A Field-Programmable Gate Array-Based Adaptive Sleep Posture Analysis Accelerator for Real-Time Monitoring.

This research presents a sleep posture monitoring system designed to assist the elderly and patient attendees. Monitoring sleep posture in real time is challenging, and this approach introduces hardware-based edge computation methods. Initially, we detected the postures using minimally optimized sensing modules and fusion techniques.

Optimal placement of renewable distributed generators and electric vehicles using multi-population evolution whale optimization algorithm.

This research takes on a crucial task- exploring the optimal placement of Renewable Distributed Generators such as Solar Photovoltaic, wind turbines and Electric Vehicles into the Radial Distribution System (RDS). This is a strategic move aimed at minimising power loss (P) and improving the voltage profile and stability index. The RDGs are integrated into RDS with and without considering the uncertainty of the different load demands for 24 h.

Comprehensive analysis of faults and diagnosis techniques in cascaded multi-level inverters.

Reliability is a crucial factor to consider for multi-level inverters (MLIs) used in industrial applications. With the increasing number of power semiconductor devices, the potential for defects to significantly degrade the overall system is heightened. A highly effective fault-detection technique is required to minimize the impact of faults.

Adaptive FPGA-Based Accelerators for Human-Robot Interaction in Indoor Environments.

This study addresses the challenges of human-robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot's intention to serve based on the human's location at static and adaptive positions.

Dual-Wide Multi-Band (DWMB) four-port flexible MIMO antenna for on-body multiple wireless applications including high diversity performance.

The single-input-single-output technology experiences loss of data in the communication channel due to the receiving antenna undergoing fading of the signal impinged on it. Today's need is faster data transfer with multiple applications in the single antenna with multiple-identical radiating elements, leading to multiple-input-multiple-outputDWMB (MIMODWMB) technology. The MIMODWMB configuration with multi-band capability is the objective of the proposed work with applications ranging between microwave-millimeterWave bands.

Molecular interaction studies of L-proline in water and ethanol at different temperatures using dielectric relaxation, refractive index and DFT methods.

The complex dielectric permittivity of L-Proline in water and ethanol solutions with molar concentrations ranging from 0.025 M to 0.15 M was measured by open-ended coaxial probe technique.

Forecasting deep learning-based risk assessment of vector-borne diseases using hybrid methodology.

Background: Dengue fever is rapidly becoming Malaysia's most pressing health concern, as the reported cases have nearly doubled over the past decade. Without efficacious antiviral medications, vector control remains the primary strategy for battling dengue, while the recently introduced tetravalent immunization is being evaluated. The most significant and dangerous risk increasing recently is vector-borne illnesses.

Hybrid deep learning and optimized clustering mechanism for load balancing and fault tolerance in cloud computing.

Cloud services are one of the most quickly developing technologies. Furthermore, load balancing is recognized as a fundamental challenge for achieving energy efficiency. The primary function of load balancing is to deliver optimal services by releasing the load over multiple resources.

A Versatile Approach for Adaptive Grid Mapping and Grid Flex-Graph Exploration with a Field-Programmable Gate Array-Based Robot Using Hardware Schemes.

Robotic exploration in dynamic and complex environments requires advanced adaptive mapping strategies to ensure accurate representation of the environments. This paper introduces an innovative grid flex-graph exploration (GFGE) algorithm designed for single-robot mapping. This hardware-scheme-based algorithm leverages a combination of quad-grid and graph structures to enhance the efficiency of both local and global mapping implemented on a field-programmable gate array (FPGA).

Hardware Schemes for Smarter Indoor Robotics to Prevent the Backing Crash Framework Using Field Programmable Gate Array-Based Multi-Robots.

The use of smart indoor robotics services is gradually increasing in real-time scenarios. This paper presents a versatile approach to multi-robot backing crash prevention in indoor environments, using hardware schemes to achieve greater competence. Here, sensor fusion was initially used to analyze the state of multi-robots and their orientation within a static or dynamic scenario.

Morphologically tuned CuO-ZnO-CeO catalyst for CO hydrogenation to methanol.

Morphologically modified composite CuO-ZnO-CeO catalysts were synthesized using a single-step hydrothermal technique. The study highlights the influence of solvent on the structural and physico-chemical properties of the catalysts. Various techniques, such as XRD, FE-SEM, BET, XPS, and H-TPR, were used to analyze the catalyst properties.

An effective Alzheimer's disease segmentation and classification using Deep ResUnet and Efficientnet.

