bluuebunny/crossref_metadata_2025_split huggingface dataset:
Carbon‐based conductive rubber composite for 3D printed flexible strain sensors
Authors: Srimongkol Siwaporn , Wiroonpochit Panithi , Utra Kittaporn , Sethayospongsa Rathanakarn , Muthitamongkol Pennapa , Methachan Boriphat , Butsri Natsaporn , Srisawadi Sasitorn 
Published: September 2022

AbstractPolymeric‐based flexible electronic devices are in high demand due to its wide range of applications. Natural rubber (NR) shows a great potential as matrix phase for flexible conductive polymer composites with its high elasticity and fatigue resistance. In this study, a new 3D printable conductive NR (CNR) composite was developed for strain sensor applications. Different contents of conductive carbon black (CCB) were mixed with NR latex to investigate the effect of the filler content on electrical and mechanical properties of the composites. The best‐known CNR composite with the CCB content of 12 phr was selected in order to produce the feedstock for the stereolithography process (SLA). The morphological, electrical, and mechanical properties of cast and 3D‐printed samples were investigated and compared. Although the 3D‐printed CNR sample had slightly lower conductivity than the cast one, it possessed comparable tensile strength and elongation at break, with values of 12.4 MPa and 703%, respectively. In addition, electrical responses of the CNR samples were investigated to demonstrate the electromechanical property of the material as a strain sensor. The 3D‐printed CNR sample exhibited the highest electromechanical sensitivity with a gauge factor (GF) of 361.4 (ε = 210%–300%) and showed good repeatability for 500 cycles. In conclusion, the development of this 3D printable functional material with great sensing capability will pave the way for innovative designs of personalized sensing textiles and other smart wearable devices.
Management of acute intra‐abdominal hemorrhage in ruptured hepatoblastoma with transarterial embolization using calibrated gelfoam particles
Authors: Fung Kin Fen Kevin , Liu Anthony Pak‐Yin , Chung Patrick Ho‐Yu , Cheng Ka King , Cho Hing Yan Danny , Kan Yee Ling Elaine , Chiang Alan Kwok Shing 
Published: September 2022

AbstractThis brief report aims to evaluate the treatment outcome of transarterial embolization in ruptured hepatoblastoma complicated with acute intra‐abdominal hemorrhage. Three children (mean age 6 years) with high‐risk hepatoblastoma presented with rupture and acute intra‐abdominal hemorrhage. In addition to aggressive fluid resuscitation and blood product support, super‐selective embolization of the arteries with active bleeding or pseudoaneurysm was performed using calibrated gelfoam particles, with a technical success rate of 100%. Hemodynamic status and hemoglobin level were normalized in all patients within 2 days postembolization. The 30‐day survival rate was 100%. No major complication was detected apart from mild elevation of alanine transaminase.
Investigation on mechanical, dynamic mechanical analysis, thermal conductivity, morphological analysis, and biodegradability properties of hybrid fiber mats reinforced HLCE resin nanocomposites
Authors: P Prabhu , B Karthikeyan , R Ravi Raja Malar Vannan , A Balaji 
Published: September 2022

AbstractHybrid composites are created by the combination of natural fibers (cotton, bamboo, jute, etc.) and synthetic fibers (glass) reinforced hybrid/natural/synthetic resin matrix composites which have good mechanical properties due to reasons of processability, reusability, renewability, and good biocompatibility compared with synthetic composites. Composites made of hybrid resin can replace epoxy resin and increase both biodegradability and environmental protection. In this work, NaOH untreated and treated hybrid fiber mats with volume (2, 4, and 6) % of nanofiller (NCF) mixed in each composition in hybrid resin to form hybrid composites are prepared by compression Hand‐lay‐up technique and then investigated. Hybrid composites are examined by utilizing the following equipment Fourier transform infrared spectrometry (FT‐IR), dynamic mechanical analysis (DMA), thermal conductivity test, and biodegradability property is also studied in this paper. The results of the experiments have demonstrated that the treated H1 fiber mats reinforced with hybrid lannea coromandelica epoxy (HLCE) matrix resin at 4% vol% of NCF have superior compression strength (67.8 MPa). The maximum thermal conductivity of 0.35 W/mK is found in hybrid composites with treated H3 fiber mat and 4% NCF reinforcement in HLCE matrix. According to the findings of dynamic mechanical analysis (DMA), the results indicate that untreated and treated hybrid composites have the highest storage modulus and the lowest damping factor in comparison with HLCE resin. FTIR analysis is used to identify the presence of chemical elements in hybrid resin and hybrid composites.
Improvement of the interfacial properties of CF/PEEK composites by introducing aminated polyphenylene sulphide (PPS‐NH2) as a sizing agent
Authors: Ren Tianning , Zhu Guangming , Zhang Chengshuang 
Published: September 2022

