bluuebunny/crossref_metadata_2025_split huggingface dataset:
Anxiety and depression emotion is involved in the pathogenesis of postural tachycardia syndrome
Authors: Shao Wei , Li Hongxia , Gao Lu , Han Zhenhui , Yuan Yue 
Published: September 2022

AbstractObjective To explore the significance of anxiety and depression in children with postural tachycardia syndrome (POTS). Study design The study enrolled seventy-one children diagnosed as POTS in Beijing Children's Hospital Affiliated to Capital Medical University and Kaifeng Children’s Hospital, aged 13 ± 2 years; The Self-Rating Depression Scale (SDS), the Self-Rating Anxiety Scale (SAS), the Hamilton Depression (HAMD) scale, and the Hamilton Anxiety (HAMA) scale were carried out in POTS children. The POTS children were divided into two groups: anxiety/ depression group and non-anxiety/ depression group according to the above scales scores. Heart rate (HR), and blood pressure (BP) were monitored by a Dash 2000 Multi-Lead Physiological Monitor. Results Twenty POTS children consisted of anxiety/ depression group. Twelve were girls and eight were boys, with mean age of 14 ± 2 years. The non-anxiety/ depression group included fifty-one POTS children, aged 12 ± 2 years. Twenty-six were girls and twenty-five were boys. There were no statically differences in weight, gender, systolic blood pressure (SBP), diastolic blood pressure (DBP), and HR in supine. The age and height of anxiety/ depression group were higher than that in non- anxiety/ depression group. The maximum HR (HRmax) in ten minutes of upright position or tilt, the change of HR from supine to upright position (ΔHR), symptom scores in anxiety/ depression group were significantly higher than that in non-anxiety/ depression group. Four scale scores were correlated with symptom scores and ΔHR. Multivariate general linear model analysis revealed that HAMD scores and SAS scores were closely associated with the OI symptom scores. Conclusions Anxiety and depression emotion might be involved in the pathogenesis of POTS.
A study of Thai word separation methods based on machine learning
Authors: Xiong Yanjiao , Zhao Zhipeng , Duan Zhigang 
Published: September 2022

AbstractThai is an analytic, isolated language where monosyllabic words occupy most of the basic vocabulary, and differences in vocabulary and grammar are distinguished by differences in tone. Traditional dictionary-based matching methods have difficulty constructing a complete dictionary. We use four machine learning algorithms, namely, a plain Bayesian classifier, a decision tree classifier, a support vector machine classifier, and a conditional random field, to slice Thai language texts for multiclass problems. In addition, we use the above four machine learning algorithms for a two-class problem slicing of Thai texts. In this study, we found that first, the accuracy, recall and F1 values of Thai text slicing using the conditional random field algorithm in the multiclass classification problem are much higher than those of the plain Bayesian classifier, decision tree classifier and support vector machine classifier. Second, the accuracy, recall, and F1 values of the plain Bayesian algorithm, J48 decision tree algorithm, and support vector machine algorithm are higher than those of the multiclass problem classification in both classes. The accuracy of the conditional random field was improved, but its recall and F1 values decreased significantly. In summary, it can be seen that the conditional random field has better results in Thai word separation.
A Comparative Study of Arima and Sarima Models to Forecast Lockdowns Due to Covid-19
Authors: Chhabra Hardik 
Published: September 2022

Abstract
The aim of this paper is to create a machine learning model that can forecast and alert users about the COVID−19 illness lockdown period. World Health Organization (WHO) data on novel coronavirus were the subject of exploratory data analysis, which used a variety of techniques to identify the proper parameters for the data so that the Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models could be trained on it. Using data from January 2020 to May 2022 during the previous two years, the machine learning model is trained. In APPENDIX G the findings for the ARIMA(5,1,5)(0,0,0)(0) and SARIMA(5,1,5)(0,0,0)(9) models are compared. The dependant variable for the Automatic ARIMA and SARIMA functions might be either new cases or death cases. The current model having these parameters can be used to work on the data of diseases that have a tendency to spread widely and quickly. This study can be extremely helpful in predicting lockdown times so that different government entities can make preparations in accordance.
Reactiveness and inclusiveness – models of regional labour markets regarding the situation of young people
Authors: Bal-Domańska Beata 
Published: September 2022

