bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigation into the effects of machining parameters on the edge quality of particleboards - Modelling and Optimization
Authors: GARRIDO NUNO MANUEL BATISTA , Martins Jorge , Davim João Paulo , Carvalho Luisa 
Published: March 2024

Abstract
It is challenging to model and optimize the variables affecting particleboard's (PB) machining edges. The lack of thorough investigation of these models presents further difficulties. However, they can directly improve the cutting-edge profile's quality and reduce production costs. Statistical and mathematical modelling tools, such as the Response Surface Methodology (RSM) are valuable tools for understanding how both qualitative and quantitative variables affect the quality performance of a process or product, which means understanding how these variables respond to the factors involved in the model. The Box-Behnken experimental design model was initially used to plan experiments in this study. By employing this technique, fewer tests are necessary to examine the predefined factors. The model's suitability and the accuracy of the results were verified using a variance analysis (ANOVA). That allowed a comparison of the variation caused by the factors with the interference caused by random errors in the generated responses. Subsequently, a mathematical-statistical model was then generated from experimental data using polynomial functions to use RSM as an optimization method. From this examination, the optimal values for each response variable were obtained. In conclusion, this study demonstrates that both experimental and prediction results show a good correlation and highlight that the use of RSM to optimize the delamination criteria (Tw), the specific cutting energy (Es), and feed per tooth (fz) as response variables when influenced by the factors feed speed (Vf) and frequency (N), is appropriate for the edge quality analysis of the specific type of PB considered in this study. Extended author information available on the last page of the article Published online: xx XXXXXXX 2024
Estimation of time–frequency-dependent lagged coherence for ground motions in simulating multipoint ground motions
Authors: Wen Pan , Bi Xirong 
Published: March 2024

Abstract
Traditional coherence functions ignore the time-varying characteristics of ground motions, influencing the accuracy of simulating multipoint nonstationary ground motions. An effective process is proposed for building the time–frequency lagged coherence expression to describe the spatial variation in both the time and frequency domains. First, the advantage of wavelet packet transform (WPT) in recognizing the time–frequency characteristics of signals is leveraged. The WPT is used to analyze the time–frequency characteristics of ground motions and estimate the time–frequency lagged coherence using the observed recordings from SMART-1 array. Then, based on the frame of simulating multipoint ground motions, a blind test for verifying the rationality of time–frequency lagged coherence is conducted to simulate spatially correlated ground motions using time–frequency and traditional lagged coherence functions. The spatial coherence of the target region is observed to have clearly lost its inherent time-varying property. The comparative results confirm the significance of using the time–frequency lagged coherence in simulating multipoint ground motions.
Predictive analytics for environmental impact: Deep learning and AI ensemble strategies in forecasting CO2 emissions
Authors: Nazir Talha , Nagra Hareem Fatima , Bhatti Matloob Hussain , Shahid Rub Nawaz , Shaukat Nadeem , Tariq Naeem ul Haq 
Published: March 2024

Abstract

The forecasting of carbon dioxide (CO2) emission trends stands as a pivotal step towards achieving environmental sustainability. As countries grapple with the challenge of curbing escalating CO2 emissions, the significance of accurate forecasting has become increasingly pronounced in recent years. In this study, to unveil the trajectory of CO2 emissions in Pakistan, forecasting was done through advanced artificial intelligence (AI) driven Artificial Neural Network (ANN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) models. Rigorous data preprocessing techniques were applied to historical CO2 emissions data for Pakistan comprising of 76 points from year 1946 to 2021. Sequences were formulated to capture temporal dependencies, paving the way for model training and validation. The ANN, GRU, and LSTM models were meticulously designed, each bearing unique attributes for time series forecasting. The obtained results yielded valuable insights, epitomized by model evaluations and predictions. The ANN model did really well with a test MAE of 8.111, a test R² of 0.8614 and a test RMSE of 10.25. The GRU model, characterized by a test MAE of 7.936, a test R² of 0.8355 and a test RMSE of 11.25, proved its worth as well. In contrast, the LSTM model demonstrated excellence with a test MAE of 7.941, a test R² of 0.8586 and a test RMSE of 10.45. A novel ensemble approach, combining these three models, yielded a test MAE of 7.876, a test R2 of 0.869, and a test RMSE of 10.5043. Further, the models were employed to forecast CO2 emissions for Pakistan from the year 2022 to 2030. The insights gained from this study not only enhance our understanding of CO₂ emissions trends in Pakistan but also provide valuable guidance for global efforts to adopt cleaner lifestyles and sustainable choices, fostering a healthier planet for all.
Cyclic loading effects and stability assessment of trees and stumps used as anchors in cable yarding operations
Authors: Marchi Luca , Mologni Omar , Byrne Ken , Grigolato Stefano , Roeser Dominik 
Published: March 2024

