bluuebunny/crossref_metadata_2025_split huggingface dataset:
CrashNet: an encoder–decoder architecture to predict crash test outcomes
Authors: Belaid Mohamed Karim , Rabus Maximilian , Krestel Ralf 
Published: May 2021

AbstractDestructive car crash tests are an elaborate, time-consuming, and expensive necessity of the automotive development process. Today, finite element method (FEM) simulations are used to reduce costs by simulating car crashes computationally. We propose CrashNet, an encoder–decoder deep neural network architecture that reduces costs further and models specific outcomes of car crashes very accurately. We achieve this by formulating car crash events as time series prediction enriched with a set of scalar features. Traditional sequence-to-sequence models are usually composed of convolutional neural network (CNN) and CNN transpose layers. We propose to concatenate those with an MLP capable of learning how to inject the given scalars into the output time series. In addition, we replace the CNN transpose with 2D CNN transpose layers in order to force the model to process the hidden state of the set of scalars as one time series. The proposed CrashNet model can be trained efficiently and is able to process scalars and time series as input in order to infer the results of crash tests. CrashNet produces results faster and at a lower cost compared to destructive tests and FEM simulations. Moreover, it represents a novel approach in the car safety management domain.
Applying machine learning to optical coherence tomography images for automated tissue classification in brain metastases
Authors: Möller Jens , Bartsch Alexander , Lenz Marcel , Tischoff Iris , Krug Robin , Welp Hubert , Hofmann Martin R. , Schmieder Kirsten , Miller Dorothea 
Published: May 2021

Abstract
Purpose
A precise resection of the entire tumor tissue during surgery for brain metastases is essential to reduce local recurrence. Conventional intraoperative imaging techniques all have limitations in detecting tumor remnants. Therefore, there is a need for innovative new imaging methods such as optical coherence tomography (OCT). The purpose of this study is to discriminate brain metastases from healthy brain tissue in an ex vivo setting by applying texture analysis and machine learning algorithms for tissue classification to OCT images.

Methods
Tumor and healthy tissue samples were collected during resection of brain metastases. Samples were imaged using OCT. Texture features were extracted from B-scans. Then, a machine learning algorithm using principal component analysis (PCA) and support vector machines (SVM) was applied to the OCT scans for classification. As a gold standard, an experienced pathologist examined the tissue samples histologically and determined the percentage of vital tumor, necrosis and healthy tissue of each sample. A total of 14.336 B-scans from 14 tissue samples were included in the classification analysis.

Results
We were able to discriminate vital tumor from healthy brain tissue with an accuracy of 95.75%. By comparing necrotic tissue and healthy tissue, a classification accuracy of 99.10% was obtained. A generalized classification between brain metastases (vital tumor and necrosis) and healthy tissue was achieved with an accuracy of 96.83%.

Conclusions
An automated classification of brain metastases and healthy brain tissue is feasible using OCT imaging, extracted texture features and machine learning with PCA and SVM. The established approach can prospectively provide the surgeon with additional information about the tissue, thus optimizing the extent of tumor resection and minimizing the risk of local recurrences.
Allergens from Edible Insects: Cross-reactivity and Effects of Processing
Authors: De Marchi Laura , Wangorsch Andrea , Zoccatelli Gianni 
Published: May 2021

AbstractPurpose of ReviewThe recent introduction of edible insects in Western countries has raised concerns about their safety in terms of allergenic reactions. The characterization of insect allergens, the sensitization and cross-reactivity mechanisms, and the effects of food processing represent crucial information for risk assessment.Recent FindingsAllergic reactions to different insects and cross-reactivity with crustacean and inhalant allergens have been described, with the identification of new IgE-binding proteins besides well-known pan-allergens. Depending on the route of sensitization, different potential allergens seem to be involved. Food processing may affect the solubility and the immunoreactivity of insect allergens, with results depending on species and type of proteins. Chemical/enzymatic hydrolysis, in some cases, abolishes immunoreactivity.SummaryMore studies based on subjects with a confirmed insect allergy are necessary to identify major and minor allergens and the role of the route of sensitization. The effects of processing need to be further investigated to assess the risk associated with the ingestion of insect-containing food products.
A review on Deep Learning approaches for low-dose Computed Tomography restoration
Authors: Kulathilake K. A. Saneera Hemantha , Abdullah Nor Aniza , Sabri Aznul Qalid Md , Lai Khin Wee 
Published: May 2021

