Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Management for Renewable Microgrid Cooperation: Theory and Algorithm

In the microgird (MG) system, cooperation of microgrids is a very essential issue. Hence, a multi-microgrid (MMG) system has been proposed to allocate the energy and information exchange for each MG. However, the uncertainties of generations and loads are very difficult to deal with when considering the utilization of renewable energy (RE) resources. To tackle this issue, we study the optimal energy management for the cooperation of multiple MGs in this work. We propose an adaptive energy scheduling algorithm for MMG systems in order to reduce the long-term time-averaged operation cost under the constraints of renewable energy resource utilization. To deal with the unused renewable energy, virtual queues are constructed and Lyapunov framework are adopted to improvement of reliability, reduction of end-user power loss, and cultivation of renewable resource penetration. The simulation results show that the proposed algorithm is effective in coordinating multiple MGs within an MMG system so as to reduce the operation cost and improve the utilization of RE resources dramatically.
Assessment of Classification Techniques for Heart Disease Prediction

Prediction of cardiac illness remains the most baffling errand in the ground of clinical disciplines. The present clinical calling has gone through a surprising progression to oblige people experiencing an assortment of ailments. One of the most essential aspects for clinical professionals is to diagnose coronary heart disease, especially if it is computer based with the goal of a quick diagnosis and a predictable outcome. Opportune screening of the presence of coronary illness can save a patient's life. Despite the fact that doctors have identified a number of triggers for heart attacks. The point of the study is distinguished between the use of Machine Learning Techniques for cardiovascular infections solicitation and presumption. In terms of clinical limits, this survey focused on datasets that incorporated formulated a model. Using Machine Learning Techniques, this framework evaluates such boundaries. According to the comparative examination of all other approaches, the Support Vector Machine technique has several advantages to be a credible manner of anticipating coronary illness.
Supernovae and supernova remnants

The basic features and astrophysics of supernovae of types Ia, Ib, and II and of SNRs are discussed, summarizing the results of recent ground-based and space observations obtained at different wavelengths. Typical data are presented in graphs and sample images, and consideration is given to their implications for shock heating by the initial explosion, radioactive energy input, circumstellar interaction, and pulsar energy input. SNRs described include Tycho's remnant, Kepler's remnant, the Cyg loop, Pup A, the Crab Nebula, the Vela and Crab pulsars, and 0540-69.3 in the LMC. The need for high-spatial-resolution IR and X-ray spectra of these objects is indicated.
Detection of Brick Kilns Using Multi-Spectral Bands of Sentinel-2 Imagery

The burning of bricks affects air quality and human health, and the toxic pollutants released into the air seriously impact the surrounding areas. In order to curb the pollution caused by these brick kilns, it is very important to identify them. As part of this study, an analysis in the Delhi- NCR region is performed. An approach for mapping brick kiln locations by using various Machine Learning (ML) algorithms is presented in this work. The Sentinel-2 imagery with 10m resolution was used to create four classes, namely, built up, field, water, and brick kiln. It is possible to distinguish brick kilns from other classes by using the said dataset due to its unique spectral and geometric features. Several ML classifiers have been trained on the sentinel-2 dataset, including the Random Forest Classifier and four built-in classifiers of Google Earth Engine, such as Smile Random Forest, Smile CART (Classification and Regression Trees), Smile Gradient Tree Boost, and Smile Naive Bayes Classifier. In order to train the model based on different classifiers, 70% of data points are used for training and 30% for validation purposes. Among the machine learning classifiers, Smile Random Forest achieved the maximum accuracy of 96.4%. As a result of this study, an ML model has been proposed for the classification of brick kilns from Sentinel-2 imagery, and a comparative analysis has been done between the various ML models. The proposed ML model can classify brick kilns from Sentinel-2 images with an accuracy of 96.4%, precision of 0.88, and recall of 0.73. The functionality of the classification model lies in its utilization by local authorities and various pollution control bodies, so that proper action can be initiated. Hence contributing to a greener environment and battling serious issues like climate change and global warming.
Assessment of the Robustness of Flexible Antennas to Complex Deformations

Wearable antennas can suffer from a variety of mechanical deformations that are induced by the body dynamic. The article analyses how these complex deformations impact the performance of a flexible antenna operating in the 5–6 GHz band. The Green Coordinates (GC) spatial manipulation technique is used to generate a range of complex 2-D deformations, namely spherical, saddle, and twisting deformation. Generating full geometries is a key enabler in this study. The results offer valuable insight into the stability of antenna performance under in situ deformations.
HYBRIDIZED UNIVARIATE AND MULTIVARIATE FILTER BASED APPROACHES FOR GENE SELECTION

