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Typically, success in the estimation of machine learning is expected to rise with increasing input parameters, whereas the noise issue may rarely arise owing to redundant input factors undesirably influencing the learning algorithm. The parameters such as overall heat transfer coefficient, pressure drop, and overall cost have been determined by two different artificial neural networks evaluated by a multi-layer perceptron model. Using the Levenberg-Marquardt training algorithm, in the first model input layer, a total of 10 input parameters ρ, np, k1, Re1, fi, Re2, fo, ns, P1 and P2 have been defined, while the second involves 8 input parameters by subtracting pumping powers from the first one, thus the noise issue has been investigated using unnecessary input parameters. Overall heat transfer coefficient, tube/annulus sides pressure drops, and overall cost have been estimated with deviations of 0.16%, 0.23%, 0.02%, and 0.003% via Model 1, 0.02%, 0.18%, 0.16%, and 0.15% via Model 2, respectively. Moreover, Model 1 results in the best mean squared errors for annulus side pressure drop and overall cost with the values of 2.54E-04 and 1.93E-04, correspondingly, whereas Model 2 yields the best values of 1.11E-04 and 1.90E-04 for overall heat transfer coefficient and tube side pressure drop, sequentially.

This study aimed to investigate the programmed cell death-ligand 1 (PD-L1) expression in cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC) and its relationship with prognostic factors in tumors that are not in the head and neck region and are therefore relatively less exposed to the sun. This retrospective cross-sectional study included 25 invasive cSCC and 42 BCC cases with a diameter ≥ 2 cm located outside the head and neck region from 2010 to 2018. The biopsy samples were examined based on the membranous PD-L1 (22C3 clone) staining. Staining results were scored as follows: 0, no staining (negative); 1, < 10% PD-L1 positivity of tumor cells; and 2, ≥ 10% PD-L1 positivity of tumor cells. PD-L1 positivity was not seen in any BCC cases, whereas 11 (44%) of cSCC cases were PD-L1 positive. No significant relationship was observed between PD-L1 expression and prognostic parameters, including tumor diameter, tumor depth, and lymphovascular or perineural invasion in the cSCC group. PD-L1 expression was not associated with prognostic factors in the early stages of BCC and SCC located outside the head and neck region. Therefore, investigating the PD-L1 expression seems to be more relevant in patients with advanced-stage disease.

Abstract In current investigation, a novel implementation of intelligent numerical computing solver based on multi-layer perceptron (MLP) feed-forward back-propagation artificial neural networks (ANN) with the Levenberg–Marquard algorithm is provided to interpret heat generation/absorption and radiation phenomenon in unsteady electrically conducting Williamson liquid flow along porous stretching surface. Heat phenomenon is investigated by taking convective boundary condition along with both velocity and thermal slip phenomena. The original nonlinear coupled PDEs representing the fluidic model are transformed to an analogous nonlinear ODEs system via incorporating appropriate transformations. A data set for proposed MLP-ANN is generated for various scenarios of fluidic model by variation of involved pertinent parameters via Galerkin weighted residual method (GWRM). In order to predict the (MLP) values, a multi-layer perceptron (MLP) artificial neural network (ANN) has been developed. There are 10 neurons in hidden layer of feed forward (FF) back propagation (BP) network model. The predictive performance of ANN model has been analyzed by comparing the results obtained from the ANN model using Levenberg-Marquard algorithm as the training algorithm with the target values. When the obtained Mean Square Error (MSE), Coefficient of Determination (R) and error rate values have been analyzed, it has been concluded that the ANN model can predict SFC and NN values with high accuracy. According to the findings of current analysis, ANN approach is accurate, effective and conveniently applicable for simulating the slip flow of Williamson fluid towards the stretching plate with heat generation/absorption. The obtained results showed that ANNs are an ideal tool that can be used to predict Skin Friction Coefficients and Nusselt Number values.

