Search Results (3,062)

Tourism performance often depends on the joint provision of built (“hard”) and environmental (“green”) infrastructure, yet their combined effects are not well established. Using official data for Saudi Arabia’s 13 regions (2023–2024), this study constructs composite hard and green indices, estimates ordinary least squares models with heteroskedasticity-consistent inference, and probes spatial heterogeneity using geographically weighted regression (exploratory) alongside k-means/hierarchical clustering. Hard infrastructure is the strongest and most consistent correlate of overnight visitors and spending, whereas green infrastructure exhibits non-positive marginal effects over the observed range of hard capacity; a negative, statistically significant Hard × Green interaction indicates diminishing returns to greening as built capacity increases. Clustering differentiates metropolitan hubs from nature-oriented regions, underscoring place-specific policy needs. Practically, results support sequencing prioritizing foundational access and basic accommodation in under-served regions, quality upgrades and public-realm enhancement in mature centers, and targeted green interventions where marginal gains are greatest. Key limitations (cross-sectional design; coarse green metrics) motivate richer environmental indicators and longitudinal data to clarify dynamics and thresholds over time. Full article

The raster is the most common type of spatio-temporal data, and it can be either regularly or irregularly spaced. Spatio-temporal prediction on regular raster data is crucial for modelling and understanding dynamics in disparate realms, such as environment, traffic, astronomy, remote sensing, gaming and video processing, to name a few. Historically, statistical and classical machine learning methods have been used to model spatio-temporal data, and, in recent years, deep learning has shown outstanding results in regular raster spatio-temporal prediction. This work provides a self-contained review about effective deep learning methods for the prediction of regular raster spatio-temporal data. Each deep learning technique is described in detail, underlining its advantages and drawbacks. Finally, a discussion of relevant aspects and further developments in deep learning for regular raster spatio-temporal prediction is presented. Full article

Lately, there has been a notable surge in the use of AI-driven dialogue systems like ChatGPT-3.5 within the realm of education. Understanding the factors that are associated with student engagement in these digital platforms is crucial for maximizing their potential and long-term efficacy. This study aims to systematically identify the key drivers behind university students’ loyalty to ChatGPT. Data gathered from university participants was analyzed using structural equation modeling. The findings indicate that novelty value is positively associated with both task attraction and hedonic value. Perceived intelligence shows significant associations with knowledge acquisition, task attraction, and hedonic value. Moreover, knowledge acquisition is positively related to task attraction and hedonic value, while creepiness is negatively related to them. Both task attraction and hedonic value demonstrate significant relationships with satisfaction and loyalty, with trust also positively associated with satisfaction. These insights provide a clearer understanding of what motivates university students to engage with AI conversational platforms like ChatGPT. This information is invaluable for stakeholders aiming to augment the adoption and effective use of such tools in educational contexts. Full article

Artificial intelligence (AI), as an emerging interdisciplinary field dedicated to simulating and extending human intelligence, is increasingly integrating into the domain of infectious disease medicine with unprecedented depth and breadth. This narrative review is based on a systematic literature search in databases such as PubMed and Web of Science for relevant studies published between 2018 and 2025, with the aim of synthesizing the current landscape. It demonstrates transformative potential, particularly in the realm of diagnostic assistance. Confronting global challenges such as pandemic control, emerging infectious diseases, and antimicrobial resistance, AI technologies offer innovative solutions to these pressing issues. Leveraging its robust capabilities in data mining, pattern recognition, and predictive analytics, AI enhances diagnostic efficiency and accuracy, enables real-time monitoring, and facilitates the early detection and intervention of outbreaks. This narrative review systematically examines the application scenarios of AI within infectious disease diagnostics, based on an analysis of recent literature. It highlights significant technological advances and demonstrated practical outcomes related to high-throughput sequencing (HTS) for pathogen surveillance, AI-driven analysis of digital and radiological images, and AI-enhanced point-of-care testing (POCT). Simultaneously, the review critically analyzes the key challenges and limitations hindering the clinical translation of current AI-based diagnostic technologies. These obstacles include data scarcity and quality constraints, limitations in model generalizability, economic and administrative burdens, as well as regulatory and integration barriers. By synthesizing existing research findings and cataloging essential data resources, this review aims to establish a valuable reference framework to guide future in-depth research, from model development and data sourcing to clinical validation and standardization of AI-assisted infectious disease diagnostics. Full article

