Miracle-dz/UniFAR-FTUs huggingface dataset:
Morphology Clustering Software for AFM Images, Based on Particle Isolation and Artificial Neural Networks

Advanced microscopy techniques currently allow scientists to visualize biomolecules at high resolution. Among them, atomic force microscopy (AFM) shows the advantage of imaging molecules in their native state, without requiring any staining or coating of the sample. Biopolymers, including proteins and structured nucleic acids, are flexible molecules that can fold into alternative conformations for any given monomer sequence, as exemplified by the different three-dimensional structures adopted by RNA in solution. Therefore, the manual analysis of images visualized by AFM and other microscopy techniques becomes very laborious and time-consuming (and may also be inadvertently biased) when large populations of biomolecules are studied. Here we present a novel morphology clustering software, based on particle isolation and artificial neural networks, which allows the automatic image analysis and classification of biomolecules that can show alternative conformations. It has been tested with a set of AFM images of RNA molecules (a 574 nucleotides-long functinal region of the hepatitis C virus genome that contains its internal ribosome entry site element) structured in folding buffers containing 0, 2, 4, 6 or 10 mM Mg2+. The developed software shows a broad applicability in the microscopy-based analysis of biopolymers and other complex biomolecules.
Antibiogram of Acinetobacter spp. isolated from various clinical specimensin a tertiary care hospital in West Bengal, India.

Acinetobacter spp. are Gram negative coccobacilli causing various nosocomial infections. They possess different types of Beta-lactamases like SHV, TEM and others which lead to treatment failure in case of infections due to this pathogen. Studying their species distribution in various infections and drug resistance pattern is hence important. Hence our study was aimed to study the different infections caused by Acinetobacter spp, in a tertiary care hospital in Eastern India and its antibiotic susceptibility pattern.
Machine Learning Enhanced NARMAX Model for Dst Index Forecasting

As many systems and equipment are sensitive to magnetic disturbances, it is important to understand the magnetosphere system, to reduce the negative effect caused by severe space weather situations. The disturbance storm time (Dst) index is used to measure the magnetic disturbances and it is correlated with solar wind variables. This study presents a new machine learning enhanced NARMAX (MLE- NARMAX) model for 3 hours ahead forecasting of Dst index. An important advantage of the MLE-NARMAX model is that it provides a transparent and explainable model structure. The model performance is tested over three typical strong storm periods, where the prediction skills are 0.9734, 0.9598 and 0.9206 in terms of correlation, and 0.9474, 0.9173, and 0.8333 in terms prediction efficiency (PE). Compared to the conventional NARX model, the MLE-NARMAX produces better model predictions.
Energy-Efficient Resource Allocation for NOMA Systems

As an aspiring multiple access scheme for future radio access, Non-Orthogonal Multiple Access (NOMA) is considered as a promising technique for the fifth-generation mobile communication networks (5G). The goal looks to improve Spectral Efficiency (SE) and flexible allocation of radio resources for users. In addition to this, Green Radio (GR), which focuses on Energy Efficiency (EE), is becoming a sought-after trend in the process of a future for an energy-efficient system. In this paper, added to SE improvement, we seek to enhance EE for downland NOMA systems. The problem is formulated as a non-convex optimization subject in which the basic idea is to maximize the EE by imposing a minimum satisfying SE while guaranteeing a Quality of Service (QoS) for each user. Optimization problem's solution is achieved when an optimal power allocation is obtained. Simulation results confirm analytical results and show the proposed power allocation scheme's efficiency to reach a flexible resources allocation.
Forecasting of software reliability using neighborhood fuzzy particle swarm optimization based novel neural network

This paper proposes an artificial neural network ( ANN ) based software reliability model trained by novel particle swarm optimization ( PSO ) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.
Effect of Sensor Motion on Time Delay and Doppler Shift Localization: Analysis and Solution

This paper investigates active localization of a stationary object using time delay alone or together with Doppler shift measurements observed by a number of dynamic monostatic sensors having collocated transmitters and receivers, where the sensor motion during the interrogation period is not negligible. Non-negligible motion effect appears when the sensor speed relative to the signal propagation speed is significant, such as in an acoustic or an underwater environment. The motion effect causes the signal sent and received locations different, with their separation proportional to the two-way propagation time delay that is dependent on the object location. We first provide new measurement models for time delay and Doppler shift in the presence of sensor motion. They come as recursive equations and their non-recursive forms are derived. Analysis of the motion effect based on the Cramér-Rao lower bound (CRLB) is conducted, and the performance loss in terms of the location bias and variance when ignoring the sensor motion effect is evaluated. Two kinds of estimators for the object location are next proposed by exploiting the non-recursive forms of the model equations. One is a closed-form solution by algebraic evaluation and the other is a semi-definite relaxation solution by convex optimization. The first is computationally efficient and is shown to achieve the CRLB performance over the small error region under Gaussian noise, while the second can handle the positioning problem better when the noise level is high. Simulations validate the theoretical analysis and the performance of the proposed estimators.
A Monitoring and Management System for Medium-Scale Photovoltaic Plants

