Search Results (51)

Wayfinding with minimal effort is essential for reducing cognitive load and emotional stress in unfamiliar environments. This exploratory quasi-experimental study investigated wayfinding challenges in a university building housing three spatially dispersed counseling centers and three academic departments that share the building entrances, lobby, and hallways. Using mobile eye tracking with concurrent think-aloud protocols and schematic mapping, we examined visual attention patterns during predefined navigation tasks performed by 24 first-time visitors. Findings revealed frequent fixations on non-informative structural features, while existing wayfinding cues were often overlooked. High rates of null gazes indicated unsuccessful visual searching. Thematic analysis of verbal data identified eight key issues, including spatial confusion, aesthetic monotony, and inadequate signage. Participants frequently described the environment as disorienting and emotionally taxing, comparing it to institutional settings such as hospitals. In response, we developed wayfinding design proposals informed by our research findings, stakeholder needs, and contextual priorities. We used an experiential digital twin that prioritized perceptual fidelity to analyze the current wayfinding challenges, develop experimental protocols, and discuss design options and costs. This study offers a transferable methodological framework for identifying wayfinding challenges through convergent analysis of gaze patterns and verbal protocols, demonstrating how empirical findings can inform targeted wayfinding design interventions. Full article

Generative Artificial Intelligence (GAI) is gaining increasing attention as a catalyst for advancing sustainability within project management for buildings and infrastructure. This paper systematically reviews 173 peer-reviewed publications, including 142 journal and conference papers, to examine the current research landscape. Bibliometric mapping and thematic synthesis reveal expanding applications of GAI in project planning, design optimization, risk management, and sustainability assessment, but adoption remains fragmented across regions and domains. This review identifies persistent challenges that constrain large-scale implementation, including data variability and interoperability gaps, high computational demand, limited regulatory alignment, and ethical and governance concerns, coupled with the absence of standardized evaluation metrics. In response, this paper outlines future research prospects through a structured agenda that emphasizes scalable and generalizable AI models, real-time integration with IoT and digital twins, explainable and secure AI systems, and policy-aligned governance frameworks. These priorities aim to strengthen environmental, social, and economic sustainability outcomes in the built environment. By clarifying current progress and knowledge gaps, this review supports both scholars and practitioners in strengthening the role of GAI in the built environment. Full article

Background: Attention-deficit/hyperactivity disorder (ADHD) affects not only children but also their families. Mothers, as primary caregivers, frequently experience high stress and reduced well-being. This scoping review mapped recent literature (2015–2025) on the quality of life (QoL) of mothers of children with ADHD and identified key factors influencing maternal QoL. Methods: Following the Arksey and O’Malley framework and Joanna Briggs Institute guidance for scoping reviews, we searched PubMed, Scopus, and ERIC in June 2025 for peer-reviewed quantitative studies in English. Eligible studies focused on mothers of children (6–18 years) with ADHD and used validated parent QoL measures. Eight studies met inclusion criteria. Results: Eight studies published between 2015 and 2025 satisfied the inclusion criteria. Mothers regularly indicated a worse quality of life relative to control groups, demonstrating shortcomings in physical, psychological, social, and environmental domains. Severe ADHD symptoms in children, accompanying disruptive disorders, parental distress or anxiety, and inadequate social support were important variables. Adaptive coping strategies correlated with enhanced outcomes, and a longitudinal study showed that effective ADHD intervention reduced familial stress over several months. Several studies have identified maternal depression, child comorbidities, and inadequate social support as key factors that adversely affect parental quality of life. Conclusions: Mothers of children with ADHD are at heightened risk for compromised QoL. Family-centered strategies that support maternal mental health, strengthen social support, and enhance coping—alongside the child’s ADHD care—are warranted. Heterogeneity in QoL measures and limited longitudinal evidence highlight priorities for future research. Full article