In this paper, we present a hybridized univariate and multivariate approach for optimal gene selection. This method seeks to find the optimal subset of genes of interest known as informative genes and maximize the accuracy of the model with reduction percentage. The merit of this approach is analyzed by experimental results on six benchmark datasets such as Leukemia, Breast Cancer and Lung Cancer, Hepatitis, Lymphoma and Embryonal Tumor and results showed an impressive accuracy. The classification accuracy of original dataset and informative genes selected using proposed model has been compared based on Naive Bayesian, Multi-layer Perceptron and Support Vector Machine classifiers. Finally, the performance of selected informative gene subsets have been measured using few probabilistic measures such as; Balanced Classification Rate (BCR), F-Measure, Jaccard Index (JI), Adjusted Random Index (ARI), Normalized Mutual Information (NMI), and Purity and the results obtained from these measures establishes our algorithm for selecting informative genes.
Stability Assessment of Renewable Energy-Based DC Microgrids for Offshore Applications

Traditional offshore microgrids such as drilling rigs and oil and gas platforms rely on diesel generators to provide their electricity needs. With the recent developments in offshore wind, solar and wave generation, and the maturity growth of the energy storage technology, the offshore microgrids are transitioning away from diesel generation. When the majority of the energy sources and loads is DC-native or rely on conversion to DC, setting up the network as a DC microgrid becomes a viable option. Using DC coupling in offshore microgrids eliminates the problems of harmonic distortion, low inertia and overcomplicated control. However, with the high penetration of intermittent generation, the provision of DC microgrid stability becomes a challenging task. This is particularly acute within practical DC microgrids since, at this stage, there are not many off-the-shelf DC components. This paper describes the experimental lab facilities set up for offshore DC microgrid research and investigates the stability issues presented in the renewable energy-based DC microgrid.
Two methods to analyze microstrip antennas for Wi-Fi bandwidth

: The paper presents a series of designed microstrip antennas with diﬀerent gain and width radiation characteristics and intended for use in Wi-Fi systems. These antennas in a multilayer system were analyzed with the use of computer programs, and then the parameters and characteristics of these antennas were measured. At the same time, to check the correctness of work, additional measurements of the temperature of the radiators were used with a thermal imaging camera. The obtained results were compared with the results of calculations and measurements. They show high compliance with both calculations and measurements. At the same time, thermovision measurements show the weaknesses of the designed power lines.
Resampling Methods for Testing a Semiparametric Random Censorship Model

This paper presents a goodness‐of‐fit test for a semiparametric random censorship model proposed by Dikta (1998 ). The test statistic is derived from a model‐based process which is asymptotically Gaussian. In addition to test consistency, the proposed test can detect local alternatives distinct n‐1/2 from the null hypothesis. Due to the intractability of the asymptotic null distribution of the test statistic, we turn to two resampling approximations. We first use the well‐known bootstrap method to approximate critical values of the test. We then introduce a so‐called random symmetrization method for carrying out the test. Both methods perform very well with a sample of moderate size. A simulation study shows that the latter possesses better empirical powers and sizes for small samples.
Autonomous Drone Navigation using Monocular Camera and Light Weight Embedded System

Autonomous robots are machines that can act without any human interference. When certain sensors and decision making algorithms are added to the control unit of a drone, the aerial vehicle is said to be autonomous. Such vehicles are capable of avoiding obstacles and correcting their local paths while staying on planned global paths. Autonomy in drones is usually achieved using many types of sensors like depth sensor, stereo camera or lidar, and intensive SLAM algorithms that require powerful processors. Though the existing methods work well, the scalability of such products is questionable as the economic and resource availability factors come into play. This paper proposes a method of achieving autonomous navigation using light weight embedded system and affordable monocular cameras by combining features of image processing and resource sharing. The proposed architecture makes use of monocular cues and midas depth estimation model to achieve obstacle avoidance and can run on any processor with basic features such as serial communication, wifi and camera ports.
Autonomous Vehicles: Security Challenges and Game theory-based Countermeasures

With the proliferation of artificial intelligence and its integration into modern infrastructure including smart cities, autonomous cyber-physical systems will form the core of any such project. Numerous smart-city projects are planned to be implemented and completed in near future. However, with a considerable amount of dependence on cyber-physical systems, safety and reliability are a concern. Due to the increase in the number of threats and the rise in the sophistication of such threats, traditional security measures seem inefficient in safeguarding safety-critical systems including autonomous vehicles (AVs). With the inability to offer reliable and safe solutions, failure in the adoption of such technology eventually may jeopardize the entirety of smart projects. Taking AV as a case study, this paper gives a brief review of the threats to self-driving cars. As a solution to such issues, we give a brief review on game theory-based solutions that could offer resilience in the context.
Salinity-Triggered Responses in Plant Apical Meristems for Developmental Plasticity

