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Agricultural greenhouses (AGs) are an important component of modern facility agriculture, and accurately mapping and dynamically monitoring their distribution are necessary for agricultural scientific management and planning. Semantic segmentation can be adopted for AG extraction from remote sensing images. However, the feature maps obtained by traditional deep convolutional neural network (DCNN)-based segmentation algorithms blur spatial details and insufficient attention is usually paid to contextual representation. Meanwhile, the maintenance of the original morphological characteristics, especially the boundaries, is still a challenge for precise identification of AGs. To alleviate these problems, this paper proposes a novel network called high-resolution boundary refined network (HBRNet). In this method, we design a new backbone with multiple paths based on HRNetV2 aiming to preserve high spatial resolution and improve feature extraction capability, in which the Pyramid Cross Channel Attention (PCCA) module is embedded to residual blocks to strengthen the interaction of multiscale information. Moreover, the Spatial Enhancement (SE) module is employed to integrate the contextual information of different scales. In addition, we introduce the Spatial Gradient Variation (SGV) unit in the Boundary Refined (BR) module to couple the segmentation task and boundary learning task, so that they can share latent high-level semantics and interact with each other, and combine this with the joint loss to refine the boundary. In our study, GaoFen-2 remote sensing images in Shouguang City, Shandong Province, China are selected to make the AG dataset. The experimental results show that HBRNet demonstrates a significant improvement in segmentation performance up to an IoU score of 94.89%, implying that this approach has advantages and potential for precise identification of AGs.

Extensive exposure to specific kinds of imagery tunes visual perception, enhancing recognition and interpretation abilities relevant to those stimuli (e.g. radiologists can rapidly extract important information from medical scans). For the first time, we tested whether specific visual expertise induced by professional training also affords domain-general perceptual advantages. Experts in medical image interpretation ( n  = 44; reporting radiographers, trainee radiologists, and certified radiologists) and a control group consisting of psychology and medical students ( n  = 107) responded to the Ebbinghaus, Ponzo, Müller-Lyer, and Shepard Tabletops visual illusions in forced-choice tasks. Our results show that medical image experts were significantly less susceptible to all illusions except for the Shepard Tabletops, demonstrating superior perceptual accuracy. These findings could possibly be attributed to a stronger local processing bias, a by-product of learning to focus on specific areas of interest by disregarding irrelevant context in their domain of expertise.

The purpose of this study was to objectively quantify the impact of implementing picture archiving and communication system-electronic medical record (PACS-EMR) integration on the time required to access data in the EMR and the frequency with which data are accessed by radiologists. Time to access a clinic note in the EMR was measured before and after integration with a stopwatch and compared by t test. An IRB-approved, HIPAA-compliant retrospective review of EMR access data from security audit logs was conducted for a 14-month period spanning the integration. Correlation of these data with report signatures identified the studies in which the radiologist accessed the EMR to obtain additional clinical data. Proportions of studies with EMR access were plotted and compared before and after integration using a chi-square test. Time to access the EMR decreased from 52 to 6 s (p < 0.001). Proportion of studies with EMR access increased from 36.7% (10,175/27,773) to 44.9% (10,843/24,153) after integration (p < 0.001). Integrating PACS and the EMR substantially decreases the time to access the EMR and is associated with a significant increase in the proportion of studies for which radiologists obtain additional clinical data.

In this article, the authors have proposed a Time Division Layered Context Integration approach that evaluates semantic information by integrating various contexts from heterogeneous sensors. It is important that the context integration approach accurately obtains semantics of large sensory data, because context integration makes a system decide to operate services by fusing plentiful, different context data from heterogeneous sensors in a large system. Thus, the authors have presented an approach that applies multiple time layers according to divided time intervals to improve the accuracy of the context integration. In the experimental result, they have proved that their approach has more accurate integration of contexts and adopts multimodal fusion methods to achieve robust results of context integration.

In modeling fractured reservoirs and the creation of a robust Static Conceptual Fracture Model, people must integrate many individual data sets from multiple disciplines. Creation of these data sets is best initiated and facilitated by a Fracture Study Champion. In the author's experience, this integration is best accomplished using 3D visualization. Most fractured reservoir modeling programs allow for multiple data sets to be co‐rendered in 3D with the ability to rapidly switch between alternative views of the data in real time. The author believes that this 3D visualization performed in a team integration context is the best and most efficient way to integrate the data and make high‐level correlations and interpretations. The Fracture Champion, and the person in charge of the computer modeling software should manage the session and the team members should drive the various displays and the direction of the integration.

