Search Results (1,505)

Project control in cost estimation, time scheduling, and resource accounting remains challenging, particularly when using the open-source Industry Foundation Classes (IFCs) format. This study aims to define the state of the art in integrating these three domains. A systematic literature review was conducted, using a bibliometric analysis to map and interpret scientific knowledge and research trajectories, and an inductive analysis for a detailed examination of relevant studies. The analysis highlights a lack of clarity in applying the IFC standard across project control domains, as current practices often rely on non-standardized procedures, including incorrect use of classes or properties, creation of unneeded user-defined PropertySets and properties, or reliance on proprietary software. Integration of cost, time, and resource management remains limited, and proposed technological solutions generally require coding skills that typical professionals do not possess. Additional challenges include fragmented data across multiple databases, manual assignment of time, cost, and resource information, and limited collaboration, all of which are time-consuming and error-prone. There is a critical need for clearer guidelines on IFC usage to enable standardized procedures and facilitate the development of IFC-based tools. Automating these labor-intensive tasks could improve efficiency, reduce errors, and support broader adoption of integrated project control practices. Full article

Generating air-conditioning system load data is crucial for tasks such as power grid scheduling and intelligent energy management. Air-conditioning load data exhibit strong non-stationarity. Their load curves are influenced by seasonal variations and highly correlated with outdoor meteorological conditions, indoor activity patterns, and equipment operational strategies. These characteristics lead to pronounced periodicity, sudden shifts, and diverse data patterns. Existing load generation models tend to produce averaged distributions, which often leads to the loss of specific temporal patterns inherent in air-conditioning loads. Moreover, as purely data-driven models, they lack explicit physical constraints, resulting in generated data with limited physical interpretability. To address these issues, this paper proposes a hybrid generation framework that integrates the DTW clustering algorithm, a physically-constrained TimeGAN model, and an LSTM-based model selection mechanism. Specifically, DTW clustering is first employed to achieve structured data partitioning, thereby enhancing the model’s ability to recognize and model diverse temporal patterns. Subsequently, to overcome the dependency on detailed building parameters and extensive sensor networks, a parameter-free physical constraint mechanism based on intrinsic temperature-load correlations is incorporated into the TimeGAN supervised loss. This design ensures thermodynamic consistency even in sensor-scarce environments where only basic operational data is available. Finally, to address adaptability challenges in long-term sequence generation, an LSTM-based selection mechanism is designed to evaluate and select from clustered submodels dynamically. This approach facilitates adaptive temporal fusion within the generation strategy. Extensive experiments on air-conditioning load datasets from Southeast China demonstrate that the framework achieves a local similarity score of 0.98, outperforming the state-of-the-art model and the original TimeGAN by 11.4% and 13.3%, respectively. Full article

The increasing deployment of hybrid renewable energy systems has introduced significant challenges in optimal energy dispatch and load balancing due to the intrinsic stochasticity and temporal variability of renewable sources, along with the multi-dimensional optimization requirements of simultaneously achieving economic efficiency, grid stability, and environmental sustainability. This paper presents GreenMind, a scalable Deep Reinforcement Learning framework designed to address these challenges through a hierarchical multi-agent architecture coupled with Long Short-Term Memory (LSTM) networks for predictive energy management. The framework employs specialized agents responsible for generation dispatch, storage management, load balancing, and grid interaction, achieving an average decision accuracy of 94.7% through coordinated decision-making enabled by hierarchical communication mechanisms. The integrated LSTM-based forecasting module delivers high predictive accuracy, achieving a 2.7% Mean Absolute Percentage Error for one-hour-ahead forecasting of solar generation, wind power, and load demand, enabling proactive rather than reactive control. A multi-objective reward formulation effectively balances economic, technical, and environmental objectives, resulting in 18.3% operational cost reduction, 23.7% improvement in energy efficiency, and 31.2% enhancement in load balancing accuracy compared to state-of-the-art baseline methods. Extensive validation using synthetic datasets representing diverse hybrid renewable energy configurations over long operational horizons confirms the practical viability of the framework, with 19.6% average cost reduction, 97.7% system availability, and 28.6% carbon emission reduction. The scalability analysis demonstrates near-linear computational growth, with performance degradation remaining below 9% for systems ranging from residential microgrids to utility-scale installations with 2000 controllable units. Overall, the results demonstrate that GreenMind provides a scalable, robust, and practically deployable solution for predictive energy dispatch and load balancing in hybrid renewable energy systems. Full article

