Heterogeneous Technologies Research Articles

Cybersecurity Maintenance in the Automotive Industry Challenges and Solutions: A Technology Adoption Approach

Numerous attempts have been made to create a secure system that meets the criteria and requirements of the automotive vehicle development life cycle. However, a critical gap exists in the secure development lifecycle, particularly concerning the development and maintenance of software after the vehicle has been sold by the manufacturer. This step is often overlooked by original equipment manufacturers (OEMs), especially after the expiration of the vehicle warranty period, given the cost that it will require to update and test the software in their vehicles. This paper addresses the issues that affect current and future vehicle cybersecurity, during the maintenance of cybersecurity, and how the neglect of it could end up creating hazards for the vehicle owner or other road users. To accomplish this, we will employ the technology adoption model (TAM) as a theoretical framework, which is used to understand and predict how organizations adopt technology. Thus, through qualitative and quantitative research, including text mining, we identify the challenges in the adoption and diffusion of cybersecurity maintenance in the automotive sector and its supply chain. In addition, we propose possible solutions on how to maintain a level of security that will benefit road users, OEMs and regulators, covering the cybersecurity needs for the vehicle’s usable life, taking into account the vehicle’s heterogeneity of components and technology, connectivity, environmental impact and cost of production and maintenance of a vehicle.

Association between central corneal thickness and systemic lupus erythematosus: a cross-sectional study protocol.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease affecting multiple systems and classified under connective tissue disorders. Ocular involvement occurs in up to 30% of SLE cases, with the cornea being particularly susceptible to thinning due to immune-complex deposits and its predominantly type I collagen composition. This corneal thinning is clinically significant in glaucoma, where patients with reduced central corneal thickness (CCT) may have up to a threefold increased risk of developing glaucoma, as well as in refractive surgery. However, existing studies on CCT in SLE are limited and marked by substantial heterogeneity in methodology, technology, criteria, and participant numbers, resulting in conflicting findings. Based in our hypothesis that SLE-related corneal lysis may result in decreased CCT, this study aims to determine and compare the mean CCT values between SLE patients and healthy controls to obtain a more precise understanding of the potential relationship. A cross-sectional observational study will be conducted, enrolling SLE patients and age-and sex-matched healthy controls recruited from ophthalmology consultations. Exclusion criteria will be applied to rule out other corneal thinning risk factors. A pilot study estimated a minimum sample size of 34 participants per group. CCT measurements will be obtained using Zeiss HD Cirrus 5,000 optical coherence tomography (OCT) on a randomly selected eye, following concordance analysis using the Kappa index. Statistical analysis will include descriptive, bivariate, and multivariate methods. The study protocol was approved by the ethics committee. The cornea's vulnerability to thinning and lysis in SLE, which impacts CCT, is crucial for the accurate assessment of glaucoma, the leading cause of irreversible blindness worldwide and the second leading cause in Europe. Given that patients with reduced CCT are at a significantly higher risk of developing glaucoma, further research is necessary to understand the association between SLE and CCT. Our study aims to enhance methodological rigor compared to prior research by determining an appropriate sample size and exclusively enrolling SLE patients to increase participant homogeneity. If a significant difference in CCT between groups and an association between CCT and SLE are found, a prospective study will be considered.

The synergetic competition of cross-regional integrated energy systems with low-carbon technology heterogeneity under carbon emission trading mechanism to achieve carbon reduction target

