Present paper aims to investigate effects of nanoparticle addition to thermal oil of parabolic trough collectors which are integrated to Kalina cycle for electricity generation to run an alkaline electrolyzer for hydrogen production. Thermodynamic models for Kalina cycle, thermal model for parabolic collectors and electrochemical models for water electrolyzer are developed for overall plant modeling. Three types of nanoparticles including: copper oxide, alumina and titanium oxide are considered and overall plant performance is investigated using these nanoparticles and is compared with basefluid. The nanofluid thermophysical properties are evaluated as a function of nanoparticles’ properties, volume fraction and the base fluid properties. The effects of key variables such as nanoparticle volume fraction, collector inlet temperature, evaporation pressure and ammonia concentration of Kalina system were evaluated on hydrogen production as well as exergetic efficiencies. Results revealed greater positive influence for CuO compared to TiO2 and Al2O3 on performance improvement for both hydrogen generation and its exergetic efficiency. Also, compared to basefluid (without nanoparticles) case, it was found that CuO particles result in a solar-to-H2 exergy efficiency improvement by 4.7%. Using this nanofluid with a volume fraction of 5%, the H2 production rate enhances from 0.633kg/hto0.664kg/h, indicating 4.9 % improvement.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Recently the energy systems' developers focused on the efficient exploitation of renewable resources and novel technologies to decrease fossil fuel consumption and environmental damage. The SOFC units are known in this regard as high-efficiency and versatile fuel-driven systems, for which the biofuel from biomass gasification is an abundant and clean fuel source. The current article investigates a biomass-driven SOFC that is hybridized with wind turbines in order to increase the hydrogen concentration of intake fuel. To do so, the generated electricity by wind turbines is given to a polymer electrolyte membrane electrolyzer for hydrogen generation and injection into the SOFC. Feasibility studies of the proposed framework are conducted based on thermodynamic laws, and its performance enhancement is appraised using three exergy-based environmental indices. A parametric study is carried out to indicate the influence of operating variables on system performance in terms of power output, exergy efficiency, and environmental indices, after which a multi-objective optimization is implemented. The results indicated significant efficiency enhancement of biomass-driven SOFC via integration with wind turbines for hydrogen injection. It is found that more power from wind turbines leads to more hydrogen input to the SOFC and increases net power as well as efficiency. Increasing the fuel utilization factor for the basic structure and the structure with HI, the exergetic efficiency decreases by 5% and 4%, respectively. Under optimal point, the overall system's environmental damage factor and net output power are found to be 0.0092 kW and 322 kW, respectively.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

The present research aims at development and design of a new high-efficiency power/hydrogen co-production framework running by biomass/geothermal renewable resources. In this regard, to attain global energy transition goals based on green hydrogen utilization of renewable resources instead of conventional fossil fuel-based routes for hydrogen production is followed. The proposed system structure consists mainly of a gas turbine coupled to a geothermal assisted Rankine unit which extracts the gas turbine wasted heat to run a water electrlyzer for H2 production. To illustrate a comprehensive performance evaluation, technical (thermodynamic), environmental and economic aspects are considered and assessed. Eight performance indices are evaluated including: power and hydrogen productions, thermal and exergetic efficiencies, environmental damage and emission index, LCOP and overall system cost. In addition, a bi-objective optimization is implemented with respect to efficiency and product cost. Results show that, the cogeneration framework under optimum condition, operates with exergetic efficiency of 42.37% and levelizedproductcostof68.52$/MWh, whose emitted CO2 is 0.7443kg/kWh. Also, compared to basic design point conditions, it is found out that optimization leads to performance enhancements by 7.5%, 9.0% and 7.7% for the three mentioned indicators, respectively.