Photovoltaic (PV) energy use is growing fast all over the world, due to new policies to reduce the application of fossil fuels. However, for achieving higher efficiency in the Renewable Power Plants (RPP), some Monitoring and Management System (MMS) are required, aiming to find the optimal operating conditions for the system. The main goal of a monitoring system is to evaluate the RPP’s performance, throughout its operating time. In the same way, a management system aims to administrate and act over the system, in order to maximize its performance. In this scenario, the main objective of this paper is the proposition of one MMS, which focuses specifically on micro-generation (medium-scale), creating the possibility for final users and for Electric Utility Companies monitoring and operating plant parameters. In order to achieve that, electrical and environmental variables are monitored and computationally analyzed, producing instantaneous and historical data, allowing to estimate the plant efficiency and some actions over the plant’s parameters, such as the power factor through the power inverter. We present the initial results of this MMS including a detailed description of each stage of the system, calibration procedures and a short-period monitoring, showing the feasibility of the proposal in a reduced PV plant of 5kW.
The Effect of Aromatherapy with Lavender Essential Oil on Depression, Anxiety and Stress in Hemodialysis Patients: A Clinical Trial

Background: Depression, anxiety and stress are common among hemodialysis patients affecting their mental health and quality of life, leading to multiple dysfunctions. Anti-anxiety effects of aromatherapy, as one of the domains of complementary medicine, have been investigated in several studies. Aim: Investigation of the effect of aromatherapy with lavender essential oil on depression, anxiety and stress rates in hemodialysis patients. Methods: This study was a randomized controlled clinical trial performed on 60 patients undergoing hemodialysis in Tehran, Iran in 2014. The patients were randomly divided into intervention and control groups. To measure the rate of depression, anxiety and stress, the Depression Anxiety Stress Scales-21 was used. In the intervention group, the patients were asked to inhale the lavender essential oil smeared on a piece of cloth (three drops of oil) for one hour during the hemodialysis procedure. After 4 weeks, the rates of depression, anxiety and stress were re-measured. Data analysis was performed by SPSS software version 14 using chi-square, Wilcoxon and Mann-Whitney U statistical tests. Results: In this study, 33(60%) of the participants were male, and 53(96.4%) were married. After the intervention, the scores of depression (P<0.001) and stress (P<0.04) significantly decreased in the intervention group in comparison with control group; but the reduction in the anxiety score was not statistically significant. Conclusion: Aromatherapy with lavender essential oil might reduce depression and stress among hemodialysis patients. Therefore, this method can be used as a complementary method with less complication to improve the quality of life of these patients.
Experimental Evaluation of a UHF-MIMO RFID System for Positioning in Multipath Channels

This paper presents an experimental evaluation of an ultra-high-frequency (UHF) multiple-input-multiple-output (MIMO) radio-frequency identification (RFID) system for positioning. To this end, we propose a set of novel parametric maximum likelihood direct-positioning algorithms capable of exploiting the coherent measurements performed by closely-spaced antennas and simultaneously exploiting the non-coherent measurements by widely-spaced antennas. The radio channel indoors for relatively small bandwidth can be characterized by a line-of-sight component plus a multitude of so-called dense multipath components (DMC). The proposed algorithm framework is able to consider the DMC, enabling more accurate positioning. We present an experimental RFID testbed, capable of performing wideband measurements up to 50MHz bandwidth. This testbed is able to query the RFID tag at the UHF band while performing the position measurements in the 2.45 GHz band, supporting such wideband signal transmission. A dual-frequency RFID tag is presented, equipped with two antennas, scattering back synchronously at the UHF and 2.45 GHz bands. Utilizing the experimental UHF-MIMO RFID testbed we show that 80% of the position errors are smaller 0.15m if the DMC process is included in the algorithm.
Advanced Synthesis of Wide-Tuning-Range Frequency-Adaptive Bandpass Filter With Constant Absolute Bandwidth

