Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect Of Leaf Cutting (P Running) On Vegetative Growth Of Red Chillies (Capsicum Annum L.)

This research was carried out in Afd II Village, West Bilah District, Labuhan Batu Regency, starting in October 2021. The aim of the study was to determine the effect on the growth of Red Chili (Capsium Annum L) plants on cutting shoots on chili leaves. Chili is a staple for consumption in everyday life. Chili peppers can be made more productive by using the right gardening techniques. Maintenance measures such as cutting shoots of chili plants with a dichotomous are one approach to increase chili yields. Pruning dichotomous shoots seeks to remove less productive plant parts to maximize. This study used a completely randomized design (CRD) with 4 treatments with 5 replications, in order to obtain 22 experimental units, each bed planted with 30 chili plants. The total number of plants is 30 x 5 = 150 plants. For the experimental unit, 3 were selected to be used as plant samples. From the observations, if the F test shows a significant effect, it will be continued with the Duncan' New Multiple Range test (DNMRT) at a 5% significance level. As treatment, namely: A = Pruning 7 DAP, B = Pruning 4th branch, C = No pruning, D = Pruning 14 DAP. Cutting the tops of leaves to increase the yield of the red chili, the results of the study concluded: 1. Red chili plants that were trimmed branches made the chili plants taller, 2. Trimming leaves caused the emergence of new leaf blades to increase 3. And cutting leaf shoots was useful for improve the yield of chili plant quality. Based on the results of the study, it is recommended to trim the 4th branch because it can increase growth.
Experimental and CFD simulation study of shell and tube heat exchangers with different baffle segment configurations

Shell and tube heat exchanger (STHX) is an implement that has tremendous applications in numerous industrial processes and research areas. In this study, the commercial software ANSYS is used for 3-D computational fluid dynamics (CFD) to compare the thermo-hydraulic performance of STHXs with recently developed tri-angular (TRI) baffles, and tri-flower (TF) baffles with conventional segmental (SG) baffles at different flow rates. Simulations have been performed to analyze the heat transfer coefficient, pressure drop, and overall thermo-hydraulic performance among the recently developed TRI-STHX, TF-STHX and conventional SG-STHX. The thermo-hydraulic performance of the numerical model of SG-STHX shows the promising results while validating it with the experimental results, Esso and Kern methods. Then the same study is carried out for comparing the two novel baffles with segmental baffle. The results depict that, novel baffles are much appreciable in increasing heat transfer coefficient. The TF-STHX offers a greater heat transfer coefficient than all others but also offers a higher pressure drop at the same flow rate. Computing the comprehensive performance (hs? p), the TRI-STHX offers a prominent increment in thermo-hydraulic performance compared to others. Moreover by inserting twisted tapes at the tube side, there is noticeable increase in heat transfer coefficient which tends to increase the thermo-hydraulic performance of STHX. By comparing the flow patterns of TRI-STHX and SG-STHX, the novel TRI-STHX shows the reduction in shell-side induced vibrations and hence helped to increase the overall efficiency of the STHX.
Coal mine subsidence: Effects of mitigation on crop yields

ABSTRACT Subsidence from longwall underground coal mining adversely impacts agricultural land by creating wet or ponded areas. While most subsided areas show little impact, some localized places, usually less than l·5 ha in size, may experience total crop failure. Coal companies mitigate subsidence damaged cropland by installing drainage waterways or by adding fill material to raise the grade. The objective of this study was to test the effectiveness of mitigation in restoring corn and soybean yields to pre-mined levels. Fourteen sites in southern Illinois were selected for study. Corn (Zea mays L.) and soybean (Glycine max L)yields from mitigated and nearby undisturbed areas were compared for four years. Results varied due to differing weather and site conditions. Mean corn yields overall, however were significantly ( α=0·05) lower on mitigated areas. There was no significant difference in overall mean soybean yields. Soil fertility levels were similar and did not account for yield differences.
The Effect of Using Different Teaching Methods on High-Level Skills in Science Lessons

