bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep Learning Approach to predict PM10 pollutant by using other Air Pollutants and Meteorological Factors
Authors: Pandita Ishaan Sunita , Bhanot Gaurav , Reddy A.V. Shanmukh 
Published: March 2024

Abstract
Particulate matter in particular poses a serious risk to both human health and the environment. For efficient pollution management and the deployment of appropriate mitigation techniques, accurate air quality forecasting is essential. The objective of the proposed work is to forecast the concentration of PM10 (particulate matter with a diameter of 10 micrometres or less) using a variety of air pollutants as predictors, such as PM2.5, NO2, CO, and other pertinent chemical species. Proposed system makes use of a sizable dataset that includes historical measurements of PM10, PM2.5, NO2, CO, and numerous other air contaminants. It is common knowledge that these pollutants are significant sources of air pollution, and metropolitan areas regularly monitor these pollutants' concentrations. The accuracy of PM10 prediction is increased by using numerous air pollutants as predictors because the intricate interactions and synergistic effects among various pollutants can be captured. This study proposes the development of a system, inspired by existing machine learning and deep learning techniques, capable of forecasting the concentration of PM10 in the atmosphere, using data about other pollutants, as mentioned previously, in the air in the region of prediction, along with meteorological factors defining the weather in the region of prediction, including but not limited to relative humidity, wind speed, wind direction, average temperature and barometric pressure. Before using data from trusted sources (elicited in the references) to build machine learning and deep learning models, the relationships of various features within the dataset were studied in comparison with the target variable – concentration of PM10. In the work done, it has been observed that the Machine Learning model based on Decision-Tree architecture, Extra Trees Regressor, delivered a 93% R2 score, with an RMSE of less than 20 parts per million with respect to PM10 concentration in the air. With this proposed system, multiple advantageous technologies emerging in the domain of air pollution control and mitigation can be built, such as early warning systems for administrative and meteorological departments; and improvement of this system to expand the forecast horizon and integrate the forecasting framework are some valuable future improvements which can be added to this work.
Multi-physics coupling simulation of GMAW arc and droplet behaviors based on CFD
Authors: Zhang Yujiao , Li Yinghao , Niu Sizhe , Wang Hongtao , Zong Ran 
Published: March 2024

Abstract

A three-dimensional transient model of gas metal arc welding (GMAW) process including the arc plasma and droplet transfer was established to investigate the complex coupling mechanism of mass transfer, heat transfer, electromagnetism, and hydrodynamics. The arc shape, current density, temperature field, electromagnetic force, arc pressure and droplet behavior were analyzed. The results showed that the iron vapor generated on the droplet surface and diffused in the arc, which changed the plasma thermal-pressure distribution. The upward surface tension maintained the forming droplet at the wire tip. The electromagnetic force promoted necking, resulting in a decrease in surface tension. Gravity and plasma drag force accelerated the droplet. The behaviors of the inner arc layer varied periodically with the droplet transfer, while the arc periphery remained stable. Droplet transfer was the result of periodic changes in its stress state over time, which also led to periodic changes in arc behavior. This study laid the foundation for further research on the influence of arc and droplet behaviors on the weld pool.
A Novel Semi-Analytical Model of Rock Compressibility for Carbonate Formations
Authors: Allawi Raed H. 
Published: March 2024

