Miracle-dz/UniFAR-FTUs huggingface dataset:
Big Data and Deep Learning Models for Automatic Dependent Surveillance Broadcast ( ADS-B )

ADS-B functions with satellite (GPS) rather than radar technology to more accurately observe and track air traffic. Aircraft equipped with an ADS-B Out transmitter sends position, altitude, heading, ground speed, vertical speed, call sign, and other aircraft information to a network of ground stations that relays the information to air traffic controllers and other aircraft. Our work in progress applies various big data tools and deep learning models such as convolutional neural networks to process and use the ADS-B big data to predict if a flight is commercial or military.
Current Status and Prospects of Health-Related Sensing Technology in Wearable Devices

The healthcare-related functions of wearable devices are very useful for continuous monitoring of biological information. Wearable devices equipped with communication function can be used for additional healthcare services. Among the wearable devices, the wristband type is most suitable for acquiring biological signals, and the wear preference of the user is high, so it is highly likely to be used more in the future. In this paper, the health-related functions of wristband were investigated and the technical limitations and prospects were also reviewed. Most current wristband-type devices are equipped with the combination of accelerometer, optical sensor, and electrodes for their health functions, and continuously measured data are expanding the possibility of discovering new medical meanings. The blood pressure measurement function without using cuff is the most useful and expected function among the health-related functions expected to be mounted on the wrist wearable device, in spite of its technical limits and difficulties.
Automated Statistical and Morphological Based Gridding Methods for Noisy Microarray Image Processing

DNA microarray technology is widely used by genetics to diagnose diseases through gene expression. The gene expression process consists of three stages: gridding, segmentation and quantification. The gridding stage is necessary to find areas in the image that contain one spot using grid lines. Also, analyzing microarray images is highly affected by image preprocessing. There are various levels of image preprocessing algorithms that require a certain level of user intervention for accurately gridding the microarray images. Grid alignment techniques can be used in terms of automation such as manual, semi-automated and fully automated. In this paper, two efficient and simple gridding algorithms were used; Statistics-Based Gridding algorithm and Morphology-Based Gridding algorithm. The first algorithm is fully automated while the second algorithm is semi-automated. Also, since noise represents a challenge in analyzing microarray images, two different noisy microarray images (high and low quality images) were used. Results obtained by applying the two gridding algorithms were compared revealing that the Statistics-Based Gridding algorithm provided higher accuracy ranges from 94% to 95.5% compared to Morphology-Based Gridding algorithm from 80% to 93.7%. The two algorithms were applied to a number of microarray images; the effect of applying preprocessing step to noisy microarray images before applying the gridding stage was also investigated. Results indicated that applying the preprocessing step before the gridding stage provided higher accuracy ranges compared to results obtained without applying the preprocessing step.
Deep convolutional neural network architecture for urban traffic flow estimation

Road traffic density estimation can be very helpful for the successful deployment of intelligent Transportation systems. In this paper, we introduce a deep convolutional neural network (DCNN) based method that learns traffic density from pre-labeled images in order to estimate the traffic flow density in highways. Our method classifies the traffic flow density into three different states: light, medium and heavy. A standard database of real videos from Seattle roads was used to develop our proposed approach. The cross-validation and the class activation mapping techniques were employed in this work, in order to evaluate the performance of our method. The results show that our model outperformed all the existing conventional methods by reaching the highest accuracy of 99,62%.
Histological assessment of the Effects of Pyrethroidsinsecticide Morteinon the Lungs of Adult WistarRats

