Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Anti-Disturbance Control of a Hydraulic System Subjected to Variable Static Loads

A hydraulic system’s lubricating oil is subject to serialized variable static loads with performance. An improved self-turbulent flow algorithm, based on the real-time acquisition and monitoring of lubricating oil static pressure in a hydraulic system to simulate variable static loads, is proposed. A mathematical model of the electrohydraulic servo control system for lubricating oil static pressure acquisition is presented, and the self-turbulent flow controller is designed for numerical analysis. The self-anti-disturbance control strategy for the lubricating oil static pressure of an electrohydraulic servo system is discussed, which is used for quadratic optimization, pole placement, PID, and self-turbulent flow control, and the lubricating oil static pressure simulation model of self-turbulent flow control is constructed by a SIMULINK module. The numerical simulation results indicate that the overshoot is significantly reduced. The proposed self-anti-disturbance control algorithm is verified by experiments, and the lubricating oil static pressure acquisition followability and monitoring accuracy are greatly improved. Variable hydraulic lubricating oil static pressure acquisition and monitoring can be effectively and stably adjusted by a predesigned electrohydraulic servo control system in the field of power hydraulic fluid lubrication.
Lightweight P2P-RPL for Efficient P2P Communication in 6TiSCH Networks

With the emergence of various IoT applications, P2P functional support in the network is essential. RPL is a routing protocol for low-power loss wireless networks, but there is an inefficient aspect of P2P communication. Although P2P-RPL has been proposed to support P2P of RPL, it is inappropriate to apply it to 6TiSCH networks as it is due to excessive overhead. So, we propose Lightweight P2P-RPL and applied to 6TiSCH Networks. Lightweight P2P-RPL includes five techniques for reducing overhead, and the performance was measured using a 6TiSCH simulator. As a result of the simulation, there was no performance reduction despite providing P2P function.
Energy Prediction and Energy Management in Kinetic Energy-Harvesting Wireless Sensors Network for Industry 4.0

Real-time control and monitoring are some of the main goals of Industry 4.0. To meet these requirements, sensors are needed at every step of the production process. Wireless sensors (WS) are better suited due to their flexibility but are limited in energy. In this work, kinetic energy harvesting using piezoelectric technologies are considered to ensure the energy autonomy of a Wireless Sensor Network (WSN). First, unlike most existing works, this paper focuses on WSN rather than a single WS since the control of the entirety of most industrial processes requires several WSs. The solution proposed here is based on deep learning of the harvestable power signals at each sensor deployed on the monitoring system. Specifically, vibration measurements were performed at 12 locations on an ore crushing mill in a mine. From there, a mechanical–electrical conversion model considering the system’s dynamics was set up to evaluate the power profile each of the WSs can harvest. Considering that the harvestable power has many peaks due to the different operating states of the engine, we first proposed a Predictor of the Harvestable Power from Vibrations (PHPV). Using a large database, compared to a state-of-the-art predictor, the Predictor of the Harvestable Energy from vibrations (PHEV) allows for significantly reducing the Root Mean Square Error (RMSE). More specifically, the lowest reduction achieved for RSME ranged from 9.4 μW (with PHEV) to 5.9 μW (with PHPV). A decrease in RMSE ranging from 18.45 to 4 μW was obtained for another measurement point. Since harvest rates differ from one location to another, a Hierarchical Energy-Balancing Protocol (HEBP) is proposed to maximize the number of WS capable of transmitting information about the system’s state, thus avoiding an interruption of the network coverage. In the HEBP, it is envisaged that some WSs, besides transmitting data, will supply other nodes with an energy deficit to allow them to communicate information about their location. For a minimum packet size of up to 1100 bits, the energy autonomy of all the WSs is ensured, unlike only 66% of the nodes with the previous protocols.
Corner Detection of the Computer VR Microscope Image Based on the 3D Reconstruction Algorithm

In order to solve the problem of multisolution and ill-formedness of the 3D reconstruction method of a single image (purpose), the author proposes a microscope image segmentation algorithm based on the Harris multiscale corner detection. Separating complex engineering images into several simple basic geometric shapes, rebuild them separately to avoid the ill-conditioned solution problem of directly recovering depth information. In order to improve the registration accuracy of the corner-based image registration algorithm, the idea of multiresolution analysis was introduced into the classic Harris corner detection, and a gray intensity variation formula based on the wavelet transform was constructed, and a scale transformation characteristic was obtained so that the improved Harris corner detection algorithm is invariant to rotation, translation, and scale. Experimental results show that after reconstruction, the error between the length of the object measured based on the point cloud data and the actual length of the object is small, and both remain within the error range of 3 mm. The experiment verifies the fast, accurate, and stable characteristics of the improved algorithm.
Peer Victimization, Peer Aggression and Depressive Symptoms over Time: A Longitudinal Study with Latent Growth Curves

