Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship Between High-Density Lipoprotein-Cholesterol and Red Cell Distribution Width in Patients With Coronary Artery Disease

Background A high red cell distribution width (RDW), which indicates ongoing inflammation, and low levels of high-density lipoprotein-cholesterol (HDL-C) are associated with increased mortality and morbidity in patients with coronary artery disease (CAD). Recent studies have suggested that HDL-C possesses anti-inflammatory and antioxidant effects, which may explain its anti-atherogenic properties. This study aims to determine the relationship between HDL-C levels and RDW in patients with CAD. Materials and methods This cross-sectional study was performed on 120 patients with CAD from July 2020 to June 2021 in the Hematology Department of Chughtai Lab Lahore. Patients were graded according to the degree of coronary artery stenosis as follows: Grade 1,30%-50%; Grade 2, 51%-70%; and Grade 3,>70%. The HDL-C level was measured from venous blood samples by a fully automated Abbot Alinity analyzer. The RDW was measured by Sysmex XN-5000. The sample size was calculated using the Select Statistics calculator. The mean RDW and HDL-C of the patients were calculated, and correlation analyses were performed using the Pearson correlation coefficient. Results The HDL-C level was inversely related to the RDW. Of the 120 patients, 38, 44, and 38 had Grade 1, Grade 2, and Grade 3 stenosis, respectively. The mean HDL-C level and RDW were 30.58 ±3.77 mg/dL and 16.04% ±1.66%, respectively. The value of r was −0.8622 (strongly negative). Data were stratified based on the degree of stenosis. The values of r in Grades 1, 2, and 3 were −0.43 (moderately negative), −0.604 (moderately negative), and −0.27 (weakly negative), respectively. Conclusion The RDW can be used as an additional marker to determine the disease status in CAD patients.
NEURAL NETWORK PREDICTION APPLIED TO THE FINITE ELEMENT ANALYSIS OF EDDY CURRENT AND TEMPERATURE FIELD IN TFIH

With neural network the eddy current and temperature distribution prediction on the continuously moving thin conducting strips in transverse flux induction heating (TFIH) equipment is presented. There were two neural networks used. One was for the eddy current field and the other for the temperature field. The average relative errors of the temperature prediction for the four groups of tested samples are 1.6~3.2%. Taking the prediction results instead of environment temperature as the initial values for solving non-linear finite element discrete equations, the iteration numbers required can be saved 55.6-67.6%.
AutoRet: A Self-Supervised Spatial Recurrent Network for Content-Based Image Retrieval

Image retrieval techniques are becoming famous due to the vast availability of multimedia data. The present image retrieval system performs excellently on labeled data. However, often, data labeling becomes costly and sometimes impossible. Therefore, self-supervised and unsupervised learning strategies are currently becoming illustrious. Most of the self/unsupervised strategies are sensitive to the number of classes and can not mix labeled data on availability. In this paper, we introduce AutoRet, a deep convolutional neural network (DCNN) based self-supervised image retrieval system. The system is trained on pairwise constraints. Therefore, it can work in self-supervision and can also be trained on a partially labeled dataset. The overall strategy includes a DCNN that extracts embeddings from multiple patches of images. Further, the embeddings are fused for quality information used for the image retrieval process. The method is benchmarked with three different datasets. From the overall benchmark, it is evident that the proposed method works better in a self-supervised manner. In addition, the evaluation exhibits the proposed method’s performance to be highly convincing while a small portion of labeled data are mixed on availability.
Local Explanations of Global Rankings: Insights for Competitive Rankings

Explaining complex algorithms and models has recently received growing attention in various domains to support informed decisions. Ranking functions are widely used for almost every form of human activity to enable effective decision-making processes. Hence, explaining ranking indicators and their importance are essential properties to enhance performance. Local explanation techniques have recently become a prominent way to interpret individual predictions of machine learning models. However, there has been limited investigation into explaining competitive rankings. This work proposes a hierarchical ranking explanation framework to capture local explanations for competitive rankings by defining a proper neighborhood construction approach. We explore various explanation techniques to identify the local contribution of ranking indicators based on the position of an instance in the ranking as well as the size of the neighborhood around the instance of interest. We evaluate the generated explanations for the Times Higher Education university ranking dataset as a benchmark of competitive ranking. The results reveal insights for a wide range of instances in the ranking list and indicate the importance of local explanations for competitive rankings.
The Characterization of a Novel Virus Discovered in the Yeast Pichia membranifaciens

