Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-agent reinforcement learning for UAV sensor management

The use of unmanned aerial vehicles (UAVs) for intelligence, surveillance, and reconnaissance (ISR) is one of the most well-known applications of the technology. UAVs performing ISR can carry out missions that would be too dangerous or otherwise too complicated for manned systems to complete. Today, explicit multi-agent information theoretic optimizations present challenges, requiring approximations and computational shortcuts to determine the expected information gain of an action. The approximation enables distributed optimization at the cost of efficiency in terms of under-utilization of certain sensors during tracking. While some techniques introduce multi-step planning to address this issue, these come at the cost of an additional computational burden. By using information theoretic metrics as reward functions in a Multi-Agent Reinforcement Learning (MARL) framework, we train RL agents to select actions which maximize expected information gain without having to estimate this quantity at runtime. This approach has potential to out-perform existing techniques, which rely on truncated estimates of expected information due to computational limitations. Our current prototype focuses on developing a cooperative learning strategy by modeling UAVs tracking down adversarial ground targets in an area of interest. Missions are expected to be highly dynamic where suboptimal conditions are likely to occur. To replicate this in our research, RL agents exist in a partial-knowledge environment where they learn to leverage various sensors and information available to complete the mission together. Our next step is to add a policy to optimize the number of UAVs needed to scan an area of interest while still tracking targets.
Evaluation of the real efficiency for selected regressors

This paper raises a problem of nonparametric estimation of the regression function and its time efficiency. Authors compare the regression accuracy but considers also the time that is needed to evaluate the value for the test object. That time takes into consideration the evaluation time, but also the time of regressor creating. Experiments were conducted with the usage of multidimensional real data.
A variant of BB84 Protocol based on quantum phase

One of the research focuses of quantum cryptography is the variants of BB84 protocol. This paper presents a variant of BB84 protocol based on quantum phase which employs two pairs of phase bases and four measurement operations. Then two different generation and measurement of quantum phases are discussed which lead to different shared keys and error probabilities introduced by the eavesdropper. The variant proposed by this paper is valuable to the research of quantum cryptography.
Development and Optimization of Wind Turbine Blade Design for Enhanced Efficiency

The improvement and optimisation of wind energy system performance and efficiency depend heavily on the design of wind turbine blades. Wind power has emerged as a viable option for environmentally friendly electricity generation in response to the rising demand for renewable energy sources. The structural properties and aerodynamic performance of a wind turbine's blades have a significant impact on its efficiency.This study focuses on the systematic creation and efficiency optimisation of wind turbine blade designs. The study uses a multidisciplinary strategy that incorporates optimisation, structural analysis, and aerodynamics. The goal is to increase power generation while maintaining the structural integrity, cost-effectiveness, and safety of the blades.The research starts with a thorough examination of the strengths and weaknesses of the current designs for wind turbine blades. To assess the performance of various blade shapes, various aerodynamic theories, computational fluid dynamics (CFD) simulations, and wind tunnel measurements are used. The issue of wind turbine blade noise is also included in the study. To lessen the influence of noise on the environment, noise reduction techniques like trailing-edge serrations are being researched. The project also investigates the use of cutting-edge materials, like carbon fibre composites, to lighten the blades without sacrificing their structural integrity.The results of this study are anticipated to have a big impact on wind turbine blade design. It is projected that the optimised blade designs will improve wind energy systems' overall efficiency by boosting power output and lowering aerodynamic loads. While the use of noise reduction measures enhances the environmental friendliness of wind turbines, structural analysis ensures the safety and dependability of the blades.
Using Old Data: When Is It Appropriate?

Researchers and gatekeepers lack clarity about the circumstances under which using old data to test hypotheses is appropriate or inappropriate. In response to this complex issue, we first define what makes data “old. ” We then suggest that using old data is justifiable (a) when examining a past event, (b) when recent data are not available, or (c) when the data were collected painstakingly. Scholars should avoid using old data if none of these conditions exist. Further, authors should be forthcoming about the age of their data and, in the case of a rejected journal submission, update the data whenever possible.
Fermentation of Date Pulp Residues Using Saccharomyces cerevisiae and Pichia kudriavzevii—Insights into Biological Activities, Phenolic and Volatile Compounds, Untargeted Metabolomics, and Carbohydrate Analysis Post In Vitro Digestion

