bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on external contour characterization of fault-karst reservoir based on deep learning and attribute fusion
Authors: Gui Zhipeng , Zhang Junhua , Zhang Yintao , Sun Chong 
Published: January 2024

Abstract

Identification of fracture dissolution has significant implications for exploration and development of fracture-controlled hydrocarbon reservoirs. Traditional methods mainly rely on well logging and seismic attribute analysis to identify dissolution features, but these methods have limited resolution and cannot meet the actual needs of reservoir mapping. In contrast, deep learning techniques can effectively overcome the complex response characteristics of seismic wave fields and demonstrate high learning capabilities. Therefore, this paper proposes a fracture dissolution identification method that involves a comparison between the U-Net++ network and traditional deep convolutional networks. Suitable training parameters are selected to separately train the networks for dissolution and fracture identification. The trained models are then applied to actual seismic data to predict the dissolution and fracture volumes in the study area. By integrating the predicted volumes, the fracture dissolution dataset for the region is obtained. The results show that: (1) The proposed method is effective in identifying dissolution and fracture features, outperforming traditional seismic attributes and coherence techniques. (2) By combining the predicted dissolution and fracture volumes, the relationship between top dissolution features and bottom fracture features in the study area can be clearly delineated. In conclusion, the fracture dissolution identification and characterization method proposed in this paper can serve as a reference for the exploration and development of fracture-controlled hydrocarbon reservoirs in the region.
Research on Multi-Group Monte Carlo Calculations Based on Group Constants Generated by RMC
Authors: Minzhi Xiong , Wenxiang Fang , Dacai Zhang , Ganglin Yu , Kan Wang 
Published: January 2024

Abstract

Nowadays, deterministic two-step or Monte Carlo methods are commonly used in core physics calculations. However, with the development of reactor core design, traditional deterministic two-step methods are becoming increasingly difficult to adapt to complex core and fuel geometry designs. However, Monte Carlo methods require high computational resources to obtain accurate full reactor simulation results. Therefore, using the Monte Carlo two-step method has become a good choice. The Monte Carlo two-step method involves obtaining group constants using the Monte Carlo method at a smaller geometric scale and conducting multi group Monte Carlo transport based on this result. This article uses the Monte Carlo software RMC to perform critical and burnup calculations on a custom core model using different group constant generation methods using the Monte Carlo two-step method. The results show that compared to continuous energy accurate simulation, the Monte Carlo two-step method requires extremely low computational resources and high computational efficiency. Therefore, the Monte Carlo two-step method can provide reference and assistance for subsequent core calculations.
Knowledge, attitude and use of Electronic Cigarettes among Cairo University Medical Students
Authors: mostafa Ola Abdelmoniem , Taha Mahmoud Ahmed 
Published: January 2024

Abstract

Background: electronic cigarettes use has increased exponentially in Egypt and all over the world. However, there is insufficient information about their use of among Cairo University medical students. Therefore, this study was performed to assess e-cigarette knowledge, attitude and use among Cairo University medical students.
Methods: A cross sectional study of 300 consented Cairo university medical students over the period of 1st of November 2023 till 15th of December 2023 using a self- administered questionnaire.
Results:
The mean age was 22.12 ± 1.87. 69.3% were males and 30.7% females. 88.3% heard about e cigarettes, with higher knowledge in clinical years. Main sources of information were the media then friends (41.8% & 37.5%). 14%, 12.7 %, and 7.3 % were cigarette, shisha, and e cigarette smokers; smoking prevalence was higher among males and clinical years’ students (12% &4.7% in clinical and preclinical years, P = 0.02).
39.3%, 10.2% and 3.3% of participants' friends, siblings and parents smoked e cigarettes respectively; compared to 16.7%, 17.9%, and 33% among e smokers. A significantly lower percentage-of e smokers believed that e cigarettes are addictive or cause respiratory problems, and a higher percentage thought that e cigarettes are less harmful, help in smoking cessation, and that their nicotine content is lower than traditional cigarettes. Predictors of e cigarette smoking were being in clinical years, cigarettes and shisha smoking and having friends who are e cigarette smokers.
Conclusion:
The study revealed several students’ misconceptions and a wide variation in their attitudes about the harmful and addictive effects of e cigarettes. This urges the need for development of medical school curriculum to provide the correct information regarding e-cigarettes in order to face the problem of increasing its use in Egypt.
Displacement prediction of fine-grained tailings ponds based on WOA-BP neural network
Authors: Liu Gaolin , Wang Guangjin , Liu Wenlian , Zhao Bing , Lan Rong , Wu Bisheng , Xin Yingming , Liu Xinjie 
Published: January 2024