Saline–alkali soils represent a significant reserve of arable land, playing a vital role in ensuring national food security. Given that saline–alkali soil has low soil organic matter (SOM) and soil nutrient contents, and that soil quality degradation poses a threat to regional high-quality agricultural development and ecological balance, this study took coastal saline–alkali land as a case study. It adopted the extreme gradient boosting (XGB) model optimized by the tree-structured Parzen estimator (TPE) algorithm, combined with in situ hyperspectral (ISH) and spaceborne hyperspectral (SBH) data, to predict and map soil organic matter and four soil nutrients: alkali nitrogen (AN), available phosphorus (AP), and available potassium (AK). From the research outputs, one can deduce that superior predictive efficacy is exhibited by the TPE-XGB construct, employing in situ hyperspectral datasets. Among these, available phosphorus (R² = 0.67) exhibits the highest prediction accuracy, followed by organic matter (R² = 0.65), alkali-hydrolyzable nitrogen (R² = 0.56), and available potassium (R² = 0.51). In addition, the spatial continuity mapping results based on spaceborne hyperspectral data show that SOM, AN, AP, and AK in soil nutrients in the study area are concentrated in the northern, eastern, southern, and riverbank and estuarine delta areas, respectively. The variability of soil nutrients from large to small is phosphorus, potassium, nitrogen, and organic matter. The SHAP (SHapley Additive exPlanations) analysis results reveal that the bands with the greatest contribution to the fitting of SOM, AN, AP, and AK are 612 nm, 571 nm, 1493 nm, and 1308 nm, respectively. Extending into realms of hierarchical partitioning (HP) and variation partitioning (VP), it is discerned that climatic factors (CLI) alongside vegetative aspects (VEG) wield dominant influence upon the spatial differentiation manifest in nutrients. Meanwhile, comparatively diminished are the contributions possessed by terrain (TER) and soil property (SOIL). In summary, this study effectively assessed the significant variation patterns of soil nutrient distribution in coastal saline–alkali soils using the TPE-XGB model, providing scientific basis for the sustainable advancement of agricultural development in saline–alkali coastal regions. Full article

Biometric authentication, such as facial recognition and fingerprint scanning, is now standard on mobile devices, offering secure and convenient access. However, the processing of biometric data is tightly regulated under the European Union’s General Data Protection Regulation (GDPR), where such data qualifies as “special category” personal data when used for uniquely identifying individuals. Compliance requires meeting strict conditions, including explicit consent and data protection by design. Passkeys, the modern name for FIDO2-based authentication credentials developed by the FIDO Alliance, enable passwordless login using public key cryptography. Its “match-on-device” architecture stores biometric data locally in secure hardware (e.g., Android’s Trusted Execution Environment, Apple’s Secure Enclave), potentially reducing the regulatory obligations associated with cloud-based biometric processing. This paper examines how Passkeys are implemented on Android and iOS platforms and their differences in architecture, API access, and hardware design, and how those differences affect compliance with the GDPR. Through a comparative analysis, we evaluate the extent to which each platform supports local processing, data minimization, and user control—key principles under GDPR. We find that while both platforms implement strong local protections, differences in developer access, trust models, and biometric isolation can influence the effectiveness and regulatory exposure of Passkeys deployment. These differences have direct implications for privacy risk, legal compliance, and implementation choices by app developers and service providers. Our findings highlight the need for platform-aware design and regulatory interpretation in the deployment of biometric authentication technologies. This work can help inform stakeholders, policymakers, and legal experts in drafting robust privacy and ethical policies—not only in the realm of biometrics but across AI technologies more broadly. By understanding platform-level implications, future frameworks can better align technical design with regulatory compliance and ethical standards. Full article