The adverse environmental impacts of fossil fuels is becoming a major concern considering the climate changes. In this respect, the H2 as a carbon-free energy carrier has absorbed interest of researchers and policy makers to pay more attention to this field. In this regard, the solar energy driven frameworks are of major importance due to the abundance of solar energy as well as being a clean energy resource. However, the solar based systems suffer from lower efficiency compared to conventional fossil fuel-based systems due to large exergy destructions. One way to solve this problem and to enhance solar-based system performance is employment of nanoparticles for improving heat transfer characteristics of solar thermal loop. Present research is an attempt in this regard in which effects of employment nanofluid instead of the pure solar salt is investigated in a solar power tower system. The solar power tower unit supplies required energy to run a steam cycle for power generation and a thermochemical H2 production unit for co-generation of power and H2. Feasibility evaluations are carried out based on thermodynamic laws and exergy-based analyses as well as economic investigations are conducted to assess the system performance and multi-objective optimization is conducted to specify the optimum operation of system. A parametric evaluation is carried out to consider how design variables affect the system performance, prior to implementation of multi-objective optimization. The results have revealed positive effect of nanofluid application instead of pure solar salt and indicated enhancement of 4−5% on technical performance. In addition considering the economic investigations, the nanofluid employment bring about a reduction by around 3−4% in unit cost of product of cogeneration plant, depending on the operating conditions.

In recent years, the rapid development of electric vehicle technology has promoted significant progress in battery technology. Concurrently, the necessity to prolong battery life has become increasingly urgent. Addressing these objectives necessitates not only advancements in battery technology itself but also effective thermal management strategies. This paper presents a novel liquid cooling plant with variable flow channels and a novel approach aimed at reducing temperature difference through a dynamic control strategy for water flow of battery liquid cooling channels based on reinforcement learning. Firstly, the thermal physical characteristics of the battery and the temperature rise curve under different conditions are experimentally determined. Then, a battery heat generation model is developed and validated. Employing the TD3 reinforcement learning algorithm, an agent is trained to dynamically regulate the flow distribution within water cooling plate channels, ensuring optimal thermal management. Through theoretical derivations, this paper establish the relationship between the target flow rate of each channel and the heat generation of individual battery cells predicted by the heat generation model, so that the heat carried away by each channel is equal to the heat production of the corresponding battery above the channel. Compared to traditional thermal management strategies under the same working condition, the thermal management strategy proposed in this paper reduced the temperature difference between battery cells .

The combination of a supercritical Brayton cycle, an absorption chiller, a Stirling engine, a reverse osmosis desalination system, and a proton exchange membrane electrolyzer is investigated for waste heat recovery of a topping gas turbine cycle. The required electricity for hydrogen and freshwater production is supplied by the Stirling engine and supercritical Brayton cycle, respectively. In addition, the output power is provided by the gas turbine cycle, and the exiting cold stream of the Stirling engine is considered hot water for domestic utilization. A comparison is made between biogas and pure methane as two possible fuels for the system. Energy and exergy analyses, economic analysis using the specific exergy costing approach, and environmental analysis considering the carbon dioxide emissions are employed in the study. The three-objective optimization of the configuration discloses an exergy efficiency (eta(ex)) of 39.49 % and 39.85 % in the cases of biogas and methane, respectively. The proposed system can improve eta(ex) by 6.18 % points compared to the stand-alone GTC. The specific cost of poly- generation (c poly ) and the total cost rate ((sic)(tot)) are obtained as 25.92 $ GJ(-1)- 1 and 249.5 $ h(-1) in the biogas mode, while these values are calculated as 36.75 $ GJ(-1) and 336.7 $ h(-1) in the methane case. The environmental cost rate ((sic)(env)) of the system is 24.78 $ h(-1) in the case of biogas and 17.47 $ h(-1) in the methane mode. The results confirm the superiority of the methane case from the environmental viewpoint over biogas. However, the low values of c poly and (sic)(tot) in the biogas case indicate that biogas is superior to methane from a general standpoint. The utilization of the present setup for waste heat recovery of biogas-driven GTCs is recommended due to the higher eta(ex) than the previous layouts.

How to better grasp students’ learning preferences in the environment of rapid development of engineering and science and technology so as to guide them to high-quality learning is one of the important research topics in the field of educational technology research today. In order to achieve this goal, this paper utilizes the LDA (Latent Dirichlet Allocation) model for text mining of the survey results on the basis of a survey on students’ self-perception evaluation. The results show that the LDA model is capable of extracting terms from text, fuzzy identifying groups of students at different levels and presenting potential logical relationships between the groups, and further analyzing the learning preferences of students at different levels for IT courses. Based on the student’s learning needs, this paper proposes recommendations for developing students’ learning effectiveness. The LDA method proposed in this paper is a feasible and effective method for assessing students’ learning dynamics as it generates cognitive content about students’ learning and allows for the timely discovery of students’ learning expectations and cutting-edge dynamics.

