Miracle-dz/UniFAR-FTUs huggingface dataset:
Wideband Unequal Power Divider With Enhanced Power Dividing Ratio, Fully Matching Bandwidth, and Filtering Performance

A new class of wideband unequal power divider with enhanced power dividing ratio, fully matching bandwidth, and filtering performance is presented. A leading triple-mode resonator is inserted in front of the power dividing junction to relax the requirement of high impedance and bring in more poles in input return loss (RL) response and isolation response. Therefore, the power dividing ratio is significantly enhanced and bandwidths of input RL and isolation are enlarged. Moreover, the cascade of the leading triple-mode resonator and the following transmission line segments corporately construct Chebyshev filtering performance. Thus, the power divider can be synthesized by the required bandwidth of RL and power dividing ratio. Examples of 4:1 and 8:1 filtering power dividers operating at 2.4 GHz are designed and fabricated. The measured power dividing ratios of two filtering power dividers are 3.8:1 and 8.1:1 with corresponding RL bandwidths of 95.8% and 97.5% (<inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert < -10$ </tex-math></inline-formula> dB), and the measured port-to-port isolation bandwidths are 100% (<inline-formula> <tex-math notation="LaTeX">$\vert S_{32}\vert < -14$ </tex-math></inline-formula> dB) and 100% (<inline-formula> <tex-math notation="LaTeX">$\vert S_{32}\vert < -20$ </tex-math></inline-formula> dB), respectively.
Robustified Kalman Filtering Using Both Dynamic Stochastic Approximation and M-Robust Performance Index

Robustified Kalman Filtering Using Both Dynamic Stochastic Approximation and M-Robust Performance Index Zoran BANJAC*, Branko KOVAČEVIĆ Abstract: In this paper, the problem of designing the feasible Kalman filter under a non-Gaussian stochastic environment characterized by spiky noise or outliers has been considered. Firstly, the similarity among a class of dynamic stochastic approximation algorithms and the standard Kalmanfilter is found. Moreover, the particular dynamic stochastic approximation algorithm is derived by minimizing the generalized M-robust performance index. The adopted robust criterion represents the conditional expectation of a suitably chosen non-linear transformation of the measurement residuals, given the predicted system states and the observation sequence. The standard Kalman filtering time-update recursions are used to account for the predicted changes in the system states at each stage. Furthermore, the speed of algorithm convergence is improved by choosing the gain matrix from the minimization of an additional criterion at each stage, resulting in an approximately minimum variance algorithm. A target tracking scenery is simulated to demonstrate the practical robustness of the proposed state estimator.
Influence factors research on control performance of synchronous control system for giant forging hydraulic press

The synchronous control system is the essential device to the giant forging hydraulic press whose synchronization control performance quality will directly determine the quality of the product.Through establishing the synchronization control system mathematical model,simulation experiments of the influence factors were carried out and the results show that owing to the hydraulic system of nonlinear,time-variant and mutual function between channels,various channel's parameters can not be achieved exactly the same,which causes the synchronization error that is produced by the system itself inherent characteristic difference.
Repurposing Plastic Wastes in Non-conventional Engineered Wood Building Bricks for Constructional Application – A Mechanical Characterization using Experimental and Statistical Analys

Plastic waste is accumulating at an alarming rate, polluting the environment due to various industrial activities. Plastic waste is also non-biodegradable, making global accumulation deteriorate. Moreover, it is observed that using sustainable building materials encourages the efficient use of wood industry waste. The study uses polyethylene terephthalate (PET) waste combined with wood fibers to make non-conventional bricks. In the present study, two distinct types of bricks were evaluated, one containing 25% weight of PET waste and the other containing 40%. All samples were subjected to compressive strength and hardness tests in accordance with the applicable American Society for Testing and Materials (ASTM) standards. Since a perfect brick composition should include both the mechanical properties on the higher sides, in the current work, Data Envelopment Analysis (DEA) was utilized to conduct a multi-response analysis and determine the optimal combination of plastic waste and wood fibers for manufacturing non-conventional bricks. The brick containing 25% by weight of plastic waste proved to be the best of the two types of wood plastic composites (WPCs) created in this study, with a grey relational grade value ranging between 2.384 and 3.045.
Prática do Pensamento Computacional e da Língua Inglesa utilizando o Scratch: uma sequência didática

