This study presents a numerical investigation of a modified conical solar thermochemical reactor for hydrogen production via steam methane reforming. Optimising reactor design is critical to enhancing hydrogen yield, lowering emissions, and advancing cleaner energy solutions. A two-dimensional simulation, employing a local thermal non-equilibrium model and the discrete ordinates radiation model, was conducted to assess the influence of porosity, mean cell size, and methane-to-steam ratio at an inlet velocity of 0.05 m/s. At a porosity of 0.8, methane and steam conversion efficiencies reached 84.95 % and 57.9 %, with a peak hydrogen mole fraction of 0.601. Increasing porosity to 0.9 elevated the reactor temperature to 1347.8 K but also increased the equilibrium thickness to 0.026 m. A mean cell size of 1 mm yielded the highest hydrogen mole fraction (0.6053), while 1.5 mm demonstrated the most balanced chemical performance within thermal constraints. Even though the reactor temperature increased at a larger cell size (2 mm), the hydrogen yield reduced to 0.596. At an inlet CH4-to-H2O ratio of 0.54, optimal methane and steam conversion efficiencies of 72.14 % and 77.4 % were obtained, with a peak hydrogen mole fraction of 0.67. Dimensionless analysis confirmed optimised performance of the modified reactor over conventional designs, achieving lower outlet methane and steam concentrations, reduced CO2, and higher hydrogen production.

Hydrogen is becoming a promising clean energy option, and improving the efficiency of steam methane reforming remains an important challenge. In this work, a solar thermochemical reactor is analysed under different conditions by comparing the Discrete Ordinate and P1 radiation models. The reactor was tested to observe the effect of varying flow velocity, porosity, mean cell size, and heat transfer coefficients on the thermal performance of the reactor under different heat transfer and radiation models. At a flow velocity of 0.05 m s−1, the Discrete Ordinate model reached a peak temperature of 1413.87 K, compared with 1342.55 K for the P1 model. Higher porosity levels also improved performance, with an average temperature of 1391.78 K, about 3

Artificial Intelligence (AI) and Machine Learning (ML) play a crucial role in developing disruptive healthcare technologies since they address the needs of consumers by providing precise and effective diagnostic and decision-making capabilities. The influence of these factors on diagnosing and making decisions is especially noteworthy when collecting valuable information from healthcare data. This work offers "AC2" (Accurate Cardiac Classification), a hybrid deep learning model that correctly detects cardiovascular disease (CVD) and provides meaningful insights. Convolutional neural networks and the Light Gradient Boosting Model integrate well in the "AC2" model, simplifying feature learning and predictions. Using the CDC's Behavioral Risk Factor Surveillance System (BRFSS) dataset, the proposed method outperforms current algorithms in accuracy, precision, recall, and F1 score. This dataset collects reliable, state-specific data on preventive health practices and risk behaviors related to chronic illnesses, injuries, and avoidable infections in adults, catering to the information needs of healthcare consumers. The "AC2" model's outstanding forecast accuracy is further improved by integrating an explainable AI methodology, specifically emphasizing SHAP's local and global explanations. These explanatory insights clarify the model's decision-making process. With healthcare technology's advancement and the requirement for efficient data analysis to gather information, the "AC2" model's accuracy and comprehensibility may substantially improve healthcare professionals' diagnosis and treatment skills, benefiting healthcare consumers.