Persistent cost overruns, schedule delays, and weak control mechanisms continue to hinder construction project delivery in Saudi Arabia, where 64% of projects exceeded their planned time and 53% experienced cost overruns. Although project control systems (PCSs) have received increasing attention, existing research lacks an empirically grounded and theory-informed framework explaining how project control system determinants (PCSDs) influence performance. This study addresses this gap by developing and testing an Input–Process–Output (IPO) model linking organisational, human, and technological inputs with operational control stages and project outcomes. Data were collected from 222 completed construction projects in Saudi Arabia using a cross-sectional survey of professionals directly involved in their delivery. Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied to test hypothesised relationships, supported by Importance–Performance Map Analysis (IPMA) to identify high-impact but underperforming areas. Seventeen of twenty hypotheses were supported, highlighting the dominant role of post-operational controls, the significant indirect influence of in-operational controls, and the most impactful total effects of organisational factors on project performance through control processes. The IPMA results identified leadership and team capacity, estimation accuracy, stakeholder integration, PMO engagement, audits, knowledge management, and corrective scheduling actions as priority areas for improvement. This study provides the first empirical tests of a multi-dimensional PCS effectiveness model in the region, contributing both to the academic literature and practical efforts aimed at improving project delivery outcomes in alignment with national development goals, such as Saudi Vision 2030. Full article

Geopolitical instability poses a significant threat to food systems by disrupting production, trade, and market access, thereby undermining both food security and long-term sustainability. Unlike peacetime food insecurity driven by poverty or climate change, conflict-related crises often involve blockades, agricultural destruction, and deliberate famine. This paper conducts a bibliometric review of the academic literature from 2010 to 2024, and partially 2025, to examine how food security and resilience under the influence of conflict have been conceptualized, focusing on their intersections with war, global food systems, and sustainability. We used the Web of Science database and tools such as VOSviewer version 1.6.18, Microsoft Excel and Bibliomagika version 2.10.0, to map thematic clusters, identify influential authors, publishers, and academic partnerships and trace the evolution of scholarly attention on this topic. Our findings reveal a growing recognition of using food as a tool of war, the increasing politicization of food aid, and heightened awareness of the fragility of agricultural systems under conflict. At the same time, significant gaps still persist, particularly in the study of “unconventional” food systems such as black markets and informal supply chains, which often sustain communities during crises but remain underexplored in mainstream scholarship. By identifying these gaps, this review outlines research priorities for developing inclusive and resilient policies, ultimately enhancing the capacity of global food systems to withstand the pressures of conflict and geopolitical instability. Full article

With the rise in motor vehicles, driving safety is a major concern, and autonomous driving technology plays a key role in enhancing safety. Vision-based lane departure warning systems are essential for accurate navigation, focusing on lane line detection. This paper reviews the development of such systems and highlights the limitations of traditional image processing. To improve lane line detection, a dataset from Roboflow Universe will be used, incorporating techniques like priority pixels, least squares fitting for positioning, and a Kalman filter for tracking. YOLOv5 will be enhanced with a di-versified branch block (DBB) for better multi-scale feature extraction and an improved segmentation head inspired by YOLACT (You Only Look At CoefficienTs) for precise lane line segmentation. A multi-scale feature fusion mechanism with self-attention will be introduced to improve robustness. Experiments will demonstrate that the improved YOLOv5 outperforms other models in accuracy, recall, and mAP@0.5. Future work will focus on optimizing the model structure and enhancing the fusion mechanism for better performance. Full article

Oxytocin (OT), traditionally associated with reproduction and social bonding, has emerged as a key modulator of gastrointestinal (GI) physiology and appetite regulation behavior through its actions within the gut–brain axis. Central to this regulation are vagal oxytocin receptors (VORs), which are located along vagal afferent and efferent fibers and within brainstem nuclei such as the nucleus tractus solitarius and dorsal motor nucleus of the vagus. This review presents a comprehensive synthesis of current knowledge on the anatomical distribution, molecular signaling, developmental plasticity, and functional roles of VORs in the regulation of GI motility, satiety, and energy homeostasis. We highlight how VORs integrate hormonal, microbial, and stress-related cues and interact with other neuropeptidergic systems including GLP-1, CCK, and nesfatin-1. Recent advances in spatial transcriptomics, single-nucleus RNA sequencing, chemogenetics, and optogenetics are discussed as transformative tools for mapping and manipulating VOR-expressing circuits. Particular attention is given to sex differences, translational challenges, and the limited understanding of VOR function in humans. This article proposes VORs as promising therapeutic targets in dysphagia, obesity, and functional GI disorders. We outline future research priorities, emphasizing the need for integrative, cross-species approaches to clarify VOR signaling and guide the development of targeted, personalized interventions. Full article