AbstractComputed Tomography (CT) is a widely use medical image modality in clinical medicine, because it produces excellent visualizations of fine structural details of the human body. In clinical procedures, it is desirable to acquire CT scans by minimizing the X-ray flux to prevent patients from being exposed to high radiation. However, these Low-Dose CT (LDCT) scanning protocols compromise the signal-to-noise ratio of the CT images because of noise and artifacts over the image space. Thus, various restoration methods have been published over the past 3 decades to produce high-quality CT images from these LDCT images. More recently, as opposed to conventional LDCT restoration methods, Deep Learning (DL)-based LDCT restoration approaches have been rather common due to their characteristics of being data-driven, high-performance, and fast execution. Thus, this study aims to elaborate on the role of DL techniques in LDCT restoration and critically review the applications of DL-based approaches for LDCT restoration. To achieve this aim, different aspects of DL-based LDCT restoration applications were analyzed. These include DL architectures, performance gains, functional requirements, and the diversity of objective functions. The outcome of the study highlights the existing limitations and future directions for DL-based LDCT restoration. To the best of our knowledge, there have been no previous reviews, which specifically address this topic.
The Influence of Sudarshan Kriya Yoga on Sleep Quality in Indian Adults: An Open Trial Pilot Study
Authors: Kanchibhotla Divya , Parekh Sheel Galada , Harsora Prateek , Kulkarni Shashank 
Published: May 2021

Abstract
Purpose
Sudarshan Kriya Yoga (SKY) is an advanced yogic breathing technique with a demonstrated impact on human physical and mental health. Even so, very few studies have observed its effect on sleep. This is the first study evaluating the impact of SKY as an intervention on sleep quality among Indian population. This study assessed both the immediate and prolonged effect of SKY on sleep quality. The secondary objective of the study was to evaluate the association between frequency of SKY practice and sleep quality.

Methods
This was a single arm open-trial study, which included 473 participants. All participants underwent a 3-day SKY workshop offered by the Art of Living, and were assessed for sleep quality using the Pittsburgh Sleep Quality Index (PSQI) questionnaire, administered to the participants before the program (pre-intervention), immediately after the program (post-intervention), and at Day 40 (D40) after the program. Responses were grouped according to age, gender, and frequency of practice. The impact of the frequency of SKY practice on sleep quality was also investigated.

Results
The quality of sleep improved with SKY practice across the study population. Women recorded inferior quality of sleep at the beginning of the study, but experienced a greater improvement in sleep quality after the SKY practice, as compared to men. The younger population seemed to benefit over the long-term, while the older population had an immediate improvement in sleep quality, however, they were not able to sustain it. The effect size varied with the frequency of practice. Sleep quality improved markedly among those who practiced SKY daily, indicating an association between the frequency of SKY practice and its efficiency in improving sleep quality.

Conclusion
The results demonstrate the efficacy of SKY in improving sleep quality across populations, irrespective of gender or age. This is indicative of the beneficial role of SKY in treating psychological disorders such as insomnia. The effect varies with the frequency of practice, with more frequent practice yielding better quality of sleep.
Ultra-high-performance cementitious composites with enhanced mechanical and durability characteristics
Authors: Sadiq Muhammad M. , Soroushian Parviz , Bakker Martin G. , Balachandra Anagi M. 
Published: May 2021

