bluuebunny/crossref_metadata_2025_split huggingface dataset:
Monitoring spatiotemporal changes in urban flood vulnerability from satellite nighttime light data
Authors: Ziarh Ghaith Falah , Chung Eun Sung , Shahid Shamsuddin , Hamed Mohammed Magdy , Iqbal Zafar 
Published: January 2023

Abstract
Urban flood vulnerability monitoring needs a large amount of socioeconomic and environmental data collected at regular time intervals. Collecting such data volume is a significant constraint in assessing changes in flood vulnerability. This study proposed a novel method to monitor spatiotemporal changes in urban flood vulnerability from satellite nighttime light (NTL) data. Peninsular Malaysia was chosen as the research region. A flood vulnerability index (FVI), estimated from socioeconomic and environmental data for a year, was linked to NTL data using a machine learning algorithm called Adaptive neuro-fuzzy inference system (ANFIS). The model was calibrated and validated with administrative unit scale data and subsequently used to predict FVI at a spatial resolution of 10 km for 2000‒2018 using NTL data. Finally, changes in estimated FVI at different grid points were evaluated using the Mann-Kendall trend method to determine changes in flood vulnerability over time and space. Results showed a nonlinear relationship between NTL and flood vulnerability factors such as population density, Gini coefficient and percentage of foreign nationals. The ANFIS technique showed a good performance in estimating FVI from NTL data with a normalized root-mean-square error of 0.68 and Kling-Gupta Efficiency of 0.73. The FVI revealed a high vulnerability in the urbanized western coastal region (FVI ~ 0.5 to 0.54), which matches well with major contributing regions to flood losses in Peninsular Malaysia. Trend assessment showed a significant increase in flood vulnerability in the study area from 2000 to 2018. The spatial distribution of the trend indicated an increase in FVI in the urbanized coastal plains, particularly in rapidly developing western and southern urban regions. The results indicate the potential of the technique in urban flood vulnerability assessment using freely available satellite NTL data.
Changes in the composition of molecular species of covalently bound and free ceramides, and their correlation with disease severity in atopic dermatitis
Authors: Kondo Akari , Takenaka Yuko , Fujiwara Anna , Takahashi Saori , Kitade-Miyayama Masami , Morifuji Masashi , Kawashima Makoto , Ishiguro Naoko 
Published: January 2023

Abstract
Ceramides (Cers) are major constituents of the stratum corneum intercellular lipids, which are involved in the barrier function of the skin. Here, we examined the ratio of molecular species of Cers and their correlation with disease severity in patients with atopic dermatitis (AD). The levels of unsaturated fatty acids (USFAs) in both covalently bound and free Cers were higher in the lesional skin of patients with AD than in the non-lesional skin of patients with AD and normal skin of healthy controls. The proportion of USFAs (C30:1, C32:1, and C34:1) was considerably higher than that of other Cer molecular species in both covalently bound and free Cers in patients with AD. The proportion of USFAs in covalently bound Cers positively correlated with the levels of transepidermal water loss (TEWL) (r = 0.542) in the lesional skin of patients with AD. Additionally, the proportion of USFAs in covalently bound Cers positively correlated with thymus and activation-regulated chemokine (TARC), which is an index of disease severity, in the non-lesional (r = 0.676) and lesional (r = 0.503) skin of patients with AD. The proportion of USFA (C32:1), which was the highest in covalently bound Cers, also positively correlated with the TARC level in non-lesional (r = 0.733) and lesional (r = 0.515) skin of patients with AD. Our study is the first to show that there is an increase in USFA in not only lesional but also non-lesional skin of patients with AD. Moreover, this increase in USFA was associated with dryness and impaired barrier function and correlated with the TARC levels, a marker for the degree of type 2 inflammation. We speculate that exacerbation of type 2 inflammation may lead to abnormal epidermal lipid metabolism in the skin of patients with AD.
Viscosity Evaluation of Carbon-Nanotube-Dispersed Phase Change Material as Latent Heat Storage Material
Authors: Morita Shin-ichi , Haniu Toshihiro , Takai Kazunori , Hayamizu Yasutaka 
Published: January 2023

