bluuebunny/crossref_metadata_2025 huggingface dataset:
Factors Influencing Primary and Secondary Implant Stability—A Retrospective Cohort Study with 582 Implants in 272 Patients
Authors: Vollmer Andreas , Saravi Babak , Lang Gernot , Adolphs Nicolai , Hazard Derek , Giers Verena , Stoll Peter
Published: November 2020

The success rate of dental implants depends on primary and secondary stability. We investigate predictive factors for future risk stratification models. We retrospectively analyze 272 patients with a total of 582 implants. Implant stability is measured with resonance frequency analysis and evaluated based on the implant stability quotient (ISQ). A linear regression model with regression coefficients (reg. coeff.) and its 95% confidence interval (95% CI) is applied to assess predictive factors for implant stability. Implant diameter (reg. coeff.: 3.28; 95% CI: 1.89–4.66, p < 0.001), implant length (reg. coeff.: 0.67, 95% CI: 0.26–1.08, p < 0.001), and implant localization (maxillary vs. mandibular, reg. coeff.: −7.45, 95% CI: −8.70–(−6.20), p < 0.001) are significant prognostic factors for primary implant stability. An increase in ISQ between insertion and exposure is significantly correlated with healing time (reg. coeff.: 0.11, 95% CI: 0.04–0.19). Patients with maxillary implants have lower ISQ at insertion but show a higher increase in ISQ after insertion than patients with mandibular implants. We observe positive associations between primary implant stability and implant diameter, implant length, and localization (mandibular vs. maxillary). An increase in implant stability between insertion and exposure is significantly correlated with healing time and is higher for maxillary implants. These predictive factors should be further evaluated in prospective cohort studies to develop future preoperative risk-stratification models.
Gemini Surfactant as a Template Agent for the Synthesis of More Eco-Friendly Silica Nanocapsules
Authors: Kaczerewska Olga , Sousa Isabel , Martins Roberto , Figueiredo Joana , Loureiro Susana , Tedim João
Published: November 2020

Silica mesoporous nanocapsules are a class of “smart” engineered nanomaterials (ENMs) applied in several fields. Recent studies have highlighted that they can exert deleterious effects into marine organisms, attributed to the use of the toxic cationic surfactant N-hexadecyl-N,N,N-trimethylammonium bromide (CTAB) during the synthesis of ENMs. The present study reports the successful synthesis and characterization of novel gemini surfactant-based silica nanocapsules. The gemini surfactant 1,4-bis-[N-(1-dodecyl)-N,N-dimethylammoniummethyl]benzene dibromide (QSB2-12) was chosen as a more environmentally-friendly replacement of CTAB. Nanocapsules were characterized by scanning electron microscopy (SEM), Fourier-transformed infrared spectroscopy (FTIR), dynamic light scattering (DLS), thermogravimetric analysis (TGA) and N2 adsorption-desorption isotherms. Short-term exposure effects of new ENMs were evaluated in four marine species (Nannochloropsis gaditana, Tetraselmis chuii and Phaeodactylum tricornutum) and the microcrustacean (Artemia salina). The replacement of the commercial cationic surfactant by the gemini surfactant does not change the structure nor the environmental behaviour in seawater of the newly synthesised silica nanocontainers. Additionally, it is demonstrated that using gemini surfactants can reduce the toxicity of novel silica nanocapsules towards the tested marine species. As a result, environmentally-friendly ENMs can be obtained based on a safe-by-design approach, thereby fitting the concept of Green Chemistry.
Improved Photoacoustic Imaging of Numerical Bone Model Based on Attention Block U-Net Deep Learning Network
Authors: Chen Panpan , Liu Chengcheng , Feng Ting , Li Yong , Ta Dean
Published: November 2020

Photoacoustic (PA) imaging can provide both chemical and micro-architectural information for biological tissues. However, photoacoustic imaging for bone tissue remains a challenging topic due to complicated ultrasonic propagations in the porous bone. In this paper, we proposed a post-processing method based on the convolution neural network (CNN) to improve the image quality of PA bone imaging in a numerical model. To be more adaptive for imaging bone samples with complex structure, an attention block U-net (AB-U-Net) network was designed from the standard U-net by integrating the attention blocks in the feature extraction part. The k-wave toolbox was used for the simulation of photoacoustic wave fields, and then the direct reconstruction algorithm—time reversal was adopted for generating a dataset of deep learning. The performance of the proposed AB-U-Net network on the reconstruction of photoacoustic bone imaging was analyzed. The results show that the AB-U-Net based deep learning method can obtain the image presented as a clear bone micro-structure. Compared with the traditional photoacoustic reconstruction method, the AB-U-Net-based reconstruction algorithm can achieve better performance, which greatly improves image quality on test set with peak signal to noise ratio (PSNR) and structural similarity increased (SSIM) by 3.83 dB and 0.17, respectively. The deep learning method holds great potential in enhancing PA imaging technology for bone disease detection.
An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features
Authors: Wang Jun , Xu Yuanyuan , Xu Hengpeng , Sun Zhe , Yang Zhenglu , Wei Jinmao
Published: November 2020

