bluuebunny/crossref_metadata_2025 huggingface dataset:
Increased screen time and its association to migraine and tension-type headache: a cross-sectional investigation among Bangladeshi students
Authors: Roy Simanta , Iktidar Mohammad Azmain , Chowdhury Sreshtha , Pulock Orindom Shing , Pinky Susmita Dey , Sharif Azaz Bin
Published: May 2024

Background

Increasing prevalence of screens among young people is a notable characteristic of the modern digital era. The study aimed to explore the prevalence and associated factors of migraine headache (MH) and tension-type headache (TTH) among Bangladeshi students continuing online education.

Methods

A total of 771 students were selected conveniently and using the quota sampling method. A pretested semistructured and self-administered questionnaire containing the background information, Headache Screening Questionnaire-English Version, Patient Health Questionnaire-9, Speech, Spatial and Qualities of Hearing scale and Insomnia Severity Index was used for data collection. Multivariate logistic regression analysis was conducted to explore the relationship between different headaches and screen use.

Results

The prevalence of MH, TTH and mixed headache (both MH and TTH) in the study population was 26.07%, 47.08% and 14.75%, respectively. Longer duration of online study (>12 months, adjusted ORs (AORs): 2.83, 95% CI 0 1.00 to 8.00), history of eye problem (AOR: 1.48, 95% CI 1.01 to 2.17), insomnia (AOR: 1.53, 95% CI 1.01 to 2.33) and moderate-to-severe depression (AOR: 2.35, 95% CI 1.55 to 3.56) were significantly associated with migraine headache. Conversely, longer duration of online study (>12 months, AOR: 2.87, 95% CI 1.40 to 5.86), moderate-to-severe depression (AOR=1.47, 95% CI 1.05 to 2.10) and use of multiple devices (AOR<1) for online study were significantly associated with TTH. In addition, longer duration of screen exposure (for >12 months, AOR: 4.56, 95% CI 0.99 to 20.93), moderate-to-severe depression (AOR: 2.25, 95% CI 1.37 to 3.72) and family history of headache (AOR: 2.66, 95% CI 1.65 to 4.29) were associated with mixed headache.

Conclusion

Considering the current prevalence of TTH and MH among students and their relationship with screentime, providing health education on the proper use of electronic devices can be a promising strategy in mitigating the negative consequences.
Fibrocartilaginous embolism after mountain cycling: a case report with clinical and radiological follow-up and almost complete recovery
Authors: Hagenkötter Sarah Sophie , Hammami Faten , Hagenkötter Beate
Published: May 2024

Introduction

Fibrocartilaginous embolism (FCE) is a rare spinal cord infarction due to embolism of fibrocartilaginous material with consecutive arterial infarction of the anterior spinal artery. Physical activity with increased axial pressure is the underlying mechanism of the retrograde migration of primarily nucleus pulposus material into the arterial system of the spinal cord. The initial severity of the clinical symptoms is supposed to be a prognostic predictor of recovery and so far, no specific treatment recommendation exists.

Methods

We present a case of spinal cord infarction due to FCE after long and sporty mountain cycling (during 6 hours and 2500 altitude difference) with detailed clinical and radiological follow-up.

Results

The clinical and radiological follow-up at month 4 showed an unexpected almost complete recovery despite the extensive initial clinical impairment.

Conclusion

Mountain cycling has not yet been described as a specific trigger of FCE with spinal cord infarction. Further observation is necessary to show if the prolonged bent posture and core muscle imbalance in cycling, in addition to the Valsalva manoeuvre during physical effort, may contribute to FCE. It is unknown if prognosis of spinal cord infarction due to FCE differs from other causes of spinal ischaemia and if anticoagulation treatment presents a therapeutic option.
Exploring strategies used following a group-based fatigue management programme for people with multiple sclerosis (FACETS) via the Fatigue Management Strategies Questionnaire (FMSQ)
Authors: Thomas S , Kersten P , Thomas P W , Slingsby V , Nock A , Jones R , Davies Smith A , Galvin K T , Baker R , Hillier C
Published: May 2024

Objectives

To explore cross-sectional patterns of use of fatigue management strategies in people with multiple sclerosis (MS) who had attended a group-based fatigue management programme,Fatigue:ApplyingCognitive behavioural andEnergy effectivenessTechniques to lifeStyle (‘FACETS’). In a multicentre randomised controlled trial (RCT) the FACETS programme was shown to reduce fatigue severity and improve self-efficacy and quality of life.

Design

A questionnaire substudy within a RCT involving the self-completed Fatigue Management Strategies Questionnaire (FMSQ). The FMSQ includes: (1) closed questions about the use and helpfulness of fatigue management strategies taught in FACETS and (2) open items about changes to lifestyle, attitudes or expectations, barriers or difficulties encountered and helpful strategies not covered in FACETS.

Participants

All had a clinical diagnosis of MS, significant fatigue, were ambulatory and had attended at least 4 of 6 scheduled FACETS sessions.

Methods

Participants (n=72) were posted the FMSQ with a prepaid return envelope 4 months after the end of the FACETS programme.

