The coupling system of two different sources has always been an important subject of research in the field of electrical grids of any voltage range. In particular, after the connection of the photovoltaic and the public grids, the voltages cannot be distinguished from each other, because after their coupling there is one voltage across the output load. In this article, we take into account the variation of the current when the load varies in order to establish the relationship between the measured current and the output AC voltage, which can be regulated using only the current. For this purpose, we employ two types of controllers, the Proportional-Integral-Derivative (PID) controller and the Artificial Neural Network (ANN) controller,using Matlab/Simulink. Despite the connection of aninverter, which increases the loss rate and the error,the results are encouraging considering that the error rate obtained for the ANN controller, which is 1.49%, is much lower compared to that of the PID controller, which is 2.4%. Based on the results obtained, it can be concluded that the ANN controller is the best choice to perform this simulation.

Our paper focuses on the discovery and analysisof a recently identified three-dimensional chaotic model. Thisresearch presents a remarkable system characterised by its easeof implementation, but which exhibits a more complex dynamicbehaviour, exceeding that of many similar chaotic systems. Byunravelling the underlying mechanisms of this system throughthe analysis of eigenvalues, bifurcation diagrams and Lyapunovexponents, its chaotic behaviour is verified by building anelectronic circuit. The experimental behaviour is in agreementwith the numerical studies. This paper paves the way for furtherexploitation of the unique interplay between simplicity andcomplexity in chaotic systems, promising applications in variousscientific disciplines.

Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.

This study set out to examine the thermo-mechanical behavior of gypsum-protected steel columns (GPSC) exposed to fire, including the cooling phase, through numerical analyses with the aim of better understanding the effect of protection materials and identifying the possibility of delayed failure of GPSC during this critical period. A parametric study has been performed with the SAFIR program using a sequentially decoupled thermal structural analysis. The examined factors are the shape of the columns, the fire intensity, and the thickness of the protection. Gypsum serves as insulation, providing passive protection to prevent the degradation of steel mechanical properties and to mitigate and delay the collapse of steel columns during fire exposure. Different thicknesses of gypsum were considered (1 mm, 3 mm, and 5 mm) in order to analyze the effect of the rate of heat storage on the delayed collapse during and after fire exposure. The simulations were performed considering ISO fire and parametric temperature–time curves, which include a cooling regime that is linear. The findings show that the failure of the GPSC over the period of cooling is a possible event where the protection acts as a cooling retarder, which leads to a delayed collapse. Columns with massive sections and thick layers of protection are the most susceptible to delayed failure. Overall, this paper provides a real assessment of the load capacity in a natural fire situation, and the results highlight the possibility of delayed collapse of GPSC.

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 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 paper presents a novel strain-based finite element (NSBPE4K) developed for the free vibration analysis of thin plates, both with and without cutouts. The element incorporates three primary degrees-of-freedom per node: a transverse displacement (w) and two normal rotations (θx, θy) about the x and y axes, respectively. The displacement field is formulated based on assumed functions for the strain components, ensuring the compatibility equations are satisfied. The non-conforming element was successfully implemented in the ABAQUS software using the UEL subroutine (user element). Free vibration analysis results demonstrate the exceptional efficiency and accuracy of the new element. The results obtained with the present element excel those obtained with standard ABAQUS elements and other non-conforming elements found in the literature. This superiority is noticeable in free vibration scenarios, demonstrating the effectiveness of the proposed finite element for accurate and reliable simulation of the vibrational behavior of thin plates.

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.

Solar power plants that incorporate parabolic trough collectors (PTC) to generate solar energy can be regarded as a viable alternative to conventional power plants. To enhance the performance and productivity of these systems, it is imperative to improve the direct steam generation process. This study proposes the implementation of a passive enhancement technique to improve steam production in the PTC absorber, with the aim of optimising the overall size and cost of solar power plants. For this purpose, longitudinal fins have been attached to the inner bottom part of the tube. A numerical investigation was conducted to examine the two-phase flow with vaporisation using the ANSYS Fluent code. The analysis of two-phase flow was carried out via the volume of fluid technique. Additionally, a phase-change model was integrated to elucidate the vaporisation process. The Monte-Carlo ray-tracing approach was employed to identify the irregular distribution of heat flux across the tube. The integration of fins within the absorber tube has been demonstrated to enhance heat transfer and vapor fraction, thereby optimising the thermal performance of the system. Furthermore, the configuration that optimised steam generation was achieved through the utilisation of an absorber tube equipped with two rectangular longitudinal fins, displaying an aspect ratio of 0.5. The optimum thermal performance factor was found to be 1.58, which is reached in the laminar regime. The study's findings indicate a reduction in the overall dimensions of the PTC absorber, leading to a decrease in the size of solar power plants and their associated costs.

This study addresses the critical challenge of reducing operating temperature in photovoltaic (PV) systems, as excessive heat generation impairs their electrical efficiency and power output. A novel mini-channel heat sink with a folded U-shaped fin design is introduced to enhance heat dissipation, offering a scalable solution for optimizing PV performance. The design increases the heat transfer area while reducing airflow velocity by narrowing the channels, and optimizing thermal management. Experiments were conducted indoors under controlled conditions, with inlet air velocity of 0.3, 0.6, 0.8, and 1 m/s and solar irradiances of 500 and 1000 W/m². The outcomes showed that the mini-channel heat sink effectively reduced the average cell temperature by 57.44 %. This significant thermal regulation increased electrical efficiency by 26.6 %, resulting in a 37.55 % increment in power output. The experimental findings were further compared to numerical simulations achieving an acceptable range of variation and ensuring the reliability of the results with an average heat transfer coefficient error percentage below 5 %. The originality of this work lies then in its unique U-shaped mini-channel design, which mitigates thermal stress and optimizes energy output. It provides a promising approach to advancing PV cooling technologies and a scalable solution for improving solar energy efficiency.

