bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning's model-agnostic interpretability on The Prediction of Students' Academic Performance in Video-Conference-Assisted Online Learning During the Covid-19 Pandemic
Authors: Miranda Eka , Aryuni Mediana , Rahmawati Mia Ika , Hiererra Siti Elda , Sano Dian 
Published: October 2023

Abstract
The Covid-19 pandemic had an immediate impact on higher education. Although online technology has made contributions to higher education, its adoption has had a significant impact on learning activities during the Covid-19 pandemic. This paper proposed a predictive model for predicting students’ academic performance in video-conference-assisted online learning (VCAOL) during Covid-19 pandemic based on machine learning approach. We investigated: Random Forest (RF), Support Vector Machine (SVM) and Gaussian Naive Bayes (GNB). There were 361 data gathered as a dataset from September 2022 to January 2023. The overall result revealed RF outperformed SVM and GNB with accuracy score of 60.27%, precision 59.46%, recall 60.27%, F1-score 59.51% and ROC AUC 87%. Understanding a machine learning model's black-box output was crucial for providing predictions that explain why and how they were developed. SHAP value of global interpretability to visualize global feature importance revealed that students' performance while using VCAOL (Performance) was the most critical attribute for predicting students' academic performance. The SHAP local interpretability bar plot revealed that ‘student academic performance was still well achieved during the learning process, despite using video conferencing during the Covid-19 pandemic’ (Performance), when Performance decreased it contributed negative impact on students’ academic performance in VCAOL during Covid-19.
Continuous Stimulation of Subcutaneously Implanted Xenogeneic Protein Thread Recruited Treg Cells and M2 Macrophages to Induce Inguinal White Adipose Tissue Browning
Authors: Jiang Shenglu , Zhu Lili , Xu Yukun , Liu Zhao , Cai Jialin , Zhu Tao , Fan Qing , Zhao Zhenxiong 
Published: October 2023

Abstract
Brown adipose tissue and beige adipose tissue have emerged as potential targets for obesity intervention, but the present strategies targeting at inducing the browning of white adipose tissue are not satisfactory. Xenogeneic protein thread implantation is an attempt of complementary and alternative medicine in the field of obesity treatment, but the specific effect or mechanism has not been well validated. This study examined the corrective effect of subcutaneous catgut or absorbable polymer thread embedding on obesity and metabolic syndrome, and aimed to explore the mechanism of subcutaneous white adipose tissue browning after catgut embedding. Embedding of catgut reduced weight gain and improved metabolic status in ob/ob mice. Browning of bilateral inguinal WAT was induced after catgut embedding, with massive infiltration of Treg cells and M2 macrophages in the tissue slices of fat pads. IL-10 and TGF-β released by Treg cells targeted the macrophages and the induced M2 macrophages promoted the secretion of norepinephrine in sympathetic nervous system, leading to the activation of β3-AR related pathways in adipocytes. This study demonstrates abdominal subcutaneous catgut embedding has the potential to combat obesity through inducing the browning of WAT mediated by the infiltrated Treg cells and macrophages.
An Analytical Methodology of Rock Burst with Fully Mechanized Top- coal Caving Mining in Steeply Inclined Thick Coal Seam
Authors: Shan Pengfei , Yan Zhongming , Lai Xingping , Xu Huicong , Hu Qinxin , Guo Zhongan 
Published: October 2023

Abstract
Rock burst disaster is still one of the most serious dynamic disasters in coal mining, seriously restricting the safety of coal mining. The b value is the main parameter for monitoring rockburst, and by analyzing its changing characteristics, it can effectively predict the dangerous period of rockburst. This article proposes a method based on deep learning that can predict rock burst using data generated from microseismic monitoring in underground mining. The method first calculates the b value from microseismic monitoring data and constructs a time series dataset, and uses the dynamic time warping algorithm (DTW) to reconstruct the established b value time series. A bidirectional short-term and short-term memory network (BiLSTM) loaded with differential evolution algorithm and attention mechanism was used for training, and a prediction model for the dangerous period of rock burst based on differential algorithm optimization was constructed. The study used microseismic monitoring data from B1 + 2 fully mechanized mining face and B3 + 6 working face in the southern mining area of Wudong Coal Mine for engineering case analysis. The commonly used residual sum of squares, mean square error, root mean square error, and correlation coefficient R2 for time series prediction were introduced, which have significant advantages compared to basic LSTM algorithms. This verifies that the prediction method proposed in this article has good prediction results and certain feasibility, and can provide technical support for the prediction and prevention of rock burst in steeply inclined thick coal seams in strong earthquake areas.
Household economic losses and community knowledge determine control strategies: A case of cerebral coenurosis in small ruminants in Northern Tanzania
Authors: Nzalawahe Jahashi Saidi , Mlanzi Dunia Saidi , Lupindu Athumani Msalale , Ngowi Helena , Eisler Mark Charles 
Published: October 2023

