Search Results (428)

“The Internet of Things” is a relatively new idea that refers to objects that can connect to the Internet and exchange data. The Internet of Things (IoT) enables novel interactions between objects and people by interconnecting billions of devices. While there are many IoT-related products, challenges pertaining to their effective implementation, particularly the lack of knowledge and confidence about security, must be addressed. To provide IoT-based enterprises with a platform for efficiency and sustainability, this study aims to identify the critical elements that influence the growth of a successful company integrated with an IoT system. This study proposes a decision support tool that evaluates the influential features of IoT using the Pythagorean Fuzzy and Interval Grey approaches within the Analytical Hierarchy Process (AHP). This study demonstrates that security, value, and connectivity are more critical than telepresence and intelligence indicators. When both strategies are used, market demand and information privacy become significant indicators. Applying the Pythagorean Fuzzy approach enables the identification of sensor networks, authorization, market demand, and data management in terms of importance. The application of the Interval Grey approach underscores the importance of data management, particularly in sensor networks. The indicators that were finally ranked are compared to obtain a good coefficient of agreement. These findings offer practical insights for promoting sustainability in enterprise operations by optimizing IoT infrastructure and decision-making processes. Full article

In the context of the global response to climate change and the active promotion of energy transformation, a number of low-carbon policies coupled with the development of synergies to help power system transformation is an important initiative. However, the insufficient articulation of the green power market, tradable green certificate (TGC) market, and carbon emission trading (CET) mechanism, and the ambiguous policy boundaries affect the trading decisions made by its market participants. Therefore, this paper systematically analyses the composition of the main players in the electricity-CET-TGC markets and their relationship with each other, and designs the synergistic mechanism of the electricity-CET-TGC markets, based on which, it constructs the optimal profit model of the thermal power plant operators, renewable energy manufacturers, power grid enterprises, power users and load aggregators under the electricity-CET-TGC markets synergy, and analyses the behavioural decision-making of the main players in the electricity-CET-TGC markets as well as the electric power system to optimise the trading strategy of each player. The results of the study show that: (1) The synergistic mechanism of electricity-CET-TGC markets can increase the proportion of green power grid-connected in the new type of power system. (2) In the selection of different environmental rights and benefits products, the direct participation of green power in the market-oriented trading is the main way, followed by applying for conversion of green power into China certified emission reduction (CCER). (3) The development of independent energy storage technology can produce greater economic and environmental benefits. This study provides policy support to promote the synergistic development of the electricity-CET-TGC markets and assist the low-carbon transformation of the power industry. Full article

The European Union Emissions Trading System (EU ETS) and its underlying market structure play a central role in the EU’s climate policy. This study analyses how the network of trading relationships within the EU ETS has evolved from a hub-dominated architecture to one marked by structural change and the emergence of new trading dynamics. Using transaction data from Phases I–IV, we apply complex network analysis to assess changes in connectivity, centrality, and community structure. We then construct a Digital Twin of the EU ETS, integrating graph neural networks and logistic regression models to simulate the entry of new participants and predict future trading links. The results indicate shifts in network composition and connectivity, especially in Phase IV, where regulatory innovations and institutional mechanisms appear to play a key role. While our analysis focuses on structural dynamics, these patterns may have broader implications for market performance and policy effectiveness. These findings underscore the importance of monitoring the evolving trading network alongside price signals to support a resilient, efficient, and environmentally credible carbon market. Full article

Education leads to building successful careers. However, different groups of students have different studying preferences. Our target group are athletes, combining their education and sports training. The main objective is to provide recommendations for an effective education system for athletes, improving their chances of finding new careers after leaving sports. Such a system must include Big Data and utilise AI possibilities currently available that support athletes’ career planning and development in a meaningful way. The main objective is specified by the following partial objectives: identifying what types of Big Data to analyse in connection with the athletes’ education; revealing what AI tools to include in the athletes’ education for their better preparation for a career after sports; determining what knowledge of AI and Big Data athletes need to stay relevant once they enter the labour market. Our study combines secondary and primary data sources. The secondary data (used in the orientation analysis) include case studies on AI and Big Data connected to education. The primary data were collected via a survey performed on over 200 Slovak junior athletes. The results show directions for the sports policymakers and sports organisations’ managers willing to improve their athletes’ career prospects. Full article

