Game Theory-Based Authentication Framework to Secure Internet of Vehicles with Blockchain.

The Internet of Vehicles (IoV) is a new paradigm for vehicular networks. Using diverse access methods, IoV enables vehicles to connect with their surroundings. However, without data security, IoV settings might be hazardous. Because of the IoV's openness and self-organization, they are prone to malevolent attack. To overcome this problem, this paper proposes a revolutionary blockchain-enabled game theory-based authentication mechanism for securing IoVs. Here, a three layer multi-trusted authorization solution is provided in which authentication of vehicles can be performed from initial entry to movement into different trusted authorities' areas without any delay by the use of Physical Unclonable Functions (PUFs) in the beginning and later through duel gaming, and a dynamic Proof-of-Work (dPoW) consensus mechanism. Formal and informal security analyses justify the framework's credibility in more depth with mathematical proofs. A rigorous comparative study demonstrates that the suggested framework achieves greater security and functionality characteristics and provides lower transaction and computation overhead than many of the available solutions so far. However, these solutions never considered the prime concerns of physical cloning and side-channel attacks. However, the framework in this paper is capable of handling them along with all the other security attacks the previous work can handle. Finally, the suggested framework has been subjected to a blockchain implementation to demonstrate its efficacy with duel gaming to achieve authentication in addition to its capability of using lower burdened blockchain at the physical layer, which current blockchain-based authentication models for IoVs do not support.

n-Player Stochastic Duel Game Model with Applied Deep Learning and Its Modern Implications.

This paper provides a conceptual foundation for stochastic duels and contains a further study of the game models based on the theory of stochastic duels. Some other combat assessment techniques are looked upon briefly; a modern outlook on the applications of the theory through video games is provided; and the possibility of usage of data generated by popular shooter-type video games is discussed. Impactful works to date are carefully chosen; a timeline of the developments in the theory of stochastic duels is provided; and a brief literature review for the same is conducted, enabling readers to have a broad outlook at the theory of stochastic duels. A new evaluation model is introduced in order to match realistic scenarios. Improvements are suggested and, additionally, a trust mechanism is introduced to identify the intent of a player in order to make the model a better fit for realistic modern problems. The concept of teaming of players is also considered in the proposed mode. A deep-learning model is developed and trained on data generated by video games to support the results of the proposed model. The proposed model is compared to previously published models in a brief comparison study. Contrary to the conventional stochastic duel game combat model, this new proposed model deals with pair-wise duels throughout the game duration. This model is explained in detail, and practical applications of it in the context of the real world are also discussed. The approach toward solving modern-day problems through the use of game theory is presented in this paper, and hence, this paper acts as a foundation for researchers looking forward to an innovation with game theory.

Knowledge Discovery Using Topological Analysis for Building Sensor Data.

Distributed sensor networks are at the heart of smart buildings, providing greater detail and valuable insights into their energy consumption patterns. The problem is particularly complex for older buildings retrofitted with Building Energy Management Systems (BEMS) where extracting useful knowledge from large sensor data streams without full understanding of the underlying system variables is challenging. This paper presents an application of Q-Analysis, a computationally simple topological approach for summarizing large sensor data sets and revealing useful relationships between different variables. Q-Analysis can be used to extract novel structural features called Q-vectors. The Q-vector magnitude visualizations are shown to be very effective in providing insights on macro behaviors, i.e., building floor behaviors in the present case, which are not evident from the use of unsupervised learning algorithms applied on individual terminal units. It has been shown that the building floors exhibited distinct behaviors that are dependent on the set-point distribution, but independent of the time and season of the year.

Cardiovascular Disease in Gout and the Protective Effect of Treatments Including Urate-Lowering Therapy.

Cardiovascular disease affects more than 90 million Americans. Recent studies support an increased cardiovascular disease risk in inflammatory conditions, such as gout. Increased serum urate levels, or hyperuricemia, are a precursor to gout. Data from meta-analyses have shown hyperuricemia to be linked to hypertension and coronary heart disease. Similarly, gout has been associated with an increased risk of myocardial infarction, cerebrovascular accidents, and death from cardiovascular disease in randomized clinical trials. Urate-lowering therapy reduces serum urate and may decrease systemic inflammation, generation of oxidative species, and reverses endothelial dysfunction through hyperuricemia-dependent or hyperuricemia-independent pathways. Cardioprotective benefits of allopurinol, a first-line agent for the treatment of gout, have been demonstrated to potentially prevent myocardial infarction, stroke, atrial fibrillation, and other cardiovascular diseases in observational studies in select populations. Randomized controlled trials (RCTs) have also examined the role of newer urate-lowering therapies, such as febuxostat and lesinurad, and their risk of cardiovascular-specific mortality in comparison to allopurinol. A large post-marketing study of febuxostat vs. allopurinol showed higher all-cause and cardiovascular-specific mortality in the febuxostat group than in the allopurinol group; a major study limitation was that large numbers of patients were lost to follow-up or discontinued treatment. RCTs are required to assess the comparative effectiveness of urate-lowering therapies, validate findings of observational studies, and to determine which subgroup populations of gout are most likely to benefit from appropriate long-term urate-lowering therapy. This review examines the data for increased cardiovascular disease in gout and potential underlying mechanisms (including hyperuricemia, inflammation, endothelial dysfunction, oxidative stress) and the effect of urate-lowering therapy on cardiovascular disease.

Technical considerations to minimize complications of inguinal lymph node dissection.

Penile cancer is a rare malignancy with a high propensity for regional dissemination. Current guidelines recommend inguinal lymphadenectomy in patients with penile cancer for palpable inguinal lymph nodes or in certain cases of nonpalpable inguinal lymph nodes. For many years, this procedure was performed with a traditional open approach and carried significant morbidity due to severe lymphedema, flap necrosis, wound infections, and seroma formation. The evolution of inguinal lymphadenectomy surgery for patients with penile cancer to a more minimally invasive approach has greatly reduced the morbidity of the procedure. Complications of inguinal lymphadenectomy can be minimized with modifications in surgical approach with the use of endoscopic, robotic, and various reconstructive methods. This review focuses on various intraoperative techniques to reduce morbidity in inguinal lymphadenectomies for penile cancer.