Abstract
Background
Cerebral coenurosis is caused by Taenia multiceps and affects sheep and goats worldwide and has zoonotic implications. Information on the financial burden, understanding of the disease and control practices by farmers may inform control strategies.
Methods
A cross-sectional study was conducted in Babati and Ngorongoro Districts, Tanzania, to determine the economic losses, farmers’ knowledge, attitude and practices on control of Taenia multiceps coenurosis in small ruminants. A structured questionnaire was administered to 558 households to collect data in the study area.
Results
The study showed that the estimated total annual direct and indirect economic loss encountered for the past one year were TZS 121.4M (USD52.3K), and TZS 1.4M (USD 609.49) for Babati and TZS 642.4M (USD 276.8K) and TZS 14.2M (USD 6.1 K) for Ngorongoro respectively. The total annual loss was TZS 122.8M (USD 52.9K) for Babati and TZS 656.5M (USD 282.9K) for Ngorongoro District. The average annual household financial loss for 2019 was TZS 356.8K (USD 153.83) for Babati and TZS 3.1M (USD1.3K) for Ngorongoro District. The questionnaire revealed that coenurosis was the most common disease of small ruminants to 50.9 - 78% of respondents followed by helminthosis and CCPP. About 85.4 – 96.7% of respondents were aware of coenurosis from their own experience while 0 - 0.6% of the respondents had no knowledge of it at all. Based on a summated index scale, the average knowledge score was 5.85 out of 16 (36.6%) which implies a low level of knowledge on coenurosis. A total of 94.5% of respondents showed willingness to own, coordinate, participate in the control plans and pay the control costs. The average attitude score on 8-point Likert scale was 32.5 out of 40. Respondents sold coenurosis suspected animals (41.4%), slaughtered them for home consumption (25.3%) or opted for treatment (33.3%).
Conclusion
Given the economic loss, favourable attitude on coenurosis control participation, delivery of an educational package on epidemiology and control of coenurosis to livestock keepers may be expected to trigger a collaborative and sustainable intervention.
Does faeces excreted by moxidectin-treated sheep impact coprophagous insects and the activity of soil microbiota in subtropical pastures?
Authors: Hentz Susana Gilaverte , Reyes Felix Guillermo Reyes , Kaschuk Glaciela , de Oliveira Leandro Bittencourt , Fernandes Maria Angela Machado , Monteiro Alda Lúcia Gomes 
Published: October 2023

Abstract

Moxidectin (MOX) is used to control helminth parasites in ruminant livestock being released through faeces into the environment. We aimed at evaluating the impact of MOX-contaminated sheep feces on soil coprophagous insects, microbial biomass, and activity. In a field experiment, faeces from MOX-treated rams (subcutaneous dose of 0.2 mg kg-1 body weight) and faeces from non-treated rams were placed on an animal-free pasture, protected or not from rain, for 88 days. Coprophagous insects were captured, identified, and counted, while N mineralization, and C e N contents were monitored. In a controlled experiment, faeces from non-treated animals were amended with MOX concentrations ranging from 75 to 3,000 ng MOX kg-1 faeces, mixed with soil from an animal-free pasture, and incubated for 28 days. Soil microbial biomass, respiration, and culture-microbe counting were determined. Diptera, Hymenoptera, Isoptera, and Coleoptera were equally found in faeces from both MOX-treated and non-treated animals. Faeces from MOX-treated animals had more N and degraded faster than those from non-treated animals. Even the lowest MOX concentration decreased soil microbial biomass, respiration, and N mineralization. Despite not harming coprophagous insects, the addition of MOX to feces from drug-free sheep has a negative impact on soil microbial biomass and activity.
Transcriptome analysis of the mechanism to ginsenoside inhibiting on Botrytis cinerea
Authors: Yang He , Xie Lu , Jin Hongyan , Di Peng , Lu Baohui , Chen Xiaolin , Wang Yingping , Xu Yonghua 
Published: October 2023

