bluuebunny/crossref_metadata_2025 huggingface dataset:
Systematic evaluation of generative machine learning capability to simulate distributions of observables at the large hadron collider
Authors: Gavranovič Jan , Kerševan Borut Paul
Published: September 2024

Monte Carlo simulations are a crucial component when analysing the Standard Model and New physics processes at the Large Hadron Collider. This paper aims to explore the performance of generative models for complementing the statistics of classical Monte Carlo simulations in the final stage of data analysis by generating additional synthetic data that follows the same kinematic distributions for a limited set of analysis-specific observables to a high precision. Several deep generative models are adapted for this task and their performance is systematically evaluated using a well-known benchmark sample containing the Higgs boson production beyond the Standard Model and the corresponding irreducible background. The paper evaluates the autoregressive models and normalizing flows and the applicability of these models using different model configurations is investigated. The best performing model is chosen for a further evaluation using a set of statistical procedures and a simplified physics analysis. By implementing and performing a series of statistical tests and evaluations we show that a machine-learning-based generative procedure can be used to generate synthetic data that matches the original samples closely enough and that it can therefore be incorporated in the final stage of a physics analysis with some given systematic uncertainty.
A quantitative approach to reflectance transformation imaging in profilometric applications
Authors: Verni Eleonora , Fiocco Giacomo , Grifoni Emanuela , Lippolis Giulia , Ludwig Nicola , Malagodi Marco , Pisani Marco , Rovetta Tommaso , Zucco Massimo , Gargano Marco
Published: September 2024

Reflectance transformation imaging (RTI) is a technique employed to assess both the intensity and directional properties of light reflected from an object, aiming to visualize an object under different incident light directions. This technique has quickly become a commonly used approach for the documentation, acquisition and deciphering for cultural heritage objects, because it enables to enhance and highlight image details. The output of this mathematical synthesis technique, which elaborates illumination information, is contained in a pseudocolour image called normal map. Starting from this image, this technique allows to obtain the reconstruction of the outline of semi-flat objects by integrating the normal map given by the RTI, i.e. normal integration. The technique of normal integration has been known for many years, being widely used in graphical modelling across various fields of 3D imaging. In the context of cultural heritage, this particular form of 3D modelling has already found application in the reconstruction of semi-flat objects, albeit with some limitations in the accurate portraying of low spatial frequencies. However, quantitative and systematic studies on the efficiency of 3D rendering of objects using RTI and normal integration have not yet been conducted. In the context of this research, the quantitative evaluation of the accuracy of the RTI technique with normal integration for profilometric applications on semi-flat objects is proposed and a study on its fidelity in reproducing the considered artefact is conducted. For this purpose, ad hoc profilometric targets were designed, realized and analysed to quantitatively compare the results of RTI and normal integration with different standardized techniques: micro-photogrammetry, laser scanning and optical profilometry.

Graphical abstract
METRIC RESULTS FOR THE EVENTUALLY ALWAYS HITTING POINTS AND LEVEL SETS IN SUBSHIFT WITH SPECIFICATION
Authors: WANG BO , LI BING
Published: September 2024

We study the set of eventually always hitting points for symbolic dynamics with specification. The measure and Hausdorff dimension of such fractal set are obtained. Moreover, we establish the stronger metric results by introducing a new quantity [Formula: see text] which describes the maximal length of string of zeros of the prefix among the first [Formula: see text] iterations of [Formula: see text] in symbolic space. The Hausdorff dimensions of the level sets for this quantity are also completely determined.
The Impact of Digital Transformation on Enterprise Ambidextrous Innovation: Evidence from Manufacturing Listed Companies in China
Authors: Jiang Yuxin , He Caifeng , Ma Wencong
Published: September 2024

In this paper, we theoretically discuss and empirically investigate the impact of digital transformation on the ambidextrous innovation of enterprises. We also examine the transmission mechanism of knowledge flows between digital transformation and ambidextrous innovation in enterprises. Based on the data of Shanghai and Shenzhen-listed companies from 2012 to 2020, using a two-way fixed-effect panel regression model, the results show that: (1) Digital transformation has a significant positive effect on both enterprise ambidextrous innovation, and its effect on exploratory innovation is greater than that on exploitative innovation. (2) Digital transformation promotes exploratory and exploitative innovation by accelerating knowledge flow. (3) The impact of digital transformation on exploratory innovation in enterprises is heterogeneous, while the impact on exploitative innovation is not heterogeneous. That is, the positive effect of digital transformation on exploratory innovation is significant in enterprises in high-tech industries, but not significant in enterprises in nonhigh-tech industries. Overall, our findings provide a deeper understanding for enterprises to grasp digital opportunities and carry out digital transformation in a timely and appropriate manner, as well as an essential policy implication for the government to strengthen the construction of digital infrastructure and improve the policies related to digital transformation.
Online Fast Assessment of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Proliferation Through Replication
Authors: Ansari Mohammad Zaheer
Published: September 2024

