This paper presents ELAREES, a task scheduling algorithm for heterogeneous multiprocessor real-time systems, designed to optimize energy savings while enhancing fault tolerance. ELAREES addresses the dual challenges of fault tolerance in task execution and communication reliability between tasks, alongside efficient power management. The algorithm employs a primary/backup strategy, assigning each task a primary execution on a low-power (LP) core and a backup on a high-performance (HP) core to ensure resilience against execution faults. Furthermore, ELAREES integrates a robust communication protocol that monitors data transmission over shared media connection buses, dynamically selecting optimal transmission paths and initiating retransmissions when necessary to mitigate communication errors. By leveraging Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Management (DPM) techniques, ELAREES achieves significant power savings while maintaining high system reliability. Simulation results demonstrate consistent power savings of approximately 30% across various scenarios, with only a minimal impact of 0.02% on reliability. This research contributes to the field of energy-efficient computing in real-time systems, offering a comprehensive solution for managing the trade-offs between energy consumption, execution fault tolerance, and communication reliability in heterogeneous multicore environments.
The growing need for clean energy has made solar panels an essential solution. However, the nonlinear behavior of photovoltaic (PV) systems under varying weather conditions necessitates advanced control strategies to ensure optimal energy harvesting. This paper presents an enhanced Maximum Power Point Tracking (MPPT) approach that integrates the conventional Perturb and Observe (P&O) method with an Indirect Adaptive Fuzzy Controller (IAFC). While P&O is known for its simplicity, it suffers from steady-state oscillations and slow response during environmental changes. To address these issues, the IAFC adaptively adjusts the perturbation step using a Lyapunov-based rule to improve convergence and minimize power fluctuations. The proposed method achieves Maximum Power Point tracking within less than 0.025 s, compared to 0.05 s for the conventional P&O algorithm. This enhances the credibility of our dynamic performance claim. Specifically, unlike prior fuzzy-P&O hybrids with fixed rule sets, our method leverages Lyapunov-based adaptation to dynamically adjust the control action, improving convergence and robustness under changing conditions. We also included a quantitative metric showing a 75% reduction in power fluctuations compared to conventional P&O. Simulation results under varying sunlight conditions demonstrate fast convergence and improved power stability. The proposed IAFC method clearly outperforms classical P&O in tracking accuracy, responsiveness, and overall energy yield.
The present work focuses on the experimental characterisation and numerical validation of the in-plane mechanical properties of IM2-12K/Epocast 50-A1 composite for structural damage prediction. Consequently, a series of tensile, compressive, shear, and flexural tests were systematically conducted on specimens prepared with specific lay-up configurations, while the fibre volume fraction was measured using the ashing method. The experimental results demonstrated that the composite under investigation exhibited high tensile strength and stiffness along the fibre direction, moderate compressive properties, and lower shear strength. This behaviour is indicative of anisotropic properties. Moreover, a three-dimensional finite element simulation of the tensile and three-point flexural tests was subsequently conducted, employing a Hashin-based failure initiation criterion. In order to achieve this objective, the key material properties were incorporated into a user-defined material subroutine (VUMAT), thereby enabling the modelling of progressive damage mechanisms, encompassing both fibre and matrix failures. The numerical predictions exhibited excellent agreement with the experimental data, thereby validating both the measured properties and the robustness of the modelling strategy. The present study establishes a validated mechanical dataset and a predictive model, providing a reliable foundation for the design and simulation of the performance of IM2-12K/Epocast 50-A1 in advanced engineering applications.
The phenomenon of swelling-shrinkage has gained widespread attention in practice owing to the generation of eroded clayey layers of soil that amplify with global climate change and the seasonal water content. This provokes several serious disorders affecting the stability of nearby constructions and consequently generating human loss. The expansive clayey soils show the phenomena of wetting and drying cycles in their natural state (undisturbed soil). Hence, classical oedometric tests are found to be unable to take into account the thermal behavior of naturally swelling soils; this is proven by the resulting asymptotic volumetric behavior, as well as the steady values of the potential of swelling and shrinkage. The main aim of this experimental analysis is to derive a test that considers the significant effect of temperature. Experimental results of an oedometric approach are represented herein for the purpose of investigating the volumetric and hydric behavior of naturally swelling soil in the region of N’Gaous (Eastern Batna province, Algeria) through drying-wetting paths. Innovative expressions are derived for the direct computations of the swelling-shrinkage potential in terms of water content, appearance time and applied loads. It is of interest to mention that those expressions are applicable to other regions in the world with similar soil geotechnical and chemical characteristics and conditions. The cyclic outputs show that the swelling pressure variation with the appearance time is mainly related to the first cycle of swelling shrinkage; as it exhibits a noticeable increase in the swelling potential with the amplification of applied loads until reaching a state of steadiness. The experimental results demonstrate a high degree of reliability and correlation with the soil behavior. Therefore, the swelling shrinkage potentials are expressed innovatively in equations that help predict the soil behavior in expansive regions in order to enhance the safety of nearby foundations.