Internists are called upon on a daily basis to address a range of women's health issues. Staying up to date with the evidence in this wide field can be challenging. This article reviews important studies published in 2015 and early 2016 pertinent to urinary tract infection, osteoporosis, ovarian cancer screening, and contraception.

Since the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was −0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.

This study focused on the analysis of partial slip effect of Arrhenius activation energy and temperature dependent viscosity on non-Newtonian Maxwell nanofluid bio-convective flow using artificial intelligence approach. Local Nusselt number, local Sherwood number and local density number values, which are dimensionless flow parameters, have been used to examine the said effect. Three different artificial neural network models have been developed using the numerically obtained data sets. Each of the feed forward back propagation multilayer perceptron network models has been developed with different input parameters. 80% of the data set has been used for training the model and 20% for the testing phase. The estimation performance of the network models developed with the Bayesian regularization training algorithm has been extensively analyzed, and the compatibility between the estimation values and the target data has been examined. The findings have shown that artificial neural network models have been developed to make predictions with high accuracy. In addition, artificial neural networks have also proven to be an ideal engineering tool that can be used to analyze the partial slip effect of non-Newtonian Maxwell nanofluids on bio-convective flow.

In this study, the viscous dissipation effects of copper-polyvinyl alcohol (Cu-PVA) Jeffrey nanofluid on magnetohydrodynamic (MHD) heat transfer flow across a stretchable surface have been analyzed with an artificial intelligence approach. The flow parameters, skin friction and Nusselt number, are numerically obtained with a closed Keller-box and partial differential equations converted to a non-linear ordinary differential equation system using the appropriate similarity transformation. Using the obtained data set, two different artificial neural network (ANN) models have been developed. In the multi-layer perceptron (MLP) network model developed with Bayesian Regularization training algorithm, solid volume fraction (φ), Deborah number (β), magnetic parameter (M), Prandtl number (Pr) and Eckert number (Ec) values have been defined as input parameters and skin friction and Nusselt number values ​​have been obtained in the output layer. R values ​​for skin friction and Nusselt number have been calculated as 0.99020 and 0.99394, respectively. The study findings show that the developed ANN model can predict with high accuracy and is a high-performance engineering tool that can be used in modeling viscous dissipation effects.

The majority of research predicted heating demand using linear regression models, but they did not give current building features enough context. Model problems such as Multicollinearity need to be checked and appropriate features must be chosen based on their significance to produce accurate load predictions and inferences. Numerous building energy efficiency features correlate with each other and with heating load in the energy efficiency dataset. The standard Ordinary Least Square regression has a problem when the dataset shows Multicollinearity. Bayesian supervised machine learning is a popular method for parameter estimation and inference when frequentist statistical assumptions fail. The prediction of the heating load as the energy efficiency output with Bayesian inference in multiple regression with a collinearity problem needs careful data analysis. The parameter estimates and hypothesis tests were significantly impacted by the Multicollinearity problem that occurred among the features in the building energy efficiency dataset. This study demonstrated several shrinkage and informative priors on likelihood in the Bayesian framework as alternative solutions or remedies to reduce the collinearity problem in multiple regression analysis. This manuscript tried to model the standard Ordinary Least Square regression and four distinct Bayesian regression models with several prior distributions using the Hamiltonian Monte Carlo algorithm in Bayesian Regression Modeling using Stan and the package used to fit linear models. Several model comparison and assessment methods were used to select the best-fit regression model for the dataset. The Bayesian regression model with weakly informative prior is the best-fitted model compared to the standard Ordinary Least Squares regression and other Bayesian regression models with shrinkage priors for collinear energy efficiency data. The numerical findings of collinearity were checked using variance inflation factor, estimates of regression coefficient and standard errors, and sensitivity of priors and likelihoods. It is suggested that applied research in science, engineering, agriculture, health, and other disciplines needs to check the Multicollinearity effect for regression modeling for better estimation and inference. [ABSTRACT FROM AUTHOR]

