Search Results (177)

Using a city-level panel for China (2011–2021), this paper estimates agricultural total factor productivity (TFP) with a stochastic-frontier approach and identifies the effect of digital finance through two-way fixed effects and instrumental-variable strategies. We document a statistically and economically significant negative association: a 1% increase in the digital finance index is linked to a decline of 1.5 in agricultural TFP. Evidence points to capital misallocation as the dominant channel, with the adverse effect most pronounced where agricultural capital markets are highly distorted. Heterogeneity analyses show stronger negative impacts in labor-intensive areas, non-major grain regions, and small-scale farming systems. Results are robust across alternative specifications and IV estimations. By moving from provincial aggregates to city-level variation, this study sharpens identification and uncovers within-province patterns that are invisible in coarser data. The findings highlight an important unintended consequence of digital financial expansion for agriculture and underscore a policy priority: improving the allocation and targeting of digital credit within rural economies to support productivity and sustainable development. Full article

Transformer-based models have accelerated EEG motor imagery (MI) decoding by using self-attention to capture long-range temporal structures while complementing spatial inductive biases. This systematic survey of Scopus-indexed works from 2020 to 2025 indicates that reported advances are concentrated in offline, protocol-heterogeneous settings; inconsistent preprocessing, non-standard data splits, and sparse efficiency frequently reporting cloud claims of generalization and real-time suitability. Under session- and subject-aware evaluation on the BCIC IV 2a/2b dataset, typical performance clusters are in the high-80% range for binary MI and the mid-70% range for multi-class tasks with gains of roughly 5–10 percentage points achieved by strong hybrids (CNN/TCN–Transformer; hierarchical attention) rather than by extreme figures often driven by leakage-prone protocols. In parallel, transformer-driven denoising—particularly diffusion–transformer hybrids—yields strong signal-level metrics but remains weakly linked to task benefit; denoise → decode validation is rarely standardized despite being the most relevant proxy when artifact-free ground truth is unavailable. Three priorities emerge for translation: protocol discipline (fixed train/test partitions, transparent preprocessing, mandatory reporting of parameters, FLOPs, per-trial latency, and acquisition-to-feedback delay); task relevance (shared denoise → decode benchmarks for MI and related paradigms); and adaptivity at scale (self-supervised pretraining on heterogeneous EEG corpora and resource-aware co-optimization of preprocessing and hybrid transformer topologies). Evidence from subject-adjusting evolutionary pipelines that jointly tune preprocessing, attention depth, and CNN–Transformer fusion demonstrates reproducible inter-subject gains over established baselines under controlled protocols. Implementing these practices positions transformer-driven BCIs to move beyond inflated offline estimates toward reliable, real-time neurointerfaces with concrete clinical and assistive relevance. Full article

Digitally controlled DC–DC converters are vulnerable to sensor-side spoofing, motivating plant-level anomaly detection that respects the converter physics. We present a physics-informed LSTM (PI–LSTM) autoencoder for a 24→12 V buck converter. The model embeds discrete-time circuit equations as residual penalties and uses a fixed decision rule ($\tau =\mu +3\sigma$, $N=3$ consecutive samples). We study three voltage-sensing attacks (DC bias, fixed-sample delay, and narrowband noise) in MATLAB/Simulink. We then validate the detection path on a TMS320F28379 DSP. The detector attains F1 scores of 96.12%, 91.91%, and 97.50% for bias, delay, and noise (simulation); on hardware, it achieves 2.9–4.2 ms latency with an alarm-wise FPR of ≤1.2%. We also define a unified safety box for DC rail quality and regulation. In simulations, we evaluate a confusion index (CI) policy for safety-bounded performance adjustment. A operating point yields $\mathrm{CI}\approx 0.25$ while remaining within the safety limits. In hardware experiments without CI actuation, the $V_{r,pp}$ and IRR stayed within the limits, whereas the ±2% regulation window was occasionally exceeded under the delay attack (up to ≈2.8%). These results indicate that physics-informed detection is deployable on resource-constrained controllers with millisecond-scale latency and a low alarm-wise FPR, while the full hardware validation of CI-guided deception (safety-bounded performance adjustment) under the complete safety box is left to future work. Full article

