Miracle-dz/UniFAR-FTUs huggingface dataset:
Adjuvant therapies in venous leg ulcer management: A scoping review

Compression therapy is the current evidence‐based approach to manage venous leg ulcers (VLU); however, adherence is a major barrier to successful treatment. Combination approaches may relieve the burden of treatment by shortening the time to ulcer healing. This scoping review conducted by Australian researchers aimed to establish the evidence of effectiveness of various adjuvant methods on wound healing and recurrence. Randomized Controlled Trials (RCTs), and Systematic Reviews (SR) and Meta‐Analyses (MA) on VLU management approaches published from January 2015 to December 2018 were included in this review. The articles included in the scoping review were grouped according to the management approaches, including (1) pharmaceutical interventions, (2) surgical interventions, (3) topical agents, (4) the use of devices, and (5) other, such as physiotherapy and psychological interventions. Results of this scoping review indicate that there is a limited high‐quality evidence of effectiveness in most adjuvant therapies on wound healing and recurrence. Given the low‐quality evidence observed in this scoping review for adjuvant treatments, the implication for practice is that current management guidelines be followed. Further rigorous studies have the potential to produce better quality evidence. Quality of evidence can be improved by ensuring large sample sizes of a single etiology wounds, standardizing reporting outcomes, and maintaining detailed and evidence‐based protocols in physiological or psychological interventions.
Routing Algorithm Based on Fuzzy Logic Optimization for Lifetime Enhancement and Packet Success in Heterogeneous Wireless Sensor Networks

The lifetime enhancement in a wireless sensor network (WSN) is an area of a lot of research. It is related to the management of energy consumption between nodes. In this context, there are many kinds of literature proposed to give a good distribution of traffic and optimizations models to enhance the network lifetime. In this paper, we used the fuzzy logic approach to obtain the maximum lifetime taking into account the residual energy parameter and studying the quality of service of the network by adding the probability of packets successful. At least we proved that our algorithm achieved lifetime with a residual energy less than 10% for all nodes.
A Novel Quad-Leg Transverse-Flux Permanent Magnet Linear Motor for 3-D Printer Applications

This paper presents a novel quadratic transverse-flux permanent magnet linear motor (QTFPMLM) with C-core modules and cylindrical PMs for 3-D printer applications. The motor topology is introduced. Then, the operating principle of the proposed motor is illustrated and the thrust and cogging torque equations are derived. The 3-D finite element analysis is adopted to obtain the main magnetic characteristics of the machine in terms of magnetic flux density distributions, flux linkage, back-EMF, cogging force, and thrust waveforms. The superiority of the proposed QTFPMLM is proved based on the simulation results. To this end, a linear transverse flux motor is considered and the results are compared. It is shown that the proposed motor has lower cogging force, normal force, and thrust ripple, which makes this motor a viable candidate for high-speed and accurate applications, especially 3-D printer applications.
Two-stage Input-Output Feedback linearization controller for AC-AC converter-based SST

Classical transformers are the most important constituent of the power system and act as a passive interface between high voltage and low voltage systems. They have undesirable characteristics such as poor voltage regulation, large size/weight, sensitivity to harmonics, and poor power flow control. Solid-state Transformers (SSTs), are an emerging power electronics-based technology, sought to replace classical transformers after a century. SSTs are equivalent to a classical transformer with embedded desired functionalities. In this paper, three-phase AC-AC converter with capacitor at the DC-link is employed as power electronic interface in MV and LV side of the SST. Input-output feedback linearization (IOFL) controller is used to control the AC-AC based SST in two stages for controlling the external and internal states. AC-AC SST is linearized by the input-output feedback linearization technique where an internal dynamics is observed. IOFL is used to control the internal dynamics and second stage of the controller in the outer loop. Simulation studies are realized to confirm the applicability of IOFL controller and the control method.
Rational Design of Modified Oxobacteriochlorins as Potential Photodynamic Therapy Photosensitizers

The modulation of the photophysical properties of a series of recently synthetized oxobacteriochlorins with the introduction of heavy atoms in the macrocycles, was investigated at density functional level of theory and by means of the time-dependent TDDFT formulation. Absorption frequencies, singlet-triplet energy gaps and spin-orbit coupling (SOC) constants values were computed for all the investigated compounds. Results show how the sulfur- selenium- and iodine-substituted compounds possess improved properties that make them suitable for application in photodynamic therapy (PDT).
Joint Deep Learning for RGB-D Action Recognition