Mycoviruses are widely distributed across fungi, including the yeasts of the Saccharomycotina subphylum. This manuscript reports the first double-stranded RNA (dsRNA) virus isolated from Pichia membranifaciens. This novel virus has been named Pichia membranifaciens virus L-A (PmV-L-A) and is a member of the Totiviridae. PmV-L-A is 4579 bp in length, with RNA secondary structures similar to the packaging, replication, and frameshift signals of totiviruses that infect Saccharomycotina yeasts. PmV-L-A was found to be part of a monophyletic group within the I-A totiviruses, implying a shared ancestry between mycoviruses isolated from the Pichiaceae and Saccharomycetaceae yeasts. Energy-minimized AlphaFold2 molecular models of the PmV-L-A Gag protein revealed structural conservation with the Gag protein of Saccharomyces cerevisiae virus L-A (ScV-L-A). The predicted tertiary structure of the PmV-L-A Pol and other homologs provided a possible mechanism for totivirus RNA replication due to structural similarities with the RNA-dependent RNA polymerases of mammalian dsRNA viruses. Insights into the structure, function, and evolution of totiviruses gained from yeasts are essential because of their emerging role in animal disease and their parallels with mammalian viruses.
Modeling and Control Design of Quadrotor UAVs Using Symbolic Regression

This paper presents a mathematic dynamic model of a quadrotor unmanned aerial vehicle (QUAV) by using the symbolic regression (SR) approach and then proposes a hierarchical control scheme for trajectory tracking. In the dynamic modeling, the uncertain partial physical parameters and unknown model structure are both taken into account. Therefore, SR is adopted to construct the dynamic model described by analytic equations, which relieves the burden of modeling. To facilitate position tracking control of a QUAV, the design of controller can be decomposed into two parts: a proportional-integral controller for the position subsystem is first designed to obtain the desired horizontal position and the backstepping method for the attitude subsystem is developed to ensure that the Euler angles and the altitude can fast converge to the reference values. Further simulation results are shown to demonstrate the effectiveness of SR for modeling.
Risk Allocation of Life Cycle Design

Compared with the conventional design standards,life cycle design requires more resources and has certain integrated innovation risks. Identifying responsibility stakeholders of risk factors is critical to life cycle design for design units based on reasonably risk allocation. Based on the principle of risk allocation,the risk allocation matrix was established by sharing the 9 key risk factors between the Employer and the designer. Questionnaire was used to verify the accuracy of the theoretical analysis. The study results indicate that identification of risk factors undertaker is beneficial to confirm their key work points,which can ensure the success of life cycle design completion.
Computational Models of Stellate Cells in Layer II of Medial Entorhinal Cortex

In vivo experiments have found that animals encode their current location through spatial neurons (such as grid cells, place cells, border cells, head-direction cells, and so on) that are usually classified according to their functions (or firing patterns). And in vitro experiments revealed that there are two types of excitatory neurons within medial entorhinal cortex according to their structural characteristics: stellate cells and pyramidal cells. It is still unclear that how neuronal types with different functions correspond to multiple structural types. Previous researches have modeled spatial neurons primarily according to their functional type. In this paper, we attempt to model spatial neurons according to their structural type and propose computational models of stellate cells in layer II of medial entorhinal cortex. We use leaky integrate-and-fire (LIF) neurons to model the soma of stellate cells which are driven by feedforward excitatory current inputs. To obtain this drive current, up-stream velocity-controlled oscillators (VCOs) are used, and the output of VCOs is encoded using an inverse hexagonal discrete Fourier transform (IHDFT). At the same time, this current is regulated by a local recurrent network consisted of stellate cells and interneurons and by backprojection signals from down-stream hippocampus. Simulation results demonstrate that our model generates grid-like and border-like firing patterns. Our model explains the causes of stellate cell discharge activity and gives a new direction for modeling spatial neurons.
New Insights into the Magnetic Properties of CoFe2O4@SiO2@Au Magnetoplasmonic Nanoparticles