Abstract

Tailing reservoir is an important auxiliary facility of mine and a dangerous source of man-made debris flow with high potential energy. China’s tailings ponds are shifting toward fine-grained high dams. Accordingly, displacement is one of the key factors affecting pond stability, and it is important to understand the displacement trend of the tailings pond to ensure its safe operation. Accordingly, this paper adopts the whale algorithm to optimize the back propagation(BP) neural network and establishes the WOA-BP neural network nonlinear prediction model to avoid the error generated by the model experiment due to the scaling effect. The infiltration line and displacement data of a tailings pond in Sichuan Province in the past two years are collected consecutively to form a learning sample, which is then used for training to predict the displacement of the tailings pond through the WOA-BP neural network model. Thereafter, these prediction results are compared with the actual monitoring values as well as the BP neural network model prediction values. The results revealed that the relative error of the WOA-BP neural network model prediction results was approximately 4.5%, and the Pearson correlation coefficients were all above 0.998. Compared with the traditional BP neural network model, the optimization model has a stronger search capability, wider application range, higher prediction accuracy, a more global optimal solution, and better response. The nonlinear fuzzy mapping provides new insights into tailings pond displacement and safety prediction.
Analysis of Chemotherapy Regimens on Survival Rate of Breast Cancer Patients
Authors: Yulistiani Yulistiani , Utomo Febriansyah Nur , Romadhon Pradana Zaky , Fiddina Nisriyati 
Published: January 2024

Abstract

Breast cancer in Indonesia is among the first with the highest number of new cancer cases with the second mortality in cancer after lung cancer in 2020. Anthracycline-taxane based regimens and taxane-based regimens are often breast cancer chemotherapy modalities that can support increased patient survival. Side effects occur more frequently in combination regimens. The selection of the right regimen is expected to support the improvement of patient survival. This study aims to analyze the survival rate of chemotherapy regimen groups and hazard ratio factors that are thought to affect survival in breast cancer patients. This study used an observational and retrospective cohort method. The statistical analyzes used in this study include Kaplan-Meier analysis to measure survival rates, Log-rank test to observe the differences between regimen groups and Cox proportional hazards regression to analyze factors that influence survival. Patients who were included in the inclusion and exclusion criteria were 71 patients, consisting of 47 patients who received anthracycline-taxane based combination and 24 patients who received taxane-based chemotherapy. The percentage of one-year and two-year survival in the anthracycline-taxane based combination was 93.6% and taxane-based 83.3% (p>0.05). Multivariate analysis showed that stage increased risk 9 times based on hazard ratio values obtained from cox regression tests (HR 9.60; 95% CI 1.60-57.54; p<0.05). Another significant factor in the multivariate analysis was that delaying chemotherapy increased the risk of survival 5 times (HR 5,58; CI 95% 1.12-27.85 ; p<0.05). Based on research, it can be concluded that anthracycline-taxane based and taxane based combination regimens can be used as a treatment modality for breast cancer patients in both early and advanced stages.
Chemically programed nucleic condensates for spatiotemporal control of mammalian gene expression
Authors: Zeng An-Ping , Wang Yukai , Jiang Jian , Xiong Qiqi , Li Shichao , Shao Jiawei , Xie Mingqi 
Published: January 2024

