Search Results (8,677)

The Nutrition and Health Claims Regulation (NHCR) has introduced a new regulatory perspective in food manufacturing, along with influencing consumers’ perception of health-related food claims. Since 2006, a new standard of science-based claims has significantly impacted the European health food market. Over the years, numerous additional decisions have been made, and the ongoing process remains challenging for policymakers striving to harmonize consumer protection and trade within and outside the European Union (EU). This paper presents the current state of the NHCR’s implementation, along with key events aimed at enhancing understanding among consumer organizations and food industry stakeholders, while also offering an insider perspective on relevant policy issues. Additionally, we address two pertinent policy issues to elucidate the associated challenges and opportunities, providing insights to support informed decision-making by policymakers. We use the nutrient profiles framework as a case study to illustrate considerations underpinning the objective of “consumer protection”, while the “probiotics” market serves as an example for exploring the goal of “facilitation of trade”. This historical perspective and reflection lead us to propose possible solutions for future food regulation. Full article

Given the increasing complexity of leadership roles in global, sustainability-driven organizations, this study examines whether Hersey and Blanchard’s Situational Leadership Model (SLM) provides a sufficiently comprehensive framework for contemporary leadership demands or requires theoretical and practical modifications. Can SLM, originally designed for adaptability in leader–follower dynamics, effectively integrate sustainability principles such as Environmental, Social, and Governance (ESG) factors; corporate social responsibility (CSR); and ethical governance? How can leadership models evolve to balance immediate responsiveness with long-term resilience and sustainability-driven decision-making? This research systematically evaluates the synthesis of empirical evidence on the application of the SLM across diverse organizational contexts while exploring its alignment with sustainability-focused leadership approaches. The study further investigates the role of Servant Leadership as a conceptual bridge between SLM and sustainability principles, emphasizing its ethical foundation, stakeholder-oriented approach, and long-term commitment to workforce well-being. Findings suggest that while SLM remains a relevant and adaptable framework, it exhibits a deficiency in explicitly addressing the sustainability dimension. However, integrating Servant Leadership’s emphasis on ethical governance and organizational resilience into SLM could enhance its applicability to sustainability-driven leadership models. By addressing these gaps, this study contributes to contemporary leadership theory by proposing an evolved SLM framework that incorporates sustainability-focused leadership competencies. Future research should focus on refining SLM to ensure its alignment with the ethical and environmental imperatives of modern organizations, equipping leaders to navigate the complexities of sustainable corporate governance while maintaining situational adaptability. Full article

This research focuses on developing a sentiment-based system to analyze public opinion on electromagnetic radiation in online networks. Issues related to EMR, such as the NIMBY effect and negative public sentiment, can lead to health crises, social conflicts, and challenges in decision-making. This study addresses limitations in existing research, including inaccurate data collection and a lack of systematic analysis. By incorporating Jieba Chinese word segmentation technology, this study introduces an innovative data collection method based on topic similarity, significantly improving data accuracy. Additionally, this research establishes a comprehensive public opinion analysis framework that integrates user follower counts, geographical distribution, and interaction data. This framework facilitates the identification of sources of negative sentiment and the development of effective response strategies. As a case study, the dissemination patterns of EMR-related public opinion on Weibo are analyzed, focusing on group sentiment and social interaction. The proposed system achieves a 65.85% improvement in data collection accuracy, demonstrating its effectiveness. Furthermore, this study provides actionable recommendations for relevant departments and governments to monitor, analyze, and respond to EMR-related public opinion. By enhancing decision-making and protecting public interests, this study highlights the role of technology in improving social governance and substantial development. Full article

The intensifying impacts of climate change induced by carbon emissions necessitate the implementation of urgent mitigation strategies. Given that the power sector is a major contributor to global carbon emissions, strategic decarbonization planning in this sector is of paramount importance. This study proposes a synergistic planning framework for low-carbon power systems that integrates coal-fired power plants (CFPPs) and a carbon and green certificate market coupling mechanism, thereby facilitating a “security–economic–low-carbon” tri-objective transition in power systems. The proposed framework facilitates dynamic decision-making regarding the retrofitting of CFPPs, investments in renewable energy resources, and energy storage systems. By evaluating three distinct CFPP retrofitting pathways, the framework enhances economic efficiency and reduces carbon emissions, achieving reductions of 28.67% in total system costs and 2.96% in CO₂ emissions. Implementing the carbon–green certificate market coupling mechanism further unlocks the market value of green certificates, thereby providing economic incentives for clean energy projects and increasing flexibility in the allocation of carbon emission quotas for enterprises. Relative to cases that consider only carbon trading or only green certificate markets, the coupled mechanism reduces the total cost by 10.96% and 15.56%, and decreases carbon emissions by 27.10% and 47.36%, respectively. The collaborative planning framework introduced in this study enhances economic performance, increases renewable energy penetration, and reduces carbon emissions, thus facilitating the low-carbon transition of power systems. Full article