This study explores a novel approach to regulate blood glucose levels in individuals with type I diabetes, employing the widely used model predictive control (MPC) strategy in type 1 diabetes mellitus therapy and clinical trials. The MPC algorithm is implemented based on Magdelaine’s long-term glucose-insulin model, which encompasses real-life characteristics often absent in other prevalent models. The control strategy is evaluated through simulations involving 10 virtual patients from existing literature. The simulations encompass fasting scenarios and a closed-loop control scenario involving three meals. MPC results are compared to those of the “optimal” conventional insulin daily injections therapy (open-loop treatment), especially under “aggressive conditions” including elevated initial blood glucose levels, substantial carbohydrate intake, closely spaced meal times, and incorporating a time delay between plasma glucose concentration and its subcutaneous measurement. The MPC algorithm demonstrated remarkable efficacy in glycemic control for 80% of patients, achieving an average time-in-range percentage exceeding 80% with no hypoglycemic episodes. This aligns with the American Diabetes Association’s recommendation of spending at least 70% of the time in the target range for effective glycemic control and maintaining an average time spent in hypoglycemia of less than 4%. However, the same MPC controller exhibited suboptimal performance for two patients, with an average time spent in hypoglycemia exceeding 8%. These findings underscore the need for individualized adjustments of MPC parameters or alternative control strategies to optimize glycemic management in all patients.
This study investigates the fatigue degradation and crack propagation in the locking mechanism of PET bottle blow molding machines, focusing on the impact of elliptical cracks on the mechanism’s performance and longevity. The locking mechanism, which plays a vital role in securing the mold during the blow molding process, is subjected to repeated loading, making it susceptible to fatigue damage. Using a combination of finite element analysis (FEA) and experimental methodologies, we examine the stress distribution, deformation, and displacement in the mechanism under operational loads. The study identifies the most stressed component and models the behavior of an elliptical crack located at the center of this component. A stress intensity factor (K) of 3.7553 MPa.mm-0.5 is found, indicating significant risk in the crack region. Fatigue analysis using Goodman’s law predicts a service life of one million cycles with a safety factor of 2.08. These findings highlight the need for targeted design and maintenance strategies to enhance the reliability and durability of PET blow molding machines.
This article focuses on improving fault tolerance and optimizing energy consumption in the context of a mining prospection mission conducted by a swarm of autonomous robots. Two major contributions are proposed. The first aims to reduce communication between robots in order to increase the system’s robustness in the presence of failures. The second focuses on minimizing the trajectory of a deminer robot to reduce overall energy consumption. To address these goals, two reinforcement learning based algorithms are proposed: Deep Q-Network (DQN) and Federated Reinforcement Learning (FRL), both derived from the Q-learning algorithm. Simulation results examining the impact of the exploration rate α on the number of detected mines show that, with 10 autonomous robots of the same architecture and 30 randomly placed mines over 30 experiments, the FRL algorithm provides better fault tolerance and ensures that the main prospection mission is accomplished even in the presence of some robotic failures or errors. Furthermore, a second series of 60 experiments involving the integration of the deminer robot, focused on optimizing energy consumption, demonstrates that the DQN algorithm is more effective in reducing energy usage, due to improved a better optimization of unnecessary deminer movements, while successfully resolving deadlock situations that the latter may encounter. These findings open the door to the development of a hybrid algorithm combining the strengths of DQN and FRL to ensure both system robustness and minimal energy consumption.
