Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Method for Breath and Blood Alcohol Determination in Rats Using a Breath Alcohol Meter: An Experimental Study

Background The use of police breath alcohol detectors in rat breath alcohol detection experiments has always been a challenge because of the small lung capacity and inability of rats to actively inhale. However, the method of using gas chromatography to detect blood alcohol concentration is time-consuming, complex, relatively expensive, and cannot achieve on-site detection and multi-point unlimited non-invasive detection. Objectives In this study, a laboratory method was validated for rat breath ethanol concentration (BrAC) measurement to estimate blood ethanol concentration (BAC) in rats. Methods The rats were placed in a gas collection bottle, the breath sample was drawn out with a syringe, and injected into the mouthpiece of the breath alcohol detector through a rubber tube. The results were immediately detected and automatically converted to BAC. Male rats were randomly divided into three groups. The control group received an intraperitoneal injection of normal saline, the liver injury group received an intraperitoneal injection of 50% Carbon tetrachloride (CCL4 1 mL.kg-1), and the induction group received an intraperitoneal injection of phenobarbital sodium (75 mg.kg-1). Western blot analysis was used to detect the protein expression of CYP2E1. Similar grouping and experimental methods were used for female rats. Results This method was reproducible. The metabolic activity of CYP2E1 was downregulated in the injury group and upregulated in the induction group, which was consistent with the results obtained for CYP2E1 protein expression. Conclusions Our results confirmed that the rat gas cylinder breath alcohol assay can be used for multiple detections with immediate and non-invasive determination of alcohol metabolizing capacity. This is important for studies that require repeated assessment of blood alcohol levels.
The Romanian Higher Educational System – before and after the Bologna moment. Case study of a Romanian university

The article represents part of a wider research initiative that aims to follow the main structural changes that happened within the Romanian universities determined by the implementation of the Bologna Process. The literature review consists in the description of the main turning points of the Romanian Higher Education System in the post-communist period, with two distinct subchapters: one that refers to the pre-Bologna period and one on the post-Bologna period until present day. We follow the main legislative changes and their effects on the Romanian Higher Education System, structural modifications, external influences, even on student number evolution. The second part of the paper is a quantitative analysis of the evolution of four selected bachelor programs from a Romanian University, starting ten years before the Bologna Process implementation until present day. Using document analysis, we follow two main parameters: the number of teaching hours for each program and the number of disciplines for each program. The aim of the research is to identify the changes that occurred within the selected programs in the investigated period, with emphasis on the Bologna Process implementation moment (2005). The data show that each of the programs selected had particularities on the evolutions of their curriculums, some differences can be noticed between pre and post Bologna periods. Due to the fact that all the bachelor programs were restructured after the Bologna moment into 6 semesters from 8 in all of the analyzed programs, we had expected to find clear decrease in the number of teaching hours per program and the number of disciplines per program, but, surprisingly, this was not a general tendency.
Mediating Effects of Attitudes, Risk Perceptions, and Negative Emotions on Coping Behaviors

Based on the perspectives of social risk amplification and the knowledge-attitudes-practice model, this study aimed to test how the level of knowledge about COVID-19 and information sources can predict people's behavioral changes and to examine the effect mechanisms through the mediating roles of attitude, risk perception, and negative emotions in a survey of 498 older Chinese adults. The results showed that (1) older people had a lower level of factual knowledge regarding the variant strains and vaccines; (2) in the information sources-behavior, information sources had a critical influence on elderly individuals' coping behaviors; and (3) in the knowledge-behavior, factual knowledge had a significant effect on elderly individuals' coping behaviors. Specifically, for prevention behaviors, both risk perception and negative emotions played full mediating roles. The findings have significant implications for the development of an effective COVID-19 prevention program to older adults coping with pandemic conditions.
Imaging with Raman photons: a novel use of mixed-mode spectroscopy

