Search Results (2,693)

China’s road transport, especially private vehicles, has experienced continuous growth in energy consumption and carbon emissions in recent years. Electrification-driven net-zero pathways and their impacts on the power sector have drawn broad concern. Current research insufficiently explores vehicle-to-grid (V2G) advantages and fails to update data and assumptions aligned with the latest policies. This study establishes a provincial bottom-up model to calculate the energy demand and carbon emissions of private vehicles and evaluates decarbonization paths and their impacts on the power sector across different scenarios. Private vehicle ownership will rise first and then fall, hitting around 453 million by 2060. Near-term improvements in energy efficiency combined with the long-term diffusion of new energy vehicles can drive private transport toward net-zero emissions after 2050. Vehicle electrification raises electricity consumption remarkably, whereas V2G effectively mitigates carbon shift and offsets over half of cumulative power generation emissions. Marked regional disparities prevail in vehicle usage and emissions, with eastern China presenting higher values compared with western regions. Decarbonization of road transport is more than just addressing carbon shifting, and V2G facilitates cross-sector coordinated emission reduction. Future research is needed to explore the technical, economic and institutional potential for deepening decarbonization. Full article

Predictive processing (PP) accounts often characterize mental illness as maladaptive and epistemically distorting due to mismatches between brain-generated top-down models and bottom-up sensory inputs, with this review identifying exceptions. First, hypervigilance in trauma survivors with PTSD or depression may sustain desirable gaps between anticipated problems and actual harms that would otherwise occur. Second, PP defenders have argued that depressive slowdowns follow from maladaptive brain-based regulatory models, yet physiological problems may make activity strenuous—in which case slowing down is adaptive. Third, PP researchers introduce tacit normative assumptions. For example, in autism and ADHD, they stipulate thresholds for how specific (hence error-prone) predictive models should be, and PP interpretations of schizophrenia sometimes presuppose Western concepts of self as normative neurocognitive ideals. Fourth, PP accounts of prediction error can tacitly invoke veridical representation, even though advocates regularly claim that cognition evolved primarily for action, not truth-seeking. While criticizing PP for its overreaches, this review also explores how greater attention to these exceptions and factors such as cultural variability may strengthen the framework’s capacity to understand and contribute to the treatment of a range of psychiatric conditions. Full article

Background/Objectives: Although food literacy is extensively studied in schools, the concept is less explored in the context of summer camps, which are also interesting learning environments. This qualitative study aimed to explore the clarity, usefulness and relevance of an adapted food literacy model for summer camps with camp counselors, camp managers and registered dietitians (RDs). Methods: Six semi-structured focus groups with counselors (n = 28) were conducted at summer camps located in the provinces of Quebec and Ontario, Canada. Semi-structured individual interviews were carried out online with camp managers (n = 5) and RDs (n = 6). Through an inductive approach, a thematic content analysis of transcribed verbatim was performed. Using a bottom-up approach, second and third final versions of the adapted food literacy model were developed. Results: Counselors and managers showed their intention to use an easy-to-use food literacy model. To improve their understanding, they proposed clarifying who will use the model and adding pictures. They expressed the need to be supported by incorporating concrete activity ideas and training to enhance the model’s usefulness. RDs highlighted that the model covered essential themes of food literacy. They expressed the need to clarify certain model components to make them easier to understand and more applicable to youth. Conclusions: This study contributed to the co-creation with participants of a clear, useful and relevant food literacy model tailored to the context of summer camps. Such a model will help camp counselors to implement relevant healthy eating actions and multiply opportunities to promote healthy habits among youth. Full article