Introduction: Pyrethroids insecticideMortein (M) composed of allethrin, imiprothrin and phenothrin is commonly used in the annihilation of household insects in Nigeria. These chemicals have been associated with the development of some respiratory diseases. This study was carried out to assess the histological effects of Pyrethroids insecticide “M” on the lungs of adult Wistar rats. Materials and Methods: twenty five healthy adult Wistar rats of both sexes were acquired from the animal house of Faculty of Basic Medical Sciences, Delta State University Abraka and used for the experiment. The rats were randomly assigned into five groups. Groups’ I-III was orally given a sub-lethal dose of 2250mg/kg/bw of M for 7, 21 and 40 days respectively, IV was given tap water as placebo while V was given olive oil. At the end of the experiment, the rats were sacrificed, lungs harvested and fixed in 10% formal saline. The lungs tissues were processed for histology and the slides were microscopically analyzed for histological changes across the groups. Results and Discussion: all the treated lung tissue sections showed similar histological architecture differing from those of the control groups. There was thickening of the connective tissue stroma, congestion of intervening blood vessels, presence of mixed inflammatory cells infiltrates and lung injuries. These features varied based on the period of treatment suggesting time-dependency. Conclusion: short to long exposure of Wistar rats to sub-lethal dose of M insecticide caused an acute to chronic lung injuries which is detrimental to their health.
A Learning Control System for Rapid Position Control with Obstacle Consideration for Aerial Hovering Vehicles

Aerial hovering vehicles, like helicopters, has the ability of remaining stationary while on air, which is useful for maintaining its position at a certain location for a period of time. Operating a rapid hovering maneuvers while avoiding obstacles is difficult and mostly depends on the skills of an expert operator. In this research, we develop a control system that learns to form a control knowledge that can perform rapid position control while considering obstacles. The system works by manipulating angular orientation of the vehicles for performing a rapid movement, while considering existing obstacles in an operation environment. The effectiveness of the system was evaluated and proven in series of simulations. I. I NTRODUCTION Aerial hovering vehicles, such as helicopters, has been useful in various applications. One of the useful application of such vehicles is in operations that needs the vehicle to remain stationary on air, hovering and maintaining its position at a certain location over a period of time. UAV that has such characteristic has been beneficial in many operation, particularly in case of observation, due to its smaller size. Operating rapid position control with hovering maneuvers around obstacles is difficult without certain interruption when making control decision. The effectiveness of such control decisions are mostly depending on the skill of an expert operator. An expert operator develops his own control knowledge from training and experience to operate such maneuvers around existing obstacles [1]. If such control knowledge can be learned by a control system autonomously, dependency on a skilled human operators can be reduce, while having possible application on developing an autonomous UAV. Previously, we have developed a learning-based control system that can perform rapid position control by manipulating a hovering UAV’s angular orientation while hovering at a certain altitude, and applied on an inverted pendulum system [2]. In this research, we develop a learning-based control system that learns the necessary control knowledge for aerial hovering vehicles, that can learn to operate the rapid position control, around obstacles, towards an assigned target position. Reinforcement Learning is applied to create control knowledges for manipulating angular orientation in order to perform a rapid position control around obstacles towards a target position. Simulations were constructed to confirm the effectiveness of the system. Control System Flight Control Se! ng • Set Flight Angular Control Reinforcement Learning Object Target Arrangement •Set Target state for control Target state Task (from human) Knowledge of Flight Angular Orienta"on Target Angles State evaluator Obstacles informa"on (from sensors) Control Part Learning Part PD Controller u
Single Stage Solar PV Array Fed Field Oriented Controlled Induction Motor Drive for Water Pump

This paper deals with three phase induction motor drive (IMD) using field oriented control (FOC) for water pumping fed by solar PV array. It is includes solar PV array, a voltage source inverter (VSI) and a motor-pump assembly. The aim is to extract maximum power from the PV array as well as control the motor in rotor reference frame. The soft start feature is used for the initial start-up of pump. For extraction of maximum power, an adaptive incremental conductance method is used. The tangent of the power versus voltage curve is tracked for good estimation of maximum power point(MPP). The vector control provides smooth startup and starting current is reduced. Proposed system is designed and simulated in MATLAB/Simulink software. Simulated results are demonstrated for verification of the system.
Build and they will come ? : Access to health facilities and maternal care usage in rural Ethiopia

