Miracle-dz/UniFAR-FTUs huggingface dataset:
Glycaemic Control among Patients with Diabetes in Primary Care Clinics in Jamaica, 1995 and 2012

Objective: To compare the quality of care received by patients with diabetes in public primary care clinics in 2012 with that reported in 1995. Methods:Patient records were audited at six randomly selected Type III health centres in the South East Health Region of Jamaica. The 2012 audit data were compared with published data from a similar audit conducted in 1995. Quality of care measures included timely screening tests and counselling of the patients. Fasting and postprandial glucose tests were used to assess glycaemic control. Results:Two hundred and forty-two patient records were analysed in 2012, and 185 in 1995. In 2012, 88% of patients were weighed within the last year compared with 43% in 1995. Advice on physical activity increased from 1% to 60% and on dietary practices from 6% to 79%. No patient had done the HBA1C in 1995 compared to 38% in 2012. In 1995, 66% had blood glucose measured at a laboratory during the last year while in 2012, 60% had a laboratory test and 90% were tested at the clinic by glucometer. Blood pressure control increased from 19% in 1995 to 41% in 2012 (p < 0.001). Poor glucose control was recorded among 61% of patients in 1995 compared with 68% in 2012. Conclusions: There was no improvement in glycaemic control. Health providers and patients must work together to improve patient outcomes. This will involve closer patient monitoring, treatment intensification where indicated, and the adoption of lifestyle practices that can lead to better control.
Research on blast furnace stability evaluation model based on neural networks using dynamic sampling approach

The blast furnace iron-making is a complicated physical and chemical reaction and heat exchange process in a closed shaft furnace. The blast furnace’s operator must judge the condition of the furnace in time and accurately, and then take the adjustment measures based on the condition to ensure that the blast furnace is running smoothly. Not handling the fluctuation of the furnace condition in time, it may cause the failure of the furnace, which leads to the decrease of the blast furnace’s output, the increase of fuel consumption, and a serious effect on the production and operation of the enterprises. In order to solve the above problem, the study implement several methods to try to judge the stability of the blast furnace in the iron-making process. Results shows that the Neural Networks with dynamic sampling method, which has a great superiority among the class imbalance problems, has the highest anomaly detection rate compared to the other methods.
NOMA-Enabled Mobile Edge Computing for Internet of Things via Joint Communication and Computation Resource Allocations

The past decades have witnessed an explosive growth of the Internet of Things (IoT) services requiring intensive computation resources. The conventional IoT devices, however, are usually equipped with very limited computation resources, which results in degraded quality of experience when executing the resource-hungry applications. Mobile edge computing (MEC), which enables smart terminals (STs) to offload parts of their computation workloads to the edge servers located at cellular base stations (BSs), has provided a promising approach to address this issue. In this article, we investigate the nonorthogonal multiple access (NOMA)-enabled multiaccess MEC. Specifically, by exploiting the advanced NOMA, an ST can simultaneously offload its computation workloads to different edge servers (ESs), which thus reduces the overall delay in completing the ST’s computation workloads. To study this problem, we formulate a joint optimization of the computation resource allocations at the ESs, the ST’s offloaded workloads and its radio resource allocations for NOMA transmission, with the objective of minimizing a system wise cost that accounts for the overall delay in finishing the ST’s total computation workload and the total computation resource usage cost at the ESs. Despite the nonconvexity of the joint optimization problem, we exploit its layered structure and propose an efficient layered algorithm to find the optimal solution. By exploiting the optimal offloading solution of a single ST, we further investigate the scenario of multiple STs and propose two algorithms to determine the optimal grouping among different ESs for serving the STs, with one algorithm aiming at minimizing the total cost of all STs and the other algorithm aiming at determining the Nash stable grouping for the ESs. Numerical results are presented to validate the effectiveness of our proposed algorithms and show the performance gain of our proposed NOMA-enabled multiaccess computation offloading.
Encoding speech through brain rhythms