Airline networks are becoming increasingly complex, particularly at mega-hub airports characterized by high transit volumes. Effective schedule design and fleet assignment are critical for an airline, as they directly influence passenger connectivity and profitability. This study addresses the challenge of introducing a new route from a mega-hub to a new destination, while maintaining the existing flight network and leveraging arrivals from spoke airports to ensure connectivity. First, a mixed-integer nonlinear mathematical model was formulated to produce a global optimal solution at a lower time granularity, but it became computationally intractable at higher granularities due to the exponential growth in constraints and variables. Second, a genetic algorithm (GA) was employed to demonstrate scalability and flexibility, delivering near-optimal, high-granularity schedules with significantly reduced computational time. Empirical validation using real-world data from 37 spoke airports revealed that, while the exact model minimized waiting times and maximized profit at lower granularity, the GA provided nearly comparable profit at higher granularity. These findings guide airline managers seeking to optimize passenger connectivity and cost efficiency in competitive global markets. Full article

Value engineering (VE), as a conceptual approach and management technique, has allowed enterprises to capture value through mass production and market expansion during the industrial economic era. The VE method has enabled companies to produce products that meet user needs at a lower cost, leading to success. However, as the complexity of society and industry development increases, the lack of theoretical expansion in VE has limited its application in today’s more complex and macro management systems. With the development and evolution of vehicle–road collaborative intelligence, the intelligent automotive industry has become a complex system with multiple entities and interwoven values across different dimensions. Intelligent connected vehicles (ICVs), along with the external intelligent environment, will jointly participate in the realization of system functions. It is no longer sufficient to apply VE methods to analyze ICVs from a single product perspective. The pursuit of “maximizing value” is always the core driving force of industrial development. This study, building on the fundamental ideas of VE, expands and extends the connotation and theory of VE in three aspects: research objects, value dimensions, and associated entities, to adapt to the current situation. It also provides a new analysis process for the VE theory to better address systemic and complex issues. Taking the intelligent automotive industry as a case study, this study analyzes it based on the expanded VE theory. It considers not only the cost of system function realization and the product value of ICVs but also the external benefits of the system across different dimensions. The social value, user value, enterprise value are introduced in entity value analysis, and the relevant indicators are organized. This approach can better guide the collaboration and division of labor among multiple participating entities such as governments, enterprises, and users, achieving overall value maximization. Full article

Ancient paintings are an intangible window to the economy, politics, and customs of the past. Their characteristics have evolved or were made obsolete, with only limited contemporary connections remaining. This research aims to preserve and to interact with characters in 2D paintings to evolve their cultural identity through combining AI and AR. The scope of this research covers traditional Chinese paintings archived by the National Palace Museum in digital collections, mainly “New Year’s Market in a Time of Peace”. About 25 characters were used for training and 3D reconstruction in RODIN^®. The models were converted into Augment^® and Sketchfab^® platforms as reactivated AR characters to interact with new urban fabrics and landscapes. Stable Diffusion^® and RODIN^® were successfully integrated to perform image training and reconstruct 3D AR models of various styles. As a result, interactions were conducted in two ways: in a mixed context with mixed characters in a painting and in a familiar context in the real world with mixed characters. It was found that AR facilitated the interpretation of how the old urban fabric was arranged. Using AI and AR is a current issue. Combining AI and AR can activate ubiquitous preservation to perform recursive processing from diffused images in order to reconstruct 3D models. This activated heritage preservation method is a reasonable alternative to redefining intangible subjects with a new and evolved contemporary cultural identity. Full article

This study examines the impact of corporate venture capital (CVC) on the labor income share of science and innovation enterprises, focusing on data from China’s Science and Technology Innovation Board (STIB) and Growth Enterprise Market (GEM) between 2010 and 2022. Empirical results reveal a significant inverted U-shaped relationship between CVC shareholding and the labor income share of invested firms. CVC increases the labor income share by enhancing corporate governance, encouraging digital transformation, and improving human capital quality, but this effect diminishes when CVC shareholding exceeds a certain threshold. The moderating role of media attention and the heterogeneity of this relationship across regions and financial conditions are further explored. Additionally, the study identifies a positive U-shaped connection between CVC shareholding and the corporate pay gap, highlighting CVC’s complex role in influencing income inequality within firms. This research contributes to the literature by unveiling the nonlinear effects of CVC on income distribution, offering new insights into its dual role in promoting innovation and equity. Practically, it provides actionable recommendations for firms to optimize CVC ownership and for policymakers to design targeted interventions that address regional and financial disparities. By bridging the gap between CVC investment strategies and labor income fairness, this study lays the foundation for a balanced approach to sustainable economic development. Full article

