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El objetivo de esta investigación fue realizar una caracterización de los residuos sólidos reciclables producidos en la Universidad de la Amazonia y desarrollar un contenedor y clasificador automático de este tipo de residuos, para contribuir por medio de las tecnologías de la información y la comunicación (TIC) al proceso de sostenibilidad ambiental ligado al concepto de economía circular en el ámbito educativo. La metodología definida se dividió en cinco fases que corresponden al conocimiento del manejo de residuos sólidos de los usuarios de las cafeterías, caracterización de los residuos sólidos, diseño y construcción del contenedor, entrenamiento del sistema de inteligencia artificial (IA) y prueba interna beta test, tras lo cual se obtuvo como resultado una generación total de residuos plásticos a la semana de 66 kg, lo que representa un ingreso económico aproximado de $138.000. Además, se desarrolló un contenedor y clasificador automático capaz de reconocer y almacenar botellas plásticas empleando una red neuronal artificial. Como conclusión, el reciclaje de residuos plásticos en una institución de educación superior (IES) aporta a la sostenibilidad ambiental. Además, el desarrollo de tecnologías como el contenedor y clasificador automático permite optimizar la selección y clasificación de elementos reciclables de forma rápida y segura.

The United Arab Emirates (UAE) and the Kingdom of Saudi Arabia have developed an increasingly active and capable space industry over recent years. Saudi Arabia sent its first astronaut to space in ...

Space missions have become diversified in recent years, where connection and separation devices play a crucial role as key components of various spacecraft. Traditional pyrotechnic devices have the advantages of large carrying capacity, rapid motion and functional reliability. However, their shortcomings such as great release shock, poor safety, unrepeatability and other prominent defects make them unsuitable for new generation spacecraft such as microsatellites to separate at low shock or lock repeatedly, etc. Therefore, it is necessary to develop space non-pyrotechnic low-shock connection and separation devices (SNLD) which are required for advanced aerospace missions. In this paper, the progress of the research on space non-pyrotechnic low-shock connection and separation technology (SNLT) is summarized and reviewed. Proceed from the principle of reducing shock for non-pyrotechnic devices, present studies are classified from the perspective of actuating technology and systematic designing methods. For non-pyrotechnic actuating techniques, according to different driving sources, the separation devices are classified into several main categories: electric, magnetic, gas and thermal actuating devices. The actuation principle and application prospect of separation techniques are introduced and the working process, dimension and mechanical properties of typical devices are compared and evaluated. For the systematic designing method, the common mechanism types of SNLDs are summarized according to the designing concept of reducing shock. Then connection configurations are classified according to the structural forms of connection devices, of which the principles, bearing capacities and general applications are discussed. This paper systematically summarizes the key problems, puts forward the future development trend of SNLT, and points out the breakthrough direction for related scholars.

This paper presents an air pluviation system, developed to facilitate 1g physical model tests in granular soils. The deposition process is fully automated and requires minimal input from the operator, thereby significantly reducing the time required to deposit large volumes of granular material, improving the uniformity of the prepared specimens and the reliability of test results. The components comprising the pluviation system have been calibrated to produce loose-to-very dense sand beds, of relative density that ranges between Dr = 7% and > 100% of the maximum density achieved with the procedures described in the pertinent standards. The testing chamber where sand is deposited is instrumented with an array of pressure sensors, and the rig is equipped with a miniature cone penetration testing (mini-CPT) device. Measurements from the earth pressure sensors and cone tip resistance profiles are used to evaluate how friction at the sand–chamber interfaces affects the distribution of geostatic stresses inside the chamber, the uniformity of sand beds and boundary effects during deposition and during mini-CPT testing. The air pluviation system allows preparing layered sand profiles by adjusting the deposition parameters on the fly, and this feature is demonstrated through the analysis of mini-CPT tests performed in layered sand beds.

With the development of neural architecture search, the performance of deep neural networks has been considerably enhanced with less human expertise. While the existing work mainly focuses on the development of optimizers, the design of encoding scheme is still in its infancy. This article thus proposes a novel encoding scheme for neural architecture search, termed action command encoding (ACEncoding). Inspired by the gene expression process, ACEncoding defines several action commands to indicate the addition and clone of layers, connections, and local modules, where an architecture grows from empty according to multiple action commands. ACEncoding provides a compact and rich search space that can be explored by various optimizers efficiently. Furthermore, a surrogate-assisted performance evaluator is tailored for ACEncoding, termed sequence-to-rank (Seq2Rank). By integrating the Seq2Seq model with RankNet, Seq2Rank embeds the variable-length encoding of ACEncoding into a continuous space, and then predicts the rankings of architectures based on the continuous representation. In the experiments, ACEncoding brings improvement to neural architecture search with existing encoding schemes and Seq2Rank shows better accuracy than existing performance evaluators. The neural architectures obtained by ACEncoding and Seq2Rank have competitive test errors and complexities on image classification tasks, and also show high transferability between different data sets.

