Development of lateral acceleration prediction models and consistency evaluation criteria for horizontal curves with gradient on two lane rural highways.

Objectives: The challenge of combined horizontal and vertical highway alignments continues to be a concern in transportation design, particularly regarding truck stability. This study analyses the stability of trucks on horizontal curves combined with ascending or descending gradients using center of gravity (CoG) height and curve geometry as parameters. It also develops models that predict lateral stability of trucks and criteria for evaluating geometric design consistency based on vehicle stability.

An integrative analysis to identify pancancer epigenetic biomarkers.

Integrating and analyzing the pancancer data collected from different experiments is crucial for gaining insights into the common mechanisms in the molecular level underlying the development and progression of cancers. Epigenetic study of the pancancer data can provide promising results in biomarker discovery. The genes that are epigenetically dysregulated in different cancers are powerful biomarkers for drug-related studies.

'Remediability Score' as a tool for selecting the most suitable remediation technology for contaminated sites.

Land gets contaminated due to different human activities like illegal industrial discharges, mining activities, and spillage of chemicals. The pollutants released through these activities can have prolonged effects on the environment and human health. Therefore, there is a need for immediate remediation of the contaminated site.

Automated Recognition of Cancer Tissues through Deep Learning Framework from the Photoacoustic Specimen.

The fast advancement of biomedical research technology has expanded and enhanced the spectrum of diagnostic instruments. Various research groups have found optical imaging, ultrasonic imaging, and magnetic resonance imaging to create multifunctional devices that are critical for biomedical activities. Multispectral photoacoustic imaging that integrates the ideas of optical and ultrasonic technologies is one of the most essential instruments.

Performance evaluation of deep neural network on malware detection: visual feature approach.

Nowadays, several malicious applications target computers and mobile users. So, malware detection plays a vital role on the internet so that the device is secure without any malicious activity affecting or gathering the useful content of the user. Researches indicate that the vulnerability of adversarial attacks is more in deep neural networks.

On the design of a compact emergency mechanical ventilator with negative expiratory exit pressure for COVID-19 patients.

The present work deals with the design of a cylinder-piston arrangement to deliver the required tidal volume (TV) of air to the patient through the respiratory tract especially in the setting of severe acute respiratory syndrome corona virus 2 (SARS CoV-2) or corona virus disease (COVID-19). The design ensures that only the desired volume of air is delivered in each breath and a negative pressure is retained at the delivery point in a separate cylinder. The frequency of piston motion is the same as that of the average human respiratory rate (RR).

Location of Early Stage Tumor Detection using Microwave Imaging in the Breast Phantom.

Background: Universally, the most predominant cause of female mortality is mainly due to breast cancer. Owing to numerous constraints in the existing imaging technique, researchers are trying out an alternative tool to detect the tumor before going to the miserable stage.

Methods: This article presents a novel method to detect the mean value system for detecting the location of the tumor in different depths by shifting the antenna anywhere in the breast tissue.

A Novel Edge-Based Trust Management System for the Smart City Environment Using Eigenvector Analysis.

The proposed Edge-based Trust Management System (E-TMS) uses an Eigenvector-based approach for eliminating the security threats present in the Internet of Things (IoT) enabled smart city environment. In most existing trust management systems, the trust aggregation process completely depends on the direct trust ratings obtained from both legitimate and malicious neighboring IoT devices. E-TMS possesses an edge-assisted two-level trust computation approach for ensuring the malicious free trust evaluation of IoT devices.

Towards Energy-Efficient and Delay-Optimized Opportunistic Routing in Underwater Acoustic Sensor Networks for IoUT Platforms: An Overview and New Suggestions.

In underwater acoustic sensor networks (UASNs), the reliable transfer of data from the source nodes located underwater to the destination nodes at the surface through the network of intermediate nodes is a significant challenge due to various unique characteristics of UASN such as continuous mobility of sensor nodes, increased propagation delay, restriction in energy, and heightened interference. Recently, the location-based opportunistic routing protocols seem to show potential by providing commendable quality of service (QoS) in the underwater environment. This study initially reviews all the latest location-based opportunistic routing protocols proposed for UASNs and discusses its possible limitations and challenges.

An AI-enabled lightweight data fusion and load optimization approach for Internet of Things.

In the densely populated Internet of Things (IoT) applications, sensing range of the nodes might overlap frequently. In these applications, the nodes gather highly correlated and redundant data in their vicinity. Processing these data depletes the energy of nodes and their upstream transmission towards remote datacentres, in the fog infrastructure, may result in an unbalanced load at the network gateways and edge servers.

Internet of Things-Based Optimized Routing and Big Data Gathering System for Landslide Detection.

An exponential progression in the miniaturization of communicating devices has proliferated the generation of a large volume of data termed as "big data." The technological advancements in the micro-electro/mechanical system has made it possible to design the low-cost, low-power consuming artificial intelligence (AI)-based wireless sensor nodes to gather the big data belonging to various attributes from their surroundings. These nodes help in the early detection and prediction for the occurrence of landslides, which are among the catastrophic hazards.

Determinants of job stress in chemical process industry: A factor analysis approach.

Background: Job stress is one of the active research domains in industrial safety research. The job stress can result in accidents and health related issues in workers in chemical process industries. Hence it is important to measure the level of job stress in workers so as to mitigate the same to avoid the worker's safety related problems in the industries.

Strong sum distance in fuzzy graphs.

In this paper the idea of strong sum distance which is a metric, in a fuzzy graph is introduced. Based on this metric the concepts of eccentricity, radius, diameter, center and self centered fuzzy graphs are studied. Some properties of eccentric nodes, peripheral nodes and central nodes are obtained.