Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian analysis in the presence of missing explanatory variables

Accurate prediction of days on feed (DOF) could be beneficial to feedlot managers in decision making as it relates directly to the profitability of the operation. To accurately predict DOF, several management factors and genetic potential of the animal should be considered. However, some of these factors affecting DOF are not always available for all animals in the feedlot. Such factors include health status, diet, or hormonal treatment. In this study, 2,449 feedlot steers and heifers with known Zilmax treatment status were used to develop a statistical model to predict Zilmax status of a given animal. In order to evaluate the ability of predicting Zilmax status, a threshold model for binary response (treated vs control) was implemented within a Bayesian framework via Gibbs sampler. At the liability scale, the model included molecular (SNP genotypes) and growth information (initial weight, hot carcass weight, backfat, marbling scores and days on feed). Different models (based on the explanatory variable included) were implemented and compared using a five-fold cross-validation approach. The validation results indicated that the model that included DOF, IW and HCW as explanatory variables yielded the highest prediction accuracy of Zilmax status. In fact, the prediction accuracy was 0.72 and 0.77 for controls and treated animals, respectively. However, adding genomic information to the model did not lead to an increase in accuracy. In fact, a decrease in accuracies was observed (0.60 for controls and 0.62 for cases) which could be explained in part by the substantial increase in number of parameters in the model.
A Digital Twin Network Approach for 6G Wireless Network Autonomy

The challenges of high energy consumption, high cost and low efficiency of mobile wireless communication network have not been effectively solved for years, and the future network will face the challenges of more diversified communication modes, service types, users and devices. Operators need a more comprehensive, intelligent, scalable and affordable network management system with a high level of autonomy. In this article, we propose a digital twin network approach to solve the above challenges and to realize a high level of autonomy for 6G wireless network. We introduce the basic concepts and components of digital twin network, clarify its technical characteristics and propose an end-to-end logical architecture, functionalities and the enabling technologies for the digital twin network. We also present two use cases where digital twin network is implemented. Finally, we put forward the key technical problems which need to be further studied.
Determining the Accuracy of Reinforced Model via Sentiment and Technical Analysis as a Stock Market Prediction Technique

The application of sentiment analysis as a stock market prediction technique has garnered significant attention in recent years, being particularly useful when catastrophic situations affect the economy. This research paper aims to determine the accuracy of sentiment analysis in predicting stock market movements by developing a reinforced model that integrates both sentiment and technical analysis. While previous studies have focused on social media sentiment for stock price prediction, this research proposes a reinforced model that combines sentiment analysis with technical analysis indicators to enhance accuracy. The study develops and tests predictive models for stock price and trend forecasting using a large-scale sample of tweets from four prominent companies: Apple, Google, Microsoft, and Netflix. The results of the study provide valuable insights into the accuracy of the reinforced model combining sentiment and technical analysis for stock market prediction, offering a more comprehensive approach to understanding market sentiment and its impact on stock prices. Further, the findings contribute to the existing knowledge on stock market prediction techniques, and underscore the value of considering multiple factors in decision-making processes.
Adversarial Attack with Genetic Algorithm against IoT Malware Detectors

The exponential growth and sophistication of Internet of Things (IoT) malware behavior have resulted in new detection technologies capable of defending IoT devices against some threats. However, their success has stimulated the interest of attackers attempting to circumvent current IoT malware detectors. Among detection technologies, the detectors trained based on Uniform Resource Locator (URL) requests have become popular. To draw attention to the safety of the detectors, we propose a grey-box method to attack detectors based on URL requests without breaking malicious functions of URL requests. The key idea is to add perturbations to the tail of URLs. Specifically, this method is based on a Genetic Algorithm (GA) to find suitable perturbations and optimizes the process of adversarial attacks through a dynamic number of evolution directions and a maximum generation limit. The effectiveness of our adversarial attack is demonstrated by experimental results based on a widely used public dataset CSIC2010 and several representative detectors. As far as we know, this is the first time an adversarial attack against IoT detectors based on URL requests has been done. The method has an attack success rate of more than 92 %. Furthermore, experiment results show that the method can reduce query numbers while maintaining the attack success rate.
Best Practices for Scaling in Cloud Based Telecommunication Systems

Like many other systems, scaling in cloud hosted telecommunication systems is very important in order to keep the existing customers in the system while growing with new customers. In such systems, scaling should be considered in various aspects such as provisioning, call processing, messaging, media services and quality of service. In this study, some of the best practices that are applied in various functionalities of a cloud hosted telecommunication system while the system was scaling in a quick way with the addition of big new customers to the system will be described.
Cabildo Elite Network as a Structure of Political Power in Colonial Buenos Aires

