bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling driver injury severity using machine learning algorithms
Authors: Sorum Neero Gumsar , Pal Dibyendu
Published: May 2024

This study planned to predict and analyze the driver injury severity (DIS) using 12 machine learning (ML) algorithms. Police reports of single- and two-vehicle accidents that occurred during 2011–2020 in the two cities of India (Itanagar and Imphal) were used in this study. The best-performing model to predict the DIS for Itanagar was Gradient Boosting Trees (GBT). “Causes of Accident” variable had shown maximum impact on the DIS. In the case of Imphal, it was the GBT, Extra Trees, and Random Forest models across all k-fold cross-validation for train ratios 0.70, 0.80, and 0.90, respectively. “Causes of Accident” and “Vehicle Type” had shown maximum impact on the DIS. These results reveal that the ML models can be applied in hilly areas to predict and identify the important factors that affect DIS. Transportation authorities can analyze road accident data using these models while implementing various road safety measures.
Quantifying marine larval dispersal to assess MPA network connectivity and inform future national and transboundary planning efforts
Authors: Cristiani John , Rubidge Emily M. , Thompson Patrick L. , Robb Carolyn K. , Hessing-Lewis Margot , O'Connor Mary I.
Published: May 2024

A marine protected area (MPA) network of multiple reserves can protect biodiversity across space, but to be effective, network configuration should support dispersal among MPAs as well as spillover to unprotected habitats. The ability of MPAs to function as an interacting network of populations connected by dispersal, however, is difficult to estimate at broad spatial scales, and therefore, connectivity is often not integrated in MPA design. Here, we simulate passive larval dispersal using a biophysical model to estimate potential transboundary network connectivity of MPAs in western Canada and United States. Drift time was varied to represent generic functional groups of nearshore species with planktonic larvae and sedentary adult stages. We found that MPAs potentially act as an interacting network and meet connectivity design criteria for nearshore invertebrate species, many MPAs (65%–90%) possibly exchange individuals, and a third of larvae from MPAs (25%–32%) source areas of the unprotected coast. This analysis provides a first approximation of multispecies connectivity to inform ongoing transboundary MPA design, and it can be used as a foundation for future model development.
Integrability of Dirac equations in static spherical space-times
Authors: Cianci Roberto , Vignolo Stefano , Fabbri Luca
Published: May 2024

We consider the Dirac equations in static spherically-symmetric space-times, and we present a type of spinor field whose structure allows the separation of elevation angle and radial coordinate in very general situations. We demonstrate that after such a separation of variables the Dirac equations reduce to two equations that can always be integrated, at least in principle. To prove that ours is a fully-working method, we find an explicit exact solution in the special case of the de Sitter universe.
Investigation of structural, dielectric and optical properties of the (Bi0.5La0.5Fe)0.5(Bi0.5Na0.5Ti)0.5O3perovskite for some electronic devices
Authors: Bhuyan R. K. , Parida S. K.
Published: May 2024

This paper outlines the investigation of structural, dielectric, complex impedance and optical properties of lanthanum-modified (Bi[Formula: see text]La[Formula: see text]Fe)[Formula: see text](Bi[Formula: see text]Na[Formula: see text]Ti)[Formula: see text]O3(BLF–BNT) single perovskite oxide synthesized by conventional solid-state reaction method. From Rietveld refinement, the crystal structure of the BLF–BNT ceramic has confirmed a tetragonal and the estimated average crystallite size is found to be 43.6[Formula: see text]nm. The dielectric properties of the La-doped BLF–BNT ceramic reveal the presence of Maxwell–Wagner-type dielectric dispersion. This suggests the occurrence of charge accumulation at grain boundaries and interfaces within the material. The complex impedance and complex electric modulus studies were employed to gain insight into the microscopic dielectric relaxations and conduction processes of the material. The electric modulus spectroscopy reveals the existence of nonDebye-type relaxation processes, including localized and long-range relaxation processes. The Nyquist and Cole–Cole plots show the semiconducting nature of the BLF–BNT ceramic. With the help of the Arrhenius method based on the imaginary portion of the electrical impedance and modulus, activation energies and relaxation times have been estimated. These parameters contribute to a deeper understanding of the electrical properties and conduction mechanisms within the material. Further, Raman spectroscopy, a nondestructive chemical analysis technique, was conducted to confirm the composition and structural integrity of the proposed system through its atomic vibrations. Also, the bandgap energy of the material has been estimated using Tauc’s relation and is found to be 1.69[Formula: see text]eV.

