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Flexible Nonenzymatic Glucose Biosensor Based on Magnetoelectrochemical Deposition of Cu
No full textDiabetes is believed to be one of the most significant public health concerns of the coming decades, with substantial efforts being devoted towards the development of new and advanced glucose monitoring systems. This paper describes the exploitation of magnetic field-assisted deposition of Cu for the fabrication of a flexible, nonenzymatic amperometric sensor for the determination of glucose levels in human sweat. Using a specially designed 3D printed electrochemical cell, Cu was deposited onto a flexible indium-tin-oxide (ITO) coated polyethylene terephthalate substrate in the presence of magnetic fields ranging from 0 to 4195 G. The amperometric responses of the Cu-ITO sensor fabricated in the presence of 4195 G (Mag-Cu-ITO) demonstrated a significantly higher sensitivity of 1052 μA mM−1 compared to the Cu-ITO sensor fabricated in the absence (i.e., 0 mT) of a magnetic field, which yielded a sensitivity of 688 μA mM−1. The Mag-Cu-ITO sensor demonstrated a linear relationship with glucose concentrations between 5 and 390 μM, which covers the physiologically relevant glucose levels in human sweat and showed no response to interferants also found in sweat (i.e. caffeine, thiocyanate, uric acid, urea, ascorbic acid). The proposed Mag-Cu-ITO sensor was successfully applied towards determining glucose levels in human sweat
Non-Invasive Glucose Monitoring Using PPG, AI, and IoT-Driven Mobile Integration for Real-Time Diabetes Management
No full textDiabetes mellitus patients must regularly monitor their blood glucose levels to manage glycaemia, typically requiring capillary tests at least three times daily and laboratory tests one to two times per month. These conventional methods involve finger pricking, causing significant discomfort and stress. This study introduces an innovative non-invasive glucose monitoring approach by integrating photoplethysmography (PPG) technology with an artificial intelligence (AI) algorithm, complemented by a mobile application using Flutter, IoT systems, and Firebase cloud for real-time data access. Among the AI models tested, polynomial regression demonstrated superior accuracy in glucose prediction, achieving a Mean Squared Error (MSE) of 14.76 and an R² score of 0.729. Despite challenges such as motion artifacts and ambient light interference affecting PPG signals, the system provides an advanced, user-friendly solution for patients and healthcare teams to monitor glucose levels effectively and conveniently
Exploring the role of online discussion forums in endorsing Egyptian undergraduates’ EFL learning
No full texteLearning Management systems (eLMS) are software applications used in universities worldwide to implement specific educational goals. eLearning platforms have several features that support traditional Face to Face (FTF) teaching and provide valuable opportunities for independent, self-paced learning. One of these features is the Online Discussion Forums (ODFs). This research paper investigates the role of ODFs in endorsing English as a Foreign Language (EFL) learning by examining students\u27 perceptions of its accessibility, peer/instructor interaction, assessment criteria, and discussion topics. The paper also investigates the influence of gender on students\u27 perceptions of the role of ODFs. This is a non-experimental research design, and 108 Egyptian undergraduates completed a semi-structured questionnaire of five dimensions. Percentages of frequency were calculated and quantitatively analysed via the SPSS. Research findings revealed male and female students’ positive perceptions of using ODFs in their learning process. Results also revealed that students highly perceived choice of topics and assessment as crucial factors for successful learning via ODFs, followed by peer/instructor interaction and accessibility of the tool. Implications for further research include employing ODFs to improve project-based teaching in different disciplines
Intelligent Multi-Agent English Auction Interaction Protocol for Logistics Service Provider Selection
No full textGlobal supply chains have become dynamic and complex over the past years, and this is expected to increase in the future. Logistics planning is a key part of supply chain management; hence it is crucial to shift towards agile and automated logistics models with the utilization of advanced information and communication technologies. The scope of this paper is the use of multi-agent systems for selection of Logistics service providers in cargo shipping. Cargos are modeled within auction-based mechanisms to automate the supplier selection and negotiation procedure between a client and multiple logistics service providers to find the best offer. FIPA English Auction Interaction Protocol is investigated to manage different actions between the agents, and a new model is proposed by applying communication acts of (Cancel, Refuse, and Failure) with different levels of credibility (Low, Moderate, and High). It was found that introducing an individual act into the interaction protocol can increase the number of interactions between agents from 24 to 26 up to 30 in case of introducing all the three acts into the interaction protocol. This means that the entire system will spend more time and energy in analyzing and responding to the additional acts. It is concluded that the higher the credibility, the lower the interactions between agents as the system will spend less time and energy in communication, which leads to enhance the performance and the efficiency of the system and the network. Therefore, a trade-off between maintaining the commutation speed and the system performance and reliability is vital