AbstractThe resin matrix and CF fibers itself of CF/PEEK composites exhibit surface inertness, resulting in poor interface bonding between CF and PEEK matrix. This phenomenon seriously affects the mechanical properties of CF/PEEK composites especially the interlaminar shear strength (ILSS). In order to improve the interfacial properties of CF/PEEK composites, a novel sizing agent of CF fibers namely PPS‐NH2 was synthesized, and its influence on the fibers–matrix interface interaction and properties of CF/PEEK composites was investigated by using scanning electron microscopy, Fourier transform infrared spectroscopy, thermogravimetric analysis, atomic force microscopy. The results showed that the interface of CF/PEEK composite was improved by coating PPS‐NH2 sizing agent on the surface of CF. When the amination degree of PPS‐NH2 reaches 31.25%, the ILSS, flexural strength and modulus of CF/PEEK Composites reach 73.9 MPa (119%), 671.4 MPa (63%) and 32.8GPa (51%) and, respectively. At the same time, the main failure mechanism of CF/PEEK composites changes from fibers–matrix debonding into interface layer deformation and resin fracture. This method can effectively improve the interface defects between CF/PEEK composites.
Singular value decomposition of protein sequences as a method to visualize sequence and residue space
Authors: Baxter‐Koenigs Autum R. , El Nesr Gina , Barrick Doug 
Published: September 2022

AbstractSingular value decomposition (SVD) of multiple sequence alignments (MSAs) is an important and rigorous method to identify subgroups of sequences within the MSA, and to extract consensus and covariance sequence features that define the alignment and distinguish the subgroups. This information can be correlated to structure, function, stability, and taxonomy. However, the mathematics of SVD is unfamiliar to many in the field of protein science. Here, we attempt to present an intuitive yet comprehensive description of SVD analysis of MSAs. We begin by describing the underlying mathematics of SVD in a way that is both rigorous and accessible. Next, we use SVD to analyze sequences generated with a simplified model in which the extent of sequence conservation and covariance between different positions is controlled, to show how conservation and covariance produce features in the decomposed coordinate system. We then use SVD to analyze alignments of two protein families, the homeodomain and the Ras superfamilies. Both families show clear evidence of sequence clustering when projected into singular value space. We use k‐means clustering to group MSA sequences into specific clusters, show how the residues that distinguish these clusters can be identified, and show how these clusters can be related to taxonomy and function. We end by providing a description a set of Python scripts that can be used for SVD analysis of MSAs, displaying results, and identifying and analyzing sequence clusters. These scripts are freely available on GitHub.
Isolation of casein for stable isotope ratio analysis of butter, cheese, and milk powder
Authors: O'Sullivan Roisin , Schmidt Olaf , O'Sullivan Michael , Cama‐Moncunill Raquel , Monahan Frank J. 
Published: September 2022