Surface enhanced Raman spectroscopy is today an established technique used for chemical fingerprinting. Here, we showcase an engineered hierarchical substrate, in which the plasmonically active regions, restricted to a micron scale, two dimensional hexagonal pattern are examined. Spatial variation of the enhanced Raman signal from any analyte, uniformly coating the substrate, consequently bears a high registry with the underlying pattern. This spatially contrasted enhancement allows optical imaging of the 2D pattern solely using the Raman scattered photons from the analyte. While the pattern brightness and contrast determine analyte identification and detection sensitivity, hyperspectral imaging can be exploited for increasing specificity. Proof of concept demonstration of the technique is carried out via the acquisition of Raman images with rhodamine and fluorescein dyes and then applied to detect glucose in 40 mM concentration. The large area optical imaging and the requirement of long-range uniformity in the detected patterns for positive analyte detection, is implemented using a machine learning based pattern recognition protocol which also increases the statistical confidence of detection. This simultaneous, large area signal detection sacrifices continuous spectral information at the cost of speed, reproducibility and minimising human error via automation of detection in the hyperspectral imaging technique presented here.
THE RELATIONSHIP BETWEEN EDUCATION AND SUSTAINABLE DEVELOPMENT IN NIGERIA

: Essential changes in any knowledgeable and dependable social society are not unconnected to educational renaissance. However, education as a potential factor in determining sustainable development in Nigeria is grossly understudied. Consequently, this study investigates the relationship between education and sustainable development in Nigeria. The study adopted the ex-post-facto research design and utilized time series data which were sourced from CBN Statistical Bulletin from 1992 to 2021. The data were analyzed using the ARDL model to determine both the long run and short run relationships between education and sustainable development. Inferences were drawn at 5% level of significance. The estimated long run ARDL model showed that education crowd-out sustainable development in Nigeria with a negative and significant relationship. Population had a positive and significant relationship with sustainable development. Hence, based on the established relationship between education and sustainable development, the study concluded that education is significant with weaken effect on sustainable development. The study therefore recommended that the government of Nigeria should ensure quality control and result oriented policies at all levels of education in Nigeria, educational funding in Nigeria should be adequate, conducive learning environment should be enhanced.
Probabilistic Matching for 3D Scan Registration

In this paper we consider the problem of three-dimensional scan registration for autonomous mobile vehicles. The problem of 3D scan matching is of enormous importance for the construction of metric representations of the environment, for localization, and for navigation planning in the three-dimensional space. We present a probabilistic technique that computes a probability density for each pair of scans and then performs the registration using a greedy hill-climbing search in the likelihood space. We compare our approach with the ICP-algorithm which currently is the most popular technique for matching 3D range scans. We present experiments illustrating that the estimation error of the ICP algorithm is significantly higher than the resulting error obtained with our probabilistic approach.
Influence of green technology, tourism, and inclusive financial development on ecological sustainability: exploring the path toward green revolution

This study demonstrates the linkages between green technological innovations, sustainable tourism, financial development, economic growth, and ecological sustainability using China ’ s regional data from 2000 to 2019. The study applies the novel estimation technique, Quantile Autoregressive Distributive Lag (QARDL) approach to examine long-run and short-run relationships between the stated variables. The initial findings confirm non-linearity in the data verified through J-B test statistics. It approves the implication of QARDL estimation for exploring ecological sustainability trends over the study period. The study outcomes confirm that tourism and green technology innovation assists in reducing ecological footprints in China in the long run. Moreover, financial development and economic growth reflect a direct role towards more ecological footprints; therefore, the sustainability dimension has been missing both in financial development and growth. Furthermore, the results in the short run cover the same phenomenon and confirm that ecological innovations and tourism would help in sustaining the natural environment. The study outcomes demonstrate that government officials in China should specifically implement long-term policies to support the natural environment from adverse shocks of more financial development and economic growth.
Performance of Gradient Boosting Learning Algorithm for Crop Stress Identification in Greenhouse Cultivation

