Theoretical Foundations of Fog and Edge Computing: Models, Algorithms, and Challenges
Cet article présente trois composés conjugués contenant des cycles thiophène et analyse leur applicabilité dans les cellules solaires à colorant (DSSC), dans le mais de contribuer au développement de nouveaux matériaux pour l'énergie et les technologies durables. Les composés correspondants ont été analysés théoriquement par la théorie de la fonctionnelle de la densité (DFT) et la DFT dépendante du temps (TD-DFT). Ces méthodes permettent de déterminer les propriétés optoélectroniques et de caractériser les processus de photogénération et de transfert de charge. L'importance des calculs théoriques en science et ingénierie des matériaux réside dans leur capacité à pallier plusieurs contraintes expérimentales et à orienter la conception de composés moléculaires performants. Les résultats obtenus révèlent des capacités photovoltaïques intéressantes, et les photosensibilisateurs étudiés présentent des caractéristiques avantageuses telles qu'une absorption favorable, une large bande interdite, un rendement de collecte de la lumière (LHE) élevé et une tension en circuit ouvert (Voc) exploitable.
The given paper describes a new method of increasing the efficiency of photovoltaic (PV) systems, such as perturbation and observation (PO) algorithm and nanofluid cooling processes. The main goal is to generate additional power and preserve the impact of thermal control. The study uses PO algorithm that is a typical MPPT algorithm to determine the maximum power point (MPP) of the PV module. The method can be facilitated by balancing the operating voltage by keeping the operating voltage constant and observe the generated power, until the MPP is reached. Nanofluids (containing nanoparticles) exhibit a greater thermal conductivity as compared to normal coolants. Dispersions of nanofluids in the PV cooling system enhance warming to the PV cooling system, lower the working temperature and improvement of the power conversion efficiency. This method has proven to be effective in the experimentation. The results of PO algorithm using nanofluid cooling (reduction of operating temperatures, increase of the power output) when compared with the conventional cooling and standard MPPT systems. The research makes an immense contribution to the existing literature in the photovoltaic (PV) technology and could be very significant in planning and implementation of renewable energy solutions infrastructure in the future
This essay discusses how the Maximum Power Point Tracking (MPPT) concepts that are offline, could be used with water cooling to improve the work of photovoltaic (PV) solar panels. In contrast to traditional real-time Maximum Power Point warping (MPPT) methods, offline approaches approximate the Maximum power point (MPPT) to different conditions including sunlight irradiance and temperature with known (pre-calculated) data or algorithms. This eliminates the requirement of constant supervision and it suggests a simpler and cheaper method of power optimization. Simultaneously, a water-cooling system is adopted that will keep the temperature of the solar panels in the best possible work conditions, since very high temperatures can significantly lower their efficiency. Water is spread across the surface of the panel absorbing excessive heat and inhibiting the degradation of performance caused by overheating. The water also serves to minimize dust build-up, raising the total energy take-up. The joint strategy of offline MPPT and water cooling show a great enhancement in generating energy, especially in the warm environments. The solution is an efficient, low maintenance approach to solar panel systems, and a bright avenue into better reliability and performance of renewable energy installations
This study introduces the unique hybrid topologies for an MLI where it is being used both symmetrical and asymmetrically for further performance enhancement at reduced usage of power electronic component count. With the offered space-efficient and relatively economical topology for lower THD and high output voltage quality. The improved performance of the topology under a wide range of operating conditions is shown by detailed studies that involve theoretical modeling, simulations, and experimental validation. Among the potential applications are inverters that are connected to the grid, electric vehicles, and systems that make use of renewable energy
This study takes a unique approach to boosting photovoltaic (PV) system performance by combining fuzzy logic with nanofluid cooling technologies. Maximizing power generation While assuring proper thermal control is the main goal, maximum power point tracking (MPPT) employs fuzzy logic, a strong technique for working with Fuzzy logic controllers use imprecision and uncertainty to produce a reliable solution. They can adapt to a variety of environmental conditions and PV module characteristics thanks to their design. It combines linguistic features with fuzzy rules, allowing for precise monitoring of the maximum power point (MPP). Nanofluids are special fluids that contain Nanofluids containing scattered nanoparticles have higher thermal conductivity than traditional refrigerants. The use of nanofluids incorporated into the powerplant can improve power generation efficiency. The cooling system of a PV system, which promotes heat dissipation and reduces operating temperatures, is one of the proposed strategies that greatly improve the efficiency of photovoltaic (PV) systems. By combining the advantages of nanofluid cooling with fuzzy logic. According to empirical findings, the Maximum Power Point Tracking (MPPT) technique based on On fuzzy logic, it effectively monitors the Maximum Power Point (MPP) under a variety of operational conditions. Improved power output and system operating efficiency. The nanofluid cooling system efficiently lowers the PV modules' operating temperature, resulting in increased power output and efficiency. To improve the performance of photovoltaic (PV) systems, a very This research makes a significant contribution to the advancement of photovoltaic (PV) technology by offering essential insights for The design and development of sustainable energy systems
