Search Results (297)

Probiotics have emerged as an important strategy to achieve improved feed efficiency and carcass quality. To evaluate the effects of a probiotic combination based on Weizmannia faecalis (formerly Bacillus coagulans) and Bacillus licheniformis on broiler performance, carcass, and intestinal health, a study was conducted. As-hatched ROSS 308 broilers were purchased from a local hatchery at day 0 and were randomly allocated to two treatments (160 birds per treatment; 8 replicates of 20 birds each): the control, which was fed a standard commercial diet throughout the experiment, and the probiotics group, where the standard diet was further supplemented with the probiotic combination. Feed and water were offered for ad libitum consumption while the feeding schedule was as follows: Starter, 1–10 days, mash; Grower, 11–24 days, mash; Finisher, 25–42, mash. The birds were challenged using re-used litter as bedding and the application of increased stocking density and mild heat stress. The final body weight of the probiotics’ supplemented group was higher than the control at 42 days of age (2822.7 g vs. 2575.4 g, respectively) (p < 0.05), and the overall feed conversion ratio was significantly reduced. The weight of all the commercial parts increased, along with the thigh and drumstick yield, thus indicating an improvement of carcass composition (p < 0.05). The European Poultry Efficiency Factor significantly improved following the probiotic dietary supplementation (409.7 vs. 344.9 of the control), while the probiotic fed birds had higher antibody titers for Bursal disease at 42 days and lower serum concentration of fatty acid binding protein 2 at 24 days (p < 0.05). Overall, the dietary supplementation of broilers with the probiotic mixture, under challenging rearing conditions, enhanced growth performance and improved carcass composition. Full article

Woody biomass crops are increasingly considered a promising alternative to conventional agricultural systems due to their potential for sustained carbon sequestration under accelerating climate change. Optimizing management practices in such systems is therefore critical to enhance biomass production and carbon storage. In this study, we investigated how management influences biomass allocation in four poplar plots differing in planting density, variety, and harvest-rotation design during their 6th and 7th year of growth. Biomass stocks were quantified for crown, stem, coarse roots, and fine roots. Management effects were most pronounced in aboveground biomass, whereas belowground responses were less consistent. The highest aboveground biomass was observed in the high-density system within the first rotation (MxHD1), reaching 55.32 Mg ha⁻¹ in 2024 and 94.91 Mg ha⁻¹ in 2025. Belowground biomass ranged from 8.12 to 18.35 Mg ha⁻¹ across plots and years. The root:shoot ratio declined with increasing shoot basal diameter and was highest in the year following harvest. Based on these data, we developed general and management-specific allometric models to predict aboveground and belowground biomass from diameter at breast height. Including management factors improved prediction accuracy, supporting more precise quantification of biomass allocation under different cultivation strategies. Full article

This paper examines how assets from emerging and developed stock markets can be efficiently allocated during periods of financial crisis by integrating traditional portfolio theory with Extreme Value Theory (EVT), using the Generalized Pareto Distribution (GPD) and Generalized Extreme Value (GEV) approaches to model tail risks. This study evaluates mean-variance portfolios constructed under each EVT framework and finds that portfolios based on GPD estimates consistently favour emerging market assets, which outperform both developed market and internationally diversified portfolios during extreme market conditions. In contrast, GEV-based portfolios indicate superior performance for developed market assets, highlighting the distinct behaviour of returns in the upper and lower tails of the distribution. These contrasting results reveal the unique nature of safe-haven characteristics associated with developed economies, the assets of which demonstrate greater stability and resilience during episodes of financial stress. By showing how tail-risk modelling alters optimal portfolio weights across market types, this paper contributes new evidence to the literature on crisis-informed asset allocation and offers practical insights for investors seeking robust diversification strategies under extreme market fluctuations. Full article

Accurately forecasting the risk matrix and constructing a well-controlled portfolio based on these forecasts is the core objective of effective asset allocation. This paper takes the Chinese stock market as the research object, employing multiple machine learning algorithms to systematically compare the predictive performance of the Financial Stress (FS) indicator and the Economic Policy Uncertainty (EPU) index in sectoral risk management. The forecast results are subsequently applied to portfolio construction and optimization. The findings indicate that, in terms of predictive dimensions, EPU demonstrates strong performance in short-term forecasts, but its explanatory power decays rapidly as the forecasting horizon extends. In contrast, the FS factor achieves forecasting accuracy that is significantly superior to both the EPU factor and traditional price series across all time horizons, exhibiting robust long-memory characteristics and cross-period stability. At the portfolio application level, the minimum variance strategy constructed based on FS forecasts effectively reduces out-of-sample portfolio variance, achieving superior risk control performance compared to strategies based on EPU factor forecasts. This result reveals the differentiated mechanisms of the two factor types: EPU acts as a driving force for short-term risk structure reshaping, while financial stress serves as the core variable driving the evolution of long-term risk structures. Machine learning methods provide an effective technical pathway for capturing these complex nonlinear relationships. The research conclusions offer new empirical evidence for investors to optimize asset allocation decisions and for regulatory authorities to improve risk monitoring systems. Full article

