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The cloud is a network of servers to share computing resources to run applications and data storage that offers services in various flavours, namely, infrastructure as a service, platform as a service and software as a service. The containers in the cloud are defined as “standalone and self-contained units that package software and its dependencies together”. Similar to virtual machines, the virtualization method facilitates the resource on a specific server that could be used by numerous appliances.

This study introduces a new Dragon Levy updated squirrel algorithm (DLU-SA) for container aware application scheduling. Furthermore, the solution of optimal resource allocation is attained via defining the objective function that considers certain criteria such as “total network distance (TND), system failure (SF), balanced cluster use (BC) and threshold distance (TD)”. Eventually, the supremacy of the presented model is confirmed over existing models in terms of cost and statistical analysis.

On observing the outcomes, the total cost of an adopted model for Experimentation 1 has attained a lesser cost value, and it was 0.97%, 10.45% and 10.37% superior to traditional velocity updated grey wolf (VU-GWO), squirrel search algorithm (SSA) and dragonfly algorithm (DA) models, respectively, for mean case scenario. Especially, under best case scenario, the implemented model has revealed a minimal cost value of 761.95, whereas, the compared models such as whale random update assisted lion algorithm, VU-GWO, SSA and DA has revealed higher cost value of 761.98, 779.46, 766.62 and 766.51, respectively. Thus, the enhancement of the developed model has been validated over the existing works.

This paper proposes a new DLU-SA for container aware application scheduling. This is the first work that uses the DLU-SA model for optimal container resource allocation by taking into consideration of certain constraints such as TND, SF, BC and TD.

This paper aims to investigate the part feeding scheduling problem with electric vehicles (EVs) for automotive assembly lines. A point-to-point part feeding model has been formulated to minimize the number of EVs and the maximum handling time by specifying the EVs and sequence of all the delivery tasks.

First, a mathematical programming model of point-to-point part feeding scheduling problem (PTPPFSP) with EVs is presented. Because the PTPPFSP is NP-hard, an improved multi-objective cuckoo search (IMCS) algorithm is developed with novel search strategies, possessing the self-adaptive Levy flights, the Gaussian mutation and elite selection strategy to strengthen the algorithm’s optimization performance. In addition, two local search operators are designed for deep optimization. The effectiveness of the IMCS algorithm is verified by dealing with the PTPPFSP in different problem scales.

Numerical experiments are used to demonstrate how the IMCS algorithm serves as an efficient method to solve the PTPPFSP with EVs. The effectiveness and feasibility of the IMCS algorithm are validated by approximate Pareto fronts obtained from the instances of different problem scales. The computational results show that the IMCS algorithm can achieve better performance than the other high-performing algorithms in terms of solution quality, convergence and diversity.

This study is applicable without regard to the breakdown of EVs. The current research contributes to the scheduling of in-plant logistics for automotive assembly lines, and it could be modified to cope with similar part feeding scheduling problems characterized by just-in-time (JIT) delivery.

Both limited electricity capacity and no earliness and tardiness constraints are considered, and the scheduling problem is solved satisfactorily and innovatively for an efficient JIT part feeding with EVs applied to in-plant logistics.

Diabetic retinopathy (DR) is a central root of blindness all over the world. Though DR is tough to diagnose in starting stages, and the detection procedure might be time-consuming even for qualified experts. Nowadays, intelligent disease detection techniques are extremely acceptable for progress analysis and recognition of various diseases. Therefore, a computer-aided diagnosis scheme based on intelligent learning approaches is intended to propose for diagnosing DR effectively using a benchmark dataset.

The proposed DR diagnostic procedure involves four main steps: (1) image pre-processing, (2) blood vessel segmentation, (3) feature extraction, and (4) classification. Initially, the retinal fundus image is taken for pre-processing with the help of Contrast Limited Adaptive Histogram Equalization (CLAHE) and average filter. In the next step, the blood vessel segmentation is carried out using a segmentation process with optimized gray-level thresholding. Once the blood vessels are extracted, feature extraction is done, using Local Binary Pattern (LBP), Texture Energy Measurement (TEM based on Laws of Texture Energy), and two entropy computations – Shanon's entropy, and Kapur's entropy. These collected features are subjected to a classifier called Neural Network (NN) with an optimized training algorithm. Both the gray-level thresholding and NN is enhanced by the Modified Levy Updated-Dragonfly Algorithm (MLU-DA), which operates to maximize the segmentation accuracy and to reduce the error difference between the predicted and actual outcomes of the NN. Finally, this classification error can correctly prove the efficiency of the proposed DR detection model.

