Search Results (10,034)

The high demand and economic value of kingfish (Scomberomorus commerson) have led to intensive fishing of this species in the Omani waters. The increased fishing pressure has made the fishery vulnerable; hence, information on the current stock status is essential for the sustainability of the kingfish stock. Three length-based stock assessment approaches (TropFishR, spawning potential ratio, and Bayesian biomass method) were used to estimate growth, mortality, exploitation, spawning potential capacity, and relative biomass in relation to maximum sustainable yield (MSY). Asymptotic length (L_∞) was 186.31 cm, and the growth coefficient (K) was 0.15 yr⁻¹ for S. commerson. Fishing mortality was 0.45 yr⁻¹, which was higher than natural mortality (M = 0.18 yr⁻¹) and optimal fishing mortalities (F40% = 0.15 yr⁻¹). The exploitation rate (E) was found to be 0.71 yr⁻¹, higher than the optimum exploitation (E = 0.50), indicating a total overfishing of 42% of the S. commerson in Oman waters. The current length at first capture (Lc50 = 74.38 cm) was significantly smaller than the length at first maturity (Lm50 = 91.25 cm), indicating growth overfishing. The current spawning potential ratio (SPR) was 10%, which was significantly below the reference point (SPR = 20%), indicating that the stock was severely overfished. Biomass was critically low (B/Bo = 0.17), and lower than the reference point of 0.20. Additionally, the current biomass was 44% of Bmsy (B/Bmsy = 0.44), which is significantly lower than the reference point of 1, indicating that the stock biomass was below the maximum sustainable yield level, suggesting recruitment overfishing. Stock indicators revealed that the fishery was primarily targeting immature/juvenile fish, as well as older and larger fish, which indicated stocks were both growth- and recruitment-overfished. Therefore, carrying out commercial fishing for an optimum size range (118 to 144 cm) and reducing fishing pressure to a sustainable level (F = M, 0.18 ^yr−1) would sustain a healthy stock biomass of kingfish in Omani waters. Full article

Stock trend prediction is a complex and crucial task due to the dynamic and nonlinear nature of stock price movements. Traditional models struggle to capture the non-stationary and volatile characteristics of financial time series. To address this challenge, we propose the Hybrid Relational Transformer (HRformer), which specifically decomposes time series into multiple components, enabling more accurate modeling of both short-term and long-term dependencies in stock data. The HRformer mainly comprises three key modules: the Multi-Component Decomposition Layer, the Component-wise Temporal Encoder (CTE), and the Inter-Stock Correlation Attention (ISCA). Our approach first employs the Multi-Component Decomposition Layer to decompose the stock sequence into trend, cyclic, and volatility components, each of which is independently modeled by the CTE to capture distinct temporal dynamics. These component representations are then adaptively integrated through the Adaptive Multi-Component Integration (AMCI) mechanism, which dynamically fuses their information. The fused output is subsequently refined by the ISCA module to incorporate inter-stock correlations, leading to more accurate and robust predictions. Extensive experiments on the NASDAQ100 and CSI300 datasets demonstrate that HRformer consistently outperforms state-of-the-art methods, e.g., achieving about 0.83% higher Accuracy and 1.78% higher F1-score than TDformer on NASDAQ100, with Sharpe Ratios of 1.5354 on NASDAQ100 and 0.5398 on CSI300, especially in volatile market conditions. Backtesting results validate its practical utility in real-world trading scenarios, showing its potential to enhance investment decisions and portfolio performance. Full article