Traditional unmanned aerial vehicle (UAV) detection and tracking methods have long faced the twin challenges of high cost and poor efficiency. In real-world battlefield environments with complex backgrounds, occlusions, and varying speeds, existing techniques struggle to track small UAVs accurately and stably. To tackle these issues, this paper presents an enhanced YOLOv8N-Drone-based algorithm for improved target tracking of small UAVs. Firstly, a novel module named C2f-DSFEM (Depthwise-Separable and Sobel Feature Enhancement Module) is designed, integrating Sobel convolution with depthwise separable convolution across layers. Edge detail extraction and multi-scale feature representation are synchronized through a bidirectional feature enhancement mechanism, and the discriminability of target features in complex backgrounds is thus significantly enhanced. For the feature confusion problem, the improved lightweight Context Anchored Attention (CAA) mechanism is integrated into the Neck network, which effectively improves the system’s adaptability to complex scenes. By employing a position-aware weight allocation strategy, this approach enables adaptive suppression of background interference and precise focus on the target region, thereby improving localization accuracy. At the level of loss function optimization, the traditional classification loss is replaced by the focal loss (Focal Loss). This mechanism effectively suppresses the contribution of easy-to-classify samples through a dynamic weight adjustment strategy, while significantly increasing the priority of difficult samples in the training process. The class imbalance that exists between the positive and negative samples is then significantly mitigated. Experimental results show the enhanced YOLOv8 boosts mean average precision (Map@0.5) by 12.3%, hitting 99.2%. In terms of tracking performance, the proposed YOLOv8 N-Drone algorithm achieves a 19.2% improvement in Multiple Object Tracking Accuracy (MOTA) under complex multi-scenario conditions. Additionally, the IDF1 score increases by 6.8%, and the number of ID switches is reduced by 85.2%, indicating significant improvements in both accuracy and stability of UAV tracking. Compared to other mainstream algorithms, the proposed improved method demonstrates significant advantages in tracking performance, offering a more effective and reliable solution for small-target tracking tasks in UAV applications. Full article

Liquid biopsy (LB) has gained attention as a valuable approach for cancer diagnostics, providing a minimally invasive option compared to conventional tissue biopsies and helping to overcome issues related to patient discomfort and procedural invasiveness. Recent advances in biosensor technologies, particularly photonic sensors, have improved the accuracy, speed, and real-time capabilities for detecting circulating biomarkers in biological fluids. Incorporating these tools into clinical practice facilitates more informed therapeutic choices and contributes to tailoring treatments to individual patient profiles. This review highlights the clinical potential of LB, examines technological limitations, and outlines future research directions. Departing from traditional biosensor focused reviews, it adopts a reverse-mapping approach grounded in clinically relevant tumor biomarkers. Specifically, biomarkers associated with prevalent cancers, such as breast, prostate, and lung cancers, serve as the starting point for identifying the most suitable photonic sensing platforms. The analysis underscores the need to align sensor design with the physicochemical properties of each biomarker and the operational requirements of the application. No photonic platform is universally optimal; rather, each exhibits specific strengths depending on performance metrics such as sensitivity, limit of detection, and easy system integration. Within this framework, the review provides a comprehensive assessment of emerging photonic biosensors and outlines key priorities to support their effective clinical translation in cancer diagnostics. Full article

