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A method to construct a stochastic differential equation describing a non-Gaussian process in a stationary state and to obtain a static potential function in terms of the temporal average is proposed. However, it has been suggested that the potential function changes temporally through some analysis of climatic change. In this paper, a mathematical model of…

The objective of this paper is to provide a new simulator framework for mobile WSN that emulate a sensor node at a laptop i.e. the laptop will model and replace a sensor node within a network. This platform can implement di?erent WSN routing protocols to simulate and validate new developed protocols in terms of energy…

Big Data Analytics (BDA) is important to utilize data from hospital systems to reduce healthcare costs. BDA enable queries of large volumes of patient data in an interactively dynamic way for healthcare. The study objective was high performance establishment of interactive BDA platform of hospital system. A Hadoop/MapReduce framework was established at University of Victoria…

In this paper, an application of artificial neural network (ANN) using bayesian regularization (BR) learning algorithm  based on multilayer perceptron (MLP) model is presented for computing the operating frequency of C-shaped patch antennas (CPAs) in UHF band.  Firstly, the operating frequencies of 144 CPAs having varied dimensions and electrical parameters were simulated by the XFDTD…

This manuscript focuses on a hybrid power system combining a solar photovoltaic array and energy storage system based on hydrogen technology (fuel cell, hydrogen tank and electrolyzer) and battery. The complete architecture is connected to the national grid through power converters to increase the continuity of power. The proposed a hybrid power system is designed…

In the present study a computer model of a pumping well has been developed and calibrated by using two slave programs of Geo-Slope software, i.e. (SIGMA/W and SEEP/W). The model has been used to study the behaviour of watertable and drawdown in an aquifer during pumping. SIGMA/W program was used to compute initial pore-pressure within…

MicroRNAs (miRNAs) are small RNA molecules that play a crucial role in regulating gene expression by binding to and degrading targeted mRNAs. miRNAs targeting a specific mRNA have a region known as the “seed sequence”, which typically has a high affinity for its complementary sequence in the targeted mRNA. Single Nucleotide Polymorphisms (SNPs) are mutations…

This study investigated the influence of a biomass additive on the thermal performance of the inner liner of fired-clay cook-stoves. Fired-clay cook-stoves are essential cooking devices, particularly in areas with limited access to modern energy resources. The study aimed to enhance the thermal efficiency of the cook-stoves by incorporating rice husk into the inner liner…

The accumulation and analysis of large-scale patient data have led to breakthrough discoveries in potential flags for diseases based on pattern recognition, highlight medication efficacy, and local population health trends that would be impossible with traditional paper-based records. However, these benefits come with unique challenges posed by the application of data sharing for research and…

Heart rate (HR) is a critical indicator in fitness monitoring, athletic performance evaluation, and injury prevention. However, traditional motion-sensitive wearable devices are highly susceptible to movement artifacts, which degrade measurement accuracy during physical activity. Remote photoplethysmography (rPPG) offers a non-contact alternative for HR measurement, though it too remains sensitive to motion. This study proposes a…

This paper presents the design and fabrication of an electromagnetically actuated PCB-based resonating scanning micro-mirror for LiDAR applications, with optimization targeted towards low-cost fabrication and a high scanning angle. Traditional silicon MEMS-based micro-mirrors, while offering high precision and compatibility with CMOS processing, are limited by fragility at low scanning frequencies and costly fabrication processes. To…

Complex networked systems provide a cornucopia of network statistics, many of which relate to the temporal behaviour of subsystems, devices, or even individual protocol layers. Different and flexible visualizations can play a crucial role in discovering and making patterns, relations, and trends tangible. We developed PerfVis, a tool that visualizes timestamp data to aid in…

The growing integration of the Internet of Medical Things (IoMT) into healthcare has amplified the need for secure and privacy-preserving artificial intelligence. Federated Learning (FL) has emerged as a pivotal paradigm for decentralized medical data processing; however, it still faces challenges concerning data confidentiality, trust management, and scalability. This review presents an extended theoretical comparison…

