Miracle-dz/UniFAR-FTUs huggingface dataset:
Antibacterial Property and Bioactive Compounds of Selected Herbal Products

Ten herbal products, made up of five powdery and five liquid samples of different brands, were assessed microbiologically for the presence and types of microorganisms. Qualitative and quantitative phytochemical screening of the samples above was also conducted to verify the presence or absence of bioactive components. Ten bacterial species, viz: Bacillus subtilis, Staphylococcus aureus, Salmonella typhi, Shigella dysenteriae, Pseudomonas aeruginosa, Corynebacterium diptheriae, Streptococcus pyogenes, Enterobacter cloacae, Klebsiella pneumoniae, and Clostridium botulinum, were isolated from these herbal products. The fungi isolated were Aspergillus Niger, Rhizopus stolonifer, Alternaria alternata, Aspergillus flavus, Fusarium verticillioides, Fusarium oxysporium, and Mucor racemosus. Results of phytochemical screening revealed the presence of flavonoids, saponins, terpenoids, and tannins. Alkaloid was the most abundant in the samples, with a value of 1070.04 mg/100g in sample F, while the least abundant in sample F was phenol (0.38 mg/100g). The GC-MS analysis revealed the presence of certain compounds such as thiophene, propanoic acid, 2,2-dimethyl-, ethyl ester, pentanoic acid, 2-methyl, toluene, and many others in sample F that exhibited significant antimicrobial effects. These compounds are known to possess antimicrobial properties. Results from this study revealed that, though these herbal products contain bioactive compounds with potential antimicrobial and antioxidant properties, they are contaminated with microorganisms of health importance. Hence, local herbalists preparing these herbal products need to be educated on good manufacturing practices (GMP). Doi: 10.28991/SciMedJ-2022-04-04-03 Full Text: PDF
Deep Learning-Based Automated Magnetic Resonance Image Segmentation of the Lumbar Structure and Its Adjacent Structures at the L4/5 Level

(1) Background: This study aims to develop a deep learning model based on a 3D Deeplab V3+ network to automatically segment multiple structures from magnetic resonance (MR) images at the L4/5 level. (2) Methods: After data preprocessing, the modified 3D Deeplab V3+ network of the deep learning model was used for the automatic segmentation of multiple structures from MR images at the L4/5 level. We performed five-fold cross-validation to evaluate the performance of the deep learning model. Subsequently, the Dice Similarity Coefficient (DSC), precision, and recall were also used to assess the deep learning model’s performance. Pearson’s correlation coefficient analysis and the Wilcoxon signed-rank test were employed to compare the morphometric measurements of 3D reconstruction models generated by manual and automatic segmentation. (3) Results: The deep learning model obtained an overall average DSC of 0.886, an average precision of 0.899, and an average recall of 0.881 on the test sets. Furthermore, all morphometry-related measurements of 3D reconstruction models revealed no significant difference between ground truth and automatic segmentation. Strong linear relationships and correlations were also obtained in the morphometry-related measurements of 3D reconstruction models between ground truth and automated segmentation. (4) Conclusions: We found it feasible to perform automated segmentation of multiple structures from MR images, which would facilitate lumbar surgical evaluation by establishing 3D reconstruction models at the L4/5 level.
The Influence of Attachment Styles On Marital Satisfaction On A Wife Who Has Been Cheated