Background/Objectives: Infective endocarditis (IE) remains a rare but increasingly complex condition, posing significant challenges for cardiologists and cardiac surgeons. Blood groups from the ABO/Rh system have been associated with susceptibility to various diseases, including infections and bacterial colonization tendencies. However, data on the distribution of ABO/Rh blood types among IE patients are lacking. We hypothesized that the prevalence of ABO/Rh blood types among IE patients differs from their frequency in the general population. This study aimed to assess the distribution of ABO/Rh blood types in the LODZ-ENDO database in comparison to general populations. Methods: LODZ-ENDO is a single-center retrospective study conducted in a tertiary cardiology and cardiac surgery facility serving 2.35 million residents. All consecutive patients with confirmed IE hospitalized between 1 January 2015 and 1 October 2025 were included. Clinical data and ABO/Rh blood types were collected and compared with national population data using Fisher’s exact and chi-square tests. Results: A total of 329 patients with IE were analyzed (median age 61 [41–68] years; 69% men), of whom 227 underwent cardiac surgery. Overall ABO/Rh distribution differed significantly from the general population (p = 0.033), driven by a tendency to an overrepresentation of B− (LODZ-ENDO 5.2% vs. Poland 2%; OR 2.88; 95% CI 1.17–7.29; p = 0.03; power 0.89; p(adj) = 0.23). Considering regional demographics and blood use (≈3 units per surgery), this represents an excess annual use of 1.9 B− units, equal to 0.23% of regional B− reserves, with additional indirect depletion of O− blood. Based on WHO data, if this overrepresentation exists elsewhere, IE-related surgeries could consume 0.2–1.3% of national B− stocks in smaller European countries such as Malta, Iceland, Luxembourg, Cyprus, Estonia, Lithuania, Latvia, and Slovenia. Conclusions: This, probably the first report of B− blood type overrepresentation in IE indicates disproportionate use of a rare blood group, highlighting the need for targeted blood management strategies, especially near specialized cardiac surgery centers. Full article

This study examines herding behavior in six MENA stock markets, with a focus on the impact of geopolitical risks and downward/upward market conditions during COVID-19/non-COVID-19 periods. Empirical results reveal significant differences among MENA countries regarding herding behavior and the impact of geopolitical events. We conclude that herding was more pronounced during the COVID-19 pandemic, especially during downward markets. Additionally, we found that geopolitical risks further amplified herding during dramatic market periods, particularly at lower quantiles. An exception was the Saudi stock market, which exhibited resilience to geopolitical risks, likely due to strong government support and robust policies. In contrast, Lebanon’s stock market has been exposed to an increased herding due to political instability. These findings suggest that regulators should enhance mechanisms to monitor market activities and introduce efficient policies to mitigate systematic risk and strengthen financial stability during downturns. Full article

With the acceleration of urbanization and the continuous expansion of building stock, building maintenance plays a critical role in ensuring structural safety, extending service life, and promoting sustainable development. In recent years, the application of artificial intelligence (AI) in building maintenance has expanded significantly, markedly improving detection accuracy and decision-making efficiency through predictive maintenance, automated defect recognition, and multi-source data integration. Although existing studies have made progress in predictive maintenance, defect identification, and data fusion, systematic quantitative analyses of the overall knowledge structure, research hotspots, and technological evolution in this field remain limited. To address this gap, this study retrieved 423 relevant publications from the Web of Science Core Collection covering the period 2000–2025 and conducted a systematic bibliometric and scientometric analysis using tools such as bibliometrix and VOSviewer. The results indicate that the field has entered a phase of rapid growth since 2017, forming four major thematic clusters: (1) intelligent construction and digital twin integration; (2) predictive maintenance and health management; (3) algorithmic innovation and performance evaluation; and (4) deep learning-driven structural inspection and automated operation and maintenance. Research hotspots are evolving from passive monitoring to proactive prediction, and further toward system-level intelligent decision-making and multi-technology integration. Emerging directions include digital twins, energy efficiency management, green buildings, cultural heritage preservation, and climate-adaptive architecture. This study constructs, for the first time, a systematic knowledge framework for AI-enabled building maintenance, revealing the research frontiers and future trends, thereby providing both data-driven support and theoretical reference for interdisciplinary collaboration and the practical implementation of intelligent maintenance. Full article

