bluuebunny/crossref_metadata_2025_split huggingface dataset:
A two-stage optimal sensor placement method for multi-type structural response reconstruction
Authors: Liu Chengyin , Jiang Zhaoshuo , Gong Yi , Xiao Yongfeng 
Published: November 2020

Abstract
Optimal multi-type sensor placement has gained considerable attention in the structural health monitoring field during the past few years. Although a structural response reconstruction-oriented optimization method for a multi-type sensor has been developed, the challenge of information redundancy in the collected signal deserves further investigation. To tackle this challenge, this paper presents a two-stage optimization framework for response reconstruction with the capability to reduce the multi-type sensor information redundancy. In the first stage, the optimization of multi-type sensor placement for response reconstruction is performed to initially determine the optimal sensor deployment scheme. After the optimal sensor locations are selected, the second-stage optimization introduces a metric, called the distance coefficient, to evaluate the information independence level between sensor locations with the goal of reducing the sensor information redundancy. A numerical study on a bridge model is first performed to evaluate the feasibility of the proposed framework, after which a lab-scale physical bridge model is tested to validate its effectiveness. Both the numerical and experimental results demonstrate that the proposed two-stage optimization framework can reduce the sensor information redundancy and, at the same time, produce a satisfying result in the response reconstruction of key locations.
Multi-task edge-recalibrated network for male pelvic multi-organ segmentation on CT images
Authors: Tong Nuo , Gou Shuiping , Chen Shuzhe , Yao Yao , Yang Shuyuan , Cao Minsong , Kishan Amar , Sheng Ke 
Published: November 2020

Abstract
Automated male pelvic multi-organ segmentation on CT images is highly desired for applications, including radiotherapy planning. To further improve the performance and efficiency of existing automated segmentation methods, in this study, we propose a multi-task edge-recalibrated network (MTER-Net), which aims to overcome the challenges, including blurry boundaries, large inter-patient appearance variations, and low soft-tissue contrast. The proposed MTER-Net is equipped with the following novel components. (a) To exploit the saliency and stability of femoral heads, we employed a light-weight localization module to locate the target region and efficiently remove the complex background. (b) We add an edge stream to the regular segmentation stream to focus on processing the edge-related information, distinguish the organs with blurry boundaries, and then boost the overall segmentation performance. Between the regular segmentation stream and edge stream, we introduce an edge recalibration module at each resolution level to connect the intermediate layers and deliver the higher-level activations from the regular stream to the edge stream to denoise the irrelevant activations. (c) Finally, using a 3D Atrous Spatial Pyramid Pooling (ASPP) feature fusion module, we fuse the features at different scales in the regular stream and the predictions from the edge stream to form the final segmentation result. The proposed segmentation network was evaluated on 200 prostate cancer patient CT images with manually delineated contours of bladder, rectum, seminal vesicle, and prostate. The segmentation performance of the proposed method was quantitatively evaluated using three metrics including Dice similarity coefficient (DSC), average surface distance (ASD), and 95% surface distance (95SD). The proposed MTER-Net achieves average DSC of 86.35%, ASD of 1.09 mm, and 95SD of 3.53 mm on the four organs, which outperforms the state-of-the-art segmentation networks by a large margin. Specifically, the quantitative DSC evaluation results of the four organs are 96.49% (bladder), 86.39% (rectum), 76.38% (seminal vesicle), and 86.14% (prostate), respectively. In conclusion, we demonstrate that the proposed MTER-Net efficiently attains superior performance to state-of-the-art pelvic organ segmentation methods.
Magnetic reconnection in 3D fusion devices: non-linear reduced equations and linear current-driven instabilities
Authors: Zocco A , Helander P , Weitzner H 
Published: November 2020

Abstract
Magnetic reconnection in 3D fusion devices is investigated. With the use of Boozer co-ordinates, we reduce the non-linear resistive magnetohydrodynamic equations in the limit of large aspect ratio and finite pressure fluctuations, to obtain a set of non-linear equations suitable for magnetic reconnection studies in stellarators. Magnetic flux unfreezing due to a finite electron mass is also considered. Equations that govern the linear regime and some of their general properties are given. We emphasise the role of magnetic geometry and identify how some aspects of stellarator optimisation could have an impact on reconnecting instabilities, in particular by exacerbating those enabled by electron inertia. The effect of 3D coupling on the linear reconnection rates and the mode structure is quantitatively addressed in the case in which the equilibrium rotational transform has one specific resonant location for which one mode can reconnect while coupled to an arbitrary number of non-resonant harmonics. The full problem is rigorously reduced to an equivalent cylindrical one, by introducing some geometrically modified plasma inertial and dissipative scales. The 3D scalings for the growth rates of reconnection instabilities and their destabilisation criteria are given.
InP membrane integrated photonics research
Authors: Jiao Yuqing , Nishiyama Nobuhiko , van der Tol Jos , van Engelen Jorn , Pogoretskiy Vadim , Reniers Sander , Kashi Amir Abbas , Wang Yi , Calzadilla Victor Dolores , Spiegelberg Marc , Cao Zizheng , Williams Kevin , Amemiya Tomohiro , Arai Shigehisa 
Published: November 2020

