The aim of our systematic review was to inspect the recently published literature on decentralized governance systems and integrate the insights it articulates on blockchain technology and smart contracts by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Throughout January and May 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was carried out, with search terms including “city” + “blockchain technology”, “smart contracts”, and “decentralized governance systems”. As the analyzed research studies were published between 2016 and 2022, only 371 sources satisfied the eligibility criteria. A Shiny app was harnessed for the PRISMA flow diagram to include evidence-based acquired and handled data. Analyzing the most recent and relevant sources and leveraging screening and quality assessment tools such as AMSTAR, Dedoose, Distiller SR, ROBIS, and SRDR, we integrated the core outcomes and robust correlations related to smart urban governance. As data visualization tools, for initial bibliometric mapping dimensions were harnessed, together with layout algorithms provided by VOSviewer. Future research should investigate smart contract governance of blockchain applications and infrastructure using decision-making tools and spatial cognition algorithms.

Relevant research has investigated how predictive modeling algorithms, deep-learning-based sensing technologies, and big urban data configure immersive hyperconnected virtual spaces in digital twin cities: digital twin modeling tools, monitoring and sensing technologies, and Internet-of-Things-based decision support systems articulate big-data-driven urban geopolitics. This systematic review aims to inspect the recently published literature on digital twin simulation tools, spatial cognition algorithms, and multi-sensor fusion technology in sustainable urban governance networks. We integrate research developing on how blockchain-based digital twins, smart infrastructure sensors, and real-time Internet of Things data assist urban computing technologies. The research problems are whether: data-driven smart sustainable urbanism requires visual recognition tools, monitoring and sensing technologies, and simulation-based digital twins; deep-learning-based sensing technologies, spatial cognition algorithms, and environment perception mechanisms configure digital twin cities; and digital twin simulation modeling, deep-learning-based sensing technologies, and urban data fusion optimize Internet-of-Things-based smart city environments. Our analyses particularly prove that virtual navigation tools, geospatial mapping technologies, and Internet of Things connected sensors enable smart urban governance. Digital twin simulation, data visualization tools, and ambient sound recognition software configure sustainable urban governance networks. Virtual simulation algorithms, deep learning neural network architectures, and cyber-physical cognitive systems articulate networked smart cities. Throughout January and March 2023, a quantitative literature review was carried out across the ProQuest, Scopus, and Web of Science databases, with search terms comprising “sustainable urban governance networks” + “digital twin simulation tools”, “spatial cognition algorithms”, and “multi-sensor fusion technology”. A Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow diagram was generated using a Shiny App. AXIS (Appraisal tool for Cross-Sectional Studies), Dedoose, MMAT (Mixed Methods Appraisal Tool), and the Systematic Review Data Repository (SRDR) were used to assess the quality of the identified scholarly sources. Dimensions and VOSviewer were employed for bibliometric mapping through spatial and data layout algorithms. The findings gathered from our analyses clarify that Internet-of-Things-based smart city environments integrate 3D virtual simulation technology, intelligent sensing devices, and digital twin modeling.

Research background: Generative artificial intelligence (AI) and machine learning algorithms support industrial Internet of Things (IoT)-based big data and enterprise asset management in multiphysics simulation environments by industrial big data processing, modeling, and monitoring, enabling business organizational and managerial practices. Machine learning-based decision support and edge generative AI sensing systems can reduce persistent labor shortages and job vacancies and power productivity growth and labor market dynamics, shaping career pathways and facilitating occupational transitions by skill gap identification and labor-intensive manufacturing job automation by path planning and spatial cognition algorithms, furthering theoretical implications for management sciences. Generative AI fintech, machine learning algorithms, and behavioral analytics can assist multi-layered payment and transaction processing screening with regard to authorized push payment, account takeover, and synthetic identity frauds, flagging suspicious activities and combating economic crimes by rigorous verification processes. Purpose of the article: We show that edge device management functionalities of cloud industrial IoT and virtual robotic simulation technologies configure plant production and route planning processes across cyber-physical production and industrial automation systems in multi-cloud immersive 3D environments, leading to tangible business outcomes by reinforcement learning and convolutional neural networks. Labor-augmenting automation and generative AI technologies can impact employment participation, increase wage and wealth inequality, and lead to potential job displacement and massive labor market disruptions. The deep learning capabilities of generative AI fintech in terms of adaptive behavioral analytics and credit scoring mechanisms can enhance financial transaction behaviors and algorithmic trading returns, identify fraudulent payment transactions swiftly, and improve financial forecasts, leading to customized investment recommendations and well-informed financial decisions. Methods: Machine learning-based study selection process and text mining systematic review management software and tools leveraged include Abstrackr, CADIMA, Colandr, DistillerSR, EPPI-Reviewer, JBI SUMARI, METAGEAR package for R, SluRp, and SWIFT-Active Screener. Such reference management systems are harnessed for methodologically rigorous evidence synthesis, study selection and characteristic extraction, predictive document classification, machine learning-based citation and record screening, bias assessment, article retrieval automation, and document classification and prioritization. Findings & value added: Industrial IoT and 3D augmented reality technologies can create business value by streamlining virtual product and remote asset management across extended reality-based navigation and robotic autonomous systems in smart factory environments by generative AI and machine learning algorithms, articulating business organizational level and theory of management implications. 3D simulation and operational modeling tools can execute and complete complex cognitive task-oriented and knowledge economy jobs, producing first-rate quality outputs swiftly while leading to unemployment spells, labor market disruptions, job displacement losses, and reduced earnings by machine learning clustering and spatial cognition algorithms. Generative AI decentralized finance, interoperable blockchain networks, cash flow management tools, and asset tokenization can mitigate fraud risks, enable digital fund and crypto investing servicing, and automate treasury operations by integrating real-time payment capabilities, routing and configurable workflows, and lending and payment technologies.

