Search Results (19)

The diffusion of green energy technological innovation based on international green energy cooperation is a critical pathway to achieving global low-carbon emission reductions. However, few studies have considered the innovation diffusion pathways of green energy technologies under bilateral policy uncertainties. This paper constructs an evolutionary game model for the diffusion of green energy technological innovation in a complex network environment, with a focus on analyzing the impacts of key parameters such as policy spillover effects, technological heterogeneity, technical leakage risks, and free-riding risks on the equilibrium outcomes of evolutionary strategies. The results of the study are as follows: (1) Technological synergy and technological heterogeneity have a significant role in promoting the diffusion of green energy technological innovation, but when technological heterogeneity is too high, it is difficult for the two parties to find more common interests and areas of technological interaction, and the cooperative innovation will be turned into an empty shell that has a name but no reality. (2) Policy uncertainty has a significant impact on the diffusion of green energy technology innovation, and the specific impact depends on the type of policy, policy intensity, policy spillover effects, and other key parameters. (3) The risk of technological obsolescence has prompted countries to deeply participate in green energy international cooperation to realize the “curved road overtaking” of green energy technology based on technological locking and latecomer advantages; due to the existence of the phenomenon of “free-riding”, the logic of value creation based on win–win cooperation is replaced by the opportunism of “enjoying the benefits”, and cooperative innovation may be turned into a one-time “handshake agreement”. The existence of the risk of technology leakage can turn collaborative innovation into a “witch hunt” by the underdog against the overdog, and the diffusion process of green energy technology innovation is led in the wrong direction. Full article

The reserves of hot dry rock (HDR) geothermal resources are huge. The main method used to develop HDR geothermal resources is called an enhanced geothermal system (EGS), and this generally uses hydraulic fracturing. After nearly 50 years of research and development, more and more countries have joined the ranks engaged in the exploration and development of HDR in the world. This paper summarizes the base technologies, key technologies, and game-changing technologies used to promote the commercialization of HDR geothermal resources. According to the present situation of the exploration, development, and utilization of HDR at home and abroad, the evaluation and site selection, efficient and low-cost drilling, and geothermal utilization of HDR geothermal resources are defined as the base technologies. Key technologies include the high-resolution exploration and characterization of HDR, efficient and complex fracture network reservoir creation, effective microseismic control, fracture network connectivity, and reservoir characterization. Game-changing technologies include downhole liquid explosion fracture creation, downhole in-situ efficient heat transfer and power generation, and the use of CO₂ and other working fluids for high-efficient power generation. Most of the base technologies already have industrial applications, but future efforts must focus on reducing costs. The majority of key technologies are still in the site demonstration and validation phase and have not yet been applied on an industrial scale. However, breakthroughs in cost reduction and application effectiveness are urgently needed for these key technologies. Game-changing technologies remain at the laboratory research stage, but any breakthroughs in this area could significantly advance the efficient development of HDR geothermal resources. In addition, we conducted a comparative analysis of the respective advantages of China and the United States in some key technologies of HDR development. On this basis, we summarized the key challenges identified throughout the discussion and highlighted the most pressing research priorities. We hope these technologies can guide new breakthroughs in HDR geothermal development in China and other countries, helping to establish a batch of HDR exploitation demonstration areas. In addition, we look forward to fostering collaboration between China and the United States through technical comparisons, jointly promoting the commercial development of HDR geothermal resources. Full article

This advanced exploration of integrating cross-modal Artificial-Intelligence-Generated Content (AIGC) within the Unity3D game engine seeks to elevate the diversity and coherence of image generation in game art creation. The theoretical framework proposed dives into the seamless incorporation of generated visuals within Unity3D, introducing a novel Generative Adversarial Network (GAN) structure. In this architecture, both the Generator and Discriminator embrace a Transformer model, adeptly managing sequential data and long-range dependencies. Furthermore, the introduction of a cross-modal attention module enables the dynamic calculation of attention weights between text descriptors and generated imagery, allowing for real-time modulation of modal inputs, ultimately refining the quality and variety of generated visuals. The experimental results show outstanding performance on technical benchmarks, with an inception score reaching 8.95 and a Frechet Inception Distance plummeting to 20.1, signifying exceptional diversity and image quality. Surveys reveal that users rated the model’s output highly, citing both its adherence to text prompts and its strong visual allure. Moreover, the model demonstrates impressive stylistic variety, producing imagery with intricate and varied aesthetics. Though training demands are extended, the payoff in quality and diversity holds substantial practical value. This method exhibits substantial transformative potential in Unity3D development, simultaneously improving development efficiency and optimizing the visual fidelity of game assets. Full article

