Search Results (1,199)

Under the global context of climate governance and sustainable development, low-carbon energy transition has become a strategic imperative. As a critical force in resource allocation, the financial system’s impact on energy transition has attracted extensive academic attention. This paper presents the first comprehensive literature review on energy transition research in the context of financial development. We develop a “Financial Functions-Energy Transition Dynamics” analytical framework to comprehensively examine the theoretical and empirical evidence regarding the relationship between financial development (covering both traditional finance and emerging finance) and energy transition. The understanding of financial development’s impact on energy transition has progressed from linear to nonlinear perspectives. Early research identified a simple linear promoting effect, whereas current studies reveal distinctly nonlinear and multidimensional effects, dynamically driven by three fundamental factors: economy, technology, and resources. Emerging finance has become a crucial driver of transition through technological innovation, risk diversification, and improved capital allocation efficiency. Notable disagreements persist in the existing literature on conceptual frameworks, measurement approaches, and empirical findings. By synthesizing cutting-edge empirical evidence, we identify three critical future research directions: (1) dynamic coupling mechanisms, (2) heterogeneity of financial instruments, and (3) stage-dependent evolutionary pathways. Our study provides a theoretical foundation for understanding the complex finance-energy transition relationship and informs policy-making and interdisciplinary research. Full article

This study proposes a novel Deep Reinforcement Learning (DRL) policy network structure for portfolio management called PortRSMs. PortRSMs employs stacked State-Space Models (SSMs) for the modeling of multi-scale continuous regime shifts in financial time series, striking a balance between exploring consistent distribution properties over short periods and maintaining sensitivity to sudden shocks in price sequences. PortRSMs also performs cross-asset regime fusion through hypergraph attention mechanisms, providing a more comprehensive state space for describing changes in asset correlations and co-integration. Experiments conducted on two different trading frequencies in the stock markets of the United States and Hong Kong show the superiority of PortRSMs compared to other approaches in terms of profitability, risk–return balancing, robustness, and the ability to handle sudden market shocks. Specifically, PortRSMs achieves up to a 0.03 improvement in the annual Sharpe ratio in the U.S. market, and up to a 0.12 improvement for the Hong Kong market compared to baseline methods. Full article

Ujimqin sheep, known for its distinctive multi-vertebrae phenotypes (T13L7, T14L6, and T14L7) and economic value, has garnered significant attention. However, conventional phenotypic detection methods suffer from low efficiency and high costs. In this study, based on a key SNP locus (ABCD4 gene, Chr7:89393414, C > T) identified through a genome-wide association study (GWAS), a TaqMan-MGB (minor groove binder) genotyping system was developed. the objective was to establish a high-throughput and efficient molecular marker-assisted selection (MAS) tool. Specific primers and dual fluorescent probes were designed to optimize the reaction system. Standard plasmids were adopted to validate genotyping accuracy. A total of 152 Ujimqin sheep were subjected to TaqMan-MGB genotyping, digital radiography (DR) imaging, and Sanger sequencing. the results showed complete concordance between TaqMan-MGB and Sanger sequencing, with an overall agreement rate of 83.6% with DR imaging. For individuals with T/T genotypes (127/139), the detection accuracy reached 91.4%. This method demonstrated high specificity, simplicity, and cost-efficiency, significantly reducing the time and financial burden associated with traditional imaging-based approaches. the findings indicate that the TaqMan-MGB technique can accurately identify the T/T genotype at the SNP site and its strong association with the multi-vertebrae phenotypes, offering an effective and reliable tool for molecular breeding of Ujimqin sheep. Full article

Marine biomass, particularly from waste streams, by-products, underutilized, invasive, or potential cultivable marine species, offers a sustainable source of high-value biopolymers such as collagen and chitin. These macromolecules have gained significant attention due to their biocompatibility, biodegradability, functional versatility, and broad applicability across health, food, wellness, and environmental fields. This review highlights recent advances in the uses of marine-derived collagen and chitin/chitosan. In alignment with the United Nations Sustainable Development Goals (SDGs), we analyze how these applications contribute to sustainability, particularly in SDGs related to responsible consumption and production, good health and well-being, and life below water. Furthermore, we contextualize the advancement of product development using marine collagen and chitin/chitosan within the European Union’s Blue bioeconomy strategies, highlighting trends in scientific research and technological innovation through bibliometric and patent data. Finally, the review addresses challenges facing the development of robust value chains for these marine biopolymers, including collaboration, regulatory hurdles, supply-chain constraints, policy and financial support, education and training, and the need for integrated marine resource management. The paper concludes with recommendations for fostering innovation and sustainability in the valorization of these marine resources. Full article

