Data grading for intelligent connected vehicles: an inference strength-driven framework

The efficiency of data retrieval from massive heterogeneous astronomical catalogs, as well as integration of various astronomical data retrieval services, are both key problems in astronomical data retrieval. This paper introduces a platform for Massive Heterogeneous Astronomical Data Retrieval (MHADR) which has been applied to the data center of National Astronomical Observatory of China. It proposes an index mechanism based on cross-matching to retrieve massive heterogeneous astronomical data resources from multiple catalogs efficiently. And on the platform it integrates various data retrieval services using the structure of four modules. ConeSearch service is implemented as a sample service of the platform. And the experiment shows the cross-matching in the retrieval costs nearly 25s, which implies the high efficiency cross-matching.

Digital data is a new factor of production in the information age. In the context of the rapiddevelopment of the latest technologies, such as big data, artifi cial intelligence, cloud computing andblockchain, as well as the continuous development of the digital economy, digital data plays an important role in the development of enterprises. Data security issues affect the development of a countryand its security, are related to public interests, and are also closely related to the personal rights of its citizens. It is necessary to regulate data protection at the legal level.July 2, 2020 the website of the National People’s Congress published the Data Security Law of the People’s Republic of China (draft), which systematically refl ects the current national point of view on data security and development, noting the improvement of data security to the level of state security. Like the Cybersecurity Law, it is a component of the general concept of national security, belongs to the highest level of law and has the highest legal force. This law is of great strategic importance for building a data protection system in the PRC.As the main law in the fi eld of data security management ,the Data Security Law has made certain breakthroughs in the defi nition of the concept of data. The concept of data in the Data Security Law is not limited to network data, but includes information recorded in electronic form and in other ways; the necessary extraterritorial consequences are provided for in the draft Data Security Law, which is of great importance for the protection of national sovereignty and civil rights of the People’s Republic of China; the relationship between data security protection, data development and use is clarifi ed, and the status of data in the development of the digital economy is clarifi ed through legislation in order to fully demonstrate the economic value of data; the main body responsible for security, supervision and overall coordination has been created, the main responsibilities of various regions and departments have been increased, supervision responsibilities have been redistributed, which is more in line with the needs and current situation in data security supervision; a data classifi cation system has been established that promotes the relevant work of data security surveillance agencies; an appropriate mechanism has been created at the state level in the system of data security risk assessment, reporting, information exchange, monitoring and early warning; a data export control system and a mutual data protection system were created, as well as the construction of a cross-border data fl ow system of the PRC was standardized; data protection obligations in organizations and for individuals performing data operations and performing data protection duties were clarifi ed; adhere to the principles of security and development and provide measures to support and promote data security and development; institutional measures have been taken to ensure the security of government data.The draft law on data security contains a comprehensive concept of information security. The upcoming Law on Data Security will contain recommendations and basic principles for ensuring data security and development. All industries will have to pay attention to an integrated management system and various supporting provisions to ensure data security and development. Gradually, a favorable environment will be created for the effective use of data and safe development, which will signifi cantly contribute to the rapid development of informatization and digitization.The Data Security Law includes all types of information in the fi eld of protection. In the future, the Law on Data Security should pay attention to the Law on Cybersecurity, Law on State Security and Law on the Protection of Personal Information, which are under development, supplement and improve today’s relatively disparate legislation related to data security. The laws and regulations of the PRC on data security will be carefully developed on the basis of these three laws in order to fully implement the development, use of data and industrial development, thereby promoting new provisions in the creation of a legal data security system.

The paper explores how blockchain technology can transform data management, especially when it comes to handling privacy and security issues. Blockchain's immutability, transparency, and decentralization make it a potentially useful tool for addressing the expanding problems associated with data security and integrity. However, the slow adoption of blockchain technology is influenced by a complex interplay of developing privacy concerns, legislative uncertainty, and technological subtleties. The current study aims to identify key data privacy, protection, and security challenges associated with blockchain adoption in various domains, assess the adequacy of the current data privacy, protection, and security laws to address these challenges, and propose potential solutions and areas for further research to mitigate data privacy, protection, and security risks in blockchain applications. Additionally, the research has examined the adaptability and dependability of the two main kinds of blockchain—public and private—as well as the variations in their reach and visibility.

