Miracle-dz/UniFAR-FTUs huggingface dataset:
The strategic interaction between business mode and store brand introduction in a platform-based supply chain

The phenomenon of store brands introduced by large-scale platforms is becoming common, and manufacturers should carefully choose the business mode of selling national brand (NB) products via platforms. Considering big-data marketing, we examine the sales mode selection in a platform-based supply chain based on the strategic interaction between the business mode decision and store brand decision. By continuous dynamic game theory, the strategies and performance under different modes are solved. We find that given the store brand (SB) decision, the business mode adopted by the manufacturer depends only on the commission rate, which is high for reselling and low for agency selling. Given the business mode, under reselling, whether the platform introduces SB depends only on the brand preference, i.e., SB is introduced when the preference for NB is low. While under agency, he introduces SB in the general or relatively passive situation, depending both on the commission rate and the brand preference. In addition, four equilibrium sales modes are obtained based on the strategic interaction. In order to achieve a “win-win-win” situation in profitability among the manufacturer, the platform and the platform-based supply chain, the manufacturer should adopt agency selling and the platform forgoes introducing SB when both the commission rates and NB preference are low.
Studying Several Virulence Factors Phenotypically and Genetically to Isolate Klebsiella Pneumonia from Diabetic Patients

Klebsiella pneumoniae is the most frequent cause of nosocomial and community-acquired infections. The virulence factors help Klebsiella pneumoniae establish an infection in the human body by contributing to its ability to survive in a variety of environmental circumstances. 42 K. pneumoniae clinical specimens were isolated from diabetic patients suffering from urinary tract infection and diabetic foot ulcer in this study. K.pneumoniae virulence factors are currently being identified using phenotypic and genotypic approaches. The K. pneumoniae isolates that were positive for several virulence factors included 33 (78.6%) capsule, 17 (40%) hypermucoviscosity development, 14 (33.3%) proteases, and none were positive for hemolysis . K. pneumoniae isolates had a virulence gene that was 15% wcaG, 60% entB, and negative for rmpA.
RISK MANAGEMENT PRACTICES AND WORKERS SAFETY IN UNIVERSITY OF MEDICAL SCIENCES TEACHING HOSPITAL, ONDO STATE NIGERIA

Healthcare facilities play an important role in safeguarding life and ensuring well-being towards attaining Sustainable Developmental Goals (SDGs) 2030. However, healthcare organizations are harmful, requiring risk management to ensure occupational safety. This study evaluated the effect of risk management on workers’ safety at the University of Medical Sciences Teaching Hospital, Ondo State. This study utilized a qualitative research methodology with a questionnaire to collect data, analysed using descriptive and inferential statistics of regression model and Analysis of Variance (ANOVA). The results revealed a positive effect of risk management on workers' safety at the University of Medical Sciences Teaching Hospital, Ondo State. Health workers had undergone risk and safety management training and were aware of risk management practices. Incident reporting was the widely used risk management strategy, while safety training was underutilized. The lack of commitment of health workers to risk management procedures was a significant constraint. The study recommended a well-designed risk management policy and systematic employee training to promote healthy and safe working conditions for health workers.
Electron-impact double ionization of the carbon atom

Electron-impact single- and double-ionization cross sections and Maxwellian rate coefficients are presented for the carbon atom. Scaling factors are introduced for the electron-impact excitation and ionization cross sections obtained in the distorted wave (DW) approximation. It is shown that the scaled DW cross sections provide good agreement with measurements for the single ionization of the C atom and C1+ ion. The direct double-ionization (DDI) process is studied using a multi-step approach. Ionization–ionization, excitation–ionization–ionization, and ionization–excitation–ionization branches are analyzed. It is demonstrated that the three-step processes contribute ≼40% of the total DDI cross sections for the case where one of the electrons takes all of the excess energy after the first ionization process.
Levitan/Bohr almost periodic and almost automorphic solutions of scalar differential equations

