Effects of hybrid post-treatments on fatigue behaviour of notched LPBF AlSi10Mg: Experimental and deep learning approaches

Laser powder bed fusion (LPBF) as one of the widely used technologies of additive manufacturing (AM), has a high capability to produce complex geometries such as notched parts in a layer-by-layer manner. LPBF parts in their as built state have inhomogeneous and anisotropic microstructure and poor surface quality. Post-treatments can play a key role in modulating these imperfections. In this study, the effects of four different post-treatments including heat treatment, shot peening and electro-chemical polishing as well as their combination as hybrid treatment were investigated on microstructure, surface and mechanical properties and finally fatigue behaviour of the LPBF V-notched AlSi10Mg samples. Afterward, a deep learning based approach was employed for modelling the fatigue behaviour via artificial neural network. Surface roughness, surface modification factor, hardness, residual stress and porosities were considered as inputs and fatigue life was considered as the output. Model function of the network was generated and the relevant parametric and sensitivity analyses were performed. The results indicated the importance of surface related properties and the notable effect of the surface post-treatments in enhancing the fatigue performance of the LPBF material

Comment on ``Solidification of a Supercooled Liquid in a Narrow Channel''

Comment on PRL v. 86, p. 5084 (2001) [cond-mat/0101016]. We point out that the authors' simulations are consistent with the known theory of steady-state solutions in this system

Thermal Investigation of Stormwater Management Ponds

Abstract Stormwater management wet ponds increase runoff temperatures in discharge waters during summer months. These increases in temperatures adversely affect receiving urban stream ecosystems. Monitoring results for three summers (2009 to 2011) from four stormwater management ponds in the cities of Guelph and Kitchener, Ontario are employed to advance our knowledge of key design parameters that influence the thermal enrichment of stormwater discharges. An artificial neural network model was developed to predict the event mean temperature at the pond outlet. The artificial neural network model explains 99% of the variability in outlet event temperature. Sensitivity analyses show that increasing the permanent pond volume from 2 000 m³ to 4 000 m³ results in an average increase of 5 °C in outlet event mean temperature. Similarly, increasing the travel path ratio from 0.6 m to 1.2 m confirmed an average increase of 6 °C in outlet event mean temperature. In addition, ponds with average depths >1.0 m can result in significant decreases in pond outlet water temperature when using bottom draw structures. The results can lead to the promotion of the design of deeper ponds with bottom draw outlets and smaller travel path ratios. However, the implications of this approach on other performance criteria should be evaluated

Examining Dark Triad traits in relation to mental toughness and physical activity in young adults

Objective: The Dark Triad (DT) describes a set of three closely related personality traits: Machiavellianism, narcissism, and psychopathy. Mental toughness (MT) refers to a psychological construct combining confidence, commitment, control, and challenge. High MT is related to greater physical activity (PA) and, relative to men, women have lower MT scores. The aims of the present study were 1) to investigate the association between DT, MT, and PA, and 2) to compare the DT, MT, and PA scores of men and women. Methods: A total of 341 adults (M=29 years; 51.6% women; range: 18–37 years) took part in the study. Participants completed a series of questionnaires assessing DT, MT, and PA. Results: Machiavellianism, narcissism, and psychopathy were all significantly associated with higher MT scores (rs =0.45, 0.50, and 0.20, respectively). DT traits and MT were associated with more vigorous PA. Compared to men, women participants had lower scores for DT traits (overall score and psychopathy), while no differences were found for MT or PA in both sexes. Conclusion: DT traits, high MT, and vigorous PA are interrelated. This pattern of results might explain why, for instance, successful professional athletes can at the same time be tough and ruthless

Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance

Purpose: Whole-body bone scintigraphy (WBS) is one of the most widely used modalities in diagnosing malignant bone diseases during the early stages. However, the procedure is time-consuming and requires vigour and experience. Moreover, interpretation of WBS scans in the early stages of the disorders might be challenging because the patterns often reflect normal appearance that is prone to subjective interpretation. To simplify the gruelling, subjective, and prone-to-error task of interpreting WBS scans, we developed deep learning (DL) models to automate two major analyses, namely (i) classification of scans into normal and abnormal and (ii) discrimination between malignant and non-neoplastic bone diseases, and compared their performance with human observers. Materials and Methods: After applying our exclusion criteria on 7188 patients from three different centers, 3772 and 2248 patients were enrolled for the first and second analyses, respectively. Data were split into two parts, including training and testing, while a fraction of training data were considered for validation. Ten different CNN models were applied to single- and dual-view input (posterior and anterior views) modes to find the optimal model for each analysis. In addition, three different methods, including squeeze-and-excitation (SE), spatial pyramid pooling (SPP), and attention-augmented (AA), were used to aggregate the features for dual-view input models. Model performance was reported through area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity and was compared with the DeLong test applied to ROC curves. The test dataset was evaluated by three nuclear medicine physicians (NMPs) with different levels of experience to compare the performance of AI and human observers. Results: DenseNet121_AA (DensNet121, with dual-view input aggregated by AA) and InceptionResNetV2_SPP achieved the highest performance (AUC = 0.72) for the first and second analyses, respectively. Moreover, on average, in the first analysis, Inception V3 and InceptionResNetV2 CNN models and dual-view input with AA aggregating method had superior performance. In addition, in the second analysis, DenseNet121 and InceptionResNetV2 as CNN methods and dual-view input with AA aggregating method achieved the best results. Conversely, the performance of AI models was significantly higher than human observers for the first analysis, whereas their performance was comparable in the second analysis, although the AI model assessed the scans in a drastically lower time. Conclusion: Using the models designed in this study, a positive step can be taken toward improving and optimizing WBS interpretation. By training DL models with larger and more diverse cohorts, AI could potentially be used to assist physicians in the assessment of WBS images. © 2023 The Author(s