This study provides a comprehensive examination of data privacy and security within the realm of environmental research, highlighting the evolving challenges and innovative solutions in safeguarding sensitive environmental data. Employing a systematic literature review and content analysis, this research scrutinizes peer-reviewed articles, legal documents, and policy papers published from 2014 to 2024. The study's objectives were to explore fundamental principles, assess key developments and cutting-edge solutions, and understand the impact of international standards and regulatory bodies on data protection in environmental studies. Key findings reveal a growing emphasis on robust data governance frameworks, driven by advancements in digital technologies and the global diffusion of regulations such as the GDPR. Innovations in encryption and anonymization techniques have emerged as critical tools in enhancing data security. However, the study identifies a pressing need for interdisciplinary collaboration and stakeholder engagement to navigate the complex landscape of data privacy and security effectively. The study concludes that the future of environmental research is intricately linked to the strategic integration of data privacy and security measures. It recommends a proactive approach to data protection, emphasizing the importance of compliance with international regulations, the adoption of privacy-by-design principles, and the development of education and training programs. Future research should focus on advancing data security technologies and exploring the socio-ethical implications of data use in environmental studies, ensuring the ethical stewardship of sensitive information. Keywords: Data Privacy, Environmental Research, Data Security Solutions, International Regulations.

Adaptive team performance is essential in high-pressure, unpredictable environments. This study investigates the impact of team coordination and team communication on adaptive team performance. Data were collected from 230 team members using structured questionnaires and analyzed through Partial Least Squares (PLS). Results reveal significant and positive relationships between coordination and communication, and adaptive team performance. These findings highlight the importance of strengthening team functional processes to enhance responsiveness and resilience. The study provides practical insights for enhancing team effectiveness in dynamic operational environments, particularly where rapid decision-making and flexibility are essential for success in unpredictable and challenging conditions.

This paper propose and test a model of how inter-firm network effect firmpsilas absorptive capacity in a dynamic operating environment. Using data of manufacturing firms, the results indicate that the intensity of network has a positive impact on potential absorptive capacity, and the intensity of weak tie network are more important; the scale of weak tiespsila network has a positive impact on potential absorptive capacity; and the location of weak tiespsila network has a negative impact on potential absorptive capacity. The empirical results suggest that the potential absorptive capacity has mediating effects on the relationships between firm network and firmpsilas realized absorptive capacity. In addition, coordination knowledge integration mechanism has moderation effects on the relationships between firmpsilas potential absorptive capacity and realized absorptive capacity.

In the past 30 years there has been a paradigmatic change in modelling, sharing and utilising knowledge from the perspective of centralised, controlled systems, to that of open, dynamic environments. This has been fueled mainly by advances and the ubiquity of the prototypical open environment - ldquoThe Webrdquo, and the increasing need for managing entities in such distributed systems. However, for these changes to occur, we need to revisit many of the underlying assumptions that have formed the basis of Knowledge Sharing, and examine how they relate to the notion of distribution. Likewise, to facilitate adaptation to changes in open environments, autonomy must be considered as assumptions relating to human intervention and offline changes are relaxed. In this paper we examine the challenges of sharing knowledge in distributed, dynamic open environments, and illustrate with examples of how independent but intelligent agents can reach consensus dynamically.

Multi-agent swarm collaboration, inspired by the collective behaviors of biological swarms in nature, has wide applications in dynamic open environments. However, hybrid action spaces in multi-agent reinforcement learning (MARL) present a critical challenge: the inherent coupling between discrete and continuous actions severely undermines policy stability and convergence, especially under dynamic topologies. Existing methods fail to decouple this coupling, leading to suboptimal policies and unstable training. This paper addresses the core problem of action coupling under dynamic topologies, proposing a Parametrized Graph Convolution Reinforcement Learning (P-DGN) method. Operating within the actor-critic framework, P-DGN decouples the optimization pathways for hybrid actions, with a biomimetic observation design inspired by starling flock behaviors: each agent only observes the states of its seven nearest neighbors to achieve efficient local interaction and global collaboration. Its actor network uses multi-head attention to build dynamic relation kernels, develops temporal relation regularization (TRR) to improve policy consistency across time steps, and generates continuous actions with a Gaussian policy. Meanwhile, P-DGN's critic network, based on deep Q-network (DQN), evaluates Q-values for discrete actions to guide optimal choices. We evaluate P-DGN in two different multi-agent cooperative environments. Experimental results show that compared with parametrized deep Q-network (P-DQN) and DQN baseline, the proposed method has faster convergence speed and stronger training stability. Moreover, with dense rewards, P-DGN agents learn emergent tactics like encirclement. Overall, P-DGN offers a new approach for optimizing hybrid action spaces in multi-agent systems within open, dynamic environments, balancing theoretical generality with practical utility, and its biomimetic design provides a biologically plausible framework for multi-agent swarm collaboration.