AbstractHaving observed the growing number of transnational shocks affecting the national and regional economies, it is worth assessing the condition of regional labour markets, primarily in the context of the situation of young people, who for many years have been one of the most vulnerable groups of employees in the labour market. Recently, we have observed an improvement in the regional labour markets situation, which compared to the situation in the period of 2009-2015 is more favourable despite the Covid19 pandemic. Unfortunately, not all age groups can enjoy the prosperity. Youth unemployment rate (March 2021 27EU: 15,9%) still significantly exceeds that recorded for adults (6.5%). The article defines models of regional labour markets with regard to the situation of youth in terms of their reactivity of youth labour markets to economic shocks, and the inclusiveness of young people compared to adults. The problem of no inclusiveness of the labour markets regarding young workers affects both markets presenting high unemployment rates and highly responsive to economic shocks, and also markets featuring low youth unemployment rates and stable economic conditions.
Delineation of groundwater potential zones of the transboundary aquifers within the semiarid Bulal catchment, Southern Ethiopia
Authors: Gebeyehu Assaminew , Ayenew Tenalem , Asrat Asfawossen 
Published: September 2022

Abstract

Groundwater is the only reliable drought-resilient water source in the semiarid Bulal transboundary catchment located close to the Kenyan border. The central and southern parts of the catchment are dominantly overlain by Bulal basalts, while the Borena basement complex outcrops in the eastern part. This work aims to identify and delineate the groundwater potential zones of the semiarid Bulal catchment within the boundary of Ethiopia using integrated GIS and RS techniques in combination with the analytical hierarchal process (AHP). Ten input parameters were selected based on their relative significance to groundwater occurrence and movement. The normalized weights were assigned to the input themes and their individual features as per Saaty’s AHP approach. A composite groundwater potential zone index (GWPZI) map was finally generated by integrating all the input layers employing the GIS-overlay analysis technique. The map was validated using the yield of wells from the catchment. The GWPZI map depicts four groundwater potential zones in the catchment: high (representing 27% of the total area), moderate (20%), low (28%) and very low (25%). The geological feature has the greatest influence on the distribution of groundwater potential in the catchment. Areas with high potential are mainly overlain by Bulal basaltic flow and alluvial sediments, while areas covered with regolith developed over the metamorphic basement are attributed to the low and very low groundwater potential zones. The GWPZI map will serve as a quick guide for effectively planning, managing, and developing the groundwater resources of the catchment.
The effect of the silver nanoparticles and platelet-rich fibrin in the healing of the severed superficial digital flexor tendon in donkeys (Equus asinus)
Authors: AbdelHakiem Mohammed A.H. , Hussein Ayman , Seleim Samia Moustafa , Abdelbaset Abdelbaset Eweda , Abd-Elkareem Mahmoud 
Published: September 2022

