bluuebunny/crossref_metadata_2025 huggingface dataset:
Perception Toward Exposure Risk of COVID-19 Among Health Workers in Vietnam: Status and Correlated Factors
Authors: Le Xuan Thi Thanh , Nguyen Quynh Thi , Onyango Brenda , Nguyen Quang Nhat , Pham Quan Thi , Ta Nhung Thi Kim , Nguyen Thao Thanh , Le Huong Thi , Vu Linh Gia , Hoang Men Thi , Vu Giang Thu , Latkins Carl A. , Ho Roger C. M. , Ho Cyrus S. H.
Published: May 2021

Background: Hospital staff are at the frontline for the prevention and control of COVID-19. Understanding their perception of exposure risk is, therefore, important during the early phase of this pandemic. In this study, we evaluated the perception regarding risk of exposure to COVID-19 among Vietnamese hospital staff in Vietnam.

Method: A cross-sectional online study was carried out to collect demographic data and risk exposure perception during the second week of the national lockdown in April 2020 in Vietnam. Seven hundred and forty two hospital staff were recruited using the snowball sampling to answer 5-point Likert scale questions regarding their risk exposure perception. Exploratory factor analysis (EFA) was used to examine the construct validity of the questionnaire. Pearson coefficient analysis and multivariable regression models were applied to identify factors associated with the perceived COVID-19 exposure risk.

Results: Participants perceived a high risk of being infected with SARS-CoV-2 (score = 3.4, SD = 0.8). They also perceived the workplace response to COVID-19 as inadequate (score = 2.0, SD = 0.5). In particular, participants who worked in the emergency or intensive care departments were more likely to perceive an exposure risk, compared to those in infectious disease control departments (Coef. = −0.38, 95%CI: −0.74; −0.02). Participants from central regions perceived a lower risk of exposure to COVID-19 than those from northern regions (OR = 0.52, 95%CI: 0.28–0.96). Nurses were less likely than doctors to report being at risk of exposure to COVID-19 (OR = 0.56, 95%CI: 0.33–0.95).

Conclusions: We identified a high level of perceived risk regarding COVID-19 exposure among hospital staff during the unprecedented lockdown period in Vietnam. A comprehensive approach, incorporating improved risk communications, safety training and psychological support programs, for all hospital staff, including nurses and those residing in high population density areas, might further strengthen the national effort to control the pandemic.
Autonomous Robotic Point-of-Care Ultrasound Imaging for Monitoring of COVID-19–Induced Pulmonary Diseases
Authors: Al-Zogbi Lidia , Singh Vivek , Teixeira Brian , Ahuja Avani , Bagherzadeh Pooyan Sahbaee , Kapoor Ankur , Saeidi Hamed , Fleiter Thorsten , Krieger Axel
Published: May 2021

The COVID-19 pandemic has emerged as a serious global health crisis, with the predominant morbidity and mortality linked to pulmonary involvement. Point-of-Care ultrasound (POCUS) scanning, becoming one of the primary determinative methods for its diagnosis and staging, requires, however, close contact of healthcare workers with patients, therefore increasing the risk of infection. This work thus proposes an autonomous robotic solution that enables POCUS scanning of COVID-19 patients’ lungs for diagnosis and staging. An algorithm was developed for approximating the optimal position of an ultrasound probe on a patient from prior CT scans to reach predefined lung infiltrates. In the absence of prior CT scans, a deep learning method was developed for predicting 3D landmark positions of a human ribcage given a torso surface model. The landmarks, combined with the surface model, are subsequently used for estimating optimal ultrasound probe position on the patient for imaging infiltrates. These algorithms, combined with a force–displacement profile collection methodology, enabled the system to successfully image all points of interest in a simulated experimental setup with an average accuracy of 20.6 ± 14.7 mm using prior CT scans, and 19.8 ± 16.9 mm using only ribcage landmark estimation. A study on a full torso ultrasound phantom showed that autonomously acquired ultrasound images were 100% interpretable when using force feedback with prior CT and 88% with landmark estimation, compared to 75 and 58% without force feedback, respectively. This demonstrates the preliminary feasibility of the system, and its potential for offering a solution to help mitigate the spread of COVID-19 in vulnerable environments.
The Efficacy of Heat Acclimatization Pre-World Cup in Female Soccer Players
Authors: Meylan César M. P. , Bowman Kimberly , Stellingwerff Trent , Pethick Wendy A. , Trewin Joshua , Koehle Michael S.
Published: May 2021

