Search Results (291)

Granulated blast furnace slag is a commonly used supplementary cementitious material in cement-based materials. The raw materials for ironmaking and the cooling process affect its composition, thereby influencing its reactivity. Three types of slag were selected and incorporated at replacement ratios of 15%, 30%, and 50% to investigate the influence of chemical composition on the activity index of slag at different ages and the mechanisms. The results indicate that in the early hydration stage, slag primarily plays a mechanical filling and dilution role (inert volumetric occupation without significant heterogeneous nucleation), while the pozzolanic effect dominates at later stages. Al₂O₃ in the slag is activated at early ages to form ettringite; at replacement ratios of 30%, C-A-S-H gel is also formed at later ages; when the replacement ratio reaches 50%, the significant reduction in cement clinker content leads to dropping in system alkalinity—corresponding to a 50% reduction in cement-derived Ca(OH)₂, the activation of Al₂O₃ in the slag is not significant at early ages. The effects of glass content, alkali content, specific surface area, CaO + MgO content, quality coefficient, and basicity coefficient on the reactivity become prominent at longer ages. No additional crystalline phases beyond those present in pure cement paste were detected in the cement paste after slag incorporation. This study provides a theoretical basis and data support for the high-value utilization of industrial solid waste in green building materials. Full article

The built environment is the core material carrier shaping urban vitality, and its impact on urban vitality constitutes a key research hotspot in urban geography and urban–rural planning. Most existing studies focus on single cities and single scales. They pay insufficient attention to the heterogeneity of their relationship across different city types and spatial scales. They also lack a systematic framework for multi-dimensional comparative analysis. This study takes Chengdu and Chongqing as cases. They are the core cities of the Chengdu–Chongqing Twin-City Economic Circle. Three grid scales are applied. Using the XGBoost–SHAP-integrated model, this paper explores the differences in indicator importance, nonlinear impacts, and threshold effects of built environment on urban vitality. The objectives of this study are as follows: (1) This study will reveal the spatiotemporal differentiation characteristics and patterns of urban vitality across multiple cities, multiple grid scales, and multiple time periods. (2) This study will identify the relative importance of built environment indicators and their heterogeneous patterns across different cities and grid scales. (3) This study will clarify the nonlinear relationship between the built environment and urban vitality, as well as grid-scale differences and city differences. The results show the following: (1) Urban vitality exhibits significant distribution differences across cities, grid scales, and times. (2) In terms of relative importance, mean building height and building density are both important influencing indicators of urban vitality at multiple grid-scales in different cities. The effects of certain built environment indicators on urban vitality vary across cities and grid scales. Road intersection density plays a prominent role in Chengdu, while commercial accessibility has a significant influence in Chongqing. As the scale changes, indicators including road density, road intersection density, and commercial accessibility demonstrate distinct variation patterns. (3) The nonlinear effects of the built environment on urban vitality are significant and differ across cities and grid scales. The nonlinear effects of certain built environment indicators in Chongqing are more complex than those in Chengdu. As the scale changes, the nonlinear effect trends and thresholds of certain built environment indicators also show significant variations. Based on multi-city and multi-scale spatial analysis, this study deepens our systematic understanding of the relationship between the built environment and urban vitality. It provides a quantitative basis for understanding the interaction between human activities and physical spaces in different types of cities and at different grid scales. It also provides a referable paradigm for multi-dimensional analysis in similar studies. Full article

The accelerating pace of technological innovation has exacerbated the spatial misalignment between the static, supply-driven provision of educational facilities and the dynamic, demand-driven patterns of contemporary pedagogical activities. Assessing and quantifying spatial demand and the operational consumption of teaching environments pose critical challenges for facility asset management in higher education. Accordingly, rigorous investigation into the determinants of classroom spatial utilization efficiency and the formulation of evidence-based spatial optimization strategies are essential to advancing the sustainable evolution of campus infrastructure. This study takes the Zhengxin Building at Harbin Institute of Technology as a descriptive case, integrating timetable data with spatial syntax at the building scale. The scheduling data for 2943 courses in the Spring semester of 2023 was selected as the research basis. Using architectural spatial analysis tools—including space syntax theory, statistical correlation methods, and in situ observational surveys—this study extracts spatial attribute variables such as classroom area (A), seating capacity (S), floor level (F), integration (I), and space utilization efficiency metrics as primary quantitative measures. The interrelationships among these variables are examined to elucidate the principal drivers of teaching space performance. The empirical results indicate that the Overall Space Utilization Rate (OSUR) of the Zhengxin Building ranged from 20% to 50% during the study. The key findings include the following: (1) spatial utilization efficiency is positively associated with classroom scale but shows no significant relationship with integration (I); (2) after controlling for classroom type (T), per capita area index (PCAI), and integration (I), floor level (F) no longer exerts a statistically significant influence on utilization outcomes; (3) teaching spaces with higher integration and spatial entropy are more adaptable to heterogeneous instructional and extracurricular uses. The classroom type (T) directly mediates occupancy patterns and activity programming. Full article

