Modeling the producibility of 3D printing in polylactic acid using artificial neural networks and fused filament fabrication

Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects

Data science in economics: Comprehensive review of advanced machine learning and deep learning methods

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models

Identification of a New Rhoptry Neck Complex RON9/RON10 in the Apicomplexa Parasite Toxoplasma gondii

Apicomplexan parasites secrete and inject into the host cell the content of specialized secretory organelles called rhoptries, which take part into critical processes such as host cell invasion and modulation of the host cell immune response. The rhoptries are structurally and functionally divided into two compartments. The apical duct contains rhoptry neck (RON) proteins that are conserved in Apicomplexa and are involved in formation of the moving junction (MJ) driving parasite invasion. The posterior bulb contains rhoptry proteins (ROPs) unique to an individual genus and, once injected in the host cell act as effector proteins to co-opt host processes and modulate parasite growth and virulence. We describe here two new RON proteins of Toxoplasma gondii, RON9 and RON10, which form a high molecular mass complex. In contrast to the other RONs described to date, this complex was not detected at the MJ during invasion and therefore was not associated to the MJ complex RON2/4/5/8. Disruptions of either RON9 or RON10 gene leads to the retention of the partner in the ER followed by subsequent degradation, suggesting that the RON9/RON10 complex formation is required for proper sorting to the rhoptries. Finally, we show that the absence of RON9/RON10 has no significant impact on the morphology of rhoptry, on the invasion and growth in fibroblasts in vitro or on virulence in vivo. The conservation of RON9 and RON10 in Coccidia and Cryptosporidia suggests a specific relation with development in intestinal epithelial cells

Comparsion of peak vertical ground reaction forces and the rate of loading during single leg drop landing between men with genu varum deformity and normal knee from different heights

Background and Objective: There are relations between rate of loading, osteoarthritis and genu varum result in osteoarthritis. This study was done to compare the peak vertical ground reaction forces and the rate of loading during single leg drop landing between men with genu varum deformity and normal knee from three heights. Methods: This quasi-experimental studywas carried out on 20 male students with genu varum deformity and 20 male students with normal knee. Genu varum deformity was measured and recorded by collis and goniometer. Subjects performed single-leg landing dropping from three heights (20, 40, 60 Centimeter) on a force platform. Results: The peak vertical ground reaction force in calcaneus contact and the rate of loading between groups significantly were different (P<0.05). No significant difference was found in the peak vertical ground reaction during toe contact. Conclusion: Frontal knee angle affect on loading rate. Maybe one of the reasons for higher injury risk and knee arthritis in genu varum population might be due to higher ground reaction forces and the rate of high loading

Knee joint muscles activity during single leg drop landing from different heights among men with genu varum and men with normal knee

Background and Objective: Genuvarum is considered as one of the risk factors for the incidence of osteoarthritis. This study was done to compare the knee joint muscles activity during single leg drop landing from different heights among men with genu varum and men with normal knee. Methods: This case – control study was done on 20 male students with genu varum deformity and 20 male students with normal knee. Genu varum deformity was measured by a kolis and goniometer. Muscle activity of lower limb was recorded with electromyography. Results: There was significant difference in muscles activity of medialis gastrucnemius, peroneus longus, biceps femoris and gluteus medius in cases and controls (P<0.05), while no significant difference was observed in other muscles. Conclusion: The changes in the knee normal structure might affect daily activities and possibly lead to in injuries due to physical training

Forecasting the discharge capacity of inflatable rubber dams using hybrid machine learning models

202303 bcwwVersion of RecordOthersKJGG004, KJGG219; Technische Universität Dresden, TUD; Natural Science Foundation of Henan Province: 182300410291Publishe

A decomposition and multi-objective evolutionary optimization model for suspended sediment load prediction in rivers

202303 bcwwVersion of RecordOthersY202147738; Technische Universität Dresden, TUD; Taif University, TU: TURSP-2020/114Publishe

State of the art survey of deep learning and machine learning models for smart cities and urban sustainability

Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems