversimplifying complex multi-parameter relationships critical to friction and wear performance. To address this challenge, this study proposes Seasonal–Trend decomposition using Loess, a Factor Attention Network, a Temporal Convolutional Network, and an Informer with Long Short-Term Memory Variational Autoencoder (SFTI-LVAE) framework for continuous tribological health assessment of diesel engine lubricants. The approach integrates Seasonal–Trend decomposition using Loess (STL) for trend–seasonal separation, a Factor Attention Network (FAN) for multidimensional feature fusion, and a Temporal Convolutional Network (TCN)-enhanced Informer for capturing long-term tribological dependencies. By combining Long Short-Term Memory (LSTM) temporal modeling with Variational Autoencoder (VAE) reconstruction, the method quantifies lubricant health through reconstruction error, establishing a direct correlation between data deviation and tribological performance degradation. Additionally, permutation importance-based feature evaluation and parameter contribution quantification techniques enable deep mechanistic analysis and fault source tracing of lubricant health degradation. Experimental validation using multi-sensor monitoring data demonstrates that SFTI-LVAE achieves a 96.67% fault detection accuracy with zero false alarms, providing early warning 6.47 h before lubrication failure. Unlike traditional anomaly detection methods that only classify conditions as abnormal or normal, the proposed continuous health index reveals gradual tribological degradation processes, capturing subtle viscosity–temperature relationships and wear particle evolution indicating early lubrication regime transitions. The health index correlates strongly with tribological performance indicators, enabling a transition from reactive maintenance to predictive tribological management, providing an innovative solution for equipment health evaluation in the digital tribology era. Full article

Diesel engines serve as critical power sources across transportation and industrial fields, and their fault diagnosis is essential for ensuring operational safety and system reliability. However, acquiring sufficient and effective operational data remains a significant challenge due to the high complexity of the systems. As a modeling method that incorporates expert knowledge, the belief rule base (BRB) demonstrates strong potential in resolving such challenges. Nevertheless, the reliance on expert knowledge constrains its practical application, particularly in complex engineering scenarios. To overcome this limitation, this study proposes a reliability fault diagnosis method for diesel engines based on the belief rule base with data-driven initialization (DI-BRB-R), which aims to improve modeling capability under conditions of limited expert knowledge. Specifically, the approach first employs fuzzy c-means clustering with the Davies–Bouldin index (DBI-FCM) to initialize attribute reference values. Then, a Gaussian membership function with Laplace smoothing (LS-GMF) is developed to initialize the rule belief degrees. Furthermore, to guarantee the reliability of the model optimization process, a group of reliability guidelines is introduced. Finally, the effectiveness of the proposed method is validated through an example of fault diagnosis of the WD615 diesel engine. Full article

This study addresses the effects of climate variability and land-use change on groundwater recharge in Major Groundwater Reservoir 406 (MGR 406) in southeastern Poland, a key strategic water resource. It focuses on how regional shifts in precipitation patterns and spatial development influence the volume and distribution of renewable groundwater resources. The analysis integrates meteorological data (1951–2024), groundwater modeling outputs, groundwater-use data, and land cover changes from CORINE datasets (1990–2018). A spatial assessment of hydrogeological conditions was performed using the Groundwater Resources Assessment Index (GRAI), combining drought frequency, recharge conditions, land-use classes, and groundwater extraction levels. Results indicate a long-term increase in annual precipitation alongside more frequent but shorter drought episodes. Urban expansion and land sealing were found to reduce infiltration efficiency, particularly in and around the city of Lublin, where the highest extraction rates were recorded. The assessment identified several zones of high threat to groundwater resources, which have no sufficient legal protection. These findings highlight the need to integrate groundwater management into local spatial planning and land management strategies. The study concludes that balancing water use and recharge potential under evolving climate and land-use pressures are essential to ensuring the sustainability of groundwater resources in MGR 406. Full article

Background/Objectives: Chronic obstructive pulmonary disease (COPD) is a progressive condition whose heterogeneous endotypes, clinical manifestations, and recovery pathways complicate the identification of reliable predictors of rehabilitation outcomes. Several respiratory and functional assessments are available with no consensus on the most predictive ones. While univariate markers may miss multifactorial interactions essential for prognosis, data-driven unsupervised clustering methods can integrate complex information from different sources. This study aimed to apply unsupervised clustering to identify pre-rehabilitation characteristics predictive of discharge outcomes for COPD patients undergoing pulmonary rehabilitation. Methods: A total of 126 COPD patients undergoing pulmonary rehabilitation were included in the analysis. Three assessments were performed at admission, namely the forced oscillation technique, spirometry, and the six-minute walk test (6MWT). The outcome was the change in 6MWT distance between admission and discharge. Unsupervised clustering methods were applied to admission variables to identify subgroups associated with outcomes. Results: Among the clustering algorithms tested, k-means (with N_cl = 2) provided the optimal solution. The resulting respiratory rehabilitation index (R2I) was significantly associated with the outcome dichotomized via the minimal clinically important difference of 30 m. Patients with R2I = 1, indicating severe functional and respiratory impairments, were associated with higher post-rehabilitation functional improvement (p = 0.032). While few functional parameters of 6MWT were statistically different between the groups identified by outcome, nearly all variables in the analysis exhibited significant distribution differences among the R2I clusters. Conclusions: These findings highlight the heterogeneity of COPD and the potential of unsupervised clustering to identify distinct patient subgroups, enabling more personalized rehabilitation strategies. Full article

Predicting short-term buy and sell signals in financial markets remains a significant challenge for algorithmic trading. This difficulty stems from the data’s inherent volatility and noise, which often leads to spurious signals and poor trading performance. This paper presents a novel algorithmic trading model for silver that combines fine-tuned Convolutional Neural Networks (CNNs) with a decision filter based on the Relative Strength Index (RSI). The technique allows for the prediction of buy and sell points by turning time series data into chart images. Daily silver price per ounce data were turned into chart images using technical analysis indicators. Four pre-trained CNNs, namely AlexNet, VGG16, GoogLeNet, and ResNet-50, were fine-tuned using the generated image dataset to find the best architecture based on classification and financial performance. The models were evaluated using walk-forward validation with an expanding window. This validation method made the tests more realistic and the performance evaluation more robust under different market conditions. Fine-tuned VGG16 with the RSI filter had the best cost-adjusted profitability, with a cumulative return of 115.03% over five years. This was nearly double the 61.62% return of a buy-and-hold strategy. This outperformance is especially impressive because the evaluation period was mostly upward, which makes it harder to beat passive benchmarks. Adding the RSI filter also helped models make more disciplined decisions. This reduced transactions with low confidence. In general, the results show that pre-trained CNNs fine-tuned on visual representations, when supplemented with domain-specific heuristics, can provide strong and cost-effective solutions for algorithmic trading, even when realistic cost assumptions are used. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article

Biosurfactants (BS) are surface-active compounds synthesized by microorganisms with broad industrial applications. Although BS-producing strains are widely reported, little is known about their production by diazotrophic bacteria. This study investigated, for the first time, the BS produced by Bradyrhizobium sp. ESA 81, a diazotrophic bacterium isolated from the Brazilian semiarid region. The strain was cultivated in the mineral medium using sunflower oil and ammonium nitrate as carbon and nitrogen sources. The compound was chemically characterized using TLC, FAME, FTIR, and mass spectrometry (MALDI-TOF). The results revealed a mixture of glycolipids composed of trehalose linked to fatty acid chains ranging from C9 to C18. The BS exhibited a surface tension of 31.8 mN/m, a critical micelle concentration of 61.2 mg/L, and an interfacial tension of 22.1 mN/m. The BS also showed an emulsification index (EI24) of 55.0%. High stability was observed under extreme conditions of temperature (−20 to 121 °C), pH (2–12), NaCl (5–20%), and sucrose (1–5%). These findings indicate that the trehalolipid BS produced by Bradyrhizobium sp. ESA 81 is a stable and efficient surface-active agent, with promising potential for use in biotechnological and industrial processes. Full article