U.S. renewable energy consumption has expanded substantially over the past five decades, but this transition cannot be adequately characterized by aggregate growth alone. This study developed an integrated empirical, forecasting, uncertainty, reconciliation, scenario, and planning framework for U.S. renewable energy consumption using a complete monthly multisector panel from January 1973 through December 2025. The analytic dataset contained 3180 sector–month observations across 636 monthly periods and five reporting sectors: Commercial, Electric Power, Industrial, Residential, and Transportation. The framework combined data harmonization, mutually exclusive source-family construction, long-run trend analysis, source-mix diversification metrics, structural-regime diagnostics, sector–source panel analysis, rolling-origin forecast benchmarking, probabilistic interval assessment, hierarchical reconciliation, future scenario analysis, and decision-focused planning evaluation. Annual reported total renewable energy consumption increased from 2475.547 trillion Btu in 1973 to 7050.214 trillion Btu in 2025, equivalent to approximately 2.476 quadrillion Btu and 7.050 quadrillion Btu, respectively. The results show that U.S. renewable energy growth was also a source-mix transformation: the portfolio became less concentrated as wind, solar, transportation biofuels, renewable diesel, waste, and other emerging sources gained importance alongside legacy wood and hydroelectric power. Sector–source heterogeneity was substantial, with Electric Power, Industrial, and Transportation showing distinct renewable-source profiles. Forecasting performance depended strongly on model family, horizon, validation window, target group, and evaluation lens. Strong statistical baselines and feature-based tree models remained competitive or superior to several deep learning architectures, while time-series foundation models provided useful modern comparators but required calibration and horizon-specific interpretation. All five selected foundation model comparators completed successfully. ChronosBolt was the fastest and strongest completed foundation model comparator, followed in runtime by TimesFM, Moirai/Uni2TS, TimeGPT, and LagLlama; however, foundation model forecasts remained too smooth for peak-sensitive planning and did not displace the strongest feature-based tree models in point-forecast benchmarking. Probabilistic diagnostics showed that nominal coverage alone was insufficient because interval width, Winkler score, CRPS, and visual inspection revealed target-specific miscalibration, underforecast bias, and weak peak coverage. Hierarchical and decision-focused evaluation changed the model-selection narrative: bottom-up and reconciled hierarchical forecasts produced stronger planning-loss and planning-value profiles than many nominally advanced alternatives, while selected tree-based models were particularly useful for preserving source-share allocation. Scenario analysis showed that solar acceleration increased projected totals but also increased concentration and coherence divergence, whereas diversification reduced concentration but required wider uncertainty buffers. Overall, U.S. renewable energy consumption should be analyzed as a dynamic, diversified, hierarchical, and planning-sensitive system. The proposed framework provides a reproducible basis for evaluating renewable energy growth, source-mix evolution, forecast reliability, uncertainty, source allocation, scenario trade-offs, and planning value beyond single-model forecasting claims. Full article

Forecast reconciliation has been widely studied in cross-sectional and temporal hierarchies, but its role in cross-temporal settings for photovoltaic (PV) forecasting remains insufficiently examined. In particular, the relative benefits of reconciliation across heterogeneous forecasting approaches, including statistical, machine learning, deep learning, and foundation models, have not been clearly established. This study addresses that gap by evaluating direct, univariate, and iterative cross-temporal reconciliation strategies applied to TBATS, LightGBM, KAN, NBEATSx, NHITS, and TimeGPT using Belgian PV generation data from 2020 to 2025 across weekly, daily, and hourly frequencies and national, regional, and provincial levels. Model efficacy is assessed through 52-week walk-forward cross-validation, which provides a full-year coverage. Under the fixed-configuration experimental protocol adopted in this study, the results show that the gains from reconciliation vary substantially across forecasting families. LightGBM achieved the largest observed gains, with its univariate and iterative schemes achieving global error reductions of up to 19.6% relative to the Bottom-Up benchmark. KAN, NHITS, and NBEATSx also benefited from reconciliation, with their best reconciled variants yielding reductions of up to 11.9%. TimeGPT and TBATS achieved reductions of up to 9.2% and 14.5%, respectively, although their global errors were higher than those obtained by the best machine learning and deep learning configurations in this evaluation. Across the fixed baseline configurations considered here, LightGBM obtained the lowest global errors before and after reconciliation. These findings show that cross-temporal reconciliation can be an effective post-processing strategy, but its impact depends strongly on the underlying base forecasting model. Therefore, the observed advantage of LightGBM should be interpreted as conditional on the adopted feature set, implementations, and baseline configurations. Full article

Small open reading frames (sORFs) encode micropeptides, which are a promising yet largely untapped resource for creating peptide design templates. Owing to their concise nature and functional efficiency, micropeptides often rely on essential structural elements and brief linear motifs, such as domains for membrane interaction, targeting sequences, and sites for protein–protein interactions, to fulfill their biological functions. This inherent simplicity makes them particularly suitable for a bottom-up design approach aimed at identifying, extracting, and systematically refining functional motifs to develop novel bioactive peptides. This review addresses the critical question of how micropeptides, particularly those involved in tumor regulation, can be explored as emerging therapeutic targets, functional templates for peptide design, and potential future therapeutic agents, by synthesizing current understanding of their mechanisms, functional significance in cancer, and the computational and design strategies for their clinical translation. We examined the current methods for analyzing the sequence and structural characteristics that underpin their functional activity and investigated how these attributes can be leveraged for drug discovery and design. Finally, we underscore the primary challenges and future prospects in converting sORF-encoded micropeptides into clinically relevant molecules with the aim of broadening the current scope of the druggable proteome. Full article