Results

82% (59/72) of participants returned the FMSQ. The fatigue management strategies most frequently used since attending FACETS were prioritisation (80%), pacing (78%), saying no to others (78%), grading tasks (75%) and challenging unhelpful thoughts (71%). Adding in those participants who were already using the respective strategies prior to FACETS, the three most used strategies at 4 months were prioritisation (55/59), grading (54/59) and pacing (53/58). Free-text comments illustrated the complex interplay between attitudes/expectations, behaviours, emotions and the environment. Issues related to expectations featured strongly in participants’ comments. Expectations (from self and others) were both facilitators and barriers to effective fatigue management.

Conclusions

Individuals’ comments highlighted the complex, multifaceted nature of fatigue management. Revising expectations and a greater acceptance of fatigue were important shifts following the programme. Findings support the relevance of a cognitive behavioural approach for fatigue management. Booster sessions might be a useful addition to the FACETS programme.

Trial registration number

Current controlled trialsISRCTN76517470; Results.
Quality improvement initiative: implementing and redefining video review of real-time neonatal procedures using action research
Authors: Heesters Veerle , van Zanten Henriette A , de Boer Linsey CC , Visser Remco , Heijstek Veerle , Te Pas Arjan B , Witlox Ruben SGM
Published: May 2024

Video review (VR) of procedures in the medical environment can be used to drive quality improvement. However, first it has to be implemented in a safe and effective way. Our primary objective was to (re)define a guideline for implementing interprofessional VR in a neonatal intensive care unit (NICU). Our secondary objective was to determine the rate of acceptance by providers attending VR. For 9 months, VR sessions were evaluated with a study group, consisting of different stakeholders. A questionnaire was embedded at the end of each session to obtain feedback from providers on the session and on the safe learning environment. In consensus meetings, success factors and preconditions were identified and divided into different factors that influenced the rate of adoption of VR. The number of providers who recorded procedures and attended VR sessions was determined. A total of 18 VR sessions could be organised, with an equal distribution of medical and nursing staff. After the 9-month period, 101/125 (81%) of all providers working on the NICU attended at least 1 session and 80/125 (64%) of all providers recorded their performance of a procedure at least 1 time. In total, 179/297 (61%) providers completed the questionnaire. Almost all providers (99%) reported to have a positive opinion about the review sessions. Preconditions and success factors related to implementation were identified and addressed, including improving the pathway for obtaining consent, preparation of VR, defining the role of the chair during the session and building a safe learning environment. Different strategies were developed to ensure findings from sessions were used for quality improvement. VR was successfully implemented on our NICU and we redefined our guideline with various preconditions and success factors. The adjusted guideline can be helpful for implementation of VR in emergency care settings.
Responsibility is not required for authorship
Authors: Levy Neil
Published: May 2024

The Committee on Publication Ethics (COPE) maintains that AIs (artificial intelligences) cannot be authors of academic papers, because they are unable to take responsibility for them. COPE appears to have theanswerabilitysense of responsibility in mind. It is true that AIs cannot be answerable for papers, but responsibility in this sense is not required for authorship in the sciences. I suggest that ethics will be forced to follow suit in dropping responsibility as a criterion for authorship or rethinking its role. I put forward three options for authorship: dropping responsibility as a criterion for authorship, retaining it and excluding AIs, but at the cost of substantial revision of our practices, or requiring only local responsibility for an intellectual contribution.
Prevalence, genetic and clinical characteristics in first-degree relatives of patients with familial cerebral cavernous malformations in China
Authors: Li Chunwang , Zhuo Lingyun , Kang Yaqing , Liu Penghui , Huang Weilin , Li Qixuan , Ma Ke , Huang Shuna , Lin Xinru , Zhuang Weiheng , Wang Haojie , Chen Darong , Wang Huimin , He Qiu , Gao Zhuyu , Niu Xuegang , Jing Yajun , Yan Lingjun , Gao Bin , Wang Dengliang , Lin Shaowei , Wu Siying , Lin Yuanxiang , Kang Dezhi , Lin Fuxin
Published: May 2024

Objective

This study aims to investigate the prevalence of familial cerebral cavernous malformations (FCCMs) in first-degree relatives (FDRs) using familial screening, to describe the distribution of initial symptoms, lesion count on cranial MRI and pathogenic gene in patients.

Methods

Patients with multiple CCMs who enrolled from the Treatments and Outcomes of Untreated Cerebral Cavernous Malformations in China database were considered as probands and FDRs were recruited. Cranial MRI was performed to screen the CCMs lesions, and whole-exome sequencing was performed to identify CCM mutations. MRI and genetic screening were combined to diagnose FCCM in FDRs, and the results were presented as prevalence and 95% CIs. The Kaplan-Meier (KM) method was used to calculate the cumulative incidence of FCCM.

Results

33 (76.74%) of the 43 families (110 FDRs) were identified as FCCM (85 FDRs). Receiver operating characteristic analysis revealed three lesions on T2-weighted imaging (T2WI) were the strong indicator for distinguishing probands with FCCM (sensitivity, 87.10%; specificity, 87.50%). Of the 85 FDRs, 31 were diagnosed with FCCM, resulting in a prevalence of 36.5% (26.2%–46.7%). In families with FCCMs, the mutation rates forCCM1,CCM2andCCM3were 45.45%, 21.21% and 9.09%, respectively. Furthermore, 53.13% of patients were asymptomatic, 17.19% were intracranial haemorrhage and 9.38% were epilepsy. The mean age of symptom onset analysed by KM was 46.67 (40.56–52.78) years.