Abstract
Latent heat storage with a high heat storage density owing to the phase change is a promising method. Melting convection is a dominant process in the phase change progression during latent heat storage. To reduce latent heat storage time, thermal conductivity and melting convection should be increased and promoted, respectively. Dispersed fluids have a continuous fluid phase that can be convected; hence, the heat exchange performance can be improved using a high thermal conductivity material as the dispersoid. Carbon nanotubes (CNTs) are promising materials for dispersoids, owing to their high thermal conductivity. However, the inclusion of dispersoids obstructs convection because of the increased viscosity and flow complications owing to the change to a non-Newtonian fluid. Therefore, understanding the viscosity properties of latent heat storage materials with dispersed systems is essential for system design. This study evaluates the viscous properties of a multi-walled CNT (MWCNT) dispersed phase change material (PCM). The test sample consists of MWCNT as the dispersoid, latent heat storage material in the continuous phase and surfactants for dispersion. Moreover, this study carries out a flow analysis of latent heat storage by melting. Shear-rate dependence of the shear stress was measured using a rotational viscometer. The experimental data are obtained for a temperature range of 60–84 °C, a shear rate of 500 1/s or below, and an MWCNT content of 0–1.0 mass%, which are arranged as an experimental equation based on the power law.
Quantum annealing for microstructure equilibration with long-range elastic interactions
Authors: Sandt Roland , Bouar Yann Le , Spatschek Robert 
Published: January 2023

Abstract
We demonstrate the use and benefits of quantum annealing approaches for the determination of equilibrated microstructures in shape memory alloys and other materials with long-range elastic interaction between coherent grains and their different martensite variants and phases.
After a one dimensional illustration of the general approach, which requires to formulate the energy of the system in terms of an Ising Hamiltonian, we use distant dependent elastic interactions between grains to predict the variant selection for different transformation eigenstrains.
The results and performance of the computations are compared to classical algorithms, demonstrating that the new approach can lead to a significant acceleration of the simulations.
Beyond a discretization using simple cuboidal elements, also a direct representation of arbitrary microstructures is possible, allowing fast simulations with currently up to several thousand grains.
Development and Validation of Nomograms to Predict Overall Survival and Cancer-Specific Survival for Non-Small Cell Lung Cancer with Chest Wall Invasion: A Population-Based Study
Authors: Yang Jie , Yin Hui , Zou Guowen , Yu Bentong 
Published: January 2023

Abstract
Background: Chest wall invasion is a relatively kind of infrequent direct tumor extension in non-small cell lung cancer (NSCLC) with a poor survival outcome. Risk factors that impact overall survival (OS) and cancer-specific survival (CSS) remain unclear. Therefore, we aimed to explore prognostic factors in NSCLC patients with chest wall invasion and construct predictive nomograms to predict both OS and CSS in NSCLC patients with chest wall invasion.
Methods: We extracted a total of 2091 patients diagnosed with primary NSCLC with chest wall invasion between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. The total patients were divided into two groups randomly, the training cohort (1463 patients) and the validation cohort (628 patients). Univariate and multivariate Cox regression analyses were applied to distinguish the independent prognostic factors. Two prognostic nomograms for OS and CSS were established based on the training cohort and were evaluated in both cohorts. The concordance index (C-index), receiver operating characteristic curves (ROC), calibration curves, and decision curve analysis (DCA) curves were applied to assess the performance of these two nomograms.
Results: After multivariate Cox analysis, age, sex, histology, grade, N stage, M stage, surgery, and chemotherapy were identified as independent prognostic factors for OS, meanwhile, age, histology, grade, N stage, M stage, surgery, and chemotherapy for CSS.
The C-index of the nomogram for OS in the training and validation cohorts was 0.711 and 0.716, respectively. The C-index of the nomogram for CSS in the training and validation cohorts was 0.721 and 0.726, respectively. The ROC curves, calibration curves, DCA curves, and K-M survival curves also exhibited good predictive performance in the training and validation cohorts of these two prognostic nomograms.
Conclusion: Two nomograms provide a useful and reliable tool to predict both OS and CSS in NSCLC patients with chest wall invasion. These nomograms can provide strong references to facilitate clinic decisions.
A Noise-free Bias, 0.2TΩ Input Impedance CMOS Ultra- Low Noise Front-end Neural Amplifier in 65nm Process
Authors: Akuri Naga Ganesh , Jatoth Deepak Naik , Kumar Sandeep , Song Hanjung , Kar Asutosh , H Kirankumar Lad 
Published: January 2023

