bluuebunny/crossref_metadata_2025 huggingface dataset:
A Generalized Dynamic Analysis Method for the Linear Structural System with Viscoelastic Elements
Authors: Gai Pan-Pan , Xu Zhao-Dong , Bi Qin-Sheng , Xie Zi-Cong , Dai Jun , Jiang Wen-An
Published: January 2024

The high-damping viscoelastic (VE) materials are widely used to reduce unwanted vibrations in civil structures. However, the significant frequency and temperature dependences from high-damping VE materials cause a challenge in efficiently analyzing the structural dynamic behaviors. The aim of this study is to propose a generalized dynamic analysis method for the linear structural system with VE elements. The implementation steps and advantages of the dynamic analysis method are presented. In this method, the frequency-dependent matrices and non-proportional damping induced by VE elements are fully considered in modal coordinates, and then the state-space representation converted from the modal-based transfer function is adopted to implement the time-domain simulation. Any popular VE constitutive models which accurately describe frequency-dependent matrices can be applied to the method. The number of the state-space representation can be adjusted according to the interested frequency range. Numerical applications performed on a viscoelastically damped structure, a VE base-isolated structure, and a structure with a VE tuned mass damper (TMD) demonstrate the numerical accuracy, efficiency, and generality of the proposed dynamic analysis method.
Products of commutators in certain rings
Authors: Son Tran Nam , Dung Truong Huu
Published: January 2024

Let [Formula: see text] be a ring. For each positive integer [Formula: see text], [Formula: see text] (respectively, [Formula: see text]) is denoted by the set of all products of (respectively, at most) [Formula: see text] additive commutators of [Formula: see text]. If [Formula: see text] is an algebra over a field [Formula: see text] such that [Formula: see text] for some [Formula: see text], then [Formula: see text]. If [Formula: see text] is a noncommutative ring such that [Formula: see text] for some [Formula: see text], then the necessary and sufficient condition for [Formula: see text] to be a division ring is that every nonzero element of [Formula: see text] is invertible. If [Formula: see text] is the matrix ring over a division ring [Formula: see text], then [Formula: see text], and in particular, if [Formula: see text] is a field, then [Formula: see text].
Relative Gorenstein flat modules and Foxby classes and their model structures
Authors: Bennis Driss , El Maaouy Rachid , García Rozas J. R. , Oyonarte Luis
Published: January 2024

We introduce the concepts of relative (strongly) cotorsion and relative Gorenstein cotorsion modules for a non-necessarily semidualizing module and prove that there exists a unique hereditary abelian model structure where the cofibrations are the monomorphisms with relative Gorenstein flat cokernel and the fibrations are the epimorphisms with relative cotorsion kernel belonging to the Bass class. In the particular case of a semidualizing module, we investigate the existence of abelian model structures on the category of left (right) R-modules where the cofibrations are the epimorphisms (monomorphisms) with kernel (cokernel) belonging to the Bass (Auslander) class. We also show that the class of relative Gorenstein flat modules and the Bass class are part of weak AB-contexts.
The Role of Technological Advancement and Multidisciplinary Collaboration in Physical Internet Towards Circular Economy
Authors: Tran Thuy Thi Phuong , Ha Minh Hien
Published: January 2024

This study contributes to determining key attributes of the physical internet (PI) in circular economy (CE) as prior studies failed to explore the interrelationships among these attributes. The perspective of a CE is suitable to adopt the PI with an objective of obtaining global logistics networks formed on physical, digital and operational interconnectivity. This study attempts to propose a set of attributes in CE which is usually with qualitative analysis. The research results reveal multidisciplinary collaboration and technological advancement influence digital interoperability and physical infrastructure. Top criteria include Internet of Things, central orchestrator, alignment of resources, big data and blockchain.
ORGANISATIONAL ADAPTABILITY: A STUDY OF THE MEDIATING ROLE OF LEADERSHIP IN THE INFLUENCE OF STRATEGIES, COMPLEXITY, AND TECHNOLOGY
Authors: RAMESH P. , BHAVIKATTI VEENA , OMNAMASIVAYA B. , CHAITANYA GORLI , TEJASWINI  , HIREMATH SONY , GONDESI H. SANTOSHI , KAMESWARI JADA
Published: January 2024

Organisational adaptation is the capacity of an organisation to recognise the need to evolve and grasp possibilities in unpredictable circumstances. To position their organisations for flexibility in a world that is becoming more complicated, leadership needs to focus on change, dispersed, and contextual characteristics. A key idea concerning the conditions necessary for organisational adaptation is the theory of dynamic capabilities. This paper intends to highlight the role that leadership plays in mediating the impact of strategy, technology, and complexity on organisational adaptation. Structural equation modelling is used to test the suggested hypothesis. Professionals at a multinational organisation in India participated in the study. This study has a sample number of 252 and employed a simple random sampling technique. The results show that strategies and complexity impact the adaptability rate of an organisation and leadership mediates the relationship between strategies, technology, and complexity of organisational adaptability. In addition, it indicates that strategic adaption also improves business performance positively and effectively.
High Efficiency Ultra-Thin Normal-Incidence Ge-On-Si Photodetector Based on Optical Metasurface
Authors: Zhang Chen , Lin Weixi , Liu Zhengtong , Wang Lei , Yue Fuyong , Chen Zhenmin , Ma Chunyang , He Zhixue
Published: January 2024