Purpose. A virus, when it infects a host organism, multiplies its ribonucleic acid (RNA) to make it more harmful. We analyze if a view-based temporal template method, the motion history image (MHI) algorithm, may be of use to extract information on the progression of viral interactions over time. Methods. We employ an alternative online processing modality to comprehend and analyze the replication dynamics of severe acute respiratory syndrome coronavirus 2 (SARS CoV 2). The data are evaluated using the computation of a quantitative algorithm, the absolute value of differences (AVD), followed by the generation of temporal template map, the MHI of viral contact. Results. We show that the MHI identifies various stages to examine cultural characteristics and information on the progression of viral interactions over time. The results are more easily obtained compared to a visual inspection of the frames constituting the recordings. MHI also has the advantage of relying on a simple algorithm. The AVD index further provides deeper insights into the dynamics of the viral interaction over time, and its measurement suggests that SARS CoV-2 virus is becoming more widespread. This study shows that the replication process damages the host cell over time, in addition to causing SARS CoV-2 to proliferate. Conclusions. The MHI provides a framework for automated feature extraction from time series data. The combined approach results in decreased computational complexity and enables online assessment of the assay. Clinical and research studies can be conducted to evaluate MHI and AVD and their utility in aiding diagnostic analysis.
Beyond "Taming Electric Scooters": Disentangling Understandings of Micromobility Naturalistic Riding
Authors: Tabatabaie Mahan , He Suining , Wang Hao , Shin Kang G.
Published: September 2024

Electric(e)-scooters have emerged as a popular, ubiquitous, and first/last-mile micromobility transportation option within and across many cities worldwide. With the increasing situation-awareness and on-board computational capability, such intelligent micromobility has become a critical means of understanding the rider's interactions with other traffic constituents (called Rider-to-X Interactions, RXIs), such as pedestrians, cars, and other micromobility vehicles, as well as road environments, including curbs, road infrastructures, and traffic signs. How to interpret these complex, dynamic, and context-dependent RXIs, particularly for the rider-centric understandings across different data modalities --- such as visual, behavioral, and textual data --- is essential for enabling safer and more comfortable micromobility riding experience and the greater good of urban transportation networks.

Under a naturalistic riding setting (i.e., without any unnatural constraint on rider's decision-making and maneuvering), we have designed, implemented, and evaluated a pilot Cross-modality E-scooter Naturalistic Riding Understanding System, namely CENRUS, from a human-centered AI perspective. We have conducted an extensive study with CENRUS in sensing, analyzing, and understanding the behavioral, visual, and textual annotation data of RXIs during naturalistic riding. We have also designed a novel, efficient, and usable disentanglement mechanism to conceptualize and understand the e-scooter naturalistic riding processes, and conducted extensive human-centered AI model studies. We have performed multiple downstream tasks enabled by the core model within CENRUS to derive the human-centered AI understandings and insights of complex RXIs, showcasing such downstream tasks as efficient information retrieval and scene understanding. CENRUS can serve as a foundational system for safe and easy-to-use micromobility rider assistance as well as accountable use of micromobility vehicles.
IrOnTex: Using Ironable 3D Printed Objects to Fabricate and Prototype Customizable Interactive Textiles
Authors: Yu Jiakun , Kuruppu Supun , Fernando Biyon , Perera Praneeth Bimsara , Sugiura Yuta , Subramanian Sriram , Withana Anusha
Published: September 2024

In this paper, we propose IrOnTex, an approach to design, fabricate, and prototype customizable interactive textiles by ironing 3D printed elements directly onto fabrics. Our method incorporates multiple filament types to demonstrate functionalities such as electrical conductivity, sensing, and thermo- and photo-sensitivity for interactive applications. Additionally, we integrate a tiling-like fragmented design to improve stretchability and flexibility to match the mechanical behavior of textiles in our 3D printed elements. We characterized the iron-on process through technical evaluations, which showed the durability and reversibility of the approach. We also suggested baseline parameters for prototyping and methods to enhance the functionality of certain materials. The empirical results from the studies were used to develop a design tool to assist the design process. A user study with 10 participants revealed the helpfulness of the tool. To demonstrate the capabilities of the approach, we show a set of example interactive applications. We envision the proposed approach will enable designers to rapidly prototype interactive textile applications.
A Reproducible Stress Prediction Pipeline with Mobile Sensor Data
Authors: Zhang Panyu , Jung Gyuwon , Alikhanov Jumabek , Ahmed Uzair , Lee Uichin
Published: September 2024

Recent efforts to predict stress in the wild using mobile technology have increased; however, the field lacks a common pipeline for assessing the impact of factors such as label encoding and feature selection on prediction performance. This gap hinders replication, especially because of a lack of common guidelines for reporting results or privacy concerns that limit access to open codes and datasets. Our study introduces a common pipeline based on a comprehensive literature review and offers comprehensive evaluations of key pipeline factors, promoting independent reproducibility. Our systematic evaluation aimed to validate the findings of previous studies. We identified overfitting and distribution shifts across users as the major reasons for performance limitations. We used K-EmoPhone, a public dataset, for experimentation and a new public dataset---DeepStress---to validate the findings. Furthermore, our results suggest that researchers should carefully consider temporal order in cross-validation settings. Additionally, self-report labels for target users are key to enhancing performance in user-independent scenarios.
Empowering IoT Developers with Privacy-Preserving End-User Development Tools
Authors: Aljeraisy Atheer , Rana Omer , Perera Charith
Published: September 2024