This study obtains a fuzzy solution to the mathematical model of a cancer tumor under Caputo-generalized Hukuhara partial differentiability by using fuzzy integral transforms. In order to solve the fuzzy partial fractional differential equations, the two-variable fuzzy Laplace—Carson transforms and the two-variable fuzzy Fourier transform under the generalized Hukuhara partial differentiability and Caputo-generalized Hukuhara partial differentiability are investigated. Following this, an algorithm for the proposed method is presented. Finally, as a practical model of fuzzy fractional diffusion equations, the fuzzy mathematical model of the net killing rate of cancer cells in tumors is investigated. This technique is powerful and essential for the development of a fuzzy analytical method for solving fuzzy partial fractional differential equations. 2-s2.0-85119302470

The research purpose of this paper is to explore some new fuzzy rough set models in the fuzzy β-covering group approximation space (FβCGAS) and study the related properties, thereby provide new thinking directions for the theoretical development and application expansion of fuzzy rough set models. Firstly, in the fuzzy β-covering approximation space (FβCAS), in light of some existing fuzzy covering rough set (FCRS) models which do not satisfy the inclusion relations between the fuzzy upper and lower approximations, we define the concepts of parameterized fuzzy β-neighborhoods and parameterized fuzzy complementary β-neighborhoods to eliminate part of the noise data and establish four types of parameterized FCRS models that satisfy the inclusion relationship between the fuzzy lower and upper approximations. Meanwhile, the properties of the models are discussed. Further, in the FβCGAS, we define the concepts of the parameterized pessimistic (optimistic) fuzzy β-fitting neighborhoods and the parameterized pessimistic (optimistic) fuzzy complementary β-fitting neighborhoods and construct the parameterized pessimistic and optimistic FCRS models that satisfy the inclusion relationship between the fuzzy lower and upper approximations. At the same time, we likewise explore the properties of the models. Besides, we further use the newly proposed parameterized fuzzy neighborhoods to study the variable precision multi-granulation fuzzy decision-theoretic rough set models in the FβCGAS. Finally, for all the models mentioned above, we recommend them for solving the criteria weight determination problem, ranking problem, optimal selection problem and qualitative classification problem in multi-criteria decision-making. Meanwhile, we complete the relationships of all the new models mentioned in this paper and discuss them.

In recent years, an increasing number of competing firms have started to integrate their product attribute development and advertising strategies by targeted advertising of the best-developed attribute of their product. With a focus on such integration and its potential effects, we establish an analytical model in which two competing firms sell multi-attribute products. Each firm can choose which attribute to focus on in its development strategy and which attribute to emphasize in its advertising strategy. By optimizing the development and advertising strategies of these firms, we first verify the integration of these two strategies as each firm advertises only its developed attribute. Second, we find that advertising, according to the firms’ development choices, plays distinctive roles on profit generation: the difference-alleviating or difference-strengthening effect. Therefore, when determined endogenously, the advertising intensities show qualitatively different changing trends: a monotonous curve in the same-attribute-development case but an inverted-U shaped curve in the different-attribute-development case. Third, we find advertising establishes a connection between firms’ attribute development choices, making them depending not only on their own development cost. A smaller difference between the development costs of these firms or a higher advertising efficiency increases the firms’ willingness to develop a same attribute. We also extend our analysis to the flexible quality level of the non-developed attribute, asymmetric substitution degrees between attributes and prices, or the endogenous attribute development levels, and study how the interactions between the product development and advertising are affected. This paper highlights the importance of making development and advertising strategies jointly.

The ongoing problem of inconsistency in the stages of system development encourages the development and elaboration of approaches and methods aimed at eliminating the causes of their occurrence. According to the research community, one of the most promising approaches is to involve users in the development process. Six directions in the implementation of the approach to user involvement in the system development process were formed and analyzed in the article. The analysis of the applicability of such approaches and methods to the task of involving the user in the process of developing an information data model is carried out. The problems of this direction were highlighted. To solve these problems, the sensemaking approach principles which is a development of the methodology for sensemaking proposed by Brenda Dervin, are proposed to use. On the basis of the proposed sensemaking approach, the implementation of the proposed approach is shown with the help of the development of the method, a method for constructing the specification of the information model was developed. The steps of the method are represented by methods that implement the idea of involving the user in the development process by analyzing his work with elements of documentation support.

