Miracle-dz/UniFAR-FTUs huggingface dataset:
Ina May's Guide to Childbirth

Getting the screenshots prepared is a good approach that might time savings. But having screenshots already prepared in addition to callouts, explanations, and annotations is an excellent approach which you will save much longer. More than likely, you've all that stuff prepared inside your ina may s guide to childbirth but it's not very polite to deal to the person: "Read that fantastic manual". User may do not know the location where the manual is on the PC as well as on what page is the looked-for solution located. On the other hand, you may also have zero time and energy to manually cut particular pages with screenshots and related instructions from your manual also to attach them to each support message.
Prediction of Air Quality Index by Extreme Learning Machines

In this study, it is aimed to predict the Air Quality Index (AQI) by the Extreme Learning Machines (ELM) algorithm. For this purpose, six parameters have been selected which can affect the AQI. These are temperature, humidity, pressure, wind speed, PM10 and SO2 respectively. First of all, “Forecast Sheet” application that presented in the Excel environment and the correlation analysis were used to determine the relationship status between these six parameters and AQI. Thus, the linearequations between each parameter and AQI were determined. Also, R values were determined for relationrate. Then, a training data set was created. 120 values belong to March, June, September and December of 2017, which includes six parameters, was determined as training data. AQI values of these six parameters were obtained by “AQI Calculator” application. The obtained AQI values were classified mathematically from 1 to 6 considering the AQI limits determined by National Air Quality Monitoring Network of Ministry of Environment and Urbanization. The first 12 day parameters of January, March, September and December of 2018 year are determined as test data. Then, ELM used 108 of the 120 generated data as training data and the remaining 12 of the data as test data. 10-fold cross validation method was used for the reliability of the result. Sine, sigmoid and hardlimit activation codes were used for classification. 6 different neurons were studied. As a result of cross-validation, the activation code and the number of neurons that achieved the highest success were applied to the actual test data. Finally, the classification values predicted by ELM and the mathematical classification values were compared. Thus, the success of the ELM was measured.
Factors associated with patient and health service delays in the management of TB in Central Equatoria State in 2008.

Background: Tuberculosis (TB) is caused by the bacterium Mycobacterium tuberculosis . Delays in diagnosis and treatment increase morbidity and mortality from tuberculosis, and the risk of transmission in the community. Methods: We conducted a cross-sectional survey at three TB treatment centres in Central Equatoria State, South Sudan. Smearpositive TB patients were enrolled in three study sites and interviewed within two days of beginning treatment using a structured questionnaire. This study was conducted to investigate factors that affect patient and health service delays in diagnosis and treatment of pulmonary tuberculosis (PTB) in Central Equatoria State. Results: 129 patients were enrolled in the study. The median patient’s, health provider’s and total pre-treatment periods are 4, 10 and 16 weeks respectively. The health care provider delay for patient diagnosis and start of treatment had the greatest contribution to overall total pre-treatment delay Conclusions and recommendations: In Central Equatoria State, health care provider delay was the most frequent type of delay observed and was a major contributor to the overall total delay. This study indicated the need for strengthening the capacity of health workers for early detection and referral of TB patients. Further research is needed to identify reasons for health provider delay. Keywords: Mycobacterium tuberculosis , health service delays, South Sudan
Fairness Based Power Allocation optimization of Cooperative NOMA with SWIPT Network

In this paper, a power allocation problem for cooperative communication network with non-orthogonal multiple access and simultaneous wireless power transmission (C-NOMA-SWIPT) is considered. We both consider the half duplex cooperative NOMA with SWIPT and the full duplex cooperative NOMA with SWIPT network. There are two users in each cooperative cluster. The strong user receives the information and energy from BS simultaneous using power splitting, and then decode and forward relay data for the weak user. On the purpose of the fairness of the data rate, we formulated the optimization problem to maximize the minimum achievable user data rate in each NOMA with SWIPT user cluster, which is a non-convex quadratic programing problem (NP-hard). We design a two step subproblem (The power allocation and the energy harvest) strategy to optimize the system model with Lagrange Dual Method and the Interior Point Method. In addition, a hybrid half/full-duplex cooperative NOMA scheme was proposed to dynamically switches between the HD-CNOMA-SWIPT and FD-CNOMA-SWIPT. Numerical results demonstrate that our proposed scheme can achieve a better performance compared with conventional schemes.
Quality of life in elderly bladder cancer patients following cystectomy and urinary diversion

