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The gig economy and gig work have grown quickly in recent years and have drawn much attention from researchers in different fields. Because the platform mediated gig economy is a relatively new phenomenon, studies have produced a range of interesting findings; of interest here are the socio‐technical issues that this work has surfaced. This systematic literature review (SLR) provides a snapshot of a range of socio‐technical issues raised in the last 12 years of literature focused on the platform mediated gig economy. Based on a sample of 515 papers gathered from nine databases in multiple disciplines, 132 were coded that specifically studied the gig economy, gig work, and gig workers. Three main socio‐technical themes were identified: (1) the digital workplace, which includes information infrastructure and digital labor that are related to the nature of gig work and the user agency; (2) algorithmic management, which includes platform governance, performance management, information asymmetry, power asymmetry, and system manipulation, relying on a diverse set of technological tools including algorithms and big data analytics; (3) ethical design, as a relevant value set that gig workers expect from the platform, which includes trust, fairness, equality, privacy, and transparency. A social informatics perspective is used to rethink the relationship between gig workers and platforms, extract the socio‐technical issues noted in prior research, and discuss the underexplored aspects of the platform mediated gig economy. The results draw attention to understudied yet critically important socio‐technical issues in the gig economy that suggest short‐ and long‐term opportunities for future research directions. [ABSTRACT FROM AUTHOR]

Purpose: The article extends the distinction of semantic from syntactic labour to comprehend all forms of mental labour. It answers a critique from de Fremery and Buckland, which required envisaging mental labour as a differentiated spectrum. Design/methodology/approach: The paper adopts a discursive approach. It first reviews the significance and extensive diffusion of the distinction of semantic from syntactic labour. Second, it integrates semantic and syntactic labour along a vertical dimension within mental labour, indicating analogies in principle with, and differences in application from, the inherited distinction of intellectual from clerical labour. Third, it develops semantic labour to the very highest level, on a consistent principle of differentiation from syntactic labour. Finally, it reintegrates the understanding developed of semantic labour with syntactic labour, confirming that they can fully and informatively occupy mental labour. Findings: The article further validates the distinction of semantic from syntactic labour. It enables to address Norbert Wiener's classic challenge of appropriately distributing activity between human and computer. Research limitations/implications: The article transforms work in progress into knowledge for diffusion. Practical implications: It has practical implications for determining what tasks to delegate to computational technology. Social implications: The paper has social implications for the understanding of appropriate human and machine computational tasks and our own distinctive humanness. Originality/value: The paper is highly original. Although based on preceding research, from the late 20th century, it is the first separately published full account of semantic and syntactic labour. [ABSTRACT FROM AUTHOR]

The ongoing coronavirus disease 2019 (COVID-19) is a pandemic causing millions of deaths, devastating social and economic disruptions. Testing individuals for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of COVID-19, is critical for mitigating and containing COVID-19. Many countries are implementing group testing strategies against COVID-19 to improve testing capacity and efficiency while saving required workloads and consumables. A group of individuals’ nasopharyngeal/oropharyngeal (NP/OP) swab samples is mixed to conduct one test. However, existing group testing methods neglect the fact that mixing samples usually leads to substantial dilution of viral ribonucleic acid (RNA) of SARS-CoV-2, which seriously impacts the sensitivity of tests. In this paper, we aim to screen individuals infected with COVID-19 with as few tests as possible, under the premise that the sensitivity of tests is high enough. To achieve this goal, we propose an Adaptive Group Testing (AdaGT) method. By collecting information on the number of positive and negative samples that have been identified during the screening process, the AdaGT method can estimate the ratio of positive samples in real-time. Based on this ratio, the AdaGT algorithm adjusts its testing strategy adaptively between an individual testing strategy and a group testing strategy. The group size of the group testing strategy is carefully selected to guarantee that the sensitivity of each test is higher than a predetermined threshold and that this group contains at most one positive sample on average. Theoretical performance analysis on the AdaGT algorithm is provided and then validated in experiments. Experimental results also show that the AdaGT algorithm outperforms existing methods in terms of efficiency and sensitivity. Note to Practitioners—Real-time reverse transcription-polymerase chain reaction (rRT-PCR) tests provide scope for automation and are one of the most widely used laboratory methods for detecting the SARS-CoV-2 virus. This paper is motivated by the following challenges: (1) Many countries are experiencing an acute shortage of professionals and consumables for conducting rRT-PCR tests; (2) Group sizes of existing group testing methods against COVID-19 may not be optimal, which adversely impacts the efficiency of the screening of the SARS-CoV-2 virus; (3) Existing group testing methods do not consider the fact that the sensitivity of rRT-PCR tests usually decreases with the group size. The objective of this paper is to improve the efficiency and sensitivity of large-scale screening against COVID-19. For achieving this goal, we propose an Adaptive Group Testing (AdaGT) algorithm, which has the following advantages: (1) It can improve the efficiency for screening the SARS-CoV-2 virus, mainly by adaptively adjusting its testing strategy between an individual testing strategy and a group testing strategy based upon an estimated ratio of positive samples during the screening process; (2) It can guarantee a high sensitivity of the rRT-PCR tests by determining the group sizes of the group testing strategy based upon some constraints; (3) We derive an appropriate threshold for the estimated ratio of positive samples such that the AdaGT algorithm can achieve a minimum average number of rRT-PCR tests and can be directly employed in practical applications. [ABSTRACT FROM AUTHOR]