In a competitive market, due to differences in the nature of various power generation entities, there is a decline in resource utilization and difficulties in ensuring a return on investment for generating units within the system. A bi-level non-cooperative game model based on the Unit Resource Return (URR) is proposed to safeguard the interests and demands of each power generation unit while improving the overall resource utilization rate of the system. Firstly, we construct a comprehensive energy-trading framework for the overall system and analyze the relationship between the Independent System Operator (ISO) and the generation units. Secondly, we propose the Unit Resource Return (URR), inspired by the concept of input-output efficiency in economics. URR evaluates the return on unit resource input by taking the maximum generation potential of each unit as the benchmark. Finally, a bi-level non-cooperative game model is established. In the lower-level non-cooperative game, the generating units safeguard their own interests, while in the upper-level, the ISO adjusts the output allocation and engages in a master–slave game between generating units to ensure the overall operational efficiency of the system. URR is adopted as the ISO’s price-clearing equilibrium criterion, enabling the optimization of both resource profitability and allocation. Ultimately, both the upper and lower-level decision variables reach a Nash equilibrium. The experimental results show that the bi-level non-cooperative game model based on the Unit Resource Return improves the overall resource utilization of the system and enhances the long-term operational motivation of the generating units. Full article

Managing risk, matching resources efficiently, and ensuring fair allocation are fundamental challenges in both finance and decision-making processes. In many scenarios, participants contribute unequally to collective outcomes, raising the question of how to distribute costs, benefits, or opportunities in a justifiable and optimal manner. This paper applies the Shapley value—a solution concept from cooperative game theory—as a principled tool in the following two specific financial settings: first, in tax cooperation games; and second, in assignment markets. In tax cooperation games, we use the Shapley value to determine the equitable tax burden distribution among three firms, A, B, and C, which operate in two countries, Italy and Poland. Our model ensures that countries participating in coalitions face a lower degree of tax evasion compared to non-members, and that cooperating firms benefit from discounted tax liabilities. This structure incentivizes coalition formation and reveals the economic advantage of joint participation. In assignment markets, we use the Shapley value to find the optimal pairing in a four-buyers and four-sellers housing market. Our findings show that the Shapley value provides a rigorous framework for capturing the relative importance of participants in the coalition, leading to more balanced tax allocations and fairer market transactions. Our theoretical insights with computational techniques highlights the Shapley value’s effectiveness in addressing complex allocation challenges across financial management domains. Full article

The deepening operation of the carbon emission trading market has reshaped the cost–benefit structure of the power generation side. In the process of participating in the market quotation, power suppliers not only need to calculate the conventional power generation cost but also need to coordinate the superimposed impact of carbon quota accounting on operating income, which causes the power suppliers a multi-time-scale decision-making collaborative optimization problem under the interaction of the carbon market, power market, and coal market. This paper focuses on the multi-market-coupling decision optimization problem of thermal power suppliers. It proposes a collaborative bidding decision framework based on a multi-agent deep deterministic policy gradient (MADDPG). Firstly, aiming at the time-scale difference of multi-sided market decision making, a decision-making cycle coordination scheme for the carbon–electricity–coal coupling market is proposed. Secondly, upper and lower optimization models for the bidding decision making of power suppliers are constructed. Then, based on the MADDPG algorithm, the multi-generator bidding scenario is simulated to solve the optimal multi-generator bidding strategy in the carbon–electricity–coal coupling market. Finally, the multi-scenario simulation based on the IEEE-5 node system shows that the model can effectively analyze the differential influence of a multi-market structure on the bidding strategy of power suppliers, verifying the superiority of the algorithm in convergence speed and revenue optimization. Full article

