Miracle-dz/UniFAR-FTUs huggingface dataset:
ARQ PROTOCOLS SUPPORTING QOS IN EMBEDDED SYSTEMS

Many efforts have been carried out to provide transmission reliability in the history of communication systems. As the demand for real-time applications increased, providing a reliable communication in a timely manner for such applications is strongly desired. Considering timing constraints makes the issue of achieving reliability more difficult. This thesis concentrates on providing reliability for real-time communication in embedded networks by achieving a timing analysis and using the ARQ concept. What is carried out in this thesis is providing retransmission in a real-time manner for embedded networks according to application request. The thesis work focuses on one packet retransmission over a point to point link, but the concept is rich and can be extended to cover application request in real-time embedded networks. Two methods have been fulfilled, and a simulation has been done on the timing analysis focusing on the performance in accepting real-time traffic in the form of separate channels for each application request. The protocol combines ARQ and a scheduling algorithm as a base to support retransmission for hard real-time applications in embedded networks.
Computational Prediction of the Isoform Specificity of Cytochrome P450 Substrates by an Improved Bayesian Method

Cytochrome P450 (CYP) is the most important drug-metabolizing enzyme in human beings. Each CYP isoform is able to metabolize a large number of compounds, and if patients take more than one drugs during the treatment, it is possible that some drugs would be metabolized by the same CYP isoform, leading to potential drug-drug interactions and side effects. Therefore, it is necessary to investigate the isoform specificity of CYP substrates. In this study, we constructed a data set consisting of 10 major CYP isoforms associated with 776 substrates, and used machine learning methods to construct the predictive models based on the features of structural and physicochemical properties of substrates. We also proposed a new method called Improved Bayesian method, which is suitable for small data sets and is able to construct more stable and accurate predictive models compared with other traditional machine learning models. Based on this method, the predictive performance of our method got the accuracy of 86% for the independent test, which was significantly better to the existing models. We believe that our proposed method will facilitate the understanding of drug metabolisms and help the large-scale analysis of drug-drug interactions.
Endoglucanase Activity of Novel Aspergillus sydowii Isolate WIU-01 and Application for Hydrophyte Degradation

Many eutrophic lakes contain rapidly growing hydrophytes. Overgrown biomass is usually me-chanically harvested and thrown away, leading to resource waste and secondary environmental pollution. Microbial degradation is an economically and environmentally friendly approach for managing hydrophytic waste, fuelling the search for efficient biomass degraders. Here, we present isolation and characterization of Aspergillus sydowii WIU-01, a novel cellulolytic fungus. Strain WIU-01 was isolated from air. The degradation rate (29.75 vs. 21.95%) and endoglucanase activi-ty (0.31 vs. 0.16 U mL–1) of the fungus were higher in Canna indica (emergent plant) medium than in Hydrilla verticillata (submerged plant) medium, accordingly. Further, fungal endoglucanase ac-tivity was significantly positively correlated with the degradation rate, neutral detergent fiber con-tent, and acid detergent fiber content of hydrophyte powder. Fungal biomass was significantly negatively correlated with reducing sugar and cellulose content of hydrophyte medium, but was significantly positively correlated with hemicellulose, acid detergent lignin, and ash content of the medium. Collectively, these observations indicate that A. sydowii decomposes emergent and sub-merged plant mass without acid–base sample pretreatment, albeit its endoglucanase activity is rela-tively low. This highlights the role of cellulolytic microorganisms in the natural environment and the notion that the environment can be a source of cellulolytic microorganisms for potential envi-ronmentally friendly applications.
Human Factors and E-Commerce

Many Web sites have been built hastily and without regard for the user. Extensive research on pre-Web user interfaces could have prevented such poor designs. It appears that both Web developers and Web users are less experienced than developers and users of other software applications. One of the most important guidelines provided by the literature is that of testing, which should be mandatory for any site to enable revision and retesting. There are several specific areas of understanding in usability that are also described, along with implications for Web designers. These areas include Fitts’ Law, site organization, difficulties of user search, ill effects of page loading delay, trade-offs between site depth and breadth, options for what is seen by the user in inputs and outputs, and problems with users’ mental models. Tools such as storyboards, checklists, and evaluation models are also described to prevent design errors. Once designers become aware of these guidelines, usability problems, and tools, Web site design quality might begin to catch up to that of systems sold in the mass market.
Crowdsourced Geospatial Data: A Report on the Emerging Phenomena of Crowdsourced and User-Generated Geospatial Data

