The difficulty of the production stages of panel radiators used for heating purposes reveals the importance of determining the heat transfer performance and panel radiator weight values, which are determined depending on the design parameters. In the present work, an artificial neural network model is proposed for predicting the heat transfer and weight values of a panel radiator as outputs depending on the design parameters of convectors. In the multilayer network model developed with 78 numerically obtained data sets, 8 different design parameters were defined as input parameters and heat transfer and in the output layer panel weight values were obtained. The design parameters of the convectors, in other words, input parameters of network model were chosen as the height of convector, thickness of convector sheet, the trapezoidal height of convector, convector base length, opposing convector distance, tip width of convector, convector vertical location and distance between convectors. For the proposed neural network model, the mean squared errors obtained for the heat transfer and panel radiator weight are −1.25E-04 and -7.54E-05 respectively. In addition, an R-value of 0.99999 has been obtained, and the average deviation value has been calculated as 0.001%. The obtained results show that, depending on the design parameters, the proposed artificial neural network model can predict the rate of heat transfer and weight of the panel radiator with high accuracy. This investigation is supposed to fill a significant gap since it is the pioneer one in open sources on machine learning modeling of panel radiators. Thus, it can possibly make a crucial contribution to the related manufacturing industry.
In this study, experimental investigations are performed in order to determine the thermal performance of NiFe2O4 nanoparticles dispersed in deionized water in a thermosyphon-type heat pipe. The effects of the magnetic flux density and nanoparticle mass fraction were analyzed at different heat inputs and cooling water flow rates. The magnetic field was generated by the current-carrying solenoid. Our results indicated that the use of NiFe2O4 nanofluid significantly improves the thermal performance of the heat pipe which also showed that the enhancement of heat transfer coefficient can be obtained with increasing mass fraction and in presence of a low parallel magnetic field. A decrease of 26.6% was achieved in the temperature difference between the evaporator and the condenser regions compared to distilled water at a 3% mass fraction and under a magnetic field. The maximum improvement in heat pipe's thermal resistance was found as 30.4%, through NiFe2O4 nanofluid usage under magnetic field. Compared to base fluid, the application of magnetic nanofluid and parallel magnetic field reduces the thermal resistance, increases heat pipe efficiency and the heat transfer coefficient.
Along with the developing technologies, the need for energy has increased day by day and negative environmental effects of fossil energy based systems increased the importance of efficient energy systems. In the recent years, shell and helically coiled type heat exchangers (SHCHEs) are extensively used in various applications because of their superior specifications in comparison with other heat exchangers. In the present work, it is targeted to raise the thermal performance of recently developed shell and helically coiled heat exchangers using single and hybrid type nanofluids. The main aim of this research is specifying the impact of hybrid CuO–TiO2/water nanofluid in comparison with single TiO2/water nanofluid. Also, the effect of adding fins as turbulators on performance enhancement of nanofluids was analyzed. In this regard, TiO2/water and CuO–TiO2/water nanofluids with 1% (wt./wt.) concentration was prepared and circulated in the hot side of both heat exchangers. TiO2/water working nanofluid application in finless and finned SHCHEs averagely upgraded overall heat transfer coefficient as 7.5% and 8.6%, respectively. CuO–TiO2/water working nanofluid application in finless and finned SHCHEs averagely upgraded overall heat transfer coefficient as 10.8% and 12%, respectively. Generally, it was observed that utilizing TiO2/water and CuO–TiO2/water nanofluid in unmodified and modified SHCHEs importantly raised the thermal performance. However, utilization of hybrid type nanofluid presented better performance than single nanofluid in both SHCHEs. Moreover, the outcomes exhibited further positive impacts of integrating fins on performance enhancement of both single and hybrid nanofluids.
