Femoral Neck Osteoporosis Is Associated with a Higher Odds of Coronary Artery Disease in Indian Postmenopausal Women: A Cross-sectional Study from a Teaching Hospital in Southern India.

Purpose: Epidemiological studies have shown an association between coronary artery disease (CAD) and osteoporosis. We studied the prevalence of CAD among postmenopausal women with osteoporosis. Factors that were significantly associated with CAD were also assessed. Methods: This was a cross-sectional study conducted over a period of 2 years. Consecutive postmenopausal women aged ≥50 years were recruited. The details of an underlying CAD were obtained. Bone biochemical parameters, bone mineral density and body composition were assessed. Results: A total of 370 postmenopausal women with mean (standard deviation [SD]) ages of 61.6 (6.2) and 60.1 (6.0) years and a body mass index of 25.3 (14.1) kg/m2 were recruited. Among them, 110 of 370 patients (29.7%) had an underlying CAD and 222 of 370 (60%) had osteoporosis at either the femoral neck or lumbar spine (LS). The odds of CAD among those with osteoporosis were 3.5 (95% confidence interval [CI]: 2.1-5.9). An LS T-score of ≤-2.2 had a sensitivity of 80% and a specificity of 45% in predicting CAD (area under the curve, AUC: 0.736; 95% CI: 0.677-0.795; p<0.001). A femoral neck T-score of ≤-1.9 had a sensitivity of 80% and a specificity of 60% in predicting CAD (AUC: 0.748; 95% CI: 0.696-0.800; p<0.001). On a logistic regression analysis after adjusting for various clinical parameters, femoral neck osteoporosis had the highest odds of CAD. Conclusion: The prevalence of CAD was higher among postmenopausal women with osteoporosis. Femoral neck osteoporosis conferred the highest odds of CAD after adjustment for other clinical factors.

Entrepreneurial education and its role in fostering sustainable communities.

Establishing sustainable communities requires bridging the gap between academic knowledge and societal requirements; this is where entrepreneurial education comes in. The first phase involved a comprehensive review of the literature and extensive consultation with experts to identify and shortlist the components of entrepreneurship education that support sustainable communities. The second phase involved Total Interpretative Structural Modelling to explore or ascertain how the elements interacted between sustainable communities and entrepreneurial education. The factors are ranked and categorized using the Matrice d'impacts croises multiplication appliquee an un classement (MICMAC) approach. The MICMAC analysis classifies partnerships and incubators as critical drivers, identifying Student Entrepreneurship Clubs and Sustainability Research Centers as dependent elements. The study emphasizes alumni networks and curriculum designs as key motivators. The results highlight the critical role that well-designed entrepreneurial education plays in developing socially conscious entrepreneurs, strengthening communities, and generating long-term job prospects. The study provides a valuable road map for stakeholders dedicated to long-term community development agendas by informing the creation of strategic initiatives, curriculum updates, and policies incorporating entrepreneurial education.

An Energy-Efficient and Blockchain-Integrated Software Defined Network for the Industrial Internet of Things.

The number of unsecured and portable Internet of Things (IoT) devices in the smart industry is growing exponentially. A diversity of centralized and distributed platforms have been implemented to defend against security attacks; however, these platforms are insecure because of their low storage capacities, high power utilization, single node failure, underutilized resources, and high end-to-end delay. Blockchain and Software-Defined Networking (SDN) are growing technologies to create a secure system and to ensure safe network connectivity. Blockchain technology offers a strong and trustworthy foundation to deal with threats and problems, including safety, privacy, adaptability, scalability, and security. However, the integration of blockchain with SDN is still in the implementation phase, which provides an efficient resource allocation and reduced latency that can overcome the issues of industrial IoT networks. We propose an energy-efficient blockchain-integrated software-defined networking architecture for Industrial IoT (IIoT) to overcome these challenges. We present a framework for implementing decentralized blockchain integrated with SDN for IIoT applications to achieve efficient energy utilization and cluster-head selection. Additionally, the blockchain-enabled distributed ledger ensures data consistency throughout the SDN controller network and keeps a record of the nodes enforced in the controller. The simulation result shows that the proposed model provides the best energy consumption, end-to-end latency, and overall throughput compared to the existing works.

Sustainable Smart Industry: A Secure and Energy Efficient Consensus Mechanism for Artificial Intelligence Enabled Industrial Internet of Things.

In recent years, the Internet of Things (IoT) has been industrializing in various real-world applications, including smart industry and smart grids, to make human existence more reliable. An overwhelming volume of sensing data is produced from numerous sensor devices as the Industrial IoT (IIoT) becomes more industrialized. Artificial Intelligence (AI) plays a vital part in big data analyses as a powerful analytic tool that provides flexible and reliable information insights in real-time. However, there are some difficulties in designing and developing a useful big data analysis tool using machine learning, such as a centralized approach, security, privacy, resource limitations, and a lack of sufficient training data. On the other hand, Blockchain promotes a decentralized architecture for IIoT applications. It encourages the secure data exchange and resources among the various nodes of the IoT network, removing centralized control and overcoming the industry's current challenges. Our proposed approach goal is to design and implement a consensus mechanism that incorporates Blockchain and AI to allow successful big data analysis. This work presents an improved Delegated Proof of Stake (DPoS) algorithm-based IIoT network that combines Blockchain and AI for real-time data transmission. To accelerate IIoT block generation, nodes use an improved DPoS to reach a consensus for selecting delegates and store block information in the trading node. The proposed approach is evaluated regarding energy consumption and transaction efficiency compared with the exciting consensus mechanism. The evaluation results reveal that the proposed consensus algorithm reduces energy consumption and addresses current security issues.

Artificial Bee Colony Reinforced Extended Kalman Filter Localization Algorithm in Internet of Things with Big Data Blending Technique for Finding the Accurate Position of Reference Nodes.

In recent years, the growth of internet of things (IoT) is immense, and the observations of their evolution need to be carried out effectively. The development of the IoT has been broadly adopted in the construction of intelligent environments. There are various challenging IoT issues such as routing messages, addressing, Localizing nodes, data blending, etc. Formerly learning eloquent information from big data systems to construct a data-gathering setup in an IoT environment is challenging. Among many viable data sources, the IoT is a rich big data source: Various IoT nodes produce a massive quantity of data. Localization is one of the crucial problems that make a significant impact inside the IoT system. It needs to be engaged with proper and effective procedures to collect all sorts of data without noise. Numerous localization procedures and schemes have been initiated by deploying the IoT sensor with wireless sensor networks for both interior and outside environments. To accomplish higher localization accuracy, with less cost for the large volume of data, it is considered a hectic task in the IoT sensor environment. By viewing the nature of the IoT, the merging of different technologies such as the internet, WiFi, etc., can aid diverse ways to acquire information about various objects' locations. Location-based service is an exceptional service of the IoT, whereas localization accuracy is a significant issue. The data generated from the sensor are available in both static and dynamic forms. In this article, a sophisticated accuracy localization scheme for big data is proposed with an optimization approach that can effectively produce proper and effective outcomes for IoT environments. The theme of the article is to develop an enriched Swarm Intelligence algorithm based on Artificial Bee Colony by using the EKF (Extended Kalman Filter) data blend technique for improving Localization in IoT for the unsuspecting environment. The performance of the proposed algorithm is evaluated by using communication consumption and Localization accuracy and its comparative advantage.

A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users.

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented.