bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of kaolin-nano-silica mixture on geomechanical properties enhancement of soils
Authors: Shalaby Ola Bakr , Elkady Hala M. , Salah Mohamed , Nagy Nabil M. , Fayed Ayman L. 
Published: September 2024

AbstractWeak soil is a major obstacle facing the urban development of any site with other exceptional merits. The current study aims to investigate the utilization of nano-silica in enhancing the mechanical properties of weak kaolin soils. Design mixes using different percentages of nano-silica were investigated in the range between 0.25–1.20% from the dry weight of the kaolin soil. Various chemical, physical, and mechanical properties of each mixture have been investigated. The obtained results indicated that nano-silica addition to such kaolin soils decreased the plasticity index and the maximum dry density while increasing the plastic limit, the Liquid limit, and the optimum moisture content. In different curing days of the tested mixtures, maximum dry density was decreased, while the optimum moisture content increased. The optimum value of added nano-silica was less than 1% of the soil dry weight. In the modified kaolin soil with 0.9% nano-silica, the plastic limit was increased by 29%, while the liquid limit decreased by 13% in comparison with the untreated sample. After 28 days of the cured sample, the unconfined compressive strength readings increased by almost 14% compared to its reading on day one. Also, the California bearing ratio results recorded significant enhancement with nano-silica additives in comparison with the untreated kaolin soil. After 28 curing days, the sonicated samples recorded enhancement in the unconfined compressive strength readings by more than 5% and 9% with the additive N-Si (0.3% and 0.9%), respectively, when compared with the unsonicated samples.
Graphical Abstract
Measuring Business Cycle Stylized Facts in Selected Oil-Producing Economies: A Comparative Study
Authors: Chukwu Chigozie , Vasilev Aleksandar , Saha Shrabani 
Published: September 2024

AbstractWe document the stylized facts relating to the contemporary business cycle in eleven oil-producing economies that account for over 65% of global oil production using a set of macroeconomic aggregates covering the period 1981 to 2021. In the context of Nigeria, a vector autoregression analysis is used to measure a one standard deviation oil price shock on time series such as trade balance, real GDP, inter alia. Our study is unique in that we compared four different detrending techniques, including Hodrick–Prescott (HP), Baxter-King, Christiano–Fitzgerald, and Butterworth filters. This is a departure from the conventional method of relying on the HP filter, which has been known to have limitations, particularly in the context of oil-producing economies. Our findings indicate that the selected oil-producing economies with countercyclical inflation patterns suggest that supply shocks drive the business cycles. Conversely, the oil-producing economies with inflation in sync (procyclical) with the business cycle suggest that demand shocks may be the primary driver of business cycle variations. Additionally, our study shows that oil price innovations lead to higher inflation in Nigeria.
Efficient path algorithms for clustered Lasso and OSCAR
Authors: Takahashi Atsumori , Nomura Shunichi 
Published: September 2024

AbstractIn high dimensional regression, clustering features according to their effects on outcomes is often as important as feature selection. For example, insurance premiums are set for each rate class pertaining to risk factors related to claim risk. To calculate reliable insurance premiums, it is often necessary to group the numerous rate classes into fewer classes. However, the combinations of ways to consolidate rate classes are vast, and it is computationally challenging to consider each combination individually. Under such circumstances, sparse regularization techniques for feature clustering are extremely useful as methods that automatically consolidate rate classes with no significant differences in risk levels during the estimation process. For this purpose, clustered Lasso and octagonal shrinkage and clustering algorithm for regression (OSCAR) can be used to generate feature groups automatically using pairwise $$L_1$$