This review aims to provide information about quality of life in elderly patients who have undergone radical cystectomy and bladder replacement for muscle-invasive bladder cancer, focusing on the best instruments for evaluation. Among patients over 75 years of age, bladder cancer is the fifth leading cause of death. Radical cystectomy is the standard treatment for muscle-invasive bladder cancer and it is also considered safe in the elderly population. Quality of life is an essential aspect following radical cystectomy and bladder replacement owing to its impact on patients’ satisfaction with body image and urinary, sexual and social functioning. Furthermore, the authors suggest that there are no significant differences between different methods of urinary diversion in the elderly.
Common Pulse Retrieval Algorithm: A Fast and Universal Method to Retrieve Ultrashort Pulses

Measuring the temporal shape of ultrashort laser pulses is an important standard task in ultrafast optics. A multitude of methods has been devised for that purpose [1]. However, in many cases a specific, iterative algorithm has to be applied to retrieve the pulse shape from the measurement. In this work we present a common pulse retrieval algorithm (COPRA) that can be universally applied to such pulse measurement methods and compares favorably in terms of speed and accuracy to existing approaches [2].
Virtual Heading Directions Based Adaptability Analysis Method for Gravity-aided Navigation

For the current matching area selection method, the matching suitability in all directions of the area is not considered, and the matching is easy to mislead in the non-matching direction. An area adaptability analysis method based on virtual heading directions is proposed. The new calculation formulas of the gravity characteristic parameters are determined according to the virtual heading directions. And the analytic hierarchy process is used to determine the unified weights of each parameter in different virtual heading directions. Parameters are fused by linear weighted to obtain the comprehensive evaluation index of matching. The experimental simulation results show that the mislead rate of the selected matching area is lower, and the guidance on the direction matching of the actual navigation is more instructive.
Absent without leave – can we predict those who go AWOL?

Abstract Objective: To quantify the number of patients absent without leave (AWOL) from an Irish psychiatric hospital in one year and to examine the clinical and demographic data of this group of patients. Method: This was a retrospective study of all patients reported as AWOL from St Brendan's Hospital in 1991. The following information was obtained from the patients' case notes: age, sex, legal status, marital status, psychiatric diagnosis, number of previous admissions, past history of absconding, length and outcome of absence. Their clinical and demographic variables were compared with a control group of 214 patients (non-absconders) randomly selected from the hospital admissions over a 12 month period. Results: A total of 48 patients absconded. The majority were male, single, compulsorily detained, with a past history of absconding. The most common psychiatric diagnoses were schizophrenia (32%), personality disorder (23%) and alcohol dependence syndrome (17%). While absent, three patients had a history of suicidal behaviour and four became involved in criminal activity. Compulsory status was statistically significant when compared to the control group. Conclusion: The characteristics of our group of absconders are similar to those in previous studies apart from the increased prevalence of personality disorder and alcohol dependence syndrome. We discuss the risks associated with absconding and recommend close follow-up of this group of patients.
Assessing comorbidity in older adults using prescription claims data

Objectives Comorbidity is an important confounder in many studies. Since self‐reports, medical records abstraction and medical claims databases present problems of practicability, comorbidity scores can be derived more conveniently from prescription claims databases. Existing scores were developed for people aged 18 years or older and use costs, hospitalizations or death as outcomes of interest. Therefore, they tend to be of limited value when studying disability in older adults. Our objective was to develop the Chronic Disease Score for Disability in Older Adults (CDS‐DOA) based on outpatient prescription claims data from community pharmacies.
Matching Drug Treatment to Those in Need: An Analysis of Correctional Service Delivery in Illinois and Ohio