It is important to find the estimation methodology with the highest accuracy in order to determine the parameters of water disinfection and to provide the most ideal disinfection. In this study, the usability of artificial neural networks in predicting response disinfection efficiency in electrochemical water disinfection processes was investigated. An artificial neural network model was developed using a total of 17 data sets and Response Disinfection Efficiency values were estimated from the model. Current density, treatment time and interelectrode spacing values are defined as input parameters in the network model, which has a multilayer perceptron architecture with 10 neurons in its hidden layer. The coefficient of determination value for the developed model was 0.98682 and the average deviation rate was −0.1%. The study findings showed that neural networks are an ideal tool that can be used to predict response disinfection efficiency in electrochemical water disinfection processes. [ABSTRACT FROM AUTHOR]

In the Vase system, the most common training system for peach-growing countries for more than a century, light distribution to the canopy is uneven, and access to the canopy for pruning, thinning, and harvest labor is difficult. It is important to identify alternative systems to the Vase system considering the cultivar and growing environment to facilitate labor and enhance productivity and quality. In Türkiye, one of the important centers of peach growing worldwide, detailed research has yet to be published on the applicability of training systems alternative to the widely used Vase system. Therefore, this study aimed to evaluate the effect of different training systems (Vase, Catalan Vase, Quad-V, Tri-V) on growth, yield, fruit quality, and labor costs of peach cultivars (ExtremeVR 314, ExtremeVR 436, ExtremeVR 568). The experiment was conducted from 2017 to 2022. Although the distance between rows in all training systems is 5 m, the distance between trees on the row is determined as 4 m in Vase, 3 m in Catalan Vase, 2.5 m in Quad-V, and 2 m in Tri-V. In the experiment, vegetative development parameters, such as canopy volume, trunk sectional area, and the amount of winter pruning weights, differed according to the training system. In the final year, the Vase system, which produces the most pruning weight, generates 48.0% more pruning weight compared with the Tri-V system, which produces the least. Concerning yield per tree and hectare, trained to the Vase system yielded higher fruit per tree regardless of cultivar, while the Quad-V and Tri-V systems yielded more fruit per hectare. The training system and cultivar affected the fruit size; the largest fruits were obtained from the ExtremeVR 568 cultivar trained according to the Vase system. The most time needed for winter pruning was obtained from the Vase (79.4 min/tree) system, and the Tri-V (57.4 min/tree) and Quad-V (60.3 min/tree) systems required the least time. The Catalan Vase (31.1 min/tree) system required the least time for summer pruning. The most fruit harvest in an hour was obtained from the trees trained according to the Tri-V (164.5 kg/h) and Quad-V (132.02 kg/h) systems. These results suggest that Quad-V and Catalan Vase systems performed well and could be alternatives to the Vase system. [ABSTRACT FROM AUTHOR]

Aim: Digital platforms such as YouTube are popular sources of health-related information. Although there are studies evaluating the quality of different online medical content, studies focusing on the quality of dementia-related content are limited. This study aimed to investigate the quality of YouTube videos related to dementia patient care. Material and Methods: Using the term "Dementia care" on the YouTube platform, 100 English videos that met the inclusion criteria were identified and analyzed. In addition to video popularity measurements, to evaluate content quality, the global quality scale (GQS), modified DISCERN scale, Journal of the American Medical Association (JAMA) quality scale, and the patient education materials assessment tool for audio/visual materials (PEMAT-A/V) are used. Results: It was observed that most of the videos were uploaded by non-academic health institutions (36%) and health professionals (23%). When the content of the videos was evaluated, it was determined that general care strategies were the most common content with 52%. Scores indicating high levels of reliability and accuracy were determined on all applied content quality scales. Videos sourced from academic healthcare institutions were found to have the highest scores on content quality scales. In correlation analyses, video metrics such as duration, view ratio, number of comments, and video power index values were positively correlated with content quality scores. Conclusion: Videos about dementia patient care on YouTube generally exhibit high popularity and content quality. Individuals seeking information about dementia care on online platforms should be directed to videos uploaded by healthcare institutions. [ABSTRACT FROM AUTHOR]