É crescente o número de iniciativas de Pensamento Computacional (PC) na escola em diversos países. Entretanto, o Brasil ainda carece de ações que implementem tal prática. O ensino de programação e da língua inglesa, já inclusos no currículo da educação básica, podem ser uma alternativa para a prática do PC. Este trabalho apresenta uma sequência didática de aulas de programação em Inglês, com Scratch, para estudantes do Ensino Fundamental I, aplicando a metodologia de Ensino de Segunda Língua Baseado em Conteúdo. Este design de experimento foi resgatado dos planos de aula de 2018, que foram reestruturados e reclassificados de acordo com a Taxonomia de Bloom, para a proposição de uma sequência didática. A partir dela e de sua reestruturação, foi possível analisar a evolução dos níveis hierárquicos do domínio cognitivo e da complexidade dos projetos desenvolvidos, além de mostrar que é possível praticar uma segunda língua ou qualquer outro conteúdo enquanto se aprende programação.
A High Static Performance Hierarchical Three-Dimensional Shifted Completely Connected Network

In recent years, there has been a significant shift toward the adoption of the “Internet of Things (IoT)” era, in which vast amounts of data are collected and processed, and Artificial intelligence (AI) is used to make critical choices. When acting in real-time, it is vital to use devices with a lot of computing power. MPC (Massively Parallel Computer) Two-dimensional (2D) design systems are the most powerful computers available. Unfortunately, they can’t meet the need for the advanced computing operation required by many applications. As a result, it is critical to invest in developing high operational capacity systems that will meet the current computational power consumption demands. This paper developed the architecture of the two-dimensional Shifted Completely Connected Network (2D-SCCN) and proposed a three-dimensional (3D) network to achieve greater capacity and performance. The shortest path protocol on three-dimensional Shifted Completely Connected Network (3D- SCCN) was established, tested by computer simulator, and compared based on “shortest diameter”, “shortest average distance”, “lowest cost”, “moderate bisection width”, and “high arc-connectivity” among other characteristics. As a result, “3D-SCCN” was the most efficient method of transmitting a message between two nodes, increasing the system’s real-time response capability.
Requirements for developing production planning and control systems for engineer-to-order industrialized building systems

Abstract Several efforts for industrializing construction have been made in different countries, and there has been a growing demand for engineer-to-order (ETO) industrialized building systems. In this context, products are unique for specific clients, as customer orders are placed at the design stage. There are many challenges for planning and controlling those building systems, due to the high level of complexity involved. The aim of this research is to propose a set of core requirements for production planning and control systems in ETO industrialized building systems. The methodological approach adopted in this investigation was design science research. It was based on a literature review on different planning and control models, and also on an empirical study carried out in a steel fabricator company. The proposed set of requirements is aligned with the management-as-organizing approach, challenging some traditional project management assumptions, including the use of metrics and practices that are not suitable for the high degree of complexity that exists in ETO environments. As a practical contribution, the requirements can be used by ETO construction supplier companies to support the conception and development of planning and control systems.
A Framework for Usability Testing using EEG Signals with Emotion Recognition

The analysis of emotions has utility in several applications that cross multiple fields, including education, medicine, psychology, software engineering, accessibility in-habitation studies, healthcare, robotics, marketing, and business. Studying emotions can play an essential role in software engineering, particularly in the domain of usability testing. For example, emotions can be used to determine whether a specific software application achieves acceptable levels of user satisfaction. Furthermore, emotions can be used to test product usability and all its aspects. Emotion detection in usability testing is a first-of-its-kind tool that has the potential to improve software production (designing and interaction), thus enabling the ongoing revolution in software development to continue onwards. This work aims to build an original framework for emotion detection using electroencephalography (EEG) brain signals, which is then applied in usability testing as a case study. This will create opportunities to gain an in-depth understanding of user satisfaction in a precise and accurate way, especially when compared to traditional approaches.
Selenium adsorption and desorption capacity and its influence on selenium uptake by green spinach

Abstract Selenium is important to human health and must be consumed in adequate amounts through a variety of food sources, particularly vegetables. In order to identify availability of soil Se to crops, Se adsorption-desorption process in soil must be understood. In this study, an adsorption-desorption study of inorganic Se in soils of various textural classes was performed with the following concentration of Se (0–2 mg L−1) applied as Se(VI) and Se(IV). A glasshouse experiment was then conducted in a factorial (2 × 5) completely randomized design, with two Se form—Se(IV) and Se(VI)—and Se at five rates (0–60 g ha−1 Se in solution form) to green spinach. First, soil texture appeared to be the main factor controlling Se adsorption in soils. Selenite was adsorbed more, however, Se(VI) was desorbed at higher rate compared to Se(IV). Contrary to other soil types, in peat, Se(IV) was desorbed at higher level compared to Se(VI), where more than 70% was retained. Higher retention of Se(IV) was found in clayey soil. Meanwhile, from glasshouse experiment, Se uptake was higher in plants when treated with Se(VI) than with Se(IV). However, higher dry matter and antioxidant activity of green spinach was observed when Se(IV) was applied. Selenium applied as Se(IV) had greater benefits to spinach yield and antioxidant activity than Se(VI) or conventionally grown spinach (0 g ha−1), even though plants treated with Se(VI) had greater Se uptake.
Ensino de habilidades socioemocionais: um estudo de caso empregando mídias audiovisuais e conversação online