ABSTRACT The aim of this paper is to prove the existence of Levitan/Bohr almost periodic, almost automorphic, recurrent and Poisson stable solutions of the scalar differential equations. The existence of at least one quasi-periodic (respectively, Bohr almost periodic, almost automorphic, recurrent, pseudo recurrent, Levitan almost periodic, almost recurrent, Poisson stable) solution of sclalar differential equations is proved under the condition that it admits at least one bounded solution on the positive semi-axis which is uniformly Lyapunov stable.
Optical and Electrical Characterization of (002)-Preferentially Oriented n-ZnO/p-Si Heterostructure

In this paper, preferentially oriented (002) ZnO thin lms have been grown on Si (100) and glass substrates using radio frequency magnetron sputtering. The dependence of the quality of the ZnO thin lms at di erent substrate temperatures on the growth is studied. A ZnO thin lm with c-axis-oriented würtzite structure is obtained at a growth temperature from 200 to 400 ◦C. X-ray di raction shows that the full width at half maximum θ 2θ of (002) ZnO/Si is located at approximately 34.42◦, which is used to infer the grain size that is found to be 17 nm to 19.7 nm. The FWHM is 9.5◦ to 8◦ in rocking curve mode, from which the crystalline quality has been determined. The texture degree demonstrates the improvement in quality with the increase of substrate temperature, which is best at 400 ◦C. The band gap extracted by UV transmittance spectrum has been identi ed as 3.2 eV at 400 ◦C. The electrical characteristics via C V and I V measurements on the basis of the heterojunction thermal emission model con rm the domination of high-density grain boundary layer existing at the interface. The transport currents indicate to the presence of space-charge-limited current and trap-charge-limited current mechanisms.
Molecular Population Genetics of Aspen Mosaic-Associated Virus in Finland and Sweden

Aspen mosaic-associated virus (AsMaV) is a newly identified Emaravirus, in the family Fimoviridae, Bunyavirales, associated with mosaic symptoms in aspen trees (Populus tremula). Aspen trees are widely distributed in Europe and understanding the population structure of AsMaV may aid in the development of better management strategies. The virus genome consists of five negative-sense single-stranded RNA (–ssRNA) molecules. To investigate the genetic diversity and population parameters of AsMaV, different regions of the genome were amplified and analyzed and full-length sequence of the divergent isolates were cloned and sequenced. The results show that RNA3 or nucleoprotein is a good representative for studying genetic diversity in AsMaV. Developed RT–PCR–RFLP was able to identify areas with a higher number of haplotypes and could be applied for screening the large number of samples. In general, AsMaV has a conserved genome and based on the phylogenetic studies, geographical structuring was observed in AsMaV isolates from Sweden and Finland, which could be attributed to founder effects. The genome of AsMaV is under purifying selection but not distributed uniformly on genomic RNAs. Distant AsMaV isolates displayed amino acid sequence variations compared to other isolates, and bioinformatic analysis predicted potential post-translational modification sites in some viral proteins.
Crop-saving with AI: latest trends in deep learning techniques for plant pathology

Plant diseases pose a major threat to agricultural production and the food supply chain, as they expose plants to potentially disruptive pathogens that can affect the lives of those who are associated with it. Deep learning has been applied in a range of fields such as object detection, autonomous vehicles, fraud detection etc. Several researchers have tried to implement deep learning techniques in precision agriculture. However, there are pros and cons to the approaches they have opted for disease detection and identification. In this survey, we have made an attempt to capture the significant advancements in machine-learning based disease detection. We have discussed prevalent datasets and techniques that have been employed as well as highlighted emerging approaches being used for plant disease detection. By exploring these advancements, we aim to present a comprehensive overview of the prominent approaches in precision agriculture, along with their associated challenges and potential improvements. This paper delves into the challenges associated with the implementation and briefly discusses the future trends. Overall, this paper presents a bird’s eye view of plant disease datasets, deep learning techniques, their accuracies and the challenges associated with them. Our insights will serve as a valuable resource for researchers and practitioners in the field. We hope that this survey will inform and inspire future research efforts, ultimately leading to improved precision agriculture practices and enhanced crop health management.
Analysis Of Fiscal Loss Compensation in Strengthening Factors Affecting Tax Avoidance