ABSTRACT Most empirical research on the relationship between peer victimization, aggression, and mental health has been conducted with correlational designs. Much of this research has also focused primarily on linking peer victimization with either the potential aggressive behaviors of victims or a deterioration in their mental health. This study analyzes the relationship between peer victimization, peer aggression, and depressive symptoms in adolescents over time. The participants are 194 adolescents (49.2% boys, 50.8% girls) aged between 10 and 13 years (M = 10.88, SD = 0.84). The results of the growth modeling analysis indicate that the trajectories are interconnected: as victimization decreases, adolescent aggression and depressive symptoms also decrease. In addition, it is observed that victimization decreased in the same way in boys and girls, while aggression and depressive symptoms showed a smaller reduction in girls. Finally, the results and their potential practical implications are discussed.
Accelerating Dense Matrix Computations with Effective Workload Partitioning on Heterogeneous Architectures

ABSTRACT The emergence of High Performance Computing (HPC) has enabled the researchers to perform large scientific computations efficiently and quickly. But as the heterogeneity of the processing units of the HPC systems increased, the utilization of all the resources became an issue. Fully harnessing the power of these systems requires efficient division of work across all the processing units. This solves the issue of under-utilization of resources and improves performance of the application. In this research work, we present a dynamic approach to workload partitioning that obtains the optimal workload partition and schedules them to processing units for parallel processing. Our workload partitioning technique is able to respond automatically to performance variation to provide good performance, it requires very negligible training and is implemented as a library. Performance results show that our dynamic approach is better than static and linear approach. By running the Dense Matrix-Matrix Multiplication kernel library by our proposed method on both CPU and Graphics Processing Unit (GPU) in parallel, we obtain average speedups from to over CPU and to over GPU. We used our method on multi-GPUs for which we obtain average speedups of over CPU and over single GPU.
Optimization Technique of Indoor-Outdoor User Pairing in DL-NOMA System

This work investigates the user pairing issue of indoor and outdoor users for a cellular downlink NOMA system, where a less complex algorithm called least and most cost method (LMCM) is proposed to maximize the achievable data rate and obtain high fairness. Furthermore, the proposed algorithm mainly depends on the diversity of the channel gains of indoor and outdoor users, where the most and least cost matrix techniques are applied. Moreover, we discuss the channel path loss for the case of indoor and outdoor users by proposing to apply particular and empirical propagation models. In addition to that, different performance metrics such as achievable data rate and fairness index are analysed to measure the efficiency of the proposed algorithm. The simulation results show that the proposed algorithm outperforms a random user pairing in terms of different performance metrics.
Versatility of the MAnure PHosphorus EXtraction (MAPHEX) System in Removing Phosphorus, Odor, Microbes, and Alkalinity from Dairy Manures: A Four-Farm Case Study

Abstract. Animal manure contains nutrients [primarily nitrogen (N) and phosphorus (P)] and organic material that are beneficial to crops. Unfortunately, for economic and logistics reasons, most manure tends to be applied to soils near where it is generated. Over time P concentrations in soils where dairy manure is applied builds up, often in excess of crop demands, and its potential loss in runoff poses a risk to water quality. We previously described, and have subsequently built, a full-scale version of a MAnure PHosphorus EXtraction (MAPHEX) System capable of removing greater than 90% of the P from manures. While originally designed to remove phosphorus, we postulated that the MAPHEX System was also capable of removing odor and microbes, and of concentrating alkalinity into a solid, economically transported form. In this study the MAPHEX System was shown to be highly versatile at removing greater than 90% of the phosphorus from a wide range of dairy manures. In addition, the study showed that the System is also capable of concentrating and recovering alkalinity from manures, while also removing over 80% of microbes and reducing the odor of the effluent applied to fields by half. Keywords: Alkalinity, Chemical treatment, Manure, Microbes, Odor, Phosphorus, Treatment systems, Solid separation.
A Comprehensive Review on Traffic Control Modeling for Obtaining Sustainable Objectives in a Freeway Traffic Environment

Traffic control strategy plays a significant role in obtaining sustainable objectives because it not only improves traffic mobility but also enhances traffic management systems. It has been developed and applied by the research community in recent years and still offers various challenges and issues that may require the attention of researchers and engineers. Recent technological developments toward connected and automated vehicles are beneficial for improving traffic safety and achieving sustainable goals. There is a need to develop a survey on traffic control techniques, which could provide the recent developments in the traffic control strategy and could be useful in obtaining sustainable goals. This survey presents a comprehensive investigation of traffic control techniques by carefully reviewing existing methods from a new perspective and reviews various traffic control strategies that play an important role in achieving sustainable objectives. First, we present traffic control modeling techniques that provide a robust solution to obtain reasonable traffic and sustainable mobilities. These techniques could be helpful for enhancing the traffic flow in a freeway traffic environment. Then, we discuss traffic control strategies that could be helpful for researchers and practitioners to design a robust freeway traffic controller. Second, we present a comprehensive review of recent state-of-the-art methods on the vehicle design control strategy, which is followed by the traffic control design strategy. They aim to reduce traffic emissions and energy consumption by a vehicle. Finally, we present the open research challenges and outline some recommendations which could be beneficial for obtaining sustainable goals in traffic systems and help researchers understand various technical aspects in the deployment of traffic control systems.
Quality in higher education: an evaluation in a graduation course in administration at a large university in the municipality of São Paulo

