Miriam Pikkemaat, Rosa de Pinho, Enrique Rodilla
et al.
Background Hypertension management guidelines provide recommendations for optimal patient care. However, the limited time available of clinicians is rarely considered. The aim of this study was to evaluate the time clinicians currently spend on different components of hypertension care during an average workday and to assess physicians’ perceptions of the time required for optimal, guideline-based care.Methods We conducted an international, cross-sectional, anonymous questionnaire-based survey among physicians involved in hypertension care. A 37-item questionnaire assessed time spent on, and time perceived as necessary for, guideline-recommended hypertension-related activities during an average workday. The survey was distributed through the European Society of Hypertension (ESH) and the European General Practice Research Network (EGPRN) between 1 June and 15 September 2025.Results 370 responses were collected from 49 countries worldwide (91.3% from Europe). After data clarification 357 responses were analysed. Of these, 153 (42.9%) were general practitioners (GPs) and 204 (57.1%) were other specialists (internists, cardiologists, nephrologists). Across all assessed activities, clinicians consistently reported a gap between the time currently spent and the time perceived as necessary for optimal care, including office blood pressure measurement, home blood pressure measurement education, lifestyle counselling, and cardiovascular risk stratification. Differences were also observed between the practice of GPs and other specialists.Conclusions This international survey demonstrates substantial perceived gaps between available and required time for several components of hypertension care, particularly in general practice. These findings highlight the importance of considering real-world clinician time constraints in the development and prioritisation of hypertension guideline recommendations and suggest that adequate health-system resources are needed to support their implementation.
Diseases of the circulatory (Cardiovascular) system
Keerththanan Vickneswaran, Mariangel Garcia Andarcia, Hugo Retief
et al.
Sustainable water resource management in transboundary river basins is challenged by fragmented data, limited real-time access, and the complexity of integrating diverse information sources. This paper presents WaterCopilot-an AI-driven virtual assistant developed through collaboration between the International Water Management Institute (IWMI) and Microsoft Research for the Limpopo River Basin (LRB) to bridge these gaps through a unified, interactive platform. Built on Retrieval-Augmented Generation (RAG) and tool-calling architectures, WaterCopilot integrates static policy documents and real-time hydrological data via two custom plugins: the iwmi-doc-plugin, which enables semantic search over indexed documents using Azure AI Search, and the iwmi-api-plugin, which queries live databases to deliver dynamic insights such as environmental-flow alerts, rainfall trends, reservoir levels, water accounting, and irrigation data. The system features guided multilingual interactions (English, Portuguese, French), transparent source referencing, automated calculations, and visualization capabilities. Evaluated using the RAGAS framework, WaterCopilot achieves an overall score of 0.8043, with high answer relevancy (0.8571) and context precision (0.8009). Key innovations include automated threshold-based alerts, integration with the LRB Digital Twin, and a scalable deployment pipeline hosted on AWS. While limitations in processing non-English technical documents and API latency remain, WaterCopilot establishes a replicable AI-augmented framework for enhancing water governance in data-scarce, transboundary contexts. The study demonstrates the potential of this AI assistant to support informed, timely decision-making and strengthen water security in complex river basins.
Michelle Hillmann, Johannes Pfeifer, Nicki Marquardt
During the COVID-19 pandemic, working from home became widespread due to contact restrictions, substantially altering work arrangements. As organizations reassess long-term work policies, this study examines whether working from home is associated with higher job satisfaction than traditional office work and which factors influence job satisfaction in a home-office context. Data were collected via an online survey of 201 em-ployees in Germany. Job satisfaction was measured using the Job Description Form (ABB), and personality traits were assessed with the Big Five Inventory (BFI-10), which was included as a control variable. Results indicate that employees working predominantly from home report significantly higher overall job satisfaction than those working mainly in traditional office settings. This effect remained stable after controlling for personality traits and age and was evident across all job satisfaction subdimensions. Furthermore, effective communication tools, adequate technical equipment, a quiet workspace, and prior expe-rience with working from home were positively associated with job satisfaction. In contrast, the presence of children or other household co-workers did not significantly reduce job satisfaction, whereas sufficient childcare arrangements showed a strong positive association. Overall, the findings highlight the importance of supportive home office conditions for sustaining job satisfaction beyond the pandemic.