The efficacy of a 14-day field-based heat acclimatization (HA) training camp in 16 international female soccer players was investigated over three phases: phase 1: 8 days moderate HA (22. 1°C); phase 2: 6 days high HA (34.5°C); and phase 3: 11 days of post-HA (18.2°C), with heart rate (HR), training load, core temp (Tc), and perceptual ratings recorded throughout. The changes from baseline (day−16) in (i) plasma volume (PV), (ii) HR during a submaximal running test (HRex) and HR recovery (HRR), and (iii) pre-to-post phase 2 (days 8–13) in a 4v4 small-sided soccer game (4V4SSG) performance were assessed. Due to high variability, PV non-significantly increased by 7.4% ± 3.6% [standardized effect (SE) = 0.63; p = 0.130] from the start of phase 1 to the end of phase 2. Resting Tc dropped significantly [p < 0.001 by −0.47 ± 0.29°C (SE = −2.45)], from day 1 to day 14. Submaximal running HRR increased over phase 2 (HRR; SE = 0.53) after having decreased significantly from baseline (p = 0.03). While not significant (p > 0.05), the greatest HR improvements from baseline were delayed, occurring 11 days into phase 3 (HRex, SE = −0.42; HRR, SE = 0.37). The 4v4SSG revealed a moderate reduction in HRex (SE = −0.32; p = 0.007) and a large increase in HRR (SE = 1.27; p < 0.001) from pre-to-post phase 2. Field-based HA can induce physiological changes beneficial to soccer performance in temperate and hot conditions in elite females, and the submaximal running test appears to show HRex responses induced by HA up to 2 weeks following heat exposure.
A New Web-Based Tool for RTO-Focused Animal Shelter Data Analysis
Authors: Kremer Tom
Published: May 2021

Animal shelters are increasingly interested in reducing their intake and helping their communities keep and care for animals. Improving Return-to-Owner (RTO) rates of stray dogs is one path to save significant shelter space, time, and costs and keep animals with their caregivers and communities. Aggregating and visualizing RTO data spatially are useful for identifying trends and highlighting areas for potential interventions. Since shelters collect similar data, an interactive web application was developed to make such an analysis easily reproducible. This paper presents the tool's capabilities via a case study of 2019 data from the Dallas Animal Services shelter, covering the relationship between stray intake and RTO rate, the distances traveled from home by RTOed strays, microchip use across the city and its relationship with RTO rate, and the length of stay of RTOs and other outcome groups. Findings include showing that 70% of RTOed strays traveled at most 1 mile away from home and 42% up to block away, and that at-large, adult strays that had a microchip had a 71% RTO rate compared with 39% without one. The results affected the shelter's hold time for strays, highlighted target areas for microchip programs, and motivated neighborhood-based methods to locate found dogs' owners. Shelters are welcome to use the tool and participate in the development of new analytical lenses and visualizations that would best suit their needs.
An Algorithm for the Broad Evaluation of Potential Matches between Actuator Concepts and Heavy-Duty Mobile Applications
Authors: Fassbender David , Minav Tatiana
Published: May 2021