This signifies that the BLF–BNT ceramic possesses a suitable bandgap for certain device applications, making it a promising candidate in various technological fields. Moreover, the overall comprehensive study of the proposed ceramic provides valuable insight and opens new possibilities for its potential applications in various electronic devices.
A computational scheme and its comparison with optical soliton solutions for the stochastic Chen–Lee–Liu equation with sensitivity analysis
Authors: Baber Muhammad Zafarullah , Ahmed Nauman , Xu Changjin , Iqbal Muhammad Sajid , Sulaiman Tukur Abdulkadir
Published: May 2024

In this study, the stochastic Chen–Lee–Liu equation is considered numerically and analytically which is forced by the multiplicative noise in the Itô sense. The Chen–Lee–Liu equation is a special type of Schrödinger’s equation which has applications in optical fibers and photonic crystal fibers. The stochastic Crank–Nicolson scheme is formed to obtain the computational results. The numerical scheme is analyzed under the mean square sense and Von-Neumann criteria to show consistence and stability, respectively. Meanwhile, stochastic optical soliton solutions are attained by using two techniques, namely, the modified auxiliary equation method and the generalized projective Riccati equation method. These methods provide us with the different types of optical soliton solutions such as hyperbolic, trigonometric, mixed trigonometric, and rational forms. Mainly, the comparison of computational results with newly constructed optical soliton solution is shown graphically. To compare these results, initial conditions and boundary conditions are constructed by selecting some soliton solutions. The 3D and line graphs are drawn by choosing different values of parameters. Additionally, the sensitivity analysis is observed for the different initial values.
H-mrk-means: Enhanced Heuristic mrk-means for Linear Time Clustering of Big Data Using Hybrid Meta-heuristic Algorithm
Authors: Puri Digvijay , Gupta Deepak
Published: May 2024

Big data is generally derived with a large volume and combined categories of attributes like categorical and numerical. Among them, [Formula: see text]-prototypes have been adopted into MapReduce structure, and thus, it provides a better solution for the huge range of data. However, [Formula: see text]-prototypes need to compute all distances among every data point and cluster centres. Moreover, the computations of distances are redundant as data points are often present in similar clusters after fewer iterations. Nowadays, to cluster huge-scale datasets, one of the efficient solutions is [Formula: see text]-means. However, [Formula: see text]-means is not intrinsically appropriate to execute in MapReduce due to the iterative nature of this technique. Moreover, for every iteration, [Formula: see text]-means should perform an independent MapReduce job but, it leads to higher Input/Output (I/O) overhead at every iteration. This research paper presents a novel enhanced linear time clustering for handling big data called Heuristic mrk-means (H-mrk-means) using optimized [Formula: see text]-means on the MapReduce model. In order to manage big data that is time series in nature, the sampling and MapReduce framework are adopted, which utilize different machines for processing data. Before initiating the main clustering process, a sampling process is adopted to get the noteworthy information. The two main phases of the developed method are the map phase (divide and conquer) and the reduce phase (final clustering). In the map phase, the data are divided into diverse chunks that should be stored in assigned machines. In the reduce phase, data clustering is performed. Here, the cluster centroid of data is tuned with the help of hybrid Tunicate-Deer Hunting Optimization (T-DHO) algorithm by attaining a newly derived objective function. This type of optimal tuning of solution enhances the efficiency of clustering when compared over normal iterative [Formula: see text]-means and mrk-means clustering. The experimental evaluation on varied counts of chunks using the proposed H-mrk-means has attained higher quality of clustering results and faster execution times evaluated with other clustering approaches.
Oroclinal arcs of the Variscan Belt: a consequence of transpression during the consolidation of Pangaea
Authors: Martínez Catalán José R. , Schulmann Karel , Ayarza Puy , Edel Jean-Bernard , Durán Oreja Manuela
Published: May 2024

Arcuate traces of large structures characterize many mountain chains. The Variscan Belt is not an exception and traces one of the tightest oroclines on Earth, the Ibero-Armorican Arc, as well as more open oroclines in the Eastern Moroccan Meseta, Central Iberia, the French Massif Central and the Bohemian Massif. All the Variscan arcs are considered to be true or secondary oroclines and are late orogenic features, but their timing and mechanisms of deformation differ. Models explaining their origin have been proposed for some individual arcs and are generally controversial. This paper aims to interprete the ensemble of Variscan arcs, highlighting their age relative to previous orogenic features as well as to those associated with arc development. Several mechanisms operated to form the arcs, the most important being ductile transcurrent shearing and rigid–plastic indentation, with shortening parallel to the orogen having a secondary role. These mechanisms acted at different time intervals, their participation or relative importance differs for each arc and they gave rise to distinct associated structures. The development of the arcs is viewed as related to late Variscan dextral transpression provoked by the displacement of Laurussia to the east relative to Gondwana during the Serpukhovian to early Permian.
SwivelTouch: Boosting Touchscreen Input with 3D Finger Rotation Gesture
Authors: Li Chentao , Yu Jinyang , He Ke , Feng Jianjiang , Zhou Jie
Published: May 2024

