bluuebunny/crossref_metadata_2025 huggingface dataset:
Innovative Cavity Modeling for Centrifugal Compressors Aeromechanical Analysis
Authors: Batisti Marco , Pinelli Lorenzo , Toni Lorenzo , Guglielmo Alberto , Marconcini Michele , Arnone Andrea
Published: November 2024

Centrifugal compressor aeromechanics is playing an increasingly important role in the energy transition scenario, especially when operating with low-molecular-weight gases, such as hydrogen. For these machines, maximum impeller tip speeds are limited by structural requirements and so aeromechanical assessment must be included from the preliminary design stages. When performing forced response analysis of centrifugal impellers, it is mandatory to consider the contribution of the unsteady forces acting within front and rear cavities. The flow field inside the cavities has different time and length scales compared with the main flow field, and from a modeling standpoint, seal regions have to be included in the computational domain: this means that a comprehensive computational fluid dynamics (CFD) simulation may require substantial efforts for the setup and high computational time and resources. In this context, this article presents an acoustic analytical model for predicting pressure perturbation within centrifugal compressor cavities without solving them with unsteady CFD approaches. The model is fed with appropriate boundary conditions to be applied at the entrance of the cavities and analytically solves the pressure perturbation distribution in an annular environment. To validate the presented analytical model, unsteady calculations were performed on impeller-vaned diffuser domains with cavities at different operating conditions, and the unsteady pressure field in the cavities was used as a target solution. The comparison between the analytical model results and the CFD solution shows a very good agreement for all the different blade passing frequencies under investigation, paving the way for accurate aeromechanical evaluations in the preliminary design phase when complete unsteady CFD simulations are not compatible with design timing.
Whole-brain spatial transcriptional analysis at cellular resolution
Authors: Kanatani Shigeaki , Kreutzmann Judith C. , Li Yue , West Zoe , Larsen Lea Lydolph , Nikou Danai Vougesi , Eidhof Ilse , Walton Abigail , Zhang Songbai , Rodríguez-Kirby Leslie Rubio , Skytte Jacob Lercke , Salinas Casper Gravesen , Takamatsu Kimiharu , Li Xiaofei , Tanaka Daisuke H. , Kaczynska Dagmara , Fukumoto Keishiro , Karamzadeh Razieh , Xiang Yujiao , Uesaka Naofumi , Tanabe Tsutomu , Adner Mikael , Hartman Johan , Miyakawa Ayako , Sundström Erik , Castelo-Branco Gonçalo , Roostalu Urmas , Hecksher-Sørensen Jacob , Uhlén Per
Published: November 2024

Recent advances in RNA analysis have deepened our understanding of cellular states in biological tissues. However, a substantial gap remains in integrating RNA expression data with spatial context across organs, primarily owing to the challenges associated with RNA detection within intact tissue volumes. Here, we developed Tris buffer–mediated retention of in situ hybridization chain reaction signal in cleared organs (TRISCO), an effective tissue-clearing method designed for whole-brain spatial three-dimensional (3D) RNA imaging. TRISCO resolved several crucial issues, including the preservation of RNA integrity, achieving uniform RNA labeling, and enhancing tissue transparency. We tested TRISCO using a broad range of cell-identity markers, noncoding and activity-dependent RNAs, within diverse organs of varying sizes and species. TRISCO thus emerges as a powerful tool for single-cell, whole-brain, 3D imaging that enables comprehensive transcriptional spatial analysis across the entire brain.
Amidination of ligands for chemical and field-effect passivation stabilizes perovskite solar cells
Authors: Yang Yi , Chen Hao , Liu Cheng , Xu Jian , Huang Chuying , Malliakas Christos D. , Wan Haoyue , Bati Abdulaziz S. R. , Wang Zaiwei , Reynolds Robert P. , Gilley Isaiah W. , Kitade Shuta , Wiggins Taylor E. , Zeiske Stefan , Suragtkhuu Selengesuren , Batmunkh Munkhbayar , Chen Lin X. , Chen Bin , Kanatzidis Mercouri G. , Sargent Edward H.
Published: November 2024

Surface passivation has driven the rapid increase in the power conversion efficiency (PCE) of perovskite solar cells (PSCs). However, state-of-the-art surface passivation techniques rely on ammonium ligands that suffer deprotonation under light and thermal stress. We developed a library of amidinium ligands, of interest for their resonance effect–enhanced N–H bonds that may resist deprotonation, to increase the thermal stability of passivation layers on perovskite surfaces. This strategy resulted in a >10-fold reduction in the ligand deprotonation equilibrium constant and a twofold increase in the maintenance of photoluminescence quantum yield after aging at 85°C under illumination in air. Implementing this approach, we achieved a certified quasi–steady-state PCE of 26.3% for inverted PSCs; and we report retention of ≥90% PCE after 1100 hours of continuous 1-sun maximum power point operation at 85°C.
Variability in cadmium tolerance of closely related
Listeria monocytogenes
isolates originating from dairy processing environments
Authors: Domen Andrea , Porter Jenna , Johnson Jared , Molyneux James , McIntyre Lorraine , Kovacevic Jovana , Waite-Cusic Joy
Published: November 2024

