Miracle-dz/UniFAR-FTUs huggingface dataset:
An observation algorithm for key motion states of skid-steered wheeled unmanned vehicle

With the increasing demand of military and civilian in the intelligent vehicles, the skid-steering theory has been widely used in unmanned ground vehicles, especially in unmanned military vehicles and unmanned surveillance platforms. Due to its driving environment complex and variable, which requires stricter dynamic control system. In order to improve the active safety performance of the skid-steering unmanned vehicle and develop the key technologies such as behavior decision planning technology, path tracking, and dynamic control technology, it is necessary to develop the dynamic state parameter observation system based on skid-steering theory. In this paper, an observation using Strong Track External Kalman Filter theory with noise matrix adaptive is designed to estimate vehicle kinematic parameters based on a 6 × 6 skid-steered unmanned vehicle. First, kinematic and dynamic model is built to analyze the characters of a skid-steered wheeled vehicle. Then a tire force estimation method based on dynamic model is presented to observe the tire longitude and vertical force. The tire force data is also used by Dugoff nonlinear model. Then an External Kalman Filter theory is designed to estimate vehicle kinematic parameters. To increase the accuracy and the robustness of the observer, the Strong Tracking EKF (STEKF) and noise adaptive adjustment is designed. Finally, a combined simulation using TruckSim and Simulink and the experiment using a 6 × 6 skid-steered unmanned vehicle verifies the efficiency of the observer. Results show that the observer is able to estimate the skid-steered wheeled vehicle states, and it also shows that the yaw rate result in the slip angle difference between each tire.
Preliminary evidence of promise of a sexual violence risk reduction intervention for female students in South African tertiary education institutions

ABSTRACT Campus sexual violence risk reduction and resistance interventions have been developed and tested among female students in the global North and proven effective. Evidence-based interventions to prevent sexual violence tested amongst female students in the global South and in South African campuses are lacking. We present preliminary evidence of promise of Ntombi Vimbela! (NV! ), a sexual violence prevention intervention piloted amongst first year female students in eight purposively selected campuses in South Africa. Focus group discussions were conducted with 118 female students who participated in NV! workshops. Most students found the content of NV! relevant and reported having experienced its positive effects. They perceived that NV! empowered them with skills to assess and deal with sexual assault risky situations. NV! changed their attitudes and beliefs about gender, shifted their acceptance of rape myths and beliefs, improved communication skills, enhanced self-esteem, and confidence to defend oneself in risky sexual assault situations. Few participants were unsure whether they will be able to use the skill in real life. These findings indicate a range of short-term positive outcomes which we anticipate would reduce the risk of sexual assault among first year female students. This suggests that NV! should be subject to further evaluation.
“Dancing Naked”: Gender, Trauma and Politics in the Mystical Poetry of Lal Ded

Abstract Lal Ded, the fourteenth century Kashmiri female mystic, is revered as a spiritual exemplar by both the Hindus and the Muslims of Kashmir. She walked away from a difficult marriage at the young age of twenty-six to pursue the Śaiva path to spiritual liberation. Lal Ded records her long and arduous spiritual journey in a Kashmiri poetic form, vākh (a short poem of four lines with four stresses in each line), which is also one of the earliest genres of Kashmiri poetry. Even though most scholarship on Lal Ded’s vākhs does invoke her difficult life as a Kashmiri woman in a turbulent political milieu, it rarely poses the question of gender and politics in relation to her mystical poetry. My paper offers a close reading of selected vākhs to argue that the figures of loss (rāvan-tyol, loss-blister), shame (mandchhi hạ̄nkal, iron fetters of shame) and joy (nangaē natsun, dancing naked) in Lal Ded’s mystical poetry can be read not merely as stages on the path of a Śaiva liberation but also as a distinctive proto-feminist perspective on political self-transformation.
FIR and IIR digital filters

A digital filter is a basic building block in any digital signal processing (DSP) system. The simulation results presented show how finite bit precisions can affect the performance of a digital filter. IIR filters are shown to be even more susceptible to finite bit precision effects than FIR filters. However, these effects can be reduced using the IIR filter with a cascaded structure.
Forecasting High Frequency Data: An ARMA-Soft RBF Network Model for Time Series

In the article we alternatively develop forecasting models based on the Box-Jenkins methodology and on the neural approach based on classic and fuzzy logic radial basis function neural networks. We evaluate statistical and neuronal forecasting models for monthly platinum price time series data. In the direct comparison between statistical and neural models, the experiment shows that the neural approach clearly improve the forecast accuracy. Following fruitful applications of neural networks to predict financial data this work goes on. Both approaches are merged into one output to predict the final forecast values. The proposed novel approach deals with nonlinear estimate of various radial basis function neural networks.
Adaptive, rule based fault diagnostician for power distribution networks