Utilizing agricultural byproducts, such as date pulp residue (DPR), can contribute significantly towards achieving sustainable food production. This paper aimed to investigate the physicochemical properties of DPR and evaluate the health benefits of nonfermented and fermented DPR samples both pre- and post-digestion (bioaccessible portions). Additionally, it aimed to analyze the carbohydrates and untargeted metabolites in the bioaccessible portions. Fermentation of DPR by Saccharomyces cerevisiae and Pichia kudriavzevii resulted in an abundance of malic acid (over 2400 mg/100 g) and the identification of 42 volatiles, with different degrees of predominance observed in the samples. Twenty phenolics were determined by UPLC in fermented DPR, with (-)-epicatechin, tyrosol, and gallic acid being the most abundant. Bioaccessibility studies revealed that fermented DPR samples retained at least ~44% of α-glucosidase inhibition and exhibited improved α-amylase inhibition compared to nonfermented and undigested samples. In vitro cytotoxicity assays showed a more potent inhibitory effect of fermented DPR against MCF-7 and Caco2 cell lines (average inhibition of 55% and 74.4% for the two types of fermented samples) compared to nonfermented DPR. The untargeted metabolomics analysis identified C5-branched dibasic acid metabolism as the most prominent pathway, with four metabolites identified. Furthermore, the analysis of bioaccessible carbohydrate metabolites in the fermented DPR using LC-QTOF showed the presence of a group of phytochemicals, including three terpenoid metabolites.
Topological Design Optimization Approach for Winding Oil Flow Paths in Oil-Natural Air-Natural Transformers Based on a Fluidic-Thermal Coupled Model

Power transformers are a critical component in transmission and distribution systems. To limit the temperature rise to extend the life-cycle while operating with enough reliability and controllability of a transformer, the design of the cooling system has attracted extensive research attention. In this article, a topology optimization (TO) methodology is presented for designing the coolant flow paths in a prototype oil-natural air-natural (ONAN) power transformer to maximize the winding heat dissipation capacity without any compromise on the flow resistance. First, the thermal and fluid flow characteristics in both the winding and the radiator of the prototype transformer are numerically investigated through a fluidic-thermal coupled field approach. The preliminary cooling effectiveness of the oil circulation system is initialized. Second, a material-density (MD)-based TO scheme is employed to realize the optimal design of the oil baffle structures. In the TO optimization, artificial Darcy frictional forces are applied to the solidified cells to block their internal flows in the fluidic calculations, while the thermal conduction coefficients in those cells are enhanced to separate the baffles from the flowing oil in thermal analyses. Finally, the fluidic and thermal coupled fields of the prototype transformer winding region with structurally optimized oil baffles are numerically investigated to validate the TO effectiveness.
Analysis of the Walt Disney's acquisition of 21st Century Fox

The film and television entertainment industry is a rapidly evolving industry, which must launch products in a short amount of time to cater to the audience. Otherwise, audience loss and a decline in profitability may result. Therefore, acquisition is a crucial business decision for film and entertainment companies, as it expedites the establishment of a production chain. This paper focuses on a series of implications for both companies and the film and entertainment industry as a result of Walt Disney's acquisition of 21st Century Fox. The impact of Disney's acquisition of Fox to launch the streaming service Disney+ on Disney's business position is analyzed in terms of financial data, box office revenues, and other aspects. According to the analysis, Disney has benefited from the acquisition. This acquisition has placed Disney at the top of the U.S. box office and given it the ability to compete with streaming companies like Netflix in the streaming service industry. However, there are also limitations and deficiencies, e.g., it may make the market less diverse. Therefore, in addition to a discussion of the limitations and flaws, this study offers substantial recommendations to allow Disney to simultaneously protect its intellectual property and expand its audience to increase viewership and prevent box office losses.
The Influence of Recovery Experience on Occupational Well-Being: A Moderated Mediating Effect

Based on the perspective of intergenerational differences, this study takes the questionnaire data of 660 enterprise employees as a sample to explore the boundary conditions of the path of “recovery experience → psychological capital → occupational well-being”. The results show that both transactional psychological capital and interpersonal psychological capital have significant mediating effects, and intergenerational differences do play a negative moderating role in the path of “recovery experience → psychological capital → occupational well-being”. And further research shows that the indirect effect of interpersonal psychological capital of Generation Z employees is not significant. Overall, this study complements the research on the boundary conditions of the path of “recovery experience → psychological capital → occupational well-being”, and provides a reference for the effective management of the new generation of employees in practice.
An Automated Method to Estimate Tropical Cyclone Intensity Using SSM/I Imagery