Zhanjiang is located at the southernmost tip of mainland China, surrounded by the sea on three sides, with mariculture being crucial to local economic growth and fishermen's income. In the context of the rural revitalization strategy, marine disasters such as storm surges, red tides and sea waves pose particularly severe challenges to the industry. This study analyzes marine disaster data in Zhanjiang City from 2014 to 2023, combined with the analysis of first-hand information obtained from on-site research, to further clarify the specific impact of marine disasters on the aquaculture industry. The results found that marine disasters caused significant economic losses to the farmers, and the existing disaster management measures were insufficient in the accuracy of early warning, response speed and technical aspects. Therefore, this article suggests strengthening the monitoring and early warning system to improve the efficiency and accuracy of early warning; encouraging governments, research institutions, enterprises, and social organizations to collaborate and build a diversified disaster management network; utilizing modern technology to enhance the disaster resistance of aquaculture infrastructure; and promoting the insurance system and cultivating professional talents to enhance the risk management capabilities of farmers. These measures can not only significantly reduce the negative impact of marine disasters but also effectively promote the sustainable development of mariculture in Zhanjiang City, continuously release new momentum of the “blue engine” in thriving towards the sea, and contribute to the rural revitalization of Zhanjiang City.
The transition in cities toward environmental sustainability requires transforming urban subsystems such as energy, transport, and waste infrastructure. Based on the frameworks of strategic spatial planning (SSP) and urban transition management (UTM), the urban transition is conceptualized as a long‐term process in which stakeholders co‐create a vision and a strategic plan, which is subsequently implemented in multiple relatively short‐term projects transforming these urban subsystems. While co‐creation is emerging in urban planning, ambiguity remains regarding the development and use of co‐creation instruments in transforming urban subsystems. This article therefore has two aims: first, to develop a typology of co‐creation instruments for urban transition planning and management; and second, to examine the institutionalization of their development and use. The article follows an iterative inductive‐deductive search method to make an inventory of instruments, after which four main types are identified: participatory planning and communication tools, expert planning support systems, urban living labs, and virtual transformation labs. Several challenges in using these instruments are identified, including the need to acquire governance and digital skills, and to keep tools and data up to date. This article subsequently examines the capabilities that need to be institutionalized to support the use and development of these instruments across multiple projects. Capabilities needed are stakeholder engagement and collaborative governance, the participatory design and updating of digital tools, maintenance of urban subsystem and city development models, definition of transition scenarios and experiments, and interpretation of (simulation) results. Additional capabilities are needed to manage the project portfolio and facilitate learning within and across projects. Ultimately, a “Transition Planning Office” is proposed to institutionalize these capabilities and, by doing so, to complement UTM’s focus on independent vision and agenda formulation with sustained involvement in long‐term planning, and to support SSP’s call for more strategic urban planning through project portfolio management and instrument use and development.
This article presents an integrated approach for superpixel segmentation (SPS) and classification, leveraging a deep learning (DL) method tailored to high-resolution remote sensing imagery (RSI). The main contributions of this method include designing a SPS approach based on a convolution-based network architecture that directly predicts superpixels on a regular grid, while adding a classification branch that leverages SPS to classify individual superpixels. The proposed method introduces a dynamic adaptive quantization framework and bit mapping modules, enabling the model to flexibly adapt to various bit-width configurations. End-to-end training integrates SPS and classification tasks within the same deep neural network. Comprehensive experiments utilized RSI datasets across three typical scenes: urban, suburban, and agricultural-pastoral areas. Quantitative and qualitative results confirm the superiority for both SPS and semantic segmentation tasks, showing strong potential for scene understanding and land cover classification. Ablation studies further confirm the efficiency and necessity of various components in the model design. This work provides new ideas and technical support for achieving high-precision, fine-grained interpretation of remote sensing scenes.
