Search Results (407)

The construction sector significantly contributes to global environmental degradation through intensive resource extraction, high energy consumption, and substantial waste generation. Addressing this unsustainable trajectory requires integrated approaches that simultaneously improve operational efficiency and material circularity. Lean Construction (LC) and Circular Economy (CE) offer complementary frameworks for enhancing process performance and reducing environmental impacts. However, their combined implementation remains underdeveloped and fragmented. This study conducts a systematic literature review (SLR) of 18 peer-reviewed articles published between 2010 and 2025, selected using PRISMA 2020 guidelines and sourced from Scopus and Web of Science databases. A mixed-method approach combines bibliometric mapping and qualitative content analysis to investigate how LC and CE are jointly operationalized in construction contexts. The findings reveal that LC improves cost, time, and workflow reliability, while CE enables reuse, modularity, and lifecycle extension. Integration is further supported by digital tools—such as Building Information Modelling (BIM), Design for Manufacture and Assembly (DfMA), and digital twins—which enhance traceability and flow optimization. Nonetheless, persistent barriers—including supply chain fragmentation, lack of standards, and regulatory gaps—continue to constrain widespread adoption. This review identifies six strategic enablers for LC-CE integration: crossdisciplinary competencies, collaborative governance, interoperable digital systems, standardized indicators, incentive-based regulation, and pilot demonstrator projects. By consolidating fragmented evidence, the study provides a structured research agenda and practical insights to guide the transition toward more circular, efficient, and sustainable construction practices. Full article

Accurate prediction of drug–drug interactions (DDIs) is essential for ensuring medication safety and optimizing combination-therapy strategies. However, existing DDI models face limitations in handling interactions related to stereochemistry and precisely locating drug interaction sites. These limitations reduce the prediction accuracy for conformation-dependent interactions and the interpretability of molecular mechanisms, potentially posing risks to clinical safety. To address these challenges, we introduce LSA-DDI, a Spatial-Contrastive-Attention-Based Drug–Drug Interaction framework. Our 3D feature extraction method captures the spatial structure of molecules through three features—coordinates, distances, and angles—and fuses them to enhance the model of molecular spatial structures. Concurrently, we design and implement a Dynamic Feature Exchange (DFE) mechanism that dynamically regulates the flow of information across modalities via an attention mechanism, achieving bidirectional enhancement and semantic alignment of 2D topological and 3D spatial structure features. Additionally, we incorporate a dynamic temperature-regulated multiscale contrastive learning framework that effectively aligns multiscale features and enhances the model’s generalizability. Experiments conducted on public drug databases under both warm-start and cold-start scenarios demonstrated that LSA-DDI achieved competitive performance, with consistent improvements over existing methods. Full article

The detection of aerial imagery captured by Unmanned Aerial Vehicles (UAVs) is widely employed across various domains, including engineering construction, traffic regulation, and precision agriculture. However, aerial images are typically characterized by numerous small targets, significant occlusion issues, and densely clustered targets, rendering traditional detection algorithms largely ineffective for such imagery. This work proposes a small target detection algorithm, SR-YOLO. It is specifically tailored to address these challenges in UAV-captured aerial images. First, the Space-to-Depth layer and Receptive Field Attention Convolution are combined, and the SR-Conv module is designed to replace the Conv module within the original backbone network. This hybrid module extracts more fine-grained information about small target features by converting image spatial information into depth information and the attention of the network to targets of different scales. Second, a small target detection layer and a bidirectional feature pyramid network mechanism are introduced to enhance the neck network, thereby strengthening the feature extraction and fusion capabilities for small targets. Finally, the model’s detection performance for small targets is improved by utilizing the Normalized Wasserstein Distance loss function to optimize the Complete Intersection over Union loss function. Empirical results demonstrate that the SR-YOLO algorithm significantly enhances the precision of small target detection in UAV aerial images. Ablation experiments and comparative experiments are conducted on the VisDrone2019 and RSOD datasets. Compared to the baseline algorithm YOLOv8s, our SR-YOLO algorithm has improved mAP@0.5 by 6.3% and 3.5% and mAP@0.5:0.95 by 3.8% and 2.3% on the datasets VisDrone2019 and RSOD, respectively. It also achieves superior detection results compared to other mainstream target detection methods. Full article