Forest fires have a great destructive impact on the Earth’s ecosystem; therefore, the top priority of current research is how to accurately and quickly monitor forest fires. Taking into account efficiency and cost-effectiveness, deep-learning-driven UAV remote sensing fire detection algorithms have emerged as a favored research trend and have seen extensive application. However, in the process of drone monitoring, fires often appear very small and are easily obstructed by trees, which greatly limits the amount of effective information that algorithms can extract. Meanwhile, considering the limitations of unmanned aerial vehicles, the algorithm model also needs to have lightweight characteristics. To address challenges such as the small targets, occlusions, and image blurriness in UAV-captured wildfire images, this paper proposes an improved UAV forest fire detection model based on YOLOv8. Firstly, we incorporate SPDConv modules, enhancing the YOLOv8 architecture and boosting its efficacy in dealing with minor objects and images with low resolution. Secondly, we introduce the C2f-PConv module, which effectively improves computational efficiency by reducing redundant calculations and memory access. Thirdly, the model boosts classification precision through the integration of a Mixed Local Channel Attention (MLCA) strategy preceding the three detection outputs. Finally, the W-IoU loss function is utilized, which adaptively modifies the weights for different target boxes within the loss computation, to efficiently address the difficulties associated with detecting small targets. The experimental results showed that the accuracy of our model increased by 2.17%, the recall increased by 5.5%, and the mAP@0.5 increased by 1.9%. In addition, the number of parameters decreased by 43.8%, with only 5.96M parameters, while the model size and GFlops decreased by 43.3% and 36.7%, respectively. Our model not only reduces the number of parameters and computational complexity, but also exhibits superior accuracy and effectiveness in UAV fire image recognition tasks, thereby offering a robust and reliable solution for UAV fire monitoring. Full article

Distribution line insulators play a key role in electrical insulation and supporting lines in distribution lines. Insulator defects due to overvoltage, thermal stress, and environmental pollution may trigger power transmission instability and line collapse, thus threatening the safe operation of distribution networks. However, distribution line insulators often present detection challenges due to their compact dimensions, diverse flaw types, and frequent installation in populated areas with visually cluttered environments. The combination of these factors, including small defect sizes, varying failure patterns, and complex background interference, in both urban and rural settings, creates significant difficulties for precise defect identification in these critical components. In response to these challenges, this paper proposes a defect recognition algorithm for distribution line insulators based on the improved YOLO11 model. Firstly, the algorithm combines the detection head of the original model with the Adaptively Spatial Feature Fusion (ASFF) module to effectively fuse defect features at different resolution levels and improve the model’s ability to recognize multi-scale defect features. Secondly, a Bidirectional Feature Pyramid Network (BiFPN) replaces the FPN + PAN structure of the original model to achieve a more effective transfer of contextual information in order to facilitate the model’s efficiency in performing defect feature fusion, and the Convolutional Block Attention Module (CBAM) Attention mechanism is embedded in the BiFPN output so that the model is able to give priority attention to defective features on insulators in complex recognition environments. Finally, the ShuffleNetV2 module is used to reduce the parameters of the improved model by replacing the large-parameter C3k2 module at the end of the backbone network for easy deployment on lightweight and small devices. The experimental results show that the improved model performs well in the distribution line insulator defect detection task, with an accuracy precision (AP) and mean accuracy precision (mAP) of 97.0% and 98.1%, respectively, which are 1.4% and 0.7% higher than the original YOLO11 model. Full article

In recent years, consensus algorithms have gained significant importance in the context of blockchain networks. These algorithms play a crucial role in allowing network participants to reach agreements on the state of the blockchain without needing a central authority. The present study focuses on carrying out a systematic mapping of these consensus algorithms to explore in detail their use, benefits, and challenges in the context of blockchain networks. Understanding consensus algorithms is essential to appreciating how blockchain networks achieve the reliability and integrity of their distributed ledgers. These algorithms allow network nodes to reach agreement on the validity of transactions and the creation of new blocks on the blockchain. In this sense, consensus algorithms are the engine that drives trust in these decentralized networks. Numerous authors have contributed to the development and understanding of consensus algorithms in the context of blockchain networks. This revolutionary concept paved the way for numerous cryptocurrencies and blockchain systems. Despite advances in this field, significant challenges remain: centralization, fair token distribution, scalability, and sustainability. The energy consumption of blockchain networks, particularly those using algorithms such as Proof of Work, Proof of Stake, Delegated Proof of Stake, Proof of Authority, and hybrid algorithms (Proof of Work/Proof of Stake), has raised concerns about their environmental impact, motivating the scientific and technological community to investigate more sustainable alternatives that promise to reduce energy consumption and contribute to climate change mitigation. Furthermore, interoperability between different blockchains and security in specific environments, such as IoT, are areas that still require significant research attention. This systematic mapping not only seeks to shed light on the current state of consensus algorithms in blockchain, but also their impact on sustainability, identifying those algorithms that, in addition to guaranteeing integrity and security, minimize the environmental footprint, promoting a more efficient use of energy resources, being a relevant approach in a context in which the adoption of sustainable technologies has become a global priority. Understanding and improving these algorithms are critical to unlocking the full potential of blockchain technology in a variety of applications and industry sectors. Full article