Abstract
Over the past years, many researchers have studied the rock compressibility of porous media because it is the most crucial factor affecting the reservoir potential and recovery factor evaluation. However, it has yet to be definitive because the researchers differ, and some suggest it is a positive relationship with porosity, while others propose the opposite. Motivated by this disagreement, this study presents an empirical equation to estimate pore volume compressibility for carbonate formations.
In this regard, this model was performed using several mathematical equations and one correlation. Thus, this equation was validated using laboratory tests. These tests were 415 laboratory-measured core porosity from the carbonate formation, which was utilized to estimate compressibility. In addition, twenty-one laboratory-measured cores compressibility were applied to test the model's accuracy. Well-log data were used to evaluate mechanical rock properties within a bulk modulus (dynamic and static). Moreover, the mechanical rock properties were estimated using derived equations suitable with three types of logs, including bulk density (ρb), compression wave velocity (DTC), and shear wave velocity (DTS).
The elastic rock properties were validated using a uniaxial lab test to test the Static Young's Modulus correlation (Esta) validity, which is essential to derive the compressibility equation. The results led to a compressibility equation that was simple, uncomplicated, and easy to use. Moreover, this equation was compared with laboratory measurement results and confirmed accurate. The results were that the equation values were very close and compatible with laboratory measurements. Therefore, this equation is correct and robust and can be relied upon to estimate compressibility in the carbonate formation of the oilfields.
The study's results could be a reference for carbonate formation to estimate compressibility and be used as effective cost tools.
Neuropsychiatric Disorders In Non-Syndromic Orofacial Clefts: A Follow-Up Study
Authors: Sándor-Bajusz Kinga Amália Dr. , Csábi Györgyi , Dávidovics Kata , Vástyán Attila , Dergez Tímea 
Published: March 2024

Abstract

Background
Children born with non-syndromic orofacial clefts are an at-risk population for neuropsychiatric disorders. In a previous study, we have observed a significantly higher proportion of previous psychiatric care, special education needs, and cognitive difficulties in children with cleft compared to their non-cleft controls. Our goal was to strengthen these observations by screening these children with a clinically well-established neuropsychiatric tool.
Methods
A follow-up study of our previous cohort was carried out in the Department of Pediatrics of the University of Pécs. The cohort included children with non.syndromic orofacial clefts and healthy controls. The M.I.N.I Kid semi-structured interview was used to screen for neuropsychiatric disorders. A parental questionnaire was used to collect demographicand clinical data. Chi2-test or Fischer's Exact test were used to analyze categorical data. Two-sided independent samples Student’s t-test were used for quantitative variables.
Results
The data of 32 non-syndromic cleft and 41 control participants were analyzed. Children with non-syndromic orofacial clefts represented a higher proportion of psychiatric diagnoses compared to their controls, significantly major depressive episode (12,5%), panic disorder (15,6%), and conduct disorder (9,4%). Subgroup analysis revealed higher proportions of diagnoses in children with complex (combined and/or bilateral) and left-sided clefts.
Conclusions
A higher proportion of neuropsychiatric diagnoses were observed in children with non-syndromic orofacial clefts compared to controls. Screening this population at an early age is an important part of their overall clinical care.
A collaborative planning model of IES containing EHs and multi-energy grids under the premise of demand response as well as wind power uncertainty
Authors: Feng Peiyun , Liu Xiang , Ding Nan , Zhao Tianyu , Sun Shijin , Fan Jinxin 
Published: March 2024

Abstract

The proliferation of renewable energy resources brings opportunities as well as new challenges to the planning and operations within integrated energy systems (IES). To make full use of the complementary transformation characteristics between energy sources, while ensuring higher flexibility, reducing wind power curtailment and cost minimization, this paper presents a collaborative planning study of IES with energy hubs (EHs) and multi-energy grids considering demand response (DR) and wind power uncertainty. We introduced Z-number into these uncertainty variables and translated them into quantifiable values. thus reflecting the actual state of this system better. Our optimization model includes EH models which decide the selection and capacity determination of energy conversion and energy storage components. In addition outside the EHs, conventional generator dispatches, and the hardening of electric-gas grid decisions are also included. Finally, the model is verified on an improved Graver-6 bus IES, and numerical results validate that an increased DR and wind power credibility can be conducive to reducing wind curtailment with 45KW and help cost-savings with 4.05%.
Microseismic fuzzy comprehensive evaluation method of coal burst - A case study in Zhaolou Coal Mine
Authors: Zhang Heng , Liang Liang , Lin Na , Zhang Shuai , Cui Miao-miao , Yang Zhong-gang 
Published: March 2024

