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Reusable Packaging Systems: Design recommendations for fostering and sustaining consumer adoption
Get PDFPresented at the 6th Product Lifetimes and the Environment Conference (PLATE2025), Aalborg, Denmark, 2-4 July 2025, Section: Track 1: Design for Longer Lasting Products and Buildings – Extended Abstracts.Multiple factors influence the success and sustainability of reusable packaging systems, including consumer behaviour (Bradley & Corsini, 2023). Recent findings from UK trials emphasise the need for continued consumer engagement to ensure reusable packaging systems are profitable and provide environmental benefits(Tesco, 2021). Specifically, product categories with high turnover (e.g., household care, personal care and food & beverage) are considered preferable (IDG, 2021; Center for the Circular Economy & US Plastics Pact, 2025), but a change in consumer behaviour is needed to ensure sufficient reuse rates are met (WRAP, 2023; Center for the Circular Economy & US Plastics Pact, 2025). ...This research has been funded by the EPSRC
DAF-DETR: A dynamic adaptation feature transformer for enhanced object detection in unmanned aerial vehicles
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Data will be made available on request.Object detection in complex environments is challenged by overlapping objects, complex spatial relationships, and dynamic variations in target scales. To address these challenges, the Dynamic Adaptation Feature DEtection TRansformer (DAF-DETR) is proposed as a novel transformer-based model optimized for real-time detection in spatially complex environments. The framework introduces four key innovations. First, a learnable position encoding mechanism is employed in place of fixed positional encoding, enhancing adaptability and flexibility when processing complex spatial layouts. Second, the Resynthetic Network (ResynNet) backbone, which consists of stacked Resynthetic Blocks (ResynBlocks) integrating ResBlock and FasterBlock feature extraction strategies, is designed to optimize multi-scale feature representation and improve computational efficiency. Third, an enhanced feature fusion module is incorporated to strengthen the detection of small, densely packed objects by integrating multi-scale contextual information. Fourth, a dynamic perception module is introduced, utilizing deformable attention to capture complex spatial relationships between overlapping objects. Extensive experiments conducted on the Vision meets Drone 2019 (VisDrone2019) and Tiny Object Detection in Aerial Images (AI-TOD) datasets demonstrate the superiority of DAF-DETR, achieving state-of-the-art detection accuracy while maintaining real-time efficiency. The results confirm its robustness in handling scale variations, occlusions, and spatial complexity, establishing it as a reliable solution for real-world applications such as aerial imagery and crowded scene analysis.This work was supported in part by the Natural Science Foundation of Shandong Province of China under Grant ZR2023MF067, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
BR-MTFL: A Novel Byzantine Resilience-Enhanced Multitask Federated Learning Framework for High-Speed Train Fault Diagnosis
Get PDFIn high-speed train systems deployed across diverse geographical regions, robust fault diagnosis techniques are essential for ensuring operational safety. This article proposes the Byzantine resilience-enhanced multitask federated learning (BR-MTFL) framework, a novel framework tailored for the complexities of fault diagnosis in traction asynchronous motors under varying operational conditions. This framework innovatively introduces multitask federated learning (MTFL) to accommodate regional law restrictions and varying fault diagnosis requirements. In addition, the Byzantine resilience of our proposed framework is specifically enhanced to address the challenges posed by inconsistent and potentially misleading feature distributions across different train networks. BR-MTFL is practically validated through experiments conducted across nine clients, each representing a distinct set of fault types and operational conditions typical of high-speed trains. The experiments demonstrate the ability of BR-MTFL to outperform conventional federated learning frameworks in terms of accuracy and resilience to Byzantine threats. BR-MTFL establishes a new standard for federated learning applications in high-speed train fault diagnosis, particularly where data diversity and privacy dominate.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62233012);
Jiangsu Provincial Qinglan Project (2021);
10.13039/501100018528-Suzhou Science and Technology Programme (Grant Number: SYG202106)