Introduction: PI-RADS 3 lesions represent a diagnostic grey zone on multiparametric MRI, with clinically significant prostate cancer (csPCa) detected in only 10–30%. Their equivocal nature leads to both unnecessary biopsies and missed cancers. Artificial intelligence (AI) has emerged as a potential tool to provide objective, reproducible risk prediction. This review summarises current evidence on AI for risk stratification in patients with indeterminate mpMRI findings, including clarification of key multicentre initiatives such as the PI-CAI (Prostate Imaging–Artificial Intelligence) study—a global benchmarking effort comparing AI systems against expert radiologists. Methods: A narrative review of PubMed and Embase (search updated to August 2025) was conducted using terms including “PI-RADS 3”, “radiomics”, “machine learning”, “deep learning”, and “artificial intelligence.” Eligible studies included those evaluating AI-based prediction of csPCa in PI-RADS 3 lesions using biopsy or long-term follow-up as reference standards. Both single-centre and multicentre studies were included, with emphasis on externally validated models. Results: Radiomics studies demonstrate that handcrafted features extracted from T2-weighted and diffusion-weighted imaging can distinguish benign tissue from csPCa, particularly in the transition zone, with area-under-the-ROC curves typically 0.75–0.82. Deep learning approaches—including convolutional neural networks and large-scale representation-learning frameworks—achieve higher performance and can reduce benign biopsy rates by 30–40%. Models that integrate imaging-based AI with clinical predictors such as PSA density further improve discrimination. The PI-CAI study, the largest international benchmark to date (>10,000 MRI exams), shows that state-of-the-art AI systems can match or exceed expert radiologists for csPCa detection across diverse scanners, centres, and populations, though prospective validation remains limited. Conclusions: AI shows strong potential to refine management of PI-RADS 3 lesions by reducing unnecessary biopsies, improving csPCa detection, and mitigating inter-reader variability. Translation into routine practice will require prospective multicentre validation, harmonised imaging protocols, and integration of AI outputs into clinical workflows with clear thresholds, decision support, and safety-net recommendations. Full article

Influence maximization is a crucial research domain in social network analysis, playing a vital role in optimizing information dissemination and managing online public opinion. Traditional IM models focus on network topology, often overlooking user heterogeneity and server-driven propagation dynamics, which often leads to limited model adaptability. To overcome these shortcomings, this study proposes the “Social–Interest Hybrid Influence Maximization” (SIHIM) problem, which explicitly models the joint influence of social topology and user interest in server-mediated propagation, aiming to enhance the effectiveness of information propagation by integrating users’ social relationships and interest preferences. To model this problem, we develop a Server-Based Independent Cascading (SB-IC) model that captures the dynamics of influence propagation. Based on this model, we further propose a novel hybrid centrality algorithm named Pascal Centrality (PaC), which integrates both topological and interest-based attributes to efficiently identify key seed nodes while minimizing influence overlap. Experimental evaluations on ten real-world social network datasets demonstrate that PaC improves influence spread by 5.22% under the standard IC model and by 7.04% under the SB-IC model, outperforming nine state-of-the-art algorithms. These findings underscore the effectiveness and adaptability of the proposed algorithm in complex scenarios. Full article