It is a major research area in mathematics, physics, engineering, and computer science to study the heat and mass transfer properties of flow. Suspensions containing multiple nanoparticles or nanocomposites have recently gained a wide range of applications in biological research and clinical trials under certain conditions. Nanofluids are important suspensions that allow nanoparticles to disseminate and behave in a homogeneous and stable environment. Therefore, here magnetohydrodynamic micropolar nanofluid flow towards the stretching surface with artificial neural network has been considered. In this study, radiation and heat source phenomena have been presented in heat convection. Brownian and thermophoresis effects and micro‐rotational particles are also taking into account. The non‐linear simplified equations have been calculated numerically via Runge‐Kutta fourth‐order shooting process. The calculation of the Sherwood number, Nusselt number, couple stress coefficient, and skin friction coefficient has been conducted utilizing diverse parameters. Furthermore, the outcomes have been employed to create four distinct artificial neural networks. Our observation indicates that an increase in the heat source quantity Q1$Q_{1}$ leads to a rise in heat generation, resulting in a greater total heat output and an increase in the temperature field. Coefficient of determination "R" values higher than 0.99 have been obtained for the artificial neural network models. The obtained findings have shown that artificial neural networks can predict thermal parameters with high accuracy. [ABSTRACT FROM AUTHOR]

This study models milk product processing and sustainable of the shelf-life extension in a dairy factory using artificial intelligence. The Cappadocia dairy factory was used to study chemical processes and computational system modeling and simulation. Levenberg--Marquardt algorithm was used to create an artificial neural network model from real-time data. An AI-based method utilizing a Multilayer Perceptron (MLP) Artificial Neural Network (ANN) model was employed to precisely analyze productivity data in dairy factories. There are 9 product types and production quantities used as input parameters, and 90 datasets of actual dairy products used as output values. The model was trained using the Levenberg--Marquardt algorithm on 62 datasets for training, 14 for validation, and 14 for testing. The accuracy of the model is affected by the optimal data segmentation. The model showed how AI algorithms can improve processes and industrial production by increasing dairy production efficiency from 20 to 40%. Model efficiency values were compared to observed values to determine prediction accuracy. Model mean squared error was 4.02E-06, and coefficient of determination was 0.99984. Model efficiency predictions and observed values differed by -0.04% on average. This study investigated using artificial intelligence to optimize salvage processes and systems to increase energy efficiency and reduce environmental impact. The results show that a neural network model trained with real data can predict dairy plant productivity. [ABSTRACT FROM AUTHOR]

Metastatic prostate cancer is the second most common cancer globally, with high mortality and morbidity rates. This study aimed to elucidate the effects of plant-derived kaempferol on the proliferation and migration of human prostate cancer cells at the molecular level. Spectrophotometric analyses proved that kaempferol was stable up to 48 h. Antioxidant properties were investigated by DPPH, CUPRAC, and ABTS methods, and kaempferol possess 3.6 mg/mL DPPH activity. Cytotoxic properties of kaempferol were investigated on various human cancerous cells, including A549(Lung), PC-3(Prostate), NCI-H295R(Adrenal Gland), HUH-7(Liver), HeLa(Cervix) using the Alamar Blue method. Western Blotting and qRT-PCR was employed to analyze Bax, BcL-2, Caspase-3, Caspase-9, Caspase-12, p53, Nf-κB, Smad-4, Kras, APC, MLH-1 expressions. Kaempferol was found the most potent inhibitor against the proliferation of PC-3 cells with an IC50 value of 16.9 µM. Confocal microscopy studies proved that kaempferol was primarily localized in the cytoplasm. Besides, PC-3 cells' migration and colony formation rates significantly (p<0.0001) inhibited 46% and 68%, respectively. Increased protein expressions of TP53 and Nf-κB due to kaempferol activated the E-cadherin, key protein in the migration process. Kaempferol treatment elevated the early rate of apoptosis by regulating apoptotic and antiapoptotic genes and proteins, including Bax, BcL-2, Caspase-3, Caspase-9, Caspase12. Protein expression of Bax was increased 2.63-fold (p<0.001), while BcL-2 protein expression was decreased 87% (p<0.05). Besides, kaempferol modulated PI3K-Akt, TGFβ, and MAPK signaling pathways. mRNA expressions of Smad-4 and Kras were inhibited while APC and MLH-1 mRNA expressions were increased. The low cost and high efficiency of kaempferol used in treating fatal and increased incidence of prostate cancer can reduce and treat prostate cancer by showing a new direction to traditional treatments. [ABSTRACT FROM AUTHOR]

