Miracle-dz/UniFAR-FTUs huggingface dataset:
Co3S4@polyaniline nanotubes as high-performance anode materials for sodium ion batteries

In this study, by employing Co3S4 nanotubes prepared by a facile self-template hydrothermal route based on the Kirkendall effect, Co3S4@polyaniline (Co3S4@PANI) nanotubes, in which polyaniline is uniformly coated on both exterior and inner surfaces of Co3S4 nanotubes, have been fabricated through in situ oxidative polymerization. Benefiting from the highly conductive and mechanically robust polyaniline shell, the conductivity and stability of the composite have been significantly improved. As a result, Co3S4@PANI nanotubes demonstrate much better electrochemical performance than bare Co3S4 nanotubes as sodium ion battery anode materials, with a high capacity of 252.5 mA h g−1 after 100 cycles at the current density of 200 mA g−1. In contrast, although a specific capacity of the bare Co3S4 nanotubes in the first cycle achieved was 815.3 mA h g−1, it rapidly decayed to 192.9 mA h g−1 at the 20th cycle and further reduced to 58.2 mA h g−1 after 100 cycles. Furthermore, Co3S4@PANI nanotubes showed a high discharge capacity of 170.1 mA h g−1 after 400 cycles with a large current density of 4000 mA g−1. The excellent electrochemical properties of Co3S4@PANI nanotubes were ascribed to the combination of Co3S4 and PANI and the stability of the composite structure.
PEDESTRIAN SIGNAL: A TECHNIQUE TO PREVENT ACCIDENT

Pedestrian signals are viable traffic control devices which helps pedestrian to cross intersections safely. Although the literature is extensive when dealing with pedestrian signals design and operations, very few studies have focused on the potential safety benefits of installing pedestrian signals at intersections throughout the worlds. This article deals with the safety technique to prevent accident, which is common nowadays in worldwide mainly huge populated countries like India. One of the past technique is before-after study. Before-after (BA) safety evaluation techniques have suffer a lot in the methodological and statistics issues. Recent studies prefer the use of Crash Modification Function to avoid the drawback of beforeafter study. Crash modification factors (CMFs) are represented in single values, Crash Modification Function used for variable treatment location characteristics (heterogeneity). The use of observational Before-After framework to develop the CM Functions avoids the cross-sectional approach. Functions are derived based on a single time period and no actual treatment intervention.
The two-loop dilatation operator of N = 4 super Yang-Mills theory in the SO(6) sector

The dilatation operator of planar N = 4 super Yang-Mills in the pure scalar SO(6) sector is derived at the two-loop order. Representation theory allows for eight free coefficients in an ansatz for the corresponding spin-chain hamiltonian acting on three adjacent scalar states. While four out of these follow from the known SU(2|3) sector two-loop dilatation operator, the remaining four coefficients are derived by diagrammatic techniques and a match to the known dimension of a length three primary operator. Finally, comments upon the use of this result for the evaluation of three-point structure functions of scalar operators at the one-loop order are given. ar X iv :1 10 6. 07 24 v1 [ he pth ] 3 J un 2 01 1
Emotion based Music player

Visual sentiment analysis, which investigates humans' emotional responses to visual stimuli such as images and videos, has been a fascinating and challenging problem. It attempts to recognize the high-level content of visual data. The success of current models can be attributed to the development of robust computer vision algorithms. The majority of existing models attempt to solve the problem by recommending either robust features or more complex models. The main proposed inputs are visual features from the entire image or video. Local areas have received little attention, which we believe is important to the emotional response of humans to the entire image. Image recognition is used to find people in images and analyze their sentiments or emotions. The CNN algorithm is used in this project to accomplish this task. Given an image, it will search for faces, identify them, place a rectangle in their positions, and describe the emotion found with a percentage of emotions displayed. The emotion output will be in audio format. After detecting emotions based on predicted emotions, songs stored in the system
COVID-19 and Delayed Cerebral Ischemia—More in Common Than First Meets the Eye

Since the arrival of the global COVID-19 pandemic scientists around the world have been working to understand the pathological mechanisms resulting from infection. There has gradually been an understanding that COVID-19 triggers a widespread endotheliopathy and that this can result in a widespread thrombosis and in particular a microthrombosis. The mechanisms involved in the microthrombosis are not confined to infection and there is evidence that patients with aneurysmal sub-arachnoid haemorrhage (SAH) also suffer from an endotheliopathy and microthrombosis. In this article we attempt to shed light on similarities in the underlying processes involved in both diseases and suggest potential treatment options.
Factors Influencing Information Systems Adoption: A Review of the Literature