Infidelity is the main cause of divorce in West Java, which harms individuals, especially wives who are victims of infidelity. In response to infidelity, wives are more likely to react with intense emotions that tend to last a long time, and it is easier to recall the incident, thus requiring more attention. The subjects of this study were 55 individuals whose wives were aged 18-40 years and had experiences of being cheated on and domiciled in the city of Bandung. This study aimed to describe the effect of Attachment Styles on the Marital Satisfaction of wives who had been cheated on in Bandung. The research method used is quantitative. The measuring instrument used to obtain individual Attachment Styles is The Experience in Close Relationships-Revised (ECR-R) Questionnaire, which consists of 36 items referring to the theory of Bartholomew and Horitz and consists of 36 items. The measuring instrument used to obtain the degree of individual Marital Satisfaction is ENRICH MARITAL SATISFACTION, compiled by Olson & Fowers (1993), consisting of 15 items with 4 answer choices. Subjects in this study will be given both questionnaires. After that, the results of the two questionnaires will be analyzed using a simple regression method. The results showed that attachment style strongly influences and can predict 31% of marital satisfaction in wives cheated on.
OPTIMIZING POTATO PRODUCTION WITH HYDROGEL TREATMENT AND IRRIGATION MANAGEMENT

Increasing global food demand due to increasing populations led to an increase in water requirements. The limited water resources and insufficient water supply for consumer for irrigation are important facing Egypt (governorate, researchers, and farmers). Water deficiency is a main environmental constraint that jeopardizes production of the crops in the world. Egypt ranks 107 of 181 countries of the world concerning. This study investigated the effect of irrigation regimes (IR) irrigation every 10 days and irrigation every 20 days and treating clay soil with three concentrations (HG=0.0, HG=2.5 and HG=5.0 per fed.) as a soil additive on plant growth, water use efficiency, productivity, and tuber quality of potato plant c.v. Lady Balfour. The experiment was conducted at the experimental farm of the Horticulture Department at Minia University's Faculty of Agriculture over two consecutive fall seasons (2018/2019 and 2019/2020). Soil analysis was performed, and the experiment was set up in split plots using a complete randomized block design with four replications (IR in the main plot and HG treatments in sub plot). The experiment demonstrated that irrigation regime had a significant effect on plant growth parameters i.e., plant height, plant fresh and dry weights. The first regime (irrigation every 10 days) shows the highest values of plant growth compared to the second regime. Hydrogel treatment also had a
Changes in tundra plant and soil organic geochemical composition in response to long-term increased winter precipitation in N. Alaska

Miller, Olivia L. M.S., Purdue University, August 2014. Changes in tundra plant and soil organic geochemical composition in response to long-term increased winter precipitation in N. Alaska. Major Professor: Timothy Filley. Tundra ecosystem soils store half of the global soil organic carbon (SOC) pool and have the potential to release large amounts of greenhouse gases, primarily CH4 and CO2. As climates warm and permafrost thaws, the emissions of these gases as organic matter decomposition rates increase may become increasingly important to the chemistry of the atmosphere. Increases in precipitation that accompany warming, may shift tundra vegetative composition from moss/graminoidto shrub-dominated systems. These vegetation shifts may result in important feedback effects as altered plant and litter chemistry may affect SOC storage in soils. Questions investigated include: 1) How does snow depth thickness influence plant distribution and the carbon (C) and nitrogen (N) primary and secondary chemistry of the dominant plant species, litter, and soil; 2) How do the changes in plant chemistry and relative interspecies input impact litter quality; and 3) Which plant products are preferentially stored versus easily degraded in litter and surface soils, and what will it ultimately suggest about carbon storage? Plant chemistries were measured in graminoids, moss, shrub leaves, and shrub stems in areas with ambient snow depth, and long-term deeper, intermediate and low snow depths which have been altered since 1994. C:N ratios, carbohydrate content, and lignin and phenol content were also used to track and relate changes in litter and soil chemistry. Results include that long-term increases in snow (Deep ~ 3 m) resulted in a ~50% increase in graminoid and shrub leaf N that was accompanied by a ~30% decrease in soil carbon in the litter (0-2 cm) and in the organic horizon (4-6 cm) layers. This response was, however, not linear as
Relevant Properties and Potential Applications of Sericin in Bone Regeneration

