Privacy-preserving distributed optimization for economic dispatch in smart grids

The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Daniele Casagrande under the direction of Editor Florian Dorfler.This paper discusses a distributed economic dispatch problem (EDP) of smart grids while preventing sensitive information from being leaked during the communication process. In response to the problem, a novel privacy-preserving distributed economic dispatch strategy is developed via adding an exponentially decaying random noise to minimize the total cost of the grid while ensuring the privacy of sensitive state information. The quantitative relationship between the privacy and the estimation accuracy of eavesdroppers is profoundly disclosed in the framework of (ς, σ)-data-privacy. Furthermore, a sufficient condition on the iteration step size is achieved to ensure that the well-designed algorithm can converge to the optimal value of the addressed EDP exactly by resorting to the classical Lyapunov stability theory. Finally, simulation results verify the effectiveness of the carefully constructed privacy-preserving scheme.This work was supported in part by the National Natural Science Foundation of China under Grants 62373251, U21A2019, 62222312 and 62473285; in part by the National Key Research and Development Program of China under Grant 2022YFB4501704; in part by the Shanghai Science and Technology Innovation Action Plan Project of China under Grant 22511100700; and in part by Fundamental Research Funds for the Central Universities

Unlocking Business Success: How Networking and Branding Capabilities Drive Performance Through Product Innovativeness

Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.In today's fast-paced market, developing innovative products with significant advantages over existing alternatives is essential for a strong market presence. This study, based on the resource-based and dynamic capability view, examines how market and technological innovativeness contribute to differentiation advantage and improved business performance. It also investigates the roles of complementary capabilities in enhancing these relationships. Primary data were collected through an on-site questionnaire survey of Iranian research and development-intensive manufacturing firms. Using 125 valid responses from senior managers, partial least squares structural equation modeling tested the proposed model. Findings indicate that networking and branding capabilities enhance technological and market innovativeness, respectively, thereby strengthening differentiation advantage. Moreover, differentiation advantage is a crucial mechanism for translating innovativeness into improved business performance. These results provide theoretical insights and practical guidance for developing effective product innovativeness strategies to augment international competitiveness and performance

Collecting real-time infant feeding and support experience: co-participatory pilot study of mobile health methodology

Data availability: The quantitative dataset supporting the conclusions of this article is available in the OSF project https://osf.io/yqsnd/ [https://doi.org/10.17605/OSF.IO/YQSND].Supplementary Information is available online at: https://internationalbreastfeedingjournal.biomedcentral.com/articles/10.1186/s13006-025-00707-7#Sec31 .Background: Breastfeeding rates in the UK have remained stubbornly low despite long-term intervention efforts. Social support is a key, theoretically grounded intervention method, yet social support has been inconsistently related to improved breastfeeding. Understanding of the dynamics between infant feeding and social support is currently limited by retrospective collection of quantitative data, which prohibits causal inferences, and by unrepresentative sampling of mothers. In this paper, we present a case-study presenting the development of a data collection methodology designed to address these challenges. Methods: In April–May 2022 we co-produced and piloted a mobile health (mHealth) data collection methodology linked to a pre-existing pregnancy and parenting app in the UK (Baby Buddy), prioritising real-time daily data collection about women's postnatal experiences. To explore the potential of mHealth in-app surveys, here we report the iterative design process and the results from a mixed-method (explorative data analysis of usage data and content analysis of interview data) four-week pilot. Results: Participants (n = 14) appreciated the feature’s simplicity and its easy integration into their daily routines, particularly valuing the reflective aspect akin to journaling. As a result, participants used the feature regularly and looked forward to doing so. We find no evidence that key sociodemographic metrics were associated with women’s enjoyment or engagement. Based on participant feedback, important next steps are to design in-feature feedback and tracking systems to help maintain motivation. Conclusions: Reflecting on future opportunities, this case-study underscores that mHealth in-app surveys may be an effective way to collect prospective real-time data on complex infant feeding behaviours and experiences during the postnatal period, with important implications for public health and social science research.We acknowledge the funding by the BA/Wellcome Trust small grants for supporting this project (reference SRG2021/210128)