An ever-increasing number of prisoners need substance abuse treatment, yet state and federal governments continue to cut funding for prison programs. Given this increased need coupled with reduced service availability, two crucial questions arise: (1) Are limited drug treatment resources being targeted to those with the greatest need? and, (2) Is the most being made of these scarce resources by providing continuity of care? Through an analysis of pre- and post-release data collected from 576 prisoners in Illinois and Ohio, this study examines the degree to which prisoners with self-reported drug problems receive in-prison substance abuse treatment services, and then receive post-release treatment. The study also identifies several individual-level predictors of successful treatment matching and continuity. Although the study finds some evidence of treatment matching, its extent is far less than desirable, and there is minimal continuity of treatment from prison to the community. Collectively, the results suggest that differences in offender motivation and readiness for treatment as well as deficiencies in correctional service delivery play a major role in the success of treatment matching. Suggestions are offered for improvements in correctional policy and practice and for future research on these topics.
Improved Multi-Agent Deep Deterministic Policy Gradient for Path Planning-Based Crowd Simulation

Deep reinforcement learning (DRL) has been proved to be more suitable than reinforcement learning for path planning in large-scale scenarios. In order to more effectively complete the DRL-based collaborative path planning in crowd evacuation, it is necessary to consider the space expansion problem brought by the increase of the number of agents. In addition, it is often faced with complicated circumstances, such as exit selection and congestion in crowd evacuation. However, few existing works have integrated these two aspects jointly. To solve this problem, we propose a planning approach for crowd evacuation based on the improved DRL algorithm, which will improve evacuation efficiency for large-scale crowd path planning. First, we propose a framework of congestion detection-based multi-agent reinforcement learning, the framework divides the crowd into leaders and followers and simulates leaders with a multi-agent system, it considers the congestion detection area is set up to evaluate the degree of congestion at each exit. Next, under the specification of this framework, we propose the improved Multi-Agent Deep Deterministic Policy Gradient (IMADDPG) algorithm, which adds the mean field network to maximize the returns of other agents, enables all agents to maximize the performance of a collaborative planning task in our training period. Then, we implement the hierarchical path planning method, which upper layer is based on the IMADDPG algorithm to solve the global path, and lower layer uses the reciprocal velocity obstacles method to avoid collisions in crowds. Finally, we simulate the proposed method with the crowd simulation system. The experimental results show the effectiveness of our method.
Synchronization of a Class of Discrete-Time Nonlinear Singularly Perturbed Complex Networks†

In this paper, the problem of synchronization is investigated for a class of discrete-time nonlinear singularly perturbed complex networks (SPCNs). A slow sampling discrete-time nonlinear SPCN model is first devised, which includes the slow and the fast states as well as the general sector-like nonlinear functions. By utilizing the Kronecker product, a new Lyapunov function dependent on singular perturbation parameter (SPP) is constructed. A sufficient condition in terms of linear matrix inequalities (LMIs) is derived under which the discrete-time nonlinear SPCN is globally asymptotically synchronized. When these LMIs have feasible solutions, the global asymptotic synchronization is ensured and the upper bound of the SPP is evaluated. A numerical example is given to illustrate the effectiveness of our results.
A First Principle Insight into Defect Assisted Contact Engineering at the Metal-Graphene and Metal-Phosphorene Interfaces

In this work we have studied bonding nature of Graphene and Phosphorene with metal (Pd) followed by carrier transport behavior and contact resistance engineering across the metal-Graphene and the metal-Phosphorene interfaces using Density Functional Theory (DFT) and Non Equilibrium Green’s Function (NEGF) computational methods. We have studied, how carrier transports at the interfaces is limited by van der Waals (vdW) gap across the interfaces and how the gap can be reduced by creating the Carbon vacancy (defect engineering) at the Graphene-Palladium interface. We have seen that the defect engineering enhances the Carbon-Palladium bond at the interface which reduces the van der Walls (vdW) gap, hence contact resistance due to corresponding reduction in the tunneling barrier width at the interface. We have also studied that the defect engineering (Phosphorous vacancy) at the Phosphorene-Palladium interface is not effective as Graphene-Palladium interface because it has less interfacial (vdW) gap than Graphene-Palladium interface intrinsically.
Different Test Site Analysis of Radiated Field Measurements of a Complex EUT

