bluuebunny/crossref_metadata_2025_split huggingface dataset:
High-resolution Raman spectroscopy reveals compositional differences between pigmented incisor enamel and unpigmented molar enamel in Rattus norvegicus
Authors: Shah Furqan A. 
Published: July 2023

AbstractDental enamel is a peculiar biological tissue devoid of any self-renewal capacity as opposed to bone. Thus, a thorough understanding of enamel composition is essential to develop novel strategies for dental enamel repair. While the mineral found in bone and dental enamel is generally viewed as the biologically-produced equivalent of hydroxy(l)apatite, the formation of these bioapatites is controlled by different organic matrix frameworks—mainly type-I collagen in bone and amelogenin in enamel. In lower vertebrates, such as rodents, two distinct types of enamel are produced. Iron-containing pigmented enamel protects the continuously growing incisor teeth while magnesium-rich unpigmented enamel covers the molar teeth. Using high-resolution Raman spectroscopy, scanning electron microscopy, and energy dispersive X-ray spectroscopy, this work explores the differences in acid phosphate (HPO42−), carbonate (CO32−), hydroxyl (OH−), iron, and magnesium content of pigmented incisor enamel and unpigmented molar enamel of Sprague Dawley rats. Bundles of hydroxy(l)apatite nanowires comprise the enamel prisms, where prisms in pigmented enamel are wider and longer than those in unpigmented molars. In contrast to magnesium-rich unpigmented enamel, higher mineral crystallinity, and higher HPO42− and OH− levels are hallmark features of iron-rich pigmented enamel. Furthermore, the apparent absence of iron oxides or oxy(hydroxides) indicates that iron is introduced into the apatite lattice at the expense of calcium, albeit in amounts that do not alter the Raman signatures of the PO43− internal modes. Compositional idiosyncrasies of iron-rich pigmented and nominally iron-free unpigmented enamel offer new insights into enamel biomineralisation supporting the notion that, in rodents, ameloblast function differs significantly between the incisors and the molars.
Sulfated and non-sulfated chondroitin affect the composition and metabolism of human colonic microbiota simulated in an in vitro fermentation system
Authors: Inokuma Kentaro , Sasaki Daisuke , Kurata Kaoru , Ichikawa Megumi , Otsuka Yuya , Kondo Akihiko 
Published: July 2023

AbstractChondroitin sulfate (CS) is a family of glycosaminoglycans and have a wide range of applications in dietary supplements and pharmaceutical drugs. In this study, we evaluated the effects of several types of CS, differing in their sulfated positions, on the human colonic microbiota and their metabolites. CS (CSA, CSC, and CSE) and non-sulfated chondroitin (CH) were added into an in vitro human colonic microbiota model with fecal samples from 10 healthy individuals. CS addition showed a tendency to increase the relative abundance of Bacteroides, Eubacterium, and Faecalibacterium, and CSC and CSE addition significantly increased the total number of eubacteria in the culture of the Kobe University Human Intestinal Microbiota Model. CSE addition also resulted in a significant increase in short-chain fatty acid (SCFA) levels. Furthermore, addition with CSC and CSE increased the levels of a wide range of metabolites including lysine, ornithine, and Ile-Pro-Pro, which could have beneficial effects on the host. However, significant increases in the total number of eubacteria, relative abundance of Bacteroides, and SCFA levels were also observed after addition with CH, and the trends in the effects of CH addition on metabolite concentrations were identical to those of CSC and CSE addition. These results provide novel insight into the contribution of the colonic microbiota to the beneficial effects of dietary CS.
Calanus finmarchicus hydrolysate improves growth performance in feeding trial with European sea bass juveniles and increases skeletal muscle growth in cell studies
Authors: Bøgwald Isak , Østbye Tone-Kari K. , Pedersen Alice Marie , Rønning Sissel Beate , Dias Jorge , Eilertsen Karl-Erik , Wubshet Sileshi Gizachew 
Published: July 2023