Feature selection has devoted a consistently great amount of effort to dimension reduction for various machine learning tasks. Existing feature selection models focus on selecting the most discriminative features for learning targets. However, this strategy is weak in handling two kinds of features, that is, the irrelevant and redundant ones, which are collectively referred to as noisy features. These features may hamper the construction of optimal low-dimensional subspaces and compromise the learning performance of downstream tasks. In this study, we propose a novel multi-label feature selection approach by embedding label correlations (dubbed ELC) to address these issues. Particularly, we extract label correlations for reliable label space structures and employ them to steer feature selection. In this way, label and feature spaces can be expected to be consistent and noisy features can be effectively eliminated. An extensive experimental evaluation on public benchmarks validated the superiority of ELC.
A Non-Framework Multilevel Surgery May Reduce Mean Heart Rate in Patients with Very Severe Obstructive Apnea Having Confined Retroglossal Space and Framework
Authors: Huang Ethan I. , Huang Shu-Yi , Lin Yu-Ching , Lin Chieh-Mo , Lin Chin-Kuo , Huang Ying-Chih , Su Jian-An
Published: November 2020

An elevated mean heart rate in untreated patients of obstructive sleep apnea (OSA) may lead to a higher risk of mortality and the development of various cardiovascular diseases. The elevation may positively relate to the severity of OSA and present in both wakefulness and sleep. A reduction in heart rate has been presented in reports of treating OSA patients with continuous positive airway pressure (CPAP). However, patients with very severe OSA may refuse use of CPAP devices and advocated surgeries, such as direct skeletal surgery or tracheostomy. It is unclear whether the non-framework multilevel surgery we reported previously can overcome the unfavorable anatomy and reduce mean heart rate, which serves as a risk factor of mortality. Here, we show that multilevel surgery reduced the mean heart rate from 68.6 to 62.7 with a mean reduction of 5.9 beats/min. The results suggest that the surgery may reduce the risk of consequences and mortality associated with an elevated mean heart rate, such as various cardiovascular diseases. We disclose these findings, along with the variations and possible risks to our future patients with very severe OSA who refuse or cannot use a CPAP device or reject direct skeletal surgery.
Factors Influencing Cloud Computing Adoption in Higher Education Institutions of Least Developed Countries: Evidence from Republic of Yemen
Authors: Hussein Alghushami Abdullah , Zakaria Nur Haryani , Mat Aji Zahurin
Published: November 2020

Cloud-based technology, which is now well established, helps in reducing costs and providing accessibility, reliability and flexibility. However, the Yemen higher educational institutions (HEIs) have not yet embraced the technology due to security and privacy concerns, lack of trust, negative cultural attitudes (i.e., tribalism), and most importantly, lack of digital devices experience in educational settings as well as lack of knowledge and technical know-how. Thus, this study proposes a conceptual model of cloud computing adoption in Yemen HEIs by investigating the influence of technology, organization and environment (TOE) factors. In addition, this study investigates the moderating effect of tribalism culture in the relationships between the identified factors and cloud computing adoption. The study employed the quantitative approach to determine the factors that influence cloud computing adoption in Yemen HEIs through a questionnaire survey. Data were collected from 328 respondents in 38 HEIs and analyzed using partial least square (PLS) structural equation modelling (SEM). The results indicate that relative advantage, reliability, compatibility, security, technology readiness, top management support, regulatory policy and competitive pressure have positive significant impacts on the cloud computing adoption, except tribalism culture with negative significant impact. The study also found that tribalism culture moderates the relationship between compatibility, reliability, security, relative advantage, regulatory policy and cloud computing adoption. This study contributes to the TOE adoption model by including the cultural factor as a moderator towards cloud computing adoption in Yemen HEIs. The study also provides a model and insights for HEIs, technology consultants, vendors and policy makers in better understanding of the factors that influence cloud computing adoption in least developed countries (LDCs), specifically, Yemen.
Improvement of Microwave Electric Field Measurement Sensitivity via Multi-Carrier Modulation in Rydberg Atoms
Authors: Li Shaohua , Yuan Jinpeng , Wang Lirong
Published: November 2020

The microwave electric field intensity is precisely measured by the Autler–Townes splitting of electromagnetically induced transparency spectrum in a 5S1/2−5P3/2−57D5/2−58P3/2 four-level ladder-type 85Rb atomic system. A robust multi-carrier modulation scheme is employed to improve the spectral signal-to-noise ratio, which determines the optical readout of Rydberg atom-based microwave electrometry. As a result, a factor of 2 measurement sensitivity improvement is clearly achieved compared with the on resonant Autler–Townes splitting case credit to the advantage of matched filtering. This research paves the way for building a high sensitivity, portable sensor and offers a platform for achieving compact and sensitive receiver.
3D CFD Analysis of Natural Ventilation in Reduced Scale Model of Compost Bedded Pack Barn for Dairy Cows
Authors: Damasceno Flávio A. , Taraba Joseph L. , Day George B. , A. O. Vega Felipe , S. O. Rocha Keller , Black Randi A. , Bewley Jeffrey M. , E. A. Oliveira Carlos , Barbari Matteo
Published: November 2020