Increased tolerance to cadmium in
Listeria monocytogenes
has been suggested to contribute to their persistence in natural and food production environments. This study investigated the phenotypic cadmium response of
L. monocytogenes
strains with efflux pump
cadAC
(variants 1–4) and related strains with
cadA1C1
. Growth of
cadAC
variant strains (
n
= 5) in 0 µM–120 µM cadmium salts (CdCl
2
, CdSO
4
) in Mueller-Hinton broth (MHB) was evaluated. Additionally, 88
L
.
monocytogenes
strains from dairy processing facilities were exposed to 43.8 µM CdCl
2
in MHB, and their lag phase duration (LPD) was measured. Strains with
cadA1
through
cadA3
showed similar growth trends in the presence of cadmium, while the
cadA4
variant (Scott A) had the highest CdCl
2
minimum inhibitory concentration (175 µM). Growth varied between the two salts, with CdSO
4
significantly increasing LPD (
P
< 0.05) compared to CdCl
2
. In 43.8 µM CdCl
2
,
cadA1
strains displayed LPDs ranging from 0.99 ± 0.14 h to 6.44 ± 0.08 h, with no clear genomic differences explaining this variability. Strains without
cadA
did not grow at 43.8 µM CdCl
2
but exhibited low tolerance (10.9 µM CdCl
2
), potentially due to non-specific soft metal ATPases (626 aa; 737 aa) and soft metal resistance proteins encoded by
czc
genes (289 aa; 291 aa; 303 aa) within their chromosomes. These findings enhance our understanding of
L. monocytogenes
cadmium tolerance and underscore the need for further research to explore the genetic and physiological factors underlying these trends.

IMPORTANCE

Mobile genetic elements in
Listeria monocytogenes
contribute to its survival in natural and food processing environments. This study focused on how different genetic variants of the efflux pump gene
cadAC
and group of closely related
cadA1C1
strains respond to cadmium exposure. When exposed to two cadmium salts, cadmium chloride and cadmium sulfate, we observed varying growth patterns, with a significantly longer lag phase in cadmium sulfate compared to cadmium chloride. Strains with
cadA1
to
cadA3
had similar growth trends, whereas a strain with the
cadA4
variant had the highest minimum inhibitory concentration value. Among 88 strains from dairy processing facilities, significant phenotypic differences were observed despite core genome similarities, indicating other underlying genetic and physiological factors contribute to cadmium tolerance. Since cadmium tolerance studies in
L. monocytogenes
are limited, with rare phenotypic comparisons between closely related strains, our study makes an important observation and contribution to understanding of
L. monocytogenes
tolerance to cadmium by providing phenotypic comparisons between numerous strains within the same clonal group (<16 single nucleotide polymorphisms).
Features of the eGFP Gene Expression in Transplastomic Tobacco Plants
Authors: Sidorchuk Yu. V. , Belavin P. A. , Zagorskaya A. A. , Marenkova T. V. , Kuznetsov V. V. , Khairulina E. S. , Deineko E. V.
Published: November 2024

Transplastomic tobacco plants expressing a reporter gene egfp and selectable marker gene aadA as part of a bicistronic operon were obtained using the bioballistics method. Features of egfp gene expression were studied in two groups of transplastomic plants: seed progeny obtained from self-pollination and plants obtained as a result of two successive regeneration cycles from the leaves of the original transformants. A comparative fluorimetric analysis of the recombinant protein accumulation in groups of transplastomic plants and nuclear transformants was carried out. It was found that the amount of recombinant eGFP protein accumulated in the leaves of transplastomic plants was unexpectedly low and did not exceed the level established for nuclear transformants. Real-time PCR results showed that the low level of recombinant eGFP accumulation was not associated with low levels of transgene expression or the presence of nontransgenic copies of the plastid genome in chloroplasts. This is most likely due to limitations imposed at the level of translation of recombinant proteins in chloroplasts.
Facilitators and barriers to survivorship support access: a qualitative study of rectal cancer survivors’ experiences in Texas
Authors: You Xinyue , Chen Qi , Fichera Alessandro , Ivatury Joga
Published: November 2024

Objective

This qualitative study aimed to analyse rectal cancer survivors’ lived experiences to identify facilitators and barriers to support access.

Design

We conducted one-on-one semi-structured interviews and employed thematic analysis to identify key themes and insights.

Setting/participants

Participants included eight rectal cancer survivors and three caregivers recruited at Texas Colorectal Collaborative sites.

Results

Results showed that adequate hospital resources, high health literacy and close connections with clinicians and peers who share similar experiences facilitate survivors’ access to social support. Conversely, ineffective healthcare team communication, financial challenges and low self-motivation hindered access.

Conclusion

Survivorship experiences were shaped by varying degrees of social support access, influenced by internal and external factors. We aim to establish a cross-institutional survivorship support network to address these factors, ensuring equitable access to support services and enhancing survivorship experiences.
Cost-effectiveness of robotic surgery compared to conventional laparoscopy for the management of early-stage cervical cancer: a model-based economic evaluation in China
Authors: Chen Chunlan , Zhang Min , Tang Junying , Pu Kexue
Published: November 2024

Objectives

The aim of this study is to assess cost-effectiveness of robotic radical hysterectomy (RRH) vs laparoscopic radical hysterectomy (LRH) in early-stage cervical cancer (ECC).

Design

Model-based cost-effectiveness analysis.