Recent approaches in RGB-based and depth-based human action recognition achieved outstanding performance respectively, which demonstrate the effectiveness of RGB and depth modalities for action classification, however it is infrequent to consider them both. Currently, available multimodal-based methods of action recognition suffer from some limitations, including non-end-to-end training, violent fusion and inefficiency. In this paper, we propose a novel joint deep learning (JDL) model which is capable of: 1) jointly optimizing the object of classification and feature extraction through a novel end-to-end two-stream deep learning model, 2) refining common-specific features via introducing the constraint of similarity loss in high-level, and 3) using 2D convolution kernel instead of 3D convolution kernel during feature extraction for gaining the efficiency. The experiments on two challenging datasets show the promising performance of our architecture.
Semi-Supervised Soft K-Means Clustering of Life Insurance Questionnaire Responses

The life insurance questionnaire is a large document containing responses in a mixture of structured and unstructured data. The unstructured data poses issues for the user, in the form of extra input effort, and the insurance company, in the form of interpretation and analysis. In this work, we aim to address these problems by proposing a semi-supervised framework for clustering responses into categories using vector space embedding of responses and soft k-means clustering. Our experiments show that our method achieves adequate results. The resulting category clusters from our method can be used for analysis and to replace free text input questions with structured questions in the questionnaire.
Soft set and Fuzzy Rules Enabled SVM Approach for Heart Attack Risk Classification Among Adolescents

Nowadays heart attack is becoming more common among adolescents due to several reasons like heavy work pressure, lack of physical activities, irregular food habits, obesity, eating high fat food, smoking, sedentary life style, heavy drinking and so on. It has been reported that above ten lakh adolescents die every year due to heart related diseases. In literature maximum number of works have been carried out to address heart attack problem among old age people, however there are very less works concentrating on the heart attack detection focusing only adolescents. Hence in this paper the focus is towards adolescents' heart attack risk detection and classification. This is achieved by proposing a novel architecture which detects chances of heart attack in early stages using soft set theory and fuzzy rules. The performance of the proposed architecture is found to be good with respect to several parameters like accuracy, delay and efficiency.
Genome Sequence of a Facklamia hominis Isolate from a Patient with Urosepsis

The genome sequence of a Facklamia hominis strain isolated from the urine of a patient with acute cystitis and sepsis is reported. The genome contains ermB and tet(M) genes, consistent with the isolate’s phenotypic resistance to macrolides and tetracycline. ABSTRACT The genome sequence of a Facklamia hominis strain isolated from the urine of a patient with acute cystitis and sepsis is reported. The genome contains ermB and tet(M) genes, consistent with the isolate’s phenotypic resistance to macrolides and tetracycline.
Isolation, characterization and analysis of bacteriophages from the haloalkaline lake Elmenteita, Kenya

As a step towards better understanding of diversity and biology of phages and their hosts in haloalkaline Lake Elmenteita, phages were isolated from sediment samples and overlying water using indigenous bacteria as hosts. 17 seemingly different phages of diverse morphotypes with different dimensions and partly exhibiting remarkably unusual ultrastructures were revealed by transmission electron microscopy. 12 clonal phage isolates were further characterized. Infection capability of the phages was optimum at 30–35°C and in alkali condition with optimum at pH 10–12. Structural protein profiles and restriction fragment length polymorphism analyses patterns were distinct for each of the phage type. Complete nucleotide sequences of phages vB-VmeM-32, vB_EauS-123 and vB_BhaS-171 genomes varied in size from 30,926–199,912 bp and G + C content of between 36.25–47.73%. A range of 56–260 potential open reading frames were identified and annotated. The results showed that the 12 phages were distinct from each other and confirmed the presence and diversity of phages in extreme environment of haloalkaline Lake Elmenteita. The phages were deposited at the German Collection of Microorganisms and Cell Cultures and three of their genomes uploaded to NCBI GenBank.
Push-Pull Chromophores Based on the Naphthalene Scaffold: Potential Candidates for Optoelectronic Applications

A series of ten push-pull chromophores comprising 1H-cyclopenta[b]naphthalene-1,3(2H)-dione as the electron-withdrawing group have been designed, synthesized, and characterized by UV-visible absorption and fluorescence spectroscopy, cyclic voltammetry and theoretical calculations. The solvatochromic behavior of the different dyes has been examined in 23 solvents and a positive solvatochromism has been found for all dyes using the Kamlet-Taft solvatochromic relationship, demonstrating the polar form to be stabilized in polar solvents. To establish the interest of this polyaromatic electron acceptor only synthesizable in a multistep procedure, a comparison with the analog series based on the benchmark indane-1,3-dione (1H-indene-1,3(2H)-dione) has been done. A significant red-shift of the intramolecular charge transfer band has been found for all dyes, at a comparable electron-donating group. Parallel to the examination of the photophysical properties of the different chromophores, a major improvement of the synthetic procedure giving access to 1H-cyclopenta[b]naphthalene-1,3(2H)-dione has been achieved.
Real-time Obstacle Detection on Embedded System

