With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks.

Research background: Fintech development shapes corporate investment efficiency and economic growth with innovative tools, and can decrease financing constraints of enterprises, enabling direct and indirect financing and furthering inter-bank competition. Crowdfunding- and blockchain-based fintech operations harness deep and maching learning algorithms, augmented and virtual reality technologies, and big data analytics in mobile payment transactions. Purpose of the article: We show that fintechs have reconfigured financial service delivery by harnessing AI-based data-driven algorithms and cloud and blockchain technologies. Fintech optimizes financial organization and services, economic structures and growth, data analysis, and digital banking performance. Machine learning algorithms can streamline payment operation capabilities and process promptness, ensuring smooth operational flows, assessing risks, and detecting frauds and money laundering by historical data and customer behavior analysis across instant payment networks and infrastructures. Methods: Quality tools: AXIS, Eppi-Reviewer, PICO Portal, and SRDR. Search period: July 2023. Search terms: “fintech” + “artificial intelligence algorithms”, “cloud computing technologies”, and “blockchain technologies”. Selected sources: 40 out of 195. Published research inspected: 2023. Data visualization tools: Dimensions and VOSviewer. Reporting quality assessment tool: PRISMA. Findings & value added: Fintech development enables organizational innovation by mitigating information asymmetry and financing limitations while providing financial assistance and tax incentives in relation to products and services. The fintech growth has influenced the dynamic intermediary function of financial institutions in terms of sustainability and economic development. Fintech and natural resources negatively influence, while green innovations and financial development further, environmental sustainability.

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

Research background: With growing evidence of consumer adoption of mobile shopping apps, there is a pivotal need for comprehending Internet-enabled consumer devices in mobile shopping behavior. Mobile shopping platform features and user technological readiness configure consumers? expectations and demands as regards mobile retailing adoption, leading to acceptance of mobile shopping apps and payment services. Purpose of the article: In this research, prior findings have been cumulated indicating that mobile social apps extend throughout consumer attitudes and behaviors by the widespread adoption of smartphones. We contribute to the literature by showing that cutting-edge technological developments associated with customer behavior in relation to mobile commerce apps have resulted in the rise of data-driven systems. Consumer behavioral intention and adoption intention in relation to mobile shopping apps/websites are developed on perceived risk and trust consequences. Methods: Throughout February and March 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was carried out, with search terms comprising ?mobile shopping app?, ?mobile commerce platform?, ?mobile payment service?, ?Internet-enabled consumer device?, ?consumer technological adoption?, and ?mobile shopping behavior?. As research published between 2018 and 2021 was analyzed, only 330 sources met the suitability criteria. By removing questionable or indeterminate findings (insubstantial/inconsequent data), results unconfirmed by replication, too imprecise content, or having quite similar titles, 66, chiefly empirical, sources were selected. A systematic review of recently published literature was carried out on technological adoption of mobile commerce apps by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. The Systematic Review Data Repository was used, a software program for the gathering, handling, and analysis of data for the systematic review. The quality of the academic articles was determined by harnessing the Mixed Method Appraisal Tool. Findings & value added: The consumer purchase decision?making process in mobile app-based marketing involves consumer engagement and willingness to adopt mobile commerce apps. Further advancements should clarify how technological-based consumer adoption of mobile shopping throughout social commerce can improve the payment for products and services.

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses. Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation. Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR. Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients.

There is a lack of data focused on the specific coagulopathic derangements in COVID‐19 versus non‐COVID‐19 acutely ill cancer patients. Our objective was to characterize features of coagulopathy in cancer patients with active COVID‐19 illness who required hospitalization at MD Anderson in the Texas Medical Center and to correlate those features with thrombotic complications, critical illness, and mortality within the first 30 days after hospital admission for COVID‐19 illness. COVID‐19 and non‐COVID‐19 hospitalized cancer patients, with at least five consecutive measures of PT, PTT, d‐dimer, and CBC during the same period, were matched 1:1 to perform a retrospective analysis. We reviewed complete blood cell counts with differential, PT, PTT, fibrinogen, D‐Dimer, serum ferritin, IL‐6, CRP, and peripheral blood smears. Clinical outcomes were thrombosis, mechanical ventilation, critical illness, and death. Compared with matched hospitalized cancer patients without COVID‐19, we found elevated neutrophil and lower lymphocyte counts in those with critical illness ( p = 0.00) or death ( p = 0.00); only neutrophils correlated with thrombosis. COVID‐19 cancer patients with a platelet count decline during the hospital stay had more frequent critical illness ( p = 0.00) and fatal outcomes ( p = 0.00). Of the inflammatory markers, interleukin‐6 showed consistently higher levels in the COVID‐19 patients with poor outcomes. The findings of unique platelet changes and coagulopathy during severe COVID‐19 illness in the cancer population are of interest to explore disease mechanisms and future risk stratification strategies to help with the management of cancer patients with COVID‐19.