Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical evidence for a nonlinear effect of galactic cosmic rays on clouds

Galactic cosmic ray (GCR) changes have been suggested to affect weather and climate, and new evidence is presented here directly linking GCRs with clouds. Clouds increase the diffuse solar radiation, measured continuously at UK surface meteorological sites since 1947. The ratio of diffuse to total solar radiation—the diffuse fraction (DF)—is used to infer cloud, and is compared with the daily mean neutron count rate measured at Climax, Colorado from 1951–2000, which provides a globally representative indicator of cosmic rays. Across the UK, on days of high cosmic ray flux (above 3600×102 neutron counts h−1, which occur 87% of the time on average) compared with low cosmic ray flux, (i) the chance of an overcast day increases by (19±4) %, and (ii) the diffuse fraction increases by (2±0.3) %. During sudden transient reductions in cosmic rays (e.g. Forbush events), simultaneous decreases occur in the diffuse fraction. The diffuse radiation changes are, therefore, unambiguously due to cosmic rays. Although the statistically significant nonlinear cosmic ray effect is small, it will have a considerably larger aggregate effect on longer timescale (e.g. centennial) climate variations when day-to-day variability averages out.
A diameter‐sensitive flow entropy method for reliability consideration in water distribution system design

Flow entropy is a measure of uniformity of pipe flows in water distribution systems. By maximizing flow entropy one can identify reliable layouts or connectivity in networks. In order to overcome the disadvantage of the common definition of flow entropy that does not consider the impact of pipe diameter on reliability, an extended definition of flow entropy, termed as diameter‐sensitive flow entropy, is proposed. This new methodology is then assessed by using other reliability methods, including Monte Carlo Simulation, a pipe failure probability model, and a surrogate measure (resilience index) integrated with water demand and pipe failure uncertainty. The reliability assessment is based on a sample of WDS designs derived from an optimization process for each of the two benchmark networks. Correlation analysis is used to evaluate quantitatively the relationship between entropy and reliability. To ensure reliability, a comparative analysis between the flow entropy and the new method is conducted. The results demonstrate that the diameter‐sensitive flow entropy shows consistently much stronger correlation with the three reliability measures than simple flow entropy. Therefore, the new flow entropy method can be taken as a better surrogate measure for reliability and could be potentially integrated into the optimal design problem of WDSs. Sensitivity analysis results show that the velocity parameters used in the new flow entropy has no significant impact on the relationship between diameter‐sensitive flow entropy and reliability.
The Impact of the Data Warehouse on Decision Making Quality and Speed in Higher Education

This paper studies the impact of implementing a data warehouse technology on decision making quality and speed in the context of higher education institutions. The model of this study uses the decision-making process phases of rational decision-making theory. The results of the survey among high level decision makers (deans, provosts, etc.) in USA higher education institutions revealed that the decision-making quality and speed are considerably improved by the presence of the data warehouse technology. This relationship is mediated by multiple mediators operating in serial, namely, system quality, information quality, intelligence phase quality, design phase quality and choice phase quality.
Active Learning Framework For Long-term Network Traffic Classification

Recent network traffic classification methods benefit from machine learning (ML) technology. However, there are many challenges due to the use of ML, such as lack of high-quality annotated datasets, data drifts and other effects causing aging of datasets and ML models, high volumes of network traffic, etc. This paper presents the benefits of augmenting traditional workflows of ML training&deployment and adaption of the Active Learning (AL) concept on network traffic analysis. The paper proposes a novel Active Learning Framework (ALF) to address this topic. ALF provides prepared software components that can be used to deploy an AL loop and maintain an ALF instance that continuously evolves a dataset and ML model automatically. Moreover, ALF includes monitoring, datasets quality evaluation, and optimization capabilities that enhance the current state of the art in the AL domain. The resulting solution is deployable for IP flow-based analysis of high-speed (100 Gb/s) networks, where it was evaluated for more than eight months. Additional use cases were evaluated on publicly available datasets.
Inference for non-linear diffusions and jump-diffusions: a Monte Carlo EM approach

We propose a simple, general and computationally efficient algorithm for maximum likelihood estimation (MLE) of parameters in diffusion and jump-diffusion processes. This is conducted within a Monte Carlo EM-algorithm, where the smoothing distribution is computed using resampling. The results are encouraging as we can approximate the MLE well for the models studied when using simulated data. We also obtain reasonable estimates, compared to other papers, when fitting the Heston and Bates model to S&P 500 and VIX data.
Abstract Pattern Image Generation using Generative Adversarial Networks