Lightweighting Building Information Modeling (BIM) models for digital-twin applications requires balancing aggressive geometric reduction with component-level engineering tolerances and mesh usability. Most geometric simplification pipelines apply uniform ratios or hand-tuned heuristics, which struggle to accommodate the strong heterogeneity of BIM components in functional role, geometric complexity, and detail distribution. End-to-end learning-based simplification can be adaptive, but it often entangles decision-making with geometric editing, making engineering constraints difficult to enforce and audit. We present Semantic-Geometric Co-driven Adaptive Budget Estimation and Reduction for BIM (SABER-BIM), which formulates lightweighting as a component-level face-budget allocation problem. Conditioned on Industry Foundation Classes (IFC) types and structure-sensitive geometric descriptors, SABER-BIM predicts target face counts for individual components and then meets a user-specified global budget through global scaling. The predicted budgets are executed by a robust geometric backend (e.g., quadric error metrics, QEM), yielding an auditable and easily deployable pipeline. To address the absence of direct supervision, we introduce an offline pseudo-ground-truth procedure that searches for the minimum feasible target face count for each component under semantic-aware tolerance and mesh-validity constraints. Experiments on the IFCNet dataset show that SABER-BIM allocates budgets more effectively under identical global constraints, improving stability in both geometric error control and engineering usability. Full article

Energy efficiency retrofits are widely promoted for public buildings, yet evidence from large-scale real-world projects remains limited compared with simulation-based assessments. This study leverages measured pre- and post-retrofit operational data from 530 public building retrofit projects across 11 provinces/municipalities in China to quantify realized energy-saving performance and screening-level cost-effectiveness across building types and climate zones. Wilcoxon and Kruskal–Wallis tests were employed to ensure statistical rigor. Retrofit measures were grouped into seven categories (e.g., HVAC, lighting, envelope, monitoring/management), and a median-based four-quadrant framework was employed to characterize investment–savings profiles by climate zone and building function. Across the full sample, mean energy use intensity decreased by 19.1%, with 99.2% of projects achieving positive savings. Savings varied markedly by building type: commercial and hotels achieved the highest savings intensities (26.5–28.0 kWh/(m²·a)), while education and cultural buildings generally showed lower gains, with some projects having < 10 kWh/(m²·a). Technology performance exhibited distinct climate and building suitability. Envelope retrofits were most effective in the Cold and Hot Summer–Cold Winter zones (13.30–22.06 kWh/(m²·a)) but yielded limited benefits in the Hot Summer–Warm Winter zone (~1.73 kWh/(m²·a)). HVAC and lighting upgrades delivered comparatively stable savings across climates and building types and dominated retrofit portfolios. Based on these findings, we propose a tiered strategy: prioritizing HVAC and envelope upgrades for high-load sectors while focusing on low-cost optimizations for educational facilities to mitigate investment risks. The findings provide large-scale empirical evidence to support climate- and building-specific retrofit prioritization and investment decision-making under real-world operating conditions. Full article

