Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduction of traffic congestion due to accidents by communicating information using VANETs

Traffic congestion is increasing day by day, and accidents are one of the primary reasons for congestion especially on roads. The secondary accidents due to collision which is a result of unawareness of the road condition ahead could reduce to a greater extent if the information about the primary accidents disseminates to the nearby vehicles. VANETs are a key component of an intelligent transport system. The research in VANETs is developing rapidly and VANETs can help in the implementation of various road safety applications. In this paper, we implement a system that detects the accident using a pi camera and broadcasts a video of the accident location to vehicles in the vicinity of the accident. This is performed with the help of python modules such as OpenCV, struct, pickle and socket programming. The video transmission occurs in real-time; this determines the credibility of the information. The system is implemented using hardware components, raspberry pi, pi camera, and LCD. Once the accident information is transmitted to other vehicles, they can decide their journey, depending upon the adversity of the accident.
Control of Autonomous Underwater Vehicles

In this thesis an overview of Autonomous Underwater Vehicles (AUV) is presented which covers the advancements in AUV technology in last two decades, different components of AUV and the applications of AUVs. A glimpse on AUV research in India is presented. A nonlinear model of AUV is obtained through kinematics and dynamics equation which is linearized about an operating point to get linearized pitch & depth plane model. A two loop controller (PI control) is used to control the pitch and in turn the depth of the AUV. After having developed, simulated and analyzed the pitch and depth controller for a single AUV, we focus our attention towards developing formation control of three AUVs. The formation control for multiple Autonomous Underwater Vehicles (AUVs) is considered in spatial motions.The objective is to drive a leader AUV along a desired trajectory, and make the follower AUVs keep a desired formation with respect to the leader’s configuration in 3-dimensional spaces (leader-follower formation control). Also, an obstacle avoidance scheme, using pitch and depth control, is used to avoid static obstacles in the path of AUV. The results of the above three control objectives such as tracking control of AUV, controller for avoiding obstacles and formation control of multiple AUVs are presented and discussed in the thesis.
Explosively formed fuse opening switches for use in flux-compression generator circuits

Explosive-driven magnetic flux compression generators (explosive generators) provide for the generation of large amounts of energy compactly stored in a magnetic field. Opening switches for use in explosive generator circuits allow the energy to be used for applications requiring higher power than can be developed by the generators themselves. We have developed a type of opening switch that we describe as an explosively formed fuse (EFF). These switches are well suited to explosive generator circuits and provide a considerable enhancement of explosive pulsed-power capability. 10 refs., 14 figs.
Automatic Detection of Bikers with No Helmet and Number Plate Detection

In a very highly populated country like India enforcing traffic rules becomes a difficult task. Many people ride motorcycles without helmets and doing so, they are risking their life. Monitoring of all vehicles by traffic police is a very tedious task and there is a need for automation for such tasks. Many of the heavily populated cities are employed by CCTV's. The available infrastructure can be used to automate the monitoring of motorcyclists. In this paper a method to detect motorcyclists without Helmet and extracting the number plate of the motorcycles whose rider is not wearing helmet is proposed. The method uses the YOLO (You Look Only Once) model to detect the helmet and license plate of the motorcyclist and motorcycle respectively. YOLO is a model which is trained on COCO dataset and is capable of detecting 80 classes. The YOLO model was trained on Helmet and number plate datasets. The results achieved by this method are very good. The accuracy is high in the detection of helmets and license plates.
Optimal Design and Control of Multi-Motor Drive System for Industrial Application