Setting

Based on long-term survival data, a three-state Markov model was constructed using TreeAge Pro 2022 to simulate the possible recurrence of ECC. Data on clinical efficacy and costs were derived from published literature and local databases.

Participants

A hypothetical cohort of 1000 individuals diagnosed with early-stage cervical cancer (FIGO 2009 stages<IIB) who underwent RRH or LRH management.

Outcome measures

The study endpoints were quality-adjusted life years (QALYs), total costs (in Chinese renminbi (RMB) adjusted to 2023-year values using the Consumer Price Index) and incremental cost-effectiveness ratio (ICER). A willingness-to-pay threshold of 268 074 RMB per QALY was used to assess cost-effectiveness.

Results

Robotic group gained more 4.84 QALYs than the laparoscopic group, but total costs for robotic strategy are substantially higher, with the incremental costs of 1 031 108 RMB. The ICER of robotic strategy is 213 054 RMB per QALY. Outcomes were robust in most one-way sensitivity and probabilistic sensitivity analyses.

Conclusions

Robotic strategy is on the efficient frontier but incurs substantial initial cost. Our findings indicated that this strategy is a cost-effective treatment option for ECC patients if assessed over a time horizon of patients’ lifetime. This study underscores the need for long-term clinical trials in early-stage cervical cancer patients with follow-up data that capture financial and quality-of-life end points.
Low effective coverage of HIV testing and counselling services during antenatal care in Ethiopia: evidence from the demographic and health survey and service provision assessment
Authors: Alemu Melaku Birhanu , Norman Richard , Dantas Jaya , Getachew Theodros , Tadele Ashenif , Tegegne Teketo Kassaw , Pereira Gavin , Tessema Gizachew A
Published: November 2024

Introduction

Prevention of mother-to-child transmission contributes to avert nearly 4000 new HIV infections in 2022. HIV testing and counselling (HTC) during antenatal care (ANC) is an effective strategy to reduce the vertical transmission of HIV. While the utilisation of HTC services in Ethiopia has been explored, there is limited evidence exploring the effective coverage of HTC during ANC. We aimed to assess the quality-adjusted (effective) coverage of HTC during ANC in Ethiopia.

Methods

We linked the 2016 Ethiopian Demographic Health Survey and the 2021–2022 Ethiopian Service Provision Assessment (ESPA) surveys to determine the crude coverage and assess the quality of HTC provision. The ESPA service quality framework, which has structural and process-related components, was used. We used a weighted additive approach of 61 indicators (38 structural and 23 process) to estimate the quality of care score. Administrative boundary linkage was used to link the two datasets. The effective coverage was calculated as the product of crude coverage and the quality of care score.

Results

During ANC contacts/visits, 17.2% (95% CI 16.0 to 18.4) of women received HTC. The effective coverage of HTC during ANC was 10.8 (95% CI 9.8 to 11.8), with substantial disparities among administrative regions, ranging from 1.6% (95% CI 1.3, 2.0) in the Somali region to 55.5% (95% CI 53.5 to 57.5) in Addis Ababa. The overall service quality was 45.2% (95% CI 42.8 to 47.1). Health facilities in Harari (31.4% (95% CI 19.0 to 43.8)), Somali (34.1% (95% CI 26.8 to 41.6)) and Dire Dawa (34.9% (95% CI 24.71 to 45.0)) demonstrated low-quality service provision. Health posts (5.8%, SD=8.4), health facilities located in rural areas (30.6%, SD=27.3) and public health facilities (44.2%, SD=29.4) had low-quality HTC provision.

Conclusion

Nearly 9 in 10 women did not receive quality HTC during ANC provision. The crude coverage, quality of services provision and effective coverage significantly vary across administrative regions. Afar and Somali regions lagged behind the Addis Ababa and Amhara administrative regions. To effectively mitigate mother-to-child transmission of HIV, policymakers need to prioritise enhancing the quality of care in HTC during ANC and expanding service delivery.
Myocardial Performance Index to assess cardiac function in autoimmune connective tissue disease: a systematic review and meta-analysis
Authors: Hidayat Rudy , Nasution Sally Aman , Parlindungan Faisal , Dalimunthe Naomi Niari , Alvianto Steven , Widjanarko Nicolas Daniel , Kultsum Ummi , Efendi Cristopher , Gotama Yovita
Published: November 2024

Objectives

This study aimed to evaluate cardiac function using Myocardial Performance Index (MPI) in autoimmune connective tissue disease (ACTD) patients without cardiovascular abnormalities.

Methods

A systematic search of databases including Medline, Google Scholar, ProQuest, Scopus and Cochrane Library was conducted to identify relevant studies on ACTD and MPI from 1995 to 2023. ACTD included in the search were rheumatoid arthritis (RA), systemic sclerosis (SSc), systemic lupus erythematosus (SLE), Sjögren syndrome (SjD), polymyositis and dermatomyositis. Quality assessment was performed using the Newcastle-Ottawa Scale, followed by meta-analysis computation of mean differences (MDs) of MPI using Review Manager V.5.4.

Results

A total of 22 studies for qualitative and 19 for quantitative synthesis were included. We found six studies on RA, eight studies on SSc, five studies on SLE, two studies on SjD and one on mixed connective tissue disorder. Conventional echocardiography and tissue Doppler imaging (TDI) were used to assess the MPI. Both conventional MPI and tissue Doppler MPI values were elevated compared with healthy control (MD=0.11, 95% CI 0.08 to 0.14, p value<0.00001 and MD=0.06, 95% CI 0.03 to 0.10, p value=0.00001, respectively).

