Miracle-dz/UniFAR-FTUs huggingface dataset:
STUDENTS’ PERCEPTION OF BLENDED ONLINE LEARNING DESIGN BASED COLLABORATIVE LEARNING MODEL AND ITS IMPACT ON LEARNING OUTCOMES

Collaborative learning is an important learning strategy applied to both face-to-face and distance education to build collaboration skills of students. Although collaborative activities in online learning are more complex, with a proper instructional design it would work well. One way to maximize the use of online collaborative learning is by implementing a combination of asynchronous and synchronous online learning which allows for better interaction and communication amongst students and between students and tutors. The research on collaborative learning models based on a combination of asynchronous and synchronous online learning called Blended Online Learning Design based Collaborative Learning Model (BOLD-CLM) aimed to analyze the role of the model (i) to analyze student perceptions in developing cooperation, improving communication, and satisfaction; and (ii) to analyze the effect of the model on student learning outcomes. This quasi-experimental research included an experimental group and a control group in the online tutorial for the Marketing Management Course in Universitas Terbuka Indonesia. A questionnaire using a Likert scale from strongly agree to strongly disagree is distributed to respondents to extract information related to the role and perceptions of respondents towards the BOLD-CLM. Respondents' perceptions of the BOLD-CLM in the aspects of communication and teamwork and student satisfaction showed positive results. The results of the Mann-Whitney test showed that the score of the assignments between students who took the tutorial with the BOLD-CLM (Experiment Group) and students who took online tutorials with standard online learning (Control Group) differed significantly (.000). It can be concluded that collaborative learning in the BOLD-CLM has a significant effect on students' abilities in achieving learning competencies. The results of the Mann-Whitney test showed that the final scores of the courses between students who took the tutorial with the BOLD-CLM (Experiment Group) and students who took the tutorial with standard online learning (Control Group) differed significantly (.026). Thus, it can be concluded that BOLD-CLM has a significant effect on the learning outcome.
Decaffeinated coffee and green tea extract inhibit foam cell atherosclerosis by lowering inflammation and improving cholesterol influx/efflux balance through upregulation of PPARγ and miR-155 [version 2; peer review: awaiting peer review

Background: Foam cells, the markers of early atherosclerosis and characterise the advanced atherosclerotic plaque, are stimulated by inflammation due to the excess of lipid in macrophages. The combination of decaffeinated coffee and green tea extract (DCGTE) has been suggested to have a role in foam cell inhibition
The Resettlement Experiences of Oromo Women Who Entered Canada as Refugees

A growing body of literature shows that gender-based experiences produce different circumstances for men and women who become refugees and thereafter. This article sought to contribute to this literature by investigating the challenges faced by Oromo women who have immigrated to Canada as refugees. Toward this end, we interviewed six Oromo women in Western Canada regarding what led them to leave Ethiopia, their experiences as refugees seeking asylum, and their struggles with resettlement and integration. The findings reveal that Oromo women share the challenges endured by their male counterparts, but also are victim of gender-based subjugation at each stage of emigration.
Computer-Aided Synthesis of the Optimum Refractive-Index Profile for a Multimode Fiber

In a multimode optical fiber, the so-called multimode dispersion (mode-delay difference) is the principal cause that widens the transmitted pulse. The multimode dispersion can be controlled by the refractive-index profile. However, the optimum profile that minimizes the multimode dispersion has not yet been determined. This paper describes the computer-aided trial-and-error synthesis of the optimum refractive-index profile. It is shown that the group delay is reduced to about 10/sup -3/ times the value obtained with the uniform core fiber, to about 10 ps/km. This value is comparable to the material dispersion obtained with an ordinary fused-silica fiber and a typical semiconductor laser. It is also shown that the optimum profile is a smoothed W-shaped one.
How Attributes of Organizational Structure Affects Knowledge Management Processes in High-Technology Companies?