PurposeThis study proposes a framework for Earthwork Ontology (EW-Onto) to support and enhance data exchange in the project and the efficient decision-making in the planning and execution phases.Design/methodology/approachThe development of EW-Onto started from defining the concepts and building taxonomies for earthwork operations and equipment following the METHONTOLOGY approach. In addition, several rules have been extracted from safety codes and implemented as SWRL rules. The ontology has been implemented using Protégé. The consistency of EW-Onto has been checked and it has been evaluated using a survey.FindingsThe assessment of EW-Onto by experts indicates an adequate level of consensus with the framework, as an initial step for explicit knowledge exchanges within the earthwork domain.Practical implicationsThe use of an ontology within the earthwork domain can help: (1) link and identify the relationships between concepts, define earthwork semantics, and classify knowledge in a hierarchical way accepted by experts and end-users; (2) facilitate the management of earthwork operations and simplify information exchange and interoperability between currently fragmented systems; and (3) increase the stakeholders' knowledge of earthwork operations through the provision of the information, which is structured in the context of robust knowledge.Originality/valueThis paper proposes a framework for Earthwork Ontology (EW-Onto) to support and enhance data exchange in the project and the efficient decision-making in the planning and execution phases. EW-Onto represents the semantic values of the entities and the relationships, which are identified and formalized based on the basic definitions available in the literature and outlined by experts.

The development and evaluation of Mission Engineering Threads (METs) require an understanding of the operational context for which a system-of-systems (SoS) will be employed as well as an assessment that the performance of a complex SoS is effective and safe. The information describing the design and performance parameters of the systems within the SoS is distributed in many different physical locations, and is represented in a variety of formats, both structured and unstructured. Because of this dynamic on the structuring of the data to include differing ontology frameworks, it is necessary to develop a framework and toolset to handle the automated extraction of information from disparate information sources. In addition, this extracted information needs to be categorized properly into defined data types as represented in the specific MET to correctly capture the appropriate context of the mission scenario. Semantic technique solutions will be researched, analysed, and applied as a means to infer new facts from existing facts and data. These techniques are particularly powerful when the amount of data and/or the relationships and constraints among data are too cumbersome and complex for human understanding and reasoning. Using the characteristics of wine as an example, we present our framework to show how it enables rapid and contextually relevant extraction, and represents complex information in a user-friendly format.

This tutorial paper presents the mathematics behind the widely observed air-gap field modulation phenomena in electric machines and derives the duality between electric machines and switching converters. A bibliometric analysis of the existing research on various machine types is presented to show the historical development of electric machines from the perspective of the number of publications, showing the necessity and urgency of unifying the analysis of operating principles/torque production mechanisms before the development of the general air-gap field modulation theory in 2017. The theoretical framework and its applications are reviewed. Detailed examples of how to use the developed theory to analyze the operating principle/torque production, conduct the quantitative analysis of parameters and basic performance, and make innovations from topologies of typical electric machines, are also included for verification as well as self-study.

A mathematical study on the traveling waves (TWs) propagation phenomena on three-phase power lines under fault conditions is presented in this work. This paper is divided into two parts, and this part focuses on the analytical development of expressions that describe fault-launched TWs as functions of line and fault parameters. From the obtained mathematical representation of TWs launched by faults, TW-based applications can be designed in a more reliable way, justifying the relevance of the presented study.

Complex decision-making problems can be conveniently decomposed in more manageable sub-problems through a hierarchical approach. Often, there are interaction effects between sub-criteria or elementary criteria in the hierarchy. In this paper, we propose a generalization of the recently published interval outranking approach to deal with this kind of problems. This new method allows an easy setting of weights (including interaction weights), and other model parameters as interval numbers. The proposal also allows to set outranking relations associated with each non-elementary criterion. On this background, the INTERCLASS-nB and INTERCLASS-nC multi-criteria ordinal classification (multi-criteria sorting) methods are generalized to cope with interacting and hierarchical criteria; these methods can suggest assignments of actions to ordered classes for each non-elementary criterion. Finally, an application of the classification methods is illustrated by evaluating research and development projects.

This study aims to develop a valid and reliable measuring instrument that can measure the level of mathematical language used by students and their teachers during the teaching of fractions to fourth-grade primary school students. This study is a methodological validity and reliability study. In total, 999 students from fourteen different secondary schools were separated into two other sampling groups, where they participated in the sample of the survey for Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). These stages are identified as the item pool stage, the testing validity of scope stage (expert opinion stage), the factor analysis stage (construct validity), and the reliability stage. Mathematical language scale in the teaching of fractions consists of the student approach, the use of fraction language and the use of symbols, and the written expressions of sub-dimensions. The exploratory factor analysis method (EFA) shows that the instrument can be built on three factors. It is also confirmed by the confirmatory factor analysis (CFA). It can be accepted according to NFI indexes. As a result, the mathematical language scale is found to be valid and reliable in fraction teaching. To this end, it can be stated that the MLSFT scale can be used in fraction teaching for elementary school students to reveal their abilities and the skills of teachers in terms of mathematical language used.

