Can China's Digital Economy and Green Economy Achieve Coordinated Development?

The coordinated development of the digital economy and green economy is a key issue that needs to be addressed. Based on the statistical data of 30 provincial-level regions in China from 2014 to 2019, this study empirically analyzed whether China's digital economy and green economy can achieve coordinated development. In this study, a coupling coordination degree model was used to evaluate the degree of coordinated development of the digital economy and green economy in provincial regions of China. A fuzzy-set qualitative comparative analysis method was adopted to identify the realization path of the coordinated development of the digital economy and green economy. The results show the following: (1) the coordinated development degree of the digital economy and green economy in China shows an upward trend from primary coordination in 2014 to intermediate-level coordination in 2019, with great differences between different regions;(2) there are five paths to achieve coordinated development of the digital economy and green economy, which are divided into two categories (technology–environment dual-drive type, and technology–organization–environment linkage drive type);(3) technological innovation capability and government financial support can substitute for one another under certain conditions to achieve the coordinated development of the digital economy and green economy. These conclusions provide a theoretical basis for countries to formulate policies to promote the coordinated development of their digital economy and green economy.

Cloud-based accounting information systems usage and its impact on Jordanian SMEs' performance: the post-COVID-19 perspective

PurposeThe purpose of this study is to examine the factors influencing the usage of cloud-based accounting information systems (AIS) in the crisis era (i.e. the COVID-19 pandemic) by expanding the unified theory of acceptance and use of technology (UTAUT) with new related critical factors.Design/methodology/approachA quantitative research approach based on a cross-sectional online questionnaire was used for collecting empirical data from 438 potential and current users of cloud-based AIS. Structural equation modeling based on analysis of a moment structures 25.0 was applied in the data analysis.FindingsThe outcome of the structural path revealed that performance expectancy, social motivation, COVID-19 risk (COV-19 PR) and trust (TR) were significantly influencing users' behavioral intention (BI) toward using cloud-based AIS and explained 71% of its variance. While, contrary to what is expected, the impact of effort expectancy and perceived security risk (SEC) on BI was insignificant. In addition, BI was revealed to influence the actual usage behaviors and explained 74% of its variance. The outcome factors: communication quality (CQ) and decision quality (DQ) were significantly influenced by the usage of cloud-based AIS.Practical implicationsThe current research would be valuable for small- and medium-sized enterprises officials and policymakers to illustrate the relatively low rates of cloud-based AIS and formulate strategies to boost the acceptance and use of cloud-based AIS by Jordanian users, where cloud-based services are still deemed as an innovation.Originality/valueTo the best of the authors' knowledge, the current study is the first academic paper that extends the UTAUT by integrating additional factors: TR, SEC and COV-19 PR. In addition to two outcome variables: CQ and DQ, to study the cloud-based AIS in the Jordanian setting beyond the COVID-19 pandemic. The current research contributes to the academic knowledge on information technology information system adoption by considering cloud accounting acceptance and use and integration into the work practices of users through the BIs and actual use of cloud-based AIS in Jordan.

Economical Utilization of Health Information with Learning Healthcare System Data Commons By Thomas

Finding, accessing, sharing, and analyzing patient data from a clinical setting for collaborative research has continually proven to be a challenge in healthcare organizations.The human and technological architecture required to perform these services exist at the largest academic institutions but are usually under-funded.At smaller, less academically focused healthcare organizations across the United States, where the majority of care is delivered, they are generally absent.Here we propose a solution called the Learning Healthcare System Data Commons where cost is usage-based and the most basic elements are designed to be extensible, allowing it to evolve with the changing landscape of healthcare.Herein we also discuss our reference implementation of this platform tailored specifically for operational sustainability and governance using the data generated in a hospital setting for research, quality, and educational purposes. Introduction Information management professionals within healthcare organizations navigate a high degree of complexity for each project and for each data source used for research and quality improvement services.?ata and data policy must be governed tightly, consistently, and transparently to meet the expectations of patients and to comply with the high ethical and legal standards in the healthcare industry.2Even prior to the pandemic, access and sharing of patient data has been of paramount importance to assess current status of medical knowledge, as well as to accelerate clinical research related to diagnosis, prognosis, and therapeutic intervention in the context of cancer care;complex, or rare disease;and in the face of rapidly changing technologies for telehealth, surveillance, engagement, and intervention.3,4 The COVID-19 pandemic has highlighted the need for unified and harmonized data sets. The diversity of patients' current health and medical history relative to various viral strains presents issues for all medical research institutions both in the capacity to access data in real time and the costs to maintain such flexible, agile analytics environments. Implementation Data Assets, and Assets Loaded (Counts of Files by Type) Rush University, operating as a major medical hospital in a diverse major city, is home to diverse troves of multimodal (i.e., wholly different information categories: medical images, genomic sequences, and clinical records) diagnostic and medical treatment outcomes data assets.