Meeting European carbon neutrality and energy performance targets requires large-scale rehabilitation of historic and traditional buildings, one of the construction sector’s key challenges by 2050. This will significantly increase demand for new materials and the production of waste, which already accounts for 39% of waste in Wallonia. From a circular economy and urban mining perspective, however, this waste can be viewed as a valuable resource for reuse and recovery. Despite this potential, Wallonia lacks detailed information on the material composition of its historic building stock, including material types, quantities, and reuse potential. Such knowledge is crucial for designing effective renovation strategies and promoting circular construction practices. The URMIBALI project addresses this gap by investigating traditional residential buildings built before 1919 in Liège (Belgium). Based on six case studies, the project develops two complementary research parts. The first part focuses on inventorying existing material stocks, estimating waste flows resulting from energy renovations, and evaluating the reuse potential of the main waste fractions. The second part proposes an initial digital methodology for the rapid and efficient acquisition of façade material data. The project’s novelty lies in its multi-material, bottom-up, and transdisciplinary approach, as well as in the creation of previously unavailable data on building-stock composition and the development of simple and flexible digital methods to acquire those data. These outputs improve knowledge of traditional buildings, support projections of renovation waste up to 2050, and facilitate urban-scale management of material flows, including transport, supply chains, and environmental impacts. This contribution presents the research methodology, key findings, and the transferability of the digital method to other building typologies and European contexts. Full article

For decades, visual perception was treated as a linear, feature-extracting mechanism driven almost exclusively by bottom-up sensory inputs. Emerging insights from affective neuroscience and cognitive psychology have systematically dismantled this view, revealing that vision operates within a continuous, bidirectional dialog with emotional systems. This review synthesizes the multi-layered neurobiological architectures underpinning this relationship. The pathways through which top–down emotional states recalibrate sensory processing are analyzed. Mechanisms including amygdalocortical feedback, frontoparietal attentional networks, and insular interoceptive monitoring are examined. These systems prioritize survival-driven motivational salience over objective accuracy. In the opposite direction, the text charts how ambient environmental features, such as lighting dynamics, spatial geometry, and structural ambiguity, immediately register along rapid subcortical and detailed cortical streams to instantiate emotional states. By situating these reciprocal dynamics within predictive coding and active inference frameworks, this paper illustrates how affective states function as precision weights that dynamically adjust internal perceptual priors. Finally, the clinical utility of these interconnected systems is evaluated, demonstrating how subtle visual aberrations like disrupted contrast suppression serve as diagnostic signatures for mood disorders, while structural retinal decay offers an accessible window into neurodegenerative pathology. Ultimately, the evidence indicates that conscious vision is fundamentally an affective construction, carrying transformative implications for early biomathematical and ocular screening in psychopathology. Full article

The increasing global demand for freshwater, together with the high energy consumption and environmental footprint of conventional desalination technologies, has stimulated growing interest in interfacial solar steam generation (ISSG). ISSG is a solar-driven water purification strategy that localizes heat at the air–water evaporation interface, thereby promoting surface evaporation without heating the entire bulk water body. The development of efficient, durable, and multifunctional ISSG systems depends strongly on substrate materials that can regulate water transport, heat localization, vapor release, and mechanical stability. This review focuses on cellulose-based substrates for ISSG and examines how their molecular structure, fibrillar assembly, and macroscopic porous architecture influence evaporation behavior and device function. The reviewed cellulose platforms are classified into three major groups: bottom–up assembled nanocellulose substrates, including cellulose nanocrystals, cellulose nanofibers, and bacterial cellulose; natural hierarchical substrates, including wood, cotton fabrics, and agricultural residues; and commercial planar substrates, including cellulose paper and membranes. Beyond evaporation performance, this review discusses multifunctional design strategies for salt regulation, antifouling and antibacterial operation, water–electricity cogeneration, and photocatalytic pollutant degradation, with emphasis on their mechanisms and functional trade-offs. Finally, we identify critical bottlenecks limiting practical deployment and propose a roadmap for future intelligent, adaptive, and multi-energy-coupled cellulose-based ISSG systems. These systems offer a promising platform for distributed and resource-efficient water treatment, but their practical and environmental benefits depend on fabrication energy, material safety, device lifetime, and end-of-life management. Full article