Authentication is a security practice that seeks to validate a user identity to various entities, yet doing so across a wireless network poses inherent risks. Wireless Body Area Networks (WBANs) are a unique form of network that contains e-healthcare patient data, thus WBANs require a high level of privacy and security. There are several anonymous WBAN authentication systems in the literature, but this is the first paper to conduct a complete review of all the various schemes, then tabulate and compare the results. Doing so reveals a number of research avenues that need either greater depth, or systems development. This article presents a thorough taxonomy and survey of the anonymous authentication methods, illustrating the security services and vulnerabilities, and then the properties of an ideal anonymous authentication scheme. The various schemes reviewed are also classified by their main encryption algorithm, such as bilinear parings-based schemes, elliptic curve cryptography-based solutions, lattice-based methods, and XOR-based approaches. The main authentication capabilities, cryptographic features, security advantages, evaluation metrics, and shortcomings are all specified and discussed. A thorough comparison of anonymous authentication methods is presented to demonstrate each scheme's resistance to a variety of security threats. The study concludes that very few schemes have addressed the group authentication problem, which is very important in healthcare systems. Also, in the multi-factor authentication context, which provides a higher level of security, only 11% of the schemes used three factors, and 1% of them utilized four factors. Besides, DDoS attacks, as a growing concern in all computing environments, are handled by a narrow number of schemes. At last, a set of recommendations for further research based on identified literature gaps.

As a result of climate change and environmental pollutants becoming more prevalent in the environment, this paper introduces an innovative approach to energy systems that uses smart building technologies and renewable energy sources to mitigate climate change and promote sustainability. The central concept of this method is to hybridize solar and biomass resources in order to meet the heating, cooling, and electricity needs of a residential neighborhood over the year. The system is driven by an intelligent design of photovoltaic thermal panels combined with an efficient heater. This idea reduces the solar system cost and increases the independency from the local heating and cooling networks when the sun is unavailable. Besides, the waste heat recovery process is introduced to utilize the additional heat to charge the chiller and space heating demand and avoid releasing excess heat into the environment as much as possible. Multiple controllers are employed to launch a bidirectional connection with the electricity grid to sell or buy energy to or from the grid based on production, demand, and usage. The system's affordability, effectiveness, and environmental sustainability are assessed comprehensively via TRNSYS software and MATLAB-developed code. According to the results, the proposed smart system achieves a very low emission rate of 14 kg.CO2/MWh with an affordable energy cost of 76.3 $/MWh. The results further reveal that while the system chiefly depends on biomass in cold hours to meet the heating demand, most of the building's cooling need is provided by the photovoltaic thermal panels in summer, signifying the importance of renewable energy resources combination. It is noteworthy that due to the combination of green energy sources, the amount of carbon dioxide emission is significantly reduced by 35%. In addition, the net electricity is positive 2300 h of the year, meeting the demand, charging the chiller and tank, and seeling the additional to the grid, offsetting a significant portion of the annual energy costs. The results eventually showed that while the minimum energy cost of 79 $/MWh was obtained in April, the performance efficiency and emission rate reached the maximum and minimum values of 41.5% and 10.5 kg.CO2/MWh in December.

This article examines the natural convection heat transfer (CNHT) in a two-dimensional square enclosure using the LBM. Nanofluid (NFD) is placed inside the enclosure and a magnetic field (MGF) is applied to the enclosure and NFD. The MGF has inclination angles ranging from 0 to 75° and its effect on the flow field and heat transfer (HTR) is evaluated. Also, the magnitude of the MGF varies by changing the Hartmann number (Ha) from 0 to 40. The left and right walls are hot and cold, respectively, and the top and bottom ones are insulated. Three baffles are placed on the hot wall, whose thickness is changed from 0.5 to 0.15 and their length is changed from 0.1 to 0.3. The results demonstrate that maximum HTR occurs on the upper part of the cold wall. The enhancement in the Ha reduces the value of Nu, and the increment of the inclination angle of the MGF enhances the value of Nu on the cold wall. An intensification in the thickness of the baffles (TOB) weakens the CNHT in the enclosure and thus reduces the amount of HTR. Also, enhancing the length of the baffles (LOB) weakens the vortex inside the enclosure.