DIn tax avoidance, company management often uses ways to reduce the tax burden. This study examines and analyzes the factors influencing tax avoidance (TA). These factors are leverage (DAR) and profitability (ROA). Another objective is to test whether fiscal compensation Loss (FLC) can strengthen the effect of DAR and ROA on ROA. The population is a consumer goods manufacturing company registered on the IDX for 2017-2021. Sample selection Data collection using purposive sampling Based on the existing criteria, 29 companies were selected as a sample with a five-year observation period, so the number of observations was 145. The test carried out was panel data regression analysis. Results This study shows that DAR does not affect TA, ROA affects TA, and DAR and ROA affect TA. FLC is proven to strengthen the impact of DAR on TA. The novelty of this study is to use Fiscal Loss Compensation as a moderating variable, not as an independent variable as in previous studies. The results of this study indicate that investors can use tax avoidance as an essential aspect in making investment decisions in companies because this aspect can measure a company's ability to generate returns on investments made by the company.
Metagenomic Study on the Influence of Enterocytozoon hepatopenaei (EHP) Infection on the Gut Microbiota in Penaeus vannamei

Background: Enterocytozoon hepatopenaei (EHP), infects P.vannamei and causes hepatopancreatic microsporidiasis associated with severe growth retardation in shrimp culture. The influence of EHP in the shrimp gut microbiota is poorly studied, and this would be an interesting area to investigate. In this study, metagenomic approach was followed to compare the overall species richness of the gut microbiota in healthy and EHP-infected P.vannamei. Methods: Bacterial genomic DNA from gut samples of EHP-infected and healthy shrimps were profiled for the 16S rRNA gene, targeting the V3-V4 conserved region. Operational Taxonomic Units (OTUs), were identified and clustered together with a cutoff of 97% identity using UCLUST. The OTUs were then used for the computation of alpha diversity and beta diversity for each sample. Result: Gut samples from EHP-infected shrimp showed lower bacterial abundance throughout the family, class, order, and genus levels. This research also highlights that EHP not only affects the hepatopancreas of the shrimp, but it also has the ability to affect the shrimp gut, predisposing them to other opportunistic infections.
The Relationship Between Work-Life Balance and Academic Burnout Levels of Academic Staff

The aim of this study is to determine the relationship between the perceptions of burnout and the work-life balance of academic staff. This research is a quantitative study using the correlational survey model. The study group consists of academic staff working in two state and two foundation universities in Istanbul, and the sampling consists of 352 academic staff. Data were collected through the Academic Burnout Scale developed by Tümkaya (2000) and the Work-Life Balance Scale developed by Apaydın (2011). Academic burnout and work-life balance levels among academic staff were analyzed according to gender and marital status variables. The arithmetic mean, standard deviation, t-test, regression analysis, and correlation analysis techniques were used to analyze the data. The findings of the study indicate that the participants experienced high levels of burnout and were able to achieve a moderate work-life balance. Academics think they can achieve work-life balance at a moderate level. There is a negative and significant relationship between academics' academic burnout and their perceptions of work-life balance in all sub-dimensions. Academics' perceptions of academic burnout are a significant predictor of their perceptions of work-life balance.
Covering arrays avoiding forbidden edges and edge clique covers

