bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of different generalized uncertainty principles on Jeans mass modification
Authors: Yang Ye-xing , Long Zheng-wen 
Published: August 2023

Abstract
Jeans mass is regarded as a crucial factor in the study of nebula collapse. Astronomical data shows that Jeans mass is larger in theory than it is in observation. Someone mentioned that Jeans mass can be modified by using the generalized uncertainty principle (GUP). However, different physical backgrounds lead to different forms of GUP expression. In order to make the theoretical values of Jeans mass and its observed values match better, we use three distinct types of GUPs to correct Jeans mass in this paper. We find that the corrected Jeans masses are smaller than the uncorrected ones, where the Pedram corrected Jeans mass is the minimum and is close to the observed value. In addition, we consider the impact of temperature T and the GUP parameters (η, β and γ) for the corrected Jeans mass.
Accuracy Improvement Based on Classic Neural Network: Voting, Restarting and Quantization
Authors: Zhu Yunxiang , Zhao Yanpeng , Liu Guanghong , Zeng Lifan 
Published: August 2023

Abstract
Since AlphaGo beat the world Go champion in 2016, which attracted wide attention, the neural network has become more and more popular in recent years, and people’s research on it has gradually improved and been applied in different fields. Today, artificial intelligence and machine learning have become an essential part of modern society and intelligent systems. We do image recognition, speech recognition, and visual learning, closely related to machine learning. However, in machine learning, unsatisfactory training results or even training failure is always encountered. Therefore, in machine learning, it is imperative to improve the accuracy of neural network training results. In this paper, speech recognition, image processing, MNIST, and other classical neural network models will be used to set the training parameters of the neural network better and improve the accuracy of training through voting, quantization, restart, and other methods. The part of research is aiming to find the relationship between restart numbers on the training process and the total extent of learning improvement. At the same time, several algorithms on utilizing these restart numbers are to be compared and selected. Finally, the conclusion is drawn that the more restart made in the training process with a convolutional neural network, the less profit on accuracy improvement we gain from restarting the process.
Simulation Study on Digital Control Model of Fiber Optic Gyro SLD Light Source
Authors: Zhao Lingxuan 
Published: August 2023

Abstract
Fiber Optic Gyroscope is an angular velocity sensitive device based on Sagnac effect. Super Luminescent Diode (SLD) is a widely used light source in fiber optic gyroscope, and its characteristics will directly affect the overall performance of gyroscope. In this paper, the working mechanism of SLD light source is studied, and the photoelectric characteristics of SLD light source and the influence of light source temperature and driving current on center wavelength are analyzed. According to the relationship between the central wavelength of the light source and the driving current and die temperature, a three-dimensional model of the central wavelength of the light source is obtained. In order to control the driving current and die temperature quickly and stably, the fuzzy PID control scheme of die temperature and incremental PID control scheme of driving current are studied and designed. Based on the three-dimensional model of the center wavelength of light source, the center wavelength of SLD light source is controlled stably and quickly.
Fault Diagnosis of High-Speed Railway Turnout System Based on Improved Group Decision-Making
Authors: Haoran Li 
Published: August 2023

Abstract
Turnout system is very important in the safety of railway system. However, the traditional maintenance method of turnouts is mainly based on human experience and basic intelligent model. Aiming at the problems existing in the current intelligent fault diagnosis method of turnout equipment, this paper uses the improved group decision-making method to dynamically and iteratively adjust and optimize the authority of each decision-making expert in the group decision-making method, so as to improve the prediction ability of the existing model, reduce the number of cases of prediction errors and further improve the safety of the turnout system.
Gated Multi-task Learning for QoS-aware Service Recommendation
Authors: Luo Haisen 
Published: August 2023

Abstract
In the ever-increasing diversity of cloud environment, the recommendation of web service-related applications based on service quality is a basic tasks that both service providers and web developers are very interested in. A key challenge is how to get personalized and accurate QoS values of web services from the multi-source information of users/services. In addition, complex contexts and multi-tasking attributes also make the collaborative services quality prediction difficult to meet. Inspired by the progress of multi-task deep learning, this paper proposes a new framework of service quality prediction called multi-task gated expert network (MGEN), which is based on gating mechanism and multi-task learning. The MGEN consists of three parts: expert-based context-source feature extraction, gated interaction learning and information fusion. First, multiple expert networks are used to capture the latent representation in raw data of web services, including both implicit features in the QoS matrix and explicit features of service descriptions. In the process, several independent experts extract features from different dimensions. Second, output embedding of different expert networks is transmitted to each task layer through different weights, learning both global and local high-order feature interactions. Third, the interactive information of different tasks is fused by embedding splicing, and then fed back to the prediction module for final service recommendation. Results of the comparative experiment demonstrate that the proposed MGEN significantly outperforms the state-of-the-art models of service recommendation, where both the implicit and explicit information of various multi-source features and interaction sequences are effectively extracted. In addition, the gating mechanism performs well in the QoS prediction by independently modeling each task.
Bipartite and tripartite continuous variable entanglement in a circular array of coupled optical waveguides
Authors: Anuradha Tonipe , Rai Amit 
Published: August 2023