The much thicker intrinsic absorption layer (IAL) in normal-incidence Ge-on-Si photodetectors (NIPD) usually causes a contradiction between responsivity and bandwidth. In response to this issue, here, we simulate the design of an NIPD with geranium (Ge) layers based on a “fishnet” metasurface, leading to a reduced device thickness as thin as 380[Formula: see text]nm. The optical simulation results show that the light field can be perfectly localized in the 210[Formula: see text]nm IAL, and the absorptivity is as high as 99.45% at 1550[Formula: see text]nm, which is even better than bulk materials. Moreover, the electrical simulation results suggest that the horizontal size of the photosensitive region can be reduced to 11.2 [Formula: see text]m, while the responsivity of the photodetector is close to 1 A/W at −1[Formula: see text]V bias voltage, which is nearly 23 times that of a bulk device with the same thickness, and the 3dB bandwidth is up to 40[Formula: see text]GHz, which can be compared with waveguide photodetectors. Besides, this device also demonstrates a high signal-to-noise ratio with a low dark current of 28.68 nA, making it an excellent PD for opto-electrical communication.
Seismic Isolation Effects on Elevated and Ground-Supported Flexible Concrete Cylindrical Tanks Under Bi-Directional Excitation Using an Advanced Mechanical Model
Authors: Hashemi Shamsedin , Aghashiri Mohammad Hossein , Ehteshami Atefeh , Kianoush Reza
Published: January 2024

In this paper, the effectiveness of seismic isolation using lead rubber bearings (LRBs) and friction pendulum systems (FPSs) for slender and broad, grounded and elevated tanks (two seat locations of isolation systems in elevated tanks) is investigated under bi-directional excitation of up to 10 records of earthquakes. An analytical mechanical model for a flexible, concrete cylindrical tank, taking into consideration the effect of wall mass and sloshing, is used. The assumptions underlying the mechanical model are basic equations of the motion according to the theory of fluid dynamics. So, the assumptions are more realistic and the results therefore are more accurate than the previous models. The results show that by selecting the best mechanical properties of base isolation systems, reductions of seismic base shear in the grounded broad tanks are around 35% and 30% and for the grounded slender tanks are around 55% and 58% in the x- and y-directions, respectively. Maximum total hydrodynamic pressure is reduced considerably by about 40% on average in all grounded and elevated models. As a result of isolation, elevated tanks are concluded to be better candidates in terms of more effective application of seismic isolation.
Can the Thermal Infrared Radiation Anomalies Reported Before Earthquakes be a Precursor?
Authors: Wang Jeen-Hwa
Published: January 2024

Twenty-seven earthquakes in Taiwan with local magnitudes [Formula: see text] and focal depths d = 8.0−215.8[Formula: see text]km were preceded by thermal infrared radiation (TIR) anomalies. The data are taken as an example to investigate the mechanisms of generating the TIR and to examine whether the TIR anomalies can be considered as a precursor or not. Let [Formula: see text] and [Formula: see text] be the largest number of days before forthcoming earthquakes when the anomalies are observed in the day-time and the night-time, respectively. The precursor time, T, for an event, is the larger value of [Formula: see text] and [Formula: see text]. The plots of both T vs. [Formula: see text] and [Formula: see text] vs. [Formula: see text] are scattered. Regardless of two data points with abnormally large [Formula: see text] values, it seems able to recognize a positive correlation between [Formula: see text] and [Formula: see text]. Among the 27 earthquakes, 8 events were proceeded by Rn concentration changes whose precursor times are [Formula: see text] days. For most of the eight events, [Formula: see text] is much longer than either [Formula: see text] or [Formula: see text]. Hence, the model of outflow of gases from the depths to the ground surface for describing the Rn concentration changes cannot interpret the TIR anomalies. Based on rock mechanics, the temperature rise, [Formula: see text], is generated by frictional heating due to preseismic slip. That the TIR anomalies discontinuously appeared before earthquakes and the occurrence times vary for different events is inconsistent with the processes of generation of frictional heating caused by preseismic slip on a few shallow faults in the source areas before forthcoming earthquakes. Since theoretical results and observations are not consistent, the answer to the title of this study is negative.
The interdependence and risk transmission between southward, northward capital and China’s stock, foreign exchange market
Authors: Cao Guangxi , Xie Wenhao
Published: January 2024

This paper mainly studies the interdependence structure and risk transmission between China’s north–south capital flow and the stock market and foreign exchange market. The results show that the correlation between southward capital and the mainland stock market is stronger, and the correlation between northward capital and the Hong Kong stock market is stronger. The correlation between northward capital and RMB exchange rate is greater than that between southward capital and RMB exchange rate. After the outbreak of COVID-19, the correlation between capital flows and the stock market has strengthened, while the correlation with the foreign exchange market has weakened. Furthermore, there is a bidirectional risk transmission between the north–south capital flow and China’s stock market and foreign exchange market. In general, the north–south capital flow has a greater impact on the stock market, and the exchange rate of RMB/US dollar has a greater impact on the north–south capital flow. This study has implications for investors and policymakers.
AI-based 3D Metrology and Defect Detection of HBMs in XRM Scans
Authors: Chang Richard , Jie Wang , Thakur Namrata , Pahwa Ramanpreet Singh
Published: January 2024