Covering arrays avoiding forbidden edges (CAFEs) are combinatorial designs which can be used to generate test suites for practical testing applications. CAFEs generate test suites in which all required pairwise interactions between any two factors are tested at least once each, with the property that a specified list of pairwise interactions, the so called forbidden interactions, are avoided by all tests generated by the CAFE. Consequently, CAFEs can be applied to testing applications wherein constraints are imposed on the factors of the tests, resulting in forbidden interactions. In this thesis, we study CAFEs, as well as their relationship to the edge clique cover problem from graph theory. We give new results and bounds for uniform edge clique covers and CAFEs. We establish the computational complexity of several problems related to CAFEs and edge clique covers. In particular, we prove that finding an optimal CAFE (as well as finding an optimal error-locating array) for a graph is NP-hard, even for the case of binary alphabets.
An Unsupervised Condition Monitoring Method for Rolling Bearings Based on Compound Feature Selection and Multistep-Aware BiGRU-VAE

Establishing a health indicator (HI) is essential for monitoring the condition of rolling bearings and mitigating failure risks. Unsupervised learning, which eliminates the necessity for manual labeling, has gained considerable attention in constructing HIs. However, existing unsupervised methods mainly suffer from two limitations. First, redundancy analysis is generally disregarded in the feature selection process. Second, temporal dependencies between distinct instants are neglected due to single time-step samples. To address the issue, this article proposes an innovative unsupervised condition monitoring method based on compound feature selection (CFS) and a multistep-aware model. The major contributions are comprehensive feature selection and effective information extraction of continuous sampling instants. Specifically, in feature selection, a novel metric is designed to effectively capture abrupt changes in features proximate to failure. Utilizing this metric, a CFS algorithm is proposed to identify a superior feature subset with a high correlation to the degradation and low redundancy among features. Additionally, a bidirectional gated recurrent unit-based variational autoencoder (BiGRU-VAE) with multiple time-step inputs is constructed to leverage the information of different sampling instants. The BiGRU-VAE is trained exclusively on healthy features within the superior subset. The dissimilarity between the input sample and its reconstruction is employed as the HI to represent bearing degradation. Comparative experiments on two run-to-failure datasets validate the superiority of the HIs constructed by the proposed method in presenting the degradation process. Ablation experiments confirm the dominance of the CFS algorithm and the necessity of multiple time-step inputs.
Extended Greenwood–Williamson Models for Rough Spheres

The current work aims to develop two extended Greenwood–Williamson (GW) models for spherical particles with surface roughness which can be incorporated into the discrete element modeling (DEM) framework. The defects of the classic GW model when directly adopted in DEM are fully addressed and illustrated by both theoretical and numerical results. The first model, the extended elastic GW (E-GW) model, which evaluates the elastic deformation of the asperities and the bulk substrate separately is developed to consider the positive overlap involved in the contact problem. The capability of incorporating the extended elastic model into the DEM is illustrated by the comparison between the classic and extended models. The second model, the extended elasto–plastic GW (EP-GW) model, is further developed to consider the plastic deformation of the asperities which reduces the pressure increased by the surface roughness. Numerical comparisons between the E-GW and EP-GW models are also conducted to demonstrate the effect of the plastic deformation on the pressure and deformation distributions in the contact region.
Probiotics and Fecal Transplant: An Intervention in Delaying Chronic Kidney Disease Progression?

Chronic kidney disease (CKD) is a global health challenge affecting nearly 700 million people worldwide. In the United States alone, the Medicare costs for CKD management has reached nearly USD 80 billion per year. While reversing CKD may be possible in the future, current strategies aim to slow its progression. For the most part, current management strategies have focused on employing Renin Angiotensin Aldosterone (RAS) inhibitors and optimizing blood pressure and diabetes mellitus control. Emerging data are showing that a disruption of the gut–kidney axis has a significant impact on delaying CKD progression. Recent investigations have documented promising results in using microbiota-based interventions to better manage CKD. This review will summarize the current evidence and explore future possibilities on the use of probiotics, prebiotics, synbiotics, and fecal microbial transplant to reduce CKD progression.
Jet-hadron correlations relative to the event plane Pb--Pb collisions at the LHC in ALICE

