Miracle-dz/UniFAR-FTUs huggingface dataset:
IMPACT OF STRATEGIC HUMAN RESOURCE MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE (A CASE STUDY OF SAFARICOM)

Purpose: The purpose of the study is to investigate the impact of strategic human resource practices on performance of Safaricom Ltd Methodology: The study adopted a cases study research design. Stratified random sampling technique was applied to select the managers from various departments in order to best inform the study results. A questionnaire was the preferred data collection instrument due to its convenience and confidential nature. The data was analyzed using descriptive statistics. Specifically, means, mode and median were the preferred descriptive statistics. The findings were presented in bar charts and graphs. Results: Findings indicated that that the human resource factors, personal factors and firm level factors contributed to the vertical and horizontal firm between strategic human resource management and competitive strategy. Consequently, it was concluded that Strategic Human management impacted on the superior performance of Safaricom Ltd. Unique contribution to theory, practice and policy: Since the study established the presence of horizontal fit among human resource function factors, the researcher recommended that Safaricom should continue to align its human recruitment policy with its motivation policy, compensation policy and training policy. For instance, Safaricom should continue recruiting highly qualified individuals, compensate the individuals highly, motivate the individuals highly and train them effectively. In addition, the Safaricom management should allow the HR function to operate flexibly such that the department is able to exercise all the available options relevant to carrying out human resource activities. Furthermore, the investment budget towards Safaricom HR function should be increased so as to enhance the strategic fit and the consequent superior organization performance. The study further recommended a causal study on the statistical relationship between investment in HR function and the performance of Safaricom using the balanced score card concept advocated by Kaplan and Norton
Analysis on the Development Strategy of Computer Education Microcourse Resources Based on a Cognitive Diagnosis Method

With the continuous deepening of the new curriculum reform, in order to meet the needs of high-efficiency and high-quality teaching, colleges and universities have carried out continuous reforms and innovations in teaching forms and methods. Microcourse is one of the main innovative forms of curriculum development in colleges at this stage, and it has a great role in promoting the quality of courses and the realization of teaching goals. At the same time, as a new discipline, computer science has its own unique properties. In addition, the traditional cognitive diagnosis model ignores the connection between knowledge points in most cases, and the use of this information is insufficient, resulting in that the traditional model cannot be reasonably applied in some practical teaching scenarios. Under the background of the continuous development of modern curriculum and the continuous innovation of educational methods, cognitive diagnosis, as an important research in educational data mining, plays a pivotal role in student evaluation. Based on this, this paper mainly takes the teaching of computer courses as an example. While introducing the current situation of teaching activities, it studies the specific strategies of microlectures in computer courses, so as to better utilize the advantages of microlectures.
Optimism, relationship quality, and problem solving discussions: A daily diary study

Problem solving is a necessary part of all successful relationships. The current study examined how naturally occurring day-to-day problem solving discussions were related to daily reports of relationship quality. In addition, we investigated whether optimism was associated with the nature and occurrence of these problems and moderated the association between problem solving discussions and relationship quality. Multilevel modeling was applied to data from 112 couples who completed daily diaries over 14 days (M age = 28.2, 95.5% heterosexual, 66.5% White). Relationship quality was higher on days when couples did not report a problem discussion, when couples reported at least somewhat solving the problem they discussed, and when the problem discussion did not involve conflict. More optimistic individuals and individuals with more optimistic partners were more likely to resolve problems, less likely to discuss problems specifically about their relationship, and less likely to have problem discussions involving conflict on days when problem discussions occurred. Individuals with partners who were high in optimism reported relatively high relationship quality regardless of whether a problem discussion occurred, whereas those with partners low in optimism reported significantly lower quality on days when such a discussion took place.
HIGH-FIDELITY CFD ANALYSIS OF IN-SERVICED SHROUDED HIGH-PRESSURE TURBINE ROTOR BLADES

In_service deterioration can lead to undesired shape variations on high-pressure turbine rotor blades. This can have a significant impact on efficiency, power generation and component life. Loss of power production from the high_pressure turbine rotor causes engine over_throttling to compensate for the lower performance. This will in turn worsen the operating conditions and ultimately reduce the life of the component. The aim of this study is to provide a high_fidelity flow simulation of in_serviced shrouded High_Pressure Turbine (HPT) blades of a modern Jet Engine. Shape variations effects on the aerodynamic performance of several shrouded HPT blades with different number of in_service hours have been investigated. In order to establish a digital model of the shape variation, a novel reverse engineering procedure is carried out, to come up with a parametrized definition of each blade's variations from nominal including any observable damage. The investigation is conducted by means of an in_house, full 3D steady_state RANS simulation of the flow around a series of damaged rotor blade geometries, which are obtained through high_resolution GOM scans. The analysis shows that the aerodynamic performance of the HPT rotor blades under investigation is primarily sensitive to shroud damage, which is found to account for efficiency losses often greater than 3%, and for more than 80% of the total performance loss. A secondary role on efficiency is found to be played by the blade shape deviation. A highly linear correlation is found between HPT stage efficiency and a combination of shroud damage parameters.
Synthesis of ZrO2:Dy3+ Nanoparticles: Photoluminescent, Photocatalytic, and Electrochemical Sensor Studies

