Miracle-dz/UniFAR-FTUs huggingface dataset:
Work Environment and the Teacher: A Qualitative Case Study of Public Secondary Schools in Addis Ababa, Ethiopia

This study examined the nexus between the public secondary school teacher and his/her work environment. To capture the nature and substance of this nexus, the study was mainly directed towards answering the following two research questions: Which attributes of work environment matter most to the public secondary school teacher? And why do they matter? The study was conducted on teachers in public secondary schools of Addis Ababa, Ethiopia. It adopted a qualitative case study design where data were collected through semi-structured interviews and then analyzed using a thematic analysis technique. The results show that basic school facilities, teacher-principal and teacher-student in-school inter-personal relations, financial and non-financial rewards, and the praxis of politics in a school setting are the attributes of the work environment that matter most to the study participants. Results also show that these attributes of work environment appeared as attributes of work environment with utmost importance to the teacher for they happened to be the basis of the teacher’s work motivation, job satisfaction, and work engagement. Findings further showed that the attributes of the work environment and the emergent causal consequences have both direct and indirect relationships. This study is expected to have empirical, methodological, and theoretical implications.
Inhomogeneities in PNIPAM Aqueous Solutions: The Inside View by Spin Probe EPR Spectroscopy

Coil to globule transition in poly(N-isopropylacrylamide) aqueous solutions was studied using spin probe continuous-wave electronic paramagnetic resonance (CW EPR) spectroscopy with an amphiphilic TEMPO radical as a guest molecule. Using Cu(II) ions as the “quencher” for fast-moving radicals in the liquid phase allowed obtaining the individual spectra of TEMPO radicals in polymer globule and observing inhomogeneities in solutions before globule collapsing. EPR spectra simulations confirm the formation of molten globules at the first step with further collapsing and water molecules coming out of the globule, making it denser.
Room-temperature demonstration of highly-functional single-hole transistor logic based on quantum mechanical effect

Novel highly-functional single-hole transistor (SHT) logic is proposed. An SHT is fabricated which shows Coulomb blockade and negative differential conductance (NDC) due to discrete quantum levels in the ultra-small dot at room temperature. By utilising gate-controllable NDC, exclusive-OR operation is successfully demonstrated in just one SHT at room temperature.
Effect of Gluconacetobacter diazotrophicus inoculation and reduced nitrogen fertilizer on yield and growth parameters of rice varieties

Abstract: This study aimed to investigate effects of nitrogen (N) fertilizer rates and inoculation of rice seeds with N-fixing bacterium Gluconacetobacter diazotrophicus on the growth parameters and yield of OM5451 and OM6976 rice varieties in the Vietnamese Mekong Delta region. Nitrogen fertilizer rates of 50 kg N.ha-1 and 100 kg N.ha-1 were used, with latter reflecting farmer practice. Three rice seed inoculation methods were also employed: Seeds soaked in water for 24 hours and allowed to stand for 30 hours (control) (B0); Seeds soaked in water for 24 hours and inoculated with G. diazotrophicus for 30 hours (B1); Seeds soaked with G. diazotrophicus in water for 24 hours and allowed to stand for 30 hours. Applying 50 kg N.ha-1 without combining with NFB bacterium in this experiment generally resulted in less tillers, shorter plants, a lower SPAD index, and lower grain yield. Combining G. diazotrophicus bacterium with reduced N fertilizer of 50 kg N.ha-1 demonstrated rice growth and yield may be maintained in both varieties compared to 100 kg N.ha-1. These results providing a firm foundation for future research of adding NFB to paddy soils to decrease the N fertilizer requirement.
A Model of Transformational Leadership in the Organisational Culture of Preschool Institution

