Search Results (2,687)

Construction of precast reinforced concrete (PRC) industrial halls in seismically active areas has been increasing in recent decades. As connections are one of the most sensitive and vulnerable zones of PRC structures, there is a need to pay special attention to their investigation and modeling in seismic analysis. Knowing that each PRC system is specific and unique, this study aims to evaluate the actual seismic performances of PRC industrial halls built in the AMONT system, which represent a significant portion of the existing industrial building stock in Italy, the Balkans, and Turkey. As there is a lack of published research data on its specific joints, the results of the quasi-static full-scale experiments carried out up to failure on the models of four characteristic connections are presented. Since the implementation of nonlinear dynamic analysis in everyday engineering practice can be demanding, a simplified model of the structure considering the effects of the connections’ stiffness is proposed in this paper. The differences in the roof top displacements between the proposed model and the model with the rigid joints of the analyzed frames are in the range from 16.53% to 66.93%. The values of inter-story drift ratios are larger by 10–100% when the real stiffness of connections is considered, which is above the limit value provided by standard EN 1998-1. These results confirm the necessity of considering the nonlinear behavior and stiffness of connections in precast frame structures when determining displacements, which is particularly important for the verification of the serviceability limit state of structures in seismic regions. Full article

The increasing need to regenerate public housing stock highlights the importance of adopting integrated evaluation tools capable of supporting transparent, sustainable, and public value-oriented investment decisions. This study compares two alternative intervention strategies—renovation with extension and demolition followed by reconstruction—by applying a Cost–Benefit Analysis (CBA) model developed in two phases. In the first phase, the analysis focuses on social benefits, with the aim of assessing their contribution to collective well-being. The second phase incorporates potential energy-related benefits, estimated on the basis of performance improvements associated with the two design scenarios. The results demonstrate that the integrated consideration of economic, social, and energy–environmental dimensions affects the relative performance differences between the examined strategies, offering a more comprehensive evaluation framework than conventional approaches based solely on monetary costs. The proposed model, which is replicable in Mediterranean contexts, contributes to the ongoing international debate on ex ante evaluation tools and provides operational insights to support urban regeneration policies oriented towards more effective, equitable, and policy-consistent solutions, in line with the objectives of the European Green Deal and the 2030 Agenda. The two-phase structure allows decision-makers to distinguish between short-term social effects and long-term energy-related benefits, offering a transparent support tool for public investment choices under fiscal constraints. Full article

Accurate quantification of forest aboveground biomass (AGB) is essential for monitoring terrestrial carbon stocks. While total AGB estimation is widely practiced, resolving component biomass such as canopy, branches, leaves, and trunks enhances the precision of carbon sink assessments and provides critical structural parameters for ecosystem modeling. Most studies rely on a single SAR sensor or a limited range of SAR features, which restricts their ability to represent vegetation structural complexity and reduces biomass estimation accuracy. Here, we propose a phased fusion strategy that integrates backscatter intensity, interferometric coherence, texture measures, and polarimetric decomposition parameters derived from dual-frequency ALOS-2, GF-3, and Sentinel-1A SAR data. These complementary multi-dimensional SAR features are incorporated into a Random Forest model optimized using an Adaptive Genetic Algorithm (RF-AGA) to estimate forest total and component estimation. The results show that the progressive incorporation of coherence and texture features markedly improved model performance, increasing the accuracy of total AGB to R² = 0.88 and canopy biomass to R² = 0.78 under leave-one-out cross-validation. Feature contribution analysis indicates strong complementarity among SAR parameters. Polarimetric decomposition yielded the largest overall contribution, while L-band volume scattering was the primary driver of trunk and canopy estimation. Coherence-enhanced trunk prediction increased R² by 13 percent, and texture improved canopy representation by capturing structural heterogeneity and reducing saturation effects. This study confirms that integrating coherence and texture information within the RF-AGA framework enhances AGB estimation, and that the differential contributions of multi-dimensional SAR parameters across total and component biomass estimation originate from their distinct structural characteristics. The proposed framework provides a robust foundation for regional carbon monitoring and highlights the value of integrating complementary SAR features with ensemble learning to achieve high-precision forest carbon assessment. Full article