The following study presents the results obtained from a comparative analysis of dry (powder and hard snow) and wet snow based on satellite data and in situ data methods for monitoring in the high mountain belt of Bulgaria. The aim of the study is to analyze the effectiveness of different spectral indices based on satellite data from Synthetic Aperture Radar (SAR), high-resolution (HR) imagery, and spectrometer data for assessing the state and dynamics of the snow cover. The methods studied and the results obtained were validated by instrument-based field observations, with instruments using thermal imaging cameras, spectrometer measurements, ground control points, and HR imagery. Satellite data offer an ever-widening view of trends in snow distribution over time. All these data combined provide a detailed picture of surface temperature and snow properties, which are crucial for understanding snowmelt processes and the energy balance in the high-altitude belt. The findings suggest that a multi-method approach, utilizing the combined advantages of SAR satellite data, offers the most comprehensive and accurate framework for satellite-based snow cover monitoring in the high mountain regions of Bulgaria, such as Rila Mountain. This integrative strategy not only improves the precision of snow cover estimates but can also support many water resource-related studies, such as snowmelt runoff studies, snow avalanche modeling, and better-informed decisions in the management and maintenance of winter tourism resorts. Full article

This study addresses the critical need for an evaluation framework for tour guides within the rapidly expanding tourism sector of Saudi Arabia. Employing the best–worst method, a robust multi-criteria decision-making technique, this study identifies and prioritizes key criteria for tour guide performance. Experts ranked local cultural and historical background as the most significant attribute, demonstrating its importance in delivering authentic and enriching visitor experiences. Results revealed the relative weights of other criteria, highlighting the significance of several factors such as language proficiency, time management, and environmental and ethical awareness. Notably, technology adaption criterion received the lowest weighting, indicating a potential area for future focus within the Saudi tourism sector. The study’s findings provide a foundational framework for developing a comprehensive tour guide evaluation system. This study contributes to the growing body of literature on tour guide evaluation and offers practical implications for training and development initiatives within the Saudi Arabian tourism industry. Full article

Due to the rapid proliferation of social media and online reviews, the accurate identification and classification of controversial texts has emerged as a significant challenge in the field of natural language processing. However, traditional text-classification methodologies frequently encounter critical limitations, such as feature sensitivity and inadequate generalization capabilities. This results in a notably suboptimal performance when confronted with diverse controversial content. To address these substantial limitations, this paper proposes a novel controversial text-detection framework based on stacked ensemble learning to enhance the accuracy and robustness of text classification. Firstly, considering the multidimensional complexity of textual features, we integrate comprehensive feature engineering, i.e., encompassing word frequency, statistical metrics, sentiment analysis, and comment tree structure features, as well as advanced feature selection methodologies, particularly lassonet, i.e., a neural network with feature sparsity, to effectively address dimensionality challenges while enhancing model interpretability and computational efficiency. Secondly, we design a two-tier stacked ensemble architecture, which not only combines the strengths of multiple machine learning algorithms, e.g., gradient-boosted decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost), with deep learning models, e.g., gated recurrent unit (GRU) and long short-term memory (LSTM), but also implements the support vector machine (SVM) for efficient meta-learning. Furthermore, we systematically compare three hyperparameter optimization algorithms, including the sparrow search algorithm (SSA), particle swarm optimization (PSO), and Bayesian optimization (BO). The experimental results demonstrate that the SSA exhibits a superior performance in exploring high-dimensional parameter spaces. Extensive experimentation across diverse topics and domains also confirms that our proposed methodology significantly outperforms the state-of-the-art approaches. Full article

Railway networks are highly susceptible to land subsidence, which can undermine their functional stability and safety, resulting in recurring failures and vulnerabilities. This paper aims to evaluate the susceptibility of the railway network due to Qanat underground channels in the city of Bafq, Iran. The criteria considered for assessing the susceptibility of Qanats subsidence on the railway network in this study are Qanat channel density, Qanat well density, discharge rate of the Qanat, depth of the Qanat, railway traffic, and the railway passing load. The subjective determination of criteria weights in Multi-Criteria Decision-Making (MCDM) for susceptibility analysis is typically a complex, time-consuming, and biased task. Furthermore, there is no comprehensive study on the impact and relative significance of Qanat-related factors on railway subsidence in Iran. To address this gap, this study developed a novel spatial objective weighting approach based on Principal Component Analysis (PCA)—as an unsupervised Machine Learning (ML) technique—within a spatial decision-making framework specifically designed for railway susceptibility assessment. In the proposed framework, the final Qanat-induced subsidence susceptibility zoning was conducted using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. This study identified 7.7 km² of the total area as a high-susceptibility zone, which encompasses 15 km of railway network requiring urgent attention. The developed framework demonstrated promising performance without deploying subjective information, providing a robust data-driven approach for susceptibility assessment in the study area. Full article