Abstract
Swing yarders in running skyline configuration using either grapples or chokers represent the most common configuration for cable yarding in coastal British Columbia. In this context, whole-tree logging and short work cycles lead to heavy and repeated loads applied to the trees and stumps when used as anchors for the rigging cables. Moreover, increased harvesting of second-growth forest stands leads to the unavailability of large and safe trees, thus introducing new challenges to identify suitable anchors and potentially increases safety risks of cable yarding operations. The present study aimed to collect evidence of the mechanical response provided by anchors for typical cable yarders used in second-growth harvesting in coastal British Columbia, and test the suitability of innovative techniques for the stability assessment based on the relationship between the anchor root-plate rotations and the related applied tensile forces. A conspicuous dataset could be derived from all the surveys, storing anchor rotations from a total of 1522 work cycles of which 1224 work cycles included also tensions measurements. The methodological approach was proven effective for monitoring different rigging configurations giving proof that repeated loading affects the stability of a tree/stump which can rapidly change over few hours of active yarding operations. Acquired data proved also that comparing theoretical failure limits with anchor rotations could be a valid approach however a considerable amount of species-specific data from tree pulling tests is required.
A Temporal Mathematical Model of Alopecia Areata: Investigating Hair Cycle Dynamics and Disease Progression
Authors: Alzubadi Hanadi 
Published: March 2024

Abstract
This paper describes the model development process in detail, including the formulation of equations and parameters based on existing knowledge of hair cycle dynamics and immune system interactions. Various analyses are conducted to gain insights into the behavior of the model. Illustrative simulations are performed to observe the temporal dynamics of the disease progression under different conditions. Sensitivity analysis using eFAST (Extended Fourier Amplitude Sensitivity Test) is employed to identify the most influential parameters affecting the length of the anagen phase in hair follicles affected by alopecia areata. The findings of the study shed light on the complex dynamics of alopecia areata and contribute to a deeper understanding of the disease mechanisms. The model provides a valuable tool for studying autoimmune hair loss diseases and may have implications for the diagnosis and treatment of such conditions. By simulating the immune response and its effects on hair follicles, the model offers insights into potential treatment strategies that can target immune dysregulation. The temporal mathematical model presented in this dissertation provides a comprehensive framework for investigating alopecia areata and understanding its underlying dynamics. The integration of hair cycle dynamics and immune system interactions enhances our knowledge of the disease and opens avenues for future advancements in diagnosis and treatment approaches.
Smoked, Smokeless, and Poly-tobacco Use during Pregnancy in relation to Infant Mortality in Cambodia: Findings from a Nationwide sample
Authors: Peng Jiahao , Petersen Anne Berit , Shavlik David , Xiao Daliao , Yel Daravuth , Kheam They , Singh Pramil N. 
Published: March 2024