This paper investigates the impacts of an ambitious program to improve access to health facilities in rural Ethiopia. In about a decade the government of Ethiopia managed to construct and staff health posts in all of the rural communities. The program was primarily geared towards provision of mother-child health services. This was motivated by the fact that even at the turn of the 21 century, ninety percent of deliveries in the country were not attended by skilled practitioners, contributing to a high maternal mortality ratio. I employ the mother fixed effects model to study the impact of the program on use of maternal care and incidence of pregnancy complications at birth. To my knowledge this is the only rigorous analysis of the Ethiopia program that takes into account the time invariant unobserved heterogeneity at the individual level. The findings suggest that the program led to an increase in utilization of antenatal care (13 percent for each additional facility). I do not find any impact on the usage of the facilities for deliveries or use of skilled birth attendants. I also do not find a reduction in incidences of pregnancy complications at birth. At the conclusion I point to findings of other descriptive studies which, together with mine, suggest that the main barrier to utilization of maternal care may lie in the culture and norms surrounding birth practices in rural Ethiopia. JEL classification: I10 ; I18 ; D12
Motor Imagery EEG Classification Method using EMD and FFT

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be used for a number of purposes in a variety of industries, such as to replace body parts like hands and feet or to improve user convenience. In this paper, we propose a method to decompose and extract motor imagery EEG signal using Empirical Mode Decomposition (EMD) and Fast Fourier Transforms (FFT). The EEG signal classification consists of the following three steps. First, during signal decomposition, the EMD is used to generate Intrinsic Mode Functions (IMFs) from the EEG signal. Then during feature extraction, the power spectral density (PSD) is used to identify the frequency band of the IMFs generated. The FFT is used to extract the features for motor imagery from an IMF that includes mu rhythm. Finally, during classification, the Support Vector Machine (SVM) is used to classify the features of the motor imagery EEG signal. 10-fold cross-validation was then used to estimate the generalization capability of the given classifier. , and the results show that the proposed method has an accuracy of 84.50% which is higher than that of other methods.
A SAS macro for generating letter displays of pairwise mean comparisons

Data from many experiments are routinely subjected to analysis of variance, followed by a multiple comparison of means. A popular way to present the result of mean comparisons is by attaching superscripted letters to the means, with a common letter on two means indicating that they are not significantly different. When the standard error of a difference is not constant, the algorithm traditionally used for generating such displays may fail to represent all significant differences. This paper reports on a so-called insert-and-absorb algorithm that is guaranteed to always truthfully represent all significant differences. This algorithm was implemented in a SAS macro %MULT. Its usage is illustrated using three examples.
The effectiveness of linking instruction on NNS speech perception and production

Despite the prevalence of connected speech processes in spoken English and their importance for communication, there has been relatively little research on them in the field of second language learning. This study investigated the effectiveness of using two methods for teaching linking to NNSs of English. Audio-visual (AV) feedback, integrating both audio and electronic visual feedback, and audio-only (AO) feedback were employed in the development of online materials to help improve learners’ perception and production of consonant-to-vowel linking such as g e and vowel-to-vowel linking such as see t. The long-term effectiveness of the instructional materials was also examined to see whether learners were able to retain improvement beyond the training period. In addition, the study explored whether improvement can transfer to novel contexts. The influence of high frequency and low frequency words on linking production was also investigated. Finally, students’ perceptions of the use of AV and AO feedback were reported to guide future implementation of the materials. Forty-five learners of English participated in the study with 15 students in each of the two experimental groups and the control group. A pretest, posttest and delayed posttest design was implemented to answer the research questions of the study. Students’ dictation of old and new sentences and their audio recordings of two read texts were analyzed. Qualitative data consisted of students’ answers to a post-training questionnaire. Findings indicate that both types of training were effective in improving learners’ perception and production of linking immediately and one month after training. Learners were also able to transfer the gained improvement to novel contexts. In addition, it was found that students’ improvement of linking high frequency words was significantly greater than that of low frequency words, which were more difficult to link. The use of audio-visual training with waveforms resulted in better improvement in linking perception and production as well as more positive learner feedback than an audio-only training. The findings of the study hold a number of pedagogical implications for language teachers, material developers, and those interested in researching connected speech and the use of technology for language learning.
FPGA IMPLEMENATION OF HIGH SPEED AND LOW POWER CARRY SAVE ADDER VS