Conclusion

Based on MRI and genetic analysis, the prevalence of CCMs in the FDRs of families with FCCMs in China was 36.5%. Genetic counselling and MRI screening are recommended for FDRs in patients with more than three CCM lesions on T2WI.
Does anaerobic speed reserve influence post-activation performance enhancement in endurance runners?
Authors: Del Rosso Sebastián , Varela-Sanz Adrián , Tuimil José L. , Boullosa Daniel
Published: May 2024

We investigated the influence of anaerobic speed reserve (ASR) on post-activation performance enhancement (PAPE). Twenty-two endurance runners and triathletes were evaluated for maximum sprinting speed (MSS) and countermovement jump (CMJ) before (non-fatigued) and after (fatigued) an incremental running test. They were allocated in LASR (low-ASR) and HASR (high-ASR) groups for comparisons between conditions. HASR showed greater CMJ and MSS (both p ≤ 0.005) performances, with enhanced CMJ in fatigued condition ( p ≤ 0.008). Significant correlations were found between ASR, CMJ, and MSS in both conditions ( p ≤ 0.01) for the entire sample, and between ∆CMJ and ∆MSS ( p ≤ 0.001) in LASR. Our results show that ASR profile influences PAPE.
Effects of forest management intensity and climate change severity on volume growth, timber yield, carbon stocks, and the amount of deadwood in Scots pine, Norway spruce, and silver birch stands in boreal conditions
Authors: Petty Aaron , Senko Sergei , Strandman Harri , Jyrkinen Essi , Tikkanen Olli-Pekka , Kilpeläinen Antti , Peltola Heli
Published: May 2024

We studied how management intensity and climate severity affect volume growth, timber yield, carbon stocks, and the amount of deadwood in Scots pine ( Pinus sylvestris (L.)), Norway spruce ( Picea abies (L.) Karst.), and silver birch ( Betula pendula Roth.) dominated stands in the Republic of Karelia and Arkhangelsk region of northwest Russia. Using the forest ecosystem model (SIMA) under different climates (current and representative concentration pathway scenarios, RCP4.5 and RCP8.5), no-thinning, low, medium, and high intensity thinning rotational forestry regimes were simulated. Under RCPs, the volume growth and timber yield (5%–53%), carbon stocks (1%–22%), and deadwood amounts (11%–75%) increased for all Scots pine and silver birch stands. The use of low intensity management increased volume growth and carbon stocks (3%–16%) and deadwood amount (up to 60%) under RCPs, but not timber yield (±3%) in these stands. For Norway spruce stands, the volume growth (5%–26%), timber yield (23%–75%), and carbon stocks (5%–15%) decreased under RCP8.5, but deadwood amount increased (up to 142%). Intensive management increased volume growth (4%–19%), timber yield (4%–63%), carbon stocks (up to 14%), and deadwood amounts (up to 49%). Our results highlight that effects of climate severity and management intensity are site and species-specific for Eurasian’s boreal forests.
Economic impacts of climate change on forests: a PICUS–LANDIS–CGE modeling approach
Authors: McMonagle Galen , Lantz Van , Taylor Anthony R. , Boulanger Yan , Sharma Chinmay , Withey Patrick , Hennigar Chris
Published: May 2024

Climate change is expected to alter both forest stand- and landscape-level dynamics through a change in environmental characteristics. While numerous studies have employed models to assess the ecological and/or economic impacts of such changes on forests throughout the world, there is need to further refine such analyses. In this paper, we contribute to this literature by coupling an ecological (PICUS–LANDIS II) modeling framework with an economic (computable general equilibrium) model to better account for the economic impact associated with climate-induced impacts on forest stand- and landscape-level structure and composition dynamics. Applying this framework to a case-study region of New Brunswick, Canada, we estimate that climate change will reduce softwood supply by 16%–73% and impact hardwood supply in the range of −2% to +4% by 2150. The change in wood supply is estimated to reduce the value of the softwood and hardwood forestry and logging sector output by up to 51% and 17%, respectively, by 2150. These sector-level impacts may lead to a 0.08%–0.88% reduction in annual gross domestic product by 2150. The methodological advances established in this study can be used to better inform future forest management and economic plans that aim to lessen both the ecological and economic impact of climate change.
Research on the thermal characteristics of motorized spindle under magnetic–thermal coupling based on digital twin
Authors: Wang Zhan , Zhu Jintao , Zhao Jinbao , Ke Zhang , Wang Zinan
Published: May 2024

In operation, traditional mechanistic models fail to iteratively update with the motorized spindle, leading to inaccuracies in dynamic data like the motorized spindle's internal magnetic and thermal fields. To address the issue, we propose a data-driven, magnetic–thermal bidirectional coupling twin model based on digital twin technology. This model, considering temperature effects on the motorized spindle motor's materials, enables dynamic updates through a twin database and service platform. Comparative analysis shows minor temperature calculation differences between this twin model and traditional mechanistic models at speeds below 9000 r/min. As the speed reaches 9000 r/min and the motorized spindle's efficiency peaks, the disparity between the two models grows. Experimental data show that the twin model outperforms the traditional model in temperature accuracy at the motorized spindle's test points, achieving up to a 4.57% improvement in front bearing temperature calculations. The proposed motorized spindle twin model demonstrates higher accuracy, providing significant assistance in the dynamic process simulation and operational monitoring of the motorized spindle.
Properties of the chalcogenide-based double perovskites Ba2NbBiS6 and Ba2TaSbS6 with respect to structural, electronic and optical aspects
Authors: Baaziz H. , Ghellab T. , Charifi Z.
Published: May 2024