The culture of an organisation refers to the common and dominant values and norms, as well as the beliefs and perceptions of its employees. Organisational culture influences employee behaviour and attitudes. The culture of the institution predetermines the tone of the environment, which can affect employee satisfaction. The basic premise of this paper is established in the connection between the characteristics of the leader and the transformational model of leadership in the organisational culture of the preschool institution. This research aims to determine whether the assessments and self-assessments of leaders and preschool teachers differ in the frequency of using the transformational leadership style and to determine the relationship between the transformational leadership style and the leader’s personality traits. The research was conducted in a private Croatian preschool institution, which consists of 10 kindergartens. A total of 51 participants in 9 kindergartens participated in the research. The study used a quantitative research approach. The research results show that preschool teachers and leaders do not differ significantly in estimates of the frequency of application of the transformational leadership style and that there is a significant correlation between some characteristics of the leader and the transformational model of preschool leadership.
Predicting the Risk of Alcohol Use Disorder Using Machine Learning: A Systematic Literature Review

The number of deaths caused by alcohol-related diseases may be reduced by predicting alcohol use disorder (AUD). Many researchers have worked on AUD prediction using machine learning (ML) techniques. However, to the best of our knowledge, there is a lack of a comprehensive systematic literature review (SLR) that summarizes the existing studies on AUD prediction using ML in the last ten years. To address this knowledge gap, this article provides an SLR of academic articles on AUD prediction using ML techniques dated from January 2010 to July 2021. This SLR highlights technical decision analysis related to five aspects: data collection site, characteristics, and type of dataset; data sampling and data pre-processing techniques; feature types and feature engineering techniques; and characteristics of ML techniques and evaluation metrics. Six bibliographic databases were searched, and the identified studies were rigorously reviewed based on the above five aspects. In the selected studies, public datasets were not used very often for AUD prediction. Given that, the current paper identified two different types of data collection sites for review. Imbalanced class distribution in datasets was the primary focus of the pre-processing and sampling steps. Various features, including demographics, family history, drinking behaviour, and electronic health records, were introduced as the more widely used AUD prediction features. The filter, wrapper, and embedded methods were identified as the primary feature selection methods. Support vector machine was the most widely employed algorithm for predicting AUD; however, the lack of deep neural network techniques is evident in this field. Moreover, considering gender disparities, early detection of AUD, and identifying trajectories towards AUD are suggested for future work. For the purpose of evaluating the performance of the prediction approaches, most studies considered the overall accuracy and the area under the receiver operating characteristic curve. Nevertheless, external validation was not performed in any of the selected studies. This paper also discusses challenges and open issues of AUD prediction for future research. This SLR represents a valuable resource for scholars investigating the prediction of AUD.
Real-Time Dorsal Hand Recognition based on Smartphone

The integration of biometric recognition with smartphones becomes necessary for increasing security, especially in financial transactions such as online payment. Vein recognition of dorsal hand is outstanding than other methods like palm, finger, and wrist as it has a wide area to be captured and does not have any wrinkles. Most of the current systems that depend on dorsal hand vein recognition are not work in real-time and have poor results. In this paper, a dorsal hand recognition system working in real-time is proposed to achieve good results with a high frame rate. A contactless device consists of a Universal Serial Bus (USB)-Camera, infrared LEDs, and connected to a smartphone used to collect our dataset. The dataset contains 2200 images collected from both hands of 100 persons. The captured images are processed with light algorithms to improve real-time performance and increase frame rate. The algorithm used for feature detection and extraction is Oriented FAST and Rotated BRIEF (ORB) with K-Nearest Neighbors (K-NN) matching to match features. Another benchmark called Poznan University of Technology (PUT) dataset is used to measure the proposed system efficiency. The result obtained from experimental testing showed that the proposed system had a low Equal Error Rate (EER) with 4.33% and a high frame rate of 29 frames per second (Fps).
Green technology licensing under platform selling

