Miracle-dz/UniFAR-FTUs huggingface dataset:
Is plagiarism really on the rise? Results from four 5-yearly surveys

ABSTRACT Historical trends in plagiarism are often estimated by comparing survey results from a diverse range of samples, institutions, and measures. However, in multi-institution multi-method comparisons changes over time are difficult to separate from differences in methods. We assessed self-reported engagement in, awareness of, and attitudes toward seven forms of plagiarism in surveys of four comparable groups of students at the same university on four occasions over 15 years, each separated by 5 years (2004, 2009, 2014, and 2019). The new 2019 sample was 1099 students. A downward trend in plagiarism from 2004 to 2014 was not continued in 2019, with similar rates of engagement in, awareness of, and attitudes toward the seven forms of plagiarism in 2019 as in 2014. These results emphasize the need to continue efforts to detect and prevent plagiarism, and to educate academics and students about academic integrity conventions.
Breast Cancer Predictive Analytics Using Supervised Machine Learning Techniques

Breast cancer unarguably has been the very prominent disease amongst women as well as the next most dangerous after lung cancer. Early diagnosis and prevention is of paramount importance. Several methods such as micro-array analysis and network analysis have been proffered but they are somewhat expensive and time consuming. There is a need to develop an automated system based on Machine learning techniques to detect breast cancer early. Benign and Malignant tumors were classified using Logistic Regression (LRO), Bayes Network (BNK), Multilayer Perceptron (MLP), Sequential Minimal Optimization (SMO), J48, Naive Bayes (NBS) and Instance Based Learner (IBK) algorithms, which were implemented in Waikato Environment for Knowledge Analysis (WEKA). The breast cancer database for this study was collected from the University of Wisconsin Hospitals, published on California College, Irvive (UCI) website. The five most critical performance metrics when selecting an algorithm in model building in the health related domain are Area Under the ROC Curve (AUC), Receiver Operating Characteristics Curve (ROC), Mean Absolute Error (MAE), Accuracy and Kappa Statistic. In relation to the results of Accuracy, Precision and Kappa Statistic which were evaluated and compared, BNK has best predictive accuracy of 97.14%, followed by SMO with 96.71%, then LOR with 96.57%. On the other hand, LOR has the highest AUC of 99.3%, followed by BNK with 99.2%, then SMO with 96.5%. Beyond accuracy, AUC should be keenly considered in algorithm selection and model building. Therefore, Logistic Regression should be chosen as the best classifier instead of Bayes network for breast cancer optimal prediction.
Psoriasis as a multisystem disease - cost of illness analysis

Introduction: Psoriasis is a chronic disease consuming many medical and non-medical resources, which should be evaluated for every healthcare system. Goal: The goal of the current study is to determine the financial burden of psoriasis and its concomitant diseases in Bulgaria for hospitalized patients with psoriasis. Materials and methods: A retrospective study among Bulgarian patients with psoriasis is conducted in the clinic of dermatology diseases of University hospital “Alexandrovska” in Sofia for the period 2014-2015. The medical dossiers of hospitalized psoriatic patients were reviewed and information regarding several patients’ characteristics was analyzed. Bottom up approach was applied in order to calculate the costs associated with treatment of psoriasis and concomitant diseases. Results: The total number of included patients in the survey was 246, 106 out of them were hospitalized in 2014, and 140 – in 2015. The average monthly pharmacotherapy costs for treatment of one patient with psoriasis in hospital are higher in 2015 than in 2014 35 BGN and 33 BGN, respectively. The average costs for pharmacotherapy of the concomitant diseases per patient are almost equally paid from the National health insurance fund (20.53 BGN) and the patient (23.10 BGN). The share of direct medical costs per patient is 58% (12% for ambulatory therapy (medications and phototherapy) and 46% in hospital settings (medications, medical care, and laboratory test), 42% indirect costs. As the age increases, the number of concomitant diseases also increases among the patients with psoriasis. Statistical significant more men (р=0.04) suffer from psoriasis and ischemic cardiac disease. Conclusion: Psoriasis is a multisystem disease with significant financial burden for Bulgarian healthcare system as a result of the costs for treatment of the concomitant diseases, hospitalization costs and phototherapy. Correspondence to: Maria Kamusheva, Dunav 2 str. , Sofia, Bulgaria, Tel: +359 2 9236 584, E-mail: maria.kamusheva@yahoo.com
Nutrients using in goats fed concentrate consist of vary levels of Muntingia calabura L leaves as maize substitution

