bluuebunny/crossref_metadata_2025_split huggingface dataset:
Drawing a materials map with an autoencoder for lithium ionic conductors
Authors: Yamaguchi Yudai , Atsumi Taruto , Kanamori Kenta , Tanibata Naoto , Takeda Hayami , Nakayama Masanobu , Karasuyama Masayuki , Takeuchi Ichiro 
Published: October 2023

AbstractEfforts to optimize known materials and enhance their performance are ongoing, driven by the advancements resulting from the discovery of novel functional materials. Traditionally, the search for and optimization of functional materials has relied on the experience and intuition of specialized researchers. However, materials informatics (MI), which integrates materials data and machine learning, has frequently been used to realize systematic and efficient materials exploration without depending on manual tasks. Nonetheless, the discovery of new materials using MI remains challenging. In this study, we propose a method for the discovery of materials outside the scope of existing databases by combining MI with the experience and intuition of researchers. Specifically, we designed a two-dimensional map that plots known materials data based on their composition and structure, facilitating researchers’ intuitive search for new materials. The materials map was implemented using an autoencoder-based neural network. We focused on the conductivity of 708 lithium oxide materials and considered the correlation with migration energy (ME), an index of lithium-ion conductivity. The distribution of existing data reflected in the materials map can contribute to the development of new lithium-ion conductive materials by enhancing the experience and intuition of material researchers.
A Benford’s law-based framework to determine the threshold of occurrence sites for species distribution modelling from ecological monitoring databases
Authors: Shim Taeyong , Kim Zhonghyun , Jung Jinho 
Published: October 2023

AbstractThe use of data-based species distribution models (SDMs) has increased significantly in recent years. However, studies of determining the minimum requirements of occurrence sites from ecological monitoring datasets used in species distribution modelling remain insufficient. Therefore, this study proposed a framework to determine the threshold of minimum occurrence sites for SDMs by assessing compliance with Benford’s law. The compliance test verified that the national-scale freshwater fish monitoring dataset was natural and reliable. Results derived from true skill statistics (TSS) determined the minimum number of occurrence sites for reliable species distribution modelling was 20 with a TSS value of 0.793 and an overall accuracy of 0.804. The Benford compliance test has shown to be a useful tool for swift and efficient evaluation of the reliability of species occurrence datasets, or the determination of the threshold of occurrence sites before species distribution modelling. Further studies regarding the evaluation of this method’s transferability to other species and validation using SDM performance are required. Overall, the framework proposed in this study demonstrates that Benford compliance test applied to species monitoring datasets can be used to derive a universal and model-independent minimum occurrence threshold for SDMs.
Litter decomposition and nutrient release are faster under secondary forests than under Chinese fir plantations with forest development
Authors: Li Shuaijun , Xu Zijun , Yu Zaipeng , Fu Yanrong , Su Xiangping , Zou Bingzhang , Wang Sirong , Huang Zhiqun , Wan Xiaohua 
Published: October 2023

AbstractIn terrestrial ecosystems, leaf litter is the main source of nutrients returning to the soil. Understanding how litter decomposition responds to stand age is critical for improving predictions of the effects of forest age structure on nutrient availability and cycling in ecosystems. However, the changes in this critical process with stand age remain poorly understood due to the complexity and diversity of litter decomposition patterns and drivers among different stand ages. In this study, we examined the effects of stand age on litter decomposition with two well-replicated age sequences of naturally occurring secondary forests and Chinese fir (Cunninghamia lanceolata) plantations in southern China. Our results showed that the litter decomposition rates in the secondary forests were significantly higher than those in the Chinese fir plantations of the same age, except for 40-year-old forests. The litter decomposition rate of the Chinese fir initially increased and then decreased with stand age, while that of secondary forests gradually decreased. The results of a structural equation model indicated that stand age, litter quality and microbial community were the primary factors driving nutrient litter loss. Overall, these findings are helpful for understanding the effects of stand age on the litter decomposition process and nutrient cycling in plantation and secondary forest ecosystems.
Functional reorganization of intranetwork and internetwork connectivity in patients with Ménière’s disease
Authors: Li Jing , Leng Yangming , Ma Hui , Yang Fan , Liu Bo , Fan Wenliang 
Published: October 2023