Abstract
This study investigated the effect of the silver nanoparticles (AgNPs) and platelet-rich fibrin (PRF) in the healing of the severed superficial digital flexor tendon in donkeys (SDFT). Twenty-seven adult donkeys were used in the study. The animals were divided into three equal groups. The 1st group (control group) in which the severed SDFT was sutured without the addition of any adjuvant. In the 2nd group, there was a suture of severed SDFT with the addition of 1ml of 1mM silver nanoparticles (AgNPs group). The 3rd group was subjected to the cutting of SDFT and then the addition of PRF after its suture. Each group of animals was divided into three equal subgroups that were examined after one, two, and three months, respectively. Each group of animals was clinically evaluated by assessing lameness. Gross and microscopic examinations of the healed tendons were performed after 1, 2, and 3 months of surgery. The results revealed that the lameness degree decreased in the PRF and AgNPs groups, in comparison to the control group, especially in the third month after surgery. As well as the lameness decreased significantly after the 3rd month relative to the 1st-month lameness in the AgNPs group. Interestingly, it was found that the PRF and AgNPs enhanced cell alignment and collagen deposition at the site of tendon injury, particularly among third-month subgroups. Therefore, it could be concluded that the PRF and AgNPs are effective materials for enhancing SDFT healing in donkeys.
Design of plasmonic coupler with germanium spacer layer for quantum well infrared photodetectors
Authors: Das Sona , Sengupta Saumya , Jayaswal Gaurav , Sharma Rahul Kumar , Hooda Manish Kumar 
Published: September 2022

Abstract
A design of plasmonic coupler, composed of metal hole arrays and germanium spacer layer, integrated with quantum well infrared photodetector structure is presented. Insertion of germanium spacer layer in this hybrid structure enhances absorption and z-component of electric field in the quantum well absorption region under both substrate-side and air-side illumination configurations. By changing thickness of the germanium spacer layer, the plasmonic resonance wavelengths can be adjusted with peak quantum well response. This plasmonic coupler is believed to be promising to improve performance of quantum well infrared photodetectors.
Modeling of infectious prediction in Human Brucellosis via metrology-driven machine learning based on routine laboratory results: a study from North China
Authors: Wang Wei , Zheng Huarong , Zhang Wei , Li Tongzeng , Yin Tao , Liang Yufang , Cui Weiqun , Wang Qingtao , Zhou Rui 
Published: September 2022

Abstract
Background:Human brucellosis shows high morbidity, severe economic losses and public health problems globally. Because of traditional cultural method shortcomings, a novel tool for assisting clinical decision to identify high-risk infectious patients is urgently required.
Methods: The data of total 2283 clinically confirmed brucellosis patients (including acute phase 816/chronic phase 989) and 13093 patients with characterized healthy outcomes was collected. Models with 3 different case groups, different sizes of variables, 7 different machine learning algorithms were tested and compared for model optimization. Metrological means combined with Shapley additive explanation (SHAP) was used for model explanation.
Results: The gradient boosting machine with acute phrase patients as case group achieved the highest accuracy (AUC=0.997, 95%CI 0.994-0.999), specificity/sensitivity of 89.6%/99.8% and positive predictive value/negative predictive value of 99.4%/96.7%. Finally, 16 variables based on Pearson's correlation coefficient scores and recursive feature reduction using random forest algorithm was selected for this model. The measurement uncertainty (MU) of percent basophil, direct count eosinophil, percent eosinophil in complete blood count accounted for a large proportion in all variables. Thus, the influence of each input feature for the accuracy and the generalization of our model was quantitative and visualized by MU together with SHAP.
Conclusions The proposed metrology-driven artificial intelligence-basedmodel, exclusive using regular laboratory results offers a promising tool to preliminarily identify high-risk brucellosis infection patients and risk stratify patients in different population, thereby promoting the health of the patients while protecting the health of the public and overcoming financial or supply constraints, especially in rural areas.
A novel procedure for measuring bacterial ATP in septic blood enables rapid antimicrobial susceptibility testing
Authors: Sugie Kazushige , Niimi Hideki , Ueno Tomohiro , Noda Hideyuki , Uchiho Yuichi , Kawabe Shunsuke , Ishimaru Masako , Tada Hiroko , Uematsu Chihiro , Hamazato Fumiaki , Kitajima Isao 
Published: September 2022

