bluuebunny/crossref_metadata_2025_split huggingface dataset:
Explainable artificial intelligence (XAI) enabled anomaly detection and fault classification of an industrial asset
Authors: Sharma Jeetesh , Mittal Murari Lal , Soni Gunjan 
Published: April 2023

Abstract

Predictive maintenance helps organizations to reduce equipment downtime, optimize maintenance schedules, and enhance operational efficiency. By leveraging machine learning algorithms to predict when equipment failure will likely occur, maintenance teams can proactively schedule maintenance activities and prevent unexpected breakdowns. Anomaly detection and fault classification are essential components of predictive maintenance. Anomaly detection involves analyzing sensor data collected from equipment to identify deviations from normal behavior. Fault classification, on the other hand, involves identifying the root cause of a fault or failure. A dataset of an industrial asset is used to evaluate the proposed study. Four distinct data-driven anomaly detection methodologies were employed after the pre-processing of the data, with the deep learning-based autoencoder producing the best results of all the techniques. Implementing machine learning-based fault categorization approaches revealed that Random Forest had the best results. Bayesian optimization and sequential model-based hyperparameter optimization technique is used for greater accuracy and optimized hyperparameters. Significant progress has been made in anomaly detection and fault classification using machine learning, but the degree of their explainability is significantly limited by the ``black-box" character of some machine learning techniques. Less emphasis has been placed on explainable artificial intelligence (XAI) approaches in the domain of maintenance. Therefore, the XAI tools have been used to acknowledge the extent of the variables to analyze the influence of respective features. A stability metric has been included to improve the explanation's overall quality. The findings of this article suggest that the utilization of eXplainable Artificial Intelligence (XAI) can offer significant contributions in terms of insights and solutions for addressing critical maintenance issues. As a result, decision-making processes can become more informed and effective.
The Application of Artificial Intelligence in Health Policy: A Scoping Review
Authors: Ramezani Maryam , Takian Amirhossein , Bakhtiari Ahad , Rabiee Hamid R. , Ghazanfari Sadegh , Mostafavi Hakimeh 
Published: April 2023

Abstract
Background Policymakers require precise, in-time information to make informed decisions in complex environments such as health systems. Artificial intelligence (AI) is a novel approach that makes collecting and analyzing data in complex systems more accessible. This study highlights recent research on AI's application and capabilities in health policymaking.
Method PubMed, Scopus, and the Web of Science databases were investigated to find relevant studies from 2000 to 2023 using the keywords of "artificial intelligence" and "policymaking." Walt and Gilson's policy triangle framework was used for charting the data.
Results The results revealed that using AI in health policy paved the way for novel analyses and innovative solutions for intelligent decision-making and data collection, potentially enhancing policymaking capacities, particularly in the evaluation phase. Moreover, it can be used to develop creative agendas with fewer political limitations and higher rationality, leading to better policies. Furthermore, AI provides the opportunity to make evidence-informed decisions by developing new platforms and toolkits. Most of the suggested AI solutions for health policy are not meant to replace experts but to make decision-making smarter.
Conclusion Numerous approaches exist for AI to influence the health policymaking process. Leading health systems can benefit from AI's potential to expand the use of evidence-based policymaking in health systems.
Characteristics of inhibitory active substances produced by Pseudoalteromonas. SW-1
Authors: Sun Xing , Xu Xin , Wang Xuhui , Tang Tongtong , Zhang Weiwei , Ma Yuhua 
Published: April 2023

Abstract
To confirm the antagonistic activity characterization of the strain Pseudoalteromonas SW-1 (P. SW-1), its cell-free supernatant (CFS) was studied against a clam pathogenic strain of Vibrio.Alginolyticu. MP-1 (V.MP-1). The CFS of P. SW-1 exhibited evident antagonistic activities against the pathogens, and the absorbance value (600 nm) of V. MP-1 remained at a lower level at 24 h when compared with the control. The results showed that the inhibitory activities of strain P. SW-1 CFS showed differences after treatment with heat, acid and alkali, and proteinase K. The CFS of P. SW-1 inhibitory activities were decreased after treatment with heat, but the inhibitory activities of strain P. SW-1 CFS were still effective after treatment with proteinase Kfor 24 h. The acid and alkali treatments could increase the inhibitory activities of strain P. SW-1 CFS. Therefore, the ammonium sulfate precipitation test also indicated that P. SW-1 could produce some active protein compounds to antagonize pathogenic V. MP-1.
Prevalence of and risk factors for obstructive pulmonary dysfunction caused by coal dust exposure: A cross-sectional study
Authors: Tang Meian , Xiao Xiongbin , Li Qi , Cui Chenyang , Chen Rou , Liu Lang , Li Xin , Huang Lei 
Published: April 2023

