Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Change Detection Technology of Equipment Appearance Defect in Substation Scene

With the continuous development of new technologies such as big data, artificial intelligence and cloud computing, intelligent terminal devices such as patrol robots, high-definition videos and unmanned aerial vehicles have been applied injoint patrol inspection of substations. In the face of the demand for appearance defect detection in the process of joint patrol inspection of substations, this paper introduces a twin differential attention change detection model (Diff-SANet) proposed by the China Electric Power Research Institute, which is used to solve the problem of defect status determination and defect area location when the equipment appearance changes to produce defects, and the defect types cannot be pre-defined and cannot be fully exemplified before the occurrence of defects. Diff -SANet network model can only use the normal state image set of observation points to complete model training, and the trained model can be used to determine appearance defects and locate areas. Finally, this paper verifies the real data set of the substation, and the experimental results show that the accuracy of the model can reach 91.6%. This method provides guidance for the promotion of intelligent patrol equipment application in substations.
Pandemic Lessons Learned: Educators’ Experiences and Tool Checklist

Abstract The Coronavirus pandemic had a significant effect on teachers when traditional face-to-face classroom instruction abruptly shifted online in the spring of 2020. It also provided educators the unique opportunity for continued growth and development in incorporating technology into their instruction. The purpose of this study was to examine educators’ s learning experiences in utilizing virtual learning technologies to provide instruction during the COVID-19 pandemic. Researchers conducted two (2) online focus group interviews with participants (N = 9). The focus group transcriptions were qualitatively analyzed for themes. The findings from the focus groups are discussed in this article. The resulting implication is the 3E (Efficient, Effective and Engaging) application checklist to utilize when selecting instructional online tools and incorporating technology in the classroom.
On Beyond Lambda: standard single field quintessence solutions for the dark energy problem department of Astronomy

OF THE THESIS On Beyond Lambda: Standard Single Field Quintessence Solutions for the Dark Energy Problem Department of Astronomy by Joseph M. Fedrow Master of Science in Astronomy San Diego State University, 2013 Observations have shown that the universe is currently expanding at an accelerating rate due to the presence of a dark energy. A slowly rolling scalar field, known as quintessence, has been proposed as the source of this dark energy. In this thesis I have developed a suite of computer codes that numerically solve the differential equations needed to describe the evolution of this dynamical field. By comparing my theoretically derived results with observations of baryon acoustic oscillations I have discovered the boundaries in solution space which divide constant dark energy solutions from dynamical dark energy solutions. The predictions made by these models can easily be verified, or falsified, by recent and upcoming experiments.
Rotation Invariant 2D Ear Recognition Using Gabor Filters and Ensemble of Pre-trained Deep Convolutional Neural Network Model

Deep learning-based approaches have become the appealing choice of researchers for invariant ear recognition which deals with the challenging tasks related to the biometric system like pose variations, illuminations, and changes in lighting conditions. Recent work of 2D ear recognition achieves good performances when the sample images are captured in a constrained environment. This paper attempts to solve the problem of pose variations, illumination changes, and rotation invariance (generally caused due to unconstrained environment) with the help of Gabor representation given as input to the ensemble of pre-trained deep neural networks. The use of multiple Gabor filters generates multiple Gabor representations of ear images which are rotation invariant and unaffected by pose and scale variation. These Gabor features when given as an input to the multiple pre-trained deep neural networks, the models are trained effectively due to the availability of various robust and discriminant features produced by Gabor filters. As a result, it improves the classification accuracy of the resultant model even when there is variation in sample images captured in unconstrained environments. The proposed work uses two pretrained models namely VGG19 and DenseNet161 which are already trained on the ImageNet dataset, so we need to perform the fine tune of these models only. In this way, it also solves the problem of the limited dataset which is one of the major challenges in the context of deep learning.
The improved H∞ controller design for nonlinear networked control systems with random communication delays

This paper is concerned with the observer‐based H∞ controller design problem for nonlinear networked control systems with random communication delays. Firstly, the dynamic observer‐based control scheme is modelled, where the control input of the observer is different from the control input of the plant. Then, a less conservative delay‐dependent H∞ stabilization criterion is derived by using an improved Lyapunov function. And the proof of stabilization criterion is completed in terms of four cases when the time delays in two communication channels are constant or time‐varying, respectively. The derived stabilization criterion is formulated in the form of a non‐convex matrix inequality, which can be solved by an optimal cone complementary linearization iteration algorithm to obtain the minimum disturbance attenuation level. Finally, several numerical examples and an illustrative example are provided to clarify the effectiveness and merits of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.
Numerical Investigation of the Influence of Hydrodynamic Dispersion on Solutal Natural Convection