The increasing adoption of Renewable Energy Sources (RESs), due to international energy policies mainly related to the decarbonization of electricity production, raises several operating issues for power systems, which need “flexibility” in order to guarantee reliable and secure operation. RESs can be considered examples of Distributed Energy Resources (DERs), which are typically electric power generators connected to distribution networks, including photovoltaic and wind systems, fuel cells, micro-turbines, etc., as well as energy storage systems. In this case, improved operation of power systems can be achieved through coordinated control of groups of DERs by “aggregators”, who also offer a “flexibility service” to power systems that need to be appropriately remunerated according to market rules. The implementation of the aggregator function requires the development of tools to optimally operate, control, and dispatch the DERs to define their overall flexibility as a “market product” in the form of bids. The contribution of the present paper in this field is to propose a new optimization strategy for flexibility bidding to maximize the profit of the aggregator in flexibility markets. The proposed optimal scheduling procedure accounts for important practical and technical aspects related to the DERs’ operation and their flexibility estimation. A case study is also presented and discussed to demonstrate the validity of the method; the results clearly highlight the efficacy of the proposed approach, showing a profit increase of 10% in comparison with the base case without the use of the proposed methodology. It is evident that quantitatively more significant results can be obtained when larger aggregations (more participants) are considered. Full article

This study investigates the evolutionary mechanisms and equilibrium character-istics of the China–US trade dispute through an improved ecological competition model. By quantifying tariff policies as competition intensity regulators and introducing trade elasticity parameters, we construct a dynamic system that captures the nonlinear feedback between economic rivals. Key findings are as follows. (1) When both nations implement reciprocal tariff measures with similar economic sensitivities, the system converges to a stable equilibrium where bilateral economic outputs stabilize at reduced levels compared to pre-conflict states, provided the product of adjusted competition coefficients remains below critical thresholds. (2) Excessive tariff escalation beyond identifiable tipping points triggers winner-takes-all outcomes, validating the “Thucydides Trap” hypothesis in eco-nomic conflicts. (3) Empirical simulations using 2018–2023 trade data demonstrate that China’s tit-for-tat tariff strategy effectively maintains competitive balance, while domestic market expansion measures (evidenced by a 6.3% average annual growth in China’s do-mestic consumption) significantly mitigate trade diversion effects. The study establishes theoretical connections with optimal tariff theory and strategic trade policy literature while providing policymakers with quantitative tools to assess trade policy impacts. Our find-ings theoretically validate China’s policy combination of calibrated reciprocity and domestic demand stimulation, offering new insights into managing great-power economic competition. Full article

Driven by the global “double carbon” goal, the volatility of renewable energy poses a challenge to the stability of power systems. Traditional methods have difficulty dealing with high-dimensional nonlinear data, and the single deep learning model has the limitations of spatiotemporal feature decoupling and being a “black box”. Aiming at the problem of insufficient accuracy and interpretability of power load forecasting in a renewable energy grid connected scenario, this study proposes an interpretable spatiotemporal feature fusion model based on an attention mechanism. Through CNN layered extraction of multi-dimensional space–time features such as meteorology and electricity price, BiLSTM bi-directional modeling time series rely on capturing the evolution rules of load series before and after, and the improved self-attention mechanism dynamically focuses on key features. Combined with the SHAP quantitative feature contribution and feature deletion experiment, a complete chain of “feature extraction time series modeling weight allocation interpretation and verification” is constructed. The experimental results show that the determination coefficient R² of the model on the Australian electricity market data set reaches 0.9935, which is 84.6% and 59.8% higher than that of the LSTM and GRU models, respectively. The prediction error (RMSE = 105.5079) is 9.7% lower than that of TCN-LSTM model and 52.1% compared to the GNN (220.6049). Cross scenario validation shows that the generalization performance is excellent (R² ≥ 0.9849). The interpretability analysis reveals that electricity price (average absolute value of SHAP 716.7761) is the core influencing factor, and its lack leads to a 0.76% decline in R². The research breaks through the limitation of time–space decoupling and the unexplainable bottleneck of traditional models, provides a transparent basis for power dispatching, and has an important reference value for the construction of new power systems. Full article