In many newly industrialized countries, the booming industrial parks have played a crucial role in propelling urban growth, promoting socioeconomic growth, and causing environmental deterioration. This study investigated land use/land cover (LULC) transformation and thermal effects of the “104 Industrial Blocks” in Shanghai, which have been the key industrial development zones since 1995. A total of 64 industrial clusters were identified by merging industrial parks with close spatial linkages. Subsequently, using a data-driven framework that contains data generated from Landsat series C2L2 images and auxiliary datasets, we analyzed land development patterns and associated anomalous thermal response across three scales: macro-level pattern, meso-level ring, and local scale. The results indicate that industrial growth in the downtown shifted from incremental expansion to stock renewal. Suburban areas became the main destinations for industrial relocation. Consequently, the thermal environment underwent a remarkable reconfiguration. Urban heat island (UHI) intensity declined in the downtown, while industrial clusters on the urban periphery emerged as newly emerging heat sources. Around 75% of suburban industrial parks have shifted from low- to medium/high-density patterns, creating new industrial heat islands. In contrast, only 20.31% of suburban industrial parks have shifted from low- to medium-density development without resulting in new urban heat islands. Full article

This study addresses the challenge of aligning inventory forecasting with sustainability and service level goals in re-order point systems. It introduces a semiparametric forecasting method based on exponential smoothing and M-estimation, designed to directly model reorder levels under fill rate (P2) constraints. The proposed approach is benchmarked against state-of-the-art techniques, including Generalized Autoregressive Score (GAS) models, volatility-adjusted smoothing, and DeepAR—a deep learning model for probabilistic time series forecasting. Using monthly demand data from the M3 competition, empirical evaluation demonstrates that the semiparametric method achieves high service level accuracy with low inventory and logistics costs, particularly under short lead times. DeepAR shows strong performance in minimizing inventory levels but tends to underestimate stock requirements under high service level targets. A hybrid strategy combining forecasts from multiple models proves robust across scenarios, reducing forecast risk. The findings highlight the potential of integrating traditional statistical methods with AI-based approaches to support resource-efficient inventory management. By minimizing excess stock and backorders, the proposed methods contribute to reducing environmental impact, offering practical solutions for organizations seeking to balance operational efficiency with sustainability. Full article

Examining the changing patterns and underlying mechanisms of soil biomass carbon stocks constitutes a fundamental aspect of soil biology. Despite the potential influence of the sulfur cycle and the life strategies of organisms on community biomass, these factors have rarely been studied in tandem. Biocrusts are model systems for studying soil ecosystems. In this study, metagenomic analysis of biocrusts related to different life strategies from five batches over four consecutive years demonstrated that, in free-living communities, microbial biomass carbon (MBC) synthesis, via assimilatory sulfate reduction (ASR), is primarily coupled with the 3-hydroxypropionate/4-hydroxybutyrate and Calvin–Benson–Bassham cycles. These pathways are affected by the oxidation-reduction potential (Eh), pH, electrical conductivity, and nutrient levels. The decomposition of organic carbon (OC) via dissimilatory sulfate reduction (DSR) was accompanied by the production of dimethyl sulfide (DMS), which was influenced by the C/S ratio and moisture, whereas the synthesis of MBC by symbiotic communities was found to be affected by Eh and pH, and decomposition was affected by the C/S ratio. The MBC stock was influenced by all strategies, with resource strategies having the greatest impacts during the growing season, and the contribution of chemotrophic energy was most significant in free-living communities. In conclusion, the MBC in biocrusts is associated with both ASR and DSR and is facilitated by the A-, S-, and P-strategies under the regulation of the stoichiometric C/S ratio. The exploration of microbial life strategies and sulfur cycling in biocrusts within arid ecosystems in this study offers a new perspective on the patterns of change in soil biomass carbon stocks. Full article