We investigate numerically the effect of hydrodynamic dispersion on convective dissolution of carbon dioxide in saline aquifers. Solutions of the transport equations were used to describe the dissolution process in the aquifer and provide a systematic parametric analysis of the influence of dispersion on two important measures of convective dissolution, the wavenumber, and dissolution flux. The results suggest that dispersion decreases the dissolution rate and reduces the finger wavenumber. Based on the simulated results, new empirical scaling laws that predict the dissolution rate and wavenumber were developed. The application of the new laws to two storage sites shows that the currently available predictions overestimate the dissolution rates by ∼30%. We have identified some discrepancies between published scaling laws that were obtained for the dissolution rate. While most numerical studies identify a linear scaling, experimental investigations often suggest a sublinear behavior. Our findings resolve the controversy by suggesting that dispersion, which was not considered in these past numerical studies, is the reason for the apparent sublinear scaling.
A Constellation Diagram Learning-Based Adaptive Sparse Nonorthogonal Wavelet Division Multiplexing for Sonar Image Underwater Acoustic Transmission

In underwater Internet of Things (IoT) applications, efficient and reliable transmission is demanding. Recently, the nonorthogonal multicarrier (MC) modulation is utilized as a promising spectrum-efficient solution in the underwater acoustic (UWA) channel, where generally, the constellation mapping (CM) methods (such as 8-PSK and 16-QAM) are utilized to further increase the transmission rate. In this article, we focus on the transmission of sonar images among IoT nodes and a constellation diagram learning-based adaptive sparse nonorthogonal wavelet division multiplexing (CDLA-SNOWDM) system is proposed to combat complex channel states between IoT nodes. We found that different CM methods contribute to unique time-frequency characteristics in constellation diagrams. We first utilize the time-frequency characteristics in designing the nonorthogonal subcarriers. The complex-valued CM signals are regarded as a new type of image. We introduce dictionary learning (DL) to nonorthogonal subcarrier designing, with the idea that designing CM signal adaptive nonorthogonal subcarriers can be regarded as designing an adaptive subcarrier dictionary. The CDLA-SNOWDM modulation is performed as a projection of the CM image to the subcarriers with an adaptive dictionary. The learned adaptive subcarriers can represent constellation diagrams more efficiently and improve transmission reliability. Both simulations and experiments show that the proposed scheme improved the reliability in transmitting sonar images over other orthogonal and nonorthogonal modulation schemes in UWA scenarios.
A Supraharmonic Measurement Method Based on Matrix Pencil with High-Order Mixed Mean Cumulant

Supraharmonics emitted from power electronic equipment can cause electromagnetic interference that may lead to EMC issues, incorrect operation of electric devices, PLC communication failure, etc. How to accurately monitor supraharmonics has become an important concern in modern power systems. In this paper, a supraharmonic measurement method based on matrix pencil with high-order mixed mean cumulant is proposed. The slice of fourth-order mixed mean cumulant of the measured signal is solved and applied to replace the original measured signal, and the matrix pencil method is used to reduce the interference of colored Gaussian noise. Numerical simulations and physical experiments are carried out and the proposal is compared with the IEC 61000-4-7 method and the TLS-ESPRIT method. It shows that the measurement method proposed in this paper has advantages in suppressing colored Gaussian noise interference with a high time resolution.
A fast Laplace-SUSAN comer detector

A novel Laplace-SUSAN corner detector is described.The original image Convolved by a Laplacian of Gaussian filter is translated to a binary or ternary image according to threshold,in which the algorithm uses the SUSAN detector to detect corners.The Laplace-SUSAN detector has excellent high-speed and location accuracy,that can perform 1.5 times faster than Harris and 6 times faster than SUSAN and whose corner is shown to be invariant to image rotation and scale and robust across a substantial range of affine distortion,addition of noise,and change in illumination.Its fast algorithm also is provided,that can substitute all multiplicative operations with shift operations for the Laplacian of Gaussian smoothing.The Laplace-SUSAN detector has a lot more corners than other detectors,so applied to SIFT it can perform more excellent for small object recogni- tion.
Community-Centered Assessment to Inform Pandemic Response in Georgia (US)