Abstract
Concrete is the most widely used construction material. It offers a desirable balance of cost, strength, moisture barrier qualities, and dimensional and chemical stability. The rising costs of aging infrastructure systems, however, point to the need for further improvements in concrete properties. Carbon-based nanomaterials (CBNs) are predicted to have excellent mechanical properties, and so are attractive candidates for addressing these issues. However, the relatively high cost of CBNs, means that only low weight fractions in cement matrices will be economically viable, which presents a significant challenge. The research presented here investigated various surface functionalization techniques for improving the compatibility of carbon nanomaterials (multi-walled carbon nanotubes, carbon nanofiber and graphene nanoplatelets) with cementitious materials in fresh and hardened state. The effects of surface functionalization on the contributions of CBNs to the performance characteristics of ultra-high-performance cementitious matrices (UHPCM) were evaluated. Functionalized multi-walled carbon nanotubes at 0.03% weight fraction increased the flexural strength by 30%, doubled the energy absorption capacity, and tripled the ductility of UHPCM. The moisture barrier qualities, abrasion resistance and toughness characteristics of UHPCM benefited significantly from introduction of CBNs at less than 0.1% weight fraction. This study demonstrates that the low weight fraction of CBNs can effectively enhance the key engineering properties of UHPCM at a viable cost. Thus, this approach has both performance advantages and economic benefits.

Article highlights

Surface functionalization of multiwalled CNTs improved dispersion in cementitious matrices at low weight fractions.

0.03 wt.% multiwalled CNT addition increased the flexural strength and the flexural toughness of UHPCM.

Abrasion resistance and moisture barrier qualities improved.

These improvements are achieved at viable cost.
Calibration of κ-ε turbulence model for thermal–hydraulic analyses in rib-roughened narrow rectangular channels using genetic algorithm
Authors: Khan Abid Hossain , Islam Md.Shafiqul , Sazzad Istiak Uddin 
Published: May 2021

Abstract
Nowadays, applications of turbulent fluid flow in removing high heat flux in rib-roughened narrow channels are drawing much interest. In this work, an improved version of the κ-ε turbulence model is proposed for better prediction of thermal–hydraulic characteristics of flow inside rib-roughened (pitch-to-rib height (p/k) ratio = 10 and 20) narrow channels (channel height, H = 1.2 mm and 3.2 mm). For this, the four turbulence model parameters, Cμ, Cε1, Cε2, and σk, are calibrated. These parameters are adjustable empirical constants provided for controlling the accuracy of the turbulence model results when needed. The simulated data are used to develop correlations between the relative errors in predicting the friction factor (f), Nusselt number (Nu), and the model parameters using a multivariate nonlinear regression method. These correlations are used to optimize the errors using genetic algorithm. Results reveal that the calibrated parameters are not the same for all the narrow channel configurations. After calibration, the overall predictive improvements are up to 35.83% and 27.30% for p/k = 10 and p/k = 20 respectively when H = 1.2 mm. Also, up to 15.48% and 18.05% improvements are obtained for p/k = 10 and p/k = 20 respectively when H = 3.2 mm. The role of the two parameters Cε1 and Cε2 are found to be of primary importance. Furthermore, three types of nanofluids i.e. Al2O3-water, CuO-water, and TiO2-water are studied using the calibrated model to check the potentiality of heat transfer enhancement. Among them, CuO-water nanofluid is predicted to have around 1.32 times higher value of Nu than pure water for the same narrow channel configuration.

Article Highlights

κ-ε turbulence model is calibrated for rib-roughened narrow rectangular channels using genetic algorithm.

Cε1 and Cε2 are the most influential parameters on the performance of the model inside rib-roughened narrow channel.

Suggested calibration process is more effective for channel height of 1.2 mm than 3.2 mm.
The effectiveness of double team for transanal total mesorectal excision in treatment of mid-low rectal cancer
Authors: Nguyen Thanh Xuan , Ho Huu Thien , Phan Hai Thanh , Vu Hoai Anh , Pham Nhu Hiep 
Published: May 2021

ABSTRACT

Introduction:
Transanal total mesorectal (TaTME) is an emerging surgical technique for mid-low rectal cancer. This study investigated the double-team TaTME on the short-term morbidity, quality of surgical specimens, and functional and oncologic outcomes by comparing them with the single-team TaTME.