For the last two decades, a number of information systems are developed for various aims, depending on business’ needs. There are a lot of organizations in the world which are using information systems in their environment, such as telecommunications organizations, universities and banks. Using information system has become crucial for most of organizations regarding with increasing the performance of work procedures and improve productivity and efficiency in general. There are many different models that have been designed and validated to explain the effect of constructs on the adoption of technologies. The aim of this research is to review the literature on information systems adoption and to analyze the different types of models which are frequently applied by researchers in their efforts to examine the factors that estimate the adoption of technologies. The research explores information systems adoption literature that focuses on development models.
A Comparative Study of Various Person Re-Identification in Videos

The various techniques for person re-identification in videos have been discussed and presented in this work. Many techniques like re-identification using neuromorphic criteria, deep metric learning with pose invariant case, transformation based on relaxation of label space, networks using multi-cameras, hybrid representation reinforced, bag of words model and log density gradient clustering are reviewed and illustrated in brief. This study can be used to further improve the existing techniques for person re-identification
Non-visual Virtual Reality: Considerations for the Pedagogical Design of Embodied Mathematical Experiences for Visually Impaired Children

: Digital developments that foreground the sensory body and movement interaction offer new ways of engaging with mathematical ideas. Theories of embodied cognition argue for the important role of sensorimotor interaction in underpinning cognition. For visually impaired children this is particularly promising, since it provides opportunities for grounding mathematical ideas in bodily experience. The use of iVR technologies for visually impaired children is not immediately evident, given the central role of vision in immersive virtual worlds. This paper presents an iterative, design-based case study with visually impaired children to inform the pedagogical design of embodied learning experiences in iVR. Drawing from embodied pedagogy, it explores the process of implementing a classroom-based non-visual VR experience, designed to give visually impaired children an embodied experience of position in terms of Cartesian co-ordinates as they move around a virtual space. Video recordings of interaction combined with feedback from teachers and children contribute to knowledge of iVR learning applications in formal settings by discerning three types of pedagogical practices: creation of a performance space introduction of performative actions and action connected diverse perspectives.
The Design of Dynamic Fog Cloud System using mDBaaS

Cloud computing has evolved into a core computing infrastructure for the internet that encompasses content, as well as communications, applications and commerce. By providing powerful computing and communications capabilities in the palm of the hand everywhere with a variety of smart devices, mobile applications such as virtual reality, sensing and navigation have emerged and radically changed the patterns people live. The data that is generated is getting bigger. Cloud computing, on the other hand, has problems with system load and speed due to the collection, processing and control of remote data. To solve this problem, fog computing has been proposed in which data is collected and processed at an edge. In this paper, we propose a system that dynamically selects a fog server that acts as a cloud in the edge. It serves as a mediator in the cloud, and provides information on the services and systems belonging to the cloud to the mobile device so that the mobile device can act as a fog. When the role of the fog system is complete, we provide it to the cloud to virtualize the fog. The heterogeneous problem of data of mobile nodes can be solved by using mDBaaS (Mobile DataBase as a Service) and we propose a system design method for this.
Secretory System Components as Potential Prophylactic Targets for Bacterial Pathogens

Bacterial secretory systems are essential for virulence in human pathogens. The systems have become a target of alternative antibacterial strategies based on small molecules and antibodies. Strategies to use components of the systems to design prophylactics have been less publicized despite vaccines being the preferred solution to dealing with bacterial infections. In the current review, strategies to design vaccines against selected pathogens are presented and connected to the biology of the system. The examples are given for Y. pestis, S. enterica, B. anthracis, S. flexneri, and other human pathogens, and discussed in terms of effectiveness and long-term protection.
ROBUST OPTIMAL PROPORTIONAL REINSURANCE AND INVESTMENT STRATEGY FOR AN INSURER WITH ORNSTEIN-UHLENBECK PROCESS

This paper analyzes a robust optimal reinsurance and investment strategy for an Ambiguity-Averse Insurer (AAI), who worries about model misspecification and insists on seeking robust optimal strategies. The AAI’s surplus process is assumed to follow a jump-diffusion model, and he is allowed to purchase proportional reinsurance or acquire new business, meanwhile invest his surplus in a risk-free asset and a riskyasset, whose price is described by an Ornstein-Uhlenbeck process. Under the criterion for maximizing the expected exponential utility of terminal wealth, robust optimal strategy and value function are derived by applying the stochastic dynamic programming approach. Serval numerical examples are given to illustrate the impact of model parameters on the robust optimal strategies and the loss utility function from ignoring the model uncertainty.
Intent-aware control in kinematically redundant systems: Towards collaborative wearable robots

