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Nanofluids possess enhanced viscous and thermal features that can be utilized to improve the heat transfer performance of several applications involving sustainable manufacturing and industrial ecology, such as in heating/cooling systems, electronics, transportation etc. Therefore, it is important to understand and optimize the flow pattern of these fluids. This research emphasizes the predictions of viscosity of water/ethylene-glycol (EG) based non-Newtonian nanofluids. Four experiment-based data sets are used to predict and validate the effective viscosity, i.e., Fe3O4-Ag/EG, MWCNT-alumina/water-EG, Fe3O4-Ag/water-EG, and MWCNT-SiO2/EG-water, via existing correlations and artificial neural network (ANN). The modeling is based on three input parameters, i.e., particle concentrations, temperatures, and shear rates, and one output parameter, i.e., viscosity. The predicted outcomes are compared to the three existing correlation structures. The error matrix consists of the coefficient of determination (R2), average absolute deviation (AAD %), the sum of squared error (SSE), that are employed to evaluate the performance of the model. Results from ANN are found to be more precise, with R2 values greater than 0.99 for all datasets, compared to data fitting into existing correlations, in which Fe3O4-Ag/water-EG resulted in an R2 value as low as 0.72, to determine the nanofluids’ effective viscosity.

The aim of this research is to investigate whether construction rubbles may be utilized as coarse aggregates in concrete. Experimentally, the slump, unit weight, compressive, tensile, and flexural strength tests were applied on concrete samples with varying percentages of recycled coarse aggregate (RCA) and compared with reference concrete produced with natural coarse aggregate. This research conducted 96 concrete samples with RCA replacement percentages of 0, 35, 65, and 100%, respectively. The control mixture produced with natural aggregate showed better results than the mixtures containing recycled aggregate; thus, compressive, tensile, and flexural strengths reduced as the amount of the recycled aggregate increased. Using 100% RCA, the compressive, tensile, and flexural strength reduction reached up to 64, 29, and 38%, respectively.

The durability and strength of concrete in construction can be significantly compromised by permeability issues, which pose considerable challenges to its long-term effectiveness and reliability. By analyzing six selected articles from the Scopus database, this study meticulously synthesizes findings on the effectiveness of CAs in improving these essential properties of concrete. The research meticulously documents and analyzes key variables such as the CA dosage, water–cement ratio, evaluation duration, and treatment conditions, providing a thorough understanding of the factors that influence the performance of CAs in concrete. The results robustly indicate that CAs significantly reduce concrete permeability, thereby enhancing its resistance to water and other detrimental substances, and simultaneously boosts the compressive strength, leading to stronger and more durable concrete structures. However, the study also reveals that the impact of CAs can vary considerably depending on the specific conditions and methodologies employed in the individual studies. This underscores the importance of standardized testing procedures to ensure consistent and comparable results across different studies. This research provides valuable insights for optimizing the use of CAs in concrete formulations, ultimately aiming to improve the durability, performance, and sustainability of concrete in construction applications.

Conventional concrete causes significant environmental problems, including resource depletion, high CO2 emissions, and high energy consumption. Steel slag aggregate (SSA), a by-product of the steelmaking industry, offers a sustainable alternative due to its environmental benefits and improved mechanical properties. This study examined the predictive power of four modeling techniques—Gene Expression Programming (GEP), an Artificial Neural Network (ANN), Random Forest Regression (RFR), and Gradient Boosting (GB)—to predict the compressive strength (CS) of SSA concrete. Using 367 datasets from the literature, six input variables (cement, water, granulated furnace slag, superplasticizer, coarse aggregate, fine aggregate, and age) were utilized to predict compressive strength. The models’ performance was evaluated using statistical measures such as the mean absolute error (MAE), root mean squared error (RMSE), mean values, and coefficient of determination (R2). Results indicated that the GB model consistently outperformed RFR, GEP, and the ANN, achieving the highest R2 values of 0.99 and 0.96 for the training and testing dataset, respectively, followed by RFR with R2 values of 0.97 (training) and 0.93 (testing), GEP with R2 values of 0.85 (training) and 0.87 (testing), and ANN with R2 values of 0.61 (training) and 0.82 (testing). Additionally, the GB model had the lowest MAE values of 0.79 MPa (training) and 2.61 MPa (testing) and RMSE values of 1.90 MPa (training) and 3.95 MPa (testing). This research aims to advance predictive modeling in sustainable construction through analysis and well-defined conclusions.

The current study seeks to explore and analyze the role of newspapers in constructing ideologies. The construction of ideologies is a discursive practice which has been given a specific worldwide focus in the field of discourse studies and corpus linguistics. Newspapers, tabloids and magazines are the immediate sources of information that have a wider influence on people all over the world. In this regard, headline articles from two Pakistani newspapers –The Dawn and The News–have been selected. 10 headline articles are selected from the Dawn and 12 headline articles are selected from The News with a particular focus on the current lockdown issue in Indian Occupied Kashmir (IOK). The selected data are processed through AntConc, a corpus tool to retrieve and analyze concordance lines. The Critical Discourse Analysis (CDA) with a particular focuson Van Dijk’s (1988) concept, which is called “Structures of News in Press,”is used as a framework for the analysis of attained concordance hits. In addition to this, some tools, namely: naming and referencing, hyperbole, implicature, generalization and reconstruction, given by Richardson (2007), have been applied in addition to the framework proposed by Van Dijk for a refined and improved analysis to explore the discursive construction of ideologies. The findings reveal that through the naming technique,the Dawn editorials have portrayed a negative image of India, while a soft image of Pakistan. However, The News editorials have remained neutral in this regard.The overall analysis depicts that both newspapers have presented Pakistan as supporter and savior of Kashmiri people and India as a manipulator inflicting cruelty in Kashmir. It is recommended that the use of corpus tools in analyzing data from newspapers and then conducting critical discourse analysis are effective ways of unearthing hidden ideologies which can be extended to future research at a broader scope.

