Heterogeneous Technologies Research Articles

Pathway to develop a low-carbon economy through energy-substitution technology in China

As China is determined to develop a low-carbon economy, this study develops a carbon index as a proxy for a low-carbon economy and employs energy-substitution patents as an important technical factor to influence the low-carbon economy. The impact of heterogeneous energy-substitution technology on the low-carbon economy in China is also investigated. The conventional analysis methods, including the generalised method of moments, propensity score matching and difference-in-differences estimator, are employed. The heterogeneity and influencing mechanism of technology are also considered. Using the 2000–2016 data collected from 30 provinces in mainland China, we confirm that energy-substitution technology effectively promotes a low-carbon economy and that this kind of technology coming from enterprises also provides a better low-carbon economy than those from research institutions, university and individuals. We also find that utility-oriented technology effectively promotes a low-carbon economy than invention-oriented ones. However, invention-oriented technology does raise the degree of the low-carbon economy when it serves to complement utility-oriented technology. The influencing mechanism analysis following a two-step method clearly shows that energy-substitution technology promotes the degrees of low-carbon economy by increasing the use of clean energy.

Operational and investment efficiency of investment trust companies: Do foreign firms outperform domestic firms?

This study examines the efficiency of investment trust companies (ITCs) from 2011 to 2020 using a meta-frontier two-stage network data envelopment analysis (DEA) based on the directional distance function (DDF). We improved the accuracy of the efficiency measurement and added a network-based ranking component to rank the top-performing entities. In the group-specific technology assessment, foreign ITCs excel in investment efficiency. Meanwhile, in the meta-technology assessment, domestic ITCs outperform foreign ITCs in terms of both investment and operational efficiencies. Group-specific technology efficiency scores were found to be lower than or equal to the meta-technology efficiency scores for both the operational and investment stages. Based on the network-based ranking approach, Yuan Ta, a domestic ITC that ranked fourth in the operational stage and first in the investment stage, can be used as a reliable benchmark. This study will enable practitioners to gain a better understanding of the performance of ITCs operating under heterogeneous technologies.

CO2 abatement costs in China and BRI countries: From the perspective of technological heterogeneity

As China cannot achieve its emission reduction target without cooperating with other countries, the international carbon trading market has become a part of China’s carbon trading market system. The Belt and Road Initiative (BRI) has brought many development opportunities to countries participating, but critics have also voiced concerns about the environmental and climate degradation it might bring. Thus China is making a great effort towards building a green and low-carbon BRI, part of which is a joint effort with other countries to cut greenhouse gas emission and achieve the 2,030 sustainable development goals. The estimation of abatement costs is the basis of regional carbon emission reduction cooperation and a prerequisite for establishing a regional carbon trading market. Taking into account the technological heterogeneity, this paper uses linear programming to estimate inefficiency level for China and BRI countries, and further calculates the marginal abatement cost (MAC) of carbon dioxide for each country. The results show that after considering technological heterogeneity, the average inefficiency level for China and BRI countries is 2.410%, which is about 26.526% lower than the traditional geographic grouping approach, indicating that the technological heterogeneity among BRI countries is significant and cannot be ignored. Most countries have a low inefficiency level, some countries show a clear trend. China has an average marginal abatement cost of 1440.183 USD/ton. As the marginal abatement cost varies greatly among countries, a large amount of abatement cost could be saved for China and BRI countries if the cost difference is exploited.

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

The Internet of Things (IoT) is a complicated security feature in which datagrams are protected by integrity, confidentiality, and authentication services. The network is protected from external interruptions and intrusions. Because IoT devices run with a range of heterogeneous technologies and process data over time, standard solutions may not be practical. It is necessary to develop intelligent procedures that can be used for multiple levels of data flow in the system. This study examines metainnovations using deep learning-based IDS. Per the findings of the earlier tests, BiLSTMs are better for binary (regular/attacker) classification; however, sequential models (LSTM or BiLSTM) are better for detecting some brutal attacks in multiclass classifiers. According to experts, deep learning-based intrusion detection systems can now recognize and select the best structure for each category. However, specific difficulties will need to be solved in the future. Two topics should be studied further in future attempts. One of the researchers’ concerns is the impact of various data processing techniques, such as artificial intelligence or metamethods, on IDS. The BiLSTM approach has chosen the safest instances with the highest accuracy among the models. According to the findings, the most reliable and suitable solution for evaluating DDoS attacks in IoT is the BiLSTM design.

