bluuebunny/crossref_metadata_2025_split huggingface dataset:
CECT and CT Perfusion Correlation with Pathological Differentiation of Pharyngeal and Laryngeal Cancers: Study from a Tertiary Care Center in Northeast India
Authors: Phukan Himraj , Lynser Donboklang , Daniala Chhunthang , Raphael Vandana , Harris Caleb , Chakraborty Suvamoy 
Published: December 2024

Abstract
Introduction Pharyngeal and laryngeal carcinomas, included under head and neck cancers, pose a significant challenge in India, accounting for around one-third of all cancer cases. Noninvasive techniques like contrast-enhanced computed tomography (CECT) and CT perfusion (CTp) can help explore the correlation between tumor differentiations, which can greatly benefit in the diagnosis, understanding of recurrence postintervention, and monitoring of the progress of the disease.
Methods A hospital-based cross-sectional study was conducted for a duration of 18 months in a tertiary care center of northeast India with a sample of 40 patients. CECT and CTp were done and the variables were correlated with pathological differentiation of tumors.
Results The mean age of the study population was 54 years. No statistically significant associations were noted between the age, size, margins, location, and T-staging of tumors with the pathological differentiation of tumors. However, significant association was found between the CTp parameters, namely blood flow (BF), blood volume, mean transit time, time to drain, and time to peak with respect to the pathological differentiation of tumors (p < 0.05).
Conclusion The addition of CTp to conventional CT sequences in the evaluation of pharyngeal and laryngeal cancers offers significant benefits in understanding the tumor physiology and behavior.
Deep Learning for Detecting and Subtyping Renal Cell Carcinoma on Contrast-Enhanced CT Scans Using 2D Neural Network with Feature Consistency Techniques
Authors: Gupta Amit , Dhanakshirur Rohan Raju , Jain Kshitiz , Garg Sanil , Yadav Neel , Seth Amlesh , Das Chandan J. 
Published: December 2024

Abstract
Objective The aim of this study was to explore an innovative approach for developing deep learning (DL) algorithm for renal cell carcinoma (RCC) detection and subtyping on computed tomography (CT): clear cell RCC (ccRCC) versus non-ccRCC using two-dimensional (2D) neural network architecture and feature consistency modules.
Materials and Methods This retrospective study included baseline CT scans from 196 histopathologically proven RCC patients: 143 ccRCCs and 53 non-ccRCCs. Manual tumor annotations were performed on axial slices of corticomedullary phase images, serving as ground truth. After image preprocessing, the dataset was divided into training, validation, and testing subsets. The study tested multiple 2D DL architectures, with the FocalNet-DINO demonstrating highest effectiveness in detecting and classifying RCC. The study further incorporated spatial and class consistency modules to enhance prediction accuracy. Models' performance was evaluated using free-response receiver operating characteristic curves, recall rates, specificity, accuracy, F1 scores, and area under the curve (AUC) scores.
Results The FocalNet-DINO architecture achieved the highest recall rate of 0.823 at 0.025 false positives per image (FPI) for RCC detection. The integration of spatial and class consistency modules into the architecture led to 0.2% increase in recall rate at 0.025 FPI, along with improvements of 0.1% in both accuracy and AUC scores for RCC classification. These enhancements allowed detection of cancer in an additional 21 slices and reduced false positives in 126 slices.
Conclusion This study demonstrates high performance for RCC detection and classification using DL algorithm leveraging 2D neural networks and spatial and class consistency modules, to offer a novel, computationally simpler, and accurate DL approach to RCC characterization.
Cervical Cancer Screening: Comparing PAPs Smear with VIA/VILI in Semiurban Women of Delhi
Authors: Sarbhai Vinita , Aggarwal Shreshtha 
Published: December 2024

