Miracle-dz/UniFAR-FTUs huggingface dataset:
NLP/Deep Learning Techniques in Healthcare for Decision Making

Day by day the health care domain is generating millions of records of patients in a structured and unstructured way. By applying deep learning techniques, it can be converted into a well-structured form i.e. Electronic Health Record (EHR). Decision making is one of the key parts in the Healthcare domain. In the decision making process doctors must refer to many data like laboratory reports, diagnosis reports, medical images, demographic information about the patient, clinical notes, and map it collectively with the concepts of medical science. Here AI especially Natural Language Processing and deep learning can be helpful in many ways. The objective of this article is to depict the importance of Deep Learning and Natural Language processing EHRs. Based on EHR, the doctor can take quick decisions in case of an emergency. Apart from that, it can also be effective in clinical predictions, detect disease at an earlier stage, forecasting future need of regular check-ups, predictions of hospitalization soon if required. The provision of an enormous amount of clinical information particularly EHR has stimulated the expansion of deep learning techniques that assist within the rapid analysis of patient data.
Efficacy of Tactile Method versus Visual Method to Define Needle Insertion Site of Inferior Alveolar Nerve Block for Pulpotomy in Children: A double-Blind, Randomized, Cross-over Study

Background Effective pain control is a prerequisite for successful child dental treatment.This study was designed to evaluate the efficacy of tactile versus visual method to define needle insertion point of IANB for pulpotomy in children. Materials and Methods: In this double-blind, cross-over, randomized clinical trial, 40 children (7-9 years old) requiring primary mandibular second molars pulpotomy were recruited. Study was performed in Zahedan Dental School in 2016. Children were randomly divided into two groups. Group I received IANB using tactile method in the right side in the first session, and using visual method in the left side in the second session. Group II received the visual method for the right and the tactile method for the left in two sessions respectively. Objective and subjective pains during injection were recorded using SEM and FIS, respectively. Reaction during cavity preparation; recorded by SEM, was evaluated as sign of anesthesia failure. Data were analyzed using the Mann-Whitney U, Wilcoxon Signed Rank and Fisher's Exact Tests at a significance level of 0.05. Results: Regarding tactile method, comparing groups I and II showed no significant difference in any of the variables (P> 0.05). About visual method two groups showed a significant difference only in subjective pain (P= 0.013). In group I, visual method showed significant differences in objective and subjective pain versus tactile method (p <0.05). In group II, no significant differences were found between the two methods in variables. Totally, all variables were significantly different in favor of visual method versus tactile method (p <0.05). Conclusion The visual method reduced objective and subjective pain and yielded superior anesthesia success compared to tactile method.
Interactive Visualization of Probability Density Functions using Clustering and Textures: A Case Study

Visualizing and understanding probability density functions (pdf) on a two-dimensional domain is non-trivial because of their high dimensionality. Things become much more complicated when the data is in a three-dimensional space, and/or if it is time-varying. A pixel-by-pixel probing to visualize the pdfs can easily overwhelm the user. It is necessary to reduce the amount of information before rendering a visualization of the data. We present a clustering approach which presents a high level view by reducing the number of pdfs to visualize. We also discuss methods of texture synthesis to show average statistical measures of pdfs of the clusters
Colorimetric Accuracy of Color Reproductions in the 3D Scenes

Color is a complex phenomenon that depends on the object, the observer and the light source, while the color reproduction additionally includes the surrounding conditions and it depends on the device that can either capture, display or print the reproduction. Colour accuracy is very important for digital reproduction of artistic work where colour represents essence of art. In our research, color reproductions of renderings of computer generated scenes, including an object, background and lights were assessed, and even more, colorimetric accuracy in renderings was analyzed. With assumption that shading in 3D computer generated scenes plays an important role in visual perception of rendered color, the colors were analyzed on renderings of shaded objects. It was determined that colorimetric accuracy depends on the position on rendered objects, either illuminated or shaded. Furthermore, it was discovered that color lightness and hue, light intensity and background influenced ΔRGB values.
Screening high potassium efficiency potato genotypes and physiological responses at different potassium levels