This study investigated the effects of dietary probiotic supplementation and stocking density on the growth performance, carcass traits, immunity, blood biochemical parameters, and ileal histomorphology of broiler chickens. A total of five hundred ten 1-day-old unsexed broiler chicks (Cobb 39) were allocated to three probiotic levels (0%, 0.1%, 0.2%) and two stocking densities (low vs. high). Results indicated that stocking density significantly influenced body weight from the third week onward, with birds reared under low density showing higher weight and better feed-to-gain ratio. Probiotic supplementation did not significantly affect weekly body weight, feed intake, or mortality, although mortality tended to be lower in probiotic-fed groups. Carcass traits and lymphoid organ indices were largely unaffected by treatments, except for a higher heart percentage in low-density birds. Cell-mediated immunity was enhanced under low stocking density, and probiotic supplementation at 0.2% increased the immune response at 48 h post-challenge. Blood biochemical analysis revealed significant effects of stocking density on total protein, globulin, and triglycerides, while probiotics reduced total lipid and LDL levels. Ileal histomorphology was significantly improved by probiotics, with increased villus height, crypt depth, and villus-to-crypt ratio. Similarly, low stocking density further enhanced these parameters. Overall, probiotic supplementation, particularly at 0.1%, combined with low stocking density, positively influenced gut morphology and immune responses, contributing to improved broiler health and performance. Full article

Micro-minerals are essential for fish, but traditional inorganic micro-minerals (IMM) have low bioavailability. This study evaluated coated inorganic micro-minerals (CIMM) in juvenile American eels under commercial recirculating aquaculture system (RAS) conditions. Three experimental groups (n = 3 tanks per group, stocking density: 138 fish/m³) were fed basal diets supplemented for 56 days with: 1000 mg/kg IMM (IMM group, providing Cu 7, Fe 200, Mn 30, Zn 70, I 1.6, Se 0.4, and Co 1.2 mg/kg diet), 1000 mg/kg CIMM (CIMM group I), or 500 mg/kg CIMM (CIMM group II). Compared to the IMM group, the CIMM group I demonstrated significantly enhanced growth performance, with the specific growth rate increasing by approximately 31.14%, higher whole-body content and retention of minerals (Ca, P, Cu, Fe, Mn, Zn), and superior intestinal health, as reflected by significantly increased activities of digestive enzymes (amylase and lipase), enhanced antioxidant capacity (elevated SOD and CAT, reduced MDA), and improved morphology (villi length and muscular thickness), an altered intestinal microbiota (increased relative abundance of Firmicutes and reduced relative abundance of Proteobacteria), and significant metabolomic alterations in purine metabolism and linoleic acid metabolism. The CIMM group II maintained growth performance, with no significant difference in WGR and SGR compared to the IMM group, while still showing significant improvements in feed intake and mineral retention (P, Cu, Fe, Zn), and antioxidant capacity. Collectively, this study not only confirms the efficacy of CIMM in commercial RAS but also reveals that the supplementation level previously shown to be effective in the laboratory (50% CIMM) is insufficient under commercial farming conditions, implying that the dietary micro-mineral requirements for juvenile American eels in commercial RAS may be higher than those established in laboratory settings. Full article

This study examined long-term changes in soil carbon stock dynamics 11 and 19 years after fire under different severities at 0–5 and 0–25 cm depths with a digital soil mapping approach. Linear (MLR) and non-linear models (RF, SVR, XGBoost) combined with feature selection methods (r < 0.8, FFS, Boruta) were used to predict bulk density (BD), total C, and C stock. Distributional biases were evaluated with Kolmogorov–Smirnov statistics and corrected by Quantile Mapping (QM). RF-FFS performed best for BD and total C at 0–5, while RF-SVR outperformed for C stock and all properties at 0–25. Total C was 49% higher at 0–5, whereas C stock was 7.57 times greater at 0–25. Both models underestimated variability, especially for C stock. At 0–25, bulk density decreased after fire, particularly under conditions of medium severity, while total C increased following the same tendency. The results showed that fire’s legacy is still present in the ecosystem after one and two decades. This is particularly evident at greater depths, where long-term C stock is lower. Full article