The rapid increase in waste electrical and electronic equipment (WEEE) creates major environmental and governance issues in developing countries like Ecuador struggle because they with minimal formal collection and recycling rates. This research presents a potential sustainable management approach that tracks products through their life cycles while following circular economy principles that include product extension and material extraction and waste minimization. A systematic literature review (SLR) using the PRISMA methodology combined with a bibliometric analysis found essential global strategies and technological frameworks and regulatory frameworks. The analysis of articles demonstrates that information management systems (IMSs) together with digital technologies and consistent regulations serve as essential elements for enhancing traceability and material recovery and formal recycling processes. A WEEE management IMS model was developed for the Ecuadorian market through an analysis of the findings; it follows a five-stage development process, starting from the technological infrastructure setup to complete data visualization integration. The proposed model is designed to enable public–private–community partnerships using digital tools that promote sustainable practices. The combination of circular strategies with traceability technologies and strong regulatory frameworks leads to improved WEEE governance, which supports sustainable system transitions in emerging economies. Full article

Antimicrobial resistance (AMR) is a growing global concern, influenced by antibiotic use in both human and veterinary medicine, especially in companion animals. In low- and middle-income countries, regulatory oversight on veterinary prescriptions is often limited, creating gaps that can accelerate AMR. This study aimed to characterize the use of antibiotics approved for human use that are prescribed by veterinarians for companion animals in Brazil, a country representative of broader regulatory challenges. We conducted a retrospective analysis of five years (2017–2021) of national sales data recorded by the National System for the Management of Controlled Products (SNGPC), maintained by the Brazilian Health Regulatory Agency (ANVISA). A total of 789,893 veterinary antibiotic prescriptions were analyzed over the five-year period, providing a comprehensive overview of prescribing patterns. The dataset included all oral and injectable antibiotics purchased in human pharmacies with veterinary prescriptions. Data wrangling and cleaning procedures were applied to extract information on volume, antibiotic classes, seasonal variation, and regional distribution. The results revealed a predominance of penicillins, first- and second-generation cephalosporins, and a marked increase in macrolide use, especially azithromycin. Notable regional disparities were observed, with the southeastern region leading in prescription volume. The findings, particularly the disproportionate use of azithromycin and the marked regional disparities, highlight the need for targeted monitoring policies and a stricter regulation of off-label antibiotic use in veterinary medicine. They also offer insights applicable to other countries facing similar AMR threats due to limited surveillance and regulatory frameworks. Full article

This study proposes a deep learning-based, non-contact method for detecting elevated intraocular pressure (IOP) by integrating Scheimpflug images with corneal biomechanical features. Glaucoma, the leading cause of irreversible blindness worldwide, requires accurate IOP monitoring for early diagnosis and effective treatment. Traditional IOP measurements are often influenced by corneal biomechanical variability, leading to inaccurate readings. To address these limitations, we present a multi-modal framework incorporating CycleGAN for data augmentation, Swin Transformer for visual feature extraction, and the Kolmogorov–Arnold Network (KAN) for efficient fusion of heterogeneous data. KAN approximates complex nonlinear relationships with fewer parameters, making it effective in small-sample scenarios with intricate variable dependencies. A diverse dataset was constructed and augmented to alleviate data scarcity and class imbalance. By combining Scheimpflug imaging with clinical parameters, the model effectively integrates multi-source information to improve high IOP prediction accuracy. Experiments on a real-world private hospital dataset show that the model achieves a diagnostic accuracy of 0.91, outperforming traditional approaches. Grad-CAM visualizations identify critical anatomical regions, such as corneal thickness and anterior chamber depth, that correlate with IOP changes. These findings underscore the role of corneal structure in IOP regulation and suggest new directions for non-invasive, biomechanics-informed IOP screening. Full article

Accurate acquisition of the phenotypic information of trunk-shaped fruit trees plays a crucial role in intelligent orchard management, pruning during dormancy, and improving fruit yield and quality. However, the precise segmentation of trunks and branches remains a significant challenge, limiting the accurate measurement of phenotypic parameters and high-precision pruning of branches. To address this issue, a novel adaptive cuboid regional growth segmentation algorithm is proposed in this study. This method integrates a growth vector that is adaptively adjusted based on the growth trend of branches and a growth cuboid that is dynamically regulated according to branch diameters. Additionally, an innovative reverse growth strategy is introduced to enhance the efficiency of the growth process. Furthermore, the algorithm can automatically and effectively identify the starting and ending points of growth based on the structural characteristics of fruit tree branches, solving the problem of where to start and when to stop. Compared with PointNet++, PointNeXt, and Point Transformer, ACRGS achieved superior performance, with F₁-scores of 95.75% and 96.21% and mIoU values of 0.927 and 0.933 for apple and cherry trees. The results show that the method enables high-precision and efficiency trunk–branch segmentation, providing data support for fruit tree phenotypic parameter extraction and pruning. Full article

