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The territorial development of the city of Brno during the 19th–21st centuries meant not only the growth of built-up areas (residential, industrial, commercial), but also the absorbing of segments of the ancient rural agricultural landscape. Within the current borders of the city of Brno, a number of green areas have been preserved, which have spontaneously developed from the original agricultural landscape, without being the result of urban planning. In half of the cases (17 out of a total of 34), they have still preserved the traditional small-scale division of land. Among the 10 medium-sized Moravian cities (between 30,000 and 400,000 inhabitants) in the historical region of Moravia in the east of the Czech Republic, the presence of 34 remnants of the ancient rural landscape in the city of Brno is quite exceptional (in Ostrava only 1; in other cities 0). The subject of the research is the inventory of such segments within the city borders and an attempt to explain their location in the city, state, focusing on the role of natural factors, land ownership and personal and recreational interests of residents. Segments of the ancient rural cultural landscape were identified by comparing the current landscape on aerial photographs with the landscape image on cadastral maps from the 1820s–1830s. Additional data on their natural and cultural properties were obtained through archival and field research. The segments were classified according to their degree of preservation and forms of threat. The results show that the remains of the ancient rural cultural landscape in the city of Brno have generally been preserved in locations that, due to the slope of the slopes, unsuitable building subsoil and poor soil, but locally on warm southern slopes, were not suitable for construction for the time being. Urban gardening contributes to their preservation and these areas are part of the city’s greenery. However, urban gardening also contributes to the destruction of these remnants. In 17 cases, the land was completely re-divided, built up with recreational facilities and overgrown with trees due to poor care. Another 17 locations are threatened by this process due to ignorance of their historical value, although this is essentially a positive development in terms of benefits for the city’s residents—land users. Although the Master Plan of the city of Brno foresees the existence of garden colonies in the future, it does not address the importance of the best-preserved segments as historical heritage. Community agriculture can play a positive role in maintaining segments of rural heritage within the city. Full article

In the modern era, demand forecasting enhances the decision-making tasks of industries for controlling production planning and reducing inventory costs. However, the dynamic nature of the fashion and apparel retail industry necessitates precise demand forecasting to optimize supply chain operations and meet customer expectations. Consequently, this research proposes the Formicary Zebra Optimization-Based Distributed Attention-Guided Convolutional Recurrent Neural Network (FZ-DACR) model for improving the demand forecasting. In the proposed approach, the combination of the Formicary Zebra Optimization and Distributed Attention mechanism enabled deep learning architectures to assist in capturing the complex patterns of the retail sales data. Specifically, the neural networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), facilitate extracting the local features and temporal dependencies to analyze the volatile demand patterns. Furthermore, the proposed model integrates visual and textual data to enhance forecasting accuracy. By leveraging the adaptive optimization capabilities of the Formicary Zebra Algorithm, the proposed model effectively extracts features from product images and historical sales data while addressing the complexities of volatile demand patterns. Based on extensive experimental analysis of the proposed model using diverse datasets, the FZ-DACR model achieves superior performance, with minimum error values including MAE of 1.34, MSE of 4.7, RMS of 2.17, and R² of 93.3% using the DRESS dataset. Moreover, the findings highlight the ability of the proposed model in managing the fluctuating trends and supporting inventory and pricing strategies effectively. This innovative approach has significant implications for retailers, enabling more agile supply chains and improved decision making in a highly competitive market. Full article

Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) techniques have received rapid advance in recent years, and the Multi-temporal InSAR (MT-InSAR) has been widely applied in various earth observations. Distributed scatterer (DS) InSAR is one of the most advanced MT-InSAR methods, and has overcome the limitation of the lack of enough measurement points in the low coherent regions for traditional methods. While the Joint-Scatterer InSAR (JS-InSAR) is the extension of DS InSAR method, which exploited the overall information of Joint Scatterers to carry out DS identification and phase optimization. And it can avoid the inaccuracy caused by the offset errors between scatterers in complex terrain areas. However, the intensive computation and low efficiency have severely restricted the application of JS-InSAR, especially when dealing with massive and long historical SAR images. As the sequential estimator has proven to successfully improve the efficiency of MT-InAR and obtain near-time deformation time series, in this work, we proposed the sequential-based JS-InSAR (S-JSInSAR) method with flexible batches. This method has adaptively divided large single look complex (SLC) stack into different batches with flexible number and certain overlaps. Then, the JS-InSAR processing is performed on each batch, respectively, and these estimated results are integrated into the final deformation time series based on the connection mode. Thus, S-JSInSAR can efficiently process large InSAR dataset, and mitigate the decorrelation effect caused by long temporal baselines. To demonstrate the effectiveness of the S-JSInSAR, a multi-year of 145 Sentinel-1 ascending SAR images in Tangshan, China, were collected to estimate the long deformation time series. And the results compared with other methods have shown the processing time has substantially decreased without the loss of deformation accuracy, and obtain deformation spatial distribution with more details in local regions, which have well validated the efficiency and reliability of the proposed method. Full article