This article proposes an advanced synthesis of the frequency-adaptive bandpass filter (FA-BPF) with constant absolute bandwidth (ABW). The proposed synthesis is based on the element-variable coupling matrix (EVCM), which is directly extracted from the classical filter polynomials. The extraction method is presented, investigated in detail, and confirmed by a set of numerical simulations. All frequency-dependent couplings between the resonators in an FA-BPF are uniquely modeled by the new coupling matrix, and the element variation of the EVCM directly accounts for the frequency tuning behavior of an FA-BPF. This one-to-one correspondence tackles the difficulty of the FA-BPF synthesis and design with a different center frequency tuning range (FTR), a different bandwidth (BW), and a different BW variation rate. This article also proposes the predefined coupling technique in the new synthesis. It allows both nonfrequency-dependent matrix and frequency-dependent matrix in an EVCM to be separately implemented in practice. For experimental demonstration, two four-pole/four-transmission zero (TZ) FA-BPFs with identical passband and constant ABW are implemented and then constitute a frequency-adaptive bandpass diplexer (FA-BPD) with two tunable channels that have identical passband and constant ABW. The measurement results exhibited performance superiority of the design and validated the proposed design method.
Robotic Inventorying and Localization of RFID Tags, Exploiting Phase-Fingerprinting

In this paper we investigate the performance of phase-based fingerprinting for the localization of RFID-tagged items in warehouses and large retail stores, by deploying ground and aerial RFID-equipped robots. The measured phases of the target RFID tags, collected along a given robot’s trajectory, are compared to the corresponding phase-measurements of reference RFID tags; i.e. tags placed at known locations. The advantage of the method is that it doesn’t need to estimate the robot’s trajectory, since estimation is carried out by comparing phase measurements collected at neighboring time-intervals. This is of paramount importance for an RFID equipped drone, destined to fly indoors, since its weight should be kept as low as possible, in order to constrain its diameter correspondingly small. The phase measurements are initially unwrapped and then fingerprinting is applied. We compare the phase-fingerprinting with RSSI based fingerprinting. Phase-fingerprinting is significantly more accurate, because of the shape of the phase-function, which is typically U-shaped, with its minimum, measured at the point of the trajectory, when the robot-tag distance is minimised. Experimental accuracy of 15cm is typically achieved, depending on the density of the reference tags’ grid.
ECG Beat Arrhythmia Classification by using 1-D CNN in case of Class Imbalance

In this study, ECG arrhythmia types of non-ectopic (N), ventricular ectopic (V), unknown (Q), supraventricular ectopic (S) and fusion (F) were classified by using the convolutional neural network (CNN) architecture. QRS detection was performed on these ECG arrhythmias that downloaded from MIT-BIH database. An imbalanced number of beats was obtained for 5 different arrhythmia types. In order to reduce the effect of imbalance in statistical performance metrics, data mining techniques, such as recall of data, were applied. It was aimed to increase the positive predictive value (PPV) rates of the classes which consist of a few instances.
Overcharged Li-Polymer Batteries: A Post-Mortem Analysis

Since safety hazards can occur during the life of a Li-ion battery, understanding its behaviour under abusive conditions is important for the development of a safe cell. In this work, overcharge tests on commercial Li-polymer cells were conducted, resulting in gas evolution. A comprehensive postmortem analysis of the abused cells was planned to be carried out by means of several analytical techniques: gas phase chromatography for the identification of the exhaust gases, scanning electron microscopy and energy X-ray spectrometry for the investigation of the cathode and anode materials, Fourier-transform infrared spectroscopy for the study of the electrolyte composition, etc.). First interesting results, regarding the characterization of a high temperature overcharged cell and the detection of flammable and toxic species produced by the cell, are presented in this paper.
Time-Domain-Signature Chipless RFID Tags: Near-Field Chipless-RFID Systems With High Data Capacity

Chipless RF identification (RFID) technology has emerged as an alternative to RFID systems that have tags equipped with silicon integrated circuits (ICs), or chips [1]-[12]. The main advantage of chipless RFID systems over their chipped counterparts is the lower cost of the tags, which results from the fact that the ID code is contained in a printed planar encoder (implementable with conductive inks) rather than stored in a silicon chip. Low-cost tags (below the US$0.01 barrier) are necessary in many RFID applications involving items that have a moderate or low price and where chip-based tagging would represent a significant penalty in terms of the overall expense.
Bidirectional Tracking Scheme for Visual Object Tracking Based on Recursive Orthogonal Least Squares