AbstractMénière’s disease (MD) is associated with functional reorganization not only in the auditory or sensory cortex but also in other control and cognitive areas. In this study, we examined intranetwork and internetwork connectivity differences between 55 MD patients and 70 healthy controls (HC) in 9 well-defined resting-state networks. Functional connectivity degree was lower in MD compared to HC in 19 brain areas involved in the somatomotor, auditory, ventral attention, default mode, limbic, and deep gray matter networks. In addition, we observed lower intranetwork connectivity in the auditory, ventral attention, and limbic networks, as well as lower internetwork connectivity between the somatomotor and limbic networks, and between the auditory and somatomotor, deep gray matter, and ventral attention networks, and between the deep gray matter and default mode network. Furthermore, we identified 81 pairs of brain areas with significant differences in functional connectivity between MD patients and HC at the edge level. Notably, the left amygdala’s functional connectivity degree was positively correlated with MD’s disease stage, and the ventral attention network’s intranetwork connectivity was positively correlated with the healthy side vestibular ratio. Our findings suggest that these functional network reorganization alterations may serve as potential biomarkers for predicting clinical progression, evaluating disease severity, and gaining a better understanding of MD’s pathophysiology. Large-scale network studies using neuroimaging techniques can provide additional insights into the underlying mechanisms of MD.
Associations among the workplace violence, burnout, depressive symptoms, suicidality, and turnover intention in training physicians: a network analysis of nationwide survey
Authors: Yun Je-Yeon , Myung Sun Jung , Kim Kyung Sik 
Published: October 2023

AbstractDepression and anxiety are the most common mental disorders among physicians, who have a greater risk of suicide than those in other professional occupations. Relationships among a demanding workload, workplace violence, burnout, and intention to turnover have also been reported. The current study examined the principal components and propagating patterns of mental health and working environment interactions in training physicians. A total of 1981 training physicians completed online self-report questionnaires during September–October (midpoint of the training year) 2020. Regularized partial correlations in a mixed graphical model (MGM) and joint probability distributions (directed acyclic graph; DAG) were estimated for four subtypes of workplace violence (verbal abuse/physical violence perpetrated by clients/hospital staff), three burnout subdomains (Maslach Burnout Inventory), thoughts about quitting, and nine depressive symptoms, including suicidality, comprising the DSM-5 diagnostic criteria for major depressive disorder (assessed using the Patient Health Questionnaire-9). Thoughts of death/self-harm showed directional dependencies on the joint probability distributions of psychomotor agitation/retardation, concentration difficulty, self-reproach, and sadness in the DAG. In the MGM, a partial correlation with psychomotor agitation/retardation (r = 0.196) accounted for 56.5% of the variance in thoughts of death/self-harm. Partial correlations with concentration difficulties (r = 0.294), self-reproach (r = 0.257), changes in appetite (r = 0.184), and worker-on-worker physical violence (r = 0.240) in the MGM accounted for 54.4% of the variance in psychomotor agitation/retardation. Thoughts about quitting were partially correlated with and dependent upon the joint probability distributions of emotional exhaustion (r = 0.222), fatigue (r = 0.142), anhedonia (r = 0.178), and sadness (r = 0.237). In contrast, worker-on-worker (r = 0.417) and client-on-physician (r = 0.167) verbal abuse had regularized partial correlations with directional dependencies on thoughts about quitting. Organization-level interventions aiming to reduce the worker-on-worker violence and individual-level approaches of clinical screening program and psychiatric counseling clinic are required. Follow-up studies to verify the effectiveness of these interventions for training physicians are needed.
An HIV-1 broadly neutralizing antibody overcomes structural and dynamic variation through highly focused epitope targeting
Authors: Hodge Edgar A. , Chatterjee Ananya , Chen Chengbo , Naika Gajendra S. , Laohajaratsang Mint , Mangala Prasad Vidya , Lee Kelly K. 
Published: October 2023