: In this paper, the topology of the adjustable speed drive with active front end rectifier is considered in terms of application in multi-motor drives. A dynamic model of the rectifier with a coupled LCL filter is presented. A simulation model has been developed for the analysis of multi-motor drive system dynamics and power flow. Based on the simulation model, the functional possibilities of reversible induction motor drive with active front end converter and multiple voltage source inverters on a common DC bus at the motor side are analysed. One example of the application of a complex full regenerative multi-motor drive system on a common DC bus for an industrial crane, implemented with Siemens converters, is shown. The motor and generator mode of operation from the aspect of energy saving as well as the influence of drives on the distribution network from the aspect of higher harmonics and power factors are analysed. Siemens's original software, Starter commissioning tool, for drives configuration and data acquisition is used.
A Power Reduction Technique Through Dynamic Runtime Algorithm For CMOS VLSI Circuits

All digital circuits have design margins for delay and power consumption. This thesis introduces an algorithm that exploits the design margin for delay to reduce power consumption instead, through the novel application of body bias to the transistors on the critical path. A runtime circuit monitors the activity of critical paths, and applies body bias to transistors on non-critical paths for specific input vectors where the value computed by the critical path is a don't care. In sub-100 nm CMOS devices, the application of adaptive body bias reduced leakage power while slightly increasing the signal propagation delay. When a portion of the circuit does not use up the whole clock cycle, the available slack can be used to reduce leakage power dissipation without compromising performance.
Update on EMEC activities, resource description, and characterisation of wave-induced velocities in a tidal flow.

The European Marine Energy Centre (EMEC) has been established in the UK as an open-sea grid-connected test facility for wave and tidal devices. Developers receive detailed site information regarding the resource available, environmental characteristics, and meteorological data. There is a range of additional support, including assistance with legislation and consenting, as well as technical and operational matters. Whilst being primarily an operational test centre serving individual device developers, there is additional scope at EMEC for investigation of a range of unknown issues which need to be addressed by the wave and tidal industries in general as they develop to commercial viability. In addition to providing an update on the current situation at EMEC, this paper also discusses some of the work being undertaken at the centre on data provision and interpretation, in particular regard to the tidal test site. For its tidal test site, EMEC is currently developing Acoustic Doppler Current Profiler (ADCP) survey methodologies in order to address developers’ needs for an appropriate picture of the raw energy. The tidal flow is not homogeneous. It is therefore necessary to capture waveinduced velocities which can be particularly important during high sea states and turbulence, especially within the seabed boundary layer. Progress in this ongoing work is also presented.
Regional transport of PM2.5 and O3 based on complex network method and source apportionment technology in the Yangtze River Delta,China

Ground-level ozone(O 3 ) and atmospheric ﬁne particulate matter (PM 2.5 ) pollution are the major challenges for continually improving air quality in the Yangtze River Delta (YRD) region of China (Lu et al., 2020; Wei et al., 2020). Understanding regional transport pattern of PM 2.5 and O 3 pollution is essential for the development of regional cooperative prevention and control strategies. This study shows annual concentration of PM 2.5 in the YRD decreased by 18.5% from 2015 to 2018, while average of daily maximum 8-h average(MDA8) O 3 concentration during March to October increased by 16.3%. A complex network method is developed to investigate regional transport of PM 2.5 and O 3 in diﬀerent grid cells (nodes). Source apportionment technology in regional transport model is applied for comparing with the results from the complex network method. Interregional and intraregional transportation play an important role in both PM 2.5 and O 3 over the YRD. The northern part of the YRD contributes much more than other areas for PM 2.5 in winter, which accounts for about 60%,while the central part of the YRD, especially the northern part of Zhejiang, is the largest contributor of O 3 in the YRD in summer,which accounts for about 70%. Intraregional transport played major role in PM 2.5 than O 3. The study focusing on heavily pollution episodes not only show results between source apportionment technology and complex network method are comparable, and also reveals both two methods pose great potential in understanding transport pattern and air pollution relationship, which is the foundation for emission mitigation in the YRD region.
Multimodal prediction of aggressive behavior occurrence using a decision-level approach

