Browse Articles

Investigation of the temperature influence on the catalytic hydrogenation upgrading of bio-oil using industrial nickel based catalyst RZ409

Experimental study on dynamic response of existing tunnel lining structure by adjacent tunnel blasting load

A Mendelian randomization study of the gut microbiota and risk of knee osteoarthritis and the mediating role of immune cells

Accelerating RRT* convergence with novel nonuniform and uniform sampling approach

Abstract Path planning plays a crucial role in autonomous mobile robotics. Sampling-based path planners are widely and frequently employed to generate collision-free paths between a start and goal location. Due to its asymptotic optimality, the optimal rapidly-exploring random tree (RRT*) algorithm is the most widely used among these. However, its reliance on uniform sampling often results in slow convergence. To address this issue, this work proposes a novel hybrid sampling method called RRT*-NUS (nonuniform–uniform sampler), which combines both uniform and nonuniform sampling to improve exploration efficiency. The proposed RRT*-NUS method is evaluated against six baseline algorithms: RRT*, Informed RRT*, RRT*-N (normal sampling RRT*), GS-RRT* (goal-oriented sampling RRT*), DR-RRT* (directional random sampling RRT*), and hybrid-RRT* in three different 384*384 2D simulation scenarios. The numerical simulation results indicate that the proposed RRT*-NUS surpasses the baseline RRT* algorithms in terms of planning time and convergence. It outperforms RRT* by 67.5% and Hybrid RRT* by 54% in time performance. Additionally, it achieves a convergence rate of 0.41 units/s, which is 3× faster than RRT* and almost 2× faster than Hybrid RRT*.

High-performance PTFE composites from industrial scrap with enhanced strength and wear resistance

Abstract Due to the high cost of raw materials, this work aims to utilize polytetrafluoroethylene (PTFE) scrap generated from industrial waste to produce composites possessing superior properties for potential use in various industrial applications. In this respect, PTFE-based composites reinforced with mono- and hybrid granite and boron carbide (B4C) nanoparticles are produced using powder metallurgy (PM) technology. The sintered composites’ physical, mechanical, tribological, and thermal properties and the phase composition and microstructure were investigated using X-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM) techniques, respectively. The results indicated that the phase composition of the prepared composites did not change. Adding granite and/or B4C to the PTFE base increased the bulk density and the total porosity, while the relative density decreased. In addition, after adding 5 vol% granite/5 vol% B4C (PTFE6 sample), there was a clear improvement in mechanical properties, including microhardness, ultimate, and Young’s modulus, reaching 123.29%, 91.33%, and 74.17% compared with the unreinforced sample (PTFE0). Moreover, there was a noticeable improvement in the wear rate, fraction coefficient, and thermal expansion coefficient (CTE) value for the same sample, which decreased by approximately 37.17%, 36.50%, and 61.64%.

Influence of capping chemotherapy prescriptions on efficacy and tolerability in medium and high-risk early-stage breast cancer

Abstract Capping body surface area (BSA) at 2.0 m 2 is a common clinical practice. This empirical practice is intended to mitigate toxicities. In this context, the objective of this study was to investigate in curative situation whether capping chemotherapy prescriptions at 2.0 m 2 had an influence on the efficacy and tolerance of treatment in patients diagnosed with early-stage breast cancer. Data from patients with a body surface area (BSA) greater than 2.0 m² who received treatment for medium and high-risk early-stage breast cancer, either in (neo)adjuvant settings, from January 1, 2010, to December 31, 2018, were examined. Patients were divided into four categories based on the percentage of chemotherapy capping throughout the treatment duration: [90–100]: the reference group, representing fully capped chemotherapy with capping exceeding 90%; [50–90[: capped chemotherapy ranging from 50 to 90%; [10–50[: capped chemotherapy between 10% and 50%; and [0–10[: representing non-capped chemotherapy. A total of 130 patients were included in the analysis, with a median age at diagnosis of 57 years (Interquartile range (IQR): 48–63) and a mean BSA of 2.07 m². Chemotherapy was provided as an adjuvant treatment to 86.9% of the participants. Depending on the capping group, the hematological toxicities were almost similar in all groups whereas non-hematological toxicities were slightly higher in the capped group between [10–50[. Similarly, chemotherapy dose reduction was also higher in capped group between [10–50[in comparison with other groups. A significant difference was observed in non-hematological toxicities of grade ≥ 2 between the reference group [90–100] and the capping group [10–50[(OR 3.59; 95% CI [1.26–10.22], p  = 0.017). Prospective studies are needed to support the practice of capping, particularly in curative situations.