Abstract
Based on a coal burst in LW 1305 and combined with the curve of stress vs. strain, six multi-parameters are proposed to forewarn coal burst induced by static high-stress concentration and impact loading, containing microseismic (MS) activity S value, fault total area A(t) value, lacking of shock b value, statistical Z value, MS entropy value and the dominant frequency. The precursors mainly characterize the enhancement trend of S value, the suddenly and sharply rise of A(t) value, the continuous and abnormal lower b value, the increasing absolute value of Z sharply and large than 2, the continuous and abnormal decline of Qt value, and the dominant frequency moving to low-frequency band, respectively. The coal burst in LW 1305 was induced by the intrinsic static high-stress concentration and the external strong impact loading generated by fracturing of the key stratum. Essentially, a lot of micro-fissures inside the key stratum had initiated, converged and connected to form the macro-fracture above LW 1305, which was verified by the attenuation rate of K value. Considering the time-varying effect of the overlying stratum movement, the curves of the six multi-parameters have a good agreement with that of stress vs. strain, which indicates it is reasonable to take the observed zone as a whole system to investigate variation of the multi-parameters and fracturing of the key stratum. It is noteworthy that the MS multi-parameters should be combined with reasonable mining arrangement and corresponding prevention measures to monitor, forecast and control coal bursts in the field.
Three-dimensional atomic interface between metal and oxide in Zr-ZrO2 nanoparticles
Authors: Zhou Jihan , Zhang Yao , Li Zezhou , Tong Xing , Xie Zhiheng , Huang Siwei , Zhang Yue-E , Ke Hai-Bo , Wang Wei-Hua 
Published: March 2024

Abstract

Metal-oxide interfaces with poor coherency have unique properties comparing to the bulk materials and offer broad applications in the fields of heterogeneous catalysis, battery, and electronics. However, current understanding of the three-dimensional (3D) atomic metal-oxide interfaces remains limited because of their inherent structural complexity and limitations of conventional two-dimensional imaging techniques. Here, we determine the 3D atomic structure of metal-oxide interfaces in zirconium-zirconia nanoparticles using atomic-resolution electron tomography. We quantitatively analyze the atomic concentration and the degree of oxidation, and find the coherency and translational symmetry of the interfaces are broken. Moreover, we observe porous structures such as Zr vacancies and nano-pores and investigate their distribution. Our findings provide a clear 3D atomic picture of metal-oxide interface with direct experimental evidence. We anticipate this work could encourage future studies on fundamental problems of oxides such as interfacial structures in semiconductor and atomic motion during oxidation process.
Microbiological study for aerobic and anaerobic bacteria influenced corrosion originated from an Egyptian petroleum company: community and corrosive activity
Authors: Labena Ahmed , Mahdy Wael , Kabel Khaled , Gado Walaa S. , Husien Shima , El-Shatoury Enas , Saeed Ali 
Published: March 2024

Abstract

This research focused on investigating a case of an Egyptian petroleum company, where their fields faced Microbiologically influenced corrosion (MIC) problems, even though they had been using corrosion inhibitors and biocides. Therefore, five samples were collected and used to study their microbial community using 16S metagenomics sequencing. The result displayed many potential corrosive bacteria that represented in families, genera and species. The samples were studied concerning total bacterial counts, acid-producing bacteria (APB), slime-forming bacteria (SFB), sulfate-reducing bacteria (SRB). The results displayed that; total bacterial counts were Nil in all samples except one. All samples did not grow in specific media for APB. All the samples displayed positive growth results on SFB specific media. Moreover, only one sample has demonstrated a positive result on SRB specific media. One sample was selected to study its corrosion activities upon its diver’s bacterial community, cultivable on microbially specific media and its real-field corrosion severity. The SFB corrosion`s activities were assessed by determining the variation in bacterial counts, redox potential and metal corrosion rate. Corrosion`s activities of SRB were assessed by examining alterations in dissolved sulfide, redox potential and metal corrosion rate. Surface morphology was examined using scanning electron microscopy.
Risk study of obstructive pulmonary dysfunction caused by silica dust exposure: A multi-center cross-sectional survey
Authors: Li Xin , Tang Mei An , Li Ying , Dai Wei Rong , Huang Lei 
Published: March 2024