Compost bedded pack (CBP) barns have been receiving increased attention as an alternative housing system for dairy cattle. To create a satisfactory environment within CBP barns that promotes a good composting process, an adequate air movement and minimal temperature fluctuations throughout the building are required. Therefore, a study based on compost barn structure model employing techniques of dimensional analysis for naturally ventilated buildings was developed. Three-dimensional computational fluid dynamic (CFD) simulations of compost barns with different ridge designs and wind direction, along with the visual demonstration of the impact on airflow through structure were performed. The results showed that the barn ventilation CFD model and simulations were in good agreement with the experimental measurements, predicting the airflow through the CBP barns structure for alternative roof ridge types adequately. The results also indicate that the best roof configuration in the winter was the open ridge with chimney for a west to east wind direction.
Optimization of Parameters for the Cutting of Wood-Based Materials by a CO2 Laser
Authors: Kubovský Ivan , Krišťák Ľuboš , Suja Juraj , Gajtanska Milada , Igaz Rastislav , Ružiak Ivan , Réh Roman
Published: November 2020

This article deals with the laser cutting of wood and wood composites. The laser cutting of wood and wood composites is widely accepted and used by the wood industry (due to its many advantages compared to, e.g., saw cutting). The goal of this research was to optimize the cutting parameters of spruce wood (Pices abies L.) by a low-power CO2 laser. The influence of three factors was investigated, namely, the effect of the laser power (100 and 150 W), cutting speed (3, 6, and 9 mm·s−1), and number of annual rings (3–11) on the width of the cutting kerf on the top board, on the width of the cutting kerf on the bottom board, on the ratio of the cutting kerf width on the top and bottom of the board, on the width of the heat-affected area on both sides of the cutting kerf (this applies to the top and bottom of the board), and on the degree of charring. Analysis of variance (ANOVA) and correlation and regression analysis were used for developing a linear regression model without interactions and a quadratic regression model with quadratic interactions. Based on the developed models, the optimization of parameter settings of the investigated process was performed in order to achieve the final kerf quality. The improvement in the quality of the part ranged from 3% to more than 30%. The results were compared with other research dealing with the laser cutting of wood and wood composites.
A Review of Health Assessment Techniques for Distribution Transformers in Smart Distribution Grids
Authors: Tran Quynh T. , Davies Kevin , Roose Leon , Wiriyakitikun Puthawat , Janjampop Jaktupong , Riva Sanseverino Eleonora , Zizzo Gaetano
Published: November 2020

Due to the large number of distribution transformers in the distribution grid, the status of distribution transformers plays an important role in ensuring the safe and reliable operation of the these grids. To evaluate the distribution transformer health, many assessment techniques have been studied and developed. These tools will support the transformer operators in predicting the status of the distribution transformer and responding effectively. This paper will review the literature in the area, analyze the latest techniques as well as highlight the advantages and disadvantages of current methodologies.
Diurnal and Seasonal Variation of Area-Fugitive Methane Advective Flux from an Open-Pit Mining Facility in Northern Canada Using WRF
Authors: Nambiar Manoj K. , Robe Françoise R. , Seguin Alison M. , Endsin Matthew , Aliabadi Amir A.
Published: November 2020

Greenhouse Gas (GHG) emissions pose a global climate challenge and the mining sector is a large contributor. Diurnal and seasonal variations of area-fugitive methane advective flux, released from an open-pit mine and a tailings pond, from a facility in northern Canada, were simulated in spring 2018 and winter 2019, using the Weather Research and Forecasting (WRF) model. The methane mixing ratio boundary conditions for the WRF model were obtained from the in-situ field measurements, using Los Gatos Research Ultra-Portable Greenhouse Gas Analyzers (LGRs), placed in various locations surrounding the mine pit and a tailings pond. The simulated advective flux was influenced by local and synoptic weather conditions in spring and winter, respectively. Overall, the average total advective flux in the spring was greater than that in the winter by 36% and 75%, for the mine and pond, respectively. Diurnal variations of flux were notable in the spring, characterized by low flux during thermally stable (nighttime) and high flux during thermally unstable (daytime) conditions. The model predictions of the methane mixing ratio were in reasonable agreement with limited aircraft observations (R2=0.68). The findings shed new light in understanding the area-fugitive advective flux from complex terrains and call for more rigorous observations in support of the findings.
Changes in Membrane Protein Structural Biology
Authors: Birch James , Cheruvara Harish , Gamage Nadisha , Harrison Peter J. , Lithgo Ryan , Quigley Andrew
Published: November 2020

