Search Results (1,675)

This study assessed consumer perceptions of high-fat, low-carbohydrate (HFLC) protein bars containing varying levels of beef tallow fat. A consumer acceptability test was conducted (n = 102) with four prepared and one commercially available HFLC bar samples. Hedonic, diagnostic (intensity), and just-about-right (JAR) questions on overall liking, texture, flavor, and purchase intent were included in the sample evaluation ballot, followed by general demographic, consumption behavior, and ingredient preference questions about the product category. Although none of the samples, including the commercial bar, were liked, the sample with the highest protein content and lowest fat content was preferred over the others. Overall flavor and aroma liking were rated significantly higher for all prepared samples compared with the commercial bar (p ≤ 0.05). The sample evaluation revealed potential pathways for improving HFLC bars by leveraging “fat-synergizing” attributes such as sweetness, saltiness, and spiciness, with texture improvements possible through higher lean-protein incorporation. The ingredient factors most important to the participants were high protein content, high satiety, minimal ingredients, natural ingredients, and no added sugar. This study’s results demonstrate a widespread desire for fewer ingredients overall, more natural ingredients, and high satiation in snack products. Full article

Objective: To synthesize current evidence on the relationships between adolescent idiopathic scoliosis (AIS), stress-related mechanisms, neuroanatomical asymmetry, and mental disorders, and to propose an integrative conceptual framework describing their interaction. Materials and Methods: A comprehensive literature review was conducted using the PubMed, Web of Science, and Scopus databases. Search terms targeted the etiology and pathogenesis of adolescent idiopathic scoliosis, hemispheric lateralization, stress responses, body schema disturbances, and associated mental disorders. The review was reported in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. A structured qualitative synthesis of 225 relevant publications was performed. Results: The analyzed studies revealed several complementary conceptual approaches to AIS pathogenesis. Emerging evidence suggests that atypical hemispheric lateralization, potentially associated with right-hemisphere (RH) dysfunction, may contribute to susceptibility to AIS. Such patterns of lateralization have been linked to specific stress-related coping strategies, including harm avoidance, as well as to disturbances of body schema and an increased prevalence of certain mental disorders. Gender-related differences in stress responses and in the development and progression of AIS were consistently reported across studies. Collectively, these findings support the hypothesis that neuropsychological and stress-related mechanisms, including phenomena described as the “distorting mirror effect”, may contribute to the persistence and progression of spinal deformity in vulnerable individuals. Conclusions: AIS appears to be a multifactorial condition in which atypical neuroanatomical asymmetry, stress-related processes, and altered body representation interact. This integrative perspective generates hypotheses suggesting that prevention and treatment strategies for AIS could benefit from incorporating approaches aimed at modulating stress responses and enhancing brain neuroplasticity. Further interdisciplinary studies integrating clinical, neuroimaging, and neurobiological methods are warranted to clarify underlying mechanisms. Full article

Vehicle repositioning is essential for improving efficiency and service quality in ride-sharing platforms, yet existing approaches typically optimize proxy rewards that fail to reflect human-centered preferences such as wait time, service coverage, and unnecessary empty travel. We propose the first two-stage Bilevel Reinforcement Learning (RL) from Human Feedback (RLHF) framework for preference-aligned vehicle repositioning. In Stage 1, a value-based Deep Q-Network (DQN)-RLHF warm start learns an initial preference-aligned reward model and stable reference policy, mitigating the reward-model drift and cold-start instability that arise when applying on-policy RLHF directly. In Stage 2, a Kullback–Leibler (KL)-regularized Proximal Policy Optimization (PPO)-RLHF algorithm, equipped with action masking, behavioral-cloning anchoring, and alternating forward–reverse KL, fine-tunes the repositioning policy using either Large Language Model (LLM)-generated or rubric-based preference labels. We develop and compare two coordination schemes, pure alternating (PPO-Alternating) and k-step alternating (PPO-k-step), demonstrating that both yield consistent improvements across all tested arrival scales. Empirically, our framework reduces wait time and empty-mile ratio while improving served rate, without inducing trade-offs or reducing platform profit. These results show that human preference alignment can be stably and effectively incorporated into large-scale ride-sharing repositioning. Full article

