bluuebunny/crossref_metadata_2025 huggingface dataset:
The effect of singing on pain and psychological well‐being in a patient population with pain‐related temporomandibular disorders
Authors: van Selms Maurits K. A. , Reda Bachar , Visscher Corine M. , Manfredini Daniele , Lobbezoo Frank
Published: May 2022

Background

Even though singing is regarded as potential jaw‐overuse behaviour, the actual evidence that underlines the idea of singing as a predisposing factor for pain‐related temporomandibular disorders (TMD) is very limited.

Objectives

The objectives were to examine if a dose–response relationship exists between the frequency of singing and TMD pain intensity, and if singing is associated with psychological well‐being.

Methods

The records of 1467 adult patients with functionally aggravated TMD pain were examined. Partial Spearman correlations were used to explore for associations between singing frequency and Characteristic Pain Intensity (CPI), somatic symptoms, depression and anxiety.

Results

The mean age of all participants was 42.8 years (±15.3), of which 79.2% were female. Contrary to the male–female ratio, no difference was found in the mean age between the response options with regard to the frequency of singing. Controlled for the influence of gender, the Spearman correlation between the frequency of singing and CPI appeared to be significant and indicated a negative association. In addition, somatic symptoms appeared to be positively associated with the frequency of singing. However, the observed correlation coefficients (ρ = −.073 and ρ = .067, respectively) imply that these are only very weak or negligible correlations. No significant associations with the frequency of singing were found for depression and anxiety.

Conclusions

The alleged negative influence of singing on TMD pain intensity was found to be virtually absent. Patients with TMD pain who sing frequently have almost equal scores on psychological well‐being compared to those who sing less.
Breakdowns and assemblages: Including machine‐actants in sociolinguistic ethnographies of blue‐collar work environments
Authors: Hovens Daan
Published: May 2022

A central concern in sociolinguistic ethnographies has been how people use language to make social distinctions. This article discusses the relevance of paying closer attention to the role of machines as actants in communication and social distinction‐making processes. It analyses audio and video‐recorded workplace interactions between humans and machines in a metal foundry in the Dutch‐German borderland. Specifically, it focuses on several cases of a breakdown of a production process, a frequently observed phenomenon in the foundry. The cases show that: (1) the production work entails many improvised human–human and human–machine interactions as opposed to Taylorised working practices; (2) the machine‐actants initiate and afford (re)negotiations of situated, hierarchical workplace relations through these interactions; and (3) the question whether these interactions should be considered ‘language‐centred’ or ‘language‐marginal’ partly depends on an ideological, conceptual distinction between what counts as ‘language’ and what not.
Sex is a major effect modifier between body composition and mortality in patients with cirrhosis assessed for liver transplantation
Authors: Benmassaoud Amine , Roccarina Davide , Arico Francesco Marcello , Cilla Marta , Donghia Rossella , Leandro Gioacchino , Iogna Prat Laura , Zuhair Mohamed , North Matthew , Kearney Orla , Ryan John , Tsochatzis Emmanuel A.
Published: May 2022

Background and Aims

Body composition predicts mortality in patients with cirrhosis. The impact of sex on this association is unknown. We investigated the impact of sex on this association in patients with cirrhosis assessed for liver transplantation.

Methods

This single‐centre retrospective cohort study included adults assessed for liver transplantation. Nutritional status was assessed using the Royal Free Hospital‐Global Assessment (RFH‐GA). Body composition at the third lumbar vertebrae was determined. SarcopeniaSMI was defined as Skeletal Muscle Index <50 cm2/m2 in males and <39 cm2/m2 in females. SarcopeniaPMI was defined as the sex‐specific 25th percentile of the Psoas Muscle Index. Patients were assessed for the occurrence of liver transplantation and death. Analyses were stratified by sex.

Results

The cohort comprised 628 patients, including 199 females and 429 males. Both groups were similar in terms of baseline liver disease severity by Model for End‐stage Liver Disease (MELD) (p = .98) and nutritional status (p = .24). SarcopeniaSMI was present in 41% of males compared to 27% of females (p < .001). In the male cohort, when adjusted for age and MELD, sarcopeniaPMI (aHR 1.74, 95% CI 1.08–2.80) and RFH‐GA (aHR 1.40, 95% CI 1.03–1.90) remained independent predictors of mortality. Adipose tissue had no impact on outcomes in males. In female patients, adipose tissue (TATI or VATI depending on the multivariable model) was independently associated with mortality, whereas sarcopenia and malnutrition were not.

Conclusions

This study demonstrates that male patients were susceptible to low muscle mass, whereas female patients were not. Future research in this patient population should minimize sex‐related bias and present data for both groups separately.
Using routine outcome measures as clinical process tools: Maximising the therapeutic yield in the IAPT programme when working remotely
Authors: Faija Cintia L. , Bee Penny , Lovell Karina , Lidbetter Nicky , Gellatly Judith , Ardern Kerry , Rushton Kelly , Brooks Helen , McMillan Dean , Armitage Christopher J. , Woodhouse Rebecca , Barkham Michael
Published: May 2022

Objectives

The objective of the study was to investigate the administration and use of routine outcome monitoring session by session in the context of improving guided‐self‐help interventions when delivered remotely at Step 2 care in the English Improving Access to Psychological Therapies (IAPT) services.