AbstractThe existence of broadly cross-reactive antibodies that can neutralize diverse HIV-1 isolates (bnAbs) has been appreciated for more than a decade. Many high-resolution structures of bnAbs, typically with one or two well-characterized HIV-1 Env glycoprotein trimers, have been reported. However, an understanding of how such antibodies grapple with variability in their antigenic targets across diverse viral isolates has remained elusive. To achieve such an understanding requires first characterizing the extent of structural and antigenic variation embodied in Env, and then identifying how a bnAb overcomes that variation at a structural level. Here, using hydrogen/deuterium-exchange mass spectrometry (HDX-MS) and quantitative measurements of antibody binding kinetics, we show that variation in structural ordering in the V1/V2 apex of Env across a globally representative panel of HIV-1 isolates has a marked effect on antibody association rates and affinities. We also report cryo-EM reconstructions of the apex-targeting PGT145 bnAb bound to two divergent Env that exhibit different degrees of structural dynamics throughout the trimer structures. Parallel HDX-MS experiments demonstrate that PGT145 bnAb has an exquisitely focused footprint at the trimer apex where binding did not yield allosteric changes throughout the rest of the structure. These results demonstrate that structural dynamics are a cryptic determinant of antigenicity, and mature antibodies that have achieved breadth and potency in some cases are able to achieve their broad cross-reactivity by “threading the needle” and binding in a highly focused fashion, thus evading and overcoming the variable properties found in Env from divergent isolates.
Polyacrylonitrile-containing amphiphilic block copolymers: self-assembly and porous membrane formation
Authors: Gemmer Lea , Niebuur Bart-Jan , Dietz Christian , Rauber Daniel , Plank Martina , Frieß Florian V. , Presser Volker , Stark Robert W. , Kraus Tobias , Gallei Markus 
Published: October 2023

Porous nanostructures were derived after self-assembly of amphiphilic poly(acrylonitrile)-containing block copolymers. The introduction of acrylonitrile moieties within the membrane matrix was shown to improve the membranes’ mechanical properties.
Improved microstrip ring resonator for dielectric response tests and moisture evaluation of oil-impregnated pressboard
Authors: Zhang Daning , Xu Lulin , Wu Ming , Jiang Jiongting , Shao Xianjun , Mu Haibao , Zhang Guanjun 
Published: October 2023

Moisture ingress is one of the major causes of aging in oil–paper insulated transformers; the measurement of the moisture content of the insulating material is thus necessary during transformer disassembly for maintenance. One critical engineering consideration is the need for rapid and nondestructive moisture content measurement of the oil–paper insulation to minimize the power-outage time. Herein, a rapid characterization method for the moisture content of oil–paper insulation based on the dielectric response obtained using a microstrip petal-like ring resonator (MPRR) is proposed. The geometric parameters of the resonator and dielectric response model were established based on simulations and theoretical analysis. The geometry of the resonant ring was optimized by modifying the originally circular ring structure of the microstrip ring resonator (MRR) into a petal-like ring structure; this increases the number of resonant peaks from five to seven in the frequency range of 0.5–6.0 GHz for the same electrode area. The effects of the coupling mode, coupling gap, and microstrip ring size on the resonance characteristics of the MPRR were simulated and analyzed. Compared with the MRR, the MPRR has the advantages of a reduced fundamental resonant frequency and a smaller resonant-ring area that improves radiation efficiency. The impact of moisture content on the dielectric response of the pressboard obtained using the MPRR was studied. A moisture-calculation equation based on the measured dielectric response for a selected type of pressboard was established by the Lasso regression. The results of verification experiments show that the error of the proposed method is sufficient for practical applications.
Magneto-optical measurement of magnetic field and electrical current on a short pulse high energy pulsed power accelerator
Authors: Owens Israel , Coffey Sean , Ulmen Ben , Harrison Richard K. , Trujillo Alex , Rhoades Elaine , McCutcheon Brandon , Grabowski Chris 
Published: October 2023