In recent years, a variety of novel actuator concepts for the implements of heavy-duty mobile machines (HDMMs) has been proposed by industry and academia. Mostly, novel concepts aim at improving the typically low energy efficiency of state-of-the-art hydraulic valve-controlled actuators. However, besides energy-efficiency, many aspects that are crucial for a successful concept integration are often neglected in studies. Furthermore, most of the time, a specific HDMM is focused as an application while other HDMM types can show very different properties that might make a novel concept less suitable. In order to take more aspects and HDMM types into account when evaluating actuator concepts, this paper proposes a novel evaluation algorithm, which calculates so-called mismatch values for each potential actuator-application match, based on different problem aspects that can indicate a potential mismatch between a certain actuator concept and an HDMM. The lower the mismatch value, which depends on actuator characteristics as well as HDMM attributes, the more potential is the match. At the same time, the modular nature of the algorithm allows to evaluate a large number of possible matches at once, with low effort. For the performance demonstration of the algorithm, 36 potential matches formed out of six actuator concepts and six HDMM types are exemplarily evaluated. The resulting actuator concept ratings for the six different HDMMs are in line with general reasoning and confirm that the evaluation algorithm is a powerful tool to get a first, quick overview of a large solution space of actuator-HDMM matches. However, analyzing the limitations of the algorithm also shows that it cannot replace conventional requirements engineering and simulation studies if detailed and reliable results are required.
Novel Loci for Kernel Hardness Appeared as a Response to Heat and Combined Heat-Drought Conditions in Wheat Harboring Aegilops tauschii Diversity
Authors: Elhadi Gamila Mohamed Idris , Kamal Nasrein Mohamed , Gorafi Yasir Serag Alnor , Yamasaki Yuji , Ban Yusuke , Kato Keita , Tahir Izzat Sidahmed Ali , Ishii Takayoshi , Tanaka Hiroyuki , Tsujimoto Hisashi
Published: May 2021

Kernel hardness influences the milling and baking quality of wheat. Stress environments such as heat and combined heat-drought can produce harder kernels, thereby affecting the overall wheat quality. Beside puroindoline genes that are known to determine hardness, other QTLs contribute to the hardness. These QTLs, especially under stress conditions, need extensive research. Moreover, understanding the modification or stabilization of hardness under stress condition and the relationship with stress tolerance will facilitate the selection of superior lines that maintain both high yield and quality even under the stress environment. Therefore, in the current work, we aimed to identify the genetic loci and marker trait associations (MTAs) that contributes for hardness under optimum conditions in Japan, and heat and combined heat-drought (HD) conditions in Sudan. We used a panel of multiple synthetic derivatives (MSD) having diverse Aegilops tauschii genome segments and investigated the association between hardness stabilization and stress tolerance. Under stress conditions, we observed that less reduction of kernel weight is associated with either low change or stable kernel hardness. We identified 47 markers associated with hardness under all conditions; the D genome was the main contributor. For the first time, we found a significant association with hardness under stress conditions on chromosome 4D. We dissected several candidate genes associated with the change of hardness under stress conditions. Our results will improve the understanding of the genetic factors that affect wheat hardness stability.
Surgical Repositioning of an Inverted Developing Incisor Assisted by 3D Technology
Authors: Seo Mi-Hyun , Yang Hoon-Joo , Han Jeong-Joon , Kwon Ik-Jae , Myoung Hoon , Kim Soung-Min
Published: May 2021

Permanent central incisor impaction is very rare, and causes of impaction include adjacent supernumerary teeth, odontoma, and trauma. Surgical repositioning is a treatment option for teeth with ectopic eruption pathways. In this case report, an inverted developing maxillary central incisor was surgically repositioned at the initial stage of root development. Three dimensional (3D) virtual images were acquired using computed tomography, and a replica was produced by LCD-based masked stereolithography 3D printing. A resin for 3D printing was approved as a medical device and used as a 3D printing replica. Further, 3D technology has been found to be useful for successful tooth repositioning surgery.
Effect of Mouth Mask and Face Shield on Speech Spectrum in Slovak Language
Authors: Chmelík Vojtech , Urbán Daniel , Zelem Lukáš , Rychtáriková Monika
Published: May 2021