Design

Qualitative research using recordings of telephone‐treatment sessions.

Method

Participants (11 patients and 11 practitioners) were recruited from four nationally funded IAPT services and one‐third sector organisation commissioned to deliver Step 2 IAPT services, in England. Data collection took place prior to the COVID‐19 pandemic. Transcripts of telephone–treatment sessions were analysed using thematic analysis.

Results

Four themes were identified: (1) lack of consistency in the administration of outcome measures (e.g. inconsistent wording); (2) outcome measures administered as a stand‐alone inflexible task (e.g. mechanical administration); (3) outcome measures as impersonal numbers (e.g. summarising, categorising and comparing total scores); and (4) missed opportunities to use outcome measures therapeutically (e.g. lack of therapeutic use of item and total scores).

Conclusions

The administration of outcome measures needs to ensure validity and reliability. Therapeutic yield from session‐by‐session outcome measures could be enhanced by focusing on three main areas: (1) adopting a collaborative conversational approach, (2) maximising the use of total and items scores and (3) integrating outcome measures with in‐session treatment decisions. Shifting the perception of outcome measures as impersonal numbers to being process clinical tools ensures a personalised delivery of psychological interventions and has the potential to enhance engagement from practitioners and patients what may reduce drop‐out rates and improve clinical outcomes.
Atopic dermatitis leading to failure to thrive: A case report
Authors: Sarver Melissa M. , Quow Krystina , Prose Neil S.
Published: May 2022

We present an infant with severe atopic dermatitis, protein loss, and subsequent failure to thrive. With proper management, the patient's laboratory findings normalized, and he gained weight appropriately. In this report, we highlight the impact that severe atopic dermatitis may have growth and development and review the genetic conditions that can result in a similar clinical presentation.
A nonasymptotic analysis for re‐solving heuristic in online matching
Authors: Wang Hao , Yan Zhenzhen , Bei Xiaohui
Published: May 2022

We investigate an online edge‐weighted bipartite matching problem with general capacity constraints. In this problem, the resources are offline and nonreplenishable with different capacities. Demands arrive online and each requests a certain amount of resources. The goal is to maximize the reward generated by successful matches. We model the offline optimization problem as a deterministic linear program (LP) and present multiple randomized online algorithms based on the solution to the offline LP. We analyze the performance guarantee of each algorithm in terms of its competitive ratio (CR). Importantly, we introduce a re‐solving heuristic that periodically recomputes the offline LP and uses the updated offline solution to guide the online algorithm decisions. We find that the algorithm's CR can be significantly improved when re‐solving at carefully selected time steps. Finally, we investigate the value of the demand distribution in further improving the algorithm efficiency. We conduct extensive numerical studies to demonstrate the efficiency of the proposed algorithms. The effect of market conditions on the algorithm performance is also investigated.
Vascular endothelial growth factor (VEGF) expression in histological endometritis in the bitch: An immunohistochemical study
Authors: Praderio Romina Gisel , García Mitacek María Carla , Gilead Tomás , de la Sota Rodolfo Luzbel , Stornelli María Alejandra
Published: May 2022

This work aimed to study the immunolocalization of vascular endothelial growth factor (VEGF) and its FLT‐1 receptor (VEGFR1) in systemically healthy bitches with and without endometritis (END). Thirty‐one uterine samples from mixed‐breed, systemically healthy diestrous bitches, were included in the study. The bitches were divided into three groups after histological evaluation (normal endometrium [n = 8]; acute‐END [n = 8]; subacute‐END [n = 5]; chronic‐END [n = 10]). The immunohistochemical localization of VEGF and VEGFR1 were detected following standard procedures. Data were analysed using SPSS 26.0. Immunoreactions were detected in cells from the luminal epithelium (LE), epithelium of the superficial glands (ESG) and epithelium of the deep glands (EDG), and some cells localized in the stroma. Immunostaining of VEGF in acute‐END was higher than chronic‐END (p < .05) and was higher in LE than EDG (p .017). Marked‐area was higher in END than normal endometrium in VEGFR‐1 (p < .004). The current results provide new information on the END in systemically healthy females. Data reported here indicate that VEGF could be involucrated in the pathogenesis of END. Future studies could provide more information, support our results and elucidate the role of VEGF in the pathogenesis of END.
Grief, Continuing Bonds, and Unreciprocated Love
Authors: Millar Becky , Lopez‐Cantero Pilar
Published: May 2022

The widely accepted “continuing bonds” model of grief tells us that rather than bereavement necessitating the cessation of one's relationship with the deceased, very often the relationship continues instead in an adapted form. However, this framework appears to conflict with philosophical approaches that treat reciprocity or mutuality of some form as central to loving relationships. Seemingly the dead cannot be active participants, rendering it puzzling how we should understand claims about continued relationships with them. In this article, we resolve this tension by highlighting two fundamental aspects of paradigmatic loving relationships that can, and often do, continue in an adapted form following bereavement: love and mutual shaping of interests, choices, and self‐concepts. Attention to these continuing features of relationships helps to capture and clarify the phenomenological and behavioral features of continuing bonds. However, love and mutual shaping must also change in important ways following bereavement. Love becomes unreciprocated, and although the dead continue to shape our interests, choices, and self‐concepts, we predominantly shape their legacies and memories in return. These changes place important constraints upon the nature of our interpersonal connections with the dead.
Differences in team performance: Gender versus ability
Authors: Städter Silvio , Roider Andreas , Dowling Michael , Helm Roland
Published: May 2022