This study examines how ENSO episodes affect maize price volatility transmission between the United States and South Africa. Using daily price data, from 1997 to 2024, for U.S. corn and South African white and yellow maize futures, the study employs GARCH models augmented with ENSO phase indicators and the Southern Oscillation Index (SOI) to determine volatility spillovers. The results show that South African maize prices respond to lagged US corn prices and exchange rate fluctuations, with price volatility of both white and yellow maize prices being more persistent during El Niño and La Niña events. This study integrates climate variability indicators, specifically different ENSO phases and the SOI, to investigate climate-driven volatility transmission between developed and emerging markets. Significant results were obtained when the Southern Oscillation Index was added in the volatility equations. Not only does the inclusion of ENSO indicators and SOI enhance the explanatory power of GARCH models beyond existing studies, it also provides evidence of climate-driven volatility spillovers between a developed and developing market. These findings highlight the role of climate variability in agricultural market dynamics and stress the need for proactive risk management strategies such as buffer stocks and climate responsive financial instruments to ensure food security and market resilience in Southern Africa. Full article

Climate change is driving the need for forest management strategies that simultaneously enhance ecosystem resilience and contribute to climate change mitigation. Voluntary carbon markets (VCMs), regulated in the European Union by the Carbon Removal Certification Framework (CRCF), offer potential financial incentives for such management, but eligibility criteria—particularly biodiversity requirements—limit the applicability of certain species. This study assessed the ecological and economic outcomes of six alternative management scenarios for a 4.7 ha, 99-year-old Scots pine (Pinus sylvestris) stand in western Hungary, comparing them against a business-as-usual (BAU) regeneration baseline. Using field inventory data, species-specific yield tables, and the Forest Industry Carbon Model, we modelled living and dead biomass carbon stocks for 2025–2050 and calculated potential CO₂ credit generation. Economic evaluation employed total discounted contribution margin (TDCM) analyses under varying carbon credit prices (€0–150/tCO₂). Results showed that an extended rotation yielded the highest carbon sequestration (958 tCO₂ above BAU) and TDCM but was deemed operationally unfeasible due to declining stand health. Black locust (Robinia pseudoacacia) regeneration provided high mitigation potential (690 tCO₂) but was ineligible under CRCF rules. Grey poplar (Populus × canescens) regeneration emerged as the most viable option, balancing biodiversity compliance, climate adaptability, and economic return (TDCM = EUR 22,900 at €50/tCO₂). The findings underscore the importance of integrating ecological suitability, market regulations, and economic performance in planning carbon farming projects, and highlight that regulatory biodiversity safeguards can significantly shape feasible mitigation pathways. Full article

Do China’s provincial digital-economy policies causally improve firm productivity and manufacturing sustainability? This paper addresses this question using a panel of Chinese manufacturers from 2008 to 2023. We first construct a novel, manually coded index of provincial policy intensity. We then use an instrumental-variable strategy, based on historical post-office density and governors’ STEM backgrounds, to identify causal effects. We find that digital-economy policy has a positive and significant impact on firm-level total factor productivity (TFP). Doubling the cumulative policy stock raises TFP by approximately 3%. This effect is transmitted through four key mechanisms: enhanced innovation quality, tax incentives, targeted digital subsidies, and knowledge spillovers. These channels support sustainable, innovation-led upgrading rather than mere input expansion. We also find the TFP gains are much larger in provinces with strong fiscal capacity and in firms with high digital absorptive capabilities. This paper contributes by providing clear causal evidence of the policy–TFP link and, crucially, by quantifying the four specific mechanisms that translate digital policy into durable, productivity-based sustainability in manufacturing. Full article