Brain oscillations are dynamic entities, rapidly varying in time and frequency that are extensively recorded in the mammalian brain. In this thesis I investigated the role of cortical rhythms in human auditory cortex during speech perception, both with computational and experimental methods. We developed a neural microcircuit model of nested oscillations for early auditory processing, involving a fast Gamma rhythm (30-100 Hz) coupled to a slow Theta rhythm (3-8 Hz). The model is capable of parsing speech into its constituents (i.e. syllables) and extracting the syllabic information for latter categorization. We then analysed EEG signals from intracranial recordings in humans using cross-frequency coupling and Granger causality. We found that Bottom-up information is dominated by Gamma oscillations, while Top-Down flow is conveyed through Theta oscillations. Both flows fluctuate over time at a rate of ~1-3 Hz, suggesting that sensory information is conveyed using distinct frequencies and via discrete time windows.
A Target Behavior Law Mining Method Based on Similar Duplicate Record Detection

In the complex marine environment, with the continuous enrichment and development of target detection methods, massive target track data is stored and accumulated in the database, which contains a large amount of information and knowledge. It is possible to analyze and predict the behavior and intention of the target by means of target behavior law mining. Most of the existing target behavior law mining methods are based on the idea of clustering. When working in the complex environment of the sea battlefield, the working steps are complicated and the effect is poor. Therefore, in combination with situation analysis requirements and based on the idea of similar duplicate record detection, this paper proposes a mining method of maritime target behavior law by defining multi-dimensional record matching similarity (MDRS). The experimental analysis is carried out on the simulated military scene. The results show that the proposed method can accurately and effectively mine the behavior of the target.
Improving classification and clustering techniques using GPUs

Classification and clustering techniques are used in different applications. Large‐scale big data applications such as social networks analysis applications need to process large data chunks in a short time. Classification and clustering tasks in such applications consume a lot of processing time. Improving the performance of classification and clustering algorithms enhances the performance of applications that use such type of algorithms. This paper introduces an approach for exploiting the graphics processing unit (GPU) platform to improve the performance of classification and clustering algorithms. The proposed approach uses two GPUs implementations, which are the pure GPU or GPU‐only implementation and the GPU‐CPU hybrid implementation. The results show that the hybrid implementation, which optimizes the subtask scheduling for both the CPU and the GPU processing elements, outperforms the approach that uses only the GPU.
Topographical Relationship of the Lymph Nodes Surrounding the Abdominal Aorta, the Inferior Mesenteric Artery, and the Superior Hypogastric Plexus

自律神経の機能を保持した直腸癌根治手術術式にともなうリンパ節の正確な郭清範囲を定めるための基礎資料として, 直腸の上方向リンパ路ならびに上下腹神経叢の構成について解剖実習体90体を精査し, 次の結果を得た. 下行結腸, S状結腸および直腸のリンパ系は下腸間膜静脈に伴走せずに, 下腸間膜動脈の根部に向かう. その大動脈周囲リンパ節への到達経路は次の4系に区分される. 右方系は大動脈大静脈間リンパ節へ, 左方系は外側大動脈リンパ節に連続する. 上方と下方系は大動脈前リンパ節を介したのち, 大動脈大静脈間リンパ節および外側大動脈リンパ節に至る. 上下腹神経叢 (仙骨前神経) を構成する最も太い左右の腰内臓神経の合流部は下腸間膜動脈起始部下端より約47. 2mmで, ほぼ大動脈分岐部に一致する. その長さは約42. 2mmであり, 第5腰椎から第1仙椎の高さで左右の下腹神経に分岐する.
HidingGAN: High Capacity Information Hiding with Generative Adversarial Network