The research was carried out to measure nutrients utilize by goats fed concentrate containig varying levels of Muntingia clabura L. as maize substitute. This research was conducted based on Double Latin Square 4 x 4 eight female Kacang goats. Four treatments and four periods used as replications. The treatments are MC0 = concentrate 100% of maize mills without the flour of Muntingia calabura L. leaves; MC20 = concentrate with 20% Muntingia calabura L. leaves flour as maize substitute; MC40 = concentrate with 40% of Muntingia calabura L. leaves flour as maize substitute and MC60 = concentrate with 60% of Muntingia calabura L. leaves flour as maize substitute. All data are tabulated and analyzed using Analysis of Variance (ANOVA) and where the influence of the treatments was recorded, Least Quare was utilized using SPSS software, version 23 for windows. The result shows that the average dry matter consumption increases with an increase in the levels of Muntingia calabura L. leaves as maize substitute but the treatments have no significant influence (p>0.05). On the contrary, the value of NFE consumption tends to decrease, but it has no significant influence. However, the consumption of other nutrients, mainly crude protein, crude fiber, and crude fat increases significantly. The increasing of these nutrients is not followed by the increasing of digestibility values, since values of dry matter, crude protein, and crude fat decreases. Actually, there is an increase of rumen fermentation condition as indicated by concentrations of total VFA, rumen NH3, and propionate acid although it has no significant influence, statistically. In conclusion, the leaves of Muntingia calabura L. can be used as a component of concentrate formulation as maize substitute up to 40% inclusion level.
Key-Performance-Indicator-Related Process Monitoring Based on Improved Kernel Partial Least Squares

Although the partial least squares approach is an effective fault detection method, some issues of nonlinear process monitoring related to key performance indicators (KPIs) still exist. To address the nonlinear characteristics in the industrial processes, kernel partial least squares (KPLS) method was proposed in the literature. However, the KPLS method also faces some difficulties in fault detection. None of the existing KPLS methods can accurately decompose measurements into KPI-related and KPI-unrelated parts, and these methods usually ignore the fact that the residual subspace still contains some KPI-related information. In this article, a new improved KPLS method, which considers the KPI-related information in the residual subspace, has been proposed for KPI-related process monitoring. First, the proposed method performs general singular value decomposition (GSVD) on the calculable loadings based on the kernel matrix. Next, the kernel matrix can be suitably divided into KPI-related and KPI-unrelated subspaces. Besides, we present the design of two statistics for process monitoring as well as a detailed algorithm performance analysis for kernel methods. Finally, a numerical case and Tennessee Eastman benchmark process demonstrate the efficacy and merits of the improved KPLS-based method.
Training Binary Neural Networks with Real-to-Binary Convolutions

This paper shows how to train binary networks to within a few percent points (~3-5 %) of the full precision counterpart with a negligible increase in the computational cost. In particular, we first show how to build a strong baseline, which already achieves state-of-the-art accuracy, by combining recently proposed advances, and carefully tuning the optimization procedure. Secondly, we show that by attempting to minimize the discrepancy between the output of the binary and the corresponding real-valued convolution additional significant accuracy gains can be obtained. We materialize this idea in two complementary ways: (1) with a loss function, during training, by matching the spatial attention maps computed at the output of the binary and real-valued convolutions, and (2) in data-driven manner, by using the real-valued activations being available during inference prior to the binarization process for re-scaling the activations right after the binary convolution. Finally, we show that, when putting all of our improvements together, the resulting model reduces the gap to its real-valued counterpart to less than 3% and 5% top-1 error on CIFAR-100 and ImageNet, respectively, when using a ResNet-18 architecture.
THE USE OF INSTAGRAM BY FOOD BRANDS: ADVERTISING CHANNEL FOR HEALTHY AND CONSCIOUS LIFESTYLE PERCEPTION