Conclusions

We found elevated MPI values in patients with ACTD compared with healthy controls. MPI assessment has the potential for early detection and management of cardiac dysfunction in patients with ACTD, but further studies are required to corroborate these findings.

PROSPERO registration number

CRD42023490643.
Impact of autoantibody status on stratifying the risk of organ involvement and mortality in SSc: experience from a multicentre French cohort of 1605 patients
Authors: Didier Kevin , Sobanski Vincent , Robbins Ailsa , Truchetet Marie-Elise , Barnetche Thomas , Contin-Bordes Cécile , Hot Arnaud , Fort Romain , Guilpain Philippe , Maria Alexandre , Agard Christian , Pennaforte Jean-Loup , Viguier Manuelle , Martin Thierry , Jolly Damien , Barbe Coralie , Giusti Delphine , Launay David , Servettaz Amélie
Published: November 2024

Introduction

Systemic sclerosis (SSc) is a rare autoimmune disease currently classified into two subgroups based on skin extension. The aim of this study was to determine in a large cohort whether the determination of autoantibody (AAb) profile among a full antinuclear AAbs panel including nine specificities had a higher impact than skin phenotype on stratifying the risk of organ involvement and mortality in SSc.

Methods

Data for patients with SSc followed in seven French university hospitals were retrospectively analysed in terms of skin phenotype, AAbs (anti-topoisomerase I (ATA), anticentromere (ACA), anti-RNA polymerase III (anti-RNAPIII), anti-U1RNP, anti-U3RNP, anti-Pm/Scl, anti-Ku, anti-Th/To, anti-NOR90), organ involvement and mortality. Multivariate analyses were performed to identify independent factors associated with organ involvement and mortality.

Results

We included 1605 patients with SSc (367 with diffuse cutaneous SSc). On multivariate analysis, ATAs were associated with interstitial lung disease and mortality (OR=3.27 (95% CI 2.42 to 4.42); HR=1.9 (95% CI 1.01 to 3.58)), anti-RNAPIII with scleroderma renal crisis and mortality (OR=7.05 (95% CI 2.98 to 16.72); HR=2.35 (95% CI 1.12 to 4.93)), anti-U1RNP with arthritis (OR=3.79 (95% CI 2.16 to 6.67)), anti-Pm/Scl and anti-Ku with myositis (OR=7.09 (95% CI 3.87 to 12.98) and 7.99 (95% CI 2.41 to 26.46)). The skin phenotype was not associated with survival or organ involvement on multivariate analysis without stepwise selection.

Conclusion

This study unravels, by contrast with skin phenotype, a strong association between AAbs specificities, organ involvement and outcome in SSc and suggests that patients’ classification based on only skin extension is not sufficient for defining prognosis and phenotype.
Impact of redshift space distortions on persistent homology of cosmic matter density field
Authors: Abedi Fatemeh , Jalali Kanafi Mohammad Hossein , Movahed Seyed Mohammad Sadegh
Published: November 2024

By employing summary statistics obtained from Persistent Homology (PH), we investigate the influence of Redshift Space Distortions (RSD) on the topology of excursion sets formed through the super-level filtration method applied to three-dimensional matter density fields. The synthetic fields simulated by the Quijote suite in both real and redshift spaces are smoothed by accounting for the Gaussian smoothing function with different scales. The RSD leads a tendency for clusters ([Formula: see text]) to shift toward higher thresholds, while filament loops ([Formula: see text]) and cosmic voids ([Formula: see text]) migrate toward lower thresholds. Notably, [Formula: see text] exhibits greater sensitivity to RSD compared to clusters and independent loops. As the smoothing scales increase, the amplitude of the reduced Betti number curve ([Formula: see text]) decreases, and the corresponding peak position shifts toward the mean threshold. Conversely, the amplitude of [Formula: see text] remains almost unchanged with variations in redshift for [Formula: see text]. The analysis of persistent entropy and the overall abundance of [Formula: see text]-holes indicates that the linear Kaiser effect plays a significant role compared to the nonlinear effect for [Formula: see text][Formula: see text]Mpc[Formula: see text][Formula: see text] at [Formula: see text], whereas persistent entropy proves to be a reliable measure against nonlinear influences.
Unveiling the physical aspects of isotropic polytropes in f(R,G) gravity
Authors: Zia Saeeda , Chaudhary Shahid , Malik Adnan , Sanaullah Usama
Published: November 2024

This paper explores isotropic polytropes within the framework of the [Formula: see text] modified gravity theory. We derive the field equations that govern these systems and develop the Lane–Emden equations for both mass (baryonic) density and total energy density, utilizing two different polytropic equations of state. Our analysis shows that the constructed models feature positive and finite density and pressure, which decrease as expected towards the outer boundary, aligning with the typical behavior of self-gravitating objects. Additionally, the energy–momentum tensor meets the required energy conditions, confirming the physical viability of the models. The compactness factor remains below the critical limit, signifying stability and providing insights into gravitational redshift. Analysis of the sound speed confirms adherence to causality, while the adiabatic index indicates a degree of stiffness within the system. These findings advance our understanding of self-gravitating objects in [Formula: see text] gravity and lay the groundwork for future research in this field.
IoT-Oriented Edge Computing-Driven Computer Vision Solution
Authors: Zhang Songning
Published: November 2024

