Search Results (2,004)

Urban form plays a crucial role in regulating urban thermal environments and air pollution patterns. However, the indirect mechanisms through which urban form influences PM2.5 concentrations via land surface temperature (LST) remain poorly understood. This study investigates these pathways by analyzing representative two- and three-dimensional urban form indices (UFIs) in the central urban area of Taiyuan, China. Multiple log-linear regression and mediation analysis were applied to evaluate the combined effects of urban form on LST and PM2.5. The results indicate that UFIs significantly influence both LST and PM2.5. The frontal area index (FAI) and sky view factor (SVF) emerged as key variables, with LST playing a significant mediating role. The indirect pathways affecting PM2.5 via LST, along with the direct LST-PM2.5 correlation, exhibit pronounced seasonal differences in direction and intensity. Moreover, different urban functional zones exhibit heterogeneous responses to the same form indices, highlighting the spatial variability of these linkages. These findings underscore the importance of incorporating seasonal and spatial differences into urban design. Accordingly, this study proposes targeted urban form optimization strategies to improve air quality and thermal comfort, offering theoretical insights and practical guidance for sustainable urban planning. Full article

Gait recognition is a reliable biometric approach that uniquely identifies individuals based on their natural walking patterns. It is widely used to recognize individuals who are challenging to camouflage and do not require a person’s cooperation. The general face-based person recognition system often fails to determine the offender’s identity when they conceal their face by wearing helmets and masks to evade identification. In such cases, gait-based recognition is ideal for identifying offenders, and most existing work leverages a deep learning (DL) model. However, a single model often fails to capture a comprehensive selection of refined patterns in input data when external factors are present, such as variation in viewing angle, clothing, and carrying conditions. In response to this, this paper introduces a fusion-based multi-model gait recognition framework that leverages the potential of convolutional neural networks (CNNs) and a vision transformer (ViT) in an ensemble manner to enhance gait recognition performance. Here, CNNs capture spatiotemporal features, and ViT features multiple attention layers that focus on a particular region of the gait image. The first step in this framework is to obtain the Gait Energy Image (GEI) by averaging a height-normalized gait silhouette sequence over a gait cycle, which can handle the left–right gait symmetry of the gait. After that, the GEI image is fed through multiple pre-trained models and fine-tuned precisely to extract the depth spatiotemporal feature. Later, three separate fusion strategies are conducted, and the first one is decision-level fusion (DLF), which takes each model’s decision and employs majority voting for the final decision. The second is feature-level fusion (FLF), which combines the features from individual models through pointwise addition before performing gait recognition. Finally, a hybrid fusion combines DLF and FLF for gait recognition. The performance of the multi-model fusion-based framework was evaluated on three publicly available gait databases: CASIA-B, OU-ISIR D, and the OU-ISIR Large Population dataset. The experimental results demonstrate that the fusion-enhanced framework achieves superior performance. Full article

Researchers are increasingly focusing on understanding the microbiota’s influence on disease susceptibility and overall health. The vast number of microorganisms in our gastrointestinal tract and their extensive surface area underscore their undeniable impact on well-being. Viewing the gut microbiome as a distinct pool of microbial genetic information that interacts with the human genome highlights its pivotal role in genetically predisposed diseases. Investigating this complex crosstalk may lead to the development of novel therapeutic strategies—such as targeting dysbiosis—to complement conventional treatments and improve patient care. Parkinson’s disease (PD) is a multifactorial condition originating from a combination of genetic and environmental risk factors. Compelling evidence points to the enteric nervous system as an initial site of pathological processes that later extend to the brain—a pattern known as the ‘body-first’ model. Furthermore, most patients with PD exhibit both qualitative and quantitative alterations in the composition of the gut microbiota, including dysbiosis and small intestinal overgrowth. Nonetheless, the existing literature predominantly addresses fecal microbiota, while knowledge of upper intestinal sections, like the duodenum, remains scarce. Given the potential for microbiota modulation to impact both motor and gastrointestinal symptoms, further research exploring the therapeutic roles of balanced diets, probiotics, and fecal transplants in PD is warranted. Full article