Abstract
Background: The control of tomato gray mold mainly relies on chemical fungicides, which can easily cause pesticide residues and environmental pollution. Therefore, a new fungicide for the control of tomato gray mold is urgently needed. Ginsenosides can inhibit the growth of diseases and are safe and non-toxic. To reveal the antibacterial mechanism of ginsenoside against Botrytis cinerea, and to provide theoretical basis for the development of ginsenoside plant pesticide.
Results: Ginsenoside showed significant inhibitory activity against B. cinerea, and the inhibition rate reached 95.45% at 1 mg·mL-1. Transcriptome results showed that there were 21 DEGs in the 3 stages, among which 17 genes were upregulated in the treatment group. Bioinformatics analysis showed that the DEGs are mainly related to metabolic pathways such as Metabolism, Cenetic Information Processing, Environmental information Processing and Cellular Processes. Quantitative real-time PCR results demonstrated the accuracy of transcriptome data. Ginsenosides seriously damaged the ultramicrostructure of B. cinerea, resulting in the exosmosis of contents, the formation of a large number of cytoplasm cavities, and the change and degradation of mitochondrial structure.
Conclusion: Transcriptomic analysis indicated that ginsenoside may inhibit the growth of B. Cinerea by decreasing the stability of cell membrane or cell wall, and B. Cinerea may respond to the stress caused by ginsenoside by increasing ergosterol synthesis and ABC transporter expression.
Optimizing a Interpretable Diagnostic Model for Colorectal Cancer Based on Yin Deficiency Pattern Characteristic Genes Using 21 Machine Learning Algorithms and Bayesian Opitimization
Authors: Li Yuqing , Huang Zhongquan , Liu Xiang , Xu Shun , Chen Zhuoni , Zhang Ning , Lin Rongrong , Cheng Hongyan , Li Geng , Liang Zhiwei 
Published: October 2023

Abstract
In this study, we aimed to identify a core set of Yin Deficiency Pattern (YDP) genes for predicting colorectal
cancer (CRC) and to construct reliable machine learning models optimized by Optuna. Comprehensive analysis was performed
on nine datasets, totaling 1,680 samples. A CRC diagnostic prediction model was developed by comparing 21 machine
learning classification models with Optuna hyperparameter optimization and validated across six independent external
cohorts. Additionally, the expression patterns of the core diagnostic genes were experimentally validated.
Linear Discriminant Analysis (LDA), along with four other machine learning models (please specify these models), ranked as the top five performing models
across six cohorts, demonstrating superior performance with AUC values exceeding 0.99 and all other performance metrics surpassing 0.899. This study marks
the first utilization of four specific novel machine learning models (again, please specify these models) in CRC diagnosis.
The robust performance of the top models across multiple external validation sets underscores the reliability and generalizability
of our diagnostic model. These results hold potential implications for the development of personalized medicine approaches
in CRC treatment, offering a new avenue for early detection and prognosis improvement.
Evidence of P. vivax in Northern Kenya, an emerging malaria control threat; An incidence report from the outcome of the mid-2023 epidemic response survey
Authors: Kimani Francis T. , Thiong’o Kelvin , Karani Lewis , Otinga Maureen A. , Ombati Mary , Gesusu Nemrod , Onchieku Noah M. , Kamau Luna , Matoke Damaris 
Published: October 2023