The novel engines nowadays with high efficiency are operated under the superpressure, supercritical, and supersonic extreme conditions that are situated in the broken reaction zone regime. In this article, the propagation and heat/radical diffusion physics of a high-pressure dimethyl ether (DME)/air turbulent lean-premixed flame are investigated numerically by direct numerical simulation (DNS). A wide range of statistical and diagnostic methods, including Lagrangian fluid tracking, Joint Probability Density Distribution (JPDF), and chemical explosive mode analysis (CEMA), are applied to reveal the local combustion modes and dynamics evolution, as well as the roles of heat/mass transport and cool/hot flame interaction in the turbulent combustion, which would be beneficial to the design of novel engines with high performances. It is found that the three-staged combustion, including cool-flame, warm-flame, and hot-flame fronts, is a unique behavior of DME flame under the elevated-pressure, lean-premixed condition. In the broken reaction zone regime, the reaction zone thickness increases remarkably, and the heat release rate (HRR) and fuel consumption rate in the cool-flame zone are increased by 16% and 19%, respectively. The diffusion effect not only enhances flame propagation, but also suppresses the local HRR or fuel consumption. The strong turbulence interplaying with diffusive transports is the underlying physics for the enhancements in cool- and hot-flame fronts. The dominating diffusive sub-processes are revealed by the aid of the diffusion index. Full article

With the advancement of precision agriculture, Unmanned Aerial Vehicle (UAV)-based remote sensing has been increasingly employed for monitoring crop water and nutrient status due to its high flexibility, fine spatial resolution, and rapid data acquisition capabilities. This review systematically examines recent research progress and key technological pathways in UAV-based remote sensing for crop water and nutrient monitoring. It provides an in-depth analysis of UAV platforms, sensor configurations, and their suitability across diverse agricultural applications. The review also highlights critical data processing steps—including radiometric correction, image stitching, segmentation, and data fusion—and compares three major modeling approaches for parameter inversion: vegetation index-based, data-driven, and physically based methods. Representative application cases across various crops and spatiotemporal scales are summarized. Furthermore, the review explores factors affecting monitoring performance, such as crop growth stages, spatial resolution, illumination and meteorological conditions, and model generalization. Despite significant advancements, current limitations include insufficient sensor versatility, labor-intensive data processing chains, and limited model scalability. Finally, the review outlines future directions, including the integration of edge intelligence, hybrid physical–data modeling, and multi-source, three-dimensional collaborative sensing. This work aims to provide theoretical insights and technical support for advancing UAV-based remote sensing in precision agriculture. Full article

Net ecosystem productivity (NEP) serves as a key indicator for assessing regional carbon sink potential, with its dynamics regulated by nonlinear interactions among multiple factors. However, its driving factors and their coupling processes remain insufficiently characterized. This study investigated terrestrial ecosystems in Yunnan Province, China, to elucidate the drivers of NEP using 14 environmental factors (including topography, meteorology, soil texture, and human activities) and 21 remote sensing features. We developed a research framework based on “Feature Selection–Machine Learning–Mechanism Interpretation.” The results demonstrated that the Variable Selection Using Random Forests (VSURF) feature selection method effectively reduced model complexity. The selected features achieved high estimation accuracy across three machine learning models, with the eXtreme Gradient Boosting Regression (XGBR) model performing optimally (R² = 0.94, RMSE = 76.82 gC/(m²·a), MAE = 55.11 gC/(m²·a)). Interpretation analysis using the SHAP (SHapley Additive exPlanations) method revealed the following: (1) The Enhanced Vegetation Index (EVI), soil pH, solar radiation, air temperature, clay content, precipitation, sand content, and vegetation type were the primary drivers of NEP in Yunnan. Notably, EVI’s importance exceeded that of other factors by approximately 3 to 10 times. (2) Significant interactions existed between soil texture and temperature: Under low-temperature conditions (−5 °C to 12.15 °C), moderate clay content (13–25%) combined with high sand content (40–55%) suppressed NEP. Conversely, within the medium to high temperature range (5 °C to 23.79 °C), high clay content (25–40%) coupled with low sand content (25–43%) enhanced NEP. These findings elucidate the complex driving mechanisms of NEP in subtropical ecosystems, confirming the dominant role of EVI in carbon sequestration and revealing nonlinear regulatory patterns in soil–temperature interactions. This study provides not only a robust “Feature Selection–Machine Learning–Mechanism Interpretation” modeling framework for assessing carbon budgets in mountainous regions but also a scientific basis for formulating regional carbon management policies. Full article