Abstract

Background: Maternal cigarette smoking during pregnancy is an established risk factor for adverse maternal, fetal, and infant outcomes. In contrast, maternal smokeless tobacco use (i.e., e-cigarettes, snus, betel quid, iqmik) during pregnancy has a more complex risk profile due to its potential use as a smoking cessation aid or to reduce the harm from smoking tobacco. In this study, our overall aim was to investigate the association between smoked, smokeless, and poly-tobacco (smoked + smokeless) use during pregnancy and infant mortality in a national sample of women in Cambodia.
Methods: Our study used the data from the National Adult Tobacco Survey of Cambodia (NATSC) that used sampling methods and tobacco survey items from the CDC Global Adult Tobacco Survey but also included a supplement on reproductive health and birthing history. For our study, we selected the 5,342 persons of the NATSC who reported complete data on at least one pregnancy, and our unit of analysis was the 15,998 pregnancies from those persons. We conducted a multivariable logistic regression to relate tobacco use to infant mortality. Taylor linearized variance estimators were used to account for clustering by sampling unit and mother.
Results: We found that smokeless tobacco in the form of a betel quid was the most common form of tobacco used during pregnancy. In multivariable logistic regression, we found increased odds of infant death for all tobacco use categories but that the strongest effects were seen for habits that included smokeless tobacco (OR[95% CI] relative to never used tobacco in any form = 5.68[1.03, 31.46] for poly-tobacco use, 2.08 [1.15, 3.76] for exclusive use of smokeless tobacco). In more detailed analyses that considered the composition of the betel quid (tobacco, areca nut/leaf, slaked lime), we found that even chewing of tobacco leaves with no processing or additives was associated with a three-fold increase in odds of infant death (OR[95% CI] relative to never user = 3.05[1.45, 6.45]).
Conclusion: We found that even among those pregnant persons who limited their nicotine habit to chewing tobacco leaves with no processing or additives, there remained a higher odds of fetal or infant death from that pregnancy.
Uncertainty-Aware Deep Learning for Segmentation of Primary Tumour and Pathologic Lymph Nodes in Oropharyngeal Cancer: Insights from a Multi-Centre Cohort
Authors: De Biase Alessia , Sijtsema Nanna Maria , Dijk Lisanne V van , Steenbakkers Roel , Langendijk Johannes A. , Ooijen Peter van 
Published: March 2024

Abstract
Purpose
Within the medical field, there is a growing demand for deep learning (DL) models which convey model certainty to the end-user, while maintaining alignment between model accuracy and certainty. For oropharyngeal cancer (OPC) primary tumour (PT) segmentation in PET/CT images, an ensemble-based DL model was developed which outputs tumour probability maps (TPM) showing voxel-level predicted probabilities. This study extended the network to generate TPMs for both PT and pathologic lymph nodes (PL) and explored whether quantified uncertainty in TPMs can predict segmentation model accuracy in an independent external cohort.
Methods
We gathered PET/CT images and manual delineations of gross tumour volume of the PT (GTVp) and PL (GTVln) of 405 OPC patients treated with (chemo)radiation in our institute in 2010-2022. The publicly available 2022 HECKTOR challenge dataset served as external test set. The existing network was adapted to perform multi-label segmentation, training 15 models and considering the ensemble average of predicted TPMs per patient. Surface and aggregate DSC were computed for the predicted contours at different probability thresholds. Uncertainty was quantified by coefficient of variation (CV) of multiple predictions.
Results
GTVln segmentation showed slightly lower performance than GTVp: aggregate DSC of 0.66 and 0.67 in the internal, 0.70 and 0.75 in the external test sets. However, a significant negative correlation (about -0.6) was observed in both test sets between Average Surface DSC and CV for both structures, indicating a significant calibration.
Conclusion
Significant accuracy versus uncertainty calibration was achieved for TPMs in both internal and external test sets, indicating the potential use of CV to identify cases with lower GTVp and GTVln segmentation accuracy, independently of the dataset.
Reporting of participant race and ethnicity from COVID-19 randomized controlled drug and biologicals trials: a scoping review
Authors: Pranić Shelly Melissa , Estevão Maria Dulce , Vasanthan Lenny T. , Pérez-Neri Iván , Pulumati Anika , Junior Fábio Antonio Serra de Lima , Malih Narges , Mishra Vinayak , Thompson Jacqueline , Nnate Daniel 
Published: March 2024

