bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of Numerical Methods for Geometric Warpage Compensation
Authors: Tillmann Steffen , Basermann Stefan , Elgeti Stefanie 
Published: February 2024

Abstract
In injection molding processes, shrinkage and warpage often cause deviations in the shape of produced parts compared to the cavity shape. These deviations arise due to uneven cooling and internal stresses within the part. One approach to mitigate these effects is by adjusting the cavity shape to anticipate the deformation. This can be achieved by simulating the expected deformation using suitable models, which then inform the optimization of the cavity shape for injection molded parts with minimal deformation. \\In this study, we evaluate various numerical algorithms from existing literature to identify the optimal cavity shape. Each method is briefly outlined regarding how it adapts the geometry, and we discuss their respective strengths and weaknesses for different scenarios. We conduct comparisons using 3D geometries of varying complexity. Our findings demonstrate that, for geometric warpage compensation, the node-based reverse geometry method yields the least warpage and is computationally cost-effective. Furthermore, it is straightforward to implement and consistently performs well across different geometries.
Association of Preoperative Sarcopenia With the Long-term Prognosis of Patients With Bladder Cancer Undergoing Radical Cystectomy
Authors: Lee Sangmin , Yoon Youngjoon , Suh Jungyo , You Dalsan , Hong Bumsik , Hong Jun Hyuk , Ahn Hanjong , Jeong In Gab , Lim Bumjin 
Published: February 2024

Abstract
Purpose
This retrospective study aimed to assess the correlation between preoperative sarcopenia and long-term oncologic outcomes in patients undergoing radical cystectomy for bladder cancer.
Methods
We included 528 patients who underwent radical cystectomy for bladder cancer between 2000 and 2010 at Asan Medical Center, Seoul, Korea. Preoperative skeletal muscle mass was quantified by analyzing computed tomography images at the third lumbar vertebra. Sarcopenia was defined based on the skeletal muscle index. We evaluated various clinical and pathological factors to analyze the association between sarcopenia and long-term oncologic outcomes.
Results
The median follow-up time was 104 months. Sarcopenia was identified in 37.9% of the patients. Although no significant differences were observed in traditional pathological factors between the sarcopenic and non-sarcopenic groups, sarcopenia was significantly associated with worse oncologic outcomes. Compared to the non-sarcopenic groups, the sarcopenic group had lower overall survival rates (52.0% vs. 67.1% at 5 years, 35.5% vs. 52.7% at 10 years) and higher cancer-specific mortality (63.3% vs. 74.3% at 5 years, 50.7% vs. 67.4% at 10 years). Multivariable Cox regression analysis demonstrated that sarcopenia was an independent predictor of cancer-specific survival (hazard ratio: 1.49, 95% confidence interval: 1.11–2.01, p = 0.008), alongside body mass index, tumor stage, lymph node metastasis, and lymphovascular invasion.
Conclusion
Sarcopenia was significantly associated with poor cancer-specific survival in patients undergoing radical cystectomy for bladder cancer. Detecting sarcopenia may assist in preoperative risk stratification and long-term management after radical cystectomy.
Gender Classification Using a Convolutional Neural Network (CNN)
Authors: Cherukuri Vyshnavi , Nookala SaiHomitha , Balabhadra Vasavi , Mareedu Bhavana , Bulla Suneetha 
Published: February 2024

Abstract
The purpose of this paper is to demonstrate an innovative convolutional neural network (also known as CNN) methodology for real-time categorization of gender via face photos. The suggested CNN architecture boasts much reduced computational complexity than the current methodologies used in pattern recognition applications. By combining convolutional and subsampling layers, the overall processing layer count is minimised to four. Notably, using cross-correlation versus standard convolution tends to alleviate computing strain. The association is programmed using extended worldwide acquisition frequencies and a second-order backpropagation learning algorithmic framework. The demonstrated CNN approach has been examined using two freely downloadable facial statistics, SUMS and AT&T, with classification accuracies of 99.38% and 98.75%, respectively. Furthermore, the neural network's algorithm demonstrates exceptional efficiency by analysing and categorising a 32 by 32-pixel face picture in just 0.27 milliseconds, resulting in an outstanding consumption of over 3700 images per second. The successful performance of the proposed CNN methodology is further demonstrated by its speedy convergence throughout the training process, which requires less than 20 epochs. The results were produced to showcase the suggested CNN's outstanding accuracy in accurately categorising data, launching it as a realistic and effective real-time identification of the gender system.
Volume-adaptive SPH simulation of structural damage subjected to underwater contact explosion
Authors: DONG XIANGWEI , Feng Long 
Published: February 2024