Background: In the present study, a parallel microchannel system is attached to the battery pack to decrease the temperature of the plate that the battery pack install on it. In fact, battery cooling is critical for electronic devices as the increasing temperature has a negative effect on performance.Methods: The impacts of several parameters like Reynolds number, and volume fraction of nanofluid are investigated on the battery surface.Significant findings: The numerical results demonstrated that the temperature of the battery surface declines dramatically up to 4 degrees as the Reynolds number increases. Moreover, the increasing volume fraction of the present nanofluid to 0.1% boosts the heat transfer up to 12.1% and decreases the thermal and viscous entropy generations up to 5.37% and 23.2%, respectively. However, increasing the Reynolds number from 400 to 2200 resulted in a 253.71% and 389.80% increase in the thermal viscous generations. optimization revealed that the best channel number is 39 in which the Nusselt number and pressure ratio intensified by 17% and 24%, respectively.

Self-regulated learning (SRL) has been an important topic in the field of global educational psychology research since the last century, and its emergence is related to researchers’ reflections on several educational reforms. To better study the research history and developmental trend of SRL, in this work, the Web of Science core collection database was used as a sample source, “self-regulated learning” was searched as the theme, and 1218 SSCI documents were collected from 30 September 1986, to 2022. We used CiteSpace software to visualize and analyze the number of publications, countries, institutions, researchers, keywords, highly cited literature, authors’ co-citations, keyword clustering, and timeline in the field of self-regulated learning research, and to draw related maps. It was found that the articles related to self-regulated learning were first published in the American Journal of Educational Research in 1986, and that self-regulated learning-related research has received increasing attention in recent decades, wherein research on self-regulated learning is roughly divided into three periods: the budding period from 1986 to 2002, the flat development period from 2003 to 2009, and the rapid development period from 2010 to 2022. The number of papers published in the United States, China, Australia, and Germany is relatively high, and the number of papers published in Spain is low compared with that in the United States. During this period, the University of North Carolina in the United States and McGill University in Canada were the institutions with the most publications; Azevedo Roger and Lajoie Susanne P were the most-published scholars in the field of self-regulated learning research; the journal publication with the highest impact factor was Computers Education; and the primary research interests in self-regulated learning mainly focused on Performance, Strategy, Students, Achievement, Motivation, and Metacognition. Furthermore, the most-cited study related to SRL research was Formative assessment and self-regulated learning: a model and seven principles of good feedback practice.

In the present study, the hydrothermal characteristics of two different a) serpentine (Conf. A), and b) double serpentine (Conf. B) heatsinks with water/silver Nano Fluid (NF) were studied. To this end, the Finite volume method was performed, and the governing equations were discretized based on the second upwind approach and solved numerically applying the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) scheme. The results demonstrated that the thermal resistance (R) of Conf. A is lower than that of Conf. B at low Re numbers, and these two configurations have an identical R as Re increases to 2000. Also, the convective heat transfer in Conf. A is slightly higher than that in Conf. B and for Re=2000 both configurations have a same h value. Moreover, the increasing of nanoparticle concentration from 0% to 1% increases h by 12.55% and decreases the pressure drop (ΔP) by 4.25%. In addition, the CPU mean temperature of Conf. A is 0.8% and 0.1% lower than that of Conf. B for Res of 500 and 2000, respectively. On the other hand, ΔP in Conf. A is 74% higher than that in Conf. B. Besides, Conf. B exhibits a better temperature uniformity as compared to other configuration. The parameter of figure of merit was obtained as 1.47 to 1.52 at Res of 500 and 2000, respectively, which showed that Conf. B provides a higher hydrothermal performance over the other configuration.The environmental impact of the designed heat sink is equal to 0.5388.