In recent years, the high speed adders play a vital role in any digital design of signal processing systems by performing much needed operations like subtraction, multiplication, and division, etc., along with the basic addition. Carry look-ahead adder (CLA), carry select adder (CSeA), carry-save adder (CSA) and carry-skip adder are the most popular high speed adders in digital signal processor and computer data path applications. This study demonstrates the efficiency of a carry-save adder with multi-operand addition using different majority voting circuit implementations in the place of carry out of a full adder. The carry is saved and propagated down to the next stage rather than passing within the same stage like in ripple carry adder (RCA). But the final sum is calculated with the help of carry ripple adder by propagating the carries appropriately. The final sum calculation stage in a carry save adder is implemented in this study using ripple carry adder (CSA-RCA), carry look-ahead adder (CSA-CLA) and carry select adder (CSA-CSeA) to compare better power, area and delay results. Addition of four 4-bit numbers, four 8-bit numbers, four 16-bit numbers, four 32-bit numbers and four 64-bit numbers are implemented on Altera EP4CE115F29C7 FPGA device using Quartus II synthesis software tool. Simulation results of adding four 32-bit numbers show that CSA-CSeA topology offers lower critical path delay as 34.823 ns compared with CSA-RCA topology (63.120 ns) and CSA-CLA topology (52.475 ns) at the expense of 221 logic elements (LEs) which is higher; the CSA-RCA topology and CSA-CLA topologies have only 192 LEs and 193 LEs respectively. It is also shown that the CSA-CLA topology has lower power dissipation as 170.54 mW compared with CLA-RCA (171.36 mW) and CSA-CSeA (174.68 mW).
Rapid Identification of Pathogens from Positive Blood Cultures by Multiplex PCR using the FilmArray System

Anne J. Blaschke, MD, PhD1,*, Caroline Heyrend, BSc1,2, Carrie L. Byington, MD1, Mark A. Fisher, PhD3,4, Elizabeth Barker, BSc2, Nicholas F. Garrone, PhD2, Stephanie A. Thatcher, MS2, Andrew T. Pavia, MD1, Trenda Barney, MT5, Garrison D. Alger, MLS5, Judy A. Daly, PhD3,5, Kirk M. Ririe, BSc2, Irene Ota, PhD2, and Mark A. Poritz, PhD2 1University of Utah, Department of Pediatrics, Salt Lake City, Utah 2Idaho Technology, Inc., Salt Lake City, Utah 3Department of Pathology, University of Utah, Salt Lake City, Utah 4Associated Regional and University Pathologists, Salt Lake City, Utah 5Primary Children’s Medical Center Microbiology Laboratory, Salt Lake City, UT
Plant spacing and mulching together affect the phenology and yield of tomato crop.