Abstract
This work proposes a CMOS two-stage ultra-low noise front-end neural amplifier (FENA) that is realized in the UMC 65nm Process. The proposed FENA consists of an operational transconductance amplifier along with incorporated low pass filter (LPF) technique. Due to this technique, FENA circuit provided best performances such as ultra-low input referred noise, ultra-high input impedance and high gain. An algorithm and mathematical noise model are employed to optimize the dimensions of LPF technique and transistor fingers which yield noise-free biasing current and ultra-low input referred noise of 18fv/√Hz at 10 KHz. The ultra-low input referred noise of FENA is achieved by reducing the gate distributed resistance method which is ignored in conventional FEA design. The FENA achieves an ultra-high input impedance of 0.2 TW, while a splendid post-layout gain of 80 dB has succeeded. FENA has layout area of 0.0023 mm2 which consumes lower power consumption of 1 mW under supply voltage of 1.2V. The FENA is found to be less prone to PVT variations as 1mHz of high-pass corner frequency is achieved for robust design. The best performance parameters of FENA could be beneficial for the purpose of deep exploration neural activities in wireless neural monitoring systems.
Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination
Authors: Grover Elise , Allshouse William , Lund Andrea , Liu Yang , Paull Sara , James Katherine , Crooks James , Carlton Elizabeth 
Published: January 2023

Abstract
Background:Although the presence of intermediate snails is a necessary condition for local schistosomiasis transmission to occur, using them as surveillance targets in areas approaching elimination is challenging because the patchy and dynamic quality of snail host habitats makes collecting and testing snails labor-intensive. Meanwhile, geospatial analyses that rely on remotely sensed data are becoming popular tools for identifying environmental conditions that contribute to pathogen emergence and persistence.
Methods: In this study, we assessed whether open-source environmental data can be used to predict the presence of human Schistosoma japonicum infections among households with a similar or improved degree of accuracy compared to prediction models developed using data from comprehensive snail surveys. To do this, we used infection data collected from rural communities in Southwestern China in 2016 to develop and compare the predictive performance of two Random Forest machine learning models: one built using snail survey data, and one using open-source environmental data.
Results: The environmental data models outperformed the snail data models in predicting household S. japonicum infection with an estimated accuracy and Cohen’s kappa value of 0.89 and 0.49, respectively, in the environmental model, compared to an accuracy and kappa of 0.86 and 0.37 for the snail model. The Normalized Difference in Water Index (NDWI) within half to one kilometer of the home and the distance from the home to the nearest road were among the top performing predictors in our final model. Homes were more likely to have infected residents if they were further from roads, or nearer to waterways.
Conclusion:Our results suggest that in low-transmission environments, investing in training geographic information systems professionals to leverage open-source environmental data could yield more accurate identification of pockets of human infection than using snail surveys. Furthermore, the variable importance measures from our models point to aspects of the local environment that may indicate increased risk of schistosomiasis. For example, households were more likely to have infected residents if they were further from roads or were surrounded by more surface water, highlighting areas to target in future surveillance and control efforts.
New accelerated preconditioner for the solution of linear systems from Stokes problem
Authors: Badahmane Achraf 
Published: January 2023