Alzheimer's disease (AD) is a degenerative neurologic condition that results in the deterioration of several brain processes (e.g. memory loss).

A Versatile Approach to Polygonal Object Avoidance in Indoor Environments with Hardware Schemes Using an FPGA-Based Multi-Robot.

Service robots perform versatile functions in indoor environments. This study focuses on obstacle avoidance using flock-type indoor-based multi-robots. Each robot was developed with rendezvous behavior and distributed intelligence to perform obstacle avoidance.

Improved graph neural network-based green anaconda optimization for segmenting and classifying the lung cancer.

Normal lung cells incur genetic damage over time, which causes unchecked cell growth and ultimately leads to lung cancer. Nearly 85% of lung cancer cases are caused by smoking, but there exists factual evidence that beta-carotene supplements and arsenic in water may raise the risk of developing the illness. Asbestos, polycyclic aromatic hydrocarbons, arsenic, radon gas, nickel, chromium and hereditary factors represent various lung cancer-causing agents.

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics.

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace.

Binding of dicoumarol analog with DNA and its antioxidant studies: A biophysical insight by in-vitro and in-silico approaches.

DNA is the major target for a number of pharmaceutical drugs. The interaction of drug molecules with DNA plays a major role in pharmacokinetics and pharmacodynamics. Bis-coumarin derivatives have diverse biological properties.

Hardware-Efficient Scheme for Trailer Robot Parking by Truck Robot in an Indoor Environment with Rendezvous.

Autonomous grounded vehicle-based social assistance/service robot parking in an indoor environment is an exciting challenge in urban cities. There are few efficient methods for parking multi-robot/agent teams in an unknown indoor environment. The primary objective of autonomous multi-robot/agent teams is to establish synchronization between them and to stay in behavioral control when static and when in motion.

Numerical study of hydrothermal and mass aspects in MHD driven Sisko-nanofluid flow including optimization analysis using response surface method.

A steady, incompressible, two-dimensional Sisko-nanofluid flow towards the horizontal direction with no movement in the vertical direction is considered on a stretching/shrinking surface. The power law component (Sisko model) is incorporated under the regime of the porous medium. A magnetic impact is included coming from the MHD in the surface normal direction.

Biophysical insights on the interaction of anticoagulant drug dicoumarol with calf thymus-DNA: deciphering the binding mode and binding force with thermodynamics.

The biological activity of drugs is exhibited due to their interactions with bio-receptors. Dicoumarol (DIC) is a natural hydroxycoumarin and a well-known anticoagulant. DNA is the genetic material and one of the targets of numerous drugs.

DNA binding studies of antifungal drug posaconazole using spectroscopic and molecular docking methods.

The binding studies of DNA with small molecules have been an emerging field of research all the time since DNA as the genetic material is a major biological target for various drugs. Interpretation of small molecule-DNA binding helps in understanding their interactions with designing new drugs of greater medicinal activity. Posaconazole is an antifungal drug in the class of triazoles which are known to possess numerous pharmacological properties.

An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach.

Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals.

Region Convolutional Neural Network for Brain Tumor Segmentation.

Gliomas are often difficult to find and distinguish using typical manual segmentation approaches because of their vast range of changes in size, shape, and appearance. Furthermore, the manual annotation of cancer tissue segmentation under the close supervision of a human professional is both time-consuming and exhausting to perform. It will be easier and faster in the future to get accurate and quick diagnoses and treatments thanks to automated segmentation and survival rate prediction models that can be used now.

Metaheuristics with Deep Transfer Learning Enabled Detection and classification model for industrial waste management.

Rapid industrialization has led to the generation of a considerable amount of waste, both solid and liquid, in industrial fields like food processing, sugar, pulp, sago or starch, dairies, paper, fruit processing, poultry, distilleries, slaughterhouses, tanneries, and so forth. Despite the requirement for pollution control measures, the waste is discharged into water bodies or generally dumped on land without appropriate management, and thus becomes a significant source of environmental pollution and health hazards. The most essential step of waste management is the segregation of waste into the various elements, and normally this process is done automatically by hand-picking.

Deciphering the nature of binding of dexlansoprazole with DNA: Biophysical and docking approaches.

Drugs, in general, exhibit their pharmacological activity in binding with intracellular targets. Numerous anticancer and antibacterial drugs target DNA as one of their primary intracellular targets. Dexlansoprazole (DLP) is a heterocyclic compound containing benzimidazole moiety and a proton pump inhibitor used to treat gastroesophageal reflux disease.