This paper develops a dynamic asset allocation strategy for defined contribution pension funds using a stochastic control framework under the Heston stochastic volatility model. By solving the associated Hamilton–Jacobi–Bellman partial differential equation, we derive optimal equity allocations that adapt to changing market volatility and investor risk aversion using a constant relative risk aversion utility function (parameter $\gamma$). The strategy increases equity exposure during stable periods and reduces it during volatile regimes, capturing both myopic and intertemporal hedging demands. We test the model using historical U.S. data from 2006 to 2025 and benchmark its performance against a traditional static 60/40 stock–bond portfolio, as well as rule-based strategies such as volatility targeting and constant proportion portfolio insurance. Our results show that with moderate risk aversion, the dynamic strategy achieves long-term wealth comparable to the 60/40 benchmark while substantially reducing drawdown risk. As risk aversion increases, drawdown risk is further reduced and risk-adjusted returns remain competitive. Although higher aversion yields lower final wealth, certainty-equivalent returns are highest at moderate aversion levels. These results demonstrate that volatility responsive dynamic policies grounded in realistic stochastic volatility modeling can substantially enhance downside protection and risk-adjusted utility, especially for long-horizon, risk-averse pension participants. Full article

The intermittent and lumpy demand of spare parts requires the choice of the right forecasting model among a variety of existing methods. Spare parts have an uneven lifecycle and mean time to failure for each individual item. As a result, they have a varied time of replacement, and consequently, a varied demand. This paper introduces a multi-cost function optimization approach that dynamically selects and adjusts forecasting models tailored to each spare part. The performance comparisons of the various demand forecasting methods led us to a new forecasting model, the Sfiris–Koulouriotis (SK) method, suited for highly lumpy and intermittent demand. A scaled version of the novel Stock-Keeping Unit-oriented Prediction Error Costs metric is also introduced. The composite negative-binomial–Bernoulli probability distribution for the stock control leveraged the replenishment policy. The best safety stock level is calculated for each individual item. Empirical validation in the automotive industry demonstrated that our approach significantly reduces safety stock while maintaining service levels, offering practical benefits for inventory management. Full article

Stock price modeling under privacy constraints presents a unique challenge at the intersection of computational economics and machine learning. Financial institutions and brokerage firms hold valuable yet sensitive data that cannot be centrally aggregated due to privacy laws and competitive concerns. To address this issue, we propose a novel Fast-Converging Federated Learning (FCFL) framework that enables decentralized and privacy-preserving stock price modeling. FCFL employs a dual-stage adaptive optimization strategy that dynamically tunes local learning rates and aggregation weights based on inter-client gradient divergence, accelerating convergence in heterogeneous financial environments. The framework integrates secure aggregation and differential privacy mechanisms to prevent information leakage during communication while maintaining model fidelity. Experimental results on multi-institutional stock datasets demonstrate that FCFL achieves up to 30% faster convergence and 2.5% lower prediction error compared to conventional federated averaging approaches, while guaranteeing strong $\epsilon$-differential privacy. Theoretical analysis further proves that the framework attains sublinear convergence in $\mathcal{O}\left(\log T\right)$ communication rounds under non-IID data distributions. This study provides a new direction for collaborative financial modeling, balancing efficiency, accuracy, and privacy in real-world economic systems. Full article

Deep learning has transformed numerous areas of data science by achieving outstanding performance in tasks such as image recognition, speech processing, and natural language understanding. Recently, the challenges of financial forecasting—marked by nonlinear dynamics, volatility, and regime shifts—have attracted increasing attention from the deep learning community. Among these approaches, Convolutional Neural Networks (CNNs), originally developed for spatial data, have shown strong potential for modelling financial time series. This study presents an interpretable CNN-based framework for stock price forecasting using the S&P 500 index as a case study. The proposed approach integrates historical price data with technical indicators within a unified experimental design and compares performance against traditional statistical (ARIMA) and sequential deep learning (LSTM) baselines. Empirical results demonstrate that the CNN model achieves superior predictive Accuracy while maintaining computational efficiency and interpretability through SHAP and Grad-CAM analyses. The findings suggest that lightweight CNN architectures can serve as effective, transparent tools for short-horizon financial forecasting, and future research may extend this framework to multimodal settings incorporating sentiment or news-based data. Full article