The integration of renewable and fossil energy in a Region Integrated Energy System (RIES) is recognized as an effective measure for carbon emission reduction. The emergence of carbon emission trading (CET) markets worldwide has raised the need to combine RIES optimal planning with CET to achieve carbon reduction targets. This study focuses on the synergetic competition of a cross-RIES system with low-carbon technology heterogeneity under the CET mechanism. A cross-RIES model is constructed, incorporating fossil Carbon Capture System (fossil-CCS), renewable Combined Cooling Heating and Power (renewable-CCHP), and cleaner production paradigms. An improved version of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to propose a bi-level multi-objective optimal planning framework for the cross-RIES model, employing a grandfather permit allocation method. Comparative analysis is conducted between cross-RIES optimal planning with and without considering the CET mechanism, with a focus on environmental indexes such as carbon emission, carbon intensity, and marginal abatement cost. Sensitivity studies are also performed to explore the impact of key CET design parameters on the system's environmental performance. The simulation results demonstrate that the CET mechanism effectively reduces carbon emission, primary energy consumption, and carbon intensity by promoting synergetic competition among the low-carbon technology heterogeneity of RIESs. Furthermore, the environmental performance varies among the three types of RIES. In terms of CET parameter design, lowering the cap constraint, reducing the allowance allocation ratio of fossil-CCS RIES, and increasing the trading price contribute significantly to reducing carbon emission and carbon intensity, albeit at the expense of higher marginal abatement cost.

Analysing group‐ and metatechnology efficiencies and technology gap of airlines for a sustainable future

AbstractAirline performance evaluation, which involves incorporating sustainable concepts, provides stakeholders and managers with an objective basis for decision‐making. This evaluation considers not only conventional metrics but also environmental impacts, contributing to the advancement of sustainable development goals. This study incorporates a two‐stage network of airline operations, which implies that airlines utilise limited resources to meet their service capacity in the first stage and generate passenger and cargo services alongside carbon emissions in the second stage. The study proposes a union dynamic network data envelopment analysis model, which integrates group technology and meta‐technology into a unified model. This model not only solves the unreasonable technology gap ratio problem but also accounts for the technological heterogeneity (whether or not to participate in a strategic alliance) and the dynamic efficiency of different airlines in the network structure during different operation periods. A sample of 24 global airlines from 2017 to 2019 is used in the study. The empirical results show that the dynamic total meta‐efficiency of the alliance group is significantly greater than that of the nonalliance group over the 3 years. This suggests that joining a strategic alliance has a positive effect on airlines' operational efficiency while reinforcing their commitment to sustainability initiatives.

Comparative efficiency of Chilean water utilities: a Bayesian stochastic frontier approach

ABSTRACT Assessing the potential relationship between ownership and performance of water utilities is relevant for water regulators. Incorporating technological heterogeneity among groups of water utilities could solve any incomparability issues and lead to more appropriate policy implications. This can be done with the use of the metafrontier framework which assumes that utilities operate under different technologies. The study applies this framework to compare the efficiency of private water utilities (PWU) and concessionary water utilities (CWU) in Chile using Bayesian Stochastic Frontier Analysis (SFA) techniques. The results indicated that PWU performed better than CWU. Nevertheless, both types of water utilities presented considerable cost inefficiency. PWU and CWU should considerably reduce costs by 34% and 48% using the industry technology as a whole, respectively. The methodological approach proposed in this study could be used to compare the performance of other groups of water utilities to support the decision making of water regulators.

Efficiency evaluation, regional technological heterogeneity and determinant of total factor productivity change in China’s healthcare system

Enhancing efficiency and productivity in countries’ healthcare systems is a global challenge. The Chinese government invested huge resources to improve the efficiency and productivity of the healthcare system across the country. To assess the success of the mission above, this research utilized DEA-SBM Meta frontier analysis alongside the Malmquist Productivity Index. These methodologies were employed to gauge Efficiency, production technology heterogeneity, and productivity of healthcare systems change across 31 mainland Chinese provinces and four distinct geographical regions throughout the study period spanning from 1997 to 2022. Results revealed that the mean efficiency score of China’s healthcare system is 0.7672. It indicates a growth potential of 23.28 percent in the operational efficiency of healthcare systems. The eastern region’s efficiency level (0.86917) is higher among all four regions. Zhejiang, Shandong, and Guangdong are the top three healthcare-efficiency performers. The technology gap ratio indicates that eastern regions witnessed a high TGR (0.9909), showing the country’s attainment of superior healthcare technologies. Beijing, Guangdong, Shanghai, Tianjin, and Zhejiang witnessed higher TGR values among all 31 mainland Chinese provinces. The total factor productivity index of the healthcare system witnessed a slight growth of 0.33%, with an average MI score of 1.0033. Efficiency change (EC) was found to be the main determinant of TFPC as technology change TC is less than EC. Moreover, the MI score of the Western region (1.033) is higher than the corresponding Eastern, northeastern, and central regions. Guizhou, Anhui, and Yunnan were found to be the top three performers in TFPC growth. Finally, the Kruskal–Wallis test confirmed the statistically significant difference among 4 Chinese regions for the healthcare system’s efficiency, TFPC, and TGR.

