Miracle-dz/UniFAR-FTUs huggingface dataset:
HYPOGLYCAEMIC ACTIVITY OF DIFFERENT FRACTIONS OF BERBERIS ARISTATA ROOT-BARK IN NORMAL AND ALLOXAN DIABETIC RABBITS

A number of plants including Berberis aristata roots are considered effective as antidiabetic agents in ethnomedical practices. In this study effects of certain isolated fractions from root-bark of Berberis aristata on blood glucose levels were determined in normal and alloxan-diabetic rabbits. In normal rabbits, ethanolic fraction in 0.5, 1.0 and 1.5 g/kg produced significant decrease (P<0.05 or 0.001) at 8 and 12 hrs. Its acidified-basified fraction showed significant lowering of blood sugar at 2, 4, 8 and 12 hrs. The chloroform: methanol fraction also produced significant hypoglycaemic effect at these intervals. In alloxan-diabetic rabbits, ethanolic fraction in doses similar to normal animals produced significant decrease in blood glucose at 2, 4, 8 and 12 hrs.
Does Self-Efficacy Correlate with Positive Emotion and Academic Performance in Collaborative Learning?

This full research paper studies the correlation of self-efficacy in computer science as well as learning and social skills with students' academic performance and their emotions in collaborative learning environments. Self-efficacy is an essential part of social cognitive theory and provides the foundation for analyzing human thoughts, motivations, and actions. Studies show that students' successful performance and accomplishment are directly affected by the level of self-efficacy. Therefore, analyzing self-efficacy in engineering education is important since it can impact the learning process in academic settings as well as provide a metric to track for improvement. Social cognitive theories also emphasize that students' interaction with each other affects their learning process and how they perform in educational settings. In previous work [5], we analyzed students' conversations in low-stake teams in an introductory programming course (CS1) and observed a strong positive correlation between students' positive emotions while interacting with each other with their performance in the course. In this study, we focus on the correlation of self-efficacy with learner's emotion and performance. We measure students' self-efficacy with a standard instrument called “Student Attitudes Toward STEM (S-STEM) Survey”. For this purpose, we asked the participants to self-report on a 5-point Likert-scaled survey including 20 questions. These 20 questions are grouped into 2 main categories of computer science and learning/social skills. Students' emotions were extracted from their speeches in teams by applying natural language processing (NLP) methods. The result of data analysis shows a statistically significant correlation between overall self-efficacy and performance in the course and positive emotions during the teamwork. We further investigate which category of self-efficacy questions most correlate with students' performance. The result shows self-efficacy in interpersonal skills and learning ability most impact students' performance.
Cost distances and least cost paths respond differently to cost scenario variations: a sensitivity analysis of ecological connectivity modeling

ABSTRACT Biodiversity conservation measures designed to ensure ecological connectivity depend on the reliable modeling of species movements. Least-cost path modeling makes it possible to identify the most likely dispersal paths within a landscape and provide two items of ecological relevance: (i) the spatial location of these least-cost paths (LCPs) and (ii) the accumulated cost along them (’cost distance’, CD). This spatial analysis requires that cost values be assigned to every type of land cover. The sensitivity of both LCPs and CDs to the cost scenarios has not been comprehensively assessed across realistic landscapes and diverging cost scenarios. We therefore assessed it in diverse landscapes sampled over metropolitan France and with widely diverging cost scenarios. The spatial overlap of the LCPs was more sensitive to the cost scenario than the CD values were. In addition, highly correlated CD matrices can be derived from very different cost scenarios. Although the range of the cost values and the properties of each cost scenario significantly influenced the outputs of LCP modeling, landscape composition and configuration variables also explained their variations. Accordingly, we provide guidelines for the use of LCP modeling in ecological studies and conservation planning.
The Kyoto Protocol and Sustainable Cities: Potential Use of Clean-Development Mechanism in Structuring Cities for Carbon-Efficient Transportation

