11351 research outputs found
Sort by
Influence Of Thermal Gradient On Mechanical Properties In Fused Deposition Modelling (Fdm) Additive Manufacturing
Get PDFFused Deposition Modeling (FDM) is a technique that constructs functional parts by extruding thermoplastic filaments layer by layer. The interplay between thermal dynamics and their subsequent effects on mechanical properties remains a field necessitating further exploration. This study introduced ongoing research focused on unraveling the connection between the thermal gradients in the FDM printing process and the resulting mechanical attributes. The primary objective was to increase quality and functionality of 3D printed components. In pursuit of this objective, a series of carefully planned experiments were devised to systematically vary FDM parameters, including print speed, layer thickness, and nozzle temperature. Through parameter manipulation, a spectrum of thermal gradients during the printing procedure we created. To assess the mechanical properties, a commercial FDM 3D printer was used to fabricate tensile specimens conforming to the ASTM D638 standard, a test method for quantifying the tensile properties of plastics. To capture the thermal gradient occurring during printing process, a high-resolution FLIR thermal camera was positioned closely to observe the area where freshly molten material was deposited to obtain temperature measurements. After the sample was printed, it was mechanically tested using Instron 5582 for tensile testing following the ASTM D638 standard, entailing the application of a uniaxial load until the specimen reached the point of fracture. Mechanical properties such as yield strength, ultimate tensile strength, and elongation at break, which offered fundamental insights into the material\u27s strength, ductility, and performance under tensile stress. The experimental results obtained through these tests were analyzed to unveil potential correlations between the thermal gradient and mechanical properties. Undersatnding the interrelationship, gained a deeper understanding of the underlying thermal relationship in the FDM 3D printing process and their impact on the mechanical behavior of printed objects. The findings derived from this research contributed to comprehension of thermal effects in FDM 3D printing and their ramifications for mechanical performance. These insights hold promise for optimizing the printing process, therefore elevating the quality and functionality of 3D-printed components. Industries reliant on FDM technology, including aerospace, automotive, and medical sectors, stand to gain from improved process control, ultimately enhancing part reliability and performance.
Index Terms – 3D printing, astm d638 standard, correlation analysis, fused deposition modeling (fdm), mechanical properties, parameter manipulation, performance under tensile stress, thermal dynamics, thermal gradients, yield strengt
Impact of Staging Concordance and Downstaging After Neoadjuvant Therapy on Survival Following Resection of Intrahepatic Cholangiocarcinoma: A Bayesian Analysis
No full textIntroduction: Concordance between clinical and pathological staging, as well as the overall survival (OS) benefit associated with neoadjuvant therapy (NAT) remain ill-defined. We sought to determine the impact of staging accuracy and NAT downstaging on OS among patients with intrahepatic cholangiocarcinoma (ICC). Methods: Patients treated for ICC between 2010 and 2018 were identified using the National Cancer Database. A Bayesian approach was applied to estimate NAT downstaging. OS was assessed relative to staging concordant/overstaged disease treated with upfront surgery, understaged disease treated with upfront surgery, no downstaging, and downstaging after NAT. Results: Among 3384 patients, 2904 (85.8%) underwent upfront surgery, whereas 480 (14.2%) received NAT and 85/480 (18.4%) were downstaged. Patients with cT3 (odds ratio [OR] 2.12, 95% confidence interval [CI] 1.34–3.34), cN1 (OR 2.47, 95% CI 1.71–3.58) disease, and patients treated at high-volume facilities (OR 1.63, 95% CI 1.13–2.36) were more likely to receive NAT (all p \u3c 0.05). Median OS was 40.1 months (95% CI 38.6–43.4). Patients with cT1-2N1 (NAT: 31.5 months vs. upfront surgery: 22.4 months; p = 0.04) and cT3-4N1 (NAT: 27.8 months vs. upfront surgery: 14.4 months; p = 0.01) disease benefited most from NAT. NAT downstaging decreased the risk of death among patients with cT3-4N1 disease (hazard ratio [HR] 0.35, 95% CI 0.15–0.82). In contrast, understaged patients with cT1-2N0/X (HR 2.15, 95% CI 1.83–2.53) and cT3-4N0/X (HR 1.71, 95% CI 1.06–2.74) disease treated with upfront surgery had increased risk of death. Conclusions: Patients with N1 ICC treated with NAT demonstrated improved OS compared with upfront surgery. Downstaging secondary to NAT conferred survival benefits among patients with cT3-4N1 versus upfront surgery. NAT should be considered in ICC patients with advanced T disease and/or nodal metastases
Disparities in NCCN Guideline-Compliant Care for Patients with Early-Stage Pancreatic Adenocarcinoma at Minority-Serving versus Non-Minority-Serving Hospitals