In this paper, with the aim of assessing the deterioration of speech intelligibility caused by a speaker wearing a mask, different face masks (surgical masks, FFP2 mask, homemade textile-based protection and two kinds of plastic shields) are compared in terms of their acoustic filtering effect, measured by placing the mask on an artificial head/mouth simulator. For investigating the additional effects on the speaker’s vocal output, speech was also recorded while people were reading a text when wearing a mask, and without a mask. In order to discriminate between effects of acoustic filtering by the mask and mask-induced effects of vocal output changes, the latter was monitored by measuring vibrations at the suprasternal notch, using an attached accelerometer. It was found that when wearing a mask, people tend to slightly increase their voice level, while when wearing plastic face shield, they reduce their vocal power. Unlike the Lombard effect, no significant change was found in the spectral content. All face mask and face shields attenuate frequencies above 1–2 kHz. In addition, plastic shields also increase frequency components to around 800 Hz, due to resonances occurring between the face and the shield. Finally, special attention was given to the Slavic languages, in particular Slovak, which contain a large variety of sibilants. Male and female speech, as well as texts with and without sibilants, was compared.
Empirical Models for the Viscoelastic Complex Modulus with an Application to Rubber Friction
Authors: Tassara Marco Furlan , Grigoriadis Kyriakos , Mavros Georgios
Published: May 2021

Up-to-date predictive rubber friction models require viscoelastic modulus information; thus, the accurate representation of storage and loss modulus components is fundamental. This study presents two separate empirical formulations for the complex moduli of viscoelastic materials such as rubber. The majority of complex modulus models found in the literature are based on tabulated dynamic testing data. A wide range of experimentally obtained rubber moduli are used in this study, such as SBR (styrene-butadiene rubber), reinforced SBR with filler particles and typical passenger car tyre rubber. The proposed formulations offer significantly faster computation times compared to tabulated/interpolated data and an accurate reconstruction of the viscoelastic frequency response. They also link the model coefficients with critical sections of the data, such as the gradient of the slope in the storage modulus, or the peak values in loss tangent and loss modulus. One of the models is based on piecewise polynomial fitting and offers versatility by increasing the number of polynomial functions used to achieve better fitting, but with additional pre-processing time. The other model uses a pair of logistic-bell functions and provides a robust fitting capability and the fastest identification, as it requires a reduced number of parameters. Both models offer good correlations with measured data, and their computational efficiency was demonstrated via implementation in Persson’s friction model.
Categorizing Touch-Input Locations from Touchscreen Device Interfaces via On-Board Mechano-Acoustic Transducers
Authors: Teo Kai Ren , B T Balamurali , Zhou Jianying , Chen Jer-Ming
Published: May 2021

Many mobile electronics devices, including smartphones and tablets, require the user to interact physically with the device via tapping the touchscreen. Conveniently, these compact devices are also equipped with high-precision transducers such as accelerometers and microphones, integrated mechanically and designed on-board to support a range of user functionalities. However, unintended access to these transducer signals (bypassing normal on-board data access controls) may allow sensitive user interaction information to be detected and thereby exploited. In this study, we show that acoustic features extracted from the on-board microphone signals, supported with accelerometer and gyroscope signals, may be used together with machine learning techniques to successfully determine the user’s touch input location on a touchscreen: our ensemble model, namely the random forest model, predicts touch input location with up to 86% accuracy in a realistic scenario. Accordingly, we present the approach and techniques used, the performance of the model developed, and also discuss limitations and possible mitigation methods to thwart possible exploitation of such unintended signal channels.
A Local Search-Based Generalized Normal Distribution Algorithm for Permutation Flow Shop Scheduling
Authors: Abdel-Basset Mohamed , Mohamed Reda , Abouhawwash Mohamed , Chang Victor , Askar S.
Published: May 2021