This research conducts a comprehensive numerical evaluation into an advanced heat dissipation system for low-concentrated photovoltaic systems, addressing the limitations of conventional minichannel heat sink designs. To overcome their inherent inefficiencies, a novel minichannel configuration with wavy surfaces and a trapezoidal inlet section (TWMC) is proposed, aiming to enhance convective heat transfer through increased surface area and induced flow turbulence. Three configurations wavy minichannel (TWMC), trapezoidal minichannel (TMC), and rectangular minichannel (RMC) are systematically compared in terms of key performance metrics, including thermal resistance, Nusselt number, pressure loss, and friction index. Water serves as the coolant, operating in a laminar flow regime (Re = 200–900) and absorbing a uniform heat flux of 100 kW/m² applied to the channel base. Results demonstrate that the TWMC configuration outperforms conventional designs, achieving a 30.82 % decline in heat resistance and a 9.2 % surge in Nusselt number at peak Reynolds numbers. The TWMC design improves the performance evaluation criterion (PEC) to 1.06, with exceptional overall thermohydraulic performance PEC(R) ranging from 1.078 to 1.271, despite higher pressure drop. These findings offer insights into optimizing CPV system performance, emphasizing the potential of innovative wavy-channel geometries to revolutionize thermal management and energy efficiency in advanced photovoltaic applications.

A numerical investigation is undertaken, employing a 3D conjugated heat transfer model to examine the impact of geometric configurations and hydrodynamical parameters on the overall thermal resistance and pumping power in mini-channels heat sinks. The aim lies in its holistic approach, integrating the non-uniform section of the mini-channel, the impact of the inlet velocity, the energy and exergy analysis, multi-objective optimization and performance evaluation criteria (PEC) evaluations, and the consideration of metal Galinstan and Cu-water nanofluid working fluids. The parametric analysis highlighted metal Galinstan as the best coolant for the five configurations involved in the present study. Furthermore, The PEC results indicate that the best performance is achieved by the Converged-Diverged Mini-channel (CDMC)heat sink. CDMC configuration with metal Galinstan performs well in terms of exergy evaluations and shows a better average temperature distribution with a maximum temperature of about 328K. The optimal inlet velocity (Uin = 0.21 m/s) is determined on the basis of the pumping power and thermal resistance profiles. The optimization process is based on the impact of the mini-channel's maximum width on the PEC. It is shown that the PEC increases with the maximum width of the CDMC and the highest (PEC = 1.31) is obtained at a maximum width of 0.95 mm.

L'écriture est une compétence fondamentale au cours de la scolarité primaire, permettant non seulement de communiquer des idées, mais aussi de développer la créativité des élèves. Cet article examine l’importance de l’enseignement de l’écriture aux élèves du primaire dans un contexte plurilingue. Nous proposerons des outils et des méthodes qui favorisent une écriture authentique et confiante.

Global sustainability initiatives increasingly rely on innovative technologies to safeguard biodiversity and mitigate environmental impacts. In this paper, we present EcoWatch, a novel framework that leverages Wireless Multimedia Sensor Networks (WMSNs) using LoRaWAN technology for efficient data transmission to enable real-time bird species detection and counting in their natural habitat. EcoWatch combines YOLOv8 You Only Look Once for object detection and Learning to Count Everything (LTCE) for precise object counting at the base station. To address the inherent limitations of WSNs in terms of energy and bandwidth, EcoWatch incorporates a multi-level ROI-based video compression technique. Extensive evaluation demonstrates that EcoWatch significantly reduces energy consumption (up to 58.7%) and data transmission load (by 69.8%) compared to other methods while maintaining acceptable image quality, detection and counting accuracy. Notably, EcoWatch exhibits robust performance across seasons and adapts well to varying environmental conditions, making it a promising solution for real-world ecological monitoring applications.

This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling.

The global wind energy industry achieved a significant milestone by reaching a total capacity of one terawatt (TW) by the end of 2023, underscoring the increasing importance of wind energy as a sustainable energy source (Global Wind Energy Outlook, 2022). This study focuses on the simulation and dynamic analysis of an H-Darrieus wind turbine rotor using 3D Finite Element Analysis (FEA). Key structural parameters, including natural frequencies, associated vibration modes, and mass participation rates, were determined to optimize the rotor performance. A novel blade design is proposed in this work, offering a lighter and more robust alternative to traditional rotor blades manufactured from composites, like fiberglass-polyester, fiberglass-epoxy, or combinations with wood and carbon. The lighter design enhances the startup performance at low wind speeds, while the improved strength and fixing mechanisms ensure resilience against the increasingly severe sandstorms reported in recent years. The vibration dynamics of the rotor under critical wind loads were analyzed using the SolidWorks Simulation software, yielding highly satisfactory results. The stability and reliability of the rotor were validated, as the dynamic performance indices, and the quality criteria meet the requirements for optimal operation.

Let (X,d,μ) be a space of homogeneous type and L be a nonnegative self-adjoint operator on L2(X) whose heat kernels satisfy Gaussian upper bounds. In this article, we introduce the weighted variable Besov space associated with the operator L and demonstrate that Peetre maximal functions can be used to characterize this space. Furthermore, we provide a detailed study of its atomic decompositions.