Membrane proteins are essential components of many biochemical processes and are important pharmaceutical targets. Membrane protein structural biology provides the molecular rationale for these biochemical process as well as being a highly useful tool for drug discovery. Unfortunately, membrane protein structural biology is a difficult area of study due to low protein yields and high levels of instability especially when membrane proteins are removed from their native environments. Despite this instability, membrane protein structural biology has made great leaps over the last fifteen years. Today, the landscape is almost unrecognisable. The numbers of available atomic resolution structures have increased 10-fold though advances in crystallography and more recently by cryo-electron microscopy. These advances in structural biology were achieved through the efforts of many researchers around the world as well as initiatives such as the Membrane Protein Laboratory (MPL) at Diamond Light Source. The MPL has helped, provided access to and contributed to advances in protein production, sample preparation and data collection. Together, these advances have enabled higher resolution structures, from less material, at a greater rate, from a more diverse range of membrane protein targets. Despite this success, significant challenges remain. Here, we review the progress made and highlight current and future challenges that will be overcome.
Fluorescent PSC-Derived Cardiomyocyte Reporter Lines: Generation Approaches and Their Applications in Cardiovascular Medicine
Authors: Sontayananon Naeramit , Redwood Charles , Davies Benjamin , Gehmlich Katja
Published: November 2020

Recent advances have made pluripotent stem cell (PSC)-derived cardiomyocytes an attractive option to model both normal and diseased cardiac function at the single-cell level. However, in vitro differentiation yields heterogeneous populations of cardiomyocytes and other cell types, potentially confounding phenotypic analyses. Fluorescent PSC-derived cardiomyocyte reporter systems allow specific cell lineages to be labelled, facilitating cell isolation for downstream applications including drug testing, disease modelling and cardiac regeneration. In this review, the different genetic strategies used to generate such reporter lines are presented with an emphasis on their relative technical advantages and disadvantages. Next, we explore how the fluorescent reporter lines have provided insights into cardiac development and cardiomyocyte physiology. Finally, we discuss how exciting new approaches using PSC-derived cardiomyocyte reporter lines are contributing to progress in cardiac cell therapy with respect to both graft adaptation and clinical safety.
Potential Use of Anti-Inflammatory Synthetic Heparan Sulfate to Attenuate Liver Damage
Authors: Arnold Katelyn , Liao Yi-En , Liu Jian
Published: November 2020

Heparan sulfate is a highly sulfated polysaccharide abundant on the surface of hepatocytes and surrounding extracellular matrix. Emerging evidence demonstrates that heparan sulfate plays an important role in neutralizing the activities of proinflammatory damage associate molecular patterns (DAMPs) that are released from hepatocytes under pathological conditions. Unlike proteins and nucleic acids, isolation of homogenous heparan sulfate polysaccharides from biological sources is not possible, adding difficulty to study the functional role of heparan sulfate. Recent advancement in the development of a chemoenzymatic approach allows production of a large number of structurally defined oligosaccharides. These oligosaccharides are used to probe the physiological functions of heparan sulfate in liver damage under different pathological conditions. The findings provide a potential new therapeutic agent to treat liver diseases that are associated with excessive inflammation.
The Influence of Workload and Work Flexibility on Work-Life Conflict and the Role of Emotional Exhaustion
Authors: Buruck Gabriele , Pfarr Anna-Lisa , Penz Marlene , Wekenborg Magdalena , Rothe Nicole , Walther Andreas
Published: November 2020

The purpose of this study is to examine the relationship between contextual work-related factors in terms of job demands (workload—WL) and job resources (work flexibility—WF), work–life conflict (WLC) and the burnout dimension emotional exhaustion (EE) in a large population-based sample. Building on the job demands resources model (JDRM), we have developed the hypothesis that WL has an indirect effect on EE that is mediated by WLC. We conducted a secondary analysis using data from the Dresden Burnout Study (DBS, N = 4246, mean age (SD) = 42.7 years (10.5); 36.4% male). Results from structural equation modelling revealed that EE is positively associated with WL (β = 0.15, p = 0.001) and negatively associated with WF (β = −0.13, p = 0.001), also after accounting for potential confounding variables (demography, depressive symptoms, and lifetime diagnosis of burnout). Both effects are mediated by WLC (β = 0.18; p = 0.001 and β = 0.08; p = 0.001, respectively) highlighting the important role of WLC in employee health. In summary, WF may help to reduce burnout symptoms in employees, whereas WL may increase them. Study results suggest that both associations depend on WLC levels.
Fe-Exchanged Small-Pore Zeolites as Ammonia Selective Catalytic Reduction (NH3-SCR) Catalysts
Authors: Gao Feng
Published: November 2020

Cu-exchanged small-pore zeolites have been extensively studied in the past decade as state-of-the-art selective catalytic reduction (SCR) catalysts for diesel engine exhaust NOx abatement for the transportation industry. During this time, Fe-exchanged small-pore zeolites, e.g., Fe/SSZ-13, Fe/SAPO-34, Fe/SSZ-39 and high-silica Fe/LTA, have also been investigated but much less extensively. In comparison to their Cu-exchanged counterparts, such Fe/zeolite catalysts display inferior low-temperature activities, but improved stability and high-temperature SCR selectivities. Such characteristics entitle these catalysts to be considered as key components of highly efficient emission control systems to improve the overall catalyst performance. In this short review, recent studies on Fe-exchanged small-pore zeolite SCR catalysts are summarized, including (1) the synthesis of small-pore Fe/zeolites; (2) nature of the SCR active Fe species in these catalysts as determined by experimental and theoretical approaches, including Fe species transformation during hydrothermal aging; (3) SCR reactions and structure-function correlations; and (4) a few aspects on industrial applications.
Influence of Pressure, Velocity and Fluid Material on Heat Transport in Structured Open-Cell Foam Reactors Investigated Using CFD Simulations
Authors: Sinn Christoph , Wentrup Jonas , Thöming Jorg , Pesch Georg R.
Published: November 2020

