bluuebunny/crossref_metadata_2025_split huggingface dataset:
Precision and convergence speed of the ensemble Kalman filter-based parameter estimation: setting parameter uncertainty for reliable and efficient estimation
Authors: Sueki Kenta , Nishizawa Seiya , Yamaura Tsuyoshi , Tomita Hirofumi 
Published: September 2022

AbstractDetermining physical process parameters in atmospheric models is critical to obtaining accurate weather and climate simulations; estimating optimal parameters is essential for reducing model error. Recently, automatic parameter estimation using the ensemble Kalman filter (EnKF) has been tested instead of conventional manual parameter tuning. To maintain uncertainty for the parameters to be estimated and avoid filter divergence in EnKF-based methods, some inflation techniques should be applied to parameter ensemble spread (ES). When ES is kept constant through the estimation using an inflation technique, the precision and convergence speed of the estimation vary depending on the ES assigned to estimated parameters. However, there is debate over how to determine an appropriate constant ES for estimated parameters in terms of precision and convergence speed. This study examined the dependence of precision and convergence speed of an estimated parameter on the ES to establish a reliable and efficient method for EnKF-based parameter estimation. This was carried out by conducting idealized experiments targeting a parameter in a cloud microphysics scheme. In the experiments, there was a threshold value for ES where any smaller values did not result in any further improvements to the estimation precision, which enabled the determination of the optimal ES in terms of precision. On the other hand, the convergence speed accelerates monotonically as ES increases. To generalize the precision and convergence speed, we approximated the time series of parameter estimation with a first-order autoregression (AR(1)) model. We demonstrated that the precision and convergence speed may be quantified by two parameters from the AR(1) model: the autoregressive parameter and the amplitude of random perturbation. As the ES increases, the autoregressive parameter decreases, while the random perturbation amplitude increases. The estimation precision was determined based on the balance between the two values. The AR(1) approximation provides quantitative guidelines to determine the optimal ES for the precision and convergence speed of the EnKF-based parameter estimation.
Biomechanical analysis of lifting on stable versus unstable surfaces—a laboratory-based proof-of-concept study
Authors: Grooten Wilhelmus Johannes Andreas , Billsten Edwin , von Stedingk Sebastian , Reimeringer Mikael 
Published: September 2022

Abstract
Background
Many workers performing manual handling tasks suffer from musculoskeletal disorders (MSD). Previous research has identified several loading aspects associated with manual handling, but it is still unknown if lifting on an unstable surface is associated with increased biomechanical loading of different body parts.

Aim
This proof-of-concept study aims to study what kinematic and kinetic movement parameters, such as movement time, joint angles, torque, and muscle activity are feasible and of importance when studying the effect of lifting on surfaces with varying degrees of stability in an experimental set-up.

Methods
Measurements were taken during three different surface conditions: stable, slightly unstable, and unstable. The participants were instructed to lift a box from the floor and place it on a table in front of them. The weight of the box varied from 0.5 to 15.5 kg. By using a motion capture system (VICON) with 28 reflective markers placed on the participants and one on the box, one Kistler force plate for measuring force levels and center of pressure movements (CoP), and four electromyographic transmitters (EMG), we analyzed the downward and upward phases of the lifting movement, using the Friedman’s test for repeated measures.

Results
Statistically significant results with less joint movements in the lower and upper back were seen with increased instability during both the downward and upward phases. The decrease in trunk movements with increased instability resulted in a somewhat more flexed knee position during the movement, a lower torque in the lower back, and a decrease in CoP movements, but no differences in movement time or muscle activity in back and knee muscles.