Cardiomyopathies represent a heterogeneous group of myocardial disorders, traditionally classified by phenotype into hypertrophic, dilated, and arrhythmogenic. Historically, these conditions have been attributed to high-penetrance rare variants in key structural genes, consistent with a classical Mendelian pattern of inheritance. However, emerging evidence suggests that this model does not fully capture the full spectrum and complexity of disease expression. Many patients do not harbor identifiable pathogenic variants, while others carrying well-known disease-causing variants remain unaffected. This highlights the role of incomplete penetrance, likely modulated by additional genetic modifiers. Recent advances in genomics have revealed a broader view of the genetic basis of cardiomyopathies, introducing new players such as common genetic variants identified as risk alleles, as well as intermediate-effect variants. This continuum of genetic risk, reflecting an overall genetic influence, interacts further with environmental and lifestyle factors, likely contributing together to the observed variability in clinical presentation. This model offers a more realistic framework for understanding genetic inheritance and helps provide a clearer picture of disease expression and penetrance. This review explores the evolving genetic architecture of cardiomyopathies, spanning from a monogenic foundation to intermediate-risk variants and complex polygenic contribution. Recognizing this continuum is essential for enhancing diagnostic accuracy, guiding family screening strategies, and enabling personalized patient management. Full article

Background: Pes planus is a condition where the arch of the foot collapses, resulting in the entire sole contacting the ground. The biomechanical implications of pes planus on gait have been widely studied; however, research specific to Black African populations, particularly recreational runners, is scarce. Aim: This study aimed to describe the forefoot centre of pressure (CoP) trajectory during the barefoot gait cycle among Black African recreational runners with pes planus. Methods: A prospective explorative and quantitative study design was employed. Participants included Black African male recreational runners aged 18 to 45 years diagnosed with pes planus. A Freemed™ 6050 force plate was used to collect gait data. Statistical analysis included cross-tabulations to identify patterns. Results: This study included 104 male participants across seven weight categories, with the majority in the 70-to-79 kg range (34.6%, n = 36). Most participants with pes planus showed a neutral foot posture (74.0%, n = 77) on the foot posture index 6 (FPI-6) scale. Flexible pes planus (94.2%, n = 98) was much more common than rigid pes planus (5.8%, n = 6). Lateral displacement of the CoP was observed in the right forefoot (90.4%, n = 94) and left forefoot (57.7%, n = 60). Load distribution patterns differed between feet, with the right foot favouring the medial heel, arch, and metatarsal heads, while the left foot favoured the lateral heel, medial heel, and lateral arch. No statistical significance was found in the cross-tabulations, but notable lateral CoP displacement in the forefoot was observed. Conclusions: The findings challenge the traditional view of pes planus causing overpronation and highlight the need for clinicians to reconsider standard diagnostic and management approaches. Further research is needed to explore the implications of these findings for injury prevention and management in this population. Full article

Stylolites are a common mineral dissolution feature in rocks that develop during compression and form distinct tooth structures. On a tectonic plate scale, mid-ocean ridges (MORs) and transform faults are a significant feature of the Earth’s surface that develop due to accretion of new material in an extensional regime. We present a comparison between the two features and argue that transform faults in MOR are similar to the sides of stylolite teeth, with both features representing kinematic faults (KFs). First, we present a numerical model of both stylolite and MOR growth and show that in both cases, KFs nucleate and grow spontaneously. In addition, we use a well-established technique (Family–Vicsek scaling) of describing fractal self-affine interfaces, which has been used for stylolites, to characterize the pattern of MOR systems in both simulations and natural examples. Our results show that stylolites and MOR have self-affine scaling characteristics with similar scaling regimes. They both show a larger roughness exponent at the small scale, a smaller exponent at the intermediate scale, followed by a flattening of the system at the largest scale. For stylolites, the physical forces behind the scaling are the surface energy at the small mineral scale, the elastic energy at the intermediate scale, followed by the system reaching the correlation length where growth stops. For MORs, the physical forces behind the scaling are not yet clear; however, the self-affine scaling shows that transform faults at MORs do not have a preferred spacing, but that the spacing is fractal. Our study offers a new perspective on the study of natural roughening phenomena on various scales, from minerals to tectonic plates, and a new view on the development of MORs. Full article