The potential of sericin, a protein derived from silkworms, is explored in bone graft applications. Sericin’s biocompatibility, hydrophilic nature, and cost-effectiveness make it a promising candidate for enhancing traditional graft materials. Its antioxidant, anti-inflammatory, and UV-resistant properties contribute to a healthier bone-healing environment, and its incorporation into 3D-printed grafts could lead to personalized medical solutions. However, despite these promising attributes, there are still gaps in our understanding. The precise mechanism through which sericin influences bone cell growth and healing is not fully understood, and more comprehensive clinical trials are needed to confirm its long-term biocompatibility in humans. Furthermore, the best methods for incorporating sericin into existing graft materials are still under investigation, and potential allergic reactions or immune responses to sericin need further study.
Identification of Planned Stroke Awareness Behavior: A Structural Equation Modeling

Introduction: Stroke is one of the most common neurological diseases, often causing death or gross physical impairment or disability. The associated risk factors such as hypertension, high cholesterol, diabetes, heart disease, and smoking should serve as warnings. However, most people are still not aware of these risks. The main aim of this study is to identify stroke awareness behavior using the construct variable from the Theory of Planned Behavior as the predictor (attitude factor, subjective norm factor, perceived behavioral factor, and intention to perform behavior). Methods: A cross-sectional study was conducted on 256 people who have a high risk of stroke at the Poncokusumo Health Center, Malang, Indonesia. The sampling technique used was purposive sampling. The authors used all the construct variables in the Theory of Planned Behavior. The stroke awareness behavior was measured using a questionnaire developed from the National Stroke Awareness Guide, while the attitude factor, subjective norm factor, perceived behavioral factor, and intention were measured using the instruments developed from standard instruments from the Theory of Planned Behavior. Structural Equation Modeling (SEM-PLS) was used to analyse the data. Result: This study found that 68.4% of respondent with high or low intention of preventing a stroke can be predicted by attitude factors, subjective norm factors, and perceived behavioral factors. While 96.1% of good or bad stroke awareness behavior can be predicted by the model used in this study, the rest (3.9%) is explained by other variables outside this research model. Conclusion: The hypothesis testing results showed that all construct variables in the Theory of Planned Behavior can be strong predictors of stroke awareness behavior. All variables in the Theory of Planned Behavior can be powerful predictors of stroke awareness behavior.
Benefits of Portland-Limestone Cement for Concrete with Rounded Gravel Aggregates and Higher Fly Ash Replacement Rates

Recently, portland-limestone cement (PLC) has garnered increased interest in the US due to potentially greater environmental sustainability and enhancement of certain concrete properties and/or performance. Combined with supplementary cementitious materials (SCMs), these benefits can be further extended. This paper builds on past works of the authors, tailored to Mississippi’s current concrete practices, by evaluating concrete mixtures with rounded gravel aggregates and greater replacement of cement with SCMs (primarily fly ash). In total, 15 different cementitious combinations were used in concrete and cement paste mixtures. Concrete specimens from 30 mixtures (360 specimens) were tested, featuring various combinations of cements, SCMs, and admixtures. Replicates of these 30 mixtures were created in cement paste mixtures (540 specimens). Major variables included SCM type, replacement rate, and cement source. Mixtures were tested for compressive strength, time of setting or thermal setting indication, and slump and air content, in the case of concrete mixtures. A small subsection of concrete mixtures was also examined using petrography. Results indicated that use of PLC vs. ordinary portland cement (OPC) resulted in notable compressive strength improvements in mixtures with high Class C fly ash replacement (especially 40%). Slump and air content were not statistically different in PLC vs. OPC mixtures; however time of setting was lower in mixtures with PLC. Hydration-related distinctions (PLC vs. OPC mixtures) were evident in petrographic images, with some observed differences in cement paste character and the paste-to-aggregate interfacial transition zone (ITZ).
Parental Involvement in Palestine