Conclusion
Lifting while standing on unstable surfaces resulted in an alteration of both kinematics and kinetics parameters; however, further studies regarding whether this is an additional risk factor for developing lower back pain are needed. Muscle activity levels were not altered due to instability and due to the complexity of the measurement, and we suggest not including EMG measures in future experiments of this type.
Prospective evaluation of sport activity and the development of femoroacetabular impingement in the adolescent hip (PREVIEW): results of the pilot study
Authors: Ayeni Olufemi R. ,   PREVIEW Pilot Investigators, Jean Pierre-Olivier , Simunovic Nicole , Duong Andrew , Foster Gary , Musahl Volker , Sim Yan , Thabane Lehana , MacLeay Callum , Skelly Matthew , Shanmugaraj Ajay , Ghanem Dana , Heels-Ansdell Diane , Buckingham Lisa , Mascarenhas Vasco V. , Ponniah Andrea , Belzile Etienne L. , Agricola Rintje , Baek Seung-Hoon , Lee Hoseok , Chang Ae-Sun 
Published: September 2022

Abstract
Background
The purpose of this pilot study was to validate the feasibility of a definitive study aimed at determining if high-intensity physical activity during adolescence impacts the development of femoroacetabular impingement (FAI).

Methods
This prospective cohort pilot study had a sample size target of 50 volunteers between 12 and 14 years old at sites in Canada, South Korea, and the Netherlands. Participants were evaluated clinically and radiographically at baseline and at 2 years. The participants’ sport and physical activity were evaluated using the Habitual Activity Estimation Scale (HAES) and the American Orthopaedic Society for Sports Medicine (AOSSM) criteria for sport specialization. The primary outcome was feasibility and secondary outcomes included the incidence of radiographic FAI and hip range of motion, function (Hip Outcome Score, HOS), and quality of life (Pediatric Quality of Life questionnaire, PedsQL) at 24 months. Study groups were defined at the completion of follow-up, given the changes in participant activity levels over time.

Results
Of the 54 participants enrolled, there were 36 (33% female) included in the final analysis. At baseline, those classified as highly active and played at least one organized sport had a higher incidence of asymptomatic radiographic FAI markers (from 6/32, 18.8% at baseline to 19/32, 59.4% at 24 months) compared to those classified as low activity (1/4, 25% maintained at baseline and 24 months). The incidence of radiographic FAI markers was higher among sport specialists (12/19, 63.2%) compared to non-sport specialists (8/17, 47.1%) at 24 months. The HOS and PedsQL scores were slightly higher (better) among those that were highly active and played a sport compared to those who did not at 2 years (mean difference (95% confidence interval): HOS-ADL subscale 4.56 (− 7.57, 16.70); HOS-Sport subscale 5.97 (− 6.91, 18.84); PedsQL Physical Function 7.42 (− 0.79, 15.64); PedsQL Psychosocial Health Summary 6.51 (− 5.75, 18.77)).

Conclusion
Our pilot study demonstrated some feasibility for a larger scale, definitive cohort study. The preliminary descriptive data suggest that adolescents engaged in higher levels of activity in sports may have a higher risk of developing asymptomatic hip deformities related to FAI but also better quality of life over the 2-year study period.
Pharmacoinformatic study of inhibitory potentials of selected flavonoids against papain-like protease and 3-chymotrypsin-like protease of SARS-CoV-2
Authors: Tijjani Habibu , Adegunloye Adegbenro P. , Uba Auwalu , Adebayo Joseph O. , Gyebi Gideon A. , Ibrahim Ibrahim M. 
Published: September 2022