In this paper, we introduce a generalization of the concept of fuzzy quantifier called fuzzy quantifiers over fuzzy domains. The development of this generalization is motivated by the impossibility to satisfactorily define the important operation of relativization for standard fuzzy quantifiers. We provide basic examples of these quantifiers and define and investigate also several semantic properties of them. Finally, we prove a theorem saying, among other things, that all these two argument fuzzy quantifiers that possess properties of extension and conservativity are relativizations of one-argument fuzzy quantifiers of this kind.

Although collecting personal information about consumers is crucial for firms and marketers, understanding of when and why consumers accept or reject information collection remains limited. The authors conceptualize a privacy calculus that represents a consumer’s trade–off of the valence and uncertainty of the consequences of the collection, storage, and use of personal information. For example, usage-based car insurance requires drivers to share data on their driving behavior in exchange for a discount (certain benefit) but at the risk of third parties intercepting location data for malicious use (uncertain disadvantage). Building on this conceptualization, the authors develop the privacy calculus (PRICAL) index. They empirically confirm the validity of the items (Study 1) and the index as a whole (Study 2). The PRICAL index is generally applicable and improves the explanation of behavioral intentions (Study 2) and actual behavior (Study 3), compared with currently used constructs (e.g., privacy concern, trust). Overall, the PRICAL index allows managers to understand consumers’ acceptance of information collection regarding financial, performance, psychological, security, social, and time-related consequences, which the authors demonstrate using the top five most valuable digital brands (Study 4).

Scientific workflows are a cornerstone of modern scientific computing. They are used to describe complex computational applications that require efficient and robust management of large volumes of data, which are typically stored/processed on heterogeneous, distributed resources. The workflow research and development community has employed a number of methods for the quantitative evaluation of existing and novel workflow algorithms and systems. In particular, a common approach is to simulate workflow executions. In previous works, we have presented a collection of tools that have been adopted by the community for conducting workflow research. Despite their popularity, they suffer from several shortcomings that prevent easy adoption, maintenance, and consistency with the evolving structures and computational requirements of production workflows. In this work, we present WfCommons, a framework that provides a collection of tools for analyzing workflow executions, for producing generators of synthetic workflows, and for simulating workflow executions. We demonstrate the realism of the generated synthetic workflows by comparing their simulated executions to real workflow executions. We also contrast these results with results obtained when using the previously available collection of tools. We find that the workflow generators that are automatically constructed by our framework not only generate representative same-scale workflows (i.e., with structures and task characteristics distributions that resemble those observed in real-world workflows), but also do so at scales larger than that of available real-world workflows. Finally, we conduct a case study to demonstrate the usefulness of our framework for estimating the energy consumption of large-scale workflow executions., arXiv admin note: substantial text overlap with arXiv:2009.00250

The capability afforded by Vehicle-to-Vehicle communication improves situational awareness and provides advantages for many of the traffic problems caused by reduced visibility or No-Line-of-Sight situations, being useful for both autonomous and non-autonomous driving. Additionally, with the traffic light Signal Phase and Timing and Map Datainformation and other advisory information provided with Vehicle-to-Infrastructure (V2I) communication, outcomes which benefit the driver in the long run, such as reducing fuel consumption with speed regulation or decreasing traffic congestion through optimal speed advisories, providing red light violation warning messages and intersection motion assist messages for collision-free intersection maneuvering are all made possible. However, developing applications to obtain these benefits requires an intensive development process within a lengthy testing period. Understanding the intersection better is a large part of this development process. Being able to see what information is broadcasted and how this information translates into the real world would both benefit the development of these highly useful applications and also ensure faster evaluation, when presented visually, using an easy to use and interactive tool. Moreover, recordings of this broadcasted information can be modified and used for repeated testing. Modification of the data makes it flexible and allows us to use it for a variety of testing scenarios at a virtually populated intersection. Based on this premise, this paper presents and demonstrates visualization tools to project SPaT, MAP and Basic Safety Message information into easy to read real-world based graphs. Also, it provides information about the modification of the real-world data to allow creation of a virtually populated intersection, along with the capability to also inject virtual vehicles at this intersection.