Forestry carbon sinks play a crucial role in mitigating climate change and protecting ecosystems, significantly contributing to the development of carbon trading systems. Remote sensing technology has become increasingly important for monitoring carbon sinks, as it allows for precise measurement of carbon storage and ecological changes, which are vital for forecasting carbon prices. Carbon prices fluctuate due to the interaction of various factors, exhibiting non-stationary characteristics and inherent uncertainties, making accurate predictions particularly challenging. To address these complexities, this study proposes a method for predicting carbon trading prices influenced by multiple factors. We introduce a Decomposition (DECOMP) module that separates carbon price data and its influencing factors into trend and cyclical components. To manage non-stationarity, we propose the KAN with Multi-Domain Diffusion (KAN-MD) module, which efficiently extracts relevant features. Furthermore, a Wave-MH attention module, based on wavelet transformation, is introduced to minimize interference from uncertainties, thereby enhancing the robustness of the model. Empirical research using data from the Hubei carbon trading market demonstrates that our model achieves superior predictive accuracy and resilience to fluctuations compared to other benchmark methods, with an MSE of 0.204% and an MAE of 0.0277. These results provide reliable support for pricing carbon financial derivatives and managing associated risks. Full article

Dietary fiber (DF) is one type of carbohydrate that cannot be digested by the gastrointestinal tract. It is widely recognized as an essential ingredient for health due to its remarkable prebiotic properties. Studies have shown that DF is important in the management of metabolic diseases, such as obesity and diabetes, by regulating the balance of gut microbiota and slowing down the absorption of glucose. It is worth noting that patients with metabolic diseases might suffer from intestinal dysfunction (such as constipation), which is triggered by factors such as the disease itself or medication. This increases the complexity of chronic disease treatment. Although medications are the most common treatment for chronic disease, long-term use might increase the financial and psychological burden. DF as a prebiotic has received significant attention not only in the therapy for constipation but also as an adjunctive treatment in metabolic disease. This review focuses on the application of DF in modulating metabolic diseases with special attention on the effect of DF on intestinal dysfunction. Furthermore, the molecular mechanisms through which DF alleviates intestinal disorders are discussed, including modulating the secretion of gastrointestinal neurotransmitters and hormones, the expression of aquaporins, and the production of short-chain fatty acids. Full article

Stock price forecasting remains a persistent challenge in time series analysis due to complex inter-stock relationships and dynamic textual signals such as financial news. While Graph Neural Networks (GNNs) can model relational structures, they often struggle with capturing higher-order dependencies and are sensitive to noise. Moreover, sentiment signals are typically aggregated using fixed time windows, which may introduce temporal bias. To address these issues, we propose DASF-Net (Diffusion-Aware Sentiment Fusion Network), a multimodal framework that integrates structural and textual information for robust prediction. DASF-Net leverages diffusion processes over two complementary financial graphs—one based on industry relationships, the other on fundamental indicators—to learn richer stock representations. Simultaneously, sentiment embeddings extracted from financial news using FinBERT are aggregated over an empirically optimized window to preserve temporal relevance. These modalities are fused via a multi-head attention mechanism and passed to a temporal forecasting module. DASF-Net integrates daily stock prices and news sentiment, using a 3-day sentiment aggregation window, to forecast stock prices over daily horizons (1–3 days). Experiments on 12 large-cap S&P 500 stocks over four years demonstrate that DASF-Net outperforms competitive baselines, achieving up to 91.6% relative reduction in Mean Squared Error (MSE). Results highlight the effectiveness of combining graph diffusion and sentiment-aware features for improved financial forecasting. Full article