We empirically study the relationship between the gender composition and the performance of teams. In our setting, teams of students are incentivized to develop business plans as part of a compulsory course in a business administration program. At first sight, and in line with earlier empirical literature, the gender composition seems to matter. Our article differs in that we have access to a measure of task‐specific individual ability. When controlling for ability, the relation between gender and team performance vanishes, highlighting the importance of controlling for other characteristics in empirical and experimental studies of gender effects.

Objective

While various studies document a relationship between the gender composition and the performance of teams, we ask whether such findings may depend on a lack of controlling for other dimensions of heterogeneity among team members.

Methods

In our setting, teams of students are incentivized to develop business plans as part of a compulsory course in a business administration program. Our article differs from the literature in that we have access to a measure of task‐specific individual ability. Using ordinary least squares regressions, controlling for year and team size fixed effects, and conducting various robustness checks, we study the association between team performance and team characteristics.

Results

At first sight, and in line with earlier empirical literature, the gender composition seems to matter for team performance. However, when controlling for the individual abilities of team members, the relation between the gender composition and team performance vanishes.

Conclusion

Our study highlights the importance of controlling for other characteristics in empirical and experimental studies of gender effects, which in our context leads them to disappear.
Performance Evaluation of Support Vector Machine Kernels in Intrusion Detection System for Wireless Sensor Network
Authors: Hamzah Muhammad Amir , Othman Siti Hajar
Published: May 2022

Wireless sensor network is very popular in the industrial application due to its characteristics of infrastructure-less wireless network and self-configured for physical and environmental conditions monitoring. However, the dynamic environments of wireless network expose WSN to network vulnerabilities. Intrusion Detection System (IDS) has been used to mitigate the vulnerability issue of network. Researches towards the efficiency improvement of WSN-IDS has been extensively done because the rapid growth of technologies influence the growth of network attacks. Implementation Support Vector Machine (SVM) was found to be one of the optimum algorithms for the improvement of WSN-IDS. Yet, classification efficiency of SVM is based on the kernel function used because different kernel gives different SVM architecture. Linear classification of SVM has limitation to maximize the margin due to the dynamic environment of wireless network which consist of nonlinear data. Since maximizing the margin is the primary goal of SVM, it is crucial to implement the optimum kernel in the classification of nonlinear data. Each SVM model in this research use different kernels which are Linear, RBF, Polynomial and Sigmoid kernels. Further, NSL-KDD dataset was used for the experiment of this research. Performance of each kernel were evaluated based on the experimental result obtained and it was found that RBF kernel provides the best classification accuracy with the score of 91%. Finally, discussion based on the findings was made.
An Improved Deep Neural Network Algorithm for the Prediction of Limited Proteolysis in Native Protein
Authors: Azmi Nur Sabrina , Hashim Haslina , Lip Hong Lim , A Samah Azurah , Abdul Majid Hairudin , Ali Shah Zuraini , Azman Nuraina Syaza
Published: May 2022

Protease is a proteolytic enzyme that hydrolyzes the amino acid where the cleavage only occurs at specific sites of the amino acid substrate.  By discovering the nick site, the prediction on the function of proteases can be identified and enable humans to control the protein's hydrolysis by their corresponding protease. It is very contributed to controlling protein production especially viral protein. The experts may alter the production of viral protein by reducing the viral proteases to undergo proteolysis. With the rise of computational methods in this era, deep learning is becoming more famous and applied in every field of study, including the biological area. Conventional techniques such as mass spectrometry and two-dimensional gel electrophoresis are being replaced by computational methods due to time-consuming. Thus, this study improves the deep learning algorithm by proposing the Hybrid model of Random Forest + Deep Neural Network (Hybrid RF+DNN) to classify nick sites. The classification in this study is compared with the other machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM),  and Deep Neural Network (DNN). The proposed method is believed to enhance the classification results in identifying the positive and negative nick sites. The RF is a feature-selector that gathers the most important feature before entering the DNN classifier. This approach reduces the data dimensionality and speeds up the execution time of the training process. The performance of the models was measured by confusion matrix, specificity, sensitivity, etc. However, the proposed method is not the best performer among the mentioned classifiers from the result. The proposed method may become the best performer as the parameter tuning is done more precisely, even after the feature selection by the RF algorithm. Thus, the proposed method with the enhancement appears to be an alternative to the researcher discovering nick site.
A Review: Deep Learning for 3D Reconstruction of Human Motion Detection
Authors: Yang Junzi , Ismail Ajune Wanis
Published: May 2022