Abstract
Objective: Occupational dust exposure is an under-appreciated risk factor for chronic obstructive pulmonary disease (COPD). This study aims to investigate the prevalence of obstructive pulmonary dysfunction in workers exposed to silica dust and analyze their risk by collecting multi-center health monitoring data, so as to provide reference for the diagnosis of occupational dust-induced chronic obstructive pulmonary disease.
Methods: From January 1, 2021 to June 30, 2022, data of 2064 workers exposed to silica dust in Hunan Occupational Disease Prevention and Control Hospital and the Second People's Hospital of Yuanling County were collected, and the incidence of obstructive pulmonary ventilation dysfunction was analyzed.
Results: The prevalence rate of obstructive pulmonary ventilation dysfunction (FEV1/FVC<70%) was 2.3% in 2064 silica dust exposed workers. The prevalence of restrictive pulmonary ventilation dysfunction (FVC/Pre<80%) was 8.1%. The prevalence of obstructive pulmonary ventilation dysfunction in the high exposure group was higher than that in the low exposure group (8.2 vs 0.9%p =0.00). High levels of silica dust exposure increased the risk of obstructive pulmonary ventilation dysfunction OR: 13.80 .
Conclusion: Silica dust exposure can cause obstructive pulmonary ventilation dysfunction and lead to chronic obstructive pulmonary disease. Early diagnosis of silica dust induced chronic obstructive pulmonary disease and timely intervention measures are very important to delay the decline of lung function and protect the health of workers.
Prevalence and Factors Associated With Substance Abuse Among Adolescents in Public and Private Secondary Schools in Katsina State, Nigeria
Authors: Lawal Aminu , Suleiman Bello Abdullahi , Olorukooba Abdulhakeem Abayomi , Hamza Khadeejah Liman , Usman Bilkisu , Abdulkadir Amir , Abba Shaimau Kabir , Yahaya Shamsuddeen , Nass Nafisa Sani , Amadu Lawal , Shuaibu Zaharaddeen Babandi , Belgore Salamatu , Iliyasu Hadiza , Umar Umar Muhammad , Jibril Bashar Muhammad , Bashir Suleiman Saidu , Ahmad Adam , Saulawa Fahad Abubakar , Babale Muawiyyah Sufiyan , Abubakar Alhaji Aliyu 
Published: March 2024

Abstract

Background
Globally, substance abuse has been identified as a major public health issue. The aim of the study was to determine and compare the prevalence, pattern, and predictors of substance abuse among adolescents in public and private day secondary schools in Katsina State
Methods
A cross sectional comparative study was employed to investigate 1126 adolescents in selected public and private day secondary schools in Katsina State. A multistage sampling technique was employed to select participants in public and private secondary schools across geopolitical zones spanning both rural and urban LGAs. Data was collected over eight weeks with the aid of pretested interviewer-administered questionnaire and was analysed using IBM SPSS version 25. Ethical approval was obtained from Katsina State Ministry of Health
Results
Respondents that were 18 years of age accounted for the majority (25.1%). Mean age of the study population was 16.98 ± 1.27 years. Most of the respondents were also in SS3 (44.1%). Proportion of adolescents who ever used any substance at least once was 22.02% (7.99% public, 14.03% private). Factors independently associated with substance abuse were being in SS3 class (p = 0.022), coming from monogamous family (p = 0.014) and peer substance abuse (p = 0.017). The logistic regression model reveals that current users in SS3 class, from monogamous setting and whose peers abuse substances are 7 times more likely (aOR = 7.12), 5 times more likely (aOR = 5.4) and 20% more likely (aOR = 0.209) to be in private than in public schools, respectively.
Conclusion
Prevalence of substance abuse was high. Major predictor was peer substance abuse. Consequently, the state Ministry of Education in collaboration with Ministry of Health and NDLEA should design a substance abuse prevention programme with a view to reducing the menace of substance abuse in the state.
Experience learning——an artificial intelligence framework suitable for exploring the unknown
Authors: He Qing , Wang Shixin , Si Juanning 
Published: March 2024