The aim of this study was to determine the relationship between students' metacognitive learning, critical thinking, scientific process skills and academic achievements after the ‘Cell and Divisions’ and ‘Force and Energy’ units have been processed according to different teaching methods. Also, in this study, it is aimed to determine the variables that predict academic achievement. In the study, five different groups were selected from 7th grade students. In these groups, lessons were taught according to the Multiple Intelligence Approach, Problem Based Learning, Peer Instruction, Combined Method and the method recommended by the Ministry of National Education (2017). The sample of the study, in which the correlational survey method, one of the quantitative research approaches, was used, consists of 185 seventh grade students studying in two secondary schools in the Yakutiye district of Erzurum. As a data collection tool in research; Metacognitive Learning Strategies Scale, Critical Thinking Tendency Scale, Scientific Process Skills Test and Academic Achievement Tests were used. As a result of the analyzes made on the data of the students in different groups and the whole study group, significant relationships between variables were determined in terms of group specific and all data. In addition, in the hierarchical regression analysis, it was determined that scientific process skills were the most predictive skills for academic achievement for each group.
How do we start? State and Directions of

: The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach to advancing our understanding of the learning process. This promise motivated the emergence of the field of learning analytics and pushed the education sector towards increased application of data for strategic decision-making. This paper addresses a commonly posed question asked by educators, managers, and administrators embarking on learning analytics in higher education – how do we start institutional learning analytics adoption? The paper first defines learning analytics and touches on lessons learned from some well-known case studies. The paper then reviews the current state of institutional adoption of learning analytics by examining evidence produced in several studies conducted worldwide. The paper next outlines directions for learning analytics adoption that should enable for a system-wide institutional transformation. The paper concludes with a summary of critical challenges that require attention in order for learning analytics to make a long-term impact on research and practice of learning and teaching. The paper emphasizes that learning analytics cannot be reduced to a simplistic rhetoric of quick technological fixes. Rather, learning analytics advocates for holistic approaches that account for and support complexities associated with specific characteristics of different educational systems and institutions.
Three-dimensional printing of orbital computed tomography scan images for use in ophthalmology teaching

Introduction: The use of tridimensional (3D) printing in healthcare has contributed to the development of instruments and implants. The 3D printing has also been used for teaching future professionals. In order to have a good 3D printed piece, it is necessary to have high quality images, such as the ones from Computerized Tomography (CT scan) exam, which shows the anatomy from different cuts and allows for a good image reconstruction. Purpose: To propose a protocol
Stakeholders’ Views about the Management of Stable Chronic Conditions in Community Pharmacies

The role of the community pharmacist has evolved to include the provision of more clinical services for patients. Those people who have stable chronic conditions will be managed in community pharmacies. This qualitative study used semi-structured in-depth interviews to understand the potential of providing additional patient-centred care for patients with stable chronic conditions in community pharmacies and identify potential limitations of this approach. Participants were recruited from Welsh Government, Local Health Boards (LHBS), Community Pharmacy Wales (CPW) and the Royal Pharmaceutical Society Wales (RPSW). The interviews were audio-recorded, transcribed verbatim, and analysed thematically. Eight interviews were conducted. The identified themes were as follows: (1) inconsistency and bureaucracy in commissioning pharmacy services; (2) availability of funding and resources; (3) disagreement and uncertainty about the contribution of the community pharmacy sector; (4) continuity of patient medical information and fragmented care; (5) accessibility, capacity and facilities in community pharmacy; (6) pharmacy education and clinical expertise, and (7) patient acceptability. It was clear that the potential benefit of managing stable chronic diseases in community pharmacies was recognised; however, several limitations expressed by stakeholders of pharmacy services need to be considered prior to moving forward.
Semi-Supervised Cloud Detection in Satellite Images by Considering the Domain Shift Problem

In terms of semi-supervised cloud detection work, efforts are being made to learn a promising cloud detection model via a limited number of pixel-wise labeled images and a large number of unlabeled ones. However, remote sensing images obtained from the same satellite sensor often show a data distribution drift problem due to the different cloud shapes and land-cover types on the Earth’s surface. Therefore, there are domain distribution gaps between labeled and unlabeled satellite images. To solve this problem, we take the domain shift problem into account for the semi-supervised learning (SSL) network. Feature-level and output-level domain adaptations are applied to reduce the domain distribution gaps between labeled and unlabeled images, thus improving predicted results accuracy of the SSL network. Experimental results on Landsat-8 OLI and GF-1 WFV multispectral images demonstrate that the proposed semi-supervised cloud detection network (SSCDnet) is able to achieve promising cloud detection performance when using a limited number of labeled samples and outperforms several state-of-the-art SSL methods.
In-Hospital Antibiotic Use for COVID-19: Facts and Rationales Assessed through a Mixed-Methods Study