Background: The gut–brain axis (GBA) has been demonstrated to be involved in normal neurodevelopment, with its dysfunction potentially contributing to the onset of mental disorders. In this systematic review and meta-analysis, we aimed to examine the relationship between levels of specific biomarkers of intestinal permeability or inflammation and scores of depressive symptoms or suicidality. Methods: All studies investigating the link between depressive symptoms and/or suicidality and biomarkers associated with intestinal permeability or inflammation were included. Studies providing data for comparisons between two groups—depressive or suicidal patients vs. healthy controls, or suicidal vs. non-suicidal patients—were included in the meta-analysis. Studies examining the correlation between depressive symptoms and biomarker levels were also included into the review. Data were independently extracted and reviewed by multiple observers. A random-effects model was employed for the analysis, and Hedge’s g was pooled for the effect size. Heterogeneity was assessed using the I² index. Results: Twenty-two studies provided data for inclusion in the meta-analysis, while nineteen studies investigated the correlation between depressive symptoms and biomarker levels. For depressive symptoms, when compared to the controls, patients showed significantly increased levels of intestinal fatty acid-binding protein (I-FABP) (ES = 0.36; 95% CI = 0.11 to 0.61; p = 0.004; I² = 71.61%), zonulin (ES = 0.69; 95% CI = 0.02 to 1.36; p = 0.044; I² = 92.12%), antibodies against bacterial endotoxins (ES = 0.75; 95% CI = 0.54 to 0.98; p < 0.001; I² = 0.00%), and sCD14 (ES = 0.11; 95% CI = 0.01 to 0.21; p = 0.038; I² = 10.28%). No significant differences were found between the patients and controls in levels of LPS-binding protein (LBP) and alpha-1 antitrypsin (A-1-AT). For suicidality, four studies were identified for quantitative analysis, three of which focused on I-FABP. No significant differences in I-FABP levels were observed between suicidal patients and the controls (ES = 0.24; 95% CI = −0.30 to 0.79; p = 0.378; I² = 86.44%). Studies investigating the correlation between depressive symptoms and levels of intestinal permeability and inflammation biomarkers did not provide conclusive results. Conclusions: A significant difference was observed between patients with depressive symptoms and controls for biomarkers of intestinal permeability (zonulin, which regulates tight junctions), inflammatory response to bacterial endotoxins (antibodies to endotoxins and sCD14—a soluble form of the CD14 protein that modulates inflammation triggered by lipopolysaccharides), and acute intestinal epithelial damage (I-FABP, released upon enterocyte injury). Studies investigating suicidality and related biomarkers were limited in number and scope, preventing definitive conclusions. Overall, these findings suggest that biomarkers of gut permeability represent a promising area for further investigation in both psychiatric and forensic pathology. They may have practical applications, such as supporting diagnostic and therapeutic decision-making in clinical settings and providing pathologists with additional information to help determine the manner of death in forensic investigations. Full article

Background/Objectives: High sodium consumption increases the risk of hypertension and cardiovascular disease. Although food remains the primary source of intake, elevated sodium levels in drinking water can further contribute to excessive intake, particularly in populations already exceeding recommendations. This review examines the extent to which national drinking water standards account for sodium-related health risks and aims to inform discussion on the need for enforceable, health-based sodium limits. Methods: National standards for unbottled drinking water in 197 countries were searched for using the WHO 2021 review of drinking water guidelines, the FAOLEX database, and targeted internet and AI searches. For each country, data were extracted for the document name, year, regulatory body, regulation type, sodium limit (if stated), and rationale. Socio-geographic data were sourced from World Bank Open Data. A descriptive analysis was conducted using Microsoft Excel. Results: Standards were identified for 164 countries. Of these, 20% (n = 32), representing 30% of the global population, had no sodium limit. Among the 132 countries with a sodium limit, 92% (n = 121) adopted the WHO’s palatability-based guideline of 200 mg/L. Upper limits ranged from 50 to 400 mg/L. Only twelve countries (9%) cited health as a rationale. Three countries—Australia, Canada, and the United States—provided a separate recommendation for at-risk populations to consume water with sodium levels below 20 mg/L. Conclusions: Globally, drinking water standards give inadequate attention to sodium’s health risks. Most either lack sodium limits or rely on palatability thresholds that are too high to protect health. Updating national and international standards to reflect current evidence is essential to support sodium reduction efforts. Health-based sodium limits would empower communities to better advocate for safe water. Amid rising water salinity, such reforms must be part of a broader global strategy to ensure universal and equitable access to safe, affordable drinking water as a basic human right. Full article