In this work, we delve into the investigation of the structural, electronic, and optical properties of Ba2NbBiS6 and Ba2TaSbS6 chalcogenide-based double perovskites, which are structured in the cubic space group [Formula: see text] form. We have performed first-principles calculations using density functional theory (DFT) to study the above properties. The electronic band structure and density of states of this compound have been investigated, and their results show that Ba2NbBiS6 and Ba2TaSbS6 exhibit a semiconducting nature with an indirect energy gap of 1.680[Formula: see text]eV and 1.529[Formula: see text]eV, respectively. Furthermore, an investigation was conducted on the optical properties of the compounds throughout the energy range spanning from 0[Formula: see text]eV to 55[Formula: see text]eV. This investigation focused on many parameters, including dielectric functions, optical reflectivity, refractive index, extinction coefficient, optical conductivity, and electron energy loss. The optical data obtained from the calculations reveals that all compounds demonstrate isotropy in optical polarization. Furthermore, it has been noted that our compounds exhibit absorption properties inside the ultraviolet (UV) region. Consequently, these materials hold promise as potential candidates for various applications, such as UV photodetectors, UV light emitters, and power electronics. This is primarily attributed to their inherent absorption limits and the presence of prominent absorption peaks in this spectral range. In brief, chemical mutation techniques have been employed to manipulate the characteristics of double-sulfide perovskites to develop durable and environmentally friendly perovskite materials suitable for solar purposes.
Equivariant Hopf Bifurcation in a Class of Partial Functional Differential Equations on a Circular Domain
Authors: Chen Yaqi , Zeng Xianyi , Niu Ben
Published: May 2024

Circular domains frequently appear in mathematical modeling in the fields of ecology, biology and chemistry. In this paper, we investigate the equivariant Hopf bifurcation of partial functional differential equations with Neumann boundary condition on a two-dimensional disk. The properties of these bifurcations at equilibriums are analyzed rigorously by studying the equivariant normal forms. Two reaction–diffusion systems with discrete time delays are selected as numerical examples to verify the theoretical results, in which spatially inhomogeneous periodic solutions including standing waves and rotating waves, and spatially homogeneous periodic solutions are found near the bifurcation points.
Pushing the Limits of Acoustic Spatial Perception via Incident Angle Encoding
Authors: Fu Yongjian , Zhang Yongzhao , Pan Hao , Lu Yu , Li Xinyi , Chen Lili , Ren Ju , Li Xiong , Zhang Xiaosong , Zhang Yaoxue
Published: May 2024

With the growing popularity of smart speakers, numerous novel acoustic sensing applications have been proposed for low-frequency human speech and high-frequency inaudible sounds. Spatial information plays a crucial role in these acoustic applications, enabling various location-based services. However, typically commercial microphone arrays face limitations in spatial perception of inaudible sounds due to their sparse array geometries optimized for low-frequency speech. In this paper, we introduce MetaAng, a system designed to augment microphone arrays by enabling wideband spatial perception across both speech signals and inaudible sounds by leveraging the spatial encoding capabilities of acoustic metasurfaces. Our design is grounded in the fact that, while sensitive to high-frequency signals, acoustic metasurfaces are almost non-responsive to low-frequency speech due to significant wavelength discrepancy. This observation allows us to integrate acoustic metasurfaces with sparse array geometry, simultaneously enhancing the spatial perception of high-frequency and low-frequency acoustic signals. To achieve this, we first utilize acoustic metasurfaces and a configuration optimization algorithm to encode the unique features for each incident angle. Then, we propose an unrolling soft thresholding network that employs neural-enhanced priors and compressive sensing for high-accuracy, high-resolution multi-source angle estimation. We implement a prototype, and experimental results demonstrate that MetaAng maintains robustness across various scenarios, facilitating multiple applications, including localization and tracking.
Let It Snow: Designing Snowfall Experience in VR
Authors: Wang Haokun , Singhal Yatharth , Kim Jin Ryong
Published: May 2024

We present Snow, a cross-modal interface that integrates cold and tactile stimuli in mid-air to create snowflakes and raindrops for VR experiences. Snow uses six Peltier packs and an ultrasound haptic display to create unique cold-tactile sensations for users to experience catching snowflakes and getting rained on their bare hands. Our approach considers humans' ability to identify tactile and cold stimuli without masking each other when projected onto the same location on their skin, making illusions of snowflakes and raindrops. We design both visual and haptic renderings to be tightly coupled to present snow melting and rain droplets for realistic visuo-tactile experiences. For multiple snowflakes and raindrops rendering, we propose an aggregated haptic scheme to simulate heavy snowfall and rainfall environments with many visual particles. The results show that the aggregated haptic rendering scheme demonstrates a more realistic experience than other schemes. We also confirm that our approach of providing cold-tactile cues enhances the user experiences in both conditions compared to other modality conditions.
AutoAugHAR: Automated Data Augmentation for Sensor-based Human Activity Recognition
Authors: Zhou Yexu , Zhao Haibin , Huang Yiran , Röddiger Tobias , Kurnaz Murat , Riedel Till , Beigl Michael
Published: May 2024