Today, touchscreens stand as the most prevalent input devices of mobile computing devices (smartphones, tablets, smartwatches). Yet, compared with desktop or laptop computers, the limited shortcut keys and physical buttons on touchscreen devices, coupled with the fat finger problem, often lead to slower and more error-prone input and navigation, especially when dealing with text editing and other complex interaction tasks. We introduce an innovative gesture set based on finger rotations in the yaw, pitch, and roll directions on a touchscreen, diverging significantly from traditional two-dimensional interactions and promising to expand the gesture library. Despite active research in estimation of finger angles, however, the previous work faces substantial challenges, including significant estimation errors and unstable sequential outputs. Variability in user behavior further complicates the isolation of movements to a single rotational axis, leading to accidental disturbances and screen coordinate shifts that interfere with the existing sliding gestures. Consequently, the direct application of finger angle estimation algorithms for recognizing three-dimensional rotational gestures is impractical. SwivelTouch leverages the analysis of finger movement characteristics on the touchscreen captured through original capacitive image sequences, which aims to rapidly and accurately identify these advanced 3D gestures, clearly differentiating them from conventional touch interactions like tapping and sliding, thus enhancing user interaction with touch devices and meanwhile compatible with existing 2D gestures. User study further confirms that the implementation of SwivelTouch significantly enhances the efficiency of text editing on smartphones.
ClassID: Enabling Student Behavior Attribution from Ambient Classroom Sensing Systems
Authors: Patidar Prasoon , Ngoon Tricia J. , Zimmerman John , Ogan Amy , Agarwal Yuvraj
Published: May 2024

Ambient classroom sensing systems offer a scalable and non-intrusive way to find connections between instructor actions and student behaviors, creating data that can improve teaching and learning. While these systems effectively provide aggregate data, getting reliable individual student-level information is difficult due to occlusion or movements. Individual data can help in understanding equitable student participation, but it requires identifiable data or individual instrumentation. We propose ClassID, a data attribution method for within a class session and across multiple sessions of a course without these constraints. For within-session, our approach assigns unique identifiers to 98% of students with 95% accuracy. It significantly reduces multiple ID assignments compared to the baseline approach (3 vs. 167) based on our testing on data from 15 classroom sessions. For across-session attributions, our approach, combined with student attendance, shows higher precision than the state-of-the-art approach (85% vs. 44%) on three courses. Finally, we present a set of four use cases to demonstrate how individual behavior attribution can enable a rich set of learning analytics, which is not possible with aggregate data alone.
Design and Fabrication of Multifunctional E-Textiles by Upcycling Waste Cotton Fabrics through Carbonization
Authors: Wicaksono Irmandy , Maheshwari Aditi , Haddad Don Derek , Paradiso Joseph , Danielescu Andreea
Published: May 2024

The merging of electronic materials and textiles has triggered the proliferation of wearables and interactive surfaces in the ubiquitous computing era. However, this leads to e-textile waste that is difficult to recycle and decompose. Instead, we demonstrate an eco-design approach to upcycle waste cotton fabrics into functional textile elements through carbonization without the need for additional materials. We identify optimal parameters for the carbonization process and develop encapsulation techniques to improve the response, durability, and washability of the carbonized textiles. We then configure these e-textiles into various 'design primitives' including sensors, interconnects, and heating elements, and evaluate their electromechanical properties against commercially available e-textiles. Using these primitives, we demonstrate several applications, including a haptic-transfer fabric, a joint-sensing wearable, and an intelligent sailcloth. Finally, we highlight how the sensors can be composted, re-carbonized and coated onto other fabrics, or repurposed into different sensors towards their end-of-life to promote a circular manufacturing process.
WatchCap: Improving Scanning Efficiency in People with Low Vision through Compensatory Head Movement Stimulation
Authors: Jo Taewoo , Yeo Dohyeon , Kim Gwangbin , Hwang Seokhyun , Kim SeungJun
Published: May 2024

Individuals with low vision (LV) frequently face challenges in scanning performance, which in turn complicates daily activities requiring visual recognition. Although those with PVL can theoretically compensate for these scanning deficiencies through the use of active head movements, few practical applications have sought to capitalize on this potential, especially during visual recognition tasks. In this paper, we present WatchCap, a novel device that leverages the hanger reflex phenomenon to naturally elicit head movements through stimulation feedback. Our user studies, conducted with both sighted individuals in a simulated environment and people with glaucoma-related PVL, demonstrated that WatchCap's scanning-contingent stimulation enhances visual exploration. This improvement is evidenced by the fixation and saccade-related features and positive feedback from participants, which did not cause discomfort to the users. This study highlights the promise of facilitating head movements to aid those with LVs in visual recognition tasks. Critically, since WatchCap functions independently of predefined or task-specific cues, it has a wide scope of applicability, even in ambient task situations. This independence positions WatchCap to complement existing tools aimed at detailed visual information acquisition, allowing integration with existing tools and facilitating a comprehensive approach to assisting individuals with LV.
AeroSense: Sensing Aerosol Emissions from Indoor Human Activities
Authors: Chhaglani Bhawana , Zakaria Camellia , Peltier Richard , Gummeson Jeremy , Shenoy Prashant
Published: May 2024