Abstract pattern is very commonly used in the textile and fashion industry. Pattern design is an area where designers need to come up with new and attractive patterns every day. It is very difficult to find employees with a sufficient creative mindset and the necessary skills to come up with new unseen attractive designs. Therefore, it would be ideal to identify a process that would allow for these patterns to be generated on their own with little to no human interaction. This can be achieved using deep learning models and techniques. One of the most recent and promising tools to solve this type of problem is Generative Adversarial Networks (GANs). In this paper, we investigate the suitability of GAN in producing abstract patterns. We achieve this by generating abstract design patterns using the two most popular GANs, namely Deep Convolutional GAN and Wasserstein GAN. By identifying the best-performing model after training using hyperparameter optimization and generating some output patterns we show that Wasserstein GAN is superior to Deep Convolutional GAN.
Critical currents of Nb/sub 3/Sn superconductors for generators under cyclic mechanical load

Critical currents of Nb/sub 3/Sn cabled superconductors were measured under cyclic mechanical compressive force of 30 MPa up to 10000 times. The conductors were developed for a superconducting generator under the Super-GM project. The conductors were designed to apply the field winding of the 70 MW-class superconducting generator. The 10000 repetitions of force application simulate 30 years of centrifugal and electromagnetic force produced by the daily start-and-stop (DSS) operation of the superconducting generator. In the test facility constructed at the Akagi Testing Center of the Central Research Institute of Electric Power Industry (CRIEPI), mechanical compressive force is generated by the cyclic fatigue test machine, which can generate 100 kN of force and is located on top of a cryostat at room temperature. Measured critical currents of two types of Nb/sub 3/Sn conductor did not change throughout the test. >
BrainNet: Improving Brainwave-based Biometric Recognition with Siamese Networks

With the advent of consumer wearables that capture brain activity, the use of brainwaves to verify a user's identity has been proposed as a convenient alternative to passwords. While recent work on brain biometrics shows feasible performance, it falls short in considering practical applicability. We propose a new solution, BrainNet, which trains a Siamese Network to measure the similarity of two electroencephalogram (EEG) inputs, and uses time-locked brain reactions instead of continuous mental activity to improve accuracy. This approach removes the need for retraining the brainwave recognition system, a common pitfall in current solutions, facilitating practical deployment. Furthermore, BrainNet achieves Equal Error Rates (EERs) of 0.14% in verification mode and 0.34% in identification mode, outperforming the state of the art even when evaluated under unseen attacker scenarios.
Pragmatic Domestic Electrical Load Disaggregation

This paper studies methods for determining the major electrical loads that contribute to aggregate electric energy consumption for a household or similar unit. It provides an alternate formulation of a well studied general problem and a framework and prototype implementation to address it. The focus is on methods that do not require any instrumentation or data beyond hourly (or similar low frequency) records of aggregate energy consumption, as is often easily available from power utility companies due to the increasing prevalence of smart meters. As well, the focus is on pragmatic approaches that are likely to provide useful information for typical household electricity consumption in contrast to methods more suited to industrial environments. Another notable feature is that disaggregation is performed not centrally at the utility company or similar entity with data from a large number of households but instead in a distributed and independent manner at each household. This feature provides two key benefits: (1) It permits the injection of information known to a household but not (easily) by others in order to simplify the problem. (2) It provides better privacy protections for such data.
Deep Learning Based An Optimized Predictive Academic Performance Approach

An early warning system is used to collect, process and analyze present data to predict possibilities that may occur in the future. This tool can be implemented in education to process relevant data to predict academic performance and threats. Several studies have been conducted in the past several decades on the use of early warning systems in education. Moreover, there are limited open datasets available in these areas of research. A remarkable dataset is the Open University Learning Analytics Dataset (OULAD). This paper proposes a deep learning-based predictive analytics model with an effective specificity score that helps predict student academic performance. Moreover, the paper analyzes the implementation timing of the model within the first two months of the academic semester. The model attains a higher success prediction accuracy rate within multicategories and a large input dataset. The best significant result achieved in the study was the 98.94 accuracy score and 93.10 specificity score in the first week of Science Technology Engineering Mathematics (STEM) domain courses of the academic term, compared to Artificial Neural Network, Naive Bayes and Support vector machine, which were applied as validators.
Effective Mobile Charging Solution for Electric Vehciles in Smart Cities

