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The metaverse, a burgeoning virtual reality realm, has garnered substantial attention owing to its multifaceted applications. Rapid advancements and widespread acceptance of metaverse technologies have birthed a dynamic and intricate digital landscape. As various platforms, virtual worlds, and social networks within the metaverse increase, there is a growing imperative for a comprehensive analysis of its implications across societal, technological, and business dimensions. Notably, existing review studies have, for the past decade, primarily overlooked a metaverse‐based multidomain approach. A meticulous examination encompassing 207 research studies delves into the technological innovation of the metaverse, elucidating its future trajectory and ethical imperatives. Additionally, the article introduces the term "MetaWarria" to conceptualize potential conflicts arising from metaverse dynamics. The study discerns that healthcare (45%) and education (22%) are pivotal sectors steering metaverse developments, while the entertainment sector (9%) reshapes the corporate landscape. Artificial intelligence (AI) plays a 9% role in enhancing the metaverse's marketing and user experience. Security, privacy, and policy concerns (11%) are addressed due to escalating threats, yielding practical solutions. The analysis underscores the metaverse's profound influence (57%) on the digital realm, a phenomenon accelerated by the COVID‐19 pandemic. The article culminates in contemplating the metaverse's role in future warfare and national security, introducing "MetaWarria" as a conceptual framework for such discussions. This article is categorized under:Application Areas > Health CareApplication Areas > Science and TechnologyFundamental Concepts of Data and Knowledge > Human Centricity and User Interaction [ABSTRACT FROM AUTHOR]

Flying an aircraft in low visibility is still a challenging task for the pilot. It requires precise and accurate situational awareness (SA) in real-time. A Headup Display (HUD) is used to project collimated internal and external flight information on a transparent screen in the pilot's forward field of view, which eliminates the change of eye position between Head-Down-Display (HDD) instruments and outer view through the windshield. Implementation of HUD increases the SA and reduces the workload for the pilot. But to provide a better flying capability for the pilot, projecting extensive information on HUD causes human factor issues that reduce pilot performance and lead to accidents in low visibility conditions. The literature shows that human error is the leading cause of more than 70% of aviation accidents. In this study, the ability of the pilot able to read background and symbology information of HUD at a different level of background seen complexity, such as symbology brightness, transition time, amount of Symbology, size etc., in low visibility conditions is discussed. The result shows that increased complexity on the HUD causes more detection errors. [ABSTRACT FROM AUTHOR]

Air pollution is one of the biggest concerns in the world but it has not been paid much attention in developing countries. It is necessary to design models and methods to understand air pollution in developing countries to reduce the rate of pollution. This paper proposes an Internet of Things (IoT) and Artificial Intelligence (AI)‐based hybrid model to predict the Air Quality Index (AQI) with a practical case study of the public data sets. The sensor node is deployed in the city to collect air quality data. Moreover, this sensor node connects to the cloud server for collecting data at the firebase real‐time database through a WiFi/5G network embedded in the raspberry controller. Carbon monoxide (CO) and fine particular matter PM2.5 sensors are integrated within a sensor node to monitor the AQI of the regions. A Kalman fis also applied to remove unwanted noise from the data collected through the sensor node. Models namely Artificial Neural Network (ANN), Support Vector Machine (SVM), k‐nearest neighbour (k‐NN), Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), CNN‐LSTM, ensemble model, and a proposed model, that is, CNN‐LSTM‐Bayesian optimization algorithm (BOA) model, have been utilised to predict the AQI. The performance evaluation of models is done through statistical parameters, such as mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and accuracy score on two different public data sets and compared with the baseline models. The performance of the CNN‐LSTM‐BOA model is better than baseline models in terms of above‐mentioned statistical parameters as the accuracy reported is more than 97 %.This study can help predict the Air Quality Index and provide sufficient time to generate warning signals in the location. [ABSTRACT FROM AUTHOR]

