Search Results (193,500)

Renewable energy plays a crucial role in achieving sustainable development and has the potential to meet humanity’s long-term energy requirements. Integrated electric–thermal energy systems are an important way to consume a high proportion of renewable energy. The intermittency and volatility of integrated electric–thermal energy systems make solving energy management optimization problems difficult. Thus, this paper proposes an energy management optimization method for an integrated electric–thermal energy system based on the improved proximal policy optimization algorithm, which effectively mitigates the problems of the traditional heuristic algorithms or mathematical planning methods with low accuracy and low solving efficiency. Meanwhile, the proposed algorithm enhances both the convergence speed and overall performance compared to the proximal policy optimization algorithm. This paper first establishes a mathematical model for the energy management of an integrated electric–thermal energy system. Then, the model is formulated as a Markov decision process, and a reward mechanism is designed to guide the agent to learn the uncertainty characteristics of renewable energy output and load consumption in the system through historical data. Finally, in the case study section, the proposed algorithm reduces the average running cost by 2.32% compared to the other algorithms discussed in this paper, thereby demonstrating its effectiveness and cost-efficiency. Full article

Mediterranean diet adherence has been decreasing during the last few decades, and non-appropriate snacking habits have also been identified among Mediterranean children and adolescents. To co-create new snacks and to explore children’s interests and preferences, a multi-method approach was used in the present study, including some qualitative and quantitative research phases. Conducted in collaboration with schools in Lebanon, Egypt, Portugal, Italy, and Spain, different snack prototypes were designed and tested in a Mediterranean cross-cultural context. The results showed significant differences among countries in snacking preferences and general food-related attitudes. Italian children exhibited higher levels of neophobia, resulting in lower acceptance of all proposed snacks. Some sensory and contextual insights were collected, such as Egyptian children favoring sweet and crunchy textures and “At school”, “With my friends”, and “As a morning/afternoon snack” being identified as linked to snack acceptance in some countries. The present study underscores the value of co-creation processes involving children to address non-recommended dietary patterns, highlighting the critical role of sensory properties, cultural differences, and contextual factors in designing healthy snacks that meet the Mediterranean diet’s principles but are highly appreciated by the young segment of the population. Full article

: Background: Eukaryotic RNA polymerase I consists of 12 or 11 core subunits and three dissociable subunits, Rrn3, A34, and A49. The A34 and A49 subunits exist as a heterodimer. In silico analysis of the A34 family of transcription factors demonstrates a commonly shared domain structure despite a lack of sequence conservation, as well as N–terminal and C-terminal disordered regions. The common structure of A34 has an N–terminal disordered region followed by a dimerization domain that, in conjunction with A49, contributes to a fold that resembles the TFIIF core. This in turn is followed by a short region that cryo-EM demonstrates resembles an arm and intimately interacts with the PolR1A, PolR1B, and PolR1C subunits of Pol I. Analyses: This Pol I–binding domain is then followed by a region that is not resolved in cryo-EM and is predicted to be intrinsically disordered. Interestingly, the size/length of this disordered structure increases from yeast to humans, and is composed of repeats with unique sequence and biochemical features that also increase in number. Further analyses of the A34 CTD (carboxy–terminal domain) indicate that it has a high probability of undergoing liquid–liquid phase separation. Conclusions: We suggest that this intrinsically disordered domain found in the A34 family of Pol I transcription factors serves a function similar to the CTD of the PolR2A subunit in coordinating transcription initiation and elongation and RNA processing. Lastly, we propose that dynamic acetylation of PAF49 may regulate interactions of the intrinsically disordered CTD and thereby specify liquid–liquid phase separations. Overall, we propose a new paradigm for a repeat-containing CTD in Pol I transcription. Full article

The usage of digital twins (DTs) is growing across a wide range of businesses. The health sector is one area where DT use has recently increased. Ultimately, the concept of digital health twins holds the potential to enhance human existence by transforming disease prevention, health preservation, diagnosis, treatment, and management. Big data’s explosive expansion, combined with ongoing developments in data science (DS) and artificial intelligence (AI), might greatly speed up research and development by supplying crucial data, a strong cyber technical infrastructure, and scientific know-how. The field of healthcare applications is still in its infancy, despite the fact that there are several DT programs in the military and industry. This review’s aim is to present this cutting-edge technology, which focuses on neurology, as one of the most exciting new developments in the medical industry. Through innovative research and development in DT technology, we anticipate the formation of a global cooperative effort among stakeholders to improve health care and the standard of living for millions of people globally. Full article