Molecular weight effects on chain pull-out fracture of reinforced polymeric interfaces

Using Brownian dynamics, we simulate the fracture of polymer interfaces reinforced by diblock connector chains. We find that for short chains the interface fracture toughness depends linearly on the degree of polymerization $N$ of the connector chains, while for longer chains the dependence becomes $N^{3/2}$. Based on the geometry of initial chain configuration, we propose a scaling argument that accounts for both short and long chain limits and crossover between them.Comment: 5 pages, 3 figure

Abdominal surgical site infections: incidence and risk factors at an Iranian teaching hospital

BACKGROUND: Abdominal surgical site infections are among the most common complications of inpatient admissions and have serious consequences for outcomes and costs. Different risk factors may be involved, including age, sex, nutrition and immunity, prophylactic antibiotics, operation type and duration, type of shaving, and secondary infections. This study aimed to determine the risk factors affecting abdominal surgical site infections and their incidence at Imam Khomeini, a major referral teaching hospital in Iran. METHODS: Patients (n = 802) who had undergone abdominal surgery were studied and the relationships among variables were analyzed by Student's t and Chi-square tests. The subjects were followed for 30 days and by a 20-item questionnaire. Data were collected through pre- and post-operative examinations and telephone follow-ups. RESULTS: Of the 802 patients, 139 suffered from SSI (17.4%). In 40.8% of the cases, the wound was dirty infected. The average age for the patients was 46.7 years. The operations were elective in 75.7% of the cases and 24.7% were urgent. The average duration of the operation was 2.24 hours, the average duration of pre-operative hospital stay 4.31 days and the average length of (pre- and post-operation) hospital stay 11.2 days. Three quarters of the cases were shaved 12 hours before the operation. The increased operation time, increased bed stay, electivity of the operation, septicity of the wound, type of incision, the administration of prophylactic antibiotic, type of operation, background disease, and the increased time lapse between shaving and operation all significantly associated with SSI with a p-value less than 0.001. CONCLUSION: In view of the high rate of SSI reported here (17.4% compared with the 14% quoted in literature), this study suggests that by reducing the average operation time to less than 2 hours, the average preoperative stay to 4 days and the overall stay to less than 11 days, and approximating the timing of shaving to the operation and substituting cefazolin for cefaluthin when prophylactic antibiotic is to be administered, the SSI may be reduced to a more acceptable level

Robustness in Glyoxylate Bypass Regulation

The glyoxylate bypass allows Escherichia coli to grow on carbon sources with only two carbons by bypassing the loss of carbons as CO2 in the tricarboxylic acid cycle. The flux toward this bypass is regulated by the phosphorylation of the enzyme isocitrate dehydrogenase (IDH) by a bifunctional kinase–phosphatase called IDHKP. In this system, IDH activity has been found to be remarkably robust with respect to wide variations in the total IDH protein concentration. Here, we examine possible mechanisms to explain this robustness. Explanations in which IDHKP works simultaneously as a first-order kinase and as a zero-order phosphatase with a single IDH binding site are found to be inconsistent with robustness. Instead, we suggest a robust mechanism where both substrates bind the bifunctional enzyme to form a ternary complex

Snazer: the simulations and networks analyzer

<p>Abstract</p> <p>Background</p> <p>Networks are widely recognized as key determinants of structure and function in systems that span the biological, physical, and social sciences. They are static pictures of the interactions among the components of complex systems. Often, much effort is required to identify networks as part of particular patterns as well as to visualize and interpret them.</p> <p>From a pure dynamical perspective, simulation represents a relevant <it>way</it>-<it>out</it>. Many simulator tools capitalized on the "noisy" behavior of some systems and used formal models to represent cellular activities as temporal trajectories. Statistical methods have been applied to a fairly large number of replicated trajectories in order to infer knowledge.</p> <p>A tool which both graphically manipulates reactive models and deals with sets of simulation time-course data by aggregation, interpretation and statistical analysis is missing and could add value to simulators.</p> <p>Results</p> <p>We designed and implemented <it>Snazer</it>, the simulations and networks analyzer. Its goal is to aid the processes of visualizing and manipulating reactive models, as well as to share and interpret time-course data produced by stochastic simulators or by any other means.</p> <p>Conclusions</p> <p><it>Snazer </it>is a solid prototype that integrates biological network and simulation time-course data analysis techniques.</p

Transcription-replication conflicts: How they occur and how they are resolved

The frequent occurrence of transcription and DNA replication in cells results in many encounters, and thus conflicts, between the transcription and replication machineries. These conflicts constitute a major intrinsic source of genome instability, which is a hallmark of cancer cells. How the replication machinery progresses along a DNA molecule occupied by an RNA polymerase is an old question. Here we review recent data on the biological relevance of transcription-replication conflicts, and the factors and mechanisms that are involved in either preventing or resolving them, mainly in eukaryotes. On the basis of these data, we provide our current view of how transcription can generate obstacles to replication, including torsional stress and non-B DNA structures, and of the different cellular processes that have evolved to solve them