This research study aims to find the relationships between parental involvement and achievements in math of Palestinian students in the fifth and ninth grades. The analysis is applied to data from 5,214 students in the fifth grade from 172 schools and 4,381 studentsin the ninth grade from 142 schools participating in national assessment testing in Palestine in 2018. The data were analyzed using descriptive analysis, Pearson correlations, and intraclass correlations. The results show strong home-based parental involvement, with a higher degree of parental involvement for students in the fifth grade than those in the ninth grade. The analysis shows weak positive relationships between how parents control their children and children's academic achievement for both grades. There were no correlations between parental support and their children's achievement. There were correlations between schoolbased parental involvement and student achievement in math in the fifth and ninth grades. Children need parental support at home and at school; however, not all parents are able to provide that support. Many schools prefer to limit parental involvement since they prefer to manage without parental help.
IMPROVING THE ACCURACY OF SATELLITE-BASED NEAR SURFACE AIR TEMPERATURE AND PRECIPITATION PRODUCTS

Abstract. In this study, we evaluate the performance of several reanalyses and satellite-based products of near-surface air temperature and precipitation to determine the best product in estimating daily and monthly variables across the complex terrain of Turkey. Each product’s performance was evaluated using 1120 ground-based gauge stations from 2015 to 2019, covering a range of complex topography with different climate classes according to the Köppen-Geiger classification scheme and land surface types according to the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, various traditional and more advanced machine learning downscaling algorithms were applied to improve the spatial resolution of the products. We used distance-based interpolation, classical Random Forest, and more innovative Random Forest Spatial Interpolation (RFSI) algorithms. We also investigated several satellite-based covariates as a proxy to downscale the precipitation and near-surface air temperature, including MODIS Land Surface Temperature, Vegetation Index (NDVI and EVI), Cloud Properties (Cloud Optical Properties, Cloud Effective Radius, Cloud Water Path), and topography-related features. The agreement between the ground observations and the different products, as well as the downscaled temperature products, was examined using a range of commonly employed measures. The results showed that AgERA5 was the best-performing product for air temperature estimation, while MSWEP V2.2 was superior for precipitation estimation. Spatial downscaling using bicubic interpolation improved air temperature product performance, and the Random Forest (RF) machine learning algorithm outperformed all other methods in certain seasons. The study suggests that combining ground-based measurements, precipitation products, and features related to topography can substantially improve the representation of spatiotemporal precipitation distribution in data-scarce regions.
Video Analysis For Human Action Recognition Using Deep Convolutional Neural Networks

In the last few years, human action recognition potential applications have been studied in many fields such as robotics, human computer interaction, and video surveillance systems and it has been evaluated as an active research area. This paper presents a recognition system using deep learning to recognize and identify human actions from video input. The proposed system has been fine-tuned by partial training and dropout of the classification layer of Alexnet and replacing it by another one that use SVM. The performance of the network is boosted by using key frames that were extracted via applying Kalman filter during dataset augmentation. The proposed system resulted in oromising performance compared to the state of the art approaches. The classification accuracy reached 92.35%.
Design of machine for onion leaf cutting.

- In a field harvester and trimmer for root crops having foliage superior growing from the crowns of the crop to be harvested and cut, like giant bulbous onions, turnips, parsnips and rutabagas. This project is meant to debate the look leaf cutting machine. This machine is used for the agricultural purpose and it is also employed in the food industries. Leaf cutting machine works on conveyer belt and cutter arrangement. Onions are fed through feeding conveyer belt into the machine. India is world's second largest Onion harvested. But yet Farmers processes onion by hand labor when harvest to get rid of the leaves and roots. This operation is referred to as topping which is time consuming and They Can't afford new techniques Because of the cost of Appraisal Our Motive is to supply them with effective and economical methodology for harvest to cut back human effort while not damaging the onion, Efforts to date have all been in the direction of large and expensive machinery and none of these has as yet been perfected so as to reach the market. An epitome aims at cutting leaf of onion victimization conveyer and cutters to cut onions which can be collected in trays for further processing or packaging.
The Effect of Task based Approach on Improving Reading Comprehension and Attitude of Pharmacy College Students in Delta University

