Beyond‐line‐of‐sight (BLOS) communication is a fundamental component of modern defense strategies, enabling secure and reliable information exchange in environments where traditional line‐of‐sight (LOS) methodologies are ineffective due to obstructions or operational constraints. This article explores the key technologies advancing BLOS communication, with a focus on nanonetworks, aerial relays, and satellite‐based defense communication. To underscore the real‐world applications and challenges of BLOS systems, we present two practical case studies, one examining tropospheric ducting for BLOS maritime communication, highlighting its advantages and implementation challenges, and another on UAV path planning in radar‐threat war zones, demonstrating the role of optimization techniques in enhancing operational efficiency. Furthermore, this study identifies several critical future research directions in BLOS defense communication, including resilience enhancement, heterogeneous network integration, contested spectrum management, advancements in multimedia communication, adaptive methodologies, and the expanding domain of the Internet of Military Things (IoMT). Addressing both technological advancements and real‐world constraints, this research lays a foundation for strengthening BLOS communication systems, fostering cross‐sector collaboration, and driving innovation at the intersection of defense, academia, and industry.

This study investigates the role of convolutional layers in deep convolutional neural networks for scenarios involving interactions between the users and road-interaction environment. The user-driving behaviour and road environment exhibit unique features, and applications often require training-specific models for each user-environment pair. This leads to significant data collection and computational demands, including model training, thus necessitating efficient solutions to minimise these requirements. The aim is to determine whether the convolutional layers of deep convolutional autoencoders (DCAEs) are more specific to the user or the environment. The distinction might lead to optimising training strategies that reduce resource requirements. Case studies and data collection are performed on multiple bicyclists navigating various road segments. We evaluated the specificity of convolutional layers using metrics such as training epochs, execution time, model and parameter loading times, and total training loss. The results confirmed that the patterns learned by the outer convolutional layers are predominantly user-specific, emphasising individual behaviour over road-environment factors. This user-specific pattern recognition enhances model efficiency, reduces data requirements, and improves accuracy in predicting user behaviour across varying environments. Furthermore, after analysing training strategies, we found that complete refinement provided higher accuracy and stability at the cost of longer training and loading times. By contrast, freezing layers allowed faster initialisation but might necessitate extended training in complex cases. Finally, we examined the implications of these findings for improving safety and performance in driver-road interactions.

MOTIVATION: The investigation of DNA methylation can shed light on the processes underlying human well-being and help determine overall human health. However, insufficient coverage makes it challenging to implement single-stranded DNA methylation sequencing technologies, highlighting the need for an efficient prediction model. Models are required to create an understanding of the underlying biological systems and to project single-cell (methylated) data accurately. RESULTS: In this study, we developed positional features for predicting CpG sites. Positional characteristics of the sequence are derived using data from CpG regions and the separation between nearby CpG sites. Multiple optimized classifiers and different ensemble learning approaches are evaluated. The OPTUNA framework is used to optimize the algorithms. The CatBoost algorithm followed by the stacking algorithm outperformed existing DNA methylation identifiers. AVAILABILITY AND IMPLEMENTATION: The data and methodologies used in this study are openly accessible to the research community. Researchers can access the positional features and algorithms used for predicting CpG site methylation patterns. To achieve superior performance, we employed the CatBoost algorithm followed by the stacking algorithm, which outperformed existing DNA methylation identifiers. The proposed iCpG-Pos approach utilizes only positional features, resulting in a substantial reduction in computational complexity compared to other known approaches for detecting CpG site methylation patterns. In conclusion, our study introduces a novel approach, iCpG-Pos, for predicting CpG site methylation patterns. By focusing on positional features, our model offers both accuracy and efficiency, making it a promising tool for advancing DNA methylation research and its applications in human health and well-being.

The industrial revolution 4.0 (I4.0), internet of things developments, and the expansion of online web services have caused exponential growth and deployment in the number of cloud data centers(CDC). Cloud computing is a paradigm that enables tenants to use storage and computing resources in the pay-per-use model. Cloud service providers maximize their profits by distributing the tenant’s demands to the reserved storage and computing servers, minimizing the reservation cost with the satisfaction of the tenant’s quality of service level agreement. Workload prediction and resource management are fundamental and critical problems due to cloud-distributed infrastructure and nonlinear dynamic workload conditions. Conventional prediction techniques in a cloud environment provide the one-dimensional output. Existing solutions mostly forecast resources, such as CPU and memory usage, each as a single output. However, the one-dimensional output in the form of resource provision and usage is not able to capture the relationship of application requirements of multiple resources such as CPU, memory, CPU cores, Disk, and network, which result in inaccurate prediction results and limited information. Efficient resource management requires predicting multiple resource parameters using multivariate state variables for efficient resource allocation. This study proposes an intelligent computing framework based on multivariate time-series bidirectional long short-term memory (BiLSTM) forecasting for predicting cloud virtual machine resources. We consider multi-dimensional resources such as CPU provisioned and usage, memory provisioned and usage, CPU cores, Disk write and read throughput, and network receives and transmit throughput. We investigate several deep learning techniques the proposed multivariate BiLSTM, LSTM, stacked BiLSTM, stacked BiLSTM with LSTM, and BiLSTM auto-encoder. Furthermore, we evaluate the effectiveness of the proposed framework on two real workload traces: Bitbrains traces f astStorage and R nd. The performance metrics used to evaluate forecasting accuracy are the root mean square error , mean absolute error and mean absolute percentage error. Furthermore, we observe the training-testing data size and the historical window size variation effects on these models.

In the vehicular ad hoc networks (VANETs), minimizing the broadcast storm that arises due to message rebroadcast during emergency message dissemination in extremely mobile environments under sparse or dense networks is a significant challenge. Proper selection of rebroadcasting vehicles guarantees acceptable end-to-end delay, high delivery ratio, and efficient bandwidth utilization. To date, many protocols have been proposed to select an appropriate rebroadcasting vehicles based on vehicle position information only. However, such approaches neglect the fact that both vehicle velocity and position information can be utilized efficiently to alleviate rebroadcast message collisions and control bandwidth consumption. In this work, we present a new broadcast suppression protocol, named, velocity and position-based broadcast suppression for VANETs (VP-CAST), which can work in both sparse and dense network situations. VP-CAST does rely on periodic beacon messages, rather the position and velocity information of broadcasting vehicle are included in a broadcast message. Moreover, the transmission range of broadcasting vehicle is divided into dynamic time slots based on velocity and position information of broadcasting and receiving vehicles.The proposed scheme assigns shorter and dynamic waiting time to the vehicles moving at high velocities and located farther from the sender vehicle that eventually reduces both the message re-transmission delay and the number of rebroadcasting vehicles. The proposed protocol is compared with the DV-CAST in terms of end-to-end delay, message delivery ratio, and message overhead.