Synergizing GIS and genetic algorithms to enhance road management and fund allocation with a comprehensive case study approach
No full textThis study identifies a critical knowledge gap, revealing how the deterioration of roads, compounded by extensive usage and additional factors, poses significant risks to the road networks’ functionality. Without a robust fund allocation and prioritization strategy, the extent of this risk may be overlooked, adversely affecting the performance of essential infrastructure elements. Our research introduces an integrated decision-making model for existing road infrastructures to address this gap. This innovative approach combines a Geographic Information System (GIS)-based road management model with a fund allocation prioritization strategy, enhanced by an optimization engine via a genetic algorithm. The primary aim is to precisely determine Maintenance and Repair (M&R) interventions tailored to the condition states, thereby improving the Pavement Condition Index (PCI) of the road segments. The research is structured around three key objectives: (1) develop a detailed GIS-based road management database incorporating inspection data and attributes of road infrastructure for proactive M&R decision-making; (2) efficiently allocate funds to maintain service delivery on deteriorated roads; and (3) pinpoint the optimal type and timing of M&R interventions to boost the condition and performance of the road segments. Anticipated results will provide asset managers with a comprehensive decision support system for executing effective M&R practices
The Impact of Green Human Resource Management (GHRM) on Employees’ Environment-Friendly Behavior (EEFB): A Comparative Analysis between Public and Private Universities in Egypt
No full textThe interface of global environmental challenges and threats like depletion of natural resources, global warming, pollution and electricity shortage reflect a new era of environmental awareness to safeguard human needs and ensure environmental sustainability within the global higher education sector (Saeed et al., 2018). The aim of this study is to align with global challenges highlighting the application of GHRM practices (Green recruitment and selection, green training, Green performance management, Green pay and rewards and green involvement and relation) on Employees’ Environmentally Friendly Behavior (EFFB) in a comparative analysis between practices in public as well as private universities in Egypt. On a global level universities are in the center of promoting sustainability towards 2030 strategy however practically Egypt has been lagging in the implementation of sustainable practices of GHRM in the higher educational sector. So, the present study fills this gap by focusing on a comparative analysis between private and public sector universities in which (EFFB) can be assessed when adopting (GHRM) practices which represent an originality in theoretical level to fill the gap in literature. The research methodology is quantitative analysis using a questionnaire to collect data from the academic and administrative staff employees working in three Egyptian public and three private universities with a sample size of 421 from faculty, administrative staff and support personnel. The findings are directed towards an overall positive significance of GHRM practices on EEFB, however with a stronger impact in private universities than in public ones
Low-Velocity Impact Behavior of 3D Printed Sandwich Composite with Polylactic Acid–Micro-crystalline Cellulose Bio-inspired Xylotus Lattice Core: Energy Absorption and Crashworthiness
No full textThis study introduces a novel bio-inspired lattice core, the Xylotus core, designed for sandwich composite structures. Inspired by lotus petals and xylem-like tubular geometries, the core is fabricated using a PLA biopolymer reinforced with 4% micro-crystalline cellulose (MCC). It is combined with PETG face sheets, and both components are additive manufactured using multi-nozzle fused filament fabrication at varying layer thicknesses (0.2 mm, 0.3 mm, and 0.4 mm) and structural orientations (in-plane and out of plane). Low-velocity impact tests were conducted at drop heights ranging from 0.5 m to 1.1 m to evaluate energy absorption, indentation depth, and crashworthiness. Results indicate that the out-of-plane Xylotus structure, with perpendicular tubular elements, absorbed 2.7% more energy than the in-plane configuration. Among all variations, the 0.2-mm layer thickness showed the highest energy absorption and crash resistance. Micro x-ray CT analysis revealed that the 0.2 mm out-of-plane samples exhibited 15.1% and 16.4% lower indentation depths compared to the 0.3-mm and 0.4-mm samples, respectively. Comparative evaluation confirms the superior performance of the PLA-4% MCC Xylotus core over existing lattice designs, especially in terms of energy absorption and crash efficiency