By adding the concept of thermoelectric cooling and Fuzzy Logic Maximum Power Point Tracking (MPPT) together with a cooling system, this paper proposes a new methodological approach toward maximizing PV-based power systems. High temperatures especially in areas where the intensity of the irradiation is very high can reduce the output of solar panels. The proposed system: Perturb and Observe (P&O) or Incremental Conductance The traditional approaches to maximum power point tracking (MPPT) as Perturb and Observe (P&O) or Incremental Conductance simply cannot effectively find and maintain optimal power point operation even ballistically with changing weather conditions, and the traditional cooling schemes may not be able to allow it to do so. To ensure that the operating level of the panel stays within an optimal temperature range to reduce thermal degradation and maximize power generation, a thermoelectric cooling system is used. This two mode method of energy harvesting has been demonstrated to enhance the whole effectiveness of the system as it makes it possible to respond climax to irradiance variations and temperature now. It offers a good alternative to bolster the stability and strength farm of solar photovoltaic systems in alternate climates. In the light of the current demand of renewable energy, this piece of work illuminates new and friendlier innovations of how the solar power can be advanced to satisfy the demand
We will show in this work how we will combine PCM cooling with the Perturbation and Observation (P&O) strategy to the Maximum Power Point Tracking (MPPT) in order to maximize the efficiency of solar panels. Regulating the operating voltage and monitoring the overall power generation, P&O algorithm guarantees best energy recovery even under the conditions of the environmental change. Absorption and storage of excess heat using phase change material (PCM) to control the temperature in the panel assists in reducing heat losses and enhances the system to work more efficiently. Regarding the results, it becomes obvious that such combined strategy can improve efficiency of energy conversion and minimize effects of temperatures on solar panels and their degradation. Maximum Power Point Tracking (MPPT) by combination of P and O cooling with Phase Change Material (PCM) cooling is a powerful and economical way of enhancing the performance and stability of solar power systems. The contribution to the design of advanced solar technology in this research has the advantages of solar technology in terms of improved energy sustainability and reliability amid diversity of climates
Plug-in Hybrid Electric Vehicles (PHEVs) play a vital role in advancing sustainable transport, offering flexibility by switching between electric and internal combustion modes. However, managing energy flow and power distribution in real-world driving remains complex. This paper presents a data-driven control framework that integrates machine learning (ML) and fuzzy logic to optimize PHEV power management. The ML model predicts battery state of charge (SOC) using real-time driving data, while fuzzy logic determines the optimal distribution of power across four modes: full electric, series hybrid, parallel hybrid, and full internal combustion. The framework dynamically adapts to diverse driving conditions, consistently minimizing fuel use and emissions through electric propulsion. Simulation using the Worldwide Harmonized Light Vehicles Test Cycle demonstrates improved fuel economy, reduced emissions, and an 84 km all-electric range with 80% battery utilization. The approach also enhances gasoline-equivalent fuel efficiency by 20%. These results underscore the framework’s potential for future PHEV applications
Efficient thermal management is very critical for the performance, safety, and longevity of lithium-ion batteries in high-demand applications such as electric vehicles. This paper proposes a compact hybrid battery thermal management system that incorporates nanofluid-based cooling, phase change materials, and novel pulsed flow function to increase heat dissipation. The proposed system is composed of U-shaped microchannels with the incorporation of PCM/aluminum foam to achieve compactness with efficient thermal regulation. A validated model of thermal-fluid dynamics evaluates the effect of coolant type and flow rate, channel dimensions, and cooling direction on thermal performance. Results show that NC+PCM+EC hybrid cooling reduces maximum battery surface temperature by as much as 3.44°C relative to conventional liquid cooling, with only a 5% penalty in power consumption. The system also increases battery charge cycles by up to 6% to 15%, therefore boosting energy efficiency and safety of the vehicle tremendously. This study forms the foundation of next-generation cooling solutions for advanced battery applications
The paper models an interactive model and fundamental signal analysis and then examines the role of battery energy storage systems (BESS) in DC microgrid applications involving two separable systems, namely, the voltage-controlled (V-BESS) and current-controlled (C-BESS) systems. The researchers came up with configurable models that compute data with efficiency they resemble real BESS systems. The approaches to the system are integrated and real-time approach to control which is able to facilitate modular grid applications. In this study small signal analysis is coupled with participation factors, to determine the stability of the system, dynamic performance and sensitivity of the parameters around converter gain and load resistance. The results have been tested using software-in-the-loop (SIL) simulation testing in OPAL-RT 5707XH hardware. Final results include higher system reliability and stability base, optimum method of design implementation and control. The work includes essential technical data on the operation of the battery energy storage system in micro grids with DC power systems in terms of energy balance and stability under varying operating conditions
We present a leakage-proof, multi-view EEG framework for ADHD classification that fuses four complementary signals: 1) aperiodic-aware spectra that separate oscillatory peaks from the 1/f background and yield a corrected θ/β* index; 2) spatial structure via Riemannian geometry on covariance (SPD→Tangent); 3) sub-second microstate dynamics (coverage, dwell, transitions, entropy); and 4) lightweight self-supervised embeddings from a compact TCN/Transformer trained strictly within the training fold. A regularized late-fusion stage aggregates calibrated probabilities (isotonic/Platt), and the full pipeline is trained/frozen under nested Group/LOSO cross-validation with a locked external holdout to prevent subject-level leakage. On pediatric EEG (N=121), the method attains balanced accuracy ≈93.5% (±3.0) with ROC–AUC ≈0.97 and PR–AUC ≈0.96; on a cross-dataset holdout, performance remains high (BA ≈91%, Δ≈−2–3 pp), indicating true out-of-subject generalization. Robustness checks show minimal sensitivity to referencing (CAR vs. linked mastoids, Δ≤0.3 pp) and modest gains with longer recordings (≥4 min → +~0.7 pp BA); Riemannian shrinkage λ≈10⁻³ is near-optimal. Label-permutation and subject-shuffle collapse to chance (BA≈50%, AUC≈0.50), supporting validity. Overall, the framework’s oscillation-aware, geometry-respecting, dynamics-sensitive, and SSL-enhanced design delivers accurate, calibrated predictions suitable for high-specificity clinical triage and prospective deployment. By advancing reliable, data-driven neurodiagnostic tools, our approach can improve early ADHD screening and equitable access to high-quality mental health assessment.