It is well known that during the coronavirus disease 2019 (COVID-19) pandemic, antibiotics were overprescribed. However, less is known regarding the arguments that have led to this overuse. Our aim was to understand the factors associated with in-hospital antibiotic prescription for COVID-19, and the rationale behind it. We chose a convergent design for this mixed-methods study. Quantitative data was prospectively obtained from 533 adult patients admitted in six hospitals (services of internal medicine, infectious diseases and pneumology). Fifty-six percent of the patients received antibiotics. The qualitative data was obtained from interviewing 14 physicians active in the same departments in which the enrolled patients were hospitalized. Thematic analysis was used for the qualitative approach. Our study revealed that doctors based their decisions to prescribe antibiotics on a complex interplay of factors regarding the simultaneous appearance of consolidation on the chest computer tomography together with a worsening of clinical conditions suggestive of bacterial infection and/or an increase in inflammatory markers. Besides these features which might suggest bacterial co-/suprainfection, doctors also prescribed antibiotics in situations of uncertainty, in patients with severe disease, or with multiple associated comorbidities.
A Systematic Review of Complementary and Alternative Veterinary Medicine in Sport and Companion Animals: Soft Tissue Mobilization

Simple Summary Soft tissue mobilization involves different massage and stretching techniques that are commonly used in animals. Despite the frequent use, there is limited knowledge of how the methods affect the animal. Therefore, this study reviews the scientific literature on massage and stretching in cats, dogs, and horses. Three core bibliographic sources were used. Relevant articles were assessed for scientific quality, and information was extracted on study characteristics, species, type of treatment, indication, and treatment effects. Of 1189 unique publications screened, 11 met the inclusion criteria and were included in the review, nine on massage and two on stretching. The risk of bias was assessed as high in eight of the studies and moderate in three of the studies. There were large differences in reported treatment effects; two studies assessed as having a moderate risk of bias indicated a decreased heart rate after massage. According to the results, the scientific documentation of massage and stretching is not of sufficient quality to draw clear conclusions about their clinical effect in horses, dogs, and cats. Abstract Soft tissue mobilization is frequently used in the treatment of sport and companion animals. There is, however, uncertainty regarding the efficacy and effectiveness of these methods. Therefore, the aim of this systematic literature review was to assess the evidence for clinical effects of massage and stretching in cats, dogs, and horses. A bibliographic search, restricted to studies in cats, dogs, and horses, was performed on Web of Science Core Collection, CABI, and PubMed. Relevant articles were assessed for scientific quality, and information was extracted on study characteristics, species, type of treatment, indication, and treatment effects. Of 1189 unique publications screened, 11 were eligible for inclusion. The risk of bias was assessed as high in eight of the studies and moderate in three of the studies, two of the latter indicating a decreased heart rate after massage. There was considerable heterogeneity in reported treatment effects. Therefore, the scientific evidence is not strong enough to define the clinical efficacy and effectiveness of massage and stretching in sport and companion animals.
Libyan Tourism Crisis Planning and Stakeholder Responses

Aims and Place of Study: A comprehensive framework is developed in conjunction with recommendations for resolving the crisis confronting national-level tourism plans, which is then applied to the Libyan case. Until now, overcoming a lack of tourism planning in unstable countries has been a lengthy, expensive process, with detailed solutions tailored to such an environment rarely being implemented systematically. the current study may contribute to developing tourism crisis management plans, thereby improving this sector and reviving tourism in Libya. Methodology: It is argued that by systematically examining the people involved in the tourism planning as well as the tourism sector in general along with the tourism planning documents against a set of criteria, much can be learned about tourism planning. While plans vary according to context, all tourism plans should incorporate certain characteristics. This dissertation identified several characteristics that should be included in national-level tourism plans, including involved stakeholders and the inclusion of scenarios and a backup plan. Results: This research determines the factors that constituted an obstacle to tourism development in Libya through the PESTEL model and then focused on a number of those factors. Based on the analysis of these factors, a model is developed that organises the practical elements that could enhance the tourism sector in developing countries. This is considered a pillar of national tourism planning, focusing on local and international stakeholders and their role in the success of the planning and identifying other factors supporting the planning process and then dividing each component. It then points to crucial points drawn from the relevant literature indicating the characteristics of a good plan and the human and financial resources that must be reserved in the plan due to their importance to the success of planning. The framework is then used to guide the examination and evaluation of documents about the Libyan Tourism Plan. Conclusion: The analysis concludes with recommendations for improving Libyan tourism planning and maximising the benefits of the current Libyan tourism plan (LNTP) 2009-2025. This research contributes to tourism planning by proposing a framework that can address tourism plans of the politically troubled nations at the national level, thereby facilitating the identification of problems and issues, allowing for the most efficient learning from mistakes, and providing a framework for conducting comparative studies.
Comparison of gesture classification methods with contact and non-contact sensors for human-computer interaction