The types of human activities occupants are engaged in within indoor spaces significantly contribute to the spread of airborne diseases through emitting aerosol particles. Today, ubiquitous computing technologies can inform users of common atmosphere pollutants for indoor air quality. However, they remain uninformed of the rate of aerosol generated directly from human respiratory activities, a fundamental parameter impacting the risk of airborne transmission. In this paper, we present AeroSense, a novel privacy-preserving approach using audio sensing to accurately predict the rate of aerosol generated from detecting the kinds of human respiratory activities and determining the loudness of these activities. Our system adopts a privacy-first as a key design choice; thus, it only extracts audio features that cannot be reconstructed into human audible signals using two omnidirectional microphone arrays. We employ a combination of binary classifiers using the Random Forest algorithm to detect simultaneous occurrences of activities with an average recall of 85%. It determines the level of all detected activities by estimating the distance between the microphone and the activity source. This level estimation technique yields an average of 7.74% error. Additionally, we developed a lightweight mask detection classifier to detect mask-wearing, which yields a recall score of 75%. These intermediary outputs are critical predictors needed for AeroSense to estimate the amounts of aerosol generated from an active human source. Our model to predict aerosol is a Random Forest regression model, which yields 2.34 MSE and 0.73 r2 value. We demonstrate the accuracy of AeroSense by validating our results in a cleanroom setup and using advanced microbiological technology. We present results on the efficacy of AeroSense in natural settings through controlled and in-the-wild experiments. The ability to estimate aerosol emissions from detected human activities is part of a more extensive indoor air system integration, which can capture the rate of aerosol dissipation and inform users of airborne transmission risks in real time.
EarSleep: In-ear Acoustic-based Physical and Physiological Activity Recognition for Sleep Stage Detection
Authors: Han Feiyu , Yang Panlong , Feng Yuanhao , Jiang Weiwei , Zhang Youwei , Li Xiang-Yang
Published: May 2024

Since sleep plays an important role in people's daily lives, sleep monitoring has attracted the attention of many researchers. Physical and physiological activities occurring in sleep exhibit unique patterns in different sleep stages. It indicates that recognizing a wide range of sleep activities (events) can provide more fine-grained information for sleep stage detection. However, most of the prior works are designed to capture limited sleep events and coarse-grained information, which cannot meet the needs of fine-grained sleep monitoring. In our work, we leverage ubiquitous in-ear microphones on sleep earbuds to design a sleep monitoring system, named EarSleep1, which interprets in-ear body sounds induced by various representative sleep events into sleep stages. Based on differences among physical occurrence mechanisms of sleep activities, EarSleep extracts unique acoustic response patterns from in-ear body sounds to recognize a wide range of sleep events, including body movements, sound activities, heartbeat, and respiration. With the help of sleep medicine knowledge, interpretable acoustic features are derived from these representative sleep activities. EarSleep leverages a carefully designed deep learning model to establish the complex correlation between acoustic features and sleep stages. We conduct extensive experiments with 48 nights of 18 participants over three months to validate the performance of our system. The experimental results show that our system can accurately detect a rich set of sleep activities. Furthermore, in terms of sleep stage detection, EarSleep outperforms state-of-the-art solutions by 7.12% and 9.32% in average precision and average recall, respectively.
Can Large Language Models Be Good Companions?
Authors: Xu Zhenyu , Xu Hailin , Lu Zhouyang , Zhao Yingying , Zhu Rui , Wang Yujiang , Dong Mingzhi , Chang Yuhu , Lv Qin , Dick Robert P. , Yang Fan , Lu Tun , Gu Ning , Shang Li
Published: May 2024

Developing chatbots as personal companions has long been a goal of artificial intelligence researchers. Recent advances in Large Language Models (LLMs) have delivered a practical solution for endowing chatbots with anthropomorphic language capabilities. However, it takes more than LLMs to enable chatbots that can act as companions. Humans use their understanding of individual personalities to drive conversations. Chatbots also require this capability to enable human-like companionship. They should act based on personalized, real-time, and time-evolving knowledge of their users. We define such essential knowledge as the common ground between chatbots and their users, and we propose to build a common-ground-aware dialogue system from an LLM-based module, named OS-1, to enable chatbot companionship. Hosted by eyewear, OS-1 can sense the visual and audio signals the user receives and extract real-time contextual semantics. Those semantics are categorized and recorded to formulate historical contexts from which the user's profile is distilled and evolves over time, i.e., OS-1 gradually learns about its user. OS-1 combines knowledge from real-time semantics, historical contexts, and user-specific profiles to produce a common-ground-aware prompt input into the LLM module. The LLM's output is converted to audio, spoken to the wearer when appropriate. We conduct laboratory and in-field studies to assess OS-1's ability to build common ground between the chatbot and its user. The technical feasibility and capabilities of the system are also evaluated. Our results show that by utilizing personal context, OS-1 progressively develops a better understanding of its users. This enhances user satisfaction and potentially leads to various personal service scenarios, such as emotional support and assistance.
PPG-Hear: A Practical Eavesdropping Attack with Photoplethysmography Sensors
Authors: Su Yuchen , Huang Shiyue , Liu Hongbo , Chen Yuefeng , Du Yicong , Wang Yan , Ren Yanzhi , Chen Yingying
Published: May 2024