Structured open-cell foam reactors are promising for managing highly exothermic reactions such as CO2 methanation due to their excellent heat transport properties. Especially at low flow rates and under dynamic operation, foam-based reactors can be advantageous over classic fixed-bed reactors. To efficiently design the catalyst carriers, a thorough understanding of heat transport mechanisms is needed. So far, studies on heat transport in foams have mostly focused on the solid phase and used air at atmospheric pressure as fluid phase. With the aid of pore-scale 3d CFD simulations, we analyze the effect of the fluid properties on heat transport under conditions close to the CO2 methanation reaction for two different foam structures. The exothermicity is mimicked via volumetric uniformly distributed heat sources. We found for foams that are designed to be used as catalyst carriers that the working pressure range and the superficial velocity influence the dominant heat removal mechanism significantly. In contrast, the influence of fluid type and gravity on heat removal is small in the range relevant for heterogeneous catalysis. The findings might help to facilitate the design-process of open-cell foam reactors and to better understand heat transport mechanisms in foams.
Pain during Cast Wedging of Forearm Shaft and Distal Forearm Fractures in Children Aged 3 to 12 Years—A Prospective, Observational Study
Authors: Freislederer Florian , Berberich Tobias , Erb Thomas O. , Mayr Johannes
Published: November 2020

Background: Although fracture displacement in children is easily treated by cast wedging, no data on pain associated with the procedure are available. We hypothesized that there is no clinically relevant difference in pain before and after cast wedging in children between 3 and 12 years of age. Patients and Methods: This international, multicenter, prospective, observational study included 68 children (39 male, 29 female) aged 3 to 12 years (median age 8 years) with forearm fractures. Cast wedging was performed 5 to 10 days after the injury. Before starting the procedure, we administered a single oral dose of sodium metamizole (10 mg/kg body weight), and the children inhaled a nitrous oxide/oxygen mixture (50%/50%) during the wedging procedure. Pain was rated on a visual analog scale (VAS) 5 to 10 min before incision of the cast as well as 3 to 5 min and 30 min (maximum remembered pain) after inhalation stop. The degree of bending was judged either by the surgeon or was determined on the basis of first signs of pain expressed by the patient. We assessed the effectiveness of the procedure by obtaining X-ray images in two planes after 3 to 9 days. Results: Among the 68 patients, median VAS score before cast wedging was 0. This increased to a score of 1 (p = 0.015) at 3 to 5 min after the procedure. Median VAS score for the maximum remembered pain measured after 30 min was 0. Median differences in angulation between proximal and distal bone fragments before and after the intervention were 0° (p < 0.0001) in the a.p. view and 8.4° (p < 0.0001) in the lateral view. Conclusion: Cast wedging improved the position of forearm fracture fragments at the expense of minimal short-term pain.
Influence of Anti-Caking Agents on the Highly Effective Organic Coatings for Preventing the Caking of Ammonium Nitrate Fertilizers
Authors: Tyc Aleksandra , Nieweś Dominik , Penkala Szymon , Grzesik Ryszard , Hoffmann Krystyna , Hoffmann Józef
Published: November 2020

Ammonium nitrate fertilizers have a tendency to cake during storage. The aim of this study was to examine the effectiveness of organic coatings for preventing the caking of ammonium nitrate fertilizers and to assess the influence of the composition and physicochemical properties of the anti-caking agents used as coatings for fertilizers on their effectiveness. CAN (calcium ammonium nitrate) and AN (ammonium nitrate) fertilizers were coated with three anti-caking agents. A GC–MS technique was used for the identification and quantitative determination of the composition of the organic coatings. The influence of the following physicochemical parameters of the preparations was assessed: density, viscosity, melting point, water content, and base number. The effectiveness of anti-caking agents was determined by measuring the force needed to crush the clumped uncoated and coated fertilizers, which were previously subjected to thermal cycles under load. Composition studies showed that all the tested preparations contained hexadecylamine and octadecylamine in comparable amounts and a slack wax. The results demonstrate that the key parameters of an effective anti-caking agent are low water content, appropriate viscosity, and appropriate content of fatty amines. This study can facilitate the development of innovative coatings with similar or higher efficiency, yet with a reduced negative impact on the environment.
CogniSoft: A Platform for the Automation of Cognitive Assessment and Rehabilitation of Multiple Sclerosis
Authors: Petrova-Antonova Dessislava , Spasov Ivaylo , Petkova Yanita , Manova Ilina , Ilieva Sylvia
Published: November 2020