In this paper, we employ the latest developments in 3D semi-supervised learning to create cutting-edge deep learning models for 3D object detection and segmentation of buried structures in high-resolution X-ray semiconductor scans. We illustrate our approach to locating the region of interest of High Bandwidth Memory (HBM) structures and their individual components and identifying various defects. We showcase how semi-supervised learning is utilized to capitalize on the vast amounts of available unlabeled data to enhance both detection and segmentation performance. Additionally, we explore the benefits of contrastive learning in the data pre-selection for our detection model and multi-scale Mean-Teacher training paradigm in 3D semantic segmentation to achieve better performance compared to the state of the art. We also provide an objective comparison for metrology-based defect detection with a 3D classification network. Our extensive experiments have shown that our approach outperforms the state of the art by up to 16% on object detection and 7.8% on semantic segmentation. Our fully-automated custom metrology package shows a mean error of less than 2 [Formula: see text]m for key features such as bond line thickness and provides better defect detection performance than the direct 3D classification approach. Overall, our method achieves state-of-the-art performance and can be used to improve the accuracy and efficiency of a wide range of failure analysis applications in semiconductor manufacturing. Finally, we also increase the segmentation models flexibility and adaptability to new data. We propose a generic training strategy and a new loss function that reduces the training time by 60% and the required amount of data by 48% making the training process more efficient.
Natural attenuation of dissolved petroleum fuel constituents in a fractured Chalk aquifer: contaminant mass balance with probabilistic analysis
Authors: Thornton Steven F. , Spence Michael J. , Bottrell Simon H. , Spence Keith H.
Published: January 2024

A plume-scale mass balance is developed to assess the natural attenuation (NA) of dissolved organic contaminants in fractured, dual-porosity aquifers. This methodology can be used to evaluate contaminant distribution within the aquifer, plume source term, contaminant biodegradation and plume status. The approach is illustrated for a site on the UK Upper Chalk aquifer impacted by petroleum fuel containing methyl
tert
-butyl ether (MTBE) and
tert
-amyl methyl ether (TAME). Variability in site investigation data and uncertainty in the mass balance were assessed using probabilistic analysis. The analysis shows that benzene, toluene, m/p-xylene and o-xylene (BTEX) compounds are biodegraded primarily by denitrification and sulfate reduction in the aquifer, with an equivalent plume-scale first-order biodegradation rate of 0.49 a
−1
. Other biodegradation processes are less important. Sorption contributes to hydrocarbon attenuation in the aquifer but is less important for MTBE and TAME. Uncertainty in the plume source term and site hydrogeological parameters had the greatest effect on the mass balance. The probabilistic analysis enabled the most likely long-term composition of the plume source term to be deduced and provided a site-specific estimate of contaminant mass flux for the prediction of plume development. The mass balance methodology provides a novel approach to improve NA assessments for petroleum hydrocarbons and other organic contaminants in these aquifer settings.

Supplementary material:
Plume organic chemistry, aquifer hydraulic conductivity and probability distribution functions used for mass balance inputs are available at
https://doi.org/10.6084/m9.figshare.c.7016429

Thematic collection:
This article is part of the Monitoring the aquifers collection available at:
https://www.lyellcollection.org/topic/collections/monitoring-the-aquifers
Soil-Powered Computing
Authors: Yen Bill , Jaliff Laura , Gutierrez Louis , Sahinidis Philothei , Bernstein Sadie , Madden John , Taylor Stephen , Josephson Colleen , Pannuto Pat , Shuai Weitao , Wells George , Arora Nivedita , Hester Josiah
Published: January 2024

Human-caused climate degradation and the explosion of electronic waste have pushed the computing community to explore fundamental alternatives to the current battery-powered, over-provisioned ubiquitous computing devices that need constant replacement and recharging. Soil Microbial Fuel Cells (SMFCs) offer promise as a renewable energy source that is biocompatible and viable in difficult environments where traditional batteries and solar panels fall short. However, SMFC development is in its infancy, and challenges like robustness to environmental factors and low power output stymie efforts to implement real-world applications in terrestrial environments. This work details a 2-year iterative process that uncovers barriers to practical SMFC design for powering electronics, which we address through a mechanistic understanding of SMFC theory from the literature. We present nine months of deployment data gathered from four SMFC experiments exploring cell geometries, resulting in an improved SMFC that generates power across a wider soil moisture range. From these experiments, we extracted key lessons and a testing framework, assessed SMFC's field performance, contextualized improvements with emerging and existing computing systems, and demonstrated the improved SMFC powering a wireless sensor for soil moisture and touch sensing. We contribute our data, methodology, and designs to establish the foundation for a sustainable, soil-powered future.
ToothFairy
Authors: Wang Yang , Hong Feng , Jiang Yufei , Bao Chenyu , Liu Chao , Guo Zhongwen
Published: January 2024

