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The purpose of this paper is to present a comparison between two well-known Lévy-based meta-heuristics called cuckoo search (CS) and flower pollination algorithm (FPA).

Both the algorithms (Lévy-based meta-heuristics called CS and Flower Pollination) are tested on selected benchmarks from CEC 2017. In addition, this study discussed all CS and FPA comparisons that were included implicitly in other works.

The experimental results show that CS is superior in global convergence to the optimal solution, while FPA outperforms CS in terms of time complexity.

This paper compares the working flow and significance of FPA and CS which seems to have many similarities in order to help the researchers deeply understand the differences between both algorithms. The experimental results are clearly shown to solve the global optimization problem.

Currently, more renewable energy resources with advanced technology levels are incorporated in the electric power networks. Under this circumstance, the attainment of optimal economic dispatch is very much essential by the power system as the system requires more power generation cost and also has a great demand for electrical energy. Therefore, one of the primary difficulties in the power system is lowering the cost of power generation, which includes both economic and environmental costs. This study/paper aims to introduce a meta-heuristic algorithm, which offers an solution to the combined economic and emission dispatch (CEED).

A novel algorithm termed Levy-based glowworm swarm optimization (LGSO) is proposed in this work, and it provides an excellent solution to the combined economic and emission dispatch (CEED) difficulties by specifying the generation of the optimal renewable energy systems (RES). Moreover, in hybrid renewable energy systems, the proposed scheme is extended by connecting the wind turbine because the thermal power plant could not control the aforementioned costs. In terms of economic cost, emission cost and transmission loss, the suggested CEED model outperforms other conventional schemes genetic algorithm, Grey wolf optimization, whale optimization algorithm (WOA), dragonfly algorithm (DA) and glowworm swarm optimization (GSO) and demonstrates its efficiency.

According to the results, the suggested model for Iteration 20 was outperformed GSO, DA and WOA by 23.46%, 97.33% and 93.33%, respectively. For Iteration 40, the proposed LGSO was 60%, 99.73% and 97.06% better than GSO, DA and WOA methods, respectively. The proposed model for Iteration 60 was 71.50% better than GSO, 96.56% better than DA and 95.25% better than WOA. As a result, the proposed LGSO was shown to be superior to other existing techniques with respect to the least cost and loss.

This research introduces the latest optimization algorithm known as LGSO to provide an excellent solution to the CEED difficulties by specifying the generation of the optimal RES. To the best of the authors’ knowledge, this is the first work that utilizes LGSO-based optimization for providing an excellent solution to the CEED difficulties by specifying the generation of the optimal RES.

It should be noted that the (inland waterway transport) IWT in Ukraine currently is in its infancy in comparison with other land based transport means (rail and road) and with other countries that possess navigable rivers. This paper is an extension of the research initiated by Grushevska and Notteboom (2015) where the concepts of intermediacy and centrality were introduced in order to assess the role of Ukraine in the global and regional transport networks. The list of key obstacles for Ukraine’s intermediacy function included IWT related barriers such as: (i) deficient inland waterway infrastructure, (ii) high IWT costs (fees for bridges, locks etc.) and (iii) pilotage charges. To date the transportation to/from ports is mainly fulfilled by road or by rail based multimodal transport solutions. We present the unutilized potential of Ukrainian IWT that needs to be efficiently exploited for the benefit of the national economy and national transport system. This study intends to enrich the limited academic research on IWT systems in a transition stage, as exemplified by the case of Ukraine.

Introduction: The main feature of modern society today is the digital way of life combined with digitalisation. It affects every aspect of life. Today’s main drivers of change are modern electronic communications and high-speed internet connections or services created by digitalisation. Thus, digitalisation stimulates economic growth by creating new jobs, especially in the information and communication sector, increasing the savings of individuals and public and private companies, increasing productivity and providing new opportunities for personal development and motivation. Therefore, the digital economy poses significant challenges to the EU tax system. The identified tax challenges are nexus, data and characterisation, standard tax avoidance practices and direct and indirect tax risks. To address these challenges, the concept of Tax Administration 3.0 should be implemented. Under this concept, digital tools will make tax administration better organised and more efficient, both in combating abuse and improving the quality of tax reporting and compliance.

Aim: This study aims to present the digital economy’s direct and indirect tax challenges from an EU perspective. In addition, it will also present the current situation of the digital levy proposed by the European Commission and the level of Europe’s digital performance among EU member states.

Method: Following the Digital Economy and Society Index (DESI) methodology, Europe’s digital performance varied widely among EU member states between 2015 and 2020.

Findings: The results show that the digital sector is highly involved in tax planning practices and significantly affects Europe’s digital performance. Moreover, there is no consensus on implementing the digital levy in EU member states since only a few of the EU member states have implemented the digital levy. Based on the findings, the author proposes introducing the digital levy in all EU member states to restore and maintain the sustainability of public finance, especially after the crisis COVID-19.

Virtual machines (VMs) are suggested by the providers of cloud services as the services for the users over the internet. The consolidation of VM is the tactic of the competent and smart utilization of resources from cloud data centers. Placement of a VM is one of the significant issues in cloud computing (CC). Physical machines in a cloud environment are aware of the way of the VM placement (VMP) as the mapping VMs. The basic target of placement of VM issue is to reduce the physical machines' items that are running or the hosts in cloud data centers. The VMP methods have an important role in the CC. However, there is no systematic and complete way to discuss and analyze the algorithms. The purpose of this paper is to present a systematic survey of VMP techniques. Also, the benefits and weaknesses connected with selected VMP techniques have been debated, and the significant issues of these techniques are addressed to develop the more efficient VMP technique for the future.

