Group Of Examples Research Articles

On Q^*-closed Sets in Fuzzy Neutrosophic Τopology: Principles, Proofs, and Examples

This paper aims to present a new concept of sets known as fuzzy neutrosophic Q^*-closed sets in fuzzy neutrosophic topology. In this study, we explore and investigate more novel properties of these classes by some new definitions, theorems, and propositions. Therefore, a group of examples is presented and discussed to clarify the relationships between the new study of Q^*-closed sets with other sets.

A Reconfigurable Single-/Dual-Bandstop Filter with Controllable Equal-Ripple Performance

Bandstop filters (BSFs) have many applications in suppressing interference signals and shielding specific bands. Among them, reconfigurable BSFs that cover more frequency bands by switching modes have great research value. In this paper, a novel synthesis approach for compact reconfigurable BSFs with controllable equal-ripple performance is proposed. By switching the ON/OFF state of the positive intrinsic negative (PIN) diode, the proposed reconfigurable BSF can be switched between single-BSF (S-BSF) and dual-BSF (D-BSF). Based on the synthesis approach, the equal-ripple levels of stopband and passband for S-BSF and D-BSF can be independently controlled. To verify, the equal-ripple levels of stopband and passband for S-BSF state and D-BSF state are independently controlled in four groups of examples. Finally, a reconfigurable BSF with a miniaturized size is designed and fabricated. The fabricated reconfigurable BSF behaves with 15 dB and 10 dB return loss (RL) in two different passbands of the D-BSF state, and 25 dB bandpass RL and controllable stopband rejection (SR) for the S-BSF state. The measured results are in good agreement with the electromagnetic (EM) simulation results.

Synthesis of New Structural Analogues of Natural Integrastatins with a Basic Epoxybenzo[7,8]oxocine Skeleton: Combined Experimental and Computational Study

AbstractIn this work, the cyclization reactivity of various 3-acetyl-2-methylpyridines (including 3-acetyl-2-methylquinoline) containing both electron donor and acceptor substituents with salicylaldehyde into epoxybenzooxocino[4,3-b]pyridine derivatives was studied. The reactions were carried out in mild (under room temperature or reflux in 2-propanol) and harsh (in a sealed glass ampoule) conditions. It was shown that 3-acetyl-2-methylpyridines with an aryl substituent in the 4-position do not react with salicylaldehyde either under normal convection heating conditions or under more severe conditions. This effect was explained by the steric hindrance of the substituents using quantum chemical calculations. It was found that electron donor substituents in 3-acetyl-2-methylpyridines significantly facilitate cyclization in epoxybenzooxocino[4,3-b]pyridines. The presence of electron acceptor substituents (NO2 group for example) in the 5-position of pyridine prevents cyclization under normal conditions, but gives a rather high conversion to oxocinopyridines under more specific conditions. This effect is quantum-chemically explained by the decrease in the basicity of pyridine. Pyridines with two pairs of methyl groups in ortho-positions to the acetyl group are capable to form mixtures of regioisomeric epoxybenzooxocinopyridines. Further, epoxybenzooxocinopyridines with methyl and acetyl groups can form a mixture of diastereomeric bisoxocins under more specific conditions. All 17 initial pyridines were studied quantum-chemically in order to understand what features of their structure and properties affect the success of the cyclization reaction and the yield of the target product. The pyridine molecules were calculated by the DFT RB3LYP/6-311++G(d,p) method taking into account the alcohol solvent within the CPCM model using Gaussian-2016 program. It was shown that the absence of steric hindrances in the form of bulky substituents in 4-position of pyridines is the main factor affecting the success of the cyclization reaction. Also, the yield of the target product is affected by the CH-acidity of the methyl group in 2-position, which, in turn, is affected by electron-donating and electron-withdrawing substituents in the 5- and 6-positions.

A biogeography-based optimization algorithm with modified migration operator for large-scale distributed scheduling with transportation time

To expand production scale and enhance enterprise competitiveness, modern supply chains and manufacturing systems have shifted from the single factory production to the multi-factory production network with the characteristics of large scale, diversification of varieties, redundant production factories, flexible production processes, and high-value products. This production mode is called distributed scheduling. The transportation time caused by geographically distributed factories in Distributed Scheduling Problem (DSP) with a series-parallel network plays an important role in the scheduling scheme. To minimize the global makespan over all factories, a two-segment representation has been taken as an encoding scheme while the decoding procedure applies the First Come First Served (FCFS) heuristic rule. A Biogeography-Based Optimization with Modified Migration Operator (BBO-MMO) is then proposed to address the heterogeneous DSP with transportation time between factories at adjacent processing stages, which improves the migration operator by modifying the immigration rate according to processing time and transportation time. A cosine migration model has been adopted to enhance the ability of BBO-MMO to break through local optimum. BBO-MMO is compared with the Improved Memetic Algorithm (IMA), and several groups of examples with various parameters have been generated to test the performance of BBO-MMO. The results show that the proposed BBO-MMO can effectively solve the large-scale heterogeneous DSP with transportation time.