Against the backdrop of rapid population aging and a high prevalence of cognitive impairment in China, identifying modifiable environmental risk factors is a public health priority. Although environmental noise is widely recognized as a significant stressor, its effects on cognitive health remain underexplored within the Chinese context. Drawing on balanced panel data from three waves of the China Health and Retirement Longitudinal Study (CHARLS), we examined 3459 individuals aged 45 and above to assess the association between noise pollution and cognitive function using a two-way fixed-effects model. Additionally, we employed a chained mediation approach to investigate whether sleep disturbances and depressive symptoms serve as intermediary mechanisms. The findings indicated a significant inverse relationship: each unit increase in the noise pollution index corresponded to a 0.41-point reduction in overall cognitive scores. These results were robust across various noise exposure measures. Sensitivity analyses using alternative noise metrics also supported this finding. Sleep duration and depression were identified as significant mediators in the relationship between noise pollution and cognitive decline. This longitudinal analysis offers compelling evidence that environmental noise constitutes a substantial risk factor for declining cognitive function in middle-aged and older adults in China. Full article

This paper aims to discuss the positive mild solutions for nonlocal fractional variable exponent differential equations with concave and convex coefficients. Based on a specifically defined order cone, even under the influence of the $p(t)$-Laplacian operator and the fractional integral operator, we avoid making many assumptions on the nonlocal coefficient $\mathcal{A}$ and just require that $\mathcal{A}>0$ on a set of positive measures. Utilizing the fixed-point index theory on cones, some new results on the existence and multiplicity of positive mild solutions were obtained, which extend and enrich some previous research findings. Finally, numerical examples are used to verify the feasibility of our main results. Full article

Accurate preoperative staging of bladder cancer on MRI remains challenging because visual reads vary across observers. We investigated a multiparametric MRI (mpMRI) radiomics approach to predict muscle invasion (≥T2) and prospectively tested it on a validation cohort. Eighty-four patients with urothelial carcinoma underwent 1.5-T mpMRI per VI-RADS (T2-weighted imaging and DWI-derived ADC maps). Two blinded radiologists performed 3D tumor segmentation; 37 features per sequence were extracted (LifeX) using absolute resampling. In the training cohort (n = 40), features that differed between non-muscle-invasive and muscle-invasive tumors (Mann–Whitney p < 0.05) underwent ROC analysis with cut-offs defined by the Youden index. A compact descriptor combining GLRLM-LRLGE from T2 and GLRLM-SRLGE from ADC was then fixed and applied without re-selection to a prospective validation cohort (n = 44). Histopathology within 6 weeks—TURBT or cystectomy—served as the reference. Eleven T2-based and fifteen ADC-based features pointed to invasion; DWI texture features were not informative. The descriptor yielded AUCs of 0.934 (training) and 0.871 (validation) with 85.7% sensitivity and 96.2% specificity in validation. Collectively, these findings indicate that combined T2/ADC radiomics can provide high diagnostic accuracy and may serve as a useful decision support tool, after multicenter, multi-vendor validation. Full article

Advancements in remote sensing technology have enabled the acquisition of high spatial and radiometric resolution imagery, offering abundant and reliable data sources for forest fire monitoring. In order to explore the ability of Sustainable Development Science Satellite 1 (SDGSAT-1) in wildfire monitoring, a systematic and comprehensive study was proposed on smoke detection during the wildfire early warning phase, fire point identification during the fire occurrence, and burned area delineation after the wildfire. The smoke detection effect of SDGSAT-1 was analyzed by machine learning and the discriminating potential of SDGSAT-1 burned area was discussed by Mid-Infrared Burn Index (MIRBI) and Normalized Burn Ratio 2 (NBR2). In addition, compared with Sentinel-2, the fixed-threshold method and the two-channel fixed-threshold plus contextual approach are further used to demonstrate the performance of SDGSAT-1 in fire point identification. The results show that the average accuracy of SDGSAT-1 fire burned area recognition is 90.21%, and a clear fire boundary can be obtained. The average smoke detection precision is 81.72%, while the fire point accuracy is 97.40%, and the minimum identified fire area is 0.0009 km², which implies SDGSAT-1 offers significant advantages in the early detection and identification of small-scale fires, which is significant in fire emergency and disposal. The performance of fire point detection is superior to that of Sentinel-2 and Landsat 8. SDGSAT-1 demonstrates great potential in monitoring the entire process of wildfire occurrence, development, and evolution. With its higher-resolution satellite imagery, it has become an important data source for monitoring in the field of remote sensing. Full article