Abstract : Crowdsourced geospatial data (CGD) is an important emerging trend that will influence future methods for geospatial data production and use. Related to broader developments in user-generated content, CGD involves the participation of end-users, many of who are untrained in the geospatial sciences but have a high degree of interest in geospatial technology. Working collectively, these end-users collect, edit, and produce datasets; create mapping applications, and develop tools for CGD. Crowdsourced geospatial data production is typically an open, lightly controlled process with few constraints, specifications, or quality assurance processes. This sharply contrasts with the less flexible and more controlled authoritative geospatial data production practices of national mapping agencies and businesses. Adoption of CGD and production methods has been a concern, especially to Government organizations, due quality concerns related to differences in production methods. We review CGD projects addressing common geospatial data collection tasks and demonstrating varied approaches to quality control, including hybrid projects that mix crowdsourced geospatial data and tools with authoritative data. The most common methods for quality assessment are summarized along with a comprehensive set of fitness-for-use considerations. Based on this information, lessons learned and future trends are summarized.
Mechanisms of axis-switching and saddle-back velocity profile in laminar and turbulent rectangular jets

Chen, Nan. M.S.M.E, Purdue University, August 2013. Mechanisms of Axis-switching and Saddle-back Velocity Profile in Laminar and Turbulent Rectangular Jets. Major Professor: Huidan (Whitney) Yu. We numerically investigate the underlying physics of two peculiar phenomena, which are axis-switching and saddle-back velocity profile, in both laminar and turbulent rectangular jets using lattice Boltzmann method (LBM). Previously developed computation protocols based on single-relaxation-time (SRT) and multiple-relaxationtime (MRT) lattice Boltzmann equations are utilized to perform direct numerical simulation (DNS) and large eddy simulation (LES) respectively. In the first study, we systematically study the axis-switching behavior in low aspect-ratio (AR), defined as the ratio of width over height, laminar rectangular jets with AR = 1 (square jet), 1.5, 2, 2.5, and 3. Focuses are on various flow properties on transverse planes downstream to investigate the correlation between the streamwise velocity and secondary flow. Three distinct regions of jet development are identified in all the five jets. The 45◦ and 90◦ axis-switching occur in characteristic decay (CD) region consecutively at the early and late stage. The half-width contour (HWC) reveals that 45◦ axis-switching is mainly contributed by the corner effect, whereas the aspect-ratio (elliptic) feature affects the shape of the jet when 45◦ axis-switching occurs. The close examinations of flow pattern and vorticity contour, as well as the correlation between streamwise velocity and vorticity, indicate that 90◦ axis-switching results from boundary effect. Specific flow patterns for 45◦ and 90◦ axis-switching reveal the mechanism of the two types of axis-switching respectively. In the second study we develop an algorithm to generate a turbulent velocity field for the boundary condition at jet inlet. The turbulent velocity field satisfies incompressible continuity equation with prescribed energy spectrum in wave space. Appli-
Macro-magnetic Modeling of the ARL Microelectromechanical System (MEMS) Flux Concentrator

Abstract : Magnetic flux concentrators are soft magnetic materials typically used to focus magnetic field lines in an effort to increase the sensitivity of a magnetic sensor. The use of magnetoresistive sensors, especially given the recent development of magnetic tunnel junctions (MTJ) with magnesium oxide (MgO) barriers exhibiting magnetoresistance values as large as 400%, has created interest in flux concentrators as an avenue to not only increase sensitivity but also mitigate the 1/function noise that is prevalent in these devices at low frequencies. Here we describe the magnetic modeling performed at the U.S. Army Research Laboratory (ARL) that has facilitated the development of the microelectromechanical system (MEMS) flux concentrator, a device that uses magnetic flux concentrators deposited on MEMS structures that modulate low frequency signals at the position of the sensor, essentially shifting the signal of interest to a frequency range in which 1/function noise is much lower. We present magnetic modeling results of various designs, including our current design, focusing on key design elements.
Water quality program elements for space station freedom

A strategy is outlined for the development of water-quality criteria and standards relevant to recycling and monitoring the in-flight water for the Space Station Freedom (SSF). The water-reclamation subsystem of the SSF's ECLSS is described, and the objectives of the water-quality are set forth with attention to contaminants. Quality parameters are listed for potable and hygiene-related water including physical and organic parameters, inorganic constituents, bactericides, and microbial content. Comparisons are made to the quality parameters established for the Shuttle's potable water and to the EPA's current standards. Specific research is required to develop in-flight monitoring techniques for unique SSF contaminants, ECLSS microbial control, and on- and off-line monitoring. After discussing some of the in-flight water-monitoring hardware it is concluded that water reclamation and recycling are necessary and feasible for the SSF.
Multi-Objective Optimization for Speed and Stability of a Sony AIBO Gait