Resolviendo ecuaciones diferenciales ordinarias con Symbolic Math Toolbox™ (Matlab) y SymPy (Python)

This paper shows solutions of ordinary differential equations (EDOS) obtained by using two symbolic packages: Symbolic Math Toolbox™ (Matlab) and SymPy (Python). The basic instructions to obtain solutions of both packages are explained step by step, through a group of examples from a traditional ordinary differential equations course. Differential equations that are solved with methods such as: separable variables, linear equations, indeterminate coefficients, variation of parameters, power series, Laplace transform, and numerical solutions are included. By means of the symbolic computation carried out with these packages it is possible to obtain the solution of linear systems, as well as the visualization of the direction field of a differential equation or of a non-linear system of differential equations. The main contribution of this work consists in providing the reader with a practical guide that allows him to start the study of differential equations assisted by Symbolic Math Toolbox™ or SymPy. Among the benefits of using these computational tools in teaching and/or learning practices, it is shown how the use of symbolic or numerical computation saves us effort in the computation of tedious calculations; focusing attention on important ideas and concepts such as: the relationship between the mathematical model and its physical counterpart, asymptotic behavior and qualitative analysis of the solutions.

Cyclic Convolutional Neural Network Model Based on Artificial Intelligence

Abstract This paper mainly discusses the internal correlation between meshless discrete data and learning samples, meshless dynamic analysis recursive operation and information transmission mode in cyclic convolutional neural networks. This paper establishes a cyclic convolutional neural network based on the meshless method. This paper demonstrates an agent model of cyclic convolutional neural network based on dynamic characteristics. This method combines the advantages of the flexible configuration of meshless nodes in the discrete model. The universality and adaptability of cyclic convolutional neural networks are improved. In addition, because of the unique historical memory characteristics of the periodic module, it can analyze continuous data efficiently. The solution of dynamic analysis is accelerated without affecting the calculation accuracy. Based on a group of examples, the accuracy and effectiveness of this method are studied experimentally.

Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics.

Supervised machine learning classification is the most common example of artificial intelligence (AI) in industry and in academic research. These technologies predict whether a series of measurements belong to one of multiple groups of examples on which the machine was previously trained. Prior to real-world deployment, all implementations need to be carefully evaluated with hold-out validation, where the algorithm is tested on different samples than it was provided for training, in order to ensure the generalizability and reliability of AI models. However, established methods for performing hold-out validation do not assess the consistency of the mistakes that the AI model makes during hold-out validation. Here, we show that in addition to standard methods, an enhanced technique for performing hold-out validation-that also assesses the consistency of the sample-wise mistakes made by the learning algorithm-can assist in the evaluation and design of reliable and predictable AI models. The technique can be applied to the validation of any supervised learning classification application, and we demonstrate the use of the technique on a variety of example biomedical diagnostic applications, which help illustrate the importance of producing reliable AI models. The validation software created is made publicly available, assisting anyone developing AI models for any supervised classification application in the creation of more reliable and predictable technologies.

Interactions of scores derived from two groups of variables: Alternating lasso regularization avoids overfitting and finds interpretable scores.

Regression models with interaction terms are common models for moderating relationships. When effects of several predictors from one group-for example, genetic variables-are potentially moderated by several predictors from another-for example, environmental variables-many interaction terms result. This complicates model interpretation, especially when coefficient signs point in different directions. By first forming a score for each group of predictors, the interaction model's dimension is severely reduced. The hierarchical score model is an elegant one-step approach: Score weights and regression model coefficients are estimated simultaneously by an alternating optimization (AO) algorithm. Especially in high dimensional settings, scores remain an effective technique to reduce interaction model dimension, and we propose regularization to ensure sparsity and interpretability of the score weights. A nontrivial extension of the original AO algorithm is presented, which adds a lasso penalty, resulting in the alternating lasso optimization algorithm (ALOA). The hierarchical score model with ALOA is an interpretable statistical learning technique for moderation in potentially high dimensional applications, and encompasses generalized linear models for the main interaction model. In addition to the lasso regularization, a screening procedure called regularization and residualization (RR) is proposed to avoid spurious interactions. ALOA tuning parameter choice and the RR screening procedure are investigated by simulations, and two illustrative applications to depression risk are provided. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Reconfigurable multiple beams forming method based on liquid crystal on silicon