Abstract An automated method to estimate tropical cyclone intensity using Special Sensor Microwave Imager (SSM/I) data is developed and tested. SSM/I images (512 km × 512 km) centered on a given tropical cyclone (TC), with a known best-track intensity, are collected for 142 different TCs (1988–98) from the North Pacific, Atlantic, and Indian Oceans. Over 100 characteristic features are computed from the 85-GHz (H-pol) imagery data and the derived rain-rate imagery data associated with each TC. Of the 1040 sample images, 942 are selected as training samples. These training samples are examined in a feature-selection algorithm to select an optimal subset of the characteristic features that could accurately estimate TC intensity on unknown samples in a K-nearest-neighbor (K-NN) algorithm. Using the 15 selected features as the representative vector and the best-track intensity as the ground truth, the 98 testing samples (taken from four TCs) are presented to the K-NN algorithm. A root-mean-square error (rmse).
Three-component measurement in transient electromagnetic method

Without considering the mutual inductance between eigencurrent and eddy current, the eigencurrent which was instead of eddy current was used to calculate the fixed-loop X, Y and Z component transient responses over a thin plate conductor in homogeneous half-space with various plate dip angles. The results show that, except for a vertical target, the cross-over of X-component is approximately the projection position of the plate’s center. For a horizontal plate, both Y and Z-component plots are symmetrical. For a vertical target Y-component is symmetrical and Z-component is anti-symmetrical. While for an inclined plate, neither component is symmetrical, it is broad and gentle in the inclination direction but narrow and steep in the other direction. So target location and orientation can be defined qualitatively by three-component measurement. The observed results are coincident with the calculated ones, which proves that the method of unitarily analyzing three-component is better than that for the interpretation of conductor body.
Quantitative Trading Strategy Based on Simplified DPG

In recent years, investment has become more and more popular, and asset management has also received more and more attention. At the same time, with the development of computer science, more and more machine learning or deep learning algorithms can be used for investment management, such as price forecasting, portfolio and quantitative trading strategies. First of all, in the trading market, most of the ups and downs are cyclical, but they are easily affected by factors such as policies and investment fever, which brings great challenges to the establishment of a reasonable price forecasting model. This paper builds a sliding price forecasting model that aims to reduce various noises such as frequent fluctuations. In order to get the best forecast results, we searched the parameters of each sliding forecast, trying to get the forecast closest to the actual price. Second, developing trading strategies based on forecast results is clearly related to goal planning. Therefore, this paper first establishes the objective programming problem, designs the objective function and constraints, and then optimizes the problem. At the same time, the article also considers investor styles, such as the investment boom in gold and bitcoin, and a more cautious approach to buying and selling. Ultimately, this paper designs a flexible feedback model based on style parameters to adjust the trading strategy, by searching for the best parameters of the final model, our trading strategy model converts assets from $1000 from September 11, 2016 to September 10, 2021 It increased to $238,869, and the cumulative return reached 23,886.9%.
MAB-DrNet: Bearing Fault Diagnosis Method Based on an Improved Dilated Convolutional Neural Network

Rolling bearing fault diagnosis is of great significance to the safe and reliable operation of manufacturing equipment. In the actual complex environment, the collected bearing signals usually contain a large amount of noises from the resonances of the environment and other components, resulting in the nonlinear characteristics of the collected data. Existing deep-learning-based solutions for bearing fault diagnosis perform poorly in classification performance under noises. To address the above problems, this paper proposes an improved dilated-convolutional-neural network-based bearing fault diagnosis method in noisy environments named MAB-DrNet. First, a basic model called the dilated residual network (DrNet) was designed based on the residual block to enlarge the model’s perceptual field to better capture the features from bearing fault signals. Then, a max-average block (MAB) module was designed to improve the feature extraction capability of the model. In addition, the global residual block (GRB) module was introduced into MAB-DrNet to further improve the performance of the proposed model, enabling the model to better handle the global information of the input data and improve the classification accuracy of the model in noisy environments. Finally, the proposed method was tested on the CWRU dataset, and the results showed that the proposed method had good noise immunity; the accuracy was 95.57% when adding Gaussian white noises with a signal-to-noise ratio of −6 dB. The proposed method was also compared with existing advanced methods to further prove its high accuracy.
Big Data Analytics Using Machine Learning Techniques for Prediction on Datasets

Data analytics is the process of performing scientific and statistical analysis on raw data in order to transform it into information that can be used for gaining knowledge. A recently emerging trend in feature abstraction is the combination of computational techniques and big data analysis. This requires gaining knowledge from trustworthy data sources, being able to digest information quickly, and making accurate predictions about the future. The primary objective of this study is to locate the machine learning strategies that produce the most accurate prediction by utilising the model that has been proposed. The supervised and unsupervised strategies have been implemented in a variety of different ways using the MapReduce methodology; however, the suggested model makes use of the Apache Spark framework in order to compare the many existing methods. In this study, the emphasis is placed on elucidating the characteristics of datasets in order to conduct the most accurate analysis possible using machine learning techniques. For the purpose of conducting an analysis of the data sets, machine learning methods such as linear regression, decision trees, random forests, and gradient boosting tree algorithms are utilised. In light of the findings of this research, it is possible to draw the conclusion that when the Spark framework is applied on top of Machine Learning methods, the efficiency of the model is improved by a factor of seventy percent in comparison to the MapReduce paradigm. Keyword : Apache Spark Framework, Big Data Analytics, Machine Learning Algorithms, MapReduce Paradigm.
Back to the Beginning: Diagnosis Experiences of Persons with Type Two Diabetes