The rise of IoT devices has led to a surge in data generation, necessitating efficient processing solutions. Traditional cloud-centric approaches face challenges like latency, bandwidth and privacy issues. Edge computing, a promising paradigm, enables data processing closer to the source, enhancing IoT-driven computer vision applications. This shift integrates edge computing frameworks with a study that proposed a novel drosophila food search-tuned convolutional neural network (DFS-CNN) and computer vision algorithms for real-time tasks like object detection and anomaly detection. We collected acquisition data from labeled Faces in the Wild (LFW) video and image dataset, the acquisition data were preprocessed using a bilateral filter for minimizing noise while maintaining sharp edges, and then filtered data were extracted using a histogram of oriented gradients (HOG) — the DFS-CNN model using the HOG feature set to detection the computer vision solution. The optimal DFS-CNN model was deployed on edge computing and is used in an IoT-based architecture to compute and transport data for real-time performance simulation using Tensor Flow Lite. The proposed method is compared to other traditional algorithms. The proposed DFS-CNN model detects objects in the LWT image with 96% accuracy. The proposed DFS-CNN model was used to address students’ inactivity status during the online exam, and that the outcome of the suggested method was tested with data latency, and real-time response, according to a comparative performance analysis.
SARS: A Personalized Federated Learning Framework Towards Fairness and Robustness against Backdoor Attacks
Authors: Zhang Weibin , Li Youpeng , An Lingling , Wan Bo , Wang Xuyu
Published: November 2024

Federated Learning (FL), an emerging distributed machine learning framework that enables each client to collaboratively train a global model by sharing local knowledge without disclosing local private data, is vulnerable to backdoor model poisoning attacks. By compromising some users, the attacker manipulates their local training process, and uploads malicious gradient updates to poison the global model, resulting in the poisoned global model behaving abnormally on the sub-tasks specified by the malicious user. Prior research has proposed various strategies to mitigate backdoor attacks. However, existing FL backdoor defense methods affect the fairness of the FL system, while fair FL performance may not be robust. Motivated by these concerns, in this paper, we propose Self-Awareness Revision (SARS), a personalized FL framework designed to resist backdoor attacks and ensure the fairness of the FL system. SARS consists of two key modules: adaptation feature extraction and knowledge mapping. In the adaptation feature extraction module, benign users can adaptively extract clean global knowledge with self-awareness and self-revision of the backdoor knowledge transferred from the global model. Based on the previous module, users can effectively ensure the correct mapping of clean sample features and labels. Through extensive experimental results, SARS can defend against backdoor attacks and improve the fairness of the FL system by comparing several state-of-the-art FL backdoor defenses or fair FL methods, including FedAvg, Ditto, WeakDP, FoolsGold, and FLAME.
Systematic Evaluation of Personalized Deep Learning Models for Affect Recognition
Authors: Han Yunjo , Zhang Panyu , Park Minseo , Lee Uichin
Published: November 2024

Understanding human affective states such as emotion and stress is crucial for both practical applications and theoretical research, driving advancements in the field of affective computing. While traditional approaches often rely on generalized models trained on aggregated data, recent studies highlight the importance of personalized models that account for individual differences in affective responses. However, there remains a significant gap in research regarding the comparative evaluation of various personalization techniques across multiple datasets. In this study, we address this gap by systematically evaluating widely-used deep learning-based personalization techniques for affect recognition across five open datasets (i.e., AMIGOS, ASCERTAIN, WESAD, CASE, and K-EmoCon). Our analysis focuses on realistic scenarios where models must adapt to new, unseen users with limited available data, reflecting real-world conditions. We emphasize the principles of reproducibility by utilizing open datasets and making our evaluation models and codebase publicly available. Our findings provide critical insights into the generalizability of personalization techniques, the data requirements for effective personalization, and the relative performance of different approaches. This work offers valuable contributions to the development of personalized affect recognition systems, fostering advancements in both methodology and practical application.
Extending EV Battery Lifetime: Digital Phenotyping Approach for Departure Time Prediction
Authors: Lee Yonggeon , Song Woojin , Song Juhyun , Noh Youngtae
Published: November 2024

Battery degradation, a gradual loss of usable capacity over time, is one of the major hurdles for widespread adoption of electric vehicles (EVs). We introduce delayed full-charging (DFC) algorithm to mitigate degradation and extend the lifetime of EV batteries in battery management systems (BMS). When the EV is plugged in, the DFC algorithm charges batteries up to approximately 80% state of charge (SOC) and delays full charging until the predicted unplug time (tunplug). This approach significantly reduces the time batteries remain fully charged (t100%), thereby mitigating degradation while ensuring charging time for EV users to utilize the full battery capacity. For predicting tunplug, we propose a novel methodology that uses digital phenotyping to predict departure times. This method leverages smartphone data to capture irregular but predictable departure patterns by reflecting relevant behavioral and environmental contexts. A case study with 48 participants was conducted to empirically evaluate the departure time prediction performance using tree-based ensemble models trained on smartphone data, compared to a baseline Long Short-Term Memory (LSTM) model trained on historical data. Results reveal that models utilizing mobile passive features achieved a Mean Absolute Error (MAE) as low as 2.18 hours on weekdays and 4.46 hours on weekends, demonstrating superior effectiveness in capturing irregular patterns compared to the baseline model trained only on historical temporal features.
DEWS: A Distributed Measurement Scheme for Efficient Wireless Sensing
Authors: Pang Mingzhi , Li Kaiying , Wang Xun , Wang Wei , Cheng Wen , Liu Dongxu , Yin Yuqing , Chen Pengpeng
Published: November 2024