Traditionally, aggressive behavior incidents have not been considered as a serious crime, but in some contexts such as Bogotá city, this type of behavior caused 70% of the reported personal injuries and homicides in 2017–2018. This phenomenon is a concern for modern cities decision-makers who require predictive models to mitigate aggressive behavior occurrence. There are different source data that can be used to model and predict aggressive behavior, for instance, legal complaints, police penalties and emergency call datasets. In this paper, we propose a decision-level data fusion to combine the prediction of the different aggressive behavior sensors and improve the model predictive capacity. Results suggest that decision-level data fusion using average and max operators improves hotspots hit rates but leads to higher mean squared errors between predicted and real events maps. A texture feature analysis over the predicted maps also revealed that maps generated using the decision-level approach have relatively high entropy, and lower energy and homogeneity values.
Development of tools and methods for scientometrics. The case of a bibliometric study on batteries and fuel cells.

3 Acknowledgement . 9 Table of figures . 10 Introduction . 11 I. Bibliometric Methods . 13 1. Power Laws in Bibliometrics . 13 a. Description . 13 b. The problem of scale dependence. 15 c. A relevant scale dependent operator . 16 d. Validation . 17 2. Probabilistic Collaborative Index . 18 a. Theory . 19 b. Validation . 20 c. Findings . 22 d. Limitations . 26 3. Spline Interpolation . 28 a. Interpolation . 28 b. Smoothing. 30 c. Results . 32 4. Moving averages . 34 a. Proof of concept . 34 b. Real-world example . 35 II. Application to batteries and fuel cells . 39 1. Institutional Collaboration . 39 a. Collaboration efforts . 39 b. Collaboration networks . 43 2. Main types of Fuel Cells . 51 a. Geographical comparison . 51 b. Growing and dying technologies . 54 c. S-curve comparison . 58 3. Time-lag before scientific impact . 60 Conclusion . 63 Annexes . 65 References . 84
An Accurate Graph Generative Model with Tunable Features

A graph is a very common and powerful data structure used for modeling communication and social networks. Models that generate graphs with arbitrary features are important basic technologies in repeated simulations of networks and prediction of topology changes. Although existing generative models for graphs are useful for providing graphs similar to real-world graphs, graph generation models with tunable features have been less explored in the field. Previously, we have proposed GraphTune, a generative model for graphs that continuously tune specific graph features of generated graphs while maintaining most of the features of a given graph dataset. However, the tuning accuracy of graph features in GraphTune has not been sufficient for practical applications. In this paper, we propose a method to improve the accuracy of GraphTune by adding a new mechanism to feed back errors of graph features of generated graphs and by training them alternately and independently. Experiments on a real-world graph dataset showed that the features in the generated graphs are accurately tuned compared with conventional models.
Calibration-Free Gait Assessment by Foot-Worn Inertial Sensors

Walking is a central activity of daily life, and there is an increasing demand for objective measurement-based gait assessment. In contrast to stationary systems, wearable inertial measurement units (IMUs) have the potential to enable non-restrictive and accurate gait assessment in daily life. We propose a set of algorithms that uses the measurements of two foot-worn IMUs to determine major spatiotemporal gait parameters that are essential for clinical gait assessment: durations of five gait phases for each side as well as stride length, walking speed, and cadence. Compared to many existing methods, the proposed algorithms neither require magnetometers nor a precise mounting of the sensor or dedicated calibration movements. They are therefore suitable for unsupervised use by non-experts in indoor as well as outdoor environments. While previously proposed methods are rarely validated in pathological gait, we evaluate the accuracy of the proposed algorithms on a very broad dataset consisting of 215 trials and three different subject groups walking on a treadmill: healthy subjects (n = 39), walking at three different speeds, as well as orthopedic (n = 62) and neurological (n = 36) patients, walking at a self-selected speed. The results show a very strong correlation of all gait parameters (Pearson's r between 0.83 and 0.99, p < 0.01) between the IMU system and the reference system. The mean absolute difference (MAD) is 1.4 % for the gait phase durations, 1.7 cm for the stride length, 0.04 km/h for the walking speed, and 0.7 steps/min for the cadence. We show that the proposed methods achieve high accuracy not only for a large range of walking speeds but also in pathological gait as it occurs in orthopedic and neurological diseases. In contrast to all previous research, we present calibration-free methods for the estimation of gait phases and spatiotemporal parameters and validate them in a large number of patients with different pathologies. The proposed methods lay the foundation for ubiquitous unsupervised gait assessment in daily-life environments.
Robust Agrobacterium-Mediated Transient Expression in Two Duckweed Species (Lemnaceae) Directed by Non-replicating, Replicating, and Cell-to-Cell Spreading Vectors