Internet auctions in open dynamic environments have been attracting increasing attention. We analyze with a bottom-up approach the competition between artificial intelligent agents in Continuous Double Auction markets. In almost all previous works agents have a fixed bidding strategy during the auction, usually under static symmetric environments. In our simulations we allow the soft-agents to learn not only about how much they should bid or ask, but also about possible switching between the alternative strategies. We examine the behaviour of strategic traders under dynamic asymmetric environments thus extending previous results. This analysis is important in the design and performance of auctions in the real world (stock exchanges, commodity markets, emission permits, and B2B exchanges) and in the applications of auction theory to many problems in management and production, far beyond market design (market oriented programming).

Life cycle management of battery powered-IoT devices in large scale deployments is difficult due to the non-existence of a compatible approach to estimate their battery health. Most existing approaches require either battery parameters, determination of which is beyond IoT devices' capability due to hardware limitation, or special applicable conditions that do not always hold due to devices' dynamic operating environments. In this paper, we propose a novel approach for facilitating the life cycle management of large-scale deployments through online estimation of battery health. Our approach is based on V-edge dynamics which capture and characterize instantaneous voltage drops. Our evaluation carried out on a dataset of battery discharge measurements demonstrate that our approach is capable of estimating the battery health up to 80% accuracy.

In order to identify cognitive error mechanisms observed in the dynamic operational environment, the following materials were analyzed giving special attention to psychological biases, together with possible cognitive tasks and these location, and internal and external performance shaping factors: (a) 13 human factors analyses of US nuclear power plant accidents, (b) 14 cases of Japanese nuclear power plant incidents, and (c) 23 cases collected in simulator experiments. In the resulting analysis, the most frequently identified cognitive process associated with error productions was situation assessment, and following varieties were KB processes and response planning, all of that were the higher cognitive activities. Over 70% of human error cases, psychological bias was affecting to cognitive errors, especially those to higher cognitive activities. In addition, several error occurrence patterns, including relations between cognitive process, biases, and PSFs were identified by the multivariate analysis. According to the identified error patterns, functions that an operator support system have to equip were discussed and specified for design base considerations.

Collision avoidance ability is very important for autonomous agricultural vehicles, but the influence of different obstacles in agricultural environment is rarely taken into account. In this paper, a velocity control strategy for collision avoidance was proposed to adjust the velocity of autonomous agricultural vehicles according to the movement state and dangerous degree of the obstacles and the distance between the obstacles and the vehicles, thus to improve intelligence and safety of the vehicles. The control strategy involved two steps: collision prediction in dynamic environments with an improved obstacle space–time grid map, and velocity generator for collision avoidance with a cloud model. Simulations were conducted on the obstacle collision prediction and the designed cloud generator for velocity control respectively. Simulation results show that the proposed strategy can effectively predict collision with anti-disturbance ability for threat-free obstacles and rapid and accurate velocity output. And it realizes the real-time operation in dynamic environments with an average time of 0.2 s to predict collision. Additionally, field experiments including five trial schemes were performed to test the proposed velocity control strategy on an agricultural robot, where a haystack, a tractor and walking persons were regarded as static or dynamic obstacles. The results of the field experiments show that the proposed velocity control strategy has strong feasibility and effectiveness.

<div>This article presents an eco-driving algorithm for electric vehicles featuring multi-speed transmissions. The proposed controller is formulated as a co-optimization problem, simultaneously optimizing both vehicle longitudinal speed and powertrain operation to maximize energy efficiency. Constraints derived from a connected vehicle–based traffic prediction algorithm are used to ensure traffic safety and smooth traffic flow in dynamic environments with multiple signalized intersections and mixed traffic. By simplifying the complex, nonlinear mixed-integer problem, the proposed controller achieves computational efficiency, enabling real-time implementation. To evaluate its performance, traffic scenarios from both Simulation of Urban MObility (SUMO) and real-world road tests are employed. The results demonstrate a notable reduction in energy consumption by up to 11.36% over an 18 km drive.</div>

Data Protection in Nigeria: A Review of the Nigerian Data Protection Regulation 2019 and the Need for Nigeria to Sign the African Convention on Data Protection

In recent years, data technology has been developing rapidly, and the free flow of data across borders has also brought about many risks and hidden dangers, such as data security issues, personal information protection, and corporate information compliance issues.This paper explores how multinational corporations manage these challenges through effective data protection strategies from a global perspective.It focuses on the current challenges of data compliance across various legal frameworks and the development of effective governance strategies.Two case studies are examined to highlight the practical difficulties and complexities encountered in international data security efforts.These examples illustrate the underlying causes of compliance failures and help set the groundwork for proposing more robust data compliance strategies.The findings from this research are intended to enrich the discourse on enhancing data security measures and compliance strategies, providing multinational corporations with actionable insights to navigate complex international data protection regulations and strengthen their data security protocols.