This paper introduces a non-conventional approach with multi-dimensional random sampling to solve a cocaine abuse model with statistical probability. The mean Latin hypercube finite difference (MLHFD) method is proposed for the first time via hybrid integration of the classical numerical finite difference (FD) formula with Latin hypercube sampling (LHS) technique to create a random distribution for the model parameters which are dependent on time [Formula: see text]. The LHS technique gives advantage to MLHFD method to produce fast variation of the parameters’ values via number of multidimensional simulations (100, 1000 and 5000). The generated Latin hypercube sample which is random or non-deterministic in nature is further integ

The Internet of Things (IoT) is an information network that connects gadgets and sensors to allow new autonomous tasks. The Industrial Internet of Things (IIoT) refers to the integration of IoT with industrial applications. Some vital infrastructures, such as water delivery networks, use IIoT. The scattered topology of IIoT and resource limits of edge computing provide new difficulties to traditional data storage, transport, and security protection with the rapid expansion of the IIoT. In this paper, a recovery mechanism to recover the edge network failure is proposed by considering repair cost and computational demands. The NP-hard problem was divided into interdependent major and minor problems that could be solved in polynomial t

<p><span>This research deals with the feasibility of a mobile robot to navigate and discover its location at unknown environments, and then constructing maps of these navigated environments for future usage. In this work, we proposed a modified Extended Kalman Filter- Simultaneous Localization and Mapping (EKF-SLAM) technique which was implemented for different unknown environments containing a different number of landmarks. Then, the detectable landmarks will play an important role in controlling the overall navigation process and EKF-SLAM technique’s performance. MATLAB simulation results of the EKF-SLAM technique come with better performance as compared with an odometry approach performance in terms of measuring the

Education specialists have differed about determining the best ways to detect the
talented. Since the appearance of the mental and psychological measurement movement, some
scholars adopted intelligence ratios as a criterion to identify the talented and others went to
rely on the degree of academic achievement. Each of these two methods has its own flaws and
mistakes and a large number of talented children were victims of these two methods.
Therefore the need to use other scales for the purpose of detection of talented children
appeared because they provide valuable information which may not be obtained easily
through objective tests and these scales are derived from consecutive studies of gifted andtalented children

The aim of this study is to develop a novel framework for managing risks in smart supply chains by enhancing business continuity and resilience against potential disruptions. This research addresses the growing uncertainty in supply chain environments, driven by both natural phenomena-such as pandemics and earthquakes—and human-induced events, including wars, political upheavals, and societal transformations. Recognizing that traditional risk management approaches are insufficient in such dynamic contexts, the study proposes an adaptive framework that integrates proactive and remedial measures for effective risk mitigation. A fuzzy risk matrix is employed to assess and analyze uncertainties, facilitating the identification of disr

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

A flexible pavement structure usually comprises more than one asphalt layer, with varying thicknesses and properties, in order to carry the traffic smoothly and safely. It is easy to characterize each asphalt layer with different tests to give a full description of that layer; however, the performance of the whole; asphalt structure needs to be properly understood. Typically, pavement analysis is carried out using multi-layer linear elastic assumptions, via equations and computer programs such as KENPAVE, BISAR, etc. These types of analysis give the response parameters including stress, strain, and deflection at any point under the wheel load. This paper aims to estimate the equivalent Resilient Modulus (MR) of the asphalt concrete

There continues to be a need for an in-situ sensor system to monitor the engine oil of internal combustion engines. Engine oil needs to be monitored for contaminants and depletion of additives. While various sensor systems have been designed and evaluated, there is still a need to develop and evaluate new sensing technologies. This study evaluated Terahertz time-domain spectroscopy (THz-TDS) for the identification and estimation of the glycol contamination of automotive engine oil. Glycol contamination is a result of a gasket or seal leak allowing coolant to enter an engine and mix with the engine oil. An engine oil intended for use in both diesel and gasoline engines was obtained. Fresh engine oil samples were contaminated with fou