We report the successful synthesis and a complete magnetic characterization of CoFe2O4@SiO2@Au magnetoplasmonic nanoparticles. The CoFe2O4 magnetic nanoparticles were prepared using the hydrothermal method. A subsequent SiO2 shell followed by a plasmonic Au shell were deposited on the magnetic core creating magnetoplasmonic nanoparticles with a core–shell architecture. A spin-glass-type magnetism was shown at the surface of the CoFe2O4 nanograins. Depending on the external magnetic field, two types of spin-glass were identified and analyzed in correlation with the exchange field acting on octahedral and tetrahedral iron sites. The magnetization per formula unit of the CoFe2O4 core is not changed in the case of CoFe2O4@SiO2@Au nanocomposites. The gold nanoparticles creating the plasmonic shell show a giant diamagnetic susceptibility, dependent on their crystallite sizes.
Multi-Scale Analysis of Brain Functional Networks in Schizophrenia Based on Spectral Graph Wavelets

Schizophrenia is a serious mental illness whose pathological mechanism is still unknown. In this paper, we study brain functional networks of schizophrenic patients from a new perspective of graph signal processing. We use fMRI data of 71 schizophrenic patients and 74 healthy subjects to represent their graph signals and calculate their Laplace matrices by Pearson correlation coefficients. Based on the maximum Laplace eigenvalue, we construct spectral graph wavelet and decompose graph signals into five frequency scales. We compare the energy distribution of brain functional network in schizophrenic patients and healthy subjects. We find that, compared with healthy subjects, the energy distribution of brain functional networks in schizophrenic patients is significantly different at different scales, especially abnormal activities in brain regions: Cortex Left, Parahippocampal Gyrus and Anterior Division Right.
Image Self-Generation Based Defect Detection Method for Train Brake Pad

The brake pads of trains will inevitably wear out with use. The most common defect is the wear, missing or damage of the brake pad’s friction block. This will directly affect the braking performance of the train and lead to potential risks to the safety of train operation. In order to detect defects on train brake pads with insufficient defect samples, an algorithm based on an image self-generation mechanism is proposed. This algorithm first identifies the brake pads at different locations in multi-view complete images of the bottom of the train. Then, utilizing multiple encoder-decoder combinations obtained by learning on normal samples, the algorithm can automatically generate defective samples and add them to the discriminator training for defect diagnosis. Experiments on real train brake pad data show that the proposed algorithm has higher performance and more usability than other advanced methods.
Modeling of the operating characteristics of photovoltaic modules based on particle swarm optimization

This work evaluates the use of particle swarm optimization to extract the parameters that represent a photovoltaic cell in the model of one diode and five parameters (1D5P) to obtain current versus voltage characteristic curves (I × V) of a photovoltaic module operating under a wide range of temperature and irradiation scenarios. In all the modeling and simulation processes, only information of the module's data sheet provided by the manufacturers was used, without depending on results from other simulations or external data collections. To validate the study, the simulation of the current versus voltage (I × V) curves of a commercial photovoltaic module was made, and this was compared with data provided by the manufacturer and also compared with Chenni's method, which is consolidated by the literature. Errors of less than 0.3% were obtained for the simulations performed under the standard test conditions and in temperature and irradiation situations commonly found in practice, as is the case, of G = 700 W/m2 and Tc = 40°C, the 1D5P method showed an average error of approximately 1%, slightly surpassing the Chenni's method where the error was approximately 1.4%.
A Memetic Algorithm for Solving the Robust Influence Maximization Problem on Complex Networks against Structural Failures