: Purpose: The article aims to identify links between attributes of organizational structure and knowledge management processes in high technology companies. Approach/Methodology/Design: The research was conducted in 100 Polish high-tech companies. Findings: The results show that high-tech enterprises carry out different number of sub-process of knowledge management in different combinations. Surveyed companies confirm that different structural features are needed depending on the phase/sub-process of knowledge management. It is recommended to redesign the organizational structure in such a way that it corresponds to the key knowledge management processes as much as possible. Flexibility was found to have the most supportive effect on five of the seven knowledge management sub-processes studied. Practical Implications: The results can serve as a guide for managers in improving the organizational structure for knowledge management needs. Originality/Value: The attributes of organizational structure supportive and inhibiting knowledge management processes for high-tech companies were identified.
Herpes Simplex Virus 2 Blepharokeratoconjunctivitis

ABSTRACT Purpose To retrospectively review the clinical characteristics of patients with herpes simplex virus type 2 (HSV-2) blepharokeratoconjunctivitis. Methods Laboratory-proven HSV-2 blepharokeratoconjunctivitis cases were reviewed between 1995 and 2021. Results Ten of 725 (1.4%) patients had HSV-2 infection. Data were available for nine patients. Associated conditions included neonatal herpes (1/9, 11%), severe atopy (1/9, 11%), genital herpes (2/9, 22%), and systemic immune disorders (2/9, 22%). The most common presenting finding was pain and blurred vision (55.5%). Two patients (22%) had dendritic lesions and one patient (11%) had reduced corneal sensation. Complete resolution was reported in four patients (44.5%). Recurrence was noted in four patients (44.5%) despite antiviral prophylaxis. Corneal complications included scarring and neovascularization. The visual acuity at the last follow-up was 20/40 or worse in four patients (44.5%). Conclusions HSV-2 is an uncommon cause of keratitis. Dendrites and loss of corneal sensation were uncommon. Recurrence was noted despite antiviral prophylaxis.
Impacts of college-level courses via Asynchronous Learning Networks: Some Preliminary Results

New Jersey Institute of Technology has been delivering college courses via an Asynchronous Learning Network (ALN) system called the Virtual ClassroomTM for a decade, using various media mixes. Currently, two complete undergraduate degree programs are available via a mix of video plus Virtual Classroom, the B.A. in Information Systems and the B.S. in Computer Science. This paper presents preliminary findings about impacts on students, and touches on some issues and potential impacts for faculty, individual universities, and the structure of higher education. Overall ratings of courses by students who complete ALN based courses are equal or superior to those for traditional courses. Dropout or Incomplete outcomes are somewhat more prevalent, while grade distributions for those who complete tend to be similar to those for traditional courses. For both students and faculty, more startup time devoted to solving the "logistics" of ALN delivery seems to be required at the beginning of courses. ALN delivery is not just a "different" way of doing the same thing, however; it is likely to change the nature and structure of higher education.
Enhanced Short-Term Load Forecasting Using Artificial Neural Networks

The modernization and optimization of current power systems are the objectives of research and development in the energy sector, which is motivated by the ever-increasing electricity demands. The goal of such research and development is to render power electronic equipment more controllable, to ensure maximal use of current circuits, system flexibility and efficiency, as well as the relatively easy integration of renewable energy resources at all voltage levels. The current revolution in communication technologies and the Internet of Things (IoT) offers us an opportunity to supervise and regulate the power grid, in order to achieve more reliable, efficient, and cost-effective services. One of the most critical aspects of efficient power system operation is the ability to predict energy load requirements, i.e., load forecasting. Load forecasting is essential for balancing demand and supply and for determining electricity prices. Typically, load forecasting has been supported through the use of Artificial Neural Networks (ANNs), which, once trained on a set of data, can predict future loads. The accuracy of the ANNs’ prediction depends on the quality and availability of the training data. In this paper, we propose novel data pre-processing strategies, which we apply to the data used to train an ANN, and subsequently evaluate the quality of the predictions it produces, to demonstrate the benefits gained. The proposed strategies and the obtained results are illustrated using consumption data from the Greek interconnected power system.
New Active Filter synthesis based on Scattering Parameters