The Georgia Community Engagement Alliance (CEAL) Against COVID-19 Disparities Project conducts community-engaged research and outreach to address misinformation and mistrust, to promote inclusion of diverse racial and ethnic populations in clinical trials and increase testing and vaccination uptake. Guided by its Community Coalition Board, The GEORGIA CEAL Survey was administered among Black and Latinx Georgia 18 years and older to learn about community knowledge, perceptions, understandings, and behaviors regarding COVID-19 testing and vaccines. Survey dissemination occurred using survey links generated through Qualtrics and disseminated among board members and other statewide networks. Characteristics of focus counties were (a) highest proportion of 18 years and older Black and Latinx residents; (b) lowest COVID-19 testing rates; and (c) highest SVI values. The final sample included 2082 surveyed respondents. The majority of participants were men (57.1%) and Latinx (62.8%). Approximately half of the sample was aged 18–30 (49.2%); the mean age of the sample was 33.2 years (SD = 9.0), ranging from 18 to 82 years of age. Trusted sources of COVID-19 information that significantly predicted the likelihood of vaccination included their doctor/health care provider (p-value: 0.0054), a clinic (p-value: 0.006), and university hospitals (p-value: 0.0024). Latinx/non-Latinx, Blacks vs. Latinx, Whites were significantly less likely to get tested and/or vaccinated. Non-Latinx, Blacks had higher mean knowledge scores than Latinx, Whites (12.1 vs. 10.9, p < 0.001) and Latinx, Blacks (12.1 vs. 9.6, respectively, p < 0.001). The mean knowledge score was significantly lower in men compared to women (10.3 vs. 11.0, p = 0.001), in those who had been previously tested for COVID-19 compared to those who had never been tested (10.5 vs. 11.5, respectively, p = 0.005), and in those who did not receive any dose of vaccination compared to those who were fully vaccinated (10.0 vs. 11.0, respectively, p < 0.001). These data provide a benchmark for future comparisons of the trajectory of public attitudes and practices related to the COVID-19 pandemic. They also point to the importance of tailoring communication strategies to specific cultural, racial, and ethnic groups to ensure that community-specific barriers to and determinants of health-seeking behaviors are appropriately addressed.
Power Quality Disturbance Recognition Method in Park Distribution Network Based on One-Dimensional VGGNet and Multi-label Classification

Aiming at the complex types of power quality disturbances in the distribution network, the lack of recognition ability of traditional algorithms, the high noise conditions that may appear in the recognition task, and the influence of unknown disturbance items on the recognition method, a one-dimensional VGGNet, and multi-label classification is proposed. A power quality disturbance identification method based on the combination of Firstly, the mathematical model of power quality disturbance is constructed according to the scene characteristics of the distribution network; secondly, the existing VGGNet model is simplified so that it can be applied to the disturbance classification scene; based on improving the network, a multi-label classifier is integrated, and determined according to the label sample Network performance evaluation system. Finally, massive disturbance data is input into the proposed network for training and learning and complete disturbance recognition. The simulation results verify the correctness and effectiveness of the method proposed in this paper, and it can be applied to complex distribution network scenarios with unknown compound disturbances.
Utility of Skeletal Muscle CT in Diagnosing Spinal Muscular Atrophy Type 3 in a Patient Who Had Been Undiagnosed for 50 Years

A 69-year-old woman presented with progressive limb weakness lasting 50 years. She denied any congenital disorders or a family history of neuromuscular disease. At ages 29, 46, and 58 years, she underwent hospitalization and evaluations including electromyogram (EMG) and muscle biopsy, but the results were inconclusive. As a result, she received a tentative diagnosis of myopathy of unknown etiology. However, at the age of 69 years, a computed tomography (CT) scan of her skeletal muscles revealed severe involvement of the triceps brachii, iliopsoas, and gastrocnemius muscles, along with preservation of the biceps brachii, gluteus maximus, and tibialis anterior muscles, which was consistent with spinal muscular atrophy (SMA). Finally, genetic testing revealed the deletion of the survival of the motor neuron 1 (SMN1) gene, confirming the diagnosis of SMA type 3. As our case suggests, SMA patients with prolonged disease duration could be underdiagnosed even after EMG and muscle biopsy. A skeletal CT scan could be useful for the diagnosis of SMA patients compared with MRI.
Lung Cancer and Pulmonary Node detection using Deep Learning: A survey

Lung cancer can present as pulmonary nodules in the early stages; however, CT screening has been shown to be useful in detecting these lesions and reducing mortality. However, because there are so few radiologists and they are already overworked, the exponential growth of picture data makes accurate appraisal of them an extremely difficult assignment. To automatically identify and categorize pulmonary nodules in medical images, a variety of techniques, particularly those based on deep learning with convolutional neural network (CNN), have been developed recently. We provide a thorough examination of these strategies’ effectiveness in this paper. First, we quickly discuss CNN’s essential concepts and the arguments for reasons why they are suitable for the interpretation of medical imagery. The environmental setup required to make lung nodule investigations with CNNs possible is then described, along with a brief overview of the relevant medical image datasets. Addition-ally, thorough analyses of current developments in lung nodule analysis using CNNs are offered. Finally, current issues and bright future options for increasing CNN’s use in medical image processing, and specifically in pulmonary nodule assessment, are discussed. It has been demonstrated that CNNs have significantly changed lung cancer early diagnosis and treatment.
Identifying liver metastasis-related hub genes in breast cancer and characterizing SPARCL1 as a potential prognostic biomarker

