Background: Real-world data (RWD) and real-world evidence (RWE) are playing increasingly important roles in clinical research and health care decision-making. To leverage RWD and generate reliable RWE, data should be well defined and structured in a way that is semantically interoperable and consistent across stakeholders. The adoption of data standards is one of the cornerstones supporting high-quality evidence for the development of clinical medicine and therapeutics. Clinical Data Interchange Standards Consortium (CDISC) data standards are mature, globally recognized, and heavily used by the pharmaceutical industry for regulatory submissions. The CDISC RWD Connect Initiative aims to better understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance needed to more easily implement them. Objective: The aim of this study is to understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance that may be needed to implement CDISC standards more easily for this purpose. Methods: We conducted a qualitative Delphi survey involving an expert advisory board with multiple key stakeholders, with 3 rounds of input and review. Results: Overall, 66 experts participated in round 1, 56 in round 2, and 49 in round 3 of the Delphi survey. Their inputs were collected and analyzed, culminating in group statements. It was widely agreed that the standardization of RWD is highly necessary, and the primary focus should be on its ability to improve data sharing and the quality of RWE. The priorities for RWD standardization included electronic health records, such as data shared using Health Level 7 Fast Health care Interoperability Resources (FHIR), and the data stemming from observational studies. With different standardization efforts already underway in these areas, a gap analysis should be performed to identify the areas where synergies and efficiencies are possible and then collaborate with stakeholders to create or extend existing mappings between CDISC and other standards, controlled terminologies, and models to represent data originating across different sources. Conclusions: There are many ongoing data standardization efforts around human health data–related activities, each with different definitions, levels of granularity, and purpose. Among these, CDISC has been successful in standardizing clinical trial-based data for regulation worldwide. However, the complexity of the CDISC standards and the fact that they were developed for different purposes, combined with the lack of awareness and incentives to use a new standard and insufficient training and implementation support, are significant barriers to setting up the use of CDISC standards for RWD. The collection and dissemination of use cases, development of tools and support systems for the RWD community, and collaboration with other standards development organizations are potential steps forward. Using CDISC will help link clinical trial data and RWD and promote innovation in health data science.

Grapes are an important cash crop that contributes to the rapid development of the agricultural economy. The harvesting of ripe fruits is one of the crucial steps in the grape production process. However, at present, the picking methods are mainly manual, resulting in wasted time and high costs. Therefore, it is particularly important to implement intelligent grape picking, in which the accurate detection of grape stems is a key step to achieve intelligent harvesting. In this study, a trellis grape stem detection model, YOLOv8n-GP, was proposed by combining the SENetV2 attention module and CARAFE upsampling operator with YOLOv8n-pose. Specifically, this study first embedded the SENetV2 attention module at the bottom of the backbone network to enhance the model’s ability to extract key feature information. Then, we utilized the CARAFE upsampling operator to replace the upsampling modules in the neck network, expanding the sensory field of the model without increasing its parameters. Finally, to validate the detection performance of YOLOv8n-GP, we examined the effectiveness of the various keypoint detection models constructed with YOLOv8n-pose, YOLOv5-pose, YOLOv7-pose, and YOLOv7-Tiny-pose. Experimental results show that the precision, recall, mAP, and mAP-kp of YOLOv8n-GP reached 91.6%, 91.3%, 97.1%, and 95.4%, which improved by 3.7%, 3.6%, 4.6%, and 4.0%, respectively, compared to YOLOv8n-pose. Furthermore, YOLOv8n-GP exhibits superior detection performance compared with the other keypoint detection models in terms of each evaluation indicator. The experimental results demonstrate that YOLOv8n-GP can detect trellis grape stems efficiently and accurately, providing technical support for advancing intelligent grape harvesting.

The benefits of virtualized IT environments, such as compute clouds, have drawn interested enterprises to migrate their applications onto new platforms to gain the advantages of reduced hardware and energy costs, increased flexibility and deployment speed, and reduced management complexity. However, the process of migrating a complex application takes a considerable amount of effort, particularly when performing post-migration testing to verify that the application still functions correctly in the target environment. The traditional approach of test case generation and execution can take weeks and synthetic test cases may not adequately reflect actual application usage. In this paper, we propose and evaluate a black-box approach for post-migration testing of Web applications without manually creating test cases. A Web proxy is put in front of the production application to intercept all requests from real users, and these requests are simultaneously sent to the production and migrated applications. Results generated by both applications are then compared, and mismatches due to migration problems can be easily detected and presented to testing teams for resolution. We implement this approach in Splitter, a software module that is deployed as a reverse Web proxy. Through our evaluation using a number of real applications, we show that it Splitter can effectively automate post-migration testing while also reduce the number of mismatches that must be manually inspected. Equally important, it imposes a relatively small performance overhead on the production environment.

