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Motor imagery drives the effects of combined action observation and motor imagery on corticospinal excitability for coordinative lower-limb actions
Get PDFData availability: The datasets generated and analyzed during the current study are publicly available on the Open Science Framework: https://osf.io/6f2g8/.A preprint version of the article is available at https://doi.org/10.31234/osf.io/g6fzk under a CC BY license. It has not been certified by peer review.Combined action observation and motor imagery (AOMI) facilitates corticospinal excitability (CSE) and may potentially induce plastic-like changes in the brain in a similar manner to physical practice. This study used transcranial magnetic stimulation (TMS) to explore changes in CSE for AOMI of coordinative lower-limb actions. Twenty-four healthy adults completed two baseline (BLH, BLNH) and three AOMI conditions, where they observed a knee extension while simultaneously imagining the same action (AOMICONG), plantarflexion (AOMICOOR-FUNC), or dorsiflexion (AOMICOOR-MOVE). Motor evoked potential (MEP) amplitudes were recorded as a marker of CSE for all conditions from two knee extensor, one dorsi flexor, and two plantar flexor muscles following TMS to the right leg representation of the left primary motor cortex. A main effect for experimental condition was reported for all three muscle groups. MEP amplitudes were significantly greater in the AOMICONG condition compared to the BLNH condition (p = .04) for the knee extensors, AOMICOOR-FUNC condition compared to the BLH condition (p = .03) for the plantar flexors, and AOMICOOR-MOVE condition compared to the two baseline conditions for the dorsi flexors (ps ≤ .01). The study findings support the notion that changes in CSE are driven by the imagined actions during coordinative AOMI
PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization
Get PDFA preprint version of the conference paper is available under a CC BY-NC-SA license at arXiv:2404.09011v1 [cs.CV], https://arxiv.org/abs/2404.09011 . It may not have been certified by peer review. Comments: Accepted to CVPR2024. You are advised to consult the final version to be published by IEEE in due course.This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.Domain Generalization (DG) aims to resolve distribution shifts between source and target domains and current DG methods are default to the setting that data from source and target domains share identical categories. Nevertheless there exists unseen classes from target domains in practical scenarios. To address this issue Open Set Domain Generalization (OSDG) has emerged and several methods have been exclusively proposed. However most existing methods adopt complex architectures with slight improvement compared with DG methods. Recently vision-language models (VLMs) have been introduced in DG following the fine-tuning paradigm but consume huge training overhead with large vision models. Therefore in this paper we innovate to transfer knowledge from VLMs to lightweight vision models and improve the robustness by introducing Perturbation Distillation (PD) from three perspectives including Score Class and Instance (SCI) named SCI-PD. Moreover previous methods are oriented by the benchmarks with identical and fixed splits ignoring the divergence between source domains. These methods are revealed to suffer from sharp performance decay with our proposed new benchmark Hybrid Domain Generalization (HDG) and a novel metric H^ 2 -CV which construct various splits to comprehensively assess the robustness of algorithms. Extensive experiments demonstrate that our method outperforms state-of-the-art algorithms on multiple datasets especially improving the robustness when confronting data scarcity.Chinese National Natural Science Foundation under Grants (62076033)
Oligogenic inheritance in severe adult obesity
Get PDFData availability:
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.Supplementary information is available onlne at: https://www.nature.com/articles/s41366-024-01476-9#Sec11 .Background/objective:
The genetic architecture of extreme non-syndromic obesity in adults remains to be elucidated. A range of genes are known to cause monogenic obesity, but even when pathogenic mutations are present, there may be variable penetrance.
Methods:
Whole-exome sequencing (WES) was carried out on a 15-year-old male proband of Pakistani ancestry who had severe obesity. This was followed by family segregation analysis, using Sanger sequencing. We also undertook re-analysis of WES data from 91 unrelated adults with severe obesity (86% white European ancestry) from the Personalised Medicine for Morbid Obesity (PMMO) cohort, recruited from the UK National Health Service.
Results:
We identified an oligogenic mode of inheritance of obesity in the proband’s family—this provided the impetus to reanalyze existing sequence data in a separate dataset. Analysis of PMMO participant data revealed two further patients who carried more than one rare, predicted-deleterious mutation in a known monogenic obesity gene. In all three cases, the genes involved had known autosomal dominant inheritance, with incomplete penetrance.