The overall accuracy of the proposed MLU-DA was 16.6% superior to conventional classifiers, and the precision of the developed MLU-DA was 22% better than LM-NN, 16.6% better than PSO-NN, GWO-NN, and DA-NN. Finally, it is concluded that the implemented MLU-DA outperformed state-of-the-art algorithms in detecting DR.

This paper adopts the latest optimization algorithm called MLU-DA-Neural Network with optimal gray-level thresholding for detecting diabetic retinopathy disease. This is the first work utilizes MLU-DA-based Neural Network for computer-aided Diabetic Retinopathy diagnosis.

Text mining has been used for various knowledge discovery based applications, and thus, a lot of research has been contributed towards it. Latest trending research in the text mining is adopting the incremental learning data, as it is economical while dealing with large volume of information.

The primary intention of this research is to design and develop a technique for incremental text categorization using optimized Support Vector Neural Network (SVNN). The proposed technique involves four major steps, such as pre-processing, feature selection, classification and feature extraction. Initially, the data is pre-processed based on stop word removal and stemming. Then, the feature extraction is done by extracting semantic word-based features and Term Frequency and Inverse Document Frequency (TF-IDF). From the extracted features, the important features are selected using Bhattacharya distance measure and the features are subjected as the input to the proposed classifier. The proposed classifier performs incremental learning using SVNN, wherein the weights are bounded in a limit using rough set theory. Moreover, for the optimal selection of weights in SVNN, Moth Search (MS) algorithm is used. Thus, the proposed classifier, named Rough set MS-SVNN, performs the text categorization for the incremental data, given as the input.

For the experimentation, the 20 News group dataset, and the Reuters dataset are used. Simulation results indicate that the proposed Rough set based MS-SVNN has achieved 0.7743, 0.7774 and 0.7745 for the precision, recall and F-measure, respectively.

In this paper, an online incremental learner is developed for the text categorization. The text categorization is done by developing the Rough set MS-SVNN classifier, which classifies the incoming texts based on the boundary condition evaluated by the Rough set theory, and the optimal weights from the MS. The proposed online text categorization scheme has the basic steps, like pre-processing, feature extraction, feature selection and classification. The pre-processing is carried out to identify the unique words from the dataset, and the features like semantic word-based features and TF-IDF are obtained from the keyword set. Feature selection is done by setting a minimum Bhattacharya distance measure, and the selected features are provided to the proposed Rough set MS-SVNN for the classification.

This paper aims to evaluate how Argentina’s Financial Information Unit (Unidad de Información Financiera, FIU) has responded after detecting noncompliance with money laundering regulations. Specifically, it identifies the main lessons that can be drawn from analyzing the sanctions that the FIU imposed between 2016 and 2019.

The issues that this article outlines suggest the need for a substantial rethinking of Argentina’s anti-money laundering regulations. Based on an analysis of the size of sanctions and the time taken to impose them, the study suggests that the regulatory framework in Argentina fails to comply with the international standards that require the imposition of effective, proportionate and dissuasive sanctions.

This analysis suggests that there are serious issues regarding the regulation of the sanctions that Argentina’s FIU is responsible for imposing. Specifically, the way the exact amount of each fine is determined urgently needs to be redesigned. In other words, the system for establishing fines needs to take the fluctuations that are typical of Argentina’s economy into account.

This paper has demonstrated that it is vital for the country to review its regulatory framework for the prevention of money laundering and to effectively apply sanctions when noncompliance is detected. A successful approach to both objectives will contribute to generate and align incentives to improve compliance levels and to fulfill international standards.

Over the past decade machine‐readable data bases have grown both in number and variety. In addition to the familiar bibliographic data bases such as MEDLINE and ERIC, one now finds data bases containing such things as properties (e.g., RTECS ‐ Registry of Toxic Effects of Chemical Substances) and full text (e.g., LEXIS, a family of files that contains the full text of court decisions, statutes, regulations, and other legal materials). As data bases increase in importance as information resources, there is a growing need for printed tools which can assist librarians in their identification and use. Available tools fall into three categories: (1) guides issued by data base producers which describe the contents of a given data base and methods of searching (e.g., INSPEC Database Users' Guide); (2) guides produced by online vendors which indicate how data bases can be searched on a particular system (e.g., Lockheed's Guide to DIALOG ‐ Databases); and (3) data base directories which include coverage of data bases produced by many different organizations and processed by a variety of online vendors. The third category is the subject of this comparative review. Readers interested in the first two categories should consult Online Reference Aids: A Directory of Manuals, Guides, and Thesauri published by the California Library Authority for Systems and Services (CLASS). This publication contains information on manuals, guides, and other search aids for over 100 online data bases, including those available through the New York Times Information Bank, National Library of Medicine (NLM), Bibliographic Retrieval Services (BRS), Lockheed DIALOG, and System Development Corporation (SDC) ORBIT. This directory is arranged by data base name, giving ordering and price information for aids available from both data base producers and online vendors. Subject and vendor indexes are also provided.