Vegetation, as a critical ecological feature and irreplaceable material resource of the Earth's surface, plays a crucial role in environmental protection, resource assessment and urban planning. The rapid advancement of remote sensing platforms and sensor technologies has facilitated the acquisition of high-resolution remote sensing imagery, providing excellent conditions for the detailed analysis of vegetation. However, vegetation segmentation in remote sensing imagery poses distinct challenges, including extreme scale variance, spectral ambiguity and complex boundary characteristics. The performance of Convolutional Neural Network (CNN)-based methods in vegetation segmentation is constrained by their limited ability to model long-range spatial dependencies. In contrast, although Transformer-based architectures effective at capturing global context, their quadratic complexity makes them impractical for processing high-resolution remote sensing images. Recently, State-Space Models (SSMs) have emerged as a promising alternative owing to their linear complexity and efficient long-range modeling capabilities. Nevertheless, existing vision SSMs have two critical limitations: (1) insufficient modeling of boundary information; and (2) the limited performance improvements offered by multi-directional scanning strategies while increasing computational cost. To address these limitations, we propose BRSMamba, a boundary-aware network that integrates two novel modules with Non-Causal State-Space Duality (NC-SSD) to enhance both boundary preservation and global context modeling. Specifically, the Boundary-Subject Fusion Perception (BFP) module extracts robust boundary features via a Laplacian-of-Gaussian Convolutional Block (LCB) and fuses them with category-centric semantics to generate a boundary-subject map. This map then directs the Boundary-Body Resolution (BBR) module to inject boundary awareness into the NC-SSD state-transition matrix, enabling selective scanning that preserves fine details and captures long-range dependencies. Experiments on four benchmark datasets demonstrated that BRSMamba achieved 78.86% mIoU on Vaihingen, 82.84% on Potsdam, 54.37% on LoveDA, and 72.24% on GID while using only 10.61 M parameters and 26.28 G FLOPs. These results validate the effectiveness of the proposed method in balancing the accuracy and efficiency of complex vegetation segmentation tasks.

Recent advances in the manufacturing and commercialization of passive optical components are now extending the capabilities of fiber to edge and campus networks. This paper presents a comparison between Passive Optical LAN (POL) and copper-based LAN solution, and demonstrate the benefits of PON such as reduced infrastructure footprint and cost, reduced power requirements, future-proof bandwidth, greener infrastructure, safer, higher security and higher reliability.

Background: Telehealth has emerged as a popular channel for providing outpatient services in many countries. However, the majority of telehealth systems focus on operational functions and offer only a sectional patient journey at most. Experiences with incorporating longitudinal real-world medical record data into telehealth are valuable but have not been widely shared. The feasibility and usability of such a telehealth platform, with comprehensive, real-world data via a live feed, for cancer patient care are yet to be studied. Objective: The primary purpose of this study is to understand the feasibility and usability of cancer patient care using a telehealth platform with longitudinal, real-world data via a live feed as a supplement to hospital electronic medical record systems specifically from physician’s perspective. Methods: A telehealth platform was constructed and launched for both physicians and patients. Real-world data were collected and curated using a comprehensive data model. Physician activities on the platform were recorded as system logs and analyzed. In February 2023, a survey was conducted among the platform’s registered physicians to assess the specific areas of patient care and to quantify their before and after experiences, including the number of patients managed, time spent, dropout rate, visit rate, and follow-up data. Descriptive and inferential statistical analyses were performed on the data sets. Results: Over a period of 15 months, 16,035 unique users (13,888 patients, 1539 friends and family members, and 174 physician groups with 608 individuals) registered on the platform. More than 382,000 messages including text, reminders, and pictures were generated by physicians when communicating with patients. The survey was completed by 78 group leaders (45% of the 174 physician groups). Of the participants, 84% (65.6/78; SD 8.7) reported a positive experience, with efficient communication, remote supervision, quicker response to questions, adverse event prevention, more complete follow-up data, patient risk reduction, cross-organization collaboration, and a reduction in in-person visits. The majority of the participants (59/78, 76% to 76/78, 97.4%) estimated improvements in time spent, number of patients managed, the drop-off rate, and access to medical history, with the average ranging from 57% to 105%. When compared with prior platforms, responses from physicians indicated better experiences in terms of time spent, the drop-off rate, and medical history, while the number of patients managed did not significantly change. Conclusions: This study suggests that a telehealth platform, equipped with comprehensive, real-world data via a live feed, is feasible and effective for cancer patient care. It enhances inpatient management by improving time efficiencies, reducing drop-off rates, and providing easy access to medical history. Moreover, it fosters a positive experience in physician-patient interactions.