Inductorless active filters which are derived from double terminated classical reactance filters, such as gyrator-capacitor filters and filters using the FDNR concept, are known to exhibit low sensitivity. Stimulated by the ideas underlying the method of wave digital filters the attempt has been made to establish a concept for the synthesis of active filters which is based on processing of wave quantities. In this paper the method leading to this new kind of active filters is presented and possibilities for practical implementation are shown. Some experimental results confirm the feasibility of the principle.
Rheological Characterization of Non-Newtonian Mixtures by Pressure Pipe Tests

The rheological behavior of non-Newtonian fluids in turbulent conditions is an important topic in several fields of engineering. Nevertheless, this topic was not deeply investigated in the past due to the complexity of the experimental tests for the assessment of the constitutive parameters. Pressure pipe tests on Herschel-Bulkley mixtures were proven to be suitable for exploring turbulent conditions, but discrepancies with the results of tests performed in laminar flow were detected. These contradictions could be attributed to the inconsistencies of the Herschel-Bulkley model (HB) for high shear rate flows, proven by Hallbom and Klein, who suggested a more general “yield plastic” model (HK). Hence, in this study, a procedure for the estimation of the rheological parameters of both HB and HK models in pressure pipe tests is defined and rated on a complete set of experiments. The HK model performed much better than HB model in the turbulent range and slightly better than the HB model in the laminar range, confirming the consistency of the “yield plastic” model. The rheological parameters obtained by the proposed procedure were used to numerically model a dam-break propagation of a non-Newtonian fluid, showing significant differences in terms of process evolution depending on the constitutive model.
Semifield root phenotyping Root traits for deep nitrate uptake

Deep rooting winter wheat genotypes can reduce nitrate leaching losses and increase N uptake. We aimed to investigate which deep root traits are correlated to deep N uptake and to estimate genetic variation in root traits and deep 15 N tracer uptake. In two years, winter wheat genotypes were grown in RadiMax, a semi-field root-screening facility. Minirhizotron root imaging was performed three times during the main growing season. At anthesis, 15 N was injected via subsurface drip-irrigation at 1.8m depth. Mature ears from above the injection area were analysed for 15 N content. From minirhizotron image-based root length data, 82 traits were constructed, describing root depth, density, distribution and growth aspects. Their ability to predict 15 N uptake was analysed with LASSO regression. Root traits predicted 24% and 14% of tracer uptake variation in the two years. Both root traits and genotype showed significant effects on tracer uptake. In 2018, genotype and the three LASSO-selected root traits predicted 41% of the variation in tracer uptake, in 2019 genotype and one root trait predicted 48%. In both years, one root trait significantly mediated the genotype effect on tracer uptake. Deep root traits from minirhizotron images can predict deep N uptake, indicating potential to breed deep-N-uptake-genotypes.
A mathematical model of tyre noise generation

The paper presents an acoustic rolling model formulated in the time domain. The model describes a smooth tyre rolling on a rough surface, and consists of three parts. The first part is a theoretical description of the vibrational behaviour of tyres. Although one can rely on fundamental work, a simplified model is presented which limits computational effort, but is sufficiently accurate in the low and middle frequency range. The second part describes the contact between tyre and road. A bedding model is used for the tread and a time iterative solution method applied to the nonlinear problem. The last step is the calculation of the radiated sound caused by a vibration of the tyre belt, taking into account the geometrical condition (i.e. tyre and road represent together a horn). This step is solved by the application of the multiple synthesis. The results of the model are compared with measurements.
UAV SYSTEMS IN SUPPORT OF LAW ENFORCEMENT FORCES