This systematic review explores the incorporation of Environmental, Social, and Governance (ESG) factors within financial risk prediction models, with a particular focus on Machine Learning (ML), Natural Language Processing (NLP), and Large Language Models (LLM). Adhering to the Preferred Reporting Items for Systematic Reviews and the Meta-Analyses (PRISMA) and PICOC frameworks, we identified 74 peer-reviewed publications disseminated between 2009 and March 2025 from the Scopus database. After excluding 10 systematic and literature reviews to avoid double-counting of evidence, we conducted quantitative analysis on 64 empirical studies. The findings indicate that traditional econometric methodologies continue to prevail (48%), followed by ML strategies (39%), NLP methodologies (8%), and Other (5%). Research that concurrently focuses on all three dimensions of ESG constitutes the most substantial category (44%), whereas the Social dimension, in isolation, receives minimal focus (5%). A geographic analysis reveals a concentration of research activity in China (13 studies), Italy (10), and the United States and India (6 each). Chi-square tests reveal no statistically significant relationship between the methodological approaches employed and the ESG dimensions examined (p = 0.62). The principal findings indicate that ML models—particularly ensemble methodologies and neural networks—exhibit enhanced predictive accuracy in the context of credit risk and default probability, whereas NLP methodologies reveal significant potential for the analysis of unstructured ESG disclosures. The review highlighted ongoing challenges, including inconsistencies in ESG data, variability in ratings across different providers, insufficient coverage of emerging markets, and the disparity between academic research and practical application in model implementation. Full article

Against the backdrop of carbon quota trading policies and Energy Performance Contracting (EPC), Energy Service Companies (ESCOs) engage in supply chain emission reduction via embedded low-carbon services. However, the impact mechanism of their financing mode selection on emission reduction efficiency and economic benefits has not been fully revealed, and there is a lack of support from a systematic theoretical and engineering design framework. Therefore, this study innovatively constructs a multi-agent Stackelberg game model with bank financing, green bond financing, and internal factoring financing. We incorporate the embedding degree, emission reduction cost coefficient, and financing mode selection into a unified analysis framework. The research findings are as follows: (1) There is a significant positive linear relationship between supply chain profit and the embedding degree. In contrast, the profit of ESCOs shows an inverted “U-shaped” change trend. Moreover, there is a sustainable cooperation threshold for each of the three financing modes. (2) Green bond financing can significantly increase the overall emission reduction rate of the industrial supply chain in high-embedding-degree scenarios. However, due to emission reduction investment cost pressure, ESCOs tend to choose bank financing. (3) The dynamic change of the emission reduction investment cost coefficient will trigger a reversal effect on the financing preferences of the supply chain and ESCOs. This study unveils the internal mechanism of multi-party decision-making in the low-carbon industrial supply chain and is supported by cross-country institutional evidence and comparative case-based analysis, providing a scientific basis and engineering design guidance for optimizing ESCO financing strategies, crafting incentive contracts, and enhancing government subsidy policies. Full article

Hot dry rock (HDR), characterized by high temperature, vast reserves, and significant development potential, is one of the most important clean energy sources for the future. This study focuses on the Jianhu Uplift and Dongtai Depression in the southern part of the Subei Basin as the research area, conducting systematic target optimization research on HDR geothermal resources within the Cambrian–Ordovician carbonate strata. By systematically compiling regional geothermal geological data, an evaluation index system for target optimization of geothermal resources was established, incorporating two categories of indicators: resource conditions (thermal reservoir temperature and roof burial depth) and environmental impact (urban area safety distance and fault safety distance). Using the Analytic Hierarchy Process (AHP) and GIS spatial overlay analysis, the study area was evaluated for HDR geothermal resource exploration zoning, ultimately delineating three levels of preferred zones. The evaluation results indicate that the target area of the Cambrian–Ordovician geothermal reservoir is extensive, with the Dongtai Depression exhibiting a larger distribution of preferred zones. This study provides a reference for the optimization of target areas in geothermal resource exploration and development. Full article

