Miracle-dz/UniFAR-FTUs huggingface dataset:
SAT: A Software for Assessing the Risk of Desertification in Spain

Desertification is a major global environmental issue exacerbated by climate change. Strategies to combat desertification include prevention which seeks to reverse the process before the system reaches the stable desertified state. One of these initiatives is to implement early warning tools. This paper presents SAT (the Spanish acronym for Early Warning System), a decision support system (DSS), for assessing the risk of desertification in Spain, where 20% of the land has already been desertified and 1% is in active degradation. SAT relies on three versions of a Generic Desertification Model (GDM) that integrates economics and ecology under the predator-prey paradigm. The models have been programmed using Vensim, a type of software used to build and simulate System Dynamics (SD) models. Through Visual Basic programming, these models are operated from the Excel environment. In addition to the basic simulation exercises, specially designed tools have been coupled to assess the risk of desertification and determine the ranking of the most influential factors of the process. The users targeted by SAT are government land-use planners as well as desertification experts. SAT tool is implemented for five case studies, each one of them representing a desertification syndrome identified in Spain. Given the general nature of the tool and the fact that all United Nations Convention to Combat Desertification (UNCCD) signatory countries are committed to developing their National Plans to Combat Desertification (NPCD), SAT could be exported to regions threatened by desertification and expanded to cover more case studies.
SNP Discovery and Mapping QTLS Associated with Root Traits and Morphological Traits in Tomato

ii DEDICATION . iii ACKNOWLEDGEMENTS . iv NOMENCLATURE . vi TABLE OF CONTENTS . vii LIST OF FIGURES . ix LIST OF TABLES . x CHAPTER I GENERAL INTRODUCTION . 1 The world tomato production statistics . 1 Tomato production in the USA . 3 Tomato origin and distribution . 6 Genetic diversity of tomato . 7 Important breeding traits . 9 GBS-SNP markers . 11 Linkage map and QTL analysis in tomato . 13 Root genetics and breeding in tomato . 14 Research objective . 16 CHAPTER II GENETIC VARIATION IN TOMATO ROOT . 17 Materials and methods . 17 Plant materials . 17 Measurement of root and other morphological traits . 19 Statistical analysis . 21 Results and discussion . 22 Traits variation among parents and environment control . 22 The correlation among traits . 23 The frequency distribution of traits . 26
Determinants of Environmental Degradation in Economy of Pakistan

The study attempts to examine various factors responsible for environmental degradation in Pakistan. Contributing towards economic growth, these factors added pollutants as a byproduct to the environment. Carbon dioxide (CO2) emission rate is one of such pollutants and has been used as a dependent variable in the study. Numerous factors are responsible for environmental damage, but the study includes the major ones. These determinants are economic growth, population, energy consumption and industrialization, and time-series data of these variables from 1972 to 2018 are utilized for empirical analysis in the study. Long run relationship is computed using Auto Regressive Distributed Lag (ARDL). Findings of the study revealed that three factors; population, energy consumption and industrialization are positively and significantly contributed to environmental degradation in Pakistan. While, economic growth is negatively contributing towards environmental degradation. The paper concluded with a finding that population growth needs to be controlled. Besides this, clean and green energy should be promoted. Lastly, EPAs must be strengthened for their effectiverole.
Mining Significant Features of Diabetes Mellitus Applying Decision Trees: A Case Study In Bangladesh

Diabetes is a chronic condition which is associated with an abnormally high level of sugar in the blood. It is a lifelong disease that causes harmful effects in human life. The goal of this research is to predict the severity of diabetes and find out significant features of it. In this work, we gathered diabetes patients records from Noakhali Diabetes Association, Noakhali, Bangladesh. Thus, We preprocessed our raw dataset by replacing and removing missing and wrong records respectively. Thus, CDT, J48, NBTree and REPtree decision tree based classification techniques were used to analyze this dataset. After this analysis, we evaluated classification outcomes of these decision tree classifiers and found the best decision tree model from them. In this work, CDT unpruned tree shows highest accuracy, precision, recall, f-measure, second highest AUROC and lowest RMSE than other models. Then, we extracted possible rules and significant features from this model and plasma glucose, plasma glucose 2hr after glucose and HDL-cholesterol have been found as the most significant features to predict the severity of Diabetes Mellitus. We hope this work will be beneficial to build a predictive system and complementary tool for diabetes treatment in future.
Low-dose CT Image Denoising Using Classification Densely Connected Residual Network