Sensor-based HAR models face challenges in cross-subject generalization due to the complexities of data collection and annotation, impacting the size and representativeness of datasets. While data augmentation has been successfully employed in domains like natural language and image processing, its application in HAR remains underexplored. This study presents AutoAugHAR, an innovative two-stage gradient-based data augmentation optimization framework. AutoAugHAR is designed to take into account the unique attributes of candidate augmentation operations and the unique nature and challenges of HAR tasks. Notably, it optimizes the augmentation pipeline during HAR model training without substantially extending the training duration. In evaluations on eight inertial-measurement-units-based benchmark datasets using five HAR models, AutoAugHAR has demonstrated superior robustness and effectiveness compared to other leading data augmentation frameworks. A salient feature of AutoAugHAR is its model-agnostic design, allowing for its seamless integration with any HAR model without the need for structural modifications. Furthermore, we also demonstrate the generalizability and flexible extensibility of AutoAugHAR on four datasets from other adjacent domains. We strongly recommend its integration as a standard protocol in HAR model training and will release it as an open-source tool1.
Sensing to Hear through Memory
Authors: Zhang Qian , Liu Ke , Wang Dong
Published: May 2024

Speech enhancement on mobile devices is a very challenging task due to the complex environmental noises. Recent works using lip-induced ultrasound signals for speech enhancement open up new possibilities to solve such a problem. However, these multi-modal methods cannot be used in many scenarios where ultrasound-based lip sensing is unreliable or completely absent. In this paper, we propose a novel paradigm that can exploit the prior learned ultrasound knowledge for multi-modal speech enhancement only with the audio input and an additional pre-enrollment speaker embedding. We design a memory network to store the ultrasound memory and learn the interrelationship between the audio and ultrasound modality. During inference, the memory network is able to recall the ultrasound representations from audio input to achieve multi-modal speech enhancement without needing real ultrasound signals. Moreover, we introduce a speaker embedding module to further boost the enhancement performance as well as avoid the degradation of the recalling when the noise level is high. We adopt an end-to-end multi-task manner to train the proposed framework and perform extensive evaluations on the collected dataset. The results show that our method yields comparable performance with audio-ultrasound methods and significantly outperforms the audio-only methods.
Accurate Blood Pressure Measurement Using Smartphone's Built-in Accelerometer
Authors: Wang Lei , Wang Xingwei , Zhang Yu , Ma Xiaolei , Dai Haipeng , Zhang Yong , Li Zhijun , Gu Tao
Published: May 2024

Efficient blood pressure (BP) monitoring in everyday contexts stands as a substantial public health challenge that has garnered considerable attention from both industry and academia. Commercial mobile phones have emerged as a promising tool for BP measurement, benefitting from their widespread popularity, portability, and ease of use. Most mobile phone-based systems leverage a combination of the built-in camera and LED to capture photoplethysmography (PPG) signals, which can be used to infer BP by analyzing the blood flow characteristics. However, due to low Signal-to-Noise (SNR), various factors such as finger motion, improper finger placement, skin tattoos, or fluctuations in environmental lighting can distort the PPG signal. These distortions consequentially affect the performance of BP estimation. In this paper, we introduce a novel sensing system that utilizes the built-in accelerometer of a mobile phone to capture seismocardiography (SCG) signals, enabling accurate BP measurement. Our system surpasses previous mobile phone-based BP measurement systems, offering advantages such as high SNR, ease of use, and power efficiency. We propose a triple-stage noise reduction scheme, integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), recursive least squares (RLS) adaptive filter, and soft-thresholding, to effectively reconstruct high-quality heartbeat waveforms from initially contaminated raw SCG signals. Moreover, we introduce a data augmentation technique encompassing normalization coupled with temporal-sliding, effectively augmenting the diversity of the training sample set. To enable battery efficiency on smartphone, we propose a resource-efficient deep learning model that incorporates resource-efficient convolution, shortcut connections, and Huber loss. We conduct extensive experiments with 70 volunteers, comprising 35 healthy individuals and 35 individuals diagnosed with hypertension, under a user-independent setting. The excellent performance of our system demonstrates its capacity for robust and accurate daily BP measurement.
CrowdBot: An Open-Environment Robot Management System for On-Campus Services
Authors: Wang Yufei , Zeng Wenting , Liu Changzhen , Ye Zhuohan , Sun Jiawei , Ji Junxiang , Jiang Zhihan , Yan Xianyi , Wu Yongyi , Wang Yigao , Yang Dingqi , Wang Leye , Zhang Daqing , Wang Cheng , Chen Longbiao
Published: May 2024

In contemporary campus environments, the provision of timely and efficient services is increasingly challenging due to limitations in accessibility and the complexity and openness of the environment. Existing service robots, while operational, often struggle with adaptability and dynamic task management, leading to inefficiencies. To overcome these limitations, we introduce CrowdBot, a robot management system that enhances service in campus environments. Our system leverages a hierarchical reinforcement learning-based cloud-edge hybrid scheduling framework (REDIS), for efficient online streaming task assignment and dynamic action scheduling. To verify the REDIS framework, we have developed a digital twin simulation platform, which integrates large language models and hot-swapping technology. This facilitates seamless human-robot interaction, efficient task allocation, and cost-effective execution through the reuse of robot equipment. Our comprehensive simulations corroborate the system's remarkable efficacy, demonstrating significant improvements with a 24.46% reduction in task completion times, a 9.37% decrease in travel distances, and up to a 3% savings in power usage. Additionally, the system achieves a 7.95% increase in the number of tasks completed and a 9.49% reduction in response time. Real-world case studies further affirm CrowdBot's capability to adeptly execute tasks and judiciously recycle resources, thereby offering a smart and viable solution for the streamlined management of campus services.
ECSkin: Tessellating Electrochromic Films for Reconfigurable On-skin Displays
Authors: Ku Pin-Sung , Jiang Shuwen , Wang Wei-Hsin , Kao Hsin-Liu Cindy
Published: May 2024