Abdominal radiography is commonly used as an initial diagnostic tool in dogs and cats with gastrointestinal (GI) signs. Historically, abdominal radiographs were considered unreliable for detecting GI masses, with detection rates below 50%. The purpose of this retrospective, case–control study was to determine the accuracy of abdominal radiographs in identifying the presence and location of GI masses and to assess the influence of the reviewer experience. Radiographs from 114 dogs and 111 cats were reviewed by two board-certified radiologists, one first year radiology resident, and one rotating intern. Patients were categorized into three groups: animals with a GI mass greater than 2 cm (dogs n = 44; cats n = 41), animals with a normal abdomen (both n = 50), and animals with abdominal disease but no GI mass (both n = 20). Reviewers demonstrated high specificity but low sensitivity for both detection and localization of GI masses. Sensitivity for detecting a mass ranged from 34 to 64% in dogs and 36 to 71% in cats; specificity exceeded 87% in dogs and 92% in cats. Sensitivity for location identification ranged from 9 to 58% in dogs and 21 to 68% in cats; specificity exceeded 76% in dogs and 81% in cats. No statistically significant differences in detection rates were found among reviewers. The accuracy of plain digital radiography for the detection of gastrointestinal masses in dogs (75%) and cats (81%) is better than previously reported film radiography but remains inferior to other imaging modalities. However, its high specificity supports its clinical utility in ruling out gastrointestinal masses. Full article

The Extended Digital Narrative (XDN) research project explores how experimental creative practice with emerging technologies generates critical insights into algorithmic narrativity—the intersection of human narrative understanding and computational data processing. This article presents five case studies demonstrating that direct engagement with AI and Extended Reality platforms is essential for humanities research on new genres of digital storytelling. Lina Harder’s Hedy Lamar Chatbot examines how generative AI chatbots construct historical personas, revealing biases in training data and platform constraints. Scott Rettberg’s Republicans in Love investigates text-to-image generation as a writing environment for political satire, documenting rapid changes in AI aesthetics and content moderation. David Jhave Johnston’s Messages to Humanity demonstrates how Runway’s Act-One enables solo filmmaking, collapsing traditional production hierarchies. Haoyuan Tang’s video game project reframes LLM integration by prioritizing player actions over dialogue, challenging assumptions about AI’s role in interactive narratives. Sérgio Galvão Roxo’s Her Name Was Gisberta employs Virtual Reality for social education against transphobia, utilizing perspective-taking techniques for empathy development. These projects demonstrate that practice-based research is not merely artistic production but a vital methodology for understanding how AI and XR platforms shape—and are shaped by—human narrative capacities. Full article

Chatbots have emerged as a promising interface for facilitating access to complex datasets, allowing users to pose questions in natural language rather than relying on specialized technical workflows. At the same time, remote sensing has transformed archaeological practice by producing vast amounts of imagery from LiDAR, drones, and satellites. While these advances have created unprecedented opportunities for discovery, they also pose significant challenges due to the scale, heterogeneity, and interpretative demands of the data. In related scientific domains, multimodal conversational systems capable of integrating natural language interaction with image-based analysis have advanced rapidly, supported by a growing body of survey and review literature documenting their architectures, datasets, and applications across multiple fields. By contrast, archaeological applications of chatbots remain limited to text-based prototypes, primarily focused on education, cultural heritage mediation or archival search. This review synthesizes the historical development of chatbots, examines their current use in remote sensing, and evaluates the barriers to adapting such systems for archaeology. Four major challenges are identified: data scale and heterogeneity, scarcity of training datasets, computational costs, and uncertainties around usability and adoption. By comparing experiences across domains, this review highlights both the opportunities and the limitations of integrating conversational AI into archaeological workflows. The central conclusion is that domain-specific adaptation is essential if multimodal chatbots are to become effective analytical partners in archaeology. Full article