Abstract
Introduction Screening with PAPs smear for screening of cervical cancer has been the gold standard for many years in high-income countries but has still not been successful in low- and middle-income country like India due to unavailability at public ground level. Thus, a simple, effective, and low-cost alternative to cervical cytology for cervical cancer prevention is urgently needed for high-risk, low-resource settings.
Objectives The aim was to compare the efficacy of visual inspection with acetic acid (VIA) and visual inspection with Lugol's iodine (VILI) with the PAP smear for screening precursor intraepithelial lesions and early signs of cervical cancer, and to evaluate their potential as alternative screening methods to the PAP smear in a semiurban population in Delhi.
Material and Method A total of 127 married, nonpregnant patients between 18 and 65 years were randomly selected from gynecology outpatient department. Then, PAP smear samples were taken from all patients followed by VIA and VILI. Biopsies were then taken from those who showed positive findings in either of the screening methods. Out of these, 50 PAP and VIA-negative women were included as a control group. The diagnostic accuracy of VIA and VILI was compared with PAP smear, for diagnosis of precancerous lesions.
Results VIA and VILI results were positive in 22 patients out of 127 (17.3%). PAP cytology report showed epithelial cell abnormality in 13 cases (10.2%) with atypical squamous cells of undetermined significance in 1 case, atypical glandular cells of undetermined significance in 4 cases, low-grade squamous intraepithelial lesion in 3 cases, and high-grade squamous intraepithelial lesions in 5 cases. PAP smear had showed better specificity (90.79%) and less sensitivity (85.71%) as compared with VIA and VILI method. Sensitivity of VIA was 100% and specificity was 80.26%. Similar parameters were seen with the VILI method also. The overall p-value for all the parameters of either screening method was > 0.05; hence, both methods are comparable for screening.
Conclusion VIA and VILI can be employed as initial screening tools in place of PAP smear, particularly in countries with limited resources or developing regions.
Enhanced red emission in perovskite light-emitting diodes via F-PEI additive
Authors: Wen Yan , Shao Yi , Guan Xuanxuan , Cao Jihua 
Published: December 2024

Mixed halogen perovskite materials show good application potential in the field of light-emitting diodes. However, due to non-radiative recombination of defect states in perovskite films, the external quantum efficiency (EQE) and stability of perovskite light-emitting diodes (PeLEDs) still lag behind traditional LEDs. Herein, we modulated the perovskite crystallization process by adding fluorinated polyethylenimine (F-PEI) into the perovskite precursor solution, resulting in perovskite films with smaller grain sizes, smoother surfaces, and lower defect density. These improvements significantly improve the photoluminescence efficiency of perovskite films and the performance of corresponding diode devices, with the peak EQE of PeLEDs reaching 20.89% at 668 nm. In addition, the F-PEI additive improves the thermal stability of the device and extends the operating life of the device. This shows the potential of this additive to improve the performance of perovskite light-emitting diodes.
Daphnia reproductive impacts following chronic exposure to micro- and nano-scale particles from three types of rubber
Authors: Cunningham Brittany E. , Harper Bryan J. , Brander Susanne M. , Harper Stacey L. 
Published: December 2024

