Search Results (8)

In the context of the “double carbon” goals and energy transformation, the integration of energy and transportation has emerged as a crucial trend in their coordinated development. Wind power prediction serves as the cornerstone technology for ensuring efficient operations within this integrated framework. This paper introduces a wind power prediction methodology based on an NGBoost–GRU fusion model and devises an innovative dynamic charging optimization strategy for electric vehicles (EVs) through deep collaboration. By integrating the dynamic feature extraction capabilities of GRU for time series data with the strengths of NGBoost in modeling nonlinear relationships and quantifying uncertainties, the proposed approach achieves enhanced performance. Specifically, the dual GRU fusion strategy effectively mitigates error accumulation and leverages spatial clustering to boost data homogeneity. These advancements collectively lead to a significant improvement in the prediction accuracy and reliability of wind power generation. Experiments on the dataset of a wind farm in Gansu Province demonstrate that the model achieves excellent performance, with an RMSE of 36.09 kW and an MAE of 29.96 kW at the 12 h prediction horizon. Based on this predictive capability, a “wind-power-charging collaborative optimization framework” is developed. This framework not only significantly enhances the local consumption rate of wind power but also effectively cuts users’ charging costs by approximately 18.7%, achieving a peak-shaving effect on grid load. As a result, it substantially improves the economic efficiency and stability of system operation. Overall, this study offers novel insights and robust support for optimizing the operation of integrated energy systems. Full article

Social farming in Italy has not developed homogeneously. In view of this, this article adopts a multivariate analysis approach to analyse the heterogeneity and the similarities in the development paths of social farming in Italian municipalities. The article takes into account the information from a representative sample of 410 interviews. The results suggest that the offer of social farming activities is highly correlated with the distinct nature of the enterprises and with the interest of local actors, who in many cases finance these activities. Regarding the characteristics of social farms, the results of principal component analysis show that the consolidated experience of offering social services and the continuation of activities are the most important organisational elements. Concerning the territorial distribution, the findings of a hierarchical cluster analysis show that Italian municipalities have distinct experiences according to the level of maturity of the social farms in each cluster, with differences in location, the agricultural system, the organisational culture and the social services provided. Assessing enterprise characteristics and recent development paths for social services in Italy can have far-reaching implications for policy. The latter should provide information and training to farmers and users to increase understanding of the social value of social farming and foster a collaborative and sustainable approach to social farming practice. Full article

Location-based services have become an important part of our daily lives, and while users enjoy convenient Internet services, they also face the risk of privacy leakage. K-anonymity is a widely used method to protect location privacy, but most existing K-anonymity location privacy protection schemes use virtual locations to construct anonymity zones, which have the problem of being vulnerable to attackers through background knowledge, while the improved collaborative K-anonymity scheme does not sufficiently consider whether collaborating users share similar attributes. We propose a distributed K-anonymity location privacy-preserving algorithm based on interest points and user social behaviors to solve these problems in existing K-anonymity schemes. The method determines the similarity of users by their interest points and social behaviors and then selects users with high similarity to build an anonymous set of collaborative users. Finally, to ensure the relatively uniform distribution of collaborative users, a homogenization algorithm is used to make the anonymous location points as dispersed as possible. The experimental results showed that our algorithm can effectively resist background attacks, and the uniformly distributed anonymous location points can achieve higher-quality anonymous regions. Full article

The incidence of type 1 diabetes (T1D) has increased by about 3% per year over the last two decades. Continuous Insulin Subcutaneous Therapy (CSII) is widely used in the pediatric population with diabetes; however, it requires more preparation by the treating team and a careful selection of its potential users. Prescriptive provisions vary from region to region, and the perspective of health workers in this regard remains an unexplored area. The aim of this research project is to explore the representations of a group of diabetologists and psychologists working in pediatric diabetology throughout the country, regarding their roles, functions, and activities as part of a multidisciplinary team; it also aims to investigate their views on the potential benefits of CSII and the types of individuals who apply for the use of this technology. A socio-anagraphic data sheet was administered, and two homogeneous focus groups were conducted, one for each profession, which were then audio recorded. The transcripts produced were analyzed using the Emotional Text Mining (ETM) methodology. Each of the two corpora generated three clusters and two factors. For diabetologists, a focus on patient care emerged at different levels, involving collaboration with other health professionals and engagement with the community, often incorporating technology in medical interventions. Similarly, psychologists’ representations highlighted interdisciplinary networking with a stronger emphasis on the psychological processes involved in managing the disease, from acceptance to the elaboration and integration of diabetes into the family narrative. Understanding the representations of health professionals working in pediatric diabetes with new technologies can contribute to the consolidation of a network of professionals through targeted work on possible critical issues that may arise. Full article