Abstract Industrial practice has shown that technology licensing is of great importance to green technology development. This study investigates licensing strategies for green technology under the background of platform selling. We consider an online platform operating through agency selling and two competitive firms one of which is an innovator investing in green technology. The innovator has an option to offer a license for the green technology to its competitor. Using No licensing scenario as a benchmark, we examine two alternative licensing strategies (Fixed-fee licensing vs. Royalty licensing) when the innovator licenses the technology. We find that licensing enhances the optimal green technology level, each firm’s optimal price, and realized demand. Royalty licensing strategy outperforms Fixed-fee licensing strategy from the viewpoint of environmental performance and the innovator’s profitability. Interestingly, we find that product price may decline with production cost, and when the platform charges firms a higher commission rate, the firms will choose a lower product price in some condition. Furthermore, by numerical examples, we observe an increasing commission rate may hurt the platform.
A THREE-DIMENSIONAL, ASYMMETRIC, AND TRANSIENT MODEL TO PREDICT GRAIN TEMPERATURES IN GRAIN STORAGE BINS

A three-dimensional, transient, combined model (headspace model + soil model + conduction model in bulk grain) was developed to predict grain temperatures in a granary. Different meshes (mesh refinement in the whole domain or at the boundary) including linear and hybrid (linear and quadratic) elements were used to simulate grain temperatures. Prediction accuracies of temperatures produced by the different meshes were compared, and the model was validated using measured temperatures in two flat bottom bins (3.76 m diameter and 5.5 m high filled with wheat up to 3 m) located side by side in the north-south orientation near Winnipeg, Manitoba. Grain temperatures predicted by the model were in close agreement with the measured temperatures throughout a 21-month test in the two bins. By using a hybrid element mesh (mesh refinement at the boundary), the mean, standard error, and maximum of the absolute difference between the measured and predicted temperatures in the south bin were 2.2°C, 0.4°C, and 7.0°C, respectively. The mean, standard error, and maximum of the absolute difference predicted by a linear element model (88 linear elements each layer) in the south bin were 2.1°C, 0.3°C, and 6.3°C, respectively. Including a headspace model improved the prediction accuracy of the conduction model at the top of the grain bulk. Mesh refinement only at the boundary produced a homogeneous distribution of errors in the whole domain; however, mesh refinement in the whole domain gave higher errors at the walls than at the center of the bins. Considering the increased computer time and slightly improved accuracy by mesh refinement at the boundary, a uniform mesh with mesh refinement in the whole domain was preferable for predicting grain temperatures in an entire grain bin.
The results of a survey on structural out-of-plumbs

Statistical characteristics obtained from measurements of out-of-plumb columns and walls in three core-braced buildings and one large industrial building under construction are presented. The new data are presented together with a summary of results of measurements previously available in the published literature. Special techniques are introduced for measuring out-of-plumbs and for reducing the data for use in design.
Prioritizing the influencing factors of utaut-2 model toward mobile network service providers

Technological developments have a major impact on user behavior. The rapidly evolving communication system and technology have provided numerous choices for people. The ever-shifting changes in the generation of communication networks have posed challenges for mobile network service providers to attract and retain customers. This study aims to prioritize the determinants of the adoption of mobile network service providers using the unified theory of acceptance and use of technology (UTAUT-2). In this study, data were collected from 660 mobile phone users in Haryana, India. A fuzzy analytical hierarchy process (F-AHP) was applied to arrange the priority or rank the factors based on the significance of the factors in explaining the adoption of mobile network service providers. Results of the study reveal that efforts expectancy is the highest-ranked and prioritized factor for the adoption of mobile network service providers followed by performance expectancy and facilitating conditions. However, social influence emerged as the least important factor. The present study provides theoretical implications for future researchers by synthesizing and prioritizing the important factors affecting technology acceptance. The practical implications offer a clearer insight to marketers for developing focused pragmatic strategies to retain customer loyalty. The study has considered only UTAUT-2 model constructs and used the F-AHP technique. Other factors may be considered in future studies. Other priority analysis techniques can also be used such as ISM and MICMAC analysis for further study. The research has been conducted in Haryana, India, and therefore, it needs to be tested in other areas/countries for generalizability. JEL Classification: O1, O2, O4
Density and elastic properties of liquid gallium up to 10 GPa using X-ray absorption method combined with externally heated diamond anvil cell

ABSTRACT The density of liquid metals at high pressure and high-temperature provides fundamental and important information for understanding their compression behavior and elastic properties. In this study, the densities of liquid gallium (Ga) were measured up to 10 GPa and 533 K using the X-ray absorption method combined with an externally heated diamond anvil cell. The elastic properties (the isothermal bulk modulus (KT0 ), and its pressure derivative (KT0’)) of liquid Ga were obtained by fitting the density data with three equations of state (EOSs) (Murnaghan, third order Birch–Murnaghan, and Vinet). The KT0 values of liquid Ga were determined to be 45.7 ± 1.0–51.7 ± 1.0 GPa at 500 K assuming KT0’ values of 4–6. The obtained KT0 or KT0 ′ showed almost the same values regardless of the EOS used. Compared with previous results, the compression curve of liquid Ga obtained in this study had a slightly stiffer trend at higher pressures.
Five-Class Classification of Cervical Pap Smear Images: A Study of CNN-Error-Correcting SVM Models

Objectives Different complex strategies of fusing handcrafted descriptors and features from convolutional neural network (CNN) models have been studied, mainly for two-class Papanicolaou (Pap) smear image classification. This paper explores a simplified system using combined binary coding for a five-class version of this problem. Methods This system extracted features from transfer learning of AlexNet, VGG19, and ResNet50 networks before reducing this problem into multiple binary sub-problems using error-correcting coding. The learners were trained using the support vector machine (SVM) method. The outputs of these classifiers were combined and compared to the true class codes for the final prediction. Results Despite the superior performance of VGG19-SVM, with mean ± standard deviation accuracy and sensitivity of 80.68% ± 2.00% and 80.86% ± 0.45%, respectively, this model required a long training time. There were also false-negative cases using both the VGGNet-SVM and ResNet-SVM models. AlexNet-SVM was more efficient in terms of running speed and prediction consistency. Our findings also showed good diagnostic ability, with an area under the curve of approximately 0.95. Further investigation also showed good agreement between our research outcomes and that of the state-of-the-art methods, with specificity ranging from 93% to 100%. Conclusions We believe that the AlexNet-SVM model can be conveniently applied for clinical use. Further research could include the implementation of an optimization algorithm for hyperparameter tuning, as well as an appropriate selection of experimental design to improve the efficiency of Pap smear image classification.
Determinan Kejadian Preeklampsia pada Ibu Hamil

Preeclampsia is an increase in blood pressure in pregnant women and excess levels of protein in the urine (proteinuria). This study aims to assess the factors associated with the incidence of preeclampsia. This is an analytical observational study with a case control study approach. This study was carried out in January-September 2018. The sample in this study was 62 people, 31 case samples and 31 control samples. Sample was selected used simple random sampling technique. Data analysis using chi-square test. The results of this study showed that there was a relationship between obesity, parity, and history of ANC with the incidence of preeclampsia with p-value <0.05. Obesity is 7.2 times more at risk of developing preeclampsia (OR = 7,200 CI 95% = 2,182-23,755). Multigravida (OR = 0.044 95% CI: 0.011-0.182) and a history of complete ANC (OR = 0.057 95% CI: 0.011-0.281) were protective factors for the incidence of preeclampsia. To prevent the incidence of preeclampsia, it is necessary to increase education and literacy on risk factor prevention, including improving clean and healthy living behavior.
Methods for Monitoring Fast and Large Gradient Subsidence in Coal Mining Areas using SAR Images: A Review

Ground subsidence caused by coal mining may lead to seriously damage to ecological environment system and economic and social development. SAR image interferometry technology has been widely used for monitoring coal mining subsidence. However, at present it is still a challenge for it to monitor fast and large gradient subsidence. In order to obtain fast and large gradient subsidence using SAR images, some scholars have resorted other methods and technology which doesn’t just depend on the interferograms, and they have obtained better results. In a summary, these methods and technology can be grouped into three main categories: (i) Combining InSAR with coal mining subsidence prediction models; (ii) Using SAR image amplitude information offset tracking technology; (iii) Combining InSAR with terrestrial 3D laser scanning technology. In this paper, the technology features, research progress, applications and limitations of these methods are analyzed; and some potential prospects of using SAR images to monitor fast and large subsidence in coal mining areas are discussed in the end.
Impact of COVID-19 on patients receiving chemotherapy for gynecological cancer