RationaleStable isotope ratio analysis (SIRA) is commonly used for the authentication of dairy commodities, providing evidence to support the geographical origin and production background of products. We set out to optimise methods for the isolation of a common constituent (casein) from three dairy commodities, which would permit easier inter‐ and intra‐commodity comparisons following SIRA.MethodsThree published methods for isolation of protein (from cheese, milk, and butter) were adapted to yield protein (casein) fractions from commercial cheddar cheese, whole milk powder (WMP), and butter samples with a high degree of purity for subsequent SIRA. The casein fractions isolated underwent elemental analysis (H, C, and N), protein determination, and some also underwent SIRA of O and S. Two‐way analysis of variance and Tukey post hoc comparisons tested differences between methods.ResultsFor each product, an optimised casein isolation method was chosen based on the C/N ratio and protein content. An optimum solvent lipid extraction (petroleum spirit–diethyl ether (2:1)) and casein precipitation method was chosen for cheddar cheese casein. A final solvent lipid extraction (heptane–isopropanol (3:2)) was necessary for WMP and butter casein extraction. δ13C and δ2H values validated the methods' abilities to remove contaminating lipid and isolate pure casein.ConclusionsCasein of high purity, for subsequent SIRA, can be isolated from cheddar cheese, WMP, and butter following modifications of previously published methods.
Imperfect detection and wildlife density estimation using aerial surveys with infrared and visible sensors
Authors: Delisle Zackary J. , McGovern Patrick G. , Dillman Brian G. , Swihart Robert K. 
Published: September 2022

AbstractAerial vehicles equipped with infrared thermal sensors facilitate quick density estimates of wildlife, but detection error can arise from the thermal sensor and viewer of the infrared video. We reviewed published research to determine how commonly these sources of error have been assessed in studies using infrared video from aerial platforms to sample wildlife. The number of annual articles pertaining to aerial sampling using infrared thermography has increased drastically since 2018, but past studies inconsistently assessed sources of imperfect detection. We illustrate the importance of accounting for some of these types of error in a case study on white‐tailed deerOdocoileus virginianusin Indiana, USA, using a simple double‐observer approach. In our case study, we found evidence of false negatives associated with the viewer of infrared video. Additionally, we found that concordance between the detections of two viewers increased when using a red‐green‐blue camera paired with the infrared thermal sensor, when altitude decreased and when more stringent criteria were used to classify thermal signatures as deer. We encourage future managers and ecologists recording infrared video from aerial platforms to use double‐observer methods to account for viewer‐induced false negatives when video is manually viewed by humans. We also recommend combining infrared video with red‐green‐blue video to reduce false positives, applying stringent verification standards to detections in infrared and red‐green‐blue video and collecting data at lower altitudes over snow when needed.
Quantification of meteorological drought risks between 1.5 °C and 4 °C of global warming in six countries
Authors: Price Jeff , Warren Rachel , Forstenhäusler Nicole , Wallace Craig , Jenkins Rhosanna , Osborn Timothy J. , Van Vuuren D. P. 
Published: September 2022

Abstract
We quantify the projected impacts of alternative levels of global warming upon the probability and length of severe drought in six countries (China, Brazil, Egypt, Ethiopia, Ghana and India). This includes an examination of different land cover classes, and a calculation of the proportion of population in 2100 (SSP2) at exposed to severe drought lasting longer than one year. Current pledges for climate change mitigation, which are projected to still result in global warming levels of 3 °C or more, would impact all of the countries in this study. For example, with 3 °C warming, more than 50% of the agricultural area in each country is projected to be exposed to severe droughts of longer than one year in a 30-year period. Using standard population projections, it is estimated that 80%-100% of the population in Brazil, China, Egypt, Ethiopia and Ghana (and nearly 50% of the population of India) are projected to be exposed to a severe drought lasting one year or longer in a 30-year period. In contrast, we find that meeting the long-term temperature goal of the Paris Agreement, that is limiting warming to 1.5 °C above pre-industrial levels, is projected to greatly benefit all of the countries in this study, greatly reducing exposure to severe drought for large percentages of the population and in all major land cover classes, with Egypt potentially benefiting the most.
Creating Insecurity Through Youth Street Groups and Applying Security for Control and Governance. A Case Study of Barcelona Latin Kings
Authors: Ballesté-Isern Eduard , Feixa Carles 
Published: September 2022

AbstractThis paper is based on an ethnographic study of the arrests and the subsequent trial and sentencing of a group of Latin Kings and Queens from Barcelona between 2015 and 2020. We analyze the actions carried out by the police, judicial institutions and media to reestablish the “hard handed” discourse in relation to these youth street groups in a time of crisis and precariousness. The concept of a “space of youth street groups” is used to construct a tool for mapping the agents who interact with these groups and the position they occupy in the social space. The interactions between these agents in Barcelona configure a new form of security governance through the creation of subjective insecurity and the promotion of punitive policies against youth street groups.
Utilizing additive manufacturing and mass customization under capacity constraints
Authors: Lacroix Rachel , Timonina-Farkas Anna , Seifert Ralf W. 
Published: September 2022