Methods:
We analyzed the surgical results of 72 patients (n = 40 double-team TaTME, n = 32 single-team TaTME) with mid-low rectal cancer who treated for mid-low rectal cancer with single-team TaTME or double-team TaTME.

Results:
The operative time was significantly shorter with a double-team TaTME (180 ± 45 min) than with a single-team TaTME (230 ± 55 min). Also, there were no intraoperative events recorded in both groups. The rate of pre-sacral abscesses complications (grade III) in the double-team (0%) was significantly lower than in the single-team group (3, 9.4%). The proportions of TME grade I, TME grade II, and TME grade III were not significantly different between the single-team and double-team groups. A positive circumferential resection margin (CRM) was not significantly different between the double-team groups (2, 5.0%) and the single-team (3, 9.3%). The mean low anterior resection syndrome (LARS) scores were lower in the double-team group (27 ± 4.5) than in the single-team group (29 ± 3.8) (p = 0.048). Simultaneously, the disease-free survival (DFS) rate was not significantly different between the two groups.

Conclusion:
The double-team TaTME in treatment of mid-low rectal cancer is effective with the shortness of operative time, the lower rate of the postoperative pre-sacral abscess, and LARS scores, while this method guaranteed the quality of mesorectal specimens and the DFS rate.
Circulation and Upwelling Induced by Coastal Trapped Waves Over a Submarine Canyon in an Idealized Eastern Boundary Margin
Authors: Saldías Gonzalo S. , Ramos‐Musalem Karina , Allen Susan E. 
Published: June 2021

AbstractWind‐driven upwelling promotes the onshore transport of dense slope waters onto the continental shelf. New observational evidence suggests that the propagation of coastal trapped waves (CTWs) over a submarine canyon can also cause upwelling within the canyon, independent of the wind forcing. Here, we use idealized numerical experiments to assess the role of CTWs in promoting the onshore transport of deeper waters onto the continental shelf. The experiments are forced with a 7‐days period CTW. Overall, there is accumulated upwelled water in time with increased onshore transport during the low sea level phase of the CTWs. Most of the onshore flow is spread over the upwave side of the canyon during the low sea level phase, but it is advected further downwave during the subsequent high sea level phase. As a result, a dense pool of upwelled water is spread over the shelf extending primarily in the downwave direction.
Genetic underpinnings of sociability in the general population
Authors: Bralten Janita , Mota Nina R. , Klemann Cornelius J. H. M. , De Witte Ward , Laing Emma , Collier David A. , de Kluiver Hilde , Bauduin Stephanie E. E. C. , Arango Celso , Ayuso-Mateos Jose L. , Fabbri Chiara , Kas Martien J. , van der Wee Nic , Penninx Brenda W. J. H. , Serretti Alessandro , Franke Barbara , Poelmans Geert 
Published: May 2021

AbstractLevels of sociability are continuously distributed in the general population, and decreased sociability represents an early manifestation of several brain disorders. Here, we investigated the genetic underpinnings of sociability in the population. We performed a genome-wide association study (GWAS) of a sociability score based on four social functioning-related self-report questions from 342,461 adults in the UK Biobank. Subsequently we performed gene-wide and functional follow-up analyses. Robustness analyses were performed in the form of GWAS split-half validation analyses, as well as analyses excluding neuropsychiatric cases. Using genetic correlation analyses as well as polygenic risk score analyses we investigated genetic links of our sociability score to brain disorders and social behavior outcomes. Individuals with autism spectrum disorders, bipolar disorder, depression, and schizophrenia had a lower sociability score. The score was significantly heritable (SNP h2 of 6%). We identified 18 independent loci and 56 gene-wide significant genes, including genes like ARNTL, DRD2, and ELAVL2. Many associated variants are thought to have deleterious effects on gene products and our results were robust. The sociability score showed negative genetic correlations with autism spectrum, disorders, depression, schizophrenia, and two sociability-related traits—loneliness and social anxiety—but not with bipolar disorder or Alzheimer’s disease. Polygenic risk scores of our sociability GWAS were associated with social behavior outcomes within individuals with bipolar disorder and with major depressive disorder. Variation in population sociability scores has a genetic component, which is relevant to several psychiatric disorders. Our findings provide clues towards biological pathways underlying sociability.
Overall survival and adverse events after treatment with darolutamide vs. apalutamide vs. enzalutamide for high-risk non-metastatic castration-resistant prostate cancer: a systematic review and network meta-analysis
Authors: Wenzel Mike , Nocera Luigi , Collà Ruvolo Claudia , Würnschimmel Christoph , Tian Zhe , Shariat Shahrokh F. , Saad Fred , Tilki Derya , Graefen Markus , Kluth Luis A. , Briganti Alberto , Mandel Philipp , Montorsi Francesco , Chun Felix K. H. , Karakiewicz Pierre I. 
Published: May 2021

