Miracle-dz/UniFAR-FTUs huggingface dataset:
RESOLUTION TIME THEORY IN THE TOPIC OF BROADBAND COMMUNICATIONS. ALGORITHM FOR DATA DEPENDENT JITTER AND CAPACITY ESTIMATIONS WITH POLYNOMIAL TIME EXECUTION

At present, due to the constant growth of the volume of transmitted information, more and more attention is paid to the issues of the possibility of increasing the transmission speed due to the use of the transmission mode "above the Nyquist rate". Despite the relevance of this research topic, due to rather large mathematical difficulties, it is difficult to obtain significant results. One of the theories that allows for a breakthrough is the rapidly developing theory of resolution time developed for phase radio engineering data transmission systems and information measuring optoelectronic systems with PAM-signals with square pulses. This paper presents a novel method for capacity and resolution time estimations with polynomial computational complexity that does not depend on the size of the channel alphabet of PAM-n-signals and is determined only by the effective memory value. The shape of pulse of PAM-n-signals has arbitrary form and amplitudes take only possitive values. The method also allows estimating the time limiting possible deviations due to the sampling error in time, at which the information about the channel symbol will be read error-free or with a given error probability.
Information technology for intellectual analysis of item descriptions in e-commerce

E-commerce is experiencing a robust surge, propelled by the worldwide digital transformation and the mutual advantages accrued by both consumers and merchants. The integration of information technologies has markedly augmented the efficacy of digital enterprise, ushering in novel prospects and shaping innovative business paradigms. Nonetheless, adopting information technology is concomitant with risks, notably concerning safeguarding personal data. This substantiates the significance of research within the domain of artificial intelligence for e-commerce, with particular emphasis on the realm of recommender systems. This paper is dedicated to the discourse surrounding the construction of information technology tailored for processing textual descriptions pertaining to commodities within the e-commerce landscape. Through a qualitative analysis, we elucidate factors that mitigate the risks inherent in unauthorized data access. The cardinal insight discerned is that the apt utilization of product matching technologies empowers the formulation of recommendations devoid of entailing customers' personal data or vendors' proprietary information. A meticulously devised structural model of this information technology is proffered, delineating the principal functional components essential for processing textual data found within electronic trading platforms. Central to our exposition is the exploration of the product comparison predicated on textual depictions. The resolution of this challenge stands to enhance the efficiency of product searches and facilitate product juxtaposition and categorization. The prospective implementation of the propounded information technology, either in its entirety or through its constituent elements, augurs well for sellers, enabling them to improve a pricing strategy and heightened responsiveness to market sales trends. Concurrently, it streamlines the procurement journey for buyers by expediting the identification of requisite goods within the intricate milieu of e-commerce platforms.
Denitrifier Community Dynamics in Soil Aggregates under Permanent Grassland and Arable Cropping Systems

A better understanding of the spatial distribution of denitrifiers and their activity may lead to an improved understanding of the denitrification process in soil. This study determined the spatial distribution of the total bacterial community (16S rRNA), components of the denitrifier community (cnorB P ; Pseudomonas mandelii and related species; nosZ), and denitrification activity across a range of soil aggregate size fractions (4-8, 1-2, and 0.25-0.5 mm) under permanent grassland (PG) and arable cropping (AC) systems. Aggregate size fraction had no significant effect on the abundance of the nosZ or cnorB P gene-bearing bacteria in soil from the AC system. The highest abundance of denitrifier bacteria was measured in the smallest size fraction in the PG system. Respiration did not differ among aggregate size fractions within the PG system; however, respiration was higher for the PG system than the AC system for all aggregate size fractions. For the AC system, higher respiration was measured in the 0.25- to 0.5-mm aggregate fraction than the 4- to 8-mm aggregate fraction. Denitrifying enzyme activity (DEA) was higher in the largest size fraction of the PG system than the AC system; however, DEA did not differ among aggregate size fractions within each management system. Cumulative denitrification during a 72-h incubation was significantly higher in the largest aggregate size fractions under both management systems. The results indicate that the differences among the aggregate size fractions were small in magnitude and that the spatial location of the denitrification activity and the abundance of the denitrifier bacteria were uncoupled across aggregate size fractions in the contrasting management systems.
Big Data Analytics for the ATLAS EventIndex Project with Apache Spark