Considering that high-dose X-ray radiation during CT scans may bring potential risks to patients, in the medical imaging industry there has been increasing emphasis on low-dose CT. Due to complex statistical characteristics of noise found in low-dose CT images, many traditional methods are difficult to preserve structural details effectively while suppressing noise and artifacts. Inspired by the deep learning techniques, we propose a densely connected residual network (DCRN) for low-dose CT image noise cancelation, which combines the ideas of dense connection with residual learning. On one hand, dense connection maximizes information flow between layers in the network, which is beneficial to maintain structural details when denoising images. On the other hand, residual learning paired with batch normalization would allow for decreased training speed and better noise reduction performance in images. The experiments are performed on the 100 CT images selected from a public medical dataset—TCIA(The Cancer Imaging Archive). Compared with the other three competitive denoising algorithms, both subjective visual effect and objective evaluation indexes which include PSNR, RMSE, MAE and SSIM show that the proposed network can improve LDCT images quality more effectively while maintaining a low computational cost. In the objective evaluation indexes, the highest PSNR 33.67, RMSE 5.659, MAE 1.965 and SSIM 0.9434 are achieved by the proposed method. Especially for RMSE, compare with the best performing algorithm in the comparison algorithms, the proposed network increases it by 7 percentage points.
Image classification with Deep Convolutional Neural Network Using Tensorflow and Transfer of Learning

حققت خوارزمية التعلم العميق مؤخرا الكثير من النجاح خاصة في مجال رؤية الكمبيوتر. يهدف البحث الحالي إلى وصف طريقة التصنيف المطبقة على مجموعة البيانات الخاصة بأنواع متعددة من الصور (صور الرادار ذي الفجوة المركبةSAR والصور ليست SAR) ، أستخدم نقل التعلم متبوعا بأساليب الضبط الدقيق في مخطط التصنيف هذا . تم استخدام بنيات مدربة مسبقا على قاعدة بيانات الصور المعروفهImageNet، تم استخدام نموذج VGG 16 بالفعل كمستخرج ميزات وتم تدريب مصنف جديد بناء على الميزات المستخرجة . تركز بيانات الإدخال بشكل أساسي على مجموعة البيانات التي تتكون من خمس فئات فئة صور الرادارSAR (المنازل) وفئات الصور ليستSAR (القطط والكلاب والخيول والبشر). تم اختيار الشبكة العصبية التلافيفية (CNN) كخيار أفضل لـعملية التدريب لانها نتجت عن دقة عالية. لقد وصلنا إلى الدقة النهائية بنسبة 91.18٪ في خمس فئات مختلفة. تتم مناقشة النتائج من حيث احتمالية الدقة لكل فئة في تصنيف الصورة بالنسبة المئوية. تحصل فئة القطط على 99.6٪ ، بينما تحصل فئة المنازل على 100٪ وتحصل انواع آخرى من الفئات بمتوسط ​​درجات 90٪ وما فوق.
Chabrolene, a Novel Norditerpene from the Bornean soft coral Nephthea sp.

Soft corals are often the research interest organism due to their structural diverse secondary metabolites with wide spectrum of biological activities range from antibacterial to anticancer potentials. The Borneo Island is rich in diversity of marine organisms including soft coral. Therefore, a population of Bornean soft coral belongs to genus Nephthea was collected from Mantanani Island (Sabah, Malaysia) which led to the isolation of a novel norditerpene, chabrolene (1) together with three known compounds (2-4). The chemical structure of 1 was determined by NMR and HREIMS data. Compound 1 exhibited repellent activity against the maize weevil Sitophilus zeamais.
Improving the quality of human resources in Ciburuy village staff to improve the quality of public services

Utilization of computer technology to improve the quality of public services in Ciburuy village cannot be carried out optimally if it is not supported by the readiness of human resources as the parties who use it. readiness of human resources in using computer technology is one of the important factors in terms of public services.Therefore we need a computer technology transfer process that is adequate, right on target and appropriate for Ciburuy village staff, as the main actor in this activity, namely service to the community.Methods of implementing human resource improvement through computer skills training activities through the Microsoft Word Application Program, Microsoft Excel, Microsoft Power Point, databases and networks are carried out through several stages. resulting in an average value of being satisfied and quite satisfied so that public services in Ciburuy village must improve service quality. but the unique thing is seen in the ability of village staff in the service. so that respondents have started to feel satisfied with the services carried out by Ciburuy village staff.
Petite Integration Factor 1 (PIF1) helicase deficiency increases weight gain in Western diet-fed female mice without increased inflammatory markers or decreased glucose clearance