A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems.

The Network Slice Selection Function (NSSF) in heterogeneous technology environments is a complex problem, which still does not have a fully acceptable solution. Thus, the implementation of new network selection strategies represents an important issue in development, mainly due to the growing demand for applications and scenarios involving 5G and future networks. This work presents an integrated solution for the NSSF problem, called the Network Slice Selection Function Decision-Aid Framework (NSSF DAF), which consists of a distributed solution in which a part is executed on the user’s equipment (for example, smartphones, Unmanned Aerial Vehicles, IoT brokers) functioning as a transparent service, and another at the Edge of the operator or service provider. It requires a low consumption of computing resources from mobile devices and offers complete independence from the network operator. For this purpose, protocols and software tools are used to classify slices, employing the following four multicriteria methods to aid decision making: VIKOR (Visekriterijumska Optimizacija i Kompromisno Resenje), COPRAS (Complex Proportional Assessment), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and Promethee II (Preference Ranking Organization Method for Enrichment Evaluations). The general objective is to verify the similarity among these methods and applications to the slice classification and selection process, considering a specific scenario in the framework. It also uses machine learning through the K-means clustering algorithm, adopting a hybrid solution in the implementation and operation of the NSSF service in multi-domain slicing environments of heterogeneous mobile networks. Testbeds were conducted to validate the proposed framework, mapping the adequate quality of service requirements. The results indicate a real possibility of offering a complete solution to the NSSF problem that can be implemented in Edge, in Core, or even in the 5G Radio Base Station itself, without the incremental computational cost of the end user’s equipment, allowing for an adequate quality of experience.

Using machine learning to model technological heterogeneity in carbon emission efficiency evaluation: The case of China's cities

Improving carbon emission efficiency is essential to address climate change. The widely used methods of modelling heterogeneity in efficiency evaluation tend to artificially classify groups based on a single variable and thus result in biased estimation. To fill this knowledge gap, this paper proposes a new method that combines machine learning and radial directional distance function (DDF) to estimate carbon emission efficiency and reduction potential, in which heterogeneity could be grouped endogenously. Furthermore, index decomposition analysis (IDA) is incorporated to explore the dynamic determinants of carbon emission reduction potential. Using China's data at city level from 2010 to 2018, we found that carbon emission efficiency considering technology heterogeneity is between 0.569–0.822. This implies an excellent emission reduction potential of around 5.9 million tons in 2018. The reduction potential is attributable to managerial failure and technology gap—the latter accounts for 46–55% of the total reduction potential. We arguably conclude that the method in this paper can capture each city's economic and environmental information more accurately than previous methods based on geographic grouping, which may underestimate the reduction potential. We anticipate the machine learning method in this paper could provide insights on clustering the technological heterogeneity and efficiency evaluation.

Two-way knowledge spillovers in the presence of heterogeneous foreign subsidiaries: evidence from an emerging country

PurposeThis paper explains the mechanisms underlying the generation of two-way knowledge spillovers through the interaction of subsidiaries with differentiated local responsibilities and domestic firms.Design/methodology/approachThe study is based on firm-level panel data from a census of Colombian manufacturing firms for the period 2003–2012. The estimation procedure involves two stages. In the first one, total factor productivity (TFP) of foreign and domestic firms is estimated. In a second step, we estimate conventional spillovers (from foreign-owned to local firms) and reverse spillovers (from local to foreign-owned firms) separately, using a random effect approach.FindingsThis study’s findings reveal that only locally creative subsidiaries enjoy positive and significant two-way knowledge spillover effects. The connectivity of subsidiaries to local and international networks is reinforced by reciprocal relationships among actors that enhance bidirectional knowledge flows, these being favored by the dynamics of clustering effects.Originality/valueThe paper contributes with new empirical evidence about the mechanism explaining how the technological heterogeneity of subsidiaries plays a determinant role in the generation of both knowledge flows from foreign to domestic firms and to the reverse, all integrated into the same framework.