Potato (Solanum tuberosum L.) growth and production is highly dependent on potassium (K) levels in the soil. Southwest China is the largest potato production region but it has low availability of soil potassium. To assess the genetic variation in K use efficiency, 20 potato genotypes were collected to compare the yield and K content in a pot experiment. Moreover, ‘Huayu-5’ and ‘Zhongshu-19’ were cultivated in five K applications to investigate the K distribution and sucrose in different organs. The results indicated that there were highly significant effects of K, genotype and K×G interactions on tuber yield, plant and tuber K content, plant K uptake efficiency and K harvest index. Cluster analysis classified 20 potato genotypes into four types: DH (high efficiency at low and high K application), LKH (high efficiency at low K application), HKH (high efficiency at high K application) and DL (low efficiency at low and high K application). The potassium distribution percentage in the tubers of the potassium-efficient genotype was higher than that of the potassium-inefficient genotype under low potassium application. The sucrose content in the tuber gently declined as the application of K rose in both cultivars, and that in the tuber of ‘Huayu-5’ was higher than that in ‘Zhongshu-19’. ‘Huayu5’ reached the highest yield when the potassium application was 159.45 kg ha, and ‘Zhongshu-19’ reached the highest yield when the potassium application was 281.4 kg ha. This study indicated that genetic variation for K utilization efficiency existed among 20 genotypes, and yield in low K application and relative yield were suitable criteria for screening K utilization efficiency genotypes.
Opleiding Informatica Q-learning with Parameterized Quantum Circuits: design, implementation and benchmarking

Existing quantum computers are still very limited in scale, making it vital to utilize them to their fullest extent. One way of doing so is by using hybrid quantum-classical algorithms. In this context, so-called parameterized quantum circuits can be regarded as machine-learning models with the potential of remarkable expressive power. The use of such parameterized quantum circuit models has been studied extensively in both a supervised and unsupervised context. But, as it is still an emerging ﬁeld, there has been little research on the use of parameterized quantum circuits in a reinforcement learning context. In this thesis, we will investigate the use of parameterized quantum circuits as Q-value approximators and attempt to train them on the FrozenLake environment. To be more precise, motivated by unstable performance of initial experiments using standard Q-learning methods, we thoroughly investigate the expressivity and optimal conﬁgurations of newly introduced quantum model hyperparameters (i.e., parameters that only appear due to the quantum model). We do so by performing supervised regression to investigate whether the parameterized quantum circuits can encode optimal Q-values and what conﬁguration of quantum model hyperparameters works best. Our experiments found that our quantum model is able to very closely represent the optimal Q-values. Moreover, we found how to best conﬁgure the quantum model hyperparameters. As an extra experiment, we investigate a training technique that is an intermediary between supervised regression and true Q-learning, namely, Bellman updates with a ﬁxed target network using the optimal parameters.
Oxygen‐Deficient BiOBr as a Highly Stable Photocatalyst for Efficient CO2 Reduction into Renewable Carbon‐Neutral Fuels

A highly facile one‐pot ethylene glycol‐assisted solvothermal process was employed to fabricate bismuth oxybromide (BiOBr) with oxygen‐deficient defects. These defects played an indispensable role for superior photocatalytic CO2 reduction, in which the as‐prepared sample demonstrated a remarkable improvement of 3.3 and 5.7‐fold for CH4 production over pristine BiOBr and P25, respectively. The enhancement could be attributed to the presence of oxygen vacancies, which acted as the active sites for CO2 adsorption and activation. In addition, the oxygen–deficiency–induced defect states could effectively trap photogenerated electrons, thus improving the separation of the electron–hole pairs and significantly slow down the recombination rate of charge carriers. On top of that, oxygen‐deficient BiOBr exhibited long term stability (>50 hours of catalytic reaction) for CO2 photoreduction under simulated solar light, where no reducing agent or any post‐treatment was needed to regenerate the oxygen vacancies.
Synthetic membranes derived from pyridinium salts with long alkyl substituents having a terminal free amino group

2,4,6-Trisubstituted pyrylium salts having either one or two linear α-alkyl side chains 1 reacts with ethylene diamine 2 affording the corresponding pyridinium salts with a free amino group 3a-3d. All new amphiphiles were fully characterized by 1 H- and 13 C-NMR (at 400 MHz for protons and 100 MHz for carbons), IR and elemental analysis. The preparation of the new compounds enabled us to examine the effects of the amphiphilic structure on the aggregation properties. Considering the dynamic light scattering (DLS) and UV spectroscopy measurements, the paper examines the surfactant properties of these pyridinim salts having a free amino group and explores the properties of the derived synthetic membranes. The reaction of these membranes with fluorescein is also reported.
ICT, Development and Poverty Nexus in Africa: Way Forward