Abstract
Background: Malaria mortality and morbidity in Kenya has all along been largely due to Plasmodium falciparum infections with no cases attributed to P.vivax. The epidemiology in northern Kenya counties is traditionally classified as zero to low-risk transmission zones. A recent upsurge in febrile illnesses was reported in the counties of Marsabit, Mandera and Turkana, in mid-2023 which required an emergency pandemic response by the county and national health teams. In addition to possible viral infection that had featured in these areas previously, malaria disease was also investigated as part of the response. An analysis determining the presence of Plasmodium infection and the species present in this rapid response are hereby reported.
Methods: A total of 89 blood samples were collected from patients suspected of malaria in the Northern Kenya counties, after which they were analyzed. Positivity and speciation was performed using 18S rRNA and qPCR TaqMan probes with species specific primers
Results: From 3 health facilities in Marsabit, Mandera and Turkana counties a total of 24, 50 and 15 dried blood spot samples were received respectively. There were 11, 40 and 15 Plasmodium positive samples in the three county health facilities respectively. This translated to a total positivity of 74.2%. Speciation analysis was done for the four common plasmodium species. This analysis recorded a total of 60 P. falciparum positive samples, 2 samples with a mixed infection of P. malariae and P. falciparum, 2 P.vivaxmono infection samples, and 2 P. vivax P. falciparum co-infected samples. No P.ovale was observed in any of the facilities. Three of the P.vivax infections were in Marsabit county while the remaining case was from Mandera County translating to an incidence rate of 12.5% (3/24)and 2%(1/50) respectively. No P.vivax infection was identified in Turkana county.
Conclusion: The findings of this analysis show that the incidence of malaria from the response sites was quite high, at 74.2%. Although the burden of P. vivax in these counties is undetermined, the presence of the observed proportions in such a small sample size is an indicator of an underlying problem of cases that are rarely diagnosed in a clinical setting and may often be asymptomatic. The presence of Anopheles stephensi that was recently identified for the first time in Kenya in the same study region, implies the potential expansion of a highly competent vector here, which could dramatically enhance the transmission of P. vivax and reverse all the malaria control gains made over the years.
Computer Model of Pump-Well-Reservoir System Based on the New Concept of Imitational Modeling of Dynamic Systems
Authors: ALIEV F. A. , MOVAHEEDI ZABIHULLAH , JAMALBAYOV M. A. , VALIYEV N. A. , HAJIYEVA N. S. , SHOKRI ALI 
Published: October 2023

Abstract
The concept of imitational modeling of dynamical systems is being developed. The fundamentals, terms of the concept and principles of creating a simulation of a physical process are outlined. The proposed concept is applied to the integrated modeling of the process of developing a volatile oil reservoir operated by a rod-pump well in the "pump-well-reservoir" system. The "pump-well-reservoir" model developed on the principles of the concept, taking into account the presence of the annulus and the fluid flow between the annulus and the lifting pipe, was used for a numerical study of the operation of the system. The dependence of the flow rate of a rod pump well and the degree of filling of the pump cylinder on the swing number was studied. It has been established that with an increase in the swing number, the pump flow and the filling degree of the cylinder increase up to a certain point, after which the pump flow almost does not change, and the degree of filling decreases sharply. Full filling of the rod pump cylinder does not always coincide with the maximum flow rate of the well. In such cases, the optimal mode can only be achieved by switching to a periodic regime of the pumping.
WITHDRAWN: A new perspective on the SDGs nexus: ethnobiology’s contribution
Authors: Arrivabene Alexandre , Lasic Lou , Blanco Julien , Carrière Stéphanie M. , Ladio Ana , Caillon Sophie , Porcher Vincent , Teixidor-Toneu Irene 
Published: October 2023

Abstract
Progress achieving the Sustainable Development Goals (SDGs) is insufficient at global scale. A thorough understanding of SDG synergies and trade-offs is needed to enhance their achievement, but such understanding is still limited. Ethnobiology has been proposed as a discipline able to contribute to sustainability and finely-tuned to study these interlinkages. In this study, we conducted a systematic review of all articles published by the Journal of Ethnobiology since 2015 (SDGs’ adoption year) and evaluated if and how they address one or more SDGs. We asked the following questions: 1) Do articles mention explicitly or implicitly the SDGs? 2) What connections does ethnobiology establish between them? 3) How do articles define sustainability? And, (4) whether articles dealing with sustainability are more inclusive to non-academics. Our study shows that ethnobiology implicitly addresses almost all SDGs and focuses particularly on the connectedness of SDGs 1–3 and 15. Biodiversity’s understanding (SDG 15) provides the basis for culture and Indigenous and local knowledge and ensures communities’ food security (SDG 2), health and well-being (SDG 3), and prosperity (SDG 1). We observe that ethnobiology studies sustainability in paradigm ethics of Indigenous peoples and local communities rather than that of the global sustainability agenda, but that local actors are still not commonly engaged in knowledge co-construction processes. While ethnobiology can provide an analysis of the SDGs complimentary to that of sustainability science and be a motor of transformative change, this potential is not fully realized because of a persisting decoupling between place-based research and global sustainability frameworks.
Properties and structural features of native and modified proteins of concentrates from white and brown rice
Authors: Kolpakova V. V. , Fan Q. Ch. , Gaivoronskaya I. S. , Chumikina L. V. 
Published: October 2023