In recent years, the application of metaheuristics in training neural network models has gained significance due to the drawbacks of deterministic algorithms. This paper aims to propose the use of a recently developed “memory based hybrid dragonfly algorithm” (MHDA) for training multi-layer perceptron (MLP) model by finding the optimal set of weight and biases.

The efficiency of MHDA in training MLPs is evaluated by applying it to classification and approximation benchmark data sets. Performance comparison between MHDA and other training algorithms is carried out and the significance of results is proved by statistical methods. The computational complexity of MHDA trained MLP is estimated.

Simulation result shows that MHDA can effectively find the near optimum set of weight and biases at a higher convergence rate when compared to other training algorithms.

This paper presents MHDA as an alternative optimization algorithm for training MLP. MHDA can effectively optimize set of weight and biases and can be a potential trainer for MLPs.

Truth matters; and the norms associated with a democratic society, such as the common good, responsibility, ethics, and civic engagement, are under attack with the emergence of the post-truth society. There are concerns worldwide that public education is failing us on pushing back on disinformation. Schools are not seen as developing skills that permit students to adequately differentiate truth from nontruths. In this context, the education system also faces some unprecedented challenges. The quality of education in most of the world is low, and only slowly improving. Also, future workers are concerned with automation's threat – or perceived threat – to jobs. In most countries, education systems are not providing workers with the skills necessary to compete in today's job markets. The growing mismatch between demand and supply of skills holds back economic growth and undermines opportunity. At the same time, the financial returns to schooling are high in most countries, and growing skill premiums are evident in much of the world. Schooling remains a good economic and social investment, and there are record numbers of children in school today. The skills that matter in the coming technological revolution are likely the same as what is needed in a media environment of disinformation. More and better education, and noncognitive skills, will not only prepare students for the future world of work; they will also prepare them to navigate the increasingly complex post-truth society. They will be able to detect fake news – or deliberate disinformation spread through news or online media. It will also allow young people to gain trust. In other words, better education is democratizing, to the extent that it promotes truth, values, and civic engagement.

With the rapid development of the Chinese economy and society, the number of international schools in China has increased sharply. As a core part of school quality, the curriculum development in international schools is facing a series of challenges due to the changing requirements from both the government and the market. In order to better understand the current practices of curriculum development in these international schools in China, this study adopts Tyler’s and Gu’s curriculum theories to design a questionnaire to collect data from 104 international schools national-wide. In addition, a semi-structured interview for teachers and principals was also conducted in nine international schools in five different cities in China.

The findings show that most international schools aim at cultivating “global citizens” or “leaders and elites.” In China, most schools attach importance to foreign language teaching, and most courses are offered in English. Group work, inquiry and discussion, and project-based learning are frequently adopted in international schools. The findings also show there is a strong integration of “Chinese culture” and “global vision,” and schools generally try to balance the two aspects. Some schools rely heavily on foreign curriculum resources, and are in urgent need of capacity building in term of curriculum development based on Chinese policy, market demands and their school realities. Compared with developed countries, international schools in China endorse the new mission, mixing the requirements of modernization and globalization at the same time. Therefore, how to reconstruct a Chinese neo-modern curriculum system is the fundamental challenge for all international schools in China.

The paper adopts an organizational perspective to explore the conditions of possibility of the recent re-emergence of overt class-based discourse on one hand, epitomized by the ‘We are the 99%’ movement, and the rise on the other hand of a populist, nativist and sometimes overtly fascist right. It is argued that these phenomena, reflecting the increasingly crisis-prone character of global capitalism, the growing gap between rich and poor and a generalized sense of insecurity, are rooted in the dismantling of socially embedded organizations through processes often described as ‘financialization’, driven by the taken-for-granted dominance of neoliberal ideology. The paper explores the rise to dominance of the neoliberal ‘thought style’ and its inherent logic in underpinning the dismantling and restructuring of capitalist organization. Its focus is upon transnational value chain capitalism which has rebalanced power relations in favour of a small elite that is able to operate and realize wealth in ways that defy and often succeed in escaping the regulation of nation states.