This study examines the efficiency trade-offs of Controllable Pitch Propeller (CPP) systems by comparing Combination and Fixed operation modes using real ship operational data. The analysis focuses on mechanical efficiency (η_mech), propulsive efficiency expressed through the normalized Relative Propulsive Efficiency Index (RPEI_norm), and fuel consumption. Combination mode consistently maintained higher η_mech across all load conditions, with pronounced advantages at low load and low speed (<50% load, <12 knots), where both propulsive efficiency and fuel economy improved. In contrast, Fixed mode outperformed Combination mode at high load and high speed, exceeding approximately 50% load and 12 knots, as propeller performance approached its optimal operating point despite some sacrifice in engine efficiency. To integrate these effects, a proxy overall efficiency index (η_{overall,proxy} = η_mech × RPEI_norm) was introduced, revealing a crossover point at 0.525 load where the efficiency dominance shifted between modes. These findings demonstrate that neither mode is universally superior, but rather their advantages depend on operating conditions. The results provide practical insights for adaptive operational strategies, enabling real-time switching between modes to optimize fuel consumption and overall propulsion performance while supporting compliance with environmental regulations. Full article

This study examines regional disparities and convergence dynamics in paediatric healthcare accessibility across Poland’s 16 provinces between 2010 and 2023. A synthetic Paediatric Service Accessibility Index (PSA Index), constructed with Hellwig’s method, is combined with socio-economic indicators such as employment, urbanisation, and disposable income to evaluate the alignment between healthcare provision and regional development. The analysis employs non-parametric regional tests (Spearman’s rank correlation, Wilcoxon signed-rank test) and national panel regression models (Fixed and Random Effects). Results demonstrate significant spatial heterogeneity: economically advanced regions, including Mazowieckie and Małopolskie, show moderate to strong convergence between socio-economic progress and healthcare access, whereas structurally weaker regions such as Lubuskie and Podkarpackie reveal persistent divergence. Disposable income and urbanisation emerge as significant predictors of healthcare availability (p < 0.01), while employment is not statistically significant. The findings highlight enduring inequalities that are relevant in the context of the European Union’s (EU) cohesion policy and indicate that economic growth alone is insufficient to ensure equitable access to paediatric care. Comparative evidence from Romania, Bulgaria, and Spain points to similar patterns and emphasises the importance of EU Structural and Investment Funds in promoting healthcare equity. The study concludes that territorially sensitive, multidimensional interventions are necessary to advance social sustainability and to align healthcare infrastructure with the Sustainable Development Goals, particularly SDG 3 (Good Health and Well-Being) and SDG 10 (Reduced Inequalities). Full article

This research explores the complex relationships between tourism, economic factors, environmental sustainability, and transportation infrastructure within the European Union (EU), as the tourist scene changes globally. Our research uses a comprehensive model to investigate the factors that influence the number of tourists arriving in the EU, focusing on the years 1990 to 2022. The model considers transportation infrastructure, environmental sustainability indices, and economic variables as major determinants of tourism flows. Economic variables encompass exchange rates, the Consumer Price Index (CPI), and per capita income, while environmental sustainability indicators include carbon footprint and renewable energy usage. Additionally, the model considers transportation infrastructure by assessing the quality and availability of transportation modes. We use a two-way fixed effect to account for any unobserved heterogeneity. Fixed effects give control over nation-specific factors that might affect tourism, as they are a reliable method to deal with potential biases in the estimated parameters. Our study aims to provide insightful information about the sustainable growth of tourism in the European Union, providing policymakers, scholars, and industry stakeholders with a comprehensive understanding of the variables influencing visitor arrivals. This research contributes to the tourism literature by integrating CE principles with behavioral insights from the theory of planned behavior, highlighting how tourists’ pro-environmental attitudes, social norms, and perceived behavioral control influence travel choices. In the framework of the circular economy, the authors hope to inform policy choices and advance a more environmentally conscious travel industry in the EU by examining the points where economic, environmental, and transportation aspects converge. Full article