Ships play a crucial role in modern society, serving purposes such as marine transportation, tourism, and exploration. Malfunctions or defects in the main engine, which is a core component of ship operations, can disrupt normal functionality and result in substantial financial losses. Consequently, early fault diagnosis of abnormal engine conditions is critical for effective maintenance. In this paper, we propose a deep hybrid model for fault diagnosis of ship main engines, utilizing exhaust gas temperature data. The proposed model utilizes both time-domain features (TDFs) and time-series raw data. In order to effectively extract features from each type of data, two distinct feature extraction networks and an attention module-based classifier are designed. The model performance is evaluated using real-world cylinder exhaust gas temperature data collected from the large ship low-speed two-stroke main engine. The experimental results demonstrate that the proposed method outperforms conventional methods in fault diagnosis accuracy. The experimental results demonstrate that the proposed method improves fault diagnosis accuracy by 6.146% compared to the best conventional method. Furthermore, the proposed method maintains superior performanceeven in noisy environments under realistic industrial conditions. This study demonstrates the potential of using exhaust gas temperature using a single sensor signal for data-driven fault detection and provides a scalable foundation for future multi-sensor diagnostic systems. Full article

Background: The migration of healthcare professionals poses a serious threat to health systems worldwide. This study examines attitudes toward brain drain and the factors influencing migration tendencies among medical and nursing students in Albania, with particular attention to nursing workforce implications. Methods: A cross-sectional study was conducted with 610 students in the 2024–2025 academic year using the 16-item Brain Drain Attitude Scale (BDAS). Socio-demographic and academic data were also collected. Results: The mean BDAS score was 53.43 ± 16.88. Pull factors (mean: 40.25 ± 12.76) were stronger motivators than push factors (mean: 13.19 ± 4.13). A total of 487 nursing, 73 midwifery-nursing, and 50 medical students participated (95% response rate). Nearly 40% expressed a desire to work abroad, citing financial prospects (48.2%), better living standards (46%), and personal freedom (42.1%) as reasons. Higher migration tendencies were seen in females (β = 0.50, p = 0.049), medical students (β = 1.01, p = 0.001), and third-year students (β = 0.46, p = 0.011). Conclusions: Migration tendencies are high among future Albanian healthcare professionals, with significant implications for the nursing workforce. Targeted policies are urgently needed to address brain drain through workforce investment and retention strategies. Full article

The confluence of financial constraints, climate change mitigation efforts, and evolving user expectations has significantly transformed the concept of facility management (FM). Traditional FM has now evolved to enhance sustainability in the built environment. Sustainable facility management (SFM) can add value to companies, organizations, and governments by balancing the financial, environmental, and social outcomes of the FM processes. The systematic literature review revealed a limited number of studies developing a performance measurement framework for SFM in office buildings and/or other building types in the literature. Given that the lack of this theoretical basis inhibits the effective deployment of SFM practices, this study aims to fill this gap by developing a performance measurement framework for SFM in office buildings. Accordingly, an in-depth literature review was initially conducted to synthesize sustainable performance measurement factors. Next, a series of focus group discussion (FGD) sessions were organized to refine and verify the factors and develop a novel performance measurement framework for SFM. Lastly, consistency analysis, the Bayesian best worst method (BBWM), and sensitivity analysis were implemented to determine the priorities of the factors. What the proposed framework introduces is the combined use of two performance measurement mechanisms, such as continuous performance measurement and comprehensive performance measurement. The continuous performance measurement is conducted using high-priority factors. On the other hand, the comprehensive performance measurement is conducted with all the factors proposed in this study. Also, the BBWM results showed that “Energy-efficient material usage”, “Percentage of energy generated from renewable energy resources to total energy consumption”, and “Promoting hybrid or remote work conditions” are the top three factors, with scores of 0.0741, 0.0598, and 0.0555, respectively. Moreover, experts should also pay the utmost attention to factors related to waste management, indoor air quality, thermal comfort, and H&S measures. In addition to its theoretical contributions, the paper makes practical contributions by enabling decision makers to measure the SFM performance of office buildings and test the outcomes of their managerial processes in terms of performance. Full article

In the era of big data, the security of information encryption systems has garnered extensive attention, particularly in critical domains such as financial transactions and medical data management. While traditional Shamir’s Secret Sharing (SSS) ensures secure integer sharing through threshold cryptography, it exhibits inherent limitations when applied to floating-point domains and high-precision numerical scenarios. To address these issues, this paper proposes an innovative algorithm to optimize SSS via type-specific coding for real numbers. By categorizing real numbers into four types—rational numbers, special irrationals, common irrationals, and general irrationals—our approach achieves lossless transmission for rational numbers, special irrationals, and common irrationals, while enabling low-loss recovery for general irrationals. The scheme leverages a type-coding system to embed data category identifiers in polynomial coefficients, combined with Bernoulli-distributed random bit injection to enhance security. The experimental results validate its effectiveness in balancing precision and security across various real-number types. Full article