Objectives/Goals: This study aimed to enhance clinical trial data management through large language model information retrieval and generation techniques within the clinical trial reporting workflow. We focused on improving compliance with reporting, reducing human labor, and promoting standardized reporting structure and data quality oversight. Methods/Study Population: We used approved study protocols from UC Davis IRB-approved investigator-initiated studies compared to the same studies reported to ClinicalTrials.gov. Our baseline data extraction system employs commercial large language models (LLMs) and retrieval augmented generation (RAG) to isolate data sources within the secure extraction environment. We stratified protocol documents into easy, complex, and random categories based on study focus, document complexity, the extent of amendments or modifications, and completion metrics from ClinicalTrials.gov. We developed a pilot web-based architecture to capture variations in categorization, labeling, and reporting style and compared generated extraction data. We primarily focused on qualitative evaluation through a review of expert staff. Results/Anticipated Results: Our results revealed significant variations in reporting quality, with dependencies stemming from multiple authors and stages throughout the clinical trial protocol lifecycle. Based on these variations, we used prompt engineering to improve the pilot application’s output compliance with the protocol registration and results system (PRS) structured data format for various study types. We piloted the assisted workflow with prospective studies by partnering with study investigators and the clinical trial office staff to assist in review and clinical trial reporting creation. Initial studies reported by our system were approved and released to the public by PRS staff. We are refining content generation and workflows to different components of studies and evaluating their use in quality and training areas. Discussion/Significance of Impact: Our system fosters collaboration, efficient review, and compliance with clinical trial reporting standards. It supports the promise of AI-driven assistance in clinical trial management, design, and reporting. We focus on the multiple stakeholders, expertise, and data flows in the organizational management of clinical and translational science.
In order to give full play to the role of "seed industry" as a high-level chip and promote the high-quality and sustainable development of modern marine aquatic seed industry and mariculture industry, this paper comprehensively analyzes the current situation of the development of marine aquatic original and seed industry in Shandong Province, as well as the existing problems, and puts forward countermeasures and suggestions. The study shows that: Shandong new varieties of marine aquatic basically cover all categories and occupy the quantitative advantage, new varieties of R & D process and categories show a marked acceleration of the trend of increasing, new varieties of the main body of the creation of a highly concentrated situation of geographic and institutional, new varieties of seawater aquaculture industry, such as the role of the economic benefits of the pulling effect of the obvious enhancement of such characteristics. At the same time, there are new varieties of seawater species coverage is low, difficult to meet the needs of the industry, the policy protection mechanism is not sound, incentives and constraints of the positive feedback effect is not strong, to build China's seawater seed industry center is facing greater pressure from external competition, insufficient power, seawater new varieties of research and development and application of the system of promotion of the "four" pattern urgently need to be perfected and other issues. To this end, it is recommended that the development of seawater seed industry in Shandong should break through the key core technology of aquatic breeding of new varieties, play a leading role in policy support, strengthen the integration of aquatic seedling industry resources, improve the "four" pattern, and promote the "conservation, measurement, propagation and promotion" system integration and development.
This paper introduces a multi-agent application system designed to enhance office collaboration efficiency and work quality. The system integrates artificial intelligence, machine learning, and natural language processing technologies, achieving functionalities such as task allocation, progress monitoring, and information sharing. The agents within the system are capable of providing personalized collaboration support based on team members' needs and incorporate data analysis tools to improve decision-making quality. The paper also proposes an intelligent agent architecture that separates Plan and Solver, and through techniques such as multi-turn query rewriting and business tool retrieval, it enhances the agent's multi-intent and multi-turn dialogue capabilities. Furthermore, the paper details the design of tools and multi-turn dialogue in the context of office collaboration scenarios, and validates the system's effectiveness through experiments and evaluations. Ultimately, the system has demonstrated outstanding performance in real business applications, particularly in query understanding, task planning, and tool calling. Looking forward, the system is expected to play a more significant role in addressing complex interaction issues within dynamic environments and large-scale multi-agent systems.
The Office of Astronomy for Development (OAD) believes that in order for astronomy-for-development activities to be effective, a scientific approach is required. Evaluation is an essential component in identifying which projects work best, for whom and under what conditions. Evidence-informed project design and selection ensures that projects build on past lessons, thereby reducing the risk of negative unintended consequences and increasing the probabilities of positive cost-effective impact. The OAD has developed an Impact Cycle that aims to enhance project design, selection and delivery systems to support such continual improvement and potential expansion. By determining what works - and, importantly, what doesn't work - the OAD can build a library of evidence on best practice and ensure a positive feedback loop for future projects.
Manoj Madushanka Perera, Adnan Mahmood, Kasun Eranda Wijethilake
et al.