The study systematizes the current state of knowledge on the morphological diversity of dispersed-phase particles in the most widely used lubricating greases, encompassing their shape, size, surface structure, and overall geometry. The extensive discussion of the diversity of grease thickener particles is supplemented with their microscopic images. Particular emphasis is placed on the influence of thickener particle morphology, the degree of their aggregation, and interparticle interactions on the rheological, mechanical, and tribological properties of grease formulations. The paper reviews recent advances in investigations of grease microstructure, with special emphasis on imaging techniques—ranging from dark-field imaging, through scanning electron microscopy, to atomic force microscopy—together with a discussion of their advantages and limitations in the assessment of particle morphology. A significant part of the work is devoted to rheological studies, which enable an indirect evaluation of the structural state of grease by analyzing its response to shear and deformation, thereby allowing inferences to be drawn about the micro- and mesostructure of lubricating greases. The historical development of rheological research on lubricating greases is also presented—from simple flow models, through the introduction of the concepts of viscoelasticity and structural rheology, to modern experimental and modeling approaches—highlighting the close relationships between rheological properties and thickener structure, manufacturing processes, composition, and in-service behavior of lubricating greases, particularly in tribological applications. It is indicated that contemporary studies confirm the feasibility of tailoring the microstructure of grease thickeners to specific lubrication conditions, as their characteristics fundamentally determine the rheological and tribological properties of the entire system. Full article

The photovoltaic field has seen significant development in recent years, with continuously expanding installation capacity and increasing grid integration. However, due to the intermittency of solar energy and meteorological variability, PV output power poses serious challenges to grid security and dispatch reliability. Traditional forecasting methods largely rely on modeling historical power and meteorological data, often neglecting the consideration of cloud movement, which constrains further improvement in prediction accuracy. To enhance prediction accuracy and model interpretability, this paper proposes a dual-branch attention-based PV power prediction model that integrates physical features from ground-based cloud images. Regarding input features, a cloud segmentation model is constructed based on the vision foundation model DINO encoder and an improved U-Net decoder to obtain cloud cover information. Based on deep feature point detection and an attention matching mechanism, cloud motion vectors are calculated to extract cloud motion speed and direction features. For feature processing, feature attention and temporal attention mechanisms are introduced, enabling the model to learn key meteorological factors and critical historical time steps. Structurally, a parallel architecture consisting of a linear branch and a nonlinear branch is adopted. A context-aware fusion module adaptively combines the prediction results from both branches, achieving collaborative modeling of linear trends and nonlinear fluctuations. Comparative experiments were conducted using two years of engineering data. Experimental results demonstrate that the proposed model outperforms the benchmarks across multiple metrics, validating the predictive advantages of the dual-branch structure that integrates physical features under complex weather conditions. Full article

Each year, around 23 million miscarriages occur worldwide, which have a substantial emotional impact on parents, and impose significant societal costs. While medical care accounts for most expenses, work productivity loss contributes significantly. Addressing underlying causes of miscarriage could improve parents’ mental health and potentially their economic impact. In most countries, investigations into miscarriage causes are only recommended after recurrent cases, focusing mainly on maternal factors. Fetal and placental tissue are rarely examined, as current guidelines do not advise routine genetic analyses of pregnancy tissue, because the impact of further clinical decision making and individual prognosis is unclear. However, this leaves over 90% of all miscarriage cases unexplained and highlights the need for alternative methods. We therefore conducted a narrative review on genetic analysis, autopsy, and imaging of products of conception (POC). Karyotyping, QF-PCR, SNP array, and aCGH were reviewed in different research settings, with QF-PCR being the most cost-effective, while obtaining the highest technical success rate. Karyotyping, historically being considered the gold standard for POC examination, was the least promising. Post-mortem imaging techniques including post-mortem ultrasound (PMUS), ultra-high-field magnetic resonance imaging (UHF-MRI), and microfocus computed tomography (micro-CT) show promising diagnostic capabilities in miscarriages, with micro-CT achieving the highest cost-effective performance. In conclusion, current guidelines do not recommend diagnostic testing for most cases, leaving the majority unexplained. Although genetic and imaging techniques show promising diagnostic potential, they should not yet be implemented in routine clinical care and require thorough evaluation within research settings—assessing not only diagnostic and psychosocial outcomes but also economic implications. Full article