Selection history is widely accepted as a vital source in attention control. Reward history indicates that a learned association captures attention even when the reward is no longer presented, while statistical learning indicates that a learned probability exerts its influence on attentional control (facilitation or inhibition). Existing research has shown that the effects of the reward history and statistical learning are additive, suggesting that these two components influence attention priority through different pathways. In the current study, leveraging the temporal resolution advantages of EEG, we explored whether these two components represent independent sources of attentional bias. The results revealed faster responses to the target at the high-probability location compared to low-probability locations. Both the target and distractor at high-probability locations elicited larger early Pd (50–150 ms) and Pd (150–250 ms) components. The reward distractor slowed the target search and elicited a larger N2pc (180–350 ms). Further, no interaction between statistical learning and the reward history was observed in RTs or N2pc. The different types of temporal progression in attention control indicate that statistical learning and the reward history independently modulate the attention priority map. Full article

The precision of electroencephalograms (EEGs) significantly impacts the performance of brain–computer interfaces (BCI). Currently, the majority of research into BCI technology gives priority to lightweight design and a reduced electrode count to make it more suitable for application in wearable environments. This paper introduces a deep learning-based time series bidirectional (BiLSTM) network that is designed to capture the inherent characteristics of EEG channels obtained from neighboring electrodes. It aims to predict the EEG data time series and facilitate the conversion process from low-density EEG signals to high-density EEG signals. BiLSTM pays more attention to the dependencies in time series data rather than mathematical maps, and the root mean square error can be effectively restricted to below 0.4μV, which is less than half the error in traditional methods. After expanding the BCI Competition III 3a dataset from 18 channels to 60 channels, we conducted classification experiments on four types of motor imagery tasks. Compared to the original low-density EEG signals (18 channels), the classification accuracy was around 82%, an increase of about 20%. When juxtaposed with real high-density signals, the increment in the error rate remained below 5%. The expansion of the EEG channels showed a substantial and notable improvement compared with the original low-density signals. Full article

The task of trademark text recognition is a fundamental component of scene text recognition (STR), which currently faces a number of challenges, including the presence of unordered, irregular or curved text, as well as text that is distorted or rotated. In applications such as trademark infringement detection and analysis of brand effects, the diversification of artistic fonts in trademarks and the complexity of the product surfaces where the trademarks are located pose major challenges for relevant research. To tackle these issues, this paper proposes a novel recognition framework named SwinCornerTR, which aims to enhance the accuracy and robustness of trademark text recognition. Firstly, a novel feature-extraction network based on SwinTransformer with EFPN (enhanced feature pyramid network) is proposed. By incorporating SwinTransformer as the backbone, efficient capture of global information in trademark images is achieved through the self-attention mechanism and enhanced feature pyramid module, providing more accurate and expressive feature representations for subsequent text extraction. Then, during the encoding stage, a novel feature point-retrieval algorithm based on corner detection is designed. The OTSU-based fast corner detector is presented to generate a corner map, achieving efficient and accurate corner detection. Furthermore, in the encoding phase, a feature point-retrieval mechanism based on corner detection is introduced to achieve priority selection of key-point regions, eliminating character-to-character lines and suppressing background interference. Finally, we conducted extensive experiments on two open-access benchmark datasets, SVT and CUTE80, as well as a self-constructed trademark dataset, to assess the effectiveness of the proposed method. Our results showed that the proposed method achieved accuracies of 92.9%, 92.3% and 84.8%, respectively, on these datasets. These results demonstrate the effectiveness and robustness of the proposed method in the analysis of trademark data. Full article