Abstract
Underwater explosions can generate substantial dynamic loads, leading to damage or failure of solid structures such as submarine pipelines. This process involves the interaction of high-pressure explosion products, water, and solid structures, characterized by transience, multi-phase interaction, and large deformations. In this study, a Lagrange mesh-free method called Smoothed Particle Hydrodynamics (SPH) is employed to establish a fluid-solid interaction (FSI) model for underwater contact explosions. The SPH discrete equations of governing equations of continuum media including fluid and solid are constructed as anti-symmetric forms based on the particle approximation technique and kernel gradient correction scheme. The equation of state is presented to describe the material response in strong interactions for the explosive, water, and solid, respectively. To simulate solid plasticity, the Johnson-Cook constitutive models are integrated into the SPH procedure to capture the behavior of large deformation and damage of metal structures. To address the issue of drastic changes in particle spacing caused by suddenly expanding gas, a modified particle regeneration technique (M-PRT) is proposed to refresh SPH particles in the gas domain according to the volume change rate. The first-order Moving Least Squares (MLS) approach is used to update the variables of refreshed particles, thus the linear variation of field variables is reproduced. The accuracy of the model is verified through several examples, including free-field underwater explosions, near-wall underwater explosions, and underwater contact explosions.
Evaluating Frontoparietal Network Topography for Diagnostic Markers of Alzheimer’s Disease
Authors: Rogers Bayard 
Published: February 2024

Abstract
Numerous prospective biomarkers are being studied for their ability to diagnose various stages of Alzheimer’s Disease (AD). High-density electroencephalogram (EEG) methods show promise as an accurate, economical, non-invasive approach to measuring the electrical potentials of brains associated with AD. These event-related potentials (ERPs) may serve as clinically useful biomarkers of AD. Through analysis of secondary data, the present study examined the performance and distribution of N4/P6 ERPs across the frontoparietal network (FPN) using EEG topographic mapping. ERP measures and memory as a function of reaction time (RT) were compared between a group of (N = 63) mild untreated AD patients and a control group of (N = 73) healthy age-matched adults. A concurrent cross-modal associative memory test and 128-channel high-density EEG facilitated data collection. By targeting select frontal and parietal EEG reference channels based on N4/P6 component time windows and positivity; our findings demonstrate statistically significant group variations between controls and patients in N4/P6 peak amplitudes and latencies during cross-modal testing, though there was no interaction effect. Our results also support that the N4 ERP might be stronger than its P6 counterpart as a possible candidate biomarker. We conclude by visually mapping FPN integration existent in healthy controls, yet absent in AD patients during cross-modal memory tasks. The implications and limitations of these findings are discussed, as are foundations for future research in exploring processes and strategies that lead to identifying clinically useful biomarkers for the detection and treatment of AD.
Electrochemical Deposition of Colored Ni/Cu2O Bilayer Films on Large Area Plastic Substrates for Decorative Applications
Authors: Can Suleyman , Colak Alime , Balci Huseyin , Kuru Cihan 
Published: February 2024

Abstract

In this study, colored Ni/Cu2O bilayer films were electrochemically deposited on large area automobile plastic parts (7x13 cm) for decorative applications. Such coatings may become an alternative to ubiquitously used Cr coatings for plastics. Purple, dark cyan and gold colors with good homogeneity and high brightness could be produced by tuning the deposition time of the Cu2O layer. Coloration of the bilayer film can be ascribed to interference of light through the metal/dielectric layer, where the thickness of the dielectric layer controls the wavelength of the reflected light. Cu2O film has a compact structure and low roughness, which is essential to obtain uniform colors. Moreover, the Ni/Cu2O bilayer film passed the cross cut adhesion test performed according to ISO 2409 standard. This process is industrially viable and enables the large scale production of such decorative coatings.
Strain-based approach to characterize mode I crack propagation in Norway Spruce directly from optical data
Authors: Kunecký Jiří , Hataj Martin , Jochman Jan , Pošta Jan , Kloiber Michal , Sebera Václav 
Published: February 2024