Addressing meso-scale sensing voids and resource misallocations, this study constructs an integrated “Performance Sensing–Bottleneck Diagnosis–Configuration Identification” framework to evaluate the spatiotemporal evolution of resilience across Xi’an’s districts (2018–2023). This research operationalizes a diagnostic-driven analytical pipeline coupling multi-source parameters with the CRITIC method to complement static stock accounting with dynamic performance sensing. This logic integrates Dagum Gini decomposition to pinpoint spatiotemporal bottlenecks and fuzzy-set QCA (fsQCA) to uncover driving pathways, utilizing an “Obstacle–Correlation” matrix to provide an objective basis for antecedent selection. The results show the following: (1) A “V-shaped” spatiotemporal trajectory and 2020 “resilience inversion” (dipping to 0.364) highlight the sensitivity of dynamic performance sensing in exposing latent vulnerabilities. (2) Persistent “center-periphery” gradients exist, with administrative siphoning driving 66.7% of inequality; diagnosis identifies distinct spatiotemporal pathologies: rigid spatial constraints in urban cores versus service imbalances in expansion zones. (3) Three equifinal pathways and an “asymmetric cancellation” effect prove that resilience hinges on configurational fit rather than linear stacking, where extreme single-dimension shortfalls neutralize collective gains. By bridging situational pathologies and governance pathways, this framework provides a robust empirical basis for the refined allocation of resources in complex environments. Full article

This study develops a novel AI-based trading framework designed to consistently generate profits across cyclical bullish and bearish futures markets. Unlike conventional strategies that rely on static rules or a single predictive model, the proposed framework introduces a dual-agent deep reinforcement learning (DRL) architecture, where one agent specializes in bullish conditions and the other in bearish conditions, while a trading decision selector dynamically predicts market regimes and allocates execution accordingly. This design enables the system to adapt to regime shifts and mitigate risks arising from market volatility and extreme events. Using Mini Taiwan Stock Exchange Index Futures (MTX) as a case study, a four-year historical backtest is conducted covering multiple disruptive periods, including the tax adjustment and the Russia–Ukraine conflict. The empirical results show that, under a monthly capital reset and loss-compensation rule with a fixed investment of TWD 500,000 per month, the proposed framework achieves an average cumulative return of 2240%, an annualized return of 109%, and a Sharpe ratio of 0.31, with the cumulative ROI exceeding twice the MTX index growth over the same period. Although the Sharpe ratio remains moderate, this outcome reflects the framework’s emphasis on directional trading and absolute return maximization, where profitable trades outweigh intermittent losses despite higher short-term volatility. These findings suggest that adaptive, regime-aware DRL architectures are particularly effective for futures trading in markets characterized by frequent trend reversals, offering both methodological innovation and practical applicability under realistic market conditions, with strong returns achieved at a moderate risk-adjusted level. Full article

Background: We study one of the most important problems in production and warehouse management: the problem of determining how to allocate the initial stock and the quantity produced to bins, and then how to manage picking operations from these bins. The objective is to minimize the total cost of the bins used. Methods: We formulate an integer linear programming model able to manage the two time periods related to assignment and picking together, and to handle the FIFO picking logic. We prove that it is NP-hard, and solve it to optimality. Then, we design a tailored heuristic algorithm, inspired by the current rule of thumb used by one of the main Italian mineral water bottling companies. Results: An extensive computational experiment allows us to show that this problem can be solved to optimality in a reasonable computational time based on real-world instances, and that the heuristic provides near-optimal solutions. Conclusions: Our approach provides a contribution to modeling and solving this problem when FIFO picking operations are taken into account. Moreover, it contributes by building important bridges between theoretical understanding and practical applications. Full article

Stock returns are a key indicator of investor confidence and capital allocation in the tourism sector, particularly during crises that compress demand and elevate liquidity risk. This study investigates firm-level determinants of stock returns among 27 Indonesian listed tourism firms over 2019–2023, covering the COVID-19 disruption and initial recovery. Operational efficiency is estimated using an input-oriented, constant returns to scale (CRS) Data Envelopment Analysis (DEA) model, and stock returns are modeled with Generalized Estimating Equations (GEE) to account for the longitudinal panel structure. The results indicate that higher DEA-based efficiency and a stronger liquidity position (current ratio) are positively and significantly associated with stock returns, whereas profitability (ROA, ROE) is not significant. Leverage, growth, and firm age also show no significant effects. In contrast, higher valuation multiples (price-to-book and price-to-sales ratios) are associated with lower subsequent returns, and larger firms exhibit lower returns over the sample horizon. The findings support signaling and resource-based interpretations, suggesting that in crisis periods investors reward operational efficiency as an indicator of disciplined resource use that helps preserve cash and sustain liquidity, while discounting firms priced at high multiples. Full article

