Search Results (9,209)

This study presents the Wood Protection Sustainability Index (WPSI), a novel decision-support tool aimed at evaluating wood preservatives utilized in Chile and facilitating a shift toward more sustainable wood protection practices. WPSI encompasses four essential attributes: protection treatment, wood durability, in-service risk, and sustainability. These are assessed under two distinct scenarios. Scenario 1 represents current market practices, where chromated copper arsenate (CCA) remains prevalent due to its accessibility and affordable cost. In contrast, Scenario 2 prioritizes sustainability, demonstrating that copper azole (CA) and alkaline copper quaternary (ACQ) surpass CCA in performance, with CCA ranking lowest due to its environmental implications. Furthermore, a SWOT analysis accompanies the index, identifying key challenges and opportunities within Chile’s wood preservation industry. The findings highlight the importance of aligning national strategies with Environmental, Social, and Governance (ESG) frameworks, as well as the Sustainable Development Goals (SDGs), through performance-based regulations and safer alternatives. The WPSI can be integrated with local standards, regional risk classifications, and national preservative approval systems, allowing for meaningful comparison across diverse global contexts. This approach promotes more sustainable construction practices while ensuring both technical and economic viability. Full article

Economic stability in emerging market economies is increasingly shaped by the interplay between global financial integration, domestic monetary dynamics, and asset price fluctuations. Yet, early detection of financial market disruptions remains a persistent challenge. This study constructs a Financial Stress Index (FSI) for Pakistan, utilizing monthly data from 2005 to 2024, to capture systemic stress in a globalized context. Using Principal Component Analysis (PCA), the FSI consolidates diverse indicators, including banking sector fragility, exchange market pressure, stock market volatility, money market spread, external debt exposure, and trade finance conditions, into a single, interpretable measure of financial instability. The index is externally validated through comparisons with the U.S. STLFSI4, the Global Economic Policy Uncertainty (EPU) Index, the Geopolitical Risk (GPR) Index, and the OECD Composite Leading Indicator (CLI). The results confirm that Pakistan’s FSI responds meaningfully to both global and domestic shocks. It successfully captures major stress episodes, including the 2008 global financial crisis, the COVID-19 pandemic, and politically driven local disruptions. A key understanding is the index’s ability to distinguish between sudden global contagion and gradually emerging domestic vulnerabilities. Empirical results show that banking sector risk, followed by trade finance constraints and exchange rate volatility, are the leading contributors to systemic stress. Granger causality analysis reveals that financial stress has a significant impact on macroeconomic performance, particularly in terms of GDP growth and trade flows. These findings emphasize the importance of monitoring sector-specific vulnerabilities in an open economy like Pakistan. The FSI offers strong potential as an early warning system to support policy design and strengthen economic resilience. Future modifications may include incorporating real-time market-based metrics indicators to better align the index with global stress patterns. Full article

Russia’s full-scale aggression against Ukraine has led to profound disruptions in local and global agri-food markets. Since Ukraine is one of the world’s largest maize exporters, the war also contributed to considerable changes in trade reallocation, as well as an increase in the significance of the European Union in Ukrainian exports. This study analyses the effects of the Russian–Ukrainian war on horizontal maize price transmission between Ukraine and the EU countries. The panel autoregressive distributed lag model (ARDL) was applied to investigate the impact of the war on the price pass-through between those countries. The econometric analysis was performed on a weekly feed maize export price series for Ukraine and 14 selected EU countries. The time frame of research, January 2019 to December 2024, was split into pre-war and war periods. The study indicates that with the outbreak of the war, the long-term relationship between Ukraine and the EU’s maize prices has weakened. At the same time, there was an increase in the short-run maize price transmission between Ukraine and the Eastern EU countries. This proves that in the face of the conflict, market participants in these countries are increasingly guided by the market situation in Ukraine when making economic decisions. Full article

Investors face the challenge of how to incorporate economic and financial forecasts into their investment strategy, especially in times of financial crisis. To model this situation, we consider a financial market consisting of a risk-free asset with a constant interest rate as well as a risky asset whose drift and volatility is influenced by a stochastic process indicating the probability of potential market downturns. We use a dynamic portfolio optimization approach in continuous time to maximize the expected utility of terminal wealth and solve the corresponding HJB equations for the general class of HARA utility functions. The resulting optimal strategy can be obtained in closed form. It corresponds to a CPPI strategy with a stochastic multiplier that depends on the information from the crisis indicator. In addition to the theoretical results, a performance analysis of the derived strategy is implemented. The specified model is fitted using historic market data and the performance is compared to the optimal portfolio strategy obtained in a Black–Scholes framework without crisis information. The new strategy clearly dominates the BS-based CPPI strategy with respect to the Sharpe Ratio and Adjusted Sharpe Ratio. Full article