Visual object tracking in unconstrained environments is a challenging task in computer vision. How to design an efficient discriminative feature representation is one challenging issue. To improve the adaptability of the tracker to large object appearance changes, the observation model needs to be updated online. However, a bad model update using inaccurate training samples can lead to model drift problem. Therefore, how to design an efficient online observation model and a model update strategy are two other challenging issues. This paper proposes the concatenation of histogram of oriented gradients variant (HOGv) and color histogram as the feature representation to balance discriminative power and efficiency. The single-hidden-layer feedforward neural network (SFNN) is used as an observation model, and the recursive orthogonal least squares (ROLS) algorithm is used to update the model online. A bidirectional tracking scheme is designed to alleviate the model drift problem during online tracking. The proposed bidirectional tracking scheme consists of three modules: the forward tracking module, the backward tracking module and the integration module. The forward tracking module first finds all the candidate regions, and then, the backward tracking module calculates the respective confidence of each candidate region according to historical information. Finally, the integration module integrates both of the first two modules’ results to determine the final tracked object and the model update strategy for the current frame. Extensive evaluations of the existing tracking benchmarks have shown that the proposed tracking framework results in significant performance improvements compared with the base tracker, and it outperforms most of the state-of-the-art trackers.
Using Mode Coupling Mechanism in Symmetrical Triple-Core Photonic Crystal Fiber for High Performance Strain Sensing

Basing on the mode coupling mechanism of symmetrical triple-core photonic crystal fiber (STCPCF), a highly sensitive strain sensor is proposed and experimentally demonstrated. Particularly, the triple-cores are arranged in an equilateral triangle on the pure silica host STCPCF. As a result, the coupling coefficient among the triple-cores of the STCPCF can increase as the fiber stretches. Moreover, the proposed sensor is designed by monitoring the wavelength shift of the coupling output spectrum. The experimental results show that the maximum strain sensitivity can be as high as −29.8 pm/μϵ, which outperforms most of mode interference theory based fiber sensors. In addition, the strain sensitivity can be predicted by using mode-coupling mechanism in triple-core fiber. Theoretical results reveal that the sensitivity can be further improved up to −58.15 pm/μϵ by optimizing the fiber structure parameters. Finally, the experimental results also show a high repeatability and stability, and a low temperature-strain cross-sensitivity of the proposed sensor.
A Case of Iliac Compression Syndrome Successfully Treated with Catheter-Directed Thrombolysis: Application of Diagnostic MRI Methods for Phase- Assessment of Venous Thromboses

A 20-year-old male with pulmonary embolism and deep venous thrombosis due to iliac compression syndrome was successfully treated with anticoagulation and catheter-directed thrombolysis (CDT). We applied a diagnostic magnetic resonance imaging method for thrombus-age assessment. In the acute phase, black blood T2-weighted (BBT2W) imaging showed very low signal intensity (SI) of the venous thrombus. However, in the subacute phase, SI was extremely high, and two months after the onset (chronic phase), BBT2W imaging showed medium SI. Thrombus-age information is essential for successful CDT. Our observations allow clinicians to distinguish between acute, subacute and chronic thromboses, allowing optimal application of CDT.
A seroepidemiological study of Parvovirus B19, Toxoplasma gondii and Chlamydia trachomatis in pregnant women referring to Obs & Gyn ward of Ahwaz Imam Khomeini Hospital

Introduction: Parvovirus B19, Toxoplasma gondii and Chlamydia trachomatis are the most common microorganisms which cause different sequela during pregnancy such as congenital aplasia, hydrops fetalis, abortion and other congenital anomalies in fetus (Such as neuroophthalmic complications). The purpose of this study was to determine the prevalence of these agents in pregnant women attending the Obs & Gyn ward of Imam Khomeini Hospital in Ahwaz, Iran. Materials & Methods: In this cross-sectional study, IgG against the mentioned microorganisms was assessed by ELISA method in the sera of 79 pregnant women attending Imam Khomeini Hospital in Ahwaz during a period of one month. In addition, a questionnaire was filled for each case and clinical data were entered. Finally, χ was calculated and the data were analyzed by SPSS (Version 11.5). P 40% of the pregnant women did not have IgG against the mentioned agents, so they were vulnerable to infection by those microorganisms in ensuing pregnancies. Therefore, health education for pregnant women and screening for the mentioned microorganisms to prevent abortions and birth of newborns with congenital anomalies should be addressed more attentively.
Reconfigurable photonic RF filters based on integrated Kerr frequency comb sources

We demonstrate two categories of photonic radio frequency (RF) filters based on integrated optical micro-combs. The first one is based on the transversal filtering structure and the second one is based on the channelization technique. The large number of wavelengths brought about by the microcomb results in a significantly increased RF spectral resolution and a large instantaneous bandwidth for the RF filters. For the RF transversal filter, we demonstrated Q factor enhancement, improved out-of-band rejection, tunable centre frequency, and reconfigurable filtering shapes. While a high resolution of 117 MHz, a large RF instantaneous bandwidth of 4.64 GHz, and programmable RF transfer functions including binary-coded notch filters and RF equalizing filters with reconfigurable slopes are demonstrated for the RF channelized filter. The microcomb-based approaches feature a potentially much smaller cost and footprint, and is promising for integrated photonic RF filters.