: Information and Communications Technologies (ICTs) have proven very important in every facet of development. It has become a critical tool in almost all areas of human endeavours that engender development through reduction of poverty. Africa has been known as an impoverished continent in spite of its abundant resources. Hence, it is imperative that an investigative study be carried out to know ICTs effects on development and poverty reduction, especially in Africa. This study was carried out to investigate the inter-relationship existing among ICT, development and poverty. It employed cross-sectional dataset of 43 African countries. The study used descriptive statistics, Cobb Douglas production function and three-stage simultaneous equation to analyse the data. It also made use of student’s t-test for the test of hypotheses. It was observed that mobile subscription, internet access and adult literacy positively and significantly influenced ICT development. Also, mobile subscription, adult literacy and per capita GNI (gross national income) positively and significantly influenced human development. However, inequality level negatively and significantly influenced both ICT and human development. Inequality level positively and significantly affected poverty, while internet access had significant negative influence on poverty. Lastly, the study showed a significant association between ICT development, human development and poverty level in Africa. Conclusively, ICT development and poverty rate were significantly inversely related. It was concluded that African governments should embark upon programs and policies that would incorporate ICT and education programs which would improve the livelihoods of their peoples with all required sincerity of purpose.
EXPLORING SOME OLD AND NEW ETHICAL ISSUES IN CRIMINOLOGICAL RESEARCH AND ITS IMPLICATIONS FOR EVIDENCE-BASED POLICY

Interest in the development of evidence-based policies for the public management of the control and treatment of crime seems to be the position that will become dominant in the coming years. In this context, it is important for policy makers to know that while social research is surrounded by many ethical dilemmas, criminological research, because of the particular sensitivity of its subject matter and the profound implications of its findings, must emphasise the responsibility of researchers and provide criteria and principles that properly guide their research. To respond to this new context, this paper introduces, as examples of its variety, some of the traditional ethical challenges of criminological research, such as the effective obtaining of consent, as well as some of the new challenges involved in the use of predictive algorithms by criminal justice system operators. We conclude by highlighting the necessity of considering the ethical dimension of criminological research as one of the necessary elements that legislators must assess to critically accept scientific evidence as legitimate.
Boron Nitride Nanotubes-Based Gas Sensors of SF\(_6\) Decomposed Components for Analyzing the Running Status of High-Voltage Insulated Equipment

Gas-insulated equipment using sulphur hexafluoride (SF\(_6\)) as insulation and arc extinguishing media such as Gas Insulated Station (GIS) has been widely used in the field of high voltage power systems. However, the internal insulation defect existed in GIS would inevitably lead to Partial Discharge (PD), and cause the composition of SF\({}_{6}\) to HF, SF\({}_{4}\), SOF\({}_{2}\), SO\({}_{2}\)F\({}_{2}\) and SO\({}_{2}\) and other characteristic component gases. The decomposition phenomenon would greatly reduce the insulation performance of SF\({}_{6}\) insulated equipment, and lead to insulation faults and power failure. Many sensing nonmaterial's such as carbon nanotube and graphene, metal oxides such as SnO\({}_{2}\) TiO\({}_{2}\), and ZnO were explored as chemical gas sensors to detect SF\({}_{6}\) decomposed species with rapid response, high sensitivity and selectivity, in order to guarantee the operation status of SF\({}_{6}\) insulation equipments. In this paper, we attempt to comprehensively understand the adsorption and sensing property of BNNT to SO\({}_{2}\), SOF\({}_{2}\) and SO\({}_{2}\)F\({}_{2}\) were investigated based on the DFT method to explore its potential as a chemical gas sensor. Our theoretical simulation results show that BNNT could be a promising sensor for sensitive detection which would be beneficial for evaluating the running status of SF\(_6\) high voltage insulated equipments.
Protective Effects of Gold Nanoparticles Against Malathion-Induced Cytotoxicity in Caco-2 Cells

Malathion is an organophosphorus insecticide widely used in agriculture, residential area, and public health programs with a known mechanism of toxicity of inhibition of acetylcholinesterase and induction of oxidative stress. Gold nanoparticles (AuNPs) represent stable and easily synthesized nanoparticles with extensive use in consumer products and medicine. Due to the antioxidant property of AuNPs, it is possible that AuNPs may prevent malathion-induced oxidative damage. In this study, the cytotoxicity of malathion and AuNPs (10 and 20 nm) were measured separately in Caco-2 cells. Then the protective effects of AuNPs were evaluated by measuring the oxidative stress (lipid peroxidation level and glutathione content) and acetylcholinesterase activity. The calculated IC50s values at 48 hr were 326.8±0.32, 43.09±0.65, and 41.46±0.24 μg/ml for malathion, AuNPs 10 and 20 nm, respectively. Then, the lowest concentration of AuNPs (1 μg/ml) and IC50 concentration of malathion (326.8 μg/ml) were selected to evaluate the effects of pretreatment of Caco-2 cells with AuNPs before exposure to malathion were evaluated. Interestingly, the results showed remarkably significant protective effects of AuNPs by attenuation the different parameters of oxidative stress and cytotoxicity induced by malathion in cells (P<0.001). It is the first report showing the protective effects of AuNPs against malathion-induced cytotoxicity in the Caco-2 cell line. © 2020 Tehran University of Medical Sciences. All rights reserved. Acta Med Iran 2020;58(11):552-561.
Electronic Document Management System