A fast, adaptive, rule based fault diagnostician for distribution networks is described. It uses two distinct types of rules. A fixed set of core rules, expressed in terms of breaker trips and bus status, is applied for all faults and networks. For particular cases in which the core rules fail, exception rules are generated by interaction with operators. These rules make use of suitable features of the faulted system, in particular, of the islands formed in the faulted network. The data used by the rules is topological information and the real time data normally available from a modern SCADA system. Test results using a distribution system protection simulator show that the diagnostician is accurate, and that speed compares very favourably with earlier schemes described.
Model-based Bayesian Seismic Monitoring

This thesis presents the underlying probabilistic model, the parameter estimation, and the inference algorithm of NET-VISA, Network Processing Vertically Integrated Seismic Analysis. NET-VISA is an Open Universe Probability Model (OUPM) for seismic events, the transmission of seismic waves through the earth, and their detection (or misdetection) at stations, as well as a model for spurious detections. The probabilistic model allows for seamless integration of various disparate sources of information. Applied in the context of the International Monitoring System (IMS), a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT), NET-VISA achieves a reduction of around 60% in the number of missed events compared to the currently deployed system. It also finds events that are missed by the human analysts who post-process the IMS output.
Integrating software process assessment models using a process meta model

Software process improvement (SPI) is motivated by the need to increase the quality and productivity of software work. It is a widely accepted fact that the quality of the software product is largely determined by the quality of the process used to develop and maintain it. SPI requires knowledge of what is needed, awareness of what is feasible, and a long-term commitment to its investment. Software process assessment helps software organizations improve themselves by identifying their critical problems and establishing improvement priorities. The assessment takes place by comparing the state of the organization's software process against the model and the improvement scale. An assessment is usually based on a software process model and an improvement roadmap. The interest in software process improvement has created dozens of international software process models and standards. CMMI and SPICE are two most widely used process assessment models in the SPI work today. In order to help software companies, a SPI support tool is currently being developed. The SPI support tool guides the users through software process improvement, diminishing the need for continuous external consultations and assessors. Software process models form the central part of this support tool. In order to implement various process models into the support tool, the structure of the models should be analyzed and integrated into one common meta model. We have previously conducted conceptual analyses of SPICE and CMMI where the structure of both models has been studied. In this paper we describe the result of the conceptual synthesis of these process models-integration of SPICE and CMMI through a meta model. The integrated meta model contributes to a more complete process assessment, since it includes elements of various process models.
Track-Before-Detect Procedures in AM Radio-Based Passive Radar

In amplitude modulation radio-based passive radar, the track-before-detect (TBD) procedures are performed to process long time observation data. This work mainly focuses on the tracking of the time-Doppler and time-azimuth traces of multiple target under real scenarios such as low signal-to-noise ratio, hybrid clutter, severe breaking points, and intersecting traces. A new original approach to deal with the TBD problem of this work is developed. Two types of linear equations according to the simplified radio wave propagation model are formulated, which is the key point of the proposed method. We make the theoretical analysis about how the linear equations can be used to track multiple target, and how multiple target’s constant velocities and initial positions can be estimated, which is an additional parameter estimation capability of the proposed method. Both the simulated data and real experimental data are performed with the proposed method and some conventional TBD methods. Several comparisons of the results are given to verify the effectiveness of the proposed method.
The Old, The New and The Future in Fecal Microbiota Transplantation

Determining the time that the Intestinal Microbiota can be conserved, without losing its efficiency, as well as evaluating the different aspects of Fecal Microbiota Transplantation, is what is intended in this work. First, the history of 2000 years to date is analyzed, regarding Microbiota Transplants, or is it 3000? And the following aspects are defined: Where did the Microbiota Transplant begin; where it was institutionalized, how many transplants have been performed, its costs. The important aspects of Recurrent Clostridiodes difficile Infection. The Intestinal Microbiota, its names; something about dysbiosis. The Methodology to carry out the IMT, excelling the donors. A list of the Conditions that improve with the Transplant is proposed. The stability time; The aim is to determine if it is necessary to use antibiotics before transplantation. The need for metagenomic and metatranscriptomic analyzes is considered. Finally, complications are evaluated and what could happen both in the immediate and distant future is proposed.
Diagnostic Utility of Smartwatch Technology for Atrial Fibrillation Detection - A Systematic Analysis.