Many human-robot collaboration scenarios can be seen as a redundant leader-follower setup where the human (i.e., the leader) can potentially perform the task without the assistance of the robot (i.e., the follower). Thus, the goal of the collaboration, beside stable execution of the task, is to reduce the human cost; e.g., ergonomic, or cognitive cost. Such system redundancies (where the same task be achieved in different manner) can also be exploited as a communication channel for the human to convey his/her intention to the robot; since it is essential for the overall performance (both execution and assistance) that the follower recognizes the intended task in an online fashion. Having an estimation for the intended task, the robot can assist the human by reducing the human cost over the task null-space; i.e., the null-space which arises from the overall system redundancies with respect to the intended task. With the prospective of supernumerary and prosthetic robots, in this work, we primarily focus on serial manipulation in which the proximal/distal part of the kinematic chain is controlled by the leader/follower respectively. By exploiting kinematic redundancies for intention-recognition and cost-minimization, our proposed control strategy (for the follower) ensures assistance under stable execution of the task. Our results (simulations and preliminary experimentation) show the efficacy of our method in providing a seamless robotic assistance (i.e., improving human posture) toward human intended tasks (i.e., reaching motions) for wearable robotics.
Finnish inserted vowels: a case of phonologized excrescence

Abstract In this paper, I examine a case of vowel insertion found in Savo and Pohjanmaa dialects of Finnish that is typically called “epenthesis”, but which demonstrates characteristics of both phonetic excrescence and phonological epenthesis. Based on a phonological analysis paired with an acoustic corpus study, I argue that Finnish vowel insertion is the mixed result of phonetic excrescence and the phonologization of these vowels, and is related to second-mora lengthening, another dialectal phenomenon. I propose a gestural model of second-mora lengthening that would generate vowel insertion in its original phonetic state. The link to second-mora lengthening provides a unified account that addresses both the dialectal and phonological distribution of the phenomenon, which have not been linked in previous literature.
METHODOLOGIES FOR EFFICIENT AND OPTIMAL IMPLEMENTATION OF 5G MMWAVE NETWORKS

: The implementation of 5G mobile networks is going to completely transform our communication systems, providing a rich and uniform experience to up to thousand times of the existing users with the help of mmWave spectrum. The mmWave band, also known as millimeter band ensures errorfree delivery at the maximum capacity and helps to increase the performance. In typical scenarios where data congestion is a problem, mmWave spectrum will help by providing better coverage and ultra-high throughput. The available bandwidth of mmWave is in the range of 25 GHz to 100 GHz which is capable of supporting the requirements of today’s world and hence is promising. These characteristics vary from that of microwave frequency bands in terms of attenuation, atmospheric absorption and the path loss that happens. So, for a point-to-point like mmWave systems have a larger overall loss in comparison to microwave systems. In this research paper we will discuss signal transmission and how connectivity can be established for a link level perspective of UE i.e., User Equipment and gNodeB i.e., radio network node for 5G in a more effective manner. By determining the signal loss in the practical environment via the pilot signal, we aim to reduce the noise by optimally positioning the minimum number of small cells. The purpose of the research is to find the optimum solution for faster and noiseless communication, most importantly in a cost-efficient manner.
Monocular 3D Reconstruction of the Colon Using CNNs Trained on Synthetic Data

INTRODUCTION Colorectal cancer is the third most common cancer worldwide and is rising in incidence. Indicators or precursors to cancerous tissue development can be detected as polyps and removed during colonoscopy. However, complete, endoscopic colon investigation is still challenging and often regions of the colon are not fully examined resulting in high polyp miss rates [1]. Improving the endoscopist’s ability to detect abnormal tissue through computational or biphotonic techniques and also to navigate within the colon and reference the position of the camera within the anatomy are significant clinical needs. Better navigation within the colon relies on the ability to map the 3D environment and localize the endoscope within it, but while computer vision advances make this possible in many applications, it has yet to be achieved reliably in endoscopic examination.
A Bayesian Analogue of Gleason’s Theorem

We introduce a novel notion of probability within quantum history theories and give a Gleasonesque proof for these assignments. This involves introducing a tentative novel axiom of probability. We also discuss how we are to interpret these generalised probabilities as partially ordered notions of preference and we introduce a tentative generalised notion of Shannon entropy. A Bayesian approach to probability theory is adopted throughout, thus the axioms we use will be minimal criteria of rationality rather than ad hoc mathematical axioms.
Anomaly Detection Methods for Log Files