This study explores the use of waste plastic oil (WPO) as a potential substitute for gasoline in gasoline direct injection (GDi) spark-ignition (SI) engines. WPO was blended with gasoline in various ratios to assess its feasibility for reducing crude oil dependency and addressing global plastic waste management challenges. The WPO was extracted using a thermal-catalytic pyrolysis process in a batch reactor, utilizing a novel catalyst and discarded plastic chairs (comprising polycarbonate and high/low-density polyethylene) sourced from the university. The chemical and physical properties of the WPO were analyzed and compared with gasoline to evaluate its compatibility as a blend component, supported by GC-MS and FTIR analyses. Experimental tests were conducted on a GDi-based SI engine with WPO/gasoline blends at different engine speeds (2000, 2500, and 3000 rpm) and load conditions (low, partial, and high). Key parameters such as Brake Thermal Efficiency (BTE), Exhaust Gas Temperature (EGT), and Cylinder Pressure (CP) were measured, with fixed spark timing at 18 degrees bTDC and injection pressure at 180 bars. The experimental design employed the Taguchi method (L18 orthogonal array) with input variables including WPO/gasoline blend ratio, engine load, and engine speed, aiming to optimize output responses such as BTE, brake-specific fuel consumption (BSFC), and emissions of nitrogen oxides (NOx), smoke, and carbon monoxide (CO). Results showed that the B05 blend (5% WPO) achieved approximately 3-5 % higher BTE than gasoline under all load conditions. Slight increases in EGT were observed for all blends, and delayed cylinder peak pressure trends were noted compared to gasoline at part load conditions. Optimization analysis identified the B05 blend, a load of 7 Nm, and an engine speed of 2000 rpm as the optimal conditions. A significant performance improvement was also observed for the B20 blend. Individual response optimizations using contour and surface plots revealed the critical influence of load and blend ratio on engine performance and emissions.

The review explores the innovative use of rice residue for developing Cellulose nanocrystals and reinforcement applications of CNCs for wastewater treatment. Rice residue, rich in lignocellulose components like cellulose, hemicellulose, and lignin, presents a sustainable resource for biocomposite fabrication. The review highlights the significant challenges of managing rice residue, particularly the environmental impact of its open field burning, which contributes to severe air pollution and health risks. By examining recent advancements in the extraction of cellulose nanocrystals (CNCs) from rice residue, the review emphasizes their potential for enhancing water treatment technologies and contributing to Sustainable Development Goal 6 (Clean Water and Sanitation). The review provides a comprehensive analysis of the current state of research such as facts and challenges related to using CNCs for water treatment, and suggests future directions for developing eco-friendly, high-performance water filtration and its reinforcement perspectives, underscoring the importance of integrating waste valorization with sustainable practices.

Along with the evolution of renewable energy technologies and some other systems such as electric vehicle, demands for batteries as storage unit has increased. Keeping the temperature of batteries in a specific range is necessary to have reliable performance and prevent degradation. In regard to the enhanced thermophysical specifications compared with pure heat transfer fluids, nanofluids would be attractive alternatives for them in thermal management of batteries. The purpose of this article is to identify the impact of using nanofluids for thermal management of batteries as heat transfer fluid with improved properties and evaluate the impactful factors in their cooling performance both as direct coolant or operating fluid of heat pipes as cooling mediums. In this regard, this article reviews the studies implemented on the thermal management of batteries by use of nanofluids. Reduction in maximum temperature of battery packs and temperature difference, due to elevation of heart transfer as a consequence of increment in the thermal conductivity, are the most remarkable outcome of using nanofluids for battery thermal management. Although nanofluids could be advantageous in term of heat transfer intensification, increment in the pressure drop can be one of the disadvantages in conditions of using liquid flow. In cases of using nanofluidic thermal mediums like heat pipes, the enhancement in the cooling performance can be attributed to some other factors, e.g. increase of nucleation sites for promotion of two-phase heat transfer, as well as thermal conductivity elevation. Concentration of the nanomaterials, operating conditions, specifications of the solid phase are among the most significant items in the effectiveness of the cooling techniques with nanofluids. In design of nanofluidic thermal management units of batteries, the mentioned influential factors must be taken into account to reach the optimal performance.