Abstract
Racial and ethnic minorities have been disproportionally burdened by hospitalization and death due to COVID-19. Participation of individuals of diverse races and ethnicities in clinical trials according to study-level characteristics of randomized controlled trials (RCT) that test effectiveness of COVID-19 drugs could be insightful for future researchers. The objective of this scoping review was to describe frequency of race and ethnicity reported as demographic variables and specific reporting of race and ethnicity according to COVID-19 RCT characteristics. We conducted comprehensive searches in Pubmed, ProQuest, WHO Database, and Cochrane Central Register of Controlled Trials, and gray literature via preprint servers from 1/1/2020 to 5/4/2022. We included RCTs on emergency- or conditionally-approved COVID-19 drug interventions (remdesivir, barcitinib, and molnupiravir) with or without comparators. Self-reported race as American Indian/Pacific Islander, Asian, Black/African American, or White, ethnicity as Hispanic/Latinx, study design characteristics, and participant-relevant data were collected. A total of 17 RCTs with 17935 participants was included. Most (n=13, 76%) reported at least one race and ethnicity and were US-based, industry-funded RCTs. Asian, Black, Latinx, and White participants were mostly enrolled in RCTs that studied remdesivir. Native American and Hawaiian participants were mostly assessed for progression to high-flow oxygen/non-invasive ventilation. Time to recovery was assessed predominantly in Black and White participants, while hospitalization or death was mostly assessed in Asian, Latinx, and multi-race participants. Trialists should be aware of RCT-level factors and characteristics that may be associated with low participation of racial and ethnic minorities, which could inform evidence-based interventions to increase minority participation.
Registration: Open Science Framework: https://osf.io/3dx4f/?view_only=d1af00d952cc472b803df5297bce9a80
Physical, social, and behavioural determinants of household energy consumption in the UK
Authors: Satre-Meloy Aven , Hampton Sam 
Published: March 2024

Abstract
Determining which attitudes and behaviours predict household energy consumption can help accelerate the low-carbon energy transition. Conventional approaches in this domain are limited, often relying on survey methods that produce data on individuals’ motivations and self-reported activities without pairing these with actual energy consumption records, which are particularly hard to collect for large, nationally representative samples. This challenge precludes the development of empirical evidence on which attitudes and behaviours influence patterns of energy consumption, thus limiting the extent to which these can inform energy interventions or conservation programs. This study demonstrates a novel methodology for estimating energy consumption in the absence of actual energy records by using a large, publicly available data set of energy consumption in the UK. We develop a predictive model using the Smart Energy Research Laboratory (SERL) data portal (with records from nearly 13,000 UK households) and then use this model to predict energy consumption (both electric and gas) for a nationally representative sample of 1,000 UK householders for which we separately collect over 200 variables relating to climate change attitudes and practices. Our approach uses a set of over 50 independent variables that are shared between the data sets, allowing us to train a model on the SERL data and use it to analyse the relationship between energy consumption and the opinions, motivations, and daily practices of survey respondents. Our results show that attitude and behavioural factors are less important in predicting both electricity and gas consumption than are dwelling characteristics, confirming previous findings in the literature. However, we also show that behaviours and attitudes related to frugality, conscientiousness, consumerism, and charitable giving demonstrate associations with energy consumption patterns. We discuss how these findings can inform efforts to decarbonize home energy use in the UK.
Performance evaluation and verification of post-processing methods for TIGGE ensemble data using machine learning approaches
Authors: Patel Anant , Yadav S. M. 
Published: March 2024