With the purpose of meeting the increasing requirement of free-space optical networking, reconfigurable multiple beams are required from a single terminal. In this paper, we demonstrate the role of co-modulation of amplitude and phase on incident laser beam using such a liquid crystal spatial light modulator. It enables arbitrary beam number, arbitrary pointing angle, and power occupation with multiple beams of excellent performance on a relative error and standard derivation when using typical groups of examples. Both numerical simulations and experimental results are presented demonstrating reconfigurable multiple beams forming via liquid crystal on silicon.

Autoencoder-Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm

This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become increasingly important due to growing availability in many industrial fields. Labeling, sorting or filtering highly transient measurement data for training Condition-based Maintenance (CbM) models is cumbersome and error-prone. For some applications it can be sufficient to filter measurements by simple thresholds or finding change-points based on changes in mean value and variation. But a robust diagnosis of a component within a component group for example, which has a complex non-linear correlation between multiple sensor values, a simple approach would not be feasible. No meaningful and coherent measurement data, which could be used for training a CbM model, would emerge. Therefore, we introduce an algorithm that uses a recurrent neural network (RNN) based Autoencoder (AE) which is iteratively trained on incoming data. The scoring function uses the reconstruction error and latent space information. A model of the identified subsequence is saved and used for recognition of repeating subsequences as well as fast offline clustering. For evaluation, we propose a new similarity measure based on the curvature for a more intuitive time-series subsequence clustering metric. A comparison with seven other state-of-the-art algorithms and eight datasets shows the capability and the increased performance of our algorithm to cluster MTSD online and offline in conjunction with mechatronic systems.

Pipeline element for upheaval buckling analysis of submarine pipelines with geometric-imperfections under high temperature high pressure

Submarine pipeline is used for transporting oil from wells to resource storage facilities. The oil inside the pipeline is heated and pressured for long-distance transportation, making the pipeline under high temperature and high pressure (HTHP). The pipeline is usually placed inside a trench where surrounding soil restraints its longitudinal expansion. The pipeline is not perfectly straight, but with geometric imperfections, so the upheaval buckling may be triggered when the accumulated compression force has reached its buckling strength. It is not easy to conduct a geometrically nonlinear analysis for the pipeline because of the nonlinear soil–pipeline interactions (SPIs). This paper proposes a new line element aiming for the geometrically nonlinear large deformation analysis of the pipeline under HTHP. The axial expansion force due to high working temperature and pressure is included in the element formulation. Furthermore, a Gauss–Legendre integral method is employed to consider continuously distributed nonlinear SPIs in both lateral and longitudinal directions. Since the pipeline might exhibit large deflections, the analysis adopts the Updated-Lagrangian approach to establish the equilibrium conditions. A Newton–Raphson procedure is developed to execute the analysis. Detailed element formulations are given. Six groups of examples are provided to examine the robustness of the numerical method.

Model architecture can transform catastrophic forgetting into positive transfer

The work of McCloskey and Cohen popularized the concept of catastrophic interference. They used a neural network that tried to learn addition using two groups of examples as two different tasks. In their case, learning the second task rapidly deteriorated the acquired knowledge about the previous one. We hypothesize that this could be a symptom of a fundamental problem: addition is an algorithmic task that should not be learned through pattern recognition. Therefore, other model architectures better suited for this task would avoid catastrophic forgetting. We use a neural network with a different architecture that can be trained to recover the correct algorithm for the addition of binary numbers. This neural network includes conditional clauses that are naturally treated within the back-propagation algorithm. We test it in the setting proposed by McCloskey and Cohen and training on random additions one by one. The neural network not only does not suffer from catastrophic forgetting but it improves its predictive power on unseen pairs of numbers as training progresses. We also show that this is a robust effect, also present when averaging many simulations. This work emphasizes the importance that neural network architecture has for the emergence of catastrophic forgetting and introduces a neural network that is able to learn an algorithm.