Abstract
Engineering nucleic condensates with chemically inducible gene switches is highly desired but challenging for precise and on-demand regulation of mammalian gene expression. Here, we harness the phase separation capability of biomolecular condensates and describe a versatile strategy to chemically program ligand-dependent gene expression at various stages of interest. By engineering synthetic anchor proteins capable of tethering various genetically encoded condensate structures towards different cellular compartments or gene products of interest, inducible regulation of transcriptional and translational activities was achieved at different endogenous and episomal loci using the same sets of anchor proteins and synthetic condensates. Using such a holistic condensates-based strategy, we not only achieved regulation performances compared favorable to state-of-the art strategies described for CRISPR/Cas9 activity and transcriptional silencing, but further show that chemically inducible retention of mRNA molecules into engineered condensate structures within the nucleus can become a remarkably efficient alternative for translational regulation. Taken together, this work describes a systematic, scalable and universal strategy to regulate mammalian gene expression with substantially reduced (re)engineering labor during gene switch design, and broadens the current toolkit for chemical and synthetic biology.
Connecting the Dots: Assessing Landscape Connectivity Algorithms for Biodiversity Conservation
Authors: Schippers Peter , Pouwels Rogier , Verboom Jana 
Published: January 2024

Abstract
Context
To understand and characterize the dispersal of organisms in a fragmented landscape, scientists often use connectivity matrices - tables that contain the probabilities of successful dispersal between different pairs of patches.
Objective
While mechanistic, individual-based correlated random walk (CRW) models are commonly used to estimate these probabilities, simpler, deterministic alternatives exist based on distance and patch size which are easier to develop, use and understand. However, the relative performance of these simpler algorithms compared to the CRW model is not well understood. We ask, how good are simplified algorithms in mimicking the CRW model?
Method
To address this gap, we compared the connectivity matrices of ten simple algorithms to those of a CRW model across 36 landscape-disperser combinations.
Results
Our results show that the frequently used exponential decay algorithm (EXP) did not perform well, with a mean R2 of 0.745 and a minimum R2 of 0.185 between the connectivities of the EXP model and the CRW model. On the other hand, the CRD-lim model - which uses a constant•radius/distance relation within a maximum inter-patch distance (dmax) - performed best, with a mean R2 of 0.918 and a minimum R2 of 0.809.
Conclusion
Overall, our results show that the CRD-lim algorithm is a good alternative to random walk models when assessing connectivity matrices for a specific landscapes and species in case a full individual-based CRW is not feasible, for example because data are scarce or a multi-species perspective is taken.
Causal relationship between type 2 diabetes and BMD: a Mendelian randomization study
Authors: Jiang Xiao-Cheng , Li Huan , Ren YangLiang , Wang Ting 
Published: January 2024

Abstract

Summary:When Mendelian randomization (MR) studies were used to investigate the causal relationship between type 2 diabetes and BMD at different sites, there was no causal relationship between type 2 diabetes and lumbar BMD, femoral neck BMD, or forearm BMD.
Purpose: The purpose of this study was to assess the causal relationship between type 2 diabetes and BMD in the lumbar spine, femoral neck, and forearm.
Methods: Based on the aggregated statistical data of a large published genome-wide association study. The IVW method, the MR-Egger method, the WM method, the Simple Mode method, and the Weighted Mode method were used to evaluate the causal relationship between type 2 diabetes and lumbar BMD, femoral neck BMD and forearm BMD. In addition, sensitivity analysis was performed using MR-Egger regression, Cochran's Q test and MR-PRESSO Global test to ensure the robustness of the results.
Results：The results of the inverse variance weighted (IVW) analysis for type 2 diabetes and lumbar BMD showed an odds ratio (OR) of 1.070997 (95% confidence interval [CI]: 0.9839422 to 1.165754), with a p-value of 0.11279766. Similarly, the IVW analysis for type 2 diabetes and femoral neck BMD showed an OR of 1.041797 (95% CI: 0.9657858 to 1.123791), with a p-value of 0.28944290. For type 2 diabetes and forearm BMD, the IVW analysis resulted in an OR of 1.102443 (95% CI: 0.9433071 to 1.288424), with a p-value of 0.22012100. Heterogeneity tests for type 2 diabetes and lumbar BMD, femoral neck BMD, and forearm BMD did not identify any outlier variables. Sensitivity analyses confirmed the robustness of the results, and no pleiotropic effects were observed.
Conclusions：There was no causal relationship between type 2 diabetes and lumbar BMD, femoral neck BMD, or forearm BMD.
Prediction of Flood-Prone zones based on Cellular Automata in GIS
Authors: shad rouzbeh , Mousavi Seyed Mojtaba , Ghaemi Marjan 
Published: January 2024