This study investigates the different mechanisms through which environmental disclosure and green accounting practices influence investment attractiveness in an emerging market context. Drawing on legitimacy theory and the resource-based view, we examine whether these environmental accountability mechanisms create value directly or through enhanced sustainability performance. Using survey data from 290 non-financial firms listed on the Saudi Stock Exchange, we employ partial least squares structural equation modeling to test a mediated-moderation model within the Saudi Vision 2030 framework. The results reveal differentiated value-creation pathways: environmental disclosure affects investment attractiveness indirectly through sustainable economic outcomes (full mediation; indirect effect β = 0.121, p < 0.001), while green accounting demonstrates both direct (β = 0.237, p < 0.001) and indirect effects (β = 0.091, p < 0.01), indicating partial mediation. Both practices are positively associated with sustainable economic outcomes (β_ED = 0.290, β_GA = 0.219, p < 0.001), which in turn are positively related to investment attractiveness (β = 0.416, p < 0.001). Unexpectedly, Vision 2030 alignment shows no significant moderating effect (β = 0.042, p = 0.498), suggesting that the sustainability–investment relationship is not significantly conditioned by perceived alignment with the national strategic framework in this sample. The model explains 25.7% of the variance in investment attractiveness and 20.0% of that in sustainable economic outcomes, indicating moderate explanatory power. These findings contribute to the environmental accounting literature by suggesting that internal management-oriented practices may be more closely associated with investment attractiveness than disclosure transparency alone. Overall, the results indicate that green accounting systems are associated with investment attractiveness, while environmental disclosure appears to require observable sustainability performance to be reflected in investment perceptions, offering measured implications for corporate strategy and regulatory policy in sustainability transitions. Full article

Background/Objectives: Vending machines in hospitals offer convenient access to snacks and beverages for employees, visitors, and patients. However, their contents are typically energy-dense and nutritionally poor, which can potentially reinforce unhealthy eating habits. This study aimed to evaluate the impact of introducing healthier vending machine options on purchasing behaviour and consumer perceptions in a hospital setting. Methods: An interventional study was conducted at a university clinical centre in Slovenia. Sales data were collected from a standard vending machine and a pilot machine stocked with healthier products over two 14-day periods. Additionally, a consumer survey assessed factors influencing purchasing decisions and opinions on the healthier offerings. Results: The proportion of healthy items purchased increased from 22% to 39% in the pilot vending machine, indicating a positive shift toward healthier choices. However, total sales declined by 18.81%, suggesting consumer hesitation toward the new product mix. Survey results identified price, ingredients, and visual appeal as the primary factors influencing purchase decisions. Conclusions: The introduction of healthier vending machine options can promote better food choices in hospital environments, though challenges remain regarding consumer acceptance and sales performance. Expanding the variety of healthy items and adopting more competitive pricing strategies may enhance uptake. Further long-term research is needed to assess the sustainability of such interventions and their broader impact on hospital food environments. Full article