The fundamental objective of this research is to learn how trade liberalization, male employment, urbanization, and foreign direct investment (FDI) affect women’s participation in the labor force. To continue, this study aims to determine the effects of trade and other factors on women’s employment in three distinct sectors (i.e., agriculture, industry, and service). From 1991 to 2021, we analyzed data from eight SAARC countries. The study’s theoretical foundation was the Cobb–Douglas production function. To better understand the connections between trade liberalization and the SAARC labor market, this paper used panel quantile regression (QR) and generalized method of moments (GMM) to empirically explore the key determinants of female employment in total and three sub-sectors. The QR method was used in the study because it looks at how variables affect each other beyond the data mean. Additionally, our data set does not follow a normal distribution, and the connection between the explained and explanatory factors is non-linear. Trade openness has a beneficial effect on total female employment throughout system GMM and all quartiles. Total female employment also benefits from an increase in GDP and FDI. However, women’s access to the workforce is hampered by urbanization. Many strategies for increasing women’s participation in the workforce across three sectors are addressed in this article. The major finding of this study is the rate of change in female employment across three industries. Women’s participation in the service and manufacturing sectors increases, whereas their participation in agriculture decreases, as a result of increased trade openness. Although these studies can assist policymakers in choosing the best feasible trade adjustments, they will also add to diverse academic and policy discussions on trade liberalization and its gender consequences. Since trade has become more accessible, more and more women are entering the workforce. Therefore, workers should acquire industrial and service-sector-related competencies.

Drivers’ response time means that drivers act after a judgment is made when an emergency action signal is needed. Drivers have different feelings while driving, and the response time to sudden situations differs. The main purpose of this study was to verify whether the mean reaction time of professional drivers is at the level of one second, which is the value usually used for practical purposes, and to verify the impact of age on the reaction times of drivers. Two different studies with a total of 120 participants—professional drivers—were conducted on the simulator, with 116 drivers participating in the first experiment and four drivers participating in the second experiment using eye-tracking technology. The determination of the mean reaction time was realized using statistical tests. The evaluation of the impact of age on the reaction time of professional drivers was carried out using statistical testing, a regression model, and clustering. The results of this study can be immediately used in practice for professional drivers, as the mean reaction time is usually used as a benchmark in several calculations in transport, for forensic and educational purposes, and for planning traffic and modelling different traffic situations.

Research background: In determining the prices in road transport, carriers usually use the calculations based on a so-called routes utilisation coefficient, which allows the carrier to also take the possibility of the return rides without load into account. Currently, it is usually used as a constant from the interval from zero to one. Purpose of the article: Considering a different offer of return transport from individual European Union (EU) countries, it can be assumed that the routes utilisation coefficient should have different values because there is a varying level of non-zero probability that the vehicle will return without a load. This study therefore proposes a new approach to determining the value of this coefficient based on transport direction. The study also aims to identify clusters of EU countries, for which the common value of the coefficient should be set. Methods: The Analysis of Variance (ANOVA) test was used to verify the assumption of the differences among the means of transport offers. Cluster analysis was used to identify the aforementioned groups of countries. This analysis is based on real data on transport offers to Slovakia from 18 different EU countries. Findings & value added: The results of the analysis can also be used in other EU countries because if significant differences in transport offers to Slovakia exist in individual countries, there is a reasonable assumption that this conclusion will also be valid in other countries. The analysis demonstrated that it is more appropriate to use the routes utilisation coefficient with various values, dependent on the transport direction. For the transport companies, implementation of the obtained results into practice is beneficial to increase their competitiveness through the more precise setting of transport prices, but also to the optimisation of the transport price itself with regard to the expected costs.