Image steganography is the technique of hiding secret information within images. It is an important research direction in the security field. Benefitting from the rapid development of deep neural networks, many steganographic algorithms based on deep learning have been proposed. However, two problems remain to be solved in which the most existing methods are limited by small image size and information capacity. In this paper, to address these problems, we propose a high capacity image steganographic model named HidingGAN. The proposed model utilizes a new secret information preprocessing method and Inception‐ResNet block to promote better integration of secret information and image features. Meanwhile, we introduce generative adversarial networks and perceptual loss to maintain the same statistical characteristics of cover images and stego images in the high‐dimensional feature space, thereby improving the undetectability. Through these manners, our model reaches higher imperceptibility, security, and capacity. Experiment results show that our HidingGAN achieves the capacity of 4 bits‐per‐pixel (bpp) at 256 × 256 pixels, improving over the previous best result of 0.4 bpp at 32 × 32 pixels.
Data Priority Assignment Technique for Opportunistic Routing

: The wireless sensor networks have within them numerous nodes. These nodes include batteries within them which are of very small sizes. Also, the deployment of nodes in these networks is at far places and so the replacement of batteries is not an option here. Thus, the efficient utilization of battery within these networks is a major concern. In the base paper, technique of opportunistic based routing is proposed for data routing in wireless sensor networks. In the opportunistic type of routing the source node store the data on the intermediate node which will move near to the base station and deliver data to the base station. The data which is stored on the intermediate node also given the priority and data which has higher priority is delivered first to base station. The simulation is performed in NS2 and it has been analyzed that proposed technique performs well in terms of various parameters.
A Unifying Theory of Driver Perception and Steering Control on Straight and Winding Roads

Novel driver support systems potentially enhance road safety by cooperating with the human driver. To optimize the design of emerging steering support systems, a profound understanding of driver steering behavior is required. This article proposes a new theory of driver steering, which unifies visual perception and control models. The theory is derived directly from measured steering data, without any a priori assumptions on driver inputs or control dynamics. Results of a human-in-the-loop simulator experiment are presented, in which drivers tracked the centerline of straight and winding roads. Multiloop frequency response function (FRF) estimates reveal how drivers use visual preview, lateral position feedback, and heading feedback for control. Classical control theory is used to model all three FRF estimates. The model has physically interpretable parameters, which indicate that drivers minimize the bearing angle to an “aim point” (located 0.25–0.75 s ahead) through simple compensatory control, both on straight and winding roads. The resulting unifying perception and control theory provides a new tool for rationalizing driver steering behavior, and for optimizing modern steering support systems.
Statistical Karyotype Analysis Using CNN and Geometric Optimization

Karyotype analysis is one of the main techniques of cytogenetics through medical image processing, which has an important role in modern medical treatment and diagnosis. The process of human karyotype analysis contains two key components: Firstly, chromosomes are segmented from metaphase chromosome digital images taken under a microscope. Chromosomes then are analyzed, compared, ordered and classified one by one carefully. Under this procedure, the operation on segmentation and classification is cumbersomely time-consuming, where traditional geometric or statistical methods only have limited effect due to low accuracy. Thus, in most conditions, human effort is still heavily required to monitor the workflow and correct the errors. In this paper, we present an integrated workflow to segment out and classify chromosomes automatically using a combination of multiple input convolutional neural networks (CNN) and geometric optimization, called mCNN_GO. We investigate Mask R-CNN to segment out chromosomes from metaphase chromosome images and train the mCNN_GO to classify the sub-images. To improve the performance of the segmentation network, we adapt a new feature-based approach to synthesize images on the labeled data. Furthermore, we develop a geometric algorithm to straighten the chromosomes before classification to ensure the consistency of the data. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art methods on automatic karyotype analysis.
NEW COMPUTER GENERATED-SCMA CODEBOOK WITH MAXIMIZED EUCLIDIAN DISTANCE FOR 5G