Many transport systems in the real world can be modeled as networked systems. Due to limited resources, only a few nodes can be selected as seeds in the system, whose role is to spread required information or control signals as widely as possible. This problem can be modeled as the influence maximization problem. Most of the existing selection strategies are based on the invariable network structure and have not touched upon the condition that the network is under structural failures. Related studies indicate that such strategies may not completely tackle complicated diffusion tasks in reality, and the robustness of the information diffusion process against perturbances is significant. To give a numerical performance criterion of seeds under structural failure, a measure has been developed to define the robust influence maximization (RIM) problem. Further, a memetic optimization algorithm (MA) which includes several problem-orientated operators to improve the search ability, termed RIMMA, has been presented to deal with the RIM problem. Experimental results on synthetic networks and real-world networks validate the effectiveness of RIMMA, its superiority over existing approaches is also shown.
Transient power quality detection and location of distribution network based on db4 wavelet transform

According to the short duration and fast change rate of transient power disturbance,a transient power disturbance detection method based on wavelet transform is proposed.Using db4 wavelet,the method realizes transient power signal sampling and multi-scale decomposition,and then judges the signal break points.It can accurately detect and locate the common transient power distanbance problems in distribution network and is also compared with other-order db wavelet transform result.The simulation results show that this method can identify and locate transient power disturbance signals quickly and accurately,it has high precision and can meet the actual engineering requirements.
PhysioChair: A Dual-Frequency Radar System for Noninvasive and Continuous Detection of Physiological Signatures

This paper presents PhysioChair which is a novel sensing technique integrated in smart chairs for noninvasive and continuous detection of multiple physiological signatures. A dual-frequency radar system is designed and integrated in the PhysioChair, including a 5.8 GHz single-antenna radar for chest movement detection and a 120 GHz miniaturized radar for pulse detection. The dual-frequency multi-radar sensing system is calibrated to ensure systematic synchronization. Besides the conventional vital signs such as respiration, heartbeat and pulse, this paper proposes techniques and approaches to make it possible for the PhysioChair to estimate additional physiological signatures including heart rate variability (HRV), pulse transit time (PTT), pulse wave velocity (PWV), and blood pressure (BP). Experiments have been launched in an office environment with Photoplethysmography (PPG) and digital sphygmomanometer as reference. The experimental results show a great performance of the proposed PhysioChair in detection of weak physiological signatures. Compared to the ground truth, the estimated physiological signatures such as HRV and BP have mean relative errors below 3%, and 6 out of 7 subjects have an over 90% probability of absolute errors less than 5mmHg. The results reveal the possibility for the PhysioChair to be employed outside the clinic, such as applications in the vast internet-of-things (IoT) and in-home health monitoring.
A Review on Conceptual Model of Cyber Attack Detection and Mitigation Using Deep Ensemble Model

Purpose: When communication networks and the internet of things are integrated into business control systems, they become more vulnerable to cyber-attacks, which can have disastrous consequences. An Intrusion Detection System is critical for identifying and blocking attacks in IoT networks. As a result, utilizing a unique Classification and Encryption approach, this article offered a novel architecture for attack node mitigation. Design/Methodology/Approach: This study reviews the current status of various cyber-attack detection models and their mitigation techniques. The proposed model works so that the system is first trained on the dataset, including the DDoS attack and ransomware components. The model examines if it contains malware from DDoS or Ransomware. When tested, we use trained information or a data set to provide the results on attack existence and what sort of attack we offer the extracted characteristics of the input. When the model identifies the attacker node, it is removed via the BAIT technique from the network. Findings/Result: Recognizing the importance of information security is critical to combating cybercrime and encouraging cyber security. There are numerous tactics, strategies, and equipment currently in use to detect intrusion in a computer network, and continuing research is being conducted to improve their ability to detect intrusion. The basic version of a cyber-assault detection and mitigation system using the BRELU-RESNET method was evaluated in this study. Originality/Value: This review-based research article examines the present state of cyber-attack detection and mitigation, as well as the research gaps and research goals. Paper Type: Review-based research analysis
Freestanding Translucent ZnO–Cellulose Nanocomposite Films for Ultraviolet Sensor Applications