Today, ship development has concentrated on electrifying ships in commercial and military applications to improve efficiency, support high-power missile systems and reduce emissions. However, the electric propulsion of the shipboard system experiences torque fluctuation, thrust, and power due to the rotation of the propeller shaft and the motion of waves. In order to meet these challenges, a new solution is needed. This paper explores hybrid energy management systems using the battery and ultracapacitor to control and optimize the electric propulsion system. The battery type and ultracapacitor are ZEBRA and MAXWELL, respectively. The 3-, 4-and 5-blade propellers are considered to produce power and move rapidly. The loss factor has been reduced, and the sea states have been found through the Elephant Herding Optimization algorithm. The efficiency of the proposed system is greatly enhanced through torque, thrust and power. The model predictive controller control strategy is activated to reduce load torque and drive system Root Average Square (RMS) error. The implementations are conducted under the MATLAB platform. The values for torque, current, power, and error are measured and plotted. Finally, the performance of the proposed methodology is compared with other available algorithms such as BAT and Dragonfly (DF). The simulation results show that the results of the proposed method are superior to those of various techniques and algorithms such as BAT and Dragonfly. [ABSTRACT FROM AUTHOR]

The current and future status of the internet is represented by the upcoming Internet of Things (IoT). The internet can connect the huge amount of data, which contains lot of processing operations and efforts to transfer the pieces of information. The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics, sensors and network connectivity. Nowadays, there are two trending technologies that take the platform i.e., Software Defined Network (SDN) and IoT (SD-IoT). The main aim of the IoT network is to connect and organize different objects with the internet, which is managed with the control panel and data panel in the SD network. The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers. It is more significant for wide area networks, because of the large packet propagation latency and the controller placement problem is more important in every network. In the proposed work, IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization (ESFO) algorithm and Pareto Optimal Controller placement tool (POCO) for the placement problem of the controller. In order to prove the efficiency of the proposed system, it is compared with other existing methods like PASIN, hybrid SD and PSO in terms of load balance, reduced number of controllers and average latency and delay. With 2 controllers, the proposed method obtains 400 miles as average latency, which is 22.2% smaller than PSO, 76.9% lesser than hybrid SD and 91.89% lesser than PASIN. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is the utmost assuring framework to facilitate human life with quality and comfort. IoT has contributed significantly to numerous application areas. The stormy expansion of smart devices and their credence for data transfer using wireless mechanics boost their susceptibility to cyberattacks. Consequently, the cybercrime rate is increasing day by day. Hence, the study of IoT security threats and possible corrective measures can benefit researchers in identifying appropriate solutions to deal with various challenges in cybercrime investigation. IoT forensics plays a vital role in cybercrime investigations. This review paper presents an overview of the IoT framework consisting of IoT architecture, protocols, and technologies. Various security issues at each layer and corrective measures are also discussed in detail. This paper also presents the role of IoT forensics in cybercrime investigation in various domains like smart homes, smart cities, automated vehicles, and healthcare. The role of advanced technologies like artificial intelligence, machine learning, cloud computing, edge computing, fog computing, and blockchain technology in cybercrime investigation is also discussed. Lastly, various open research challenges in IoT to assist cybercrime investigation are explained to provide a new direction for further research. [ABSTRACT FROM AUTHOR]

An effective machine learning implementation means that artificial intelligence has tremendous potential to help and automate financial threat assessment for commercial firms and credit agencies. The scope of this study is to build a predictive framework to help the credit bureau by modelling/assessing the credit card delinquency risk. Machine learning enables risk assessment by predicting deception in large imbalanced data by classifying the transaction as normal or fraudster. In case of fraud transaction, an alert can be sent to the related financial organization that can suspend the release of payment for particular transaction. Of all the machine learning models such as RUSBoost, decision tree, logistic regression, multilayer perceptron, K-nearest neighbor, random forest, and support vector machine, the overall predictive performance of customized RUSBoost is the most impressive. The evaluation metrics used in the experimentation are sensitivity, specificity, precision, F scores, and area under receiver operating characteristic and precision recall curves. Datasets used for training and testing of the models have been taken from kaggle.com. [ABSTRACT FROM AUTHOR]