Solution combustion was employed to create a series of ZrO2:Dy3+ (1-11 mol percent) nanoparticles (NPs) using oxalyl dihydrazide (ODH) as the fuel. ZrO2:Dy3+ NPs were subjected to calcination at about 700°C. ZrO2:Dy3+ NPs comprised of 1 to 11 mol% of Dy3+ were characterized by employing the X-ray diffraction (XRD), transmission electron microscopic (TEM), UV-visible, and X-ray photoelectron spectroscopic (XPS) techniques. The crystallite diameters of 1 to 11 mol% ZrO2:Dy3+ NPs were observed to range from 8.1 nm to 16.3 nm, exhibiting spherical shape. According to BET tests, the pore volume of ZrO2:Dy3+ NPs was determined to be 100.129 cm3/g. The mean pore diameter of ZrO2:Dy3+ NPs was determined to be 4.803 nm from the Barrett-Joyner-Halenda (BJH) plot. The photoluminescence and photocatalytic dye degradation properties of ZrO2:Dy3+ NPs were investigated. The acid red 88 (AR88) dye was applied to appraise the photocatalytic activities of the NPs under UV irradiation. ZrO2:Dy3+ NPs with 3 mol% Dy3+ exhibited improvised photocatalytic activity due to the operative departure of charge carriers. The electrochemical examination of ZrO2:Dy3+ NP modified carbon paste electrode in 0.1 N HCl demonstrated considerable redox potential output, as evidenced by cyclic voltammetric and amperometric measurements. The electrochemical sensor studies on ZrO2:Dy3+ NPs exhibited potentiality towards sensing of highly toxic metals like mercury and lead.
Stripline Y-Junction Circulators: Accurate Model and Electromagnetic Analysis Based on Gaussian Field Distribution Boundary Conditions

The conventional analysis of the circulator usually ignores ferrite disk thickness by assuming no axial variation, where the outcomes are limited to the case of small thickness ferrite disks. However, a typical scenario of a stripline Y-junction circulator has a ferrite disk thickness equal to that of the substrate. As a result, the assumption of a constant field along the feed-linewidth does not account for a realistic boundary condition. Increasing the ferrite thickness results in a significant variation in the field along the perimeter of the ferrite disk, which must be considered for accurate modeling and analysis. In this article, modeling of the Y-junction stripline circulator is proposed and investigated using Gaussian field distribution (GFD) boundary conditions. The proposed analysis takes into account the effects of ferrite thickness that controls both the field distribution and the demagnetization factor. The proposed analysis is applied to the case of a single ferrite disk and validated through a comparison between the analytical and simulation results conducted using a 3-D electromagnetic solver in different frequency bands. An excellent agreement is observed between the simulated and analytical results, highlighting the limitations of the conventional analysis methodology.
A comparison of different Malaise trap types

Recent reports on insect decline have highlighted the need for long‐term data on insect communities towards identifying their trends and drivers. With the launch of many new insect monitoring schemes to investigate insect communities over large spatial and temporal scales, Malaise traps have become one of the most important tools due to the broad spectrum of species collected and reduced capture bias through passive sampling of insects day and night. However, Malaise traps can vary in size, shape, and colour, and it is unknown how these differences affect biomass, species richness, and composition of trap catch, making it difficult to compare results between studies. We compared five Malaise trap types (three variations of the Townes and two variations of the Bartak Malaise trap) to determine their effects on biomass and species richness as identified by metabarcoding. Insect biomass varied by 20%–55%, not strictly following trap size but varying with trap type. Total species richness was 20%–38% higher in the three Townes trap models compared to the Bartak traps. Bartak traps captured lower richness of highly mobile taxa but increased richness of ground‐dwelling taxa. The white roofed Townes trap captured a higher richness of pollinators. We find that biomass, total richness, and taxa group specific richness are all sensitive to Malaise trap type. Trap type should be carefully considered and aligned to match monitoring and research questions. Additionally, our estimates of trap type effects can be used to adjust results to facilitate comparisons across studies.
Recent Advances in Cellulose-Based Hydrogels for Tissue Engineering Applications