The present study attempts to investigate the impact of conducting the Task based Approach (TBA) to improve reading comprehension skills of Delta University Students. A quantitative technique was conducted by applying pre-and post-test to measure enhancement of students. Sixty medical students, who studied "English2" course in the academic year 2019\2020, were participating in the study. They were split into two equivalent groups represented Control Group (CG) and Experimental Group (EG) with thirty students each. To find out the upgrading of students‟ reading comprehension, pre-posttest and t-test were used. And to evaluate students’ attitude towards employing TBA in studying English and their complications within the medical context, a semi-structured questionnaire also was administered. The findings reveal that t-test was 2.042 (significant at 0.05). This showed that by conducting TBA, the students' attainment has been enhanced. The results of the questionnaire revealed positive attitude towards applying TBA for enhancing their reading comprehension skills.
Research progress of liquid cooling technology for high heat flux electronic equipment

Heat flux of electronic equipment shows an increasing trend with the increasing demand for miniaturization and high performance. Low heat dissipation capacity makes it difficult to apply air cooling technology to high heat flux electronic devices in the future. At present, liquid cooling technology is the research hotspot of cooling solutions for electronics, mainly because liquid cooling has the advantages of strong fluidity and high heat transfer coefficient. Several kinds of liquid cooling technologies which can be used for cooling electronic equipment are introduced in this paper, among which micro-channel cooling, spray cooling, jet cooling and immersion cooling are emphasized. The working principle and progress in research of each liquid cooling technology are introduced and analyzed. Finally, the future development direction of liquid-based cooling technology is discussed. The problems in the research direction of liquid cooling technology are pointed out. Some suggestions for further research on liquid cooling technology are put forward.
The effect of applying the blended learning model with the Moodle application on student cognitive improvement

This study aims to investigate the effect of implementing the blended learning model with Moodle application on improving students' cognitive abilities. Blended learning is a learning approach that integrates the use of digital technology and face-to-face interaction between teachers and students. Moodle is one of the popular e-learning platforms used in implementing blended learning. This study used a pre-test and post-test experimental control group design, where the research sample consisted of two groups of students, each of which was given different treatments: a control group and an experimental group. Students' cognitive data was measured through knowledge tests before and after treatment. Data analysis was performed using t-test and ANOVA. The results of this study are expected to provide useful information for educational institutions in choosing effective and efficient learning strategies to improve the quality of higher education.
Using TLC-MALDI-TOF to Interrogate In Vitro Peptidyl Proximal Preferences of PARP14 and Glycohydrolase Specificity

The transfer of ADP–ribose (ADPr) from nicotinamide adenine dinucleotide (NAD+) to target proteins is mediated by a class of human diphtheria toxin-like ADP-ribosyltransferases (ARTDs; previously referred to as poly-ADP–ribose polymerases or PARPs) and the removal of ADPr is catalyzed by a family of glycohydrolases. Although thousands of potential ADPr modification sites have been identified using high-throughput mass-spectrometry, relatively little is known about the sequence specificity encoded near the modification site. Herein, we present a matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) method that facilitates the in vitro analysis of proximal factors that guide ARTD target selection. We identify a minimal 5-mer peptide sequence that is necessary and sufficient to drive glutamate/aspartate targeting using PARP14 while highlighting the importance of the adjacent residues in PARP14 targeting. We measure the stability of the resultant ester bond and show that non-enzymatic removal is pH and temperature dependent, sequence independent, and occurs within hours. Finally, we use the ADPr–peptides to highlight differential activities within the glycohydrolase family and their sequence preferences. Our results highlight (1) the utility of MALDI-TOF in analyzing proximal ARTD–substrate interactions and (2) the importance of peptide sequences in governing ADPr transfer and removal.