Petite Integration Factor 1 (PIF1) is a multifunctional helicase present in nuclei and mitochondria. PIF1 knock out (KO) mice exhibit accelerated weight gain and decreased wheel running on a normal chow diet. In the current study, we investigated whether Pif1 removal alters whole body metabolism in response to weight gain. PIF1 KO and wild type (WT) C57BL/6J mice were fed a Western diet (WD) rich in fat and carbohydrates before evaluation of their metabolic phenotype. Compared with weight gain-resistant WT female mice, WD-fed PIF1 KO females, but not males, showed accelerated adipose deposition, decreased locomotor activity, and reduced whole-body energy expenditure without increased dietary intake. Surprisingly, PIF1 KO females were protected against obesity-induced alterations in fasting blood glucose and glucose clearance. WD-fed PIF1 KO females developed mild hepatic steatosis and associated changes in liver gene expression that were absent in weight-matched, WD-fed female controls, linking hepatic steatosis to Pif1 ablation rather than increased body weight. WD-fed PIF1 KO females also showed decreased gene expression of inflammatory markers in adipose tissue. Collectively, these data separated weight gain from inflammation and impaired glucose homeostasis. They also support a role for Pif1 in weight gain resistance and liver metabolic dysregulation during nutrient stress.
Understanding the Impact of COVID-19 Intervention Policies on the Labor Market of the Hospitality and Retail Industries

Using new high-frequency data that covers a representative sample of small business owners, we investigate the effects of the COVID-19 pandemic and the resulting state policies on the retail, hospitality, food, and accommodation sectors. First, we find that business closure policies are associated with a 20-30% reduction of non-salaried workers in the hospitality industry and a 10-20% decline for the retail sector from March-April of 2020. Second, business reopening policies play a statistically significant role in reviving the labor market, though at a slower pace. Third, considerable differences exist in the impact of policies on the labor market by state. Fourth, the rise of new cases on a daily basis is associated with the continued deterioration of the labor market. We use these results to provide policy and managerial recommendations for restoring the labor market and curbing the transmission of COVID-19.
Liquidity and Asset Pricing: Evidence from Indian Stock Market

In this research article, we present a liquidity premium based asset pricing model and test it in the Indian stock market. Using high-frequency data of stocks listed in the National Stock Exchange, we show that observed illiquidity has a significant negative impact on realized stock returns even after controlling for the up and down market, volatility, and effects of derivatives trading. The illiquidity measure is modified for its time variations, and then the modified measure is used to assess its impact on returns. Using a cross-section of stocks, we show the year wise results of the model and extend it to show that it has some role in explaining returns across industries. Findings show that the down market has contemporaneous systematic risk at higher levels, and the market risk premium is higher in down markets. Finance, utility and real estate sector companies have higher systematic risk in both up and down market and investors of these sectors has relatively higher expected higher returns in comparison to companies from the rest of the segments.
A fast alignment of marine strapdown inertial navigation system based on adaptive unscented Kalman Filter

This study has presented an efficient adaptive unscented Kalman filter (AUKF) with the new measurement model for the strapdown inertial navigation system (SINS) to improve the initial alignment under the marine mooring conditions. Conventional methods of the accurate alignment in the ship’s SINS usually fail to succeed within an acceptable period of time due to the components of external perturbations caused by the movement of sea waves and wind waves. To speed up convergence, AUKF takes into account the impact of the dynamic acceleration on the filter and its gain adaptively tuned by considering the dynamic scale sensed by accelerometers. This approach considerably improved the corrections of the current residual error on the SINS and decreased the influence due to the external perturbations caused by the ship’s movement. Initial alignment algorithm based on AUKF is designed for large misalignment angles and verified by experimental data. The experimental test results show that the proposed algorithm enhanced the convergence speed of SINS initial alignment compared with some state-of-the-art existing approaches.
Multi Task Mutual Learning for Joint Sentiment Classification and Topic Detection

Recently, advances in neural network approaches have achieved many successes in both sentiment classification and probabilistic topic modeling. On the one hand, latent topics derived from the global context of documents could be helpful in capturing more accurate word semantics and hence could potentially improve the sentiment classification accuracy. On the other hand, the word-level attention vectors obtained during the learning of sentiment classifiers could carry word-level polarity information and can be used to guide the discovery of topics in topic modeling. This paper proposes a multi-task learning framework which jointly learns a sentiment classifier and a topic model by making the word-level latent topic distributions in the topic model to be similar to the word-level attention vectors in sentiment classifiers through mutual learning. Experimental results on the Yelp and IMDB datasets verify the superior performance of the proposed framework over strong baselines on both sentiment classification and topic modeling. The proposed framework also extracts more interpretable topics compared to other conventional topic models and neural topic models.
Determinants of geospatial distribution of parasitaemia among patients infected with Plasmodium falciparum malaria in Ondo State, Southwest, Nigeria