Abstract
Background: Although previous studies have shown that coal dust exposure can lead to decreased lung function, little is known regarding the prevalence of and risk factors for obstructive pulmonary dysfunction (OPD) in Chinese coal workers. The present study investigated the prevalence of and risk factors for OPD in Chinese coal workers.
Methods: Health monitoring data were collected from 3534 workers in 8 different state-owned coal mines from January 1, 2021, to June 30, 2022. Ultimately, data from 3450 workers were eligible for analysis. OPD was defined as FEV1/FVC less than 70%. FVC dicline was defined as FVC/Pre less than 80%. High-intensity exposure refers to underground workers who basically work underground all day; correspondingly, low-intensity exposure refers to workers who only work underground for part of the day. Logistic regression analyses were used to analyze the relationships between clinical variables and OPD. The attributable risk percentage for each risk factor was calculated as follows: (（OR-1）/OR)x100%.
Results: The overall prevalence of OPD was 2.6%, and the prevalence of FVC decline (FVC/Pre<80%) was 21.8%. The prevalence of OPD was higher in the high-intensity exposure group than in the low-intensity exposure group , higher in the smoking group than in the no smoking group, and higher among workers aged 50 years or older than among those under the age of 50. Risk factors for OPD included age greater than 50 years old, high-intensity exposure, duration of exposure more than 20 years, smoking, and dust concentration exceeding 2 mg/m3. High-intensity exposures were considered to contribute approximately equally to the prevalence of OPD as smoking.
Conclusion: Coal dust exposure can lead to OPD. High-intensity exposure, a duration of exposure of more than 20 years, smoking, and dust concentrations exceeding 2 mg/m3 are major risk factors for OPD. Cigarette smoking and coal dust exposure are major preventable risk factors for the disease. Enhancing occupational protection, strengthening smoking control in underground workplaces and regular monitoring of lung function for early detection of abnormal lung function are important for protecting workers' health.
Bioinformatic screening of genes associated with giant cell arteritis and therapeutic agents
Authors: Su Zhaohui , Yao Menghui , Zhang Chunyi , Gao Congcong , Dai Mengmeng , Yue Runzhi , Li Chaoying , Zheng Zhaohui 
Published: April 2023

Abstract
Objective
To analyze the high-throughput sequencing data of giant cell arteritis by bioinformatics technology, to initially identify the core genes associated with giant cell arteritis and to explore potential therapeutic agents.
Methods
Gene expression profile (GSE174694) was obtained from the Gene Expression Database (GEO), and the differential genes were calculated, the differentially expressed genes were analyzed by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG), and the protein interaction network was constructed to obtain the core genes. Finally, drug correlation analysis (connectivity map, CMap) was used to identify small molecule drugs with potential therapeutic effects on giant cell arteritis.
Results
A total of 771 differentially expressed genes were screened, including 481 up-regulated and 290 down-regulated. The GO analysis showed that the differentially expressed genes were mainly involved in cell surface receptor signaling pathway, T cell receptor signaling pathway, cell adhesion and intrinsic immune response, and the KEGG pathway analysis showed that the differentially expressed genes were mainly involved in chemokine signaling pathway, Th17 cell differentiation and Th1 and Th2 cell differentiation. The KEGG pathway analysis showed that the differential genes were mainly involved in chemokine signaling pathway, Th17 cell differentiation and Th1 and Th2 cell differentiation. The protein interaction network was constructed to screen five core genes, PTPRC, FCGR2B, ITGAM, SPI1 and ITGB2, which were mainly involved in promoting T cell value-added and differentiation, inhibiting apoptosis, increasing cell adhesion and promoting inflammatory response. CMap analysis suggested that small molecules such as warfarin A and anisomycin have potential therapeutic effects on giant cell arteritis. The CMap analysis suggested the potential therapeutic effects of small molecules such as warfarin A and anisomycin on giant cell arteritis.
Conclusion
This study provides a holistic view of the gene transcriptome in giant cell arteritis, and the core genes and small molecule drugs screened may provide new ideas for the pathogenesis of giant cells and drug development.
Biochemical and Microbiological Interactions of Molecularly Detected Blastocystis hominis: a cross-sectional study.
Authors: Saftawy Enas A. El , Ghallab Marwa M. I. , Haydara Tamer , Ibrahim Asmaa , Atta Shimaa Attia , Allah Reham Abdelmonem Mohamed Gad , Kamel Marwa N. , Morsy Salwa M 
Published: April 2023