The prompt and precise detection of road damage is vital for effective infrastructure management, forming the foundation for intelligent transportation systems and cost-effective pavement maintenance. While current convolutional neural network (CNN)-based methodologies have made progress, they are fundamentally limited by treating damages as independent, isolated entities, thereby ignoring the intrinsic spatial symmetry and topological organization inherent in complex damage patterns like alligator cracking. This conceptual asymmetry in modeling leads to two major deficiencies: “context blindness,” which overlooks essential structural interrelations, and “temporal inconsistency” in video analysis, resulting in unstable, flickering predictions. To address this, we propose a Spatio-Temporal Graph Mamba You-Only-Look-Once (STG-Mamba-YOLO) network, a novel architecture that introduces a symmetry-informed, hierarchical reasoning process. Our approach explicitly models and integrates contextual dependencies across three levels to restore a holistic and consistent structural representation. First, at the pixel level, a Mamba state-space model within the YOLO backbone enhances the modeling of long-range spatial dependencies, capturing the elongated symmetry of linear cracks. Second, at the object level, an intra-frame damage Graph Network enables explicit reasoning over the topological symmetry among damage candidates, effectively reducing false positives by leveraging their relational structure. Third, at the sequence level, a Temporal Graph Mamba module tracks the evolution of this damage graph, enforcing temporal symmetry across frames to ensure stable, non-flickering results in video streams. Comprehensive evaluations on multiple public benchmarks demonstrate that our method outperforms existing state-of-the-art approaches. STG-Mamba-YOLO shows significant advantages in identifying intricate damage topologies while ensuring robust temporal stability, thereby validating the effectiveness of our symmetry-guided, multi-level contextual fusion paradigm for structural health monitoring. Full article

Leaf area index (LAI) is a pivotal biophysical parameter linking vegetation physiological processes and macro-ecological functions. Accurate large-scale LAI estimation is indispensable for agricultural management, climate change research, and ecosystem modeling. However, existing methods fail to efficiently extract integrated spatial-spectral-temporal features and lack targeted modeling of spatio-temporal dependencies, compromising the accuracy of LAI products. To address this gap, we propose STC-DeepLAINet, a Transformer-GCN hybrid deep learning architecture integrating spatio-temporal correlations via the following three synergistic modules: (1) a 3D convolutional neural networks (CNNs)-based spectral-spatial embedding module capturing intrinsic correlations between multi-spectral bands and local spatial features; (2) a spatio-temporal correlation-aware module that models temporal dynamics (by “time periods”) and spatial heterogeneity (by “spatial slices”) simultaneously; (3) a spatio-temporal pattern memory attention module that retrieves historically similar spatio-temporal patterns via an attention-based mechanism to improve inversion accuracy. Experimental results demonstrate that STC-DeepLAINet outperforms eight state-of-the-art methods (including traditional machine learning and deep learning networks) in a 500 m resolution LAI inversion task over China. Validated against ground-based measurements, it achieves a coefficient of determination (R²) of 0.827 and a root mean square error (RMSE) of 0.718, outperforming the GLASS LAI product. Furthermore, STC-DeepLAINet effectively captures LAI variability across typical vegetation types (e.g., forests and croplands). This work establishes an operational solution for generating large-scale high-precision LAI products, which can provide reliable data support for agricultural yield estimation and ecosystem carbon cycle simulation, while offering a new methodological reference for spatio-temporal correlation modeling in remote sensing inversion. Full article

Localising leaks in pressurised water distribution networks (WDNs) is crucial for reducing water loss but remains challenging because of model uncertainties and limited sensor data. Nevertheless, many state-of-the-art methods rely on idealised assumptions that are perfectly known, like time-invariant demands, noise-free pressure sensors, a single, stationary leak, and a known leak-free baseline. These assumptions rarely hold in practice, creating a gap between expected performance and field reality. This article provides a comprehensive review of current leak localisation techniques based on sensor data and hydraulic or data-driven models. This study critically examines how recent studies have addressed these unrealistic assumptions. Advanced methods incorporate demand uncertainty and sensor noise into leak detection algorithms to improve robustness, estimate unknown demand variations using physics-informed machine learning, and employ Bayesian inference to locate multiple simultaneous leaks. The analysis indicates that accounting for such real-world complexities markedly improves localisation accuracy; for instance, even minor demand estimation errors or sensor noise can dramatically degrade performance if not addressed. Finally, bridging the gap between the models and reality is essential for the practical deployment of water utilities. Thus, this review recommends that future studies integrate uncertainty quantification, adaptive modelling, and enhanced sensing into leak localisation frameworks, thereby guiding the development of more resilient and field-ready leak management solutions. Full article