The aim of this paper is to show the relationship between the nominations for seats in the Cabildo of Buenos Aires between 1776 and 1810, and the network status of council individuals and families. We test hypothesis on the importance of networks metrics of elite individuals and families with political positions in the council. We find that actors and families with higher degree are statistically associated with diverse indicators of political power.
Reinforcement Learning-Based Robust Tracking Control for Unknown Markov Jump Systems and its Application

This brief presents a novel reinforcement learning-based robust tracking control method for discrete-time unknown Markov jump systems. First, the optimal tracking and robust controller design problem is formulated as an optimal output regulation problem. In particular, we reconstruct the stochastic coupled algebraic Riccati equation to decouple the jumping mode and approximate the optimal control policy, where the knowledge of system dynamics should be known as a priori. To solve this problem, by employing the online reinforcement learning approach, the optimal output regulator is learned within a novel data-based parallel learning framework. On this basis, the solutions of the stochastic coupled algebraic Riccati equation and the output regulation equation of Markov jump systems are obtained by using online system data. Moreover, the convergence of the proposed algorithms is analyzed. Finally, a PWM-driven DC-DC boost converter model is provided to show the effectiveness of the proposed method and the main theoretical results.
Resensitisation of Methicillin-Resistant Staphylococcus aureus to Conventional Antibiotics in the Presence of an Engineered Enzybiotic

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most dreadful pathogens relevant in community and nosocomial-related infections around the world. Resensitising MRSA to antibiotics, once it became resistant, was a tough choice due to the high adaptability of this bacteria to savage conditions. This study aimed to create a chimeric enzybiotic against MRSA and test its efficiency, either individually or in combination with antibiotics. The novel enzybiotic BAC100 was constructed by fusing the catalytic domain from the bacteriocin BacL1 from Enterococcus faecalis with the cell-wall-binding domain from protein P17 of Staphylococcus aureus bacteriophage ϕ44AHJD. Apart from its partial lone activity, BAC100 was found to resensitise the MRSA strain to traditional antibiotics, including ampicillin and tetracycline. Both drugs were able to reduce live MRSA cells by 85 and 90%, respectively, within 60 min of treatment together with BAC100. However, no significant activity was observed against MRSA when these drugs were tested independently, pointing to the inherent resistance of MRSA against these conventional antibiotics. To our knowledge, this is one of the first instances where an engineered enzybiotic was found to resensitise MRSA to conventional antibiotics. This study will pave the way for the development of similar peptides that can be used together with antibiotics against gruesome pathogens of clinical importance.
Rhoptromeris haywardi (Blanchard, 1947) is not Rhoptromeris Förster, 1869: new combination in Leptopilina Förster, 1869 (Hymenoptera: Cynipoidea: Figitidae: Eucoilinae)

Rhoptromeris haywardi (Blanchard, 1947) is reviewed and the new combination Leptopilina haywardi (Blanchard, 1947) is proposed. Leptopilina haywardi comb. nov. is redescribed and illustrated. Leptopilina Förster, 1869 is cited for the first time for Uruguay and Tephritidae (Diptera) is reported for the first time as its host.
An Examination of the Attitudes of Teacher Candidates towards Mobile Learning

This study aims to ascertain how potential Turkish language and literature and Turkish teacher candidates tend to mobile learning. In this context, it investigates whether there are any notable differences in these aspiring teachers' attitudes toward mobile learning based on elements like gender, age, major field of study, grade level, personal tablet ownership, computer ownership, income level, social media usage, and enrollment in computer courses. A relational survey model, a quantitative research method, is used in this study. 209 females and 65 males who willingly joined the study at a university in Turkey's east comprise its participant pool. The findings reveal that prospective teachers of Turkish language and literature, as well as Turkish language teaching, strongly endorse the statements in the Mobile Learning Attitude Scale. Furthermore, their attitudes towards mobile learning do not exhibit significant variations concerning variables such as gender, major field of study, grade level, personal tablet ownership, possession of a computer, income level, social media usage, and participation in a computer course. Nevertheless, a noteworthy difference is identified in attitudes towards mobile learning across different age groups. The outcomes imply that while substantial distinctions exist among age groups, the attitudes of prospective teachers in the aforementioned fields do not significantly differ in terms of demographic or technological aspects. These results underscore the importance of integrating mobile learning into teaching and learning. Encouraging positive attitudes towards mobile learning and conducting additional research on this subject are strongly recommended.
A Frequency-Tunable Dielectric Resonator Antenna With Rotatable Anisotropic Metasurface