: Greenhouse cultivation is one of the most crucial circular economy systems in agriculture that allows the maximum production in less cultivation area, with minimum inputs and low environmental impact. The data generated in high-tech and sophisticated greenhouse operations are provided by a variety of different sensors that enable a better understanding of the operational environment. In this study a learning algorithm namely Gradient Boosting Machine was tested using the generated data-base in order to estimate different type of stress in tomato crop. The examined model perform qualitative classification of the data, depending on the type of stress (such as no stress, water stress and cold stress). For the comparison was selected 10-fold cross validation strat-egy on the 10,763 samples from the training set. The dataset was divided in two parts, one for train-ing-validation 80% (8610) and a second one for testing 20% (2152). The cross-validation process was repeated 50 times. Among the data entries was used to build the model, the leaf temperature was one of the highest in the feature importance with ratio 0.51. According to the results, the Gradient Boosting algorithm defined all the cases with high accuracy. Particularly, the model find correct all the 372 samples of the cold stress plants, the 1305 out of 1321 samples of the no stress plants and the 431 out of 452 samples of the water stress plants. In these results, the model preserved accuracy of 98% in the testing performance and more than 98% in the validation performance. This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project “Reinforcement of Postdoctoral Researchers—2nd Cycle” (MIS-5033021), implemented by the State Scholarships Foundation (ΙΚΥ).
Combinatorial screening of ternary NiO–Mn2O3–CuO composition spreads

We describe the screening of large combinatorial oxide libraries for their electric transport characteristics. Complete ternary composition spreads of NiO–Mn2O3–CuO oxide alloys were deposited onto conducting Nb-doped SrTiO3 substrates using the combinatorial pulsed laser deposition technique. Resistance-temperature relations for each composition in the spread were determined using a custom-designed scanning probe. Ternary and binary electrically conducting alloys are located around Ni0.45Cu0. 75Mn1.65O4 (the lowest resistance point) and CuMn2O4 compositions, respectively. The temperature sensitivities of the compositions Ni0.3Cu0. 3Mn2.4O4,Cu0.84Mn2. 16O4, and Cu1.35Mn1. 65O4 were 5.0%/°C, 3.5%/°C, and 3.0%/°C, respectively. From this screening we propose compounds for thin-film negative temperature coefficient thermistors with standardized characteristics.
A retrospective study of real-world effectiveness and safety of rivaroxaban in patients with non-valvular atrial fibrillation and venous thromboembolism in Saudi Arabia

Background Real-world evidence on factor Xa inhibitor (rivaroxaban) prescribing patterns, safety, and efficacy in patients with non-valvular atrial fibrillation (NVAF) and venous thromboembolism (VTE) is rare. Herein, we sought to examine the above outcomes in the largest academic center in the Kingdom of Saudi Arabia (KSA). Methods This is a retrospective observational study designed to examine the prescribing pattern, safety and real-world effectiveness of the factor Xa inhibitor rivaroxaban in patients with NVAF and VTE. Data on rivaroxaban prescriptions were collected and analyzed. Bleeding outcomes were defined as per the International Society on Thrombosis and Hemostasis (ISTH) definition. Results A total of 2,316 patients taking rivaroxaban recruited through several departments of King Saud University Medical City (KSUMC). The mean age was 61 years (±17.8) with 55% above the age of 60 and 58% were females. Deep vein thrombosis and pulmonary embolism (VTE) was the most prevalent reason for prescribing rivaroxaban, followed by NVAF. A total daily dosage of 15 mg was given to 23% of the patients. The incidence rate of recurrent thrombosis and recurrent stroke was 0.2%. Furthermore, rivaroxaban had a 0.04 percent incidence rate of myocardial infarction. Half of the patients with recurrent thrombosis and stroke were taking 15 mg per day. The incidence rate of major bleeding was 1.1%. More over half of the patients who experienced significant bleeding were taking rivaroxaban at a dosage of 20 mg per day. According to the HAS-BLED Score (>2 score), 48 percent of patients who experienced significant bleeding had a high risk of bleeding. Non-major bleeding occurred in 0.6% of cases. Similarly, 40% of patients with non-major bleeding were taking rivaroxaban at a dosage of 20 mg per day. According to the HAS-BLED Score, just 6.6% of these individuals had a high risk of bleeding. 93.4% of the patients, on the other hand, were at intermediate risk. Conclusion The prescription of rivaroxaban in this real-life cohort study differs from the prescribing label and the outcomes of a phase 3 randomised clinical trial. However, for individuals with VTE and NVAF, the 20 mg dose looked to be more efficacious than the pivotal trial outcomes. Furthermore, among patients with VTE and NVAF, rivaroxaban was linked to a decreased incidence of safety events such as recurrent thrombosis, recurrent stroke, MI, major bleeding, and non-major haemorrhage in a real-world environment.
Design Considerations of Magnet Configurations in Interior Permanent Magnet Machines for High-Speed Traction Applications: A Comprehensive Study