With the increasing demand for privacy preservation and strategy sharing in global financial markets, traditional centralized modeling approaches have become inadequate for multi-institutional collaborative tasks, particularly under the realistic challenges of multi-source heterogeneity and non-independent and identically distributed (non-IID) data. To address these limitations, a heterogeneity-aware Federated Quantitative Learning framework, Federated Quantitative Learning, is proposed to enable efficient cross-market financial strategy modeling while preserving data privacy. This framework integrates a Path Quality-Aware Aggregation Mechanism, a Gradient Clipping and Compression Module, and a Heterogeneity-Adaptive Optimizer, collectively enhancing model robustness and generalization. Empirical studies conducted on multiple real-world financial datasets, including those from the United States, European Union, and Asia-Pacific markets, demonstrate that Federated Quantitative Learning outperforms existing mainstream methods in key performance indicators such as annualized return, Sharpe ratio, maximum drawdown, and volatility. Under the full model configuration, Federated Quantitative Learning achieves an annualized return of 12.72%, a Sharpe ratio of 1.12, a maximum drawdown limited to 10.3%, and a reduced volatility of 9.7%, showing significant improvements over methods such as Federated Averaging, Federated Proximal Optimization, and Model-Contrastive Federated Learning. Moreover, module ablation studies and attention mechanism comparisons further validate the effectiveness of each core component in enhancing model performance. This study introduces a novel paradigm for secure strategy sharing and high-quality modeling in multi-institutional quantitative systems, offering practical feasibility and broad applicability. Full article

Background/Objectives: Self-adhesive flowable resins (SAFR) entered the market, eliminating the adhesive system application due to their self-adhesive technology. Guided by the PICO framework (Population, Intervention, Comparison, Outcome), the aim was to conduct a systematic review of clinical studies to compare the clinical performance of Self Adhesive Flowable Resin (SAFRs) with conventional flowable resins used for direct restorations. Methods: The protocol of this systematic review was registered in the International Prospective Register of Systematic Reviews (CRD42023394297) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Five databases (PubMed, Embase, Web of Science, Scopus, and Cochrane Library) were searched from inception to July 2025. Nine randomized clinical trials were included, totaling 493 restorations in 232 patients. Clinical performance was assessed using USPHS or FDI criteria, with follow-up periods ranging from 6 months to 5 years. Data were pooled using a random-effects meta-analysis to calculate risk differences (RD) and 95% confidence intervals (CI) for marginal adaptation, retention, marginal staining, post-operative sensitivity, color stability, surface roughness, secondary caries, and anatomical form. Results: Meta-analysis showed no significant differences between SAFRs and CFRCs for in terms of: marginal adaptation (RD = 0.01; 95% CI: −0.02 to 0.04; p = 0.53; I² = 0%), retention (RD = 0.00; 95% CI: −0.02 to 0.03; p = 0.81; I² = 0%), marginal staining (RD = 0.01; 95% CI: −0.01 to 0.02; p = 0.51; I² = 0%), and post-operative sensitivity (RD = −0.01; 95% CI: −0.03 to 0.02; p = 0.62; I² = 0%). The certainty of the evidence for all outcomes was rated as moderate to high according to the GRADE assessment. Conclusions: SAFR restorations demonstrated comparable clinical performance to conventional resins; however, heterogeneity in follow-up duration and the scarcity of long-term data (>5 years) warrant caution. 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

Amid accelerating climate change, climate-related hazards—such as floods, wildfires, hurricanes, and sea-level rise—increasingly drive land transformations and pose growing risks to housing markets by affecting property valuations, insurance availability, mortgage performance, and broader financial stability. This review synthesizes recent progress in two distinct domains and their linkage: (1) assessing climate-related financial risks in housing markets, and (2) applying AI-driven remote sensing for hazard detection and land transformation monitoring. While both areas have advanced significantly, important limitations remain. Existing housing finance studies often rely on static models and coarse spatial data, lacking integration with real-time environmental information, thereby reducing their predictive power and policy relevance. In parallel, remote sensing studies using AI primarily focus on detecting physical hazards and land surface changes, yet rarely connect these spatial transformations to financial outcomes. To address these gaps, this review proposes an integrative framework that combines AI-enhanced remote sensing technologies with financial econometric modeling to improve the accuracy, timeliness, and policy relevance of climate-related risk assessment in housing markets. By bridging environmental hazard data—including land-based indicators of exposure and damage—with financial indicators, the framework enables more granular, dynamic, and equitable assessments than conventional approaches. Nonetheless, its implementation faces technical and institutional barriers, including spatial and temporal mismatches between datasets, fragmented regulatory and behavioral inputs, and the limitations of current single-task AI models, which often lack transparency. Overcoming these challenges will require innovation in AI modeling, improved data-sharing infrastructures, and stronger cross-disciplinary collaboration. Full article