Plant-based transient expression systems have recognized potential for use as rapid and cost-effective alternatives to expression systems based on bacteria, yeast, insect, or mammalian cells. The free-floating aquatic plants of the Lemnaceae family (duckweed) have compact architecture and can be vegetatively propagated on low-cost nutrient solutions in aseptic conditions. These features provide an economically feasible opportunity for duckweed-based production of high-value products via transient expression of recombinant products in fully contained, controlled, aseptic and bio-safe conditions in accordance with the requirements for pharmaceutical manufacturing and environmental biosafety. Here, we demonstrated Agrobacterium-mediated high-yield transient expression of a reporter green fluorescent protein using deconstructed vectors based on potato virus X and sweet potato leaf curl virus, as well as conventional binary vectors, in two representatives of the Lemnaceae (Spirodela polyrhiza and Landoltia punctata). Aseptically cultivated duckweed populations yielded reporter protein accumulation of >1 mg/g fresh biomass, when the protein was expressed from a deconstructed potato virus X-based vector, which is capable of replication and cell-to-cell movement of the replicons in duckweed. The expression efficiency demonstrated here places duckweed among the most efficient host organisms for plant-based transient expression systems, with the additional benefits of easy scale-up and full containment.
An ROV toolbox for optical and acoustical seabed investigations

An archaeologist's primary interest is to extract as much information as possible from a sunken ship. An archaeological investigation should therefore not rely on one single method but utilize sensor fusion as much as possible. Various survey equipment complement each other and by combining more than one tool the archaeologist's capability for interpretation is enhanced. This paper presents the use of sensor fusion on the Ormen Lange deepwater archaeology project, where a number of sensors were combined to document the site with improved precision and standards as a result.
Internet Addiction and Emotional and Behavioral Maladjustment in Mainland Chinese Adolescents: Cross-Lagged Panel Analyses

Adolescence is a developmental stage when adolescents are vulnerable to addictive behaviors, such as Internet addiction (IA), which refers to pathological use of the Internet. Although there are views proposing that the links between IA and adolescent problem behavior may be bidirectional in nature, few studies have examined the reciprocal relationships between IA and other maladjustment indicators, and even fewer studies have simultaneously employed both emotional and behavioral maladjustment indicators in a single study. To address the above research gaps, the present study investigated how IA is associated with both depression and delinquency among Chinese adolescents. Two waves of data were collected at two consecutive years, respectively, with 1year apart, from 3,010 students (Mean age=13.16, SD=0.81; 57.48% boys) in four junior high schools in mainland China. These students completed the same questionnaire containing measures of IA, depression, and delinquency at each wave. The proposed cross-lagged panel model fitted the data very well, and there were significant positive reciprocal effects between IA and depression as well as delinquency after controlling for background socio-demographic factors. Gender differences were also observed in multi-group comparisons. Specifically, IA showed a stronger longitudinal impact on delinquency among boys than among girls. While depression significantly predicted IA in 1year among boys, such a prediction was not significant among girls. These findings delineate the bidirectionality of the associations between IA and emotional and behavioral maladjustment indexed by depression and delinquency, respectively. The findings also suggest that researchers and practitioners have to take gender differences as well as different developmental indicators in understanding the bidirectional influences between IA and adolescent behavioral and emotional development.
AFIKS DALAM BERITA UTAMA SURAT KABAR LAMPUNG POST

