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FE-SpikeFormer: A Camera-Based Facial Expression Recognition Method for Hospital Health Monitoring
Get PDFFacial expression recognition has emerged as a critical research area in health monitoring, enabling healthcare professionals to assess patients' emotional and psychological states for timely intervention and personalized care. However, existing methods often struggle to balance computational accuracy with energy efficiency. To address this challenge, this paper proposes FE-SpikeFormer — a high-accuracy, low-energy, and deployment-friendly Spiking Neural Network (SNN) for facial emotion recognition. The proposed architecture comprises three key components: the initial convolution module, the spiking extraction block, and the spiking integration block. These three modules collectively support detailed and contextual feature extraction, promote spatial feature integration, and strengthen the representational capacity of spiking signals. Meanwhile, a joint verification is conducted in both controlled laboratory settings and real-world hospital scenarios. Experimental results demonstrate that FE-SpikeFormer achieves top-three recognition accuracy among state-of-the-art methods, while utilizing only 6.93 million parameters. Moreover, it exhibits strong robustness against various noise conditions, underscoring its potential for practical deployment in healthcare environments.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62401326);
10.13039/501100002858-China Postdoctoral Science Foundation (Grant Number: 2024T170463 and 2024M751676);
Ministry of Science and Technology-Yangtze River Delta Science and Technology Innovation Program (Grant Number: 2023CSJGG1300);
Hangzhou Dianzi University Graduate Research Innovation Fund Project (Grant Number: CXJJ2024044)
Learning Accurate Representation to Nonstandard Tensors via a Mode-Aware Tucker Network
Get PDFA nonstandard tensor is frequently adopted to model a large-sale complex dynamic network. A Tensor Representation Learning (TRL) model enables extracting valuable knowledge form a dynamic network via learning low-dimensional representation of a target nonstandard tensor. Nevertheless, the representation learning ability of existing TRL models are limited for a nonstandard tensor due to its inability to accurately represent the specific nature of the nonstandard tensor, i.e., mode imbalance, high-dimension, and incompleteness. To address this issue, this study innovatively proposes a Mode-Aware Tucker Networkbased Tensor Representation Learning (MTN-TRL) model with three-fold ideas: a) designing a mode-aware Tucker network to accurately represent the imbalanced mode of a nonstandard tensor, b) building an MTN-based high-efficient TRL model that fuses both data density-oriented modeling principle and adaptive parameters learning scheme, and c) theoretically proving the MTN-TRL model's convergence. Extensive experiments on eight nonstandard tensors generating from real-world dynamic networks demonstrate that MTN-TRL significantly outperforms state-of-the-art models in terms of representation accuracy.This work is supported by the National Natural Science Foundation of China under grant 62302402, 62272078, and the Science and Technology Re- search Program of Chongqing Municipal Education Commission under grant KJQN202403017, KJZD-K202400209
Memorandum submitted to the House of Commons Energy Security and Net-zero Select Committee for their inquiry on: Revisiting the nuclear roadmap
Get PDFWritten evidence submitted to the House of Commons Energy Security and Net-zero Select Committee for their inquiry on: Workforce planning to deliver clean, secure energy, 7 April 2025.Summary: Under the assumptions currently driving HM Government policy, it is impossible to meet the stated target of 24 GW of new nuclear by 2050. We estimate that 15.9 GW is the likely maximum possible installed capacity for large-scale nuclear. Furthermore, the investment required is high, amounting to 2.4% annually of UK gross fixed capital formation. In the light of extensive development and construction over-runs on current nuclear projects, it is very hard to recommend that the Government commission additional projects before addressing the causes of risk leading to these problems
How the rights-based approach can help social work deliver social justice outcomes: Lessons from housing rights activism in Scotland