This paper assesses the possibility for changing urban development patterns to reduce transportation greenhouse gas (GHG) emissions. The analysis was carried out as part of a larger project exploring the possibility of using the clean development mechanism (CDM) to reduce transportation GHG emissions in Santiago, Chile. The paper provides an overview of the analytical approach, which includes an integrated travel demand model with sensitivity to mesolevel land use variations, a method to generate optimal land use scenarios that relate to emission reductions, and a process to estimate the level of subsidies needed to produce those land use scenarios. Limitations to the approach and suggestions for future research are discussed. The paper concludes with an assessment of the results in the face of the fairly strict requirements for project development and implementation implied by the CDM.
Random noise attenuation by a selective hybrid approach using f-x empirical mode decomposition

Empirical mode decomposition (EMD) becomes attractive recently for random noise attenuation because of its convenient implementation and ability in dealing with non-stationary seismic data. In this paper, we summarize the existing use of EMD in seismic data denoising and introduce a general hybrid scheme which combines f? x EMD with a dipping-events retrieving operator. The novel hybrid scheme can achieve a better denoising performance compared with the conventional f? x EMD and selected dipping event retriever. We demonstrate the strong horizontal-preservation capability of f? x EMD that makes the EMD based hybrid approach attractive. When f? x EMD is applied to a seismic profile, all the horizontal events will be preserved, while leaving few dipping events and random noise in the noise section, which can be dealt with easily by applying a dipping-events retrieving operator to a specific region for preserving the useful dipping signal. This type of incomplete hybrid approach is termed a selective hybrid approach. Two synthetic and one post-stack field data examples demonstrate a better performance of the proposed approach.
Transcriptomic and Proteomic Characterizations of the Molecular Response to Blue Light and Salicylic Acid in Haematococcus pluvialis

Haematococcus pluvialis accumulates a large amount of astaxanthin under various stresses, e.g., blue light and salicylic acid (SA). However, the metabolic response of H. pluvialis to blue light and SA is still unclear. We investigate the effects of blue light and SA on the metabolic response in H. pluvialis using both transcriptomic and proteomic sequencing analyses. The largest numbers of differentially expressed proteins (DEPs; 324) and differentially expressed genes (DEGs; 13,555) were identified on day 2 and day 7 of the treatment with blue light irradiation (150 μmol photons m−2s−1), respectively. With the addition of SA (2.5 mg/L), a total of 63 DEPs and 11,638 DEGs were revealed on day 2 and day 7, respectively. We further analyzed the molecular response in five metabolic pathways related to astaxanthin synthesis, including the astaxanthin synthesis pathway, the fatty acid synthesis pathway, the heme synthesis pathway, the reactive oxygen species (ROS) clearance pathway, and the cell wall biosynthesis pathway. Results show that blue light causes a significant down-regulation of the expression of key genes involved in astaxanthin synthesis and significantly increases the expression of heme oxygenase, which shows decreased expression by the treatment with SA. Our study provides novel insights into the production of astaxanthin by H. pluvialis treated with blue light and SA.
Electrical impedance tomography of known targets.

Electrical impedance tomographs (showing magnitude and phase) of known, laboratory‐scale targets are reported. Also presented is a generalization of a proven electrical resistance tomography (ERT) algorithm, to account for electrical impedance. Three methods are used to reconstruct electrical impedance data and their results compared. The first two methods employ approximate relationships between the measurements and reconstructed tomogragh while the third approach employs fully complex algebra to account for the real and imaginary components of electrical impedance data.The electrical impedance tomography (EIT) and the perturbation analysis (PA) algorithms provided useful magnitude and phase images for the frequency range of 0.0625 to 64 Hz; images for 256 and 1024 Hz are also presented but these were not reliable. The frequency effect (FE) approach did not produce reliable phase images. Comparisons of the ERT and EIT magnitude images show that both methods provided equivalent results for the water blank.
Experimental Characterization of Lift on a Rigid Flapping Wing