This study presents a transnational comparative analysis of two UNESCO World Heritage complexes and their sculptural programmes: the Borobudur Temple Complex (Java, Indonesia; built c. 8th-9th centuries CE) and the Dazu Rock Carvings (Chongqing, China; major carving activity late 9th-13th centuries CE). Using qualitative methods that integrate stylistic, iconographic, and technical analyses based on visual and photographic surveys and published conservation studies, the paper examines how stone type, carving techniques, compositional strategies, and ritual frameworks shaped divergent sculptural languages. Results show that Borobudur's mandala-based architectural order and narrative bas-reliefs-carved in volcanic andesite and largely experienced frontally along a prescribed circumambulatory route-favour shallow, sequential narrative modelling that supports didactic pilgrimage. By contrast, Dazu's sandstone reliefs and near-full-round modelling enable deeper undercutting, more pronounced chiaroscuro, and a syncretic iconography that fuses Buddhist, Daoist and Confucian elements for localized devotional practice; traces of polychromy and gilding further differentiate its visual effect. The study demonstrates that material and technical constraints critically inform form, viewing geometry, and ritual use, and argues for context-sensitive conservation approaches. These findings advance comparative understandings of Mahayana sculptural practice and contribute to broader debates in global art history.

Ubiquitous computing (UC) encompasses sensor-instrumented environments, which are often endowed with wireless network interfaces, in which devices, software agents, and services are integrated in a seamless and transparent way and cooperate to meet high-level goals of human users. This chapter introduces some background concepts on UC. It presents the state-of-the-art for the field of middleware for UC. The requirement of the integration of UC middleware is addressed and an existent solution is described - the Open Context Platform Integration (OpenCOPI) platform. OpenCOPI provides an automatic service composition, a standardized context and communication model, and a fault tolerance mechanism that handles service failures. Another important feature of OpenCOPI is the use of semantic workflows. Finally, the chapter presents a new integration platform for UC. It talks about OpenCOPI as an example of a software system developed to integrate different context provision middleware, with the goal of facilitating the development of context-aware ubiquitous applications.

Since the start of the 21st century, humanity has been a predominantly urban species. This Special Issue is about the future of cities and how urbanization will develop when based on principles of sustainability. It explores the underlying dimensions of the transformation of existing cities and the design of low carbon green precincts and their urban systems. The view of the papers presented in this Special Issue is holistic and takes questions of social sustainability into account. This editorial highlights the contents and methodologies of 13 selected papers, while presenting diverse issues in strategies, concepts and policies for sustainable urban development.

From the late 1970s rising interrelated interests in Japanese mountain asceticism, Esoteric Buddhism and \"New Age\" spirituality and healing were spurred by oil shocks and concerns about the fragility of human and ecological health during a time of unprecedented economic prosperity. When the bubble economy burst in the 1990s, certain individuals shifted focus to inner rather than outer wealth and to greater quality of life over income. They found a growing body of print, audio-visual and on-line media produced by charismatic Shugendō priests Tanaka Riten and Tateishi Kōshō, who condensed and abbreviated traditional ascetic mountain initiation rituals of Shugendō (literally \"The Way of Acquiring Power\"). They adapted these practices to suit the needs and work schedules of busy urban lay participants. In 2007 filmmaker Jean-Marc Abela and I traveled to Yoshino and Shingu (south of Kyoto) to create a participatory documentary film, Shugendō Now. By disseminating research about the experiences of Shugendō priests and lay practitioners in an accessible documentary and Ph.D. thesis, we have sought to contribute to a new understanding of how a mountain ascetic tradition is being creatively reinvented in the 21st Century. Bringing a camera and professional filmmaker into the field and seeking direction and feedback from research co-participants enabled unanticipated discoveries and my most productive and collaborative fieldwork experiences to date.

Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits spatial transcriptomics data to outperform existing methods. It combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. We demonstrated GraphST on multiple tissue types and technology platforms. GraphST achieved 10% higher clustering accuracy and better delineated fine-grained tissue structures in brain and embryo tissues. GraphST is also the only method that can jointly analyze multiple tissue slices in vertical or horizontal integration while correcting batch effects. Lastly, GraphST demonstrated superior cell-type deconvolution to capture spatial niches like lymph node germinal centers and exhausted tumor infiltrating T cells in breast tumor tissue. Advances in spatial transcriptomics technologies have enabled the gene expression profiling of tissues while retaining spatial context. Here the authors present GraphST, a graph self-supervised contrastive learning method that learns informative and discriminative spot representations from spatial transcriptomics data.