Cognitive disorders remain a major cause of disability in Multiple Sclerosis (MS). They lead to unemployment, the need for daily assistance, and a poor quality of life. The understanding of the origin, factors, processes, and consequences of cognitive disfunction is key to its prevention, early diagnosis, and rehabilitation. The neuropsychological testing and continuous monitoring of cognitive status as part of the overall evaluation of patients with MS in parallel with clinical and paraclinical examinations are highly recommended. In order to improve health and disease understanding, a close linkage between fundamental, clinical, epidemiological, and socio-economic research is required. The effective sharing of data, standardized data processing, and the linkage of such data with large-scale cohort studies is a prerequisite for the translation of research findings into the clinical setting. In this context, this paper proposes a software platform for the cognitive assessment and rehabilitation of patients with MS called CogniSoft. The platform automates the Beck Depression Inventory (BDI-II) test and diagnostic tests for the evaluation of memory and executive functions based on the nature of Brief International Cognitive Assessment for MS (BICAMS), as well as implementing a set of games for cognitive rehabilitation based on BICAMS. The software architecture, core modules, and technologies used for their implementation are presented. Special attention is given to the development of cognitive tests for diagnostics and rehabilitation. Their automation enables better perception, avoids bias as a result of conducting the classic paper tests of various neurophysiologists, provides easy administration, and allows data collection in a uniform manner, which further enables analysis using statistical and machine learning algorithms. The CogniSoft platform is registered as medical software by the Bulgarian Drug Agency and it is currently deployed in the Neurological Clinic of the National Hospital of Cardiology in Sofia, Bulgaria. The first experiments prove the feasibility of the platform, showing that it saves time and financial resources while providing subjectivity in the interpretation of the cognitive test results.
Parity-Time Symmetry and Exceptional Points for Flexural-Gravity Waves in Buoyant Thin-Plates
Authors: Farhat Mohamed , Guenneau Sebastien , Chen Pai-Yen , Wu Ying
Published: November 2020

We derive and apply a transfer matrix method (M-matrix) coupling liquid surface waves and flexural-gravity waves in buoyant thin elastic plates. We analyze the scattering matrix (S-matrix) formalism for such waves propagating within a Fabry-Perot like system, which are solutions of a sixth order partial differential equation (PDE) supplied with adequate boundary conditions. We develop a parity-time (PT)-symmetry theory and its applications to thin elastic floating plates. The sixth order PDE governing the propagation of these waves leads to six by six M and S matrices, and results in specific physical properties of the PT-symmetric elastic plate systems. We show the effect of geometry and gain/loss on the asymmetric propagation of flexural-gravity waves, as well as a Fano-like line-shape of the reflection signature. Importantly, we show the possibility of obtaining coherent perfect absorber-laser (CPAL) using simple thin structures.
The Milk Fat-to-Protein Ratio as Indicator for Ruminal pH Parameters in Dairy Cows: A Meta-Analysis
Authors: Zschiesche Marleen , Mensching André , Sharifi A. Reza , Hummel Jürgen
Published: November 2020

Subacute ruminal acidosis (SARA) represents one of the most important nutritional disorders in high-producing dairy farms. The determination of ruminal pH is a key factor for the diagnosis of SARA. However, measuring ruminal pH in the field is not practicable. Therefore, indicators that reflect the ruminal pH are in demand. The main objective of this study was to examine the relationship between the milk fat-to-protein ratio (FPR) and ruminal pH parameters (daily mean pH, daily time with pH < 5.8, and pH range) on a meta-analytical level including 47 studies with 189 treatment means. Besides the FPR, it was examined how a stepwise extension of further indicators (milk yield, rumination time, and dietary starch and structure effectiveness) can improve the prediction of ruminal pH parameters. Significant associations between milk FPR and ruminal pH parameters were found. The inclusion of further on-farm indicators improved the prediction of daily mean ruminal pH up to Rm2 = 0.46 and time with pH < 5.8 up to Rm2=0.58. Still, a considerable part of variability was explained by the random factor study. Additional information (dietary PUFA content) may improve the models in further investigations.
Spontaneous Remission and Concomitant Progression in a Patient with DLBCL
Authors: Han Eun Ji , Kim Jihyun , Park Suk Young , O Joo Hyun
Published: November 2020

Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoma. Although DLBCL can be cured in more than half of all patients, up to 50% of patients become refractory to initial treatment or relapse after complete remission. We present a case of complete spontaneous remission of some tumors and concomitant newly developed tumors observed in a patient with relapsed DLBCL. Spontaneous remission of lymphoma without treatment is a rare phenomenon and can occur at baseline as well as in relapsed DLBCL. However, most patients who initially experience spontaneous remission later develop relapse. Thus, careful follow-up is required, and fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) allows monitoring of multiple lesions.
Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region
Authors: Naghavi Azam , Teismann Tobias , Asgari Zahra , Mohebbian Mohammad Reza , Mansourian Marjan , Mañanas Miguel Ángel
Published: November 2020