Returns to scale, technical and efficiency changes in the Spanish hotel industry using technological heterogeneity models

This paper analyses returns to scale, productivity growth and its decomposition in the Spanish hotel industry (period 1997–2019). To do so, we consider that hotels can have technological heterogeneity and, therefore, parameters in their production function can differ between them. Also, we use a multiple input and output production function based on an output distance stochastic frontier approach with random parameters in a Bayesian framework. Results indicate that the percentage of hotels working under increasing returns to scale decreased during the period considered. Furthermore, productivity growth was low, indicating signs of stagnation. Efficiency change appears to be the main driving force behind productivity growth, although technical change had a positive effect on productivity after the start of the global financial crisis. Several practical implications are proposed based on these results.

Urban green total factor energy efficiency and its decomposition under a multidimensional heterogeneity framework: a case study of the Yellow River Basin, China.

Assessing and monitoring the green total factor energy efficiency (GTFEE) of cities while considering technology heterogeneity is crucial for the development of energy-conservation and emission-reduction policies. Considering that the heterogeneity of production technologies encompasses several dimensions, this paper proposes a 3E3S (Economy-Environment-Energy-Society-Science-Space) heterogeneity framework and integrates it with the improved meta-frontier global SBM-undesirable to analyze GTFEE and its decomposition. Empirical analysis of cities in the Yellow River Basin of China (YRBC) highlights the following: (1) The 3E3S heterogeneity framework facilitates the classification of all cities into three distinct groups, a finding that contrasts significantly with previous outcomes documented in the literature that relied solely on criteria such as geographic location. (2) The three groups identified under the meta-frontier exhibit substantial energy-saving potentials of 24.49%, 35.17%, and 52.46%, respectively. Additionally, there are spatiotemporal variations in GTFEE, with cities located in the central part of YRBC, particularly those in Shanxi province, demonstrating poor GTFEE performance. (3) The decomposition analysis of GTFEE indicates that technological progress plays a pivotal role in enhancing GTFEE on the whole, albeit with varying approaches for improving GTFEE depending on individual cities.

Towards China's dual-carbon target: Energy efficiency analysis of cities in the Yellow River Basin based on a “geography and high-quality development” heterogeneity framework

The Yellow River Basin (YRB) serves as a critical energy hub in China. Enhancing energy efficiency in cities across the YRB is pivotal for achieving high-quality development and meeting carbon peaking and neutrality targets. This paper proposes a multidimensional technology heterogeneity assessment framework to refine the classification of production technologies. It leverages this framework to develop a data envelopment analysis model for evaluating green total factor energy efficiency (GTFEE) in 78 cities within the YRB from 2006 to 2022. The key findings are as follows: (1) Grouping outcomes from the multidimensional heterogeneity framework significantly differ from existing literature. (2) GTFEE shows notable spatiotemporal variations within the YRB, with a substantial overall increase since 2011. However, cities in the central YRB exhibit lower GTFEE performance. (3) Energy inefficiency in groups 1, 2, and 3 is attributed to developmental inefficiency, management inefficiency, and practical inefficiency, respectively. (4) Evaluation of energy-saving potential reveals an average annual potential of around 8 million tce per city during 2006–2022. The study concludes with strategies for enhancing GTFEE based on dynamic GTFEE decomposition outcomes, supporting policy design for achieving dual-carbon goals through improved energy efficiency in the YRB region.