Objective: to assess the quality perceived by students in the on-site undergraduate course in administration at a private university located in the city of São Paulo, using a new scale developed from the skills expected in the training of future administrators. Design/Methodology/Approach: a 46-item questionnaire was developed and tested, with multivariate statistical techniques (exploratory factor analysis and Cronbach's alpha). In total, 629 students participated in the study. Results: the study identified four blocks of competences: managerial skills, problem solving, proactivity, communication, and creativity and techniques. In the global assessment of the quality of educational services, the institution achieved 76.19%, which indicates that the Administration course offered by the institution can be considered of good quality. The work delivers an instrument and an evaluation methodology that can be used to improve administration courses in the country. Originality/value: the study is relevant when assessing the quality of higher education from a new scale based on the skills required to exercise the profession of an administrator defined by Resolution No. 4 of July 13th, 2005.
The effect of transport infrastructure, congestion and reliability on mental wellbeing: a systematic review of empirical studies

ABSTRACT There has been a growing interest in understanding the interdependencies between urbanisation and mental health. Although transportation in cities is complex and of foremost importance to support the mobility of goods and passengers, little is known about how it relates to individual psychological distress. This review aims to provide an up-to-date synthesis of research evidence about the influence of transport infrastructure and operational performance (congestion, delays and reliability) on mental health/wellbeing. It is structured around three main interacting concepts that determined the search/selection of articles: identification of the above-mentioned transport-related exposures; use of psychological and physiological validated instruments; and the outcome on mental health/wellbeing. Ultimately, 69 studies were identified involving an empirical quantitative focus that met the inclusion criteria. We summarise the instruments most reported in these studies and the findings linking transport indicators and psychological and physiological outcomes. Across the review, we identified evidence of the contribution of key transport infrastructure, congestion and delay indicators on negative affective states and psychophysiological distress. Regarding transport reliability, the scarce number of studies identified did not allow for drawing similar firm conclusions. We conclude by discussing some limitations and providing recommendations for future research and policy-making agendas.
Prenatal Exposure to Delta-9-tetrahydrocannabinol (THC) Alters the Expression of miR-122-5p and Its Target Igf1r in the Adult Rat Ovary

As cannabis use during pregnancy increases, it is important to understand its effects on the developing fetus. Particularly, the long-term effects of its psychoactive component, delta-9-tetrahydrocannabinol (THC), on the offspring’s reproductive health are not fully understood. This study examined the impact of gestational THC exposure on the miRNA profile in adult rat ovaries and the possible consequences on ovarian health. Prenatal THC exposure resulted in the differential expression of 12 out of 420 evaluated miRNAs. From the differentially expressed miRNAs, miR-122-5p, which is highly conserved among species, was the only upregulated target and had the greatest fold change. The upregulation of miR-122-5p and the downregulation of its target insulin-like growth factor 1 receptor (Igf1r) were confirmed by RT-qPCR. Prenatally THC-exposed ovaries had decreased IGF-1R-positive follicular cells and increased follicular apoptosis. Furthermore, THC decreased Igf1r expression in ovarian explants and granulosa cells after 48 h. As decreased IGF-1R has been associated with diminished ovarian health and fertility, we propose that these THC-induced changes may partially explain the altered ovarian follicle dynamics observed in THC-exposed offspring. Taken together, our data suggests that prenatal THC exposure may impact key pathways in the developing ovary, which could lead to subfertility or premature reproductive senescence.
End-to-end autonomous driving using the Ape-X algorithm in Carla simulation environment

Despite great advances in controlling vehicles for autonomous driving and in deep reinforcement learning (DRL) techniques, designing an end-to-end architecture that supports autonomous driving using DRL techniques while facing uncertainties in complex and dynamic environments still remains challenging. By examining the state-of-the-art works in the domain of DRL for autonomous driving and inspired from the work of [1], we have designed an end-to-end autonomous driving system using the Ape-X algorithm [2] in Carla simulation environment [3] and have evaluated the performance by comparing its results to those that are obtained using other DRL techniques.
Peanut shaped titanium oxide micro-particles achieved by cathode plasma electrolysis and their electrorheological characteristics