Flowers, as integral elements of urban landscapes, are critical not only for aesthetic purposes but also for fostering human–nature interactions in green spaces. However, research on flower color preferences has largely been descriptive, and there is a lack of exploration of potential mechanisms influencing flower color preferences, such as economic and social factors. This study created visual samples through precise color adjustment techniques and introduced the L*, a*, and b* parameters from the CIELAB color system to quantify the flower colors of the survey samples, conducting an online survey with 354 Chinese residents. The complex aesthetic process’s driving factors were unveiled through a comprehensive analysis using a Generalized Additive Model (GAM), a piecewise Structural Equation Model (SEM), and linear regression models. The results show that the public’s flower color preference is primarily related to the a* and b* parameters, which represent color dimensions in the CIELAB color space, and it is not significantly related to L* (lightness). Factors such as age, annual household income level (AI), personal income sources (PI), nature experience, and emotional state (TMD) significantly influence color preferences, with emotional state identified as the most critical factor. Lastly, linear regression models further explain the potential mechanism of the influencing factors. This study proposes a framework to assist urban planners in selecting flower colors that resonate with diverse populations, enhancing both the attractiveness of urban green spaces and their potential to promote pro-environmental behavior. By aligning flower color design with public preferences, this study contributes to sustainable urban planning practices aimed at improving human well-being and fostering deeper connections with nature. Full article

Individuals in social housing heavily depend on the surrounding infrastructure and services. There is a gap in understanding the needs of vulnerable groups regarding access to these resources. From the lens of spatial justice, we explored the specific needs of financially limited residents, considering affordability, service levels, and service quality. Surveying 660 individuals across 13 communities in Guangzhou, we firstly assessed the accessibility to facilities by using travel data. Subsequently, a principal component analysis revealed key factors influencing individuals’ satisfaction, including proximity to essential amenities, efficient service, high-quality education, affordability of commercial establishments, and access to healthcare services. While proximity to basic facilities achieves satisfaction, it falls short of ensuring fulfilment in healthcare, commerce, and education service quality. Satisfaction varies by community location and age, with newer communities, especially those established post-2010 in outlying areas, struggling to uphold spatial justice. Full article

Urbanization poses significant challenges to ecosystems, resources, and human well-being, necessitating sustainable planning. Urban vegetation, particularly trees, provides critical ecosystem services such as carbon sequestration, air quality improvement, and biodiversity conservation. Traditional tree assessments are resource-intensive and time-consuming. Recent advances in remote sensing, especially high-resolution multispectral imagery and object-based image analysis (OBIA), offer efficient alternatives for mapping urban vegetation. This study evaluates and compares the efficacy of Sentinel-2 and Pléiades satellite imagery in classifying tree species within historic urban parks in Rome—Villa Borghese, Villa Ada Savoia, and Villa Doria Pamphilj. Pléiades imagery demonstrated superior classification accuracy, achieving an overall accuracy (OA) of 89% and a Kappa index of 0.84 in Villa Ada Savoia, compared to Sentinel-2’s OA of 66% and Kappa index of 0.47. Specific tree species, such as Pinus pinea (Stone Pine), reached a user accuracy (UA) of 84% with Pléiades versus 53% with Sentinel-2. These insights underscore the potential of integrating high-resolution remote sensing data into urban forestry practices to support sustainable urban management and planning. Full article

Strawberries (Fragaria × ananassa) are highly valued for their colorful fruit and flavorful taste. Anthocyanins provide much of the red fruit color, and the flavor is highly affected by soluble sugars and non-volatile organic acids. These fruit composition parameters impact consumer decisions. In this study, strawberry fruits from 17 commercial cultivars and advanced selections were collected weekly from replicated trials at three locations in North Carolina. The relative effects of the location and germplasm on fruit composition were determined, including the soluble solid concentration (SSC), titratable acidity (Tacid), and total anthocyanin content (TAC). The breeding criteria of at least 8.0% SSC and 0.80% Tacid were met by eight and six genotypes, respectively and five of these met both criteria. The fruit TAC ranged from 24.0 to 45.7 mg pelargonidin-3-O-glucoside (P3G) equivalents/100 g FWT. P3G was the dominant pigment in all genotypes, followed by pelargonidin-3-O-rutinoside (P3R). As harvest dates advanced, pH, TAC, P3G, P3R, and pelargonidin-3-O-(6″-malonylglucoside) (P3MG) generally decreased, while Tacid, SSC, and cyanidin-3-O-glucoside (C3G) increased. Composition of field-grown strawberries in this mid-Atlantic location were more influenced by the genotype and harvest date than by the growing location. Full article