Achieving targeted reliability for complex products in real-world field environments remains a persistent challenge, even when laboratory validation suggests high performance. A significant reliability gap often emerges during the initial deployment phase, typically within the first one to five years where field failure rates can be up to twice those predicted in controlled settings. Compounding…

This study establishes a method for determining whether learners have an understanding of data science. Data science requires knowledge in various fields, which makes many learners give up. To prevent learners from being discouraged, it is necessary to judge the comprehension of the principles in each specified skill. It is important to assess not only…

Deep neural networks (DNNs) have demonstrated remarkable success across a wide range of machine learning tasks. However, determining an effective network architecture, particularly the sizes of the hidden layers, remains a significant challenge and often relies on inefficient trial-and-error experimentation. In this paper, we propose an automated architecture design approach based on structurally adaptive DNNs,…

We address irregular polygon nesting on sheet materials with a lightweight evolutionary framework that operates directly in the layout space. The method formalizes multi-term fitness combining utilization, overlap penalties, spacing regularity, and local alignment, with all components normalized before aggregation. Feasibility is enforced by an AABB– SAT pipeline and validated via analytic ground-truth cases, degenerate…

In this work, we present CIRB-Edge, a novel integer compression method designed specifically to overcome the limitations of traditional techniques such as Huffman coding, Delta encoding, and dictionary-based algorithms. These legacy methods often fall short in meeting the stringent requirements of secure, energy-efficient, and real-time edge computing due to their high computational overhead, memory demands,…

Investment decision-making plays a pivotal role in shaping both individual and institutional economic outcomes. Given the increasing complexity and uncertainty in global markets, optimizing investment decisions has become essential for maximizing returns while managing risks. This work explores modern optimization approaches in investment decision-making, focusing on mathematical modeling techniques such as linear programming (LP), mixed-integer…

While prior research has focused on siloed approaches to equipment replacement, this study introduces an integrated decision-making framework that synergizes predictive maintenance (IoT/M), dynamic multi-criteria analysis (MCDM), and sustainability-driven material selection. By validating this model through cross-sector case studies and strategic operational planning across various industrial sectors. We demonstrate a 30% improvement in replacement timing…

As models based in artificial intelligence increase in sophistication, there is a higher demand for the integration of hardware components to heighten real-world implementations. Both facial feature tracking and shape-from-focus are known techniques in computer vision. However, the combination of these two elements, particularly in a cost-effective configuration, has not been extensively explored. In this…

The rapid evolution of global technologies and industrial restructuring demands innovative pedagogical approaches to foster interdisciplinary engineering expertise. This research pioneers a blended instructional framework anchored in micro-topic pedagogy under the New Engineering Education (NEE) paradigm, orchestrating case studies, heuristic scaffolding, and research-driven inquiry strategies within digitally augmented learning ecosystems. A quasi-experimental study was conducted…

Persistent anomalies in modern photovoltaic (PV) systems present a formidable challenge, impeding optimal power output and system resilience. Artificial Intelligence (AI) has surfaced as a game-changing solution, yet existing research has merely scratched the surface of solar panel prognosis, leaving a critical void in leveraging AI’s explainable nature and active learning capabilities. This pioneering study…

This study presents a novel alternative to traditional Net Energy Metering (NEM) by proposing a set of innovative pricing schemes for solar customers participating in utility-led grid service programs through the aggregation of Distributed Energy Resources (DERs). Grounded in cooperative game theory, the proposed framework facilitates equitable and efficient value allocation among key stakeholders, namely…

Gallium nitride (GaN) is expected to be used as a material for power semiconductor devices. However, it is crucial to focus on the dielectric properties of GaN. In this study, we investigated the transient response of the drain current during high-frequency application after intentionally maintaining the current collapse in the AlGaN/GaN high electron mobility transistors.…