Photoplethysmography (PPG) sensors have become integral components of wearable and portable health devices in the current technological landscape. These sensors offer easy access to heart rate and blood oxygenation, facilitating continuous long-term health monitoring in clinic and non-clinic environments. While people understand that health-related information provided by PPG is private, no existing research has demonstrated that PPG sensors are dangerous devices capable of obtaining sensitive information other than health-related data. This work introduces PPG-Hear, a novel side-channel attack that exploits PPG sensors to intercept nearby audio information covertly. Specifically, PPG-Hear exploits low-frequency PPG measurements to discern and reconstruct human speech emitted from proximate speakers. This technology allows attackers to eavesdrop on sensitive conversations (e.g., audio passwords, business decisions, or intellectual properties) without being noticed. To achieve this non-trivial attack on commodity PPG-enabled devices, we employ differentiation and filtering techniques to mitigate the impact of temperature drift and user-specific gestures. We develop the first PPG-based speech reconstructor, which can identify speech patterns in the PPG spectrogram and establish the correlation between PPG and speech spectrograms. By integrating a MiniRocket-based classifier with a PixelGAN model, PPG-Hear can reconstruct human speech using low-sampling-rate PPG measurements. Through an array of real-world experiments, encompassing common eavesdropping scenarios such as surrounding speakers and the device's own speakers, we show that PPG-Hear can achieve remarkable accuracy of 90% for recognizing human speech, surpassing the current state-of-the-art side-channel eavesdropping attacks using motion sensors operating at equivalent sampling rates (i.e., 50Hz to 500Hz).
WiFi-CSI Difference Paradigm
Authors: Li Wenwei , Gao Ruiyang , Xiong Jie , Zhou Jiarun , Wang Leye , Mao Xingjian , Yi Enze , Zhang Daqing
Published: May 2024

Passive tracking plays a fundamental role in numerous applications such as elderly care, security surveillance, and smart home. To utilize ubiquitous WiFi signals for passive tracking, the Doppler speed extracted from WiFi CSI (Channel State Information) is the key information. Despite the progress made, existing approaches still require a large number of samples to achieve accurate Doppler speed estimation. To enable WiFi sensing with minimum amount of interference on WiFi communication, accurate Doppler speed estimation with fewer CSI samples is crucial. To achieve this, we build a passive WiFi tracking system which employs a novel CSI difference paradigm instead of CSI for Doppler speed estimation. In this paper, we provide the first deep dive into the potential of CSI difference for fine-grained Doppler speed estimation. Theoretically, our new design allows us to estimate Doppler speed with just three samples. While conventional methods only adopt phase information for Doppler estimation, we creatively fuse both phase and amplitude information to improve Doppler estimation accuracy. Extensive experiments show that our solution outperforms the state-of-the-art approaches, achieving higher accuracy with fewer CSI samples. Based on this proposed WiFi-CSI difference paradigm, we build a prototype passive tracking system which can accurately track a person with a median error lower than 34 cm, achieving similar accuracy compared to the state-of-the-art systems, while significantly reducing the required number of samples to only 5%.
Lipwatch: Enabling Silent Speech Recognition on Smartwatches using Acoustic Sensing
Authors: Zhang Qian , Lan Yubin , Guo Kaiyi , Wang Dong
Published: May 2024

Silent Speech Interfaces (SSI) on mobile devices offer a privacy-friendly alternative to conventional voice input methods. Previous research has primarily focused on smartphones. In this paper, we introduce Lipwatch, a novel system that utilizes acoustic sensing techniques to enable SSI on smartwatches. Lipwatch leverages the inaudible waves emitted by the watch's speaker to capture lip movements and then analyzes the echo to enable SSI. In contrast to acoustic sensing-based SSI on smartphones, our development of Lipwatch takes into full consideration the specific scenarios and requirements associated with smartwatches. Firstly, we elaborate a wake-up-free mechanism, allowing users to interact without the need for a wake-up phrase or button presses. The mechanism utilizes the inertial sensors on the smartwatch to detect gestures, in combination with acoustic signals that detecting lip movements to determine whether SSI should be activated. Secondly, we design a flexible silent speech recognition mechanism that explores limited vocabulary recognition to comprehend a broader range of user commands, even those not present in the training dataset, relieving users from strict adherence to predefined commands. We evaluate Lipwatch on 15 individuals using a set of the 80 most common interaction commands on smartwatches. The system achieves a Word Error Rate (WER) of 13.7% in user-independent test. Even when users utter commands containing words absent in the training set, Lipwatch still demonstrates a remarkable 88.7% top-3 accuracy. We implement a real-time version of Lipwatch on a commercial smartwatch. The user study shows that Lipwatch can be a practical and promising option to enable SSI on smartwatches.
Model Touch Pointing and Detect Parkinson's Disease via a Mobile Game
Authors: Ling Kaiyan , Zhao Hang , Fan Xiangmin , Niu Xiaohui , Yin Wenchao , Liu Yue , Wang Cui , Bi Xiaojun
Published: May 2024