Background: Co-infections with HIV, HBV, and HDV pose significant public health challenges, especially in populations exposed parenterally. Romania hosts a unique pediatric HIV cohort of individuals born 1987–1995 who acquired HIV iatrogenically. This study assessed the prevalence, hepatic impact, and management of HIV–HBV–HDV co-infection in 130 long-term survivors from Galați County. Methods: Patients underwent clinical, laboratory, and FibroScan assessments. HBV and HDV serology and viral loads were measured, and antiretroviral therapy regimens, including tenofovir-based therapies, were reviewed. Entecavir or Bulevirtide was applied when indicated. Results: HBV infection was present in 57.7% of cohort patients versus 20% in non-cohort PLWH, and HDV co-infection in 7.7% of cohort patients. Hepatic fibrosis increased from HBV-uninfected to HBV/HDV co-infected individuals. HIV impairs viral clearance and exacerbates liver injury via immune dysregulation and chronic inflammation. Despite TDF-based ART, replicative HBV was detected in eight patients, managed with Entecavir. Bulevirtide therapy for HDV was initiated in eligible patients, with minor adverse events. Conclusions: Pediatric HIV cohort survivors show high rates of HBV and HDV co-infection and progressive hepatic fibrosis. Optimized antiviral therapy and adherence support are essential to control viral replication and reduce liver-related complications. Full article

Emerging evidence indicates a high rate (>10%) of drug resistance (DR) associated with integrase strand transfer inhibitors (INSTIs) in developed countries, although there is limited information on DR during INSTI treatment in Uganda. With the increased use of INSTIs as standard first-line treatment, monitoring for DR using next-generation sequencing (NGS) has become essential. NGS can detect the lower-frequency variants that may be missed by traditional Sanger sequencing (SS). This study evaluates the diagnostic accuracy of next-generation sequencing (NGS) compared to Sanger sequencing for detecting HIV-1 INSTI resistance mutations and estimates the prevalence and factors associated with drug resistance among adults with virologic failure on INSTI-based regimens in Uganda. Utilizing the Illumina MiSeq platform for NGS, data was analyzed using STATA V.18 and a logistic regression model at 5% level of significance. This study demonstrates that NGS achieved 100% sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy in detecting major mutations. NGS identified INSTI DRMs in 4% of adults at a ≥20% threshold and was able to detect both high- and low-abundance variants, which could have important implications for clinical outcomes. This study emphasizes the need for HIVDR testing before antiretroviral therapy (ART) initiation, given the increasing use of INSTIs. We recommend that healthcare providers adopt more sensitive diagnostics such as NGS and use detailed resistance profiles to tailor antiretroviral therapies. This approach is critical for effectively managing and preventing drug-resistant HIV strains. Full article

The increasing complexity of water distribution systems (WDSs) and the growing demand for sustainable infrastructure management have spurred interest in Building Information Modelling (BIM) and Digital Twin (DT) technologies. This study presents a comprehensive bibliometric and thematic literature review aiming to identify key trends, research clusters, and knowledge gaps at the intersection of BIM, DT, and WDSs. Using the Scopus database, 95 relevant publications from 2004 to 2024 were systematically analyzed. VOSviewer was applied to create, visualize, and analyze maps of countries, journals, documents, and keywords based on citation, co-citation, collaboration, and co-occurrence data. The results indicate a sharp rise in scholarly attention after 2020, with dominant contributions from European institutions. Co-authorship networks show limited global interconnectedness, suggesting that developing countries should especially prioritize integrated DT and BIM for more inclusive and diverse research partnerships. This study characterizes the state of the art and future requirements for research on the use of DT and BIM technologies in WDSs and makes a noteworthy contribution to the body of knowledge. Future research should focus on integrating DT and BIM technologies with ML, which represents scalability challenges of real-time anomaly detection integration models, advancing decision-making and operational resilience in WDNs. Full article

Physical fatigue is a major cause of work-related accidents and musculoskeletal injuries in the construction industry, and additional means are needed for their identification and management to prevent long-term consequences. Based on recent scientific literature, fatigue can be detected with wearable inertial measurement units (IMUs). However, IMUs for detecting fatigue have been so far tested mainly in the laboratory; therefore, a research gap exists in application of IMU sensors for detecting fatigue in real-life work settings. The aim of this paper is to bring the fatigue trend detection with IMUs closer to real-life context by using wearable IMU sensor data from an actual construction site measuring actual workers with simulated work tasks. The paper also presents advancements in fatigue trend detection with frequency domain investigations to gain access to more detailed fatigue relevant features. Machine-learning methods are used to predict fatigue trends based on IMU data, resulting in fatigue trend detection accuracy that advances the state of the art. More knowledge is also unearthed about relevant sensor locations and features. Full article