This paper introduces a new framework for high-dimensional covariance matrix estimation, the Blockwise Exponential Covariance Model (BECM), which extends the traditional block-partitioned representation to the log-covariance domain. By exploiting the block-preserving properties of the matrix logarithm and exponential transformations, the proposed model guarantees strict positive definiteness while substantially reducing the number of parameters to be estimated through a blockwise log-covariance parameterization, without imposing any rank constraint. Within each block, intra- and inter-group dependencies are parameterized through interpretable coefficients and kernel-based similarity measures of factor loadings, enabling a data-driven representation of nonlinear groupwise associations. Using monthly stock return data from the U.S. stock market, we conduct extensive rolling-window tests to evaluate the empirical performance of the BECM in minimum-variance portfolio construction. The results reveal three main findings. First, the BECM consistently outperforms the Canonical Block Representation Model (CBRM) and the native 1/N benchmark in terms of out-of-sample Sharpe ratios and risk-adjusted returns. Second, adaptive determination of the number of clusters through cross-validation effectively balances structural flexibility and estimation stability. Third, the model maintains numerical robustness under fine-grained partitions, avoiding the loss of positive definiteness common in high-dimensional covariance estimators. Overall, the BECM offers a theoretically grounded and empirically effective approach to modeling complex covariance structures in high-dimensional financial applications. Full article

This study examines the tension between “substance” and “form” in standardized sustainability reporting within an emerging market context. Using 21,964 firm-year observations from Chinese A-share listed companies (2018–2023), we investigate whether the adoption of the Global Reporting Initiative (GRI) framework enhances substantive Environmental, Social, and Governance (ESG) and creates firm value. While baseline regressions suggest a positive link between GRI and ESG performance, rigorously applying Propensity Score Matching (PSM) reveals a critical nuance: the effect of mere framework adoption attenuates after controlling for selection bias, whereas independent assurance remains a robust driver of substantive governance quality. Furthermore, mediation analysis using bootstrap resampling documents a distinct “Labeling Effect”: GRI adoption directly enhances market valuation (Tobin’s Q), yet the indirect path via ESG scores is statistically insignificant. This indicates that investors utilize GRI as a heuristic signal of legitimacy rather than pricing granular performance metrics. We also identify a “Valuation Latency”, where substantive ESG improvements significantly boost operational profitability (ROA) but are not yet fully incorporated into stock prices. Heterogeneity analysis shows these effects are stronger for non-state-owned enterprises (Non-SOEs), supporting the view that private firms leverage standardized reporting and verification to mitigate legitimacy deficits. These findings provide empirical evidence for regulators and investors to distinguish between the “label” of adoption and the “substance” of verification. Full article

Housing stock is observed to be associated with high carbon emissions, high fuel poverty and low comfort levels in the UK. Retrofitting the housing stock is one of the best solutions to address these problems. This paper directly corresponds with human and planetary health in terms of climate change, human health and mental health by addressing the challenges of housing retrofit at scale. Retrofitting houses can also contribute to social equity, reduced use of planetary resources and better financial and physical comfort. Despite the availability of the right technology, government grants and the potential to acquire supply chain and skilled labour, the progress of retrofit is extremely poor. Importantly, the UK is off track to achieve net zero by 2050, and the housing stock contributes 18.72% of the total emissions. The problem is further exacerbated by the 30.4 million units of housing stock. Robust strategies are required to retrofit the housing stock at scale. The study uses a qualitative modelling method under the diffusion of innovations theory to formulate a retrofit-at-scale strategy for the UK. Findings recommend focusing on skill development, show homes, research and innovation, supply chain development, business models, government grants and regulatory tools in a trajectory from 2025 to 2050. The proposed strategy is aligned with the segments of the diffusion of innovation theory. Although the analysis was performed with reference to the UK, the findings are transferable, considering the broader and urgent concerns related to human and planetary health. Full article

The debate over CEO compensation persists despite extensive efforts by academics and technocrats to understand its determinants. Most research has focused on how firm-specific characteristics and CEO-specific traits influence CEO compensation. However, the results have been contradictory, indicating that other factors may also play a role. This study examines the impact of macroeconomic factors on the compensation of CEOs. It examines how price variables such as interest rates, inflation, and exchange rates affect the fixed salaries and total compensation of CEOs at six South African banks listed on the Johannesburg Stock Exchange. Conducted over a 15-year period, this quantitative longitudinal study utilized secondary data from annual reports and the IRESS database. Panel data regression analysis was employed to interpret the data. The findings reveal a positive relationship between interest rates and fixed salaries, as well as between exchange rates and fixed salaries. Additionally, interest rates and total compensation are positively related, and exchange rates also have a positive relationship with fixed salaries. Understanding how macroeconomic conditions influence CEO pay helps Compensation Committees contextualize performance. It allows them to differentiate between achievement driven by a CEO’s abilities and that resulting from external factors, ensuring fair compensation and minimizing excessive rewards for “luck”. This knowledge supports the adjustment of incentive plans based on relative performance and economic-adjusted metrics, reducing the cyclical influence of macroeconomic variables on firm performance and, ultimately, CEO compensation. Full article