3D reconstruction of human motion is an important research topic in VR/AR content creation, virtual fitting, human-computer interaction and other fields. Deep learning theory has made important achievements in human motion detection, recognition, tracking and other aspects, and human motion detection and recognition is an important link in 3D reconstruction. In this paper, the deep learning algorithms in recent years, mainly used for human motion detection and recognition, are reviewed, and the existing methods are divided into three types: CNN-based, RNN-based and GNN-based. At the same time, the main stream data sets and frameworks adopted in the references are summarized. The content of this paper provides some references for the research of 3D reconstruction of human motion.
Forecasting Model of Air Pollution Index using Generalized Autoregressive Conditional Heteroskedasticity Family (GARCH)
Authors: Zamrus Nurul Asyikin , Mohd Rodzhan Mohd Hirzie , Mohamad Nurul Najihah
Published: May 2022

The Air Pollution Index (API) of Malaysia has increased consistently in recent decades, becoming a serious environment issue concern. In this paper, we analyzed daily integer value time series data for API in Sarawak from January to June in 2019 using generalized autoregressive conditional heteroskedasticity (GARCH) family for discrete case namely poisson integer value GARCH (INGARCH), negative binomial integer value GARCH (NBINGARCH) and integer value autoregressive conditional heteroskedasticity (INARCH) models. The parameters of the models will be estimated using quasi likelihood estimator (QLE) and we compare their Akaike information criterion (AIC) to determine the best model fitted the data. The results showed that   INGARCH (1,1) and INARCH (1,0) performed inconsistent results since the conventional methods of NBINGARCH (1,1) outperformed the performance of  INGARCH (1,1) and INARCH (1,0). However, consistet results were achieved as the NBINGARCH (1,1) gave the smallest forecasting error compared to INGARCH (1,1) and INARCH (1,0).  The findings are very important for controlling the API results in future and taking protection measure for conservation of the air.
General Health of Pregnant Sprague-Dawley Rats and Neonates’ Small Intestine Morphology Upon Maternal Bisphenol A Exposure: A Preliminary Study
Authors: Zulkifli Sarah , Mohd Nor Noor Shafina , Sheikh Abdul Kadir Siti Hamimah , Mohd Ranai Norashikin , Mohd Kornain Noor Kaslina , Rasdi Zatilfarihiah , Wan Mohamad Zain Wan Nor I'zzah , Abdul Rahman Amirah , Khan Jesmine , Jalaludin Muhammad Yazid
Published: May 2022

Associations between xenoestrogen bisphenol A (BPA) and multiple types of diseases, including metabolic syndrome, have been recorded in various studies. However, certain subsets of the human population are particularly more vulnerable to BPA repercussions, such as pregnant women, neonates, and children. This study was conducted to investigate the effects of BPA exposure during pregnancy on the general health of mothers and the histopathology of neonates’ small intestines. Eighteen Sprague-Dawley rats were divided into three groups: control, vehicle Tween-80, and 5mg/kg/day BPA after positive mating was confirmed. Physiological parameters consisted of body weight, waist circumference, water, and food intake, and blood pressures were measured at pregnancy day -1 or 2, 7, and 14 to see whether BPA exposure could exert obesogenic impacts on pregnant rats. Newborns were sacrificed to collect blood plasma for BPA analysis and intestinal samples for histopathological examination. Maternal BPA exposure did not affect the physiological parameters of pregnant rats. The number of pups delivered per litter and the sex ratio of BPA offsprings was not significantly different to those of control and vehicle groups (p>0.05). Likewise, the small intestine morphology of BPA neonates was comparable to those of controls and vehicles (preserved structure and absence of inflammatory cells infiltration). The nonsignificant difference in plasma BPA levels of control and BPA-exposed mothers and neonates may explain these findings. Future longitudinal studies which include the dose-dependent impacts of BPA on pregnant mothers’ health and neonates’ small intestine would be more beneficial.
Mechanics-Driven Anode Material Failure in Battery Safety and Capacity Deterioration Issues: A Review
Authors: Gao Xiang , Jia Yikai , Zhang Wen , Yuan Chunhao , Xu Jun
Published: May 2022

High-capacity anodes, such as Si, have attracted tremendous research interest over the last two decades because of the requirement for the high energy density of next-generation lithium-ion batteries (LIBs). The mechanical integrity and stability of such materials during cycling are critical because their volume considerably changes. The volume changes/deformation result in mechanical stresses, which lead to mechanical failures, including cracks, fragmentation, and debonding. These phenomena accelerate capacity fading during electrochemical cycling and thus limit the application of high-capacity anodes. Experimental studies have been performed to characterize the deformation and failure behavior of these high-capacity materials directly, providing fundamental insights into the degradation processes. Modeling works have focused on elucidating the underlying mechanisms and providing design tools for next-generation battery design. This review presents an overview of the fundamental understanding and theoretical analysis of the electrochemical degradation and safety issues of LIBs where mechanics dominates. We first introduce the stress generation and failure behavior of high-capacity anodes from the experimental and computational aspects, respectively. Then, we summarize and discuss the strategies of stress mitigation and failure suppression. Finally, we conclude the significant points and outlook critical bottlenecks in further developing and spreading high-capacity materials of LIBs.
The NaHCO
3
-Responsive Phenotype in Methicillin-Resistant Staphylococcus aureus (MRSA) Is Influenced by
mecA
Genotype
Authors: Ersoy Selvi C. , Manna Adhar C. , Proctor Richard A. , Chambers Henry F. , Harrison Ewan M. , Bayer Arnold S. , Cheung Ambrose
Published: May 2022