Tooth brushing monitors have the potential to enhance oral hygiene and encourage the development of healthy brushing habits. However, previous studies fall short of recognizing each tooth due to limitations in external sensors and variations among users. To address these challenges, we present ToothFairy, a real-time tooth-by-tooth brushing monitor that uses earphone reverse signals captured within the oral cavity to identify each tooth during brushing. The key component of ToothFairy is a novel bone-conducted acoustic attenuation model, which quantifies sound propagation within the oral cavity. This model eliminates the need for machine learning and can be calibrated with just one second of brushing data for each tooth by a new user. ToothFairy also addresses practical issues such as brushing detection and tooth region determination. Results from extensive experiments, involving 10 volunteers and 25 combinations of five commercial off-the-shelf toothbrush and earphone models each, show that ToothFairy achieves tooth recognition with an average accuracy of 90.5%.
ADA-SHARK
Authors: Martin Marvin , Meunier Etienne , Moreau Pierre , Gadenne Jean , Dautel Julien , Catherin Félicien , Pinsky Eugene , Rawassizadeh Reza
Published: January 2024

Due to global warming, sharks are moving closer to the beaches, affecting the risk to humans and their own lives. Within the past decade, several technologies were developed to reduce the risks for swimmers and surfers. This study proposes a robust method based on computer vision to detect sharks using an underwater camera monitoring system to secure coastlines. The system is autonomous, environment-friendly, and requires low maintenance. 43,679 images extracted from 175 hours of videos of marine life were used to train our algorithms. Our approach allows the collection and analysis of videos in real-time using an autonomous underwater camera connected to a smart buoy charged with solar panels. The videos are processed by a Domain Adversarial Convolutional Neural Network to discern sharks regardless of the background environment with an F2-score of 83.2% and a recall of 90.9%, while human experts have an F2-score of 94% and a recall of 95.7%.
MagDot
Authors: Chen Dongyao , Luo Qing , Chen Xiaomeng , Wang Xinbing , Zhou Chenghu
Published: January 2024

Tracking the angular movement of body joints has been a critical enabler for various applications, such as virtual and augmented reality, sports monitoring, and medical rehabilitation. Despite the strong demand for accurate joint tracking, existing techniques, such as cameras, IMUs, and flex sensors, suffer from major limitations that include occlusion, cumulative error, and high cost. These issues collectively undermine the practicality of joint tracking.

We introduce MagDot, a new magnetic-based joint tracking method that enables high-accuracy, drift-free, and wearable joint angle tracking. To overcome the limitations of existing techniques, MagDot employs a novel tracking scheme that compensates for various real-world impacts, achieving high tracking accuracy. We tested MagDot on eight participants with a professional motion capture system, i.e., Qualisys motion capture system with nine Arqus A12 cameras. The results indicate MagDot can accurately track major body joints. For example, MagDot can achieve tracking accuracy of 2.72°, 4.14°, and 4.61° for elbow, knee, and shoulder, respectively. With a power consumption of only 98 mW, MagDot can support one-day usage with a small battery pack.
CAvatar
Authors: Chen Wenqiang , Hu Yexin , Song Wei , Liu Yingcheng , Torralba Antonio , Matusik Wojciech
Published: January 2024

Human mesh reconstruction is essential for various applications, including virtual reality, motion capture, sports performance analysis, and healthcare monitoring. In healthcare contexts such as nursing homes, it is crucial to employ plausible and non-invasive methods for human mesh reconstruction that preserve privacy and dignity. Traditional vision-based techniques encounter challenges related to occlusion, viewpoint limitations, lighting conditions, and privacy concerns. In this research, we present CAvatar, a real-time human mesh reconstruction approach that innovatively utilizes pressure maps recorded by a tactile carpet as input. This advanced, non-intrusive technology obviates the need for cameras during usage, thereby safeguarding privacy. Our approach addresses several challenges, such as the limited spatial resolution of tactile sensors, extracting meaningful information from noisy pressure maps, and accommodating user variations and multiple users. We have developed an attention-based deep learning network, complemented by a discriminator network, to predict 3D human pose and shape from 2D pressure maps with notable accuracy. Our model demonstrates promising results, with a mean per joint position error (MPJPE) of 5.89 cm and a per vertex error (PVE) of 6.88 cm. To the best of our knowledge, we are the first to generate 3D mesh of human activities solely using tactile carpet signals, offering a novel approach that addresses privacy concerns and surpasses the limitations of existing vision-based and wearable solutions. The demonstration of CAvatar is shown at https://youtu.be/ZpO3LEsgV7Y.
UniFi
Authors: Liu Yan , Yu Anlan , Wang Leye , Guo Bin , Li Yang , Yi Enze , Zhang Daqing
Published: January 2024