In recent years, cellulose has attracted much attention because of its excellent properties, such as its hydrophilicity, mechanical properties, biodegradability, biocompatibility, low cost and low toxicity. In addition, cellulose and its derivatives contain abundant hydrophilic functional groups (such as hydroxyl, carboxyl and aldehyde groups), which are good raw materials for synthesizing biocompatible hydrogels. In this paper, the application prospects of cellulose and its derivatives-based hydrogels in biomedical tissue engineering are summarized and discussed through the analysis of recent research. Firstly, we discuss the structure and properties of cellulose, nano celluloses (NC) from different sources (including cellulose nanocrystals (CNC), cellulose nanofibrils (CNF) and bacterial nano celluloses (BNC)) and cellulose derivatives (including cellulose ethers and cellulose esters) obtained by different modification methods. Then, the properties and preparation methods of physical and chemical cellulose hydrogels are described, respectively. The application of cellulose-based hydrogels as a tissue engineering scaffold (skin, bone and cartilage) in the biomedical field is introduced. Finally, the challenges and prospects of cellulose-based hydrogels in tissue engineering are summarized.
Corrosion of Carbon Steel in Extreme Environments by Acid Mine Water: Experimental Study of the Process Using a Factorial Analysis Tool

Acid mine drainage (AMD) is a process resulting from mining activity, which has a potential degrading effect on metallic materials used in machinery and structural installations, mainly carbon steel composites. This work shows how steel is affected and degraded by AMD, as well as the physicochemical changes that occur in the solvent as a consequence of the metal corrosion process. For this purpose, thirty specimens were immersed in AMD for thirty weeks and were removed once per week, observing the changes that had occurred both in the metal and in the solvent to which it was exposed. The results show a material degradation with a loss of weight and alterations in the acid drainage with an increase in pH, total dissolved solids (TDS) and modifications in the rest of the solvent characteristics. The data from the measurements of the plates under study, together with the physicochemical data of the resulting reagent solution, were integrated into matrices for subsequent graphical–statistical processing using Statgraphics Centurion software, a powerful tool for exploratory data analysis, statistical summary, analysis of variance, statistical control, multivariate analysis, time series, etc., and which allows the different variables studied to be classified into categories or proximity ratios.
Near Infrared Spectroscopy as a Traceability Tool to Monitor Black Soldier Fly Larvae (Hermetia illucens) Intended as Animal Feed

The demand for animal proteins, especially from pork and poultry, is projected to increase significantly due to rapid growth in population and underlying socio-economic conditions. Livestock rearing using conventional feed ingredients is becoming challenging due to climate change and several other factors, thereby suggesting the need for alternative, viable and sustainable animal feed sources. The use of black soldier fly larvae (BSFL) (Hermetia illucens) as a component in animal feed is a promising candidate due to their ability to valorise different organic waste streams. The nutrient composition of BSFL reared on organic waste streams is also comparable to that of several conventional animal feed ingredients and varies depending upon the feed, rearing conditions, and the morphological stage of the larvae. The identification of organic waste is of importance as it can determine not only the composition but also the safety issues of BSFL as an animal feed ingredient. The objective of this study was to determine the ability of near-infrared (NIR) spectroscopy to trace the food waste used to grow BSFL. Samples of BSFL (5th and 6th instar BSFL; n = 50) obtained from a commercial production facility were analysed using NIR spectroscopy. Partial least squares discriminant analysis (PLS-DA) was employed to develop the models. The outcomes of this study revealed that NIR spectroscopy could distinguish different larval instars and suggested the importance of larval instars in developing calibration models for traceability applications. The developed PLS-DA model could predict the feed source used for rearing the 5th instar larvae (R2 value: 0.89) and 6th instar pre-pupae (R2 value: 0.91). This suggests that NIR spectroscopy could be used as a non-invasive traceability tool for BSFL and to assist in selecting the suitable time frame for larvae harvesting in commercial facilities.
Advances in thermal contact resistance studies

The demand for accurate Thermal Contact Resistance analysis and prediction continues to increase with the advancement of technology. In applications such as manufacturing, it is beneficial to understand the Thermal Contact resistance between the workpiece and tool, or between the tool and debris or shavings to accurately predict the tool wear rate. It is essential to understand and analyze this parameter in aerospace, nuclear power generation, supercar braking systems, and similar applications where close tolerances are used. The continued technological advancements indicate further opportunities for research in Thermal Contact Resistance. This article surveys and summarizes industrial applications’ as well as experimental and theoretical studies. In addition recent advances in both experimental methods and predictive studies are also comprehensively summarized and highlight the trends and future needs of studies in Thermal Contact Resistance, these included recent advances in transient and steady state experimental methods. There is a clear need for optimization, versatility, and economical improvement of these methods. Moreover, the rise in advanced metrological technologies, instrumentation, and thermal measurement systems.
Green Finance Evaluation Based on Neural Network Model