Abstract
The paper focuses on assessment and utilization of strain-based criterion obtained using the digital image correlation in characterization of fracture behavior of Norway spruce wood. The study employed a single-edge notched beam loaded in three-point bending (SEN-TPB) to examine mode I at three anatomical directions of crack propagation (radial, tangential, tangential-radial - R, T and TR). The criterion is evaluated at the maximal load (Fmax), where the compliance-based beam method (CBBM) provides critical strain energy (Gc), which ensures the proper criteria representing equivalent crack length growth is described. The novel approach also enables one to determine the fracture process zone (FPZ) length using an algorithm which finds the onset of the nonlinear region. Uniqueness of the approach lies in processing a big set of optical data and simultaneous tracking of crack length on both sides of medium-size specimens. Results indicate that crack length is dependent on the anatomical direction, for instance in T direction the criterion ε1crit is 2.5e-3 producing crack length equal to ac =23.9 mm, whilst in R direction, the ε1crit is least and equals 1.3e-3 producing crack length of 22.1 mm. The highest ε1crit is attained in TR (on average ε1crit = 3.4e-3) and distance from the place where the crack started is 19.4 mm. Size of the non-linear region here attributed to FPZ length reaches the value of 38.4 mm in T, 30.1 mm in R and 36.3 mm in TR directions, respectively. The study presents a novel approach in characterization of fracture properties by coupling optical and energetical data and may find its usage in evaluation of other fracture modes.
Incorporating movement behavior into connectivity assessments
Authors: Schumaker Nathan H. 
Published: February 2024

Abstract
Context
The number of publications that evaluate or utilize landscape connectivity has grown dramatically in recent years. In contrast, the biological realism and defensibility of common connectivity assessments has advanced slowly.
Objectives
I introduce a flexible methodology for evaluating landscape connectivity that accounts for potentially complex movement behavior and nuanced species-landscape interactions.
Methods
Making use of a forested landscape map, I develop the concepts and mechanics behind my connectivity assessment tools. I then describe a case study involving the Fender’s blue butterfly, and utilize this example to demonstrate the character and utility of my methods.
Results
My methods are able to identify clusters of connected resource patches, quantify and visualize movement rates between these patches, and identify connectivity-related opportunities and vulnerabilities. My results include an emergent dispersal kernel that captures the influence of movement behavior on connectivity.
Conclusions
The methods I introduce are capable of generating detailed yet practical connectivity analyses that can incorporate considerable biological and behavioral realism. My approach is straightforward, simple to implement, and the requisite data can be modest. Conclusions drawn using my methods will help identify limitations to connectivity analyses developed using circuit models.
Prognostic and predictive value of tertiary lymphoid structures in TURBT materials; Should it be seated in routine pathological examination and can it be used in deciding on the treatment method?
Authors: Yilmaz Fatih , Sagir Suleyman 
Published: February 2024

Abstract
Purpose: This study aims to reveal the importance of tertiary lymphoid structures (TLS) in TURBT materials with a practical and applicable method in which the effect of a certain threshold value on survival and treatment response can be implicated.
Methods: TURBT materials that had not previously received any treatment (chemotherapy, radiotherapy, or immunotherapy) and were diagnosed for the first time at Mardin Training and Research Hospital between 2014 and 2022 were included in the study. The maximum number of TLS per 4x magnification field (Field diameter: 4,5 mm) was recorded. Grouping and statistical analysis of the TLS number were performed using threshold values of “≥1”, “≥2” and “≥3”.
Results
TLSs were more frequently found in high-grade tumors (p=0,008) and showed a strong association with stage progression (p<0,001). It was also significantly associated with many adverse histopathological parameters. Conversely, high TLS (≥1, ≥2, and ≥3) appeared to be associated with fewer recurrences (p=0,032, p=0,001, and p=0,018, respectively), and cases with higher TLS showed longer recurrence-free survival (p=0,089, p=0,023, p=0,037, respectively). TLS≥3 was found to be an independent parameter that was negatively associated with RFS (p=0,019, HR=0,401) and multifocality was found to be an independent risk factor for RFS (p=0,023, HR=2,302).
Conclusion
This study is the first to demonstrate the relationship between the presence and specific thresholds of TLS in TURBT materials with prognostic parameters. Including this information in the routine pathological examination of TURBT materials will allow a more accurate approach to treatment and follow-up, especially in patients with NMIBC.
Diabetes knowledge, attitudes and practices among Chinese primary care physicians: A cross-sectional study
Authors: Pi Linhua , He BinBin , Fei Dongxue , Shi Xiejie , Zhou Zhiguang 
Published: February 2024