Touch pointing is one of the primary interaction actions on mobile devices. In this research, we aim to (1) model touch pointing for people with Parkinson's Disease (PD), and (2) detect PD via touch pointing. We created a mobile game called MoleBuster in which a user performs a sequence of pointing actions. Our study with 40 participants shows that PD participants exhibited distinct pointing behavior. PD participants were much slower and had greater variances in movement time (MT), while their error rate was slightly lower than age-matched non-PD participants, indicating PD participants traded speed for accuracy. The nominal width Finger-Fitts law showed greater fitness than Fitts' law, suggesting this model should be adopted in lieu of Fitts' law to guide mobile interface design for PD users. We also proposed a CNN-Transformer-based neural network model to detect PD. Taking touch pointing data and comfort rating of finger movement as input, this model achieved an AUC of 0.97 and sensitivity of 0.95 in leave-one-user-out cross-validation. Overall, our research contributes models that reveal the temporal and spatial characteristics of touch pointing for PD users, and provide a new method (CNN-Transformer model) and a mobile game (MoleBuster) for convenient PD detection.
A Survey on Variational Autoencoders in Recommender Systems
Authors: Liang Shangsong , Pan Zhou , liu wei , Yin Jian , de Rijke Maarten
Published: May 2024

Recommender systems have become an important instrument to connect people to information. Sparse, complex, and rapidly growing data presents new challenges to traditional recommendation algorithms. To overcome these challenges, various deep learning-based recommendation algorithms have been proposed. Among these,
Variational AutoEncoder (VAE)
-based recommendation methods stand out. VAEs are based on a flexible probabilistic framework, which is robust for data sparsity and compatible with other deep learning-based models for dealing with multimodal data. In addition, the deep generative structure of VAEs helps to perform Bayesian inference in an efficient manner. VAE-based recommendation algorithms have given rise to many novel graphical models, and they have achieved promising performance. In this article, we conduct a survey to systematically summarize recent VAE-based recommendation algorithms. Four frequently used characteristics of VAE-based recommendation algorithms are summarized, and a taxonomy of VAE-based recommendation algorithms is proposed. We also identify future research directions for, advanced perspectives on, and the application of VAEs in recommendation algorithms, to inspire future work on VAEs for recommender systems.
A Tale of Two Comprehensions? Analyzing Student Programmer Attention during Code Summarization
Authors: Karas Zachary , Bansal Aakash , Zhang Yifan , Li Toby , McMillan Collin , Huang Yu
Published: May 2024

Code summarization is the task of creating short, natural language descriptions of source code. It is an important part of code comprehension and a powerful method of documentation. Previous work has made progress in identifying where programmers focus in code as they write their own summaries (i.e., Writing). However, there is currently a gap in studying programmers’ attention as they read code with pre-written summaries (i.e., Reading). As a result, it is currently unknown how these two forms of code comprehension compare: Reading and Writing. Also, there is a limited understanding of programmer attention with respect to program semantics. We address these shortcomings with a human eye-tracking study (
n
= 27) comparing Reading and Writing. We examined programmers’ attention with respect to fine-grained program semantics, including their attention sequences (i.e., scan paths). We find distinctions in programmer attention across the comprehension tasks, similarities in reading patterns between them, and differences mediated by demographic factors. This can help guide code comprehension in both computer science education and automated code summarization. Furthermore, we mapped programmers’ gaze data onto the Abstract Syntax Tree to explore another representation of human attention. We find that visual behavior on this structure is not always consistent with that on source code.
Graph Artificial Intelligence in Medicine
Authors: Johnson Ruth , Li Michelle M. , Noori Ayush , Queen Owen , Zitnik Marinka
Published: May 2024

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.
The Archaeal Cell Cycle
Authors: Cezanne Alice , Foo Sherman , Kuo Yin-Wei , Baum Buzz
Published: May 2024

Since first identified as a separate domain of life in the 1970s, it has become clear that archaea differ profoundly from both eukaryotes and bacteria. In this review, we look across the archaeal domain and discuss the diverse mechanisms by which archaea control cell cycle progression, DNA replication, and cell division. While the molecular and cellular processes archaea use to govern these critical cell biological processes often differ markedly from those described in bacteria and eukaryotes, there are also striking similarities that highlight both unique and common principles of cell cycle control across the different domains of life. Since much of the eukaryotic cell cycle machinery has its origins in archaea, exploration of the mechanisms of archaeal cell division also promises to illuminate the evolution of the eukaryotic cell cycle.
The Politics of Expertise in Genomics Policy and Law
Authors: Parthasarathy Shobita
Published: May 2024