Suicide is one of the most critical public health concerns in the world and the second cause of death among young people in many countries. However, to date, no study can diagnose suicide ideation/behavior among university students in the Middle East and North Africa (MENA) region using a machine learning approach. Therefore, stability feature selection and stacked ensembled decision trees were employed in this classification problem. A total of 573 university students responded to a battery of questionnaires. Three-fold cross-validation with a variety of performance indices was sued. The proposed diagnostic system had excellent balanced diagnosis accuracy (AUC = 0.90 [CI 95%: 0.86–0.93]) with a high correlation between predicted and observed class labels, fair discriminant power, and excellent class labeling agreement rate. Results showed that 23 items out of all items could accurately diagnose suicide ideation/behavior. These items were psychological problems and how to experience trauma, from the demographic variables, nine items from Post-Traumatic Stress Disorder Checklist (PCL-5), two items from Post Traumatic Growth (PTG), two items from the Patient Health Questionnaire (PHQ), six items from the Positive Mental Health (PMH) questionnaire, and one item related to social support. Such features could be used as a screening tool to identify young adults who are at risk of suicide ideation/behavior.
Convolutional Neural Network Approach for Multispectral Facial Presentation Attack Detection in Automated Border Control Systems
Authors: Sánchez-Sánchez M. Araceli , Conde Cristina , Gómez-Ayllón Beatriz , Ortega-DelCampo David , Tsitiridis Aristeidis , Palacios-Alonso Daniel , Cabello Enrique
Published: November 2020

Automated border control systems are the first critical infrastructure point when crossing a border country. Crossing border lines for unauthorized passengers is a high security risk to any country. This paper presents a multispectral analysis of presentation attack detection for facial biometrics using the learned features from a convolutional neural network. Three sensors are considered to design and develop a new database that is composed of visible (VIS), near-infrared (NIR), and thermal images. Most studies are based on laboratory or ideal conditions-controlled environments. However, in a real scenario, a subject’s situation is completely modified due to diverse physiological conditions, such as stress, temperature changes, sweating, and increased blood pressure. For this reason, the added value of this study is that this database was acquired in situ. The attacks considered were printed, masked, and displayed images. In addition, five classifiers were used to detect the presentation attack. Note that thermal sensors provide better performance than other solutions. The results present better outputs when all sensors are used together, regardless of whether classifier or feature-level fusion is considered. Finally, classifiers such as KNN or SVM show high performance and low computational level.
Null Wave Front and Ryu–Takayanagi Surface
Authors: Tsujimura Jun , Nambu Yasusada
Published: November 2020

The Ryu–Takayanagi formula provides the entanglement entropy of quantum field theory as an area of the minimal surface (Ryu–Takayanagi surface) in a corresponding gravity theory. There are some attempts to understand the formula as a flow rather than as a surface. In this paper, we consider null rays emitted from the AdS boundary and construct a flow representing the causal holographic information. We present a sufficient and necessary condition that the causal information surface coincides with Ryu–Takayanagi surface. In particular, we show that, in spherical symmetric static spacetimes with a negative cosmological constant, wave fronts of null geodesics from a point on the AdS boundary become extremal surfaces and therefore they can be regarded as the Ryu–Takayanagi surfaces. In addition, from the viewpoint of flow, we propose a wave optical formula to calculate the causal holographic information.
What Potential Entrepreneurs from Generation Y and Z Lack-IEO and the Role of EE
Authors: Wasilczuk Julita Eleonora , Richert-Kaźmierska Anita
Published: November 2020

This paper addresses the issue of individual entrepreneurship orientation (IEO) and entrepreneurship education (EE), which are both important for modern economic development. Intergenerational differences in these areas were discussed, especially characteristics of Generations Y and Z. The results of research conducted among 757 Polish students showing their IEO are presented. 80% of respondents displayed high levels of proactivity (primarily directed at meeting their own needs), whilst only 56% exhibited innovativeness and only 47% risk-taking. On the basis of the research results, the authors formulated recommendations for entrepreneurship education, aimed at strengthening IEO among representatives of Generations Y and Z.
A Distributed Observer-Based Cyber-Attack Identification Scheme in Cooperative Networked Systems under Switching Communication Topologies
Authors: Taoufik Anass , Defoort Michael , Busawon Krishna , Dala Laurent , Djemai Mohamed
Published: November 2020

This paper studies an approach for detecting cyber attacks against networked cooperative systems (NCS) that are assumed to be working in a cyber-physical environment. NCS are prone to anomalies both due to cyber and physical attacks and faults. Cyber-attacks being more hazardous given the cooperative nature of the NCS may lead to disastrous consequences and thus need to be detected as soon as they occur by all systems in the network. Our approach deals with two types of malicious attacks aimed at compromising the stability of the NCS: intrusion attacks/local malfunctions on individual systems and deception/cyber-attacks on the communication between the systems. In order to detect and identify such attacks under switching communication topologies, this paper proposes a new distributed methodology that solves global state estimation of the NCS where the aim is identifying anomalies in the networked system using residuals generated by monitoring agents such that coverage of the entire network is assured. A cascade of predefined-time sliding mode switched observers is introduced for each agent to achieve a fast estimate of the global state whereby the settling time is an a priori defined parameter independently of the initial conditions. Then, using the conventional consensus algorithm, a set of residuals are generated by the agents that is capable of detecting and isolating local intrusion attacks and communication cyber-attacks in the network using only locally exchanged information. In order to prove the effectiveness of the proposed method, the framework is tested for a velocity synchronization seeking network of mobile robots.
The Path Planning of Synchronous Cooperative Motion Control between Robot and Positioner for Complex Space Curve Processing
Authors: Chen Lin , Wang Ziwei , Mo Yuliang , Pan Haihong
Published: November 2020