Classification of signals that are received from the human body and control systems is one of the most important subjects of the machine learning application. In this study, classification algorithms were used to classify electromyography and depth sensor data. First, electromyography and joint angle data were obtained from software developed in Python environment. Five different types of movements have been identified for classification and thousand different samples have been collected as training for each of these movements. Support Vector Machine, Random Forest, and K-Nearest Neighbour algorithms were used for classification. To measure success algorithms, results have been compared for achieving criteria. The results show which of three different algorithms was the most successful on two different sensors. While Random Forest provides the best results for noncontact sensor, KNearest Neighbour produces the best results for contact sensors. This paper evaluated the classification success of two different sensors. The results will be utilized in online c lassification to control a graphical user interface. Cite this article as: E , K , B . Comparison of gesture classification methods with contact and non-contact sensors for human-computer interaction. Sigma J Eng Nat Sci 2021;40(2):219–226. Sigma Journal of Engineering and Natural Sciences Web page info: https://sigma.yildiz. edu.tr DOI: 6
The moral of the story: ‘populism’ and ‘activism’ in entrepreneurship

ABSTRACT This paper engages with the concepts of ‘populism’ and ‘activism’ in entrepreneurial storytelling in order to explain how entrepreneurship may be both an individual and a collective endeavour. Through a case study of a moose park, we show how entrepreneurs move back and forth between individualism and collectivism with what seems to be little forethought. Our findings suggest that populism and activism function as a duality that essentially serves two purposes: populism reinforces the entrepreneur stereotype, highlighting the individual entrepreneur’s business venture; whilst activism challenges stereotypes, initiating new meaning, and social and ecological value change. Embeddedness appears a necessary condition for both these processes – the social connections in context affect the possibilities to initiate change, whether individualist or collectivist. Thus, we contribute to entrepreneurship as practice by showing how storytelling both strengthens and changes social context; and how storytelling alters depending on the social context, and the different forms of embeddedness in it.
Comparative Study of Two Localization Approaches for Mobile Robots in an Indoor Environment

In the last years, mobile robot localization has been developed significantly due to the need for accurate solutions to determine the position and orientation of the wheeled mobile robot (WMR) in a given environment. Many different sensors have been used to solve the problem. For instance, ultrasonic sensors, laser, or infrared sensors are also used to determine the pose of the WMR. However, sensors are sensitive to noise measurements and disturbances, which can distort the acquired information. For this reason, adequate algorithms should be used to reduce these uncertainties and determine the optimal pose of the WMR. In this research work, we focus on the comparative study of the most used algorithms, using landmarks as sensors, which are the extended Kalman filter and particle filter. Further, for an effective comparison, the simulation results were conducted and analyzed using different performance criteria. The simulations results showed better estimation performance achieved by the particle filter being compared to the extended Kalman filter when the sensors are subject to non-Gaussian noises.
Estimating the Water Quality Class of a Major Irrigation Canal in Odisha, India: A Supervised Machine Learning Approach

Contamination of surface water by rapid industrialization, natural and anthropogenic activities is of great concern over the last few decades. Nowadays, canal water systems are no exception to this form of contamination, which results in water quality degradation. To classify the canal water based on the Bureau of Indian Standards (BIS), it was thought to develop a quick and inexpensive approach as an alternative to the time-consuming analysis approach. With this motivation, the present study explores building a machine learning model for water quality classification of a major canal namely the Talaldanda canal operating in the state of Odisha, India. The water quality class is predicted using supervised machine learning (ML) prediction models for the new canal water input parameters. The water quality parameters such as pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), and total coliform (TC) at six strategic locations of the canal from the year 2013-2020 were collected from Odisha State Pollution Control Board for the training phase. The supervised ML models used in the study are Decision Tree (DT), Neural Network (NN), k-NN (k-Nearest Neighbor), Naïve Bayes (NV), Support Vector Machine (SVM), and Random Forest (RF). The predictions of the models are evaluated using the Orange-3.29. 3 data analytics tool. When analyzing the performance parameters by sampling the training data into training and testing using cross-validation, the results show that DT has a higher classification accuracy (CA) of 96.6 percent than other ML models. In addition, the likelihood of DT correctly predicting water quality class for the testing dataset is higher than that of other prediction models.