L
1

norm and pairwise $$L_\infty $$

L
∞

norm, respectively. This paper proposes efficient path algorithms for clustered Lasso and OSCAR to construct solution paths with respect to their regularization parameters. Despite the excessive terms in exhaustive pairwise regularization, their computational costs are reduced by using the symmetry of those terms. Simple equivalent conditions to check subgradient equations in each feature group are derived using the graph theory. The proposed algorithms are shown to be more efficient than existing algorithms via numerical experiments.
Distributed hybrid quantum-classical performance prediction for hyperparameter optimization
Authors: Wulff Eric , Garcia Amboage Juan Pablo , Aach Marcel , Gislason Thorsteinn Eli , Ingolfsson Thorsteinn Kristinn , Ingolfsson Tomas Kristinn , Pasetto Edoardo , Delilbasic Amer , Riedel Morris , Sarma Rakesh , Girone Maria , Lintermann Andreas 
Published: September 2024

AbstractHyperparameter optimization (HPO) of neural networks is a computationally expensive procedure, which requires a large number of different model configurations to be trained. To reduce such costs, this work presents a distributed, hybrid workflow, that runs the training of the neural networks on multiple graphics processing units (GPUs) on a classical supercomputer, while predicting the configurations’ performance with quantum-trained support vector regression (QT-SVR) on a quantum annealer (QA). The workflow is shown to run on up to 50 GPUs and a QA at the same time, completely automating the communication between the classical and the quantum systems. The approach is evaluated extensively on several benchmarking datasets from the computer vision (CV), high-energy physics (HEP), and natural language processing (NLP) domains. Empirical results show that resource costs for performing HPO can be reduced by up to 9% when using the hybrid workflow with performance prediction, compared to using a plain HPO algorithm without performance prediction. Additionally, the workflow obtains similar and in some cases even better accuracy of the final hyperparameter configuration, when combining multiple heuristically obtained predictions from the QA, compared to using just a single classically obtained prediction. The results highlight the potential of hybrid quantum-classical machine learning algorithms. The workflow code is made available open-source to foster adoption in the community.
The role of adsorbent microstructure and its packing arrangement in optimising the performance of an adsorption column
Authors: Valverde Abel , Griffiths Ian M. 
Published: September 2024

AbstractPhysical adsorption takes place inside narrow pores where the attractive interaction between the surface of the adsorbent and the contaminant molecules is strong enough to retain the molecules. Adsorption columns involve a range of mass-transport mechanisms: advection through the free spaces between the adsorbent pellets, diffusion through the macro/mesopores of the adsorbent, and adsorption at the surface, where the micropores exist. The adsorbent specifications along with its assembly within the sorption column are key factors when optimizing the filtration of pollutants. In this work we present a mathematical model based on advection–diffusion equations coupled with Langmuir kinetics that accounts for a geometrical approach to the porosity structure inside the adsorbent, formed by a radial lattice of cylinders, and the channels through which the fluid flows surrounding the cylindrical pellets. The model is tested using typical lab-scale values based on VOC adsorption, and we use it to identify optimal macro/mesopore size and column porosity for energetic considerations.
Time-restricted feeding relieves high temperature-induced impairment on meat quality by activating the Nrf2/HO-1 pathway, modification of muscle fiber composition, and enriching the polyunsaturated fatty acids in pigs
Authors: Li Zhaojian , Wang Yiting , Yuan Peng , Zhu Yanli , Hu Ping , Song Tongxing , Liu Rui , Liu Hao-Yu , Cai Demin 
Published: September 2024