Get PDFDrawing upon the findings of a qualitative case study of a housing rights community development project in Scotland, this article explores how a rights-based approach can help social work deliver social justice outcomes. Social work is often described as a ‘human rights profession’. However, there remains a gap between how rights are enacted within individual-focused practice and structural change efforts. This gap stems from the persistent divide between bottom-up and top-down rights-based approaches in micro and macro social work practice. Study findings suggest that a bottom-up rights-based approach can help social workers achieve individual-level improvements while simultaneously advancing wider social change. To do so, social workers need to reposition themselves from ‘the centre’ to ‘the side’ when applying human rights frameworks. Only then, can the rights-based approach help revitalize social work’s commitment to both individual well-being and social justice, particularly in contexts of increased participation, prevention, and early intervention.The empirical research this article draws upon was supported by the Economic and Social Research Council
Search for vector-like leptons with long-lived particle decays in the CMS muon system in proton-proton collisions at √ = 13 TeV
Get PDFData Availability Statement: Release and preservation of data used by the CMS Collaboration as the basis for publications is guided by the CMS data preservation, re-use and open access policy (http://opendata.cern.ch/record/415).Code Availability Statement. The CMS core software is publicly available on GitHub (https://github.com/cms-sw/cmssw).A version of the article is available at arXiv:2503.16699v2 [hep-ex] (https://arxiv.org/abs/2503.16699). Comments: Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at https://cms-results.web.cern.ch/cms-results/public-results/publications/EXO-23-015 (CMS Public Pages). Report number: CMS-EXO-23-015, CERN-EP-2025-021. Journal reference: JHEP 08 (2025) 156.A first search is presented for vector-like leptons (VLLs) exclusively decaying into a light long-lived pseudoscalar boson and a standard model τ lepton. The pseudoscalar boson is assumed to have a mass below the τ + τ − threshold, so that it decays exclusively into two photons. It is identified using the CMS muon system. The analysis is carried out using a data set of proton-proton collisions at a center-of-mass energy of 13 TeV collected by the CMS experiment in 2016–2018, corresponding to an integrated luminosity of 138 fb −1. Selected events contain at least one pseudoscalar boson decaying electromagnetically in the muon system and at least one hadronically decaying τ lepton. No significant excess of data events is observed compared to the background expectation. Upper limits are set at 95% confidence level on the vector-like lepton production cross section as a function of the VLL mass and the pseudoscalar boson mean proper decay length. The observed and expected exclusion ranges of the VLL mass extend up to 700 and 670 GeV, respectively, depending on the pseudoscalar boson lifetime.SCOAP³
Reclaiming the well-being agenda in community development
Get PDFData Availability: No new data were generated or analysed in support of this research.Improving well-being did not use to be a controversial idea in community development. Yet, in recent years, the growing focus on well-being at the policy level has made many become critical of the term. Well-being has been employed to support government neo-liberal agendas by emphasizing individual responsibility over social justice. On this framing, improving well-being is thought to shift community development practice from challenging injustice to helping people feel and cope better with their lives. This article argues that, despite attempts to associate well-being with individual responsibility, the greater focus on well-being at the policy level is something to celebrate. This article draws upon the philosophy, psychology and sociology of well-being to make two arguments. The first argument is that conceptualizations of well-being are diverse and contested, and as such, it is important not to associate well-being with the narrow conception one is critical of. The second argument is that a greater focus on well-being can help communities challenge the reduction of welfare spending. Well-being, instead of de-politicizing development, can help reinforce its political stand. This article advocates for the use of pluralistic understandings of well-being within the framework of the capabilities approach to ensure community development advances social change
Wireless-powered Multi-access Edge Computing with Cascaded zeRISs