Internet of Things applications (IoT) have the potential to derive sensitive user data, necessitating adherence to privacy and data protection laws. However, developers often struggle with privacy issues, resulting in personal data misuse. Despite the proposed Privacy by Design (PbD) approach, criticism arises due to its ambiguity and lack of practical tools for educating software engineers. We introduce Canella, an integrated IoT development ecosystem with privacy-preserving components leveraging End-User Development (EUD) tools Blockly@rduino and Node-RED, to help developers build end-to-end IoT applications that prioritize privacy and comply with regulations. It helps developers integrate privacy during the development process and rapid prototyping phases, offering real-time feedback on privacy concerns. We start by conducting a focus group study to explore the applicability of designing and implementing PbD schemes within different development environments. Based on this, we implemented a proof-of-concept prototype of Canella and evaluated it in controlled lab studies with 18 software developers. The findings reveal that developers using Canella created more privacy-preserving applications, gained a deeper understanding of personal data management, and achieved better privacy compliance. Our results also highlight Canella's role in educating and promoting privacy awareness, enhancing productivity, streamlining privacy implementation, and significantly reducing cognitive load. Overall, developers found Canella and its privacy-preserving components useful, easy to use, and easy to learn, which could potentially improve IoT application privacy. Watch the demo video.
GrainSense: A Wireless Grain Moisture Sensing System Based on Wi-Fi Signals
Authors: Wang Zhu , Chen Zhen , Zhang Yao , Geng Chendi , Song Wenchao , Sun Zhuo , Guo Bin , Yu Zhiwen , Chen Liming
Published: September 2024

Grain moisture sensing plays a critical role in ensuring grain quality and reducing grain losses. However, existing commercial off-the-shelf (COTS) grain moisture sensing systems are either expensive, inconvenient or inaccurate, which greatly limit their widespread deployment in real-world scenarios. To fill this gap, we develop a system called GrainSense which leverages COTS Wi-Fi devices to detect the grain moisture without the need for dedicated sensors. Specifically, we propose a wireless grain moisture detection model based on the refraction phenomenon of Wi-Fi signals and the Multiple-Input-Multiple-Output (MIMO) technology. On one hand, we correlate the grain moisture with the phase difference between two refracted Wi-Fi signals that propagate along different paths, based on which grain moisture can be deduced accordingly. On the other hand, to reduce the multi-path interference in indoor environments (e.g., the granary), we adopt Wi-Fi beamforming to enhance the refracted signal. In particular, a new signal feature (i.e., the Wi-Fi CSI beamforming ratio) is designed to eliminate the effect of sub-carrier frequency bias and cumulative phase bias. To validate the effectiveness of the developed system, we conduct extensive experiments with different types of grains in both the laboratory and the granary. Results show that the system can accurately estimate the grain moisture with an mean absolute error smaller than 5%, which meets the requirements for commercial usage. To the best of our knowledge, this is the first model-based work that achieves accurate grain moisture detection based on wireless sensing.
Explaining the (non-) Use of Voting Advice Applications
Authors: Walder Maxime , Fivaz Jan , Schwarz Daniel , Giger Nathalie
Published: September 2024

Voting requires lots of information. In most democracies, elections leave voters with dozens, if not thousands, of voting possibilities. Voting Advice Applications (VAAs) have primarily been recognized as a guide for voters through their electoral decision-making. However, the use of such digital tools is still limited. In this article, we analyze how voters’ characteristics affect the use of VAAs and what makes individuals not use the application. We present the results of a panel survey where we randomly invited part of the survey respondents to use a VAA during an electoral campaign in the canton of Bern, Switzerland. Our results show that while significant differences exist between respondents in and out of the experimental setting, political knowledge, and interest are consistent drivers of VAA use. Additionally, we show that more than half of non-VAA users indicate that they do not need assistance from a VAA as a reason for not using the tool and that factors influencing this reason are similar to factors that affect the use of VAAs. In doing so, we add to the literature on the use and biases of VAAs and the general understanding of why people use or do not use digital tools.
Modeling and Controlling Many-Core HPC Processors: an Alternative to PID and Moving Average Algorithms
Authors: Bambini Giovanni , Ottaviano Alessandro , Conficoni Christian , Tilli Andrea , Benini Luca , Bartolini Andrea
Published: September 2024