Objective: The treatment of acromioclavicular joint (ACJ) dislocations offers numerous options, and ongoing debates persist regarding their comparative effectiveness. Among these options, the suspensory loop device (SLD) is one of the most favored treatment modalities. Despite the observed high reduction loss rate associated with SLD, the treatment yields favorable clinical outcomes. This study aimed to investigate the clinical outcomes of patients with acute type 3 and 5 ACJ dislocations who underwent open and arthroscopic procedures using a single‐bundle SLD, and to evaluate the effect of clavicular tunnel position on reduction loss. Methods: Thirty‐seven eligible patients diagnosed with acute type 3 and type 5 ACJ dislocation who underwent open and arthroscopic surgery with a single‐bundle SLD between January 2015 and March 2022 were evaluated retrospectively. Demographic data and radiological measurements including coracoclavicular (CC) interval, clavicle length (CL), and implant distance (ID) were recorded. The ID/CL ratio was calculated and a value between 0.17 and 0.24 was considered as "acceptable implant position". Reduction loss and other complications were noted. Patients were divided into two groups: open (Group 1) and arthroscopic (Group 2). Constant Murray Score (CMS) and Visual Analog Scale (VAS) were used for clinical and functional outcomes. Non‐parametric tests were used for statistical analysis of variables. Results: The study included six females (16.2%) and 31 males (83.8%) with a mean age of 40.2 ± 14.7 years (range: 20–75). The mean follow‐up period was 22.3 ± 16.7 months (range: 6–72). The average time from trauma to surgery was 6.3 ± 5.3 days (range: 1–18). At the last follow‐up, the CMS was 89.3 ± 8.8 and the VAS score was 2.1 ± 0.9. The mean ID/CL ratio was 0.19 ± 0.1 and 19 patients (51.4%) were between 0.17 and 0.24. Reduction loss was observed in nine patients (24.3%). There were no significant differences between Group 1 and Group 2 regarding operation time (p = 0.998), ID/CL ratio (p = 0.442), reduction loss (p = 0.458), CMS (p = 0.325), and VAS score (p = 0.699). Of the 28 patients without reduction loss, 16 had an ID/CL ratio between 0.17 and 0.24 (p = 0.43). Furthermore, within the 0.17–0.24 interval, CMS was higher with an average of 91.8 ± 5.1 compared to the other intervals (p = 0.559). Conclusion: The clinical and functional outcomes of acute type 3 and type 5 ACJ dislocation operated open and arthroscopically with single‐bundle SLD are similar and satisfactory. A clavicular tunnel position in the range of 0.17–0.24 (ID/CL ratio) is recommended to maintain postoperative reduction. [ABSTRACT FROM AUTHOR]