In this paper, we focus on developing self-organizing algorithms aimed at solving, in a distributed way, the coverage problem in Wireless Sensor Networks (WSNs). For this purpose, we apply a game-theoretical framework based on an application of a variant of the Spatial Prisoner’s Dilemma game. The framework is used to build a multi-agent system, where agent-players in the process of iterated games tend to achieve a Nash equilibrium, providing them the possible maximal values of payoffs. A reached equilibrium corresponds to a global solution for the coverage problem represented by the following two objectives: coverage and the corresponding number of sensors that need to be turned on. A multi-agent system using the game-theoretic framework assumes the creation of a graph model of WSNs and the further interpretation of nodes of the WSN graph as agents participating in iterated games. We use the following two types of reinforcement learning machines as agents: Learning Automata (LA) and Cellular Automata (CA). The main novelty of the paper is the development of a specialized reinforcement learning machine based on the application of ($ϵ,h$)-learning automata. As the second model of an agent, we use the adaptive CA that we recently proposed. While both agent models operate in discrete time, they differ in the way they store and use available information. LA-based agents store in their memories the current information obtained in the last h-time steps and only use this information to make a decision in the next time step. CA-based agents only retain information from the last time step. To make a decision in the next time step, they participate in local evolutionary competitions that determine their subsequent actions. We show that agent-players reaching the Nash equilibria corresponds to the system achieving a global optimization criterion related to the coverage problem, in a fully distributed way, without the agents’ knowledge of the global optimization criterion and without any central coordinator. We perform an extensive experimental study of both models and show that the proposed learning automata-based model significantly outperforms the cellular automata-based model. Full article

Given the significant impact of air pollution on global health, the continuous and precise monitoring of air quality in all populated environments is crucial. Unfortunately, even in the most developed economies, current air quality monitoring networks are largely inadequate. The high cost of monitoring stations has been identified as a key barrier to widespread coverage, making cost-effective air quality monitoring devices a potential game changer. However, the accuracy of the measurements obtained from low-cost sensors is affected by many factors, including gas cross-sensitivity, environmental conditions, and production inconsistencies. Fortunately, machine learning models can capture complex interdependent relationships in sensor responses and thus can enhance their readings and sensor accuracy. After gathering measurements from cost-effective air pollution monitoring devices placed alongside a reference station, the data were used to train such models. Assessments of their performance showed that models tailored to individual sensor units greatly improved measurement accuracy, boosting their correlation with reference-grade instruments by up to 10%. Nonetheless, this research also revealed that inconsistencies in the performance of similar sensor units can prevent the creation of a unified correction model for a given sensor type. Full article