Audio forgery has emerged as a significant security and forensic challenge, driven by rapid advances in generative artificial intelligence and the widespread availability of audio editing tools, which enable the creation of highly realistic manipulated speech with minimal technical expertise. Existing approaches predominantly operate at the file level, providing only coarse binary decisions without identifying when or where manipulation occurs. This study addresses fine-grained temporal localization through a unified frame-level localization framework. We introduce a controlled forgery generation framework derived from the TIMIT speech corpus, applying atomic, localized manipulations under strict temporal constraints and producing precise frame-level annotations across diverse manipulation types. Building on this dataset, we then propose a transform-agnostic localization-driven detection approach using temporal inconsistency modeling, enabling unified analysis across heterogeneous manipulations at frame-level resolution. To analyze forensic evidence, we present an evidence-stratified modeling paradigm comparing three complementary strategies: a handcrafted anomaly-based method, a deep localization model leveraging pretrained wav2vec 2.0 representations, and a hybrid approach combining both through confidence-aware fusion and temporal consistency reinforcement. A systematic experimental analysis evaluates the effects of representation adaptation, hybrid fusion, and manipulation type on detection and localization performance. Results show that handcrafted features are insufficient for reliable frame-level localization, while task-adapted wav2vec 2.0 achieves strong and consistent performance. The hybrid approach does not consistently improve frame-level accuracy but yields substantial gains in segment-level localization by enforcing temporal coherence. Per-transform analysis confirms robust performance across most manipulations, with deletion-based operations remaining the most challenging. Full article

Autism spectrum disorder (ASD) is highly heterogeneous in symptom onset and severity, comorbidities, and treatment responsiveness, challenging the notion of a single pathogenic mechanism. Increasing evidence indicates that some individuals with ASD exhibit prominent peripheral physiological alterations, including gastrointestinal (GI) dysfunction, gut microbial dysbiosis, immune imbalance, oxidative stress, and mitochondrial/energy metabolic vulnerability. In this context, gut-derived metabolites—particularly short-chain fatty acids (SCFAs)—have emerged as plausible modulators of the neurodevelopmental milieu through the expanded gut–immune–metabolic–brain axis. This review synthesizes: (i) SCFAs’ biogenesis and physiological roles, (ii) context- and developmental stage-dependent effects, (iii) the clinical heterogeneity of reported microbiome and SCFA alterations in ASD, and (iv) propionate as a frequently discussed candidate signal and the interpretive boundaries of preclinical evidence. Human studies show substantial inter-study variability in SCFA alterations (increases, decreases, or no differences), influenced by factors such as sample type (stool vs. blood), GI symptoms, diet, medication exposure, and analytical variability. Accordingly, SCFAs should not be treated as universal ASD biomarkers but rather as context-dependent metabolic signals relevant under specific clinical and biological conditions. Building on this premise, we propose the conceptual framework of “metabolic ASD” representing a metabolically informed dimension of biological variability in which peripheral metabolic–immune perturbations may contribute to neurodevelopmental vulnerability. To avoid premature causal claims, we outline design requirements for future research, including stratified study designs, longitudinal cohorts, and integrative multi-layer analyses. Ultimately, metabolic ASD should be positioned as a testable precision medicine research framework rather than a universal etiological model. Full article

Digital twin platforms for smart cities must continuously receive different types of data from sensors, gateways, and services, but in real situations these data are heterogeneous in terms of indicator names, measurement units, time rules, and object identification, which makes integrations expensive and fragile, while second verification becomes complicated. In this paper, a minimal semantic core for “first-stage” telemetry receiving of the DTwin platform, where semantics are used as operational rules during data ingestion. The core includes a machine-readable model of entities and relationships, dictionaries of metrics and measurement units, a unified event format with separation into a stable envelope and payload, formal validation against data schemas, a mapping table for transforming raw fields into standardized measurements [name, value, unit], as well as an ingestion service with canonicalization of the event record and integrity control through the SHA-256 cryptographic hash. The implementation ensures ingestion of correct events, rejection of incorrect ones without recording, and reproducible verification through control examples, a testing protocol, and evidence snapshots. In smart city settings, such a telemetry ingestion foundation can support reliable monitoring of municipal buildings and infrastructure, including energy efficiency, indoor environmental quality, and data-driven operational decision-making. The proposed approach establishes a core for the stable integration of different sensor data into digital twins and further scaling of the platform. Full article

In Seoul, neighborhood-scale commercial districts, known as Golmok commercial districts, are small-scale retail areas focused on local daily life but also play a significant role in the city’s economy. Existing classification strategies for supporting Seoul’s Golmok commercial districts primarily rely on static, administrative data, failing to sufficiently capture actual citizen usage patterns. This deficiency limits the effectiveness of revitalization efforts. This study employs a two-timescale analysis of de facto population data to build a more dynamic typology of Seoul’s Golmok commercial districts. An unsupervised machine learning approach, specifically time-series K-means clustering, was applied to both weekly (short-term) and multi-year (long-term) time series data. This enabled us to classify 1090 districts into 16 distinct types. This more granular typology reveals significant heterogeneity masked by the Seoul Metropolitan Government’s current system, which groups these districts into only four broad categories. Our results show that while a minority of districts maintain stable activity, many exhibit patterns of long-term decline or significant fluctuation, underscoring the diverse and dynamic nature of these areas. The short-term analysis also captures temporal variations in population activity. The proposed typology may offer a foundation for near real-time monitoring and more proactive policy interventions to support urban economic sustainability. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