Hip fractures contribute significantly to mortality in older adults. New methods to identify those at risk use dual-energy X-ray absorptiometry (DXA) images and advanced image processing. However, DXA images have an overlapping femur and pelvis and may contain boundary lines, making automation challenging. Herein, a 5-layer U-net convolutional neural network (CNN) was developed to segment the femur from hip DXA images. Images were used from the Canadian Longitudinal Study on Aging (CLSA, N=104) and Canadian Multicentre Osteoporosis Study (CaMos, N=105) databases for training, with manual contour drawing defining the ‘true output’ of each image. An algorithm was then developed to mask each hip image, and trained to predict subsequent masks. The CNN was tested with 44 additional CLSA images and 42 CaMos images. This proposed approach had an accuracy and intersection over union (IoU) of 97% and 0.57, and 93% and 0.51, for CaMos and CLSA scans, respectively. Furthermore, a series of augmentation techniques was applied to increase the data size, with accuracies of 96% and 94%, and IoU of 0.53 and 0.50. Overall, our strategy automatically determined the contour of the proximal femur using various clinical DXA images, a key step to automate fracture risk assessment in clinical practice.

Vortex generators and pin fins are conventionally used to deliver fluid mixing and improved convective heat transfer. The increased pressure loss following a fractional increase in heat transfer, as well as the complex manufacturing design, leave room for improvement. The present work proposes a novel diverging–converging base corrugation model coupled with vortex generation using simple geometrical modifications across rectangular microchannels to ensure a superior performance. The Nusselt number, friction factor, and flow phenomenon were numerically studied across a Reynolds number range of 50–1000. The optimum cross-section of the microchannel-generating vortices was determined after thorough study, and base corrugation was further added to improve heat transfer. For the vortex–corrugation modeling, the heat transfer enhancement was verified in two optimized cases: (1) curved corrugated model, (2) interacting corrugated model. In the first case, an optimized curve generating Dean vortices was coupled with base corrugation. An overall increase in the Nusselt number of up to 32.69% and the thermal performance of “1.285 TPF” were observed at a high Reynolds number. The interacting channels with connecting bridges of varying width were found to generate vortices in the counter-flow configuration. The thermal performance of “1.25 TPF” was almost identical to the curved corrugated model; however, a major decrease in pressure, with a loss of 26.88%, was observed for this configuration.

ركز هذا البحث على تحديد المحتوى البروتيني في عينات من السرطان الأزرق السابح Portunus pelagicus، التي تم جمعها من شاطئ اللاذقية خلال صيف وخريف العام 2020 باستخدام طريقة التهضيم الرطب باستخدام حمض الكبريت. تراوحت نسب البروتين ما بين (20.43-17.95%) عند الاناث في فصل الصيف و(20.3-19.37%) خلال الخريف، وما بين (12.95-19.37%)عند الذكور خلال فصل الصيف و(19.56-21%) خلال الخريف. أظهرت الدراسة وجود علاقات ارتباط قوية بين النسبة المئوية للبروتين ووزن الجسم لعينات ذكور واناث السرطان الأزرق السابح P. pelagicusفي فصل الصيف، حيث بلغت قيمة معامل الارتباط عند الذكور R² = 0.9058 وعند الاناث R² = 0.8358. بالإضافة الى وجود علاقات ارتباط قوية بين النسبة المئوية للبروتين ووزن الجسم لعينات ذكور واناث السرطان الأزرق السابح P. pelagicusفي فصل الخريف، حيث بلغت قيمة معامل الارتباط عند الذكور R² = 0.9225 وعند الاناث R² = 0.9551. بينت الدراسة الحالية أن السرطان البحري P. pelagicus يمكن اعتباره مصدراً جيداً للبروتين ويمكن التوصية باستهلاك لحومه كعنصر غذائي غني يالبروتين. الكلمات المفتاحية: السرطان الأزرق، القشريات، الشاطئ السوري، قاعيات حيوانية، المحتوى البروتيني.

Abstract Although numerous works have been done, most of the studies in fault diagnosis are limited to single fault type at a time. Majority of the works reported in the literature do not extend the diagnosis of the root cause of the fault for simultaneous faults specifically in the distillation column. However, an industrial system is susceptible to more than one fault at a time, which may or may not be interrelated. These faults not only reduce the diagnosis performance but also increase the computational complexity of the diagnosis algorithm. In this work, therefore, a multiple kernel support vector machine (MK‐SVM) algorithm is proposed to diagnose simultaneous faults in the distillation column. In the developed MK‐SVM algorithm, multilabel approach based on various kernel functions has been utilized for the classification of simultaneous faults. Dynamic simulation of a pilot‐scale distillation column using Aspen Plus® is used for generating data in normal and faulty operation. Eight different fault types are considered, including valve sticking at reflux and reboiler, tray upsets, loss of feed flow, feed composition, and feed temperature changes. In the classification of simultaneous faults, a combination of two, three, and four faults is introduced for the performance evaluation of the proposed MK‐SVM algorithm. The result showed that the proposed MK‐SVM has a high fault detection rate (FDR) of 99.51% and a very low misclassification rate (MR) of 0.49%. The MK‐SVM–based classification is better with the F1 score of >97% for all combinations of faults. Moreover, it is observed that the proposed MK‐SVM shows better fault diagnosis for single, multiple, and simultaneous faults as compared to other established machine‐learning algorithms.