With the European Union (EU) Health Technology Assessment (HTA) regulation, Joint Clinical Assessments (JCA) are now required for oncological and advanced therapy medicinal products. The JCA assessment scope is determined through the PICO framework (Population, Intervention, Comparator, Outcome). Given the tight JCA timelines, Health Technology Developers (HTD) must anticipate PICO elements early to prepare dossiers effectively. This study investigates whether PICO can be predicted across EU member states using publicly available information. A systematic literature review was conducted to identify relevant peer-reviewed articles. Additionally, an extensive search of publicly available HTA documents, including reports, methodological guidelines, submission templates, and market access information was performed across 29 European countries. Relevant information for PICO anticipation was extracted. For many member states, a wealth of relevant information is publicly accessible: 66% have HTA reports publicly available, 79% have HTA methodological guidelines, 69% have dossier templates, and 100% have market access status lists. Between countries, the requirements for population and outcomes are largely aligned, making comparator the central element in PICO anticipation. PICO can be anticipated reliably based on public information. HTDs must be prepared to adjust their strategies as national procedures adapt, ensuring alignment with both current and emerging EU and national requirements. Full article

Cuscutae Semen (CS), a traditional herb recognized as a nutraceutical food in China, has been widely utilized in managing aging-related diseases throughout history. However, whether this mechanism is associated with mitochondrial stress tolerance remains unclear. In the present study, Caenorhabditis elegans (C. elegans) was used to investigate the effects of CS on their longevity. The data demonstrated that CS prolonged the average lifespan of the nematodes by 15.26%, reducing lipofuscin accumulation by 61.46%, as well as improving spontaneous motility. CS treatment significantly enhanced the resistance of C. elegans to hydrogen peroxide-induced oxidative stress and 37 °C induced heat stress, reducing reactive oxygen species (ROS) production by 71.45%. Additionally, membrane potential (MMP) and adenosine triphosphate (ATP) were increased by 354.72% and 69.64%, respectively. However, mitochondrion-specific ROS and calcium flux were significantly reduced to 45.86% and 63.25%, respectively, in C. elegans treated with CS. Consistently, the polymerase chain reaction data revealed that CS significantly up-regulated the expressions of the antioxidant-related genes skn-1, ctl-1, sod-3, and gst-4; the heat shock gene hsp-16.2; and the autophagy-related genes lgg-1 and bec-1. Considering the crucial role of the silent information regulator sirtuin 1 (SIR-2.1/SIRT1) in aging-related mitochondrial oxidative stress, we examined its expression and transcriptional activity. As expected, treatment with CS induced SIRT1 expression, and isorhamnetin identified from CS extract significantly enhanced SIRT1 transcriptional activity in HEK293T cells. Collectively, our results provided evidence that CS prolonged the lifespan of C. elegans by ameliorating oxidative stress damage and mitochondrial dysfunction via SIRT1. Full article

The rapid development and deployment of artificial intelligence (AI) technologies have sparked intense public interest and debate. While these innovations promise to revolutionise various aspects of human life, it is crucial to understand the complex emotional responses they elicit from potential adopters and users. Such findings can offer crucial guidance for stakeholders involved in the development, implementation, and governance of AI technologies like OpenAI’s ChatGPT, a large language model (LLM) that garnered significant attention upon its release, enabling more informed decision-making regarding potential challenges and opportunities. While previous studies have employed data-driven approaches towards investigating public reactions to emerging technologies, they often relied on sentiment polarity analysis, which categorises responses as positive or negative. However, this binary approach fails to capture the nuanced emotional landscape surrounding technological adoption. This paper overcomes this limitation by presenting a comprehensive analysis for investigating the emotional landscape surrounding technology adoption by using the launch of ChatGPT as a case study. In particular, a large corpus of social media texts containing references to ChatGPT was compiled. Text mining techniques were applied to extract emotions, capturing a more nuanced and multifaceted representation of public reactions. This approach allows the identification of specific emotions such as excitement, fear, surprise, and frustration, providing deeper insights into user acceptance, integration, and potential adoption of the technology. By analysing this emotional landscape, we aim to provide a more comprehensive understanding of the factors influencing ChatGPT’s reception and potential long-term impact. Furthermore, we employ topic modelling to identify and extract the common themes discussed across the dataset. This additional layer of analysis allows us to understand the specific aspects of ChatGPT driving different emotional responses. By linking emotions to particular topics, we gain a more contextual understanding of public reaction, which can inform decision-making processes in the development, deployment, and regulation of AI technologies. Full article