Transport SDN architecture for multi-layer transport slicing

The proposed architecture advances the concept of network slicing, crucial for beyond 5G services, by enabling dynamic resource allocation and customized partitioning in managed network infrastructures. This architecture addresses the challenges of provisioning end-to-end (E2E) slices across diverse network domains, which is complicated by technological heterogeneity and the variety of vendor solutions. By introducing a standardized transport network solution, we ensure seamless integration, equitable treatment of service requests, and the ability to meet diverse demands. The architecture is centered around a multi-layer transport network slicing architecture, which allows for the division of transport networks into virtual autonomous segments, each tailored for specific services or applications. This segmentation is essential for providing differentiated and personalized 5G services, optimizing network performance, and maximizing resource application. A key component of this architecture is the transport slice controller (TSC), which controls the provision and life-cycle management of transport slices, ensuring a standardized approach in the industry for the definition and realization of slices.

Heterogeneous technology-induced global CO2 emission reduction and emission forecasting since the Kyoto era

While identifying the drivers of global CO2 emission is crucial for climate change mitigation, the heterogeneous technology-related drivers (e.g., technological change and efficiency of energy and CO2 emission) were ignored to a large extent at a global scale, hindering to formulate heterogenous climate policies. Moreover, the projection of CO2 emission was also not well compared for countries. Here, the study investigated the heterogeneous technology-related drivers of CO2 emissions in time and space simultaneously in 42 major emitter countries over 1998–2020 by extending the spatiotemporal production-theoretical decomposition models, and compared the different performances for forecasting CO2 emission by traditional time-series models and several machine learning models. Key findings as follows: first, drivers of CO2 emissions exhibit significant heterogeneity across countries where the effects of energy usage technology gap and CO2 emission technology gap were negative drivers for USA, South Korea, and the Czech Republic and potential energy intensity effect was the negative driver in countries like China, Russia, Japan, and India. Second, the effects of within-GDP per capita and within- population size were the important drivers affecting global CO2 emission difference. Third, general regression neural network achieved the best forecasting performance on average compared with other models in the study. The study highlights the importance of formulating climate policies based on heterogeneous technology and emission forecast modeling.

Evaluation and optimization of mariculture ecological efficiency under technological heterogeneity — New evidence from China's coastal regions

This paper combines the meta-frontier analysis framework with the SBM-DEA model based on undesirable outputs to thoroughly reveal the mechanism of ecological efficiency differences in China's mariculture. This paper accurately identifies the weak links in ecological efficiency in various regions, thereby achieving a precise evaluation of mariculture ecological efficiency within the framework of technological heterogeneity. The results indicate that under the meta-frontier, the annual average ecological efficiency of mariculture in coastal areas of China during the sample period is 0.586, with a 41.4 % efficiency improvement space. However, under the group frontier, the annual average ecological efficiency is 0.783. The MEI in the BR economic zone and the YRD economic zone respectively stem from MI and TI, whereas in the PRD economic zone, the difference in the proportions of management inefficiency and technological inefficiency is not significant. Finally, this paper proposes four types of improvement paths.

Accounting for technology heterogeneity in the measurement of persistent and transient inefficiency

This article tackles the problem of ignoring firm technology heterogeneity when measuring persistent and transient inefficiencies, which can lead to distorted estimates. Given that firms’ technologies exert a greater influence on long-run rather than short-run management, accounting for technology heterogeneity should prevent distortions in persistent inefficiencies and, to a lesser extent, in transient inefficiencies, which has been overlooked in the literature. Employing data from Dutch dairy farms spanning the years 2009 to 2016, I estimate random and fixed coefficients generalized true random effects models in a Bayesian framework, subsequently comparing the derived inefficiency scores. The findings indicate that average persistent inefficiency and its dispersion are inflated when technology heterogeneity is ignored, whilst transient inefficiency remains virtually unaffected. This finding is crucial to prevent firms and policy-makers from receiving misleading information about long-run performance metrics, whilst highlighting the need for future studies to account for technology heterogeneity when analyzing firms’ decisions.