With the purpose of understanding the trophic relationships between three herbivores that use humid high-mountain grassland and evaluating a possible interspecific competition between them and depending on the importance of the hydromorphic vegetation formations of high-mountain areas, relations were established between the attributes of these grasslands and the botanical composition of the diet of grey-breasted seedsnipe (Thinocorus orbignyianus), brown hares (Lepus europaeus), and horses (Equus ferus caballus). For two summer seasons, the botanical composition of the grassland and dry matter availability were assessed. In parallel, the botanical composition of the diets of the three herbivores was estimated through fecal microhistology. Based on the botanical composition data for both the grasslands and herbivores’ diets, their relative diversity was estimated. The Pianka index was established among the three herbivores. Hares showed greater dietary diversity (J) than horses and grey-breasted seedsnipes, factors that were negatively correlated in all three cases with the vegetation diversity patch. The same response amplitude was found when analyzing the food web. The dietary diversity for all species showed no relation to the dry matter productivity of the vegetable patches. Through analyzing the correlation of the abundance of two species of Cyperaceae in the grassland with the presence of the same in the diet of herbivores, we found a negative relationship between the abundance of Carex sp. and grey-breasted seedsnipe diet, and a positive relationship between the Eleocharis pseudoalbibracteata species abundance and frequency in the diet of hares and horses. About the group of species content of graminoids in the diet, a dietary overlap of 30% was determined in the animal species assessed; depending on that, it could identify the existence of interspecific competition between herbivores, which would be conditioned by the response of individuals to the environment. However, and according to the magnitude of the dietary overlap, a low probability of interspecific trophic competition among the studied herbivore species can be expected, which enables the use of the highland wet grassland habitat in sympatry. Full article

The mechanisms underlying groundwater microbial community assembly have long attracted attention in earth, environmental, and ecological studies. Nevertheless, limited knowledge is available regarding microbial community assembly within the intact groundwater flow systems in arid regions. In this study, long-term hydrochemical data and microbial community profiles were integrated to unravel the assembly processes and driving forces mediating microbial communities in the Golmud River watershed. Our results indicated that hydrochemical conditions gradually transitioned from oxidizing to reducing environments along the groundwater flow path, as evidenced by a 28.57% and 65.45% decrease in DO and ORP, respectively. Major ions, represented by TDS, displayed minimal variations in phreatic (519.72 ± 16.83 mg/L) and artesian groundwater (486.01 ± 27.71 mg/L), followed by pronounced enrichment in high-salinity groundwater (TDS: 316,112.74 ± 12,452.19 mg/L). Gammaproteobacteria and Actinobacteria declined markedly from phreatic (51.69 ± 6.83% and 9.54 ± 3.40%, respectively) to high-salinity groundwater (13.97 ± 3.70% and 4.77 ± 2.46%). Conversely, halophiles such as Halobacteria and Parcubacteria were rarely detected in low-TDS groundwater, but increased sharply in high-salinity groundwater, reaching 23.22 ± 10.42% and 8.34 ± 3.71%, respectively. Deterministic processes primarily controlled groundwater microbial communities across hydrochemical conditions (relative importance > 50%, NST index < 50%). Microbial co-occurrence networks revealed increasingly tight interactions and intensified competition among communities, driven by accumulated salinity–oxygen stress along the groundwater flow path. This study emphasizes the role of deterministic processes in shaping groundwater microbial community structure, particularly the impact of salinity–oxygen stress. Our findings advance the current understanding of the mechanisms by which hydrochemical processes shape groundwater microbial assemblages. Full article