In this paper, we propose a synchronous cooperative path planning (SCPP) algorithm for the robot and the positioner to process complex space curve workpieces. The specific algorithm design is illustrated by using the intersecting line welding as an example. The robot and positioner are regarded as an 8-degree-of-freedom (DOF) system to plan the whole synchronous cooperative motion path, and the constraint for the Y-axis of the welding torch coordination system is added for solving the intersecting line orientation information. SCPP is used to process the intersecting line. The changes of welding torch orientation and robot joint rotation angles during welding of the intersecting line by using the improved method and compared to the traditional method. The experimental results show that 8-DOF keeps synchronous cooperative in the whole movement. There is no interference happening during the entire cooperative movement and the welding torch’s orientation remains basically unchanged during the welding intersecting line.
Siamese High-Level Feature Refine Network for Visual Object Tracking
Authors: Rahman Md. Maklachur , Ahmed Md Rishad , Laishram Lamyanba , Kim Seock Ho , Jung Soon Ki
Published: November 2020

Siamese network-based trackers are broadly applied to solve visual tracking problems due to its balanced performance in terms of speed and accuracy. Tracking desired objects in challenging scenarios is still one of the fundamental concerns during visual tracking. This research paper proposes a feature refined end-to-end tracking framework with real-time tracking speed and considerable performance. The feature refine network has been incorporated to enhance the target feature representation power, utilizing high-level semantic information. Besides, it allows the network to capture the salient information to locate the target and learns to represent the target feature in a more generalized way advancing the overall tracking performance, particularly in the challenging sequences. But, only the feature refine module is unable to handle such challenges because of its less discriminative ability. To overcome this difficulty, we employ an attention module inside the feature refine network that strengths the tracker discrimination ability between the target and background. Furthermore, we conduct extensive experiments to ensure the proposed tracker’s effectiveness using several popular tracking benchmarks, demonstrating that our proposed model achieves state-of-the-art performance over other trackers.
Design and Application of Biomedical Circuits and Systems
Authors: Yúfera Alberto , Huertas Gloria , Calvo Belen
Published: November 2020

The development of new sensing technologies, biomaterials, microelectronic devices, microfluidic systems and micro-electro-mechanical systems (MEMs) etc [...]
The Sentiment Analysis Model of Services Providers’ Feedback
Authors: Shakhovska Khrystyna , Shakhovska Nataliya , Veselý Peter
Published: November 2020

The purpose of this paper is to develop a hybrid model Ukrainian language sentiment analyzer, which should improve the accuracy of the mood definition to expand the Ukrainian language among the instruments on the market. The object of research is the processes of determining the language of the text and predicting its sentiment score. The subject of the study is Ukrainian comments posted by Google Maps users. The following text categories are taken into account: food, hotels, museums, and shops. The new method was built as an ensemble of support vector machine, logistic regression, and XGBoost, in combination with a rule-based algorithm. The practical use of the algorithm makes it possible to analyze the Ukrainian text in accordance with the category with the visualization of the research results. The accuracy of the proposed method is bigger than 0.88 in the worst case. The mining procedure of the positive and negative sides of service providers based on users’ feedback is developed. It allows electronics business to make improvements based on frequent positive and negative words.
Optimization of a Steam Reforming Plant Modeled with Artificial Neural Networks
Authors: Pardo Eduardo G. , Blanco-Linares Jaime , Velázquez David , Serradilla Francisco
Published: November 2020

The objective of this research is to improve the hydrogen production and total profit of a real Steam Reforming plant. Given the impossibility of tuning the real factory to optimize its operation, we propose modelling the plant using Artificial Neural Networks (ANNs). Particularly, we combine a set of independent ANNs into a single model. Each ANN uses different sets of inputs depending on the physical processes simulated. The model is then optimized as a black-box system using metaheuristics (Genetic and Memetic Algorithms). We demonstrate that the proposed ANN model presents a high correlation between the real output and the predicted one. Additionally, the performance of the proposed optimization techniques has been validated by the engineers of the plant, who reported a significant increase in the benefit that was obtained after optimization. Furthermore, this approach has been favorably compared with the results that were provided by a general black-box solver. All methods were tested over real data that were provided by the factory.
Clinical Perspective on Internet of Things Applications for Care of the Elderly
Authors: Tun Soe Ye Yint , Madanian Samaneh , Parry Dave
Published: November 2020

The elderly population is increasing globally, putting more pressure on aged care and public health systems. To address this issue and help increase the independence of older people, different digital technologies, including the Internet of things (IoT), can play an important role. Although there has been an increase in the number of studies in this field, most of them concentrate on IoT applications in elderly care from a technology perspective, with very little contribution from the clinical side. Therefore, this paper aims to investigate and identify the available IoT applications and their clinical utility for common diseases in elderly people. The results of this study could be useful for information technology professionals in developing and understanding the clinical requirements for IoT applications in healthcare for older people. Clinicians will also be informed about the clinical possibilities of using IoT devices in this area. Based on our findings, future research should focus on enhancing the clinical utility of current IoT applications in different settings and on developing new applications to support practitioners and older people.