Abstract
Integrated optical systems have evolved into suitable platforms in the field of photonic quantum technologies. New technologies open up new possibilities for multimode quantum operations. Here we study how circularly coupled waveguide arrays generate bipartite and tripartite continuous-variable (CV) entanglement. We focus on the single-mode squeezed state as input to the circular array of the waveguide system. Our findings suggest that the circularly coupled arrays can be used to generate entangled sources in CV quantum technologies. So the generation of entanglement makes the circular arrays more critical for further investigation and in the applications of photonic CV quantum-information processing.
The power of empathy and positive emotions in enhancing the communication of environmental issues: a case study of ‘wandering elephant in Yunnan’ on twitter
Authors: Xue Ke , Li Sichen , Wen Anna Maria 
Published: August 2023

Abstract
Media narratives in environmental communication often broadcast scientific and complex information from the perspective of professional experts, and while focusing on emotions, fear and worry are being among the most frequently studied. However, the effectiveness of these strategies in raising public awareness has been questioned. Animals are recognized as one of the critical indicators of the ecological environment, therefore defining the most effective strategies that will draw public’s attention to wildlife conservation is an important issue. Actions aimed at increasing the public’s interest and dissemination of related content are the key elements of environmental communication. Based on that, we focus our empirical research on the event known as the Wild Elephant Northward Migration in Yunnan, which gained global popularity in 2021. Choosing Twitter to collect our data, we used text mining methods to investigate 1230 tweets, exploring the mechanism of empathetic narrative in wildlife protection. The study has found that the themes such as warm feelings, diverse subjects, and scientific presentation can help to attract attention, enhance participation, and stimulate sympathetic responses. However, it is also necessary to pay attention to the lack of public participation in environmental issues, being a consequence of audience’s preference for entertaining content, among others. Instead of focusing on negative emotions or scientific language, we aim to provide a new perspective on environmental communication narratives in the era of social media by emphasizing the role of warm emotions in triggering audience’s interaction.
Peptide epimerase-dehydratase complex responsible for biosynthesis of the linaridin class ribosomal peptides
Authors: Xiao Wanlu , Tsunoda Takeshi , Maruyama Chitose , Hamano Yoshimitsu , Ogasawara Yasushi , Dairi Tohru 
Published: August 2023

ABSTRACT
Grisemycin, salinipeptin, and cypemycin belong to the linaridin class of ribosomally synthesized and posttranslationally modified peptides that contain multiple dehydrobutyrine and D-amino acid residues. The biosynthetic gene clusters of these linaridins lack obvious candidate genes for the dehydratase and epimerase required to introduce dehydrobutyrine and D-amino acid residues, respectively. However, we previously demonstrated that the grisemycin (grm) cluster contained cryptic dehydratase and epimerase genes by heterologous expression of this biosynthetic gene cluster in Streptomyces lividans and proposed that two genes (grmH and grmL) with unknown functions catalyze dehydration and epimerization reactions. In this study, we confirmed that both GrmH and GrmL, which were shown to constitute a protein complex by a co-purification experiment, were required to catalyze the dehydration, epimerization, and proteolytic cleavage of a precursor peptide GrmA by in vivo experiments. Furthermore, we demonstrated that GrmH/GrmL complex accepted salinipeptin and cypemycin precursor peptides, which possess three additional amino acids.
Toward computing attributions for dimensionality reduction techniques
Authors: Scicluna Matthew , Grenier Jean-Christophe , Poujol Raphaël , Lemieux Sébastien , Hussin Julie G 
Published: August 2023

AbstractSummaryWe describe the problem of computing local feature attributions for dimensionality reduction methods. We use one such method that is well established within the context of supervised classification—using the gradients of target outputs with respect to the inputs—on the popular dimensionality reduction technique t-SNE, widely used in analyses of biological data. We provide an efficient implementation for the gradient computation for this dimensionality reduction technique. We show that our explanations identify significant features using novel validation methodology; using synthetic datasets and the popular MNIST benchmark dataset. We then demonstrate the practical utility of our algorithm by showing that it can produce explanations that agree with domain knowledge on a SARS-CoV-2 sequence dataset. Throughout, we provide a road map so that similar explanation methods could be applied to other dimensionality reduction techniques to rigorously analyze biological datasets.Availability and implementationWe have created a Python package that can be installed using the following command: pip install interpretable_tsne. All code used can be found at github.com/MattScicluna/interpretable_tsne.
Practitioner Vulnerability in Children and Families Social Work—Identity, Impact and Implications
Authors: Dore Ian , Teverson Paul , Wilkins Sarah 
Published: August 2023