This letter introduces a frequency-tunable dielectric resonator (DR) antenna (DRA) with a top-loaded rotatable anisotropic metasurface. The metasurface at different angles can be equivalent to materials with different permittivity. As a result, the frequency of the DRA operating at TMδ11 mode can be continuously adjusted. The working mechanism of the proposed tunable DRA is given in detail. Since both the mechanical tuning scheme and DR are adopted, the proposed design shows high efficiency (low loss) and small size. For demonstration, a differential-feeding prototype is fabricated and measured. The center frequency can be tuned from 6.64 to 6.96 GHz as the metasurface is rotated by 90°. Within it, the radiation pattern is stable, and the radiation efficiency is more than 76%.
Planar Polarization-Rotation Pattern Manipulation Surfaces With Patch-Microstrip Line-Patch Structure Under Different Incidences

This article presents a method to design and implement pattern manipulation surfaces (PMSs) with polarization rotation by employing a patch-microstrip line-patch (PMP) structure. The polarization-rotation PMSs (PRPMSs) receive plane waves from different angles and reradiate polarization-rotated plane waves to a wanted direction. The impedance transforming characteristic of the patch under different incident angles between spatial wave to guided wave is studied with an equivalent circuit model (ECM). All the parameters in the ECM are obtained through one full-wave simulation with periodic boundary conditions (PBCs) under normal incidence in CST. The polarization rotation ability is realized by converting TM10 mode to TM01 mode on the patch through a microstrip line. The polarization conversion ratio (PCR) of the PMS with different incidences, scan angles, and polarizations is modeled by the ECM. Then, PRPMS elements with different microstrip line lengths are studied for phase tuning. The relationship between the microstrip line length and the phase shift is calculated via ECM and verified via full-wave simulation, both of which show that a continuous phase shifting range of 360° can be easily provided. In this way, the scattered waves can be steered in an arbitrarily prescribed direction. To validate the design concept under different incident angles, three PRPMS prototypes with the main beam steered to 0°, 30°, and 50° are fabricated and tested. Measured normalized RCS results agree well with the calculated and simulated ones, which verifies the validity of the proposed ECM and the PRPMS. Therefore, the proposed PRPMS is a promising method for future smart radio environments (SREs).
Transformer-Aided Parametric CSI Feedback for mmWave Massive MIMO Systems

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have been considered as a promising solution to provide high-quality data services. To get the most of beamforming gain, an acquisition of accurate channel information is crucial for the success of massive MIMO systems. In recent years, numerous approaches to reduce the excessive feedback overhead due to the large number of antennas have been suggested. However, they do not consider the long-range dependency of the channel state information (CSI) resulting from the correlation between the large-scale antennas and subcarriers. In this paper, we propose a deep learning-based parametric CSI feedback technique for mmWave massive MIMO systems to improve the performance of CSI compression and reconstruction with low feedback overhead. To be specific, the proposed scheme learns the correlation between channel parameters by exploiting Transformer architecture. From the numerical results, we demonstrate that the proposed scheme outperforms the conventional channel feedback schemes in terms of the normalized mean square error (NMSE) and the feedback overhead reduction.
When Local Optimization is Bad: Learning What to (Not) Maximize in the Null-Space for Redundant Robot Control

Redundancy in robot structures allows the implementation of control algorithms in which it is possible to add secondary control objectives. Those are typically functions to be minimized/maximized and projected onto the null-space of the primary control objectives. As an example, typical metrics to maximize are the robot manipulability or the distance from its mechanical joint limits. Usually, designer's heuristics is used to decide which function eventually to optimize. This paper shows that heuristics may lead to counter-intuitive results such as, for example, reducing the dexterous workspace with respect to, e.g., avoiding optimization at all. A learning algorithm is proposed to allow the robot to dynamically select the function to optimize in a way to increase the overall dexterous workspace with respect to the static, heuristic choice. As a result, the robot will be able to increase its dexterous workspace by selecting the proper lower-priority task via the use of a neural network trained during a proper supervised learning process. A 3-link planar manipulator is used as numerical case study.
Classification Type of Asynchrony Breathing Image Using 2-Dimensional Convolutional Neural Network

