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Sorting is a very important algorithm to solve problems in computer science. The most well-known divide and conquer sorting algorithm is quicksort. It starts with dividing the data into subarrays and finally sorting them.

In this paper, the algorithm named Dual Parallel Partition Sorting (DPPSort) is analyzed and optimized. It consists of a partitioning algorithm named Dual Parallel Partition (DPPartition). The DPPartition is analyzed and optimized in this paper and sorted with standard sorting functions named qsort and STLSort which are quicksort, and introsort algorithms, respectively. This algorithm is run on any shared memory/multicore systems. OpenMP library which supports multiprocessing programming is developed to be compatible with C/C++ standard library function. The authors’ algorithm recursively divides an unsorted array into two halves equally in parallel with Lomuto's partitioning and merge without compare-and-swap instructions. Then, qsort/STLSort is executed in parallel while the subarray is smaller than the sorting cutoff.

In the authors’ experiments, the 4-core Intel i7-6770 with Ubuntu Linux system is implemented. DPPSort is faster than qsort and STLSort up to 6.82× and 5.88× on Uint64 random distributions, respectively.

The authors can improve the performance of the parallel sorting algorithm by reducing the compare-and-swap instructions in the algorithm. This concept can be used to develop related problems to increase speedup of algorithms.

This paper aims to assess the comparative position of the national innovation system of Chinese state entrepreneurship versus liberal market entrepreneurship. Based on the comparative institutional framework, it asks whether Chinese state entrepreneurship has a comparative disadvantage because of its incoherent institutions in liberal or coordinated economies. Hence, does the Chinese institutional system of innovation lag behind that of US or liberal countries of Organisation for Economic Co-operation and Development (OECD) economies in the transformation of national science into economic products measured as high-technology exports?

This study uses panel data analysis based on 29 OECD economies and the Chinese economy over 23 years. Regarding national science productivity (explorative capabilities), it includes published and patented science streams; regarding technological transformation (exploitative capabilities), it measures the percentage of high-technology exports in gross domestic product (GDP). The interactions between the types of entrepreneurship and national science institutions serve as predictors in the design.

The results show that Chinese state entrepreneurship has a comparative advantage over liberal economies in published science. However, Chinese state entrepreneurship has a comparative disadvantage compared to liberal entrepreneurship in patent science. Regarding the dyadic level of comparability between the national economies, there are mixed results in the transformation of national science.

This study supports the three following theoretical points: national institutions differ regardless of the pressure of convergence through globalization; national science contingencies influence different paths of the transformation of national science to technology; and mixed economies, such as state entrepreneurship, can achieve high performance without fully conforming to liberal markets.

This study emphasizes institutional mechanisms for future research to support the innovation of incoherent institutions and suggests the benefit of cross-pollination of senior managers between state and private organizations for a defined duration.

Theoretically, this research combines an interdisciplinary and interinstitutional level of analysis, and in so doing, it deals with the transformation of national science in scientific publications and patents in the vertical value chain. Empirically, this study links the national published and patented science with the national economic artifacts in high-technology sectors. This novel approach to assess the national and discipline-level interaction sets a context for the future research in other settings. It also informs policy decisions regarding the growth of science, innovation and development.

The most prominent and persistent problems of our global monetary system are instability and imbalances. We propose an international monetary model to solve these problems while at the same time move the model closer to Maqāṣid Sharīʿah (objectives of Sharīʿah). We name this an organic global monetary model or abbreviated as OGM. OGM is an international monetary model directly built on the national monetary system of each member country so that the two can co-exist.

Model design, theory and literature.

The model can eliminate interest rates at the central bank level, create non-tradable international money, and make a more stable international monetary system.

Original.

The purpose of this paper is to illuminate the ethical concerns associated with the use of artificial intelligence (AI) in the medical sector and to provide solutions that allow deriving maximum benefits from this technology without compromising ethical principles.

This paper provides a comprehensive overview of AI in medicine, exploring its technical capabilities, practical applications, and ethical implications. Based on our expertise, we offer insights from both technical and practical perspectives.

The study identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery. However, there are pending ethical issues such as algorithmic bias, lack of transparency, data privacy issues, and the potential for AI to deskill healthcare professionals and erode humanistic values in patient care. Therefore, it is important to address these issues as promptly as possible to make sure that we benefit from the AI’s implementation without causing any serious drawbacks.