Environmental context Tyre rubber particles, from both driving and reuse of tyre rubber, are pollutants that carry toxic chemicals into the environment. We investigated the long-term effects that these particles have on small aquatic organisms and found that they drastically reduce their ability to reproduce. Continued exposure of aquatic invertebrates to tyre-related pollutants, has the potential to affect the population by inhibiting reproduction into future generations. Rationale High levels of rubber microplastics in aquatic environments are often attributed to particles from driven tyres; however, the use of recycled or crumb rubber particles in outdoor surfaces is another source. Chronic toxicity assessments with tyres are limited, and there is a need to evaluate effects of rubber particles from different sources to better understand their role in conferring toxicity. Methodology We investigated the effect of chronic exposure of Daphnia magna to micro-sized (1–20 µm, 3.13 × 104–1.25 × 105 particles mL–1) and nano-sized (<1 µm, 1.25 × 105–1.00 × 107 particles mL–1) synthetic rubber particles. These included tyre particles (TPs) and two types of rubber from recycled tyres (called recycled rubber, RR, and crumb rubber, CR). Mortality, reproduction and moulting were assessed daily, and growth was measured at the end of the exposure. Additionally, the F1 generation was reared to assess multigenerational effects. Results Chronic exposure to micro-rubber particles had severe effects, delaying, decreasing and even eliminating reproduction starting at 6.25 × 105 particles mL–1. Chronic exposure to nano-rubber particles had less severe effects, but delayed and decreased reproduction at the highest exposure level, 5.00 × 107 particles mL–1. Exposure to nano-rubber in the parental generation affected reproduction in the F1 generation. Discussion This is the first comparison of chronic and generational toxicity between different sizes and compositions of rubber particles. The reproductive affects of chronic exposure to rubber particles could have devastating impacts on populations of Daphnia. Research is needed to identify which components leached from rubber particles affect reproductive ability.
Entanglement entropy in quantum black holes
Authors: Belfiglio Alessio , Luongo Orlando , Mancini Stefano , Tomasi Sebastiano 
Published: December 2024

Abstract
We discuss the entanglement entropy for a massive scalar field in two Schwarzschild-like quantum black hole spacetimes, also including a nonminimal coupling term with the background scalar curvature. To compute the entanglement entropy, we start from the standard spherical shell discretization procedure, tracing over the degrees of freedom residing inside an imaginary surface. We estimate the free parameters for such quantum metrics through a simple physical argument based on Heisenberg uncertainty principle, along with alternative proposals as asymptotic safety, trace anomaly, and graviton corpuscular scaling. Our findings reveal a significant decrease in entropy compared to the area law near the origin for the quantum metrics. In both scenarios, the entanglement entropy converges to the expected area law sufficiently far from the origin. We then compare these results to the entropy scaling in regular Hayward and corrected-Hayward spacetimes to highlight the main differences with such regular approaches.
Model updating of engineering structures based on modified subset simulation methods and measured data
Authors: Xia Zhiyuan , Tang Baijian , Shi Huiyuan , Chen Peng , Chen Xin , Gu Sheng 
Published: December 2024

Abstract
Subset simulation (SuS) as a reliability method is used to solve optimization issues in areas of civil engineering for relatively simple engineering structures, and several advancements in the generation approaches of samples in the subset to alleviate the generation of repeat samples have been made. However, the application of the SuS method to real, complicated engineering structures is rare. In this paper, firstly, the feasibility of four typical SuS-based methods — namely, SuS-modified Metropolis-Hasting (SuS-MMH), SuS-MMH with delayed rejection (SuS-MMHDR), SuS-adaptive conditional sampling (SuS-ACS) and SuS-Hamiltonian Monte Carlo — was assessed based on the updating of a damaged simulated cantilever beam. The differences in the first 10 frequencies of the beam between the updated finite element model (FEM) and the real FEM were all reduced from above 5% to within 1%, with the methods of SuS-MMHDR and SuS-ACS showing better efficiency with fewer iteration steps. Then, the application of SuS-MMHDR and SuS-ACS to the model updating of a real steel corridor structure was analyzed. The results show that the differences in the first five vertical vibration frequencies are reduced from approximately 20% to within 10%, and the result of SuS-MMHDR was around 5%. Finally, the updated model under human-induced vibration for the steel corridor structure was verified. Based on the experiment, the difference in the largest acceleration between the updated model and real structure was controlled at around 10%, which shows relatively good agreement with each other. The above model updating applications show the feasibility of the SuS-based optimization method for real engineering optimization, and SuS-MMHDR shows the strongest performance.
A target recognition algorithm using machine learning based on millimeter wave radar on intelligent connected vehicles
Authors: Fan Likang , Liu Haichao , Wei Hongqian , He Zhuoyu , Chen Xu 
Published: December 2024