Abstract

A novel framework of machine learning--experience learning (EL) is proposed for observing new objects and mastering new skills, which can typically be applied to artificial intelligence robots (AIR) exploring the unknown. Unlike traditional approaches, it is not necessary to prepare a large training sample set prior to model training. Instead, an experience chain is established by continuously observing or stimulating the researched objects and recording these experiences, which is inspired by early human learning behavior. Through continuous observation and attempts, the experience chain is updated and gradually converges toward the actual output probability of the researched object. The current experience unit serves as the basis of EL judgment while past experiences can be discarded using a forget coefficient. The application mode of this framework is illustrated with two simple examples. The cat and dog generator experiment represents the self-exploration for new objects. The virtual basketball machine experiment demonstrates the ability of this method to learn a new skill and to effectively mitigate random interference. By comparison, the similarities and differences between the proposed method and the related algorithms are analyzed. Ultimately, this approach proves valuable in enabling artificial intelligence systems to study and explore the unknown territories.
Serial dependence in the auditory numerical estimation task
Authors: Hashimoto Takuma , Morimoto Yukihiro , Makioka Shogo 
Published: March 2024

Abstract

Serial dependence, the phenomenon wherein current perceptions are modulated by preceding perceptions, is posited to play a pivotal role in facilitating perceptual stability. While the exploration of serial dependence has been extensive across various paradigms employing numerical stimuli, its manifestation in tasks requiring auditory numerical estimation remains unexplored. This study aimed to ascertain the presence of serial dependence in auditory numerical estimation tasks. Specifically, it examined participant estimations of object numbers based on the sound of objects colliding with a floor (Experiment 1) and estimations of the number of brief white noises (Experiment 2). Both experiments revealed serial dependence effects, whereby participants' numerical estimates were influenced to align more closely with the number of stimuli presented in the trial immediately prior.
Identification of deep intronic variants in junctional epidermolysis bullosa using whole genome sequencing and splicing assays
Authors: Li Ming , Chen Fuying , Wei Ruoqu , Wang Yumeng , Cao Qiaoyu , Wang Jianbo , Wang Chenfei , Yao Dingjin , Yao Zhirong , Ni Cheng 
Published: March 2024

Abstract

Junctional epidermolysis bullosa (JEB) are a group of recessive dermatoses characterized by mucocutaneous fragility and blister formation. We enrolled 69 recessive JEB cases, 13.0% JEB cases remain genetically undiagnosed following an initial whole exome sequencing. In cases carried COL17A1 mutations, this proportion can reach 31.6%. The aim of our study was to genetically diagnosis the JEB cases by employing whole genome sequencing where only one recessive mutation in targeted gene has been identified. Here, four distinct novel deep intronic mutations (c.4156+117G>A, c.2039-104G>A and c.1267+237dupC in COL17A1 gene and c.-38+2T>C promoter mutation in LAMB3 gene) had been identified in six cases. Of these, the c.4156+117G>A variant was found three out of five cases and may be a COL17A1 hotspot deep intronic variants in Chinese JEB. Minigene assays and in vivo studies revealed that these intronic mutations were indeed shown to exert a splicing defect by causes exon skipping or pseudoexon insertion into the transcript. Moreover, the three COL17A1 deep intronic variants demonstrated effect upon mRNA processing in HaCaT cells, not in HEK293 cells. We show that deep intronic mutations are not a rare occurrence, and firstly described to occur in JEB. Our results suggest the importance of selecting the right cell line for mRNA analysis.