Graph-based multi-view clustering is a fundamental analysis method that learns the similarity matrix of multi-view data. Despite its success, it has two main limitations: (1) complementary information is not fully utilized by directly combining graphs from different views; (2) existing multi-view clustering methods do not adequately address redundancy and noise in the data, significantly affecting performance. To address these issues, we propose the Enhanced Similarity Matrix Learning (ES-MVC) for multi-view clustering, which dynamically integrates global graphs from all views with local graphs from each view to create an improved similarity matrix. Specifically, the global graph captures cross-view consistency, while the local graph preserves view-specific geometric patterns. The balance between global and local graphs is controlled through an adaptive weighting strategy, where hyperparameters adjust the relative importance of each graph, effectively capturing complementary information. In this way, our method can learn the clustering structure that contains fully complementary information, leveraging both global and local graphs. Meanwhile, we utilize a robust similarity matrix initialization to reduce the negative effects caused by noisy data. For model optimization, we derive an effective optimization algorithm that converges quickly, typically requiring fewer than five iterations for most datasets. Extensive experimental results on diverse real-world datasets demonstrate the superiority of our method over state-of-the-art multi-view clustering methods. In our experiments on datasets such as MSRC-v1, Caltech101, and HW, our proposed method achieves superior clustering performance with average accuracy (ACC) values of 0.7643, 0.6097, and 0.9745, respectively, outperforming the most advanced multi-view clustering methods such as OMVFC-LICAG, which yield ACC values of 0.7284, 0.4512, and 0.8372 on the same datasets. Full article

This study proposes a computational framework that transforms eye-tracking analysis from statistical description to cognitive structure modeling, aiming to reveal the organizational features embedded in the viewing process. Using the designers’ observation of a traditional Chinese landscape painting as an example, the study draws on the goal-oriented nature of design thinking to suggest that such visual exploration may exhibit latent structural tendencies, reflected in patterns of fixation and transition. Rather than focusing on traditional fixation hotspots, our four-dimensional framework (Region, Relation, Weight, Time) treats viewing behavior as structured cognitive networks. To operationalize this framework, we developed a data-driven computational approach that integrates fixation coordinate transformation, K-means clustering, extremum point detection, and linear interpolation. These techniques identify regions of concentrated visual attention and define their spatial boundaries, allowing for the modeling of inter-regional relationships and cognitive organization among visual areas. An adaptive buffer zone method is further employed to quantify the strength of connections between regions and to delineate potential visual nodes and transition pathways. Three design-trained participants were invited to observe the same painting while performing a think-aloud task, with one participant selected for the detailed demonstration of the analytical process. The framework’s applicability across different viewers was validated through consistent structural patterns observed across all three participants, while simultaneously revealing individual differences in their visual exploration strategies. These findings demonstrate that the proposed framework provides a replicable and generalizable method for systematically analyzing viewing behavior across individuals, enabling rapid identification of both common patterns and individual differences in visual exploration. This approach opens new possibilities for discovering structural organization within visual exploration data and analyzing goal-directed viewing behaviors. Although this study focuses on method demonstration, it proposes a preliminary hypothesis that designers’ gaze structures are significantly more clustered and hierarchically organized than those of novices, providing a foundation for future confirmatory testing. Full article

Typo-morphology, as interpreted by the Italian School of Planning, provides an approach to investigate the relationship between built form and socio-cultural patterns in vernacular settlements. This study examines Harran, a heritage site in southeastern Türkiye known for its distinctive conic domed dwellings, to explore how typo-morphological analysis can inform culturally sensitive design and adaptive reuse approaches. Despite its historical significance and inclusion in the UNESCO tentative list, Harran faces insufficient documentation, fragmented governance, limited conservation, and increasing pressure from urbanization and natural disasters. Using multiple sources and fieldwork, the research reconstructs the morphological evolution of Harran through diachronic maps across compound, district, and town scales. Reflections from Alberobello, Italy, i.e., the sister city of Harran and a UNESCO-listed town with a similarly unique vernacular fabric, provide a comparative view to explore different heritage management approaches. Harran evolved through informal, culture-driven growth, whereas Alberobello followed a regulated path. While Alberobello benefits from planned development and institutional preservation, Harran faces partial abandonment and neglect. By positioning typo-morphology as a conceptual planning tool, this paper emphasizes the need for context-responsive, ethically grounded, and inclusive approaches to heritage planning and conservation. It argues for planning practices that are not only technically competent but also attuned to place-based knowledge, local identities, and the long-term sustainability of living heritage. Full article

