Miracle-dz/UniFAR-FTUs huggingface dataset:
Navigation and Speed Regulation Aimed at Travel through Immersive Virtual Environments: A Review

The recent technical and interaction breakthroughs within the virtual reality (VR) sector have marked a new age. This has also led to a huge advancement of technology in the arena of Virtual environment locomotion. Fixed movement speeds are impractical while virtually exploring huge areas because of the long distances involved and various details. However, in order for clients to manually modify the journey speed, they will be required to regulate different factors that can make them awkward and will take intellectual work on their behalf. Therefore, there is a need for more investigative and comparative research on present VR navigation techniques. This is necessary so that the distinctive interaction aspects of these methods can be documented and used to lead the process of designing new techniques. In this article, a comprehensive, objective evaluation study of existing and widespread VR navigation and automatic speedadjustment techniques is presented, with the goal of assessing the user experience that each methodology provides.
An approximation theory for optimum sheets in unilateral contact

In this paper we give an approximation theory for the optimum variable thickness sheet problem considered in [1] and [2], This problem, which is a stiffness maximization of an elastic continuum in unilateral contact, admits complete material removal, i.e., the design variable is allowed to take zero values. The original saddle-point problem is replaced by a sequence of approximating problems, the solutions of which are shown to converge weakly to exact solutions. In the case that complete material removal is not admissible, the state variable is shown to converge strongly in the (Hl )2-norm to the unique exact state solution. We consider a particular finite-element discretization that fits into the general theory and present the mathematical programming problem that results from it.
IoT enabled obstruction evasion Robots for enhancing the security of the Systems

The surveillance of homes and other places like homes, restaurants, clubs, cafes, schools etc have become a major task in this technological era. There are various reasons required for this surveillance. One being the increasing crimes like that of theft. The major reason being the unattended homes by parents, either alone or under the care of babysitter or nanny. We don’t know how these house service keeps on working and what they do at our homes, while we are away, leaving our homes completely unattended. So, to keep a check on it, we came on an idea of Mobile Controlled Surveillance Robot. This paper is based on the concept of Internet of Things, creatively called as IoT. The aim of this paper is to establish the communication between the owner and is/her unattended belongings, also to keep an eye on the surroundings. Thus, this research work will act as a game changer, i.e. nothing would go un-noticed now.
Skill and Return of Skill in Dynamic Extended-Range Forecasts

Abstract The skill of a set of extended-range dynamical forecasts made with a modern numerical forecast model is examined. A forecast is said to be skillful if it produces a high quality forecast by correctly modeling some aspects of the dynamics of the real atmosphere; high quality forecasts may also occur by chance. The dangers of making a conclusion about model skill by verifying a single long-range forecast are pointed out by examples of apparently high “skill” verifications between extended-range forecasts and observed fields from entirely different years. To avoid these problems, the entire distribution of forecast quality for a large set of forecasts as a function of lead time is examined. A set of control forecasts that clearly have no skill is presented. The quality distribution for the extended-range forecasts is compared to the distributions of quality for the no-skill control forecast set. The extended-range forecast quality distributions are found to be essentially indistinguishable from thos.
Positive and Negative Sequence Components Separation Control Method for PV Inverters Based on Second-order Generalized Integrator

The negative sequence components generated by the grid during asymmetric faults cause deviations and fluctuations of the output frequency from phase-locked loop and the output current and power from grid-connected PV inverter. To solve these problems, a phase-locked loop based on the second-order generalized integrator quadrature signal generator (SOGI-QSG) is designed to achieve the phase locking of the grid-connected inverter. A positive and negative sequence separation method is proposed to eliminate the influence of its DC component using a single SOGI-QSG unit, and then a control strategy is designed to suppress the negative sequence current. Finally, a grid-connected PV generation system simulation model is built in PSCAD/EMTDC to verify the rapidity of phase locking and negative sequence current suppression. The proposed control strategy can achieve the separation of positive and negative sequence current components rapidly and accurately, and improve the accuracy of output power of the grid-connected inverter.
Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter

High−precision and robust localization is critical for intelligent vehicle and transportation systems, while the sensor signal loss or variance could dramatically affect the localization performance. The vehicle localization problem in an environment with Global Navigation Satellite System (GNSS) signal errors is investigated in this study. The error state Kalman filtering (ESKF) and Rauch–Tung–Striebel (RTS) smoother are integrated using the data from Inertial Measurement Unit (IMU) and GNSS sensors. A segmented RTS smoothing algorithm is proposed in order to estimate the error state, which is typically close to zero and mostly linear, which allows more accurate linearization and improved state estimation accuracy. The proposed algorithm is evaluated using simulated GNSS signals with and without signal errors. The simulation results demonstrate its superior accuracy and stability for state estimation. The designed ESKF algorithm yielded an approximate 3% improvement in long straight line and turning scenarios compared to classical EKF algorithm. Additionally, the ESKF−RTS algorithm exhibited a 10% increase in the localization accuracy compared to the ESKF algorithm. In the double turning scenarios, the ESKF algorithm resulted in an improvement of about 50% in comparison to the EKF algorithm, while the ESKF−RTS algorithm improved by about 50% compared to the ESKF algorithm. These results indicated that the proposed ESKF−RTS algorithm is more robust and provides more accurate localization.
Efficacy of intracytoplasmic sperm injection in women with non‐male factor infertility: A systematic review and meta‐analysis

The use of intracytoplasmic sperm injection (ICSI) has dramatically increased in patients with non‐male factor infertility during the last decades. However, whether ICSI provides a significant benefit over in vitro fertilization (IVF) in these patients is still controversial. In this study, we aimed to investigate the efficacy of ICSI on reproductive outcomes with non‐male factor infertility and to provide updated evidence for clinical practice.
Dynamic attention deconvolutional single shot detector for polyp detection and classification in narrow-band imaging

To deal with multitask segmentation, detection and classification of colon polyps, and solve the clinical problems of small polyps with similar background, missed detection and difficult classification, we have realized the method of supporting the early diagnosis and correct treatment of gastrointestinal endoscopy on the computer. We apply the residual U-structure network with image processing to segment polyps, and a Dynamic Attention Deconvolutional Single Shot Detector (DAD-SSD) to classify various polyps on colonic narrow-band images. The residual U-structure network is a two-level nested U-structure that is able to capture more contextual information, and the image processing improves the segmentation problem. DAD-SSD consists of Attention Deconvolutional Module (ADM) and Dynamic Convolutional Prediction Module (DCPM) to extract and fuse context features. We evaluated narrow-band images, and the experimental results validate the effectiveness of the method in dealing with such multi-task detection and classification. Particularly, the mean average precision (mAP) and accuracy are superior to other methods in our experiment, which are 76.55% and 74.4% respectively.
Research on emotion classification technology of movie reviews based on topic attention mechanism and dual channel long short term memory

In recent years, with the popularity of the Internet, more and more people like to comment on movies they have watched on the film platform after watching them. These reviews hide the reviewers’ feedback on films. Mining the emotional orientation information in these reviews can provide consumers with shopping references and help businesses optimize film works and improve business strategies. Therefore, the emotional classification of film reviews has high research value because few emotion dictionaries and analysis tools are available for reference and use in film reviews. The accuracy of emotion classification still needs to be improved. This study introduces the attention mechanism and dual channel long short term memory (DC-LSTM) while building the emotion dictionary in the field of Chinese film review. It classifies Chinese film reviews in terms of topic-based fine-grained emotion. First, the emotion vector is constructed using the constructed movie review emotion lexicon. The semantic vector obtained by the Word2vector tool is input to LSTM to encode the comment text. Then, the topic attention module is used to decode. Finally, the final emotion classification result is obtained through the softmax function of the entire link layer and the output layer. The thematic attention modules constructed in this study are independent of each other for attention parameter adjustment and learning. One attention module corresponds to one film theme. In this study, eight themes, including “plot,” “special effects,” “original work,” “music,” “thought,” “theme,” “acting skills,” and “joke,” were extracted, and each theme was classified into three types of emotions: “positive,” “neutral,” and “negative. ” The experimental results on the crawled Chinese film review dataset show that the proposed algorithm is superior to some existing algorithms and models in accuracy, precision, recall and F1 measure. The DCLSTM based on the thematic attention mechanism (DCLSTM-TAM) model constructed in this study introduces the emotion vector into the network and adds the theme attention mechanism. It can not only classify the emotion for different topics of a film review but also effectively deal with film reviews with fuzzy emotional tendencies. It realizes the fine-grained emotion classification of film topics and improves the accuracy of emotion classification of film reviews. The emotion classification method and model proposed in this study have good transferability, and the change of training corpus is also applicable to other short text fields.
Macroscopic models of magnetization

Four magnetization models, now considered as classical, are presented: the Stoner-Wolhfarth model, the Jiles-Atherton model, the Globus model, and the Preisach model. The paper describes the methods each model uses to simulate the magnetization mechanisms, reversible and irreversible processes, major and minor loops, and anhysteretic behavior. Improvements and changes are proposed to the Stoner-Wolhfarth and Globus models. The necessary simplifying assumptions define the limits of applicability of the models. The paper concludes with a table of the main characteristics of each model to help a potential user to make a suitable choice.
New method of estimating model parameters of modified GM(1,1)