Decomposition of China’s Carbon Emissions Responsibility from the Perspective of Technological Heterogeneity

A global agreement has been reached on the reduction in greenhouse gas emissions. Worldwide, countries have implemented measures to tackle carbon emission issues by establishing aggregate targets and decomposing responsibilities. This study aims to decompose carbon emissions by creating an input–output model that incorporates multivariate factors like energy consumption and water consumption, together with a ZSG-DEA (zero-sum data envelopment analysis) model considering technological heterogeneity (Tech-ZSG-DEA). Based on the total carbon emission data predicted using the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the carbon emission efficiency of 30 provinces in China is evaluated according to multiple principles. This is achieved by considering variations in population, economy, energy consumption, and water consumption across different locations. The efficiency findings indicate a discrepancy between the initial allocation and the highest efficiency value of 1. The traditional ZSG-DEA model overlooks regional disparities and may worsen carbon emission pressures in less developed areas. In contrast, the Tech-ZSG-DEA model, which considers regional technological diversity, allows more efficient regions to help alleviate some of the carbon emission burden and considers economic and social benefits. There is a large difference in the emission responsibility of the provinces based on the different decomposition principles. Finally, relevant policy recommendations are provided, such as the formulation of differentiated and inclusively coordinated emission plans. In addition, there are also mechanisms for coordinating interests and joint prevention among different regions.

Structural and functional connectivity in hydrocephalus: a scoping review.

Optimizing the treatment of hydrocephalus remains a major challenge in adult and pediatric neurosurgery. Currently, clinical treatment relies heavily on anatomic imaging of ventricular size and clinical presentation. The emergence of functional and structural brain connectivity imaging has provided the basis for a new paradigm in the management of hydrocephalus. Here we review the pertinent advances in this field. Following PRISMA-ScR guidelines for scoping reviews, we searched PubMed for relevant literature from 1994 to April 2023 using hydrocephalus and MRI-related terms. Included articles reported original MRI data on human subjects with hydrocephalus, while excluding non-English or pre-1994 publications that didn't match the study framework. The review identified 44 studies that investigated functional and/or structural connectivity using various MRI techniques across different hydrocephalus populations. While there is significant heterogeneity in imaging technology and connectivity analysis, there is broad consensus in the literature that 1) hydrocephalus is associated with disruption of functional and structural connectivity, 2) this disruption in cerebral connectivity can be further associated with neurologic compromise 3) timely treatment of hydrocephalus restores both cerebral connectivity and neurologic compromise. The robustness and consistency of these findings vary as a function of patient age, hydrocephalus etiology, and the connectivity region of interest studied. Functional and structural brain connectivity imaging shows potential as an imaging biomarker that may facilitate optimization of hydrocephalus treatment. Future research should focus on standardizing regions of interest as well as identifying connectivity analysis most pertinent to clinical outcome.

Pricing carbon in a multi-sector economy with social discounting

Economists tend to view a uniform emissions price as the most cost-effective approach to reducing greenhouse gas emissions. This paper scrutinizes the assumptions in general equilibrium which underlie the established view that uniform emissions pricing is optimal, focusing on economies where society values the well-being of future generations more than private actors. When social and private discount rates differ, we show that a uniform carbon price is optimal only under restrictive assumptions about technology homogeneity and intertemporal decision-making. Non-uniform pricing spurs capital accumulation and benefits future generations. Depending on sectoral heterogeneity in the substitutability between capital and energy inputs, we find that optimal carbon prices differ widely across sectors and yield substantial welfare gains relative to uniform pricing. Realizing these welfare gains, however, requires that the regulator has information on the technology heterogeneity across sectors. Differentiated carbon pricing based on imperfect estimates of technology heterogeneity can yield significant welfare losses.

The Effects of The Multi-Target Policy on Green Productivity: Evidence from China’s Fossil Fuel Power Plants

We propose the non-radial meta-frontier global Luenberger productivity index, which addresses the issues of technology heterogeneity, slack variable, and linear programming infeasibility, and decompose it based on the technology perspective and factor perspective. With the dataset of China’s power plants from 2005 to 2015, we identify the effects of the multi-targets, including energy-saving targets and [Formula: see text] reduction targets, in the 12th Five-Year Plan on green productivity encompassing all channels of effects. A [Formula: see text] increase in the average energy-saving target cumulatively increases green productivity by [Formula: see text], while a [Formula: see text] increase in the average [Formula: see text] reduction target cumulatively decreases that by [Formula: see text]. In all channels, the technology leadership effect and generation efficiency exhibit sensitivity. Differential effects of the multi-targets on state-owned versus non-stateowned plants imply significant differences in observed production technology.