Salt stress severely affects plant growth and development. The plant growth and development of a sessile organism are continuously regulated and reformed in response to surrounding environmental stress stimuli, including salinity. In plants, postembryonic development is derived mainly from primary apical meristems of shoots and roots. Therefore, to understand plant tolerance and adaptation under salt stress conditions, it is essential to determine the stress response mechanisms related to growth and development based on the primary apical meristems. This paper reports that the biological roles of microRNAs, redox status, reactive oxygen species (ROS), nitric oxide (NO), and phytohormones, such as auxin and cytokinin, are important for salt tolerance, and are associated with growth and development in apical meristems. Moreover, the mutual relationship between the salt stress response and signaling associated with stem cell homeostasis in meristems is also considered.
Research on dual-mode switching of the new dual full-bridge topology of the beam supply

: In order to meet the operation requirements of the beam supply with multi-working conditions, multi-modes and high eﬃciency, a dual-mode hybrid output control method combining phase-shifting and pulse-width dual-mode modulation technology with secondary side series-parallel operation is proposed. In this paper, the structure and working mode of the new dual full-bridge topology are ﬁrstly analyzed. Secondly, the main circuit parameters are designed according to the power performance indicators, and the losses under two control modes of phase shift and pulse width are calculated. Finally, comparing the losses of these two control methods, and combining the series-parallel operation mode of the secondary side of the transformer, a dual-mode switching control method of the beam supply is designed. In order to verify the rationality of the dual-mode mixed output control method, a principle prototype with a rated capacity of 2 kW, a rated voltage of 1800 V and a switching frequency of 50 kHz was used for veriﬁcation. Experiments show the eﬀectiveness and superiority of the dual-mode hybrid output control method.
Evaluation of surface plate flatness measurements

The method of determining the flatness deviation of a surface plate, using differential measurements based on electronic levels, an autocollimator or laser interferometer, is well established. However, for the transformation of these measured values into absolute heights relative to a reference plane, various methods are used which do not utilize the redundancy of the measurements in a proper way. This paper gives the theoretical background for a proper calculation method and a treatment of the uncertainties in the results. This is illustrated by examples which have been met in practice.
Synthesis and Characterization of Sulfur Nanoparticles of Citrus limon Extract Embedded in Nanohydrogel Formulation: In Vitro and In Vivo Studies

The study aimed to synthesize non-noxious, clean, reliable, and green sulfur nanoparticles (SNPs) from Citrus limon leaves. The synthesized SNPs were used to analyze particle size, zeta potential, UV–visible spectroscopy, SEM, and ATR-FTIR. The prepared SNPs exhibited a globule size of 55.32 ± 2.15 nm, PDI value of 0.365 ± 0.06, and zeta potential of −12.32 ± 0.23 mV. The presence of SNPs was confirmed by UV–visible spectroscopy in the range of 290 nm. The SEM image showed that the particles were spherical with a size of 40 nm. The ATR-FTIR study showed no interaction, and all the major peaks were preserved in the formulations. An antimicrobial and antifungal study of SNPs was carried out against Gram-positive bacteria (Staph. aureus, Bacillus), Gram-negative bacteria (E. coli and Bordetella), and fungal strains (Candida albicans). The study showed that Citrus limon extract SNPs exhibited better antimicrobial and antifungal activities against Staph. aureus, Bacillus, E. coli, Bordetella, and Candida albicans at a minimal inhibitory concentration of 50 μg/mL. Different antibiotics were used alone and in combination with SNPs of Citrus limon extract to evaluate their activity against various strains of bacteria and fungal strains. The study showed that using SNPs of Citrus limon extract with antibiotics has a synergistic effect against Staph.aureus, Bacillus, E. coli, Bordetella, and Candida albicans. SNPs were embedded in nanohydrogel formulations for in vivo (wound healing) studies. In preclinical studies, SNPs of Citrus limon extract embedded within a nanohydrogel formulation (NHGF4) have shown promising results. To be widely used in clinical settings, further studies are needed to evaluate their safety and efficacy in human volunteers.
Research on anti single event transient multi-objective optimization technology based on sensitive flip-flop replacement

The decreasing characteristic size of transistors leads to a large increase in the Soft Error Rate of circuits. In view of the Single Event Transient generated by the combined circuit, this paper proposes a Triple Modular Redundancy structure Flip-Flop with filtering function to replace the standard Flip-Flop, and the Flip-Flop to be replaced is divided according to the soft error sensitivity. At the same time, aiming at the problem of automatic equalization of various indicators of the generated circuit in the replacement process, this paper explores an optimization architecture based on the "BP-NSGA-III" algorithm, which can automatically determine the position, number and radiation level of the Flip-Flop to be replaced in the circuit, and achieve the comprehensive optimization of the three design indicators of radiation hardening performance, area and power consumption.
Fuzzy Sparse Subspace Clustering for Infrared Image Segmentation