The rapidly advancing technology of wearable and miniaturized electronics has increased the demand for low-cost high-performance flexible sensors. Herein, the preparation of translucent freestanding films consisting of cellulose nanofibers (CNFs) and ZnO nanoparticles (NPs) via a simple spray coating method is presented. The obtained nanocomposite films were thin (~10 µm) and flexible. The scanning electron microscopy and atomic force microscopy analysis revealed that the nanocomposite film was composed of regions of ZnO NP-modified CNFs and regions of aggregation of ZnO NPs with each other. The electrical conductance of the films was rapidly increased beyond 40 wt. % ZnO and reached up to >50 nA at 60 wt. % ZnO. This was attributed to the increased number of conductive paths formed by the ZnO NPs in the nanocomposite film when a certain threshold was crossed. The ZnO–CNF nanocomposite film exhibited a stable response over on/off cycles of UV light exposure. The responsivity and sensitivity of the nanocomposite film with 60 wt. % ZnO were 36.5 mA/W and 247, respectively. Even when the device was curved (radius of curvature: 3 mm), the response and sensitivity remained high. The developed nanocomposite films are expected to be applied as environmentally friendly flexible UV sensors.
MORPHOLOGY OF TOMATO PLANTS UNDER NEMATODE ATTACK AND SALICYLIC ACID APPLICATION

Root-knot nematodes are the main soil-dwelling phytopathogens that cause severe damages to plants, especially tomato plants. Exogenous application of salicylic acid (SA) can mitigate such pathogenicity. This work aimed to evaluate the growth of tomato plants submitted to Meloidogyne javanica population densities (PD) and application of SA. The experiment was a randomized block design, in an incomplete factorial scheme (central composite design), with five PD (0, 5815, 20000, 34184, and 40,000 eggs per pot) and five SA doses (0.0, 0.29, 1.0, 1.71, and 2.0 mM), with four replicates containing two plants each. Number of leaves, plant height, stem diameter, shoot dry mass, root dry mass and total dry mass, Dickson's quality index, leaf area, specific leaf area, specific leaf weight, root volume, absolute and relative growth rates for plant height, number of eggs, number of galls, and nematode reproduction factor were evaluated at 50 days after soil inoculation (DAI). Results showed the application of 0.97, 2.0, and 0.88 mM SA increased, respectively, the RGR, SLA and SLW. On the other hand, 0.91 and 0.93 mM SA decreased, respectively, the number of eggs and reproduction factor of nematodes. Also, M. javanica did not affect the growth of tomato plants until 50 DAI. Keywords: Meloidogyne javanica; phytohormone; Solanum lycopersicum. Morfologia do tomateiro sob ataque de nematoides e aplicação de ácido salicílico RESUMO: Nematoides das galhas são uns dos principais patógenos de solo que causam danos severos nas plantas, especialmente em plantas de tomate. A aplicação exógena de ácido salicílico (AS) pode minimizar os efeitos desses patógenos. O objetivo deste trabalho foi avaliar o crescimento de plantas de tomate submetidas à densidades populacionais de Meloidogyne javanica (DP) e aplicação de AS. O delineamento em blocos casualizados em esquema fatorial incompleto (Composto Central de Box) com cinco DP (0, 5815, 20000, 34184 e 40000 ovos por planta) e cinco doses de AS (0.0, 0.29, 1.0, 1.71 e 2.0 mM), com quatro repetições e duas plantas por repetição foi utilizado. O número de folhas, altura de planta, diâmetro do caule, massa seca da parte aérea, raiz e total, índice de qualidade de Dickson, área foliar, área foliar específica, peso específico de folha, volume de raiz, taxas de crescimento absoluto e relativo para altura, número de ovos, número de galhas e fator de reprodução dos nematoides foram avaliados aos 50 dias após a inoculação do solo (DAI). A aplicação de 0.97, 2.0 e 0.88 mM de AS aumentam a taxa de crescimento relativo de altura, área foliar específica e peso específico de folhas, respectivamente. A aplicação de 0.91 e 0.93 mM de AS diminuem o número de ovos por grama de raiz e fator de reprodução, respectivamente. M. javanica não influenciou o crescimento de plantas de tomate até 50 DAI. Palavras-chave: Meloidogyne javanica; fitohormônio; Solanum lycopersicum.