This study explores how classical painting techniques, particularly those rooted in the Renaissance tradition, can produce illusions of depth that vary with the viewer’s position. Focusing on a work rich in soft shading and subtle tonal transitions, we investigate how movement across the frontal plane influences the perception of spatial structure. A sequence of high-resolution photographs was taken from slightly offset viewpoints, simulating natural viewer motion. Using image alignment and pixel-wise difference mapping, we reveal perceptual shifts that suggest the presence of latent three-dimensional cues embedded within the painted surface. The findings offer visual and empirical support for concepts such as and dynamic engagement, where depth is constructed not solely by the image, but by the interaction between the artwork and the observer. Our approach demonstrates how digital analysis can enrich art historical interpretation, offering new insight into how still images can evoke the illusion of spatial presence. Full article

Background: Stage B heart failure (SBHF) increases the risk of symptomatic HF. Current guideline criteria for SBHF lack sex and ethnic thresholding and cardiac magnetic resonance (CMR) imaging cut-offs. We aimed to assess the prevalence of SBHF in a large cohort of people with type 2 diabetes (T2D) and healthy controls and propose a refined CMR definition for SBHF. Methods: Sex- and ethnic-specific thresholds for imaging criteria were derived from 373 healthy controls, who underwent CMR cine imaging. The current definition for SBHF and refined criteria was applied to our prospectively recruited and intensively phenotyped cohort of asymptomatic people with T2D and no evidence of cardiovascular disease. The prevalence of SBHF by different definitions was calculated and patient characteristics, including exercise capacity, were compared between those classified as Stage A vs. B HF. Finally, the refined criteria were also applied to the following two historical cohorts with symptomatic cardiovascular disease: severe aortic stenosis (AS n = 70) and HF with preserved ejection fraction (HFpEF n = 136). Results: A total of 423 people with T2D and a subset of 102 healthy controls who underwent echocardiography were prospectively recruited. Current guideline criteria classified 91% of those with T2D and 69% of the healthy controls as SBHF, suggesting a lack of specificity. Applying derived sex- and ethnicity-specific thresholds, combining echo and CMR measures, the prevalence of SBHF was reduced to 30% in those with T2D. Using the refined definition, those with Stage B HF had lower exercise capacity than those with Stage A HF (percentage predicted maximal oxygen consumption 81 ± 16% vs. 91 ± 20%, p < 0.001). Applying the refined definition to symptomatic AS and HFpEF participants classified 89% and 85% with abnormal cardiac remodelling. Conclusion: Current guideline criteria for SBHF are non-specific and likely of limited value in clinical practice. Refining these criteria with sex- and ethnic-specific thresholds may improve identification of those at risk of developing symptomatic disease. Further research is required to validate these criteria. Full article

A cultural heritage augmented reality system overlays virtual information onto real-world heritage sites, enabling intuitive exploration and interpretation with spatial and temporal contexts. This study presents the design and implementation of a cognitive Mobile Augmented Reality (MAR) system that integrates KCHDM-based ontologies with large language models (LLMs) to facilitate intelligent exploration of urban heritage. While conventional AR guides often rely on static data, our system introduces a Semantic Retrieval-Augmented Generation (RAG) pipeline anchored in a structured knowledge base modeled after the Korean Cultural Heritage Data Model (KCHDM). This architecture enables the LLM to perform dynamic contextual reasoning, transforming heritage data into adaptive question-answering (Q&A) and interactive knowledge-testing quizzes that are precisely grounded in both historical and spatial contexts. The system supports on-site AR exploration and map-based remote exploration to ensure robust usability and precise spatial alignment of virtual content. To deliver a rich, multisensory experience, the system provides multimodal outputs, integrating text, images, models, and audio narration. Furthermore, the integration of a knowledge sharing repository allows users to review and learn from others’ inquires. This ontology-driven LLM-integrated MAR design enhances semantic accuracy and contextual relevance, demonstrating the potential of MAR for socially enriched urban heritage experiences. Full article