The ability of subsurface dams to protect freshwater abstraction wells against seawater intrusion in heterogeneous aquifers
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No data was used for the research described in the article.The main purpose of this study was to examine the ability of subsurface dams to protect freshwater abstraction against seawater intrusion in both homogeneous and layered aquifers. Laboratory experiments were conducted in a synthetic aquifer where a subsurface dam was simulated in a homogeneous scenario (case H), and in another scenario where a top low-permeability (low-K) layer was placed in the upper part of the aquifer (case LH). We then conducted numerical simulations using the SEAWAT model to validate the experimental results. We also examined other numerical cases where a low-K layer existed at the middle (case HLH) and the bottom of the aquifer (case HL). The existence of a low-K layer has generally delayed the upconing, and it took longer for the SWI to contaminate the abstraction well. The top low-K layer case needed 52 % more pumping than the homogeneous aquifer for the wedge to spill over the dam into the landward side. The clean-up time varied substantially from one case to another, with the case HL taking longer than the other cases for SWI removal. The cleanup time was reduced by 23 % in the presence of a top low-K layer compared to the homogeneous aquifer. The study demonstrates that a low-K layer on the top of the aquifer contributed positively to improving the ability of the subsurface dams to obstruct SWI, limit saltwater upconing and, therefore, allow more optimal freshwater abstraction. A feature of this study was that it examined the ability of dams to prevent seawater intrusion in the presence of freshwater pumping, which has not been investigated in previous studies, at least in laboratory experiments
Assessing Data Reliability for AI-Driven Volcanic Rock Dating: A Comparison of Electron Microprobe and Laser Ablation Mass Spectroscopy
Get PDFData and code availability section:
The dataset used in this study, including Electron Microprobe and Laser Ablation Mass Spectroscopy results for volcanic rock dating, is available at Zenodo with DOI (10.5281/zenodo.1493329) under the CC-BY 4.0 license.
The Python scripts and Jupiter Notebooks used for data preprocessing, AI model training, and figure generation are available at GitHub and archived in Zenodo with DOI (10.5281/zenodo.14933482.
All datasets and software are shared under the MIT License, ensuring unrestricted access. Further methodological details, including data processing steps and AI model implementation, are provided in the Methods section.This study explores the integrationof artificial intelligence (AI) and modern data analytics for accurately predicting and classifying three distinct periods of volcanic activity. By leveraging previously dated volcanic samples, we assess whether existing age and geochemical data can reliably group and predict volcanic episodes. Our study focuses on the Kula Volcanic Province (Turkey). We compare the effectiveness of two analytical techniques—Electron Microprobe Analysis (EPMA) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS)—in producing high-quality datasets for training deep learning models. While EPMA provides major and minor elemental compositions, LA-ICP-MS offers a broader range of trace elements, which may improve classification accuracy. Two experiments were conducted to evaluate the feasibility of AI-based volcanic rock age estimation. In the first experiment, an autoencoder and unsupervised clustering were applied to reduce dimensionality and group samples based on their elemental composition. The results revealed that EPMA data lacked sufficient detail to form well-defined clusters, whereas LA-ICP-MS data produced clusters that closely aligned with true age classes due to their higher sensitivity to trace elements. In the second experiment, a deep neural network (DNN) was trained to classify rock ages. The LA-ICP-MS-based model achieved a classification accuracy of 95 %, significantly outperforming the EPMA-based model (72 %). These findings underscore the importance of data quality and analytical technique selection in AI-powered geochronology, demonstrating that high-quality trace element data enhances AI model performance for volcanic rock age estimation.Quaternary Research Association, and the Mineralogical Society for their financial support
Transforming seaweed into bioplastics: a review of cultivation, harvesting and processing methods
