Distributed ledger technology (DLT) that stores data (usually immutable and sequenced transaction records) in a decentralized way through cryptography and consensus algorithms. The first widely recognized implementation of the blockchain took place in 2009 on the Bitcoin public blockchain. Since then, other types of blockchain have been developed for a wide range of applications and features built on common principles such as decentralization, encryption, consensus, and immutability. In particular, blockchain technology is most widely used in transaction settlement and digital currency banks and the financial sector, as well as in supply chain applications that help participants solve problems quickly and efficiently. Other use cases continue to be developed. As a form of information management, blockchain and related DLTs offer advantages over traditional databases and may help develop certain new technologies such as the Internet of Things. Blockchain regulation is currently restricted at the international and federal levels, but state-level legislation provides support and awareness of aspects of blockchain technology. Most of the current regulations are in the form of self-regulation by blockchain developers and related communities, but many challenges and risks such as data privacy and security need to be addressed in the near future.

Ongoing studies have anticipated that in 2030, car crashes will be the fifth driving reason for death around the world. The main cause of car crashes is difficult to decide these days because of a complex mix of qualities like the mental condition of the driver, road conditions, climate conditions, traffic, and infringement of traffic rules to give some examples. The expenses of fatalities and driver wounds because of car crashes incredibly influence the general public. The use of machine learning methods in the field of road accidents is picking up speed nowadays. The organization of machine learning classifiers has swapped conventional data mining methods for creating higher outcomes and exactness. This work presents a review of different existing businesses related to accident prediction utilizing the machine learning area. Wounds because of road accidents are one of the most pervasive reasons for death separated from health-related issues. The investigation of road accident seriousness was finished by running an accident dataset through a few machine learning arrangement calculations to see which model played out the best in characterizing the accidents into severity classes, for example, slight, extreme, and fatal. It was seen that calculated relapse to perform multilevel order gave the most noteworthy exactness score. It was additionally seen that variables, for example, the number of vehicles, lighting conditions, and road highlights assumed a part in deciding the seriousness of the accident. Engineers and analysts in the car business have attempted to plan and manufacture more secure vehicles, yet auto collisions are unavoidable. Examples associated with hazardous accidents could be identified by building up a prediction model that naturally orders the sort of injury severity of different traffic accidents. These social and roadway designs are valuable in the improvement of traffic security control strategies. Significantly, estimates be founded on logical and target reviews of the reasons for accidents and the seriousness of injuries. This paper presents a few models to predict the seriousness of the injury that happened during traffic accidents utilizing machine-learning paradigms. We considered networks prepared to utilize machine learning methods. Analysis results uncover that among the machine learning ideal models considered different standards paradigm approaches.

The advancement in Artificial Intelligence (AI) is surging and it is at the center of improved output. Consequent to the advancement in the science of AI, human-to-machine interactions are facilitated, business models and logic now vary, and the lifestyle, as well as the living standards of humans, has changed. Artificial Intelligence (AI) is often regarded as a complex unit. There are many descriptions and numerous more impressions of what makes up Artificial Intelligence. The risk implications of Artificial Intelligence vary and it’s dependent on the degree of its usage. The advantages of artificial intelligence outnumber the risks it conveys, particularly in the world of business usage and its application is resulting in a world that is innovative and smarter. With the expectation that professions will be impacted, it conveys immediate effect on firms, their workers, and organizational management. The current study sought to answer some research questions; Is there knowledge about the effects of Artificial Intelligence on management? What is the current prediction for the future of management as it relates to the emergence of Artificial Intelligence? Does the current study specify the duties of middle managers in the future following the rise in AI? It adopted the systematic literature review approach where relevant literature is selected and used for the analysis.  Following Jesson, Matheson & Lacey (2011) approach to systematic review to include; (a) Mapping the area through a scoping review, b) Detailed domain search, c) Quality appraisal, d) Data gathering e) Synthesis, and f) Report writing, over 100 papers on the subject were selected. On screening, it was reduced to about 15 papers with a direct relationship on the subject. The study found out that; AI is a necessary evil to and for the future of firms.  It will have varying degrees of effect on management. AI technology will discharge managerial assignments and will be helpful in addressing management challenges. Managers and management, therefore, need to get new competencies and skills to tap into new frontiers that are AI-based.

Hybrid automata strategies have advanced as a vital tool to design, check and direct the execution of hybrid systems. Any way they can – and we assume should – be utilized to communicate quantitative models about hybrid systems in different areas, for example, experimental sciences. Since the conventional design of hybrid automata compares well to consecutively integrate behavioral chains in living creatures, we look for a use of hybrid modeling procedures in the social sciences and, particularly, brain research. We attempt to address the question related to how human drivers move onto an expressway and simultaneously utilize this study as our test-bed for utilizing hybrid automata inside behavioral sciences. Hybrid automata give a language to displaying and exploring advanced and simple calculations in real-time systems. Hybrid automata are studied here from a dynamical systems point of view. Essential and adequate conditions for the presence and uniqueness of arrangements are inferred and a class of hybrid automata whose arrangements rely consistently upon the underlying state is described. The outcomes on presence, uniqueness, and progression fill in as a beginning stage for solid study. In this paper, we present the structure of hybrid automata as a model and detailed language for hybrid systems. Hybrid automata can be seen as a theory of timed automata, in which the behavior of factors is represented in each state by a bunch of differential conditions. We show that a large number of the models considered in the workshop can be characterized by hybrid automata. While the reachability issue is undecidable in any event, for extremely confined classes of hybrid automata, we present two semi-decision techniques for checking security properties of piecewise-straight hybrid automata, in which all factors change at steady rates. The two techniques are based, individually, on limiting and figuring fix points on for the most part endless state spaces. We show that if the end of the method, at that point they offer the right responses. We then show that for a significant number of the run of the mill workshop models, the strategies do end and hence give an algorithmic approach to confirming their properties.

Defensive in the first few decades, the use of space for military purposes is now aggressive. The concept of militarized outer space has been replaced by the concept of "weaponized" outer space. International law in space only strictly prohibits the putting of weapons of mass destruction into orbit. The threat of conventional weapons development, which is primarily aimed at destroying operating military satellites, may not be prevented by France's earnest diplomatic activities. In this context, "European Space Deterrence" is a statement of strong European foreign policy, the development of independent means for trajectory monitoring of ballistics and space launches, and the ability to respond quickly in the event of an actual attack. May be based on including retaliation. And a small dedicated launcher. This seems to secure the space used for the social and economic development of France and Europe and will become increasingly important in the coming decades. Such assets will put Europe on an equal footing in dialogue with the United States in the context of NATO with interoperable space assets.