Carpal tunnel syndrome (CTS) is the most common peripheral nerve entrapment disorder, with a lifetime prevalence estimated at approximately 10%. This narrative review explores the historical evolution, current management strategies, and emerging trends in CTS diagnosis and treatment. Early recognition of CTS led to the development of conservative interventions, including splinting, corticosteroid injections, and physical therapy, aimed at alleviating median nerve compression and associated symptoms. The advent of open carpal tunnel release established surgery as the definitive treatment for moderate-to-severe CTS, with subsequent refinements—such as mini-open and endoscopic techniques—focused on minimizing tissue trauma and expediting recovery. Comparative studies demonstrate similar long-term efficacy between surgical modalities, though endoscopic approaches often provide faster short-term recovery. Advances in diagnostic imaging, including high-resolution ultrasound, have improved early detection and dynamic assessment of median nerve compression. Emerging therapies, such as regenerative biologics, neuromobilization, and minimally invasive surgical innovations, offer promising adjuncts to current care. Despite substantial progress, further research is needed to clarify optimal patient selection, refine minimally invasive techniques, and explore regenerative interventions. This review underscores the importance of individualized, evidence-based, and patient-centered approaches to CTS management, integrating both established and emerging strategies to optimize functional outcomes and quality of life. Full article

Vista landscapes of historic cities embody unique spatial order and cultural memory, and the scientific quantification of their visual quality presents a common challenge for both heritage conservation and urban renewal. Focusing on the Beijing Central Axis, this study integrates VR panoramic technology with the SBE-SD evaluation method to develop a visual quality assessment framework suitable for vista landscapes of historic cities, systematically evaluating sectional differences in scenic beauty and identifying their key influencing factors. Thirteen typical viewing places and 17 assessment points were selected, and panoramic images were captured at each point. The evaluation framework comprising 3 first-level factors, 11 secondary factors, and 24 third-level factors was established, and a corresponding scoring table was designed through which students from related disciplines were recruited to conduct the evaluation. After obtaining valid data, scenic beauty values and landscape factor scores were analyzed, followed by correlation tests and backward stepwise regression. The results show the following: (1) The scenic beauty of the vista landscapes along the Central Axis shows sectional differentiation, with the middle section achieving the highest scenic beauty value, followed by the northern section, with the southern section scoring the lowest; specifically, Wanchunting Pavilion South scored the highest, while Tianqiao Bridge scored the lowest. (2) In terms of landscape factor scores, within spatial form, color scored the highest, followed by texture and scale, with volume scoring the lowest; within marginal profile, integrity scored higher than visual dominance; within visual structure, visual organization scored the highest, followed by visual patches, with visual hierarchy scoring the lowest. (3) Regression analysis identified six key influencing factors, ranked in descending order of significance as follows: color coordination degree of traditional buildings, spatial openness, spatial symmetry, hierarchy sense of buildings, texture regularity of traditional buildings, and visual dominance of historical landmark buildings. This study establishes a quantitative assessment pathway that connects subjective perception and objective environment with a replicable process, providing methodological support for the refined conservation and optimization of vista landscapes in historic cities while demonstrating the application potential of VR panoramic technology in urban landscape evaluation. Full article

Urban perception research has long focused on global metropolises, but has overlooked many cities with complex functions and spatial structures, resulting in insufficient universality of existing theories when facing diverse urban contexts. This study constructed an analytical framework that integrates street scene images and interpretable machine learning. Taking Zhengzhou City as the research object, it extracted street visual elements based on deep learning technology and systematically analyzed the formation mechanism of multi-dimensional urban perception by combining the LightGBM model and SHAP method. The main findings of the research are as follows: (1) The urban perception of Zhengzhou City shows a significant east–west difference with Zhongzhou Avenue as the boundary. Positive perceptions such as safety and vitality are concentrated in the central business district and historical districts, while negative perceptions are more common in the urban fringe areas with chaotic built environments and single functions. (2) The visibility of greenery, the openness of the sky and the continuity of the building interface are identified as key visual elements affecting perception, and their directions and intensifies of action show significant differences due to different perception dimensions. (3) The influence of visual elements on perception has a complex mechanism of action. For instance, the promoting effect of greenery visibility on beauty perception tends to level off after reaching a certain threshold. The research results of this study can provide quantitative basis and strategic reference for the improvement in urban space quality and humanized street design. Full article