This paper studies the generalized normal distribution algorithm (GNDO) performance for tackling the permutation flow shop scheduling problem (PFSSP). Because PFSSP is a discrete problem and GNDO generates continuous values, the largest ranked value rule is used to convert those continuous values into discrete ones to make GNDO applicable for solving this discrete problem. Additionally, the discrete GNDO is effectively integrated with a local search strategy to improve the quality of the best-so-far solution in an abbreviated version of HGNDO. More than that, a new improvement using the swap mutation operator applied on the best-so-far solution to avoid being stuck into local optima by accelerating the convergence speed is effectively applied to HGNDO to propose a new version, namely a hybrid-improved GNDO (HIGNDO). Last but not least, the local search strategy is improved using the scramble mutation operator to utilize each trial as ideally as possible for reaching better outcomes. This improved local search strategy is integrated with IGNDO to produce a new strong algorithm abbreviated as IHGNDO. Those proposed algorithms are extensively compared with a number of well-established optimization algorithms using various statistical analyses to estimate the optimal makespan for 41 well-known instances in a reasonable time. The findings show the benefits and speedup of both IHGNDO and HIGNDO over all the compared algorithms, in addition to HGNDO.
Research on 3D Improved Extended Finite Element Method for Electric Field of Liquid Nitrogen with Bubbles
Authors: Duan Nana , Ma Xinyu , Lu Shaocong , Xu Weijie , Wang Shuhong
Published: May 2021

In this paper, the improved extended finite element method (XFEM) for analyzing the three-dimensional (3D) electric field is presented. The interface between two media is described by using a four-dimensional (4D) level set function. For elements with multiple interfaces, the local level set method is used to improve the accuracy. By using weak discontinuous enrichment function and moving level set function, the interpolation function is modified. The new interpolation function makes it unnecessary to repeat the mesh generation when a moving interface occurs. The cost of calculation is greatly reduced. The reliability of 3D improved XFEM in the electric field is verified through numerical calculation examples of single bubble, multi-bubbles, and moving deformed bubble in liquid nitrogen.
Mechanical Properties of Green Synthesized Graphene Nano-Composite Samples
Authors: Safari Mehdi , de Sousa Ricardo Alves , Salamat-Talab Mazaher , Joudaki Jalal , Ghanbari Davood , Bakhtiari Amir
Published: May 2021

Graphene quantum dots are zero-dimensional nanoparticles that are used widely in advanced composite materials such as filtration membranes, adsorbent materials, optical devices, biomedical applications (especially biosensors), flame retardancy, and automotive, aerospace, agricultural and environmental applications. In this article, the mechanical properties (flexural strength, flexural strain and elastic modulus) of polymer-based nanocomposites will be investigated. The main novelty of the current work is the green synthesis of graphene quantum dots which were extracted from lemon juice. XRD and FTIR tests have been conducted to determine the composition of the prepared powder. The polyester resin and graphene quantum dots were mixed with different weight percentages (0.25%, 0.5% and 1% wt. graphene) and processed to fabricate nanocomposite samples. The mechanical properties of the prepared samples were measured according to the ASTM D790-17 standard testing method. The experimental results show that the strength increased from 80 MPa to about 112 MPa (40% increase in strength) by adding 0.25% wt. graphene quantum dots. The flexural modulus decreased from 2.70 GPa to 2.06 GPa by adding 1% wt. graphene content (23% decrease). The flexural strain increased considerably (up to 14.2%) by adding 1% wt. graphene quantum dots. Consequently, the ductility of the nanocomposites increased by adding green synthesized graphene quantum dots. The fracture behavior changed from brittle fracture mode to ductile fracture mode by adding the graphene quantum dots. Additionally, a flame retardancy test has been carried out by implementing the UL-94 test. The fabricated nanocomposites showed fire retardancy due to char barrier formation on the surface of the nanocomposites.
Data Embedding in SHVC Video Using Threshold-Controlled Block Splitting
Authors: Pang LieLin , Wong KokSheik , Tew Yiqi , Rahardja Susanto
Published: May 2021