Abstract
We developed a novel pretreatment procedure that destroys the plasma membrane of platelets but not the bacterial cell wall, allowing viable bacterial ATP in septic blood samples to be measured with minimal human-derived ATP background. After the pretreatment procedure with this approach, the plasma membranes of platelets are completely destroyed, and human-derived ATP background is dramatically decreased. To test the recovery rates of viable bacteria after pretreatment, we evaluated 19 bacterial genera. As a result, the mean recovery rates of viable bacteria were 93.6% using ATP bioluminescence and 71.9% using the colony-counting method. Finally, using the pretreatment procedure and ATP bioluminescence, rapid antimicrobial susceptibility testing of pathogenic bacteria in septic blood samples was performed, and LVFX resistance was determined within 6.5 h of whole blood collection. This novel pretreatment procedure will open up new possibilities concerning the utility of ATP bioluminescence in medicine.
Identification of differentially methylated regions in rare diseases from a single patient perspective
Authors: Grolaux Robin , Hardy Alexis , Olsen Catharina , Dooren Sonia Van , Smits Guillaume , Defrance Matthieu 
Published: September 2022

Abstract
Background:
DNA methylation (5-mC) is being widely recognized as an alternative in the detection of sequence variants in the diagnosis of some rare neurodevelopmental and imprinting disorders. Identification of alterations in DNA methylation plays an important role in the diagnosis and understanding of the etiology of those disorders. Canonical pipelines for the detection of differentially methylated regions (DMRs) usually rely on inter-group (e.g. case versus control) comparisons. However, in the context of rare diseases and ii-locus imprinting disturbances, these tools might perform suboptimal due to small cohort sizes and inter-patient heterogeneity. Therefore, there is a need to provide a simple but statistically robust pipeline for scientists and clinicians to perform differential methylation analyses at the single patient level as well as to evaluate how parameter fine-tuning may affect differentially methylated region detection.
Result:
In this paper, we describe an improved statistical method to detect differentially methylated regions in correlated datasets based on the Z-score and empirical Brown aggregation methods from a single-patient perspective. To accurately assess the predictive power of our method, we generated semi-simulated data using a public control population of 521 samples and assessed how the size of the control population, the effect size and region size affect DMRs detection. In addition, we have validated the detection of methylation events in patients suffering from rare multi-locus imprinting disturbance and discuss how this method could complement existing tools in the context of clinical diagnosis.
Conclusion:
We present a robust statistical method to perform differential methylation analysis at the single patient level and evaluated its optimal parameters to increase DMRs identification performance and show its diagnostic utility when applied to rare disorders.
Spatiotemporal characteristics of precipitation processes in the Dabie Mountains of China and their response to ENSO
Authors: Zhao Yongmei , Sun Wenjun , Chen Ting , Zhu Changming , Luo Yuan , Zhou Fang 
Published: September 2022

Abstract
Using daily precipitation data (1960–2019) from five meteorological observation stations in Lu’an City (China), this study analyzed the impact of various El Niño–Southern Oscillation (ENSO) events on the spatiotemporal characteristics of regional precipitation. Results showed that precipitation in Lu’an City increased in intensity and became more dispersed temporally over the 60-year period. Such characteristics of precipitation were especially prominent in Jinzhai, Huoshan, and Lu’an counties. In comparison with precipitation indexes of normal years, the annual total and maximum consecutive precipitation amounts increased in years with central Pacific warming. Additionally, the annual maximum numbers of consecutive days with/without precipitation were extended in years with eastern Pacific cooling, whereas the annual total and maximum numbers of consecutive days with precipitation were extended in years with eastern Pacific warming. Various ENSO events had distinct impact on rainstorms and 6-d consecutive precipitation. During ENSO years, the high-value centers of days with rainstorms and 6-d consecutive precipitation were distributed predominantly in the southwestern mountainous regions of Jinzhai and Huoshan. Flood hazards were more likely to occur during ENSO years with an extreme value of the rainstorm anomaly index, or during years with eastern Pacific cooling and central Pacific warming with an extreme value of the 6-d consecutive precipitation anomaly index. For improved flood relief in Lu’an City, particular attention should be paid to precipitation in years with such climatic extremes.
Graphene Oxide Fibers: Synthesis Method and Correlation among Compositional, Vibrational, and Electrical Properties
Authors: Castro-Ladino J. R. , Prías-Barragán J. J. 
Published: September 2022