Methicillin-resistant
Staphylococcus aureus
(MRSA) strains are a leading cause of many invasive clinical syndromes, and pose treatment difficulties due to their
in vitro
resistance to most β-lactams on standard laboratory testing. A novel phenotype frequently identified in MRSA strains, termed ‘NaHCO
3
-responsiveness’, is a property whereby strains are susceptible
in vitro
to many β-lactams in the presence of NaHCO
3
.
Multicenter Population Pharmacokinetic Study of Unbound Ceftriaxone in Critically Ill Patients
Authors: Heffernan Aaron J. , Sime Fekade B. , Kumta Nilesh , Wallis Steven C. , McWhinney Brett , Ungerer Jacobus , Wong Gloria , Joynt Gavin M. , Lipman Jeffrey , Roberts Jason A.
Published: May 2022

The objective of this study was to describe the total and unbound population pharmacokinetics of ceftriaxone in critically ill adult patients and to define optimized dosing regimens. Total and unbound ceftriaxone concentrations were obtained from two pharmacokinetic studies and from a therapeutic drug monitoring (TDM) program at a tertiary hospital intensive care unit. Population pharmacokinetic analysis and Monte Carlo simulations were used to assess the probability of achieving a free trough concentration/MIC ratio of ≥1 using Pmetrics for R. A total of 474 samples (267 total and 207 unbound) were available from 36 patients. A two-compartment model describing ceftriaxone-albumin binding with both nonrenal and renal elimination incorporating creatinine clearance to explain the between-patient variability best described the data.
The WalRK Two-Component System Is Essential for Proper Cell Envelope Biogenesis in Clostridioides difficile
Authors: Müh Ute , Ellermeier Craig D. , Weiss David S.
Published: May 2022

The WalRK two-component system (TCS) is essential for coordinating synthesis and turnover of peptidoglycan in
Firmicutes
. We investigated the WalRK TCS in
Clostridioides difficile
, an important bacterial pathogen with an atypical cell envelope.
Diversity and Long-Term Dynamics of Human Blood Anelloviruses
Authors: Kaczorowska Joanna , Deijs Martin , Klein Michelle , Bakker Margreet , Jebbink Maarten F. , Sparreboom Mila , Kinsella Cormac M. , Timmerman Anne L. , van der Hoek Lia
Published: May 2022

Knowledge of the persistence of AVs in humans is crucial to our understanding of the nature of AV infection (chronic or acute) and the role of AV in the host. We therefore investigated the dynamics of anellovirus infection in two healthy people followed up for 30 years. Our findings suggest that the human blood anellovirus virome (anellome) remains stable and personal for decades.
On Fruits and Fungi: A Risk of Antifungal Usage in Food Storage and Distribution in Driving Drug Resistance in Candida auris
Authors: Yadav Vikas , Heitman Joseph
Published: May 2022

The continuous emergence of antifungal drug resistance is a mounting concern for the treatment of fungal infections worldwide. While many pathogenic fungi exhibit some level of antifungal drug resistance, the identification of
Candida auris
has brought this phenomenon to the fore in recent years.
C. auris
exhibits resistance to all antifungal drugs used for treatment, and it does so at a very high rate, with more than 90% of isolates being resistant to at least one drug and roughly 4% being panresistant.
Draft Genome Sequence of the Novel, Moderately Thermophilic, Iron- and Sulfur-Oxidizing Firmicute Strain Y002, Isolated from an Extremely Acidic Geothermal Environment
Authors: Bitencourt José , Scholte Larissa , Oliveira Renato , Morais Daniel , Nunes Gisele , Nancucheo Ivan , Valadares Rafael , Holmes David S. , Johnson D. Barrie , Alves Ronnie , Oliveira Guilherme
Published: May 2022

We report the draft genome sequence of the Firmicute strain Y002, a facultatively anaerobic, acidophilic bacterium that catalyzes the dissimilatory oxidation of iron and sulfur and the reduction of ferric iron. Analysis of the genome (2.9 Mb; G+C content, 46 mol%) provided insights into its ability to grow in extremely acidic geothermal environments.
Measuring user engagement with low credibility media sources in a controversial online debate
Authors: Vilella Salvatore , Semeraro Alfonso , Paolotti Daniela , Ruffo Giancarlo
Published: May 2022