The world in which we live can often be dangerous. Then, law enforcement officials must use the best technologies to protect us, but many times they don't have the budget to buy such advanced equipment. The UAV is the best airborne platform for surveying areas in which unpleasant things might occur. Instead of using an expensive helicopter or airplane, or sending human beings into a potentially dangerous situation, you can use a UAV platform to capture video evidence you need. Due to its silent flight and small dimensions, compared with a normal aircraft, UAV won't disturb the scene being surveyed. Having video, low light or FLIR cameras on board, you get the picture that your organization need, without exposing your people. The expenses are also at a fraction level from those inferred by a conventional manned aircraft mission. The response plan of the street cops in uniform to a call is based on their experience and information acquired from various sources and on scene observations. In such situations they may benefit also from the live images obtained with the help of a non-expensive eye in the sky. Strategies and decisions based on accurate information in hostage taking act or barricaded subject can save lives and prevents injuries. With a UAV help you may have a big picture instead of visual limited recording from the ground level. Also, once the event and activities are recorded, you have valuable information available for review as evidence or as an authentic training tool.
DEVELOPMENT OF A SURFACE ROUGHNESS PREDICTION SYSTEM FOR MACHINING OF HOT CHROMIUM STEEL (AISI H11) BASED ON ARTIFICIAL NEURAL NETWORK

An attempt have been made to apply the principles of artificial neural networks (ANN) towards developing a prediction model for surface roughness during the machining of high chromium steel through face milling process. Now a days, hot chromium steel is prominently used in die and mould industry as well as in press tools, helicopter rotor blades, etc. Initially, Taguchi design of experiments was applied while conducting the experiments to reduce the time and cost of experiment. Multilayer perceptron (MLP) network using Feed Forward Error Back propagation was chosen as the neural network architecture to describe the process model. The experiments were conducted on a C.N. C milling machine using carbide cutters. Pearson correlation coefficient was also calculated to analyze the correlation between the system inputs and selected system output i.e. surface roughness. The results of ANN modeling were substantiated by testing and validation of the resulting surface roughness values and the results have been encouraging. The outputs of Pearson correlation coefficient also showed a strong correlation between the feed per tooth and surface roughness, followed by cutting speed.
The Geography of Covid-19 Spread in Italy Using Social Media and Geospatial Data Analytics

ABSTRACT Since December 2019, Coronavirus has been spreading rapidly across the world. From its early stages, while the rest of the world was still new to COVID-19, Italy was one of the first and most severely hit countries. The chronic nature of the COVID-19 outbreak and untimely treatments were translated into an unstable social and emotional environment. This study explores the societal response to the first wave of the pandemic outbreak in Italy, looking at geographic variations, social and policy perspectives, and sentiments across northern, central, and southern regions. For this study, we collected nearly three million tweets associated with the coronavirus outbreak in the Italian regions of Lombardy, Lazio, and Sicily, during the months of March-May, 2020. A sentiment analysis was developed to (i) identify the spatio-temporal distribution of the polarity patterns of anger, fear, and joy; (ii) test correlation between these sentiments and COVID-19 cases; and (iii) determine the statistical significance between the sentiments across geographies. Findings provide insights on the relationship between sentiment variations and spatio-temporal case distribution associated with regional differences. These findings align with those of the “Track IT. COVID-19 International Survey,” that investigated the societal response of Italians to the new measures enforced by local governments. Overall, the findings of this study can be weaved together into important implications for emotional wellbeing and economic perspective.
Horizontal Skills Mismatch and Vocational Education

We analyze horizontal mismatch in Switzerland dened as a mismatch between the type of skills acquired by students and the skills required for their job. We investigate the argument in the literature that the more specic an education system is, the higher are the wage penalties due to horizontal mismatch. Switzerland is an ideal case to test this hypothesis because it relies heavily on vocational education and training. The data stems from the longitudinal Swiss Household Panel in the years 1999 to 2012 and contains subjective and objective measures of mismatch. Controlling for time-invariant heterogeneity in xed eects regressions, the wage penalty for self-reported horizontal
Activity-dependent circuitry plasticity via the regulation of the histamine receptor level in the Drosophila visual system