Thermopower waves (TWs) are based on a heat transfer process that occurs by combusting chemical fuels with the help of nanomaterials. As a result of this process, electrical energy is generated. Within the scope of this article, studies were carried out to increase the energy production performance of the prepared nano yarns, and catalysts were used to increase the catalytic reaction. For this, in the first step, nano yarn was prepared using multi-walled carbon nanotubes (MWCNT) and polyvinyl alcohol (PVA). The nano yarn fibers were characterized by a scanning electron microscope (SEM). In this test system, formic acid and methanol were used as liquid fuels and potassium nitrate and sucrose were used as solid fuels. In most of the experimental outputs, a minimum of 2 mV, and a maximum of 8 mV electrical energy were obtained. According to the experimental data of the study, it was observed that the used catalyst activates the thermopower-based energy system. This study is a study that will guide future studies on electrical energy-based energy production from derivatives of carbon-based nanomaterial.
COVID-19 pandemic created a need to improvise and redefine blended learning to be executed fully online. Background information on the effectiveness of fully online blended learning activities, especially for surgical disciplines is limited. This study describes a fully online blended learning course format on spinal surgery and aims to provide data regarding it effectiveness. Fully online blended courses on three topics of spinal surgery designed as six-week asynchronous and followed by 3-day live parts. Learning gaps (LGs) were identified with a survey at the beginning of asynchronous part, at its end, and at the end of the live part. The effectiveness of the asynchronous and live parts was assessed by LGs and a quiz, login statistics of learners and faculty and a post-course survey. Participants' LGs decreased in all courses, statistically significant in two. Faculty and learner login rates significantly correlated with each other. Faculty and learner satisfaction was very high. A fully online blended learning course can be delivered effectively on spine surgery with a high participant and faculty satisfaction rate. The asynchronous part contributes to learning significantly.
Background: We aimed to evaluate the features of primary membranous nephropathy (MNP) in Turkish people. Methods: This is a retrospective analysis of patients with biopsy-proven primary MNP. We obtained the data collected between 2009 and 2019 in the primary glomerulonephritis registry of the Turkish Society of Nephrology Glomerular Diseases Study Group (TSN-GOLD). Patients with a secondary cause for MNP were excluded. Clinical, demographic, laboratory, and histopathological findings were analyzed. Results: A total of 995 patients with primary MNP were included in the analyses. Males constituted the majority (58.8%). The mean age was 48.4 ± 13.9 years. The most common presentation was the presence of nephrotic syndrome (81.7%) and sub nephrotic proteinuria (10.3%). Microscopic hematuria was detected in one-third of patients. The median estimated glomerular filtration rate (eGFR) was 100.6 mL/min/1.73 m2 (IQR, 75.4-116.3), and median proteinuria was 6000 mg/d (IQR, 3656-9457). Serum C3 and C4 complement levels were decreased in 3.7 and 1.7% of patients, respectively. Twenty-four (2.4%) patients had glomerular crescents in their kidney biopsy samples. Basal membrane thickening was detected in 93.8% of cases under light microscopy. Mesangial proliferation and interstitial inflammation were evident in 32.8 and 55.9% of the patients, respectively. The most commonly detected depositions were IgG (93%), C3 complement (68.8%), and kappa and lambda immunoglobulin light chains (70%). Although renal functions were normal at presentation, vascular, interstitial, and glomerular findings were more prominent on biopsy in hypertensive patients. No significant effect of BMI on biopsy findings was observed. Conclusions: Despite some atypical findings, the main features of primary MNP in Turkey were similar to the published literature. This is the largest MNP study to date conducted in Turkish people.
Poor chromatographic resolution is one of the main challenges in chromatographic analysis. Partially separated chromatographic peaks frequently occur, due to the nature of analytes and the demand for fast analysis using high flow rates and shorter columns. Modelling of chromatographic three‐way data using suitable chemometric tools enables resolving co‐eluted peaks without using additional experimental efforts. In this paper, parallel factor analysis (PARAFAC) was applied to chromatographic data for the quantitative resolution of a quaternary mixture at the co‐elution condition of acetaminophen, aspirin, ascorbic acid, and guaifenesin in a spectrochromatogram. The spectrochromatograms of the calibration set, validation set and real samples were arranged as a three‐way array. In the next step, the PARAFAC model was implemented to decompose the spectrochromatographic array into trilinear components, corresponding to spectral, chromatographic and relative concentration profiles of the analytes. The chromatographic and spectral modes were used for the qualitative analysis of components, while the analytes in commercial tablets were quantified from their individual profiles in their concentration mode. This study indicated that the application of PARAFAC model provided a novel strategy for solving overlapping peaks in a chromatogram to perform the analysis of multicomponent mixtures with a very short runtime and without additional efforts.