Research on Multi-Center Mixed Fleet Distribution Path Considering Dynamic Energy Consumption Integrated Reverse Logistics

The fleet operation model in which electric vehicles coexist with traditional vehicles is becoming increasingly popular. Because electric vehicles have certain disadvantages and usage limitations, the multi-center management of the distribution of mixed fleets is very complex. There is no research on the multi-center mixed vehicle routing problem based on the integration of reverse logistics and dynamic energy consumption. In response to this challenge, this study proposes a solution to the multi-center mixed vehicle routing problem considering dynamic energy consumption and integrated reverse logistics. Specifically, three studies were carried out: (1) Considering the influencing factors of the operating cost system of the mixed fleet, a system dynamics model was constructed. (2) On the basis of considering delaying the aging of electric vehicle batteries, a new charging station insertion strategy was designed. (3) Based on a novel charging station insertion strategy, a fast non-dominated sorting multi-objective genetic algorithm with an elite strategy was designed to solve this problem. We designed 15 groups of examples to prove the effectiveness of the model and algorithm. The experimental results show that 46.67% of the cases have more than 60% customer satisfaction. The average expenditure cost of 15 groups of cases is CNY 2018.33, which can improve the average customer satisfaction by 22.94%. This method helps companies to formulate transportation plans according to the actual situation, including providing a cost model that considers multiple influencing factors and improving the average customer satisfaction while reducing the total cost expenditure. We believe that the results of this research can provide methods and ideas for logistics companies with multiple distribution centers to formulate large-scale distribution plans.

Peer Navigators for the Health Needs of People of Color With Serious Mental Illness.

People with serious mental illness die up to 20 years younger than others in the same age group, with people of color showing even earlier death. The social disadvantages experienced by this group-for example, poverty, criminal legal involvement, and immigration-are further determinants of this disparity. Peer health navigators (PHNs) are people with experiences of recovery who may share a racial-ethnic background, history of poverty, experience with criminal legal systems, or immigration status with current patients and who assist in practical and timely tasks that help people engage with fragmented health systems. This column describes the PHN practice, including the putative components that define it.

Histopathological Evaluation of Ficus Capensis Aqueous Extract on Liver, Kidney, Intestine and its Potentials in Treatment of Anaemia

Ficus capensi is a nature of Nigerian multi ethnic group and it is been identified and called differently dy ethnic groups for example, igbo call it akporo, hausa -uwaryara,etc. The aim of the this research titlled; Histopathologica evaluation of ficus capensis ethanol extract on liver, kidney, intestine and it potentials in the treatment of anaemia is to evaluate the effect of leaf and stem back extract of ficus capensis in the treatment potential of anaemia and it’s histopathological effect on liver, kidney, and large intestine of wister rat. The animals were grouped into 5(A, B, C, D & E) and anaemia induced with cirfuroxime in all the groups except group E which is the negative control whereas other groups were treated with vitamins and leaf & stem bark of Ficuscapensis ethanol extract; group C wasn’t treated and it’s regarded as positive control. The blood sample were collected suing cardiac puncture and liver,kidney and large intestine were all harvested following dissection upon 14 days treatment and experimental termination. All the samples collected were analysed using standard methods. There was significant difference in body weight, same was observed in multiple comparism test on packed cell volume and haemoglobin after treatment with Vitamins, leaf and stem bark of Ficus capensis ethanol extract. It was also observed that there was relative distortion of large intestinal lumen, kidney and hepatocytes. Ficusis capensis leaf and bark has shown antianaemic potentials, however; its toxic effect on the selected organs (liver, Kidney and large intestine) called for caution among users.

Extension of the kinematic approximations to the multilayered elastic orthorhombic medium

Understanding the kinematics of horizontally layered reservoir rocks is important to their proper characterization; to accomplish this, it is necessary to specify the explicit model for these kinematic properties. Accurate approximations for traveltime and relative geometric spreading in an elastic homogeneous orthorhombic (ORT) model have been investigated with different forms: shifted hyperbola form (SHF), Taylor series, and the rational form. We have extended these approximations to the multilayered ORT model by adopting composite coefficients and effective model parameters. The multilayered model is characterized without and with the azimuthal variation among layers. There is an overdetermined problem when the azimuthal variation exists; to address that case, the least-squares method is adopted. To check the feasibility of the expansion, we select the SHF approximation specified in the homogeneous elastic ORT model for the calculation in the numerical example. Four groups of examples are analyzed to investigate the influence on the accuracy of the approximation with the change in rotation angle, degree of anisotropy, and the direction of the orientation. The results indicate that, for the multilayer, the accuracy of the approximation is proportional to the degree of anisotropy and the value of the angle of rotation. The relative errors in traveltime and relative geometric spreading in this multilayered extension are very small and can be implemented in practical applications.