Besides evolving to one of the most popular social platforms reaching 1 billion active users per monthly, Instagram has become an effective advertising channel used by businesses to not only increase visibility and traffic but also to play a role in shaping consumers’ beliefs and attitudes towards brands. Parallel to that, the growth of consumers' concerns regarding global issues has led to the emergence of a new lifestyles of health and sustainability (LOHAS) trend, which involves new values and more responsible and sustainable consumption behavior. Food represents one main aspect that contributes to this holistic way of life. Therefore, aligning with this increasing demand represents business opportunities for food companies. As such, the implementation of healthy and conscious lifestyle branding has been used in marketing and appears to be a powerful strategy at the moment. Instagram as an advertising channel appears to represent a substantial means to reach this aim, so it is relevant to study the ways brands make use of Instagram. By combining in-depth interviews and textual analysis research methods, and making use of an interdisciplinary theoretical approach, this thesis empirically investigates how food companies use Instagram as an advertising channel to be perceived as healthy and conscious lifestyle brands. The interviews with brand representatives and a selection of Instagram posts serve to research the problem based on six food brands. A thematic analysis method is used to analyze the complete data. The findings indicate that several aspects play a role in the advertising of healthy and conscious food brands on Instagram. First, the platform appears to be highly valuable to reach specific consumer segments, most notably the possibility to select the target of LOHAS audience, and to tailor Instagram content relevant to health, environmental and ethical perspectives. Second, the promotion of products through advertising appeals is implemented by incorporating emotional and rational product benefits. Third, the opportunity is taken to address major themes connecting to lifestyle trend values, such as the display of utopian atmosphere, the demonstration of transparency and sustainable commitments, the wellness dimension, and authenticity.
The cultural and morphological variability among Rhizoctonia solani isolates causing banded leaf and sheath blight of maize in Indonesia

Abstract Banded leaf and sheath blight caused by Rhizoctonia solani Kuhn has become a constraint on maize production in Indonesia. Forty-one isolates from 11 provinces in Indonesia were characterized based on cultural and morphological characteristics. Most isolates were characterized with sparse fluffy hyphae and pale yellow mycelium. They were categorized into two, three and four groups based on hyphal width, their growth rate and sclerotia production, respectively. The number of sclerotia was highly variable across different isolates. They were arranged in subcentral-peripheral, irregular, scattered, and peripheral manner on the Petri dish. The UPGMA analysis showed four major clusters, designated as cluster I, II, III, and IV at 0.75 similarity coefficient according to cultural and morphological characters. Cluster III was the largest cluster comprising 34 isolates; while cluster I, II, and IV comprised of four, one, and two isolate, respectively. Our results indicated that the cultural and morphological variability among R. solani isolates in Indonesia was not related to geographic origins.
Evaluation of the Therapeutic Effectiveness of Polymyxin B Versus Colistin in Carbapenem-resistanct Klebsiella pneumoniae-Associated Bacteremia

Polymyxins are the last resort antibiotics for treating carbapenem-resistance Klebsiella pneumoniae (CR-Kp)-associated bacteremia. The main aim of this study was to demonstrate the therapeutic effectiveness of polymyxin B versus colistin in the treatment of CR-Kp-associated Bacteremia. This 12-month long cross-sectional study was conducted on 54 and 47 patients suffering from CR-Kp-associated Bacteremia, and residing into two separate groups, they were treated with polymyxin B and colistin, respectively. After 5 day-treatment, the microbiological eradication rate was found 96.30% (52; n=54) in polymyxin B group and 87.23% (41; n=47) in colistin group. In the 30-day mortality rate-comparison, 51.03% more death was found in colistin group (34.04 %, n= 47) than the polymyxin B group (16.67 %, n= 54). In this study, Polymyxin B showed superior therapeutic potentiality with less number of secondary infections and 30-day mortality rate in CR-Kp-associated bacteremia than colistin. Dhaka Univ. J. Pharm. Sci. 18(2): 209-215, 2019 (December)
The addictive appetite model of Bulimia Nervosa and Binge Eating Disorder : a synthesis of basic science and clinical evidence for a new maintenance model of recurrent binge eating