AbstractThe world will be dependent on the development of novel feed ingredients from renewable sources to ensure sustainable growth of the aquaculture industry. Zooplankton like Calanus finmarchicus are viable new raw material candidates, as they have optimal nutrient profiles for aquatic animals and may be sustainably harvested in large volumes. In this study, the aim was to investigate if a protein hydrolysate of C. finmarchicus was able to influence the growth performance of fish. The effect of dietary inclusion of hydrolysates was tested in a feeding trial with European sea bass (Dicentrarchus labrax) juveniles, benchmarking calanus hydrolysate (CH) against commercially available hydrolysates. The diet with CH inclusion yielded increased growth, with significantly higher body weight than hydrolysates of sardine and tuna fish at the end of the trial. The observed growth-promoting effects were further examined using an in vitro model with skeletal muscle cells from Atlantic salmon. Through bioactivity experiments with muscle cells grown in media containing CH, low-molecular fractions were found to have the greatest positive effect on proliferation, viability, and expression of muscle-specific genes. Characterization of the most potent fraction revealed an abundance of small peptides, along with amino acids and marine metabolites associated with increased muscle growth.
Daily rhythms, light exposure and social jetlag correlate with demographic characteristics and health in a nationally representative survey
Authors: Ujma Péter P. , Horváth Csenge G. , Bódizs Róbert 
Published: July 2023

AbstractThe timing of daily activity in humans have been associated with various demographic and health-related factors, but the possibly complex patterns of confounding and interaction between these has not been systematically explored. We use data from Hungarostudy 2021, a nationally representative survey of 7000 Hungarian adults to assess the relationship between self-reported chronotype, social jetlag (using the Munich Chronotype Questionnaire), demographic variables and self-reported health and demographic variables, including ethnic minority membership. Supporting the validity of self-reports, participants with later chronotypes reported the lowest daytime sleepiness at a later clock time. We found that older age, female sex, a more eastward and southward geographical position, residence in a smaller settlement, less education and income, religiousness and cohabiting with small children were associated with an earlier chronotype. Younger age, higher education and income, and cohabiting with small children were associated with increased social jetlag. Of the 48 health-related variables surveyed, the relationship with both chronotype and social jetlag were mostly accounted for by age, sex, and socioeconomic effects, but we identified alcohol consumption, smoking, and physical activity as predictors of both social jetlag and chronotype, while a number of disorders were either positively or negatively associated with chronotype and social jetlag. Our findings from a large, nationally representative sample indicate that both biological and social factors influence chronotype and identified both demographic and health-related variables as risk factors for social jetlag. Our results, however, do not support a causal relationship between light exposure and mental health.
Role of hybrid nanofiller GNPs/Al2O3 on enhancing the mechanical and tribological performance of HDPE composite
Authors: A. Nabhan , Taha Mohamed , Ibrahim Ahmed Mohamed Mahmoud , A. K. Ameer 
Published: August 2023

AbstractThe unique mechanical properties and wear resistance of HDPE give it the potential as an alternative to frictional material. The current research focuses on using hybrid nanoparticles with various loading fillers to determine the best additive contents. The mechanical and tribological characteristics were examined and evaluated. The HDPE nanocomposite samples containing 0.5, 1.0, 1.5, and 2.0 wt.% filling content of Al2O3nanoparticles (NPs) and 0.5, and 1.0 wt.% of graphene nanoplatelets (GNPs) were fabricated. The results showed a good enhancement in the mechanical and tribological properties of HDPE composites with the presence of nano additives. The HDPE nanocomposites recorded the best performance with a loading amount of 2.0 wt.% with an equal ratio of hybrid nanofiller Al2O3NPs and GNPs.
Nuclear shell-model simulation in digital quantum computers
Authors: Pérez-Obiol A. , Romero A. M. , Menéndez J. , Rios A. , García-Sáez A. , Juliá-Díaz B. 
Published: July 2023

AbstractThe nuclear shell model is one of the prime many-body methods to study the structure of atomic nuclei, but it is hampered by an exponential scaling on the basis size as the number of particles increases. We present a shell-model quantum circuit design strategy to find nuclear ground states by exploiting an adaptive variational quantum eigensolver algorithm. Our circuit implementation is in excellent agreement with classical shell-model simulations for a dozen of light and medium-mass nuclei, including neon and calcium isotopes. We quantify the circuit depth, width and number of gates to encode realistic shell-model wavefunctions. Our strategy also addresses explicitly energy measurements and the required number of circuits to perform them. Our simulated circuits approach the benchmark results exponentially with a polynomial scaling in quantum resources for each nucleus. This work paves the way for quantum computing shell-model studies across the nuclear chart and our quantum resource quantification may be used in configuration-interaction calculations of other fermionic systems.
LXGB: a machine learning algorithm for estimating the discharge coefficient of pseudo-cosine labyrinth weir
Authors: Emami Somayeh , Emami Hojjat , Parsa Javad 
Published: July 2023