Stellar isotropic model in the symmetric teleparallel equivalent of general relativity theory
No full textRecently, the theory of symmetric teleparallel equivalent of general relativity (STEGR) has gained much interest in the cosmology and astrophysics community. Within this theory, we discuss the method of deriving a stellar isotropic model. In this respect, we implement the equations of motion of STEGR theory to a spacetime that is symmetric in a spherical manner, resulting in a set of nonlinear differential equations with more unknowns than equations. To solve this issue, we assume a special form of gtt, and suppose a null value of the anisotropy to obtain the form of grr. We then investigate the possibility of obtaining an isotropic stellar model consistent with observational data. To test the stability of our model, we apply the adiabatic index and the Tolman–Oppenheimer–Volkoff equation. Furthermore, we examine our model using different observed values of radii and masses of pulsars, showing that all of them fit in a consistent way
Statistical inference for dependent competing-risk failures in land-based radar detection: A PHW model under generalized progressive hybrid censoring
No full textDependent competing risks usually arise in modern reliability and survival studies, but remain under‑explored because of the mathematical and computational complexity they introduce. This paper developed a flexible inferential framework for systems based on mutually dependent failure causes when the lifetimes are governed by the proportional hazard Weibull (PHW) distribution. Data were collected through the generalized progressive hybrid censoring scheme (GPHCS), which reduced test duration while preserving information with a prefixed number of failures. From a computational perspective, the maximum likelihood estimators (MLEs) were derived via numerical optimization, such as the Newton-Raphson algorithm. To incorporate prior knowledge and quantify parameter uncertainty, Bayesian estimates were produced using conjugate gamma priors and a Metropolis within Gibbs sampler. Estimator performance was assessed through an extensive Monte Carlo simulation study. Results show that MLE and Bayesian procedures were unbiased, and Bayesian credible intervals were noticeably shorter than their asymptotic counterparts. The procedure was applied to a land-based surveillance radar data set in which the target loss risks are dependent. The fitted PHW model accurately captures the dynamics of radar return signals, and posterior analyses revealed how each covariate modulates detection reliability
On estimation of the quadratic hazard rate model parameters: Simulation and application
No full textThis study applies both Bayesian and non-Bayesian methods to estimate the lifetime parameters of the quadratic hazard rate distribution using incomplete lifetime data under progressive Type-II censoring with binomial removal. The three-parameter quadratic hazard rate model generalizes traditional distributions, including linear hazard rate, exponential, and Rayleigh distributions. In the non-Bayesian framework, parameters, as well as reliability and hazard rate functions, are estimated using maximum likelihood estimation (MLE) and maximum product of spacing (MPS). Asymptotic confidence intervals are derived, with a focus on the delta method. Bayesian inference is then performed under both MLE and MPS approaches using independent informative priors (normal and gamma) and both symmetric (squared error) and asymmetric (linear exponential) loss functions to obtain point estimates and highest posterior density credible intervals. Given the complexity of closed-form Bayesian estimates, Markov chain Monte Carlo methods are employed to sample from the posterior distribution. The precision and consistency of point and interval estimates are assessed using four performance metrics: root mean squared error, mean relative absolute bias, average confidence interval length, and coverage probability. A simulation study explores these criteria across varying sample sizes and censoring schemes. The proposed methods are further validated on real-world data concerning the remaining service time of aircraft windshields. In addition, we analyzed the existence and uniqueness of the estimates before confirming them graphically. Finally, the results indicate that Bayesian approaches outperform non-Bayesian methods in terms of accuracy and robustness, offering valuable insights into reliability testing and decision-making in engineering applications