Epileptic seizure (ES) detection is critical for improving clinical outcomes in epilepsy management. While intracranial EEG (iEEG) provides high-quality neural recordings, existing detection methods often rely on large amounts of data, involve high computational complexity, or fail to generalize in low-data settings. In this paper, we propose a lightweight, data-efficient, and high-performance approach for ES detection based on hyperdimensional computing (HDC). Our method first extracts local binary patterns (LBPs) from each iEEG channel to capture temporal–spatial dynamics. These binary sequences are then mapped into a high-dimensional space via HDC for robust representation, followed by a binary Naive Bayes classifier to distinguish ictal and inter-ictal states. The proposed design enables fast inference, low memory requirements, and suitability for hardware implementation. We evaluate the method on the SWEC-ETHZ iEEG short-term dataset. In one-shot learning, it achieves 100% sensitivity and specificity for most patients. In few-shot learning, it maintains 98.88% sensitivity and 93.09% specificity on average. The average latency is 4.31 s, demonstrating that it is much better than state-of-the-art methods. These results demonstrate the method’s potential for efficient, low-resource, and high-performance ES detection. Full article

Home energy management systems (HEMSs) have attracted considerable research interest in residential appliance management. Although optimal scheduling of home appliances has been extensively studied, these problems are fundamentally dynamic multi-objective optimization problems. This paper proposes a dynamic appliance scheduling model under time-of-use electricity pricing based on user’s preferences, to minimize energy costs and user dissatisfaction. A knee point-based manifold transfer algorithm (KPMT-DMOEA) is proposed to solve the scheduling problem. This approach leverages high-quality knee points from previous environments to generate optimized initial populations in response to environmental changes, thereby improving solution quality and convergence speed. The experimental results validate the effectiveness and feasibility of the proposed scheduling framework. By making a comparison with state-of-the-art algorithms, the experimental results demonstrate that the proposed method outperforms others and is able to efficiently generate optimal schedules for each appliance under different environments. Full article

Biodegradable bioplastics have emerged as a promising sustainable alternative to minimize the environmental impact of traditional plastics. Nevertheless, many of them degrade slowly under natural or industrial conditions, raising concerns about their practical biodegradability. This fact is related to the high-order structure of the polymer backbones, i.e., high molar mass and high crystallinity. Research efforts are being devoted to the development of technologies capable of reducing the length of polymer segments by accelerated chain scission, which could help improve biodegradation rates upon disposal of bioplastic products. The objective of this review is to examine the current state of the art of abiotic degradation techniques, physically driven by temperature, mechanical stress, UV/gamma/microwave irradiation, or plasma or dielectric barrier discharge, and chemically induced by ozone, water, or acidic/basic solutions, with the aim of enhancing the subsequent biodegradation of bioplastics in controlled valorization scenarios such as composting and anaerobic digestors. Particular attention is given to pretreatment degradation technologies that modify surface properties to enhance microbial adhesion and enzymatic activity. Technologies such as ozonation and plasma-driven treatments increase surface hydrophilicity and introduce functional groups with oxygen bonds, facilitating subsequent microbial colonization and biodegradation. Irradiation-based techniques directly alter the chemical bonds at the polymer surface, promoting the formation of free radicals, chain scission, and crosslinking, thereby modifying the polymer structure. Pretreatments involving immersion in aqueous solutions may induce solution sorption and diffusion, together with hydrolytic chain breakage in bulk, with a relevant contribution to the ulterior biodegradation performance. By promoting abiotic degradation and increasing the accessibility of biopolymers to microbial systems, these pretreatment strategies can offer effective tools to enhance biodegradation and, therefore, the end-of-life management of bioplastics, supporting the transition toward sustainable cradle-to-cradle pathways within a biocircular economy. Full article