The extensive utilization of consumer electronic devices such as smartphones, smart wearables, and smart home technology has resulted in significant surge in data production. Due to storage and data transfer limitations, traditional machine learning techniques are sometimes impracticable for these distributed systems and can cause serious privacy problems. Federated Learning mitigates these issues by maintaining data on local devices. It updates models by consolidating locally trained outcomes on central server, which can be crucial in case of consumer electronic devices. Thus, to tackle these issues, this article presents new method named Dynamic inertia weight-based federated advanced particle swarm optimization (DIW-FedAPSO). It uses dynamic inertia weight strategy in advanced particle swarm optimization to select inertia weight dynamically for providing optimal velocity to consumer electronic devices and transmitting obtained optimal score after performing the local training instead of sending and averaging weights of devices as traditional federated learning method does. The experimental evaluations on different datasets (CelebA, FFHQ) under different non-iid data heterogeneity settings shows that proposed method attains better accuracy, while maintaining data privacy and enhances communication efficiency while minimizing number of communication rounds required to attain targeted accuracy over all datasets than other currently existing methods.

Tunnel Field-Effect Transistors (TFETs) have emerged as promising alternatives to conventional Metal–Oxide–Semiconductor Field-Effect Transistors (MOSFETs) for next-generation low-power electronic applications, owing to their steep subthreshold swing (SS), low leakage currents, and scalability to advanced nanoscale architectures. This review presents a detailed exploration of the fundamental principles, design innovations, and material strategies employed to enhance TFET performance. Emphasis is placed on Band-to-Band tunneling (BTBT) mechanisms, the impact of novel materials such as III-V semiconductors, GeSn, InAs, and two-dimensional materials, as well as bandgap and gate engineering techniques. The paper evaluates advanced TFET structures, including doping-less, junction-less, vertical, and gate-all-around configurations, and their integration into analog, RF, and biosensing applications. Recent simulation models and fabrication challenges are also discussed. By examining state-of-the-art TFET research, this work highlights the transformative potential of TFETs in enabling ultra-low power devices and neuromorphic systems in the post-CMOS era.

In recent years, in response to an increased demand for renewable energy sources, there has been a rise in the rate of energy recovery from municipal solid trash. This study analyses the feasibility of employing a variety of energy recovery methods to produce clean power from municipal solid waste (MSW). The conversion of MSW into a variety of useable sources of energy, such as fuel, heat and electricity, is required for the process of energy recovery. Other strategies for the recuperation of lost energy include gasification, incineration, anaerobic digestion, and the recovery of landfill gas. This article provides a high-level assessment of the advantages and disadvantages associated with each technology that is currently being utilised in India. According to the findings of the study, recovering energy from municipal solid waste is a sustainable and cost-effective option that can fulfil the growing demand for power while simultaneously lowering emissions of greenhouse gases and the amount of rubbish that ends up in landfills.

A notable development in photovoltaic (PV) technology, quantum dot solar cells (QDSCs), provides viable answers to the drawbacks of conventional silicon-based solar cells. Quantum dots (QDs) are tiny semiconductor particles with unique optical and electrical properties due to their quantum confinement, which allows for programmable bandgaps and broad-spectrum light absorption. This review explores the integration of QDs into various solar cell architectures, such as quantum dot-sensitized solar cells (QDSSCs), quantum dot heterojunction solar cells (QD-HJSCs), colloidal quantum dot solar cells (CQDSCs), and quantum dot-polymer hybrid solar cells (QD-PHSCs). Innovations in these areas have improved power conversion efficiency (PCE), stability, and scalability. Additionally, the commercial applications and market potential of QDSCs are discussed, focusing on building-integrated photovoltaics (BIPV) and portable solar technologies. Despite these advancements, challenges such as material stability, cost-effectiveness, and scalability remain, necessitating further research and development. The future of QDSCs will likely involve exploring non-toxic materials, improved encapsulation techniques, and developing hybrid devices that combine the strengths of different PV materials.