The ATLAS EventIndex was designed to provide a global event catalogue and limited event-level metadata for ATLAS experiment of the Large Hadron Collider (LHC) and their analysis groups and users during Run 2 (2015-2018) and has been running in production since. The LHC Run 3, started in 2022, has seen increased data-taking and simulation production rates, with which the current infrastructure would still cope but may be stretched to its limits by the end of Run 3. A new core storage service is being developed in HBase/Phoenix, and there is work in progress to provide at least the same functionality as the current one for increased data ingestion and search rates and with increasing volumes of stored data. In addition, new tools are being developed for solving the needed access cases within the new storage. This paper describes a new tool using Spark and implemented in Scala for accessing the big data quantities of the EventIndex project stored in HBase/Phoenix. With this tool, we can offer data discovery capabilities at different granularities, providing Spark Dataframes that can be used or refined within the same framework. Data analytic cases of the EventIndex project are implemented, like the search for duplicates of events from the same or different datasets. An algorithm and implementation for the calculation of overlap matrices of events across different datasets are presented. Our approach can be used by other higher-level tools and users, to ease access to the data in a performant and standard way using Spark abstractions. The provided tools decouple data access from the actual data schema, which makes it convenient to hide complexity and possible changes on the backed storage.
Joint Discriminative Adversarial Domain Adaptation for Cross-Domain Fault Diagnosis

The automatic feature extraction capability of deep learning has led to its extensive usage in fault diagnosis applications. In engineering scenarios where the distribution between training and test sets is inconsistent, deep domain adaptation (DA) methods are commonly used to solve cross-domain fault diagnosis problems. Despite achieving good performance for cross-domain diagnosis, there are some limitations to DA models. First, most existing research has only focused on domain alignment between source and target domains while neglecting class information, which can result in incorrect alignment between classes of the two domains. Second, target samples that are distributed close to the boundaries of the clusters are easily misclassified by the classification decision boundary learned from the source domain. To address these issues, joint discriminative adversarial DA (JDADA) is proposed in this article. The proposed method combines domain alignment and class alignment by introducing a class alignment module into the domain adversarial network. Furthermore, the discriminative discrepancy module is proposed to compact features of the same class and separate features of different classes to extract more discriminative features. In addition, we propose a new pseudolabeling strategy to address the problem of target training samples without labels. The proposed method is evaluated on the gearbox dataset and bearing dataset, and the results demonstrate its effectiveness and superiority over state-of-the-art DA methods. Specifically, JDADA achieves up to 5.0% accuracy improvement on the gearbox dataset and 3.4% accuracy improvement on the bearing dataset.
Design and Flight Control of a Novel Tilt-Rotor Octocopter Using Passive Hinges

This letter presents a novel tilt-rotor octocopter that can generate tiltable thrust without the need for servo-driven mechanisms. The octocopter's eight rotors are divided into pairs and each pair is mounted on an arm, which is connected to the airframe through passive hinges. Each pair is also equipped with a sub-controller that uses the differential thrust of the rotor pair to control the arm's tilting angle. Then, these sub-controllers work together with the main controller installed on the frame body to regulate the attitude and position of the octocopter. This octocopter can be controlled via a remote controller in a manner similar to a classic quadrotor, operating in stabilized or position mode both indoors and outdoors, also capable of executing flight missions following predefined trajectories. Considering that the communication delays between the main controller and sub-controllers would degrade the control performance due to the delayed response of the vectored thrust. We also propose a decentralized control allocation strategy that directly assigns parts of control loops to the sub-controllers so that the communication delays can be bypassed. Finally, we demonstrate the advantages of the new octocopter with the proposed method through simulations and real flight experiments.
Long‐term InHM simulations of hydrologic response and sediment transport for the R‐5 catchment

The physics‐based model known as the Integrated Hydrology Model (InHM) is used to simulate continuous hydrologic response and event‐based sediment transport for the R‐5 catchment (Oklahoma, USA). For the simulations reported herein the R‐5 boundary‐value problem was refined, from that reported by Loague et al. (2005), to include (i) an improved conceptualization of the local hydrogeologic setting, (ii) a more accurate topographical representation of the catchment, (iii) improved boundary conditions for surface‐water outflow, subsurface‐water outflow and evapotranspiration, (iv) improved characterization of surface and subsurface hydraulic parameters and (v) improved initial conditions. The hydrologic‐response simulations were conducted in one‐year periods, for a total of six years. The sediment‐transport simulations were conducted for six selected events. The multi‐year water‐balance results from the hydrologic‐response simulations match the observed aggregate behavior of the catchment. Event hydrographs were generally simulated best for the larger events. Soil‐water content was over‐estimated during dry periods compared with the observed data. The sediment‐transport simulations were more successful in reproducing the total sediment mass than the peak sediment discharge rate. The results from the effort reported here reinforce the contention that comprehensive and detailed datasets are crucial for testing physics‐based hydrologic‐response models. Copyright © 2007 John Wiley & Sons, Ltd.
Development and Assessment of 30-Meter Pine Density Maps for Landscape-Level Modeling of Mountain Pine Beetle Dynamics