Cross-border cooperation (CBC) is defined as the structured, institutionalized, or informal collaboration between adjacent regional and local authorities, economic actors, and civil society groups across international state borders. Within contemporary border studies, CBC has transitioned from traditional top-down, state-centric diplomatic containment toward bottom-up, grassroots territorial integration. This entry synthesizes the multidisciplinary evolution of CBC across geography, economics, jurisprudence, sociology, and political science, structuring the analysis around four core dimensions: spatial, political, economic, and socio-cultural. It categorizes diverse territorial and governance mechanisms of cooperation, ranging from localized town twinnings to formalized Euroregions and European Groupings of Territorial Cooperation (EGTCs), and introduces quantitative performance metrics such as the Cross-Border Activity Index (CBAI). Examining how these structures operate along both the internal and external borders of the European Union, this entry analyzes the cyclical, non-linear dynamics of the bordering–debordering–rebordering framework. By evaluating diverse theoretical models across varying geopolitical contexts, it identifies the universal characteristics of contemporary border dynamics, conceptualizing borders not merely as physical or political demarcations, but as analytical lenses reflecting broader processes of globalization, regionalization, and territorial resilience. Full article

Carbon dots (CDs) have been widely employed in diverse fields by virtue of their excellent water solubility, low toxicity, high fluorescence stability, and favorable biocompatibility. Nevertheless, traditional preparation methods for CDs generally suffer from drawbacks that run counter to the concept of green chemistry. This review comprehensively summarizes the green synthesis technologies, machine learning (ML)-assisted synthesis strategies, and diversified application fields of fluorescent CDs. Specifically, it discusses the characteristics of synthetic organic molecular/polymeric materials and natural sources (e.g., plants and fruit peels, etc.) and elaborates on the top-down and bottom-up green synthesis methods, analyzing their advantages. It also focuses on ML’s core role in precisely regulating CD emission wavelengths, enhancing and predicting fluorescence quantum yields to optimize synthesis processes. Additionally, this review highlights the representative biological applications of CDs, including biosensing and biomedicine (e.g., bioimaging, drug delivery, and photodynamic therapy), while briefly covering their applications in other fields. Finally, the review points out current challenges in green synthesis, ML-assisted applications and industrial translation, and puts forward future research directions, aiming to promote the greenization, intellectualization and large-scale development of CDs. Full article

Ultra-deep excavations in soft soil pose major challenges for deformation control. Based on field monitoring of a 38.3 m deep five-story metro excavation in Tianjin, this study systematically investigates the spatiotemporal deformation of the diaphragm wall, columns, and surrounding environment. Key innovations include the proposal of an extended ground settlement influence model and the quantification of stage-wise deformation development ratios. The maximum lateral wall displacement is about 40 mm, ranging from 0.028% H_e to 0.184% H_e (average 0.087% H_e), outperforming comparable bottom-up excavations in Shanghai. Wall top vertical displacement varies from −0.23% H_emax to 0.04% H_emax, and column rebound averages 3.5 mm, is significantly lower than that of excavations using the bottom-up method. The extended settlement model shows that the maximum settlement occurs at H_e/3 from the wall, the primary influence zone extends to 3H_e, and the secondary zone reaches 5H_e. Building settlement strongly depends on distance and foundation type, with raft foundations settling much more than pile-raft foundations. Stage-by-stage analysis reveals that, immediately after the completion of diaphragm wall construction, the settlement already exceeded 60% of the final maximum ground settlement. Furthermore, the deformation on the long side developed at a faster rate than that on the short side. These findings provide quantitative benchmarks for designing ultra-deep excavations in soft soil. Full article