AbstractRecently a novel photonic integration technology, based on a thin InP-based membrane, is emerging. This technology offers monolithic integration of active and passive functions in a sub-micron thick membrane. The enhanced optical confinement in the membrane results in ultracompact active and passive devices. The membrane also enables approaches to converge with electronics. It has shown high potential in breaking the speed, energy and density bottlenecks in conventional photonic integration technologies. This paper explains the concept of the InP membrane, discusses the versatility of various technology approaches and reviews the recent advancement in this field.
Effect of working gas on physicochemical and biological properties of CaP coatings deposited by RFMS
Authors: Kozelskaya Anna , Fedotkin Alexander , Khlusov Igor , Litvinova Larisa , Tverdokhlebov Sergei 
Published: November 2020

Abstract
Thin calcium phosphate (CaP) coatings were deposited on titanium substrates by radio frequency magnetron sputtering of hydroxyapatite target in neon (Ne), argon (Ar), krypton (Kr) and xenon (Xe). The influence of the working gas (Ne, Ar, Kr and Xe) on the wettability and biodegradation in the RPMI 1640 synthetic culture medium of the CaP coatings was investigated. This paper is the first comprehensive study of working gas effect on properties of the CaP coatings. There was an increase in the polar component of surface free energy (SFE) and a decrease in the dispersion component of SFE with an increase in the atomic mass of the working gas. All CaP coatings had a pronounced protective effect, reducing double the number of dead cells in culture compared to the Ti control. The most soluble CaP coatings formed in the atmosphere of Xe stimulated the hAMMSCs division, which led to an increase in the total number of cells (208% of the initial culture). Samples with CaP coatings formed in an inert gas atmosphere increased the gene expression (ALPL, BMP6, BMP2)
in vitro
. The most biocompatible coatings were those formed in the atmosphere of Xe and Ar.
Average skew information-based coherence and its typicality for random quantum states
Authors: Wu Zhaoqi , Zhang Lin , Fei Shao-Ming , Li-Jost Xianqing 
Published: November 2020

Abstract
We study the average skew information-based coherence for both random pure and mixed states. The explicit formulae of the average skew information-based coherence are derived and shown to be the functions of the dimension N of the state space. We demonstrate that as N approaches to infinity, the average coherence is 1 for random pure states, and a positive constant less than 1/2 for random mixed states. We also explore the typicality of average skew information-based coherence of random quantum states. Furthermore, we identify a coherent subspace such that the amount of the skew information-based coherence for each pure state in this subspace can be bounded from below almost always by a fixed number that is arbitrarily close to the typical value of coherence.
Theoretical-field description of the critical behavior in ferromagnetic nanoparticles structured ensemble
Authors: Belim S V 
Published: November 2020

Abstract
The paper explores the critical behaviour of a ferromagnetic nanoparticle structured ensemble within a theoretical-field approach in three-dimensional space. Critical behaviour has been investigated in the transition from the superparamagnetic to the ferromagnetic phases. Individual nanoparticles are considered in the investigated system instead of point spins. Interaction between spins is carried out only by means of dipole-dipole forces. In this case, the interaction is anisotropic. The calculations are made in a two-loop approximation. The method Pade-Borel for summing asymptotic series is used. Critical exponents are calculated (ν = 0.635, η = 0.347, γ = 1.050, β = 0.427, α = 0.095, δ = 3.456). This system had a new class of critical behaviour. A comparison was made with the results of actual experiments.
Fabrication and characterization of forcespun polycaprolactone microfiber scaffolds
Authors: Kodali Deepa , Syed Farooq , Jeelani Shaik , Rangari Vijaya K 
Published: November 2020

Abstract
Forcespinning technique was used to fabricate sub-micron size polycaprolactone (PCL) fibers. Forcespinning method uses centrifugal forces for the generation of fibers unlike the electrospinning method which uses electrostatic force. PCL has been extensively used as scaffolds for cell regeneration, substrates for tissue engineering and in drug delivery systems. The aim of this study is to qualitatively analyze the force spun fiber mats and investigate the effect of the spinneret rotational speed on the fiber morphology, thermal and mechanical properties. The extracted fibers were characterized by scanning electron microscopy differential scanning calorimetry, tensile testing and dynamic mechanical analysis. The results showed that higher rotational speeds produced uniform fibers with less number of beads. The crystallinity of the fibers decreased with increase in rotational speeds. The Young’s modulus of the forcespun fibers was found to be in the range of 3.5 to 6 MPa. Storage and loss moduli decreased with the increase in the fiber diameter. The fibers collected at farther distance from spinneret exhibited optimal mechanical properties compared to the fibers collected at shorter distances. This study will aid in extracting fibers with uniform geometries and lower beads to achieve the desired nanofiber drug release properties.
Aluminium composites prepared by laser cladding assisted by friction stir processing
Authors: Wei Yanni , Sun Fu , Gao Huaibao , Peng Xiao , Zou Juntao 
Published: November 2020