Increasing the implemented SAE level of autonomy in road vehicles requires extensive simulations and verifications in a realistic simulation environment before proving ground and public road testing. The level of detail in the simulation environment helps ensure the safety of a real-world implementation and reduces algorithm development cost by allowing developers to complete most of the validation in the simulation environment. Considering sensors like camera, LIDAR, radar, and V2X used in autonomous vehicles, it is essential to create a simulation environment that can provide these sensor simulations as realistically as possible. While sensor simulations are of crucial importance for perception algorithm development, the simulation environment will be incomplete for the simulation of holistic AV operation without being complemented by a realistic vehicle dynamic model and traffic cosimulation. Therefore, this paper investigates existing simulation environments, identifies use case scenarios, and creates a cosimulation environment to satisfy the simulation requirements for autonomous driving function development using the Carla simulator based on the Unreal game engine for the environment, Sumo or Vissim for traffic co-simulation, Carsim or Matlab, Simulink for vehicle dynamics co-simulation and Autoware or the author or user routines for autonomous driving algorithm co-simulation. As a result of this work, a model-based vehicle dynamics simulation with realistic sensor simulation and traffic simulation is presented. A sensor fusion methodology is implemented in the created simulation environment as a use case scenario. The results of this work will be a valuable resource for researchers who need a comprehensive co-simulation environment to develop connected and autonomous driving algorithms.

During the last decade, the confidence in many social sciences, including consumer research, has been undermined by doubts about the replicability of empirical research findings. These doubts have led to increased calls to improve research practices and adopt new measures to increase the replicability of published work from various stakeholders such as funding agencies, journals, and scholars themselves. Despite these demands, it is unclear to which the research published in the leading consumer research journals has adhered to these calls for change. This article provides the first systematic empirical analysis of this question by surveying three crucial statistics of published consumer research over time: sample sizes, effect sizes, and the distribution of published p-values. The authors compile a hand-coded sample of N = 258 articles published between 2008 and 2020 in the Journal of Consumer Psychology, the Journal of Consumer Research and the Journal of Marketing Research. An automated text analysis across all publications in these three journals corroborates the representativeness of the hand-coded sample. Results reveal a substantial increase in sample sizes above and beyond the use of online samples along with a decrease in reported effect sizes. Yet, despite increased sample sizes, the expected replicability of published results in the last 12 years has not increased.

Carbohydrate chains are ubiquitous in the complex molecular processes of life. These highly diverse chains are recognized by a variety of protein receptors, enabling glycans to regulate many biological functions. High-resolution structures of protein-glycoligand complexes reveal the atomic details necessary to understand this level of molecular recognition and inform application-focused scientific and engineering pursuits. When experimental challenges hinder high-throughput determination of quality structures, computational tools can, in principle, fill the gap. In this work, we introduce GlycanDock, a residue-centric protein-glycoligand docking refinement algorithm developed within the Rosetta macromolecular modeling and design software suite. We performed a benchmark docking assessment using a set of 109 experimentally determined protein-glycoligand complexes as well as 62 unbound protein structures. The GlycanDock algorithm can sample and discriminate among protein-glycoligand models of native-like structural accuracy with statistical reliability from starting structures of up to 7 A root-mean-square deviation in the glycoligand ring atoms. We show that GlycanDock-refined models qualitatively replicated the known binding specificity of a bacterial carbohydrate-binding module. Finally, we present a protein-glycoligand docking pipeline for generating putative protein-glycoligand complexes when only the glycoligand sequence and unbound protein structure are known. In combination with other carbohydrate modeling tools, the GlycanDock docking refinement algorithm will accelerate research in the glycosciences.

Solid phase extraction is widely used in sample pretreatment, concentration and analysis processes due to high selectivity and suitability for low concentration sample system. In this review, we systematically summarized and discussed the development trends of solid phase extraction by bibliometrics method. By analyzing papers output scale, the research and development direction of solid phase extraction technology is prospected. We also give an overview on current strategies of novel solid phase extraction from the separation medium and separation technology. The paper aims to describe the global research profile and the development trends of solid phase extraction, to help researchers to accurately grasp the research trend and to provide support for scientific research institutions to formulate scientific policies and strategic plans. Furthermore, the prospect of the development and application of solid phase extraction is also discussed.

In the article, the authors describe their approach to creating regional nanotechnology development roadmaps. Original visualization and roadmaps for selected nanotechnologies are showcased. The creation of roadmaps was preceded by technology mapping and the selection of priority technologies, for which technology characteristics sheets were developed. Human resources (skills), financial resources, and tangible assets were a basis for the creation of the roadmaps specifying the development of priority nanotechnologies in the studied region (Podlaskie, Poland). The development of the technologies described in the roadmaps is stimulated by the R&D domain, which constitutes one of the roadmap layers. Potential technology application areas form the background for the “market” layer of the roadmaps. Segments related to the general development of a particular technology in time are additional elements enriching the roadmaps. All layers were presented in three-time perspectives. The conducted analysis served as a basis for the integration of the selected priority nanotechnologies into four nanotechnology development scenarios of the region. The obtained results contributed to the formulation of the Regional Nanotechnology Development Strategy. The research was conducted in a framework of the “Technological foresight ‘NT FOR Podlaskie 2020’ Regional strategy of nanotechnology development” project.