The race towards performance increase and computing power has led to chips with heterogeneous and complex designs, integrating an ever-growing number of cores on the same monolithic chip or chiplet silicon die. Higher integration density, compounded with the slowdown of technology-driven power reduction, implies that power and thermal management become increasingly relevant. Unfortunately, existing research lacks a detailed analysis and modeling of thermal, power, and electrical coupling effects and how they have to be jointly considered to perform dynamic control of complex and heterogeneous mpsoc. To close the gap, in this work, we first provide a detailed thermal and power model targeting a modern hpc mpsoc. We consider real-world coupling effects such as actuators’ non-idealities and the exponential relation between the dissipated power, the temperature state, and the voltage level in a single processing element. We analyze how these factors affect the control algorithm behavior and the type of challenges that they pose. Based on the analysis, we propose a thermal capping strategy inspired by Fuzzy control theory to replace the state-of-the-art PID controller, as well as a root-finding iterative method to optimally choose the shared voltage value among cores grouped in the same voltage domain. We evaluate the proposed controller with model-in-the-loop and hardware-in-the-loop co-simulations. We show an improvement over state-of-the-art methods of up to

\(5\times\)

the maximum exceeded temperature while providing an average of

\(3.56\%\)

faster application execution runtime across all the evaluation scenarios.
Vulnerabilities and Security Patches Detection in OSS: A Survey
Authors: Lin Ruyan , Fu Yulong , Yi Wei , Yang Jincheng , Cao Jin , Dong Zhiqiang , Xie Fei , Li Hui
Published: September 2024

Over the past decade, Open Source Software (OSS) has experienced rapid growth and widespread adoption, attributed to its openness and editability. However, this expansion has also brought significant security challenges, particularly introducing and propagating software vulnerabilities. Despite the use of machine learning and formal methods to tackle these issues, there remains a notable gap in comprehensive surveys that summarize and analyze both Vulnerability Detection (VD) and Security Patch Detection (SPD) in OSS. This article seeks to bridge this gap through an extensive survey that evaluates 127 technical studies published between 2014 and 2023, structured around the Vulnerability-Patch lifecycle. We begin by delineating the six critical events that constitute the Vulnerability-Patch lifecycle, leading to an in-depth exploration of the Vulnerability-Patch ecosystem. We then systematically review the databases commonly used in VD and SPD, and analyze their characteristics. Subsequently, we examine existing VD methods, focusing on traditional and deep learning based approaches. Additionally, we organize current security patch identification methods by kernel type and discuss techniques for detecting the presence of security patches. Based on our comprehensive review, we identify open research questions and propose future research directions that merit further exploration.
Transformer-Based Topic Modeling for Urdu Translations of the Holy Quran
Authors: Zafar Amna , Wasim Muhammad , Zulfiqar Shaista , Waheed Talha , Siddique AbuBakar
Published: September 2024

Topic modeling enables the discovery of concealed themes and patterns in extensive text collections. It facilitates a thorough examination of the messages present in religious texts. Topic modeling for Quranic verses is a trending study area, with various translations already explored including Bahasa, English, and Arabic. Yet, there is a need for further research, particularly in Urdu translations of the Quran. In this study, we propose applying the BERTopic framework to Urdu translations of the Holy Quran. By leveraging the BERTopic approach, which incorporates a fine-tuned BERT model, we aim to capture the contextual nuances and linguistic complexities unique to the Quran. In this study, we utilized existing Urdu translations of the Quran from eight different translators sourced from Tanzil, a renowned resource for Quranic text and translations. We assessed the performance of our proposed BERTopic model compared to traditional techniques like LDA and NMF, using coherence and diversity metrics. The results indicate that our BERT-based approach outperforms these conventional methods, achieving an average coherence improvement of 0.03 and a diversity score of 0.83. These findings highlight the effectiveness of BERTopic in extracting meaningful topics from Urdu translations of the Holy Quran and contribute to the computational analysis of religious texts, supporting scholarly endeavors in comparative studies of Quranic translations in Urdu.
Transparent Depth Completion Using Segmentation Features
Authors: Liu Boqian , Li Haojie , Wang Zhihui , Xue Tianfan
Published: September 2024

Estimating the depth of transparent objects is one of the well-known challenges of RGB-D cameras due to the reflection and refraction effects. Previously, researchers propose to correct the depth of transparent objects by using their estimated segmentation masks because it is possible to recover the internal depth of an object just from its boundary, as illustrated by those depth-from-silhouette methods. However, these algorithms only use segmentation masks. They ignore the internal structure information from the mask segmentation features, which we argue are more useful for transparent depth estimation. In this work, we demonstrate the effectiveness of segmentation features for transparent object depth estimation. We show that it is even possible to recover the depth map just from segmentation features, without any RGB or depth map as input. Based on this observation, we propose DualTransNet which uses segmentation features for transparent depth completion. In our DualTransNet, we feed segmentation features from an extra module to the main network for better depth completion quality. Extensive experiments have shown the superiority of segmentation features as well as the state-of-the-art performance of our network.
A novel tree-based method for interpretable reinforcement learning
Authors: Li Yifan , Qi Shuhan , Wang Xuan , Zhang Jiajia , Cui Lei
Published: September 2024