Genomics and biotechnology have generated controversy for decades, about the moral limits of tinkering with and commodifying life, the boundary between nature and technology, the respectful treatment of research participants, and the proper evaluation of emerging technologies. Socio-legal and science and technology studies scholars have demonstrated how the resolution of these questions has serious implications for both science and the law, including the appropriate conduct and direction of scientific research and the construction of legal categories and rights. This article demonstrates that these conflicts also have profound impacts on the politics of science and technology, and particularly approaches to relevant knowledge and expertise. Humanities, social scientific, legal, and lay knowledge asserted themselves through the establishment of human genetics research and battles over the ownership of biological materials and data. However, US political culture—which favors technical and market knowledge—constrained their authority. Cross-national comparison highlights this finding, as other jurisdictions have been more inclusive in their approaches to science and technology governance.
The Economics of Geographical Indications: An Update
Authors: Menapace Luisa , Moschini GianCarlo
Published: May 2024

We discuss the economics of geographical indications (GIs), a form of collective branding of products based on their geographic origin. GIs serve as essential tools in addressing market failures stemming from information asymmetries and supporting the provision of high-quality products in competitive settings. They are increasingly used in wine and food markets. We briefly review the institutional development of GIs, discuss the basic economic theory arguments that rationalize the use of GIs, and characterize their key functions. We then examine the empirical evidence from studies that have probed various features of GIs. We find consensus on the potential for GIs to address a very real market failure and the positive role that policies and supporting institutions can play. We also uncover limitations and unresolved problems. GIs have both efficiency and distributional consequences, their international implications remain controversial, and tensions between tradition and innovation emerge while confronting new challenges such as climate change.
Interracial Unions and Racial Assortative Mating in an Age of Growing Diversity, Shifting Intimate Relationships, and Emerging Technologies
Authors: Lundquist Jennifer , Lin Ken-Hou , Curington Celeste
Published: May 2024

While racial assortative mating and interracial unions have been a central interest in the study of race relations and family demography since the early twentieth century, there have been marked changes in the social contexts in which these processes have taken place in recent decades. This review article examines three important shifts: (a) the rise of population diversity and its impact on traditional views of racial integration, (b) the changing institution of marriage in American life, and (c) the increasing centrality of technology. We discuss how these societal shifts have challenged traditional understandings of preferences, opportunities, and intermediaries in the mate selection process, as well as new opportunities for interracial intimacy that these changes have introduced. We conclude with a discussion on conceptual issues and promising future research directions.
Characterization of a novel adipose tissue located between abdominal lymph nodes and cervix/prostate in mice
Authors: Hu Lihao , Zhang Gaochen , Tong Xiaoyu , Wang Lulu , Qiu Xiang , Yang Hongbo , Liu Xinmei , Huang Hefeng
Published: May 2024

A novel perigonadal brown-like AT (LNCAT) of female mice was identified. Abdominal lymph nodes were involved in cold-induced browning in this newly discovered adipose tissue. The UCP1 expression in LNCAT/LNPAT was also related to ages, sexes, and reproductive stages, in which androgen acted as an inhibitor role.
Anatomical distribution of µ-opioid receptors, neurokinin-1 receptors, and vesicular glutamate transporter 2 in the mouse brainstem respiratory network
Authors: Furdui Andreea , da Silveira Scarpellini Carolina , Montandon Gaspard
Published: May 2024

Opioid drugs can cause serious respiratory side-effects by binding to µ-opioid receptors (MORs) in brainstem regions that control breathing. To better understand the regions and their cellular subpopulations that may be vulnerable to modulation by opioids, we provide a comprehensive map of Oprm1 (gene encoding MORs) mRNA expression throughout brainstem regions that control and modulate breathing. Notably, we identify glutamatergic and neurokinin-1 receptor-expressing cells as potentially vulnerable to modulation by opioid drugs and worthy of further investigation using targeted approaches.
Harbor Sedimentation Management Using Numerical Modeling and Exploratory Data Analysis
Authors: Elnabwy Mohamed T. , Elbeltagi Emad , El Banna Mahmoud M. , Alshahri Abdullah H. , Hu Jong Wan , Choi Byoung Gil , Kwon Yong Hee , Kaloop Mosbeh R.
Published: May 2024

Sedimentation in the harbors’ basins is an environmental phenomenon that frequently disrupts safe shipping and necessitates costly dredging operations. The layout of harbors and the permeability of protective structures such as breakwaters influence sediment transport within harbor basins. Thus, through a multistep framework, this study investigates the sedimentation management issues for the Egyptian proposed Ezbet Elborg fishing harbor based on field measurements and a numerical morphodynamic coastal modeling system (CMS). First, field measurements were analyzed and evaluated for acquiring a full grasp of the research area’s bathymetry and hydrodynamics. Second, a two‐dimensional (2D) numerical simulation CMS model was set up and calibrated against field measurements wherein the developed CMS model highly correlated with actual measurements by 97%. CMS results demonstrate that the predominant NNW wave with the formed longshore current on both the harbor’s sides affects sediment accumulation within the harbor’s basin. Third, 100 simulations for the proposed harbor including different structural modulation scenarios affecting the sedimentation issue were investigated via the calibrated CMS model. Finally, an exploratory data analysis (EDA) is performed via correlation matrix and ANOVA test for the CMS’s scenarios’ results to gain an in‐depth view of the relation between the harbors’ layout and the structural characteristics with the sedimentation volumes. Results showed that breakwaters’ orientation affects sediment accumulation more than its length. Also, breakwater permeability and basin width are significantly affecting sediment accumulation. Ultimately, the current study makes a substantial contribution to integrated coastal structure management (ICSM) by helping coastal stakeholders to mitigate the negative impacts of the harbors’ sediment deposition aiming at sustaining both environmental and economic aspects.
Prediction of Adverse Maternal Outcomes in Women with Early‐Onset Preeclampsia with Severe Features in Mexico: Validation of the Full‐PIERS Model
Authors: Ponce Nájera Eduardo , Montoya Hinojosa Marcela , Lozano Galván Rogelio Armando , González Oropeza Diego
Published: May 2024