Building Information Modeling (BIM) has fundamentally changed the way interdisciplinary coordination works in construction projects; however, the theoretical mechanisms underlying open collaboration standards in this field remain insufficiently explored. This article fills this gap by presenting a systematic analysis of the BIM Collaboration Format (BCF) through the lens of reification and serialization, two fundamental concepts in information systems theory. Although the BCF format is widely used in the industry and implemented in major BIM tools for clash detection and issue tracking, the existing literature treats it primarily as an operational tool, overlooking the deeper information systems principles that govern its architecture. The analysis demonstrates that BCF achieves reification by transforming informal coordination knowledge—such as verbally communicated clashes, scattered email threads, and undocumented design decisions—into first-class objects (Topic, Comment, Viewpoint) equipped with unique identifiers, typed attributes, ownership, temporal metadata, and formalized inter-object relationships. Further analysis was conducted on BCF’s serialization mechanisms, including XML encoding for file exchange, JSON for RESTful API communication, and ZIP archiving as a distribution container, each of which was selected to balance human readability, schema validation, compression, and cross-platform portability. The complementarity of these two mechanisms was examined: reification determines what to preserve and in what structure, while serialization determines how to encode and in what format, which together enable interoperable, auditable, and automatable coordination workflows in heterogeneous software environments. The analysis was illustrated with a real-world BCF example from a major infrastructure project in Poland, demonstrating practical alignment between theoretical constructs and their implementation. The research results provide both a conceptual foundation for researchers working on openBIM standards and practical guidance for practitioners seeking to optimize issue management, the implementation of a Common Data Environment (CDE), and the specification of Exchange Information Requirements (EIR). The study contributes new knowledge in three areas: (1) To the best of the authors’ knowledge, it provides the first systematic theoretical analysis of BCF through the lens of reification and serialization, filling a gap between the format’s widespread practical use and its limited theoretical understanding. (2) It demonstrates how the formal criteria of reification (unique identity, typed attributes, ownership, temporal metadata, and inter-object relationships) map onto specific BCF entities, offering a transferable analytical framework for evaluating other openBIM standards. (3) It identifies the complementarity of reification and serialization as a design principle that can guide the development of future standards for digital twins and IoT-based facility management. Full article

Brownfield-transformed urban parks, particularly those derived from industrial heritage, play a critical role in both cultural preservation and public-space provision. However, existing studies often rely on linear models and general urban contexts, limiting their ability to capture nonlinear, interaction-driven perception and translate analytical results into design-oriented insights. To address this gap, this study develops an interpretable data-driven framework integrating NLP (natural language processing) with explainable machine learning. Using social media reviews from Shougang Park in Beijing, built environmental elements are identified and structured into four dimensions—Accessibility, Safety, Comfort, and Enjoyment. An XGBoost model combined with SHAP analysis is employed to examine variable importance, nonlinear relationships, and interaction effects. The results reveal that visitor satisfaction is governed by heterogeneous and nonlinear relationships rather than independent additive effects. Several variables exhibit threshold-like, diminishing, and inverted-U-shaped patterns, indicating sensitivity to intensity ranges. More importantly, spatial perception emerges from the nonlinear coupling of multiple elements, forming four representative interaction types: compensatory, inverted-U-shaped, context-dependent, and threshold-like relationships. Key interactions are concentrated around industrial landscape, leisure activities, and supporting facilities. Building on these findings, the study translates interactions into design-oriented strategies, emphasizing synergistic configuration, functional balance, moderated development intensity, and context- sensitive programming. By linking interpretable machine learning with spatial design, this research advances an interaction-oriented paradigm and provides a transferable framework for satisfaction-informed evaluation and optimization of brownfields. Full article