Background: Cancer patients’ increased susceptibility to serious COVID-19 complications can be attributed to the immunosuppressed state caused by the disease and anticancer treatments such as chemotherapy or surgery. Objectives: To assess the effect of COVID-19 pandemic on gynecological cancer patients receiving chemotherapy. Methods: A cross-sectional study was conducted on patients receiving chemotherapy for gynecological cancer between (March 2020 to February 2021) at King Abdulaziz University Hospital (KAUH) in Jeddah, Saudi Arabia. Clinical data collected from medical records included patients’ ages, medical history data, cycles of chemotherapy, COVID-19 infection, complications and death. Results: Total of 84 patients were identified. The mean age of studied patients was 53.81 ± 13.76 years, and the most common chronic diseases were HTN (35.7%) and DM (23.8%). The majority of diagnoses were ovarian cancer (41.7%) followed by uterine cancer (33.3%). Of studied patients, 17.9%, 19.1%, 27.4 and 33.3% had I, II , III and IV cancer stages respectively. The mean number of cycles of chemotherapy was 7.14 ± 5.55. 52.4% had first line chemotherapy. 57 percent of patients had delays due to various causes, including COVID-19 infection, and 9 percent of patients had COVID-19 while on therapy. 15 percent of the delays were caused by patients who were affected by Covid-19 while receiving chemotherapy and 2% of the patients died as a result of COVID-19 . Patients with recurrent disease had a significantly higher percentage of patients detected with COVID-19, and all cases detected with COVID-19 died with respiratory failure. Patients who had their chemotherapy delayed had a significantly higher mean number of cycles. Conclusion: Improved communication and management programs are required to keep cancer patients and their healthcare providers connected, as well as to allow cancer patients to survive a pandemic. Key words: Impact, COVID-19, patients, chemotherapy, Jeddah, Saudi Arabia.
Examination of the Influence of Cobalt Substitution on the Properties of Barium Titanate Ceramics

Cobalt (Co) doped Barium titanate (BaTiO3) powders, with Co concentration (0,5 and 10 mol%), are synthesized by the sol-gel technique and characterized by Thermogravimetric analysis (TGA), and Differential thermal analysis (DTA), X-Ray diffraction (XRD), Fourier Transform Infrared (FTIR) and scanning electron microscopy (SEM). X-ray diffraction (XRD) patterns of the obtained powders, calcined at a relatively low temperature (1000 °C/3 h), found their crystallization in the pure perovskite structure without the appearance of secondary phases. XRD results reveal that the Co decreases the lattice parameters, the volume of the unit cell and the crystallite size of BaTiO3. The investigations carried out by FT-IR spectroscopy allow the investigation of the substitution procedure behavior associated to the Co incorporation into BaTiO3 lattice. The evolution of the physical parameters as functions of the dopant content have been examined based on XRD and FT-IR results. Furthermore, the morphology and the shape variation of particle size were studied through SEM.
Multimode coupled flutter and buffeting analysis of the Akashi-Kaikyo bridge

Flutter and buffeting are both issues of utmost concern in the wind-resistant design of long-span bridges. Multimode flutter and buffeting analysis of the Akashi-Kaikyo Bridge, which is the world's longest suspension bridge with a center span of 1,991 m, were performed, and the analytical results were compared with wind-tunnel test data obtained from an aeroelastic full model of the bridge. The multimode flutter analysis was in good agreement with the measurements and exhibited a significant coupling among modes. The multimode buffeting analysis also showed excellent agreement between the analysis and measurements in vertical and torsional response. However, for reasons likely associated with the assessment of lateral aerodynamic damping, the analysis overestimated lateral response. Significant coupling among modes was also observed in the buffeting analysis, and the multimode analysis predicted the measurement better than an alternate single-mode analysis method.