Abstract
Background
The most recent overall survival (OS) and adverse event (AE) data have not been compared for the three guideline-recommended high-risk non-metastatic castration-resistant prostate cancer (nmCRPC) treatment alternatives.

Methods
We performed a systematic review and network meta-analysis focusing on OS and AE according to the most recent apalutamide, enzalutamide, and darolutamide reports. We systematically examined and compared apalutamide vs. enzalutamide vs. darolutamide efficacy and toxicity, relative to ADT according to PRISMA. We relied on PubMed search for most recent reports addressing prospective randomized trials with proven predefined OS benefit, relative to ADT: SPARTAN, PROSPER, and ARAMIS. OS represented the primary outcome and AEs represented secondary outcomes.

Results
Overall, data originated from 4117 observations made within the three trials that were analyzed. Regarding OS benefit relative to ADT, darolutamide ranked first, followed by enzalutamide and apalutamide, in that order. In the subgroup of PSA-doubling time (PSA-DT) ≤ 6 months patients, enzalutamide ranked first, followed by darolutamide and apalutamide in that order. Conversely, in the subgroup of PSA-DT 6–10 months patients, darolutamide ranked first, followed by apalutamide and enzalutamide, in that order. Regarding grade 3+ AEs, darolutamide was most favorable, followed by enzalutamide and apalutamide, in that order.

Conclusion
The current network meta-analysis suggests the highest OS efficacy and lowest grade 3+ toxicity for darolutamide. However, in the PSA-DT ≤ 6 months subgroup, the highest efficacy was recorded for enzalutamide. It is noteworthy that study design, study population, and follow-up duration represent some of the potentially critical differences that distinguish between the three studies and remained statistically unaccounted for using the network meta-analysis methodology. Those differences should be strongly considered in the interpretation of the current and any network meta-analyses.
A mobile edge–cloud collaboration outlier detection framework in wireless sensor networks
Authors: Gao Cong , Song Guohao , Wang Zhongmin , Chen Yanping 
Published: May 2021

AbstractWireless sensor networks (WSNs) are extensively deployed to collect various data. Due to harsh environments and limitation of computing and communication capabilities of sensor nodes, the quality and reliability of sensor data are compromised by outliers. With the advent of 5G, sensors tend to generate increasingly more complex data. When faced with big data, traditional outlier detection methods relied on sensor nodes and remote cloud are unable to accord satisfactory performance in terms of delay and energy consumption. To address this problem, we propose a mobile edge–cloud collaboration outlier detection framework. Outlier detection is performed by edge nodes between the remote cloud and the underlying WSNs, while the training and updating of detection model are conducted on the cloud. A fast angle‐based outlier detection method is developed to obtain training data. The detection model is constructed based on support vector data description. An on‐line learning‐based iterative optimization scheme is devised to update the detection model. Besides, a fuzzy concept is incorporated into the detection model to alleviate the problem of loose decision boundary. Extensive experiments are conducted on real‐world data set. Simulation results show that our model is superior to three popular methods in terms of delay and energy consumption. In addition, when the percentage of operational nodes is 60%, our proposal prolongs the network lifetime by 14.2% to 69.8% compared to the three methods.