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

A Multi-Objective Genetic Programming with Size Diversity for Symbolic Regression Problem

Genetic programming has been positioned as a fit-for-purpose approach for symbolic regression. Researchers tend to select algorithms that produce a model with low complexity and high accuracy. Multi-objective genetic programming (MOGP) is a promising approach for finding appropriate models by considering tradeoffs between accuracy and complexity. The MOGP has gained significant attention for non-dominated sorting genetic algorithm II (NSGA-II). However, NSGA-II tends to excessively select individuals of lower complexity, making NSGA-II inefficient in real world applications. SD can be a strategy to promote the evolutionary process by adapting selection pressures for individuals of various size. It deals with the excessive tendency to select low complexity individuals in NSGA-II.We also introduce a practical industrial case of defect detection for dispensing machines. By modeling the dispensing volume of the fluid dispensing systems, defects in the dispensing machine can be detected under different external environmental factors.For the validation of SD, other MOGP algorithms are compared with the improved NSGA-II algorithm, NSGA-II with SD. By comparing multi-objective optimization methods tested on seven general datasets and an industrial case about defect prediction, the experimental results show that performance of the proposed approach is superior or same to other models in terms of accuracy. In terms of complexity, performance of the proposed approach is satisfactory.10.13039/501100001809-National Natural Science Foundation of China

Rising compound hot-dry extremes engendering more inequality in human exposure risks

Data availability: Data analyzed during the current study is the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) gridded dataset distributed data archive [https://ds.nccs.nasa.gov/].Supplementary information is available online at: https://www.nature.com/articles/s44304-025-00119-x#Sec16 .Compound hot-dry events, with their amplified negative impacts on ecosystems and societies, are attracting growing attention. This study investigates the global-scale inequality and risks of hot-dry compound events under various shared socioeconomic pathways (SSP) scenarios, considering hazards, exposure, and vulnerability. Results show a worldwide increase in hot-dry extreme events and population exposure by mid-century (2041–2070), with variations among scenarios and regions. Climate factors are identified as the primary contributors to future changes in population exposure. SSP1-2.6 shows lower risks than SSP5-8.5 notably. Spatially, ASIA and the Middle East and Africa (MAF) will likely face higher exposure risks due to large populations, lower income levels and aging demographics, which amplify climate impacts. Under SSP3-7.0, rapid population growth introduces greater uncertainty in exposure estimates, particularly in ASIA, MAF, Latin America and the Caribbean (LAM). Aging populations, especially under SSP3-7.0 and SSP5-8.5 scenarios, exacerbate exposure risks through climate-demographic interactions.This paper is supported by the Academy of Medical Sciences, British Academy, Royal Academy of Engineering, Royal Society and the International Science Partnerships Fund (NGR2\1867)

Formulation and Structural Optimisation of PVA-Fibre Biopolymer Composites for 3D Printing in Drug Delivery Applications

Data Availability Statement: The dataset is available upon request from the authors.Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/polym17182502/s1, Figure S1: Macroscopic images of M10, S10, P10, P10F5, P10F5T5, P10F5E5, and P10F5E5T5 filaments.Additive manufacturing using fused deposition modelling (FDM) is increasingly explored for personalised drug delivery, but the lack of suitable biodegradable and printable filaments limits its pharmaceutical application. In this study, we investigated the influence of formulation and structural design on the performance of polyvinyl alcohol (PVA)-based filaments doped with theophylline anhydrous for 3D printing. To address the intrinsic brittleness and poor printability of PVA, cassava pulp-derived fibres—a sustainable and underutilised agricultural by-product—were incorporated together with polyethylene glycol (PEG 400), Eudragit® NE 30 D, and calcium stearate. The addition of fibres modified the mechanical properties of PVA filaments through hydrogen bonding, improving flexibility but increasing surface roughness. This drawback was mitigated by Eudragit® NE 30 D, which enhanced surface smoothness and drug distribution uniformity. The optimised composite formulation (P10F5E5T5) was successfully extruded and used to fabricate 3D-printed constructs. Release studies demonstrated that drug release could be modulated by pore geometry and construct thickness: wider pores enabled rapid Fickian diffusion, while narrower pores and thicker constructs shifted release kinetics toward anomalous transport governed by polymer swelling. These findings demonstrate, for the first time, the potential of cassava fibre as a functional additive in pharmaceutical FDM and provide a rational formulation–structure–performance framework for developing sustainable, geometry-tuneable drug delivery systems.This research project was supported by the Fundamental Fund 2025, Chiang Mai University and Thailand Science Research and Innovation (TSRI) (FRB680102/0162)