Morphological differences between sea-caught and farmed fish reflect environmental conditions and long-term domestication. However, standardized and objective quantification of these differences remains limited for many commercially important species. The large yellow croaker (Larimichthys crocea) represents a typical marine fish with clear contrasts between natural and aquaculture production systems. In this study, an image-based phenotyping workflow was developed to quantify external morphological traits of sea-caught and farmed L. crocea. Visible-light images were acquired under standardized conditions. A YOLOv11-based instance segmentation model was applied to automatically delineate major anatomical regions, including the body, head, eyes, pectoral fins, and tail. Surface areas and proportional indices were calculated following geometric calibration to ensure measurement consistency. The segmentation model achieved high accuracy on the test dataset (mAP@50 > 98%). Morphometric analyses revealed clear differences between the two groups. Farmed individuals exhibited larger body-related surface areas, whereas the relative proportions of pectoral fins and tail regions were reduced. Sea-caught fish showed higher proportional investment in locomotor structures, consistent with the physical demands of natural marine environments. These results indicate a shift in morphological allocation associated with aquaculture, characterized by enhanced trunk growth and reduced relative development of propulsion-related structures. The proposed workflow provides a rapid, non-invasive, and reproducible approach for fish morphometric analysis. It offers practical potential for phenotypic monitoring and stock assessment, while contributing quantitative evidence for domestication-driven morphological divergence in marine fishes. Full article

The rational allocation of talent resources is significant to regional transformation and upgrading high-quality development. Focusing on urban agglomerations in China, this study examines the spatial patterns and underlying mechanisms of graduate talent mobility using employment data from the Ministry of Education Graduate Employment Quality Reports. We utilized the social network analysis method, stickiness rate, external attractiveness index, and directed migration model. The results reveal the following. (1) Spatial Stratification and Typology: A significant “Matthew Effect” characterizes China’s talent landscape. While the Yangtze River Delta and Pearl River Delta exhibit a “high stickiness–high attractiveness” dual-drive pattern, emerging inland agglomerations like Chengdu–Chongqing rely on high internal stickiness as a critical “stabilizer,” maintaining regional resilience through local stock retention despite limited external pull. (2) Complexity of Driving Mechanisms: Ridge regression indicates that while economic development (GDP per capita) and innovation capacity remain core drivers of external attractiveness, public services and institutional costs exert stronger constraints on mobility. (3) Policy Implications: In contrast, monetary talent policies show limited marginal utility. The study concludes that talent governance in urban agglomerations must shift from homogenous “talent wars” to differentiated sustainable strategies. Advanced regions should foster polycentric networks to mitigate overcrowding, while emerging regions should prioritize “soft infrastructure” to lower social costs, leveraging endogenous stickiness for long-term human capital accumulation and spatial equity. Full article

This paper presents a hybrid framework for multi-stock trading that combines the decision-making ability of Deep Q-Networks (DQN) with the allocation precision of portfolio optimization models. Realistic markets are noisy and non-stationary, and complex action spaces can hinder reinforcement learning (RL) performance. The DQN generates buy/sell signals based on market conditions. The framework passes buy-listed assets to an optimizer, which computes portfolio weights. Five allocation strategies are examined: naïve 1/N, Markowitz Mean–Variance, Global Minimum Variance, Risk Parity, and Sharpe Ratio Maximization. Empirical evaluations on emerging-market exchange-traded funds (ETFs), as well as additional tests on U.S. equities, show that even the baseline DQN outperforms traditional technical indicators. Furthermore, integrating any of the optimization approaches with DQN yields measurable improvements in return-risk performance metrics. Among the hybrid frameworks, DQN combined with Sharpe Ratio Maximization delivers the most consistent gains. The findings highlight the value of decomposing stock selection from capital allocation and demonstrate the effectiveness of the proposed DQN-optimization framework on our testbed. Full article