Emerging electrochromic (EC) materials have advanced the frontier of thin-film, low-power, and non-emissive display technologies. While suitable for wearable or textile-based applications, current EC display systems are manufactured in fixed, pre-designed patterns that hinder the potential of reconfigurable display technologies desired by on-skin interactions. To realize the customizable and scalable EC display for skin wear, this paper introduces ECSkin, a construction toolkit composed of modular EC films. Our approach enables reconfigurable designs that display customized patterns by arranging combinations of premade EC modules. An ECSkin device can pixelate patterns and expand the display area through tessellating congruent modules. We present the fabrication of flexible EC display modules with accessible materials and tools. We performed technical evaluations to characterize the electrochromic performance and conducted user evaluations to verify the toolkit's usability and feasibility. Two example applications demonstrate the adaptiveness of the modular display on different body locations and user scenarios.
The EarSAVAS Dataset
Authors: Zhang Xiyuxing , Wang Yuntao , Han Yuxuan , Liang Chen , Chatterjee Ishan , Tang Jiankai , Yi Xin , Patel Shwetak , Shi Yuanchun
Published: May 2024

Subject-aware vocal activity sensing on wearables, which specifically recognizes and monitors the wearer's distinct vocal activities, is essential in advancing personal health monitoring and enabling context-aware applications. While recent advancements in earables present new opportunities, the absence of relevant datasets and effective methods remains a significant challenge. In this paper, we introduce EarSAVAS, the first publicly available dataset constructed specifically for subject-aware human vocal activity sensing on earables. EarSAVAS encompasses eight distinct vocal activities from both the earphone wearer and bystanders, including synchronous two-channel audio and motion data collected from 42 participants totaling 44.5 hours. Further, we propose EarVAS, a lightweight multi-modal deep learning architecture that enables efficient subject-aware vocal activity recognition on earables. To validate the reliability of EarSAVAS and the efficiency of EarVAS, we implemented two advanced benchmark models. Evaluation results on EarSAVAS reveal EarVAS's effectiveness with an accuracy of 90.84% and a Macro-AUC of 89.03%. Comprehensive ablation experiments were conducted on benchmark models and demonstrated the effectiveness of feedback microphone audio and highlighted the potential value of sensor fusion in subject-aware vocal activity sensing on earables. We hope that the proposed EarSAVAS and benchmark models can inspire other researchers to further explore efficient subject-aware human vocal activity sensing on earables.
Beamforming for Sensing: Hybrid Beamforming based on Transmitter-Receiver Collaboration for Millimeter-Wave Sensing
Authors: Fan Long , Xie Lei , Zhou Wenhui , Wang Chuyu , Bu Yanling , Lu Sanglu
Published: May 2024

Previous mmWave sensing solutions assumed good signal quality. Ensuring an unblocked or strengthened LoS path is challenging. Therefore, finding an NLoS path is crucial to enhancing perceived signal quality. This paper proposes Trebsen, a Transmitter-REceiver collaboration-based Beamforming scheme SENsing using commercial mmWave radars. Specifically, we define the hybrid beamforming problem as an optimization challenge involving beamforming angle search based on transmitter-receiver collaboration. We derive a comprehensive expression for parameter optimization by modeling the signal attenuation variations resulting from the propagation path. To comprehensively assess the perception signal quality, we design a novel metric perceived signal-to-interference-plus-noise ratio (PSINR), combining the carrier signal and baseband signal to quantify the fine-grained sensing motion signal quality. Considering the high time cost of traversing or randomly searching methods, we employ a search method based on deep reinforcement learning to quickly explore optimal beamforming angles at both transmitter and receiver. We implement Trebsen and evaluate its performance in a fine-grained sensing application (i.e., heartbeat). Experimental results show that Trebsen significantly enhances heartbeat sensing performance in blocked or misaligned LoS scenes. Comparing non-beamforming, Trebsen demonstrates a reduction of 23.6% in HR error and 27.47% in IBI error. Moreover, comparing random search, Trebsen exhibits a 90% increase in search speed.
ShuffleFL: Addressing Heterogeneity in Multi-Device Federated Learning
Authors: Zhu Ran , Yang Mingkun , Wang Qing
Published: May 2024

Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative deep learning model training across distributed data silos. Despite its importance, FL faces challenges such as high latency and less effective global models. In this paper, we propose ShuffleFL, an innovative framework stemming from the hierarchical FL, which introduces a user layer between the FL devices and the FL server. ShuffleFL naturally groups devices based on their affiliations, e.g., belonging to the same user, to ease the strict privacy restriction-"data at the FL devices cannot be shared with others", thereby enabling the exchange of local samples among them. The user layer assumes a multi-faceted role, not just aggregating local updates but also coordinating data shuffling within affiliated devices. We formulate this data shuffling as an optimization problem, detailing our objectives to align local data closely with device computing capabilities and to ensure a more balanced data distribution at the intra-user devices. Through extensive experiments using realistic device profiles and five non-IID datasets, we demonstrate that ShuffleFL can improve inference accuracy by 2.81% to 7.85% and speed up the convergence by 4.11x to 36.56x when reaching the target accuracy.
The Personality Dimensions GPT-3 Expresses During Human-Chatbot Interactions
Authors: Kovačević Nikola , Holz Christian , Gross Markus , Wampfler Rafael
Published: May 2024