The physicochemical properties and structural features of proteins of concentrates (PC) from white and brown rice are described. Differences in a degree of proteolysis and the relationship between the functional and technological properties of proteins and their structure were established. Proteins from white rice had lower molecular weights (MW) than proteins from brown rice: 1.3–199.5 kDa versus 1.5–251.1 kDa. Most proteins were grouped in three low-molecular-weight fractions (МW 11.7–27.5 kDa) in PC from white rice and in one fraction with the high molecular weight (182–251.1 kDa) in PC from brown rice. In the process of hydrolysis, the MW of proteins (high-molecular-weight) of PC from white rice decreased from 199.5 to 120.2 kDa with generation of peptides with a molecular weight of <1.3 kDa, while the MW remained unchanged upon hydrolysis of proteins of PC from brown rice. In the composition of PC from white rice, flavonoids interacted with protein fractions with molecular weights of 131, 10, and 4.0 kDa; while in PC from brown rice, they interacted with only one fraction with a molecular weight of 216 kDa. The quantity of flavonoids in PC from white rice was 2.3 times less than that in PC from brown rice. Elements of protein secondary structure were established for PC: α–helix, 310 — helix, β-structure, β-bends, and irregular shape. Proteolysis of proteins was accompanied by a decrease in the number of α-helices, increase in the proportion of β-structures and irregular regions, weakening of the hydrophobic properties of proteins and an increase in the number of S–S bonds; PC from brown rice was characterized by a higher content of –СН2 groups from the flavonoid family, as well as by a higher degree of unsaturation of groups of benzene nuclei, ketone and ester groups. A negative correlation was found between the foaming ability of PC and the upper boundaries of molecular weights (r = — 0.95), quantity of high-molecular weight proteins (r = — 0.80) and aggregation constants, and a positive correlation (r = + 0.8) with the number of S–S bonds. The high foaming ability is interrelated with proteins with a molecular weight of no more than 120 kDa. The results are intended to regulate the functional properties of protein products based on the characteristics of the physicochemical properties of native and modified proteins.
Application of machine learning in river water quality management: a review
Authors: Cojbasic Sanja , Dmitrasinovic Sonja , Kostic Marija , Turk Sekulic Maja , Radonic Jelena , Dodig Ana , Stojkovic Milan 
Published: October 2023

Abstract

Machine learning (ML), a branch of artificial intelligence (AI), has been increasingly used in environmental engineering due to the ability to analyze complex nonlinear problems (such as ones connected with water quality management) through a data-driven approach. This study provides an overview of different ML algorithms applied for monitoring and predicting river water quality. Different parameters could be monitored or predicted, such as dissolved oxygen (DO), biological and chemical oxygen demand (BOD and COD), turbidity levels, the concentration of different ions (such as Mg2+ and Ca2+), heavy metal or other pollutant's concentration, pH, temperature, and many more. Although many algorithms have been investigated for the prediction of river water quality, there are several which are most commonly used in engineering practice. These models mostly include so-called supervised learning algorithms, such as artificial neural network (ANN), support vector machine (SVM), random forest (RF), decision tree (DT), and deep learning (DL). To further enhance prediction power, novel hybrid algorithms, could be used. However, the quality of prediction is not only dependent on the applied algorithm but also on the availability of previously mentioned water quality parameters, their selection, and the combination of input data used to train the ML model.
Recent Advances in the Synthesis and Analysis of Atorvastatin and its Intermediates
Authors: Li Shu-Fang , Zhang Wei , Zhang Wen , Huang An , Zhu Jia-Qi , Wang Ya-Jun , Zheng Yu-Guo 
Published: October 2023

:
Atorvastatin, a lipid-lowering drug that is widely used in the treatment of cardiovascular
diseases, has significant clinical significance. This article focuses on the synthetic
procedures of atorvastatin, including Paal-Knorr synthesis and several new synthetic
strategies. It also outlines chemical and chemo-enzymatic methods for synthesizing optically
active side chain of atorvastatin. In addition, a comprehensive overview of the analytical
monitoring techniques for atorvastatin and its metabolites and impurities is reported,
alongside a discussion of their strengths and limitations.