This article presents a comprehensive comparison and the design considerations of three magnet configurations in interior permanent magnet (IPM) machines for high-speed traction applications. The study investigates single-V, double-V, and single-U magnet configurations. A multiphysic multiobjective optimization is used to enhance the three machines, which are designed with a constraint to fit in the same housing and have the same torque-speed curve. The optimized configurations are then compared with respect to multiple performance indices according to common design criteria for traction motors, including no-load characteristics, short circuit characteristics, demagnetization, torque, efficiency, loss distributions, continuous performance, and magnet volume. The study presents how multiphysics design requirements affect the selection of magnet configurations: a single-V rotor configuration can be selected to have the lowest magnet usage as well as high torque density; while the double-V design is suitable for noise-sensitive, high-efficiency applications but requires a rotor-cooling system; and the single-U design is a good candidate for managing tradeoffs among the other options while maintaining a high degree of freedom in the design.
Hitting sets : VC-dimension and Multicut

In this manuscript we study hitting sets both from a combinatorial and from an algorithmic point of view. A hitting set is a subset of vertices of a hypergraph which intersects all the hyperedges. A packing is a subset of pairwise disjoint hyperedges. In the general case, there is no function linking the minimum size of a hitting set and a maximum size of a packing.The first part of this thesis is devoted to present upper bounds on the size of hitting sets, in particular this upper bounds are expressed in the size of the maximum packing. Most of them are satisfied when the dimension of Vapnik-Chervonenkis of the hypergraph is bounded. The originality of this thesis consists in using these hypergraph tools in order to obtain several results on graph problems. First we prove that a conjecture of Scott holds for maximal triangle-free graphs. Then we generalize a result of Chepoi, Estellon and Vaxes on dominating sets at large distance. We finally study a conjecture of Yannakakis and prove that it holds for several graph subclasses using VC-dimension.The second part of this thesis explores algorithmic aspects of hitting sets. More precisely we focus on parameterized complexity of graph separation problems where we are looking for hitting sets of a set of paths. Combining connectivity tools, important separator technique and Dilworth's theorem, we design an FPT algorithm for the Multicut problem parameterized by the size of the solution.
In-Silico Molecular Modeling Studies to Identify Novel Potential Inhibitors of HPV E6 Protein

The etiological agent of some anogenital tract cancers is infection with the high-risk human papillomavirus (HPV). Currently, prophylactic vaccines against HPV have been validated, but the presence of drug treatment directed against the infection and its oncogenic effects remain essential. Among the best drug targets, viral oncoprotein E6 has been identified as a key factor in cell immortalization and tumor progression in HPV-positive cells. E6, through interaction with the cellular ubiquitin ligase E6AP, can promote the degradation of p53, a tumor suppressor protein. Therefore, suppression of the creation of the E6-E6AP complex is one of the essential strategies to inhibit the survival and proliferation of infected cells. In the present study, we proposed an in-silico approach for the discovery of small molecules with inhibitory activity on the E6-E6AP interaction. The first three compounds (F0679-0355, F33774-0275, and F3345-0326) were selected on the basis of virtual screening and prediction of the molecules’ ADMET properties and docking with E6 protein, these molecules were selected for further study by investigating their stability in the E6 complex and their inhibitory effect on the E6-E6AP interaction by molecular dynamics (MD) simulation. The identified molecules thus represent a good starting point for the development of anti-HPV drugs.
Herbal Treatment for Infertility in Women: A Systematic Review