Abstract
Due to climate change and rapid urbanization, urban flooding is on the rise, necessitating effective flood control measures in urban areas. Predicting potential flood-prone areas undergoing Land Use (LU) changes could significantly aid in planning for risk reduction and sustainable urban design. However, there's a scarcity of studies that consider both climate change and LU alterations. This research introduces a novel basin-scale framework utilizing a Future LU Simulation (FLUS) model to evaluate disaster-prone areas' risk from 20-year flood scenarios projected for 2040 and 2060. The Markov-FLUS model was developed and validated using historical data from 2000 to 2020. This model was then employed to simulate LU changes from 2020 to 2060 based on natural scenarios. Focusing on Khorasan Razavi as a case study, it investigates the potential consequences of LU transformations due to ongoing urbanization and vegetation changes in connection with predicted environmental shifts. The findings indicate an anticipated increase in accident-prone areas and constructed land in the studied area in the future. Spatially, this heightened flood risk primarily occurs on the periphery of existing developed areas or converted land. This framework's insights into future flood-prone areas' spatio-temporal characteristics offer valuable guidance for implementing rational flood mitigation measures in the most critical regions for development.
Evaluating adhesion properties of transdermal adhesives using novel skin mimetic substrate
Authors: Bagde Arvind , Mosley-Kellum Keb , Salau Oluwaseyi , Dev Satyanarayan , Modi Nisarg , Singh Mandip 
Published: January 2024

Abstract
Even though, the transdermal patches are evaluated for adhesion testing using current adhesion protocols (based on stainless steel (SS) probe), they fail to correlate to in vivo studies. Therefore, our objective was to print the probes (skin mimetic substrate) using 3D Fused Deposition Modeling (FDM) printing using materials with similar surface energy as skin and evaluate the adhesion property of pressure sensitive adhesives (PSAs). Additionally, the objective of the present study was to study the effect of various intrinsic factors including coat weight, elastic properties of backing membranes and viscosity of PSAs on adhesion properties of PSAs. Our novel 3D printed polypropylene (PP) probe was successfully printed and postprocessed using FDM printing coupled with automated robotic arm setup for adhesion testing. Probe tack test results showed no significant difference in peak adhesive force in case of skin adhered to SS probe and PP probe. However, PP probe showed about 10.26-fold decrease (p<0.0001) in the adhesive force as compared to SS probe. Effect of coat weight using PP robe results revealed that both amine (PSA-4302) and non-amine (PSA-4501) compatible silicone PSAs showed significant (p<0.05) increase in their adhesion prosperities at 10 mg/cm2 as compared to 5 mg/cm2 coating. Further, choice of backing membrane and viscosity of silicone PSAs also showed significant (p<0.0001) effect on PSAs adhesion property. In conclusion, PP probe could be a promising approach for in vitro adhesion testing of TDS products. Further, intrinsic properties including coat weight and viscosity of silicone PSAs significantly affected their adhesion properties.
Construction and validation of a bioinformatics-based screen for Cuproptosis-related genes and risk model for Alzheimer's disease
Authors: Hu Rui , Xiao Zhen , Qiao Mingyu , Liu Chaoyu , Wu Guiyou , Wang Yunyi , Huang Zhongshi , Dong Mingyou 
Published: January 2024