Abstract

Background:
Type 2 diabetes mellitus (T2DM) is a public health crisis that requires adequate knowledge, attitudes, and practices (KAP) by health care providers to prevent or delay the progression of the disease. This study aimed to assess the KAP regarding T2DM among primary care providers (PCPs) in Central China.
Methods:
This multicenter cross-sectional study was conducted among 971 PCPs using self-administered KAP questionnaires. Questionnaires were designed to evaluate KAP regarding T2DM among PCPs, and was measured with SPSS software.
Results:
A total of 971 PCPs with a mean age of 44.0 ± 10.2 years were evaluated. 620 (63.9%) PCPs worked at village clinic and 605 (62.3%) PCPs have been working more than 20 years. Only 26.3% of the respondents participated in CME programs regarding diabetes in the past year due to Covid-19 pandemic. Overall, despite positive attitudes toward diabetes, there were substantial gaps in knowledge and practices. The PCPs scored 7.25 out of 14 points on the knowledge subscales, 7.13 out of 8 on the attitude subscales, and 4.85 out of 11 on the practice subscales. Gender, age, practice setting, professional titles, duration of practice and CME attendance were significant predictors of knowledge; Age, practice setting and duration of practice were significant predictors of attitudes; and family history of diabetes affected PCP practices.
Conclusions:
Despite positive attitudes toward diabetes, there were substantial gaps in knowledge and practices. These findings call for action from relevant health authorities and policy makers to improve PCPs' KAP regarding diabetes in Central China.
The comparison of handpiece anti-surge system and main body in cataract surgery
Authors: Kim Hyo Won , Kim Inkee , Kim Hyun Seung , Kim Eun Chul 
Published: February 2024

Abstract
Abstract
Background: To compare the efficacy of the Centrion handpiece anti-surge system (Active Sentry®) and main body, and evaluate the frequency of the anti-surge system in the Centrion handpiece in cataract surgery according to the grade of nucleosclerosis.
Methods: 600 eyes of 415 patients who underwent cataract surgery was retrospectively enrolled. Intraoperative parameters included phacoemulsification time (seconds), cumulative dissipated energy (CDE), and balanced salt solution (BSS) use (ml). Clinical measurements were made preoperatively and at one day, one month, two, and three months postoperatively, including the best corrected visual acuity (BCVA), and the corneal endothelial cell (CEC) count in the handpiece group (N=300) and main body group (N=300). Within the anti-surge group, the number of anti-surge system activated was collected. Cataract nucleosclerosis grading was done by the Lens Opacities Classification System III.
Results: Anti-surge (times), phacoemulsification time, CDE, and BSS use (ml) significantly increased with increasing nucleosclerosis grades and in patients with zonule weakness, poor mydriasis, and pseudoexfoliation syndrome in the handpiece group, respectively (p < 0.05). Phacoemulsification time, CDE, and BSS use of the handpiece group were significantly lower than those of the main body group (p < 0.05). Phacoemulsification time, CDE, and CEC loss of handpiece group were significantly lower than those of the main body group in nucleosclerosis grade 5 and 6 (p < 0.05). BSS uses of the handpiece group were significantly lower than those of the main body group in all nucleosclerosis grade (p < 0.05).
Conclusion: Because of the fast-reacting anti-surge, phacoemulsification time, CDE, and BSS use were saved in the handpiece anti-surge system rather than in the main body. Therefore, the anti-surge system in the Centrion handpiece helps the surgeon perform safer cataract surgery in higher nucleosclerosis grade, zonule weakness, poor mydriasis, and pseudoexfoliation syndrome.
Selective Bias Virtual Screening for Discovery of Promising Antimalarial Candidates targeting Plasmodium N-Myristoyltransferase
Authors: Andrade Carolina , Sousa Bruna Katiele de Paula , Sigurdardóttir Sunniva , Bourgard Catarina , Borba Joyce , Clementino Leandro , Salazar-Alvarez Luis Carlos , Groustra Sophia , Zigweid Rachael , Khim Monique , Staker Bart , Costa Fabio , Eriksson Leif , Sunnerhagen Per 
Published: February 2024