This study presents a novel framework for the optimal design of an off-grid residential energy system, applied to the hyper-arid region of Tamanrasset, Algeria. The proposed hybrid renewable energy system (HRES) integrates photovoltaic panels, wind turbines, battery storage, and diesel generators. A key innovation is the integration of a green energy forecasting module within a multi-objective techno-economic optimization process. Various machine learning and deep learning models suited for time series prediction were evaluated, and the best-performing models for each meteorological parameter were selected which enables precise long-term hourly forecasts, improving system design and operational efficiency compared to conventional methods based on historical averages. The optimization targets three objectives: minimizing the Levelized Cost of Energy (LCOE), reducing the Loss of Power Supply Probability (LPSP), and maximizing the Reliability Factor (RF). Using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, the system achieves an LCOE of $0.05433/kWh, an LPSP of 3.1 %, and an RF of 98 %, indicating a strong balance between cost and reliability. Energy contributions are 47 % from solar PV, 35 % from batteries, 12 % from wind, and 6 % from diesel. Comparison with HOMER Pro simulations confirms the superior economic performance of the MOPSO-based configuration. Sensitivity analyses underscore the critical role of forecast accuracy in HRES performance, while environmental assessments show an 80 % reduction in CO2 emissions compared to diesel-only systems. The integrated forecasting module serves as a valuable decision-support tool for rural electrification, particularly in resource-constrained and climate-challenged regions.
Introduction. A new global maximum power point tracking (GMPPT) control strategy for a solar photovoltaic (PV) system, based on the combination of Takagi-Sugeno (T-S) fuzzy models and an ANFIS, is presented. The novelty of this paper lies in the integration of T-S fuzzy models and the ANFIS approach to develop an efficient GMPPT controller for a PV system operating under partial shading conditions.
Purpose. The new GMPPT control strategy aims to extract maximum power from the PV system under varying weather conditions or partial shading.
Methods. An ANFIS algorithm is used to determine the maximum voltage, which corresponds to the actual maximum power point, based on PV voltage and current. Next, the nonlinear model of the PV system is employed to design the T-S fuzzy controller. A reference model is then derived based on the maximum voltage. Finally, a tracking controller is developed using the reference model and the T-S fuzzy controller. The stability of the overall system is evaluated using Lyapunov's method and is represented through linear matrix inequalities expressions.
The results clearly demonstrate the advantages of the proposed GMPPT-based fuzzy control strategy, showcasing its high performance in effectively reducing oscillations in various steady states of the PV system, ensuring minimal overshoot and a faster response time. In addition, a comparative analysis of the proposed GMPPT controller against conventional algorithms, such as Incremental Conductance, Perturb & Observe and Particle Swarm Optimization, shows that it offers a fast dynamic response in finding the maximum power with significantly less oscillation around the maximum power point.
Phenolic compounds form the largest group of phytochemical compounds in plants. They comprise nearly 8.000 molecules divided into around ten chemical classes, and more than ten classes have already been shown to have pharmacological potential. In this context, this study tested the in-vitro bioactivity, antimicrobial aspect and vegetal compounds of Artemisia herba-alba using HPLC-DAD test. Data indicated that the hemostatic effect of the chloroform fraction was more pronounced and reached a reducing rate of clotting time of 70.91% for the highest tested dose. The photoprotective effect of this fraction was considered high when compared to petroleum ether. The initial phytoscreening revealed the presence of several classes of secondary metabolites. The quantification showed a high amount of flavonoids (88.05±1.94 ?g quercetin/mg fraction) in chloroform fraction, while petroleum ether fraction contained more tannins (39.55±1.46 ?g tannic acid /mg fraction). HPLC DAD screening revealed the presence of several flavonoids and phenolic acids compounds while the antimicrobial simulation showed a non-negligible effect of 3 tested compounds on several microbial strains. This work revealed the phytochemical aspect of certain extracts of A. herba-alba but also its procoagulant, photoprotective and antimicrobial properties.
The article presents a hybrid controller based on the Incremental Conductance (Inc-Cond) and Interval Type-2 Fuzzy Logic (IT-2FL) algorithms as a Maximum Power Point Tracker (MPPT). The controller employs a three-phase Interleaved Boost Converter (IBC), which operates based on the pulses generated by the MPPT to ensure that the photovoltaic (PV) system operates at or near its Maximum Power Point (MPP). IT-2FL enhances the tracking process by applying rule fuzzification and managing uncertainties in response to significant fluctuations in climatic conditions. The proposed controller demonstrates precise and rapid convergence to the MPP, outperforming the individual application of both component methods, as well as traditional fuzzy logic, even when combined with Inc-Cond. The fault tolerance of the proposed tracker is validated through MATLAB simulations under various operational scenarios, evaluating response time, MPP tracking accuracy, efficiency, and other parameters.