AbstractBackgroundInhibition of papain-like protease (PLpro) and 3-chymotrypsin-like protease (3CLpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is projected to terminate its replication. Hence, these proteases represent viable therapeutic targets.MethodsSixty-one flavonoids with reported activities against other RNA viruses were selected and docked in PLpro and 3CLpro. Flavonoids with better binding energies compared to reference inhibitors (lopinavir and ritonavir) in their interaction with PLpro and 3CLpro were selected for drug-likeness and ADMET analysis. The best representative flavonoid for each protease from the ADMET filtering analysis was subjected to molecular dynamics simulations (MDS) and clustering analysis of the trajectory files.ResultsLicorice, ugonin M, procyanidin, silymarin, and gallocatechin gallate had better binding energies (-11.8, -10.1, -9.8, -9.7 and -9.6 kcal/mol respectively) with PLpro compared to lopinavir and ritonavir (-9.1 and -8.5 kcal/mol respectively). Also, isonymphaeol B, baicalin, abyssinone II, tomentin A, and apigetrin had better binding energies (-8.7, -8.3, -8.2, -8.1, and -8.1 kcal/mol respectively) with 3CLpro compared to lopinavir and ritonavir (-7.3 and -7.1 kcal/mol respectively). These flavonoids interacted with the proteases via hydrogen and non-hydrogen bonding. Of these flavonoids, silymarin and isonymphaeol B demonstrated most favourable combination of attributes in terms of binding energies, compliance with Lipinski rule for drug-likeness and favourable pharmacokinetics in silico. These two flavonoids exhibited appreciable degree of structural stability, maintaining strong interaction with residues in the different representative clusters selected during the MDS run.ConclusionSilymarin and isonymphaeol B are proposed for further studies as compounds with potential activities against SARS-CoV-2.
Biomineralization of bone tissue: calcium phosphate-based inorganics in collagen fibrillar organic matrices
Authors: Hong Min-Ho , Lee Jung Heon , Jung Hyun Suk , Shin Heungsoo , Shin Hyunjung 
Published: September 2022

Abstract

Background
Bone regeneration research is currently ongoing in the scientific community. Materials approved for clinical use, and applied to patients, have been developed and produced. However, rather than directly affecting bone regeneration, these materials support bone induction, which regenerates bone. Therefore, the research community is still researching bone tissue regeneration. In the papers published so far, it is hard to find an improvement in the theory of bone regeneration. This review discusses the relationship between the existing theories on hard tissue growth and regeneration and the biomaterials developed so far for this purpose and future research directions.

Mainbody
Highly complex nucleation and crystallization in hard tissue involves the coordinated action of ions and/or molecules that can produce different organic and inorganic composite biomaterials. In addition, the healing of bone defects is also affected by the dynamic conditions of ions and nutrients in the bone regeneration process. Inorganics in the human body, especially calcium- and/or phosphorus-based materials, play an important role in hard tissues. Inorganic crystal growth is important for treating or remodeling the bone matrix. Biomaterials used in bone tissue regeneration require expertise in various fields of the scientific community. Chemical knowledge is indispensable for interpreting the relationship between biological factors and their formation. In addition, sources of energy for the nucleation and crystallization processes of such chemical bonds and minerals that make up the bone tissue must be considered. However, the exact mechanism for this process has not yet been elucidated. Therefore, a convergence of broader scientific fields such as chemistry, materials, and biology is urgently needed to induce a distinct bone tissue regeneration mechanism.

Conclusion
This review provides an overview of calcium- and/or phosphorus-based inorganic properties and processes combined with organics that can be regarded as matrices of these minerals, namely collagen molecules and collagen fibrils. Furthermore, we discuss how this strategy can be applied to future bone tissue regenerative medicine in combination with other academic perspectives.
The role of cellular traction forces in deciphering nuclear mechanics
Authors: Joshi Rakesh , Han Seong-Beom , Cho Won-Ki , Kim Dong-Hwee 
Published: September 2022

Abstract
Cellular forces exerted on the extracellular matrix (ECM) during adhesion and migration under physiological and pathological conditions regulate not only the overall cell morphology but also nuclear deformation. Nuclear deformation can alter gene expression, integrity of the nuclear envelope, nucleus-cytoskeletal connection, chromatin architecture, and, in some cases, DNA damage responses. Although nuclear deformation is caused by the transfer of forces from the ECM to the nucleus, the role of intracellular organelles in force transfer remains unclear and a challenging area of study. To elucidate nuclear mechanics, various factors such as appropriate biomaterial properties, processing route, cellular force measurement technique, and micromanipulation of nuclear forces must be understood. In the initial phase of this review, we focused on various engineered biomaterials (natural and synthetic extracellular matrices) and their manufacturing routes along with the properties required to mimic the tumor microenvironment. Furthermore, we discussed the principle of tools used to measure the cellular traction force generated during cell adhesion and migration, followed by recently developed techniques to gauge nuclear mechanics. In the last phase of this review, we outlined the principle of traction force microscopy (TFM), challenges in the remodeling of traction forces, microbead displacement tracking algorithm, data transformation from bead movement, and extension of 2-dimensional TFM to multiscale TFM.
Comprehensive summary of solid oxide fuel cell control: a state-of-the-art review
Authors: Yang Bo , Li Yulin , Li Jiale , Shu Hongchun , Zhao Xinyu , Ren Yaxing , Li Qiang 
Published: September 2022