We describe a direct magneto-optical approach to measuring the magnetic field driven by a narrow pulse width (&lt;10 ns), 20 kA electrical current flow in the transmission line of a high energy pulsed power accelerator. The magnetic field and electrical current are among the most important operating parameters in a pulsed power accelerator and are critical to understanding the properties of the radiation output. However, accurately measuring these fields and electrical currents using conventional pulsed power diagnostics is difficult due to the strength of ionizing radiation and electromagnetic interference. Our approach uses a fiber coupled laser beam with a rare earth element sensing crystal sensor that is highly resistant to electromagnetic interference and does not require external calibration. Here, we focus on device theory, operating parameters, results from an experiment on a high energy pulsed power accelerator, and comparison to a conventional electrical current shunt sensor.
Visual art, discourse, and Cognitive Linguistics
Authors: Boubakri Awatef 
Published: October 2023

Abstract
A live-show painting is a unique art experience and more unique is the live-show painting that takes place amid an interaction between the artist and his audience. The discourse taking place consists in an artist who is receiving propositions from his audience while painting. The present study is a cognitive discourse analysis of the interaction artist/audience in line with a semiotic analysis of the content of the resulting painting. Since the resulting painting blends the mental representations of the artist with those of the audience, this live-show painting is framed within a complex conceptual integration network that is a triple-scope rather than a double-scope network. The framework is Gill Fauconnier & Mark Turner’s (2002) Conceptual Blending Theory that fits into the understanding of the interaction artist/audience during the live-show painting. Thus the present study investigates a perceived interface between visual art, discourse, and Cognitive Linguistics.
When happiness can be luck
Authors: Lasnik Mateja , Chiang Wen-yu 
Published: October 2023

AbstractPrevious studies have mainly focused on orientational, structural and ontological metaphors of happiness, and have not distinguished between luck and happiness; the latter in many languages originates from the former. This research aims to bridge these gaps by examining event-structure andobject(possession) metaphors of 8000 hits for happiness and luck in the corpora of English, German, Greek, and Slovene. Our results suggest that luck is cross-linguistically perceived as non-pursuable and asan entity outside a personthrough numerousobject(possession) metaphors ofluck, or as a deity based on manystationary-egometaphors ofluck. In contrast, happiness is understood as pursuable (through frequentquestmetaphors ofhappiness) and asan entity within a person. This research proposes an embodied cognition model which includes orientational, psychological, and culture-specific embodiments to account for the cross-linguistic universalities and differences. Our study could contribute to overall human understanding of these two important concepts.
Application of kalman filter for radar target tracking.
Authors: Uwigize Patrick , Rao S. Koteswara , Divya G. Naga 
Published: October 2023

Abstract
In this paper, the principles of recursive tracking and prediction filters are used to track a single maneuvering aerial target and implemented using instantaneous noisy measurements. Kalman filter is used to estimate (predict) the system state (bearing, range, target course, and speed) as they cannot be measured directly from measurements. Furthermore, after mathematical formulation of the equations for the Kalman filter fitting the application, it was then applied and evaluated. Monte Carlo Simulation technique was applied to quantify the results and the simulation results are obtained using a python program.
Automated Bone Fracture Detection Using Convolutional Neural Network
Authors: Thaiyalnayaki K , Kavyaa L , Sugumar Joshua 
Published: October 2023