Applying Meta-Frontier Approaches to Profitability Estimation: In Case of the Effects of Official Independent Directors

The meta-frontier approach can mathematically understand the production dynamics. With the help of meta-frontier approach differences, the efficiency of income and earnings between companies with different structure can be estimated. Considering the great influence of independent directors on the economic market, this study estimated such effects from the perspective of production function models. It is found that the revenue efficiency results can be optimized without considering the technology heterogeneity for listed companies. When considering the common production boundary, there is an issue that the revenue efficiency of listed companies that own official independent directors is lower than other listed companies that without official independent directors, and the empirical results show that the withdrawal of official independent directors in the market has a positive impact on the profitability of the company.

Distant Reach, Focused Investing: An Examination of CVC Portfolio Configuration

While prior research has focused mainly on the consequences of firm CVC activities, little attention has been paid to how firms configure their CVC portfolios. We conceptualize firm CVC portfolio configuration in two dimensions – technological distance between a firm and its CVC portfolio and technological heterogeneity of the portfolio. We theorize that when a firm looks for new investment targets, a larger technological distance between the firm and its CVC portfolio directs the firm to choose an investee that will narrow its portfolio’s technological heterogeneity while a smaller technological distance encourages the firm to increase its portfolio’s technological heterogeneity. We further examine how CVC portfolio configuration may affect the firm’s learning from its CVC portfolio. We propose that a firm learns more from a more technologically heterogeneous portfolio, however this effect is reduced if the firm has a larger technological distance from its portfolio. Using a sample that consists of 106 U.S. firms with CVC arms and their CVC investments, we find support for these hypotheses.

Multi-Radio Access Software-Defined Vehicular Network

With the promising advances in the telecommunication and automotive industries, there are increasing concerns on the reliability of Intelligent Transportation Systems (ITS) applications and standardization efforts. Nowadays, vehicles are empowered with innovative communications and sensing capabilities to accelerate next-generation Connected and Autonomous Vehicles (CAV) or Vehicle-to-Everything (V2X) communications. Furthermore, V2X is based on many radio access technologies including Dedicated Short-Range Communications (DSRC), Cellular-V2X, and millimeter-wave. It is expected that a single technology is incapable to support the diverse QoS and reliability requirements of vehicular safety and non-safety applications. Therefore, recent research works have investigated and developed different approaches to enable multi-radio access technologies (Multi-RAT) in V2X networks to support these multitudes of requirements. In addition, the heterogeneity of diverse radio access technologies and rigidity in their deployment increases the complexity of the network management. Complications may also arise from inefficient resource utilization due to the complexity of an optimum RAT selection or multi-link establishment through Multi-RATs. Therefore, a global monitoring paradigm such as Software-Defined Vehicular Network (SDVN) has been identified as a solution to reduce this bottleneck, by adding flexibility and agility to the vehicular network. This paper extensively reviews recent related works on Multi-RAT V2X networks focusing on the opportunistic selection mechanism of RAT, allocation of radio resources, and simultaneous establishment of multi-link over a heterogeneous network. Consequently, we have outlined the SDVN architecture, layers, features, and significance towards enhancing the performance of such Multi-RAT networks. Finally, several potential challenges with probable solutions and opportunities are discussed.