Background The liver is the third most common metastatic site for advanced breast cancer (BC), and liver metastases predict poor prognoses. However, the characteristic biomarkers of BC liver metastases and the biological role of secreted protein acidic and rich in cysteine-like 1 (SPARCL1) in BC remain unclear. The present study aimed to identify potential biomarkers for liver metastasis of BC and to investigate the effect of SPARCL1 on BC. Methods The publicly available GSE124648 dataset was used to identify differentially expressed genes (DEGs) between BC and liver metastases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to annotate these DEGs and understand the biological functions in which they are involved. A protein–protein interaction (PPI) network was constructed to identify metastasis-related hub genes and further validated in a second independent dataset (GSE58708). Clinicopathological correlation of hub gene expression in patients with BC was determined. Gene set enrichment analysis (GSEA) was performed to explore DEG-related signaling pathways. SPARCL1 expression in BC tissues and cell lines was verified by RT-qPCR. Further in vitro experiments were performed to investigate the biological functions of SPARCL1 in BC cells. Results We identified 332 liver metastasis-related DEGs from GSE124648 and 30 hub genes, including SPARCL1, from the PPI network. GO and KEGG enrichment analyses of liver-metastasis-related DEGs revealed several enriched terms associated with the extracellular matrix and pathways in cancer. Clinicopathological correlation analysis of SPARCL1 revealed that its expression in BC was associated with age, TNM stage, estrogen receptor status, progesterone receptor status, histological type, molecular type, and living status of patients. GSEA results suggested that low SPARCL1 expression in BC was related to the cell cycle, DNA replication, oxidative phosphorylation, and homologous recombination. Lower expression levels of SPARCL1 were detected in BC tissues compared to adjacent tissues. The in vitro experiments showed that SPARCL1 knockdown significantly increased the proliferation and migration of BC cells, whereas the proliferation and migration were suppressed after elevating the expression of SPARCL1. Conclusion We identified SPARCL1 as a tumor suppressor in BC, which shows potential as a target for BC and liver metastasis therapy and diagnosis.
Epileptic Seizure Prediction Using 1D-MobileNet

Automatic seizure prediction is helpful to Epilepsy patients to identify the possibility of seizure events momentarily. This could mitigate the risk faced by them while driving, climbing a staircase, or in similar situations in public. Since the olden days, clinicians have widely used electroencephalography (EEG) for examining epilepsy patients' brain activities. Yet, manually inspecting seizure episodes from EEG recordings is a time-consuming operation that may be reduced with the assistance of an automated seizure prediction system. The proposed work focuses on implementing a seizure detection mechanism, which inturn benefit the lives of both Epilepsy sufferers and clinicians. To do so, seizure phases must be identified, and a 1D-MobileNet architecture is used in this study to classify the ictal and preictal stages of the seizure. Prior to feeding the EEG data into the model, we employed fewer channels and data augmentation based on the sliding window technique. We assessed the proposed model on the CHB-MIT dataset, which yielded an accuracy of 98.50%, average precision of 99.00%, recall of 98.06%, and F1-score of 98.00%.
Reconstructing a Computable and Computationally Complex Theoretic Path Towards Simon's Behavioural Economics

This paper aims to interpret and formalize Herbert Simon's notions of bounded rationality, satisficing and heuristics in terms of computability theory and computational complexity theory. Simon's theory of human problem solving is analysed in the light of Turing's work on Solvable and Unsolvable Problems. It is suggested here that bounded rationality results from the fact that the deliberations required for searching computationally complex spaces exceed the actual complexity that human beings can handle. The immediate consequence is that satisficing becomes the general criterion of decision makers and heuristics are the procedures used for achieving their goals. In such decision problems, it is demonstrated that bounded rationality and satisficing are more general than Olympian rationality and optimization, respectively, and not the other way about.
IgA Vasculitis Associated With COVID-19

IgA vasculitis, also known as Henoch-Schonlein Purpura (HSP), is an inflammatory disorder of small blood vessels that can present with palpable purpura, arthralgias, abdominal pain, and kidney disease. It is most commonly found in pediatric patients after an inciting infection but has been seen across all ages and associated with certain drugs and vaccines. COVID-19 has been associated with various cutaneous manifestations, but HSP is a rarely reported one. We present a case of a 21-year-old female presenting with a petechial rash found to be seronegative IgA vasculitis presenting concurrently with dyspnea secondary to COVID-19. She was initially seen by an outside practitioner, tested negative for COVID, and was prescribed a course of oral prednisone. Shortly thereafter, she visited the ED for worsening shortness of breath and tested positive for COVID-19, for which she received Paxlovid. Biopsy after a visit to a dermatologist confirmed intramural IgA deposition on immunofluorescence, and she was tapered off prednisone and started on azathioprine.