In order to improve the forecast precision of modified GM(1,1) model,a new parameter estimation formula based on accumulating method to modified GM(1,1) model is proposed.This new method reduces the calculation quantity and complexity because it avoids large numbers of matrix operations.The computation results show that accumulating method modified GM(1,1) model has higher forecast precision than other GM(1,1) model.So the accumulating method modified GM(1,1) model can be applied extensively.
Frequency of F508del Variant in Patients with Cystic Fibrosis from Paraguay

Cystic fibrosis (CF) is an autosomal recessive disorder and is caused by variants in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene. We aimed to study the frequency of the F508del variant, the most common variant worldwide, in patients with CF from Paraguay. The frequency of the F508del variant in Paraguayan patients with a clinical diagnosis of CF was assessed using a polymerase chain reaction followed by the sequencing of the PCR products. 43 of the 86 patients (50%) were homozygous for the F508del variant, 28 were heterozygous (32.56%), and the remaining 15 (17.44%) were non-carriers. In terms of alleles, there were 114 mutated (114/172 or 66.28%) and 58 did not correspond to this variant (58/172 or 33.72%). This is the first study of the frequency of the F508del variant in patients with CF in Paraguay. This information is of utmost relevance when planning and offering treatments from health services.
Bank Fixed Term Deposit analysis using Bayesian Logistic Regression

Marketing is the biggest aspect of any business. Proper analysis on marketing strategies can help expand and improve a business up another notch. Direct marketing campaigns are direct interactions with the particular client base for achieving a particular business objective. Distant clients can also be targeted with proper use of available communication channels like telephones, social media marketing, and any kind of digital marketing. Every sector needs marketing to let people know about their product or service. The Banking sector is also one of these and marketing analysis is of utmost importance when it comes to loan sanctioning, insurance policies or even fixed deposits. Banks target different customers based on several distinct factors which also include their past transactions. This study aims at analyzing Bank Fixed Term Deposit sanctioning by using Bayesian Logistic Regression. Only after a thorough analysis, a person is considered as a potential customer for loan or insurance. The amount might still differ for different customers based on their transaction history and loyalty i.e., timely payback of the loans provided. All these factors affect the customer’s chance of getting a loan or fixed deposit or even an insurance.
AF-FDS: An Accurate, Fast, and Fine-Grained Detection Scheme for DDoS Attacks in High-Speed Networks With Asymmetric Routing

Distributed Denial of Service (DDoS) attacks have posed severe threats to the Internet. Although researchers have proposed many DDoS detection schemes, there are still some challenging issues. Traditional per-flow-based DDoS methods are impractical for massive amounts of high-speed network traffic due to the huge resource consumption. In addition, existing methods are not designed to take into account the widespread asymmetric routing in high-speed networks, resulting in false positives when these methods are deployed on the Internet. Furthermore, existing methods can not achieve a good trade-off between detection accuracy and granularity when detecting hybrid DDoS attacks. This paper proposes an Accurate, Fast, and Fine-grained Detection Scheme (AF-FDS) for DDoS attacks in high-speed networks with asymmetric routing. We select features based on the characteristics of DDoS attacks and design a data structure Double Composite Structure Sketch (DCSS). DCSS can achieve fast recording and extraction of the selected features from the sampled traffic. Experimental results using real-world traces in a 10Gbps network with asymmetric routing show that AF-FDS can detect nine types of DDoS attacks at a fine-grained level within 15 seconds with over 98.0% precision and recall, even at a sampling rate of 1/1024. Furthermore, the comparison with several state-of-the-art methods illustrates that AF-FDS can detect DDoS attacks with a lower false positive rate (FPR) and shorter alarm time in asymmetric routing scenarios.
Particle-in-cell analysis of beam-wave interaction in gyrotron cavity with tapered magnetic field

A commercially available electromagnetic simulator — MAGIC, a particle-in-cell (PIC) code — has been used to carry out a comparative study of the beam-wave interaction under uniform and tapered magnetic field profiles of a 42 GHz, 200 kW gyrotron. The magnetic field profile across the resonant cavity varies by ±6.5% with a peak value of 1.615 T. The MAGIC simulation shows the desire performance of the gyrotron under both magnetic field conditions with an operating mode TE03 and a pitch factor of 1.26. The analysis of the simulated results show that stability in the power growth was reached more quickly and achieved higher output power in the case of a tapered magnetic field.
Master Thesis Evaluation of Interface Tracking Schemes with Finite Element Discretizations