Physical and Augmented Dynamics of a Cultural Event

The Taiwan Lantern Festival (TLF) is a specific cultural tradition that has evolved over many years. It is a large-scale festival as determined by the large number of installations and visitors—that is, 20 million visitors in a period of two weeks. The aim of this study is to combine the TLF-related physical dynamics of land use and lantern installations with the augmented dynamics of lantern installations at reallocated sites. We compared five cities in Taiwan with regard to land alterations between 2016 and 2020. The TLF land assessment identified 34 cross-referred types of land use between aerial imagery and GIS surveys in a small area of 2 km × 2 km, in total. The change in land use by year varied between 2% and 499%, up to three times. The complexity of physical dynamics was re-experienced by a more sustainable dynamic of augmented reality (AR) using a scan-to-AR approach to reactivate the installations and fabrics at redeployed sites. The installations of the 2016 TLF were applied. We found that the land use, 3D scan, and AR reshaped the spatio-temporal festivalscape by both types of dynamics. The simulation demonstrated that the fabric retrieved by heterogeneous technologies had equal importance in assessing the host city and in enabling a reactivation for more diversified scales and characters, even with a smartphone AR.

Performance of Nigerian Rice Farms from 2010 to 2019: A Stochastic Metafrontier Approach

Rice has become the most rapidly growing staple food crop in Nigeria. Despite the country favorable rice production ecology, there is still a huge gap between domestic rice production and consumption patterns. This study uses a two-step stochastic metafrontier model to estimate the performance of rice farms from 2010 to 2019, considering the differences between the North and South rice farms. The results indicate heterogeneity in production technology across farms located in different regions, and that both regions have low technical efficiency. However, Nigeria’s South rice farms were ranked better based on managerial issues rather than technological gaps. To attain self-sufficiency in rice production as proposed in its national plan, there is room for improvement of technical efficiency and output production at current production levels in the country’s rice farms.

Developing Microservice-Based Applications Using the Silvera Domain-Specific Language

Microservice Architecture (MSA) is a rising trend in software architecture design. Applications based on MSA are distributed applications whose components are microservices. MSA has already been adopted with great success by numerous companies, and a significant number of published papers discuss its advantages. However, the results of recent studies show that there are several important challenges in the adoption of microservices such as finding the right decomposition approach, heterogeneous technology stacks, lack of relevant skills, out-of-date documentation, etc. In this paper, we present Silvera, a Domain-Specific Language (DSL), and a compiler for accelerating the development of microservices. Silvera is a declarative language that allows users to model the architecture of microservice-based systems. It is designed so that it can be used both by inexperienced and experienced developers. The following characteristics distinguish Silvera from similar tools: (i) lightweight and editor-agnostic language, (ii) built with heterogeneity in mind, (iii) uses microservice-tailored metrics to evaluate the architecture of the designed system, and (iv) automatically generates the documentation. Silvera’s retargetable compiler transforms models into runnable code and produces the documentation for each microservice in the model. The compiler can produce code for any programming language or framework since code generators are registered as plugins. We present a case study that illustrates the use of Silvera and also discuss some current limitations and development directions. To evaluate Silvera, we conducted a survey based on A Framework for Qualitative Assessment of DSLs (FQAD), where we focused on the following DSL characteristics: functional suitability, usability, reliability, productivity, extendability, and expressiveness. Overall, the survey results show that Silvera satisfies these characteristics.

Internet of Things (IOT): Research Challenges and Future Applications

Abstract: With the Internet of Things (IoT) gradually evolving as the subsequent phase of the evolution of the Internet, it becomes crucial to recognize the various potential domains for application of IoT, and the research challenges that are associated with these applications. Ranging from smart cities, to health care, smart agriculture, logistics and retail, to even smart living and smart environments IoT is expected to infiltrate into virtually all aspects of daily life. Even though the current IoT enabling technologies have greatly improved in the recent years, there are still numerous problems that require attention. Since the IoT concept ensues from heterogeneous technologies, many research challenges are bound to arise. The fact that IoT is so expansive and affects practically all areas of our lives, makes it a significant research topic for studies in various related fields such as information technology and computer science. Thus, IoT is paving the way for new dimensions of research to be carried out. This paper presents the recent development of IoT technologies and discusses future applications and research challenges.