Conclusion:
Oligogenic inheritance may explain some of the variable penetrance in Mendelian forms of obesity. We caution clinicians and researchers to avoid confining sequence analysis to individual genes and, in particular, not to stop looking when the first potentially-causative mutation is found
Aeroacoustic assessment of porous blade treatment applied to centrifugal fans
Get PDFData Availability: The data that support the findings of this study are available from the corresponding author upon reasonable request.Heavy-duty centrifugal fans account for a significant share of energy consumption in the process and manufacturing industries. As a result, these machines are under increasing pressure to operate at maximum efficiency to reduce costs, pollutants and noise: only combined optimization is considered competitive for future generations of fans. Preliminary studies have shown that applying structured porosity to aerofoil rear parts can lead to a reduction in self noise and trailing edge shedding noise in the mid-to-high frequency range. With this in mind, a porous surface cover is applied to a prototype centrifugal fan to evaluate the aeroacoustic potential in a complex rotating machinery. The optimal geometric characteristics of the perforation are derived from experiments with single aerofoils, while the perimeter of the covered area is varied in eight steps. The centrifugal fan specimen is rapid-prototyped and tested at different fan speeds along the complete characteristic curves, while both aerodynamic and aeroacoustic performances are simultaneously recorded. The results obtained show a significant reduction in overall noise level while aerodynamic performance is maintained. Spectral analysis shows that the noise reduction is due to a broadband effect, where the upper and lower cut-off frequencies are determined by the rotational speed and the location of the applied porosity along the blade chord. However, the maximum noise reduction is obtained as a clear function of the minimum distance between the perforation and the trailing edge of the blade, indicating that the underlying working mechanisms are a combination of broadband dissipation effects due to porosity and destructive interference.This study is supported by the Engineering and Physical Sciences Research Council inthe United Kingdom through research grant No. EP/V006886/1, and the PhD studentship sponsored by theDoctoral Training Partnership
Many-Objective Simulation Optimization for Camp Location Problems in Humanitarian Logistics
Get PDFData Availability Statement: The Integrated Food Security Phase Classification (IPC) datasets can be downloaded from: https://www.ipcinfo.org/ipc-country-analysis/, the JAXA ALOS Global 30m DSM 2021 dataset can be downloaded from https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AW3D30_V3_2, the Esri Land Cover 2020 source can be downloaded from https://www.arcgis.com/apps/instant/media/index.html?appid=fc92d38533d440078f17678ebc20e8e2.Humanitarian organizations face a rising number of people fleeing violence or persecution, people who need their protection and support. When this support is given in the right locations, it can be timely, effective and cost-efficient. Successful refugee settlement planning not only considers the support needs of displaced people, but also local environmental conditions and available resources for ensuring survival and health. It is indeed very challenging to find optimal locations for establishing a new refugee camp that satisfy all these objectives. In this paper, we present a novel formulation of the facility location problem with a simulation-based evolutionary many-objective optimization approach to address this problem. We show how this approach, applied to migration simulations, can inform camp selection decisions by demonstrating it for a recent conflict in South Sudan. Our approach may be applicable to diverse humanitarian contexts, and the experimental results have shown it is capable of providing a set of solutions that effectively balance up to five objectives.This work was supported by the ITFLOWS and HiDALGO projects, which have received funding from the European Union Horizon 2020 research and innovation programme under grant agreement nos 882986 and 824115. It was also supported by UKRI through the ExCALIBUR-funded SEAVEA project initiative (grant agreement number EP/W007762/1)
Walking the (Infrastructural) Line: Mobile and Embodied Explorations of Infrastructures and Their Impact on the Urban Landscape
Get PDFDrawing on a series of Infrastructural Exploration ‘walkshops’ hosted at the Centre for Urban and Community Research (Goldsmiths), this article reflects on the possibilities offered by walking infrastructural lines to critically engage with urban infrastructure. In these walkshops, we invite participants – academic researchers, students, activists, members of the public – to join in moving through the city and to consider their embodied and emotional contact with the infrastructure we encounter. Traversing different spaces and opening our sociological imaginations to the city, we place an emphasis on collective experiences, happenstance conversations and different forms of knowing. We aim to foster a corporeal, mobile and multisensory attention to infrastructure and its impacts on the urban landscape. In this article, we propose that these embodied and affective encounters with infrastructure can attune us to questions of infrastructure’s social life, the politics of its siting, urban power dynamics, distributional (in)justice and forms of (infra)structural violence. Inspired by Shannon Mattern’s work, the article ends by offering a provocation. We ask readers, as we ask walkshop participants: then what? What are the socio-political potentials in these collective, peripatetic and visceral engagements with infrastructure?The author(s) received no financial support for the research, authorship, and/or publication of this article
Assessment of primary care services operational resilience by patients: Implications for COVID-19 recovery