The increasing demand for plant-based alternatives, driven by veganism, lactose intolerance, and greater health consciousness, has intensified research into dairy-free frozen desserts. This study investigates the development of a plant-based ice cream alternative utilizing oleosomes extracted from sunflower seed kernels as natural emulsifiers, eliminating the need for synthetic additives. Oleosomes were obtained through aqueous extraction from raw kernels, incorporated into emulsions in three levels (0, 12, and 24%), and combined with sunflower seed oil, tahini, date paste, and water to create the ice cream (IC) formulations. The physicochemical properties of three formulations of a sugar-free frozen dessert were studied. Physicochemical analyses assessed nutritional value, color (CIELab), melting time, stability, overrun, viscosity, and texture profile (TPA). Sensory evaluation was conducted using a hedonic test to assess the impact of tahini type (sunflower seed tahini or pumpkin seed kernel tahini) on the product acceptance. Results showed that higher oleosome content improved emulsion stability and melting resistance, while also producing a softer (30.74 ± 0.28 N), less adhesive (1.87 ± 0.20 mJ) texture, suitable for plant-based ice cream. Sensory analysis revealed a clear preference for the pumpkin tahini formulation, which scored 8.21 ± 0.62 for overall appreciation. The findings demonstrate that the addition of oleosome might improve textural attributes of the products, while the consumer preference could also be influenced by the type of tahini involved in the formulation. However, further studies are necessary to corroborate the proposed interaction mechanisms of ingredients. Full article

The cultivation of blueberry (Vaccinium spp.) has undergone an unprecedented global expansion, driven by its nutraceutical value and the diversification of production zones across the Americas, Europe, and Asia. Its consolidation as a strategic crop has prompted intensive scientific activity aimed at optimizing every stage of management from propagation and physiology to harvest, postharvest, and environmental sustainability. However, the available evidence remains fragmented, limiting the integration of results and the formulation of knowledge-based, comparative production strategies. The objective of this systematic review was to synthesize scientific and technological advances related to the integrated management of blueberry cultivation, incorporating physiological, agronomic, technological, and environmental dimensions. The PRISMA 2020 methodology (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was applied to ensure transparency and reproducibility in the search, selection, and analysis of scientific literature indexed in the Scopus database. After screening, 367 articles met the inclusion criteria and were analyzed comparatively and thematically. The results reveal significant progress in propagation using hydrogel and micropropagation techniques, efficient fertigation practices, and the integration of climate control operations within greenhouses, leading to improved yield and fruit quality. Likewise, non-thermal technologies, edible coatings, and harvest automation enhance postharvest quality and reduce losses. In terms of sustainability, the incorporation of water reuse and waste biorefinery has emerged as key strategies to reduce the environmental footprint and promote circular systems. Among the main limitations are the lack of methodological standardization, the scarce economic evaluation of innovations, and the weak linkage between experimental and commercial scales. It is concluded that integrating physiology, technology, and sustainability within a unified management framework is essential to consolidate a resilient, low-carbon, and technologically advanced fruit-growing system. Full article

To achieve carbon neutrality goals, large amounts of renewable energy sources (RESs) are being integrated into power systems. In particular, high penetration of distributed photovoltaic (PV) makes distribution networks highly stochastic, calling for dynamic distribution network reconfiguration (DNR). Existing DNR approaches can be broadly categorized into model-driven optimization-based methods and learning-based methods, with deep reinforcement learning (DRL) being a representative paradigm for fast online decision-making. Existing DNR models typically belong to mixed-integer linear programming, which requires solution methods such as deep reinforcement learning (DRL). However, existing methods commonly struggle to account for human factors, i.e., the time-varying preferences of distribution network operators in DRL decisions. To this end, this paper proposes a natural language-driven, human-in-the-loop DNR framework, which combines a DRL base policy for hour-level dynamic reconfiguration with a large language model (LLM)-based instruction supervision layer. Based on this human-in-the-loop framework, commands from operators in natural language are translated into online adjustments of safety-screened DRL switching actions. Therefore, the framework demonstrates the fast, model-free decision capability of DRL while providing an explicit and interpretable interface for incorporating temporary and context-dependent operator requirements without retraining. Case studies on IEEE 16-bus and 33-bus distribution networks show that the proposed framework reduces network losses, improves voltage profiles, and limits switching operations. It also achieves markedly higher compliance with operator instructions than a conventional model-based method and a pure DRL baseline. These results highlight a viable path to embedding natural language guidance into the data-driven operation of active distribution networks. Full article