This thesis is concerned with the evaluation of performance of different numerical methods to track moving and deforming interfaces with finite element discretizations. We compare and contrast the behavior of the following interface tracking methods: the Volume of Fluid (VOF) method, the level set (LS) method, and the hybrid particle level set (PLS) method. We demonstrate the capabilities of these interface tracking methods to model topology changes by evaluating and comparing the performance of the three different methods applied to four different test cases: simple translation, solid body rotation, a single vortex, and a complex deformation field. Our results indicate that the level set method is accurate, robust and good at smoothly capturing interfaces, but suffer an excessive amount of mass loss in under-resolved regions of the flow. The VOF method turned out to be stable but is in general less accurate than the level set method. The particle level set method is easily the most accurate of those tested. It employs massless marker particles along interface to provide characteristic information. The method maintains the nice geometric properties of the level set method along with the robustness and the ease-of-implementation. Although the particle level set method failed to work in the practical application of the rising bubble problem, some suggestions were given how to construct a velocity field for which particles do not stray significantly from the interface.
A Novel Dual Time-Point Unified Amyloid MRI/PET Data Analysis Technique

There is disagreement regarding the best method for performing semi-quantified of amyloid in the preliminary assessment of sufferers with suspicious Alzheimer's & dementia illness in order to: (1) enable picture subjective explanation; (2) account for the propulsive actions of the detector, especially when it comes to at partly adjusting for blood circulation reliance; (3) evaluate the amyloid burden utilizing precise segmentation of subcortical & cortical region.Methodologies Comprehensive testing of a technique that included all of these features was performed on eighty-six patients who had amyloid (18F-florbetaben) MRI/PET in a medical context . After injecting the detector, early photos were taken within 0 & 10 minutes later, while subsequent pictures were taken around 90 & 110 minutes later. Utilizing the geometrical transferring matrix technique, PVEC of the PET information was done. 2 novel Bdual time-point indices as well as parameterized pictures as well as certain local expected outputs were established.According to the is cortical phase of the topographical arrangement of the amyloid plaques, the main sensor, neurological, & retinal regions of patients who were categorised subjectively as amyloid +Ve displayed a scant tracer pickup. The technique identified trace levels of tracer take - up in the basal parts of the temporal & frontal lobes in sick people who were visibly determined to be amyloid—Ve. These areas are thought to be locations of beginning amyloid plaque accumulation & likely symbolised beginning concentration , which is usual of aging process. In individuals who tested negative for amyloid, the results showed a significant association within age as well as the indices of the novel dual time-point amyloid picturing technique.The approach will standardise information on amyloid concentration, making it a useful instrument for both ordinary clinical practise as well as the research context. In younger people, when therapy would hypothetically be more successful, it might also be utilised to detect beginning amyloid plaque formation.
Early Recognition and Risk Stratification in Cardiogenic Shock: Well Begun Is Half Done

Cardiogenic shock is a complex syndrome manifesting with distinct phenotypes depending on the severity of the primary cardiac insult and the underlying status. As long as therapeutic interventions fail to divert its unopposed rapid evolution, poor outcomes will continue challenging health care systems. Thus, early recognition in the emergency setting is a priority, in order to avoid delays in appropriate management and to ensure immediate initial stabilization. Since advanced therapeutic strategies and specialized shock centers may provide beneficial support, it seems that directing patients towards the recently described shock network may improve survival rates. A multidisciplinary approach strategy commands the interconnections between the strategic role of the ED in affiliation with cardiac shock centers. This review outlines critical features of early recognition and initial therapeutic management, as well as the utility of diagnostic tools and risk stratification models regarding the facilitation of patient trajectories through the shock network. Further, it proposes the implementation of precise criteria for shock team activation and the establishment of definite exclusion criteria for streaming the right patient to the right place at the right time.
PI4P-Containing Vesicles from Golgi Contribute to Mitochondrial Division by Coordinating with Polymerized Actin

Golgi-derived PI4P-containing vesicles play important roles in mitochondrial division, which is essential for maintaining cellular homeostasis. However, the mechanism of the PI4P-containing vesicle effect on mitochondrial division is unclear. Here, we found that actin appeared to polymerize at the contact site between PI4P-containing vesicles and mitochondria, causing mitochondrial division. Increasing the content of PI4P derived from the Golgi apparatus increased actin polymerization and reduced the length of the mitochondria, suggesting that actin polymerization through PI4P-containing vesicles is involved in PI4P vesicle-related mitochondrial division. Collectively, our results support a model in which PI4P-containing vesicles derived from the Golgi apparatus cooperate with actin filaments to participate in mitochondrial division by contributing to actin polymerization, which regulates mitochondrial dynamics. This study enriches the understanding of the pathways that regulate mitochondrial division and provides new insight into mitochondrial dynamics.