Human factor causes most of the road accidents; for improving traffic safety, advanced driver assistance system and autonomous driving have become active research. The most significant challenge of these systems is the development of a reliable obstacle detection which can recognize pedestrians and pedestrian-like objects on the road from color images and thermal images. Consequently, the object detection based on neural networks, such as YOLO [1] , is emerging as a solution for identifying obstacles with various appearance. Although the convolutional neural network provides a robust and reliable result, it still involves substantial computational costs and hard to port to an embedded device. This work intends to improve the performance of YOLO and aims at presenting a real-time obstacle detection system on an integrated device.
Towards a Complete Character Set Meitei Mayek Handwritten Character Recognition

Off-line Handwritten Character Recognition (HCR) is the automatic conversion of text in an image into letter codes which are usable within computer and text-processing applications. In order to have a complete HCR system of a script, it is essential to consider all the characters present in the character set of the concerned script. This is essentially important for Indian scripts which have a rich set of characters including modifiers and compound characters. Although it is important to consider all the characters, most works have focused mainly on consonants and numerals of the character sets. This paper reports recognition of complete character set of Meitei Mayek script and the issues and challenges faced. A Convolutional Neural Network (CNN) model is proposed to recognize characters of a fairly large dataset consisting of 38,500 samples. Proposed model is also trained and tested against five mini subsets of the dataset to highlight the complexity in terms of recognition accuracy when complete character set is taken into account. It achieves an accuracy of 93.64% and 92.29% on the dataset of 54 and 55 classes respectively.
Effective Similarity Measurement for Video-based Person Re-identification

Learning discriminative spatial-temporal feature representation and distance metric is crucial for video-based person re-identification. Most of current approaches directly use the extracted feature vectors to compute similarity, while a single feature vector is not sufficient enough to overcome the noise caused by background clutters as well as larger variations in poses and viewpoints. To this end, we incorporate learning spatial-temporal feature representation and similarity measurement into a unified framework for video-based person re-identification. We propose a similarity measurement layer, which measures the implicit similarity of two video sequences in different regions. This strategy makes the network more robust to noise. Meanwhile, in order to alleviate the imbalance in the number of positive and negative samples, we propose a matching sampling loss to help training the similarity measurement layer. We extensively conduct comparative experiments on three challenging datasets iLIDS-VID, PRID-2011 and MARS. The experimental results demonstrate that the proposed approach can achieve favorable/superior performance compared with the state-of-the-art methods for the video-based person re-identification.
Simple Iterative Clustering on Graphs for Robust Model Fitting

In this paper, we propose a novel method, simple iterative clustering on graphs (SICG), to deal with robust model fitting problems. Specifically, we first construct a graph, where each vertex denotes a model hypothesis and each edge represents the similarity between two model hypotheses, for model fitting. We then propose a simple iterative clustering algorithm, which adapts the k-medoids clustering algorithm, to intuitively estimate model instances in data. The proposed SICG method is able to effectively fit and segment multiple-structure data contaminated with a large number of outliers and noises. Experimental results show that SICG achieves superior fitting results over several state-of-the-art model fitting methods on real images.
Machine Learning Based Approach for Person Identification in Group Photos

In this digital era, capturing photos using smartphone camera is very handy. Especially when we are hanging out with friends or family or attending a wedding and so on, we end up taking many group photos. But when browsing through these group photos, most of the times, a person is interested in only those photos in which he himself is present. So, currently we manually browse through such group photos in the phone gallery and then identify the pictures in which the specific person is present. For group photos, this procedure needs to be repeated for every person in the group. In this paper, we have designed and implemented an Android platform based photo grouping application named “EuphoriaGrouping” (EUG) using Neural Networks. EUG application automates the process of detecting faces and identifying persons from a group photo. It maintains a catalogue of group photos for every person present in the group photo. For person identification, two different convolutional neural network models, viz, Custom Built and OpenFace CNN are used. Implementation and performance comparison of these models is presented.
An Investigation of High-Throughput Biometric Systems: Results of the 2018 Department of Homeland Security Biometric Technology Rally