In response to the challenges in UAV-oriented ground small-object localization and detection, including the easy loss of tiny target features, insufficient scale adaptability, severe interference from complex backgrounds, as well as high missed and false detection rates and the inadequate localization accuracy of the conventional YOLOv11n model in such scenarios, this paper takes YOLOv11n as the basic framework and performs systematic optimization from three aspects, network structure, core modules, and feature enhancement, proposing a lightweight small-object-enhanced detection algorithm named YOLOSO for UAV applications. By introducing a P2 high-resolution feature branch with a stride of 4, a four-scale detection structure consisting of P2-P3-P4-P5 is constructed, which reduces the minimum detection stride from 8 to 4 and alleviates the loss of detailed feature information for ultra-tiny targets. A bidirectional “top-down + bottom-up” multi-scale feature fusion strategy is utilized to improve the complementation between deep semantic information and shallow detailed features, while the core modules C3k2SO and C2PSASO are optimized and redesigned, respectively; by adjusting the channel compression ratio (0.25 for shallow modules and 0.75 for deep modules in C3k2SO; 0.25 in C2PSASO), optimizing the convolution kernel configuration (combining 1 × 3 and 3 × 1 convolutions), increasing the number of attention heads (from 4 to 8), and introducing residual connections with a 1 × 1 convolutional branch, the refinement and focusing ability of small-object feature extraction are improved. Additionally, an Enhanced Dual-branch Convolutional Block Attention Module (ED-CBAM) is proposed to further suppress background interference. Experimental results on the VisDrone2019-DET dataset demonstrate that the proposed YOLOSO contains 3.56M parameters and maintains a lightweight structure, attaining P, R, and mAP50 values of 47.2%, 36.8%, and 37.3% in the test set, which are 4.5 percentage points, 4.8 percentage points, and 3.7 percentage points higher than those of the baseline YOLOv11n (42.7%, 32.0% and 33.6%), respectively. Meanwhile, the medium-to-large version YOLOSO-S (14.85M parameters, 45.3% mAP50) reduces the number of parameters by 53.6% compared with the same-scale Rtdetr-L (32.0M) while achieving significantly better performance (37.8% mAP50). Experiments on the DOTAv1 dataset further confirm the generalization of YOLOSO, achieving 62.2% precision and 27.3% mAP50, outperforming all compared YOLO models. Evaluated on the DOTA-v1 dataset, YOLOSO achieves a feasible FPS of 20.53. Although slightly slower than mainstream lightweight YOLO models, the substantial accuracy gains fully offset the minor inference speed loss, and such performance trade-off is acceptable for practical UAV deployment. Ablation experiments verify that structural optimization (2.8 percentage points mAP50 improvement, from 33.6% to 36.4%) and the proposed C2PSASO (0.7 percentage points mAP50 improvement to 34.3%) and C3k2SO (1.4 percentage points mAP50 improvement to 35.0%) modules all contribute positive performance gains with favorable complementarity. While retaining lightweight characteristics, the model effectively enhances the detection accuracy of small objects in unmanned aerial vehicle scenarios and can provide technical references for practical applications such as remote sensing monitoring and security patrolling. Full article

This data descriptor presents CINES-REC-CITY, an open synthetic dataset providing high-resolution load profiles for energy community research. The dataset represents a typical German urban district with 70 apartments across eight multi-family buildings, including diverse socioeconomic characteristics. Three main components are provided at 15 min resolution for a full year: non-controllable residential electricity consumption for all apartments, charging profiles for 17 battery electric vehicles with trip information, and heat pump operation data for both variable-speed and hysteresis-controlled ground-source systems. All profiles were generated using validated bottom-up stochastic simulation models accounting for realistic user behavior, mobility patterns, and thermal building physics. The modular structure allows for selective combination of components, enabling investigation of different technology penetration scenarios. The dataset serves as a reference benchmark for reproducible research, allowing for direct comparison of optimization approaches, business models, and control strategies using identical underlying consumption patterns. It is suitable for techno-economic analysis, algorithm development for flexible load control, and grid impact assessment. All data is provided in CSV format with weather data for consistent extensions. Full article

Accurate urban building energy modeling (UBEM) is constrained by mismatches between standard climate inputs and actual urban microclimate conditions. This study introduces a scalable, bottom-up, framework that integrates EnergyPlus building energy modeling simulation outputs with Earth observational and geographical-based urban morphology data, which are enhanced through machine learning techniques to improve energy demand predictions in urban settings. Applied to Los Angeles (LA), California, we evaluate the representativeness of typical meteorological year (TMYx) sampling sites against actual urban environmental conditions. We find that while satellite-derived surface temperatures show reasonable alignment with average city conditions, significant discrepancies are observed in urban form metrics such as tree cover, street cover, and building density, suggesting that TMYx stations should be placed in denser urban areas. We augment EnergyPlus simulations for 19 single-family buildings, with remote sensing data using machine learning models, to generate city-wide residential energy consumption heatmaps corrected for microclimate conditions. Models capture substantial intra-urban variation, with predicted energy use differing by approximately 10% between neighborhoods. Feature importance analysis highlights land surface temperature as a key predictor, underscoring its relevance to building energy research. We also find the majority of TMY3 sampling sites to be in low-vulnerability areas, underscoring the structural mismatch that is embedded in urban form and climate. This framework offers a scalable path for integrating urban microclimate effects into energy modeling to enable more precise and equitable energy policy and planning. Full article