Forecasting spatial patterns of mountain pine beetle (MPB) population success requires spatially explicit information on host pine distribution. We developed a means of producing spatially explicit datasets of pine density at 30-m resolution using existing geospatial datasets of vegetation composition and structure. Because our ultimate goal is to model MPB population success, three study areas in the western United States that have experienced recent MPB outbreaks were used for evaluation. Pine density estimates for each study area were compared to measures of cumulative MPB-caused pine mortality summarized from annual Aerial Detection Surveys (ADS). ADS data provide spatial and temporal representations of MPB-caused pine mortality collected by observers in fixed wing aircraft and are the most readily available estimates of landscape-scale impacts of MPB. Regression analyses using LANDFIRE ecological systems classifications (EVTs) as units of analysis showed that the best pine density estimates explained 75 to 98% of cumulative MPB-caused tree mortality. LANDFIRE EVTs, which provide an index of the plant communities growing in a particular 30-m cell, effectively delineate distinct vegetation types that are meaningful suitability indicators for MPB-caused tree mortality. Our analyses suggested that available geospatial vegetation datasets derived from field data and remotely sensed imagery are useful for producing spatially explicit measures of pine density for use in landscape-level modeling of MPB dynamics.
Adaptive Super-Twisting Sliding Mode Control of Underwater Mechanical Leg with Extended State Observer

Underwater manipulation is one of the most significant functions of the deep-sea crawling and swimming robot (DCSR), which relies on the high-accuracy control of the body posture. As the actuator of body posture control, the position control performance of the underwater mechanical leg (UWML) thus determines the performance of the underwater manipulation. An adaptive super-twisting sliding mode control method based on the extended state observer (ASTSMC-ESO) is proposed to enhance the position control performance of the UWML by taking into account the system’s inherent nonlinear dynamics, uncertainties, and the external disturbances from hydrodynamics, dynamic seal resistance, and compensation oil viscous resistance. This newly designed controller incorporates sliding mode (SMC) feedback control with feedforward compensation of the system uncertainties estimated by the ESO, and the external disturbances of the hydrodynamics by fitting the parameters, the dynamic seal resistance, and the compensation oil viscous resistance to the tested results. Additionally, an adaptive super-twisting algorithm (AST) with integral action is introduced to eliminate the SMC’s chattering phenomenon and reduce the system’s steady-state error. The stability of the proposed controller is proved via the Lyapunov method, and the effectiveness is verified via simulation and comparative experimental studies with SMC and the adaptive fuzzy sliding mode control method (AFSMC).
The Siberian Connection: A case for Precambrian separation of the North American and Siberian cratons

The hypothesis that the Cordilleran geosyncline originated as an Atlantic-type continental margin by the rifting of an older Precambrian continental mass and the opening of a new ocean basin leads to the question of where the counterpart of the North American Precambrian craton may be. The Siberian platform—a large, discrete, older Precambrian craton, lodged in northeast Asia and surrounded by younger fold belts—is a likely candidate for the missing Precambrian continental fragment. The outline of its northeast margin fits the southwest margin of the North American Precambrian craton to produce a congruence of tectonic grain and age provinces. Both margins are overlapped by sediments of the same general type that began to accumulate about 1,500 m.y. ago, after the initiation of the separation.
Grey Wolf Optimization Algorithm for Tuning PI Controller Based Speed Control of Switched Reluctance Motor for Ship Propulsion Systems

Electric ship propulsion is no longer a revolutionary idea in the marine sector today. Lights were the first thing that needed electricity on board a ship, and it later applied to an electric ship propeller that is powered by an electric motor. Eventually, the idea of all-electric ships enters the picture, allowing all ship power sources to run both auxiliary and propulsion loads. Due to the great electric power constraints, numerous electric motors are used in industrial and naval ships, and the development of power electronics has made it easier to manage and regulate these motors. The switching resistance motor is becoming more popular despite its low dependability and straightforward design. Such benefits make SR motors superior to conventional adjustable speed devices. Due to their significant torque fluctuations, variable reluctance motors have recently been used in restriction traction systems. On the other side, torque ripple reduces motor performance by generating repeated noise and vibration. The suggested system controls the speed of an 8/6 pole SRM using a metaheuristic Grey Wolf Optimization Algorithm (GWO), Proportional Integral (PI) controller, and a (n+1) Semiconductor (n+1) Diode power conversion. This article's goal is to increase the proposed Switched Reluctance Motor's output cogging torque. GWO algorithm has been chosen as a promising strategy when compared to other algorithms because of its generality, decreased complexity, stability, and accuracy. MATLAB/Simulink was used to create the simulation tool.
WATER WAVE RUN-UP ON A BEACH