Background Smartphone technologies have been recently developed to assess heart rate and rhythm, but their role in accurately detecting atrial fibrillation (AF) remains unknown. Objective We sought to perform a meta-analysis using prospective studies comparing Smartwatch technology with current monitoring standards for AF detection (ECG, Holter, Patch Monitor, ILR). Methods We performed a comprehensive literature search for prospective studies comparing Smartwatch technology simultaneously with current monitoring standards (ECG, Holter, and Patch monitor) for AF detection since inception to November 25th, 2019. The outcome studied was the accuracy of AF detection. Accuracy was determined with concomitant usage of ECG monitoring, Holter monitoring, loop recorder, or patch monitoring. Results A total of 9 observational studies were included comparing smartwatch technology, 3 using single-lead ECG monitoring, and six studies using photoplethysmography with routine AF monitoring strategies. A total of 1559 patients were enrolled (mean age 63.5 years, 39.5% had an AF history). The mean monitoring time was 75.6 days. Smartwatch was non-inferior to composite ECG monitoring strategies (OR 1.06, 95% CI 0.93 - 1.21, p=0.37), composite 12 lead ECG/Holter monitoring (OR 0.90, 95% CI 0.62 - 1.30, p=0.57) and patch monitoring (OR 1.28, 95% CI 0.84 - 1.94, p=0.24) for AF detection. The sensitivity and specificity for AF detection using a smartwatch was 95% and 94%, respectively. Conclusions Smartwatch based single-lead ECG and photoplethysmography appear to be reasonable alternatives for AF monitoring.
New control algorithms for series and shunt three-phase four-wire active power filters

This paper presents the general equations that relate the new concepts of instantaneous active and reactive power theory and the well known theory of Symmetrical Components, for the case of 3-phase 4-wire systems, where zero sequence components and harmonics may be present in both voltages and currents. Based on these equations, new control algorithms for 3-phase 4-wire shunt and series active filters for unbalanced power systems are proposed and validated through simulations. The control algorithms presented here are very important for active power filter design and also for FACTS technology reactive power compensators. >
Maternal and Fetal Outcomes in Women with Diabetes in Pregnancy Treated before and after the Introduction of a Standardized Multidisciplinary Management Protocol

Background Diabetes in pregnancy is associated with an increased risk to the woman and to the developing fetus. Currently, there is no consensus on the optimal management strategies for the follow-up and the timing of delivery of pregnancies affected by gestational and pregestational diabetes, with different international guidelines suggesting different management options. Materials and Methods We conducted a retrospective cohort study from January 2017 to January 2021, to compare maternal and neonatal outcomes of pregnancies complicated by gestational and pregestational diabetes, followed-up and delivered in a third level referral center before and after the introduction of a standardized multidisciplinary management protocol including diagnostic, screening, and management criteria. Results Of the 131 women included, 55 were managed before the introduction of the multidisciplinary management protocol and included in group 1 (preprotocol), while 76 were managed according to the newly introduced multidisciplinary protocol and included in group 2 (after protocol). We observed an increase in the rates of vaginal delivery, rising from 32.7% to 64.5% (<0.001), and the rate of successful induction of labor improved from 28.6% to 86.2% (P < 0.001). No differences were found in neonatal outcomes, and the only significant difference was demonstrated for the rates of fetal macrosomia (20% versus 5.3%, P: 0.012). Therefore, the improvements observed in the maternal outcomes did not impact negatively on fetal and neonatal outcomes. Conclusion The introduction of a standardized multidisciplinary management protocol led to an improvement in the rates of vaginal delivery and in the rate of successful induction of labor in our center. A strong cooperation between obstetricians, diabetologists, and neonatologists is crucial to obtain a successful outcome in women with diabetes in pregnancy.
AgriBot-Farmer Friendly Agricultural Robot with Watering System

Aim of this paper is to build a multitasking agricultural robot that can perform tasks such as detecting, extinguishing fires and water spraying for irrigation systems. The emphasis of this paper is on the improvement of agricultural methods. In this dissertation we investigate that farmers spend a lot of manpower on field. India, as we all know, is a country where agricultural production is the most significant sector. Our main goal is to reduce their strenuous activity and struggling by recreating an improved irrigation system that could save all or most of the effort and time. This paper proposes a comprehensive agricultural approach based on the farmer’s needs. The proposed system is based on embedded system. Keywords— Micro-controller, Embedded System, DC motor, Watering, H-Bridge motor driver.
Chemical and Microbial Analysis of Bedding Material (Wood Shavings) Treated with Graded Levels of Aluminium Sulphate (Alum)