This paper presents a microsimulation-driven framework to analyze the performance of connected and automated vehicles (CAVs) alongside vehicles with human drivers (VHDs), channeled towards assessing project alternatives in road intersection design. The transition to fully automated mobility is driving the development of new intersection geometries and traffic configurations, influenced by increasing market entry rates (MERs) for CAVs (CAV-MERs), which were analyzed in a microsimulation environment. A suburban signalized intersection from the Polish road network was selected as a representative case study. Two alternative design hypotheses regarding the intersection’s geometric configurations were proposed. The Aimsun micro-simulator was used to hone the driving model parameters by calibrating the simulated data with reference capacity functions (RCFs) based on CAV factors derived from the Highway Capacity Manual 2022. Cross-referencing the conceptualized geometric design solutions, including a two-lane roundabout and an innovative knee-turbo roundabout, allowed the experimental results to demonstrate that CAV operation is influenced by the intersection layout and CAV-MERs. The research provides an overview of potential future traffic settings featuring CAVs and VHDs operating within various intersection designs. Additionally, the findings can support project proposals for the geometric and functional design of intersections by highlighting the potential benefits expected from smart driving. Full article

Fast-growing technology and the development of IT services give the idea of founding a new application of stochastic processes and their properties. We give a new connection between electronic process management and a stochastic process named the bivariate Pólya–Aeppli counting process. This process is applied as a counting process in the mathematical construction of the given model and it has been introduced as a counting process in electronic process management. In our current study, we assume a company that has two locations in two countries—Bulgaria and Romania. For seamless communication and data sharing, the integrated factories leverage the System Applications and Products in Data Processing (SAP) system. By combining these functions into one structure, we optimize coordination, streamline operations, and improve the company’s productivity and profitability. The automated tools within the system facilitate uninterrupted production and secure supply chains and thus the decision making is improved. A key benefit of these tools is to boost production and procurement activities for success in today’s competitive market which results in cost savings, will increase visibility, and also will improve rapid decision making. Full article

Neurotechnology is a rapidly emerging field with vast potential within healthcare, but also has inherent concerns. There is, therefore, a need to ensure the responsible and ethical development and regulation of these technologies. This horizon scan aimed to provide an overview of neurotechnologies in development and those approved by the FDA as of June 2024 for a range of conditions relating to mental health, healthy ageing, and physical disability. Searches of clinical trials, conferences, journals, and news were performed in March 2024. Relevant technologies were identified through a process of screening, data extraction and synthesis. A total of 81 unique neurotechnologies were identified, with 23 relating to mental health, 31 to healthy ageing, and 42 to physical disability. A total of 79% percent did not yet have FDA approval and 77.4% were at earlier stages of development (pilot/feasibility studies), with 22.6% at pivotal or post-market stages. Digital elements were common features of the technologies, including software, apps, and connectivity to other devices; however, there were only three technologies with an identifiable AI component. A complex regulatory landscape and unique ethical and safety concerns associated with neurotechnology could pose challenges to innovators, though the emerging nature of the field also provides an opportunity to pre-emptively address potential issues. Full article

With the acceleration of globalization, the connections between emerging market economies are becoming increasingly intricate, making it crucial to understand the mechanisms of risk transmission. This study employs the transfer entropy model to analyze risk diffusion and network resilience across ten emerging market countries. The findings reveal that Brazil, Mexico, and Saudi Arabia are the primary risk exporters, while countries such as India, South Africa, and Indonesia predominantly act as risk receivers. The research highlights the profound impact of major events such as the 2008 global financial crisis and the 2020 COVID-19 pandemic on risk diffusion, with risk diffusion peaking during the pandemic. Additionally, the study underscores the importance of network resilience, suggesting that certain levels of noise and shocks can enhance resilience and improve network stability. While the global economy gradually recovered following the 2008 financial crisis, the post-pandemic recovery has been slower, with external shocks and noise presenting long-term challenges to network resilience. This study emphasizes the importance of understanding network resilience and risk diffusion mechanisms, offering new insights for managing risk transmission in future global economic crises. Full article