COMPARATIVE STUDY OF DEEP CO-AXIAL CLOSED LOOP AND U-SHAPED WELLBORE GEOTHERMAL SYSTEMS

Until recently, geothermal energy has been limited to regions with favorable subsurface conditions. Most installed geothermal systems are open, using two wells for fluid injection and extraction. Closed-loop systems have historically been used in low-depth installations as ground source heat pumps. However, climate changes and energy market volatility have catalyzed the development of deep-borehole heat exchangers (DBHE) - a potentially cost-competitive technology for direct heat applications and electricity generation. The closed-loop systems can be built in any area with a sustainable geothermal gradient, while millions of abandoned wells worldwide offer low-cost repurposing as closed-loop DBHEs, producing revenue without the large cost linked with drilling. This study provides a technical assessment of coaxial and U-shaped DBHE systems, with a primary focus on the effects of pipe insulation. Results indicate that the installation of proper insulation is a vital part of the system, with a particular emphasis on the return line insulation of U-shaped systems

UK Live Comedy Sector Survey Report 2024

The UK Live Comedy Sector Survey 2024 was jointly conducted by the Centre for Comedy Studies Research at Brunel University, the Live Comedy Association, and British Comedy Guide. The UK Live Comedy Sector Survey was administered by Brunel University of London and ethical approval to conduct the survey was received from the College of Business, Arts and Social Sciences Research Ethics Committee at Brunel University of London.This report outlines the main findings of the UK Live Comedy Sector Survey 2024 conducted by the Centre for Comedy Studies Research (CCSR), the Live Comedy Association (LCA) and British Comedy Guide (BCG). Until now very little was known about the size, scale and impact of the UK live comedy sector. The survey provides detailed insights about the economics of the live comedy sector including its size and its longevity, numbers of shows and ticket sales, and turnover. It also provides insights into regional variations, venues used and performance types supported, and reveals inequalities and inequities prevalent in the sector. The survey serves to support and advocate live comedy in the UK politically, economically and socially. 366 people working in UK live comedy completed the survey. 67% of respondents were comedians. 33% of respondents were people working as comedy promoters, producers, venue managers, festival organisers or agentsLive Comedy Association; Brunel University of London. Centre for Comedy Studies Research (CCSR); British Comedy Guide

State of the evidence on the impacts of fishing plastic waste to coastal communities: protocol for a Systematic Evidence Map

Data sharing The interactive Tableau dashboards will be hosted on Tableau Public a free online platform to share interactive visualisations of public data. The underpinning database will also be made publicly available as supplementary material to an open access peer-reviewed scientific article as an Excel file.Supplemental material is available online at: https://www.tandfonline.com/doi/full/10.1080/2833373X.2025.2554973# .Background: Fishing plastic waste (FPW) is known to cause multidimensional impacts to coastal communities globally. Detailed information on the environmental, socioeconomic and technical dimensions of effects to coastal communities caused by FPW has yet to be collated and considered in one place. Methods: The main aim of this study is to identify, organise and group existing primary evidence of the environmental, social, economic, political, and technical impacts of FPW on coastal communities and identify gaps in our knowledge about which types of FPW are most problematic. Search Strategy: We will search several databases across four electronic academic indexes (Web of Science, Scopus, PubMed and EBSCOhost [Business Source Complete, CINAHL Plus, EconLit, GreenFile, and Humanities International Index]). Eligibility Criteria: Eligible studies must contain primary research investigating an environmental, social, economic, political, or technical impact of fragments of any size of plastic polymers (macro-, micro-, or nano-) originating from fishing equipment (i.e., capture and ancillary) that has been abandoned, lost, or otherwise discarded in the marine environment, affecting any defined human or non-human (vertebrates, invertebrates, micro-organisms) individual, group or assemblage of individuals, relying on coastal and ocean resources. Environmental impacts include physical and physiological effects to biotic and abiotic elements of marine ecosystems. Social impacts include impacts to community health and wellbeing. Economic impacts include impacts to livelihood and trade. Political impacts include responses from local or regional governments to address FPW. Technical impacts include effects to techniques employed by fisherfolk or to the management of FPW at the local level. Screening & Extraction: Our search was optimised on Cadima. Articles will be screened at title and abstract, before a full-text review. All articles will be screened by a single reviewer, with two additional reviewers assessing articles for consistency. One out of ten articles will be screened by two additional reviewers in duplicate as a quality control. Data extraction will be performed on all articles included at full text, and articles that do not meet the eligibility criteria will be excluded. All articles excluded at full text will be confirmed by the two additional reviewers. Study Mapping & Reporting: Results will be published in a narrative summary and visualised in a publicly available, user-friendly, interactive and interrogable evidence map on Tableau.This work was supported by the Natural Environment Research Council (NERC) Doctoral Training Partnership grant Partnership grant [NE/S007229/1]

Correction of misalignment errors in the magnetic gradient tensor measurement system and its application in localization