The growing demand for sustainable livestock systems requires efficient methods for monitoring forage biomass. This study evaluated spectral (RGB and multispectral), textural (GLCM), and area attributes derived from unmanned aerial vehicle (UAV) imagery to predict buffelgrass (Cenchrus ciliaris L.) biomass, also testing the effect of soil pixel removal. A comprehensive machine learning pipeline (12 algorithms and 6 feature selection methods) was applied to 14 data combinations. Our results demonstrated that soil removal consistently improved the performance of the applied models. Multispectral (MSI) sensors were the most robust individually, whereas textural (GLCM) attributes did not contribute significantly. Although the MSI and RGB data combination proved complementary, the model with the highest accuracy was obtained with CatBoost using only RGB information after Boruta feature selection, achieving a CCC of 0.83, RMSE of 0.214 kg, and R² of 0.81 in the test set. The most important variable was vegetation cover area (19.94%), surpassing spectral indices. We conclude that integrating RGB UAVs with robust processing can generate accessible and effective tools for forage monitoring. This approach can support pasture management by optimizing stocking rates, enhancing natural resource efficiency, and supporting data-driven decisions in precision silvopastoral systems. Full article

This study explores the relationship between board gender diversity and corporate social responsibility (CSR) in explaining the financial performance of firms listed on the Lima Stock Exchange during 2022–2023, using 242 firm year observations for 121 firms. The research addresses a broader question on how gender representation in corporate governance and engagement in social and environmental policies influence firms’ profitability and liquidity in an emerging market context. Using a multiple linear regression model, financial performance was measured through return on assets (ROA), return on equity (ROE), asset turnover (ATO), and the current liquidity ratio (LIQ). The results indicate that CSR is positively associated with profitability indicators (ROA, ROE, ATO), while board gender diversity shows a negative short term relationship with these variables. Both CSR and board gender diversity are negatively associated with liquidity, reflecting short term financial commitments arising from sustainability and inclusion initiatives. These findings suggest that the financial implications of diversity and CSR initiatives may vary across temporal horizons and institutional contexts. The study contributes empirical evidence from a Latin American emerging market and underscores the importance of evaluating corporate governance and sustainability practices by considering the short term financial trade-offs of diversity and CSR initiatives and their potential longer term implications. Full article

The stock market serves as an important channel for investors to preserve and increase their assets and has attracted significant attention. However, stock price is affected by multiple factors and represents complex characteristics such as high volatility, nonlinearity, and non-stationarity, making accurate prediction highly challenging. To improve forecasting accuracy, this study proposes FT-iTransformer, a stock price prediction model based on time–frequency domain collaborative analysis. The model integrates a frequency domain feature extraction module and a multi-scale temporal convolution network module to comprehensively capture both time and frequency domain features, and then the extracted features are fused and input into iTransformer. It models the complex relationships among multiple variables through the self-attention mechanism, utilizes the feedforward network to capture temporal dependencies, and finally the prediction results are output through the projection layer. This study conducts both comparative and ablation experiments on six stock datasets to evaluate the proposed FT-iTransformer model. The results of comparative experiments show that, compared with seven mainstream baseline models, such as LSTM, Informer, and FEDformer, FT-iTransformer achieves superior performance on all evaluation metrics. Furthermore, the results of ablation experiments exhibit the contributions of each core module to the overall predictive performance, and confirming the validity of the model’s design. In summary, FT-iTransformer provides an effective framework for predicting stock price accurately. Full article