Range anxiety has become a major stumbling block for the adoption of electric vehicles (EVs) when it comes to long journeys. Aside from the extended charging period, most of the existing charging techniques require EVs to remain stationary while being charged. With the major advance in dynamic wireless charging technology, the Mobile Energy Disseminator (MED) has been introduced as a means to supply energy to an EV on the road. MED can provide energy to the vehicle during its motion without the need for larger batteries or an expensive change in infrastructure. However, MED is often associated with vehicles with predefined travel paths which forces an EV to alter its route to take advantage of the MED charging option. In this paper, we present RIMEC, a -Routing for -Increased Mobile Energy Charging algorithm that determines an optimized EV travel route for utilizing MEDs in order to maximize the amount of potential energy that can be harvested while minimizing the impact on travel time. The experiment results conducted in a realistic simulation scenario indicate that RIMEC not only achieves its design goals but also boosts the system throughput by serving a larger number of EV charging requests.
FoCA: Failure-oriented Class Augmentation for Robust Image Classification

Image classification with classes of varying difficulty can cause performance disparity in deep learning models and reduce the overall performance and reliability of the predictions. In this paper, we introduce a failure-oriented class augmentation (FoCA) technique to address the problem of imbalanced performance in image classification, where the trained model has performance deficits in some of the dataset's classes. By employing Generative Adversarial Networks (GANs) to augment these deficit classes, we finetune the model towards a balanced performance among the different classes and an overall better performance on the whole dataset. Unlike earlier works, during training, our method focuses on those classes with the lowest accuracy after the initial training phase. Only these classes are augmented to boost the accuracy, which leads to better performance. FoCA is designed to be used with a light-weight GAN method to make the GAN-based augmentation viable and effective, even for datasets with only few images per class, while simultaneously requiring less computation than other, more complex GAN methods. Our implementation of FoCA combines this light-weight GAN method for class-wise data augmentation with state-of-the-art deep neural network techniques for training. Experiments show an overall improvement from FoCA with competitive or better accuracy than the previous state-of-the-art on five datasets with different sizes and image resolutions.
Deep Learning Approaches for the Protein Scaffold Filling Problem

We are on the verge of a post-genomics era in which whole protein sequencing will be quickly carried out. Protein se-quencing plays an important role in identifying protein functions, analyzing protein-protein interactions, and characterizing post-translational modifications, etc. The protein sequencing problem is to determine the complete sequence of amino acids in proteins. De novo protein sequencing using top-down and bottom-up tandem mass spectrometry suffers from the problem of producing only partial sequences of target proteins, namely scaffold. In this paper, we explore the possibility of using deep learning techniques to perform the task of predicting amino acids in partially sequenced proteins by two phases. First, our methods involve querying the NCBI Protein Blast server to find closest matching homologous sequences to a scaffold as a training dataset. Second, we train several deep learning models based on a convolutional neural network and long short term memory to predict missing amino acids in the scaffold in the forward and reverse directions. We comprehensively evaluate our proposed methods on an alemtuzumab dataset and our results show that the proposed methods achieve high sequence coverage and high sequence accuracy with 100 % on the the light chain of alemtuzumab scaffold data.
CNN Aided Surface Inspection for SMT Manufacturing

Automated optical inspection (AOI) is a visual defect inspection system. The semiconductor industry has a strong dependency on AOI for defects screening. Conventional AOI is inadequate for some inspections, especially surface defects like crack, chip and void, and the algorithms are inefficient in isolating the defects from product variants. Convolutional Neural Network (CNN) had been broadly studied and adopted to replace the conventional AOI in surface inspection. There are many CNN architectures developed in the past decade for image classification, such as AlexNet, GoogLeNet, ResNet, VGGNet, etc.; each with its own strength in terms of accuracy and speed. The training process could be speeded up too using techniques such as transfer learning from pre-trained CNN models. Newer techniques in vector programming on kernels, e.g., Single Instruction Multiple Data (SIMD) and depth wise separable method can further increase the efficiency of convolutional layer activation functions. CNN algorithms for surface inspection are found to be very promising, with defect classification able to achieve accuracies of 91-99% on the wide range of products. The CNN result outperforms conventional surface inspection methods like edge detection and machine learning algorithms.
Adaptive Token Circulation to Avoid Malicious UEs Hoarding Tokens and Assure D2D Relay Efficiency