This study investigates how bank mergers and acquisitions (M&As) reshape the monitoring architecture of syndicated loans and, by extension, borrowers’ financing conditions. Using a global panel of 20,299 syndicated loan contracts, originating in 43 countries between 1982 and 2020, we link LPC DealScan data to Securities Data Company M&A records to trace each loan’s lead arrangers before and after consolidation events. Fixed-effects regressions, enriched with borrower- and loan-level controls, reveal three key patterns. First, post-merger loans exhibit significantly more concentrated syndicates: the Herfindahl–Hirschman Index rises by roughly 130 points and lead arrangers retain an additional 0.8–1.1 percentage points of the loan, consistent with heightened monitoring incentives. Second, these effects are amplified when information asymmetry is acute, i.e., for opaque or unrated firms, supporting moral hazard theory predictions that lenders internalize greater risk by holding larger stakes. Third, relational capital tempers the impact of consolidation: borrowers with repeated pre-merger relationships face smaller increases in syndicate concentration, while switchers experience the most significant jumps. Robustness checks using lead arranger market share, alternative spread measures, and lag structures confirm the findings. Overall, the results suggest that bank consolidation strengthens lead arrangers’ incentives to monitor but simultaneously reduces risk-sharing among participant lenders. For borrowers, the net effect is a trade-off between potentially tighter oversight and reduced syndicate diversification, with the balance hinging on transparency and prior ties to the lender. These insights refine our understanding of how structural shifts in the banking sector cascade into corporate credit markets and should inform both antitrust assessments and borrower funding strategies. Full article

Soil–structure contacts often govern deformation and stability in foundations and buried infrastructure. Rubber waste is used in soil mixtures to enhance geotechnical performance and promote environmental sustainability. This study investigates the peak and residual shear strength of sand–steel interfaces, where the sand is mixed with recycled rubber. It also develops predictive machine learning (ML) models based on the experimental data. Two silica sands, medium and coarse, were mixed with two rubber gradations; however, Rubber B was included only in limited comparative tests at a fixed content. Ring-shear tests were performed against smooth and rough steel plates under normal stresses of 25 to 200 kPa to capture the full τ–δ response. Nine input variables were considered: median particle size (D₅₀), regularity index (RI), porosity (n), coefficients of uniformity (C_u) and curvature (C_c), rubber content (RC), applied normal stress (σ_n), normalised roughness (R_n), and surface hardness (HD). These variables were used to train multiple linear regression (MLR) and random forest regression (RFR) models. The models were trained and validated on 96 experimental data points derived from ring-shear tests across varied material and loading conditions. The machine learning models facilitated the exploration of complex, non-linear relationships between the input variables and both peak and residual interfacial shear strength. Experimental findings demonstrated that particle size compatibility, rubber content, and surface roughness significantly influence interface behaviour, with optimal conditions varying depending on the surface type. Moderate inclusion of rubber was found to enhance strength under certain conditions, while excessive content could lead to performance reduction. The MLR model demonstrated superior generalisation in predicting peak strength, whereas the RFR model yielded higher accuracy for residual strength. Feature importance analyses from both models identified the most influential parameters governing the shear response at the sand–steel interface. Full article