AbstractAdditive manufacturing (AM), originally used for prototyping, is increasingly adopted for custom final part production across different industries. However, printing speed and production volume are two barriers for the adoption of AM for product customization at large scale. Nevertheless, manufacturers could aim to combine the benefits of AM for product customization with traditional mass customization (MC) technologies over the product life cycle (PLC). This approach is showcased in our paper as a manufacturing opportunity and is addressed via a non convex-concave optimization model that considers a monopolist manufacturer producing horizontally differentiated products at scale. To satisfy individual customer preferences under capacity considerations, the firm jointly decides on the inventory, production quantity, product variety, optimal technology-switching times (between AM and MC) and pricing strategy. Our approach can be implemented by decision-makers to leverage customer-centricity and benefit from this novel hybrid manufacturing practice. By deriving a closed-form solution for the production quantity based on an adaptive inventory policy, the resulting optimization problem is solved using the Sample Average Approximation framework grounded by analytical results. Our results demonstrate that the new usage of AM with MC can benefit a manufacturer for customer-centric driven strategies. Significant profit improvements can be achieved with an AM–MC–AM technology-switching scenario under certain capacity conditions and with an increasing-decreasing pricing strategy. Our results also indicate that the benefits of pricing flexibility are highest when capacity is unlimited or when the firm does not hold inventory. Under capacity constraints, a simple decreasing pricing policy combined with inventory performs very well.
Tuning the Structural and Magnetic Properties of the Stuffed Framework Structures MeFe2O4 (Me = Ni, Ca, and Sr)
Authors: Ateia Ebtesam. E. , Hussien Sara , Mohamed Amira. T. 
Published: September 2022

AbstractSpinel ferrite nanoparticles (NPs), have received a lot of attention in medical applications. Therefore, facile synthesis of ferrite NPs of numerous shapes and sizes using the citrate autocombustion technique was utilized in this article. A series of ferrite with the general formula MeFe2O4 [Me = nickle (Ni), calcium (Ca), and strontium (Sr)] are synthesized with varying average ionic radii and cation disorder on the A-site. The structural and morphological characterization of the prepared samples was performed using XRD, HRTEM, FESEM, EDAX, XPS, and Raman analyses. The phase transformation from cubic (Ni) to orthorhombic (Ca) to monoclinic (Sr) was also revealed by XRD. Accordingly, HRTEM images demonstrated nanoparticles in orthorhombic and monoclinic shapes, which are inconsistent with XRD analyses. The coercive field HC for monoclinic SrFe2O4 is ≈ 42 times larger than the Hc for NiFe2O4 with a cubic structure. This deviation in HC compared to the cubic shape particles can be coupled to the shape anisotropy present in SrFe2O4 and refers to the presence of a preferred magnetization direction within the material. The use of monoclinic SrFe2O4 NPs as antifungal activity agents is noteworthy due to their advantages in terms of surface area, efficacy, and biodegradability.
Multimodal recognition of frustration during game-play with deep neural networks
Authors: Fuente Carlos de la , Castellanos Francisco J. , Valero-Mas Jose J. , Calvo-Zaragoza Jorge 
Published: September 2022

AbstractFrustration, which is one aspect of the field of emotional recognition, is of particular interest to the video game industry as it provides information concerning each individual player’s level of engagement. The use of non-invasive strategies to estimate this emotion is, therefore, a relevant line of research with a direct application to real-world scenarios. While several proposals regarding the performance of non-invasive frustration recognition can be found in literature, they usually rely on hand-crafted features and rarely exploit the potential inherent to the combination of different sources of information. This work, therefore, presents a new approach that automatically extracts meaningful descriptors from individual audio and video sources of information using Deep Neural Networks (DNN) in order to then combine them, with the objective of detecting frustration in Game-Play scenarios. More precisely, two fusion modalities, namelydecision-levelandfeature-level, are presented and compared with state-of-the-art methods, along with different DNN architectures optimized for each type of data. Experiments performed with a real-world audiovisual benchmarking corpus revealed that the multimodal proposals introduced herein are more suitable than those of a unimodal nature, and that their performance also surpasses that of other state-of-the–art approaches, with error rate improvements of between 40%and 90%.
Approximating the linear response of physical chaos
Authors: Śliwiak Adam A. , Wang Qiqi 
Published: September 2022