This article investigates subject-related research metrics from the most popular scientometrics platforms with the capability of citation count. Quantitative analyses have been carried out to determine the most- to the least-cited subject areas to indicate the most active and dynamic fields in research. Subject areas are classified based on statistical analyses and the number of citations received in the period of search. Numerous articles were analyzed from top-ranked journals in all research fields covered by SCImago journal classification. A comprehensive data set was generated in 27 research subject areas, including 313 subject categories and 81 high-ranked journals. Results showed that the highest number of citations were related to the sciences and science-related subject areas. The subject areas of computer science and mathematics had the lowest research metrics and the smallest number of highly cited publications. Web of Science performed better than Scopus for subject-related evaluations. Analysis of frequency data showed that Google Scholar cited fewer papers for low-cited publications than did Scopus or Web of Science. For highly cited publications, however, Google Scholar was found to have better performance. Web of Science showed the best consistency in citation coverage in most of the investigated subject areas. [ABSTRACT FROM AUTHOR]

Distributed optimization algorithms for security-constrained economic dispatch (SCED) problems have been the subject of significant research interest in recent years. However, existing distributed SCED algorithms can be ineffective in the presence of malicious participants and inefficient in the absence of a coordinator. On the other hand, blockchain, an emerging technique known as the trust machine, has not shown its potential to address the above challenges in state-of-the-art literature. This paper proposes a blockchain-based distributed SCED algorithm. Using blockchain to form a coordination committee and enable balance among committee members, the proposed method allows the use of hierarchical SCED algorithms in the absence of a coordinator and can disable malicious participants. Numerical results show the robustness and necessity of the proposed blockchain-based SCED algorithm, by comparing the SCED results with and without blockchain. [ABSTRACT FROM AUTHOR]

Temporal decomposition is a potential approach to relieve the computation cost of power system multi-interval scheduling problems, such as economic dispatch. In this form of decomposition, the considered scheduling horizon is partitioned into several subhorizons. A subproblem is formulated for each subhorizon, and a distributed optimization algorithm strategy is used to coordinate subproblems. The main existing challenge is decomposing the scheduling horizon to gain the most time saving from distributed computing. This paper serves as an extension to our previous work and presents a machine learning-aided temporal decomposition strategy to partition a scheduling horizon optimally. We have found that the load profile, known before solving economic dispatch, significantly affects the best number of subhorizons. We have used load profiles as inputs to a learner whose goal is to assign a temporal decomposition class to each load profile. Possible decomposition classes are divisors of the considered scheduling horizon. Thus, the proposed learning procedure is a multiclass classification. We have selected Extreme Gradient Boosting that is a tree-based classification learner. Simulation results using real-world load profiles show the promising performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of distributed energy resources (DER) due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of flexible demand (FD) and energy storage (ES) resources. This paper demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability. [ABSTRACT FROM AUTHOR]