Background: One in ten adults in the United States has diabetes; the vast majority are diagnosed with type two diabetes mellitus (T2DM). Even though diabetes care has significantly evolved, and a wide variety of pharmacological treatments are available, approximately one-half of people with type two diabetes do not achieve a hemoglobin A1C of less than seven percent. In addition, self-management is a consequential contributor to glycemic control. In light of the mortality, morbidity, decreased quality of life, and financial burden caused by T2DM, the author wanted to explore the diagnosis experiences of type two diabetics. Could their experiences be a contributing factor to less-than-optimal treatment outcomes? Methods: The author interviewed nine consenting adults with T2DM in person, over the telephone, and via videoconference over four weeks in March and April 2023. The data from each transcript was analyzed independently and then compared, identifying meanings and clustering themes found throughout utilizing descriptive phenomenology as the underpinning for this study.
The potential distribution in a layered anisotropic spheroidal volume conductor

Formulas are derived that express the potential distribution in a layered spherical and spheroidal anisotropic volume conductor: the number of layers is arbitrary and each layer may be anisotropic. These expressions are a generalization of existing formulas for localization of sources underlying evoked potentials or electro‐encephalographic data. The formulas are presented in a convenient form so that if they are applied in practice the likelihood of software errors is minimal.
Combining Infinity Number Of Neural Networks Into One

One of the important aspects of a neural network is its generalization property, which is measured by its ability to make correct prediction on unseen samples. One option to improve generalization is to combine results from multiple networks, which is unfortunately a time-consuming process. In this paper, a new approach is presented to combine infinity number of neural networks in analytic way to produce a small, fast and reliable neural network.
Internet addiction and depression among Chinese adolescents: anxiety as a mediator and social support as a moderator

ABSTRACT This study constructed a moderated mediation model to investigate the mediating effect of anxiety and the moderating effect of social support between Internet addiction and depression. A sample of 17 058 middle school students in one district of Chengdu were selected. The Internet Addiction Test (IAT), Generalized Anxiety Disorder (GAD−7), Patient Health Questionnaire (PHQ−9), and Social Support Scale for adolescents were used to investigate their Internet addiction, anxiety, depression and social support. The descriptive statistics and Spearman correlation analysis were performed with SPSS 25.0. An SPSS macro process was used to analyze the data from complex models that contained mediators and moderators. The results show that adolescents with Internet addiction are more likely to suffer from depression. Anxiety partially mediated the relations between Internet addiction and depression. Social support moderated both direct and indirect pathways leading from Internet addiction to depression, and these two effects were stronger for adolescents with low social support than for those with high social support. It will be possible for researchers to gain a better understanding of the conditions, pathways, and effects of Internet addiction on depression in adolescents through the results of this study.
Sample size considerations for assessing treatment effect heterogeneity in randomized trials with heterogeneous intracluster correlations and variances

An important consideration in the design and analysis of randomized trials is the need to account for outcome observations being positively correlated within groups or clusters. Two notable types of designs with this consideration are individually randomized group treatment trials and cluster randomized trials. While sample size methods for testing the average treatment effect are available for both types of designs, methods for detecting treatment effect modification are relatively limited. In this article, we present new sample size formulas for testing treatment effect modification based on either a univariate or multivariate effect modifier in both individually randomized group treatment and cluster randomized trials with a continuous outcome but any types of effect modifier, while accounting for differences across study arms in the outcome variance, outcome intracluster correlation coefficient (ICC) and the cluster size. We consider cases where the effect modifier can be measured at either the individual level or cluster level, and with a univariate effect modifier, our closed‐form sample size expressions provide insights into the optimal allocation of groups or clusters to maximize design efficiency. Overall, our results show that the required sample size for testing treatment effect heterogeneity with an individual‐level effect modifier can be affected by unequal ICCs and variances between arms, and accounting for such between‐arm heterogeneity can lead to more accurate sample size determination. We use simulations to validate our sample size formulas and illustrate their application in the context of two real trials: an individually randomized group treatment trial (the AWARE study) and a cluster randomized trial (the K‐DPP study).