Abstract : Locomotion is a fundamental facet of mobile robotics that many higher level aspects rely on. However, this is not a simple problem for legged robots with many degrees of freedom. For this reason, machine learning techniques have been applied to the domain. Although impressive results have been achieved, there remains a fundamental problem with using most machine learning methods. The learning algorithms usually require a large dataset which is prohibitively hard to collect on an actual robot. Further, learning in simulation has had limited success transitioning to the real world. Also, many learning algorithms optimize for a single fitness function, neglecting many of the effects on other parts of the system. As part of the RoboCup 4-legged league, many researchers have worked on increasing the walking/gait speed of Sony AIBO robots. Recently, the effort shifted from developing a quick gait, to developing a gait that also provides a stable sensing platform. However, to date, optimization of both velocity and camera stability has only occurred using a single fitness function that incorporates the two objectives with a weighting that defines the desired tradeoff between them. However, the true nature of this tradeoff is not understood because the pareto front has never been charted, so this a priori decision is uninformed. This project applies the Nondominated Sorting Genetic Algorithm-II (NSGA-II) to find a pareto set of fast, stable gait parameters. This allows a user to select the best tradeoff between balance and speed for a given application. Three fitness functions are defined: one speed measure and two stability measures. A plot of evolved gaits shows a pareto front that indicates speed and stability are indeed conflicting goals. Interestingly, the results also show that tradeoffs also exist between different measures of stability.
Extension worker as a leader to farmers: Influence of extension leadership competencies and organisational commitment on extension workers' performance in Yemen

Agriculture extension primarily deals with human resource development (HRD) and the transfer of technology and knowledge from agricultural research centres to farmers. Improving human resource development (HRD) within rural community is essential for agriculture and community development. Extension workers are professionals in the extension system responsible for developing individuals in the community. Hence, as the profession of extension work continues to expand, it is necessary to identify leadership skills possessed by the agricultural extension workers in order to gauge their performance in the extension system. Therefore, there is a need first to gauge performance of extension workers and a need to determine predictors of performance. This article examined the relationship between leadership competencies and organizational commitment with performance as perceived by the agricultural extension workers and the predictors of performance. The leadership competencies variables and commitment are important attributes of a leader. Data were collected from 290 respondents who were selected based on stratified sampling technique. The stratification was based on highlands, coastal and desert regions. The findings showed that four variables were significantly contributed to the level of extension workers’ performance. The variables are competencies in program implementation, program evaluation and program planning as well as organizational commitment. These predictor variables explain 45.3 % of the variance in the job performance of extension workers. The study suggests that relevant ministry such as the ministry of agriculture should take into account the leadership characteristics of extension workers and how to improve those competencies as well as organizational commitment in order to upgrade their performance in developing rural communities through extension services.
Extensions of Royal Road Functions for Use in SELEX in silico

The field of Genetic Algorithms (GA) in modern computer sciences has increasingly become a source of algorithms and methods for the computational systems biology. In this report we extend the definition of the Royal Road functions (specifically, their Royal Staircase version), used in GA, to BioRS functions, purposively devised to be applied in the computational biology. This approach is motivated by the necessity of taking into account the modular structure of biological macromolecules that has clear analogies with the theory of building blocks (BB) in GA. We demonstrate computationally how the BioRS functions can be applied to test the efficiency of different GA approaches with the BB preservation, using a multi-modular RNA-device as a test object. Particularly, we have shown that a simple version of the Hill Climbing algorithm (with random mutagenesis), RMHC, is extremely efficient. The impressively high efficiency of the RMHC algorithm in numerical experiments makes the question of its implementation in synthetic biology quite relevant. Our results can serve as a promising basis for the search for new effective techniques of the directed evolution in biology.
Serpentine Diffuser Performance with Emphasis on Future Introduction to a Transonic Fan (Postprint)

Abstract : A serpentine diffuser for embedded propulsion systems was characterized in terms of pressure recovery and total pressure distortion generation within the discharge flow field. A new experimental facility was constructed and also characterized for this investigation to provide detailed, high fidelity measurements. Diffuser performance was characterized for five different Mach numbers with 195 axial and circumferential static pressure measurements within the diffuser flow path. Pressure distortion at the diffuser discharge was quantified with detailed static and steady total pressure measurements at the 17-inch aerodynamic interface plane (AIP). These measurements were acquired using eight total pressure rakes with ten radial probes each while rotating the diffuser in 5-degree increments, yielding highly detailed pressure distortion measurements at the AIP. The wall static pressure plots of diffuser performance display signs of detached flow along the wall at design conditions. Total pressure contour plots at the AIP show a concentrated pressure distortion pattern at the top center exit of the diffuser resulting in an overall recovery factor of 0.983. Distortion descriptors are presented and discussed.
Research Progress of Electrochemical Energy Storage Materials