Unless another licence is stated on the immediately following page this work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ You are free to copy, distribute and transmit the work Under the following conditions:  Attribution: You must attribute the work in the manner specified by the author (but not in any way that suggests that they endorse you or your use of the work).  Non Commercial: You may not use this work for commercial purposes.  No Derivative Works You may not alter, transform, or build upon this work.
Mental disorder and disability: A cross-sectional study of disability variance in severe mental disorders

Background: Severe mental disorders are associated with long-standing functional impairment and disability. The degree of disability varies with diagnosis. However, limited evidence are available on the association of psychiatric diagnosis and disability, especially from community settings in India. Methods: We examined the association of psychiatric diagnosis and disability in 711 persons suffering from severe mental illness. The patients were recruited from 12 community psychiatry outreach clinics at Wayanad District, South India. The Indian Disability Evaluation and Assessment Scale was administered to measure disability. ANOVA was applied to examine the extent of disability variance in diagnosis. Results: Disability varied significantly with diagnosis (F = 3.866; P = 0.000). Persons with schizophrenia experienced higher disability than bipolar affective disorder and depressive disorder. However, the disability was found nonsignificant in other diagnosis groups. Conclusion: The result implies the need for illness-specific programs and rehabilitative measures for persons with disability.
Response in carcass yield, organ weights, and gut morphology of broiler chickens to incremental levels of Vachellia tortilis leaf meal

Abstract: The objective of this study was to assess the carcass yield, organ weights, and gut morphology response to Vachellia tortilis inclusion in broilers. Unsexed 14-d-old Cobb500™ chicks were randomly assigned to six diets containing 0, 30, 60, 90, 120, and 150 g kg-1 dry matter of V. tortilis leaf meal. The slaughter weight (SW), dressed carcass weight (DCW), breast weight, thigh weight (TW), drumstick weight (DW), and internal organs were measured. Villus height (VH), crypt depth (CD), epithelial, submucosa, and muscularis thickness were taken. The apparent villus surface area (AVSA) and villus to crypt ratio were calculated. There was a linear decrease in SW and DCW (P < 0.05) with the increase of V. tortilis leaf meal. The SW and DCW decreased by 0.77 and 5.25 g for each g kg-1 increase in V. tortilis, respectively. The TW and DW showed no relationship with levels of V. tortilis leaf meal. The relative weight of gizzard, intestine, stomach, heart, and kidney increased linearly (P < 0.05) as levels of V. tortilis increased. The relative weight of the liver was not related to V. tortilis inclusion. The VH, AVSA, VH to CD ratio, as well as submucosa, muscularis, and epithelial thicknesses linearly increased as the V. tortilis inclusion level increased. There was no relationship between V. tortilis leaf meal inclusion and CD (P > 0.05). The increased morphological measurements suggest that leaf meal inclusion alters gut physiology and could influence the digestion and absorption of nutrients in broilers.
The Mobile Payment Fintech Continuance Usage Intention in Indonesia