This particular paper introduces an Adaptive Context Management (ACM) framework for the Conversational Question Answering (ConvQA) systems. The key objective of the ACM framework is to optimize the use of the conversation history by dynamically managing context for maximizing the relevant information provided to a ConvQA model within its token limit. Our approach incorporates a Context Manager (CM) Module, a Summarization (SM) Module, and an Entity Extraction (EE) Module in a bid to handle the conversation history efficaciously. The CM Module dynamically adjusts the context size, thereby preserving the most relevant and recent information within a model's token limit. The SM Module summarizes the older parts of the conversation history via a sliding window. When the summarization window exceeds its limit, the EE Module identifies and retains key entities from the oldest conversation turns. Experimental results demonstrate the effectiveness of our envisaged framework in generating accurate and contextually appropriate responses, thereby highlighting the potential of the ACM framework to enhance the robustness and scalability of the ConvQA systems.
This paper presents comprehensive findings on the characterization of Indoor Hotspot channel parameters, derived from an extensive experimental campaign conducted at 6.9, 8.3, and 14.5 GHz in a commercial office building. Extensive measurements were carried out in diverse indoor office settings, including cubicles, conference rooms, hallways, and laboratory spaces across four floors. The path loss, shadow fading, delay spread, and angular spread was modeled. Our results offer significant insights into the attenuation and dispersion characteristics of wireless signals in diverse indoor settings in the centimeter-wave frequency band, and can be used for improving indoor network design and performance in commercial buildings.
This study aims to understand the roles and functions of village heads in managing the finances of Madani Village and the factors influencing them. The village, as the lowest administrative unit, plays a strategic role in development and public service provision as stipulated by Law No. 6 of 2014. Public accountability is the main foundation in village governance. This research uses a descriptive analytical method with normative and empirical legal approaches. Data were collected through interviews and documentation studies at the Madani Village Office, Wotu District, East Luwu Regency. The results show that village heads hold significant positions in carrying out governance, development, and community guidance. Financial management in the village faces several issues such as delayed payments and asset management. Improvement efforts are made through monitoring, evaluation, and enhancing financial management practices. Implementing public accountability principles is necessary to improve transparency and the performance of village governance.
Morphological (e.g. shape, size, and height) and function (e.g. working, living, and shopping) information of buildings is highly needed for urban planning and management as well as other applications such as city-scale building energy use modeling. Due to the limited availability of socio-economic geospatial data, it is more challenging to map building functions than building morphological information, especially over large areas. In this study, we proposed an integrated framework to map building functions in 50 U.S. cities by integrating multi-source web-based geospatial data. First, a web crawler was developed to extract Points of Interest (POIs) from Tripadvisor.com, and a map crawler was developed to extract POIs and land use parcels from Google Maps. Second, an unsupervised machine learning algorithm named OneClassSVM was used to identify residential buildings based on landscape features derived from Microsoft building footprints. Third, the type ratio of POIs and the area ratio of land use parcels were used to identify six non-residential functions (i.e. hospital, hotel, school, shop, restaurant, and office). The accuracy assessment indicates that the proposed framework performed well, with an average overall accuracy of 94% and a kappa coefficient of 0.63. With the worldwide coverage of Google Maps and Tripadvisor.com, the proposed framework is transferable to other cities over the world. The data products generated from this study are of great use for quantitative city-scale urban studies, such as building energy use modeling at the single building level over large areas.
The developed system aims to incorporate a private blockchain technology in the procurement process for the supply office. The procurement process includes the canvassing, purchasing, delivery and inspection of items, inventory, and disposal. The blockchain-based system includes a distributed ledger technology, peer-to-peer network, Proof-of-Authority consensus mechanism, and SHA3-512 cryptographic hash function algorithm. This will ensure trust and proper accountability to the custodian of the property while safeguarding sensitive information in the procurement records. The extreme prototyping model will be used as software development life cycle. It is mostly used for web-based applications and has an increased user involvement. The prototype version of the system allows the users get a better understanding of the system being developed. It also reduces the time and cost, has quicker user feedback, missing and difficult functions can be recognized, and confusing processes can be addressed on an early stage. The implementation of a private blockchain technology has an increased privacy, enhanced security, improved efficiency, and reduced complexity over traditional blockchain network. The use of SHA3-512 as cryptographic hash function algorithm is much faster than its predecessors when cryptography is handled by hardware components. Furthermore, it is not vulnerable to length extension attacks making it reliable in terms of security of data. The study recommends the use of private blockchain-based technology with the procurement and asset management system in the supply office. The procurement records will be protected against tampering using this technology. This will promote trust and confidence of the stakeholders. The implementation of blockchain technology in developing a system served as advancement and innovation in terms of securing data.