The weights of green finance indicators are established in accordance with the AHP in order to suggest an evaluation system that is more thorough and reasonable and to construct an evaluation index system. The findings indicate that the growth of urban green finance is more closely correlated with the development of environmental protection businesses, capital allocation efficiency, and governmental and social capital support. Regulation of consumption also has a significant impact. In order to encourage the growth of urban green finance, this paper analyzes the scoring outcomes and changes for each city and offers solutions and recommendations.
Ricinus communis: An Efficient Biological Tool for Heavy Metal Removal from Contaminated Soil

Heavy metal contamination often occurs when there is an abnormal discharge of metal in the environment. This is caused mainly by human activities such as agriculture, mining and industrialization. When this heavy metal gets into the environment, it adversely affects plants, humans and aquatic biodiversity. Conventional treatment methods have been applied extensively, hence these methods are inefficient and expensive to carry out. This study was conducted to assess whether castor plants could be able to absorb heavy metal actively from the soil. Metal ions determined are lead and copper at different concentrations of 1.5, 2 and 2.5 g/dm3. The soil located at Gombe State University Botanical Garden was used for the cultivation of the plant. Acid digestion and Atomic Absorption Spectroscopy were used for metal determination. The results of the study showed that Copper and Lead were absorbed the highest in the leaves than in stem and root by the absorption efficiency of 100, 91 and 87% respectively. The plant leaves and roots accumulate high Lead content of 2.661, 2.43, in the root, 1.26, 1.52, and 0.031, 0.3 in the stem for both lead and copper respectively. Results of translocation factor revealed the highest absorption of metal from Root to stem of 1.83, 1.98, and the least was 0.36, 0.34, for copper and lead respectively.
HPV High-risk Multiple Infection Is a Key Predictor of Cervical Dysplasia in Diagnostic LEEPs: a Retrospective Cohort Analysis

Abstract Objective This study aimed to identify predictors for the presence of cervical dysplasia in diagnostic LEEPs (Loop Electrical Excision Procedure) of the cervix. Materials/Methods The study was designed as a retrospective single-institution cohort analysis of all patients who underwent LEEP without prior proof of high-grade intraepithelial lesion (diagnostic LEEP) between 2015 and 2020 in the Department of Obstetrics and Gynecology of University Hospital Aachen. In order to identify the most meaningful predictive variables for CIN status (CIN2+ or non-CIN2+), multivariate logistic regression was performed and a machine-learning method was used. Results A total of 849 patients with an indication for loop excision of the cervix were assessed for eligibility. Finally, 125 patients without prior proof of CIN2+ were included into the study. Based on the final multivariate logistic regression model, multiple high-risk HPV infections (p = 0.001), the presence of a T2 transformation zone (p = 0.003) and major lesion changes (p = 0.015) as a result of the colposcopy examination were found to be statistically significant for CIN status based on the diagnostic LEEP. Subsequent ROC analysis showed a high predictive value for the model of 88.35% (AUC). The machine-learning technique (recursive partitioning) identified similar variables as important for CIN status with an accuracy of 75%. Conclusion For clinical decision-making, the result of the colposcopy examination (T2, major change) as well as the results of HPV testing (multiple high-risk HPV infections) are stronger indicators for clinicians to perform diagnostic excisional procedures of the cervix than the presence of high-grade cytological abnormalities.
Automatic Mushroom Species Classification Model for Foodborne Disease Prevention Based on Vision Transformer

Mushrooms are the fleshy, spore-bearing structure of certain fungi, produced by a group of mycelia and buried in a substratum. Mushrooms are classified as edible, medicinal, and poisonous. However, many poisoning incidents occur yearly by consuming wild mushrooms. Thousands of poisoning incidents are reported each year globally, and 80% of these are from unidentified species of mushrooms. Mushroom poisoning is one of the most serious food safety issues worldwide. Motivated by this problem, this study uses an open-source mushroom dataset and employs several data augmentation approaches to decrease the probability of model overfitting. We propose a novel deep learning pipeline (ViT-Mushroom) for mushroom classification using the Vision Transformer large network (ViT-L/32). We compared the performance of our method against that of a convolutional neural network (CNN). We visualized the high-dimensional outputs of the ViT-L/32 model to achieve the interpretability of ViT-L/32 using the t-distributed stochastic neighbor embedding (t-SNE) method. The results show that ViT-L/32 is the best on the testing dataset, with an accuracy score of 95.97%. These results surpass previous approaches in reducing intraclass variability and generating well-separated feature embeddings. The proposed method is a promising deep learning model capable of automatically classifying mushroom species, helping wild mushroom consumers avoid eating toxic mushrooms, safeguarding food safety, and preventing public health incidents of food poisoning. The results will offer valuable resources for food scientists, nutritionists, and the public health sector regarding the safety and quality of mushrooms.