The proposed research work aims to discuss and explore various constraints of traditional web page search and ranking systems primarily in the present generation of big data. The primary objective is to facilitate a web user by presenting a most personalized web page ranking as a response to a user’s search query by considering tastes and browsing history of the user while previously searching on the web. This research intends to design and develop a machine learning based next generation of web page ranking algorithm, i.e., Advanced Cluster Vector Page Ranking algorithm (ACVPR). This ACVPR algorithm is implemented in the form of an Intelligent Mata Search System-Personalized tool to evaluate the performance of the algorithm. The ACVPR algorithm arm the user with a powerful meta-search tool to facilitate the user by providing a web page ranking order to quickly satisfy the personalized needs especially when the search query is erroneous or incomplete. An extensive mathematical and experimental evaluation of the developed logistic regression model by calculating and comparing various evaluation metrics such as specificity, sensitivity, precision, recall using R statistical tool shows the improved efficiency as compared to other popular search engines.

This study was conducted to corroborate in understanding the teachers’ beliefs about assessment practices. The prior studies related to teachers’ assessment beliefs in mathematics have been done to assess teachers’ beliefs in the general context of mathematics teaching. This study developed an instrument to assess teachers’ assessment beliefs of mathematical thinking. The research aimed to develop and validate a scale of assessment beliefs of mathematical thinking by using the confirmatory factor analysis. The first draft of the scale contained 25 items. The sample of the study consisted of 537 mathematics teachers from public schools in Oman. The instrument was a questionnaire with a 5-point Likert scale. The scale was validated by asking a number of experts in mathematics educational measurement and evaluation. Exploratory and confirmatory factor analysis was applied to test the model of assessment beliefs of mathematical thinking scale using AMOS 25.0. All constructs had acceptable reliability. The model had a good model fit for the assessment beliefs of mathematical thinking scale which obtainable from the fit indices tests. The findings revealed that all fit criteria indices were realized. The results also showed acceptable validity and construct reliability for the scale.

Accurate topology is the basis of fine management and safe operation of distribution network. With the development of distributed generation, more and more users participate in the distribution network, which makes the flow direction of distribution network more complex and brings some difficulties to the topology identification of distribution network. The existing distribution network topology identification methods lack of multi period measurement data, and the utilization rate of multi-user data is low, which leads to the low accuracy of distribution network topology identification. To solve this problem, a distribution network topology identification method based on multi-user fusion data is proposed. Firstly, the existing distribution network measurement system is analyzed, and the data and characteristics that can be collected in the actual project are obtained. Then, according to the characteristics of different data sampling frequency and accuracy, multi-user data fusion is carried out, and a PMU based multi-user data time scale alignment method and a pseudo measurement generation method based on linear extrapolation are proposed. Finally, an optimization model of distribution network topology identification based on multi-user and multi period fusion data is proposed. By minimizing the error between the measured value and the estimated value in several time periods, the model realizes the identification of distribution network topology. Simulation results show that the method is correct and effective.

The data volume of various smart devices has exploded with the advent of the 5G communication era and the rapid development of Internet of Things technology. A large amount of data has higher requirements on the transmission protocol in the network. Most of the existing research focuses on studying a single node as the basic information transmission unit. However, social networks have complex topological structures, and such methods are difficult to grasp the evolutionary rules of the network. It usually leads to higher resource waste and problem complexity. A triad (a network subgraph containing three nodes) can not only provide accurate local topology information, but also its conversion rules are easy to describe, which can reduce the complexity of the problem. The mutual conversion between triads constitutes the evolution of the entire network. Based on this, this article first proposes a triad-based social network evolution analysis method (SNEA). SNEA includes a prediction algorithm (TPMPA) that learns the evolution of the triad transition probability matrix through time series and a quantization algorithm (TTHQA) that quantifies the impact of triad transformations on the network-based on random walks. SNEA integrates the advantages of the two algorithms to dynamically grasp the evolutionary rules of the network. Then this paper proposes a triad link prediction algorithm (TLPA) to quantitatively evaluate the results of the evolution analysis of the SNEA method. The TLPA algorithm reduces the blindness of message forwarding and unnecessary waste of resources by predicting the probability of a connection between nodes in the network. Experimental results show that compared with Epidemic, SECM, CRDNT, ICMT algorithms, our method has a prominent advantage in improving message delivery rate and reducing resource consumption.

This study aims to develop a mathematics learning application, namely Android-based mobile learning to increase students' High Order Thinking Skills (HOTs). The result of mathematics learning media is a valid and practical mobile learning application product. "Mastering Math" is the name of a mathematics e-learning application designed as a mobile or smartphone application, with specifications for the OS Android. The procedure for the development of virtual mathematical media used the development of the 4D model of Thiagarajan: (1) define; (2) design; (3) develop, and (4) disseminate. The trials conducted included five expert judgments and a small group. The research instruments used were a validation sheet, a practical assessment sheet by the teacher, a practical assessment sheet by students, and a media effectiveness test instrument. Data analysis was performed using Cochran's Q test for similarity of expert validation and qualitative analysis. The teaching materials used are junior high school teaching materials with validity and practicality in the good category to increase students' HOTs. This research implies that the learning of mathematics is more effective and efficient, students' divergent thinking develops, and their learning motivation for mathematics increases.