The goal of the COST IC1407 Action is to investigate the radiated emission characteristics in densely integrated technologies. One of the promising techniques for this application is the Near Field Scanning (NFS), as it allows to measure the near field intensity with a very high resolution, which is especially useful for tracking down critical components, even on a board level. The results can additionally be translated to the far field equivalents, comparable with the conventional emissions measurement standards. In this study, we investigate this technique with respect to another, commonly recognized emission measurement techniques, such as semi-anechoic chamber (SAC), fully anechoic room (FAR), gigahertz transverse-electromagnetic (GTEM) cell, and reverberation chamber (RC). As opposed to classical round-robin tests, where a simple reference radiator is measured, in order to increase the harshness of our tests, we used as a test sample with very complex, unpredictable and unstable behavior, an Intel Galileo board- a perfect example of real equipment.
Pulmonary Image Classification Based on Inception-v3 Transfer Learning Model

Chest X-ray film is the most widely used and common method of clinical examination for pulmonary nodules. However, the number of radiologists obviously cannot keep up with this outburst due to the sharp increase in the number of pulmonary diseases, which increases the rate of missed diagnosis and misdiagnosis. The method based on deep learning is the most appropriate way to deal with such problems so far. The main research in this paper was using inception-v3 transfer learning model to classify pulmonary images, and finally to get a practical and feasible computer-aided diagnostic model. The computer-aided diagnostic model could improve the accuracy and rapidity of doctors in the diagnosis of thoracic diseases. In this experiment, we augmented the data of pulmonary images, then used the fine-tuned Inception-v3 model based on transfer learning to extract features automatically, and used different classifiers (Softmax, Logistic, SVM) to classify the pulmonary images. Finally, it was compared with various models based on the original Deep Convolution Neural Network (DCNN) model. The experiment proved that the experiment based on transfer learning was meaningful for pulmonary image classification. The highest sensitivity and specificity are 95.41% and 80.09% respectively in the experiment, and the better pulmonary image classification performance was obtained than other methods.
Performance Analysis of Different Level Modular Multilevel Converters

This paper deals with the comparison of the different output levels of three phase modular multilevel converter [MMC]. The analysis of three level, five level and nine level MMC is performed by considering THD (total harmonic distortion) of output voltage and current with 2N+1 level PD-PWM switching strategy. Simulation of all levels of MMC is done in MATLAB/SIMULINK and the results are presented. It is found that THD of voltage and current are less in 9 level MMC. In addition, prototype of the three level MMC with redundant switching is developed in the laboratory and results are presented.
Maternofetal outcome in obstructed labour in a tertiary care hospital