The magnetic gradient tensor measurement system (MGTMS) is critical for detecting and localizing ferromagnetic targets. However, its localization accuracy is closely linked to measurement precision, which is often compromised by sensor misalignment errors. To mitigate these errors, this study proposes a practical calibration method using a standard magnetic source, achieving effective misalignment correction with minimal system alteration. The proposed method is validated through both simulations and localization experiments. Simulation results indicate that the root mean square error (RMSE) of the calibrated tensor values is reduced by three orders of magnitude compared to the uncalibrated state. Experimental results further demonstrate that the average localization error decreases from 0.040 m to 0.019 m after calibration, corresponding to a 52.5% improvement in positioning accuracy. These results highlight the potential of improving the accuracy of MGTMS-based target localization in practical applications

Eco-Driving with Deep Reinforcement Learning at Signalized Intersections Considering On-the-fly Queue Dissipation Estimation and Lane-Merging Disturbances

Eco-driving research has grown significantly over the past decade, increasingly incorporating real-world traffic and road conditions such as road gradients, lane changes, and queue effects. However, most existing studies that account for queue effects are limited to single-lane scenarios, without considering lane-merging disturbances, and can only estimate queue length or discharge time within restricted regions. To address these limitations, this paper proposes a novel deep reinforcement learning (DRL) based eco-driving algorithm that simultaneously considers on-the-fly queue dissipation time estimation and lane-merging disturbances. The approach integrates a practical and cost-effective navigation-app-based traffic data sharing framework with a data-driven dissipation time estimation model, enabling the reinforcement learning agent to continuously receive accurate modified reference speeds that reflect both queueing and merging vehicle effects. Five comprehensive case studies, benchmarked against conventional and state-of-the-art eco-driving methods, were conducted to evaluate the effectiveness of the proposed approach. Simulation results demonstrate that the proposed method consistently achieves the best energy performance across all scenarios, reducing energy consumption by an average of 37.5% compared with the Intelligent Driver Model (IDM) baseline.Tianjin Municipal Science and Technology Bureau Science and Technology; Natural Science Foundation (Grant Number: 24JCQNJC00280)

Prototyping and Evaluating TWIRL: A Temperature-Controlled DIY Airflow System for Enhancing Immersive Media

The text was partially generated with the help of ChatGPT [OpenAI, 2025].This study presents the design, prototyping, and user-centered evaluation of TWIRL (Thermal Wind Right and Left), a low-cost, temperature-controlled airflow system aimed at enhancing immersion in multisensory media experiences. TWIRL integrates hot and cold airflow generation, via Peltier-based cooling modules and modified hairdryer heating units, synchronized with audiovisual content using Sensory Effects Metadata (MPEG-V). A preliminary user study (N = 12) evaluated perceived realism, enjoyment, comfort, and engagement while experiencing thermally congruent video stimuli. The results indicated high participant acceptance, with thermal effects classified as realistic, pleasant, and nonintrusive, supported by strong internal consistency metrics. The participants expressed a willingness to use TWIRL in the future and recommend it, suggesting the potential of TWIRL to increase the presence and enjoyment of multisensory systems. The findings contribute to the understanding of thermal-wind integration in mulsemedia and offer design guidelines for future scalable, multisensory systems in the entertainment, education, and accessibility domains.This study was financed in part by the Coordination for the Improvement of Higher Education Personnel (CAPES, Brazil) – Finance Code 7.570688/2020-00 and 88881.689984/2022-01, National Council for Scientific and Technological Development (CNPQ, Brazil) – Finance Code 307718/2020-4 and Fundação de Amparo à Pesquisa e Inovação do Espírito Santo (FAPES, Brazil) – Finance Code 2021-GL60J

Conceptual Aircraft Design and AI: Developing a functional relationship for the rapid realisation of future drone concepts

The use of Unmanned Aerial Vehicles(UAVs) has expanded rapidly over the last decade. These systems have an almost limitless scope of application with resupply, surveillance, monitoring, and logistics representing but a few. Having such a wide scope, a means to rapidly, efficiently and accurately develop new designs fit-for-purpose would offer a significant advantage to developers given their inherent need to maximise potential within a competitive marketplace. This paper attempts to leverage the capabilities of Artificial Intelligence(AI) for this purpose through the development of functional synergies to predict maximum rated engine power from limited inputs and datasets. Overall, the use of AI techniques was found to offer the potential to substantial improve and enhance the design process with also the possibility for the creation of more cost-effective and efficient software tools that could significantly streamline the process.The work was financially supported under project “DATA3: Drone Design using AI for Transport Applications 3(Grant No 10126519)” as part of the UKRI Innovate UK Feasibility studies for AI solutions: Series 2 competition