Deep reinforcement learning (DRL) has garnered remarkable success across various domains, propelled by advancements in deep learning (DL) technologies. However, the opacity of DL presents significant challenges, limiting the application of DRL in critical systems. In response, decision tree (DT)-based methods, known for their transparent decision-making mechanisms, have shown promise in making interpretable policies for decision-making problems. Existing methods often employ differential DTs to model RL policies and discretize them to conventional DTs for higher interpretability. Yet, this method leads to discrepancies between the trained differential DTs and the discretized DTs. To address this issue, we introduce Generative Consistent Trees (GCTs), a novel solution that circumvents the information loss typically associated with the
argmax
operation in prior research. By implementing a reparameterization technique to approximate the categorical distribution, GCTs ensure the consistencies between trained GCTs and discretized counterparts. Moreover, we have developed an imitation-learning-based framework for interpretable reinforcement learning. This framework is designed to train GCTs by efficiently mimicking expert policies. Our extensive experiments across multiple environments have validated the effectiveness of this approach, highlighting the potential of GCTs in enhancing the interpretability and applicability of DRL.
Lateral NiO/AlN Heterojunction Rectifiers with Breakdown Voltage >11 kV
Authors: Wan Hsiao-Hsuan , Li Jian-Sian , Chiang Chiao-Ching , Rahman Md Hafijur , Haque Aman , Ren Fan , Pearton Stephen J.
Published: September 2024

Lateral NiO/AIN heterojunction diodes (HJDs) with breakdown voltage up to 11.6 kV and Ni/Au/AlN Schottky barrier diodes (SBDs) with VB of 8.6 kV were fabricated on layers grown on sapphire substrates by metalorganic chemical vapor phase deposition. The power figure-of-merits VB
2/RON where RON is the on-resistance were 0.31 MW·cm−2 for HJD and 0.16 MW·cm−2 for SBD. The lowest turn-on voltages were ∼2.03 and 1.91 V for HJDs and SBDs, respectively, with ON/OFF ratios up to 102. The maximum field before breakdown was 0.45 MV·cm−1 in HJDs and 0.31 MV·cm−1 in SBDs. These correspond to <3% of the critical field in AlN of ∼15 MV·cm−1. This work demonstrates there is still significant optimization to be done in the overall quality of the AlN, including purity, crystal perfection, and defect density to realize the potential of this material as an ultra-wide bandgap semiconductor for efficient multi-kV power switching applications. Our results also demonstrate the promise of NiO as a p-type conducting oxide for forming heterojunctions with AlN.
KLF3 impacts insulin sensitivity and glucose uptake in skeletal muscle
Authors: Fu Shuying , Gong Xiaocheng , Liang Keying , Ding Ke , Qiu Li , Cen Huice , Du Hongli
Published: September 2024

The knockdown of Klf3 increased glucose uptake and improved insulin sensitivity in L6 myotubes, whereas its overexpression had the opposite effect. To explore the underlying mechanisms, we evaluated the transcriptional profiles of L6 myotubes after Klf3 knockdown and overexpression and revealed that metabolism and insulin-related pathways were significantly impacted. Klf3 also influenced the expression or modification of glucose transporter 4 (GLUT4), protein kinase B (AKT), TBC1 domain family member 1 (TBC1D1), and TBC1 domain family member 4 (TBC1D4) in the insulin signaling pathway, affecting insulin sensitivity and glucose uptake.
PALMD haploinsufficiency aggravates extracellular matrix remodeling in vascular smooth muscle cells and promotes calcification
Authors: Zhang Jichao , Yang Zhao , Zhang Congcong , Gao Shijuan , Liu Yan , Li Yingkai , He Songyuan , Yao Jing , Du Jie , You Bin , Han Yingchun
Published: September 2024

We found that PALMD haploinsufficiency causes extracellular matrix dysregulation, reduced PALMD expression links to vascular calcification, and PALMD mutations may lead to the risk of both calcific aortic valve stenosis and vascular calcification.
Preworkout dietary nitrate magnifies training-induced benefits to physical function in late postmenopausal women: a randomized pilot study
Authors: Carter Stephen J. , Blechschmid Tyler H. , Baranauskas Marissa N. , Long Emily B. , Gruber Allison H. , Raglin John S. , Lim Kenneth , Coggan Andrew R.
Published: September 2024

Though exercise training represents the principal strategy to combat age-related decline, the attendant effects of menopause weaken aspects of exercise adaptation compared with premenopausal women and age-matched men. Here we provide important initial evidence that preworkout (i.e., an acute dose before training) beetroot juice coupled with an 8-wk, circuit-based exercise training intervention may uniquely benefit late postmenopausal women by enhancing indices of physical function including aerobic capacity and recovery.
Now and then in eukaryotic DNA methylation
Authors: Stein Richard A. , Gomaa Faris E. , Raparla Pranaya , Riber Leise
Published: September 2024