In recent years, considerable endeavors have been devoted to exploring Wi-Fi-based sensing technologies by modeling the intricate mapping between received signals and corresponding human activities. However, the inherent complexity of Wi-Fi signals poses significant challenges for practical applications due to their pronounced susceptibility to deployment environments. To address this challenge, we delve into the distinctive characteristics of Wi-Fi signals and distill three pivotal factors that can be leveraged to enhance generalization capabilities of deep learning-based Wi-Fi sensing models: 1) effectively capture valuable input to mitigate the adverse impact of noisy measurements; 2) adaptively fuse complementary information from multiple Wi-Fi devices to boost the distinguishability of signal patterns associated with different activities; 3) extract generalizable features that can overcome the inconsistent representations of activities under different environmental conditions (e.g., locations, orientations). Leveraging these insights, we design a novel and unified sensing framework based on Wi-Fi signals, dubbed UniFi, and use gesture recognition as an application to demonstrate its effectiveness. UniFi achieves robust and generalizable gesture recognition in real-world scenarios by extracting discriminative and consistent features unrelated to environmental factors from pre-denoised signals collected by multiple transceivers. To achieve this, we first introduce an effective signal preprocessing approach that captures the applicable input data from noisy received signals for the deep learning model. Second, we propose a multi-view deep network based on spatio-temporal cross-view attention that integrates multi-carrier and multi-device signals to extract distinguishable information. Finally, we present the mutual information maximization as a regularizer to learn environment-invariant representations via contrastive loss without requiring access to any signals from unseen environments for practical adaptation. Extensive experiments on the Widar 3.0 dataset demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in different settings (99% and 90%-98% accuracy for in-domain and cross-domain recognition without additional data collection and model training).
PmTrack
Authors: Liu Hankai , Liu Xiulong , Xie Xin , Tong Xinyu , Li Keqiu
Published: January 2024

The difficulty in obtaining targets' identity poses a significant obstacle to the pursuit of personalized and customized millimeter-wave (mmWave) sensing. Existing solutions that learn individual differences from signal features have limitations in practical applications. This paper presents a Personalized mmWave-based human Tracking system, PmTrack, by introducing inertial measurement units (IMUs) as identity indicators. Widely available in portable devices such as smartwatches and smartphones, IMUs utilize existing wireless networks for data uploading of identity and data, and are therefore able to assist in radar target identification in a lightweight manner with little deployment and carrying burden for users. PmTrack innovatively adopts orientation as the matching feature, thus well overcoming the data heterogeneity between radar and IMU while avoiding the effect of cumulative errors. In the implementation of PmTrack, we propose a comprehensive set of optimization methods in detection enhancement, interference suppression, continuity maintenance, and trajectory correction, which successfully solved a series of practical problems caused by the three major challenges of weak reflection, point cloud overlap, and body-bounce ghost in multi-person tracking. In addition, an orientation correction method is proposed to overcome the IMU gimbal lock. Extensive experimental results demonstrate that PmTrack achieves an identification accuracy of 98% and 95% with five people in the hall and meeting room, respectively.
Orientation-Aware 3D SLAM in Alternating Magnetic Field from Powerlines
Authors: Wang Rongrong , Tan Rui , Yan Zhenyu , Lu Chris Xiaoxuan
Published: January 2024

Identifying new sensing modalities for indoor localization is an interest of research. This paper studies powerline-induced alternating magnetic field (AMF) that fills the indoor space for the orientation-aware three-dimensional (3D) simultaneous localization and mapping (SLAM). While an existing study has adopted a uniaxial AMF sensor for SLAM in a plane surface, the design falls short of addressing the vector field nature of AMF and is therefore susceptible to sensor orientation variations. Moreover, although the higher spatial variability of AMF in comparison with indoor geomagnetism promotes location sensing resolution, extra SLAM algorithm designs are needed to achieve robustness to trajectory deviations from the constructed map. To address the above issues, we design a new triaxial AMF sensor and a new SLAM algorithm that constructs a 3D AMF intensity map regularized and augmented by a Gaussian process. The triaxial sensor's orientation estimation is free of the error accumulation problem faced by inertial sensing. From extensive evaluation in eight indoor environments, our AMF-based 3D SLAM achieves sub-1m to 3m median localization errors in spaces of up to 500 m2, sub-2° mean error in orientation sensing, and outperforms the SLAM systems based on Wi-Fi, geomagnetism, and uniaxial AMF by more than 30%.
HapticPilot
Authors: Sung Youjin , Kim Rachel , Song Kun Woo , Shao Yitian , Yoon Sang Ho
Published: January 2024

The emergence of vibrotactile feedback in hand wearables enables immersive virtual reality (VR) experience with whole-hand haptic rendering. However, existing haptic rendering neglects inconsistent sensations caused by hand postures. In our study, we observed that changing hand postures alters the distribution of vibrotactile signals which might degrade one's haptic perception. To address the issues, we present HapticPilot which allows an in-situ haptic experience design for hand wearables in VR. We developed an in-situ authoring system supporting instant haptic design. In the authoring tool, we applied our posture-adaptive haptic rendering algorithm with a novel haptic design abstraction called phantom grid. The algorithm adapts phantom grid to the target posture and incorporates 1D & 2D phantom sensation with a unique actuator arrangement to provide a whole-hand experience. With this method, HapticPilot provides a consistent haptic experience across various hand postures is available. Through measuring perceptual haptic performance and collecting qualitative feedback, we validated the usability of the system. In the end, we demonstrated our system with prospective VR scenarios showing how it enables an intuitive, empowering, and responsive haptic authoring framework.
Bias Mitigation in Federated Learning for Edge Computing
Authors: Djebrouni Yasmine , Benarba Nawel , Touat Ousmane , De Rosa Pasquale , Bouchenak Sara , Bonifati Angela , Felber Pascal , Marangozova Vania , Schiavoni Valerio
Published: January 2024