Get PDFData availability statement: The data that support the findings of this study are available from the corresponding author, [YX], upon reasonable request.Supplementary data is available online at: https://www.sciencedirect.com/science/article/pii/S0263237324001518?via%3Dihub#appsec1 .While the National Health Service of the United Kingdom recovers from COVID-19, it's crucial to assess the impact of the dynamic capabilities within its healthcare services to ensure future public health protection. This study adopts mixed methods of literature review and surveys. Survey findings reveal that agility, flexibility, and building redundancy proved instrumental in reconfiguring resource foundations swiftly and fostering new partnerships. These actions were essential for sustaining service quality and efficiency. The analysis recommends that patients and healthcare professionals should co-design a technology-driven primary care service provision that is person-centric and digitally inclusive. Furthermore, primary care service stakeholders should develop targeted collaborations, and workforce development should be a priority to increase medical reserve in the healthcare system. This research provides empirical evidence, enabling the National Health Service to persist in enhancing dynamic capabilities and reinforcing resilience for anticipated and unforeseen future challenges
On the Analysis of GAN-based Image-to-Image Translation with Gaussian Noise Injection
Get PDFImage-to-image (I2I) translation is vital in computer vision tasks like style transfer and domain adaptation. While recent advances in GAN have enabled high-quality sample generation, real-world challenges such as noise and distortion remain significant obstacles. Although Gaussian noise injection during training has been utilized, its theoretical underpinnings have been unclear. This work provides a robust theoretical framework elucidating the role of Gaussian noise injection in I2I translation models. We address critical questions on the influence of noise variance on distribution divergence, resilience to unseen noise types, and optimal noise intensity selection. Our contributions include connecting
-divergence and score matching, unveiling insights into the impact of Gaussian noise on aligning probability distributions, and demonstrating generalized robustness implications. We also explore choosing an optimal training noise level for consistent performance in noisy environments. Extensive experiments validate our theoretical findings, showing substantial improvements over various I2I baseline models in noisy settings. Our research rigorously grounds Gaussian noise injection for I2I translation, offering a sophisticated theoretical understanding beyond heuristic applications.National Natural Science Foundation of China under Grants U22A2096, 62036007 and 62106184; in part by the Fundamental Research Funds for the Central Universities under Grants QTZX23042 and YJSJ24011; in part by the Young Talent Fund of Association for Science and Technology in Shaanxi China under Grant 20230121; in part by the Youth Innovation Team of Shaanxi Universities; in part by the Technology Innovation Leading Program of Shaanxi under Grant 2022QFY01-15 and in part by the Innovation Fund of Xidian University
Prediction of long-term photovoltaic power generation in the context of climate change
No full textAccurate long-term prediction of power generation in photovoltaic (PV) power stations is crucial for preparing generation plans and future planning. Quantitative prediction of future power generation from PV stations not only contributes to the stable operation of the local power system but also assists managers in formulating regional energy policies to promote renewable energy consumption. We utilized the NEX-GDDP-CMIP6 high-resolution climate dataset and employed the Vine Copula method for post-downscaling. This approach enabled high-resolution forecasts of key meteorological factors under different shared socioeconomic pathways (SSPs) scenarios (SSP245 and SSP585) for a PV power station in Yunnan, China. Additionally, we developed the KM-PSO-SVR power generation prediction model, which enables future accurate long-term PV power generation prediction. The results show that the Vine Copula multi-model ensemble downscaling model can effectively simulate the changes in key meteorological factors in the PV power station area. The KM-PSO-SVR model exhibited good simulation performance, with a mean absolute error of 0.843, root mean square error of 1.136, and correlation coefficient of 0.874 during the validation period. The results indicate that during the decade spanning from January 1, 2025, to December 31, 2034, radiation and wind speed will be decrease, while the temperature is expected to increase. In the SSP245 scenario, there is a 1.585 % increase in the average annual power generation during the future carbon peaking period (2025–2034). However, the SSP585 scenario, representing higher future emissions, shows a lower increase of 1.479 %.National Key Research and Development Program of China (Grant No. 2018YFE0208400); Science and Technology Project of State Grid Corporation of China (Key Technologies of Novel Integrated Energy System Considering Cross-border Interconnection)
Emotion Recognition Using EEG Signals through the Design of a Dry Electrode Based on the Combination of Type 2 Fuzzy Sets and Deep Convolutional Graph Networks
Get PDFData Availability Statement: The data are private and the University Ethics Committee does not allow public access to the data.Emotion is an intricate cognitive state that, when identified, can serve as a crucial component of the brain–computer interface. This study examines the identification of two categories of positive and negative emotions through the development and implementation of a dry electrode electroencephalogram (EEG). To achieve this objective, a dry EEG electrode is created using the silver-copper sintering technique, which is assessed through Scanning Electron Microscope (SEM) and Energy Dispersive X-ray Analysis (EDXA) evaluations. Subsequently, a database is generated utilizing the designated electrode, which is based on the musical stimulus. The collected data are fed into an improved deep network for automatic feature selection/extraction and classification. The deep network architecture is structured by combining type 2 fuzzy sets (FT2) and deep convolutional graph networks. The fabricated electrode demonstrated superior performance, efficiency, and affordability compared to other electrodes (both wet and dry) in this study. Furthermore, the dry EEG electrode was examined in noisy environments and demonstrated robust resistance across a diverse range of Signal-To-Noise ratios (SNRs). Furthermore, the proposed model achieved a classification accuracy of 99% for distinguishing between positive and negative emotions, an improvement of approximately 2% over previous studies. The manufactured dry EEG electrode is very economical and cost-effective in terms of manufacturing costs when compared to recent studies. The proposed deep network, combined with the fabricated dry EEG electrode, can be used in real-time applications for long-term recordings that do not require gel.This research received no external funding
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