Explainability and interpretability have emerged as essential considerations in machine learning, particularly as models become more complex and integral to a wide range of applications. In response to increasing concerns over opaque “black-box” solutions, the literature has seen a shift toward two distinct yet often conflated paradigms: explainable AI (XAI), which refers to post hoc techniques that provide external explanations for model predictions, and interpretable AI, which emphasizes models whose internal mechanisms are understandable by design. Meanwhile, the phenomenon of concept and data drift—where models lose relevance due to evolving conditions—demands renewed attention. High-impact events, such as financial crises or natural disasters, have highlighted the need for robust interpretable or explainable models capable of adapting to changing circumstances. Against this backdrop, our systematic review aims to consolidate current research on explainability and interpretability with a focus on concept and data drift. We gather a comprehensive range of proposed models, available datasets, and other technical aspects. By synthesizing these diverse resources into a clear taxonomy, we intend to provide researchers and practitioners with actionable insights and guidance for model selection, implementation, and ongoing evaluation. Ultimately, this work aspires to serve as a practical roadmap for future studies, fostering further advancements in transparent, adaptable machine learning systems that can meet the evolving needs of real-world applications. Full article

This study proposes a hybrid approach to analyze and forecast non-stationary financial time series by combining statistical models with deep neural networks. A model is introduced that integrates three key components: the Generalized Autoregressive Score (GAS) model, which captures volatility dynamics; an attention mechanism (ATT), which identifies the most relevant features within the sequence; and a Long Short-Term Memory (LSTM) neural network, which receives the outputs of the previous modules to generate price forecasts. This architecture is referred to as GAS-ATT-LSTM. Both unidirectional and bidirectional variants were evaluated using real financial data from the Nasdaq Composite Index, Invesco QQQ Trust, ProShares UltraPro QQQ, Bitcoin, and gold and silver futures. The proposed model’s performance was compared against five benchmark architectures: LSTM Bidirectional, GARCH-LSTM Bidirectional, ATT-LSTM, GAS-LSTM, and GAS-LSTM Bidirectional, under sliding windows of 3, 5, and 7 days. The results show that GAS-ATT-LSTM, particularly in its bidirectional form, consistently outperforms the benchmark models across most assets and forecasting horizons. It stands out for its adaptability to varying volatility levels and temporal structures, achieving significant improvements in both accuracy and stability. These findings confirm the effectiveness of the proposed hybrid model as a robust tool for forecasting complex financial time series. Full article

Emerging economies such as China have benefited from rapid growth but now face acute carbon risk amid worsening environmental conditions. Carbon-intensive firms—major emitters—face rising carbon risk that pervades operations and threatens efficient capital allocation. To advance global climate-change mitigation, help China meet its dual-carbon goals, and enhance corporate financial sustainability, we analyze panel data on 575 Chinese carbon-intensive companies from 2012 to 2022 and estimate OLS models to assess how carbon risk influences capital mismatch. Results show that higher carbon risk significantly widens capital mismatch, whereas higher media attention and better corporate governance each weaken this effect. These findings suggest that regulators and the media should monitor carbon-intensive firms more closely to improve information transparency and guide capital to its most productive uses, while firms themselves need to strengthen governance to limit the damage carbon risk inflicts on capital allocation. Full article

This article focuses on how rising nativism, manifested through immigrants’ experiences of everyday discrimination, and Florida’s legal context (ascertained through immigrants’ fears of deportation), are related to immigrants’ hesitation when seeking healthcare services. Hesitation to seek healthcare, or healthcare hesitancy, is examined in the context of Florida’s SB1718, a law passed in 2023 that criminalized many aspects of being an immigrant. Based on a survey of 466 Florida immigrants and U.S. citizen adult children of immigrants, logistic regression analysis reveals that everyday experiences with discrimination are associated with a reluctance to seek healthcare services among this population. In particular, those with insecure legal immigrant status (i.e., undocumented and temporary statuses), those with financial hardship, and women demonstrate reluctance to engage with healthcare systems when controlling for other sociodemographic factors. Findings from this study exemplify how immigration policies that restrict access to healthcare and social services not only create logistical barriers to seeking care but also foster a climate of fear and exclusion that deters even those with legal status from seeking medical attention. Full article