Federated learning (FL) is a distributed machine learning paradigm that enables data owners to collaborate on training models while preserving data privacy. As FL effectively leverages decentralized and sensitive data sources, it is increasingly used in ubiquitous computing including remote healthcare, activity recognition, and mobile applications. However, FL raises ethical and social concerns as it may introduce bias with regard to sensitive attributes such as race, gender, and location. Mitigating FL bias is thus a major research challenge. In this paper, we propose Astral, a novel bias mitigation system for FL. Astral provides a novel model aggregation approach to select the most effective aggregation weights to combine FL clients' models. It guarantees a predefined fairness objective by constraining bias below a given threshold while keeping model accuracy as high as possible. Astral handles the bias of single and multiple sensitive attributes and supports all bias metrics. Our comprehensive evaluation on seven real-world datasets with three popular bias metrics shows that Astral outperforms state-of-the-art FL bias mitigation techniques in terms of bias mitigation and model accuracy. Moreover, we show that Astral is robust against data heterogeneity and scalable in terms of data size and number of FL clients. Astral's code base is publicly available.
Enabling WiFi Sensing on New-generation WiFi Cards
Authors: Yi Enze , Zhang Fusang , Xiong Jie , Niu Kai , Yao Zhiyun , Zhang Daqing
Published: January 2024

The last few years have witnessed the rapid development of WiFi sensing with a large spectrum of applications enabled. However, existing works mainly leverage the obsolete 802.11n WiFi cards (i.e., Intel 5300 and Atheros AR9k series cards) for sensing. On the other hand, the mainstream WiFi protocols currently in use are 802.11ac/ax and commodity WiFi products on the market are equipped with new-generation WiFi chips such as Broadcom BCM43794 and Qualcomm QCN5054. After conducting some benchmark experiments, we find that WiFi sensing has problems working on these new cards. The new communication features (e.g., MU-MIMO) designed to facilitate data transmissions negatively impact WiFi sensing. Conventional CSI base signals such as CSI amplitude and/or CSI phase difference between antennas which worked well on Intel 5300 802.11n WiFi card may fail on new cards. In this paper, we propose delicate signal processing schemes to make wireless sensing work well on these new WiFi cards. We employ two typical sensing applications, i.e., human respiration monitoring and human trajectory tracking to demonstrate the effectiveness of the proposed schemes. We believe it is critical to ensure WiFi sensing compatible with the latest WiFi protocols and this work moves one important step towards real-life adoption of WiFi sensing.
Wi-Painter
Authors: Yan Dawei , Yang Panlong , Shang Fei , Jiang Weiwei , Li Xiang-Yang
Published: January 2024

WiFi has gradually developed into one of the main candidate technologies for indoor environment sensing. In this paper, we are interested in using COTS WiFi devices to identify material details, including location, material type, and shape, of stationary objects in the surrounding environment, which may open up new opportunities for many applications. Specifically, we present Wi-Painter, a model-driven system that can accurately detects smooth-surfaced material types and their edges using COTS WiFi devices without modification. Different from previous arts for material identification, Wi-Painter subdivides the target into individual 2D pixels, and simultaneously forms a 2D image based on identifying the material type of each pixel. The key idea of Wi-Painter is to exploit the complex permittivity of the object surface which can be estimated by the different reflectivity of signals with different polarization directions. In particular, we construct the multi-incident angle model to characterize the material, using only the power ratios of the vertically and horizontally polarized signals measured at several different incident angles, which avoids the use of inaccurate WiFi signal phases. We implement and evaluate Wi-Painter in the real world, showing an average classification accuracy of 93.4% for different material types including metal, wood, rubber and plastic of different sizes and thicknesses, and across different environments. In addition, Wi-Painter can accurately detect the material type and edge of the word "LOVE" spliced with different materials, with an average size of 60cm × 80cm, and material edges with different orientations.
How to Design an ISA
Authors: Chisnall David
Published: January 2024

Over the past decade I've been involved in several projects that have designed either ISA (instruction set architecture) extensions or clean-slate ISAs for various kinds of processors (you'll even find my name in the acknowledgments for the RISC-V spec, right back to the first public version). When I started, I had very little idea about what makes a good ISA, and, as far as I can tell, this isn't formally taught anywhere. With the rise of RISC-V as an open base for custom instruction sets, however, the barrier to entry has become much lower and the number of people trying to design some or all of an instruction set has grown immeasurably.
A Module-Level Configuration Methodology for Programmable Camouflaged Logic
Authors: Wang Jianfeng , Chen Zhonghao , Zhang Jiahao , Xu Yixin , Yu Tongguang , Zheng Ziheng , Ye Enze , George Sumitha , Yang Huazhong , Liu Yongpan , Ni Kai , Narayanan Vijaykrishnan , Li Xueqing
Published: January 2024