Infrared image segmentation is a challenging task, due to interference of complex background and appearance inhomogeneity of foreground objects. A critical defect of fuzzy clustering for infrared image segmentation is that the method treats image pixels or fragments in isolation. In this paper, we propose to adopt self-representation from sparse subspace clustering in fuzzy clustering, aiming to introduce global correlation information into fuzzy clustering. Meanwhile, to apply sparse subspace clustering for non-linear samples from an infrared image, we leverage membership from fuzzy clustering to improve conventional sparse subspace clustering. The contributions of this paper are fourfold. First, by introducing self-representation coefficients modeled in sparse subspace clustering based on high-dimensional features, fuzzy clustering is capable of utilizing global information to resist complex background as well as intensity inhomogeneity of objects, so as to improve clustering accuracy. Second, fuzzy membership is tactfully exploited in the sparse subspace clustering framework. Thereby, the bottleneck of conventional sparse subspace clustering methods, that they could be barely applied to nonlinear samples, can be surmounted. Third, as we integrate fuzzy clustering and subspace clustering in a unified framework, features from two different aspects are employed, contributing to precise clustering results. Finally, we further incorporate neighbor information into clustering, thus effectively solving the uneven intensity problem in infrared image segmentation. Experiments examine the feasibility of proposed methods on various infrared images. Segmentation results demonstrate the effectiveness and efficiency of the proposed methods, which proves the superiority compared to other fuzzy clustering methods and sparse space clustering methods.
Optimal PI Controller with Position Feedback for Vibration Suppression

In vibration control of distributed-parameter mechanical systems the main problem arises from endeavoring to achieve even damping in the desired frequency range and robustness simultaneously. These two important properties usually compromise each other so that more sophisticated techniques are needed to improve the vibration control of large structures. In the case of classical velocity feedback, for example, some kind of low-pass filter should be used to achieve better stability. In the case of position feedback use of dynamic controllers like Positive Position Feedback (PPF) or Linear Quadratic Gaussian (LQG) is an alternative, but considering their low robustness and more complicated design there are no advantages within the control of large structures with variable parameters. The control method suggested in this work, i.e. PI control with position feedback, is based on the fact that integral of the position can replace the velocity sensing with the idea of suppressing the influence of higher frequencies (which are responsible for instability) while phase shift in working low-frequency range remains in desired bounds. Numerical root-loci analysis as well as experimental examples presented in this paper can show that PI control with position feedback is able to add damping to vibrating structure within low-frequency bands at comparable level as velocity feedback can do, but with a smaller influence on higher frequencies.
Measures are not invariant across groups without error variance homogeneity

The comparison of both naturally occurring and experimentally derived groups is a fundamental process in psychological inquiry. The accuracy of inferences concerning group differences is highly dependent upon the assumption that measure of the focal constructs function equivalently across the groups being compared. This research addresses the need to demonstrate homogeneity of error variances across groups when assessing measurement invariance. It is argued that, contrary to popular belief, the primary component of error variance is not a random error process, but rather reflects the effect of unmodeled sources of systematic variance that effect measured responses. The effect of these unmodeled sources of systematic variance on the evaluation of measurement invariance is demonstrated fo the single indicator case. The inability if multi-indicator models to overcome the problem of heterogeneous error variance is presented. It is concluded that comparing means, variance, or scale intercorrelation across groups is not meaningful unless strict metric invariance can be demonstrated.
Lung nodule false positive reduction using a central attention convolutional neural network on imbalanced data

Computer-aided detection systems for lung nodules play an important role in the early diagnosis and treatment process. False positive reduction is a significant component in pulmonary nodule detection. To address the visual similarities between nodules and false positives in CT images and the problem of two-class imbalanced learning, we propose a Central Attention Convolutional Neural Network on Imbalanced Data (CACNNID) to distinguish nodules from a large number of false positive candidates. To solve the imbalanced data problem, we consider density distribution, data augmentation, noise reduction, and balanced sampling for making the network well-learned. During the network training, we design the model to pay high attention to the central information and minimize the influence of irrelevant edge information for extracting the discriminant features. The proposed model has been evaluated on the public dataset LUNA16 and achieved a mean sensitivity of 92.64%, specificity of 98.71%, accuracy of 98.69%, and AUC of 95.67%. The experimental results indicate that our model can achieve satisfactory performance in false positive reduction.