Background/Objectives: Subtalar arthroereisis (STA) is a widely used surgical procedure for symptomatic pediatric flexible flatfoot. However, implant migration remains a concern due to its potential impact on long-term correction and complications. This study evaluated the migration pattern of STA implants and assessed long-term clinical and radiographic outcomes. Methods: This retrospective cohort study included 47 feet from children aged 8–13 years who underwent STA with adjunctive soft tissue procedures between 2014 and 2018, following ≥6 months of failed conservative treatment, with a minimum follow-up of 5 years. Exclusion criteria included neuromuscular or rigid flatfoot. Weight-bearing radiographs assessed anteroposterior (AP) and lateral Meary’s angles, reflecting forefoot-to-hindfoot alignment, and calcaneal pitch, indicative of longitudinal arch height. Implant migration was recorded and clinical outcomes were measured by the American Orthopedic Foot and Ankle Society (AOFAS) score. Measurements were recorded preoperatively, immediately postoperatively, and at 1 month, 3 months, 6 months, 1 year, and 5 years. Results: Radiographic correction was significant and sustained at 5 years. The AP Meary’s angle improved from 13.09° to 5.26° at 1 month and 6.69° at 5 years (p < 0.001); lateral Meary’s angle from 9.77° to 4.06° and 4.88° (p < 0.001); and calcaneal pitch from 14.52° to 16.87° and 16.89° (p < 0.001), respectively. AOFAS scores increased from 67.52 to 90.86 at 1 month and 96.33 at 5 years (p < 0.001). Implant migration peaked within the first postoperative month (mean: 3.2 mm on ankle AP view; 3.0 mm on foot AP view) and stabilized thereafter. Four cases of complications included implant dislodgement, subsidence, and persistent sinus tarsi tenderness, which were successfully resolved after appropriate management. No recurrence of deformity was observed. Conclusions: STA implant migration is most pronounced during the first month, likely due to physiological settling as the foot adapts to altered biomechanics. With appropriate implant selection, technique, and follow-up, migration does not compromise long-term correction or outcomes. In general, symptomatic cases can often be managed conservatively prior to implant removal. Full article

The evaluation of climate change effects on airport infrastructures is important to maintain safety and flexibility in air travel operations. Airports are particularly vulnerable to extreme weather events and temperature fluctuations, which can disrupt operations, compromise passenger safety, and cause economic losses. Issues such as flooded runways and the disruption of power supplies highlight the need for strong adaptation strategies. The study focuses on the application of the high-resolution regional model COSMO-CLM to assess climate change impacts on Roma Fiumicino airport (Italy) under the IPCC RCP8.5 scenario. The complex topography of Italy requires fine-scale simulation to catch localized climate dynamics. By employing advanced statistical methods, such as fractal analysis, this research aims to increase an understanding of climate change and improve the model prediction capability. The findings provide valuable insights for designing resilient airport infrastructures and updating operational protocols in view of evolving climate risks. A consistent increase in daily temperatures is projected, along with a modest positive trend in annual precipitation. The use of advanced statistical methods revealed insights into the fractal dimensions and frequency components of climate variables, showing an increasing complexity and variability of future climatic patterns. Full article