Supercapacitors have many advantages that electrostatic capacitors and batteries do not such as fast charge and discharge speed, high efficiency, long cycle life, wide operating temperature range, and good reliability. In recent years, they have become a research hotspot in the field of electrochemical energy storage. Supercapacitors have been successfully applied in many fields. Such as: acting as a backup power supply for electronic products such as memory, computers, timers, used in electric vehicles and hybrid vehicles, used as auxiliary power supplies for solar and wind power generation devices, and can also be used in military, aerospace and other fields.
Study on an Advanced Low Voltage Ride-Through Control Strategy of Energy Router

With the drastic growth of the energy internet in recent years, the energy router, an important energy hub, has seen wide application and unprecedented value due to its many functions such as energy transmission and coordination management. However, the energy router's ability to cope with the grid voltage sag is critical to its stable operation. This paper presents an advanced low voltage ride-through (LVRT) control strategy for the energy router when the symmetrical grid voltage sag occurs. The presented LVRT strategy can assist in voltage recovery and active power maintenance. When detecting a voltage sag, the inner loop regulator is directly controlled which contributes to the amelioration of the response speed as the outer loop regulator is disconnected. Voltage sags of different severities require different reactive and active power injections to support the voltage recovery and reduce the impact on the energy router's power delivery. Simulation results confirm the effectiveness of the presented LVRT strategy.
Helmet Detection Using Detectron2 and EfficientDet

In the past few years, there has been great technological advancement in the field of deep learning and its application in image classification, image segmentation and object detection. One of those applications is detecting helmets on bike riders. Since road accidents are increasing day by day, it is important that riders wear helmets all the time so that injuries can be avoided. In the past few years, many deep learning approaches have been made to solve this problem of detection of helmets on bike riders. A lot of research has been conducted in this field using primitive technologies like CNNs. But the recent advancement in computer vision techniques has shown a lot of promise. This paper acts as an analytical study of various different approaches and also experiments the new state of the art object detection models such as Detectron2 and EfficientDet. The results from these state of the art models show a lot of promise.
Detection of Parkinsons Disease Via a Multi-Modal Approach

Parkinson's affects 700000 plus people in India. It primarily affects the nervous system. Its various symptoms manifest in the form of lack of motor skills, verbal and mental abilities, affected speech and memory. The disease has no cure and the best remedy is early diagnosis and treatment to reduce the severity of the symptoms. The lack of easy access to medical facilities and doctors is a major deterrent to early diagnosis of Parkinson's. Therefore, we propose here a solution in the form of a mobile application that allows people to test themselves for symptoms of Parkinson's anywhere, anytime. We use a two-way approach to detect the probability of the patient having Parkinson's. The authors posit image processing and machine learning techniques to detect anomalies in speech and tracing patterns. The model for the Trace test achieves a test accuracy of 97.39% (VGG-19)architecture and the Speech test achieves 93.2% (LGBM Classifier).
Genre Based Music Classification using Machine Learning and Convolutional Neural Networks

In this digital era, as the quantity of music being released on a regular basis continues to grow, Music Information Retrieval (MIR) has become a challenging yet important task. Music Classification is a crucial part of MIR. This involves the task of defining and implementing measurements of music similarity, which is quite complex. Pattern recognition and machine learning techniques offer appropriate solutions to deal with such issues. In this paper, we have built various machine learning models for genre classification and trained them on the GTZAN dataset. We have used signal processing to extract informative features from raw audio and have analyzed them for usage. Along with traditional models, we have used ensemble tree models, which result in higher accuracy. A deep learning approach is also taken using convolutional neural networks for the classification task. The proposed CNN model achieves the best performance among all. We have compared the models using various metrics and logged their results along with suitable graphs.
Vehicle-NnA Cnn Based Local Vehicle Detection Classifier

In this technological era, computer vision accomplishes superior performance and it introduced to the Convolutional neural network (CNN) which is acquainted for recognize an object. In addition to object detection and classification are considered difficult tasks in computer vision. From the ancient times vehicle is the only communication media to move from one place to another place. In this automation period local vehicles are decreasing day by day. Because these type of vehicles motion are slow. We want to preserve our culture with the help of vehicle-NN model. We presented our own CNN model name Vehicle-NN which will be a convenient effect on the vehicle sector of Bangladesh. We compared our CNN model with other pre-trained models like MobileNet, VGG16, InceptionV3. This proposed model plays an effective role in the future automation vehicle identification sector. Besides we want to hold on to our traditional roots and preserving our local culture. Our work approach is novel for the detection of local vehicles in Bangladesh. In this experiment, we have used 7 classes (Bus, CNG, Leguna, Pickup, Rickshaw, Thelagari, Van). We trained our model with our own dataset and our CNN model acquired accuracy is 96%.