Large language models such as GPT-3 and ChatGPT can mimic human-to-human conversation with unprecedented fidelity, which enables many applications such as conversational agents for education and non-player characters in video games. In this work, we investigate the underlying personality structure that a GPT-3-based chatbot expresses during conversations with a human. We conducted a user study to collect 147 chatbot personality descriptors from 86 participants while they interacted with the GPT-3-based chatbot for three weeks. Then, 425 new participants rated the 147 personality descriptors in an online survey. We conducted an exploratory factor analysis on the collected descriptors and show that, though overlapping, human personality models do not fully transfer to the chatbot's personality as perceived by humans. We also show that the perceived personality is significantly different from that of virtual personal assistants, where users focus rather on serviceability and functionality. We discuss the implications of ever-evolving large language models and the change they affect in users' perception of agent personalities.
Optimization of Sparse Matrix Computation for Algebraic Multigrid on GPUs
Authors: Wang Yizhuo , Chang Fangli , Wei Bingxin , Gao Jianhua , Ji Weixing
Published: May 2024

AMG is one of the most efficient and widely used methods for solving sparse linear systems. The computational process of AMG mainly consists of a series of iterative calculations of generalized sparse matrix-matrix multiplication (SpGEMM) and sparse matrix-vector multiplication (SpMV). Optimizing these sparse matrix calculations is crucial for accelerating solving linear systems. In this paper, we first focus on optimizing the SpGEMM algorithm in AmgX, a popular AMG library for GPUs. We propose a new algorithm called SpGEMM-upper, which achieves an average speedup of 2.02× on Tesla V100 and 1.96× on RTX 3090 against the original algorithm. Next, through experimental investigation, we conclude that no single SpGEMM library or algorithm performs optimally for most sparse matrices, and the same holds true for SpMV. Therefore, we build machine learning-based models to predict the optimal SpGEMM and SpMV used in the AMG calculation process. Finally, we integrate the prediction models, SpGEMM-upper, and other selected algorithms into a framework for adaptive sparse matrix computation in AMG. Our experimental results prove that the framework achieves promising performance improvements on the test set.
Detail-preserving Joint Image Upsampling
Authors: Yang Yang , Qiu Shuailong , Zeng Lanling , Pan Zhigeng
Published: May 2024

Image operators can be instrumental to computational imaging and photography. However, many of them are computationally intensive. In this article, we propose an effective yet efficient joint upsampling method to accelerate various image operators. We show that edge-preserving filtering can be facilitated with a downsampling-and-upsampling process. Moreover, when the extent of smoothing is mild, the process is detail preserving, i.e., the fine details lost in the low-resolution (LR) images can be accurately restored in the high-resolution (HR) images. Given an HR input and an LR output of an operator, we downsample the HR input and calculate its affinities to the HR input. By applying the affinities to the LR output, we promote its resolution. Due to the strong detail-preserving property, the HR output derived in the previous step may exhibit aliasing artifacts around the salient edges. We further refine it based on the linear relations in a small neighborhood to rid the artifacts. Experiments on various image operators show that our method achieves superior quality over the state-of-the-art joint upsampling methods. Furthermore, the running time of our method is linear to the number of pixels. Our naive implementation derives 1080P images in real time (24 fps) on an NVIDIA GTX 3070 GPU.
Recent Developments in Financial Risk and the Real Economy
Authors: Dew-Becker Ian , Giglio Stefano
Published: May 2024

In this article, we review recent developments in macroeconomics and finance on the relationship between financial risk and the real economy. We focus on three specific topics: (a) the term structure of uncertainty, (b) time variation—specifically, the long-term decline—in the variance risk premium, and (c) time variation in conditional skewness. We also introduce two new data series: implied volatility from one-day options on grains for the period 1906–1936 and prices of cliquet options, which provide insurance against single-day crashes on the S&P 500. Both series give some context to the recent rise in trade in extremely short-dated options. Finally, we discuss new avenues for future research.
Review—Recent Advancements in Molybdenum Carbides for Efficient Hydrogen Evolution Reaction
Authors: Tran Vinh Van , Lee Daeho , Bui Vu Khac Hoang , Tran Nguyen Tien , Truong Hai Bang , Do Ha Huu
Published: May 2024

The quest for economical and sustainable electrocatalysts to facilitate the hydrogen evolution reaction (HER) is paramount in addressing the pressing challenges associated with carbon dioxide emissions. Molybdenum carbide-based nanomaterials have emerged as highly promising electrocatalysts for HER due to their Pt-like catalytic proficiency, exceptional stability, and the versatility of their crystal phases. Within this comprehensive review, we explore the diverse methodologies for synthesizing molybdenum carbides, including solid-gas, solid-solid, and solid-liquid phase reactions. In addition, a thorough elucidation of the hydrogen generation process through water electrolysis is provided. Furthermore, a spectrum of innovative strategies aimed at augmenting the performance of molybdenum carbides in the HER milieu is introduced, encompassing cutting-edge techniques such as phase-transition engineering, the construction of heterostructures, hetero-atom doping, the integration of hybrid structures with carbon materials, defect engineering, and meticulous surface modification. The review culminates by underscoring the current challenges and the promising prospects in the advancement of electrocatalysts for hydrogen production, with a dedicated focus on molybdenum carbide-based catalysts.
Electrochemical Characterization of Nickel/Gadolinia Doped Ceria Fuel Electrodes under H2/H2O/CO/CO2-Atmospheres
Authors: Esau D. , Grosselindemann C. , Sckuhr S. P. , Kullmann F. , Lindner A. , Liang Z. , Fuchs F. M. , Weber A.
Published: May 2024