Get PDFIn this paper, we develop an energy minimization framework for wireless-powered multi-access edge computing (WP-MEC) networks, where two zero-energy reconfigurable intelligent surfaces (zeRISs) are employed to support energy harvesting and task offloading. In the downlink energy harvesting period, the hybrid access point (HAP) provides energy beamforming for multiple zero-energy devices and two zeRISs, where the harvested energy is used to offload tasks in the uplink period. Specifically, an optimization problem is formulated to minimize the energy consumption at the HAP, jointly considering the nonlinear energy harvesting model and cascaded RIS link. Next, an efficient iterative solution is designed to realize joint time allocation, HAP energy beamforming, and reflection coefficients for two-RIS by employing alternating optimization and semidefinite relaxation methods. Numerical results demonstrate the superiority of the algorithm. Compared to the corresponding single RIS setup, the energy consumption of HAP under the proposed two-zeRIS scheme is significantly reduced.This work was supported in part by the National Key R&D Program of China under Grant 2023YFB2603500, in part by the National Natural Science Foundation of China under Grant 62361136810 and 62301462, and in part by NSFC 62350710217, international cooperation project S20240364 and Sichuan HT 2024JDHJ0042
Epitalon increases telomere length in human cell lines through telomerase upregulation or ALT activity
Get PDFThis article has been updated.Data availability:
No datasets were generated or analysed during the current study.Supplementary Information is available online at: https://link.springer.com/article/10.1007/s10522-025-10315-x#Sec17 .Epitalon, a naturally occurring tetrapeptide, is known for its anti-aging effects on mammalian cells. This happens through the induction of telomerase enzyme activity, resulting in the extension of telomere length. A strong link exists between telomere length and aging-related diseases. Therefore, telomeres are considered to be one of the biomarkers of aging, and increasing or maintaining telomere length may contribute to healthy aging and longevity. Epitalon has been the subject of several anti-aging studies however, quantitative data on the biomolecular pathway leading to telomere length increase, hTERT mRNA expression, telomerase enzyme activity, and ALT activation have not been extensively studied in different cell types. In this article, the breast cancer cell lines 21NT, BT474, and normal epithelial and fibroblast cells were treated with epitalon then DNA, RNA, and proteins were extracted. qPCR and Immunofluorescence analysis demonstrated dose-dependent telomere length extension in normal cells through hTERT and telomerase upregulation. In cancer cells, significant telomere length extension also occurred through ALT (Alternative Lengthening of Telomeres) activation. Only a minor increase in ALT activity was observed in Normal cells, thereby showing that it was specific to cancer cells. Our data suggests that epitalon can extend telomere length in normal healthy mammalian cells through the upregulation of hTERT mRNA expression and telomerase enzyme activity.Self-funding PhD students, and funding from the department of biosciences for final year project students and masters’ students
Formulation and Structural Optimisation of PVA-Fibre Biopolymer Composites for 3D Printing in Drug Delivery Applications
Get PDFData 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)
Social Entropy Informer: A Multi-Scale Model-Data Dual-Driven Approach for Pedestrian Trajectory Prediction
Get PDFPedestrian trajectory prediction is fundamental in various applications, such as autonomous driving, intelligent surveillance, and traffic management. Existing methods generally fall into two categories: model-driven approaches and data-driven approaches. However, both approaches have inherent limitations when applied to real-world scenarios, particularly in capturing the complex interactions between pedestrians and modeling the stochastic nature of human motion. Notably, there is a lack of research on integrating the strengths of model-driven and data-driven paradigms, which can better address these challenges. This paper aims to fill these limitations by proposing a novel model-data dual-driven approach, called Social Entropy Informer (SEI), for pedestrian trajectory prediction. SEI simultaneously models local and global pedestrian interactions while incorporating information entropy to capture human motion’s inherent randomness and uncertainty quantitatively, which provides a robust framework for predicting pedestrian trajectories. Furthermore, we propose a new loss function derived from information theory, which accounts for the stochasticity of pedestrian movement and enhances the model’s ability to generalize across diverse scenarios. The SEI framework integrates feature extraction, entropy-based stochastic modeling, and the new loss function, improving prediction accuracy and model interpretability. Experimental results demonstrate that SEI outperforms other benchmark methods in prediction accuracy.10.13039/501100013088-Qinglan Project of Jiangsu Province of China;
Natural Science Foundation of Jiangsu Higher Education Institutions of China (Grant Number: 23KJB520038);
Research Enhancement Fund of Xi’an Jiaotong-Liverpool University (XJTLU) (Grant Number: REF-23-01-008);
Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia (Grant Number: GPIP: 108-135-2024)
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