Toward the Web of Industrial Things: A Publish-Subscribe Oriented Architecture for Data and Power Management

The foundation of an energy sustainable Web of Industrial Things (WoIT) is facing several open issues due to the constraints imposed by the involved devices, the technological heterogeneity and the complex interactions and, hence, communications patterns. Towards this goal, in this paper, a general framework inspired by the Publish-Subscribe principle have been proposed, in order to jointly optimize the service requirements and the network availability. In particular, in this paper we focus on a holistic design with the objective to manage power budget distribution, in order to support applications that extend the basic publish-and-subscribe scheme.The involved WoIT nodes functionalities, interfaces and hardware architectures have been designed, with a special focus on control protocols. The introduced integrated solution has been validated in scenarios minimising and possibly balancing the power consumption. The achieved results show an average improvement of 45% for the communications performance with the wireless power management.

Leveraging the potential of a technologically heterogeneous suppliers – a dynamic approach

PurposeIn today’s turbulent environment, it is important that companies effectively leverage resources available both in-house and in their interorganizational ties. The purpose of this study is to focus on technology as a key resource and aims to analyse contextual factors of the relationship between the technological heterogeneity of suppliers and the buyer firm’s short- and long-term performance in a dynamic approach.Design/methodology/approachThis paper applies a mixed-method approach. The theoretical model has been developed based on extant literature, validated by a workshop with practitioners, and tested using structural equation modelling on a sample of 157 companies.FindingsSuppliers’ technological heterogeneity has a significant positive effect on both the buyer’s actual business performance and its research and development capability. Business performance captures the short term, while the long-term performance implications of heterogeneity are understood as a precondition for future, technology-based competitiveness. The results show that both short- and long-term consequences are mediated by the buyer’s supply chain management efforts (SCMEs), while the relationship between technological heterogeneity and these efforts is moderated by the buyer’s networking capability (NC).Research limitations/implicationsThis study provides the first evidence of potential long-term positive performance consequences of technologically heterogeneous suppliers. Additionally, it develops new insights into how the internal abilities of the focal firm might facilitate or hinder the positive implications of such heterogeneity. Specifically, the role of the buyer’s SCMEs and its NC is analysed. Industry-specific analyses offer new opportunities for future scholarship and future studies could extend research with other contextual factors.Practical implicationsManagers at different levels of the buyer firm should be aware of the organizational capabilities through which they can leverage the potential embedded in technologically heterogeneous suppliers. The results contribute to this understanding, which is especially important when a change in the environment (and the consequent changes in the level of technological heterogeneity) is constant.Originality/valueThis paper reflects on an important critique of the extant literature by applying a dynamic approach. Dynamization is twofold. Firstly, this study does not limit empirical analysis to short-term performance consequences. Secondly, this study discusses contextual factors that capture some aspects of the buyer’s ability to dynamically adapt to the changing environment. SCMEs align the supply chain of the buyer along customer requirements that change over time, while the NC is responsible for the ongoing reconfiguration of the supplier’s base.

A Microservice Resilience Deployment Mechanism Based on Diversity

The microservice architecture has many advantages, such as technology heterogeneity, isolation, scalability, simple deployment, and convenient optimization. These advantages are important as we can use diversity and redundancy to improve the resilience of software system. It is necessary to study the method of improving the resilience of software system by diversity implementation and redundant deployment of software core components based on microservice framework. How to optimize the diversity deployment of microservices is a key problem to maximize system resilience and make full use of resources. To solve this problem, an efficient microservice diversity deployment mechanism is proposed in this paper. Firstly, we creatively defined a load balancing indicator and a diversity indicator. Based on this, a diversified microservices deployment model is established to maximize the resilience and the resource utilization of the system. Secondly, combined with load balancing, a microservice deployment algorithm based on load balance and diversity is proposed, which reduces the service’s dependence on the underlying mirror by enriching diversity and avoids the overreliance of microservices on a single node through decentralized deployment, while taking into account load balancing. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm outperforms other algorithms.