In recent years, there has been an ever increasing interest in UxVs and the technology surrounding them. A more recent area of interest within the UxV ecosystem is the development of UxV swarms. In these systems, multiple UxVs synchronize, continuously exchange information, and operate as a cohesive unit. This evolution requires a higher level of autonomy, enhanced coordination, and more efficient communication channels. In this survey, we present relevant research on swarms of UxVs, always considering artificial intelligence (AI) as the key technological enabler for the swarm operations. We view the swarm from three distinct perspectives; these are intelligence-wise, communication-wise, and security-wise. Our main goal is to explore in which ways and to what extent AI has been integrated in these aspects. We aim to identify which of these aspects are the most researched and which need deeper investigation, the types of AI that are mainly used, and which types of vehicles are preferred. We then discuss the results of our work and present current limitations as well as areas of future research in the realm of UxVs, AI, swarm intelligence, communications, and security. Full article

As a major contributor to environmental pollution in recent years, solid waste has become an increasingly significant concern in the realm of sustainable development. Unmanned Aerial Vehicle (UAV) imagery, known for its high spatial resolution, has become a valuable data source for solid waste detection. However, manually interpreting solid waste in UAV images is inefficient, and object detection methods encounter serious challenges due to the patchy distribution, varied textures and colors, and fragmented edges of solid waste. In this study, we proposed an improved instance segmentation approach called Watershed Mask Network for Solid Waste (WMNet-SW) to accurately retrieve solid waste with precise edges from UAV images. This approach combined the well-established Mask R-CNN segmentation framework with the watershed transform edge detection algorithm. The benchmark Mask R-CNN was improved by optimizing the anchor size and Region of Interest (RoI) and integrating a new mask head of Layer Feature Aggregation (LFA) to initially detect solid waste. Subsequently, edges of the detected solid waste were precisely adjusted by overlaying the segments generated by the watershed transform algorithm. Experimental results show that WMNet-SW significantly enhances the performance of Mask R-CNN in solid waste retrieval, increasing the average precision from 36.91% to 58.10%, F1-score from 0.5 to 0.65, and AP from 63.04% to 64.42%. Furthermore, our method efficiently detects the details of solid waste edges, even overcoming the limitations of training Ground Truth (GT). This study provides a solution for retrieving solid waste with precise edges from UAV images, thereby contributing to the protection of the regional environment and ecosystem health. Full article

What does one learn about religion through a study of Syrian Christian family cultures? How do religion and family—both as historically shaped ideological frames and social classifiers—inflect each other in Syrian Christian articulations about their past and present? What do these inflections tell us about being Christian in Kerala and beyond? Does it offer a critique of religion and family as sui generis categories? Based on select examples of Syrian Christian articulations from digital family displays produced by family associations (or kudumbayogam), I will argue that the religion–family node (or more appropriately nexus) hovers over the muddle of social relations. On one hand, the triumph of religion as a state-cushioned, universal category separated from the realm of the social and the historical (or religion with a capital ‘R’) has meant a neat tucking away of Syrian Christian households under the rubric of a reified Christianity. Similarly, the invocation of the patrilineal, patriarchal family as a universal category bereft of specificities works in tandem with this ironed-out Christianity in Syrian Christian family cultures. On the other hand, beyond their function as easy explainers, the religion–family nexus includes particular details which complicate the universality of the categories. These details recover family and religion in their heterogeneous elements expressed in place-sensitive caste idioms. I argue that the ‘universal’ in family and religion is sustained by the ‘particular’. A dialectical process of differentiation and homogenisation is critical to the Syrian Christian embrace of the religion–family nexus. Full article