The problem of this research was how the use of affix in newspaper headlines Lampung Post. The purpose of this research was to describe the affix in newspaper headlines Lampung Post and its implications for indonesian learning in high school. This research used a qualitative descriptive method. The data source of this research is newspaper headlines lampung post. Based on the analysis of data, it was found that the use of affixes include prefixes, suffixes, konfiks, and the combination of affixes. The result of this research can also be used as teaching materials of indonesian language learning competencies releated to writing text news. Masalah penelitian ini adalah bagaimana penggunaan afiks dalam berita utama surat kabar Lampung Post. Tujuan penelitian ini untuk mendeskripsikan afiks dalam berita utama surat kabar Lampung Post dan implikasinya terhadap pembelajaran Bahasa Indonesia di SMA. Penelitian ini menggunakan metode deskriptif kualitatif. Sumber data penelitian ini adalah berita utama surat kabar Lampung Post. Teknik pengumpulan dan analisis data yang digunakan adalah analisis teks. Berdasarkan hasil analisis data ditemukan afiks berupa prefiks, sufiks, konfiks, dan kombinasi afiks. Afiks pada kajian ini dapat digunkana sebagai bahan ajar pembelajaran bahasa Indonesia di SMA yang berkaitan dengan kompetensi menulis teks berita. Kata kunci: afiks, headline, implikasi bahan ajar.
Prevalence of Newcastle Disease Virus in Commercial and Backyard Poultry in Haryana, India

Newcastle disease virus (NDV) causes Newcastle disease (ND) in poultry. The ND is a highly contagious disease, which is endemic in several countries despite regular vaccination with live or killed vaccines. Studies on NDV in India are mostly targeted toward its detection and characterization from disease outbreaks. A surveillance study was undertaken to determine NDV prevalence throughout the state of Haryana from March 2018 to March 2020 using a stratified sampling scheme. The state was divided into three different zones and a total of 4,001 choanal swab samples were collected from backyard poultry, commercial broilers, and layers. These samples were tested for the M gene of NDV using real-time RT-PCR. Of the 4,001 samples tested, 392 were positive (9.8% apparent prevalence; 95% CI: 8.9–10.8%) for the M gene. Of these 392 M gene positive samples, 35 (8.9%; 95% CI: 6.4–12.3%) were found to be positive based on F gene real-time RT-PCR. Circulation of NDV in commercial and backyard poultry highlights the importance of surveillance studies even in apparently healthy flocks. The information generated in this study should contribute to better understanding of NDV epidemiology in India and may help formulate appropriate disease control strategies for commercial and backyard birds.
Effect of Engagement With Digital Interventions on Mental Health Outcomes: A Systematic Review and Meta-Analysis

Digital mental health interventions (DMHIs) present a promising way to address gaps in mental health service provision. However, the relationship between user engagement and outcomes in the context of these interventions has not been established. This study addressed the current state of evidence on the relationship between engagement with DMHIs and mental health outcomes. MEDLINE, PsycINFO, and EmBASE databases were searched from inception to August 1, 2021. Original or secondary analyses of randomized controlled trials (RCTs) were included if they examined the relationship between DMHI engagement and post-intervention outcome(s). Thirty-five studies were eligible for inclusion in the narrative review and 25 studies had sufficient data for meta-analysis. Random-effects meta-analyses indicated that greater engagement was significantly associated with post-intervention mental health improvements, regardless of whether this relationship was explored using correlational [r = 0.24, 95% CI (0.17, 0.32), Z = 6.29, p < 0.001] or between-groups designs [Hedges' g = 0.40, 95% CI (0.097, 0.705), p = 0.010]. This association was also consistent regardless of intervention type (unguided/guided), diagnostic status, or mental health condition targeted. This is the first review providing empirical evidence that engagement with DMHIs is associated with therapeutic gains. Implications and future directions are discussed. Systematic Review Registration: PROSPERO, identifier: CRD 42020184706.