Peanut shaped TiO2 micro-particles were prepared by using the huge energy of the plasma and controlling the Ti(SO4)2 concentrations in aqueous solutions via a one-step method, cathode plasma electrolysis. The morphology and structure of the micro-particles were characterized, and the tiny nanostructures about 3–10 nm were obtained on their surface, which obviously increased their surface roughness and specific surface area. These peanut shaped TiO2 particles with nanostructures on their surface were considered to be formed by the collision of two molten TiO2 particles and then the sharply cooling in Ti(SO4)2 solution. Most of all, the electrorheological (ER) behaviors of the peanut shaped TiO2 micro-particles were characterized using a rotational rheometer. Compared with the traditional TiO2 powders, the better ER effect of the peanut shaped micro-particles was attributed to their unique shape and special surface nanostructures.
Component mode synthesis for multibody systems with absolute coordinates

In the present paper, the concept of the float- ing frame of reference is transformed to a description based on absolute coordinates. The equations are linearized for a certain configuration of the body based on a corotational strain tensor. Because of the transformation to absolute coordinates, the mass matrix is constant and the stiffness matrix can be inverted efficiently. Pairs of perpendicular static and dynamic modes are used to describe large rota- tions and small flexible deformations. A numerical example shows the computational advantages compared to the stan- dard component mode synthesis in multibody dynamics.
The automatization of social media communication - Exploring the development of bot codes on GitHub and the use of open-source bots on Twitter

The production and deployment of social media bots signal the emergence of new political economies that redistribute agency around new technological actors. This has implications for marketing, political action and even private lives. Yet, we have very little systematic knowledge about how bots are produced, and how open-source developers use collaborative platforms for sharing their code online. In my PhD research project, I proposed a novel way to study Twitter bots by examining both the open-source bot codes on GitHub and the bots deployed on Twitter. Traditionally, research projects have examined bots as black boxes and attempted to “reverse engineer” the algorithms behind automated accounts. Instead, I tracked bots to their source code on GitHub and connect the algorithm (code) running behind the bot to data about bot activity on a social media platform. By using this approach, I combined data about automated accounts deployed on a social media platform with the source code behind those same bots – this creates a unique lens on bots and provides important insights about how they were produced and how they operate. Combining API-based, data driven research with a classic social science approach helps to understand current practices of developing bots and deploying them on online social media platforms. As part of my PhD research, I examined bot repositories on GitHub and conducted a survey with the same bot developers to gain further insights into the nitty-gritty of writing bot code, including the most important challenges during the development phase and major barriers to deploying and operating bots on Twitter over an extensive period of time. The survey results also shed light on the motivations behind creating automated social media accounts. I also studied how programmers deploy their bots on Twitter and how other, mostly human, users react to the bot activity. The final part of my thesis contributes to a typology of open-source social media bots by systematically examining bot codes shared on GitHub and the activity of some of these bots on Twitter in tandem. My dissertation contributes to scientific knowledge about open source Twitter bots in two distinct ways. First, it provides a framework to study both the development and the use of open source bots on Twitter. Secondly, it explores the practices of developing open source bots and shed lights on the motivation behind developing Twitter bots, the long-term use of such bots including the challenges of running bots for an extensive period of time and the bots contribution to the Twitter eco-system.
Physicochemical Characteristics, Fatty Acids Profile and Lipid Oxidation during Ripening of Graviera Cheese Produced with Raw and Pasteurized Milk

The production of cheese can be made from either pasteurized or non-pasteurized milk, depending on the country or dietary habits. In this work, the effect of pasteurization of milk on the progress of the physicochemical properties, fatty acids profile and lipid oxidation of cheese throughout a maturation period of 90 days is presented. This research was carried out on two types of Graviera cheese produced in Greece, one made from raw milk and the other from pasteurized milk. The proximal composition of each sample was evaluated, the fatty acids profile was analyzed by Gas Chromatography, whereas lipid oxidation was determined on the basis of the formation of malondialdehyde (MDA). Significant differences (p < 0.05) in the values of pH, fat and density between raw and pasteurized milk were observed. The physicochemical parameters during the ripening of the cheeses showed significant differences according to the type and the stage of maturation. Specifically, the two types of cheese differed significantly (p < 0.05) in terms of pH, protein, fat in dry matter (FDM), and water-soluble nitrogen/total nitrogen (WSN/TN). Although the fatty acids profile was similar for the two types of cheese, differences were observed during the ripening stages as well as between the milk and the final product. The lipid oxidation levels increased during maturation, whereas they seemed to be lower in the pasteurized cheeses. Therefore, it can be concluded that the use of raw or pasteurized milk affects the physicochemical characteristics, fatty acids profile and lipid oxidation of Graviera cheese during ripening.