Abstract: This research aimed to determine both positive and negative factors influencing the continuance usage intention of mobile payment FinTech in Indonesia. This research used an exploratory sequential mixed methods research design with a questionnaire as the first research instrument followed by semi-structured interviews. This research found that both the perceived benefit and perceived risk significantly influenced the mobile payment FinTech continuance usage intention in Indonesia but, the perceived benefit had a stronger impact than the perceived risk. Convenience had the most substantial positive impact on the perceived benefit, which later increased the mobile payment FinTech continuance usage intention. Financial risk had the strongest influence on the perceived risk, which later reduces the mobile payment FinTech continuance usage intention. Keywords: mobile payment FinTech, continuance usage intention, Indonesian consumerNiat Penggunaan Berkelanjutan dari Pembayaran Seluler Fintech di Indonesia Abstrak: Tujuan dari penelitian ini adalah untuk menentukan faktor-faktor positif dan negatif yang mempengaruhi niat penggunaan berkelanjutan dari pembayaran seluler FinTech di Indonesia. Penelitian ini menggunakan desain penelitian metode campuran sekuensial eksploratori dengan kuesioner sebagai instrumen penelitian pertama diikuti oleh wawancara semi-terstruktur. Penelitian ini menemukan bahwa baik manfaat yang dirasakan maupun risiko yang dirasakan secara signifikan mempengaruhi kelanjutan penggunaan pembayaran seluler FinTech di Indonesia tetapi manfaat yang dirasakan memiliki dampak yang lebih kuat daripada risiko yang dirasakan. Kenyamanan memiliki dampak positif yang paling kuat pada manfaat yang dirasakan, yang kemudian meningkatkan niat kelanjutan penggunaan pembayaran seluler FinTech. Risiko keuangan memiliki pengaruh paling kuat pada risiko yang dirasakan, yang kemudian mengurangi niat kelanjutan penggunaan pembayaran seluler FinTech. Kata kunci: mobile payment FinTech, niat kelanjutan penggunaan, konsumen Indonesia
Transfer Learning for Predicting Conversion from Mild Cognitive Impairment to Dementia of Alzheimer’s Type based on 3D-Convolutional Neural Network

Dementia of Alzheimer’s Type (DAT) is associated with a devastating and irreversible cognitive decline. As a pharmacological intervention has not yet been developed to reverse disease progression, preventive medicine will play a crucial role for patient care and treatment planning. However, predicting which patients will progress to DAT is difficult as patients with Mild Cognitive Impairment (MCI) could either convert to DAT (MCI-C) or not (MCI-NC). In this paper, we develop a deep learning model to address the heterogeneous nature of DAT development. Structural magnetic resonance imaging was utilized as a single biomarker, and a three-dimensional convolutional neural network (3D-CNN) was developed. The 3D-CNN was trained using transfer learning from the classification of Normal Control and DAT scans at the source task. This was applied to the target task of classifying MCI-C and MCI-NC scans. The model results in 82.4% classification accuracy, which outperforms current models in the field. Furthermore, by implementing an occlusion map approach, we visualize key brain regions that significantly contribute to the prediction of MCI-C and MCI-NC. Results show the hippocampus, amygdala, cerebellum, and pons regions as significant to prediction, which are consistent with current understanding of disease. Finally, the model’s prediction value is significantly correlated with rates of change in clinical assessment scores, indicating the model is able to predict an individual patient’s future cognitive decline. This information, in conjunction with the identified anatomical features, will aid in building a personalized therapeutic strategy for individuals with MCI. This model could also be useful for selection of participants for clinical trials.
Inspiring Creativity in Diverse Organizational Cultures: An Expatriate Integrity Dilemma

Abstract Employee creativity can bolster organizational competitiveness and survival. Although, when in host countries, expatriate top management leaders (ETML) are often challenged to constantly exhibit integrity that positively impacts the creativity of employees, despite prevalent organizational cultures’ (OCs) influences. Varying influences of distinct OCs and questionable ETML integrity have also been argued to have unpredictable influences on the creativity prowess of several emerging economies like Nigeria. It is, thus, unclear from the literature how ETML and distinct OCs act to inspire employee creativity. This study investigated the relationship between ETML integrity, OC and employee creativity. A cross-sectional survey design was administered to 439 participants from 22 manufacturing organizations in Nigeria, and data analysis was executed by leveraging partial least square path modelling (SmartPLS 3). Results indicated that ETML integrity and adhocracy OC have positive associations with employee creativity. Equally, clan and market OCS reflect negative associations with employee creativity. Surprisingly, ETML integrity dampens the positive relationships between adhocracy OC and hierarchy OC, and employee creativity. Furthermore, ETML integrity reinforces the association of clan OC and market OC with employee creativity. This study offers substantive and significant contributions that can be applied to emerging economies with similar concerns and context.