As the importance of emotional interaction between humans and robots continues to gain attention, numerous studies have been conducted to identify the characteristics and effects of emotional HRI (Human–Robot Interaction) elements applied to robots. However, no study has yet combined various HRI elements into a single robot and conducted large-scale user experiments to determine which HRI element users prefer the most. This study selected four characteristics that facilitate attachment and emotional bonding between humans and animals: grooming, emotional transfer, imprinting, and cooperative hunting (play). These four characteristics were incorporated into the design and behavioral patterns of the robot EDIE as HRI elements. To allow users to effectively experience these elements, a 30 min runtime robot performance content featuring EDIE as the main character was developed. This large-scale experiment in the form of a performance enabled participants to engage with all four HRI elements and then respond to a survey identifying their most preferred element. Over two experiments involving a total of 3760 participants, this study examined trends in user preferences regarding the robot’s characteristics. By identifying the most effective HRI elements for fostering user attachment to robots, the findings aim to contribute to the harmonious coexistence of humans and robots. Full article

Background: Melanoma outcomes have improved in recent years as a result of modern systemic therapies. A major molecular feature of melanoma is abnormal telomerase activation; this is most often caused by telomerase reverse transcriptase (TERT) promoter mutations, which occur in 50–82% of cases and are the most common noncoding alteration in this cancer. Telomerase maintains telomere length, allowing melanoma cells to avoid senescence and continue dividing. However, how telomerase activity influences melanoma cell doubling time remains unclear, and the pathways linking TERT expression to faster cell-cycle progression require further study. Although telomerase inhibitors show promise in preclinical models, their clinical use is limited by delayed cytotoxicity and resistance. Materials and Methods: A scoping review was conducted using Scopus, ScienceDirect, MEDLINE/PubMed, and CINAHL (Cumulative Index to Nursing and Allied Health Literature). Keywords included “telomerase,” “melanoma,” “cancer,” “cell proliferation,” and “doubling time,” using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: Telomerase-related biomarkers were found to correlate with disease stage and survival. Suggested therapeutic strategies include enzyme inhibitors, cytotoxic nucleotide incorporation, telomere destabilization, and immunotherapies such as peptide or dendritic cell vaccines, etc. Conclusions: Understanding both telomere-dependent and -independent TERT functions is essential for developing effective biomarkers and therapies that overcome resistance and slow melanoma progression. Full article

Objective: This study aimed to design and clinically evaluate a bespoke cosmetic formulation tailored to individual skin characteristics and user preferences, focusing on hydration and barrier recovery in mature, therapy-affected skin. In addition, this study aimed to explore the feasibility and short-term outcomes of a structured, biometry-driven personalization approach applied within a single-subject case study design. Materials and Methods: A personalized dermato-cosmetic formulation incorporating melatonin, astaxanthin, low-molecular-weight hyaluronic acid, allantoin, yarrow oil (Achillea millefolium), lecithin, cholesterol, and arginine was developed based on objective biophysical assessment of the skin. A clinical case evaluation was conducted in a male subject over 55 years of age (Fitzpatrick phototype III) presenting persistent xerosis and dehydration following completed oncologic therapy. Quantitative skin biometry was performed at baseline and after 28 days of daily application, assessing hydration at six anatomical sites, sebum secretion, pigmentation and erythema indices, elasticity, and stratum corneum turnover and scaling. Results: After 28 days, sebum secretion increased by more than 100%, indicating partial restoration of the lipid barrier. Hyperpigmented areas decreased from 7.2% to 2.3%, while skin elasticity improved from 25% to 44%. A reduction of 8% in the erythema index suggested decreased vascular reactivity. Hydration levels improved consistently across all evaluated sites, and epidermal renewal was enhanced, as evidenced by reduced scaling and smoother skin surface. The melanin index remained stable throughout the study period. Conclusions: This pilot evaluation shows that bespoke cosmetic formulations, customized to individual skin biometry and preferences, can yield measurable improvements in hydration, barrier repair, elasticity, pigmentation uniformity, and epidermal renewal within 28 days, even in skin compromised by previous oncologic therapy. Given the single-subject nature of this pilot evaluation, these findings cannot be generalized to broader populations but rather highlight the importance of personalization and objective skin assessment in guiding individualized dermato-cosmetic formulation strategies. Personalized dermato-cosmetology using objective biophysical assessment may be a promising future strategy for effective, consumer-centered skincare. Full article