Since the mid-1970s, increasingly innovative methods to detect DNA methylation provided detailed information about its distribution, functions, and dynamics. As a result, new concepts were formulated and older ones were revised, transforming our understanding of the associated biology and catalyzing unprecedented advances in biomedical research, drug development, anthropology, and evolutionary biology. In this review, we discuss a few of the most notable advances, which are intimately intertwined with the study of DNA methylation, with a particular emphasis on the past three decades. Examples of these strides include elucidating the intricacies of 5-methylcytosine (5-mC) oxidation, which are at the core of the reversibility of this epigenetic modification; the three-dimensional structural characterization of eukaryotic DNA methyltransferases, which offered insights into the mechanisms that explain several disease-associated mutations; a more in-depth understanding of DNA methylation in development and disease; the possibility to learn about the biology of extinct species; the development of epigenetic clocks and their use to interrogate aging and disease; and the emergence of epigenetic biomarkers and therapies.
Study of Liquid‐Cooled Fuel Cell Stack Uniformity considering Global and Local Characteristics under Air Starvation Conditions
Authors: Wang Wanteng , Li Nan , Pan Sanjiang , Zhang Caihong , Zhang Liang , Li Guijing
Published: September 2024

Gas starvation significantly affects the uniformity of stack. To reduce this phenomenon, a three‐dimensional, nonisothermal, and two‐phase full‐size liquid‐cooled stack model was developed, and the characteristics of the global and local nonuniformities of oxygen and liquid water in the flow field, membrane temperature, and current density were analyzed under sufficient oxygen supply and three different degrees of gas shortage. Advanced uniformity evaluation criteria (AUEC) were proposed to evaluate the overall uniformity of the stack, which can effectively characterize the local high nonuniformity of the cell units and the differences between edge and intermediate cell units. The results showed that with a decrease in cathode stoichiometric ratio, the AUEC of oxygen, liquid water, membrane temperature, and current density all decreased. The increase in the cooling liquid velocity increased the AUEC of the membrane temperature and current density in the stack, and the AUEC increased further when the oxygen supply was sufficient. In addition, the improvement scheme of GDL gradient porosity was proposed, which can effectively improve the uniformity of each parameter in the stack.
Development and Structural Evaluation of Dog Bone Particle–Reinforced Epoxy Composites for Biomedical Applications
Authors: Owa Adebayo Felix , Olubambi Peter Apata
Published: September 2024

There has been great interest in developing high‐performance, sustainable and eco‐friendly materials. This study presents an innovative development and research of the mechanical properties of dog bone–reinforced epoxy composites to determine their suitability for biomedical applications. Good surface compatibility for the matrix and reinforcement was the target, complementing the structural compatibility needed for such applications. Hand‐lay open mould technique was used to fabricate the composites with varying weight percentage of dog bone particles (DBPs), and mechanical properties (tensile strength, Young’s modulus, percentage elongation and flexural strength) were determined. The results show that the developed composites’ mechanical properties were enhanced, and the tensile strength of 21.10 MPa–27.97 MPa, Young’s modulus of 1531.00 MPa–1732.90 MPa, ductility of 2.10%–2.60% and flexural strength of 27.53 MPa–35.67 MPa were recorded for the biocomposites. Energy‐dispersive X‐ray spectroscopy (EDS) revealed the main elements’ contents of the DBPs as calcium (50.3%), carbon (20.77%) and oxygen (11.76%). Scanning electron microscopy (SEM) examination of the fractured surfaces revealed the homogeneous distribution of the dog bone particulates within the epoxy matrix, which caused the property enhancement. The findings of this research underline the potency of processed animal waste as filler materials.
2D‐Action Asynchronous Cooperative Lane Change Trajectory Planning Method for Connected and Automated Vehicles
Authors: Wei Liyang , Zhang Weihua , Bai Haijian , Li Jingyu
Published: September 2024

The ability to change lanes safely, efficiently, and comfortably is an important prerequisite for the application of Connected‐Automated Vehicles (CAVs). Based on the five‐order polynomial trajectory planning for CAVs, the 2D‐Action Asynchronous Lane Change (AALC) trajectory planning model is constructed by further considering the longitudinal and lateral driving action execution time parameters. This is done to improve the applicability of the lane change model and increase the CAV lane change success rate. The continuous collision space algorithm is constructed by determining the continuity condition of collision trajectory parameter solution space through the monotonicity of trajectory curve parameters and collision form classification. AALC trajectory safety judgment is realized through this algorithm. A cooperative lane change trajectory evaluation objective function is constructed, considering multivehicle comfort and efficiency. Finally, the AALC model is solved in the continuous collision space according to the optimal objective function, and the lane change is divided into free, cooperative, and refused according to the optimization. The results indicate that the AALC model achieves the transfer of collision space between lanes through asynchronous process of behavior execution time window, thereby reducing the possibility of vehicle collision. The AALC model reduces the degree of change of cooperative lane change parameters by asynchronous process of behavior, increasing the number of free lane change trajectories by about 17%, effectively reducing the occurrence of lane change refusal, improving the successful rate of lane change, and enhancing the overall evaluation of the lane change. The AALC model realizes the reallocation of collision space between different lanes through asynchronous process, making it more suitable for environments with large differences in vehicle gaps such as ramp merging. The collision‐based trajectory optimization algorithm can quickly obtain the corresponding safety space and optimal trajectory. The maximum calculation time for a single cooperative lane change is 0.073 s, thus enabling real‐time trajectory planning.
Investigation of Indoor Airborne Bacteria in the Severe Cold Region in China: Genera, Levels, and the Influencing Factors of Concentration
Authors: Duan Xiaojian , Shen Chao , Chen Guozheng , Deng Xi , Jones Phil
Published: September 2024