Abstract
Recognition technology based on millimeter wave radar (MMW) can operate in all-weather conditions and has received much attention in the field of intelligent connected vehicles (ICVs). However, the label information of the targets cannot be directly obtained from the original radar point clouds, making it necessary to develop advanced recognition algorithms. This paper proposes a target recognition algorithm based on machine learning that utilizes radar point clouds and leverages the radar reflection intensity to improve target recognition accuracy. Firstly, regional division and density clustering techniques are employed to preprocess the original point clouds from the MMW and segment them into meaningful regions, thereby reducing the computational burden; Secondly, relevant features are extracted from the processed radar point cloud, including radar scattering cross section and its related features. Finally, to improve target recognition accuracy, this paper proposes a grid search optimization principal component analysis support vector machine (GS-PCA-SVM) classification algorithm. The algorithm uses PCA to reduce the dimensionality of the data while preserving key information; then, it optimizes the parameters and kernel function of SVM by using the GS method to improve the performance of the classifier. The experimental results indicate that the recognition algorithm proposed in this paper achieves accuracies of 80%, 93%, and 95% on static, dynamic, and mixed datasets, respectively. Real vehicle experiments also prove that this algorithm has high accuracy and reliability when applied to ICV.
Surface defect detection of steel strip at low resolution based on SAC-YOLOv5
Authors: Rui Changxin , Wu Zhantao , Liu Chenzhen , Li Baoqing , Cheng Junsheng 
Published: December 2024

Abstract
Surface defects are common occurrences in the production process of strip steel. The development of automatic and efficient intelligent detection algorithms for strip steel is crucial for enhancing product quality and operational safety. While deep learning-based defect detection techniques have achieved satisfactory accuracy when applied to high-resolution images, obtaining a sufficient number of high-resolution images in practical engineering scenarios is challenging. The degradation of image quality often results in a significant decline in the performance of existing detection techniques. To address these challenges, this paper proposes SAC-you only look once (YOLO)v5-based surface defect detection model specifically designed for low-resolution strip steel images. SAC, SIoU based K-Means++, asymmetric convolutional neural networks (ACNNs) and composite down-sampling feature fusion block (CDFFB). Firstly, we introduce an anchor boxes clustering algorithm called Shape-IoU based K-Means++ (SIoU based K-Means++) to enhance the efficiency of anchor boxes regression. Secondly, we construct an ACNNs for multi-level feature extraction. Utilizing reparameterization techniques, we reduce the inference resources required by the model. Additionally, we propose a CDFFB which enhances key texture information and improves the model’s nonlinear fitting ability. Experimental analysis using NEU-DET surface defect data demonstrates the superiority of the proposed model in processing low-resolution strip steel surface defect images. In comparison to YOLOv8, YOLOv7, YOLOX, YOLOv3SPP, CenterNet and Faster RCNN, SAC-YOLOv5 outperforms all other models in terms of both speed and accuracy.
Electrospun poly(vinylidene fluoride) nanofibers: a review on its utilization in energy harvesting devices
Authors: Fadzallah Iman Aris , Sabran Nuur Syahidah , Hassan Hazirah Che , Ono Takahito , Sabri Mohd Faizul Mohd 
Published: December 2024