Abstract
A laser cladding and friction stir processing hybrid method was employed to produce an Al matrix composite layer. The microstructure, phase composition, microhardness and conductivity of the composites were investigated. A laser cladding layer with a thickness of approximately 200 μm was prepared on a 1060 aluminium plate and it was broken up and distributed on the Al matrix after friction stir processing. The particle/Al interfaces exhibited extremely good interfacial integrity. Microstructural observations revealed that an obvious in situ reaction occurred at the particle/Al interfaces, which effectively improved the bonding between the reinforcement phase and the matrix. TEM analysis and selected area diffraction enabled the identification of the intermetallic compounds and confirmed them to be Al5Fe2 and Al3Fe. The average microhardness values of the friction stir processed composites reached approximately 85 HV. The electrical resistivity of the friction stir processed composites is slightly higher than that of the aluminium matrix.
The Justinianic Plague and Global Pandemics: The Making of the Plague Concept
Authors: Eisenberg Merle , Mordechai Lee 
Published: November 2020

Abstract
This article explores how plague—as an idea—became an ahistorical independent agent of historical change. It focuses on the case of the Justinianic Plague (ca. 541–750 c.e.), the first major recorded plague pandemic in Mediterranean history, which has increasingly been used to explain significant demographic, political, social, economic, and cultural change during late antiquity (ca. 300–800 c.e.). We argue that the Justinianic Plague retains its great historiographic power—namely, its supposed destructive impact over two centuries—because it evokes a terrifying myth of what plague should do rather than because of conclusive evidence of what it did. We define this historiographic power as the plague concept. It includes three key features: extensive chronology (lasting for two centuries), mortality (catastrophic death toll), and geography (global). The plague concept is built on three interdisciplinary types of evidence (here termed truisms): rats, climate, and paleogenetics. Our article traces how scientists constructed the plague concept in the first half of the twentieth century, and how historians entered the discussion in the last third of that century. As historians engaged in Justinianic Plague research, they used the plague concept to frame their arguments without problematizing its presence or contesting features that scientists had constructed decades earlier.
Morphing projections: a new visual technique for fast and interactive large-scale analysis of biomedical datasets
Authors: Díaz Ignacio , Enguita José M , González Ana , García Diego , Cuadrado Abel A , Chiara María D , Valdés Nuria 
Published: November 2020

Abstract

Motivation
Biomedical research entails analyzing high dimensional records of biomedical features with hundreds or thousands of samples each. This often involves using also complementary clinical metadata, as well as a broad user domain knowledge. Common data analytics software makes use of machine learning algorithms or data visualization tools. However, they are frequently one-way analyses, providing little room for the user to reconfigure the steps in light of the observed results. In other cases, reconfigurations involve large latencies, requiring a retraining of algorithms or a large pipeline of actions. The complex and multiway nature of the problem, nonetheless, suggests that user interaction feedback is a key element to boost the cognitive process of analysis, and must be both broad and fluid.

Results
In this article, we present a technique for biomedical data analytics, based on blending meaningful views in an efficient manner, allowing to provide a natural smooth way to transition among different but complementary representations of data and knowledge. Our hypothesis is that the confluence of diverse complementary information from different domains on a highly interactive interface allows the user to discover relevant relationships or generate new hypotheses to be investigated by other means. We illustrate the potential of this approach with three case studies involving gene expression data and clinical metadata, as representative examples of high dimensional, multidomain, biomedical data.

Availability and implementation
Code and demo app to reproduce the results available at https://gitlab.com/idiazblanco/morphing-projections-demo-and-dataset-preparation.