We quantify social media user engagement with low-credibility online news media sources using a simple and intuitive methodology, that we showcase with an empirical case study of the Twitter debate on immigration in Italy. By assigning the Twitter users an Untrustworthiness (U) score based on how frequently they engage with unreliable media outlets and cross-checking it with a qualitative political annotation of the communities, we show that such information consumption is not equally distributed across the Twitter users. Indeed, we identify clusters characterised by a very high presence of accounts that frequently share content from less reliable news sources. The users with high U are more keen to interact with bot-like accounts that tend to inject more unreliable content into the network and to retweet that content. Thus, our methodology applied to this real-world network provides evidence, in an easy and straightforward way, that there is strong interplay between accounts that display higher bot-like activity and users more focused on news from unreliable sources and that this influences the diffusion of this information across the network.
BOHR’S FORMULA FOR ONE-DIMENSIONAL SCHRÖDINGER OPERATORS DEFINED BY SELF-SIMILAR MEASURES WITH OVERLAPS
Authors: TANG WEI
Published: May 2022

We study Bohr’s formula for fractal Schrödinger operators on the half line. These fractal Schrödinger operators are defined by fractal measures with overlaps and a locally bounded potential that tends to infinity. We first derive an analog of Bohr’s formula for these Schrödinger operators under some suitable conditions. Then we demonstrate how this result can be applied to self-similar measures with overlaps, including the infinite Bernoulli convolution associated with the golden ratio, m-fold convolution of Cantor-type measures, and a family of graph-directed self-similar measures that are essentially of finite type.
Minimal excludant over partitions into distinct parts
Authors: Kaur Prabh Simrat , Bhoria Subhash Chand , Eyyunni Pramod , Maji Bibekananda
Published: May 2022

The average size of the “smallest gap” of a partition was studied by Grabner and Knopfmacher in 2006. Recently, Andrews and Newman, motivated by the work of Fraenkel and Peled, studied the concept of the “smallest gap” under the name “minimal excludant” of a partition and rediscovered a result of Grabner and Knopfmacher. In this paper, we study the sum of the minimal excludants over partitions into distinct parts, and interestingly we observe that it has a nice connection with Ramanujan’s function [Formula: see text]. As an application, we derive a stronger version of a result of Uncu.
Elliptic curves with few bad primes
Authors: Wong Siman
Published: May 2022

We show that there are [Formula: see text] semistable elliptic curves over Q with conductor [Formula: see text] and at most two bad primes, and that there are [Formula: see text] semistable elliptic curves over Q with conductor [Formula: see text], at most 12 bad primes, and positive Mordell–Weil rank. We also show that there are [Formula: see text] elliptic curves over Q with conductor [Formula: see text] for some prime [Formula: see text].
Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models
Authors: Topham Luke K. , Khan Wasiq , Al-Jumeily Dhiya , Hussain Abir
Published: May 2022

Person identification is a problem that has received substantial attention, particularly in security domains. Gait recognition is one of the most convenient approaches enabling person identification at a distance without the need for high-quality images. There are several review studies addressing person identification such as the utilization of facial images, silhouette images, and wearable sensor. Despite skeleton-based person identification gaining popularity while overcoming the challenges of traditional approaches, existing survey studies lack the comprehensive review of skeleton-based approaches to gait identification. We present a detailed review of the human pose estimation and gait analysis that makes the skeleton-based approaches possible. The study covers various types of related datasets, tools, methodologies, and evaluation metrics with associated challenges, limitations, and application domains. Detailed comparisons are presented for each of these aspects with recommendations for potential research and alternatives. A common trend throughout this paper is the positive impact that deep learning techniques are beginning to have on topics such as human pose estimation and gait identification. The survey outcomes might be useful for the related research community and other stakeholders in terms of performance analysis of existing methodologies, potential research gaps, application domains, and possible contributions in the future.
Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine
Authors: Ju Dan , Hui Daniel , Hammond Dorothy A. , Wonkam Ambroise , Tishkoff Sarah A.
Published: May 2022

One goal of genomic medicine is to uncover an individual's genetic risk for disease, which generally requires data connecting genotype to phenotype, as done in genome-wide association studies (GWAS). While there may be clinical promise to employing prediction tools such as polygenic risk scores (PRS), it currently stands that individuals of non-European ancestry may not reap the benefits of genomic medicine because of underrepresentation in large-scale genetics studies. Here, we discuss why this inequity poses a problem for genomic medicine and the reasons for the low transferability of PRS across populations. We also survey the ancestry representation of published GWAS and investigate how estimates of ancestry diversity in GWASparticipants might be biased. We highlight the importance of expanding genetic research in Africa, one of the most underrepresented regions in human genomics research, and discuss issues of ethics, resources, and technology for equitable advancement of genomic medicine.
Energy Justice, Decarbonization, and the Clean Energy Transformation
Authors: Bennear Lori Snyder
Published: May 2022

Addressing climate change will require significant reductions in carbon emissions. Decarbonization will likely lead to increases in energy prices, which are regressive. Poorer households spend a higher percentage of income on energy and also have less access to energy efficient options in housing, transportation (including electric vehicles), and household durables. This review summarizes the state of knowledge on the energy justice implications of the clean energy transformation along four dimensions—production of energy, energy insecurity/energy poverty, access to clean energy technologies, and impacts of policy instrument choices for achieving decarbonization. Along each dimension there is evidence of greater negative impacts on lower-income households and on Black, Indigenous, and people of color (BIPOC) households, even controlling for income. But there is also evidence that these impacts can be mitigated through policy choices. Together these findings highlight that centering justice concerns in policy debates is critical for a just and clean energy transformation.
Optimization of Finite-Zone Implanted Edge Termination for β-Ga2O3 SBD
Authors: Wang Boyi , Feng Chun , Jiang Lijuan , Xiao Hongling , Li Wei , Wang Xiaoliang
Published: May 2022