Consumer Internet of Things (CIoT) interconnects multiple devices over internet, like smartphones, wearables, and smart gadgets to simplify tasks and provide convenience. However, it encounters obstacles such as privacy apprehensions arising from data aggregation, security flaws, interoperability discrepancies. Federated learning (FL) mitigates these issues by localizing data, reducing privacy risks, and securing IoT networks. It stores and updates learnt models on a central server, which is necessary for CIoT networks. Nonetheless, its application confronts problems like non-independent and identically distributed (non-IID) data, communication efficiency, and privacy issues. According to recent research, training models using non-IID data have detrimental influence on performance, convergence, and overall model quality in FL. Moreover, traditional FL approaches, including clustered federated learning (CFL), have problems with client training and fixed hyperparameter use. This paper introduces new approach, Artificial Bee Colony Clustered Federated Learning (ABC-CFL). ABC-CFL uses Density-based spatial clustering of applications with noise (DBSCAN) to cluster client devices based on training hyperparameters, followed by hyperparameter optimization for each cluster to better suit each cluster using artificial bee colony algorithm. This method outperforms static hyperparameter utilization problems and improves model performance and communication efficiency in CFL as demonstrated by experiments on the CIFAR-10, MNIST and CelebA datasets.

There is enormous potential in the Middle East region for power generation by employment of renewable energies, particularly solar. The purpose of this article is representation of the status of power generation by use of different renewable energy systems in some Middle Eastern countries and the challenges and opportunities related to the development of these systems. In this study, five resource-rich Middle Eastern countries including Iran, Saudi Arabia, Oman, Iraq and the United Arab Emirates (UAE) are considered to be analyzed in term of status of renewable energies for power generation. In this regard, the electricity generation status of these countries is focused. Furthermore, the policies and potentials of these countries are represented and discussed. Moreover, the challenges for development of renewable energy technologies for electricity development in these countries are represented and investigated. Among the considered countries for assessment of power generation by renewable energy sources, the UAE has the highest share of solar energy in generation of electricity. Moreover, there are ambitious plans and policies for clean electricity generation in Saudi Arabia and the UAE, while in Iran and Iraq the development of renewable energies for power generation is not very significant. The reasons that hinder development of renewable energy systems in these countries can be different. For example, war and political issues in Iraq and sanctions in Iran could be among the main reasons that hinder development of new energy systems based on renewable energy sources. It can be stated that despite the considerable potential in these countries for exploitation of renewable energy sources for power generation, utilization of these sources is limited. In this regard, promotion of social awareness, incrementing economic incentives and advanced planning are needed to accelerate the renewable energies development.

Introduction This study aimed to investigate the structural alterations of nanoparticles due to external forces. These forces, both direct and indirect, are crucial in changing the structures and characteristics of nanoparticles, which may have an impact on important variables and results.Methods The main focus of this study was on how researchers might modify the characteristics of nanoparticles by using a simple technique and adding precursor chemicals. The employed methodology, referred to as the simple bath method, made it easier to prepare and characterize composite nanoparticles using high-resolution TEM, XRD, SEM, and UV. To obtain important information, a comparative examination was carried out against standard market combinations.Results This study explored the size and shape fluctuations of nanoparticles as identified by XRD and SEM investigations. Using Tauc plots for UV-Vis spectroscopy, the refractive indices of the nanoparticles were calculated, and energy gaps, extinction coefficients, and dielectric constants were visualized. Moreover, ZnO nanoparticles were tested against Gram-positive (S pneumonia, Bacillus subtilis, and Bacillus megaterium) and Gram-negative (Klebsiella pneumonia, Shigella dysenteries, 'E-coli') bacteria using an agar well diffusion process. Region reserve values (mm) were measured after twenty-four hours at thirty-seven degrees Celsius.Conclusion The common antibiotic amoxicillin (ten 'mu 'g 'disc') was used as a standard. The activity of IN, ISB, ISC, and ISN on bacteria and fungi was examined. It was found that ZnO nanoparticles exhibited antibacterial capabilities, such as ion release and rupture, as well as the generation of antibacterial properties of IN, ISB, ISC, and ISN.