AbstractOne of the practical and financial solutions to increase the efficiency of weirs is to modify the geometry of the plan and increase the length of the weir to a specific width. This increases the discharge coefficient (Cd) of the weir. In this study, a new weir referred to pseudo-cosine labyrinth weir (PCLW) was introduced. A hybrid machine learning LXGB algorithm was introduced to estimate theCdof the PCLW. The LXGB is a combination of the linear population size reduction history-based adaptive differential evolution (LSHADE) and extreme gradient boosting (XGB) algorithm. Seven different input scenarios were presented to estimate the discharge coefficient of the PCLW weir. To train and test the proposed method, 132 data series, including geometric and hydraulic parameters from PCLW1 and PCLW2 models were used. The root mean square error (RMSE), relative root mean square error (RRMSE), and Nash–Sutcliffe model efficiency coefficient (NSE) indices were used to evaluate the proposed approach. The results showed that the input variables were the ratio of the radius to the weir height (R/W), the ratio of the length of the weir to the weir height (L/W), and the ratio of the hydraulic head to the weir height (H/W), with the average values ofRMSE = 0.009,RRMSE = 0.010, andNSE = 0.977 provided better results in estimating the Cd of PCLW1 and PCLW2 models. The improvement compared to SAELM, ANFIS-FFA, GEP, and ANN in terms ofR2is 2.06%, 3.09%, 1.03%, and 5.15%. In general, intelligent hybrid approaches can be introduced as the most suitable method for estimating the Cd of PCLW weirs.
An innovative ensemble model based on deep learning for predicting COVID-19 infection
Authors: Su Xiaoying , Sun Yanfeng , Liu Hongxi , Lang Qiuling , Zhang Yichen , Zhang Jiquan , Wang Chaoyong , Chen Yanan 
Published: July 2023

AbstractNowadays, global public health crises are occurring more frequently, and accurate prediction of these diseases can reduce the burden on the healthcare system. Taking COVID-19 as an example, accurate prediction of infection can assist experts in effectively allocating medical resources and diagnosing diseases. Currently, scholars worldwide use single model approaches or epidemiology models more often to predict the outbreak trend of COVID-19, resulting in poor prediction accuracy. Although a few studies have employed ensemble models, there is still room for improvement in their performance. In addition, there are only a few models that use the laboratory results of patients to predict COVID-19 infection. To address these issues, research efforts should focus on improving disease prediction performance and expanding the use of medical disease prediction models. In this paper, we propose an innovative deep learning model Whale Optimization Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN) called WOCLSA which incorporates three models ANN, CNN and LSTM. The WOCLSA model utilizes the Whale Optimization Algorithm to optimize the neuron number, dropout and batch size parameters in the integrated model of ANN, CNN and LSTM, thereby finding the global optimal solution parameters. WOCLSA employs 18 patient indicators as predictors, and compares its results with three other ensemble deep learning models. All models were validated with train-test split approaches. We evaluate and compare our proposed model and other models using accuracy, F1 score, recall, AUC and precision metrics. Through many studies and tests, our results show that our prediction models can identify patients with COVID-19 infection at the AUC of 91%, 91%, and 93% respectively. Other prediction results achieve a respectable accuracy of 92.82%, 92.79%, and 91.66% respectively, f1-score of 93.41%, 92.79%, and 92.33% respectively, precision of 93.41%, 92.79%, and 92.33% respectively, recall of 93.41%, 92.79%, and 92.33% respectively. All of these exceed 91%, surpassing those of comparable models. The execution time of WOCLSA is also an advantage. Therefore, the WOCLSA ensemble model can be used to assist in verifying laboratory research results and predict and to judge various diseases in public health events.
A dietary perspective of cat-human interactions in two medieval harbors in Iran and Oman revealed through stable isotope analysis
Authors: Brozou Anastasia , Fuller Benjamin T. , De Cupere Bea , Marrast Anaïs , Monchot Hervé , Peters Joris , Van de Vijver Katrien , Lambert Olivier , Mannino Marcello A. , Ottoni Claudio , Van Neer Wim 
Published: July 2023