: Electronic Document Management System (EDMS) is needed by many organizations to properly managed large volumes of physical documents. One of them is Supply Chain Management (SCM) department of Oil and Gas (O&G) company in Malaysia. The objective of this research is to develop a framework of EDMS that is tailor-made to the SCM department, which is believed could help the department to manage their physical documents which are now located in their rented external storage due to the lack of space in the department itself. The requirements gathering is conducted by using interview method. The current business flow which involved the usage of the documents from one unit to another needed to be fully understood before developing the EDMS framework. This research has successfully developed an EDMS framework of SCM department and it could be used in developing the EDMS for future research.
Prevalence of suspected peste des petits ruminants infection and complicating bacteria in goats in Abeokuta, Ogun State, Nigeria

Nig. J. Anim. Prod. 2017, 44(3):43 48 Nigerian Society for Animal Production Nigerian Journal of Animal Production © Prevalence of suspected peste des petits ruminants infection and complicating bacteria in goats in Abeokuta, Ogun State, Nigeria *Okwelum, N. , Adewumi, O. O. , Akinduti, P. A. , Mshelbwala, F. M. , and Williams, T. J. Federal University of Agriculture, Abeokuta, Ogun State, Nigeria. *Corresponding E-mail: drjhephzi@yahoo.com Peste des Petits Ruminants (PPR) is endemic in Nigeria and causes major economic losses due to the high rates of mortality and morbidity in infected domestic sheep and goats. The outbreak of PPR in some goat herds was observed in the Abeokuta area of Ogun State, Nigeria, between January and August, 2014. Amongst the herds affected were those of the research animal in the Federal University of Agriculture Abeokuta. The goat herds were located in the Directorate of the University Farms (DUFARMS), the University environments and Apakila. The goats were intensively managed on concentrates and Panicum maximiun grass. The morbidity rates were 80-100 per cent in all the herds while mortality rates varied from 10% to 70%. Disease diagnosis was based on history, clinical signs and Post-mortem lesions. Complicating secondary bacteria were isolated from nasal and ocular discharges, which isolated, identified and sensitivity results obtained from laboratory tests. Treatments were carried out with sensitive antibiotics, analgesics and fluid therapy in a symptomatic approach. We conclude that PPR is endemic in this area of study and is responsible for a great loss in goat production and poverty of the rural goat farmers. We recommend that vaccination against PPR in this study area be instituted to avert the deleterious effects of the disease.
IMPROVED INDOOR LOCALIZATION WITH DIVERSITY AND FILTERING BASED ON RECEIVED SIGNAL STRENGTH MEASUREMENTS

: Distance estimation by the evaluation of RSSI measurements is a simple and well-known technique to predict the position of an unknown node. Therefore the infrastructure does not have to be extended by expensive hardware for synchronization or direction approximation. However, with the localization based on RSSI measurements common and proven systems can be used for the infrastructure. For indoor environments the distance-pending path loss is affected by strong variations, especially appearing as frequency specific signal dropouts. A diversity concept with redundant data transmission in different frequency bands can reduce the dropout probability. If also space diversity and plausibility filtering are used, the Location Estimation Error can be reduced significantly. The investigations show that a good performance for precision and availability can also be reached with low infrastructural costs.
Towards Crowd-aware Indoor Path Planning

Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people's routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path encountering the least objects en route. To process the queries, we design a unified framework with three major components. First, an indoor crowd model organizes indoor topology and captures object flows between rooms. Second, a time-evolving population estimator derives room populations for a future timestamp to support crowd-aware routing cost computations in query processing. Third, two exact and two approximate query processing algorithms process each type of query. All algorithms are based on graph traversal over the indoor crowd model and use the same search framework with different strategies of updating the populations during the search process. All proposals are evaluated experimentally on synthetic and real data. The experimental results demonstrate the efficiency and scalability of our framework and query processing algorithms.
Querying Deep Web Data Sources: A Structured Keyword Query Approach

(cid:151) A popular trend in data dissemination involves online data sources that are hidden behind query forms, thus forming the deep web . Extracting information across multiple deep web data sources is challenging, but increasingly crucial in many areas. Keyword search, which is a popular information discovery method, has been studied extensively on the surface web and relational databases. Keyword-based queries can provide a powerful yet intuitive means for accessing information from the deep web as well. However, this involves many dif(cid:2)cult challenges. For example, deep web data sources often contain redundant and/or incomplete data, there is often inter-dependence among data sources, and most data sources only support non-predicate keyword queries on their input interfaces. Thus, it is very hard to automatically execute complex queries. In this paper, we present a structured keyword query approach. We support keyword search over deep web data sources for complex queries, which can include constraint predicates , aggregation functions , group-by operations , and nested sub-queries . We have developed three query planning algorithms to generate query plans for different types of queries. Several optimization techniques are also used for optimizing the query plans and their execution.