AbstractHydrogen energy is a promising renewable resource for the sustainable development of society. As a key member of the fuel cell (FC) family, the solid oxide fuel cell (SOFC) has attracted a lot of attention because of characteristics such as having various sources as fuel and high energy conversion efficiency, and being pollution-free. SOFC is a highly coupled, nonlinear, and multivariable complex system, and thus it is very important to design an appropriate control strategy for an SOFC system to ensure its safe, reliable, and efficient operation. This paper undertakes a comprehensive review and detailed summary of the state-of-the-art control approaches of SOFC. These approaches are divided into eight categories of control: proportional integral differential (PID), adaptive (APC), robust, model predictive (MPC), fuzzy logic (FLC), fault-tolerant (FTC), intelligent and observer-based. The SOFC control approaches are carefully evaluated in terms of objective, design, application/scenario, robustness, complexity, and accuracy. Finally, five perspectives are proposed for future research directions.
Iron deposition in multiple sclerosis: overall load or distribution alteration?
Authors: Hamdy Eman , Galeel Aya Abdel , Ramadan Ismail , Gaber Dina , Mustafa Haytham , Mekky Jaidaa 
Published: September 2022

AbstractBackgroundThough abnormal iron deposition has been reported in specific brain regions in multiple sclerosis (MS), no data exist about whether the overall quantity of iron in the brain is altered or not. We aimed to determine whether the noted aberrant iron deposition in MS brains was a problem of overall load or regional distribution in a cohort of MS patients.MethodsAn experienced neuroradiologist, a radiology software engineer, and four neurologists analysed data from quantitative susceptibility maps reconstructed from 3-T magnetic resonance brain images of 30 MS patients and 15 age- and sex-matched healthy controls. Global brain iron load was calculated, and the regional iron concentrations were assessed in 1,000 regions of interest placed in MS lesions in different locations, normal appearing white matter, thalami, and basal ganglia.ResultsGlobal brain iron load was comparable between patients and controls after adjustment for volume (p= 0.660), whereas the regional iron concentrations were significantly different in patients than in control (p≤ 0.031). There was no significant correlation between global iron load and clinical parameters, whereas regional iron concentrations correlated with patients’ age, disease duration, and disability grade (p≤ 0.039).ConclusionsThe aberrant iron deposition noted in MS seems to be a problem of regional distribution rather than an altered global brain iron load.
Prediction of lipomatous soft tissue malignancy on MRI: comparison between machine learning applied to radiomics and deep learning
Authors: Fradet Guillaume , Ayde Reina , Bottois Hugo , El Harchaoui Mohamed , Khaled Wassef , Drapé Jean-Luc , Pilleul Frank , Bouhamama Amine , Beuf Olivier , Leporq Benjamin 
Published: September 2022

Abstract
Objectives
Malignancy of lipomatous soft-tissue tumours diagnosis is suspected on magnetic resonance imaging (MRI) and requires a biopsy. The aim of this study is to compare the performances of MRI radiomic machine learning (ML) analysis with deep learning (DL) to predict malignancy in patients with lipomas oratypical lipomatous tumours.

Methods
Cohort include 145 patients affected by lipomatous soft tissue tumours with histology and fat-suppressed gadolinium contrast-enhanced T1-weighted MRI pulse sequence. Images were collected between 2010 and 2019 over 78 centres with non-uniform protocols (three different magnetic field strengths (1.0, 1.5 and 3.0 T) on 16 MR systems commercialised by four vendors (General Electric, Siemens, Philips, Toshiba)).
Two approaches have been compared: (i) ML from radiomic features with and without batch correction; and (ii) DL from images. Performances were assessed using 10 cross-validation folds from a test set and next in external validation data.