In relativistic heavy ion collisions at the Large Hadron Collider (LHC), a hot, dense and strongly interacting medium known as the Quark Gluon Plasma (QGP) is produced. Quarks and gluons from incoming nuclei collide to produce partons at high momenta early in the collisions. By fragmenting into collimated sprays of hadrons, these partons form 'jets'. Within the framework of perturbative Quantum Chromodynamics (pQCD), jet production is well understood in pp collisions. We can use jets measured in pp interactions as a baseline reference for comparing to heavy ion collision systems to detect and study jet quenching. The jet quenching mechanism .
CHARACTERIZATION OF SOLANUM NIGRUM L. VARIANTS BACK AND ORANGE FRUITS BY RAPD MARKER

Randaom Amplified Polimorphic DNA (RAPD) technique was used as a tool for assessing genetic diversity and species relationship among two variants of Solanum nigrum (black and orange fruited) . Four primers were used in this study. A total of 48 bands were observed among the variants of Solanum nigrum L. In which 8% were polymorphic among the S. nigrum variants whilst, 92% of these bands were shared by the two variants. A total of 25 and 23 DNA products were scored for Solanum nigrum black and orange fruited respectively. Genetically distinct genotypes identified using RAPD markers could be potential sources of germplasm for Solanum nigrum improvement. Thus, RAPD markers combined with morphological analysis proved to be a quick, simple and significant testing methods to assess genetic diversity among S. nigrum variants (black and orange fruited).
Diophantine m -tuples and elliptic curves

RESUME. Un m-uplet diophantien est un ensemble de m entiers naturels non nuls tel que le produit quelconque de deux d’entre eux augment6 de 1 est un carr6 parfait. Dans cet article, nous nous int6ressons a certaines propri6t6s de courbes elliptiques d’6quation du
Identi�cation of Function Genes regulating Gastric Cancer progression using Integrated Bioinformatics Analysis

Gastric cancer(GC)is the most frequent cause of cancer with poor prognosis. Treatment for GC remains unchanged in the past few years. This study was done to investigate the potential therapeutic targets and related regulatory biomarkers of Gastric cancer. We obtained the public GC transcriptome sequencing dataset from the GEO database. The datasets contains 348 GC tissues and 141 healthy tissues. In total, 251 DEGs were identi�ed, containing 187 down-regulated genes and 64 up-regulated genes. The DEGs' enriched functions and pathways include Progesterone-mediated oocyte maturation, cell cycle, and oocyte meiosis, Hepatitis B, and Hippo signaling pathway. Survival analysis showed that BUB1, MAD2L1, CCNA2, CCNB1, and BIRC5 might be associated with the regulation the cell cycle phase mitotic spindle checkpoint pathway. We selected 26 regulated genes with the PPI networks' aid analyzed by the Molecular Complex Detection. Then we focused on three critical gene, which is high expressed in gastric cancer, while negatively related to patients survival. Furthermore, we found that knockdown either BIRC5, TRIP13 or UBE2C signi�cantly inhibited cell proliferation, induced cell apoptosis. Knockdown of BIRC5, TRIP13 or UBE2C increased cellular sensitivity to cisplatin. Together, our present study identi�ed signi�cantly upregulated genes with worse prognosis in GC using integrated bioinformatics methods.
Vertical Integration and Costly Demand Information in Regulated Network Industries Elisabetta Iossa & Francesca Stroffolini

We study how vertical integration affects the acquisition and transmission of demand information in regulated network industries. Demand information helps to set the access price, incentivize infrastructure investment, and foster competition in the unregulated downstream market. We show that when demand information is costly and private, the optimal access prices are independent of demand levels. Vertical integration then secures greater welfare in new markets where little demand information is available or where infrastructure cost is low, or when investing is highly risky. In the remaining cases, vertical separation is preferable.