One of the key challenges for wireless sensing systems is how to efficiently enable wireless sensing capabilities for multiple devices while leveraging existing wireless communication resources. In this paper, we propose DEWS, a distributed channel measurement scheme that allows multiple transmitters to perform sensing tasks simultaneously, which considers three key issues in wireless sensing tasks: multi-device sensing resolution, multi-device sensing reliability, and multi-device sensing accuracy. First, we use a carefully designed distributed Resource Unit (dRU) allocation scheme based on OFDMA to ensure that multiple devices perform sensing tasks simultaneously with the entire bandwidth, thereby improving the sensing resolution. Then, a subcarrier linear phase shift scheme is designed to avoid signal interference between different transmitting devices and improve the reliability of multi-device sensing. This scheme realizes the fine-grained Time Division Multiplexing (TDM) of multiple devices, which can also increase the number of simultaneous access of sensing devices. Finally, a sensing accuracy improvement algorithm combining a single antenna-based motion recovery method utilizing Independent Component Analysis (ICA) and sampling frequency offset (SFO) correction is proposed, to further help DEWS to break the inherent bandwidth limits and recover different motions at similar distances. We verify the effectiveness of DEWS through extensive experimental testing using commercial USRPs.
ESATED: Leveraging Extra-weak Supervision with Auxiliary Task for Enhanced Non-intrusiveness in Energy Disaggregation
Authors: Xia Peng , Zhou Hao , Yang Tianjian , Zhou Wangqiu , Liu Zhi , Wang Xiaoyan , Li Xiang-Yang
Published: November 2024

Non-intrusive load monitoring (NILM) is crucial to smart grid, which enables applications such as energy conservation and human activity recognition. As a typical task of NILM, energy disaggregation is to decompose total power consumption into appliance-level ones. Despite the remarkable achievements of deep-learning-based methods, their training phase still requires intrusively collected appliance-level power data as strong labels, which are directly used for supervising predictions.

In this paper, we present ESATED, a novel energy disaggregation system which instead utilizes non-intrusively collected binary on-off states of appliances as labels, thus enhancing non-intrusiveness throughout the life cycle. However, our labels are inherently weak labels due to the weak correlation between labels (binary states) and predictions (real-valued power), thus making our model struggle in terms of feasible supervision and acceptable performance. To tackle this challenge, we first explore the feasibility of binary-state-based weak supervision, and then integrate it into an auxiliary learning system, where an auxiliary subtask (i.e., state classification) is introduced to further enhance the performance of the primary task (i.e., energy disaggregation). We conduct extensive experiments on two real-world public datasets, and also implement the prototype system in a practical scenario. Corresponding results reveal that even using weak labels, ESATED could achieve performance and transferability second only to the state-of-the-art model. This result demonstrates the effectiveness of the proposed approach to extract information and train the model from extra weak labels.
CalibRead: Unobtrusive Eye Tracking Calibration from Natural Reading Behavior
Authors: Liu Change , Yu Chun , Wang Xiangyang , Jiang Jianxiao , Yang Tiaoao , Tang Bingda , Shi Yingtian , Liang Chen , Shi Yuanchun
Published: November 2024

In this paper, we present a novel, unobtrusive calibration method that leverages the association between eye-movement and text to calibrate eye-tracking devices during natural reading. The calibration process involves an iterative sequence of 3 steps: (1) matching the points of eye-tracking data with the text grids and boundary grids, (2) computing the weight for each point pair, and (3) optimizing the calibration parameters that best align point pairs through gradient descent. During this process, we assume that, from a holistic perspective, the gaze will cover the text area, effectively filling it after sufficient reading. Meanwhile, on a granular level, the gaze duration is influenced by the semantic and positional features of the text. Therefore, factors such as the presence of empty space, the positional features of tokens, and the depth of constituency parsing play important roles in calibration. Our method achieves accuracy error comparable to traditional 7-point mehtod after naturally reading 3 texts, which takes about 51.75 seconds. Moreover, we analyse the impact of different holistic and granular features on the calibration results.
EMooly: Supporting Autistic Children in Collaborative Social-Emotional Learning with Caregiver Participation through Interactive AI-infused and AR Activities
Authors: Lyu Yue , Liu Di , An Pengcheng , Tong Xin , Zhang Huan , Katsuragawa Keiko , Zhao Jian
Published: November 2024