Measuring and Clustering Heterogeneous Chatbot Designs

Conversational agents, or chatbots, have become popular to access all kind of software services. They provide an intuitive natural language interface for interaction, available from a wide range of channels including social networks, web pages, intelligent speakers or cars. In response to this demand, many chatbot development platforms and tools have emerged. However, they typically lack support to statically measure properties of the chatbots being built, as indicators of their size, complexity, quality or usability. Similarly, there are hardly any mechanisms to compare and cluster chatbots developed with heterogeneous technologies. To overcome this limitation, we propose a suite of 21 metrics for chatbot designs, as well as two clustering methods that help in grouping chatbots along their conversation topics and design features. Both the metrics and the clustering methods are defined on a neutral chatbot design language, becoming independent of the implementation platform. We provide automatic translations of chatbots defined on some major platforms into this neutral notation to perform the measurement and clustering. The approach is supported by our tool Asymob , which we have used to evaluate the metrics and the clustering methods over a set of 259 Dialogflow and Rasa chatbots from open-source repositories. The results open the door to incorporating the metrics within chatbot development processes for the early detection of quality issues, and to exploit clustering to organise large collections of chatbots into significant groups to ease chatbot comprehension, search and comparison.

Investigating the effects of ICT, education, and R&D on economic efficiency and technology heterogeneity: A cross-country analysis

This study explores the impact of information and communication technology (ICT), education, and research and development (R&D) on countries' economic efficiency and technology heterogeneity. A panel of 52 countries, classified into developed (31 sample countries) and developing (21 sample countries) during 2011–2019, were the data sources for the analysis. We obtained relative country-level economic efficiency and technology gap inefficiency estimates from a novel Bayesian data envelopment analysis (DEA) approach. Bias-corrected estimates yielded by this technique have proven to be valid. We then regressed the estimates on ICT, education, and R&D proxy variables using a two-step and an iterative generalized method of moments (GMM) for linear dynamic panel data. Our analysis mitigates possible feedback effects between the explanatory and response variables, as well as possible endogeneity. The novelty of this work goes beyond the use of a new data analysis technique, investigating the impact of the three factors mentioned above and the country classification on technology heterogeneity caused by differences in countries' market structures, regulatory frameworks, economic and knowledge ecosystems, and cultures. Based on this study's findings, fixed broadband subscriptions have the most significant effect on economic efficiency improvement, while R&D is the main driver for reducing technology gap inefficiency. Specifically, ICT-facilitated knowledge spillovers within and across country groups through R&D cooperation play a significant positive role in closing the technology gap. This finding is consistent with the catch-up theory.

How to establish input and output targets for airlines with heterogeneous production technologies: A nash bargaining DEA approach within the meta-frontier framework

In order to take into consideration the presence of technological heterogeneity in the aviation industry, numerous studies over the past ten years have embraced the data envelopment analysis (DEA)-based meta-frontier framework. Since group targets and meta targets are calculated independently to evaluate the technology gap in airlines, there is a possibility of obtaining unrealistic targets. Estimating the projected inputs and outputs of the evaluated units separately at both the group and meta-frontier does not automatically ensure superiority on the meta-frontier compared to the group frontier. Our proposed combined Nash bargaining model that integrates group technology and metatechnology presents a potential answer to the current unreasonable targets issue and gives more reliable outcomes. The results of the empirical application on a dataset of Alliance and Non-alliance airlines show that the proposed approach generates targets for inputs and outputs which are realistic and optimal, as well as a valid and reasonable technology gap. The finding thus validates the utilisation of our suggested model and it can be used by airline managers to improve the airline operational process.