This article improves the breakdown voltage of Ga2O3 SBD with finite implanted edge termination through simulation. The influence of implanted acceptor concentration, implanted depth and implanted length on the breakdown voltage of SBD is investigated. The breakdown voltage (BV) can be increased to 2500 V when the implanted concentration is 5 × 1017 cm−3 and implanted depth is 0.5 μm, more than 5 times than that of regular Ga2O3 SBD. However, the breakdown voltage reduces to below 1000 V when the implanted concentration is higher than 1 × 1018 cm−3. An extra trench isolation structure combined with finite implanted edge termination can improve this phenomenon. The breakdown voltage at high implanted concentration can be stabilized above 1500 V.
Tunable Dielectric Properties in Paraffin-Matrix Metacomposites via Macro-Scale Periodic Stacking of the Functional Fillers
Authors: Wang Yuding , Guo Xinyue , Han Bingchen , Zhao Yehao , Qiu Tianyu , Zhang Zidong
Published: May 2022

As electronic devices play an increasingly important role in industry and daily life, the demand for materials with excellent dielectric properties to meet different needs is also increasing. In this paper, the effects of the composition and diameter of functional phases on the dielectric properties are investigated, in which the periodically arranged composites are prepared by gravitational self-assembly and the volume fraction of functional phases is controlled. The results show that the micro capacitor introduced by the periodically arranged functional phase leads to a substantial increase in the dielectric constant of the composites when the content is the same. Changing the particle size of the functional phase can change the micro capacitor’s polarity to modulate the dielectric properties. It is worth noting that the introduction of the ceramic phase can effectively regulate the dielectric loss of the composite by introducing a new loss mechanism while maintaining a high dielectric constant. This paper provides a deeper understanding of the dielectric property regulation, which provides new ideas for the special dielectric regulation of electronic devices.
Entropy profiling for the diagnosis of NCA/Gr-SiOx Li-ion battery health
Authors: Wojtala Malgorzata Ewa , Zulke Alana , Burrell Robert , Nagarathinam Mangayarkarasi , Li Guanchen , Hoster Harry , Howey David , Mercer Michael
Published: May 2022

Graphite-silicon (Gr-Si) blends have become common in commercial Li-ion battery negative electrodes, offering increased capacity over pure graphite. Lithiation/delithiation of the silicon particles results in volume changes, which may be associated with increased hysteresis of the open circuit potential (OCP). The OCP is a function of both concentration and temperature. Entropy change measurement--which probes the response of the OCP to temperature--offers a unique battery diagnostics tool. While entropy change measurements have previously been applied to study degradation, the implications of Si additives on the entropy profiles of commercial cells have not been explored.Here, we use entropy profiling to track ageing markers in the same way as differential voltage analysis. In addition to lithiation/delithiation hysteresis in the OCP of Gr-Si blends, cells with Gr-Si anodes also exhibit differences in entropy profile depending on cycling direction, reflecting degradation-related morphological changes. For cycled cells, entropy change decreased during discharge, likely corresponding to graphite particles breaking and cracking. However, entropy change during charge increased with cycling, likely due to the volume change of silicon. Over a broad voltage range, these combined effects led to the observed rise in entropy hysteresis with age. Conversely, for calendar aged cells entropy hysteresis remained stable.
Cold Exposure and the Metabolism of Mice, Men, and Other Wonderful Creatures
Authors: Kowaltowski Alicia J.
Published: May 2022

Laboratory rodents and cold-adapted animals in the wild use a significant amount of the energy derived from food intake for heat generation. Thermogenesis involving mitochondrial uncoupling in the brown adipose tissue differs quantitatively in mice, humans, and cold-adapted animals and could be an important ally to combat obesity if humans were prepared to deviate slightly from thermoneutral living conditions to activate this pathway.
An Intelligent Digital Media Asset Management Model Based on Business Ecosystem
Authors: Liu Yutong , Song Peiyi
Published: May 2022

In the context of media convergence, it is of great significance to study and discuss the intelligent digital media asset management model and build a digital media asset management ecosystem model to carry out effective digital media asset management for media organizations and promote the value creation of digital media content assets. Based on the business ecosystem, this study adopts a system dynamics approach to construct a system dynamics model through theoretical research and simulation analysis. By studying the positioning of intelligent digital media asset management and service patterns, the basic framework of digital media asset management ecosystems is proposed. To explore the interaction between different factors, the system dynamics model of value creation in digital media asset management ecosystems is built in this paper. This study provides new insights for media organizations on building an intelligent digital media asset management model and promoting the development and utilization of media content resources.
Application of Intelligent Education Using Interactive Modeling in Higher Ideological and Political Education Platform
Authors: Jiang Shaoying , Sohail Mishal
Published: May 2022