AbstractParametric derivatives of statistics are highly desired quantities in prediction, design optimization and uncertainty quantification. In the presence of chaos, the rigorous computation of these quantities is certainly possible, but mathematically complicated and computationally expensive. Based on Ruelle’s formalism, this paper shows that the sophisticated linear response algorithm can be dramatically simplified in higher-dimensional systems featuring statistical homogeneity in the physical space. We argue that the contribution of the SRB (Sinai–Ruelle–Bowen) measure gradient, which is an integral yet the most cumbersome part of the full algorithm, is negligible if the objective function is appropriately aligned with unstable manifolds. This abstract condition could potentially be satisfied by a vast family of real-world chaotic systems, regardless of the physical meaning and mathematical form of the objective function and perturbed parameter. We demonstrate several numerical examples that support these conclusions and that present the use and performance of a simplified linear response algorithm. In the numerical experiments, we consider physical models described by differential equations, including Lorenz 96 and Kuramoto–Sivashinsky.
One-dimentional periodic structure infiltrated by (PVA/CV + CF)-polymer for high-performance sensitivity
Authors: Sayed Fatma A. , Elsayed Hussein A. , Mehaney Ahmed , Eissa M. F. , Aly Arafa H. 
Published: September 2022

AbstractIn the current work, we demonstrate a design to act as a Gamma-ray radiation dosimeter based on the one-dimensional photonic crystal (1D-PhC). The basic concept of the present dosimeter is based on a Porous Silicon (PSi) infiltrated by poly-vinyl alcohol (PVA)-polymer doped with crystal violet (CV) and carbol-fuchsine (CF) dyes. The mechanism of suggested dosimeter is based on the shift of the photonic bandgap (PBG) to higher wavelengths as exposed to gamma-ray radiation doses from 0 to 70 Gray (Gy). The basic axes of the current theoretical treatment are the transfers matrix method (TMM), Bruggeman's effective medium equation, and the fitted experimental data to the refractive index of the doped PVA-Polymer. The obtained results showed the proposed sensor is characterized by high stable sensitivity varied from (178–186 nm/ RIU) along an applied γ-dose from (10–70 Gy) in the visible range. In addition, we compared these results with previous researches. In addition, based on the our knowledge may be it is the first time that a 1D-PhC has been used for gamma-ray detection by using (PVA/CV + CF) based on Porous Silicon.
Reading and math skills development among Finnish primary school children before and after COVID-19 school closure
Authors: Lerkkanen Marja-Kristiina , Pakarinen Eija , Salminen Jenni , Torppa Minna 
Published: September 2022

AbstractThis study quantified the possible learning losses in reading and math skills among a sample of Finnish Grade 3 children (n = 198) who spent 8 weeks in distance learning during the first wave of the COVID-19 pandemic in spring 2020. We compared their reading and math skill development trajectories across Grades 1, 2, and 4 to a pre-COVID sample (N = 378). We also examined if gender, parental education, maternal homework involvement, and child’s task-avoidant behavior predict children’s academic skills at Grade 4 differently in the pre-COVID sample compared with the COVID sample. Children’s reading and math skills were tested, mothers reported their education and homework involvement, and teachers rated children’s task-avoidant behavior. The results showed, on average, lower reading skills in the COVID sample than in the pre-COVID sample but there were no differences in math skills. Although the COVID sample had lower levels in reading, their developmental trajectories in reading and math skills were not different from the pre-COVID sample before the pandemic in Grades 1 and 2. From Grade 2 to 4, however, the development was slower in reading fluency and comprehension in the COVID sample, but not in math. The predictors of change from Grade 2 to 4 in reading and math skills were not different in the samples. The results showed that the development of reading skills in particular may have been affected by the COVID-19 pandemic.