Abstract
Many applications in fluid mechanics require the numerical solution of sequences of linear systems typically issued from finite element discretization of the Stokes equations. The resulting matrices, then exhibit a saddle point structure and the iterative solution of such preconditioned linear systems is considered as challenging. The involved linear systems being large scale and ill-conditioned, effective linear solvers must be implemented. Here we proposes a direct approach with multiple right-hand sides, considering in particular the multi-frolintal massively parallel sparse solver (MUMPS), as a new strategy to improve the performance of Hermitian and skew-Hermitian precondi-tioner (P HSS) and accelerated Hermitian and skew-Hermitian preconditioner (P AHSS). The proposed preconditioners can be used as a preconditioner corresponding to the stationary iterative method or to accelerate the convergence of the generalized minimal residual method (GMRES). Several numerical experiments are run on a Linux cluster. We assess the performance of the preconditioned iterative solvers in terms of computational time and numbers of GMRES iterations. We find that the use of MUMPS to improve the preconconditioners P HSS and P AHSS is particularly efficient.
Evaluation Of The RDW Index (Red Cell Distribution Width) In The Prognosis Of Patients With Breast Cancer
Authors: Ferreira Amanda , Silva Luciana , Sabino Adriano , Carvalho Maria das Graças , Gomes Karina , Simões Ricardo 
Published: January 2023

Abstract
Background: Breast cancer is the most common cancer and the most frequent cause of death in women. The tumor inflammatory process is associated with the initiation, progression and maintenance of cancer and Red cell distribution width (RDW) index is one of the parameters that is altered in progressive inflammatory states.
Objective: To correlate RDW and inflammatory markers in women with breast cancer as aid in monitoring adverse events associated with the use of doxorubicin.
Methods: Database analysis of a prospective study of 80 women with breast malignancy undergoing doxorubicin-based chemotherapy, in a public hospital, from June 2015 to June 2018. To assess the significance of RDW in cancer and to correlate it with the variables: - plasma levels of C-reactive protein (CRP), galectin 3, myeloperoxidase, NT-terminal B-type natriuretic peptide and troponin I (cTnI).
Results: Analysis by marginal logistic regression, significant differences were found over time for the variables - RDW (p<0.001), PCR (p=0.036). In the comparative analysis of correlation between the different variables, there was a significant, negative difference between RDW and CRP at time T1, with a decrease in CRP over time, while RDW showed an increase.
Conclusion: The RDW is a simple test, easy to obtain and economical as part of the hemogram. The present study presented significant differences in providing additional information regarding the laboratory evolution of breast cancer and variations in its evolution regarding the therapeutic response.
Opioid Reduction and Enhanced Recovery in Orthopaedic Surgery (OREOS): A Protocol for a Feasibility Randomized Controlled Trial in Knee Replacement Patients
Authors: Madden Kim , Pallapothu Sushmitha , Shing Darren Young , Adili Anthony , Bhandari Mohit , Carlesso Lisa , Khan Moin , Kleinlugtenbelt Ydo V. , Krsmanovic Adrijana , Nowakowski Matilda , Packham Tara , Romeril Eric , Tarride Jean-Eric , Thabane Lehana , Tushinski Daniel M. , Wallace Christine , Winemaker Mitchell , Shanthanna Harsha 
Published: January 2023

Abstract
Background: Knee arthritis is a leading cause of limited function and long-term disability in older adults. Despite a technically successful total knee arthroplasty (TKA), around 20% of patients continue to have persisting pain with reduced function, and low quality of life. Many of them continue using opioids for pain control, which puts them at risk for potential long-term adverse effects such as dependence, overdose, and risk of falls. Although persisting pain and opioid use after TKA have been recognised to be important issues, individual strategies to decrease their burden have limitations and multi-component interventions, despite their potential, have not been well studied. In this study, we propose a multi-component pathway including personalized pain management, facilitated by a pain management coordinator. The objectives of this pilot trial are to evaluate feasibility (recruitment, retention, and adherence), along with opioid-free pain control at 8 weeks after TKA.
Methods: This is a protocol for a multicentre pilot randomized controlled trial using a 2-arm parallel group design. Participants in the intervention group will receive support from a pain management coordinator who will facilitate a multicomponent pain management pathway including: 1) preoperative education on pain and opioid use; 2) preoperative risk identification and mitigation; 3) personalized post-discharge analgesic prescriptions; 4) continued support for pain control and recovery up to 8 weeks post-op. Participants in the control group will undergo usual care. The primary outcomes of this pilot trial are to assess the feasibility of participant recruitment, retention, and adherence to the interventions, and key secondary outcomes are persisting pain and opioid use.
Discussion: The results of this trial will inform implementation of a coordinated approach, integrating a multicomponent pain pathway to improve pain control and reduce harms, while keeping an emphasis on patient centered care and shared decision making.
Trial Registration: Prospectively registered in clinicaltrials.gov (NCT04968132) on 21 July 2021. https://clinicaltrials.gov/ct2/show/NCT04968132
Alpha synuclein by RT-QuIC in dementia with Lewy Bodies: a systematic review and meta-analysis
Authors: PEÑA-BAUTISTA Carmen , KUMAR Rakesh , BAQUERO Miguel , CHÁFER-PERICÁS Consuelo , ABELEIN Axel , FERREIRA Daniel 
Published: January 2023