The 2018 Biometric Technology Rally was an evaluation, sponsored by the U.S. Department of Homeland Security, Science and Technology Directorate (DHS S&T), that challenged industry to provide face or face/iris systems capable of unmanned, traveler identification in a high-throughput security environment. Selected systems were installed at the Maryland Test Facility (MdTF), a DHS S&T affiliated bio-metrics testing laboratory, and evaluated using a population of 363 naive human subjects recruited from the general public. The performance of each system was examined based on measured throughput, capture capability, matching capability, and user satisfaction metrics. This research documents the performance of unmanned face and face/iris systems required to maintain an average total subject interaction time of less than 10 seconds. The results highlight discrepancies between the performance of biometric systems as anticipated by the system designers and the measured performance, indicating an incomplete understanding of the main determinants of system performance. Our research shows that failure-to-acquire errors, unpredicted by system designers, were the main driver of non-identification rates instead of failure-to-match errors, which were better predicted. This outcome indicates the need for a renewed focus on reducing the failure-to-acquire rate in high-throughput, unmanned biometric systems.
Addition of chitosan to Calcium-Alginate Membranes for Seawater NaCl Adsorption

Initial research was focused on the production of calcium-based alginate-chitosan membranes from coral skeletons collected from the Gulf of Prigi. The coral skeleton's composition was analyzed using XRF, revealing a calcium oxide content ranging from 90.86% to 93.41%. These membranes showed the significant potential for salt adsorption, as evidenced by FTIR analysis, which showed the presence of functional groups such as -OH, C = O, C-O, and N-H involved in the NaCl binding process. SEM analysis showed the particle size diameter of 185.96 nm, indicating a relatively rough and porous morphology. Under optimized conditions, the resulting calcium-based alginate-chitosan membrane achieved 40.5% Na+ and 48.39% Cl- adsorptions, using 13.3 mL of 2% (w/v) chitosan and 26.6 mL of 2% (w/v) alginate with a 40-minutes contact time. The subsequent we applied for the desalination potential of calcium alginate, revealing the efficient reduction of NaCl levels in seawater. The calcium of coral skeletons collected was 90.86% and 93.41% before and after calcination, respectively, affirming the dominant calcium composition suitable for calcium alginate production. We identified an optimal 8-minute contact time for calcium alginate to effectively absorb NaCl, resulting in an 88.17% and 50% for Na+ and Cl- absorptions. We applied the addition of chitosan into calcium-alginate membranes and its impact on enhancing salt adsorption efficiency for seawater desalination.
An Interpretable CNN With Wavelet Group Policy Embedded for Intelligent Fault Diagnosis

Interpretable convolutional neural networks (CNNs) are key to reliable industrial fault diagnosis by elucidating model decision-making processes and extracting high-dimensional data features. Presently, interpretable CNNs include time-frequency transformation methods in convolutional layers, but their hyperparameter setting (e.g., window size, overlap, and type of wavelet) depends largely on expert knowledge. This article focuses on the problem of wavelet type selection, we propose a wavelet shrinkage convolutional network (GP-WSCN) based on a group policy to solve this problem. Initially, GP-WSCN creates a pretrained network with wavelet convolution layers and soft-threshold learning, quickly providing a basic prior for diverse wavelet waveforms. This prior knowledge is combined with reinforcement learning (RL), allowing GP-WSCN to independently select suitable wavelet convolution kernels, reducing expert dependency. The pretrained network is then repurposed and fine-tuned to selected kernels for swift GP-WSCN deployment in fault diagnosis tasks. Experimental results confirm the diagnostic precision and interpretability of GP-WSCN, as proven through multiple trials.
Fire Detection System Using Color and Flickering Behaviour of Fire with Kekre's LUV Color Space

This paper presents a vision based fire detection system based on Kekre's LUV color space for flame detection and uses the flickering nature of fire to confirm the existence of fire. Traditional fire detection system based on sensors needs to be positioned at certain distance and works on certain trigger condition. The popularity and use of video surveillance at various places has facilitated the popularity of vision based fire detection system in both indoor and outdoor locations. The most important feature of fire is the flame color which varies from red, orange, yellow to white. In order to improve the accuracy of fire detection system, fire like non fire object needs to be differentiated from actual fire objects. The proposed technique uses Kekre's LUV color space to detect the presence of fire pixels. L gives luminance and U and V gives chromaticity values of color image. Negative value of U indicates prominence of red component in color image and negative value of V indicates prominence of green component over blue. Once the fire pixels are detected, the flickering nature of fire is used further to eliminate the fire like non fire objects and hence reducing the false alarm rate. Experimentation is performed on subset of MIVIA database with 21 fire videos is tested with RGB, HSV, YCbCr and Kekre's LUV color space to compare the accuracy and efficiency of the system. The accuracy of fire detection with RGB color space was 66.67%. The use of HSV and YCbCr color model provided an accuracy of 80%. Our method using Kekre's LUV color space for fire detection provided an overall average accuracy of 93.33%.