No full textBackground: Racial/ethnic disparities in pancreatic adenocarcinoma (PDAC) outcomes may relate to receipt of National Comprehensive Cancer Network (NCCN) guideline-compliant care. We assessed the association between treatment at minority-serving hospitals (MSH) and receipt of NCCN-compliant care. Patients and Methods: Patients who underwent resection of early-stage PDAC between 2006 and 2019 were identified from the National Cancer Database (NCDB). MSH was defined as the top decile of facilities treating minority ethnicities (Black and/or Hispanic). Factors associated with receipt of NCCN-compliant care and its impact on overall survival (OS) were assessed. Results: Among 44,873 patients who underwent resection of PDAC, most were treated at non-MSH (n = 42,571, 94.9%), while a smaller subset were treated at MSH (n = 2302, 5.1%). Patients treated at MSH were more likely to be at a younger median age (MSH 66 years versus non-MSH 67 years), Black or Hispanic (MSH 58.4% versus non-MSH 12.0%), and not insured (MSH 7.8% versus non-MSH 1.6%). While 71.7% (n = 31,182) of patients were compliant with NCCN care, guideline-compliant care was lower at MSH (MSH 62.5% versus non-MSH 72.2%). On multivariable analysis, receiving care at MSH was associated with not receiving guideline-compliant care [odds ratio (OR) 0.63, 95% confidence interval (CI) 0.53–0.74]. At non-MSH, non-white patients had lower odds of receiving guideline-compliant PDCA care (OR 0.85, 95% CI 0.78–0.91). Failure to comply was associated with worse overall survival (OS) [hazard ratio (HR) 1.50, 95% CI 1.46–1.54, all p \u3c 0.001]. Conclusions: Patients with PDAC treated at MSH and minorities treated at non-MSH were less likely to receive NCCN-compliant care. Failure to comply with guideline-based PDAC treatment was associated with worse OS
Racial Segregation Among Patients with Cholangiocarcinoma—Impact on Diagnosis, Treatment, and Outcomes
No full textBackground: Racial segregation, an effect of historical marginalization, may impact cancer care and outcomes. We sought to examine the impact of racial segregation on the diagnosis, treatment, and outcomes of patients with cholangiocarcinoma (CCA). Patients and Methods: Data on Black and White patients with CCA were obtained from the linked SEER-Medicare database (2004–2015) and 2010 Census data. The index of dissimilarity (IoD), a validated measure of segregation, was used to assess Black–White disparities in stage disease presentation, surgery for localized disease, and cancer-specific mortality. Multivariable Poisson regression was performed, and competing risk regression analysis was used to determine cancer-specific survival. Results: Among 7480 patients with CCA, 90.2% (n = 6748) were White and 9.8% (n = 732) were Black. Overall, Black patients were more likely to reside in segregated areas compared with White patients (IoD, 0.42 vs. 0.38; p \u3c 0.05). On multivariable Poisson regression, Black patients were more likely to present with advanced-stage disease [relative risk (RR) 1.17, 95% confidence interval (CI) 1.08–1.27; p \u3c 0.001] and were less likely to undergo surgery for localized disease (RR 0.62, 95% CI 0.51–0.76; p \u3c 0.001). Black patients also had worse cancer-specific survival (CSS) compared with White patients (median CSS: 4 vs. 8 months; p \u3c 0.01). Black patients living in the highest areas of segregation had 40% increased hazard of mortality versus White patients residing in the lowest IoD areas (hazard ratio 1.40, 95% CI 1.10–1.80; p \u3c 0.01). Conclusion: Racial segregation, as a proxy for structural racism, had a marked effect on Black–White disparities among patients with CCA
Cell Type Classification Via Deep Learning On Single-Cell Gene Expression Data
Get PDFSingle-cell sequencing is a recently advanced revolutionary technology which enables researchers to obtain genomic, transcriptomic, or multi-omics information through gene expression analysis. It gives the advantage of analyzing highly heterogenous cell type information compared to traditional sequencing methods, which is gaining popularity in the biomedical area. Moreover, this analysis can help for early diagnosis and drug development of tumor cells, and cancer cell types. In the workflow of gene expression data profiling, identification of the cell types is an important task, but it faces many challenges like the curse of dimensionality, sparsity, batch effect, and overfitting. However, these challenges can be overcome by performing a feature selection technique which selects more relevant features by reducing feature dimensions. In this research work, recurrent neural network-based feature selection model is proposed to extract relevant features from high dimensional, and low sample size data. Moreover, a deep learning-based gene embedding model is also proposed to reduce data sparsity of single-cell data for cell type identification. The proposed frameworks have been implemented with different architectures of recurrent neural networks, and demonstrated via real-world micro-array datasets and single-cell RNA-seq data and observed that the proposed models perform better than other feature selection models. A semi-supervised model is also implemented using the same workflow of gene embedding concept since labeling data is very cumbersome, time consuming, and requires manual effort and expertise in the field. Therefore, different ratios of labeled data are used in the experiment to validate the concept. Experimental results show that the proposed semi-supervised approach represents very encouraging performance even though a limited number of labeled data is used via the gene embedding concept. In addition, graph attention based autoencoder model has also been studied to learn the latent features by incorporating prior knowledge with gene expression data for cell type classification.