Aiming to mitigate engineering risks such as tunnel face collapse and equipment jamming caused by poor geological conditions during the construction of tunnel boring machines (TBMs), this study proposes a TBM surrounding rock grade prediction method based on multi-source feature fusion. Firstly, a multi-source dataset is established by systematically integrating TBM tunnelling parameters, horizontal acoustic profile (HSP) detection data and three-dimensional geological spatial information. In the data preprocessing stage, the TBM data is cleaned and divided according to the mileage section, the statistical characteristics of key tunnelling parameters (thrust, torque, penetration, etc.) are extracted, and the rock fragmentation index (TPI, FPI, WR) is fused to construct a composite feature vector. The Direct-LiNGAM causal discovery algorithm is innovatively introduced to analyse the nonlinear correlation mechanism between multi-source features, and then a hybrid model, TRNet, which combines the local feature extraction ability of convolutional neural networks and the nonlinear approximation advantages of Kolmogorov–Arnold networks, is constructed. Verified by a real tunnel project in western Sichuan, China, the prediction accuracy of TRNet for surrounding rock grade on the test set reaches an average of 92.15%, which is higher than other data-driven methods. The results show that the prediction method proposed in this paper can effectively predict the surrounding rock grade of the tunnel face during TBM tunnelling, and provide decision support for the dynamic regulation of tunnelling parameters. Full article

E-Waste management in Nigeria remains predominantly informal, with unlicensed collectors focusing on extracting valuable materials, primarily for export. Despite policy interventions, including the revised 2022 E-Waste framework and the Global Environment Facility (GEF) project, which introduced collection centers in Lagos and bolstered Extended Producer Responsibility (EPR), progress has been uneven. This comparative longitudinal study examined informal E-Waste processing practices over a six-year period (2017–2023) to evaluate the impact of these initiatives. Using a mixed-methods approach, including content analysis and field interviews with informal collectors, government officials, and NGOs, our findings reveal that profit is the primary motivator for informal collectors, while E-Waste fractions that are not considered profitable are often discarded in environmentally harmful ways. The findings indicate persistent noncompliance with regulations and stagnant or declining income levels for informal collectors. The revised 2022 regulation resulted in a significant increase in registrations, with EPRON recording its highest number of producers, with 39 in total, including 25.6% renewals and 74.4% new registrations. Although the revised framework and EPR efforts have achieved limited success, critical gaps in implementation and outreach remain, with minimal improvements in collectors’ awareness of health and environmental risks. This study underscores the need for targeted training and financial incentives to redirect E-Waste flows toward formal channels, thereby more effectively safeguarding the environment and wellbeing. Full article

High-frequency (HF) injection is a widely used technique for low-speed implementation of position sensorless permanent magnet synchronous motor control. A key component of this technique is the tracking loop control system, which extracts rotor position error and utilizes proportional–integral regulation as a position observer for estimating the rotor position. Generally, this process relies on band-pass filters (BPFs) and low-pass filters (LPFs) to modulate signals in the quadrature current to obtain rotor position error information. However, limitations in filter accuracy and dynamic response lead to prolonged convergence times and timing inconsistencies in the estimation process, which affects real-time motor control performance. To address these issues, this study proposes an exponential moving average (EMA)-based scheme for rotor position error extraction, offering a rapid response under dynamic conditions such as direction reversals, step speed changes, and varying loads. EMA is used to pass the original rotor position information carried by the quadrature current signal, which contains HF components, with a specified smoothing factor. Then, after the synchronous demodulation process, EMA is employed to extract rotor position error information for the position observer to estimate the rotor position. Due to its computational simplicity and fast response in handling dynamic conditions, the proposed method can serve as an alternative to BPF and LPF, which are commonly used for rotor position information extraction, while also reducing computational burden and improving performance. Finally, to demonstrate its feasibility and effectiveness in improving rotor position estimation accuracy, the proposed system is experimentally validated by comparing it with a conventional system. Full article