This study evaluates the inhibition mechanisms of CaCO3 scaling in reverse osmosis using Zn++ which has been superficially investigated in RO, with only three papers published. In order to do that a novel experimental approach was used; this approach involved a synthetic calco-carbonic solution, with an initial hardness of 60 °F (240 mg/l Ca++) and saturated with CO2, The CO2 leakage through the RO membrane allowed the interfacial pH to increase and, thus, accelerated the scaling occurrence. The condition for CaCO3 precipitation is the solubility product verification: (Ca++)* (CO3=) ≥ KS. In saturation pH, the CO3= concentration remained at ppm level; masking the CO3= ligand by Zn++, for scaling prevention, was the main research hypothesis. This approach is very different from conventional kinetic and crystallographic theories. Furthermore, the synthetic solution was desalted in batch mode using various Zn++ concentrations (0–1.5 ppm); the results showed a 288 minutes induction time without Zn++ and 402 mn with Zn++ at 1.5 ppm, the saturation pH increased from 7.33 to 8.18, confirming the Zn++ efficiency. Also, the pH–time, conductivity–time, [Ca++]–time and turbidity–time plotting allowed scaling detection in the fluid bulk; their comparison showed a good correlation. SEM and EDS spectro were used.
This book provides an introduction to the fundamental principles and applications of medical physics, specifically designed for students beginning their studies in health sciences and related fields. It covers essential topics such as aqueous solutions and colligative properties, geometrical and physical optics, and radiation physics, emphasizing their significance in medical and biological contexts. To enhance learning, the book includes exercises with answers, designed to clarify and reinforce the concepts presented in each chapter.
For any additional information or to purchase a copy, please contact Afak Bookstore (Mr. Tayeb, Tel. 0770647239)
Conductors serve as essential components in various electrical and electronic applications (steel, aircraft, and nuclear industries). Therefore, an accurate evaluation of their electrical parameters, in particular their electrical conductivity (σ), remains critical for assessing their performance in industrial processes. Although numerous eddy current based methods exist for conductivity measurement, this study approaches the problem through inverse problem solving. A novel approach integrating Eddy Current Testing (ECT) with Artificial Neural Networks (ANNs) is proposed to determine electrical conductivity from probe impedance measurements. An experimental setup has been developed that includes a custom-designed bobbin coil probe used in conjunction with metal plate samples (targets) and data acquisition and signal processing systems. To validate the introduced approach, conductivity values predicted by the ANN model were rigorously compared with reference measurements obtained using the four-point Direct Current Potential Drop (DCPD) technique. This comparative analysis demonstrates the robustness and measurement fidelity of the proposed approach.
This paper investigates the relationship between the extended spectrum of a bounded linear operator and its group inverse. We also establish a connection between the extended spectrum of the bounded linear operator and that of its Drazin inverse. As part of our analysis, we prove the following equality: σext((BA)D) = σext((AB)D), where (BA)D and (AB)D represent the Drazin inverses of BA and AB, respectively. 2020 Mathematics Subject Classification. 35K15; 35K55; 35K65; 35B40. Key words and phrases. extended spectrum; operator group inverse; Drazin inverse.
The usual use of value stream mapping is studying to improve production lines that are already running. In this study, we used value stream mapping and the PDCA cycle on a production line that is still being finished and not yet operational. This work is important and unique because it uses a proactive approach to improve processes. The used method aims to create a waste-free production chain from the start. This is a big plus because it means avoiding losses with high costs and getting a very efficient production line from the start. The findings demonstrate that lean manufacturing tool (VSM) can be used on current and future production lines, and this strategy enhances production line efficiency from the outset by minimising non-value-added activities and maximising value-added activities.
Le présent article propose une étude portant sur "Les Porteurs de Feu" de Salah Stétié. Dans cet essai, Stétié s'appuie sur la voix du mythe, qui s'imprègne du texte pour en devenir le cœur battant, afin de dresser un vibrant hommage à la poésie arabe. Il y attribue également aux poètes arabes le rôle d’alchimistes qui transforment l’imaginaire en réalité en se servant du pouvoir de la création verbale Mots-clés : mythe , alchimistes, imaginaire, poésie arabe.
