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LW-DETR: a lightweight transformer-based object detection algorithm for efficient railway crossing surveillance
Get PDFObject detection at coal transportation railway crossings is crucial for accident prevention and traffic efficiency improvement. However, the application of existing methods on resource-constrained devices has seldom been considered. To address these challenges, in this paper, we propose a lightweight railway crossing object detection algorithm based on the Transformer framework, referred to as Light-Weight DEtection TRansformer (LW-DETR). In this algorithm, the Paddle Paddle-Lightweight CPU Convolutional Network (PP-LCNet) is employed as the backbone network, where standard convolution is combined with depthwise separable convolution for multi-scale feature extraction. Furthermore, the cross-scale feature fusion module is optimized to reduce redundant calculations and enhance feature fusion efficiency. Moreover, the Scylla-Intersection over Union loss function is introduced to comprehensively evaluate bounding box similarity, thereby improving object detection accuracy. Ablation experiments conducted on a modified Pascal Visual Object Classes (Pascal VOC) dataset demonstrate that LW-DETR, while maintaining acceptable detection accuracy, achieves a 135.3% increase in frames per second, a 71.7% reduction in parameters, and a 73.7% decrease in computational load, leading to effective lightweight performance. Comparative experiments with other popular object detection algorithms further confirm that LW-DETR significantly enhances detection speed while maintaining high accuracy, considerably reducing model size and validating the effectiveness of these improvements.This work was supported in part by the Natural Science Foundation of Shandong Province of China under Grant ZR2023MF067, the European Union’s Horizon 2020 Research and Innovation Programme under Grant 820776 (INTEGRADDE), the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Graphene-Based Biosensors: Enabling the Next Generation of Diagnostic Technologies—A Review
Get PDFData Availability Statement:
Data sharing is not applicable.Graphene, a two-dimensional carbon material with a hexagonal lattice structure, possesses remarkable properties. Exceptional electrical conductivity, mechanical strength, and high surface area that make it a powerful platform for biosensing applications. Its sp2-hybridised network facilitates efficient electron mobility and enables diverse surface functionalisation through bio-interfacing. This review highlights the core detection mechanisms in graphene-based biosensors. Optical sensing techniques, such as surface plasmon resonance (SPR) and surface-enhanced Raman scattering (SERS), benefit significantly from graphene’s strong light–matter interaction, which enhances signal sensitivity. Although graphene itself lacks intrinsic piezoelectricity, its integration with piezoelectric substrates can augment the performance of piezoelectric biosensors. In electrochemical sensing, graphene-based electrodes support rapid electron transfer, enabling fast response times across a range of techniques, including impedance spectroscopy, amperometry, and voltammetry. Graphene field-effect transistors (GFETs), which leverage graphene’s high carrier mobility, offer real-time, label-free, and highly sensitive detection of biomolecules. In addition, the review also explores multiplexed detection strategies vital for point-of-care diagnostics. Graphene’s nanoscale dimensions and tunable surface chemistry facilitate both array-based configurations and the simultaneous detection of multiple biomarkers. This adaptability makes graphene an ideal material for compact, scalable, and accurate biosensor platforms. Continued advancements in graphene biofunctionalisation, sensing modalities, and integrated multiplexing are driving the development of next-generation biosensors with superior sensitivity, selectivity, and diagnostic reliability.This research received no external funding
Operationalising sustainability in professional kitchens: The interplay of chef competencies, environmental values and human resource management strategies