Children with autism spectrum disorder (ASD) have social-emotional deficits that lead to difficulties in recognizing emotions as well as understanding and responding to social interactions. This study presents EMooly, a tablet game that actively involves caregivers and leverages augmented reality (AR) and generative AI (GenAI) to enhance social-emotional learning for autistic children. Through a year of collaborative effort with five domain experts, we developed EMooly that engages children through personalized social stories, interactive and fun activities, and enhanced caregiver participation, focusing on emotion understanding and facial expression recognition. Compared with a baseline, a controlled study with 24 autistic children and their caregivers showed EMooly significantly improved children's emotion recognition skills and its novel features were preferred and appreciated. EMooly demonstrates the potential of AI and AR in enhancing social-emotional development for autistic children via prompt personalizing and engagement, and highlights the importance of caregiver involvement for optimal learning outcomes.
Investigating Perspectives of and Experiences with Low Cost Commercial Fitness Wearables
Authors: Kouaho Whitney-Jocelyn , Epstein Daniel A.
Published: November 2024

Consumer fitness wearables account for a growing body of personal mobile devices in the U.S. As a result, there has been increasing interest in Ubicomp in designing sensing devices which incorporate the needs of people of a broad range of backgrounds, especially of lower socioeconomic (SES) status. However, fitness trackers are expensive, and although low cost versions exist, there has not been much research on the viability of these devices for promoting their goals. To further the conversation of device use, we review a corpus of over 1,700 product reviews of 9 low cost consumer trackers, to determine perceptions and expectations of quality and general use. From this, we find that the low cost device is not currently necessarily increasing access, and that people have a wide range of expectations for what tracking will allow and represents at this price point, which colors how they describe their use experiences. We suggest that there is a need for a focused design effort for low cost devices which rectifies such discrepancies, and presents the low cost device as a counterpart to those of a higher consumer price, not an alternative.
Ring-a-Pose: A Ring for Continuous Hand Pose Tracking
Authors: Yu Tianhong Catherine , Hu Guilin , Zhang Ruidong , Lim Hyunchul , Mahmud Saif , Lee Chi-Jung , Li Ke , Agarwal Devansh , Nie Shuyang , Oh Jinseok , Guimbretière François , Zhang Cheng
Published: November 2024

We present Ring-a-Pose, a single untethered ring that tracks continuous 3D hand poses. Located in the center of the hand, the ring emits an inaudible acoustic signal that each hand pose reflects differently. Ring-a-Pose imposes minimal obtrusions on the hand, unlike multi-ring or glove systems. It is not affected by the choice of clothing that may cover wrist-worn systems. In a series of three user studies with a total of 36 participants, we evaluate Ring-a-Pose's performance on pose tracking and micro-finger gesture recognition. Without collecting any training data from a user, Ring-a-Pose tracks continuous hand poses with a joint error of 14.1mm. The joint error decreases to 10.3mm for fine-tuned user-dependent models. Ring-a-Pose recognizes 7-class micro-gestures with a 90.60% and 99.27% accuracy for user-independent and user-dependent models, respectively. Furthermore, the ring exhibits promising performance when worn on any finger. Ring-a-Pose enables the future of smart rings to track and recognize hand poses using relatively low-power acoustic sensing.
EyeTrAES: Fine-grained, Low-Latency Eye Tracking via Adaptive Event Slicing
Authors: Sen Argha , Bandara Nuwan Sriyantha , Gokarn Ila , Kandappu Thivya , Misra Archan
Published: November 2024

Eye-tracking technology has gained significant attention in recent years due to its wide range of applications in human-computer interaction, virtual and augmented reality, and wearable health. Traditional RGB camera-based eye-tracking systems often struggle with poor temporal resolution and computational constraints, limiting their effectiveness in capturing rapid eye movements. To address these limitations, we propose EyeTrAES, a novel approach using neuromorphic event cameras for high-fidelity tracking of natural pupillary movement that shows significant kinematic variance. One of EyeTrAES's highlights is the use of a novel adaptive windowing/slicing algorithm that ensures just the right amount of descriptive asynchronous event data accumulation within an event frame, across a wide range of eye movement patterns. EyeTrAES then applies lightweight image processing functions over accumulated event frames from just a single eye to perform pupil segmentation and tracking (as opposed to gaze-based techniques that require simultaneous tracking of both eyes). We show that these two techniques boost pupil tracking fidelity by 6+%, achieving IoU~=92%, while incurring at least 3x lower latency than competing pure event-based eye tracking alternatives [38]. We additionally demonstrate that the microscopic pupillary motion captured by EyeTrAES exhibits distinctive variations across individuals and can thus serve as a biometric fingerprint. For robust user authentication, we train a lightweight per-user Random Forest classifier using a novel feature vector of short-term pupillary kinematics, comprising a sliding window of pupil (location, velocity, acceleration) triples. Experimental studies with two different datasets (capturing eye movement across a range of environmental contexts) demonstrate that the EyeTrAES-based authentication technique can simultaneously achieve high authentication accuracy (~=0.82) and low processing latency (~=12ms), and significantly outperform multiple state-of-the-art competitive baselines.
Densor: An Intraoral Battery-Free Sensing Platform
Authors: Dsouza Vivian , Pronk Jeffrey , Peppelman Christian , Madariaga Víctor Ignacio , Pereira-Cenci Tatiana , Loomans Bas , Pawełczak Przemysław
Published: November 2024