AbstractTo assess the effects of a time-restricted feeding (TRF) regimen on meat quality of pigs exposed to high ambient temperature, a two-month feeding and heat treatment (HT) trial was conducted using a 2 × 2 factorial design. A total of 24 growing pigs (11.0 ± 1.9 kg) were randomly divided into four groups: thermal neutral group (NT, 24 ± 3 °C), HT group (exposed to a high temperature at 35 ± 2 °C from 11:00 to 15:00), TRF group and HT + TRF group (HT and TRF co-treatment group, n = 6 for each group). Pigs in TRF groups got access to feed within 5 h from 9:00 to14:00, while the others were fed at 6:00, 11:30, and 16:00. All pigs received the same diet during the trail. The results showed that HT increased the drip loss, shear force, lightness, and malondialdehyde production in Longissimus thoracis et lumborum (LTL) muscle. TRF reversely reduced the shear force and drip loss, accompanied by decreased intramuscular fat and increased moisture content. Enhanced fiber transformation from type 1 to type 2b and down-regulated expression of muscle growth-related genes were observed by HT, while TRF suppressed the fiber transformation and expression of muscle atrophy-related genes. Furthermore, TRF restored the diminished protein expressions of Nrf2 and HO-1 in LTL muscle by chronic HT. Accumulation of HSP70 in muscle of HT group was reduced by treatment of TRF. HT declined the expression of vital genes involved in fatty acids poly-desaturation and the proportion of (polyunsaturated fatty acids) PUFAs, mainly omega-6 in LTL muscle, while TRF group promoted the expression of poly-desaturation pathway and displayed the highest proportion of PUFAs. These results demonstrated that TRF relieved the chronic high temperature affected meat quality by the restored expression of Nrf2/HO-1 anti-oxidative cascade, modified muscle fiber composition, and enriched PUFAs in LTL muscle.
Urban system equilibrium model in an asymmetric polycentric city and the influence of rail transit investment
Authors: Chen Yajuan , Tian Xin , Huang Han 
Published: September 2024

AbstractTaking bus and subway into account, an asymmetric polycentric urban system equilibrium model is constructed to explore the impacts of rail transit on the distribution of residents and labor force in a linear polycentric urban system. By comparing the urban system equilibria before and after the introduction of rail transit, the influence of rail transit on urban performance is analyzed. The results show that the introduction of rail transit makes the advantaged center more attractive for the labor force, and further widens the economic level gap between the two business centers. Specifically, larger distance between the two commercial centers and the higher subway travel cost would strengthen the polarization effect caused by rail transit investment. Since the improvement of transportation makes it easier for residents to accept long-distance commuting and they are more willing to choose the jobs in the advanced center.
Class size and its influence on examination results in higher education
Authors: Abrahamsen Eirik Bjorheim , Moen Vegard , Asche Frank 
Published: September 2024

AbstractIn this article, we analyze the relationship between class sizes and examination results in higher education. Data from the University of Stavanger, collected in the Common Student System (FS) in the period from 2011 to 2021, shows that there is a negative correlation between the number of students enrolled on courses and examination results. An increase in the number of students taking a course reduces the average scores. We also show that the difference in examination results for different class sizes decreases when the course level increases.
Virtual Reflection Height of Nighttime Equatorial Ionosphere Estimated With Low‐Frequency Magnetic Sferics Measured in Malacca
Authors: Wang Yongping , Lu Gaopeng , Cheng Zhengwei , Niu Ziru , Chen Yazhou , Peng Kang‐Ming , York Seah Boon , Ahmad Mohd Riduan 
Published: September 2024

AbstractThe return stroke of cloud‐to‐ground (CG) lightning is an impulsive radiator of very low‐frequency/low‐frequency (VLF/LF) electromagnetic signals allowing for the remote sensing of lower ionosphere over large spatial coverage. In this study, we examined the LF magnetic fields measured in Malacca, Malaysia, to probe reflection heights of the lower ionosphere near the equator on three different nights in 2021. The results show that the virtual ionospheric height at nighttime typically ranged from 82.0 to 90.0 km, with a mean value of 85.3 km. Our measurements also revealed significant variations in the virtual ionospheric height across different regions over a spatial scale of about 800 km. The maximum height difference was about 5.0 km. Moreover, the fluctuation characteristics are observed in both estimated ionospheric height and calculated peak reflection ratio during similar periods. This fluctuation may be related to atmospheric gravity waves in the nighttime ionosphere. In addition, we compared the virtual ionospheric height estimated from CG strokes of different polarities, and the results showed that the virtual reflection height for positive CG strokes is lower than that for negative ones.
Streamflow Prediction in Human‐Regulated Catchments Using Multiscale Deep Learning Modeling With Anthropogenic Similarities
Authors: Tursun Arken , Xie Xianhong , Wang Yibing , Peng Dawei , Liu Yao , Zheng Buyun , Wu Xinran , Nie Cong 
Published: September 2024