Na atualidade, dispomos de diversas opções midiáticas que podem ser utilizadas como recurso tecnológico para o ensino. Redes sociais, ebooks, quadrinhos, vídeos, curtas e aplicativos móveis são alguns exemplos. Nesse contexto, as diversas mídias podem ser adotadas como estratégias para auxiliar estudantes no desenvolvimento de competências socioemocionais no ambiente de ensino. Este trabalho tem como objetivo observar o uso de curta-metragem como recurso tecnológico na compreensão de competências socioemocionais em estudantes de ensino superior. Os resultados demonstraram que o uso de curta-metragem compreende importante instrumento pedagógico na compreensão de habilidades como empatia, percepção de contexto e colaboração.
Roles of Physicochemical and Structural Properties of RNA-Binding Proteins in Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning

Trans-acting splicing factors play a pivotal role in modulating alternative splicing by specifically binding to cis-elements in pre-mRNAs. There are approximately 1500 RNA-binding proteins (RBPs) in the human genome, but the activities of these RBPs in alternative splicing are unknown. Since determining RBP activities through experimental methods is expensive and time consuming, the development of an efficient computational method for predicting the activities of RBPs in alternative splicing from their sequences is of great practical importance. Recently, a machine learning model for predicting the activities of splicing factors was built based on features of single and dual amino acid compositions. Here, we explored the role of physicochemical and structural properties in predicting their activities in alternative splicing using machine learning approaches and found that the prediction performance is significantly improved by including these properties. By combining the minimum redundancy–maximum relevance (mRMR) method and forward feature searching strategy, a promising feature subset with 24 features was obtained to predict the activities of RBPs. The feature subset consists of 16 dual amino acid compositions, 5 physicochemical features, and 3 structural features. The physicochemical and structural properties were as important as the sequence composition features for an accurate prediction of the activities of splicing factors. The hydrophobicity and distribution of coil are suggested to be the key physicochemical and structural features, respectively.
The Dilemma of Resolving the Low-Frequency Breakdown Problem in Microwave Components via Traditional and Improved Finite-Element Time-Domain Techniques

This paper developed an improved numerical methods for calculating electromagnetic field at low frequencies, and compared the performance with the traditional method. Traditional finite-element time-domain (FETD) is known to suffer from low frequency breakdown (LFB) problem, where system matrix becomes ill-conditioned, and leads to instability of small size electrical structures. In this paper, we proposed a mixed FETD (mFETD) method in resolving the LFB anomaly in the traditional FEM, with a particular reference to transmission line, inductor, and coaxial cable. We considered wave equation of E-field with incorporation of divergence constraint equation (DCE) as a function of Lagrange multiplier using current continuity equation (CCE) and Gauss’s law. For spatial discretization, both nodal basis functions and Curl conforming vector basis functions were selected, and we employed implicit Newmark beta algorithm (NBA) for integration of time. We describe how the components constructed via DCE in system matrix mitigate the singularity impact of the stiffness matrix, which consequently led to significant improvement of the system matrix. Numerical experiment and results demonstrate how the mFETD obtains stable numerical solution and attains faster rate of convergence while using an iterative solver, as such, improves the computational efficiency. Therefore, the mFETD method handles the transient problems in transmission line, which causes LFB anomaly in the traditional FETD, but not efficient in terms of computational time and iteration at solving the coaxial cable problem.
Reporting incidental findings from non-biological assessments in human subject research

Incidental findings in research with human participants may have implications for a person’s present health or future health outcomes. Current guidelines focus on methods for handling and reporting incidental findings from biological test data but incidental findings might also arise from non-biological tests. This article presents three examples in which the results from non-biological test data can be predictive of future disease and should be disclosed to research participants. It is intended to increase awareness and facilitate further discussion about the reporting of incidental findings from non-biological data.
A compact twisted string actuation system for robotic applications

Rafael José Correia Batista v Abstract The research presented here, focus on the development of a compact twisted string actuation system for robotic applications. When coupled to a motor, a twisted string system acts as a high ratio rotary to linear motion convertor. This dissertation starts with a brief description of existing applications of twisted string mechanisms. Then, the first prototype of the twisted string actuation system, built for this research, is presented. The prototype was used for the development of a better understanding of the mechanism and to find its capabilities. Afterwards, and based on experiments performed with the first prototype, a two-phase twisted string actuation system that benefits from an overtwist phase is proposed. In this way, a higher contraction ratio compared to the state of the art could be achieved. Furthermore, since none of the previous mathematical models could predict the linear displacement of the system, for the overtwist phase, an improved mathematical model was developed that can accurately predict the linear displacement over the entire range of motion. Afterwards, the twisted string system was applied for the actuation of a robotic hand. The high contraction ratio that was achieved with the two-phase twisted string system, resulted in a compact and light rotary to linear motion conversion mechanism that could fit inside the palm of a robotic hand, which has around the same size of an adult human hand. Finally, there will be an overview of the most important achievements and conclusions of this research work, along with some future plans for further investigation.
Science teaching in kindergartens: factors associated with teachers’ self-efficacy and outcome expectations for integrating science into teaching