Abstract
Background: Blastocystis hominis (B. hominis) is a cosmopolitan intestinal protozoan that has been related to several gastrointestinal disturbances simulating irritable bowel syndrome (IBS). However, the underlying pathogenicity of blastocystosis in human studies remains indistinct.
Methods: In a cross-sectional study, 167 stool samples from patients attending internal medicine department, Kafrelsheik university hospital were examined. Polymerase chain reaction (PCR) -based identification using known sequenced-tagged site (STS) primers allowed the isolation of the positive samples and genotyping of the parasite. Reducing sugar and pH were investigated in patients’ stool samples.
Results: Patients who participated in the study were from both sexes where blastocystosis infection was most prevalent in the age group 20 – 29. Of 167 cases, twenty-seven (16.1%) were molecularly confirmed blastocystosis infections. Genotype 3 was solely detected. Of these, 26 (19%) cases presented with diarrhea, and 27 (17.1%) cases suffered abdominal pain. Additionally, 20 (16.8%) cases had increased flatulence, and only two patients manifested vomiting. The seasonal cycle of the parasite was explored being higher in summer and spring. Our results highlight the consistent chemical association of carbohydrate intolerance and acidic fecal pH with genotype-3 of B. hominis that seemed to augment the irritable bowel syndrome (IBS) -like manifestations of the parasitic infection. Specimens positive for B. hominis showed dominant growth of E. coli. Co-culture of B. hominis with E. coli and Candidashowed their eminent growth whereas the parasite was suppressed. B. hominiswith Giardia species co-infections showed a significant rise in lactate dehydrogenase (LDH) enzyme in fecal samples compared with B. hominissolely.
Conclusion: there is an association between Blastocystis hominis infections and carbohydrate intolerance and fecal acidity. B. hominis was observed to be closely related to IBS-like manifestations with the dominatingly isolated genotype-3. Also, B. hominis appeared to have an enhancing effect on the growth of E. coli and C. non-albicans. Blastocystosis seemed to heve damaging effects on the cells of the intestinal brush border especially when co-existing with Giardia sp. thus increasing levels of LDH.
Influence of surface pre-treatments on porosity and microstructure of laser welding in AlSi10Mg alloys sheets fabricated by selective laser melting
Authors: Wang Can , He Dingyong , Cui Li , Guo Xingye , Tan Zhen , Wu Xu 
Published: April 2023

Abstract
The surface pre-treatment is of great significance for improving the welding quality of metal materials, especially aluminum alloys. At present, the welding process is introduced to achieve the joining of the selective laser melting (SLM) AlSi10Mg alloys or their repair during manufacturing or service. However, the high susceptibility of hydrogen pores is one of the most important problems encountered in the fusion welding process of SLM AlSi10Mg alloys. To clarify the influence of the surface state of the base metal on the hydrogen pore characteristics, the SLM AlSi10Mg alloys sheets were pretreated by chemical cleaning treatment (CCT), mechanical polishing treatment (MPT) and chemical cleaning after mechanical polishing (MPT-CCT) before autogenous laser welding. Subsequently, the hydrogen pore characteristics, microstructure and micro-hardness of the weld joints were investigated. The experimental results show that the autogenous laser welding with MPT-CCT pre-treatment conspicuously reduces the porosity and pore diameter. In addition, TEM results show that MPT-CCT pre-treatment can eliminate most of the broken and discontinuous surface oxide film and Mg enrichment. In summary, the necessity of MPT-CCT surface pre-treatment for SLM aluminum alloy parts is analyzed systematically, which is of great significance to improve the welding quality.
The soybean CEP6 signaling peptides positively regulates nodulation
Authors: Wang Xiaoli , Qin Jie , Tian Wenqing , miao Bai , Wang Min , Du Weijun , Wang Lixiang 
Published: April 2023