Supplementary information
Supplementary data are available at Bioinformatics online.
Transfer of Technology for Youth Civic Engagement in Pond Restoration: An Intervention Study Using the Social Group Work Method
Authors: Lalitha S 
Published: November 2020

Abstract
Youth role is significant in the conservation of water resources. However, youth civic engagement in pond restoration is less explored. This article discusses the research process and outcome of an intervention study conducted by the researcher for her Postdoctoral Research. Mixed methods were used to understand community needs, power structures, problems of youth and youth culture. Social group work method was used to motivate youth to restore a pond called Vairakuttai in Katchipattu Village, Tamil Nadu, India. The youth received training in pond restoration, and the impact of the training was analysed using one-sample ‘t’-test. The study revealed that transfer of technology enhances youth civic engagement in pond restoration and raised awareness on the protection of water resources amongst the community. As an outcome, training content and theoretical framework for enhancing youth civic engagement in the restoration of water bodies were developed by the researcher.
Outcomes for Families Referred to Family Centres: Using Validated Instruments to Chart Changes in Psychological Functioning, Relationships and Children’s Coping Strategies over Time
Authors: Spratt Trevor , Swords Lorraine , Vilda Dovile 
Published: November 2020

AbstractThere is growing interest in the development of evidence supporting therapeutic interventions in social work. Few examples, however, exist, of the use of validated instrumentation in measuring the impact of services upon children and families. We report here on the use of a suite of validated instruments to measure the impact of services on children and their parents in receipt of services provided by an Irish Non-Governmental Organisation (NGO) across their seven family centres. The NGO engaged a team of university-based researchers who provided training in the use of validated instruments, monitored implementation of their use and analysed the resulting data. Over a two-year period, 968 families were surveyed at Time 1 (entering the service), with 452 completing surveys at Time 2 (leaving the service). The results indicate a decrease in children’s emotional and social problems, with those with highest scores at Time 1 making most progress by Time 2. Similarly, it was evident that interventions had a positive impact upon parental mental health and, with regard to child–parent relationships, both children and their parents reported positive changes. Such findings lay emphasis on the importance of measuring the impact of services on individual psychological functioning and interfamilial relationships.
Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders
Authors: Barron Daniel S , Gao Siyuan , Dadashkarimi Javid , Greene Abigail S , Spann Marisa N , Noble Stephanie , Lake Evelyn M R , Krystal John H , Constable R Todd , Scheinost Dustin 
Published: November 2020

Abstract
Memory deficits are observed in a range of psychiatric disorders, but it is unclear whether memory deficits arise from a shared brain correlate across disorders or from various dysfunctions unique to each disorder. Connectome-based predictive modeling is a computational method that captures individual differences in functional connectomes associated with behavioral phenotypes such as memory. We used publicly available task-based functional MRI data from patients with schizophrenia (n = 33), bipolar disorder (n = 34), attention deficit hyper-activity disorder (n = 32), and healthy controls (n = 73) to model the macroscale brain networks associated with working, short- and long-term memory. First, we use 10-fold and leave-group-out analyses to demonstrate that the same macroscale brain networks subserve memory across diagnostic groups and that individual differences in memory performance are related to individual differences within networks distributed throughout the brain, including the subcortex, default mode network, limbic network, and cerebellum. Next, we show that diagnostic groups are associated with significant differences in whole-brain functional connectivity that are distinct from the predictive models of memory. Finally, we show that models trained on the transdiagnostic sample generalize to novel, healthy participants (n = 515) from the Human Connectome Project. These results suggest that despite significant differences in whole-brain patterns of functional connectivity between diagnostic groups, the core macroscale brain networks that subserve memory are shared.
Connectome-Based Prediction of Optimal Weight Loss Six Months After Bariatric Surgery
Authors: Zhang Wenchao , Ji Gang , Manza Peter , Li Guanya , Hu Yang , Wang Jia , Lv Ganggang , He Yang , von Deneen Karen M , Han Yu , Cui Guangbin , Tomasi Dardo , Volkow Nora D , Nie Yongzhan , Wang Gene-Jack , Zhang Yi 
Published: November 2020

Abstract
Despite bariatric surgery being the most effective treatment for obesity, a proportion of subjects have suboptimal weight loss post-surgery. Therefore, it is necessary to understand the mechanisms behind the variance in weight loss and identify specific baseline biomarkers to predict optimal weight loss. Here, we employed functional magnetic resonance imaging (fMRI) with baseline whole-brain resting-state functional connectivity (RSFC) and a multivariate prediction framework integrating feature selection, feature transformation, and classification to prospectively identify obese patients that exhibited optimal weight loss at 6 months post-surgery. Siamese network, which is a multivariate machine learning method suitable for small sample analysis, and K-nearest neighbor (KNN) were cascaded as the classifier (Siamese-KNN). In the leave-one-out cross-validation, the Siamese-KNN achieved an accuracy of 83.78%, which was substantially higher than results from traditional classifiers. RSFC patterns contributing to the prediction consisted of brain networks related to salience, reward, self-referential, and cognitive processing. Further RSFC feature analysis indicated that the connection strength between frontal and parietal cortices was stronger in the optimal versus the suboptimal weight loss group. These findings show that specific RSFC patterns could be used as neuroimaging biomarkers to predict individual weight loss post-surgery and assist in personalized diagnosis for treatment of obesity.