With the heterogeneity and collaboration of many wireless operators (2G/3G/4G/5G/Wi-Fi), the priority is to effectively manage shared radio resources and ensure transparent user movement, which includes mechanisms such as mobility support, handover, quality of service (QoS), security and pricing. This requires considering the transition from the current mobile network architecture to a new paradigm based on collecting and storing information in big data for further analysis and decision making. For this reason, the management of big data analytics-driven networks in a cloud environment is an urgent issue, as the growth of its volume is becoming a challenge for today’s mobile infrastructure. Thus, we have formalized the problem of access network selection to improve the quality of mobile services through the efficient use of heterogeneous wireless network resources and optimal horizontal–vertical handover procedures. We proposed a method for adaptive selection of a wireless access node in a heterogeneous environment. A structural diagram of the optimization stages for wireless heterogeneous networks was developed, making it possible to improve the efficiency of their functioning. A model for studying the processes of functioning of a heterogeneous network environment is proposed. This model uses the methodology of big data evaluation to perform data transmission monitoring, analysis of tasks generated by network users, and statistical output of vertical handover initiation in (2G/3G/4G/5G/Wi-Fi) mobile communication infrastructure. The model allows studying the issues of optimization of operators’ networks by implementing the algorithm of redistribution of its network resources and providing flexible load balancing with QoS users in mind. The effectiveness of the proposed solutions is evaluated, and the performance of the heterogeneous network is increased by 16% when using the method of static reservation of network resources, compared to homogeneous networks, and another 13% when using a uniform distribution of resources and a dynamic process of their reservation, as well as compared to the previous method. An appropriate self-optimizing technique based on vertical handover for load balancing in heterogeneous wireless networks, using big data analytics, improves the QoS for users. Full article

Internet of Things (IoT) projects are increasing in size over time, and some of them are growing to reach the whole world. Sensor arrays are deployed world-wide and their data is sent to the cloud, making use of the Internet. These huge networks can be used to improve the quality of life of the humanity by continuously monitoring many useful indicators, like the health of the users, the air quality or the population movements. Nevertheless, in this scalable context, a percentage of the sensor data readings can fail due to several reasons like sensor reliabilities, network quality of service or extreme weather conditions, among others. Moreover, sensors are not homogeneously replaced and readings from some areas can be more precise than others. In order to address this problem, in this paper we propose to use collaborative filtering techniques to predict missing readings, by making use of the whole set of collected data from the IoT network. State of the art recommender systems methods have been chosen to accomplish this task, and two real sensor array datasets and a synthetic dataset have been used to test this idea. Experiments have been carried out varying the percentage of failed sensors. Results show a good level of prediction accuracy which, as expected, decreases as the failure rate increases. Results also point out a failure rate threshold below which is better to make use of memory-based approaches, and above which is better to choose model-based methods. Full article

Several heterogeneous, intelligent, and distributed devices can be connected to interact with one another over the Internet in what is termed internet of things (IoT). Also, the concept of IoT can be exploited in the industrial environment for enhancing the production of goods and services and for mitigating the risk of disaster occurrences. This application of IoT for enhancing industrial production is known as industrial IoT (IIoT). Emergent configuration (EC) is a technology that can be adopted to enhance the operation and collaboration of IoT connected devices in order to improve the efficiency of the connected IoT systems for maximum user satisfaction. To meet user goals, the connected devices are required to cooperate with one another in an adaptive, interoperable, and homogeneous manner. In this paper, a survey of the concept of IoT is presented in addition to a review of IIoT systems. The application of ubiquitous computing-aided software define networking (SDN)-based EC architecture is propounded for enhancing the throughput of oil and gas production in the maritime ecosystems by managing the exploration process especially in emergency situations that involve anthropogenic oil and gas spillages. Full article

Amidst collaborative consumption and developments in information and communication technology, the sharing economy has attracted worldwide attention, being considered sustainable consumption as it shares time, resources, and materials with others. However, because sharing-economy platforms offer nearly homogeneous assets to traditional business firms, enhancing consumer loyalty presents a huge challenge. This study provides a theoretical view for understanding the mechanisms behind user loyalty in the sharing economy. It identifies consumer satisfaction and trust in Airbnb as the key antecedents of consumer loyalty. Moreover, this study investigates the different effects of economic, hedonic, and symbolic benefits on consumers’ decision-making processes. A structural equation modeling method was used to check the research hypotheses based on a sample of 317 Airbnb consumers in South Korea. The analysis results reveal that in the case of Airbnb, consumer loyalty is jointly shaped by consumer satisfaction and trust, with entertainment and recognition significantly influencing both consumer satisfaction and trust. Moreover, money savings and exploration are not significantly related to consumers’ decision-making processes. Although social benefits significantly influence trust in Airbnb, these have no significant effect on consumer satisfaction. The findings provide theoretical and practical implications and future research direction. Full article