Abstract
Nodulation is the most efficient nitrate assimilation system in the ecosystem while excessive fertilization increased nitrate inhibition effect, deciphering the nitrate signal transduction mechanism in the process is of the utmost importance. In this study, genome-wide analysis of the GmCEP genes were applied to identify nodulation related CEPgenes, 22 GmCEP family members were identified while GmCEP6 mainly expressed in nodule and significantly responded to nitrate treatment and rhizobium infection, especially in later stages. Overexpression and CRISPR-Cas9 were used to validate its role in nodulation. We found GmCEP6overexpression significantly increased the nodule number while GmCEP6knock out significantly decreased nodule number suggests GmCEP6 function as a positive regulator in soybean nodulation. qRT-PCR shown that alterations in the expression of GmCEP6 affected the expression of marker genes in the Nod factor signaling pathway. Lastly the function of GmCEP6 in nitrate inhibition of nodulation was analyzed, nodule number in the GmCEP6 overexpressed roots significantly increased under nitrogen treatments suggests GmCEP6 functions in resistance to nitrate inhibition. The study will help us understand that GmCEP6 promotes nodulation and participates in the regulation of nitrate inhibition of nodulation, which is of great significance for high efficiency utilization of nitrogen in soybean.
Pattern dynamics of a predator-prey system with Ivlev-type functional response
Authors: Liu Yang , Shen Zuolin , Wei Junjie 
Published: April 2023

Abstract

In this paper, we study the dynamics and pattern formation of a reaction-diffusion system with Ivlev-type functional response and homogeneous Neumann boundary conditions. We first consider the global existence and boundedness of nonnegative solutions and then discuss the global stability of nonnegative steady states. By using the energy estimates and Leray-Schauder degree theory, we prove the nonexistence and existence of nonconstant positive steady states. We show some interesting spatiotemporal dynamical behaviors in numerical simulations. Our results suggest that the predator-prey system with Ivlev-type functional response enriches spatiotemporal dynamical behaviors.
Health care Providersàttitude and associated factors to safe abortion in Ethiopia, 2023: A systematic review and meta-analysis
Authors: Bante Simachew Animen , Balcha Wondu Feyisa , Chekole Fentahun Alemnew , Kassahun Eden Asmare , Getu Alemwork Abie , Awoke Amlaku Mulat , Tariku Mengistie Kassahun 
Published: April 2023