ABSTRACT

Ensemble modelling has become a significant technique in the field of machine learning, as it utilises the combined knowledge of multiple base models to improve the accuracy of predictions in different domains. Nevertheless, the effectiveness of ensemble predictions relies on the implementation of post-processing techniques that enhance and optimize the outputs of the ensemble. This study explores the domain of ensemble data post-processing, utilizing a machine learning-focused methodology to thoroughly assess and contrast a variety of post-processing methods. TIGGE Ensemble data from ECMWF and NCEP were used from 2010 to 2020. Research covers machine learning approaches post-processing methods such as BMA, cNLR, HXLR, OLR, logreg, hlogreg, QM were applied. The probabilistic forecasts were validated using the Brier Score (BS), Area Under Curve (AUC) of Receiver Operator Characteristics (ROC) plots and reliability plots. The cNLR and BMA strategies for post-processing performed exceptionally well with BS value of 0.10 and RPS value of 0.11 at all grid points for both methods. The ROC–AUC values for the cNLR and BMA methods were found to be 91.87 and 91.82%, respectively. The results show that improved post-processing techniques can be helpful to predict the flood in advance with accurate precision and warning.
Nano-delivery of Silibinin Potentiate the Induction of Immunogenic Cell Death (ICD) in Melanoma Cells
Authors: Amiri Mina , Jafari Sevda , Lavasanifar Afsaneh , Molavi Ommoleila , Montazersaheb Soheila 
Published: March 2024

Background:
Induction of immunogenic cell death (ICD) in tumors can enhance antitumor
immunity and modulate immunosuppression in the tumor microenvironment (TME).

Objective:
In the current study, we investigated the effect of silibinin, a natural compound with
anticancer activity, and its polymer-based nanoformulations on the induction of apoptosis and
ICD in cancer cells.

Methods:
Free and nanoparticulate silibinin were evaluated for their growth-inhibitory effects
using an MTT assay. Annexin V/PI staining was used to analyze apoptosis. Calreticulin (CRT)
expression was measured by flow cytometry. Western blotting was conducted to examine the
levels of elf2α, which plays a role in the ICD pathway. The HSP90 and ATP levels were determined
using specific detection kits.

Results:
Compared to the free drug, silibinin-loaded nanocarriers significantly increased the
induction of apoptosis and ICD in B16F10 cells. ICD induction was characterized by significantly
increased levels of ICD biomarkers, including CRT, HSP90, and ATP. We also observed
an increased expression of p-elf-2α/ elf-2α in B16F10 cells treated with silibinin-loaded micelles
compared to cells that received free silibinin.

Conclusion:
Our findings showed that the encapsulation of silibinin in polymeric nanocarriers
can potentiate the effects of this drug on the induction of apoptosis and ICD in B16F10 melanoma
cells.
Tanshinone IIA Against Cerebral Ischemic Stroke and Ischemia-
Reperfusion Injury: A Review of the Current Documents
Authors: Arefnezhad Reza , Nejabat Alireza , Behjati Fatemeh , Torkamanche Mona , Zarei Hooman , Yekkehbash Motahhareh , Afsharmanesh Fatemeh , Niknam Zahra , Jamialahmadi Tannaz , Sahebkar Amirhossein 
Published: March 2024

Abstract::
Stroke is a well-known neurological disorder that carries significant morbidity and mortality
rates worldwide. Cerebral Ischemic Stroke (CIS), the most common subtype of stroke, occurs
when thrombosis or emboli form elsewhere in the body and travel to the brain, leading to reduced
blood perfusion. Cerebral Ischemia/Reperfusion Injury (CIRI) is a common complication of CIS and
arises when blood flow is rapidly restored to the brain tissue after a period of ischemia. The therapeutic
approaches currently recognized for CIS, such as thrombolysis and thrombectomy, have notable
side effects that limit their clinical application. Recently, there has been growing interest
among researchers in exploring the potential of herbal agents for treating various disorders and malignancies.
One such herbal agent with medicinal applications is tanshinone IIA, an active diterpene
quinone extracted from Salvia miltiorrhiza Bunge. Tanshinone IIA has shown several pharmacological
benefits, including anti-inflammatory, antioxidant, anti-apoptotic, and neuroprotective properties.
Multiple studies have indicated the protective role of tanshinone IIA in CIS and CIRI. This literature
review aims to summarize the current findings regarding the molecular mechanisms through
which this herbal compound improves CIS and CIRI.