The accurate upscaling of peatland carbon stocks is fundamentally limited by fine-scale microrelief (hummocks/depressions), which has not yet been resolved by conventional satellite or field methods. We demonstrate the critical advantage of using Uncrewed Aerial System LiDAR (UAS-LiDAR) for mapping the hierarchical microrelief of a Western Siberian ombrotrophic bog to enhance ground-layer phytomass estimation. The rule-based classification of a normalized digital terrain model generated a high-resolution microform map (overall accuracy = 79%, Kappa = 0.72). This map was used to upscale field-measured phytomass and compared against estimates generated through satellite imagery (SuperView-2) and traditional field-visual extrapolation. While total landscape-level phytomass stocks were similar across methods (~93–97 t ha⁻¹), their spatial allocation differed fundamentally. The satellite-based method exhibited a predictable, landscape-dependent systematic bias (overestimation by 7–25% in some units) and a substantially lower microtopography accuracy (OA = 77%, Kappa = 0.53) compared to the aggregated LiDAR map (OA = 95%, Kappa = 0.89). Crucially, only the LiDAR-based approach accurately resolved the biomasses of key microforms (e.g., hummocks within hollows contributing up to 6.2 ± 1.4 tonnes per unit), which were missed or misaggregated when using traditional techniques. We conclude that objective, high-resolution microrelief mapping via UAS-LiDAR is essential for spatially explicit and ecologically coherent phytomass upscaling, providing an indispensable structural template for credible carbon accounting in heterogeneous peatlands. Full article

This paper develops a novel hybrid framework that integrates clustering-enhanced Particle Swarm Optimization (PSO) with stretching techniques to solve Markowitz’s quadratic portfolio optimization problem. The proposed approach avoids local optima traps that plague traditional optimization methods, while the stretching function modifications enhance the algorithm’s global search capabilities. The framework comprises four distinct algorithmic variants: a baseline SWARM PSO with stretching algorithm, and three clustering-enhanced extensions incorporating Hierarchical, K-means, and DBSCAN techniques. These clustering enhancements strategically group assets based on risk–return characteristics to improve portfolio diversification and risk management. Implementation in R enables comprehensive analysis of portfolio weight allocation patterns and diversification metrics across varying market structures. Empirical validation using daily price data from six major international stock market indices spanning January 2020 to December 2025 demonstrates the framework’s generalization capability in constructing buy-and-hold investment portfolios. The results reveal significant market-specific algorithmic effectiveness, with K-means variants achieving competitive efficacy in Eurostoxx and Belgian markets, DBSCAN demonstrating strong effectiveness in Chinese equity markets, Hierarchical clustering showing robust results in Indian market conditions, and the baseline SWARM algorithm exhibiting relative efficiency in French and Danish indices. Performance evaluation encompasses comprehensive risk-adjusted metrics, including Portfolio Return, Volatility, Sharpe Ratio, Calmar Ratio, and Value at Risk, providing portfolio managers with an adaptive, market-responsive optimization toolkit. Full article

This study examines the extent to which Indian technology equities generate sufficient returns relative to their inherent volatility and assesses whether intra-sector diversification can improve outcomes in this dynamic, high-risk sector. Drawing on data from January 2020 to April 2025, ten leading firms are analyzed using an integrated approach that incorporates traditional risk-adjusted indicators, downside-sensitive metrics, and a six-factor model featuring momentum. The results show clear heterogeneity in performance. Mid-cap innovators such as Persistent Systems and Coforge deliver positive and, in some cases, statistically significant alphas, while large-cap stocks including Infosys, Tata Consultancy Services (TCS), and Wipro provide stability but limited excess returns. At the portfolio level, an equally weighted allocation improves downside protection. However, factor-model analysis finds no statistically significant portfolio alpha once systematic exposures are accounted for. These findings highlight the importance of active firm-level selection within the Indian technology sector, while also underscoring the role of intra-sector diversification in mitigating extreme losses. Full article

As a key component of sustainable economic development, digital transformation has become a fundamental driver for developing and upgrading the modern economic system. While existing research has identified resources and dynamic capabilities as foundational elements, a critical yet underexplored factor lies in the cognitive foundations that enable firms to strategically direct and leverage these assets. Based on 19,062 observation samples of more than 3000 listed companies in Shanghai and Shenzhen stock markets from 2010 to 2023, this paper constructs a theoretical framework of entrepreneurship, organizational attention and digital transformation from the Attention-Based View, and examines a moderated mediation model of the relationship between entrepreneurship and digital transformation. The results show that entrepreneurship significantly promotes digital transformation; organizational attention to “cooperation orientation” and “future orientation” plays a mediating role in it; and the regional innovation atmosphere positively strengthens the “cooperation orientation” path, facilitating the diffusion of innovative knowledge and technologies within the region. Meanwhile, online media reports negatively regulate the “future orientation” path, reflecting that short-term public pressure may weaken enterprises’ attention to long-term sustainable technology investment. In addition, different dimensions of entrepreneurship have varied effects on digital transformation. Heterogeneity analysis revealed significant variations across ownership type, scale, region, industry competition intensity, and technological intensity. This study expands the theoretical mechanism of entrepreneurship and digital transformation from the perspective of attention allocation, and provides theoretical and empirical foundation for fostering a strategic cognitive orientation and advancing digital transformation. Full article