Measurements of aerodynamic forces over flapping wings are challenging due to unsteady flowfields and rapidly changing structural loads. This represents an important limitation to the aerodynamic data currently available to researchers working on the aerodynamics of bioinspired flapping wings. This paper proposes the use of pressure-based instrumentation for flapping-wing testing and documents the comparison of two experimental methods to measure the aerodynamic lift generated during the stroke of a rigid flapping wing. The first approach uses a force transducer to measure the forces in air and vacuum, and the second approach uses pressure measurements taken on the surfaces of the wing. The results from both methods are used to validate each other and provide experimental data for a test case with easily simulated conditions. This paper describes the design of the test hardware required, including a custom flap stand, flap mechanics, and instrumented wing construction techniques. A series of flap tests we.
Endocannabinoids and Heart Rate Variability Alterations after Exposure to Prolonged Intensive Physical Exercise of the Hellenic Navy SEALs

Background: Recent research indicates that both endocannabinoids (eCB) and heart rate variability (HRV) are associated with stress-induced experiences. However, these underlying mechanisms are not elucidated. The present study aims to investigate whether exposure to acute and chronic stress conditions can give rise to measurable changes, both to the peripheral eCB ligands and HRV. Methods: Thirteen candidates under intense preparation for their enlistment in the Hellenic Navy SEALs (HNS) participated in the study. All subjects underwent mental state examination, while HRV variables in time and frequency domain recordings were acquired. Furthermore, at baseline and 30 days after prolonged and intensive physical exercise, hair was collected to measure eCB ligands, such as anandamide (AEA), 2-arachidonoylglycerol (2-AG), and the N-acyl ethanolamine (NAE) molecules: palmitoylethanolamide (PEA) and oleoylethanolamide (OEA). Results: Comparing basal hair concentrations of eCB ligands before and after intense physical exercise, we found that AEA, PEA, and OEA were notably increased, whereas no differences were observed regarding the ligand 2-AG. Furthermore, there were observed associations between the concentrations of peripheral eCB ligands, both at baseline and after the prolonged physical exercise and the time and frequency domains of HRV. Conclusions: These findings suggest that endocannabinoid–HRV interrelations might share a short-term, and long-term adaptability of the changes in self-regulation associated with stress. Further studies will be required to determine the validity of peripheral eCB signaling and HRV as a biomarker for different aspects of the stress response.
A study of variations of areal parameters on machined surfaces

This paper presents an analysis of variations of areal parameters on surfaces. Textures were measured using stylus equipment. After form removal, parameters from the ISO 25178 standard were computed using TalyMap Gold software. Then measured areas of these topographies were divided into smaller sub-areas, for which the parameters were calculated again. Relative deviations between parameters from measured areas and sub-areas were calculated as well as the coefficients of variation of parameters in sub-areas. The tendencies of parameter variations for various types of measured surfaces were studied. Parameters that were stable for surfaces and sensitive to surface irregularities were selected.
A structural equation model of activity participation and travel behavior using longitudinal data

Abstract This paper presents a dynamic structural equation model (SEM) that explicitly addresses complicated causal relationships among socio-demographics, activity participation, and travel behavior. The model assumes that activity participation and travel patterns in the current year are affected by those in previous years. Using the longitudinal dataset collected from Puget sound transportation panel ‘wave 3’ and ‘wave 4,’ these assumptions are tested with suggested SEMs. Within each wave, the model is structured to have a three-level causal relationship that describes interactions among endogenous variables under time-budget constraints. The resulting coefficients representing the activity durations indicate that people tend to allocate their time according to the importance and the obligation of the activity level. Results from the dynamic SEM confirm the fact that people's current activity and travel behavior do have effects on those in the future. The resulting model also shows that activity participation and travel behavior in ‘wave 3’ are closely related to those in ‘wave 4. ’ These explicit explanations of relationships among variables could provide important perspectives in the activity-based approach which becomes recognized as a better analytical tool for the transportation planning and policy making process.
Deep Learning-Based Object Detection System for Identifying Weeds Using UAS Imagery