Device-to-device (D2D) relay can be used to expand the service coverage of a base station (BS) and improve network throughput. In D2D relay, when a user equipment (UE) has bad channel quality, it solicits a neighbor to relay its data from the BS. In view that UEs are usually owned by self-interested users, token-based incentive methods adopt tokens as virtual currencies to let UEs sell and buy relay services, which encourages UEs to help relay data for other UEs. However, malicious UEs may keep gathering tokens from others but never spend tokens, which we call hoarders. Hence, negotiable tokens would reduce gradually, and many UEs cannot afford relay services due to lack of tokens. In this case, these token-based incentive methods will incur bad performance or even collapse. To this end, we propose an adaptive token circulation (ATC) scheme to tax the UEs that have ample tokens and subsidize the UEs in need of tokens. ATC works well in a multi-cell environment and does not cause the inflation of tokens. Simulation results reveal that the ATC scheme keeps high D2D throughput when there exist hoarders, thereby assuring the efficiency of D2D relay.
Stability Analysis for Impulsive Systems: 2D Vector Lyapunov Function Approach

This paper contributes to the stability analysis for impulsive dynamical systems based on a vector Lyapunov function and its divergence operator. The new method relies on a 2D time domain representation. The result is illustrated for the exponential stability of linear impulsive systems based on LMIs. The obtained results provide some notions of minimum and maximum dwell-time. Some examples illustrate the feasibility of the proposed approach.
Strategic implants and bone morphogenic changes: Survival and clinical success in long-term

Dental implants are one of the established treatment protocols to replace/restore lost tooth/teeth structures. Ihde and Ihde redefined concept of strategic implantology based on immediate loading. The aim of present case series was to evaluate efficacy of strategic implants regarding primary stability, quality of bone, survival, and clinical success in long-term. The present case series included a total of 26 patients aged 40 to 70 years restored with strategic implants after an immediate loading protocol. On follow-up visits, clinical assessment of implant stability was done by absence of implant mobility and absence of pain and dysesthesia while radiographically, immediate postoperative crestal bone level was compared with that observed on follow-up visits. Also, integration of apical and/or crestal threads of implants were assessed radiographically for any signs of radiolucency along with healing of sockets and crestal bone levels in relation to abutment margins.
Prevalence of mesio-distal dilaceration in patients presenting for initial orthodontic care: A retrospective study

OBJECTIVE: To determine the prevalence of dilaceration in a sample of patients presenting for initial orthodontic care. METHODS: Examining radiographs from a random sample of orthopantomogram images was used to acquire the data. In all, 2,801 dental records were evaluated at Oman Dental College (ODC), Oman. A dental X-ray processing software was utilized to view the images. A tooth was classified as having a mesial/distal dilaceration if its long axis exhibited an angle of 90 degrees or greater. Dilacerated roots in the buccal/lingual direction were diagnosed by observing the appearance of a spherical opaque area with a dark shadow in its central region, projected by the apical foramen, which gave the root canal a “bull's-eye” appearance. RESULTS: Dilacerations were found in 17.32% of the records examined. The maxillary second molars (22.71%) were the most commonly affected, followed by the mandibular third molars and mandibular lateral incisors (21.90% and 17.23%, respectively). The central incisors and canines were the least affected, with dilaceration affecting less than 0.2% of the teeth. The mandible was found to have more dilacerations than the maxilla (53.78% and 46.22%, respectively). 61.03% of dilacerations occurred in molars, 43.12% of which occurred in third molars. CONCLUSION: Dilaceration is a notable dental anomaly that can affect any tooth, with some teeth being more affected than others. Dilaceration in maxillary second molars and mandibular lateral incisors is more common in the population of this study than in other populations reported in the literature. Recognizing the condition will allow for more effective orthodontic treatment.
Comparison between neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio as predictors of mortality on Fournier's gangrene cases

Introduction: Fournier's gangrene (FG) is an infection of the subcutaneous tissue and fascia that progresses quickly and leads to necrosis. It is more prevalent in male patients and immunocompromised individuals, such as those suffering from uncontrolled diabetes. It has a high mortality rate, which makes its early identification and clinical suspicion critical. This study aimed to compare two laboratory parameters, neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), and to predict the mortality of FG in a tertiary care hospital. Methods: In a retrospective study, data was retrieved from medical records for the period from January 2014 to December 2020, of patients diagnosed with FG. Recorded data that is age, sex, comorbidities, mortality, and laboratory results (PLR and NLR) were used to assess determinants of survival. Results: There were 23 (17.04%) nonsurvivors among the 135 subjects studied. The mean age was 50.9 ± 14.9 years and men were 103 (83%) patients. Among the participants, diabetes mellitus was the most frequent comorbidity at 74 (54.81%) patients. NLR ≥8 was statistically significant (P = 0.013) for identifying mortality, while PLR >140 was not. In multivariate analysis, NLR ≥8 was found to be a reliable predictor of the FG mortality rate (adjusted odds ratio 12.062, confidence interval 95% 2.115–68.778, P = 0.005). Conclusion: NLR had prognosis predictive value for FG, whereas PLR did not.