Results
The best DL model was obtained using ResNet50 (resulting into an area under the curve (AUC) of 0.87 ± 0.11 (95% CI 0.65−1). For ML/radiomics, performances reached AUCs equal to 0.83 ± 0.12 (95% CI 0.59−1) and 0.99 ± 0.02 (95% CI 0.95−1) on test cohort using gradient boosting without and with batch effect correction, respectively. On the external cohort, the AUC of the gradient boosting model was equal to 0.80 and for an optimised decision threshold sensitivity and specificity were equal to 100% and 32% respectively.

Conclusions
In this context of limited observations, batch-effect corrected ML/radiomics approaches outperformed DL-based models.
A stochastic deep reinforcement learning agent for grid-friendly electric vehicle charging management
Authors: Heendeniya Charitha Buddhika , Nespoli Lorenzo 
Published: September 2022

AbstractElectrification of the transportation sector provides several advantages in favor of climate protection and a shared economy. At the same time, the rapid growth of electric vehicles also demands innovative solutions to mitigate risks to the low-voltage network due to unpredictable charging patterns of electric vehicles. This article conceptualizes a stochastic reinforcement learning agent that learns the optimal policy for regulating the charging power. The optimization objective intends to reduce charging time, thus charging faster while minimizing the expected voltage violations in the distribution network. The problem is formulated as a two-stage optimization routine where the stochastic policy gradient agent predicts the boundary condition of the inner non-linear optimization problem. The results confirm the performance of the proposed architecture to control the charging power as intended. The article also provides extensive theoretical background and directions for future research in this discipline.
A framework for researching energy optimization of factory operations
Authors: Grosch Benedikt , Ranzau Heiko , Dietrich Bastian , Kohne Thomas , Fuhrländer-Völker Daniel , Sossenheimer Johannes , Lindner Martin , Weigold Matthias 
Published: September 2022

AbstractEnergy optimization of factory operations has gained increasing importance over recent years since it is understood as one way to counteract climate change. At the same time, the number of research teams working on energy-optimized factory operations has also increased. While many tools are useful in this area, our team has recognized the importance of a comprehensive framework to combine functionality for optimization, simulation, and communication with devices in the factory. Therefore, we developed a framework that provides a standardized interface to research energy-optimized factory operations with a rolling horizon approach. The optimization part of the framework is based on the OpenAI gym environment. The framework also provides connectors for multiple communication protocols including Open Platform Communication Unified Architecture and Modbus via Transmission Control Protocol. These facilities can be utilized to implement rolling horizon optimizations for factory systems easily and directly control devices in the factory with the optimization results. In this article, we present the framework and show some examples to prove the effectiveness of our approach.
Net load forecasting using different aggregation levels
Authors: Beichter Maximilian , Phipps Kaleb , Frysztacki Martha Maria , Mikut Ralf , Hagenmeyer Veit , Ludwig Nicole 
Published: September 2022

AbstractIn the electricity grid, constantly balancing the supply and demand is critical for the network’s stability and any expected deviations require balancing efforts. This balancing becomes more challenging in future energy systems characterised by a high proportion of renewable generation due to the increased volatility of these renewables. In order to know when any balancing efforts are required, it is essential to predict the so-called net load, the difference between forecast energy demand and renewable supply. Although various forecasting approaches exist for both the individual components of the net load and the net load itself, it is unclear if it is more beneficial to aggregate several specialised forecasts to obtain the net load or to aggregate the input data to forecast the net load with one approach directly. Therefore, the present paper compares three net load forecasting approaches that exploit different levels of aggregation. We compare an aggregated strategy that directly forecasts the net load, a partially aggregated strategy that forecasts demand and supply separately, and a disaggregated strategy that forecasts demand and supply from each generator separately. We evaluate the forecast performance of all strategies with a simple and a complex forecasting model, both for deterministic and probabilistic forecasts, using one year of data from a simulated realistic future energy system characterised by a high share of renewable energy sources. We find that the partially aggregated strategy performs best, suggesting that a balance between specifically tailored forecasting models and aggregation is advantageous.