Activity-dependent synaptic plasticity is crucial for responses to the environment. Although the plasticity mechanisms of presynaptic photoreceptor neurons in the Drosophila visual system have been well studied, postsynaptic modifications remain elusive. In addition, further studies on the adaption of the visual system to different light experiences at a circuitry scale are required. Using the modified transcriptional reporter of intracellular Ca2+ method, we describe a way to visualize circuitry changes according to different light experiences. We found enhanced postsynaptic neuronal activity responses in lamina monopolar neuron L2 after prolonged light treatment. Although L1 also has connections with photoreceptors, there were no enhanced activity responses in L1. We also report in this study that activity-dependent transcriptional downregulation of inhibitory histamine receptors (HRs) occurs in postsynaptic neuron L2, but not in L1, during continuous light conditions. We expressed exogenous HR proteins in L2 neurons and found that it attenuated the enhanced activity response caused by constant light exposure. These findings, together with the fact that histamine is the main inhibitory neurotransmitter released by photoreceptors in the Drosophila visual system, confirmed our hypothesis that the activity-dependent transcriptional downregulation of HRs is responsible for the constant light exposure-induced circuitry response changes in L2. The results successfully demonstrated the selective circuit change after synaptic remodeling evoked by long-term activation and provided in vivo evidence of circuitry plasticity upon long-term environmental stimulation.
Tinjauan Kepuasan Pelanggan Terhadap Kualitas Pelayanan Parkir Basement di Pusat Perbelanjaan Bandung Supermall

Pusat perbelanjaan dengan konsep one stop shopping selalu menyediakan area parkir yang cukup luaspada lahannya karena konsumen dalam jumlah besar sangat tertarik untuk berkunjung ke tempattersebut. Oleh karena itu para pengelola parkir di pusat perbelanjaan berusaha untuk menciptakankualitas pelayanan parkir terbaiknya agar para pelanggan tetap berkunjung dan berbelanja di pusatperbelanjaan tersebut. Sebuah studi menyatakan bahwa daerah yang memiliki tempat parkir yangaman, nyaman, dan murah lebih mampu untuk mempertahankan kehadirannya. Untuk itu perlu adanyatinjauan kepuasan pelanggan terhadap kualitas pelayanan parkir yang diberikan. Penelitian ini berupasurvei kepada pengunjung di pusat perbelanjaan Bandung Supermall yang menggunakan jasapenyedia parkir. Pengunjung pusat perbelanjaan diminta pendapatnya mengenai kualitas pelayananparkir yang berupa kemudahan parkir, kinerja petugas parkir, kinerja geometrik ruang parkir,keamanan, kenyamanan, dan kesesuaian tarif parkir. Hasil penelitian menunjukkan bahwa pelangganpada umumnya merasa puas dengan kualitas pelayanan yang diberikan oleh pengelola parkir di pusatperbelanjaan tersebut.
Improving quality of breast cancer data through pre-processing

Using data mining for medical prognosis becomes a promising approach recently. In the mining process, the raw data are commonly suffering from outlier and imbalanced problems which affect the performance of the model in predicting the unseen data. Thus, choosing appropriate data mining algorithms has a straight forward impact on the prediction model. The objective of this study is to investigate the use of three kinds of data pre-processing techniques including outlier filtering, Synthetic Minority Over-sampling TEchnique (SMOTE) and attribute selections for improving the quality of breast cancer data at Srinagarind Hospital in Thailand. Three types of decision rule building techniques, i.e. Decision Table with Naive Bays (DTNB), Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and PART Decision List were employed. The performance of proposed approaches was evaluated through the Area Under the receiver operating characteristics Curve (AUC) of the decision rules. Experimental results have shown that applying the suitable data pre-processing, especially the outlier filtering method, can lead to the significant improvement of the prediction performance of decision rule models.