Environmental technology spillovers and green start-up emergence: the moderating role of patent commercialization policy and patent enforcement.

While research on the creation of green start-ups recognizes the influence of technology spillovers, less attention has been given to the relative importance of heterogeneous technology spillover sources and how they contribute to green start-up emergence. This study aims to fill this lacuna in the existing literature by investigating the relationship between green start-up entry and environmental technology in universities, research institutes, and firms. We primarily focus on the moderating effects of patent commercialization policy and patent enforcement in the relationship. The results show that environmental technology in universities, research institutes, and firms has a positive influence on both of green and non-green start-up emergences. Among them, firms play the most important role, followed by research institutes and universities. Besides, patent commercialization policy plays a negative role in shaping the relationship between green start-ups and environmental technology of universities and firms and the relationship between non-green start-ups and university environmental technology. However, the role of patent commercialization policy does not hold in other cases. Moreover, patent enforcement is negatively related to the relationship between both of green and non-green start-ups and environmental technology of universities and firms, except for research institutes. It indicates that regions should emphasize the dominant position of firms in environmental technology innovation and formulate specific innovation measures for different organizations to better foster local green entrepreneurship.

Coordinating knowledge work across technologies: Evidence from critical care practices

This paper examines how heterogeneous technologies impact the coordination of knowledge work in complex socio-technical settings. It is based on an in-depth field study of critical care practices characterized by intensive knowledge work and technological heterogeneity. We observe that heterogeneous technologies create workflow gaps within which health professionals adapt technology use to contingencies and local needs, prioritize interventions, and identify problems before they become detrimental to patient care. These adaptations provide opportunities for health professionals to continuously align work across heterogeneous technologies and to accomplish broader professional and ideological goals. Our analysis shows that health professionals use three coordination practices when working across heterogeneous technologies: controlling and enhancing information, reconstructing workflows, and circumventing requirements. We theorize how these practices address coordination needs associated with heterogeneous technologies and discuss implications for knowledge work. We provide a more complete understanding of coordination practices in complex, socio-technical settings which contributes to both knowledge work and coordination literatures.

Empirical Analysis of Enterprise Financial Management Risk Prediction in View of Associative Memory Neural Network

Enterprise financial analysis has a far-reaching impact on modern enterprise management decision-making and plays a role that cannot be ignored. Financial status is related to the life and death of an enterprise and is the lifeline of an enterprise. Fast and efficient financial analysis can provide reliable and accurate decision-making information support for enterprise investors, operators, creditors, and other organizations and individuals to understand and evaluate the enterprise status and future development potential. With the development of intelligent methods such as associative memory neural network, the research of financial analysis decision support based on artificial intelligence has been paid more and more attention by academia and management, and has made new progress. Efficient and accurate financial risk prediction can help enterprises predict the possible financial risks in the future earlier, facilitate the early detection of problems, and take effective measures to avoid risks or minimize losses. However, most of the existing mature financial risk prediction studies are based on balanced data sets. The research on the classification of unbalanced data sets is not mature and perfect and needs to be further studied. Generally speaking, this paper mainly adopts the research method of cross integration of various disciplines and organically integrates the key theories, methods, and technologies such as default risk management theory, financial index analysis theory, data mining principle, prediction and decision theory, computer technology, multiclassifier integration technology, a variety of enterprise financial risk early warning technology and statistical sampling, carrying out systematic research on enterprise financial risk prediction. This review constructs the enterprise financial risk prediction method system based on heterogeneous data mining technology, mainly including data preprocessing layer, improved nearest neighbor delta increment layer, heterogeneous nearest neighbor extraction layer, and case-based reasoning prediction layer, so as to improve the traditional financial risk prediction method and obtain a new risk classification prediction model. The case-based method has some significant advantages in risk prediction performance, and it also helps to reduce the probability of financial risk.