The increasing need for observation in seawater or ocean monitoring systems has ignited a considerable amount of interest and the necessity for enabling advancements in technology for underwater wireless tracking and underwater sensor networks for wireless communication. This type of communication can also play an important role in investigating ecological changes in the sea or ocean-like climate change, monitoring of biogeochemical, biological, and evolutionary changes. This can help in controlling and maintaining the production facilities of outer underwater grid blasting by deploying unmanned underwater vehicles (UUVs). Underwater tracking-based wireless networks can also help in maintaining communication between ships and divers, submarines, and between multiple divers. At present, the underwater acoustic communication system is unable to provide the data rate required to monitor and investigate the aquatic environment for various industrial applications like oil facilities or underwater grit blasting. To meet this challenge, an optical and magnetic tracking-based wireless communication system has been proposed as an effective alternative. Either optical or magnetic tracking-based wireless communication can be opted for according to the requirement of the potential application in sea or ocean. However, the hybrid version of optical and wireless tracking-based wireless communication can also be deployed to reduce the latency and improve the data rate for effective communication. It is concluded from the discussion that high data rate optical, magnetic or hybrid mode of wireless communication can be a feasible solution in applications like UUV-to-UUV and networks of aquatic sensors. The range of the proposed wireless communication can be extended using the concept of multihop. [ABSTRACT FROM AUTHOR]

With an increasing number of Internet of Things (IoT) devices in the digital world, the attack surface for consumer networks has been increasing exponentially. Most of the compromised devices are used as zombies for attacks such as Distributed Denial of Services (DDoS). Consumer networks, unlike most commercial networks, lack the infrastructure such as managed switches and firewalls to easily monitor and block undesired network traffic. To counter such a problem with limited resources, this article proposes a hybrid anomaly detection approach that detects irregularities in the network traffic implicating compromised devices by using only elementary network information like Packet Size, Source, and Destination Ports, Time between subsequent packets, Transmission Control Protocol (TCP) Flags, etc. Essential features can be extracted from the available data, which can further be used to detect zero-day attacks. The paper also provides the taxonomy of various approaches to classify anomalies and description on capturing network packets inside consumer networks. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study is to assess morphological variations of the coronoid process, condyle, and sigmoid notch as an adjunct in personal identification using orthopantomograms among the North Indian population., Methodology: The study sample (n=240) was distributed into four age groups: Group I: 30 males and 30 females aged 10-19 years, Group II: 30 males and 30 females aged 20-29 years, Group III: 30 males and 30 females aged 30-39 years, and Group IV: 30 males and 30 females aged 40-59 years. All were subjected to panoramic radiographs. The different morphological forms of the coronoid process, condyle, and sigmoid notch were evaluated., Results: The results showed that across all age groups, angular condyles were the most common kind of condyle in males, followed by round and convex types. The present study found that the coronoid process typically takes on a triangle shape across all ages and sexes. Additionally, the vast majority of cases were triangular on both sides, and this was true across both sexes. It was found in this study that the sigmoid notch most commonly took the form of a larger notch, followed by a rounder notch., Conclusion: Using panoramic photos to portray the different morphologies of the coronoid process, condyle, and sigmoid notch can be a much simpler and faster method of identifying an individual, especially in the event of a mass disaster, so long as antemortem data are kept. The method of radiographic identification of individuals has recently gained prominence due to its efficacy. Radiographs like these can be invaluable in forensic dentistry, where they can help unearth previously hidden evidence if premortem records are retained. As a potential approach for individual identification among our population, panoramic radiographs were used to investigate the varying morphological forms of the coronoid process, condyle, and sigmoid notch., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Bains et al.)