Abstract
Based on in-depth qualitative interviews, this article explores the experiences of vulnerability for nineteen children and families Social Workers in England and discusses how this emotional state manifests and impacts upon practice. As this study shows, workers frequently harbour personal vulnerabilities that can motivate them in their work and act as an enabler within relationships with service users; these same feelings can also provoke a sense of threat and discomfort, resulting in self-doubt and reduced confidence. Whether carried from the past or housed in the present, feelings of vulnerability often rise to the surface when provoked by routine encounters with clients and the knotty issues that surround them; what is valued by Social Workers encountering such feelings is a sense of validation and recognition. In taking an explicit focus on practitioner vulnerability, this article adds an original contribution to the literature concerning the emotional world of Social Workers. It reveals vulnerability to be an important component of identity and a driver for practice, which needs to be given greater consideration in social work training and employment, arguing that enhanced attention to worker histories, emotional challenges and self-care are not only necessary but also essential for practitioner and client well-being.
Reversible Data Hiding With Pattern Adaptive Prediction
Authors: Yuan Junying , Zheng Huicheng , Ni Jiangqun 
Published: August 2023

Abstract
In the area of reversible data hiding (RDH), one of the most popular techniques is prediction-error expansion (PEE), which hides data in the prediction errors with well-preserved image fidelity. The key to a successful PEE-based RDH implementation usually lies in prediction algorithms with high accuracy. Existing PEE-based RDH works often employ one single prediction algorithm, which is usually globally optimized, but with less consideration of the pixel distribution characteristics within local neighborhoods. In this manuscript, the technique of pattern adaptive prediction is proposed for pixel estimation according to the type of local binary pattern (LBP), which is obtained from the pixel’s eight neighborhood. Theoretically speaking, pattern-based predictors can be designed for each and every LBP patterns to create multiple prediction-error histograms (PEHs). However, the process of performance optimization with multiple PEHs requires extremely heavy computing power. To speed up the optimization process, LBP patterns are classified into various groups based on the degree of histogram concentration. Experiments demonstrate that the prediction accuracy is obviously improved and the image fidelity is well preserved.
Development of a real-time PCR assay for detection of hemp russet mite (Aculops cannabicola)
Authors: Carter Olivia , MacWilliams Jacob , Nachappa Punya 
Published: August 2023

Abstract
Of the many arthropod species affecting hemp (Cannabis sativa L.) cultivation in the United States, one species of particular importance is the hemp russet mite (Aculops cannabicola, HRM). Hemp russet mite is a microscopic arthropod which feeds on all parts of hemp plants. Due to its minute size, HRM can proliferate undetected for a long time, complicating management efforts and causing serious economic losses. DNA sequencing and PCR assays can facilitate accurate identification and early detection of HRM in infested-plants. Therefore, a real-time SYBR Green based species-specific PCR assay (quantitative PCR, qPCR) was developed for the identification of HRM DNA by amplification of a 104 bp Internal Transcribed Spacer 1 (ITS1) sequence. The detection limit was estimated to be approximately 48 copies of the HRM marker gene sequence. The real-time-PCR assay is rapid, detects all life stages of mite under 2 hours. A 10-fold serial dilution of the plasmid DNA containing the ITS1 insert were used as standards in the real-time PCR assay. The quantification cycle (Cq) value of the assay showed a strong linear relationship with HRM DNA with R2 of 0.96. The assay was tested against several commonly found hemp pests including two-spotted spider mite and western flower thrips to determine specificity of the assay and to show that no non-target species DNA was amplified. The outcomes of this research will have important applications for agricultural biosecurity through accurate identification of HRM, early detection and timely deployment of management tactics to manage and prevent pest outbreaks.
Effect of dose and trap type on captures of striped and spotted cucumber beetles using synthetic vittatalactone
Authors: Pasteur Kayla , Haber Ariela I , Weber Donald C 
Published: August 2023

Abstract
Striped cucumber beetle, Acalymma vittatum (F.) and spotted cucumber beetle (Diabrotica undecimpunctata howardi Barber) are key pests of cucurbits. Pesticide applications directed against cucumber beetles can jeopardize bee pollination which is essential to crop yield. Alternative selective tactics, including behavioral control for managing cucumber beetles, are needed in order to improve pest management. The striped cucumber beetle pheromone vittatalactone is attractive to both male and female adults, and also to spotted cucumber beetles. We tested the response of both species to different doses of synthetic vittatalactone, and to different trap types, in field trapping experiments in Maryland. Both species showed strong dose–response in May–June and October 2020. Among 12 trap types tested during a 2-wk experiment in June, using 1 mg mixed vittatalactone, sticky panel traps caught the most of each species, with vittatalactone-baited traps exceeding unbaited traps by &gt;8 fold for striped and &gt;2 fold for spotted, and with significant response to yellow versus clear color absent in striped, but strong in spotted. Among nonsticky traps, those with yellow or yellow-green exceeded those without these colors for both beetle species, and a ground-placed boll weevil trap captured the most striped cucumber beetles. Bucket-style traps caught excessive nontarget bumblebees (Bombus spp.) if yellow, and a few beetles, if all green. Results allow field monitoring with a choice of sticky or nonsticky traps and suggested lure loading of 1 mg mixed vittatalactone, containing ~90 μg active isomer. These findings contribute to the development of multispecies behavioral monitoring and control as a component of cucurbit IPM.
Dynamics of unipolar J-ST elevation coupled to bipolar delayed potentials on the epicardium in Brugada syndrome: a case report
Authors: Kataoka Naoya , Nagase Satoshi , Imamura Teruhiko , Kinugawa Koichiro 
Published: August 2023

Abstract

Background
The area of abnormal bipolar potentials in the right ventricular epicardium is recognized as an arrhythmogenic substrate in patients with Brugada syndrome (BrS); however, the correlation between local potentials and Brugada-type surface electrocardiograms (ECGs) remains unclear.

Case summary
A 49-year-old man with BrS who was hospitalized for refractory ventricular fibrillation underwent an electrocardiographic study with unipolar electrodes with the same bandwidth as surface ECGs. The right ventricular outflow tract epicardium showed abnormal bipolar potentials composed of split sharp and delayed dull components with coved-type J-ST elevation in the unipolar electrodes. The additional stimuli from the atrium gradually decreased the number of unipolar electrodes showing coved-type J-ST elevation along with a shortening of the local bipolar activation time. The pilsicainide provocation test induced a change in unipolar morphology from coved type to convex type and an intermittent local block of the divided and sharp components in bipolar electrodes. Of note, the unipolar J-ST elevation was not changed along with the localized conduction block in bipolar leads.

Discussion
The unipolar electrode waveforms during sinus rhythm change together with bipolar electrodes, consisting of sharp and blunt components in BrS. However, the convex-type J-ST elevation in unipolar leads persisted irrespective of the local conduction block in bipolar leads after pilsicainide provocation. These findings suggest the complexity of BrS mechanisms.
Is the association between financial scarcity and health behaviours moderated by cultural capital? The GLOBE study
Authors: Oosterwegel Sigrid L , Boderie Nienke W , van Lenthe Frank J 
Published: August 2023

Abstract

Background
Experiencing financial scarcity taxes cognitive bandwidth. This leaves less capacity to withhold temptations and makes relying on easiest default options more likely. Whether this default option is (un)healthy may depend on the amount of cultural capital acquired during life course. This study examined whether the association between financial scarcity and health behaviours is moderated by cultural capital.

Methods
Self-reported data were used from Dutch adults of the 2014-survey of the GLOBE study (N = 2466). Using linear regression analysis, financial strain (no, some, great) and cultural capital (institutionalized, objectivized, incorporated) were related to body mass index (BMI), alcohol intake, sports participation, cycling and walking, fruit intake and vegetable consumption. The interaction between financial strain and cultural capital was used to assess moderation.

Results
Experiencing some financial strain was associated with a higher BMI (0.7 kg/m2) and less sport participation (−31.8 min/week). Great financial strain was associated with less sport participation (−41.4 min/week). Being in the lowest tertile of cultural capital was associated with a higher BMI (1.3 kg/m2), drinking less alcohol (−10.0 units/week), less sport participation (−31.5 min/week) and consuming less fruit (−2.9 pieces/week). Cultural capital had no significant moderating effect on the relationship between financial strain and these health behaviours.

Conclusion
Financial strain and cultural capital seem associated with different health behaviours. Cultural capital had no moderating effect on the relationship between financial strain and different health behaviours. While financial strain and cultural capital could both be entry points for interventions to improve health behaviour, underlying mechanisms require further attention.