Background Patients are the most important stakeholders in the care of any disease and have an educational need to learn about their condition and the treatment they should receive. Considering this need for patient-focused materials, we present a directed approach for mucopolysaccharidosis (MPS) VI and MPS IVA, a pair of rare, inherited diseases that affects multiple organs and parts of the body. Independent guidelines on the treatment of these diseases were recently published, providing evidence- and expertise-driven recommendations to optimize patient management. However, while healthcare providers may have the training and knowledge to understand these guidelines, patients and their caregivers can find the technical content challenging. Hence, we aimed to develop plain language summaries (PLS) of the MPS VI and MPS IVA guidelines with patients as the primary audience. Results A review of the guidelines by an expert team identified six domains of information relevant to patients: The multidisciplinary team, regular tests and check-ups, disease-modifying and supportive treatments, general anesthetics, ear-nose-throat/respiratory care, and surgeries. This information was adapted into a series of infographics specific to either MPS VI or MPS IVA, designed to appeal to patients and clearly present information in a concise manner. Conclusions The use of patient-friendly materials, like the infographics we have developed, has the potential to better inform patients and engage them in their care. We issue a “call to arms” to the medical community for the development of similar PLS materials in rare diseases intended to inform and empower patients.
This consensus statement by a panel of Fabry experts aimed to identify areas of consensus on conceptual, clinical and therapeutic aspects of Fabry disease (FD) and to provide guidance to healthcare providers on best practice in the management of pediatric and adult patients with FD. This consensus statement indicated the clinical heterogeneity of FD as well as a large number of pathogenic variants in the GLA gene, emphasizing a need for an individualized approach to patient care. The experts reached consensus on the critical role of a high index of suspicion in symptomatic patients and screening of certain at-risk groups to reveal timely and accurate diagnosis of FD along with an increased awareness of the treating physician about the different kinds of pathogenic variants and their clinical implications. The experts emphasized the crucial role of timely recognition of FD with minimal delay from symptom onset to definite diagnosis in better management of FD patients, given the likelihood of changing the disease’s natural history, improving the patients’ quality of life and the prognosis after enzyme replacement therapy (ERT) administered through a coordinated, multidisciplinary care approach. In this regard, this consensus document is expected to increase awareness among physicians about unique characteristics of FD to assist clinicians in recognizing FD with a well-established clinical suspicion consistent with pathogenic variants and gender-based heterogeneous clinical manifestations of FD and in translating this information into their clinical practice for best practice in the management of patients with FD.
Background/aim Certain constituents in migraine food triggers and non-steroidal anti-inflammatory drugs (NSAIDs) inhibit sulfotransferases (SULTs) that detoxify drugs/chemicals and play role in the metabolism of neurotransmitters. We aimed to dissect SULT1A1 modulation of CSD susceptibility and behavior in an in vivo experimental model using hesperidin, a SULT1A1 inhibitor found in citrus fruits (known migraine triggers) and mefenamic acid (SULT1A1 inhibitor), an NSAID to simulate medication overuse. Methods Hesperidin was used as SULT1A1 inhibitor found in citrus fruits, known migraine triggers and mefenamic acid (NSAID), another SULT1A1 inhibitor, was used to induce MO in rats. The groups were; 1) Hesperidin (ip) or its vehicle-DMSO (ip) 2) Chronic (4 weeks) mefenamic acid (ip) or its vehicle (ip) 3) Chronic mefenamic acid+hesperidin (ip) or DMSO (ip). CSD susceptibility was evaluated and behavioral testing was performed. SULT1A1 enzyme activity was measured in brain samples. Results Single-dose of hesperidin neither changed CSD susceptibility nor resulted in any behavioral change. Chronic mefenamic acid exposure resulted in increased CSD susceptibility, mechanical-thermal hypersensitivity, increased head shake, grooming and freezing and decreased locomotion. Single dose hesperidin administration after chronic mefenamic acid exposure resulted in increased CSD susceptibility and mechanical-thermal hypersensitivity, increased freezing and decreased locomotion. SULT1A1 enzyme activity was lower in mefenamic acid and mefenamic acid+hesperidin groups compared to their vehicles. Conclusion Mefenamic acid and hesperidin have synergistic effect in modulating CSD susceptibility and pain behavior. Sulfotransferase inhibition may be the common mechanism by which food triggers and NSAIDs modulate migraine susceptibility. Further investigations regarding human provocation studies using hesperidin in migraine patients with medication overuse are needed.