Sparse code multiple access (SCMA) is one of the Non-Orthogonal Multiple Access (NOMA) that attracts many researchers for its promising future uses in 5G systems. In this paper, a Computer Generated-Sparse Code Multiple Access (CG-SCMA) is proposed. CG-SCMA generates a complex SCMA codebook that maximizes the Minimum Euclidian Distance (MinED) of 16-point star-QAM. This codebook is generated using computer program to specify the most appropriate values for this constellation. Then Trellis Coded Modulation (TCM) is used to divide the constellation into four sub constellations to maximize MinED. The new codebook reaches MinED for four sub constellations {3.46, 2.16, 2.16, 3.46} and achieves increment over SCMA codebook based 16-point star-QAM by 10.1%. The multiplexer and de-multiplexer for proposed codebook and traditional SCAM codebook is implemented using netFPGA-1G-CML Kintex-7. it can speed up the process by 597.6 times over MATLAB simulator and decreasing BER compared to open literature codebooks.
On the Performance of Cell-Free Massive MIMO Relying on Adaptive NOMA/OMA Mode-Switching

The downlink (DL) of a non-orthogonal-multiple-access (NOMA)-based cell-free massive multiple-input multiple-output (MIMO) system is analyzed, where the channel state information (CSI) is estimated using pilots. It is assumed that the users are grouped into multiple clusters. The same pilot sequences are assigned to the users within the same clusters whereas the pilots allocated to all clusters are mutually orthogonal. First, a user’s bandwidth efficiency (BE) is derived based on his/her channel statistics under the assumption of employing successive interference cancellation (SIC) at the users’ end with no DL training. Next, the classic max-min optimization framework is invoked for maximizing the minimum BE of a user under per-access point (AP) power constraints. The max-min user BE of NOMA-based cell-free massive MIMO is compared to that of its orthogonal multiple-access (OMA) counter part, where all users employ orthogonal pilots. Finally, our numerical results are presented and an operating mode switching scheme is proposed based on the average per-user BE of the system, where the mode set is given by Mode = { OMA, NOMA }. Our numerical results confirm that the switching point between the NOMA and OMA modes depends both on the length of the channel’s coherence time and on the total number of users.
Prediction of Missing Tubing Head Pressure Using Recurrent Neural Network

This study suggests a method to restore missing pressure data in production wells using a recurrent neural network (RNN). The use of RNNs with long short-term memory (LSTM) cells is considered to be an efficient method for predicting time series data. Pressure data measured by gauges at the bottomhole and wellhead often provide abnormal values due to various reasons such as gauge malfunction, noise, outliers, and non-operations. In this study, pre-treatment of the abnormal measurement data was carried out, and then missing wellhead pressure data were restored through the LSTM-RNN method. The applicability of the LSTM-RNN was investigated in comparison with the pressures estimated by the production well model of PipeSim.
Mentoring , Education , and Training Corner Introduction to the Mentoring , Education , and Training Corner

The Mentoring, Education and Training (MET) corner is a special section of Gastroenterology that started in July 2011. It was designed to provide its readership a collection of expert perspectives that would contribute to the shaping of persciences, law, or business will provide assurance that the social expectations of the future are being met. Mentoring needs will change while climbing up the ladder of career development. Knowing oneself, what one is most passionate about, and who the role models are who have served to illustrate the potential path of interest are important in helping the individual to decide if a certain career is the right one. It is essential to find a mentor who will provide guidance through the intricacies of the chosen direction. Drive, passion, hard work, and several other attributes are constant requirements for success independent of the path chosen. What will vary is the specific steps required to reach one’s ultimate goal.
Active Adaptive Battery Aging Management for Electric Vehicles

The battery pack accounts for a large share of an electric vehicle cost. In this context, making sure that the battery pack life matches the lifetime of the vehicle is critical. The present work proposes a battery aging management framework which is capable of controlling the battery capacity degradation while guaranteeing acceptable vehicle performance in terms of driving range, recharge time, and drivability. The strategy acts on the maximum battery current, and on the depth of discharge. The formalization of the battery management issue leads to a multi-objective, multi-input optimization problem for which we propose an online solution. The algorithm, given the current battery residual capacity and a prediction of the driver's behavior, iteratively selects the best control variables over a suitable control discretization step. We show that the best aging strategy depends on the driving style. The strategy is thus made adaptive by including a self-learnt, Markov-chain-based driving style model in the optimization routine. Extensive simulations demonstrate the advantages of the proposed strategy against a trivial strategy and an offline benchmark policy over a life of 200 000 (km).
Prevalence Of Metabolic Syndrome And Its Components In Jimma Town, South West Ethiopia.