Modelling of the co-electrolysis process requires understanding of the underlying reaction pathways under H2/H2O/CO/CO2-atmospheres. These include the electrochemical steam reduction/hydrogen oxidation, the electrochemical CO2 reduction/CO oxidation and their coupling via the catalytic (reverse) water gas shift reaction ((R)WGS). The assumption of a very fast RWGS and therefore neglectable electrochemical CO2 conversion is commonly used to model the co-electrolysis process. In contrast, previous studies on Ni/GDC fuel electrodes suggest that the electrochemical conversion of CO/CO2 can be present in H2/H2O/CO/CO2-atmospheres. To deconvolute surface-related and non-surface-related processes in the impedance response we present results from a complex variation of operating parameters for process identification by the use of electrochemical impedance spectroscopy and the subsequent impedance analysis by the distribution of relaxation times. A physically meaningful equivalent circuit model, based on a single channel transmission line, is then derived. The model enables quantification of the surface reaction resistance under varied C/H-ratios. From a kinetic analysis it is shown that the electrochemical H2/H2O conversion is dominant for

y

CO

+

y

C

O

2

≤

50% and electrochemical CO/CO2-conversion onsets from

y

CO

+

y

C

O

2

 ≥ 60%.
Impacts of Catalyst Ink Composition and Wet Film Thickness on Fuel Cell Catalyst Layers Fabricated by Direct Film Coating Method
Authors: Stoll Jonas , Jeong Jisung , Huynh Philip , Kjeang Erik
Published: May 2024

Utilizing a direct film coating method (DFCM), such as doctor blade coating, offers a promising approach for efficient and scalable catalyst layer (CL) production for fuel cells. To further widen the understanding of lab-scale DFCM, the present research investigates how different Pt-based catalyst ink formulations coated via doctor blade coating with varying blade gap thickness (BGT) affect the CL quality and catalyst loading. In total, 120 CL samples were prepared by coating 20 different catalyst ink formulations with varying solids content, ionomer-to-carbon (I/C) ratio, and water-to-isopropanol solvent ratio with BGTs of 75, 125, and 200 μm. Inspection of these samples showed that the solvent ratio affects the coating uniformity, with the most uniform films achieved with a ratio of 1.67 or greater. Furthermore, increasing the I/C ratio for a given solids content ink formulation decreases the Pt loading, whereas an I/C ratio above or below 1.0 reduces cell performance due to mass transport or proton conductivity impacts, respectively. In addition, a relationship factor and equations are presented to estimate the solid weight and catalyst loading of the fabricated CL based on the ink formulation and BGT. Overall, this work provides important guidance for lab-scale DFCM fabrication of industrially relevant CLs.
Correlations in abnormal synergies between the upper and lower extremities across various phases of stroke
Authors: Kim Dongwon , Ko Sung-Hwa , Han Junhee , Kim Young-Taek , Kim Yun-Hee , Chang Won Hyuk , Shin Yong-Il
Published: May 2024

For the first time, this article addresses the asynchronous relationships in the strengths of flexion and extension synergy expressions between the paretic upper extremity and lower extremity across various phases of stroke.
Cassava Starch-Based Multifunctional Coating Incorporated with Zinc Oxide Nanoparticle to Enhance the Shelf Life of Passion Fruit
Authors: Congying Han , Meifang Wang , Islam Md. Nahidul , Cancan Shi , Shengli Guo , Hossain Afsana , Xiaohuang Cao
Published: May 2024

Passion fruits are susceptible to numerous postharvest challenges including weight loss, ethylene production, peel shrinkage, microbial growth, and pulp liquefaction. To mitigate these issues, yellow passion fruits were treated with hydroxypropyl cassava starch zinc oxide (HCS-ZnO) nanoparticles at varying concentrations. Fruits were stored at 10°C for 42 days, and the treated fruits underwent periodic assessments for weight loss, electrical conductivity, reducing sugar, total soluble solids (TSS), titratable acidity, and peel color. The results showed that a 0.8% HCS-ZnO nanoparticle coating could significantly reduce the weight loss of passion fruit during storage. However, compared to other treatments, a 0.2% HCS-ZnO nanoparticle coating demonstrated superior preservation of physicochemical properties, delayed discoloration, slowed ripening, maintained cell membrane integrity (electrical conductivity 1337 μs/cm), and reduced nutrient loss (titrable acidity 3.35 g/100 mL, TSS 17.9%, reducing sugar 5.1 g/100 g) at 42 days of storage. This innovative approach holds promise for commercial application, offering a sustainable solution to mitigate postharvest losses of passion fruit. The study underscores the potential of HCS-ZnO nanoparticles as effective coatings to uphold fruit quality and extend shelf life, presenting compelling insights for future fruit preservation strategies.