Asynchrony breathing (AB) refers to a situation where the patient's breathing does not align with the mechanical ventilator (MV), which can have a detrimental effect on the patient's recovery. A few types of AB make it difficult for clinicians to identify and manage MV properly. Hence, there is a need to develop a method that can classify the type of AB in MV patients. In this study, a 2- dimensional (2D) convolutional neural network (CNN) method is presented to classify the type of AB based on the input image of the airway pressure. A total of 866 images of airway pressure were analysed in this study, and 4 types of AB were classified: 1) double triggering (DT); 2) reverse triggering (RT); 3) delayed triggering (DC); and 4) premature cycling (PC). Two types of activation functions for classification purposes, SoftMax and Sigmoid, were compared based on performances. Results show SoftMax produced a higher accuracy of 98.5% with a training dataset of 70% and a testing dataset of 30% of the data. In contrast, the Sigmoid function produced an accuracy of 98.1 % when trained and tested with the same dataset. Furthermore, this 2D-CNN model produced a range of accuracy between 89% and 96% in classifying the type of AB, with the highest accuracy of 96% in classifying DT. Overall, the developed CNN model, based on the input image of airway pressure, accurately extracts critical and unique features to precisely classify various types of AB, which could help clinicians in managing MV patients.
Sensitivity analysis of numerical modeling input parameters on floating offshore wind turbine loads

Abstract. Floating wind turbines must withstand a unique and challenging set of loads from the wind and ocean environment. To de-risk development, accurate predictions of these loads are necessary. Uncertainty in modeling predictions leads to larger required safety factors, increasing project costs and the levelized cost of energy. Complex aero-hydro-elastic modeling tools use many input parameters to represent the wind, waves, current, aerodynamic loads, hydrodynamic loads, and structural properties. It is helpful to understand which of these parameters ultimately drives a design. In this work, an ultimate and fatigue-proxy load sensitivity analysis was performed with 35 different input parameters, using an elementary effects approach to identify the most influential parameters for a case study involving the National Renewable Energy Laboratory (NREL) 5 MW baseline wind turbine atop the OC4-DeepCwind semisubmersible during normal operation. The importance of each parameter was evaluated using 14 response quantities of interest across three operational wind speed conditions. The study concludes that turbulent wind velocity standard deviation is the parameter with the strongest sensitivity; this value is important not just for turbine loads, but also for the global system response. The system center of mass in the wind direction is found to have the highest impact on the system rotation and tower loads. The current velocity is found to be the most dominating parameter for the system global motion and consequently the mooring loads. All tested wind turbulence parameters in addition to the standard deviation are also found to be influential. Wave characteristics are influential for some fatigue-proxy loading but do not significantly impact the extreme ultimate loads in these operational load cases. The required number of random seeds for stochastic environmental conditions is considered to ensure that the sensitivities are due to the input parameters and not due to the seed. The required number of analysis points in the parameter space is identified so that the conclusions represent a global sensitivity. The results are specific to the platform, turbine, and choice of parameter ranges, but the demonstrated approach can be applied widely to guide focus in parameter uncertainty.
Coffee consumption and all-cause and cardiovascular mortality in older adults: should we consider cognitive function?

Background The association between coffee and mortality risk has been found in most previous studies, and recent studies have found an association between coffee consumption and cognition. However, there is still a lack of research exploring whether the association between coffee and mortality is influenced by cognitive function. Objective The purpose of this study was to explore the association of coffee, caffeine intake in coffee and decaffeinated coffee with all-cause mortality and cardiovascular disease (CVD) mortality in older adults with different cognitive performances. Methods The study was based on data from the National Health and Nutrition Examination Survey (NHANES) 2011–2014. Coffee and caffeine consumption data were obtained from two 24-h dietary recalls. Individual cognitive functions were assessed by CERAD-word learning test (CERAD-WLT), animal fluency test (AFT), and digit symbol substitution test (DSST). In addition, principal component analysis (PCA) was performed with the above test scores to create global cognitive score. The lowest quartile of scores was used to classify cognitive performance. Cox regression and restricted cubic spline (RCS) were applied to assess the relationship between coffee and caffeine consumption and mortality. Results In the joint effects analysis, we found that those with cognitive impairment and who reported without drinking coffee had the highest risk of all-cause and cardiovascular mortality compared with others. In the analysis of population with cognitive impairment, for all-cause mortality, those who showed cognitive impairment in the AFT displayed a significant negative association between their total coffee consumption and mortality {T3 (HR [95% CI]), 0.495 [0.291–0.840], p = 0.021 (trend analysis)}. For DSST and global cognition, similar results were observed. Whereas for CERAD-WLT, restricted cubic spline (RCS) showed a “U-shaped” association between coffee consumption and mortality. For CVD mortality, a significant negative trend in coffee consumption and death was observed only in people with cognitive impairment in AFT or DSST. In addition, we observed that decaffeinated coffee was associated with reduced mortality in people with cognitive impairment. Conclusion Our study suggested that the association between coffee consumption and mortality is influenced by cognition and varies with cognitive impairment in different cognitive domains.