AbstractCats are hypercarnivorous, opportunistic animals that have adjusted to anthropogenic environments since the Neolithic period. Through humans, either by direct feeding and/or scavenging on food scraps, the diet of cats has been enriched with animals that they cannot kill themselves (e.g., large mammals, fish). Here, we conducted carbon and nitrogen stable isotope ratio analysis to reconstruct the diet of medieval cats and investigate cat-human interactions in two medieval harbor sites (Qalhât, Oman and Siraf, Iran). The analysis included 28 cat individuals and 100 associated marine and terrestrial faunal samples pertaining to > 30 taxa. The isotopic results indicate a high marine protein-based diet for the cats from Qalhât and a mixed marine-terrestrial (C4) diet for the cats from Siraf. Cats at these sites most likely scavenged on both human food scraps and refuse related to fishing activities, with differences in the two sites most likely associated with the availability of marine resources and/or the living conditions of the cats. By shedding light on the dietary habits of cats from two medieval harbors in the Arabian Gulf and Gulf of Oman, this study illustrates the potential of stable isotope analysis in reconstructing human-cat interactions in the past.
The experiences of counselors caring for children and adolescents with problematic smartphone use
Authors: Joung Jaewon , Oh Eunhee , Lee Eun Jee 
Published: July 2023

AbstractThis study examines field experts’ experiences to ascertain the actual circumstances and strategies to increase the efficacy of intervention programs for children and adolescents with problematic smartphone use. Three focus group interviews were conducted via video conferencing. The data were grouped into three major themes: (1) the screening and inflow phase, which included the inaccuracy of the screening tests, barriers in the inflow process, and the importance of school cooperation; (2) the intervention phase, which included the necessity of developing a program tailored to the target audience, the importance of parental participation, and concerns about the vulnerable; and (3) the maintenance phase, which included the lack of a long-term strategy, the need to re-establish the purpose of the counseling/intervention programs, and the need for systematic maintenance of the system. To improve the reliability of the screening test for children and adolescents with problematic smartphone use, it is necessary to improve the measurement tools and environment. To increase parental involvement, education should be provided on perceiving the severity of problematic smartphone use, and program running hours should be varied. The findings offer information necessary for improving counseling and interventions for children and adolescents with problematic smartphone use.
Overlap-ADAPT-VQE: practical quantum chemistry on quantum computers via overlap-guided compact Ansätze
Authors: Feniou César , Hassan Muhammad , Traoré Diata , Giner Emmanuel , Maday Yvon , Piquemal Jean-Philip 
Published: July 2023

AbstractADAPT-VQE is a robust algorithm for hybrid quantum-classical simulations of quantum chemical systems on near-term quantum computers. While its iterative process systematically reaches the ground state energy, practical implementations of ADAPT-VQE are sensitive to local energy minima, leading to over-parameterized ansätze. We introduce the Overlap-ADAPT-VQE to grow wave-functions by maximizing their overlap with any intermediate target wave-function that already captures some electronic correlation. By avoiding building the ansatz in the energy landscape strewn with local minima, the Overlap-ADAPT-VQE produces ultra-compact ansätze suitable for high-accuracy initialization of a new ADAPT procedure. Significant advantages over ADAPT-VQE are observed for strongly correlated systems including substantial savings in circuit depth. Since this compression strategy can also be initialized with accurate Selected-Configuration Interaction (SCI) classical target wave-functions, it paves the way for chemically accurate simulations of larger systems, and strengthens the promise of decisively surpassing classical quantum chemistry through the power of quantum computing.
Class relationship‐based knowledge distillation for efficient human parsing
Authors: Lang Yuqi , Liu Kunliang , Wang Jianming , Hwang Wonjun 
Published: July 2023