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Data will be made available on request.Food waste reduction and lowering greenhouse gas emissions (GHGE) of diets are key focus areas of the food systems transition. Inspired by the ecological systems theory, this exploratory study assesses how chefs' competencies, environmental values, human resource management (HRM) practices interact in a microsystem to reduce food waste and GHGE of food offers. A participant selection framework was developed to explore four perspectives: kitchen, sustainability, industry support and catering education. Twenty-three stakeholders, 9 (39 %) chefs, 6 (26 %) industry stakeholders, 5 (22 %) chef educators and 3 (13 %) sustainability professionals in the sector were interviewed. While the study set out to examine the role of HRM in shaping environmental kitchen practices, the findings suggest that HRM does not directly influence behaviours related to food waste and GHGE reduction. Instead, kitchen leadership, as part of the microsystem, where daily interactions and operational decisions occur, emerges as a potentially more influential factor. Within this immediate environment, daily interactions and skill application, such as culinary techniques, product knowledge, logistics, creativity, and innovation, play a central role in shaping sustainable practices. These competencies not only support operational efficiency but also foster entrepreneurial thinking. While broader societal discourse reflects a macrosystem shift in environmental attitudes, the study stresses the need to translate this awareness into applied skills within the microsystem, where behaviour change is most effectively enacted.The research was funded by the UK Food Systems Centre for Doctoral Training (The Partnership for Sustainable Food Future Centre for Doctoral Training (PSFF-CDT); Project Reference: BB/V011391/1
Closed-Loop Parameter Optimization for Robotic Machining Using Physics-Informed Machine Learning and Multiobjective Optimization
Get PDFIn practical applications, the simultaneous optimization of numerous design parameters in time-consuming multi-objective optimization experiments is recognized as a significant bottleneck across various scientific and engineering disciplines. A prominent example is the optimization of machining parameters for achieving efficient and precise robotic belt grinding (RBG). This paper presents a closed-loop machining parameter optimization approach, which comprises two key stages: forward multi-task prediction and backward multi-objective parameter optimization. In the first stage, a physics-informed neural network (PINN) method is introduced, which integrates the multi-gate mixture-of-experts multi-task learning method with an RBG mechanism model to simultaneously predict material removal depth and averaged surface roughness. In the second stage, a powered multi-objective particle swarm optimization (MOPSO) method is developed, which combines a standard MOPSO method with a non-linear Powerball technique, to efficiently optimize the RBG machining parameters with a limited number of training iterations based on the learned PINN model. Two optimal machining parameter solutions are generated and recommended for the RBG machining process. The effectiveness and superiority of the proposed closed-loop parameter optimization method are validated through comparative experiments, which demonstrate its advantages in both coprediction accuracy and optimization efficiency. Note to Practitioners—This paper addresses the challenge of identifying robotic machining parameters that effectively balance machining efficiency and surface quality. Traditional trial-and-error methods for adjusting these parameters are both time-intensive and costly, given the vast number of possible combinations. To overcome these limitations, this paper proposes an intelligent optimization approach that leverages historical machining data to automatically determine optimal machining parameters. Our approach integrates artificial intelligence techniques with robotic machining mechanisms, ultimately recommending two sets of parameters for robotic machining. Preliminary experiments demonstrate the feasibility of this approach, but it has not yet been incorporated into a robotic machining system or tested in production. Future research will focus on dynamically optimizing robotic machining parameters by combining dynamic time-series signals with static machining parameters.National Natural Science Foundation of China (Grant Number: 52188102, 62503185 and 52505554);
10.13039/501100001809-China Post-Doctoral Science Foundation (Grant Number: 2024M750991);
Post-Doctoral Project of Hubei Province of China (Grant Number: 2024HBBHCXA010)
Search for dark matter produced in association with one or two top quarks in proton-proton collisions at √s = 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 (https://opendata.cern.ch/record/415).Code Availability Statement. The CMS core software is publicly available on GitHub (https://github.com/cms-sw/cmssw).A preprint version of the article is available at arXiv:2505.05300v2 [hep-ex], https://arxiv.org/abs/2505.05300 . 