This paper gains its value from the combined practical experience of Professor Elhassan gained through his practice at top hospitals worldwide, and the theoretical expertise of Dr. Arabi acquired from international institutes. The shared experiences of the authors provide valuable insights that are beneficial for raising awareness and guiding action in addressing the ethical concerns associated with the integration of artificial intelligence in medicine.

This study aims to map the conceptual structure and evolution of the recent scientific literature published in marketing journals to identify the areas of interest and potential future research directions.

The 100 most influential marketing academic papers published between 2018 and 2022 were identified and scrutinized through a bibliometric analysis.

The findings further upheld the critical role of emerging technologies such as Blockchain in marketing and identified artificial intelligence and live streaming as emerging trends, reinforcing the importance of data-driven marketing in the discipline.

The data collection included only the 100 most cited documents between 2018 and 2022, and data were limited only to Scopus database and restrained to the Scopus-indexed marketing journals. Moreover, documents were selected based on the number of citations. Nevertheless, the data set may still provide significant insight into the marketing field.

Influential authors, papers and journals identified in this study will facilitate future literature searches and scientific dissemination in the field. This study makes an essential contribution to the marketing literature by identifying hot topics and suggesting future research themes. Also, the important role of emerging technologies and the shift of marketing toward a more data-driven approach will have significant practical implications for marketers.

To the best of the authors’ knowledge, this is the first comprehensive study offering a general overview of the leading trends and researchers in marketing state-of-the-art research.

Artificial intelligence (AI) reasoning is fuelled by high-quality, detailed behavioural data. These can usually be obtained by the biometrical sensors embedded in smart devices. The currently used data collecting approach, where data ownership and property rights are taken by the data scientists, designers of a device or a related application, delivers multiple ethical, sociological and governance concerns. In this paper, the author is opening a systemic examination of a data sharing concept in which data producers execute their data property rights.

Since data sharing concept delivers a substantially different alternative, it needs to be thoroughly examined from multiple perspectives, among them: the ethical, social and feasibility. At this stage, theoretical examination modes in the form of literature analysis and mental model development are being performed.

Data sharing concepts, framework, mechanisms and swift viability are examined. The author determined that data sharing could lead to virtuous data science by augmenting data producers' capacity to govern their data and regulators' capacity to interact in the process. Truly interdisciplinary research is proposed to follow up on this research.

Since the research proposal is theoretical, the proposal may not provide direct applicative value but is largely focussed on fuelling the research directions.

For the researchers, data sharing concepts will provide an alternative approach and help resolve multiple ethical considerations related to the internet of things (IoT) data collecting approach. For the practitioners in data science, it will provide numerous new challenges, such as distributed data storing, distributed data analysis and intelligent data sharing protocols.

Data sharing may post significant implications in research and development. Since ethical, legislative moral and trust-related issues are managed in the negotiation process, data can be shared freely, which in a practical sense expands the data pool for virtuous research in social sciences.

The paper opens new research directions of data sharing concepts and space for a new field of research.

Privacy scholars appear to struggle in conceptualizing blockchain from a privacy perspective: is it a privacy-enhancing mechanism like differential privacy, a privacy-intruding tool like third-party cookies or a technology orthogonal to the issue of privacy? Blockchain does not seem to neatly fit into any of these buckets that we traditionally use to gauge the privacy implications of information technologies. In this article, the authors argue that blockchain transcends the extant conceptualization of privacy because it modifies the nature of data flow upon which the modern concept of privacy is based.

The authors introduce a conceptualization of blockchain as a new mechanism for data management. Then, following this conceptualization, the authors present a functional review of blockchain, summarizing the features it provides for the data it manages. This review sets up the discussion of how blockchain redefines data flow by separating the power of collection, access and query of data to different entities. After illustrating how this change regrounds privacy concerns in a blockchain system, the authors conclude with a discussion of the recommendations for future privacy research on blockchain.

The authors demonstrate that blockchain, by design, separates three core data-centric operations that are assumed to be inextricably linked in the canonical conceptualization of privacy: the collection, access and query of data. Collection means to capture and then store the data; access means to modify or augment the data and query means the ability to test or verify certain properties of the data (e.g. whether a bank account has a zero balance). Traditionally, any entities that collect data can evidently read, modify or query the same data as they wish. With blockchain, however, an entity that stores the data may not be able to modify the data, yet an entity that cannot even read the data may be able to verify certain properties of the data.