Get PDFSeaweed, once overlooked as a low-value resource, is emerging as a promising feedstock for bioplastic production. This review examines in seaweed cultivation, harvesting and processing techniques, highlighting innovative approaches to overcoming current challenges and emphasizing the seaweed’s potential to revolutionize the bioplastics industry. Seaweed offers numerous advantages over traditional bioplastic sources, including rapid growth in marine environments, no competition for arable land or freshwater and the ability to sequester carbon dioxide and absorb excess nutrients, contributing to climate change mitigation. The unique biochemical composition of seaweed, rich in hydrocolloids such as agar, carrageenan alginate and other biopolymers like ulvan and starch, enhances its suitability for bioplastic production. However, despite these benefits, seaweed-based bioplastics are still in their infancy, constrained by economic and logistical challenges, such as high production costs, technological limitations and supply chain integration issues. The findings underscore the significant potential of seaweed to contribute to sustainable development, emphasizing the need for continued innovation, collaborative efforts and investment to realize this potential fully.This work was supported by the Guarantee funding for Horizon European MSCA for the project ‘Development of compostable and renewable foams from macroalgae-based sources and identification of strategies for their successful implementation in the waste management system (CORAL), [grant EP/Y027701/1]
Joint Optimization of Data Urgency and Freshness in Wireless Body Area Networks for Enhanced eHealth Monitoring
Get PDFWireless Body Area Networks (WBANs), as an effective technology for electronic health monitoring, have transformed traditional consumer electronics (CE) into the next generation of devices with enhanced connectivity and intelligence. The improved interconnectivity between sensor nodes, coordinators, and other consumer devices has increased data availability and enabled autonomous monitoring within CE networks. However, due to the time-sensitive nature of physiological data transmission in WBANs and the urgency of sensor node data, addressing real-time data transmission under dynamic link conditions remains a significant challenge. To tackle this issue, we propose a joint optimization scheduling strategy that considers both data urgency and freshness. Our proposed strategy consists of two key components: a Sink Channel Allocation (SCA) strategy and a Node Scheduling Selection (NSS) strategy. By integrating deep reinforcement learning (DRL), we overcome the challenges posed by the large action space in channel allocation and timeslot selection, thereby improving scheduling efficiency. Both theoretical analysis and simulation results demonstrate that our method significantly outperforms traditional approaches in terms of real-time data transmission and scheduling optimization.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62171073, 62311530103 and U21A20447);
Research Foundation of the Education Bureau of Hunan Province (Grant Number: 20A112);
10.13039/501100004502-Chongqing University of Posts and Telecommunications (Grant Number: BYJS202206)
Redox active bio-ionic liquid electrolyte for high energy density Zn-ion capacitor
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The link to the data is provided in 10.17633/rd.brunel.27901281Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0378775325006792?via%3Dihub#appsec1 .Zinc-ion capacitors are attracting significant interest due to their safety, recyclability, and high theoretical capacity (820 mAh g−1). Most studies on Zn-ion capacitors focus on aqueous electrolytes, but these limit the electrochemical window and often lead to dendrite formation. In contrast, aprotic ionic liquid electrolytes extend the electrochemical window but suffer from slow diffusion kinetics of Zn species, which can reduce power density. In this work, we introduce a sustainable and biocompatible redox electrolyte based on bio-ionic liquids (Choline acetate and Choline iodide) for Zn-graphene capacitors. This electrolyte results in a high storage capacity of 350 F g−1 at a current density of 0.5 A g−1. Electrochemical tests, in situ Atomic Force Microscopy (AFM), and Density Functional Theory (DFT) studies reveal that choline in the electrolyte interacts with graphene, altering its local electronic structure and enhancing its capacity. The presence of choline iodide further improves the capacity through a redox reaction on the graphene surface. Stability tests at 3 A g−1 show an initial capacity of 160 F g−1, which decreases to 130 F g−1 after 5000 cycles, yielding a capacity retention of 81.5 %. This study paves the way for the development of biocompatible hybrid capacitors for a range of applications.We thank EPSRC (EP/W015129/1) and The Royal Society (IES/R3/233252), for funding this research. We thank Mr Pranay Hirani for Raman measurements and Dr Shaoliang Guan for XPS measurements. The authors acknowledge the use of ARCHER2 (via UK's HEC Materials Chemistry Consortium; EP/X035859) and Sulis (EP/T022108/1 and the HPC Midlands + consortium). AP thanks DST-Inspire PhD fellowship. VK acknowledge the funding support from the Indian Institute of Science, DST-INSPIRE (DST/INSPIRE/04/2018/002983), SERB-Core research grant (CRG/2020/002302)