The supply chain is a critical tool for enterprises to withstand risks and ensure sustainable development. Integrating green and environmentally friendly practices into the supply chain has become an increasingly prominent trend. This study examines the impact of green supply chain management (GSCM) on supply chain resilience, using the green supply chain pilot projects implemented in China as a quasi-natural experiment, employing a multi-period difference-in-difference (DID) model. Based on panel data from manufacturing enterprises listed on the A-share market in China from 2014 to 2022, the findings reveal three key insights. First, GSCM significantly improves the resilience of enterprise supply chains. Second, GSCM has both signaling and cost effects, as it can reduce corporate financing costs and enhance market value, lower market transaction costs, and improve productivity. These are potential channels through which GSCM exerts a positive influence. Third, the positive impact of GSCM on supply chain resilience is more pronounced in enterprises with third-party environmental certifications and higher institutional shareholder ratios. Additionally, this study also extends to demonstrate that GSCM directly and positively influences corporate environmental performance. These findings provide policy recommendations for enhancing green supply chain development and offer managerial insights to help enterprises proactively embrace green transformation. Full article

This study investigates the contributions of five key sectors—insurance, materials, utilities, real estate, and transport—to the financial markets of six Gulf Cooperation Council (GCC) countries from 2004 to 2023. Grounded in the Sectoral Linkage Theory and Endogenous Growth Theory, the study employs a Panel Autoregressive Distributed Lag (Panel ARDL) model to examine both short-term and long-term sectoral impacts on financial market resilience. The findings reveal that the insurance and transport sectors offer short-term market stimulation, but lack persistent effects. Conversely, the materials, utilities, and real estate sectors exhibit strong, long-run contributions to financial stability and economic diversification. These results highlight the asymmetric impact of sectoral dynamics on market performance in resource-rich contexts. This research contributes to the literature by providing empirical evidence on sectoral interdependence in oil-dependent economies and highlights the importance of structural diversification for sustainable financial resilience. The study provides actionable insights for policymakers and investors seeking to enhance market resilience and reduce reliance on hydrocarbon revenues through targeted sectoral development. Full article

This study investigates the optimal sizing of photovoltaic (PV) and battery storage (BAT) systems for Estonian households operating under grid constraints that prevent selling surplus energy. We develop and compare three sizing models of increasing complexity, ranging from a simple heuristic to a full simulation based optimization. Their performance is evaluated using a multi-criteria decision analysis (MCDA) framework that integrates Net Present Value (NPV), Internal Rate of Return (IRR), Profitability Index Ratio (PIR), and payback period. Sensitivity analyses are used to test the robustness of each configuration against electricity price shifts and market volatility. Our findings reveal that standalone PV-only systems are the most economically robust investment. They consistently outperform combined PV + BAT and BAT-only configurations in terms of investment efficiency and overall financial attractiveness. Key results demonstrate that the simplest heuristic-based model (Model 1) identifies configurations with a better balance of financial returns and capital efficiency than the more complex simulation-based approach (Model 3). While the optimization model achieves the highest absolute NPV, it requires significantly higher investment and results in lower overall efficiency. The economic case for batteries remains weak, with viability depending heavily on price volatility and arbitrage potential. These results provide practical guidance, suggesting that for grid constrained households, a well-sized PV-only system identified with a simple model offers the most effective path to cost savings and energy self-sufficiency. Full article