Logic camouflage is a widely adopted technique that mitigates the threat of intellectual property (IP) piracy and overproduction in the integrated circuit (IC) supply chain. Camouflaged logic achieves functional obfuscation through physical-level ambiguity and post-manufacturing programmability. However, discussions on programmability are confined to the level of logic cells/gates, limiting the broader-scale application of logic camouflage. In this work, we propose a novel module-level configuration methodology for programmable camouflaged logic that can be implemented without additional hardware ports and with negligible resources. We prove theoretically that the configuration of the programmable camouflaged logic cells can be achieved through the inputs and netlist of the original module. Further, we propose a novel lightweight ferroelectric FET (FeFET)-based reconfigurable logic gate (rGate) family and apply it to the proposed methodology. With the flexible replacement and the proposed configuration-aware conversion algorithm, this work is characterized by the input-only programming scheme as well as the combination of high output error rate and point-function-like defense. Evaluations show an average of >95% of the alternative rGate location for camouflage, which is sufficient for the security-aware design. We illustrate the exponential complexity in function state traversal and the enhanced defense capability of locked blackbox against Boolean Satisfiability (SAT) attacks compared with key-based methods. We also preserve an evident output Hamming distance and introduce negligible hardware overheads in both gate-level and module-level evaluations under typical benchmarks.
Next-Generation Therapies for Multiple Myeloma
Authors: Meermeier Erin W. , Bergsagel P. Leif , Chesi Marta
Published: January 2024

Recent therapeutic advances have significantly improved the outcome for patients with multiple myeloma (MM). The backbone of successful standard therapy is the combination of Ikaros degraders, glucocorticoids, and proteasome inhibitors that interfere with the integrity of myeloma-specific superenhancers by directly or indirectly targeting enhancer-bound transcription factors and coactivators that control expression of MM dependency genes. T cell engagers and chimeric antigen receptor T cells redirect patients’ own T cells onto defined tumor antigens to kill MM cells. They have induced complete remissions even in end-stage patients. Unfortunately, responses to both conventional therapy and immunotherapy are not durable, and tumor heterogeneity, antigen loss, and lack of T cell fitness lead to therapy resistance and relapse. Novel approaches are under development to target myeloma-specific vulnerabilities, as is the design of multimodality immunological approaches, including and beyond T cells, that simultaneously recognize multiple epitopes to prevent antigen escape and tumor relapse.
Achieving CoAl2O4@Li1.2Ni0.13Co0.13Mn0.54O2 with Long Cyclic and High-Rate Performance via Citric Acid Combustion Method
Authors: Gao Lvjin , Su Yinli , Zou Yi , Wang Yuting , Zhu Bowen , Ma Yu , Chen Li
Published: January 2024

Lithium-rich manganese-based cathode materials (LNCM) have high specific capacities and operating voltages. However, they also have intractable deficiencies such as low capacity retention and unsatisfactory rate capability. Herein, CoAl2O4 with spinel structure and chemically stable is adopted to decorate the surface of LNCM to modify the cyclic and high-rate performance of final product. 3CoAl2O4@LNCM synthesized by self combustion of citric acid shows the best electrochemical properties such as lower voltage drop (4.7 mV per cycle) and higher capacity retention of 81.33% (1 C) after 100 cycles, which is exceed to bare LNCM (7.7 mV per cycle, 47.65%). Besides, the charge transfer resistance R
ct of the modified sample is lower, and its lithium ion diffusion coefficient D
Li

+
is higher. Consequently, 3CoAl2O4@LNCM is able to demonstrate high rate performance, whereby a capacity retention of 65.84% (5 C) is achieved after 100 cycles. The improved electrochemical properties of 3CoAl2O4@LNCM because of the 3D diffusion channel of CoAl2O4 benefits lithium ion diffusion; and the chemical stability of CoAl2O4 enhances the corrosion resistance of LNCM bulk to the electrolyte. As such, this results in the suppression of TM ion dissolution, and the enhancement of cyclic stability and initial coulomb efficiency.
Modeling Lithium Plating Onset on Porous Graphite Electrodes Under Fast Charging with Hierarchical Multiphase Porous Electrode Theory
Authors: Lian Huada , Bazant Martin Z.
Published: January 2024

Lithium plating during fast charging of porous graphite electrodes in lithium-ion batteries accelerates degradation and raises safety concerns. Predicting lithium plating is challenging due to the close redox potentials of lithium reduction and intercalation, obscured by the nonlinear dynamics of electrochemically driven phase separation in hierarchical pore structures. To resolve dynamical resistance of realistic porous graphite electrodes, we introduce a model of porous secondary graphite particles to the multiphase porous electrode theory (MPET), based on electrochemical nonequilibrium thermodynamics and volume averaging. The resulting computational framework of “hierarchical MPET” is validated and tested against experimental data over a wide range of fast charging conditions and capacities. With all parameters estimated from independent sources, the model is able to quantitatively predict the measured cell voltages, and, more importantly, the experimentally determined capacity for lithium plating onset at fast 2C to 6C rates. Spatial and temporal heterogeneities in the lithiation of porous graphite electrodes are revealed and explained theoretically, including key features, such as idle graphite particles and non-uniform plating, which have been observed experimentally.
Impact of Polymer Interlayers on All-Solid-State Battery Performance Using a Physicochemical Modeling Approach
Authors: Scheller M. , Durdel A. , Frank A. , Kriegler J. , Jossen A.
Published: January 2024