With the high-frequency trading process, which is a subclass of algorithmic trading transactions, intraday information has increasing importance. Traditional statistical methods often fall short in capturing the intricate patterns and volatility inherent in such high-frequency data. In contrast, artificial neural network (ANN) models demonstrate remarkable capability in handling these challenges, and vector autoregressive (VAR) models provide insights into short-term relationships among variables. This study highlights the importance of using both ANN and VAR models for processing these short time intervals. BIST100 index, which is the main index of Borsa Istanbul, is predicted with two different models in different data ranges with ANN models and VAR models. Both generated ANN models successfully complete the training stages, with extremely high precision, and exhibit exceptionally low error values in their predictions. Although both models are effective, the evidence favors the model evaluated using 5-min data for both the training and prediction phases of ANN models. However, the relative importance of 15-min data in explaining the variation of BIST100 is higher. Moreover, the VAR model results indicate that the short-term relationship between variables can be influenced by the range of data and the 15-min interval data of the variables play a more significant role in explaining the BIST100 index over the longer term. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National Education / Millî Eğitim Dergisi is the property of Milli Egitim Bakanligi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: This study evaluated the use of metformin or pioglitazone in preventing or reducing the development of postoperative intra-abdominal adhesion (PIAA) by employing histopathological, immunohistochemical, and biochemical analyses in an experimental adhesion model. METHODS: Fifty Wistar-Albino rats were divided into five groups: Group I (Control), Group II (Sham Treatment), Group III (Hyaluronic Acid), Group IV (Metformin), and Group V (Pioglitazone). Adhesions were induced in the experimental groups, except for the sham group, using the scraping method. After 10 days, rats were euthanized for evaluation. Macroscopic adhesion degrees were assessed using Nair's scoring system. Immunohistochemical and enzyme-linked immunosorbent assay (ELISA) methods were utilized to assess serum, peritoneal lavage, and intestinal tissue samples. Fructosamine, interleukin-6 (IL-6), transforming growth factor-beta (TGF-β), and fibronectin levels were measured in serum and peritoneal lavage samples. RESULTS: The groups exhibited similar Nair scores and Type I or Type III Collagen staining scores (all, p>0.05). Pioglitazone significantly reduced serum IL-6 and TGF-β levels compared to controls (p=0.002 and p=0.008, respectively). Both metformin and pioglitazone groups showed elevated IL-6 in peritoneal lavage relative to controls, while fibronectin levels in the lavage were lower in pioglitazone-treated rats compared to the sham group (all, p<0.005). CONCLUSION: Pioglitazone, but not metformin, demonstrated a positive biochemical impact on preventing PIAA formation in an experimental rat model, although histological impacts were not observed. Further experimental studies employing different dose/ duration regimens of pioglitazone are needed to enhance our understanding of its effect on PIAA formation. [ABSTRACT FROM AUTHOR]

Due to their durability and efficiency, hermetic reciprocating compressors (HRCs) are used in refrigeration and air conditioning. Compressor performance and reliability concerns reduce system efficiency and raise maintenance costs. Machine learning (ML) is being used to improve hermetic reciprocating compressor performance, reliability, and energy economy. ML is used in hermetic reciprocating compressors for issue identification, performance improvement, predictive maintenance, and energy management. This research compared HRC performance factors such as mass flow rate, cooling capacity, compression power, coefficient of performance, exhaust line losses, and volumetric efficiency. Simple regression, probabilistic neural network, gradient boosted, polynomial regression, and random forest (RF) were used to examine and evaluate these parameters as outputs. Over three cycles, the Fluid-Structure Interaction (FSI) approach assessed compressor performance parameters. For compressor speeds of 1300, 2100, and 3000 rpm, mass flow rate, compression power, cooling efficiency coefficient, and exhaust line energy losses varied by 10%, 4%, 5%, and 6%. To gather ML algorithm inputs, the research used experimental, fluid-structure interaction, and ML methodologies. Experimental and FSI approaches produced 108 data points. These data points were randomly assigned, with 70% for learning and 30% for prediction. The mean convergence criterion for mass flow rate, cooling capacity, compression power, cooling efficiency coefficient, exhaust line energy losses, and volumetric efficiency parameters was 0.9966, 0.9969, 0.9572, 0.0561, 0.9925, and 0.4640 for all ML methods. Simple regression, probabilistic neural networks, gradient boosted, polynomial regression, and RF convergence criteria were 0.8978, 0.9999, 0.6016, 0.4439, and 0.7761. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Academic Social Resources is the property of Journal of Academic Social Resources and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)