The integral role of wild fungi in ecosystems, including provisioning, regulating, cultural, and supporting services, is well recognized. However, quantifying and predicting wild mushroom yields is challenging due to spatial and temporal variability. In Mediterranean forests, climate-change-induced droughts further impact mushroom production. Fungal fruiting is influenced by factors such as climate, soil, topography, and forest structure. This study aims to quantify and predict the mycological potential of Lactarius deliciosus in sustainably managed Mediterranean pine forests using machine learning models. We utilize a long-term dataset of Lactarius deliciosus yields from 17 Pinus pinaster plots in Soria, Spain, integrating forest-derived structural data, NASA Landsat mission vegetation indices, and climatic data. The resulting multisource database facilitates the creation of a two-stage ‘mycological exploitability’ index, crucial for incorporating anticipated mycological production into sustainable forest management, in line with what is usually done for other uses such as timber or game. Various Machine Learning (ML) techniques, such as classification trees, random forest, linear and radial support vector machine, and neural networks, were employed to construct models for classification and prediction. The sample was always divided into training and validation sets (70-30%), while the differences were found in terms of Overall Accuracy (OA). Neural networks, incorporating critical variables like climatic data (precipitation in January and humidity in November), remote sensing indices (Enhanced Vegetation Index, Green Normalization Difference Vegetation Index), and structural forest variables (mean height, site index and basal area), produced the most accurate and unbiased models (OA_training = 0.8398; OA_validation = 0.7190). This research emphasizes the importance of considering a diverse array of ecosystem variables for quantifying wild mushroom yields and underscores the pivotal role of Artificial Intelligence (AI) tools and remotely sensed observations in modeling non-wood forest products. Integrating such models into sustainable forest management plans is crucial for recognizing the ecosystem services provided by them. Full article

The gaming industry is one of the most important and innovative subfields in the field of technology, which boasts a staggering USD 200 billion in annual revenue and stands as a behemoth. It has an immense effect on popular culture, social networking, and the entertainment industry. Continuous advances in technology are the primary factor fueling the industry’s expansion, and these innovations are also revolutionizing the design of games and improving the overall gaming experience for players. The growing number of people who have access to the internet, the widespread use of smartphones, and the introduction of high-bandwidth networks such as 5G have all contributed to an increase in the demand for gaming around the world. It is essential to perform consumer feedback analysis if one wants to appreciate market requirements, evaluate game performance, and realize the effect that games have on players. On the other hand, short-text reviews frequently lack grammatical syntax, which makes it difficult for standard natural language processing (NLP) models to effectively capture underlying values and, as a result, compromises the accuracy of these models. This research focuses on determining which natural language processing model is the most accurate at forecasting five-star ratings of video games based on brief reviews. We make use of natural language processing (NLP) to avoid the constraints that are imposed on us by the linguistic structure of short-text reviews. The findings of our research have led to several important contributions, one of which is the creation of an innovative model for reviewing and grading short writings. The accuracy is improved by employing different machine learning models, which enables game creators and other industry stakeholders to identify patterns about the behavior and preferences of the users. Full article

As the digitization of the supply side continues to advance, the takeaway industry has made a significant contribution to economic growth. However, the rapid expansion of the scale has also brought many social problems, merchants provide low-quality goods out of the psychology of opportunity, and the uneven quality of goods and asymmetric information not only bring great regulatory problems for the takeaway platform, but also make it difficult for consumers to identify the platform, merchants, and consumers as takeaway platform stakeholders, it is difficult to integrate resources to achieve value co-creation. Therefore, in order to realize the value co-creation among the stakeholders of the takeaway platform, a three-party evolutionary game model was constructed to analyze and simulate the strategic choices of stakeholders under different situations through simulation experiments and to explore the sensitive influence of each factor. The results of the study show the following: shaping a scientific reward and punishment system and setting reasonable rewards and punishments within a limited threshold; platforms, consumers using word-of-mouth effects to amplify the loss of network externalities that merchants have to bear when they provide low-quality services, and improving the consumer feedback mechanism to reduce the cost of feedback are all effective measures to promote the active participation of takeaway platform stakeholders in value co-creation and promote the sustainable and healthy development of the takeaway industry. Full article

With the emergence of new internet traffic types in modern transport networks, it has become critical for service providers to understand the structure of that traffic and predict peaks of that load for planning infrastructure expansion. Several studies have investigated traffic parameters for Virtual Reality (VR) applications. Still, most of them test only a partial range of user activities during a limited time interval. This work creates a dataset of captures from a broader spectrum of VR activities performed with a Meta Quest 2 headset, with the duration of each real residential user session recorded for at least half an hour. Newly collected data helped show that some gaming VR traffic activities have a high share of uplink traffic and require symmetric user links. Also, we have figured out that the gaming phase of the overall gameplay is more sensitive to the channel resources reduction than the higher bitrate game launch phase. Hence, we recommend it as a source of traffic distribution for channel sizing model creation. From the gaming phase, capture intervals of more than 100 s contain the most representative information for modeling activity. Full article