Because of the importance of VMP in the cloud environments, in this paper, the articles and important mechanisms in this domain have been investigated systematically. The VMP mechanisms have been categorized into two major groups, including static and dynamic mechanisms.

The results have indicated that an appropriate VMP has the capacity to decrease the resource consumption rate, energy consumption and carbon emission rate. VMP approaches in computing environment still need improvements in terms of reducing related overhead, consolidation of the cloud environment to become an extremely on-demand mechanism, balancing the load between physical machines, power consumption and refining performance.

This study aimed to be comprehensive, but there were some limitations. Some perfect work may be eliminated because of applying some filters to choose the original articles. Surveying all the papers on the topic of VMP is impossible, too. Nevertheless, the authors are trying to present a complete survey over the VMP.

The consequences of this research will be valuable for academicians, and it can provide good ideas for future research in this domain. By providing comparative information and analyzing the contemporary developments in this area, this research will directly support academics and working professionals for better knowing the growth in the VMP area.

The gathered information in this paper helps to inform the researchers with the state of the art in the VMP area. Totally, the VMP's principal intention, current challenges, open issues, strategies and mechanisms in cloud systems are summarized by explaining the answers.

This paper aims to examine the indirect tax reform process in China. Specifically, it examines the reform of business tax to value-added tax. Inefficiencies within the new tax system are identified and discussed. The “business tax to value-added tax” reform was seen as an essential element in promoting the economic transition and stimulating the service industries (Jin and Jin, 2013).

The paper uses archival and current literature. In undertaking the study, the different periods of indirect tax are examined, prior to 1994, 1994-2012, the changes from 2012 culminating in the new 2017 regime. Attributes of “good” value-added tax (VAT) systems are covered as well as a comparison with New Zealand’s goods and services tax (GST).

The paper finds that to align with the international trend of indirect tax development and more efficiently accomplish the economic transition China needs to build a more neutral VAT system with fewer reduced rates and exemptions and the tax system have created tax inefficiencies and increased the compliance cost. VAT is imposing an increasingly significant impact on China’s national economy and industrial structure as well as accountants.

This is the first study that analyses the indirect tax reforms that are currently being implemented in China and as such has lessons for China but also for VAT/GST in general. We should not forget how special New Zealand’s GST is and the clarity of focus of those who implemented it!

FT unveils its shot at the end‐user market. Online/CD‐ROM Information 94 saw a host of new products and it is difficult to pick out the main highlights. For me, however, there was no contest for the slickest and most user‐friendly product unveiled: FT Discovery. This is a point‐and‐click online service offering end‐users a combination of news, company and country information, along‐side graphing tools. A launch is pencilled in for the middle of the year.

In 2021, nonfungible token (NFT) has emerged and grown as a new digital asset and became a carrier for cryptocurrency holders in China. NFT opens the door of the digital world for creators’ rights and the realization of economic interests. However, potential problems such as money laundering, terrorist financing and tax avoidance risks have increased in China due to the lack of regulations. As tax control is an important tool used by the government to adjust the economy and market, this study aims to investigate the future market capitalization of NFT and provide value orientations to control the NFT market in China with a tax control approach based on the positive experience of other countries.

In this study, least squares and expert estimation are applied to predict the future market capitalization based on the global market, which can provide an understanding of the current NFT market and the significance of its tax control. In addition, the tax control and interpretation of Chinese taxation institutions and structures are also explored.

Results include the probable tax structure or policy that national institutions can carry out over different transactions. Conclusions show that introducing tax control to regulate and monitor the rise of state revenue and decline of illegal financing activities. Establishing tax control in the Chinese NFT market can provide a centralized guarantee to ensure the safety and legality of transactions and enable further progress.

This study puts forward new ideas on the future development of nonprofitable tokens based on blockchain technology from the perspective of taxation in China.

Training is an important component of successful business concerns. However, although there is growing acceptance amongst scholars that small- and medium-sized enterprises (SMEs) are engines that drive economies across nations, through their contribution in terms of job creation and poverty reduction; extant research portray that these organisations often lack the resources required to undertake training. Mauritius, as a small island developing state, is not an exception. Similar to other economies, the Mauritian business landscape is characterised by a larger number of SMEs, representing 97 per cent of the business stock and accounting for nearly 47 per cent of the national workforce. To ensure a smooth transition along the business life cycle and fulfil their objectives, it becomes important to gauge into the training practices of these SMEs. This paper therefore aims to investigate into the barriers which SMEs face in the provision of training to their employees in the Republic of Mauritius.

Through a survey questionnaire, the study attempted, inter alia, to mainly identify the importance and perception that owner managers of SMEs attach to the concept of training and, consequently, identifies the various barriers faced by the SMEs to impart training.

Data obtained were analysed using SPSS 20.0 through descriptive statistics and inferential statistics. The analysis of the findings showed that training practices among SMEs in Mauritius rhymes with extant general literature.

Research pertaining to SMEs is still in a state of infancy in the Republic of Mauritius, without mentioning that it is practically non-existent as far as the training function is concerned. This paper thus attempts to provide both policymakers and researchers’ scientific data regarding the barriers which SMEs face when indulging in training.

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.