: Malaria is a disease caused by Plasmodium parasites which is transmitted by female Anopheles mosquitoes. It remains one of the most deadly diseases globally and especially in the tropical nations of the world. In the clinical setting, the level of parasitaemia is useful as one of the criteria in defining “severe Plasmodium falciparum malaria” and to monitor the effect of anti-malarial therapy. Mapping is a known vital tool in the control and elimination of malaria globally. In this study, blood samples were collected from 415 malaria infected patients in the outpatient department of 10 randomly selected government hospitals. Coordinates of the patients’ address were taken, geocoding, mapping and cartographic display were carried out. Thin and thick blood smear preparations and microscopy were conducted on the blood samples collected from outpatients attending some selected government hospitals and percentage parasitaemia were determined. Ondo North Senatorial District had the highest mean parasitaemia of 1.06% while Ondo South had the least mean parasitaemia (0.89%). The risk map revealed that majority of patients in Ondo North 26.05% (31/119) and few in Ondo Central 12.73% (21/165) senatorial district had high malaria burden with high parasitaemia (2.62 to 5.1% parasitaemia). Based on the malaria map developed, this study could be used as guide in monitoring the efficacy of antimalarial therapy and provide information for malaria control intervention in Ondo State.
Revealing the Core Transcriptome Modulating Plant Growth Phase in Arabidopsis thaliana by RNA Sequencing and Coexpression Analysis of the fhy3 far1 Mutant

Plants must continually calibrate their growth in response to the environment throughout their whole life cycle. Revealing the regularity of plant early growth and development is of great significance to plant genetic modification. It was previously demonstrated that loss of two key light signaling transcription factors, FHY3 and FAR1, can cause a stunted stature in the plant adult stage, and numerous defense response genes can be continuously activated. In this study, we performed a time-course transcriptome analysis of the early 4 weeks of leaf samples from wild plants and their fhy3 and far1 transcription factors. By comparative transcriptome analysis, we found that during the early 4 weeks of plant growth, plants primarily promoted morphogenesis by organizing their microtubules in the second week. In the third week, plants began to trigger largescale defense responses to resist various external stresses. In the fourth week, increased photosynthetic efficiency promoted rapid biomass accumulation. Weighted gene coexpression network analysis of FHY3 and FAR1 revealed that the two light signaling transcription factors may be originally involved in the regulation of genes during embryonic development, and in the later growth stage, they might regulate gene expression of some defense-related genes to balance plant growth and immunity. Remarkably, our yeast two-hybrid and bimolecular fluorescence complementation experiments showed that FAR1 interacts with the immune signaling factor EDS1. Taken together, this study demonstrates the major biological processes occurring during the early 4 weeks of plant growth. The light signaling transcription factors, FHY3 and FAR1, may integrate light signals with immune signals to widely regulate plant growth by directly interacting with EDS1.
The Impact of Emergency Call Taking on the Mental Health and Wellbeing of Ambulance Call-Takers: A Systematic Thematic Narrative of Qualitative Research

Background Over the past decade there has been significant focus on the mental health and wellbeing of emergency service workers in Australia, evidenced by the 2018 Senate Inquiry into the role of Commonwealth, state and territory Governments in addressing the high rates of mental health conditions experienced by first responders, emergency service workers and volunteers. Call-takers as an occupational group within this domain are at risk of cumulative and vicarious trauma, yet there is little research on their work-related mental illness. This systematic thematic narative literature review of qualitative articles reports on the mental health and wellbeing of emergency call-takers. Methods Both published peer review (2000–2018) and grey literature (2009–2018) that examined the impact of emergency work on call-takers was retrieved. Papers that focussed on call-takers’ psychological and psychosocial health were selected. Databases included Ovid Medline, CINAHL, Ovid EMcare, PsychInfo, Scopus as well as Google Scholar. Results Fourteen articles met the eligibility criteria; five peer review and nine grey literature studies. Thematic analysis identified issues around surveillance versus lack of supervision; role denial versus advocacy; and failure to acknowledge vicarious trauma. Suggestions for improvement required recognition of the stressful nature of the work, improvements in workplace culture and the provision of support and counselling services. Conclusion Workplace stressors for call-takers arise from their work in dealing with trauma related communication with the public as well workplace culture, particularly the response of management to issues such as shift work, poorly managed rosters and long hours of work with little time for recovery. Compounding these issues is the invisibility of call-taker work.
Nonlinear rotating viscoelastic liquid convection with temperature modulation

In this study, heat transport in thermal convection of rotating viscoelastic liquids heated from below with temperature modulation is analyzed. The study is performed using Fourier series with a minimal representation. The constitutive relationship of the Oldroyd liquid B model is taken into consideration. The resulting Khayat–Lorenz model in the generalized form is solved using the numerical technique of Runge–Kutta–Fehlberg 45 with the adaptive grid method, and this solution is used to quantify the heat transport. The combined effect of the temperature modulation and Coriolis force on the thermal convection is investigated for various values of the amplitude of modulation and Taylor number. It is shown that the temperature modulation and Coriolis force lead to the enhancement of heat transport. As particular cases, the results of three liquids, namely Maxwell, Newtonian, and Rivlin–Ericksen, are obtained in the current study, which are found to have a good agreement with the available results.