This paper focuses on image and video content analysis of handball scenes and applying deep learning methods for detecting and tracking the players and recognizing their activities. Handball is a team sport of two teams played indoors with the ball with well-defined goals and rules. The game is dynamic, with fourteen players moving quickly throughout the field in different directions, changing positions and roles from defensive to offensive, and performing different techniques and actions. Such dynamic team sports present challenging and demanding scenarios for both the object detector and the tracking algorithms and other computer vision tasks, such as action recognition and localization, with much room for improvement of existing algorithms. The aim of the paper is to explore the computer vision-based solutions for recognizing player actions that can be applied in unconstrained handball scenes with no additional sensors and with modest requirements, allowing a broader adoption of computer vision applications in both professional and amateur settings. This paper presents semi-manual creation of custom handball action dataset based on automatic player detection and tracking, and models for handball action recognition and localization using Inflated 3D Networks (I3D). For the task of player and ball detection, different configurations of You Only Look Once (YOLO) and Mask Region-Based Convolutional Neural Network (Mask R-CNN) models fine-tuned on custom handball datasets are compared to original YOLOv7 model to select the best detector that will be used for tracking-by-detection algorithms. For the player tracking, DeepSORT and Bag of tricks for SORT (BoT SORT) algorithms with Mask R-CNN and YOLO detectors were tested and compared. For the task of action recognition, I3D multi-class model and ensemble of binary I3D models are trained with different input frame lengths and frame selection strategies, and the best solution is proposed for handball action recognition. The obtained action recognition models perform well on the test set with nine handball action classes, with average F1 measures of 0.69 and 0.75 for ensemble and multi-class classifiers, respectively. They can be used to index handball videos to facilitate retrieval automatically. Finally, some open issues, challenges in applying deep learning methods in such a dynamic sports environment, and direction for future development will be discussed. Full article

Social e-commerce is an emerging e-commerce mode in response to the upgrading of consumption, which has become an important engine for the development of the digital economy. Knowledge transfer and sharing play vital roles in improving the competitiveness and the sustainability of social e-commerce platform enterprises. However, academic research on knowledge transfer for the social e-commerce platform enterprise’s operation team remains deficient. To help social e-commerce platform enterprises to improve performance and better seek survival and sustainable development, this paper constructs a knowledge transfer model for the social e-commerce platform enterprise’s operation team, in the self-centered sustainable ecological business mode, from the relationship between intra-organizational operation knowledge transfer and cross-organizational knowledge sharing for value co-creation, and explores knowledge transfer behaviors from the perspective of complex network-based evolutionary game under strategy imitation preferences. Simulation results indicate that relationships among knowledge transfer cost, knowledge synergy benefit, cross-organizational value co-creation benefit rate, and reward and punishment, along with strategy imitation preferences, significantly impact knowledge transfer behaviors of the social e-commerce platform enterprise’s operation team. When all the members of the social e-commerce platform enterprise’s operation team prefer to imitate the knowledge transfer strategies of the operation members with smaller knowledge transfer costs, the operation team is more likely to show a high proportion adopting the transfer strategy, requiring low knowledge synergy coefficient, reward, punishment, and cross-organizational value co-creation benefit rate to achieve stable and sustainable knowledge transfer. Conversely, the operation team is more likely to show a low proportion adopting the transfer strategy, requiring high knowledge synergy coefficient, reward, punishment, and cross-organizational value co-creation benefit rate to achieve stable and sustainable knowledge transfer. This study has significance as a guide for social e-commerce platform enterprises in deploying the self-centered sustainable ecological business mode. Full article