AbstractAccurate streamflow prediction in human‐regulated catchments remains a formidable challenge due to the complex disturbance of hydrological processes. To consider human disturbance in hydrological modeling, this study introduces a novel static attribute collection that combines river‐reach attributes with catchment attributes, referred to as multiscale attributes. The attribute collection is assembled into two deep learning (DL) methods, that is, the Long Short‐Term Memory (named as Multiscale LSTM) and the Differentiable Parameter Learning (DPL) model, and the performance is evaluated across 95 human‐regulated catchments in the United States (USA) and 24 catchments in the Yellow River Basin in China. In the USA, the Multiscale LSTM and the DPL models achieve similar performance with median Kling‐Gupta Efficiency (KGE) of 0.78 and 0.71, respectively. However, in the Yellow River Basin, the KGE values are 0.58 for Multiscale LSTM and 0.24 for DPL. These results highlight the DL models' ability to leverage multiscale attributes for improved performance compared to traditional catchment attributes. The performance of Multiscale LSTM and DPL models is predominantly influenced by river‐scale attributes, encompassing factors such as connectivity status index (CSI), degree of regulation (DOR), sediment trapping (SED), and number of dams. Additionally, satellite‐derived attributes such as mean and maximum river width (Width), slope and mean water surface elevation (WSE) from the Surface Water and Ocean Topography River Database (SWORD) contribute valuable insights into anthropogenic influences. Moreover, our study highlights the significance of selecting the appropriate training data period, which emerges as the most dominant factor affecting model performance across human‐regulated catchments. The diversity of data during the training period enables the model to capture a broad spectrum of hydrological signatures within these catchments. Consequently, this study emphasizes the advantages of Multiscale LSTM and underscores the significance of considering both natural and anthropogenic signatures to enhance hydrological predictions within human‐regulated environments.
The Response of the Venusian Upper Atmosphere During the Passage of Interplanetary Coronal Mass Ejections
Authors: Rout Diptiranjan , Thampi Smitha V. , Miyoshi Yoshizumi , Pant Tarun Kumar , Bhardwaj Anil 
Published: September 2024

AbstractThe current study explores the dynamic interaction between Interplanetary coronal mass ejections (ICMEs) and the induced magnetosphere of Venus, utilizing measurements from the Venus Express (VEX) mission. We have investigated 16 ICME events during the period 2006–2013. The altitude of the inbound bow shock and ionopause at Venus are comprehensively studied during the passage of these ICMEs. The ionosphere is found to be highly magnetized due to the very high magnetic pressure of the induced magnetosphere. Remarkably, the altitude of the ionopause is found to be significantly changed as compared to the previous quiet day due to the increased solar wind dynamic pressure . The ratio of the altitude of ionopause and magnitude of the magnetic field at ionopause on the event days to the quiet days shows a strong anti‐correlation which indicates the ionopause height is inversely related to the magnetic field. Intriguingly, the position of the bow shock exhibited minimal deviations compared to typical quiet days, underscoring that, during ICME events, the ionopause location is more responsive to solar wind pressure fluctuations than the bow shock location. Additionally, the heavy‐ion density near and above the ionopause is found to be significantly higher than that observed on previous quiet days. This substantial increase implies that ICMEs can induce atmospheric loss in Venus's atmosphere and also cause a significant reduction in the ionopause location.
Spatial Distribution and Wave Property of Dual‐Frequency EMIC Waves: Swarm Observations
Authors: Wang C. Z. , Wang H. , Zhang K. D. , Zhong Y. F. , Xia H. , Sun Y. , Cheng Q. H. , Leng Z. Y. 
Published: September 2024