Hearing function and ossicular deformities and fractures in the oim mouse model of brittle bone disease
No full textData availability:
Data will be made available on request.Supplementary materials are available online at: https://www.sciencedirect.com/science/article/pii/S0378595525001698#sec0015 .Hearing loss is a prevalent symptom of osteogenesis imperfecta (OI), a group of collagen type I-related skeletal disorders, commonly known as brittle bone disease. Clinical manifestation of hearing loss in OI often presents with stapes footplate fixation and hypodense foci in the otic capsule. However, the etiology and evolution of OI-hearing loss and its relation to bone abnormalities are still unknown. This study investigates the onset, severity, and progression of hearing loss in the homozygous oim mouse model of severe OI Type III, which is reported to exhibit hearing loss at 11-12 weeks of age (Chen et al., 2007), using auditory brainstem responses up to 26 weeks of age. We further examine the presence of deformities, microcracks, and fractures of the ossicular chain using synchrotron microtomography. Our results demonstrate that oim/oim mice have normal hearing, regardless of i) their parental lineage, ii) their husbandry in isolation or with other animals, iii) their mastication with powder or chow food, and iv) their anesthesia with single or multiple ketamine injections. Bone abnormalities like excessive formations, fusions, and fractures, were observed in up to 33 % of wild-type and up to 43 % of oim/oim mice in each group. Among these, joint and bone-tendon abnormalities were twice as frequent in the oim/oim mice compared to the wild-type mice. Notably, these abnormalities did not impact the hearing response in mice. Whether such bone abnormalities occur and alter auditory function in humans with OI remains uncertain.This study was supported by the National Science Foundation (CBET 1829310) and Diamond Light Source, UK (MG33620–2, MG38900–1, and MG36899–2)
Production of liquid smoke by consecutive electroporation and microwave-assisted pyrolysis of empty fruit bunches
Get PDFData availability statement: All data generated or analysed during this study are included in this published article (and its supplementary information files).Supplementary Material is available online at: https://www.degruyterbrill.com/document/doi/10.1515/gps-2025-0022/downloadAsset/suppl/gps-2025-0022_sm.pdf .This study investigated the combined effects of electroporation and microwave-assisted pyrolysis (MAP) on the characteristics of liquid smoke produced from empty fruit bunches (EFBs). Ground EFBs were subjected to electroporation in an electroporation chamber at varying electric fields of 15 and 20 kV·cm−1 and subsequently pyrolysed at varying temperatures of 300°C and 400°C in a modified microwave oven connected to a condensation system. Electroporation significantly altered EFB structure, with higher electric fields (20 kV·cm−1) causing greater structural disruptions, as evidenced by reduced pore sizes. MAP of EFBs electroporated at 15 kV·cm−1 produced a higher liquid smoke yield, while severe electroporation (20 kV·cm−1) resulted in lower yields but higher antioxidant capacity and lower pH. This was attributed to improved chemical selectivity under high electric fields and phenolic enrichment. A proposed lignocellulose degradation mechanism outlined the roles of MAP temperature and electric fields in influencing chemical conversion pathways. These findings highlight the potential for optimising electroporation and MAP to enhance the valorisation of EFBs, providing a sustainable green approach for producing high-quality liquid smoke from agricultural waste.This work was funded by the Directorate of Research and Community Service at Institut Teknologi Sepuluh Nopember via the scheme of Dana Keilmuan ITS (1171/PKS/ITS/2024) and the National Research and Innovation Agency and the Endowment Fund for Education Agency via the scheme of Riset dan Inovasi untuk Indonesia Maju (3871/II.7.5/KS.00/4/2025 and 2599/III.10/FR.06.00/4/2025)
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