A Sustainability Improvement Strategy of Interconnected Data Centers Based on Dispatching Potential of Electric Vehicle Charging Stations

With the rapid development of information technology, the electricity consumption of Internet Data Centers (IDCs) increases drastically, resulting in considerable carbon emissions that need to be reduced urgently. In addition to the introduction of Renewable Energy Sources (RES), the joint use of the spatial migration capacity of IDC workload and the temporal flexibility of the demand of Electric Vehicle Charging Stations (EVCSs) provides an important means to change the carbon footprint of the IDC. In this paper, a sustainability improvement strategy for the IDC carbon emission reduction was developed by coordinating the spatial-temporal dispatch flexibilities of the IDC workload and the EVCS demand. Based on the Minkowski sum algorithm, a generalized flexible load model of the EVCSs, considering traffic flow and Road Impedance (RI) was formulated. The case studies show that the proposed method can effectively increase the renewable energy consumption, reduce the overall carbon emissions of multi-IDCs, reduce the energy cost of the DCO, and utilize the EV dispatching potential. Discussions are also provided on the relationship between workload processing time delay and the renewable energy consumption rate.

Build This House: Real Estate Opportunities in The U.S. Under Biden

In the face of sudden economic stops, monetary policy has demonstrated its limitations, and successive waves of fiscal relief packages disbursed by governments have provided critical liquidity support to help shore up balance sheets for households and for companies hard-hit by the pandemic.2 As multiple vaccines are disseminated across the globe, an uneven economic recovery has taken shape, with countries such as Italy, Spain, and the U.K. having experienced severe contractions in 2020, and with China actually posting real GDP growth in 2020.3 While investors remain exuberant about the prospects of additional stimulus measures-fueling the unleashing of pent-up demand and the onset of the 'roaring twenties'-expectations for higher inflation have sent shudders through bond and equity markets in the U.S. Nevertheless, considerable slack in the labor market remains in the U.S., with segments of the economy still far behind recovery.4 In early 2021, the real unemployment rate hovered around 10%.5 The aggregate unemployment rate for those at the bottom part of the wage quartile far exceeds that of the top-with many minimum wage jobs in sectors such as leisure and hospitality having been decimated during the crisis, whilst higher-earning white-collar workers have, for the most part, been able to retain their jobs working from home.6 Indeed, even thinking beyond unemployment numbers and focusing on income, many households within the bottom three quintiles of the income distribution came into the crisis in a situation of stagnation, or deep distress. [...]these opportunities are also to be found across global markets. [...]with central banks and governments committed to expansionary monetary policy in the wake of the pandemic, a lower interest rate-induced surge in buying homes (for those who can afford to) has unfolded within advanced economies across the globe, including Singapore, Canada, and the U.K.18, 19, 20 In December 2020, U.S. housing starts jumped to their highest level in 13 years.21 In early 2021, U.S. existing home sales reached the highest level in 14 years.22 Similarly, with the rush of pent-up demand emerging from initial lockdowns, house prices in the U.K. hit a six-year zenith.23 Looking beyond lower interest rates as a causal factor, purchases have been stepped up throughout the pandemic by those who have been able to work from home, and might have transitioned into a more amenable living arrangement, potentially with more space to live and work and play. [...]homeowners in the U.S. spent an average of $17,140 on their homes in the first eight months of the pandemic.24 Due to the surge in demand, a shortage of supply, and ongoing tariffs and disruptions to supply chains, the price of lumber has spiked by 180% since the spring of 2020, eating into developers' margins, and also contributing to the rise in house prices.25 It is important to note in the debates about the potential return of high inflation, house prices are not included in

A Fuzzy Grouping Genetic Algorithm for Solving a Real-World Virtual Machine Placement Problem in a Healthcare-Cloud

Due to the large-scale development of cloud computing, data center electricity energy costs have increased rapidly. Energy saving has become a major research direction of virtual machine placement problems. At the same time, the multi-dimensional resources on the cloud should be used in a balanced manner in order to avoid resources waste. In this context, this paper addresses a real-world virtual machine placement problem arising in a Healthcare-Cloud (H-Cloud) of hospitals chain in Saudi Arabia, considering server power consumption and resource utilization. As a part of optimizing both objectives, user service quality has to be taken into account. In fact, user quality of service (QoS) is also considered by measuring the Service-Level Agreement (SLA) violation rate. This problem is modeled as a multi-objective virtual machine placement problem with the objective of minimizing power consumption, resource utilization, and SLA violation rate. To solve this challenging problem, a fuzzy grouping genetic algorithm (FGGA) is proposed. Considering that multiple optimization objectives may have different degrees of influence on the problem, the fitness function of the proposed algorithm is calculated with fuzzy logic-based function. The experimental results show the effectiveness of the proposed algorithm.