Abstract
This review provides a comprehensive exploration of small-scale energy harvesting (EH) for low-power devices, covering various ambient energy sources such as human activities, solar, thermal, mechanical vibration, radio frequency (RF), magnetism, and temperature differentials. It explains the use of conversion mechanisms like piezoelectric, thermoelectric, pyroelectric, and triboelectric. The focus is on piezoelectric materials, particularly pyroelectric materials, delving into the fundamental principles and equations governing their operation. The mechanisms of piezoelectric and pyroelectric effects under mechanical loadings and temperature changes are also explained. The review addresses material selection for small-scale EH, discussing both inorganic and organic piezoelectric materials. It justifies the preference for lead-free materials like poly(vinylidene fluoride) (PVDF) due to its biocompatibility, mechanical flexibility, ease of thin film production, and cost-effective implementation, replacing toxic lead-based materials. The various polymorphs within PVDF are explained, emphasizing the β-phase as the one responsible for its highest piezoelectric property. Different methods to enhance β-phase content in PVDF are reviewed, with electrospinning highlighted as a one-step process eliminating the need for post-treatment steps. The research effort to fabricate PVDF-based EH devices with various techniques, dimensions, mechanical loadings, and excitations is thoroughly examined. Recent advancements in the Internet of Things and low-power devices have driven interest in device miniaturization and complex circuit module fabrication using microelectromechanical systems (MEMS) technologies. The review explores approaches for fabricating PVDF-based EH devices using MEMS techniques and discusses hybrid systems combining piezoelectric and pyroelectric effects, with PVDF as the conversion medium.
One trick to treat them all: SuperEasy linear response for any hot dark matter in N-body simulations
Authors: Pierobon Giovanni , Mosbech Markus R. , Upadhye Amol , Wong Yvonne Y.Y. 
Published: December 2024

Abstract
We generalise the SuperEasy linear response method, originally developed to describe
massive neutrinos in cosmological N-body simulations, to any subdominant hot dark matter (HDM)
species with arbitrary momentum distributions. The method uses analytical solutions of the HDM
phase space perturbations in various limits and constructs from them a modification factor to the
gravitational potential that tricks the cold particles into trajectories as if HDM particles were
present in the simulation box. The modification factor is algebraic in the cosmological
parameters and requires no fitting. Implementing the method in a Particle-Mesh simulation code
and testing it on subdominant HDM cosmologies up to the equivalent effect of ∑ m

ν
=
0.315 eV-mass neutrinos, we find that the generalised SuperEasy approach is able to predict the
total matter and cold matter power spectra to ≲ 0.1% relative to other linear response
methods and to ≲ 0.25% relative to particle HDM simulations. Applying the method to
cosmologies with mixed neutrinos+thermal QCD axions and
neutrinos+generic thermal bosons, we find that non-standard subdominant
HDM cosmologies have no intrinsically different non-linear signature in the total matter power
spectrum from standard neutrino cosmologies. However, because they predict different time
dependencies even at the linear level and the differences are augmented by non-linear evolution,
it remains a possibility that observations at multiple redshifts may help distinguish
between them.
An NDVI analysis is conducted to determine the arrangement of green open space in the riparian zone of Sungai Kahayan, Palangka Raya City
Authors: Sutrisno Herwin , Susi Theresia , Hartanto Singgih , Perkasa Petrisly , Supriadi Benong , Mantana Handri 
Published: December 2024

Abstract
The configuration of riparian zones is predominantly characterized by moderate vegetation, comprising 43.4%. Deforestation in the riparian zone of the Kahayan River diminishes habitat quality and exacerbates soil erosion. Human activities, such deforestation and mining, can intensify soil erosion along riverbanks, hence increasing flood risks and harming river ecosystems. Riverside communities are susceptible to inundation, particularly during the monsoon season. Floods can impair infrastructure, devastate residences, and result in economic detriment. This study employs a quantitative methodology utilizing a descriptive technique, revealing that the research site comprises water bodies covering 16.6%, low vegetation at 12.2%, medium vegetation at 43.4%, and high vegetation at 27.7%. Based on these findings, the riparian zone of the Kahayan River is characterized by moderate vegetation; therefore, to preserve the ecosystem and hydrological functions, it is essential to plant large tree seedlings to enhance soil protection and augment biodiversity. This study aims to investigate the configuration of riparian tree habitat (RTH) along the Kahayan River in Palangka Raya City, utilizing Geographic Information System (GIS) analysis alongside the Normalized Difference Vegetation Index (NDVI) for assessing vegetation density and the spatial distribution of settlements in the area. The findings of this analysis influence the management of flood risk, environmental degradation, and improved RTH planning in Palangka Raya.