Abstract
Malaria remains a significant public health challenge, with Plasmodium vivax being the species responsible for the most prevalent form of the disease. Given the limited therapeutic options available, the search for new antimalarials against P. vivax is urgent. This study aims to identify new inhibitors for P. vivax N-myristoyltransferase (PvNMT), an essential drug target against malaria. Through a validated virtual screening campaign, we prioritized 23 candidates for further testing. In the yeast NMT system, seven compounds exhibit a potential inhibitor phenotype. In vitro antimalarial phenotypic assays confirmed the activity of four candidates while demonstrating an absence of cytotoxicity. Enzymatic assays reveal LabMol-394 as the most promising inhibitor, displaying selectivity against the parasite and a strong correlation within the yeast system. Furthermore, molecular dynamics simulations shed some light into its binding mode. This study constitutes a substantial contribution to the exploration of a selective quinoline scaffold and provides valuable insights into the development of new antimalarial candidates.
Automatic BECT Spike Detection Algorithm Based on Adaptive Template Matching and Convolution Neural Network
Authors: Wu Jiang , Xiong Zhao , He Xuelan , Wu Duanpo , Jiang Tiejia , Jiang Lurong 
Published: February 2024

Abstract
Spike is an important and widely accepted biomarker used in the diagnosis of epilepsy based on scalp electroencephalogram (sEEG). Due to the similarity of its waveform with non-epileptic brain activity and artifacts, it is essential to develop an automated spike detection method based on waveform characteristics to assist physicians. Therefore, this paper proposes a novel algorithm for automatic spike detection that uses an adaptive template matching method as a pre-processor to optimise the selection of candidate spikes for efficient segmentation of sEEG signals. The process aims to eliminate the impact of data overlap caused by sliding window segmentation. The 1D convolutional neural network with a simple structure achieves excellent performance. Besides, it requires fewer trainable parameters and imposes a smaller demand on computational resources. The proposed method is evaluated on sEEG data recorded at Zhejiang University Children's Hospital. Moreover, the experimental results demonstrate that our model achieves average accuracy, sensitivity, specificity, F1 score, and AUC of 98.7\%, 97.39\%, 100\%, 98.67\%, and 99.3\%, respectively.
GNN Graph Classification In Action: Uncovering Patterns in Time Series Data
Authors: Romanova Alex 
Published: February 2024

Abstract
This study pioneers a technique for analyzing time series data through Graph Neural Network (GNN) Graph Classification, extending its application from the traditional arenas of biology and chemistry to the intricate domain of time series analysis. It elaborates on the process of constructing and examining graphs derived from time series data, utilizing both single graphs and sliding window graphs. This methodology proves effective in discerning complex interrelations within the data.
A critical insight from applying GNN Graph Classification to single graphs is the models’ acute sensitivity to graph structures. This attribute significantly enhances their capacity to identify outliers and decode complex patterns that hint at systemic shifts. Such sensitivity underscores the utility of GNN Graph Classification in elucidating time series data dynamics, indicating its broad potential across diverse analytical endeavors.
Integrating sliding window techniques with GNN models, our approach emerges as a versatile analysis tool. It adeptly identifies temporal variations and nuanced patterns that elude traditional static analysis techniques. The findings affirm the adaptability and promise of GNN Graph Classification in conducting thorough time series analysis.
By spotlighting the models’ structural sensitivity and leveraging advanced sliding window methodologies, this research broadens the application spectrum of GNN Graph Classification in data analysis. It lays the groundwork for future explorations in time series and affiliated domains. Marking a significant leap forward, this study illuminates the capacity of GNN Graph Classification models to dissect and interpret complex data patterns, paving the way for novel time series analysis applications and beyond.