The transition from Industry 4.0 to Industry 5.0 gives more prominence to human-centered and sustainable manufacturing practices. This paper proposes a conceptual design framework based on Vision Transformers (ViTs) and digital twins, to meet the demands of Industry 5.0. ViTs, known for their advanced visual data analysis capabilities, complement the simulation and optimization capabilities of digital twins, which in turn can enhance predictive maintenance, quality control, and human–machine symbiosis. The applied framework is capable of analyzing multidimensional data, integrating operational and visual streams for real-time tracking and application in decision making. Its main characteristics are anomaly detection, predictive analytics, and adaptive optimization, which are in line with the objectives of Industry 5.0 for sustainability, resilience, and personalization. Use cases, including predictive maintenance and quality control, demonstrate higher efficiency, waste reduction, and reliable operator interaction. In this work, the emergent role of ViTs and digital twins in the development of intelligent, dynamic, and human-centric industrial ecosystems is discussed. Full article

Arbitrary translational Six Degrees of Freedom (6DoF) video represents a transitional stage towards immersive terminal videos, allowing users to freely switch viewpoints for a 3D scene experience. However, the increased freedom of movement introduces new distortions that significantly impact human visual perception quality. Therefore, it is crucial to explore quality assessment (QA) to validate its application feasibility. In this study, we conduct subjective and objective QAs of arbitrary translational 6DoF videos. Subjectively, we establish an arbitrary translational 6DoF synthesized video quality database, specifically exploring path navigation in 3D space, which has often been limited to planar navigation in previous studies. We simulate path navigation distortion, rendering distortion, and compression distortion to create a subjective QA database. Objectively, based on the spatio-temporal distribution characteristics of various distortions, we propose a no-reference video quality assessment (VQA) metric for arbitrary translational 6DoF videos. The experimental results on the established subjective dataset fully demonstrate the effectiveness and superiority of the proposed objective method. Full article

Objectives: This study investigates the potential of artificial intelligence (AI), specifically large language models (LLMs) like ChatGPT, to enhance decision support in diagnosing epilepsy. AI tools can improve diagnostic accuracy, efficiency, and decision-making speed. The aim of this study was to compare the level of agreement in epilepsy diagnosis between human experts (epileptologists) and AI (ChatGPT), using the 2014 International League Against Epilepsy (ILAE) criteria, and to identify potential predictors of diagnostic errors made by ChatGPT. Methods: A retrospective analysis was conducted on data from 597 patients who visited the emergency department for either a first epileptic seizure or a recurrence. Diagnoses made by experienced epileptologists were compared with those made by ChatGPT 4.0, which was trained on the 2014 ILAE epilepsy definition. The agreement between human and AI diagnoses was assessed using Cohen’s kappa statistic. Sensitivity and specificity were compared using 2 × 2 contingency tables, and multivariate analyses were performed to identify variables associated with diagnostic errors. Results: Neurologists diagnosed epilepsy in 216 patients (36.2%), while ChatGPT diagnosed it in 109 patients (18.2%). The agreement between neurologists and ChatGPT was very low, with a Cohen’s kappa value of −0.01 (95% confidence intervals, CI: −0.08 to 0.06). ChatGPT’s sensitivity was 17.6% (95% CI: 14.5–20.6), specificity was 81.4% (95% CI: 78.2–84.5), positive predictive value was 34.8% (95% CI: 31.0–38.6), and negative predictive value was 63.5% (95% CI: 59.6–67.4). ChatGPT made diagnostic errors in 41.7% of the cases, with errors more frequent in older patients and those with specific medical conditions. The correct classification was associated with acute symptomatic seizures of unknown etiology. Conclusions: ChatGPT 4.0 does not reach human clinicians’ performance in diagnosing epilepsy, showing poor performance in identifying epilepsy but better at recognizing non-epileptic cases. The overall concordance between human clinicians and AI is extremely low. Further research is needed to improve the diagnostic accuracy of ChatGPT and other LLMs. Full article