The mouth offers valuable insights into the condition of the human body. Yet, deploying intraoral sensors to measure oral temperature or jaw movements poses challenges in safety and acceptability. Consequently, real-world data for intraoral research is scarce. To address this gap, we leverage the widespread use of dental retainers and enhance them with Densor: an electronic sensing platform requiring only a standard smartphone for charging and data retrieval using a Near Field Communication interface. Its low power architecture enables prolonged sensing on a single charge, making it suitable for sleep studies. It can provide practitioners with feedback on treatment compliance, and is even able to detect if the user is speaking or drinking water. Densor presents an intraoral, actively powered, battery-free platform featuring multi-modal sensors and an extended lifespan.
Wind of Change: Investigating Information Visualizations for Passengers and Residents' Perception of Automated Urban Air Mobility
Authors: Meinhardt Luca-Maxim , Colley Mark , Tahmid Marjana , Rädler Max , Rukzio Enrico
Published: November 2024

Automated Urban Air Mobility (UAM) is expected to improve passenger transportation but raises concerns about passenger trust and its impact on residents. We address these issues through three online studies using 360-degree videos. The study on adverse lighting and weather conditions (N=31) shows that bounding box visualizations of other air taxis positively impact passengers' perceived safety. Especially during fog, passengers' perceived safety is reduced when no additional information besides the path is shown. The second study (N=29) extends these findings to emergency scenarios (wind gusts, bird flocks, and rotor breakdown) and shows that visualizations of the alternate path increase perceived safety and trust. Finally, studying the impact of UAM on residents (N=29), we found significant concerns about visual pollution and privacy, especially in the suburbs but also in offices. Our findings provide actionable insights into UAM perception and societal acceptability for future aerial transportation design and infrastructure.
3D Bounding Box Estimation Based on COTS mmWave Radar via Moving Scanning
Authors: Feng Yiwen , Zhao Jiayang , Wang Chuyu , Xie Lei , Lu Sanglu
Published: November 2024

Object boundary estimation, usually achieved by bounding box estimation, is crucial in various applications, such as intelligent driving, which facilitates further interactions like obstacle avoidance and navigation. Existing solutions mainly rely on computer vision, which often performs poorly in low-visibility conditions, e.g., harsh weather, and has limited resolution for depth estimation. Recent studies show the potential of mmWave radar for object detection. However, due to the inherent drawbacks, conventional mmWave techniques suffer from the severe interference of noise points in the points cloud, leading to the position vagueness, as well as sparsity and limited spatial resolution, which leads to the boundary vagueness. In this paper, we propose a novel bounding box estimation system based on mmWave radar that sufficiently leverages the spatial features of the antenna array and the temporal features of moving scanning to detect objects and estimate their 3D bounding boxes. To mitigate the interference from noise points, we introduce a new integration metric, Reflection Saliency, which evaluates the effectiveness of each point based on signal-to-noise ratio (SNR), speed, and spatial domains, successfully reducing the majority of noise points. Moreover, we propose the Prior-Time Heuristic Point Cloud Augmentation method to enrich the point representation of objects based on the previous data. To obtain boundary information, we propose a beamforming-based model to extract the Angle-Reflection Profile (ARP), which depicts the spatial distribution of the object's reflection. Furthermore, a generative neural network is used to refine the boundary and estimate the 3D bounding box by incorporating the ARP features, SNR of cloud points, and depth information. We have implemented an actual system prototype using a robot car in real scenarios and extensive experiments show that the average position error of the proposed system in 3D bounding box estimation is 0.11m.
Governing Open Vocabulary Data Leaks Using an Edge LLM through Programming by Example
Authors: Li Qiyu , Wen Jinhe , Jin Haojian
Published: November 2024

A major concern with integrating large language model (LLM) services (e.g., ChatGPT) into workplaces is that employees may inadvertently leak sensitive information through their prompts. Since user prompts can involve arbitrary vocabularies, conventional data leak mitigation solutions, such as string-matching-based filtering, often fall short. We present GPTWall, a privacy firewall that helps internal admins create and manage policies to mitigate data leaks in prompts sent to external LLM services. GPTWall's key innovations are (1) introducing a lightweight LLM running on the edge to obfuscate target information in prompts and restore the information after receiving responses, and (2) helping admins author fine-grained disclosure policies through programming by example. We evaluated GPTWall with 12 participants and found that they could create an average of 17.7 policies within 30 minutes, achieving an increase of 29% in precision and 22% in recall over the state-of-the-art data de-identification tool.
MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences
Authors: Nepal Subigya , Pillai Arvind , Campbell William , Massachi Talie , Heinz Michael V. , Kunwar Ashmita , Choi Eunsol Soul , Xu Xuhai , Kuc Joanna , Huckins Jeremy F. , Holden Jason , Preum Sarah M. , Depp Colin , Jacobson Nicholas , Czerwinski Mary P. , Granholm Eric , Campbell Andrew T.
Published: November 2024

Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape explores a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI. We present an 8-week exploratory study with 20 college students, demonstrating the MindScape app's efficacy in enhancing positive affect (7%), reducing negative affect (11%), loneliness (6%), and anxiety and depression, with a significant week-over-week decrease in PHQ-4 scores (-0.25 coefficient). The study highlights the advantages of contextual AI journaling, with participants particularly appreciating the tailored prompts and insights provided by the MindScape app. Our analysis also includes a comparison of responses to AI-driven contextual versus generic prompts, participant feedback insights, and proposed strategies for leveraging contextual AI journaling to improve well-being on college campuses. By showcasing the potential of contextual AI journaling to support mental health, we provide a foundation for further investigation into the effects of contextual AI journaling on mental health and well-being.