Distribution System Operators (DSOs) increasingly need planning tools that coordinate utility-influenced assets—such as electric-vehicle charging stations (EVCS) and voltage-support resources—with customer-sited distributed generation (DG). We present a Nash-equilibrium-based Iterative Best Response Algorithm (IBRA-NE) for joint planning of DG and EVCS in radial distribution networks. The framework supports two applicability modes: (i) a DSO-plannable mode that co-optimizes EVCS siting/sizing and utility-controlled reactive support (DG operated as VAR resources or functionally equivalent devices), and (ii) a customer-sited mode that treats DG locations as fixed while optimizing DG reactive set-points/sizes and EVCS siting. The objective minimizes network losses and voltage deviation while incorporating deployment costs and EV charging service penalties, subject to standard operating limits. A backward/forward sweep (BFS) load flow with Monte Carlo simulation (MCS) captures load and generation uncertainty; a Bus Voltage Deviation Index (BVDI) helps identify weak buses. On the EEU 114-bus system, the method reduces base-case losses by up to 57.9% and improves minimum bus voltage from 0.757 p.u. to 0.931 p.u.; performance remains robust under a 20% load increase. The framework explicitly accommodates regulatory contexts where DG siting is customer-driven by treating DG locations as fixed in such cases while optimizing EVCS siting and sizing under DSO planning authority. A mixed scenario with 5 DGs and 3 EVCS demonstrates coordinated benefits and convergence properties relative to PSO, GWO, RFO, and ARFO. Additionally, the proposed algorithm is also tested on the IEEE 69-bus system and results in acceptable performance. The results indicate that game-theoretic coordination, applied in a manner consistent with regulatory roles, provides a practical pathway for DSOs to plan EV infrastructure and reactive support in networks with uncertain DER behavior. Full article

Urbanization affects bird communities by reducing habitat and fragmenting ecosystems. Urban parks can help counteract these effects. However, anthropogenic noise can further alter bird composition. We examined the distribution and abundance of bird species in nine urban parks in Mexico City. We used a ten minute fixed-radius (25 m) point-counting technique to count birds along their annual cycle, with ten minutes allocated for bird counts. The quality of green areas was analyzed in terms of vegetation (Normalized Difference Vegetation Index), park size, and mean noise level dB(A) (based on MIN and MAX values), and species were grouped into trophic guilds. A total of 108 bird species were recorded, 5 of which are under special protection; we grouped all species into 14 trophic guilds, showing different responses to environmental gradients. Redundancy analysis (RDA) explained 89.98% of the variance, with noise and park size being the most influential variables. Granivores and omnivores were more tolerant to noise, while insectivores and frugivores preferred quieter areas with more vegetation. A positive association was observed between the presence of winter resident species and the park size. On the other hand, mean noise level dB(A) was negatively related to permanent resident species, winter resident species, and those with protected status. Conservation efforts should focus on maintaining ample green spaces and reducing noise pollution, as recorded high mean noise levels (>53 dB(A)) exceed the recommended thresholds for avifauna conservation. Full article

This study explores how FinTech and artificial intelligence (AI) adoption shape efficiency and financial stability in dual-banking systems. It focuses on 26 listed Islamic and conventional banks across 11 countries in the MENA and Southeast Asia regions between 2020 and 2024. To measure digital adoption, we create a seven-component FinTech Adoption Index. We use fixed-effects regressions to examine its impact on cost efficiency, profitability, solvency stability, and credit risk. This analysis also controls bank size, capitalization, and macroeconomic conditions. The results show a clear adoption gap. Conventional banks consistently score 0.5–0.8 points higher on the FinTech Index compared to Islamic banks. Each additional FinTech component raised operating costs by about 0.8%, but improved profitability slightly by only 0.03%. This suggests that technological integration creates upfront costs before any real efficiency gains are seen. However, the stability benefits are stronger. FinTech adoption increases the Z-score by 3.6 points and lowers the non-performing loan ratio by 0.1%. Islamic banks gain more stability benefits due to their risk-sharing contracts and asset-backed financing structures. Overall, an efficiency–stability trade-off emerges. Conventional banks focus more on profitability, while Islamic banks gain resilience, but face slower efficiency improvements. By combining the Resource-Based View and Financial Stability Theory, this study provides the first multi-country evidence of how governance structures shape digital transformation in dual-banking markets. The findings offer practical guidance for regulators and bank managers around balancing innovation, efficiency, and stability. Full article