Abstract
This study aimed to validate the correlation between core cuproptosis genes (CRGs) and Alzheimer's disease (AD) from both bioinformatics and experimental perspectives and also to develop a risk prediction model. To this end, 78 human-derived temporal back samples were analyzed in GSE109887, and then the biological functions of the resulting CRGs were explored by cluster analysis, weighted gene co-expression network analysis (WGCNA), and similar methods to identify the best machine model. Moreover, a nomogram was developed to validate the model. The mRNA and protein expression of CRGs were validated using the SH-SY5Y cell model and SD rat animal model. The RT-qPCR and western blot results showed that the mRNA and protein expression content of DLD, FDX1, GLS, and PDHB decreased, and the DBT expression content increased in AD, which supported the bioinformatic analysis results. CRGs expression alterations affected the aggregation and infiltration of certain immune cells. The study results also confirmed the accuracy and validity of AD diagnostic models and nomograms. This study validated the correlation between five CRGs and AD, indicating a significant difference between AD patients and healthy individuals. Therefore, CRGs are expected to serve as relevant biomarkers for the diagnosis and prognostic monitoring of AD.
Individualized Physiology-Based Digital Twin Model for Sports Performance Prediction : A Reinterpretation of the Margaria-Morton Model
Authors: Boillet Alice , Messonnier Laurent A. , Cohen Caroline 
Published: January 2024

Abstract
Performance in many racing sports depends on the ability of the athletes to produce and maintain the highest possible worki.e., the highest power for the duration of the race. To model this energy production in an individualized way, an adaptation anda reinterpretation (including a physiological meaning of parameters) of the three-component Margaria-Morton model wereperformed. The model is applied to the muscles involved in a given task. The introduction of physiological meanings waspossible thanks to the measurement of physiological characteristics for a given athlete. A method for creating a digital twin wastherefore proposed. The twins thus created were validated by comparison with field performance, experimental observations,and literature data. Simulations of record times and 3-minute all-out tests were consistent with experimental data. Consideringthe literature, the model provided good estimates of the time course of muscle metabolite concentrations (e.g., lactate andphosphocreatine). It also simulated the behavior of oxygen kinetics at exercise onset and during recovery. This methodologyhas a wide range of applications, including prediction and optimization of the performance of individually modeled athletes.
In-target Production of [11C]CH4 from a Nitrogen/Hydrogen Gas Target as a Function of Beam Current, Irradiation Time, and Target Temperature
Authors: Helin Semi , Rajander Johan , Aromaa Jussi , Arponen Eveliina , Helin Jatta S , Solin Olof 
Published: January 2024

Abstract
Background
Production of [11C]CH4 from gas targets is notorious for weak performance with respect to yield, especially when using high beam currents. However, airborne carrier carbon especially is better in control than in [11C]CO2 production, so that in-target–produced [11C]CH4 is superior with respect to molar activity. Post-target conversion of [11C]CO2 to [11C]CH4 is widely used in 11C-radiochemistry, but this roundabout method retains the low molar activity challenges of the original [11C]CO2. We studied the in-target production of [11C]CO2 and [11C]CH4 from nitrogen gas targets as a function of beam current, irradiation time, and target temperature.
Results
[11C]CO2 production was practically unchanged across the range of varied parameters, but the [11C]CH4 yield, presented in terms of saturation yield YSAT(11CH4), had a negative correlation with beam current and a positive correlation with target chamber temperature. A formulated model equation indicates behavior where the [11C]CH4 formation follows a parabolic graph as a function of beam current. The negative square term, i.e., the yield loss, is postulated to arise from Haber-Bosch–like NH3 formation: N2 + 3H2 → 2NH3. The studied conditions suggest that the NH3 (liq.) would be condensed on the target chamber walls, thus depleting the hydrogen reserve needed for the conversion of nascent 11C to [11C]CH4.
Conclusions
[11C]CH4 production can be improved by increasing the target chamber temperature, which is presented in a mathematical formula. Our observations have implications for targetry design (geometry, gas volume and composition, pressure) and irradiation conditions, providing specific knowledge to enhance [11C]CH4 production at high beam currents. Increased [11C]CH4 radioactivity is an obvious benefit in radiosynthesis in terms of product yield and molar radioactivity.