The study was carried out at the poultry unit of the Department of Animal Science teaching and research farm, Ahmadu Bello University, Zaria to determine the chemical and microbial analysis of poultry litter (wood shavings) treated with graded levels of alum. The alum used was obtained from the Sabon-gari market in Zaria, Kaduna State. The rates of alum application (prior to keeping the birds) was as follows: T1 control (normal wood shavings with no alum), T2 (5% alum by kg weight of wood shavings), T3 (10% alum by kg weight of wood shavings) and T4 (15% alum by kg weight of wood shavings). Five sets of litter samples were obtained fortnightly from each pen from different locations i.e. the four corners and center from which the microbial load, pH, total nitrogen (N), soluble reactive phosphorus, VFA and NH4+ concentration were measured. The result shows significantly (P<0.05) lower pH value in all the alum treated wood shavings groups (5%, 10% and 15% alum treated wood shavings) compared to the control. The result showed that significantly (P<0.05) lower total volatile fatty acid level was obtained in all the alum treated wood shavings groups (5%, 10% and 15% alum treated wood shavings) compared to the control untreated wood shaving group. The results showed a decrease in total bacteria, E. coli and Salmonella spp. load in alum treated wood shavings groups (5%, 10% and 15% alum treated wood shavings) compared to the control, while mould and yeast load was increased in all the alum treated wood shavings groups (5%, 10% and 15% alum treated wood shavings) compared to the control. The study conclude that treating wood shavings with alum can reduce microbial load of the litter, hence improve health and reduce mortality. Treating wood shavings with alum tends reduce the microbial load of the litter.
Adaptive Friction Compensation Control of Linear Slider with Considering a Periodic Reference Signal

An adaptive friction compensation method based on notion of H∞ optimality is proposed. It is assumed that the static and dynamic characteristics of friction are captured by the dynamic LuGre model. Neural–Network (NN) is used to parameterize the nonlinear characteristic function of the friction model. An adaptive NN based controller is given, and an approximation error in NN is regarded as exogenous disturbance to the system. Consequently, in the resulting control system, the L2 gains from the disturbance to generalized outputs are made less than prescribed positive constants. For the practical applications, σ–modification method and dead–zone method are applied to the estimation strategies. To illustrate the effectiveness of our proposed method, experimental results are shown.
Analysis of Arbitrarily Oriented Microstrip Transmission Lines in Arbitrarily Shaped Dielectric Media Over a Finite Ground Plane

A numerical analysis is presented for a multiconductor transmission line in multilayered Iossy, dielectric regions where the ground plane is of finite extent. The transmission fines are infinitely long and vary in cross section from finite to infinitesimally thin. The Green's function for such a two-dimensional transmission line involves an arbitrary constant. If the ground plane is infinite, the method of images could be used where this constant cancels out. However, in the case of a finite ground plane, the constant has to be evaluated. Here a numerical method is presented where the constant could be eliminated rather than evaluated by imposing the condition for the total charge to be zero. The transmission lines, dielectric regions, and the ground plane can have arbitrary cross sections.
LOCALIS: Locally-adaptive Line Simpliﬁcation for GPU-based Geographic Vector Data Visualization

Visualization of large vector line data is a core task in geographic and cartographic systems. Vector maps are often displayed at different cartographic generalization levels, traditionally by using several discrete levels-of-detail (LODs). This limits the generalization levels to a ﬁxed and predeﬁned set of LODs, and generally does not support smooth LOD transitions. However, fast GPUs and novel line rendering techniques can be exploited to integrate dynamic vector map LOD management into GPU-based algorithms for locally-adaptive line simpliﬁcation and real-time rendering. We propose a new technique that inter-actively visualizes large line vector datasets at variable LODs. It is based on the Douglas-Peucker line simpliﬁcation principle, generating an exhaustive set of line segments whose speciﬁc subsets represent the lines at any variable LOD. At run time, an appropriate and view-dependent error metric supports screen-space adaptive LOD levels and the display of the correct subset of line segments accordingly. Our implementation shows that we can simplify and display large line datasets interactively. We can successfully apply line style patterns, dynamic LOD selection lenses, and anti-aliasing techniques to our line rendering.
SENTIMENT ANALYSIS ON RSS NEWS FEED USING NAÏVE BAYES AND SVM

: As we know that stock market depends on the stock news. This paper is to build a model that predicts news polarity which may affect changes in stock price movement trends . Sentiment analysis is to understand people’s actions, feelings, emotions etc. The novelty of our approach is efficient prediction of model that scores emotions from all relevant real time stock news available in public domain. Our paper uses news feeds from Really Simple Syndication (RSS) news feed that has an impact on stock market values. Hence stock market RSS news feed data is collected for a period of time. We use machine learning Framework for processing data set is available on the RSS news feed. Results of sentiment analysis will be positive or negative or neutral based on the polarity. In our experimental study, Results of sentimental analysis on RSS news feed will be displayed as different sections predicting stock price fluctuations, whether up or down.
Cross-Cultural Differences in Responses to News Videos

Continuous globalization makes it so that any event might be reported by multiple news sources. These sources range across different affinity groups in the world and are spread over diverse types of media, and each affinity group might have different opinions on the same topic or piece of news. We are studying the differences and similarities in the portrayals of and reactions to the same event across different affinity groups, specifically through video.