Abstract
Bones are an important part of the human body. Humans are prone to bone fractures, which can occur as a result of tremendous pressure being applied to the bone, or as a result of a simple accident. As a result, in the medical field, an accurate diagnosis of a bone fracture is crucial. In this work, bone fractures are examined using X-ray/CT scans. The purpose of this research is to develop an image processing based system that can classify bone fractures rapidly and accurately using data from x-ray and CT images. In many diagnostic and therapeutic applications, automatic fault detection in MRI and CT images is critical. Tumor segmentation and classification are difficult due to the large amount of data in MR images and the fuzzy boundaries. This project proposed an automatic bone fracture detection system that improves accuracy and yield while cutting down on diagnosis time. The aim is to classify into two categories: normal, fractured. The amount of data in MR and CT pictures is too great for human interpretation and analysis. Bone fracture segmentation in Magnetic Resonance Imaging (MRI) has appeared as an emerging subject study in the realm of medical imaging systems in recent years. The potential to accurately recognise the size and location of a bone fracture is critical in the diagnosis of a fracture. Pre-processing of MR images, feature extraction, and classification are the four stages of the diagnosis procedure. The features are extracted using wavelet transformation after the image has been preprocessed (DWT). Automated fracture identification is essential in a computer-aided telemedicine system. Human arbitrary bones frequently fracture as a consequence of accidental traumas like slipping. Computer-aided diagnosis (CAD) relieves doctors’ workload while also detecting fractures. A new classification network sensitive to fracture lines is described, called Crack-Sensitive Convolutional Neural Network (CrackNet). Faster Region with Convolutional Neural Network (Faster R-CNN) is used to identify 20 various types of bone areas in X-ray pictures, and CrackNet is used to assess incase each bone area is broken. The classification accuracy for 100 training and test sets is 99.5%.
Energy Harvesting Using Piezoelectric Materials
Authors: Lakshmi Prasanna J , Santhosh Chella , Ravi Kuamr M , Ram Kumar I. 
Published: October 2023

Abstract
The need for new energy sources has been growing as a result of the rise in energy consumption and the depletion of non-renewable energy sources. One such method is the use of piezoelectric sensors, which can provide valuable electrical energy by converting mechanical stress into electric potential when pressure is applied to the sensors. The main challenges in designing the system are selection of the materials to be used, environmental that it is being used the type of pressure being applied are all the factors that play vital role in the result. Thus, all these factors should be taken into consideration while developing the system. A sophisticated simulator was used to create the system’s proportions and shape. The system’s chosen model is unimorph; to create potential, a force is applied to the surface. For the harvester to provide output electric potential, the pressure applied needs to be dynamic (i.e., continually changing). The rectified voltage can be used to power electronics, recharge batteries, or store energy in capacitors.
MCSO-MW: An Energy Optimized Data Switching Scheme for Multi-hop Wireless Sensor Networks
Authors: Alagarsamy Aravindhan , Mahilmaran Sundarakannan , Sureshkumar K , Vimala P 
Published: October 2023

Abstract
Due to continuous growth in the field of wireless sensor networks (WSN), it holds enormous growth in the multidisciplinary domains to provide a promising solution. Especially, on the internet of things (IoT) based edge device network, WSN plays a significant area in the field of data collection and physical entity monitoring. The effectiveness of the routing scheme in WSN can be directly measured through the amount of data transmission, network topology, intermediate hop counts, and data transmission distance. Similarly, an ideal energy model can improve the entire system’s performance and sustainability over successive data transmission. In this paper, a multi-objective chicken swarm optimization (CSO) algorithm is adopted to optimize energy consumption over WSN and improve the system performance through an effective routing approach. Furthermore, the proposed approach is adopted to partition the entire sensor network into multiple clusters and select the partitioned head among the sensor nodes. The competence of the proposed approach is validated against other existing approaches.
A self-organized shape formation in swarm robots
Authors: Rooban S , Bhargav M , Vamsi Reddy A , Sai Teja M , Probin Kumar V 
Published: October 2023

Abstract
This work proposes a functional method-based approach for shape formation. The core principle in swarm intelligence is that simple swarm behaviour can be established with assigning the rules. To keep all the agents in the given shape and to build the set of motion, desired locations must be assigned to govern the leader and the other followers. Given the restrictions of communication, collision avoidance, and computation challenges, the proposed work is ideal for swarm robots shape formation. The proposed dynamic shape formation uses the shortest path method and the obstacle avoidance algorithm. Natural swarm inspired method based on the Particle Swarm Optimization (PSO) model is used to form the robots in the assigned position. A set of simulation analysis demonstrate that the convergence and scalability of our proposed technique. The effectiveness of pattern formation is increased further by adjusting specific parameters in the PSO model with the aid of the MATLAB simulator.