This article examines public preferences for a proposed West Coast submarine high-voltage direct current (HVDC) transmission network in South Korea, installed by trenching and burying the cables in the seabed, which is essential for facilitating renewable energy integration and ensuring a stable electricity supply to the Seoul Metropolitan Area. The purpose of this study is to estimate South Korean households’ willingness to pay (WTP) for the proposed West Coast submarine HVDC network using contingent valuation (CV), thereby assessing its social acceptability amid renewable energy integration challenges. Employing a CV survey with a nationally representative sample of 1000 households conducted from late May to late June 2025, the research applies the one-and-one-half-bound spike model to address zero WTP responses and incorporates socio-demographic covariates to account for preference heterogeneity. The analysis estimates an average monthly WTP of KRW 1832 (USD 1.33) per household for the HVDC infrastructure. Results demonstrate statistically significant public support for the submarine HVDC project despite its high capital investment and potential electricity rate increases. These findings underscore notable consumer acceptance and provide valuable welfare insights for policymakers, reinforcing the prioritization of this project within South Korea’s energy transition framework. This paper contributes to the field of energy infrastructure valuation by advancing methodological approaches and offering policy-relevant recommendations for sustainable grid expansion. Full article

Anime pilgrimage refers to the act of fans visiting real-world locations featured in anime works, offering visual familiarity alongside cultural depth. However, existing studies on anime tourism provide limited computational support for selecting pilgrimage sites based on contextual and experiential factors. This study proposes an intelligent recommendation framework based on multi-source data fusion that integrates three key elements: transportation accessibility, seasonal alignment between the current environment and the anime’s depicted scene, and a Cross-Platform Popularity Index (CPPI) derived from major global platforms. We evaluate each pilgrimage location using route-based accessibility analysis, season-scene discrepancy scoring, and robustly normalized popularity metrics. These factors are combined into a weighted Multi-Criteria Decision Making (MCDM) model to generate context-aware recommendations. To rigorously validate the proposed approach, a user study was conducted using a ranking task involving popular destinations in Tokyo. Participants were presented with travel conditions, spatial relationships, and popularity scores and then asked to rank their preferences. We used standard ranking-based metrics to compare system-generated rankings with participant choices. Furthermore, we conducted an ablation study to quantify the individual contribution of accessibility, seasonality, and popularity. The results demonstrate strong alignment between the model and user preferences, confirming that incorporating these three dimensions significantly enhances the reliability and satisfaction of real-world anime pilgrimage planning. Full article

Previous studies, constrained by the overly rigid stability requirements, often fail to adapt to complex systems and struggle to identify stable outcomes that align with the practical context of multi-agent resource allocation. To address the three-sided matching problem in complex socio-technical and business management systems, this paper proposes a fuzzy stable matching method for three-sided agents under a framework of combinatorial preference relations, integrating network and decision theory. First, we construct a membership function to measure the degree of preference satisfaction between elements of different agents, and then define the concept of fuzzy stability. By incorporating preference satisfaction, we introduce the notion of fuzzy blocking strength and derive the generation conditions for blocking triples and fuzzy stability under the fuzzy stable criterion. Furthermore, we abstract the three-sided matching problem with combined preference relations into a shortest path problem. Second, we prove the equivalence between the shortest path solution and the stable matching outcome. We adopt Dijkstra’s algorithm for problem-solving and derive the time complexity of the algorithm under the pruning strategy. Finally, we apply the proposed model and algorithm to a case study of project assignment in software companies, thereby verifying the feasibility and effectiveness of this three-sided matching method. Compared with existing approaches, the fuzzy stable matching method developed in this study demonstrates distinct advantages in handling preference uncertainty and system complexity. It provides a more universal theoretical tool and computational approach for solving flexible resource allocation problems prevalent in real-world scenarios. Full article