Cereals, especially wheat, have always been one of the main bases of the global food chain. Food security in the future will depend on more production or production with higher yields. So, it is critical to provide approaches to identify factors affecting wheat yield. Prior research has partially overcome the limitations of previous methods (field surveys, empirical statistical models, and crop models). A combination of climate and satellite inputs has been considered in previous research. But the main missing part is insufficient attention to crop-specific vari-ables. A user-friendly interface is another issue that has been neglected because using satellite images requires time and expert effort. Here, to cover these gaps, the variables affecting the wheat yield in different stages of growth were identified and analyzed. The data of 17 years (2001-2017) from 31 provinces of Iran were used to evaluate the proposed approach. Six climatic variables (maximum and minimum temperature, relative humidity, wind speed, precipitation, and sunshine hours) from 238 weather stations and one variable for geographic yield bias (Ybaseline) were considered. Also, growing degree days (GDD) and crop coefficient (Kc) were introduced as crop-specific variables. First, the relative contribution of each variable was determined based on grey systems theory (GST) in different growth stages. Finally, the sensitivity analysis of input variables in each growth stage was performed based on eight scenarios and machine learning models, including artificial neural network (ANN), least squares support vector regression (LS-SVR), and adaptive neuro fuzzy inference system (NF). The results revealed that the mid-season stage suitable time window and the Kc, maximum temperature, relative humidity, Ybaseline, precipitation, and GDD with R2 = 0.958, WI = 0.989, and SI = 0.051 were the most influential variables in wheat yield estimation with ANN model. Estimating wheat yield before the harvest season can confirm the ability of the applied models in real-time applications.

The very existence of smart cities forms the stepping stone in the evolution of many technological advancements in the future era. While smart cities have already grown in their way, the tremendous amount of data generated from them paves the way for new perspectives of development. This is because security and privacy remain to be the major constraint across smart city applications. Further, smart city applications such as smart homes, smart transportation, and smart healthcare are generating a huge amount of data every day and it is often complex to collect and manage all the data together at a single location. To address these constraints, this paper presents a novel and innovative blockchain-assisted federated learning approach for secure data sharing in IoT Smart Cities. Here, we implement a federated learning approach, where the process of learning is made in a distributed fashion. The use of blockchain in turn adds more security and resilience to smart city applications. The security analysis proves that the proposed approach offers comparatively better performance and remains more resistant to various security threats and vulnerabilities.

The frictional and thermal entropy generation rates (S˙fr and S˙th) of two heatsinks with serpentine (Conf. A) and double serpentine (Conf. B) channels with water/silver Nano fluid (NF) were numerically investigated. The results demonstrated that the augmentation of nanoparticle concentration (φ) from 0% to 1% leads to decrease S˙th by 6.36% and 3.51% in Conf. A and Conf. B, respectively, for Re=500. The escalation of Re from 500 to 2000 increases this percentages to 9.93% and 7.51%, respectively. Also, the increase of Re from 500 to 2000 at a constant φ reduces S˙th by 63% and 68% in Conf. A and Conf. B, respectively, due to enhance the heat dissipation. Moreover, the increasing of φ from 0% to 1% decreases S˙fr by 12.41% in both configurations and at four studied Res due to decreasing the NF velocity. Besides, the escalation of Re from 500-2000 intensifies S˙fr nearly by 62% and 67% in Conf. A and Conf. B, respectively. The entropy contour plots reveal that S˙fr intensifies near the turn regions of the serpentine channels. Also, S˙th is intensifies at the entrance of heatsink channels due to the high temperature gradient at these points. Conversely, S˙fr escalates at the exit of the channels due to intensification of the vortexes formed at the turn parts and along the serpentine channel.

In the foreseeable future, heat exchangers will continue to play an important role in environmental management and have numerous applications, their geometry has been the subject of interest for many researchers. The main goal of this research is improving the heat exchangers' thermal performance and investigating the exergy using SIMPLE algorithm, k-w turbulent model and the Eulerian-Eulerian method for multiphase flow. Hence, the operation of Al2O3-CuO-water hybrid nanofluid in a 3D shell-and-tube heat exchanger is simulated to enhance the contact surface of hot and cold fluid streams using computation fluid mechanics techniques. Reynolds number is 10,000, 15,000, 20,000, and 25,000 and volume fraction of nanoparticle is 2-6%. The innovations of this research are the use of the use of hybrid nanofluid and turbulator. The hybrid nanoparticles are employed to enhance the thermal conductivity and the turbulator is utilized to increase flow turbulence. The results demonstrated that the hybrid nanofluid, the turbulator, and high amount of Reynolds number can have a remarkable impact on increasing the thermal performance. The obtained results revealed that a 6% increment in hybrid nanoparticles volume fraction and an enhancement in Reynolds number from minimum to maximum lead to a 126% improvement in thermal performance in the presence of the turbulator. Lastly, environmental damage, energy consumption, and emissions are reduced by nanofluids.