This thesis is dedicated to methods of anomaly detection applied to log files. The current state-of-the-art anomaly detection methods usually follow the traditional approach for log processing. Firstly, log files are processed by a log parsing technique which transforms text information into non-specific structured data. Next, the data is converted into a numerical representation. The feature extraction is often related to natural language processing techniques. However, the traditional approach requires extensive domain knowledge and retraining a particular model when new log messages become available. Thanks to the recent advancements in the natural language processing domain, we can directly learn embedding vectors instead of the feature extraction based on log parsing. We propose novel autoencoder-based models leveraging the embedding vectors since autoencoders are a recommended choice in the field of anomaly detection. Moreover, we experiment with various techniques which are incorporated into autoencoders, such as convolutional layers and the self-attention mechanism. We verify that the autoencoders utilizing convolutional layers are effective for anomaly detection in log files. Furthermore, we demonstrate that boosting the models with the self-attention mechanism might be advantageous and open room for future work and further research. Finally, we can conclude that the traditional approach combined with an autoencoder may achieve impressive results on the provided testing data set. Nonetheless, the AECNN1D model achieves the most promising results among models leveraging the embedding representation of logs — the F1-score is 0.8597 on the testing data set. The AECNN1D model is generally applicable to deploying into the production since no additional requirements or periodic retraining is necessary.
Species-rich bark and ambrosia beetle fauna (Coleoptera, Curculionidae, Scolytinae) of the Ecuadorian Amazonian Forest Canopy

Abstract Canopy fogging was used to sample the diversity of bark and ambrosia beetles (Coleoptera, Curculionidae, Scolytinae) at two western Amazonian rainforest sites in Ecuador. Sampling was conducted by Dr Terry Erwin and assistants from 1994–2006 and yielded 1158 samples containing 2500 scolytine specimens representing more than 400 morphospecies. Here, we analyze a subset of these data representing two ecological groups: true bark beetles (52 morphospecies) and ambrosia beetles (69 morphospecies). A high percentage of these taxa occurred as singletons and doubletons and their species accumulation curves did not reach an asymptote. Diversity estimates placed the total scolytine species richness for this taxon subset present at the two sites between 260 and 323 species. The α-diversity was remarkably high at each site, while the apparently high β-diversity was an artifact of undersampling, as shown by a Monte Carlo resampling analysis. This study demonstrates the utility of canopy fogging for the discovery of new scolytine taxa and for approximate diversity assessment, but a substantially greater sampling effort would be needed for conclusive alpha as well as beta diversity estimates.
BUCCAL DRUG DELIVERY SYSTEMS: A SMART WAY TO DELIVER DRUGS

A considerable fraction of orally administered drug undergoes extensive first pass metabolism which poses a major challenge of bioavailability with certain drugs. The solution to this problem seems to exist with delivery of drug through buccal route. Owing to highly vascular area and relatively higher permeability, the buccal mucosa offers a smart option for local as well as systemic delivery of various drugs. In addition, it may prolong the contact time of drug with affected site. This review attempts to provide a brief idea about the buccal drug delivery including structural features of oral cavity, mechanism of buccal drug absorption, concept of bio adhesion, pharmaceutical considerations for dosage forms, methods of preparation and evaluation of buccal delivery systems along with some marketed products and patents granted thereon.
Optimal Transmission Line Switching to Improve the Reliability of the Power System Considering AC Power Flows

The reliability of the electrical system is a fundamental study that is carried out to determine the possible deficiencies that an electrical system can have in case of failures, since a failure can cause disturbances, power cuts, and load disconnections. For this reason, Optimal Transmission Switching (OTS) with Optimal AC Power Flows (OPF-AC) is used to reduce disturbances when faults occur and minimize equipment load and disconnections, but OTS offers possible switches in order to make it possible to reduce the damage that can be done for a fault with operating limitations in voltage, power, and angular deviation. However, to have a complete study, it is proposed to use a reliability analysis through contingency ranking to know the risks that a switched system may have at the time of simultaneous or consecutive failures. In addition, a load capacity investigation is conducted to determine if the transmission lines are within their operating limits. The study presents an analysis of the behavior of the switched system and an adequate operation for the mitigation of failures in the system through the switching of transmission lines with analysis of load capacity and reliability. The results presented by the proposed methodology will be compared with Matlab’s Matpower simulation package.
Selection of car model through TOPSIS using intuitionistic fuzzy entropy weights

: This paper proposes a new fuzzy TOPSIS decision making model using entropy weights for dealing with multiple attribute decision making (MADM) under intuitionistic fuzzy environment. The assignment of attribute weights is one of the most important issues in fuzzy multiple attribute decision making (MADM). In this study, attribute values are considered as intuitionistic fuzzy numbers while the information about attribute weights are completely unknown. Entropy is one of the weight measures based on objective evaluation. Non-probabilistic-type entropy measures for intuitionistic fuzzy sets have been developed and applied to measure attribute weights. Performance ratings are used to give a numerical measure of each attribute value. We have applied Intuitionistic Fuzzy TOPSIS to analyze performance matrix based on performance ratings and ranked them in order of preference.