Objective. Validate the full‐PIERS model in predicting adverse maternal outcomes in women with early‐onset preeclampsia with severe features in our population. Methods. Retrospective cohort study. We applied the full‐PIERS model on 130 women with severe early‐onset preeclampsia who were treated at a second‐level hospital in Nuevo León, México. We validated the full‐PIERS model in its ability to discriminate through the AUROC. Results. The full‐PIERS model applied to the data obtained in our study had good discrimination, revealing an AUC of 0.718 (95% CI 0.515–0.921; P = 0.017). A cut‐off of 7.95 was identified as the cut‐off point with the best diagnostic performance, with the highest Youden index, presenting a sensitivity of 54.5% and specificity of 99.2% for the development of complications. Conclusion. The full‐PIERS model can predict adverse maternal outcomes in women admitted to our hospital with severe early‐onset preeclampsia within 48 hours of admission.
The Burden of Obesity in Cardiac Surgery: A 14 years’ Follow-Up of 14.754 Patients
Authors: Beckmann Alexander , Poehlmann Maximilian , Mayr Patrick , Krane Markus , Boehm Johannes
Published: May 2024

Aims. The prevalence of obesity is rapidly increasing during the past decades. While previous research has focused on the early outcome after cardiac surgery or specific complications, the current study covers the whole burden of obesity in the field of cardiac surgery over short term and long term. Endpoints of the study were all-cause mortality, perioperative outcome, and wound-healing disorders (WHDs). Methods. 14.754 consecutive patients who underwent cardiac surgery over a 14 years’ time period were analyzed. BMI classifications were used according to the WHO definition. Results. Mean survival was 11.95 years ± 0.1; CI 95% [12.04–12.14]. After adjustment for clinical baseline characteristics, obesity classes’ I–III (obesity) did not affect 30-day mortality or all-cause mortality during the whole observational period. After adjustment for known risk factors, the risk for WHDs doubled at least in obesity patients as follows: obesity I (OR = 2.06; CI 95% [1.7–2.5]; p<0.0001), obesity II (OR = 2.5; CI 95% [1.83–3.41]; p<0.0001), and obesity III (OR = 4.12; CI 95% [2.52–6.74]; p<0.0001). The same applies to the risk for sternal reconstruction that is substantially elevated in obesity I (OR = 2.23; CI 95% [1.75–2.83]; p<0.0001), obesity II (OR = 2.81; CI 95% [1.91–4.13]; p<0.0001), and obesity III (OR = 2.31; CI 95% [1.08–4.97]; p=0.03). No significant correlation could be found between obesity and major adverse events in the perioperative course like renal failure, ventilation >24 h, re-exploration, or cerebrovascular events. Conclusions. Cardiac surgery is safe in obesity as short- and long-term mortality are not increased, and major adverse events during the perioperative course are similar to control patients. The burden of obesity lies in substantially increased rates of wound-healing disorders and sternal reconstructions.
Characterization and Pathogenicity of the Novel Porcine Reproductive and Respiratory Syndrome Virus 1 Strain SL‐01 in China
Authors: Zheng Jiaying , Wu Yu , Gao Xiaopeng , Lin Limiao , Chang Hao , Zhu Guojun , Fang Siyun , Li Wei , Ren Bohua , Li Qunhui , Zhang Xiangbin
Published: May 2024

Currently, PRRSV‐1 causes a large number of clinical infections in Chinese swine herds, and the prevalence of new strains has presented great challenges. In this study, the novel PRRSV‐1 strain SL‐01 was isolated, with a genome length of 14,978 bp, and genetic evolution analysis revealed that it belonged to a new subtype branch. Sequence homology analysis showed that the strain was only 82.2%–86.7% identical to the current classical PRRSV‐1 strains. In particular, the novel strain exhibited a unique deletion pattern in Nsp2. In addition, GP3 and GP4 of the SL‐01 strain showed four consecutive amino acid deletions in the highly variable regions at amino acids 243–248 and 63–68, respectively. Further challenges in piglet and pregnant sow demonstrated that the SL‐01 strain could cause the piglet fever and death but less pathogenic to pregnant sow. Overall, the characterization and pathogenicity of a novel PRRSV‐1 strain were first explored and provide a prevention for pig farms.