Current methods of broadcast herbicide application cause a negative environmental and economic impact. Computer vision methods, specifically those related to object detection, have been reported to aid in site-specific weed management procedures for targeted herbicide application within a field. However, a major challenge to developing a weed detection system is the requirement for a properly annotated database to differentiate between weeds and crops under field conditions. This research involved creating an annotated database of 374 red, green, and blue (RGB) color images organized into monocot and dicot weed classes. The images were acquired from corn and soybean research plots located in north-central Indiana using an unmanned aerial system (UAS) flown at 30 and 10 m heights above ground level (AGL). A total of 25,560 individual weed instances were manually annotated. The annotated database consisted of four different subsets (Training Image Sets 1–4) to train the You Only Look Once version 3 (YOLOv3) deep learning model for five separate experiments. The best results were observed with Training Image Set 4, consisting of images acquired at 10 m AGL. For monocot and dicot weeds, respectively, an average precision (AP) score of 91.48 % and 86.13% was observed at a 25% IoU threshold (AP @ T = 0.25), as well as 63.37% and 45.13% at a 50% IoU threshold (AP @ T = 0.5). This research has demonstrated a need to develop large, annotated weed databases to evaluate deep learning models for weed identification under field conditions. It also affirms the findings of other limited research studies utilizing object detection for weed identification under field conditions.
Assessment of Knowledge, Attitude, and Practice of Adverse Drug Reaction Reporting Among Community Pharmacy in Gondar Town, Ethiopia, 2021

Background: Drugs can treat diseases, reduce symptoms, and enhance patients’ health and quality of life. However, taking a drug is not always as easy as just swallowing a pill. This is because drugs have some unwanted effects. Adverse Drug Reactions (ADRs) are one of the leading causes of morbidity and mortality and contribute to excessive health care costs. Objectives: To assess knowledge, attitude, and practice of health care providers on adverse drug reporting among community pharmacists, Gondar, Ethiopia Methods: Community-based cross-sectional study designs were used from July - August 2021. The sampling method was a survey from community pharmacists. Self-administered questioner was used, and the collected data was entered into Epi-info Version 7.0 and exported to SPSS version 20 software for analysis. Result: The study included 215 community pharmacy professionals to assess the knowledge, attitudes, and practices of adverse drug reaction reporting. From a total of 215 community pharmacy professionals, 34 (15.8%) of the respondents were able to differentiate ADR from side effects and only 56(26%) respondents felt that they are adequately trained in ADR reporting. Out of 215 respondents, 78(36.3%) and 146(67.9%) knew the availability of the national reporting system and ADR reporting form in Ethiopia respectively. The study revealed that 21.4% [95% CI: 18.6-35.9] and 42.3% [95% CI: 38.2-65.9] of community pharmacists have poor knowledge and negative attitude towards ADR reporting respectively. Conclusion: Underreporting of ADR by community pharmacists was identified in this study. Training sessions to clarify the role of various community pharmacists in ADR reporting will hopefully fill the observed gap in knowledge and practices. The community pharmacy should formulate strategies to enhance the detection and reporting of ADRs.
Effects of Temperature on the Evolution of Yield Surface and Stress Asymmetry in A356–T7 Cast Aluminium Alloy