Abstract : While investigating the Tsunami wave project, a numerical method was devised to solve the initial boundary value problem for the equations of the non-linear shallow water theory. The case of one horizontal dimension was considered in a domain bounded by a shore at one end, with the motion of the shoreline taken into account. In addition, this method enabled the incident wave to be introduced at the seaward end of the domain. The water was assumed to be at rest in the domain until the incident wave arrived, and the bottom was assumed to slope uniformly.
Association between Lifestyle Factors and Weight Gain among University Students in Japan during COVID-19 Mild Lockdown: A Quantitative Study

We aimed to investigate the lifestyle factors influencing weight gain among university students in Japan during the mild lockdown imposed due to the novel coronavirus disease pandemic. In this cross-sectional study, we conducted a questionnaire survey of students who underwent health examinations at Nagasaki University in 2021. Students reporting a weight gain of ≥3 kg were included in the weight gain group; the remaining students were included in the non-weight-gain group. Fisher’s exact test and binary logistic regression were performed to determine the association between weight gain and each lifestyle factor. We included 3059 respondents (response rate: 45.7%), and 9.5% of them reported a weight gain of ≥3 kg. The following factors were associated with weight gain (odds ratio (95% confidence interval), p value based on Fisher’s exact test): dining out for four times or more/week (2.16 (1.40, 3.32), p = 8.7 × 10−4) and gaming time of ≥4 h/day (2.26 (1.45, 3.47), p = 2.4 × 10−4). Binary logistic regression among the four highest odds ratios revealed that after adjusting for other factors, frequent dining out and prolonged gaming time were significantly associated with weight gain in students during the mild lockdown.
Continental recycling: The oxygen isotope point of view

Mass balance calculations based on a data compilation lead us to estimate a δ18O value of 8.9 ± 0.7‰ for the continental crust. This isotopic composition is the product of two competing processes, namely, the erosion and growth of continental masses. Erosion tends to enrich the continental crust in 18O through low‐temperature isotopic exchange with the hydrosphere, whereas continental growth maintains the composition of the crust close to its mantle‐derived precursor (δ18O = 5.7‰). Box modeling of the oxygen isotope exchange between the continents, mantle, and seawater leads us to calculate a flux of subducted sediments averaged over the Earth's history of 0.4 km3 yr−1, significantly lower than most other recent estimates.
Umbilical Cord Diseases Affecting Obstetric and Perinatal Outcomes

Background: (1) The aim of this article is to describe the physiopathology underlying umbilical cord diseases and their relationship with obstetric and perinatal outcomes. (2) Methods: Multicenter case series of umbilical cord diseases with illustrations from contributing institutions are presented. (3) Results: Clinical presentations of prenatal ultrasound findings, clinical prenatal features and postnatal outcomes are described. (4) Conclusions: Analysis of our series presents and discusses how umbilical cord diseases are associated with a wide variety of obstetric complications leading to a higher risk of poor perinatal outcomes in pregnancies. Knowing the physiopathology, prenatal clinical presentations and outcomes related to umbilical diseases allow for better prenatal counseling and management to potentially avoid severe obstetric and perinatal complications.
The interplay of Turkish EFL teachers’ academic optimism, psychological well-being, and self-efficacy

This study examined the relationships between Turkish EFL (English as a Foreign Language) teachers' sense of self-efficacy, psychological well-being, and academic optimism. The study's strength lies in its exploration of this question within the context of the COVID-19 pandemic, which provides a background that adds depth to the examination. Researchers collected data from 272 English as a Foreign Language (EFL) teachers during the pandemic. The study intended to identify the motivating factors underlying self-efficacy in this particular teacher set using the regression analysis perspective. The findings provided significant understanding; a strong link between self-efficacy and academic optimism was found. This shows that EFL teachers were more likely to demonstrate a better level of self-efficacy in their teaching responsibilities if they had a more positive outlook on academic outcomes. However, psychological well-being was not shown to be a significant predictor of self-efficacy. These findings shed light on a crucial aspect of classroom teacher efficiency and well-being. It shows that there is an intense desire to put teachers' health first, particularly in challenging circumstances like the pandemic. Creating a climate of intellectual positivity should be a priority in all educational initiatives; the study can operate as guidance, directing the creation of solutions to counteract possible adverse effects on teachers' psychological health and, consequently, their ability to teach effectively. It is concluded that this study provides insight into the complexity of teacher self-efficacy, highlighting the impact of psychological wellness, academic optimism, and crisis-related environments.