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-22-014 (CMS Public Pages). Report number: CMS-EXO-22-014, CERN-EP-2024-338. Journal reference: JHEP 08 (2025) 085.A search is performed for dark matter (DM) produced in association with a single top quark or a pair of top quarks using the data collected with the CMS detector at the LHC from proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to 138 fb −1 of integrated luminosity. An excess of events with a large imbalance of transverse momentum is searched for across 0, 1 and 2 lepton final states. Novel multivariate techniques are used to take advantage of the differences in kinematic properties between the two DM production mechanisms. No significant deviations with respect to the standard model predictions are observed. The results are interpreted considering a simplified model in which the mediator is either a scalar or pseudoscalar particle and couples to top quarks and to DM fermions. Axion-like particles that are coupled to top quarks and DM fermions are also considered. Expected exclusion limits of 410 and 380 GeV for scalar and pseudoscalar mediator masses, respectively, are set at the 95% confidence level. A DM particle mass of 1 GeV is assumed, with mediator couplings to fermions and DM particles set to unity. A small signal-like excess is observed in data, with the largest local significance observed to be 1.9 standard deviations for the 150 GeV pseudoscalar mediator hypothesis. Because of this excess, mediator masses are only excluded below 310 (320) GeV for the scalar (pseudoscalar) mediator. The results are also translated into model-independent 95% confidence level upper limits on the visible cross section of DM production in association with top quarks, ranging from 1 pb to 0.02 pb.SCOAP3
Measurements of inclusive and differential Higgs boson production cross sections at √ = 13.6 TeV in the H → γγ decay channel
Get PDFData Availability Statement: This article has no associated data or the data will not be deposited. 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.Code Availability Statement: This article has no associated code or the code will not be deposited. The CMS core software is publicly available on GitHub.A version of the article is available at arXiv:2504.17755v2 [hep-ex] (https://arxiv.org/abs/2504.17755). Comments: Replaced with the published version. Added the journal reference and the DOI. All the figures and tables, including additional supplementary figures, can be found at https://cms-results.web.cern.ch/cms-results/public-results/publications/HIG-23-014 (CMS Public Pages). Report number: CMS-HIG-23-014, CERN-EP-2025-067. Journal reference: JHEP 09 (2025) 070.Inclusive and differential cross sections for Higgs boson production in proton-proton collisions at a centre-of-mass energy of 13.6 TeV are measured using data collected with the CMS detector at the LHC in 2022, corresponding to an integrated luminosity of 34.7 fb^-1. Events with the diphoton final state are selected, and the measured inclusive fiducial cross section is σfid = 74 ± 11 (stat) [+5 -4] (syst) fb, in agreement with the standard model prediction of 67.8 ± 3.8 fb. Differential cross sections are measured as functions of several observables: the Higgs boson transverse momentum and rapidity, the number of associated jets, and the transverse momentum of the leading jet in the event. Within the uncertainties, the differential cross sections agree with the standard model predictions.SCOAP³
Does a knowledge targeted integrated translation intervention influence the practice behaviours of children’s occupational therapists?
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonBackground: Research shows it can take between 10-20 years for new evidence to be
routinely implemented into occupational therapy practice. This ‘knowledge-to-practice gap’
has serious implications on quality of care, particularly in services for children and young
people, where early interventions can significantly influence life outcomes. This thesis
evaluated the impact of a targeted, integrated knowledge translation intervention on
evidence-based practice behaviours of children's occupational therapists working in
England’s community-based National Health Services (NHS).
Methods: Multiple research designs were used, including a scoping review, qualitative
enquiry, intervention development protocol, qualitative process evaluation, a before-andafter
study and cross-sectional study. Forty-nine occupational therapists from five sites,
including one comparison site, participated.
Results: Early findings identified 77 beliefs grouped into seven key determinants influencing
evidence-based practice, with self-efficacy, social influence, and attitudes most prominent.
An integrated knowledge translation intervention was developed informed by Intervention
Mapping and delivered online. The intervention included educational outreach, file auditing
and feedback, and the creation of an ‘evidence library’. Data collection included focus
groups, file audits, Canadian Occupational Performance Measure scores, and service user
length of stay. Thematic analysis and generalised linear mixed-effects models were primarily
used to assess impact. Results showed nuanced outcomes at a clinician behaviour level,
service user level and an organisational level. Key mechanisms of impact included expert
facilitation, reflection, peer learning, multimodal feedback, and strengthened accountability.