In the realm of image classification, the capsule network is a network topology that packs the extracted features into many capsules, performs sophisticated capsule screening using a dynamic routing mechanism, and finally recognizes that each capsule corresponds to a category feature. Compared with previous network topologies, the capsule network has more sophisticated operations, uses a large number of parameter matrices and vectors to express picture attributes, and has more powerful image classification capabilities. However, in the practical application field, the capsule network has always been constrained by the quantity of calculation produced by the complicated structure. In the face of basic datasets, it is prone to over-fitting and poor generalization and often cannot satisfy the high computational overhead when facing complex datasets. Based on the aforesaid problems, this research proposes a novel enhanced capsule network topology. The upgraded network boosts the feature extraction ability of the network by incorporating a multi-scale feature extraction module based on proprietary star structure convolution into the standard capsule network. At the same time, additional structural portions of the capsule network are changed, and a variety of optimization approaches such as dense connection, attention mechanism, and low-rank matrix operation are combined. Image classification studies are carried out on different datasets, and the novel structure suggested in this paper has good classification performance on CIFAR-10, CIFAR-100, and CUB datasets. At the same time, we also achieved 98.21% and 95.38% classification accuracy on two complicated datasets of skin cancer ISIC derived and Forged Face EXP. Full article

The background of the article refers to the diagnosis of focal liver lesions (FLLs) through contrast-enhanced ultrasound (CEUS) based on the integration of spatial and temporal information. Traditional computer-aided diagnosis (CAD) systems predominantly rely on static images, which limits the characterization of lesion dynamics. This study aims to assess the effectiveness of Transformer-based architectures in enhancing CAD performance within the realm of liver pathology. The methodology involved a systematic comparison of deep learning models for the analysis of CEUS images and videos. For image-based classification, a Hybrid Transformer Neural Network (HTNN) was employed. It combines Vision Transformer (ViT) modules with lightweight convolutional features. For video-based tasks, we evaluated a custom spatio-temporal Convolutional Neural Network (CNN), a CNN with Long Short-Term Memory (LSTM), and a Video Vision Transformer (ViViT). The experimental results show that the HTNN achieved an outstanding accuracy of 97.77% in classifying various types of FLLs, although it required manual selection of the region of interest (ROI). The video-based models produced accuracies of 83%, 88%, and 88%, respectively, without the need for ROI selection. In conclusion, the findings indicate that Transformer-based models exhibit high accuracy in CEUS-based liver diagnosis. This study highlights the potential of attention mechanisms to identify subtle inter-class differences, thereby reducing the reliance on manual intervention. Full article

In the context of public space research, numerous studies highlight its vital role in fostering public life and social interaction. With urbanization on the rise and most people living in cities, acknowledging public spaces, and especially public squares, as key components of the urban realm is more important than ever. The success of space is frequently determined by its capacity to meet human needs, a condition that, in turn, is largely contingent upon specific design qualities. Literature identifies key qualities such as inclusiveness, accessibility and connectivity, sociability, vitality, perceptual and esthetic satisfaction, and participatory characteristics. While many studies explore these factors, little attention has been given to whether users and designers assign equal importance to them. This research addresses the question: To what extent do experts’ and users’ perceptions converge regarding the variables that determine the success of public spaces? To explore this, the study applies MICMAC method structural analysis that prioritizes variables based on their interdependence and dependence. The method is used with both design experts and public space users. Findings reveal convergence in perceptions regarding key parameters; specifically, strong convergence is observed in the qualities of participation and vitality, followed by sociability and perceptual and esthetic satisfaction. Moreover, the expert group prioritizes parameters related to sociability, accessibility and connectivity, and inclusiveness, reflecting contemporary design principles aimed at creating equitable, easily accessible, and inclusive spaces. In contrast, the user group focuses more on the experiential and esthetic dimension of space, adding variables related to perceptual and esthetic satisfaction and vitality. The study aims to inform more user-responsive public space design by bridging gaps between expert and user perspectives. Full article