Exploiting Redundancy in Direct Synthesis for Inline Filters

The generalized Chebyshev (pseudoelliptical) filter functions may not be optimal for certain applications due to physical constrains. With redundancy in filter polynomials, this article breaks down the “mental barrier” in conventional synthesis and directly synthesizes the “Chebyshev-like” functions to achieve desired filter performance under physical limitations. By exploiting the redundant restrictions on exact equal-ripple response, the reflection zeros can be moved to the complex plane, thus forming the “Chebyshev-like functions.” A class of inline filters with a block of second-order dangling resonators is synthesized, which can generate a pair of symmetric transmission zeros (Tzs) but is considered unrealizable using standard Chebyshev functions. With the proposed theories, an accurate and much improved filter prototype is directly derived from the “Chebyshev-like” polynomials, avoiding additional optimization in the design process. For verification, a group of examples with synthesis results are elucidated. Eight ten-pole filters with four Tzs are synthesized and fabricated. The simulated and measured results demonstrate the effectiveness and application of the proposed theories.

Web-Based Collaborative Learning: A proposed Learning Platform for the School of ICT, Auchi Polytechnic

Technological advancements in the World Wide Web continue to gain widespread adoption, with various kinds of applications finding their use in education, including those for 'digital collaboration'. The term has become widely known for its extension of our traditional means of distributed learning and workflow. In present-day Nigeria, collaboration between students and educators in most tertiary institutions still involves the most basic use of instant messaging tools provided via online chat forums, google groups, WhatsApp, for knowledge sharing over the internet. However, these tools come with their limitations. Take 'Yahoo' and 'google' groups for example; yahoo groups are meant solely for students possessing yahoo mail accounts, and the same applies for google groups. Overall, the user-access convention on these platforms makes it difficult for students to enjoy any cross-platform privileges in the absence of email ownership on the particular service domain. Owing to this, our research project sought to bridge this identified gap, with a pilot project in the School of ICT, Federal Polytechnic, Auchi. We proposed the development and implementation of this unified platform to meet the salient and underlying collaborative need within our learning community. The system (PolyCollab) is a cross-platform web application, hosted on an online server and designed using MVC model architecture. The framework was developed using PHP, CSS3, HTML5, and MySQL for the database. The system was subjected to evaluation by potential users within the institution, and on the whole met with predefined user-specific requirements. Keywords: W eb-Based Learning, Collaborative Learning, Distributed Learning Environment DOI: 10.7176/CEIS/12-2-05 Publication date: April 30 th 2021

Customer Review on Confidence, Readiness and Security in Electronic Commerce

Nowadays, the world of technologies is evolving. We used to do business within big companies and now, there is e-commerce. The revolution of e-commerce is only just getting started. Over the next few years, e-Commerce is predicted to be widely accessible. E-commerce would have a significant effect on the formation of a truly mass-market phenomenon. It will also have a profound impact on the way trade is done by each person and company. In addition, e-commerce is responsible for implementing digital interactive TV that will bring further e-commerce beyond the realms of PC literacy. The fact that e-commerce is a future way of business really change the landscape of business. The proliferation of e-commerce companies saw the rise of the worldwide number of online companies. Consumers are now opted for business online, which in contrast to conventional transactions is dramatically convenient. E-commerce firms have, in any case, more advantages compared to conventional brick and mortal companies, but the difficulties and problems facing e-commerce businesses cannot be missed. This study illustrated the difficulties and problems of e-commerce and a range of solutions. Trust, readiness and protection are among the problems. Proposed solutions are proposed for each of the problems and challenges. Alibaba Group for example also face this common challenges. Alibaba Group being chosen in this case study as Alibaba was exceptionally skilled in establishing credibility. Despite its revealed vulnerability in intellectual property rights, Alibaba keep on continued give efforts to create credibility and will help to facilitate the evolution of e-commerce platform. Such attempts have made Alibaba is a standard model for e-business but overall, the challenges in this study are collected among all e-commerce platform, not only Alibaba Group.

Optimal design of accelerated degradation test with multiple optimization objectives

ABSTRACT In this article, a multi-objective optimization method for the accelerated degradation test is proposed to solve the problem that the single objective optimization cannot meet the needs in practice gradually. Firstly, the Gamma process is assumed to describe the degradation path of product. Then, three optimization objectives are considered to establish the multi-objective optimization model based on the analytic hierarchy process, under the constraint that the total experimental cost does not exceed a predetermined budget. To solve the established optimization model, the self-adaption searching algorithm is proposed to search the optimal test plan, in which the searching interval of the decision variable is dynamically adjusted according to the feedback of the optimization function. Finally, the effectiveness of the proposed method is illustrated by a group of examples of motorized spindles.