ABSTRACT Recently, much importance has been placed on early science education. This study examined the associations between kindergarten teachers' science teaching self-efficacy, outcome expectations, and their integration of science into teaching, and investigated how four potential factors (i.e. mastery experience, interpersonal support, previous science learning experience, and perceptions of the importance of science) predict kindergarten teachers' self-efficacy and outcome expectations for science teaching. Structural equation modeling was employed to analyze questionnaire data collected from 813 teachers in public and private kindergartens (teaching children aged 4–6). The results confirmed the association between teachers' self-efficacy and their outcome expectations. When both self-efficacy and outcome expectations were taken into account to predict the degree of science integration into teaching, only science teaching self-efficacy was significantly influential. Moreover, this study revealed that all four factors were significantly associated with teachers' science teaching self-efficacy, of which mastery experience was the most potent. On the other hand, only teachers' perceptions of the importance of science and the interpersonal support they received played a significant role in directly predicting their science teaching outcome expectations. The results could shed light on how to better prepare and support early childhood teachers to integrate science into their classroom teaching.
Research on Positioning and Monitoring Method of Pipeline Abnormal State Based on Sliding Window Outlier Analysis

In recent years, various accidents caused by pipeline corrosion and groundwater infiltration have brought irreparable losses to people’s lives and property safety. Therefore, it is very important to lay sensors on the pipeline for real-time monitoring and alarm. Aiming at the problem of distributed optical fiber sensing in the real-time pipeline monitoring process, this study proposes a pipeline invasion detection analysis method based on sliding window outliers. By continuously optimizing the value of the sliding window and the length of the abnormal state diagnosis window, the false alarm rate of the system is greatly reduced. This method only uses amplitude data to realize the intelligent identification of abnormal pipeline conditions. In addition, a simulation experiment of pipeline groundwater invasion monitoring has been carried out, and the results show that the algorithm does not generate false positives when the device is in good condition. Once the pipeline is invaded, the algorithm can quickly determine the location, and the error range is stable at 0.5 m. This method is an unsupervised artificial intelligence pipeline invasion detection processing method, and it has a good application prospect in the monitoring of abnormal pipeline conditions.
Aquaporin-4 in Neuromyelitis Optica Spectrum Disorders: A Target of Autoimmunity in the Central Nervous System

Since the discovery of a specific autoantibody in patients with neuromyelitis optica spectrum disorder (NMOSD) in 2004, the water channel aquaporin-4 (AQP4) has attracted attention as a target of autoimmune diseases of the central nervous system. In NMOSD, the autoantibody (NMO-IgG) binds to the extracellular loops of AQP4 as expressed in perivascular astrocytic end-feet and disrupts astrocytes in a complement-dependent manner. NMO-IgG is an excellent marker for distinguishing the disease from other inflammatory demyelinating diseases, such as multiple sclerosis. The unique higher-order structure of AQP4—called orthogonal arrays of particles (OAPs)—as well as its subcellular localization may play a crucial role in the pathogenesis of the disease. Recent studies have also demonstrated complement-independent cytotoxic effects of NMO-IgG. Antibody-induced endocytosis of AQP4 has been suggested to be involved in this mechanism. This review focuses on the binding properties of antibodies that recognize the extracellular region of AQP4 and the characteristics of AQP4 that are implicated in the pathogenesis of NMOSD.
Cost-Aware Resource Optimization for Efficient Cloud Application in Smart Cities

In this generation of smart computing environment, every device in the system and multple system are interconnected to each other, which allows users to view, analyze data, and make smart decisions. Smart cities are one example of smart environments where every device is connected and computing is performed on the cloud. In such a situation, the system requires an efficient system to handle huge requests and deliver data. Cloud computing plays an essential role in solving this issue but suffers from resource optimization, cost optimization, and load balancing. This work is aimed at solving the issue of resource and cost optimization in cloud infrastructure to provide a high service rate and sustainable infrastructure to cloud applications in smart cities. The proposed model is inspired by artificial neural networks and nature-inspired algorithm to reduce execution cost, average start time, and finish time and to make the system power efficient at the same time to improve the utilization of the system. The result shows that the proposed model completes more tasks in the least time and execution cost as compared to the existing models. This showcases the smart cloud applications are cost efficient and can complete the tasks in less time.