BACKGROUND-Obstructed labour is still a major cause of maternal morbidity and mortality and adverse outcome of newborn in low income countries . It is the leading cause of hospitalization, comprising of 39% of all obstetric patients in developing countries OBJECTIVES: To study frequency, causes outcome and complications of obstructed labour. Materials and methods: 402 patients admitted with feature of obstructed labour were studied. Detailed history included sociodemographic factors, obstetric history, features of obstruction, intrapartum events were recorded. Condition of patients, mode of delivery, preoperative and postoperative complications, maternal and fetal outcomes was recorded . RESULTS: A total of 23381 deliveries were conducted during one year, 402 cases of obstructed labour were found with incidence of 1.71%. 86.5 % of the patients were from rural areas and 78.1 % of patients were unbooked.73. 3% patients were primigravida, 15.67% were multigravida and 10.94% were grandmultipara. The commonest cause of obstructed labour was cephalopelvic disproportion (55%). Other causes were Malposition (22.9%), Malpresentation (17.9%), fetal congenital anomaly (1.38), myoma (0.83%), and non dilatation of cervix (0.83%). The commonest mode of delivery was cesarean section (83.8%). Instrumental deliveries were conducted in 10.5% of cases. Destructive procedures are discouraged in out set up. Rupture uterus was seen in 16 cases (4.16%) out of which repair was done in 11 cases and subtotal hysterectomy was performed in 5 patients. The common maternal complications were sepsis [pyrexia (15.1%), wound infections (12.8%),urinary tract infection(7%)], , abdominal distention(11.2%), postpartum hemorrhage (9.7%). Other complications were subinvolution (10%),rupture uterus(4.16%), hysterectomy (2.9%),bladder injury(1.49%). vesicovaginal fistula (1.49%),peritonitis (1.38%),broad ligament hematoma (0.83%),maternal death (0.74%). Perinatal mortality was 107/402 (26.6%),live birth rate 316/402 (78.7%),still birth rate 86/402 (21.3%). Perinatal morbidity was most commonly due to birth asphyxia (28.8%),jaundice(16.9%),septicemia(14.75%), meconium aspiration syndrome (9.9%) CONCLUSION: Obstructed labour is a preventable condition prevalent in developing countries. Improving nutrition, antenatal care, early diagnosis and timely intervention may result in decrease in incidence of morbidity and mortality
Anempirical Analysis of Classification Models for Detection of Fake News Articles

Fake news has been at the centre of the debate raging on how humans consume information in the digital era. The rise in use of social media has made it easier to blur the line between what is and what isn’t a reliable source of information, making this one of the most pressing issues of our time. This paper approaches the issue from a data-oriented perspective by investigating whether automatic computational approaches in NLP and Machine Learning can be used to detect falsehoods in written text. Performance of features like n-grams and word vectors used with five supervised learning techniques in detecting Fake News articles are compared. The impact of certain changes in the parameters of feature extraction on classifier performance are also analysed in this paper.
Benchmark for Kitchen20, a daily life dataset for audio-based human action recognition

This paper introduces a new raw-audio, environmental, kitchen-related, non-vocal dataset to fill what we consider as a gap in the context of audio datasets. Our so-called Kitchen20 dataset is compared to other datasets such as ESC-50 and shown that both datasets can be merged together in what we call ESC-70. A human quantitave appreciation of the audio samples contained in Kitchen20 is provided as well as several machine learning benchmarks on both Kitchen20 and ESC-70.
Design and Optimization of a New 3-DOF Energy Harvester With Single Piezoelectric Stack

This paper presents the design and optimization of a novel piezoelectric energy harvester with a single piezo-stack for harvesting energy from excitations with 3-DOF direction. To scavenge the energy with 3-DOF direction of input force, the proposed energy harvester is composed of a two-stage force amplifier with single piezo-stack, several force transmission mechanisms, and slider mechanisms. Analytical model is established to predict the force amplification ratio of the proposed energy harvester. In order to maximize the output performance of the proposed energy harvester, key parameters of the mechanism are selected and optimally designed. Optimization result indicates that the developed harvester has the merits in terms of high safety factor, large force-amplification ratio, and compact size.
A String of Tethered Drones - System Dynamics and Control

In this paper we present a novel concept of a tethered-drones system. The system includes an arbitrary number of drones connected serially to an active ground station. The considered drones are of quadrotor type. Utilizing a unique pulley-gimbal mechanism, each drone can freely move along the tether and its position is measured with respect to the ground station without the use of standard onboard inertial sensors. The proposed system can be thought of as a robotic arm where each tether section acts as a variable-length link and each drone is a joint actuator. We model the coupled behavior of the ground station and the string, taking into account an arbitrary number of drones. Then, a controller that combines tools from geometric-control and linear-control is suggested. Finally, the concept is demonstrated using numerical simulations, which also illustrate its potential effectiveness.