AbstractThe spatial distributions of single‐frequency and dual‐frequency Electromagnetic ion cyclotron (EMIC) waves in the subauroral ionosphere are investigated under varying geomagnetic activities, using high‐resolution magnetic field data from dual Swarm satellites spanning from 2015 to 2017. Single‐frequency EMIC waves predominantly occur in the dawn sector, whereas dual‐frequency waves exhibit peaks around both dawn and dusk. The occurrence rate of dual‐frequency waves shows a more pronounced increase with increasing geomagnetic activity. As magnetic storms evolve, both types of EMIC waves shift from dusk to dawn. The South Atlantic Anomaly (SAA) emerges as a high‐incidence region for ionospheric EMIC waves. Dual‐frequency EMIC waves display lower frequencies compared to other regions. Additionally, the low‐frequency components of dual‐frequency waves observed at higher latitudes demonstrate greater power density and longer durations than their high‐frequency counterparts. This suggests that higher frequency waves experience more significant damping during propagation. Most dual‐frequency EMIC waves observed in the ionosphere belong to the O‐band and He‐band waves, indicating that magnetospheric bands below the cyclotron frequency of H+ are more likely to propagate into the ionosphere.
Tropical Cyclone‐Induced Ecological Responses and Their Feedback on Physical Fields: A Case Study for Hurricane Fernanda (2017)
Authors: Ye Shuoni , Zhang Rong‐Hua , Wang Hongna , Tian Feng , Shi Qidong 
Published: September 2024

AbstractTropical cyclones (TCs) are known to trigger chlorophyll bloom and boost local primary productivity. The characteristics of ocean ecological responses to TCs and their feedback on physical fields in the northeastern Pacific are investigated using a coupled ocean general circulation model‐ocean ecosystem model. A case study is carried out on Hurricane Fernanda (2017), which formed in the northeastern tropical Pacific. TC‐induced mixing and upwelling act to transport subsurface cold and nutrient‐rich waters into the mixed layer, leading to surface cooling and chlorophyll (CHL) bloom. The phytoplankton budget analyses indicate that the CHL bloom is dominated by small phytoplankton (PS) growth term, which is modulated by biological consumption terms (i.e., small zooplankton grazing and PS mortality) and physical processes‐related terms (i.e., advection and vertical mixing); biological consumption terms and physical processes tend to mainly offset the contributions from the PS growth term. Furthermore, CHL is found to exert feedback on physical fields. The CHL bloom mainly contributes to a decrease in surface temperature, thereby enhancing the temperature structure induced physically by TCs. This biofeedback on physical fields involves two mechanisms: a direct heating (OBH) due to ocean biology‐induced effect, and an indirect cooling effect due to dynamic processes associated with vertical mixing and advection. In particular, the CHL bloom‐induced sea surface temperature cooling is dominated by vertical mixing and modulated by the OBH effect and advection. These findings offer novel perspectives on TC‐induced ecological responses, as well as the related mechanisms for biofeedback on physical fields.
Large Eddy Simulations of the Interaction Between the Atmospheric Boundary Layer and Degrading Arctic Permafrost
Authors: Schlutow M. , Stacke T. , Doerffel T. , Smolarkiewicz P. K. , Göckede M. 
Published: September 2024

AbstractArctic permafrost thaw holds the potential to drastically alter the Earth's surface in Northern high latitudes. We utilize high‐resolution large eddy simulations to investigate the impact of the changing surfaces onto the neutrally stratified atmospheric boundary layer (ABL). A stochastic surface model based on Gaussian Random Fields modeling typical permafrost landscapes is established in terms of two land cover classes: grass land and open water bodies, which exhibit different surface roughness length and surface sensible heat flux. A set of experiments is conducted where two parameters, the lake areal fraction and the surface correlation length, are varied to study the sensitivity of the boundary layer with respect to surface heterogeneity. Our key findings from the simulations are the following: The lake areal fraction has a substantial impact on the aggregated sensible heat flux at the blending height where surface heterogeneities become horizontally homogenized. The larger the lake areal fraction, the smaller the sensible heat flux. This result gives rise to a potential feedback mechanism. When the Arctic dries due to climate heating, the interaction with the ABL may accelerate permafrost thaw. Furthermore, the blending height shows significant dependency on the correlation length of the surface features. A longer surface correlation length causes an increased blending height. This finding is of relevance for land surface models concerned with Arctic permafrost as they usually do not consider a heterogeneity metric comparable to the surface correlation length.