Index Terms — Single-Cell Gene Expression Data, Gene Embedding, Semi-Supervised model, Incorporate Prior Knowledge, Gene-gene Interaction Network, Deep Learning, Graph Auto Encode
Motivating Factors For Murder With Rape Of Minor Girls In India: A Study Using Systematic Content Analysis
Get PDFGender-related homicides of young women and minor girls have reached alarming proportions in India. The 2020 crime statistics reported 1,582 homicides of children, with 119 (7.5%) minor victims raped and murdered. However, no empirical studies address the motives nor offer incident-based details of the murders and rape/gang-rape of minors. The current study used systematic content analysis of news media sources published in India. The search included five primary print media—The Times of India, Hindustan Times, The Hindu, The Indian Express, and The New Indian Express, published in English and reported from 2017 to 2018. The search also included digital news sources from newswire, web based, and other online news sources. Specifically, the study (1) compared official statistics from the National Crime Records Bureau (NCRB) with the systematic search results on murder with rape/gang-rape incidents to identify trends and state and regional variations, (2) analyzed the bias-motivating factors such as social, political, religious, and caste-based discrimination, (3) assessed the trend between non-bias motivating factors such as revenge/anger, sexual sadism, or opportunity with bias factors, (4) reviewed the criminal/juvenile justice systems’ response, and (5) assessed public reaction to these incidents.
The results showed discrepancies and variations in reported incidents between NCRB and the systematic search data. In addition, social bias was noted as the most common bias factor, followed by political, religious, and caste biases. Among the non-bias motivating factors, opportunity was the most common factor, followed by revenge/anger and sexual sadism. Along with motivating factors, the criminal justice agencies failed to act. Instead, they blamed the victim, destroyed the evidence, or supported the accused. The public reactions also varied widely from public anger, protests, and rallies in support of the victims, mob violence against the perpetrators, or supporting the perpetrator to victim-blaming. The research provides a broader understanding of motivations for committing child rapes and murders in India. Additionally, this research might assist in discussing patriarchal views, educating youth on social justice advocacy, and organizing community initiatives to protect the victims. Finally, the outcomes would guide Indian juvenile and criminal justice reforms.
Keywords: motivating factor, murder, rape/gang-rape, girls, India, content analysi
(R1965) Some More Properties on Generalized Double Fuzzy Z Alpha Open Sets
Get PDFIn this paper, a new class of sets termed as double fuzzy generalized Z alpha closed sets and double fuzzy generalized Z alpha open sets are introduced with the help of double fuzzy Z alpha open and double fuzzy Z alpha closed sets, respectively. Using these sets double fuzzy generalized Z alpha border, double fuzzy generalized Z alpha exterior and double fuzzy generalized Z alpha frontier of a fuzzy set in double fuzzy topological spaces are introduced. Also, the topological properties and characterizations of these sets and operators are studied. Furthermore, suitable examples have been provided to illustrate the theory
(R1987) Hermite Wavelets Method for System of Linear Differential Equations
Get PDFIn this research paper, we present an accurate technique for solving the system of linear differential equations. Such equations often arise as a result of modeling in many systems and applications of engineering and science. The proposed scheme is based on Hermite wavelets basis functions and operational matrices of integration. The demonstrated scheme is simple as it converts the problem into algebraic matrix equation. To validate the applicability and efficacy of the developed scheme, some illustrative examples are also considered. The results so obtained with the help of the present proposed numerical technique by using Hermite wavelets are observed to be more accurate and favorable in comparison with those by using Haar wavelets
Digital Twin Modeling And Optimal Control Of Soft-Bodied Robotics Using Reservoir Computing
Get PDFSoft-bodied robots have become increasingly popular due to their ability to per- form tasks that are difficult or impossible for traditional rigid robots. However, accurately modeling and controlling the movement and behavior of soft robots are very challenging due to their complex and dynamic nature. In recent years, Reservoir Computing has emerged as a promising approach to modeling and controlling soft robots. In this thesis, reservoir computing was used to create a digital twin of soft-bodied robots. Specifically, a digital twin of a spring-mass system was created using echo state network, a popular reservoir computing model. Furthermore, an optimal controller was trained using reservoir computing to drive the spring-mass system to follow a desired trajectory. Extensive simulations were carried out to validate the proposed methods. The results demonstrate the effectiveness of the proposed approach. For example, the digital twin model achieved 2% MAPE and the optimal controller achieved 8.7% MAPE for a 20-node 54-spring system.
Index Terms: Deep Learning, Digital Twin Modeling, Echo State Network, Optimal control, Soft Bodied Robotics, Time series predictio