Introduction: The metabolic syndrome has become one of the major public-health challenges worldwide [35]. Metabolic syndrome (Met S) is a cluster of metabolically related cardiovascular disease (CVD) risk factors such as obesity, atherogenic dyslipidemia, elevated blood pressure and abnormal glucose. Metabolic syndrome is highly prevalent among adults in developed countries and is an emerging health problem in developing countries. The criteria used for defining Metabolic syndrome are those proposed by International Diabetes Federation (IDF), National Cholesterol Education ProgrammeAdult Treatment Pannel-III (NCEP-ATP III). Objective: The objective of this study was to assess the prevalence of metabolic syndrome, its components, and its major risk factors among adults ≥ 20 in Jimma Town, South West Ethiopia according to IDF and NCEP ATP III criteria. The purpose of these definitions is to find out the edge group in the population who are at increased risk of developing cardio-metabolic diseases (CVDs / CHDs). Methods: A community based cross-sectional study was conducted to determine prevalence of Met S in accordance with the stepwise approach of WHO. Met S prevalence was determined from the anthropometric measurements and biochemical estimations by using IDF and NCEP ATP -III definitions. Results and discussion: The overall prevalence of Met S in adults, ≥ 20 years was estimated to be 16.7% (IDF criteria) and 10.5 % (NCEP ATPIII criteria). The prevalence rate gradually increased as on aging with a decline in the old age. The prevalence rate was observed to be the highest among the 45-54 years age category in both men and women (Met S IDF=32.5% and Met S ATP III=20.7%). This might be a result of an age-related variation in different components of the Met S cluster such as WC, HDL-C and TG. Generally, women of the study population were more prone to Met S as per both definitions (Met S-IDF: Men=11%, Women=21.7% and Met S ATP III: Men=6%, Women =14.5%). Further, within the Met S category majority were females (Met S IDF-68.6% and Met S ATP- III-73.2%). Among the occupational categories house wives were at more risk of Met S (Met S IDF: 31.1% and Met S ATP III: 19.8%) and might be because of insufficient physical exercise, child delivery /menopause associated changes, the general reclining nature. No significant association was found between income, educational status and Met S. Logistic regression analysis revealed that sex( female), age (45-54 years), occupation ( house wife category), are directly associated with the development of Met S according to IDF as well as ATP-III criteria. Conclusions: The overall prevalence of Met S in Jimma Town was estimated to be 16.7% and 10.5 % by IDF and NCEP ATP III criteria respectively. Prevalence increases with age with a slight decline in the old age population; middle aged individuals (45-54 years age category) were at more risk; women were at greater risk compared to men in all age groups.
OlloBot - Towards A Text-Based Arabic Health Conversational Agent: Evaluation and Results

We introduce OlloBot, an Arabic conversational agent that assists physicians and supports patients with the care process. It doesn’t replace the physicians, instead provides health tracking and support and assists physicians with the care delivery through a conversation medium. The current model comprises healthy diet, physical activity, mental health, in addition to food logging. Not only OlloBot tracks user daily food, it also offers useful tips for healthier living. We will discuss the design, development and testing of OlloBot, and highlight the findings and limitations arose from the testing.
Automatic Propbank Generation for Turkish

Semantic role labeling (SRL) is an important task for understanding natural languages, where the objective is to analyse propositions expressed by the verb and to identify each word that bears a semantic role. It provides an extensive dataset to enhance NLP applications such as information retrieval, machine translation, information extraction, and question answering. However, creating SRL models are difficult. Even in some languages, it is infeasible to create SRL models that have predicate-argument structure due to lack of linguistic resources. In this paper, we present our method to create an automatic Turkish PropBank by exploiting parallel data from the translated sentences of English PropBank. Experiments show that our method gives promising results.