Field trials were carried out in the farm of the University of Agriculture, Peshawar Pakistan in the years 2012 and 2013 in order to investigate the effect of line spacings, mulching materials, and herbicides on phenology of the tomato crop. The design of the experiments was RCBD, with split-plot arrangement. Three various line spacings viz. 40, 60, and 80 cm between the adjacent tomato lines were maintained in main plots; whereas the mulches and herbicides were arranged in the sub-plots. The mulches of polyethylene (white), polyethylene (black), wheat straw at the rate of 1.0 kg, saw dust at 1.0 kg, and paper mulch at the rate of 0.5 kg m-2, and the herbicides of Puma super 7.5 EW (fenoxaprop-p-ethyl) at the rate of 2 kg, Dual Gold 960 EC (s-metolachlor) at 1.5 kg, and Stomp 330 EC (pendimethalin) at the rate of 1.44 kg a.i ha1, a hand weeding (weed free) and a weedy check (control) treatments were used. The data recording parameters were fresh weed biomass (kg ha-1), days to flowering, and fruiting, leaf area (LA), leaf area index (LAI), and yield of tomato fruit (t ha-1). All the treatments significantly affected the studied parameters. The line spacing of 40 cm among the tomato lines had the lowest weed biomass and that of 80 cm spacing had the highest weed biomass. The number of days to first flowering and first fruiting was declined in 40 cm line spacing and enhanced in 80 cm line spacing plots. The leaf area and leaf area index were the uppermost in 40 cm line spacing and lower most in 80 cm spacing. Finally, the highest total fruit yields were 12.12 and 15.15 t ha-1 during 2012 and 2013, respectively in 60 cm line spacing. Among the subplot treatments, the control plots showed maximum weed biomass while minimum in the weed free plots. However, number of days to flowering and fruiting were highest in hand weeded treatments followed by pendimethalin and polyethylene-white plots which were statistically at par in both the years. Similarly, the LA, LAI and total fruit yield of tomato were best in the plots of hand weeding in comparison with the lowest values in weedy check plots. In conclusion, line spacing of 60 cm, the application of pendimethalin herbicide as pre-emergence, once manual weeding, and black plastic mulch proved to be the best integrated weed management strategy for enhancing the phenology of tomato crop.
Determining Language for Human to Robot Navigational Commands

Robots that collaborate with humans must be equipped with interfaces that support deeper and richer interaction. Such interfaces may involve the understanding and production of speech. This calls for an understanding of speech and natural language in various contexts. The present study investigates the preferred words and phrases used in giving directions to a robot teammate in an intelligence and surveillance reconnaissance (ISR) mission. Results indicate that participants may have had a perceptual mental model that influenced choice of words or phrases. Recommendations for future research include examining the factors that affect development of schemas when interacting with robots.
Knee Injuries in the Growing Athlete

Sports-related injuries in young athletes are increasing. Approximately 95% of acute knee injuries with a hemarthrosis have evidence of an intraarticular injury (eg, anterior cruciate ligament tear, meniscus tear, patellar dislocation). Magnetic resonance imaging (MRI) should be the next step in an athlete with a combination of an acute knee injury, a hemarthrosis, or new onset knee effusion and negative radiographs. Non-operative treatment of complete anterior cruciate ligament (ACL) tears in young athletes results in significant increases in irreparable injuries to other structures in the knee. ACL reconstructions can be done safely in growing athletes and should be growthplate–respecting surgeries.
A PHENOMENOLOGICAL INQUIRY INTO ENGINEERS ' MOTIVATION TO LEAD

The present paper describes a qualitative (phenomenological) study of six engineering managers who were, within the past two years, promoted into management roles at a medical device manufacturer in Southern California. The objective of this study was to understand their motivation to lead. Each engineer participated in an interview which was approximately 30 minutes long, semi-structured, and guided by an eleven-question interview guide based on the literature. The transcriptions of each interview were then analyzed using first and second cycle coding. In all, eight themes emerged to explain why these engineers accepted leadership roles: (a) some simply enjoy leading, (b) some felt a social obligation, (c) some were motivated by values higher than mere money, (d) some were motivated by a sense of benevolence, (e) some were motivated by observing other leaders in the organization, (f) some found additional motivation to lead as they experienced success as managers, (g) some were motivated by their engineering skills, and, most significantly, (h) some found motivation to lead in a desire to teach and mentor. The study contributes to the literature by identifying a previously unrecognized motivation to lead (motivation to teach and mentor). Because a qualitative design was used, this study demonstrates only that this motivation to lead was present in these six engineers; no claim regarding generalizability is made. Additionally, three tentative practical implications for training potential engineering managers are described. However, a follow-on quantitative study is required before generalizing these findings.