This article illuminates the cosmology of Raa Haqi (often called Dersim Alevism or Kurdish Alevism), a rarely examined strand within Alevi Studies. Existing scholarship’s emphasis on identity politics and sparse ethnography has left Raa Haqi’s mythological and cosmological dimensions underexplored. This paper approaches Raa Haqi through a dual authority framework: (1) Ocak lineages and Ocak–talip relations—sustained by kinship institutions like kirvelik, musahiplik, and communal rites such as the cem—and (2) jiares, non-human agents from the Batın realm that manifest in Zahir as sacred places, objects, and animals. Methodologically, I conduct a close, motif-based reading of folktales compiled by Caner Canerik (2019, Dersim Masalları I), treating them as ethnographic windows into living theology. The analysis shows that tales encode core principles—rızalık (mutual consent), ikrar (vow), sır (the secret knowledge), fasting and calendrical rites, ritual kinship, and moral economies involving humans, animals, and Batın beings. Dreams, metamorphosis, and jiare-centered orientations structure time–space, ethics, and authority beyond the Ocak, including in individual re-sacralizations of objects and sites. I conclude that these narratives do not merely reflect belief; they actively transmit, test, and renew Raa Haqi’s cosmological order, offering Alevi Studies a theory-grounded, source-proximate account of Kurdish Alevi mythic thought. Full article

In the context of digital school development, the leadership practices of school leadership teams play a significant role. If leadership teams want to enact leadership practices effectively, they require strong connections to the entire teaching staff as well as close contact with other key actors in the digital process. Since little is known about these connection patterns of school leadership teams, this study aims to uncover them. The aim is to provide practical advice to school administrators and schools regarding digital school development, and to derive concrete recommendations for action concerning their relationships and management. To this end, we examined the social networks of the teaching staff of 13 German secondary schools (N = 817 teachers) by asking all the teachers to complete a questionnaire about their contacts in relation to digital school development. We conducted a social network analysis and extracted various network metrics pertaining to the school leadership teams of these institutions, considering not only their integration within the overall network but also their connections with a pivotal stakeholder: the digital coordinator. To contextualize our findings, we compared the network metrics of the two different professional target groups using t-tests. The results reveal significant variability in the connection patterns of school leadership teams across different schools. Furthermore, our analysis indicates that digital coordinators consistently exhibit higher levels of connectedness within the realm of digital school development than the members of the school leadership teams. These findings highlight the importance of close collaboration between school leadership teams and the digital coordinator in order to advance digital school development. It is also suggested that school leadership teams should consider delegating more responsibilities to the digital coordinator, particularly those necessitating close collaboration with the teaching staff. Full article

The educational philosophy of the Nuns’ Buddhist Academy at Pushou Monastery, Mount Wutai, is based on the principles of “Hua Yan as the foundation, precepts as the practice, and Pure Land as the destination.” This philosophy draws upon Buddhist scriptures, integrating descriptions of the Pure Land practice found in the Avatamsaka Sūtra and the Amitābha Sūtra. This approach translates the textual teachings of Buddhist classics into real-life practice, expressing the concept of “the non-obstruction of principle and phenomenon” in the tangible activities of practitioners. It also allows for the experiential understanding of the spiritual realms revealed in the scriptures during theoretical learning and practice. The philosophy of the Nuns’ Academy embodies the practical emphasis of Chinese Buddhism, guiding all aspects of learning and practice. This paper argues that the pure land practice is living. In order to understand pure land practice, there should be a comprehensive viewpoint. It is needed to explore this way of practice through the analysis of textual analysis, figuring its root in Buddhis sūtra, as well as a sociological method to investigate its manifestation at the present society. Moreover, the spiritual dimension should not be neglected for a full-scale study. In this sense, the pure land school is living at present. Full article