The thermal conductivity and dynamic viscosity of nanofluids are essential factors in determining heat transfer and fluid flow characteristics. Intelligent methods have demonstrated great effectiveness for the precise estimation and modeling of these properties. The purpose of this study is to model both thermal conductivity and dynamic viscosity of a hybrid nanofluid, TiO2-SiO2/water-ethylene glycol, by application of three intelligent approaches namely Group Method of Data Handling (GMDH), Particle Swarm Optimization-Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and Genetic Algorithm-Adaptive Neuro Fuzzy Inference System (GA-ANFIS). The outcome of the study shows significant precision of the proposed models in estimation of the thermophysical properties. The most accurate models for thermal conductivity and dynamic viscosity are PSO-ANFIS and GMDH, respectively. R2 & and Average Absolute Relative Deviation (AARD) for the thermal conductivity and dynamic viscosity of the nanofluids with the most accurate models are 0.9907 & 0.41% and 0.9889 & 2.45%, respectively. Furthermore, sensitivity analysis is conducted on both properties of the nanofluid by considering temperature, concentration, and mixture ratio of the hybrid nanofluids and it is found that for both properties, temperature has the highest effect and is followed by the concentration.

BACKGROUND: Accurate neuro-registration is important as the success of the surgical procedure highly depends on it. This article deals with neuro-registration using tele-manipulation (Master-Slave Manipulation) to facilitate tele-surgery and enhance the overall accuracy and reach of the robot-assisted neurosurgery. METHODS: A 6 degrees-of-freedom parallel kinematic mechanism (6D-PKM) master-slave robot in tele-manipulation mode is utilized for both neuro-registration and neurosurgery. Real-time kinematic control of 6D-PKM is made possible by solving its forward kinematics using the trajectory modifier algorithm with an accuracy of 1mm and 0.001 degrees in translation and orientation, respectively, in real time. The master operator using the 6D-PKM master mechanism moves the 6D-PKM slave robot equipped with a touch probe stylus (4 mm diameter) in tele-manipulation mode. In neuro-registration, the slave is remotely guided to touch the fiducial marker in a predetermined order. A correlation between the medical image space and the real patient space is made to establish the neuro-registration. The accuracy of neuro-registration is validated through experiments on skull phantoms. These phantoms are designed to simulate the neurosurgical process. RESULTS: The neuro-registration process successfully registers the phantoms, and maximum registration error is found to be 0.6 mm. The accuracy of neurosurgery is validated using several target points in phantom. The accuracy of registration is also verified by robot piercing a 2-mm- diameter surgical needle through a predesignated 3-mm- diameter cylindrical target hole with radial clearance of 500 mm. CONCLUSION: Accurate neuro-registration using telemanipulation has been demonstrated. The overall accuracy of the robot-based neurosurgery is tabulated. This approach eliminates line-of-sight issue and the requirement of an additional unit for neuro-registration. This minimizes the registration time and makes intraoperative registration feasible.

Abstract The search for an effective solution to improve performance and emission characteristics of internal combustion (IC) engines used in the commercial sector is regarded as one of the most important and essential issues in recent years due to increasing levels of pollution. Nanoparticles with their additive ability to increase fuel reactivity and atomization, due to their large surface area and high heat transfer coefficient, can improve the performance and emission characteristics of a fuel. This review highlights the use of nanoparticles as fuel additives to enhance the emission and performance characteristics of IC engines. Detailed comparisons of performance, emission, and combustion characteristics of IC engines using fuels blended with nanoparticles have been done. Nanoparticles were observed to be an oxygen buffer for fuel combustion and boost fuel atomization, thus enhancing engine performance. While alumina exhibited a decrease in levels of HC and CO but a considerable increase in NOx, graphene nanoparticles and ceria were found to be particularly effective in enhancing engine performance. Detailed study has been done on other nanoparticles, including metal‐oxide, nonmetal‐oxide as well as carbon nanoparticles. Overall, the use of nanoparticles can enhance the thermophysical characteristics of fuels, improving the emission and performance characteristics of engines. The review suggests that selecting the right dosage and variety of nanoparticles is crucial for optimizing engine performance, and thus directly helps in tackling the ongoing pollution problem.