As the electrification of vehicle powertrains takes prominence to meet stringent emission norms, parts of internal combustion engines like cylinder heads are subjected to an increased number of thermal load cycles. The cost-effective design of such structures subjected to cyclic thermo-mechanical loads relies on the development of accurate material models capable of describing the continuum deformation behaviour of the material. This study investigates the effect of temperature on the evolution of flow stress under cyclic loading in A356-T7 + 0.5% Cu cast aluminium alloy commonly used in modern internal combustion engine cylinder heads. The material exhibits peak stress and flow stress asymmetry with the stress response and flow stress of the material under compressive loading higher than under tension. This peak and flow stress asymmetry decrease with an increase in temperature. To compare this stress asymmetry against conventional steel, cyclic strain-controlled fatigue tests are run on fully pearlitic R260 railway steel material. To study the effect of mean strain on the cyclic mean stress evolution and fatigue behaviour of the alloy, tests with tensile and compressive mean strains of +0.2% and −0.2% are compared against fully reversed (Rε = −1) strain-controlled tests. The material exhibits greater stress asymmetry between the peak tensile and peak compressive stresses for the strain-controlled tests with a compressive mean strain than the tests with an identical magnitude tensile mean strain. The material exhibits mean stress relaxation at all temperatures. Reduced durability of the material is observed for the tests with tensile mean strains at lower test temperatures of up to 150 °C. The tensile mean strains at elevated temperatures do not exhibit such a detrimental effect on the endurance limit of the material.
The 2018 Global Point Prevalence Survey of antimicrobial consumption and resistance in 47 Canadian hospitals: a cross-sectional survey

Background: Patient-level surveillance of antimicrobial use (AMU) in Canadian hospitals empowers the reduction of inappropriate AMU and was piloted in 2017 among 14 hospitals in Canada. We aimed to describe AMU on the basis of patient-level data in Canadian hospitals in 2018 in terms of antimicrobial prescribing prevalence and proportions, antimicrobial indications, and agent selection in medical, surgical and intensive care wards. Methods: Canadian adult, pediatric and neonatal hospitals were invited to participate in the standardized web-based cross-sectional Global Point Prevalence Survey of Antimicrobial Consumption and Resistance (Global-PPS) conducted in 2018. An identified site administrator assigned all wards admitting inpatients to specific surveyors. A physician, pharmacist or nurse with infectious disease training performed the survey. The primary outcomes were point prevalence rates for AMU over the study period regarding prescriptions, indications and agent selection in medical, surgical and intensive care wards. The secondary outcomes were AMU for resistant organisms and practice appropriateness evaluated on the basis of quality indicators. Antimicrobial consumption is presented in terms of prevalence and proportions. Results: Forty-seven of 118 (39.8%) hospitals participated in the survey; 9 hospitals were primary care centres, 15 were secondary care centres and 23 were tertiary or specialized care centres. Of 13 272 patients included, 33.5% (n = 4447) received a total of 6525 antimicrobials. Overall, 74.1% (4832/6525) of antimicrobials were for therapeutic use, 12.6% (n = 825) were for medical prophylaxis, 8.9% (n = 578) were for surgical prophylaxis, 2.2% (n = 143) were for other use and 2.3% (n = 147) were for unidentified reasons. A diagnosis or indication was documented in the patient’s file at the initiation for 87.3% (n = 5699) of antimicrobials; 62.9% (n = 4106) of antimicrobials had a stop or review date; and 72.0% (n = 4697) of prescriptions were guided by local guidelines. Interpretation: Overall, three-quarters of AMU was for therapeutic use across participating hospitals. Canadian hospitals should be further incentivized to create and adapt local guidelines on the basis of recent antimicrobial resistance data.
Optimization of Biodiesel Production from Waste Cooking Oil

Energy is basic need for growth of any country. The world energy demand is increasing so rapidly because of increases in industrialization and population that limited reservoirs will soon be depleted at the current rate of consumption. Both the energy needs and increased environmental consciousness have stimulated the researching of an alternative solution. So an attempted has been made to investigation of biodiesel production using transesterification reaction with solid or heterogeneous catalyst at laboratory scale and to compare the physical properties with the standard biodiesel properties. The selected process parameters are temperature ranged from 318 K to 333 K, molar ratio of methanol to oil from 4:1 to 8:1, mass ratio of catalyst to oil from 3% to 5% and rotation speed at optimum biodiesel yield was produced at 600 rpm.