Abstract

BackgroundMaternal deaths due to unsafe abortion have increased steadily in sub- Saharan Africa. In Ethiopia, 25% of the pregnancy is unintended pregnancy. This contributes from 6–9% maternal deaths from unsafe abortion .Previously, many fragmented cross-sectional studies were conducted but there is no cumulative evidence on health care providers’ attitude and associated factors to safe abortion in Ethiopia. This study aimed to measure pooled health care Providers’ attitude and determinants of safe abortion in EthiopiaMethodsIn this review the databases used were Google Scholar, Medline/PubMed, EMBASE, Science Direct, HINARI and African Journals Online. The quality of articles that met the inclusion criteria was assessed. The studies were critically appraised by using the Joanna Briggs Critical Appraisal tools and the preferred reporting item for systematic review was used for quality assessment. Data were extracted in an excel spreadsheet and imported to STATA version 17 software for meta-analysis. The random- effect model was used to pooled the health care Providers’ attitude to safe abortion. The I2statistics were used to test heterogeneity and Egger’s tests were used to assess publication bias. Forest plot were used to present the odds ratio (OR) with a 95% confidence interval (CI).ResultsA total of eight studies with a total sample size of 2,826 were included for this review and meta-analysis. The overall pooled health care providers’ favorable attitude towards safe abortion in Ethiopia was 65.49% (95%CI; 49.64, 81.34; I2 = 99.20%, P = 0.000). Familiarize with abortion law, OR = 2.25 (95% CI: 1.06, 3.43), being male provider, OR = 1.89 (95% CI: 1.23, 2.54), taking training on abortion, OR = 2.91 (95% CI: 1.17, 4.65), being midwives profession, OR = 3.029 (95% CI: 1.605, 4.453) and practicing abortion procedure, OR = 2.55 (95% CI: 1.32, 3.78) were positively associated with providers’ favorable attitude to safe abortion.ConclusionHealth care provider who trained on abortion service and who were familiarized with abortion law were more likely to have favorable attitude to safe abortion services in Ethiopia. Therefore, all health institutions and other stakeholders should facilitate training on safe abortion services and enable providers familiarize with abortion law in Ethiopia.
Cohort profile: Chinese moyamoya disease cohort study
Authors: Hao Fangbin , Han Cong , Gao Gan , Liu Simeng , Wang MinJie , Yang Rimiao , Zou Zhengxing , Yu Dan , Sun Caihong , Zhang Qian , Zhang Houdi , Guo Qing-Bao , Wang Xiaopeng , Shen XuXuan , Fu Heguan , Li JingJie , Zhang Zhengshan , Bao Xiangyang , Feng Jie , Li Bin , Ren Bin , Wang Hui , Wang Qiannan , Pei Songtao , Zhao Bo , Zou Zhenglong , Han Yiqin , Tong Huaiyu , Sun Zhenghui , Liu Miao , Duan Lian 
Published: April 2023

Abstract
Background: The etiology and natural course of moyamoya disease (MMD) remain unknown. This study evaluated the natural course and etiology of MMD using data from the Chinese MMD (C-MMD) cohort study.
Methods: The C-MMD cohort consisted of 6,167 patients with MMD treated at our center over the past 20 years. We analyzed the medical history and laboratory and imaging examination results of the patients at different stages of the disease to identify common features of MMD.
Results: The median age for symptom onset was 32 years. The age distribution was bimodal; the highest peak was between ages 35 and 45 years, with a smaller peak between ages 3 and 9 years. The female-to-male ratio was 1:1. The disease occurred mainly in the Han people and was rarely observed in minority nationalities. In our cohort, a transient ischemic attack was the most common initial clinical manifestation (46.3%); others included infarction (25.0%), hemorrhage (15.1%), and headache (7.9%).
Conclusions: The C-MMD cohort is currently one of the largest single-center MMD cohorts in the world. This study provides baseline data for future research on the etiology and natural course of MMD.
Clinical Trial Registration: This study has been registered in the Chinese Clinical trial registry (registration number: ChiCTR2200064160) on September 28, 2022.
Comparison and Statistical analysis of various machine learning techniques for daily prediction of solar GHI representing India’s overall solar radiation
Authors: Girdhani Bharat , Agrawal Meena 
Published: April 2023

Abstract
Solar energy integration into the grid is a significant challenge because of its varying and unpredictable nature. Therefore, accurate solar energy prediction is vital in ensuring grid stability. To achieve this, the present study uses machine and deep learning methods to estimate the solar global horizontal irradiance. This study aims to predict daily solar GHI for four Indian states (Rajasthan, Madhya Pradesh, Assam, and Meghalaya) with different solar radiation distributions ranging from very high to very low. Four machine-learning techniques (linear regression, support vector machine, ANN and random forest) are used in the present study. Specific sites (Bhadla - Rajasthan, Rewa - Madhya Pradesh, Amguri-Assam, and Shillong-Meghalaya) were chosen in the respective states. The results of the sites represent the overall results for the entire state in this study. The dataset utilized for the study pertains to the selected sites and encompasses the period from January 2019 to November 2022. The study has focused on evaluating the success of machine learning techniques based on seven statistical metrics, including MBE, MAE, MSE, RMSE, Max. Error, R2, and MAPE. The result analysis indicates that all ML techniques' R2, MAPE, and MBE values lie between 0.6108 to 0.9152, 0.0432 to 0.2248, and − 0.2271 to 0.63704 MJ/m2, respectively. The study concludes that all of the machine learning techniques can accurately predict daily solar GHI, with ANN being the best-performing model.