Although epidermal growth factor (EGF) has potential wide applications in the cosmetic industry, it still has limitations, such as a costly purification process and low stability in the surrounding environment. To overcome these limitations, we developed genetically modified Pediococcus pentosaceus CBT SL4, which can secrete EGF protein in growth media, thereby producing probiotic-derived PP-EGF culture medium supernatant (PP-EGF-SUP). Even at low EGF concentrations, PP-EGF-SUP exhibited EGF activities, such as cell scratch wound healing, tyrosinase inhibition, and improvements in anti-wrinkle factors, similar to or stronger than those of recombinant human EGF (rhEGF), which was used as a positive control. PP-EGF-SUP exhibited strong additional biological activities, such as antioxidant, anti-inflammatory, and anti-microbial activities, even though rhEGF did not have such properties. PP-EGF-SUP could be easily transformed to PP-EGF-SUP dried powder (PP-EGF-DP) using the freeze-drying method, and it could also be well resolved in water up to 20 mg/mL; furthermore, it still maintained its bioactivity after the manufacturing process. To determine melasma improvement efficacy, a human application test was performed using melasma ampoules containing 1% or 5% PP-EGF-DP formulations for four weeks. When comparing the melasma values before and after treatment, it was found that the light melasma value statistically decreased by 3.38% and 3.79% and that the dark melasma value statistically decreased by 1.74% and 2.93% in the test groups applying the 1% and 5% PP-EGF-DP melasma ampoules, respectively. In addition, the melasma area also decreased by 21.21% and 29.1%, while the control group showed no statistical difference. During the study period, no significant adverse skin reactions were observed due to the application of the PP-EGF-DP melasma ampoule. In conclusion, PP-EGF-DP may offer unique advantages in the cosmetic ingredient market, such as safety (as a probiotic derivative), stability (postbiotics protect EGF activity), and diverse bioactivities (activity potentiation and postbiotic-derived biological activities). Full article

Short-term wind turbine energy yield forecasting is crucial for effectively integrating wind energy into the electricity grid and fulfilling day-ahead scheduling obligations in electricity markets such as Nord Pool and EPEX SPOT. This study presents a forecasting approach utilising operational data from two wind turbines in Latvia, as well as meteorological inputs from the NORA 3 reanalysis dataset, sensor measurements from the turbines, and data provided by the Latvian Environment, Geology and Meteorology Centre (LEGMC). Forecasts with lead times of 1 to 36 h are generated to support accurate day-ahead generation estimates. Several modelling techniques, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), artificial neural networks (ANNs), XGBoost, CatBoost, LightGBM, linear regression, and Ridge regression, are evaluated, incorporating wind and atmospheric parameters from three datasets: operational turbine data, meteorological measurements from LEGMC, and the NORA 3 reanalysis dataset. Model performance is assessed using standard error metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). This study demonstrates the effectiveness of integrating reanalysis-based meteorological data with turbine-level operational measurements to enhance the accuracy and reliability of short-term wind energy forecasting, thereby supporting efficient day-ahead market scheduling and the integration of clean energy. Full article

In the present era where digitalization, FinTech, and blockchain technologies are reshaping the global financial landscape, traditional measures of bank performance need to evolve. This paper introduces a hybrid approach that combines multi-criteria efficiency assessment and econometric modeling to evaluate bank performance within the context of digital transformation in emerging economies. Focusing on a panel of banks across selected emerging markets, this study first applies a multi-criteria decision-making technique (Data Envelopment Analysis) to assess operational efficiency using both conventional indicators and digitalization-driven metrics, such as mobile banking penetration and blockchain adoption. We then employ a panel econometric model to investigate the factors that shape efficiency outcomes, with special attention to FinTech and blockchain innovations as potential drivers. The results reveal a nuanced picture of how digital technologies can influence bank performance, highlighting both opportunities and constraints for financial institutions in less developed markets. The findings offer actionable insights for bank managers, regulators, and policymakers striving to balance traditional operational priorities with the demands of digital transformation. By linking efficiency measurement with an examination of the digitalization process, this paper provides a timely contribution to the literature on banking and financial innovation, serving as a foundation for future research and strategic decision-making in the FinTech and blockchain era. Full article

This study aimed to produce and evaluate artisanal garrafa ice cream made with goat milk, performing microbiological analysis and sensory evaluation, and comparing it with cow milk-based ice cream. Pasteurized goat and cow milk were used to prepare pecan and chocolate cookie-flavored ice creams. Microbiological tests confirmed the absence of total and fecal coliforms, as well as aerobic mesophiles, indicating adherence to Good Manufacturing and Hygiene Practices. All products complied with the microbiological safety limits established by Mexican Official Standards. Sensory evaluation techniques are essential for assessing how attributes such as appearance, aroma, color, flavor, and texture influence consumer preferences for dairy products. A sensory evaluation was conducted with 72 untrained panelists. Among all samples, pecan-flavored goat milk ice cream received the highest preference, particularly for its taste and texture. Panelists also noted differences in color and odor between goat and cow milk ice creams. The sensory analysis highlighted the distinctive organoleptic properties of goat milk ice cream and its potential for consumer acceptance. These findings suggest that artisanal goat milk ice cream, especially the pecan variety, can successfully compete with traditional cow milk products in the expanding market for innovative and high-quality dairy foods. Full article