The development of a green hydrogen industry is a strategic priority for Russia’s energy transition, yet the dynamics of scaling up this nascent sector remain poorly understood. This study uses agent-based modeling (ABM) to simulate the co-evolution of Russia’s electricity, hydrogen, and electrolyzer sectors over 2024–2050. The model incorporates three types of heterogeneous agents (power producers, hydrogen producers, and electrolyzer manufacturers) operating under bounded rationality. Four scenarios are examined across 50 Monte Carlo runs each, varying the electrolyzer learning rate (10–25%), willingness to pay for green hydrogen (2–6 $/kg), and government support intensity. The results reveal an endogenous three-phase development pattern: Phase I (2024–2028) dominated by renewable capacity build-up reaching ~30 GW; Phase II (2029–2040) characterized by rapid electrolyzer deployment scaling to 14.5 GW; and Phase III (2041–2050) marked by stabilization at approximately 30 GW producing 1.12 Mt/year at 3.1 $/kg. Two critical thresholds are identified: renewable capacity exceeding 30–38 GW and low-cost electricity above 4–7 TWh/year. The electrolyzer learning rate emerges as the most influential parameter, while the pessimistic scenario confirms market failure without a green premium (WTP < 2 $/kg). Strategic investment losses of 2–6 billion USD are necessary catalysts for industry emergence. Russia’s 2030 production target (0.55 Mt) is found structurally infeasible under all scenarios. Full article

Background: Pancreatic adenocarcinoma (PAAD) is a highly malignant cancer posing severe clinical challenges. Although the dual role of pyroptosis in tumor progression is increasingly recognized, the prognostic value of its molecular heterogeneity in PAAD remains underexplored. Methods: We integrated multi-omics data and applied interpretable machine learning to construct a predictive framework centered on pyroptosis heterogeneity. Using non-negative matrix factorization (NMF) on pyroptosis-related genes (PRGs), patients were classified into distinct molecular subtypes. Evaluating 117 machine learning combinations, we employed random survival forest (RSF) to build the final model, followed by comprehensive internal and external validation. SHapley Additive exPlanations (SHAP) analysis provided global and local interpretability. Clinical potential was assessed via nomogram, drug sensitivity prediction, single-cell analysis, and immunohistochemical validation. Results: We identified two biologically distinct pyroptosis subtypes and developed a ten-gene pyroptosis subtype-associated gene signature (PSAGS). PSAGS demonstrated robust performance across training, test, and multiple external validation cohorts, outperforming most published models. Multivariate analysis confirmed its independent prognostic value, and a PSAGS-based nomogram exhibited clinical utility. PSAGS-stratified subgroups showed differential responses to immunotherapy, chemotherapy, and targeted agents. Single-cell analysis revealed cell type-specific links between PSAGS scores and pyroptosis activity, indicating that high-PSAGS malignant cells foster an immunosuppressive microenvironment through extracellular matrix (ECM)-mediated signaling. Protein-level validation confirmed upregulation of signature genes in PAAD tissues. Conclusions: This work presents a biologically reliable prognostic model for personalized PAAD management and elucidates how pyroptosis heterogeneity drives tumor progression through cellular interactions. Full article

Improving urban land use efficiency is a critical pathway toward sustainable urban development, particularly in large countries undergoing rapid urbanization such as China. However, significant disparities in land use efficiency exist across cities, largely due to differences in economic development, resource endowments, and governance practices. These disparities highlight the necessity of conducting a systematic spatiotemporal assessment of land use mismatch at the city level to identify regional weaknesses and inform differentiated policy mechanisms. This study extends the land use mismatch (LUM) model, which introduces a supply–demand framework for analyzing the mismatch phenomenon of urban land use. Building on the LUM model, this study innovatively develops a classification system of five mismatch zones across eight construction land types, which provides a more systematic and comprehensive approach to identifying land use mismatch patterns. The empirical analysis is conducted using data from 283 prefecture-level cities in China. The results reveal substantial spatial heterogeneity in land use mismatch across Chinese cities. Most of the cities in East China generally fall within acceptable mismatch zones, where market mechanisms play a more effective role in land allocation. Cities in Western China exhibit more serious mismatch levels, where policy intervention seems more significant in land use planning. Cities in Central China demonstrate mixed patterns, ranging from acceptable to severe mismatch. The findings further indicate that these disparities are associated not only with economic and geographical differences but also with variations in governance practices, particularly the interaction between policy intervention and market mechanisms. This study introduces a new approach to examining the patterns of land use mismatch and provides evidence-based policy recommendations for cities in different regions to reduce land mismatch and promote more efficient use of urban land. Full article