Long term efficacy and safety of MICT of cardiopulmonary function in patients after TAVR extended follow up of ENERGY study

Delays reduce culprit-presence detection but do not affect guessing-based selection in response to lineups

Abstract Police lineups are conducted with varying delays between the crime and the lineup. Crime-to-lineup delays may adversely affect the detection of the presence and absence of the culprit in the lineup and may potentially affect guessing-based selection. In the present study we examined how these processes change across four crime-to-lineup delays. Participants viewed a staged-crime video and then completed simultaneous photo lineups after no delay or after a delay of one day, one week or one month. The results showed a significant decline in the probability of culprit-presence detection. The form of the decline is best described by a power function with the most rapid decline occurring at short crime-to-lineup delays. Eyewitnesses did not compensate the decline in culprit-presence detection by increasing guessing-based selection, as demonstrated by the fact that the probability of guessing-based selection remained constant across crime-to-lineup delays. The findings underscore the critical importance of conducting lineups as soon as possible after a crime to maximize the probability of memory-based-culprit detection.

Women’s empowerment and nutritional outcomes in India

Multilingual sentiment analysis in restaurant reviews using aspect focused learning

Mathematical methods to model double strip cylindrical fruit preserver using water as heat absorber

Comparison of Niosomal formulation of citrus limon peel extracts and doxorubicin effects on MCF-7 and MDA-MB-231 human breast cancer cells

Real-world study of first-line immunotherapy combined with chemoradiotherapy in esophageal squamous cell carcinoma

Abstract This study aimed to assess the effectiveness, safety, and recurrence patterns of first-line immunotherapy combined with chemoradiotherapy in esophageal squamous cell carcinoma (ESCC) patients. A retrospective analysis of 79 eligible ESCC patients was conducted. Primary outcomes included overall survival (OS) and progression-free survival (PFS), with secondary outcomes being objective response rate (ORR), disease control rate (DOR), treatment-related adverse events (trAEs), and treatment failure patterns. The median follow-up was 29.4 months, with median OS unreached and median PFS of 14.6 months (95% CI 10.7–18.5). ORR was 82.3%, and DOR was 96.2%. Factors affecting OS were clinical stage, immunotherapy cycles, immunotherapy and chemoradiotherapy sequence, radiation coverage, mid-treatment lymphocyte count, and short-term efficacy (HR = 2.254, 0.374, 2.653, 2.957, 2.309, 2.789; P  = 0.030, 0.019, 0.009, 0.004, 0.001, 0.014). Factors impacting PFS were clinical stage, immunotherapy and chemoradiotherapy, and post-treatment lymphocyte count (HR = 2.135, 2.048, 1.911; P  = 0.007, 0.010, 0.001). Among the cohort, 46.8% experienced treatment failure, with 33 receiving second-line treatment, resulting in a median OS of 14.17 months (95% CI 7.303–21.037) and 1- and 2-year OS rates of 56.7% and 24.3%. Notably, 36.7% experienced grade ≥ 2 trAEs, bone marrow suppression most commonly happened, and 5.1% developed esophageal fistulas. Immunotherapy combined with chemoradiotherapy demonstrates strong anti-tumor activity and tolerability in ESCC patients, The radiation for all leisions, sequential immunotherapy combined with chemoradiotherapy, and higher levels of lymph node cell counts are associated with a better prognosis. Large-scale randomized controlled trials are needed for further validation.