AbstractIn computer vision, human parsing is challenging due to its demand for accurate human region location and semantic partitioning. This dense prediction task needs powerful computation and high‐precision models. To enable real‐time parsing on resource‐limited devices, the authors introduced a lightweight model using ResNet18 as a core network . The authors simplified the pyramid module, improving context clarity and reducing complexity. The authors integrated a spatial attention fusion strategy to counter precision loss in the light‐weighting process. Traditional models, despite their segmentation precision, are limited by their computational complexity and extensive parameters. The authors implemented knowledge distillation (KD) techniques to enhance the authors’ lightweight network's accuracy. Traditional methods can fail to learn useful knowledge with significant network differences. Hence, the authors used a novel distillation approach based on inter‐class and intra‐class relations in prediction outcomes, noticeably improving parsing accuracy. The authors’ experiments on the Look into Person (LIP) dataset show that their lightweight model significantly reduces parameters while maintaining parsing precision and enhancing inference speed.
SSTNet: Saliency sparse transformers network with tokenized dilation for salient object detection
Authors: Yang Mo , Liu Ziyan , Dong Wen , Wu Ying 
Published: July 2023

AbstractThe vision Transformer structure performs better in salient object detection than the convolutional neural network (CNN)‐based approach. Vision Transformer predicts saliency by modelling long‐range dependencies from sequence to sequence with convolution‐free. It is challenging to distinguish the salient objects' location and obtain structural details for the influence of extracting irrelevant contextual information. A novel saliency sparse Transformer network is proposed to exploit sparse attention to guide saliency prediction. The convolution‐like with dilation in the token to token (T2T) module is replaced to achieve relationships in larger regions and to improve contextual information fusion. An adaptive position bias module is designed for the Vision Transformer to make position bias suitable for variable‐sized RGB images. A saliency sparse Transformer module is designed to improve the concentration of attention on the global context by selecting the Top‐k of the most relevant segments to improve the detection results further. Besides, cross‐modality to exploit the complementary RGB and depth modality fusion module (CMF) is used to take advantage of the complementary RGB image features and spatial depth information to enhance the feature fusion performance. Extensive experiments on multiple benchmark datasets demonstrate this method's effectiveness and superiority that it is suitable for saliency prediction comparable to state‐of‐the‐art RGB and RGB‐D saliency methods.
Stochastic planning of integrated energy system based on correlation scenario generation method via Copula function considering multiple uncertainties in renewable energy sources and demands
Authors: Chen Jinfan , Mao Chengxiong , Liu Zhe , Ma Chunyan , Sha Guanglin , Duan Qing , Fan Hua , Qiu Shushan , Wang Dan 
Published: July 2023

AbstractThe main goal of integrated energy service providers (IESP) in the future is to pursue profitability and reduce carbon emissions while ensuring sufficient energy supply. However, the multiple uncertainties associated with renewable energy resources and the multi‐energy consumers pose significant challenges in the optimal planning of the integrated energy system (IES) based on energy hubs (EH). This includes the global optimization of device types, EH capacity, and the optional interconnections between adjacent EHs. This study aims to develop a comprehensive mathematical model of EH that considers the temperature limitations on device operation. The thermal bus and associated devices are divided into high and low temperature parts, and a multi‐scenario stochastic programming model is proposed for IES planning. Scenarios are generated using non‐parametric kernel density estimation and the Copula function, a scenario reduction technique is implemented to alleviate computational burden. Additionally, case studies illustrate effectiveness of the proposed approach, the operation and forms of EHs, effect of the interconnections and the trade‐off between carbon emissions and economic costs are further studied by extensive simulations. This study can be a good guide for IESPs to invest in the construction of the EH‐based IES.
Detection by radar of a slow‐moving target on the sea surface
Authors: Watts Simon 
Published: July 2023

AbstractA simple model is proposed for the radar returns from a slow‐moving target with a wake on the sea surface, based on measurements of returns from a submarine mast. This model is used to investigate the effect of a wake with a broad Doppler spectrum on the detection of a slow‐moving target in sea clutter. It is shown how the relative magnitudes of the returns from the target and its wake affect detection performance. Non‐coherent and coherent detectors are compared and it is shown that in some circumstance non‐coherent detectors may give better performance, dependent on the choice of radar waveform parameters.