Unmanned aerial systems, commonly known as drones, present new opportunities to perform autonomous tasks. Handling qualities requirements for manned vertical lift aircraft have been well defined and documented. The need to define handling qualities requirements for vertical take-off and landing (VTOL) unmanned aerial systems (UAS) to meet mission demands is of paramount importance for all potential operators and procurement agencies. One way to relate handling qualities specifications of large-scale manned and subscale unmanned aircraft is through Froude dynamic scaling. Froude dynamic scaling based on hub-to-hub distance has shown great promise in relating the natural frequencies of scaled multicopters. There have been recent efforts to develop a VTOL-UAS handling qualities standard by scaling mission task elements and rating their performance through a Trajectory, Tracking, and Aggression (TTA) score. This paper proposes a new performance standard adapted from the TTA scoring method, along with a modified Cooper–Harper scale as a VTOL-UAS handling qualities framework that is consistent with the spirit of Aeronautical Design Standard 33 (ADS-33). These newly proposed performance standards were then validated through simulation and flight testing on a small hexacopter UAS, flown at the University of Portland. A key outcome of this work is the flight verification of a key dynamic response metric, the disturbance rejection bandwidth, and associated validation of Froude scaling for predicted handling qualities metrics.

Summary Living systems are studied using three complementary approaches: living cells, cell‐free systems and computer‐mediated modelling. Progresses in understanding, allowing researchers to create novel chassis and industrial processes rest on a cycle that combines in vivo, in vitro and in silico studies. This design–build–test–learn iteration loop cycle between experiments and analyses combines together physiology, genetics, biochemistry and bioinformatics in a way that keeps going forward. Because computer‐aided approaches are not directly constrained by the material nature of the entities of interest, we illustrate here how this virtuous cycle allows researchers to explore chemistry which is foreign to that present in extant life, from whole chassis to novel metabolic cycles. Particular emphasis is placed on the importance of evolution., The iteration cycle: design–build–test–learn, between experiments and analyses combining physiology, genetics, biochemistry and bioinformatics in an ever more advanced way, generates the most interesting future developments in microbial synthetic biology of industrial interest.

Process optimization in equation-oriented (EO) modeling environments favors the gradient-based optimization algorithms by their abilities to provide accurate Jacobian matrices via automatic or symbolic differentiation. However, computational inefficiencies including that in initial-point-finding for Newton type methods have significantly limited its application. Recently, progress has been made in using a pseudo-transient (PT) modeling method to address these difficulties, providing a fresh way forward in EO-based optimization. Nevertheless, research in this area remains open, and challenges need to be addressed. Therefore, understanding the state-of-the-art research on the PT method, its principle, and the strategies in composing effective methodologies using the PT modeling method is necessary for further developing EO-based methods for process optimization. For this purpose, the basic concepts for the PT modeling and the optimization framework based on the PT model are reviewed in this paper. Several typical applications, e.g., complex distillation processes, cryogenic processes, and optimizations under uncertainty, are presented as well. Finally, we identify several main challenges and give prospects for the development of the PT based optimization methods.

Data is flying around us and there is a constant flow. Data traffic, based on a predictable and controllable system, is promising with its contributions to many areas of life and the solutions it offers to social problems. With the growth of technology, the internet continues to facilitate communication channels that are an integral part of our lives. The widespread applications of algorithms using artificial intelligence (AI) and the gradual increase in the use of 'Internet of Things' (IoT) technologies are stunning examples of how the internet has become a ubiquitous part of everyday life and how it permeates our lives. While the extensive use of digital platforms benefits human rights, facilitating greater diversity of voices, greater access to information, and stronger social movements than ever before, there is also an increase in the abuse of society by malicious actors. Political microtargeting campaigns, mass spread of disinformation, foreign intervention in elections, and polarized 'echo chambers' during election periods, cyber techniques used directly or indirectly by such actors or institutions, we are subjected to destructive information bombardment. Therefore, within the framework of international human rights, there is a need for new local or international legislation to guide digital technology. However, due to the complexity of the problem, a multidimensional approach is needed to deal with cyber techniques that threaten democracy. Finding adequate solutions to disruptive cyber techniques is directly linked to maximizing accountability in the context of digital technology. The purpose of this article is; It is an overview of why regulations that will solve this problem are vital while addressing the current and possible consequences of the algorithmic accountability problem in digital platforms created by governments and various institutions. In light of these, the first part of this article will attempt to explain current cyber techniques and how digital platforms facilitate their use. Then, the adequacy of current approaches to the disruptive aspects of technology will be examined within the scope of relevant human rights laws. Finally, the article will be concluded with a multidimensional approach to ensure internet freedom and protect democracy and rights.