Mathematical understanding is considered to have a multidimensional structure and an important place in mathematics teaching. The focus of the assessments in mathematics courses is on the multidimensional evaluation of mathematical understanding. However, a review of related literature shows the lack of studies on assessing mathematical understanding using multidimensional structures. For this reason, the current study attempted to develop a scale for assessing teachers' mathematical understanding. The instrument development model, an exploratory sequential mixed methods model, has been used in line with this aim during the scale’s development. The study’s qualitative data were collected through a mathematical understanding assessment form and semi-structured interviews developed by the authors. Qualitative data emerged from 17 mathematics teachers working in middle schools or high schools who were determined using the easy access sampling method. Five themes were arrived at from the content analysis with the obtained qualitative data: applying rules, transferring knowledge, generating questions and solutions, generalizing, and exploring information. The initial version of the instrument prepared in accordance with these themes was applied to a total of 501 primary school mathematics preservice teachers from nine different universities in Turkey during the 2018 spring semester. Cronbach’s alpha (α) assessment was performed for the scale’s reliability. Item-total correlations were calculated as item statistics. Exploratory and confirmatory factor analyses were applied to test the scale’s construct validity. The item loadings for the scale were seen to vary between.431 and.759 in this process; the reliability of the sub-dimensions and the scale’s fit indices were seen to be at sufficient levels. As a result of all the performed analyses, a scale form consisting of 21 items and five sub-dimension was obtained. The results from the scale development process show that the preservice teachers took into account the factor of transferring knowledge the most and the factor of producing questions and solution pathways the least.
Shell and Helically Coiled Tube Heat Exchangers (SHCTHEXs) are utilized in energy conversion applications in industry and in various engineering systems. They are generally composed of a helically coiled tube and a shell covering it. This coiled structure of tubes, provides better heat transfer and takes less space. There is an ongoing interest in research on this type of heat exchangers. In this study, a new design was created modifying a simple type of conventional shell and helically coiled heat exchanger, by integrating discs and rings. These rings and discs were attached to the helically coiled tubes with the aim of performing as baffles restricting the shell side flow and creating turbulence. The thermal performance of a conventional heat exchanger was improved by this modification. The study was carried out both numerically and experimentally. At first step, two SHCTHEXs, one conventional; one modified, were designed with same overall geometric dimensions. Then created solid models were numerically simulated with same boundary conditions using ANSYS Fluent. Simulations were performed with various flow rates and the results were reported. According to the simulations, compared to the conventional one, with the modified heat exchanger 7.1% increase in average amount of heat transfer rate and around 20% increase in overall heat transfer coefficient were obtained. With the promising results taken by simulations, the modified heat exchanger was fabricated with the same dimensions and it was experimentally tested with same conditions in laboratory to verify the simulation results. Experimental results were in harmony with the simulations with little differences. The average differences between simulation and experimental values in terms of average amount of heat transfer rate were obtained as 2.4% for 3 l/min hot fluid flow rate and 3.5% for 4 l/min hot fluid flow rate. Overall heat transfer coefficient of modified SHCTHEX with circular baffles achieved in the range of 1050–1400 W/m²K. General outcomes of this study exhibited successful design of baffled SHCTHEX.