Conclusion: This research contributes to a growing evidence-base in children’s
occupational therapy knowledge translation. It highlights the importance of integrating theory
with practical strategies and clinician collaboration. The findings support the use of multifaceted
knowledge translation strategies to address therapist behaviour change, that in turn
can influence organisational aspects and service user outcomes
Multi-recycling of different concrete products: Effects on recycled aggregate’s physical characteristics and compressive strength
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Data will be made available on request.The utilisation of recycled aggregate from construction and demolition waste (CDW) as a replacement for fine and coarse natural aggregate has been increasing in recent years. The purpose of this study is to examine the feasibility and limitations of multiple recycling of concrete aggregates, which represents a novel contribution in understanding the extent to which CDW can be repeatedly reused. This research aims to reduce the amount of construction waste sent to landfill and reduce carbon emissions. An experimental investigation was carried out on eleven randomly selected natural aggregate concrete products available on the market. The parent concrete was used to create the first-generation recycled concrete aggregates by crushing with a hammer. Within the concrete products happened to have two different fibre-reinforced composites and one manufactured aggregate were examined as well. The investigation assessed the aggregate morphology, density and particle size distribution through three recycling cycles. The investigation found that increasing the number of recycling cycles for all types of aggregates increased the angularity, the volume of coarse aggregates and water absorption while fine particles was reduced giving way to mortar paste and the compressive strength of each subsequent concrete was reduced. By the end of the third recycling cycle, all aggregates turned into 80 % cement paste. The rate of physical and mechanical performance change decreased with each cycle but did not settle by the third cycle, thus a conclusive conclusion could not be formed, although the trend was noticed. The decrease was asymptotic with the number of recycling cycles. It was also discovered that the multiple recycling procedure replaced 80 % of the parent aggregate volume by the third recycling cycle and, for mixes containing fibres, it damaged 98 % of the fibres resulting in a full loss of fibre performance. These findings demonstrate that it is only possible to recycle concrete by a finite number of times before significant deterioration in quality occurs, limiting its long-term reuse potential
Investigating the effects of hybrid PVA/BF fibers in low-carbon 3D printed concrete: rheology, strength, and anisotropy
No full textData availability:
The data that has been used is confidential.Balancing low-carbon content with performance in 3D printed concrete (3DPC) remains a key challenge for wider application. This study demonstrates the researchers’ self-developed low‑carbon mix design combining 20 % recycled sand replacement, a low binder-to-sand ratio of 1:2, and a hybrid fiber system using Polyvinyl Alcohol (PVA) and Basalt Fiber (BF) for 3DPC. The effects of hybrid fibers on the rheology, mechanical properties, and anisotropy of low‑carbon 3DPC were systematically investigated through two experimental groups, namely: Group I where the PVA content was fixed at 0.25 % while BF content varied from 0 % to 0.4 %; and Group II where the total fiber content was fixed at 0.5 % whilst the optimal PVA/BF ratio was explored. In addition, a cradle-to-gate (A1-A3) life-cycle assessment was performed to quantify embodied carbon. The results showed that appropriate proportions of hybrid PVA/BF fibers significantly increased static yield stress (up to 40.3 %), while the hydrophobic BF fibers reduced dynamic yield stress and plastic viscosity, optimizing the balance between buildability and extrudability. Optimal mixes reached compressive strengths near 50 MPa, about a 42.2 % increase over the control group. Furthermore, hybrid fibers reduced the compressive anisotropy index from 8.8 to 1.1. Failure mode analysis showed that 3DPC had obvious directional weaknesses, among which the interlayer bonding was the main weak point. It was further observed that PVA fibers were mainly pulled out, while BF fibers were fractured. However, when the two fibers were added in similar amounts, agglomeration occurred, reducing their synergistic effect. The cradle-to-gate Life-cycle impact assessment (LCA) indicates that the proposed P40B10 mix-through substantial cement reduction, partial replacement of natural sand with recycled sand, and hybrid PVA/BF reinforcement-markedly improves carbon efficiency of 3DPC, roughly halving the strength-normalized carbon intensity compared with reference mixes. The research provides theoretical support and practical guidance for the rheological regulation, mechanical strengthening and isotropic optimization of low-carbon 3DPC.This work was supported by the National Nature Science Foundation of China [Grant No. 51908253] and the Natural Resources Development Special Foundation of Jiangsu Province [Grant No. JSZRHYKJ202113]
Transient stability analysis and optimal control strategy of grid-forming converters based on circular current limiter
Get PDFTo limit transient current overshoot in grid-forming (GFM) converters caused by grid voltage sags, a variety of current limiting strategies, including priority-based limiter and circular current limiter (CCL) are employed. Existing research predominantly focuses on the transient stability analysis of priority-based limiting strategies. In this paper, an equivalent model and transient analysis method for GFM converters utilizing CCL is proposed, in which the CCL strategy is equivalent to a resistor inserted in series within the control loop. Based on this model, transient stability analysis under current saturation conditions is further conducted by constructing power-angle curves. An optimal control strategy is proposed to enhance the system’s transient stability by modifying the power references and incorporating an anti-windup loop for the integrator. Finally, simulation results verify the effectiveness of the proposed transient stability analysis method and the optimized control strategy.10.13039/100006190-Research and Development;
10.13039/501100001809-National Natural Science Foundation of Chin
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