Informal groundwater markets, where farmers with wells sell surplus water to neighbors, are a widespread adaptive response to water scarcity, particularly in South Asia where they are most prevalent and well-documented. This review (1990–2025) examines the evolving patterns of these markets by synthesizing global literature and viewing them through the lens of three transformative trends: energy transition (especially solar pumps), digital innovations (e.g., blockchain and IoT), and new policy pathways. We synthesize literature to evaluate market structures, contract forms, efficiency and equity outcomes, environmental impacts, and the influence of energy policies and digital tools. The review assesses whether these informal trades fulfill their promise of enhancing water productivity and equity or if new challenges are creating pitfalls. Key objectives include documenting historical evolution, analyzing market performance, discussing externalities like aquifer depletion, examining policy interactions, reviewing digital pilots, exploring social inclusion, comparing governance frameworks, identifying research gaps linked to SDGs, and proposing a policy roadmap for harnessing benefits while ensuring sustainability. Full article

Digital transformation (DT) has become a critical focus in both academia and industry. However, its rapid evolution complicates our understanding of its core concepts and developmental patterns. Understanding the development path of DT is crucial for both scholars and practitioners because it provides a structured view of how the field has progressed over time. This study employs main path analysis (MPA), a citation-based scientometric method, to systematically review and trace the intellectual trajectory of DT research over the past 30 years. Drawing on 1790 academic articles from the Web of Science database, the study identifies key influential works and maps the primary citation paths that shape the field. The analysis reveals three major developmental phases of DT research—engagement, enablement, and enhancement—each characterized by distinct thematic and conceptual shifts. Furthermore, five emerging research trends are uncovered: reinventing digital innovation affordance, value-creation paths of DT, synergistic DT with business and management practices, disciplinary boundaries of DT, and digital leadership. Understanding the intellectual trajectory and emerging trends of DT helps practitioners anticipate technological shifts and align transformation efforts, guiding decision-makers in effectively managing their DT processes. Also, these findings provide a structured framework for understanding the evolution of DT and offer valuable directions for future research in information systems and digital innovation. Full article

With the widespread adoption of mobile devices and the increasing availability of user trajectory data, accurately predicting the next location a user will visit has become a pivotal task in location-based services. Despite recent progress, existing methods often fail to effectively disentangle the diverse and entangled behavioral signals, such as collaborative user preferences, global transition mobility patterns, and geographical influences, embedded in user trajectories. To address these challenges, we propose a novel framework named Multi-Perspective Hypergraphs with Contrastive Learning (MPHCL), which explicitly captures and disentangles user preferences from three complementary perspectives. Specifically, MPHCL constructs a global transition flow graph and two specialized hypergraphs: a collective preference hypergraph to model collaborative check-in behavior and a geospatial-context hypergraph to reflect geographical proximity relationships. A unified hypergraph representation learning network is developed to preserve semantic independence across views through a dual propagation mechanism. Furthermore, we introduce a cross-view contrastive learning strategy that aligns multi-perspective embeddings by maximizing agreement between corresponding user and location representations across views while enhancing discriminability through negative sampling. Extensive experiments conducted on two real-world datasets demonstrate that MPHCL consistently outperforms state-of-the-art baselines. These results validate the effectiveness of our multi-perspective learning paradigm for next-location prediction. Full article

This paper explores the application of deep learning techniques for evaluating and classifying deadlift weightlifting techniques from video input. The increasing popularity of weightlifting, coupled with the injury risks associated with improper form, has heightened interest in this area of research. To address these concerns, we developed an application designed to classify three distinct styles of deadlifts: conventional, Romanian, and sumo. In addition to style classification, our application identifies common mistakes such as a rounded back, overextension at the top of the lift, and premature lifting of the hips in relation to the back. To build our model, we created a comprehensive custom dataset comprising lateral-view videos of lifters performing deadlifts, which we meticulously annotated to ensure accuracy. We adapted the MoveNet model to track keypoints on the lifter’s joints, which effectively represented their motion patterns. These keypoints not only served as visualization aids in the training of Convolutional Neural Networks (CNNs) but also acted as the primary features for Long Short-Term Memory (LSTM) models, both of which we employed to classify the various deadlift techniques. Our experimental results showed that both models achieved impressive F1-scores, reaching up to 0.99 for style and 1.00 for execution form classifications on the test dataset. Furthermore, we designed an application that integrates keypoint visualizations with motion pattern classifications. This tool provides users with valuable feedback on their performance and includes a replay feature for self-assessment, helping lifters refine their technique and reduce the risk of injury. Full article