Direct synthesis of dimethyl ether (DME) from syngas was investigated in the presence of a bifunctional catalyst pair containing silicotungstic acid (STA) incorporated SBA-15 (5, 10, 25% by wt. STA) for the in-situ dehydration of synthesized methanol. The methanol synthesis component of this catalyst mixture is a Cu/ZnO-based commercial (HF) catalytic material. The new [email protected] type solid acid catalysts synthesized in this study indicated ordered mesoporous structures with surface area values in the range of 493–804 m²/g. They possess both strong Brønsted and Lewis acid sites. Activity tests performed at 50 bar and 275 °C with a molar feed ratio of CO/H2 being 1/1 showed the highest CO conversion and DME selectivity values in the presence of HF/[email protected] admixed catalyst, as 41.1% and 56.6%, respectively. Activity test results showed that the incorporation of STA into SBA-15 had caused a significant increase in DME selectivity and CO conversion. TGA/DTG and XRD results of the spent catalysts showed not very high coke formation (between 2.26 and 3.13%). In order to test the effect of CO2 concentration on the product distribution, a set of sorption-enhanced reaction tests were performed by in-situ removal of carbon dioxide from the reaction medium via a Huntite adsorbent. These tests showed an increase of DME selectivity to 60.3% during the first 50 min of the reaction. Another significant result obtained in the presence of Huntite was the presence of quite a high CH4 mole fraction in the product stream during the first 50 min, which decreased to less than 1% at 200 min reaction time. These results indicated the shift of reverse dry reforming and the water gas shift reactions to the product side as a result of the removal of CO2 from the reaction zone. Hence, some positive effect of CO2 sorption was observed on DME selectivity due to the appearance of CO2 in the product side of the overall DME synthesis stoichiometry. As the reaction period increased Huntite was saturated with CO2 and the effects of its sorption on product distribution diminished.
The multi-floor facility layout problem (MFLP) is one of the most important and complex facility layout problems that has many applications in designing the facilities of manufacturing and service sectors. In this study, a hybrid version of the Dantzig-Wolfe decomposition algorithm is proposed to solve the MFLP for the first time. The proposed solution approach is performed in two steps. In the first step, a mathematical formulation is applied to assign the departments to the floors in a way that the departments with higher material flow between them be located on the same or closer floors. In the second step, the output of the first step is considered and the MFLP is decomposed into a master problem and some sub-problems to form the Dantzig-Wolfe decomposition algorithm and find the optimal layout of each floor separately. Then the integrated layout of multiple floors is formed easily. The proposed algorithm is evaluated using some sample problems from the literature and some newly generated test problems. The obtained results show the superiority of the proposed algorithm compared to the approaches of the literature.
In this study, it was aimed to control the HCCI combustion phase. For this purpose, low and high reactivity pure fuels and their mixtures in various ratios were tested. Reference n-heptane was chosen as the high reactivity fuel, while heavy naphtha was used as the low reactivity fuel. Detailed combustion analysis was performed using in-cylinder pressure data. IMEP, the start of combustion, combustion duration, indicated thermal efficiency, MPRR and COVimep maps were examined at different engine speeds and lambda values. Almost zero NOx and soot emissions were observed under all test conditions. On the other hand, CO and HC emissions were analyzed comparatively. It has been determined that HN75 fuel provides optimum operating conditions for HCCI combustion and also causes low CO and HC emissions. Indicated thermal efficiency was obtained at about 36% and in a wide operating range in the use of HN75 and HN100 fuels. This study shows that heavy naphtha fuel significantly improved combustion phase control in HCCI engines. Particularly remarkable results were obtained in terms of performance and emission values at 50% and 75% mixing ratios with n-heptane.