Abstract
The synthesis method and correlation among compositional, vibrational, and electrical properties in graphene oxide fibers (GOF) are presented and discussed here. The GOF samples were synthesized from rice husk (RH) as source material, via a thermal decomposition method employing an automated pyrolysis system with a controlled nitrogen atmosphere, varying carbonization temperature (TCA) from 773 to 1273 K. The samples were characterized through scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and current-voltage curves at four points of collinear electrical contacts. Oxide concentration (OC) of samples varied from 0.21 (at TCA = 1273 K) to 0.28 (TCA = 773 K), influenced by TCA. The GOF samples exhibited fiber morphology composed of porous structures with sizes between 5 and 30 μm; peaks in the XPS spectrum at ≈ 538 and 284 eV were associated with O1s and C1s, respectively. Analysis of the results corroborates the graphite oxide vibrational behavior with crystal sizes varying from 3.52 to 4.88 nm, and boundary defects density of 3.12-3.6 x 10-4 cm-2, as expected. The electrical response shows that OC decreases and increases electrical conductivity from 4.66 x 10-2 to 4.45 S/m at the polycrystalline phase, possibly attributed to the desorption of some oxides and organic compounds. Likewise, the physical correlations between OC and vibrational response revealed that decreased OC generates an increase in boundary defects density and decreased crystal size, as a consequence of thermal decomposition processes. The correlations between electrical and vibrational properties revealed that increased electrical conductivity increases defects density and decreases crystal size in GOF samples, possibly attributed to hydroxyl and epoxy bridges getting carbons atoms out of plane and modifying the band-gap energy (Eg) and graphitic structure. These results suggest that by controlling the TCA and OC in the synthesis of GOF samples, modification of vibrational and electrical properties, of great interest in the electronic development of sensors and devices, has been made possible.
Implementation of FPGA-based Phase Detection and Improving the Accuracy Using Machine Learning
Authors: Salehi Seyedeh Akram , AFARIDEH Hossein , Asadi Mohammad Saleh Sharifi , Rahimi Mohammad Sadegh , Ghergherehchi Mitra , Chai Jong Seo 
Published: September 2022

Abstract

In cyclotron accelerators, Digital Low-Level RF or DLLRF system plays a major role in the detection and control of the RF amplitude and phase which means it is necessary to utilize accurate, reliable, stable, and high-speed digital hardware. In this project, Field- Programmable Gate Array or FPGA was selected to design and implement a special phase detection algorithm that is not only independent of the RF amplitude changes, but inde- pendent of the sampling speed of analog-to-digital converter or ADC and the RF frequency. Thus, it will be proved that using the ADCs is possible, without considering the RF amplitude changes, which enables us to detect and control the RF phase in a broad range of frequencies. Finally, the accuracy of this project is maximized by using a linear regression model in machine learning to predict phase differences precisely.
Ultrastrong coupling regime of the two-photon quantum Rabi model: a Numerical Approach
Authors: Hammani M , Jineral R El , Bennai M 
Published: September 2022

Abstract
In this paper, some physical observables such as the energy spectra and time dynamics as well as the photon statistics of the two-photon quantum Rabi model in the ultrastrong coupling regime are numerically explored. The main aim of this work is to show the effect of the number of cavity fock states N on the spectral properties, particularly the special feature spectral collapse at critical coupling gc=ꞷc/2. We have shown that for a large number of cavity fock states the numberof discrete energy levels decreases down to only one at the collapse point. Secondly, we have simulated the time dynamics behaviors as well as the photon statistics, at the final state, of the two-photon quantum Rabi model in the ultrastrong coupling regime.
PACS numbers: 03.67.Lx, 85.25.Dq, 42.50.Dv