In regions experiencing severe cold, inadequate ventilation during winter months often leads to increased concentrations of indoor pollutants. While there have been several studies on indoor particulate matter and inorganic pollutants in such regions, bioaerosol pollution has not been as extensively investigated. This study examines the indoor bioaerosol situation in a university located in one of the severe cold regions in China, focusing on bacteria as a representative pollutant. It investigated random samples of an office and a dormitory (including washrooms) and spanned heating and nonheating periods. The findings indicated that bacterial abundance in the dormitory and office was approximately equivalent. The predominant airborne bacterial communities identified were Proteobacteria, Bacteroidota, Actinobacteriota, Firmicutes, and Myxococcota. Opening windows effectively reduced bacterial concentrations during both heating and nonheating periods. When windows remained closed, bacterial concentrations exceeded the standard by 9.1% during the nonheating period and by 14.3% during the heating period. Furthermore, temperature and relative humidity influenced bacterial particle size, activity, and consequently, aerosol concentrations. In the office, the highest percentage of bioaerosols was observed in particle sizes <1.1 and 1.1–2.1 μm, with smaller percentages observed in other particle sizes. Conversely, the percentage of particle sizes 2.1–3.3 μm in the dormitory was higher. The highest bacterial aerosol concentrations were detected in the morning in both the dormitory and office, during heating and nonheating periods. Bacterial concentrations in the office were lower on weekends than on weekdays, whereas in the dormitory, concentrations were higher on weekends than on weekdays. The above results indicate that indoor bacterial aerosol pollution is serious in winter in severe cold regions, which needs more attention.
Supplementary Data from Quantitative Spatial Profiling of Immune Populations in Pancreatic Ductal Adenocarcinoma Reveals Tumor Microenvironment Heterogeneity and Prognostic Biomarkers
Authors: Mi Haoyang , Sivagnanam Shamilene , Betts Courtney B. , Liudahl Shannon M. , Jaffee Elizabeth M. , Coussens Lisa M. , Popel Aleksander S.
Published: September 2024

Supplementary Materials: Supplementary Figures S1-S5 and Tables S1-S7
Cancer-Associated Fibroblasts Expressing Sulfatase 1 Facilitate VEGFA-Dependent Microenvironmental Remodeling to Support Colorectal Cancer
Authors: Wang Huijuan , Chen Jiaxin , Chen Xiaoyu , Liu Yingqiang , Wang Jiawei , Meng Qing , Wang Huogang , He Ying , Song Yujia , Li Jingyun , Ju Zhenyu , Xiao Peng , Qian Junbin , Song Zhangfa
Published: September 2024

Tumor stroma plays a critical role in fostering tumor progression and metastasis. Cancer-associated fibroblasts (CAF) are a major component of the tumor stroma. Identifying the key molecular determinants for the protumor properties of CAFs could enable the development of more effective treatment strategies. In this study, through analyses of single-cell sequencing data, we identified a population of CAFs expressing high levels of sulfatase 1 (SULF1), which was associated with poor prognosis in patients with colorectal cancer. Colorectal cancer models using mice with conditional SULF1 knockout in fibroblasts revealed the tumor-supportive function of SULF1+ CAFs. Mechanistically, SULF1+ CAFs enhanced the release of VEGFA from heparan sulfate proteoglycan. The increased bioavailability of VEGFA initiated the deposition of extracellular matrix and enhanced angiogenesis. In addition, intestinal microbiota–produced butyrate suppressed SULF1 expression in CAFs through its histone deacetylase (HDAC) inhibitory activity. The insufficient butyrate production in patients with colorectal cancer increased the abundance of SULF1+ CAFs, thereby promoting tumor progression. Importantly, tumor growth inhibition by HDAC was dependent on SULF1 expression in CAFs, and patients with colorectal cancer with more SULF1+ CAFs were more responsive to treatment with the HDAC inhibitor chidamide. Collectively, these findings unveil the critical role of SULF1+ CAFs in colorectal cancer and provide a strategy to stratify patients with colorectal cancer for HDAC inhibitor treatment.

 Significance: SULF1+ cancer-associated fibroblasts play a tumor-promoting role in colorectal cancer by stimulating extracellular matrix deposition and angiogenesis and can serve as a biomarker for the therapeutic response to HDAC inhibitors in patients.
Figure S5 from Activation of Cell-Intrinsic Signaling in CAR-T Cells via a Chimeric IL7R Domain
Authors: Vorri Stamatia C. , Holl Natalie J. , Leeming Michael , Apostolova Petya , Marple Andrew , Ravich Jonas W. , Canbaz Ata , Rahnama Ruyan , Choe Jun , Modi Arjun , Fearnow Adam D. , Walsh Scott T.R. , Pearce Erika L. , Varadhan Ravi , Bonifant Challice L.
Published: September 2024