Recent studies presented the advantages of incorporating solid-polymer-electrolyte (SPE) interlayers in all-solid-state batteries (ASSB). Still, drawbacks regarding the cell performance are expected due to additional polymer-related overpotentials. The pseudo-two-dimensional (p2D) physicochemical model is extended to account for Li-ion transport in the SPE interlayer and in the ceramic LLZO solid electrolyte (SE), as well as for the charge transfer at the SPE∣LLZO interface using Butler-Volmer-like kinetics. The overpotential analysis for a reference parameterization disclosed a dominant overpotential contribution from the SPE∣LLZO charge transfer and a facilitation with increasing discharge C-rate. Variance-based global sensitivity analyses demonstrate that as the exchange current density between SPE and LLZO increases, polarization losses exhibit an exponential-like reduction. Additionally, the radius of the active material (AM) particles within the composite cathode exerts a significant and dominant influence on cell performance. With an optimization of the SPE∣LLZO exchange current density, the accessible capacity could be increased compared to the reference parameterization from 41% to 61% for a 2C discharge.
Development of a Voltammetric Methodology Based on an Artificial Neural Network for the Quantification of Sodium Diclofenac in Pharmaceutical Samples
Authors: Franco-Guzmán Miriam , Romero-Romo Mario Alberto , Cobos-Murcia José Ángel , Palomar-Pardavé Manuel Eduardo , Hernández-Ramírez Daniel , Álvarez-Romero Giaan Arturo , Aguilar-Lira Guadalupe Yoselin , Gutiérrez-Salgado Juan Manuel
Published: January 2024

Sodium diclofenac is a widely used anti-inflammatory drug that can cause heart diseases if consumed constantly in high doses. Consequently, it is essential to have strict control of the amounts of this active principle in pharmaceutical products. The combination of electroanalytical techniques with advanced chemometrics has risen as a viable alternative for the exact and precise determination of active principles even in the presence of chemical interferences. In this research, an artificial neural network (ANN) for the voltammetric quantification of diclofenac in the presence of paracetamol, pyridoxine, and caffeine is presented, using a carbon paste electrode modified with multilayer carbon nanotubes and titanium dioxide nanoparticles. Cyclic voltammetry is performed to study the effect of the interferences on diclofenac response. Subsequently, a set of diclofenac standards and interferents was prepared using a fractional factorial design to build the response model and perform differential pulse voltammetry to produce the data of the input layer of the ANN. The ANN developed was able to predict the concentration of diclofenac even in the presence of the interferences, since multiple correlation coefficients of 0.9917 and 0.8387 were obtained for training and test data in the analysis of pharmaceutical samples with a recovery percentage of 95.9%.
State-of-Health Estimation of Lithium-ion Batteries Based on Singular Value Decomposition and an Improved Group Method of Data Handling
Authors: Li Junhong , Bai Guixiang , Yan Jun , Hua Liang
Published: January 2024

Lithium-ion batteries are complex electrochemical systems, and the degradation of their state of health (SOH) is a nonlinear process. Accurate SOH estimation is critical to lithium-ion battery life and safety. This paper uses a data-driven approach to study SOH estimation of lithium-ion batteries. Firstly, this paper uses the singular value decomposition (SVD) method to extract features from the battery charging history data. Secondly, the particle swarm optimization (PSO) algorithm is used to optimize the parameter configuration of the group method of data handling (GMDH). Finally, the SOH estimation is completed using the optimized GMDH. The results show that the proposed PSO-GMDH estimation model maintains an error within 0.89% for estimating its subsequent SOH using historical data of a certain battery, and maintains an error within 0.5% for estimating the SOH of another battery of the same model using historical data of multiple batteries. At the same time, the results also show that the PSO-GMDH estimation model has higher estimation accuracy than the GMDH model without parameter optimization.
Mannitol and hyponatremia regulate cardiac ventricular conduction in the context of sodium channel loss of function
Authors: Blair Grace A. , Wu Xiaobo , Bain Chandra , Warren Mark , Hoeker Gregory S. , Poelzing Steven
Published: January 2024

Cardiac sodium channel loss of function (LOF) diseases such as Brugada syndrome (BrS) are often concealed. We optically mapped mouse hearts with reduced sodium channel expression ( Scn5a+/−) to evaluate whether reduced ephaptic coupling (EpC) can unmask conduction deficits. Data suggest that conduction deficits in the Scn5a+/− mouse may be unmasked by treatment with hyponatremia and perinexal widening via mannitol. These data support further investigation of hyponatremia and mannitol as novel diagnostics for sodium channel loss of function diseases.
An in silico study of the effects of cardiovascular aging on carotid flow waveforms and indexes in a virtual population
Authors: Suriani Irene , Bouwman R. Arthur , Mischi Massimo , Lau Kevin D.
Published: January 2024

An age-specific virtual population was generated based on a 1-D model of the arterial circulation, including newly defined literature-based specific age variations in carotid vessel properties. Simulated carotid flow/velocity waveforms, indexes, and age trends were statistically validated against in vivo data from the literature. A comprehensive study of the impact of aging on carotid flow waveform morphology was performed, and the mechanisms influencing different carotid indexes were elucidated. Notably, flow augmentation index (FAI) was found to be a strong indicator of local carotid stiffness.