Sistema para a identificação de aglomerações operando em Redes IoT e Fog Computing

: This paper presents an IoT network system, using fog computing to identify agglomerations from IP camera images, processing for pattern recognition and distance calculations. [...]monitoring is done efficiently, as there is no need to send the images to be processed by a centralized system (data center or cloud), bringing savings in terms of sending and storing data. Having said that, situations that need attitude from a monitoring manager to avoid breaks in social distance can easily be managed. O sistema é composto por por dispositivos inteligentes, ou SBC (Single Board Computers), que sao responsáveis por processar e identificar as aglomeraçoes nas imagens enviadas pelas câmeras IP, assim como também um computador, que é responsável por receber todo o fluxo de imagens das possíveis aglomeraçoes detectadas pelos dispositivos, validar as imagens e notificar as quebras de distanciamento social. 3.1.Arquitetura do Projeto O sistema foi construido em cima de paradigmas IoT, Fog/Cloud Computing e Vis&acaron;o Computacional.

A flexible approach for virtual machine selection in cloud data centers with AHP: Practice & Experience

Increasing resource efficiency and reducing the energy consumption of cloud data centers is critical, especially during the global CORONA virus pandemic. Virtual machines' consolidation using live migration maximizes the hosts' and the reduction of energy consumption. An increase in the host's virtual machines in the consolidation process and the dynamic workload of the virtual machines may cause the overloading in the hosts. One approach to overcome this problem is reducing the hosts' virtual machines. One crucial issue to improve the quality of the consolidation process's quality is determining the best virtual machine for the migration process. Although the selection process has lower computational complexity than other challenges (like placement and overload prediction) in the consolidation process, this issue has received less attention. This article aims to present an efficient algorithm for the selection process. We first considered five main criteria for the selection process: migration time, migration risk, virtual machine connectivity, releasable resources, and penalty for SLA violation. Then, we propose an algorithm based on analytic hierarchy process multi‐criteria decision‐making technique. Next, to determine the weight of the proposed criteria, we simulate thousands of virtual machines of the PlanetLab workloads. These weights are tunable based on the data center preferences. The results of the suggested approach results show 23% reduction in the hosts' energy consumption, 49% reduction in the number of migrations, and 18% reduction in the SLA violation compared with other techniques. So, using the proposed method may significantly reduce the overall cost of the data centers.

Data-Center Farming: Exploring the Potential of Industrial Symbiosis in a Subarctic Region

As our world becomes increasingly digitalized, data centers as operational bases for these technologies lead to a consequent increased release of excess heat into the surrounding environment. This paper studies the challenges and opportunities of industrial symbiosis between data centers’ excess heat and greenhouse farming, specifically utilizing the north of Sweden as a case study region. The region was selected in a bid to tackle the urgent urban issue of self-sufficiency in local food production. A synergetic approach towards engaging stakeholders from different sectors is presented through a mix of qualitative and quantitative methods to facilitate resilient data-center-enabled food production. The paper delivers on possible future solutions on implementing resource efficiency in subarctic regions.

Detection and Localization of Failures in Hybrid Fiber–Coaxial Network Using Big Data Platform

Modern HFC (Hybrid Fiber–Coaxial) networks comprise millions of users. It is of great importance for HFC network operators to provide high network access availability to their users. This requirement is becoming even more important given the increasing trend of remote working. Therefore, network failures need to be detected and localized as soon as possible. This is not an easy task given that there is a large number of devices in typical HFC networks. However, the large number of devices also enable HFC network operators to collect enormous amounts of data that can be used for various purposes. Thus, there is also a trend of introducing big data technologies in HFC networks to be able to efficiently cope with the huge amounts of data. In this paper, we propose a novel mechanism for efficient failure detection and localization in HFC networks using a big data platform. The proposed mechanism utilizes the already present big data platform and collected data to add one more feature to big data platform—efficient failure detection and localization. The proposed mechanism has been successfully deployed in a real HFC network that serves more than one million users.