Designing a successful and efficient collaborative governance mechanism to promote the value co-creation of complementors has become critical to platform owners. Therefore, using an evolutionary game theory approach, we first constructed a conceptual model of collaborative governance, analyzing the conditions of collaborative governance of multiple subjects. This was based on the belief that the design of a collaborative governance mechanism needs to nurture collaborative culture and internalize it into the practice of platform governance. Secondly, this paper built a tripartite evolutionary game model of platform enterprises, complementary enterprises, and users, systematically illustrating the strategy evolution process of the three parties under incentive and penalty mechanisms, and simulated the influence of parameters, such as cost, culture, and cooperative willingness, on the evolutionary results. The results showed that: (1) A culture of trust and encouragement of innovation was more conducive to collaborative innovation; (2) Platform enterprises are more sensitive to joint cost investment, work culture environment, and benefit distribution; (3) Complementary enterprises and users have a solid ambition to respond to the impulses of digital technology. In particular, when the initial desire to collaborate is low, the evolutionary process of platform enterprises presents an asymmetric ‘U’ shape. To enable stakeholders of the platform to formally, prudently, and deeply participate in the ecological governance process, platform enterprises should fully use network resources and digital technology to build a platform for high-intensity interaction and communication between complementary enterprises and users, and improve their identification with the innovation culture. Full article

This study examined the impact of digital transformation (DT) on supply chain procurement (SCP) for the creation of competitive advantage (CAD). This study adopted a quantitative approach using a survey administered to 221 supply chain (SC) professionals through the professional networking website LinkedIn. The conceptual model was evaluated with the partial least squares-based structural equation model (PLS-SEM) using SmartPLS. The findings showed that DT has significant positive impacts on SCP and CAD, and that SCP has a significant positive impact on CAD. Supply chain procurement plays a significant mediating role in the relationship between DT and CAD. The findings are useful for decision-makers investing in digitally modernising their SC processes. The study recommends starting the DT of an SC with procurement, as procurement is a complex process involving a wide range of internal and external stakeholders. The results show that digital procurement may be an SC game changer in a competitive market. The study provides initial guidelines for a transition from traditional to smart procurement (procurement 4.0). Despite the prevalence of studies on SCP, there is a lack of empirical evidence on how DT of procurement functions can lead to sustainable CAD. Full article

Creative thinking is an inherent process in the creation of innovations. Imagination is employed to seek creative solutions. This article presents research results on the use of inventive methods to develop an eco-friendly product. A household appliance was selected as the object of research. The article deals with issues relating to eco-design, eco-innovation, and inventory. The process of selecting inventive methods was presented. Selected inventive methods used to develop the product concept were briefly characterized. Creativity sessions were conducted using the methods of brainstorming, stimulating, reverse brainstorming, word games, and superpositions. The effect of these activities is the concept for an eco-innovative product. A product design was developed that is highly recyclable and environmentally friendly. An ecological analysis of the designed product, including AI-based (artificial neural networks), was carried out, which showed the legitimacy of the actions taken to develop an environmentally friendly product. The novelty of the proposed approach consists of combining the use of research data, with new methods for their analysis using both traditional and artificial intelligent tools, to create a transparent and scalable product design. To date, this approach is unique and has no equivalent in the literature. Despite higher manufacturing costs, the more environmentally friendly refrigerator is cheaper in operation (consumes less energy) due to the ecological solutions incorporated into its design. Full article

Considering the Social Network Analysis approach and based on the creation of mathematical models, the aim of this study is to analyze the players’ interactions of professional football teams in critical moments of the game. The sample consists in the analysis of a 2019/2020 season UEFA Champions League match. The mathematical models adopted in the analysis of the players (micro analysis) and the game (macro analysis) were obtained through the uPATO software. The results of the networks indicated a performance pattern trend more robust in terms of the mathematical model: Network Density. As far as it concerned, we found that the Centroid Players had a decisive role in the level of connectivity and interaction of the team. Regarding the main critical moments of the game, the results showed that these were preceded by periods of great instability, obtaining a differentiated performance in the following mathematical models: Centrality, Degree Centrality, Closeness Centrality, and Degree Prestige. We concluded that the networks approach, in concomitance with the dynamic properties of mathematical models, and the critical moments of the game, can help coaches to better evaluate the level of interaction and connectivity of their players toward the actions imposed by opponents. Full article