Infertility is a disease of the male or female reproductive system defined by the failure to achieve pregnancy after 12 months or more of regular unprotected sexual intercourse. This study aimed to systematically review the mechanism, benefits and causes of using herbal treatment in treating infertile women of reproductive age. The search for suitable studies was carried out using Google Scholar, PubMed, Scopus and Science Direct using the search string “Herbal Treatment'' AND (“Infertility Women” OR “Infertility Female” AND (“Mechanism” OR “benefits” OR “cause”). The inclusion criteria were herbal treatment, herbal medicine or alternative treatment and women in the reproductive age. The exclusion criteria were men, older women (beyond reproductive age) and females before menarche. A total of 7 articles were systematically reviewed and appraised. The herbal treatment of interest includes Huoxue Xiaoyi Granule (HXG), Bushen Zhuyun Granule (BZG), Dan’e mixture, Ziyin recipe (ZR), Liuwei Dihuang Granule (LDG), Erzhi Tianghui Granule (ETG), Jiutengzhuyu tablets and Macrophage – activating Chinese mixed herbs (MACH). The herbal treatments had many benefits such as to improve female infertility in terms of increasing the number of high-quality oocytes and embryos, improving follicular development, ovulation, clinical pregnancy rates, blood circulation, clinical symptoms, live birth rate and endometrial receptivity. Many evidence-based traditional Chinese medicine practices were made. Before conducting more investigations, closer monitoring, toxicology studies, standardized dosage and extract used, adequate design of multicentred, randomized, parallel-controlled and blinded trials are required to deliver improved evidence for future research.
Vector bending sensor for measuring spine curvature

Abstract. A three-core photonic crystal fiber (TCPCF) vector bending sensor for measuring spine curvature is proposed. Refractive index variations of the TCPCF due to the wavelength differences in the range of 1.25 to 1.85 μm are studied. We show a linear relation between refractive index and operating wavelength. The wavelength of 1.55 μm is chosen for simulation with high energy modules to compensate for bending loss. An asymmetrical fiber is designed to sense both the direction and magnitude of the curve. The asymmetrical geometry of fiber creates an asymmetrical refractive index profile that leads to shifts in light speed in different fiber sections. This geometry asymmetry, in turn, provides the information needed to calculate both variables. Bending effects on the TCPCF has been numerically studied by the finite element method. Data analysis shows that the proposed sensor is sensitive to bending with a sensitivity of 1.2199 pm.m. It also reveals high directional sensing.
The Computational Complexity of Probabilistic Networks

In this thesis, the computational complexity of a number of problems related to probabilistic networks is studied that combine probabilistic inference, finding, verifying, and enumerating solutions. In particular parameter tuning, sensitivity analysis, monotonicity, enumerating solutions, and problems related to qualitative abstractions of probabilistic networks are studied. These problems are not ‘merely’ NP-hard, but are complete for a variety of complexity classes in the Counting Hierarchy (CH). It is shown that these problems often remain hard under a number of constraints on the problem structure, e.g., when the treewidth of the network is bounded. This suggests, that practical applications must restrict themselves to limited degrees of freedom (e.g. a restricted number of parameters to tune or variables to determine monotonicity constraints on) in order to be tractable. Some of the problems are complete for complexity classes that have no other ‘real world’ complete problems and may be interested also from a complexity-theoretical point of view.