Qualitative data collection and analysis approaches, such as those employing interviews and focus groups, provide rich insights into customer attitudes, sentiment, and behavior. However, manually analyzing qualitative data requires extensive time and effort to identify relevant topics and thematic insights. This study proposes a novel approach to address this challenge by leveraging Retrieval Augmented Generation (RAG) based Large Language Models (LLMs) for analyzing interview transcripts. The novelty of this work lies in strategizing the research inquiry as one that is augmented by an LLM that serves as a novice research assistant. This research explores the mental model of LLMs to serve as novice qualitative research assistants for researchers in the talent management space. A RAG-based LLM approach is extended to enable topic modeling of semi-structured interview data, showcasing the versatility of these models beyond their traditional use in information retrieval and search. Our findings demonstrate that the LLM-augmented RAG approach can successfully extract topics of interest, with significant coverage compared to manually generated topics from the same dataset. This establishes the viability of employing LLMs as novice qualitative research assistants. Additionally, the study recommends that researchers leveraging such models lean heavily on quality criteria used in traditional qualitative research to ensure rigor and trustworthiness of their approach. Finally, the paper presents key recommendations for industry practitioners seeking to reconcile the use of LLMs with established qualitative research paradigms, providing a roadmap for the effective integration of these powerful, albeit novice, AI tools in the analysis of qualitative datasets within talent
Faced with the burgeoning volume of academic literature, researchers often need help with uncertain article quality and mismatches in term searches using traditional academic engines. We introduce IntellectSeeker, an innovative and personalized intelligent academic literature management platform to address these challenges. This platform integrates a Large Language Model (LLM)--based semantic enhancement bot with a sophisticated probability model to personalize and streamline literature searches. We adopted the GPT-3.5-turbo model to transform everyday language into professional academic terms across various scenarios using multiple rounds of few-shot learning. This adaptation mainly benefits academic newcomers, effectively bridging the gap between general inquiries and academic terminology. The probabilistic model intelligently filters academic articles to align closely with the specific interests of users, which are derived from explicit needs and behavioral patterns. Moreover, IntellectSeeker incorporates an advanced recommendation system and text compression tools. These features enable intelligent article recommendations based on user interactions and present search results through concise one-line summaries and innovative word cloud visualizations, significantly enhancing research efficiency and user experience. IntellectSeeker offers academic researchers a highly customizable literature management solution with exceptional search precision and matching capabilities. The code can be found here: https://github.com/LuckyBian/ISY5001
Marcel Bluhm, Adrian Cachinero Vasiljević, Sébastien Derivaux
et al.
Stablecoins have turned out to be the "killer" use case of the growing digital asset space. However, risk management frameworks, including regulatory ones, have been largely absent. In this paper, we address the critical question of measuring and managing risk in stablecoin protocols, which operate on public blockchain infrastructure. The on-chain environment makes it possible to monitor risk and automate its management via transparent smart-contracts in real-time. We propose two risk metrics covering capitalization and liquidity of stablecoin protocols. We then explore in a case-study type analysis how our risk management framework can be applied to DAI, the biggest decentralized stablecoin by market capitalisation to-date, governed by MakerDAO. Based on our findings, we recommend that the protocol explores implementing automatic capital buffer adjustments and dynamic maturity gap matching. Our analysis demonstrates the practical benefits for scalable (prudential) risk management stemming from real-time availability of high-quality, granular, tamper-resistant on-chain data in the digital asset space. We name this approach Crypto Asset-Liability Management (CALM).
Zeb S, Akbar A, Gul A, Haider SA, Poulova P, Yasmin F. Psychol Res Behav Manag. 2021;14:1867–1876.
We, the Editors and Publisher of Psychology Research and Behaviour Management, have retracted the following article.
Following publication of the article, the editorial office received notification from a researcher with concerns that, aside from the first author, the remaining co-authors did not contribute to the reported study or the drafting of the original manuscript. The researcher provided evidence demonstrating that the article had been derived directly from their BSc thesis.
When approached for an explanation, the authors were cooperative but were unable to provide sufficient evidence to show they had contributed to the reported study or the drafting of the original manuscript.
Our editorial policies are clear that authors are expected to fulfil specific criteria to warrant authorship and in light of this, the decision was made to retract the article and the authors were notified of this.
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