Centralna koherencja pozwala na integrowanie bodźców w spójną całość i przetwarzanie informacji w określonym kontekście. Jest terminem wprowadzonym w celu usystematyzowania obserwacji dotyczących funkcjonowania poznawczego osób z rozpoznaniem zaburzeń ze spektrum autyzmu (Autism Spectrum Disorder - ASD). Słaba centralna koherencja charakterystyczna dla ASD jest rozumiana jako odmienny styl poznawczy, charakteryzujący się tendencją dla lokalnego przetwarzania informacji. Pozwala to na przedstawienie centralnej koherencji jako kontinuum cechy. Pomimo wielu badań, jak dotąd nie dysponujemy ostateczną konceptualizacją powyższego konstruktu. Aktualnym jest pytanie, czy słaba centralna koherencja jest ograniczoną zdolności do integracji bodźców, czy też zwiększoną umiejętnością skupiania się na danych szczegółowych. Ważny postulat dotyczy ustalenia standardu pomiaru centralnej koherencji i jej poszczególnych wymiarów. Poza licznymi badaniami wśród osób z zaburzeniami ze spektrum autyzmu, centralną koherencję badano także w zaburzeniach odżywiania, zespole Williamsa, zespole nadpobudliwości psychoruchowej i zaburzeń koncentracji uwagi oraz specyficznych zaburzeniach rozwoju umiejętności szkolnych (dysleksji). Poniższa praca stanowi podsumowanie dotychczas przeprowadzonych badań.

With the sub-skills covered, there are many studies aimed at providing students with computational thinking skills that are known to be an important skill for today's students. In this study, it is aimed to investigate the effect of code.org applications on the development of computer thinking and algorithm development skills of the students. In this study, experimental research design with pre-test and post-test control group was used. A total of 67 middle school of 6th grade students, 32 of who were in the control group and 35 in the experimental group, participated in the study. The study was planned to cover 6 weeks of information technology and software courses with students. The course was enriched with the applications in Code.Org site for the experimental group students. The control group was treated appropriately course curriculum to their students. In the study, the scale of computer thinking skill levels and algorithm development achievement test were applied to the students as pre-test and post-test. When the data obtained in the study is examined, it is seen that there is no significant difference between the pre-test results of algorithm development achievement test and computer thinking skill levels scale. However, when the differences between pre-test and post-test scores of both tests were examined, it was seen that there was a significant difference in favor of the experimental group. As a result, it can be said that code.org applications used by experimental group students have positive effect on developing algorithms and computer thinking skills of students.

Lean maintenance concept is crucial to increase the reliability and availability of maintenance equipment in the manufacturing companies. Due the elimination of losses in maintenance processes this concept reduce the number of unplanned downtime and unexpected failures, simultaneously influence a company’s operational and economic performance. Despite the widespread use of lean maintenance, there is no structured approach to support the choice of methods and tools used for the maintenance function improvement. Therefore, in this paper by using machine learning methods and rough set theory a new approach was proposed. This approach supports the decision makers in the selection of methods and tools for the effective implementation of Lean Maintenance.

The ability to relate geophysical measurements to the material properties of the subsurface is fundamental for the successful application of geophysical methods. Estimating the electrical resistivity from material properties can be challenging at many hydrogeologic field sites, which typically lack the spatial density and resolution of the measurements needed to develop an accurate rock-physics relationship. We have developed rock-physics transforms using the machine-learning method of gradient-boosted decision trees (GBDTs). We adopt as our study area the coastal Salinas Valley, in which saltwater intrusion results in changes in resistivity. We use measurements available in boreholes, including salinity and sediment type, to predict the resistivity. In some transforms, we include as predictors in the GBDT algorithm the location of each measurement and the aquifer corresponding to each measurement. We also explore incorporating the predictions of a baseline rock-physics transform as a prior term within the objective function of the GBDT algorithm to guide the predictions made by the machine-learning algorithm. The use of location and aquifer information improves the predictions of the GBDT transform by 28% compared with when location and aquifer information are not included. After the salinity, the easting of each measurement is the most important predictor, due to the spatial pattern of salinity changes in the area. The next most important predictor is the aquifer corresponding to each measurement. The benefit of including the baseline transform in the objective function is greatest for small data sets and when the accuracy of the baseline transform is already high. Finally, using the resistivity predicted by the GBDT, we generate 1D resistivity models, which we use to simulate the acquisition of airborne electromagnetic (AEM) data. In most cases, the 1D resistivity models and the corresponding AEM data match well with the models and data corresponding to the resistivity measured in boreholes.