DFT investigation of therapeutic potential of benzimidazolone capsule as a drug delivery vehicle for anticancer drug

A truth inference scheme for crowdsourcing using NLP and swin transformers

Abstract Crowdsourcing has become a prevalent method for data collection across various domains, offering a scalable and cost-effective solution. However, ensuring the reliability of crowdsourced data remains a significant challenge due to the varying expertise of contributors and the complexity of tasks. Truth inference aims to derive high-quality and accurate answers from heterogeneous and noisy responses for crowdsourcing tasks. In order to address these challenges, we propose a truth inference model that integrates Natural Language Processing with transfer learning using Swin transformers. Unlike traditional transformer architectures, the Swin transformer employs a shifted windowing technique that effectively captures both local and global contextual features in textual data. This approach helps to generate more accurate embedding representations, specifically fine-tuned for nuances of crowdsourced tasks. By incorporating the Swin transformer, our model dynamically refines contributor reliability scores and task difficulty estimates, resulting in a more accurate truth inference. Experimental evaluations on multiple crowdsourcing datasets demonstrate that our approach consistently outperforms state-of-the-art methods in accuracy, scalability, and robustness, particularly under noisy and complex task conditions.

Enhancing 1D ionic conductivity in lithium manganese iron phosphate with low-energy optical phonons

Rendering algorithm for 3D model of goods in power warehouse based on linear interpolation and 2D texture mapping

Elevated plasma level of PAI-1 is associated with severe COVID-19

Abstract Evidence indicates endothelial dysfunction in severe coronavirus disease (COVID-19). Plasminogen activator inhibitor-1 (PAI-1) is a marker of endothelial injury and could be a prognostic marker for COVID-19-related hospitalization and outcomes. The association between PAI-1 levels and the severity of COVID-19-related outcomes was investigated in this study. This single-center retrospective chart review included 113 hospitalized adults from 6.29.2020 to 8.1.2021 with confirmed COVID-19. Plasma PAI-1 levels were measured by ELISA. The primary endpoint was the difference in PAI-1 levels between severe and non-severe COVID-19 groups. Severe COVID-19 was defined as the need for ventilator assistance and/or death. The mean age was 60.78 (22 to 103, SD ± 16.93), and 52 were female and 63 male. There was a significant positive correlation between age and PAI-1 levels. PAI-1 levels were significantly higher in patients with hyperlipidemia. PAI-1 levels in patients requiring ventilator assistance and who died versus those who did not are significantly higher. High plasma PAI-1 levels are associated with severe COVID-19, defined as requiring ventilator use and/or death. Thus, PAI-1 may be a biological marker for severe COVID-19.

Non-linear relationship between serum iron levels and 28-day mortality in sepsis patients: a retrospective study

A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature

Abstract This research paper delves into the realm of quantum machine learning (QML) by conducting a comprehensive study on time-series data. The primary objective is to compare the results and time complexity of classical machine learning algorithms on traditional hardware to their quantum counterparts on quantum computers. As the amount and complexity of time-series data in numerous fields continues to expand, the investigation of advanced computational models becomes critical for efficient analysis and prediction. We employ a time-series dataset that include temperature records from different nations throughout the world spanning the previous half of the century. The study compares the performance of classical machine learning algorithms to quantum algorithms, which use the concepts of superposition and entanglement to handle subtle temporal patterns in time-series data. This study attempts to reveal the different benefits and drawbacks of quantum machine learning in the time-series domain through rigorous empirical analysis. The findings of this study not only help to comprehend the applicability of quantum algorithms in real-world contexts, but they also open the way for future advances in utilizing quantum computing for increased time-series analysis and prediction. This study’s findings could have ramifications in industries ranging from finance to healthcare, where precise forecasting using time-series data is critical for informed decision-making.