Privacy scholars appear to struggle in conceptualizing blockchain from a privacy perspective: is it a privacy-enhancing mechanism like differential privacy, a privacy-intruding tool like third-party cookies or a technology orthogonal to the issue of privacy? In this article, the authors aim to respond to this important question.

The aim of this research is to analyze the knowledge transfer process in technological innovation clusters. The problem of the study addresses how organizations can act in a network to enhance experiences and gains, particularly in the aspect of knowledge management.

The study is qualitative, applied through a case study, cross-sectional and multiple sources of evidence – semistructured interviews, nonparticipant observation and analysis of documents and secondary institutional data. The case analyzed was the Technology Park of São José dos Campos, in Brazil, involving private companies, governmental organizations, universities and research institutions.

The results reinforce the arguments that the transfer of knowledge is influenced by factors, facilitators or inhibitors such as: cooperation, relationship with institutions, workforce mobility and geographical proximity, influencing the competitiveness and performance of the organizations in the cluster.

This study advances the knowledge management literature in network environments, especially in technological innovation clusters, systematizing and highlighting the facilitating and inhibiting dimensions of knowledge transfer.

The present work has a direct dialogue with the managers and actors involved in the governance of these organizational arrangements with regard to increasing the capacity for creation and the dissemination of knowledge among organizations, educational institutions, government and companies.

There is a presence of aspects indicating that knowledge goes beyond borders through dynamic and collaborative structures, reinforcing the premise that clusters must be perceived as an evolutionary system, whose result of interactions leads to a superior joint capacity.

Human or machine, which one is more intelligent and powerful for performing computing and processing tasks? Over the years, researchers and scientists have spent significant amounts of money and effort to answer this question. Nonetheless, despite some outstanding achievements, replacing humans in the intellectual tasks is not yet a reality. Instead, to compensate for the weakness of machines in some (mostly cognitive) tasks, the idea of putting human in the loop has been introduced and widely accepted. In this paper, the notion of collective hybrid intelligence as a new computing framework and comprehensive.

According to the extensive acceptance and efficiency of crowdsourcing, hybrid intelligence and distributed computing concepts, the authors have come up with the (complementary) idea of collective hybrid intelligence. In this regard, besides providing a brief review of the efforts made in the related contexts, conceptual foundations and building blocks of the proposed framework are delineated. Moreover, some discussion on architectural and realization issues are presented.

The paper describes the conceptual architecture, workflow and schematic representation of a new hybrid computing concept. Moreover, by introducing three sample scenarios, its benefits, requirements, practical roadmap and architectural notes are explained.

The major contribution of this work is introducing the conceptual foundations to combine and integrate collective intelligence of humans and machines to achieve higher efficiency and (computing) performance. To the best of the authors’ knowledge, this the first study in which such a blessing integration is considered. Therefore, it is believed that the proposed computing concept could inspire researchers toward realizing such unprecedented possibilities in practical and theoretical contexts.

Gridded management in the public service supply is still in the experience exploratory stage, and this paper aims to analyze the inherent logic and operation mode of the gridding mechanism of the public supply based on the existing theory study and practices, and verify its efficiency so as to come to the conclusion whether it could be promoted to a wider range.

The methodology applied in this paper was case study/deductive induction.

The grid model in the public service supply needs to be demonstrated completely in theoretical logic and operation principles before it is promoted across the country. Meanwhile, full support of the government is required in terms of service concept, function distribution, technical parameters and infrastructure.

The inherent logic and operation mode of the gridding mechanism of the public service supply needs enough practice tests. The practical test of efficiency analysis of the gridding mechanism of the public service supply is not enough.

This paper validated whether the gridding mechanism that originated from China’s urban management can be promoted to all over the country in the public service supply. It provides references for government policy.

This paper constructs a gridded management model for public service provision in urban and rural areas on the basis of an analysis of the plight of traditional model of public service provision, thus delivering the same standard of public service for both urban and rural areas through optimization of resource allocation without requiring more supply and fundamental change to the content of service.