The aim of this study is to examine the effects of commercial rhizobacteria inoculant on eggplant plants grown under drought stress conditions. Commercial inoculant containing Azotobacter chroococum and Azotobacter vinelandii rhizobacteria species was applied to eggplant plants by root inoculation and the plants were exposed to different levels of drought stress (moderate drought stress-MS and severe drought stress-SS). To determine the growth-promoting ability of inoculation with rhizobacteria, changes in plant morphology (shoot-root fresh and dry weights, shoot length and diameter) and physiology (relative water content-RWC, stomatal conductivity-gs, K, Ca, Mg and Na accumulations in shoot and root, photosynthetic pigment contents) were investigated. To determine the impacts of the inoculant on the potential of increasing the drought tolerance of eggplant, besides the enzyme activities of superoxide dismutase (SOD), catalase (CAT) and glutathione reductase (GR), non-enzymatic antioxidant activities such as antochiyanin, total phenolic substance, proline were investigated. In addition, H2O2 and malondialdehyde (MDA) contents were analyzed to resolve whether drought stress causes oxidative damage in eggplant. The increase in the severity of drought caused a decrease in plant growth and shoot-root fresh and dry weights. Nevertheless, these adverse effects of drought stress were alleviated by inoculation. Decreased RWC, gs values of plants under drought stress, and especially K, Ca and Mg accumulations and protein contents in the root increased significantly with inoculation. On the other hand, Chlorophyll (Chl) (Chl a, Chl b, Chl a + b) and carotenoid contents were significantly increased in leaves under uninoculated MS and SS. Inoculation with rhizobacteria reduced the increase in photosynthetic pigment contents. Depending on the severity of stress, higher levels of total phenolic compounds and proline were accumulated in inoculated plants compared to uninoculated plants. However, higher SOD, CAT, and GR enzymatic activities were observed in inoculated stressed plants, and membrane lipid peroxidation was reduced. These results were found to be important in that the commercial bacterial inoculant has the potential to diminish the negative effects of drought stress in eggplant and supports the stress tolerance of the plant by mitigating the drought-related oxidative damage.
The variant perovskites are considered novel materials for studying solar cells and other electro-optic applications. Born’s stability criteria confirm the mechanical stability, while Poisson coefficient (ν > 0.26) and Pugh ratio (B0/G > 1.75) certify the ductile nature of the compounds by investigating elastic properties. For Cs2KGaI6 and Rb2KGaI6, the computed bandgap is direct 1.81 eV and 1.85 eV, respectively, in terms of exploring the band structures. Their band gaps may capture electromagnetic waves in the visible spectrum, excelling them for solar cell devices. The optical properties have been studied against photon energy (eV) with maximum transition and absorption in the visible region. BoltzTrap code is employed for the thermoelectric properties executed by electrical conductivity, thermal conductivity, power factor, and figure of merit (ZT), which shows a slight variation in the temperature. Hence, the considered lead-free double perovskite compounds offer applications in solar cell devices and p-type semiconducting behavior predictive in transport investigations.
Deep learning has emerged as a promising tool in time-series prediction tasks such as weather forecasting, and adaptive models can deal with dynamic data more effectively. In this work, we first investigate how successfully meteorological features can be predicted by a deep learning model based on long-short-term memory (LSTM). Then, we endeavor to improve the prediction model’s performance by employing various LSTM types and choosing a model type that provides robust and accurate results. After that, we extend the proposed model to deal with univariate and multivariate problems, and we compare them. Finally, we apply the adaptive learning concept to the selected model by retraining and updating the model periodically to improve its accuracy. The experimental findings demonstrate that applying adaptive learning in the bidirectional LSTM-based model decreases the prediction error by 45% compared to the baseline models. Moreover, the results reveal that exploiting only the univariate model leads to learning from fewer features; and thus, less time and memory consumption for the model construction and usage.
In this study, the combustion, performance, and emission results of the HCCI engine under different fuel and engine parameters conditions were examined experimentally and statistically. Engine speed, excess air ratio, and fuel types with different fusel oil concentrations were used as variable parameters. The engine speed was determined as 1000 and 1200 rpm, excess air ratio 1.7 and 2.1, and fusel oil ratio in fuel was determined as 15% and 30%. When the HCCI engine was operated with these input parameters, the effective torque indicated thermal efficiency, maximum pressure increase rate, COVimep, HC, CO, and NOx values were examined. Experiments were carried out in line with the determined experimental series, and the data obtained were analyzed. Optimization has been made to determine the optimum input parameters by inputting the targeted response parameters from the HCCI engine. After the optimization study, it was concluded that the optimum response parameters, engine speed was 1262.44 rpm, excess air ratio was 1.91631, and was obtained by using F30 fuel. Under optimum input parameters, the effective torque is 5.751 Nm, ITE 34.089%, MPRR 7.257%, COVimep 4.009%, CA50 7 ° CA, HC 454.185 ppm, CO 0.0727%, and NOx 0.000169486 ppm.
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