In order to improve the efficiency of ideological and political intelligent teaching, this paper uses interactive modeling technology to build an ideological and political education platform. In the classroom interactive image processing, this paper uses the maximum circumscribed moment of the contour to extract the rectangular block area and uses the convolutional neural network to distinguish the defect and the text for the extracted rectangular block area. In addition, this paper comprehensively judges the detection results of each detection area and designs a corresponding detection algorithm for each part to detect defects. From the experimental research results, it can be seen that the interactive ideological and political teaching platform proposed in this paper has a good interactive teaching effect and can effectively improve the effect of ideological and political teaching.
Salicylic Acid Treatment Reduces Lipid Peroxidation and Chlorophyll Degradation and Preserves Quality Attributes of Pointed Gourd Fruit
Authors: Yadav Nitin , Singh Anil K. , Emran Talha Bin , Chaudhary Ratiram G. , Sharma Rohit , Sharma Swati , Barman Kalyan
Published: May 2022

The marketability of pointed gourd fruit is drastically reduced after harvest due to moisture loss, chlorophyll degradation, yellowing of the skin, and shriveling. The present investigation studied the effect of exogenous salicylic acid (SA) treatment on senescence and fruit quality attributes of pointed gourd during storage under ambient conditions. Fruits were treated by immersing them in different concentrations of SA (1.0 mM, 2.0 mM, and 3.0 mM) and distilled water (control) for 5 minutes. The investigation showed beneficial effects of 3.0 mM SA treatment in lowering weight loss (16.8%), maintaining higher chlorophyll (32.8%) in the skin, and reducing lipid peroxidation (20.2%) compared to the control. SA (3.0 mM)-treated fruits retained 15.3% higher ascorbic acid and about 18% higher total phenol, flavonoids, and radical scavenging activity over pointed gourd fruits in the control group. However, significant difference in the total antioxidant capacity after 6 days of storage was not noted between SA-treated and control fruit. Thus, postharvest salicylic acid treatment can beneficially be used to extend marketability and delay quality deterioration of pointed gourd fruits stored under ambient conditions.
Connections between Various Disorders: Combination Pattern Mining Using Apriori Algorithm Based on Diagnosis Information from Electronic Medical Records
Authors: Ma He , Ding Jingjing , Liu Mei , Liu Ying
Published: May 2022

Objective. Short‐term or long‐term connections between different diseases have not been fully acknowledged. This study was aimed at exploring the network association pattern between disorders that occurred in the same individual by using the association rule mining technique. Methods. Raw data were extracted from the large‐scale electronic medical record database of the affiliated hospital of Xuzhou Medical University. 1551732 pieces of diagnosis information from 144207 patients were collected from 2015 to 2020. Clinic diagnoses were categorized according to “International Classification of Diseases, 10th revision”. The Apriori algorithm was used to explore the association patterns among those diagnoses. Results. 12889 rules were generated after running the algorithm at first. After threshold filtering and manual examination, 110 disease combinations (support ≥ 0.001, confidence ≥ 60%, lift > 1) with strong association strength were obtained eventually. Association rules about the circulatory system and metabolic diseases accounted for a significant part of the results. Conclusion. This research elucidated the network associations between disorders from different body systems in the same individual and demonstrated the usefulness of the Apriori algorithm in comorbidity or multimorbidity studies. The mined combinations will be helpful in improving prevention strategies, early identification of high‐risk populations, and reducing mortality.
Construction of Enterprise Financial Early Warning Model Based on Intelligent Mathematical Model
Authors: Cheng Jing , Lu Xiaofan , Zhang Xionggang
Published: May 2022

In order to improve the ability of enterprises to cope with financial risks, this paper analyzes the financial early warning of enterprises combined with intelligent mathematical models and builds an intelligent financial early warning model to assist in analyzing the financial status of enterprises. Moreover, this paper combines the existing information technology and expenditure business scenarios to construct an intelligent early warning frame for enterprise financial control based on an intelligent mathematical model. In addition, this paper combines the K-means clustering algorithm to design a financial approval process integrity control early warning method to warn the integrity of the financial approval process. Finally, this paper presents the early warning of financial standard compliance control based on the C4.5 decision tree algorithm. The experimental research shows that the enterprise financial early warning model based on the intelligent mathematical model proposed in this paper can play an important role in the enterprise financial management and effectively improve the ability of the enterprise to cope with financial risks.
Agile Teaching Framework for Music Education Software Development Curriculum Based on Deep Learning
Authors: Seng Wenli
Published: May 2022

Music learning is changing as a result of high-quality instruction, progressing from shallow to deep learning over time. In-depth learning is a revolutionary teaching paradigm that focuses entirely on the perception and investigation of music by students, allowing them to completely experience the allure of music. It can assist students not only to learn more about music and improve their skills but also to grow their music literacy and improve their musical talent. In this study, a deep-learning-based agile teaching framework is proposed for music education software development courses. The framework is built on the Internet of Things (IoT), and each device is considered an IoT device. Data is recorded and transmitted via the IoT network using the wireless sensor network (WSN). With the use of a genetic algorithm, the WSN Randomized Search Method is employed to execute intelligent music data transfer. For online classes, various music datasets are used, and the performance is examined. The findings of this study demonstrate that the proposed method is effective in teaching music education to students.