Abstract
Dementia with Lewy Bodies (DLB) is the second most common cause of neurodegenerative dementia but the field is still lacking a specific biomarker for its core pathology: alpha-synuclein. Real-time quaking induced conversion (RT-QuIC) has recently emerged as a strong candidate of alpha-synuclein biomarker. However, the variability in the technique parameters and the heterogeneity of DLB patients makes complex the reproducibility of the results. We provide an overview of the state-of-the-art research with regard to RT-QuIC in DLB focused on: (1) the capacity of RT-QuIC to discriminate DLB from controls, Parkinson’s disease (PD) and Alzheimer’s disease (AD); (2) the capacity of RT-QuIC to identify prodromal stages of DLB; (3) and the influence of co-pathologies on the RT-QuIC’s performance. We also assessed the influence of different factors on the RT-QuIC’s performance, such as technical conditions (e.g. temperature, pH, shaking-rest cycles), sample type, and clinical diagnosis versus autopsy confirmation. Our meta-analysis shows that RT-QuIC reaches very high diagnostic performance in discriminating DLB from both controls (pooled sensitivity and specificity of 0.94 and 0.96, respectively) and AD (pooled sensitivity and specificity of 0.95 and 0.88), and is promising for prodromal phases of DLB). However, the RT-QuIC’s performance to discriminate DLB from PD is currently low due to low specificity (pooled sensitivity and specificity of 0.94 and 0.11). Further, our analyses showed that the RT-QuIC’s performance is not substantialy influenced by sample type or clinical diagnosis versus autopsy confirmation. Co-pathologies did not seem to influence the RT-QuIC’s performance, but the number of studies is currently limited. We observed technical variability across studies but, for the published articles, we could not find that this variability has a clear effect on the reported results. We conclude that there is currently enough evidence to test alpha-synuclein by RT-QuIC for clinical use. We anticipate that harmonization of protocols across centres and advances in standardization will facilitate the clinical establishment and generalization of alpha-synuclein by RT-QuIC.
Super-forecasting the ‘technological singularity’ risks from artificial intelligence
Authors: Radanliev Petar , De Roure David , Maple Carsten , Ani Uchenna 
Published: January 2023

AbstractThis article investigates cybersecurity (and risk) in the context of ‘technological singularity’ from artificial intelligence. The investigation constructs multiple risk forecasts that are synthesised in a new framework for counteracting risks from AI itself. In other words, the research concern in this article is not just with securing a system, but to analyse how the system responds when (internally and externally caused) failure and compromise occur. This is an important methodological principle because not all systems can be secured, and we need to construct algorithms that will enable systems to continue operating even when parts of the system have been compromised. Furthermore, the article forecasts emerging cyber-risks from the integration of artificial intelligence in cybersecurity. Based on the forecasts, the article is concentrated on creating synergies between the existing literature, the data sources identified in the survey and forecasts. The forecasts are used to increase the feasibility of the overall research and enable the development of novel methodology that uses AI to defend from cyber risk. The methodology is focused on addressing the risk of AI attack, as well as to forecast the value of AI in defence and in the prevention of AI rogue devices acting independently.