The aim of the present research is to analyze community participation in disaster risk management due to tailings dam failures (DRM-TDF). Conceição do Mato Dentro, Minas Gerais State (Brazil) was used as case study. The aims of the study are to help developing more effective DRM-TDF strategies and to strengthen community participation in decision making, and in mapping and categorizing vulnerabilities (criticality and support capacity) by assessing current practices and prioritizing future strategies. Semi-structured questionnaires were applied to community leaders and open interviews were carried out with DRM experts for information collection purpose. The collected responses were categorized based on vulnerabilities by taking into account criticality (communities) and support capacity (public management and mining entrepreneurs). SWOT analysis identified “Weaknesses” (criticality) and “Threats” (support capacity), whereas Pareto analysis highlighted the most critical aspects. The results indicate that public policies and the Brazilian legal framework have made limited contributions toward achieving the Sendai Framework guidelines and the Sustainable Development Goals. A review of current practices is necessary to safeguard the rights of affected communities through their meaningful participation in decision-making processes. Full article

Primary thyroid lymphoma (PTL) is a rare malignancy, and this study aimed to develop a prognostic prediction model for PTL using deep learning algorithms while providing interpretable analyses. Machine learning models were employed for mortality risk prediction, with the SHAP framework introduced for feature interpretation, and a DeepSurv model was constructed for comparison with the Cox proportional hazards (Cox-PH) model. Model performance was evaluated using Harrell’s c-index, ROC curves, AUC, calibration curves, and decision curve analysis (DCA). Results revealed that age, ‘B’ symptoms, histological type, and marital status were the most influential factors affecting patient mortality risk, as identified through SHAP analysis, and the DeepSurv model outperformed the Cox model in predicting the test set (consistency indices 0.758 vs. 0.739 and 0.789 vs. 0.779). Additionally, a web application platform was developed based on the DeepSurv model to predict the 5-year survival rate of PTL patients, facilitating the transition from theoretical research to clinical application. This study highlights the potential of deep learning models, particularly DeepSurv, in improving prognostic predictions for PTL and provides a practical tool for guiding clinical treatment decisions. The findings underscore the value of integrating interpretable machine learning frameworks into survival analysis for rare cancers. Full article

This study develops a comprehensive roadmap for Ecuador’s energy transition using a hybrid governance model that balances top–down and bottom–up approaches. By integrating national directives with local participation, this framework aims to enhance energy consumption and drive sustainable transitions. This research employs a mixed methodology, combining bibliometric analysis and governance structure assessment to evaluate Ecuador’s centralized energy system and its challenges. A three-phase strategy is proposed: Phase 1 introduces short-term interventions such as efficiency improvements and public awareness campaigns. Phase 2 focuses on decentralization, fostering local renewable energy production and community involvement. Phase 3 envisions a fully decentralized system where local entities operate autonomously within a supportive regulatory framework. The central research question is, how can a balanced governance framework foster sustainable ECB in Ecuador? By aligning national policies with local needs, this approach enhances policy adaptability, inclusivity, and long-term sustainability. Anticipated outcomes include improved energy efficiency, reduced reliance on fossil fuels, and increased community engagement in decision making. The findings contribute to global discussions on energy governance, demonstrating how hybrid models can facilitate sustainable energy transitions, particularly in developing countries with historically centralized systems. Full article

Background/Objectives: Liver cirrhosis is a critical chronic condition with increasing global mortality and morbidity rates, emphasizing the necessity for early and accurate diagnosis. This study proposes a comprehensive deep-learning framework for the automatic diagnosis and staging of liver cirrhosis using T2-weighted MRI images. Methods: The methodology integrates stacked ensemble learning, multi-task learning (MTL), and transfer learning within an explainable artificial intelligence (XAI) context to improve diagnostic accuracy, reliability, and transparency. A hybrid model combining multiple pre-trained convolutional neural networks (VGG16, MobileNet, and DenseNet121) with XGBoost as a meta-classifier demonstrated robust performance in binary classification between healthy and cirrhotic cases. Results: The model achieved a mean accuracy of 96.92%, precision of 95.12%, recall of 98.93%, and F1-score of 96.98% across 10-fold cross-validation. For staging (mild, moderate, and severe), the MTL framework reached a main task accuracy of 96.71% and an average AUC of 99.81%, with a powerful performance in identifying severe cases. Grad-CAM visualizations reveal class-specific activation regions, enhancing the transparency and trust in the model’s decision-making. The proposed system was validated using the CirrMRI600+ dataset with a 10-fold cross-validation strategy, achieving high accuracy (AUC: 99.7%) and consistent results across folds. Conclusions: This research not only advances State-of-the-Art diagnostic methods but also addresses the black-box nature of deep learning in clinical applications. The framework offers potential as a decision-support system for radiologists, contributing to early detection, effective staging, personalized treatment planning, and better-informed treatment planning for liver cirrhosis. Full article