Supplementary Figure 5
Figure 3 from Activation of Cell-Intrinsic Signaling in CAR-T Cells via a Chimeric IL7R Domain
Authors: Vorri Stamatia C. , Holl Natalie J. , Leeming Michael , Apostolova Petya , Marple Andrew , Ravich Jonas W. , Canbaz Ata , Rahnama Ruyan , Choe Jun , Modi Arjun , Fearnow Adam D. , Walsh Scott T.R. , Pearce Erika L. , Varadhan Ravi , Bonifant Challice L.
Published: September 2024

CD123-targeted CAR and CCR can be coexpressed with maintained functionality. A, Schematic showing CAR and CCR structures. B, Transduction efficiency measured using flow cytometric staining of Halo and SNAP tags. n = 3 to 6 unique T-cell donors, unless noted no significant difference between groups. C, Measurement of phosphorylated STAT5 percentage in cytokine starved, engineered T cells measured with and without activation on the immobilized rhCD123 target. n = 3 to 4 unique T-cell donors, comparison of CCR + CAR+ and CAR+ to NT (black) and with stimulation (colored). D, Soluble IL2 and IFNγ measured in the supernatant following coculture of unmodified (NT), CCR+, CAR+, or CCR + CAR+ T cells with CD123-negative (K562) and CD123-positive (K562.CD123, MV-4-11, and Molm-13) targets. n = 3 to 6 unique T-cell donors; data represented as mean ± SD. Significance noted is in comparison to NT (black asterisks) and/or CCR+ cells (green asterisks) or as noted. NT vs. CCR+ comparison was nonsignificant in all instances. E, Bioluminescence-based cytotoxicity assays performed using K562, K562.CD123, MV-4-11, and Molm-13 stably expressing ffLuc; n = 3 to 5 donors. For B–E, *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001. Unless noted, comparisons were nonsignificant. NT, nontransduced.
Characteristics of Alzheimer’s Disease and Mild Cognitive Impairment Influenced by the Time of Onset
Authors: Sato Hiroyuki , Ota Miho , Kitabatake Ayako , Numata Yuriko , Takahashi Takumi , Tamura Masashi , Nemoto Kiyotaka , Arai Tetsuaki
Published: September 2024

Introduction: Although the prevalence of Alzheimer’s disease (AD) is higher in older people compared to younger people, dementia has also been documented in younger adults. Although early-onset dementia and late-onset dementia had been considered a single disease in pathological investigations, many studies revealed differences in cognitive and neuroimaging changes between them. We evaluated differences in cognitive and neuroimaging changes among the following groups: individuals with early-onset AD (EOAD), late-onset AD (LOAD), early-onset mild cognitive impairment (EOMCI), or late-onset MCI (LOMCI), and healthy controls (HCs). Methods: Patients underwent both a 1.5 Tesla magnetic resonance imaging scan and the Mini-Mental State Examination (MMSE). Differences in regional gray matter volumes and MMSE subscales were investigated among the five diagnostic groups. Results: Compared to the EOAD group, the LOAD group had significantly higher scores on orientation in place. Compared to the LOMCI patients, the EOMCI patients achieved significantly higher recall scores. The LOAD and LOMC groups showed significant volume reductions in bilateral medial temporal regions compared to the HCs. The EOAD and EOMCI groups did not show significant atrophy of the medial temporal region compared to the HC group. Conclusions: The hippocampal volume and memory were preserved in the patients with EOMCI or EOAD compared to those with LOMCI or LOAD. These findings may indicate that the distinct and differing patterns of neuropsychological changes between EOAD and LOAD are also common in MCI, which is intermediate between normal cognition and AD.
Impact of Silver Dressings on Wound Healing Rate in Patients with Lower Extremity Ulcers: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Authors: Yi Qingling , Huang Zhongkui , Tang Bangli
Published: September 2024

Objective: The ulcers of lower extremities, both venous and diabetic, impose a massive burden on sufferers globally. This meta-analysis evaluates the efficacy of silver dressings in improving healing rate of these ulcers. Methods: We carried out comprehensive searches in PubMed, Embase, Web of Science, the Cochrane Library, and China National Knowledge Internet (CNKI) up to March 2024. Two reviewers independently screened the study and extracted data. While the primary outcome was wound healing rate, secondary outcomes included wound healing area and recurrence rate. Data were analyzed using Stata 16.0 software. Results: A total of 18 studies involving 1,825 participants were included. Silver dressings demonstrated statistically significant effects in improving healing rates for diabetic foot ulcers (DFUs) (OR 2.14, 95% CI: 1.52, 3.00, p = 0.00), whereas the effects were not statistically significant for venous leg ulcers (VLUs) (OR 1.32, 95% CI: 0.97, 1.78, p = 0.07). Silver dressings also reduced ulcer area by 27.44 cm2 (95% CI: 6.96–47.92) and recurrence rate by 45% (95% CI: 0.11–1.79). Subgroup analysis revealed that the Asia-Pacific region and treatments of 6 weeks or less exhibited higher healing rates than their respective counterparts, although there was no statistical significance. Conclusions: This study indicated a statistically significant benefit of the treatment for DFU. However, the effect on VLU, though positive, did not reach statistical significance. This distinction should be considered in clinical application and further research.