The tissue specificity of DNA methylation refers to the significant differences in DNA methylation patterns in different tissues. This specificity regulates gene expression, thereby supporting the specific functions of each tissue and the maintenance of normal physiological activities. Abnormal tissue-specific patterns of DNA methylation are closely related to age-related diseases. This abnormal methylation pattern affects the regulation of gene expression, which may lead to changes in cell function and promote the occurrence of pathological conditions. By analyzing the differences in these methylation patterns, key CpG sites for disease diagnosis can be effectively screened. The main goal of this paper is to use the characteristics associated with tissue-specific abnormal expression and disease to construct an age-related disease diagnosis model. First, we combined chi-square tests and logistic regression to identify tissue-specific and disease-specific CpG sites, laying the foundation for accurate medical diagnosis, and verified the biological relevance of these CpG sites through enrichment analysis. Then we used the Transformer model to fit these CpG sites and realized the automatic diagnosis of age-related diseases. Our work proves that the tissue specificity of DNA methylation has the potential to diagnose age-related diseases, and proves the scientific nature of our proposed diagnostic method from a biological perspective. Full article

Taraxacum officinale (dandelion) is a perennial flowering plant of the Asteraceae family that has spread globally and is well-known for its traditional uses. The aim of this work is to provide a detailed review of scientific literature on the genus Taraxacum from the last two decades, with particular emphasis on the biological and pharmacological characteristics of dandelions. The traditional use of Taraxacum species and their potential use in medicine are assessed. In addition, individual papers describing principal pathways and molecules modulated by Taraxacum in antitumoral, anti-inflammatory, antidiabetic, hepatoprotective, immunomodulatory, antimicrobial, and antioxidant activities are presented. This review of phytochemical studies reveals that dandelions contain a wide range of bioactive compounds, such as polyphenols, phytosterols, flavonoids, carotenoids, terpene, and coumarins, whose biological activities are actively explored in various areas of human health, some constituents having synergistic activities, including antioxidant, antimicrobial, anti-inflammatory and anticancer activities. The study provides a screening of Taraxacum sp. chemical composition, an assessment of the main pharmacological properties, and a description of relevant studies supporting the use of dandelion for its particularly valuable and diversified therapeutic potential in different diseases. Full article

Flavonoids, being prevalent in fruits and vegetables, are essential to the diverse stages of plant growth, development, and storage. Furthermore, flavonoids have been shown to exert substantial beneficial effects on human health, prompting heightened scientific interest in their potential advantages. This review elucidates the functions of flavonoids in fruits and vegetables, confirming their position as natural sources of these compounds, despite the differences in type and concentration among various species. This review elucidates the significance of flavonoids in the growth and development of fruits and vegetables, highlighting their roles in enhancing pigmentation and providing protection against both biotic and abiotic stresses. In relation to human health, flavonoids are recognized for their ability to combat aging, mitigate inflammation, safeguard the nervous system, and promote overall well-being. Additionally, this review proposes avenues for future research in the domain of flavonoids, underscoring the necessity for ongoing exploration of their potential applications and benefits. Full article

This study proposed a vision-based underwater vertical docking guidance and positioning method to address docking control challenges for human-operated vehicles (HOVs) and unmanned underwater vehicles (UUVs) under complex underwater visual conditions. A cascaded detection and positioning strategy incorporating fused active and passive markers enabled real-time detection of the relative position and pose between the UUV and docking station (DS). A novel deep learning-based network model, YOLO-D, was developed to detect docking markers in real time. YOLO-D employed the Adaptive Kernel Convolution Module (AKConv) to dynamically adjust the sample shapes and sizes and optimize the target feature detection across various scales and regions. It integrated the Context Aggregation Network (CONTAINER) to enhance small-target detection and overall image accuracy, while the bidirectional feature pyramid network (BiFPN) facilitated effective cross-scale feature fusion, improving detection precision for multi-scale and fuzzy targets. In addition, an underwater docking positioning algorithm leveraging multiple markers was implemented. Tests on an underwater docking markers dataset demonstrated that YOLO-D achieved a detection accuracy of [email protected] to 94.5%, surpassing the baseline YOLOv11n with improvements of 1.5% in precision, 5% in recall, and 4.2% in [email protected]. Pool experiments verified the feasibility of the method, achieving a 90% success rate for single-attempt docking and recovery. The proposed approach offered an accurate and efficient solution for underwater docking guidance and target detection, which is of great significance for improving the safety of docking. Full article