With the increasing number of video applications, it is essential to resolve issues such as ineffective search of video content, tampered/forged video content, packet loss, to name a few. Data embedding is typically utilized as one of the solutions to address the aforementioned issues. One of the important requirements of data embedding is to maximize embedding capacity with minimal bit rate overhead while ensuring imperceptibility of the inserted data. However, embedding capacity varies depending on the video content and increasing the embedding capacity usually leads to video quality degradation. In this work, a threshold-controlled block splitting technique is proposed for embedding data into SHVC video. Specifically, the embedding capacity can be increased by coding the host video by using more small blocks, which can be achieved by tuning a threshold-controlled parameter in the rate distortion optimization process. Subsequently, the predictive syntax elements in both intra and inter-coded blocks are jointly utilized to embed data, which ensures that data can be embedded regardless of the prediction mode used in coding a block. Results suggest that the proposed method can achieve a trade-off between the increase in embedding capacity and bit rate overhead while maintaining video quality. In the best case scenario, the sequence PartyScene can embed 516.9 kbps with an average bit rate overhead of +7.0% for the Low Delay P configuration, while the same video can embed 1578.6 kbps with an average bit rate overhead of +2.9% for the All Intra configuration.
A New Computational Method for Arabic Calligraphy Style Representation and Classification
Authors: Kaoudja Zineb , Kherfi Mohammed Lamine , Khaldi Belal
Published: May 2021

Despite the importance of recognizing Arabic calligraphy styles and their potential usefulness for many applications, a very limited number of Arabic calligraphy style recognition works have been established. Thus, we propose a new computational tool for Arabic calligraphy style recognition (ACSR). The present work aims to identify Arabic calligraphy style (ACS) from images where text images are captured by different tools from different resources. To this end, we were inspired by the indices used by human experts to distinguish different calligraphy styles. These indices were transformed into a descriptor that defines, for each calligraphy style, a set of specific features. Three scenarios have been considered in the experimental part to prove the effectiveness of the proposed tool. The results confirmed the outperformance of both individual and combine features coded by our descriptor. The proposed work demonstrated outstanding performance, even with few training samples, compared to other related works for Arabic calligraphy recognition.
Contextual Information Helps Understand Messages Written with Textisms
Authors: Jacquet Baptiste , Jaraud Caline , Jamet Frank , Guéraud Sabine , Baratgin Jean
Published: May 2021

The present study investigated the influence of the use of textisms, a form of written language used in phone-mediated conversations, on the cognitive cost of French participants in an online conversation. Basing our thinking on the relevance theory of Sperber and Wilson, we tried to assess whether knowing the context and topic of a conversation can produce a significant decrease in the cognitive cost required to read messages written in textism by giving additional clues to help infer the meaning of these messages. In order to do so, participants played the judges in a Turing test between a normal conversation (written with the traditional writing style) and a conversation in which the experimenter was conversing with textisms, in a random order. The results indicated that participants answered messages written in textism faster when they were in the second conversation. We concluded that prior knowledge about the conversation can help interpret the messages written in textisms by decreasing the cognitive cost required to infer their meaning.
Coupling Effect of Air Flow Rate and Operating Conditions on the Performance of Electric Vehicle R744 Air Conditioning System
Authors: Wang Anci , Fang Jianmin , Yin Xiang , Song Yulong , Cao Feng , Gullo Paride
Published: May 2021

The air flow rate on the gas cooler side is one of the key parameters affecting the performance and running safety of transcritical CO2 electric vehicle air conditioning systems. After experimentally analyzing the effects of the air volume flow rate in the gas cooler on the cycle parameters and system performance, a novel method to evaluate the optimal air flow rate was proposed. In addition, the effect of the gas cooler air volume flow rate on the key performance parameters of the system (e.g., optimal discharge pressure) was explored. Finally, the coupling effects of the compressor speed, ambient temperature and optimal air flow rate on the system performance was also exhaustively assessed. It was found that as the discharge temperature, the CO2 temperature at the gas cooler outlet and the discharge pressure did not vary more than ±2%, the corresponding gas cooler air volume flow rate was optimal. For the single-row and dual-process microchannel evaporator used in this work, the recommended value of the optimal gas cooler air volume flow rate was 2500 m3·h−1. The results could provide reference for the fan speed design of electric vehicle CO2 air conditioning systems, especially for the performance under idling model.
The Steelmaking Process Parameter Optimization with a Surrogate Model Based on Convolutional Neural Networks and the Firefly Algorithm
Authors: Liu Yung-Chun , Horng Ming-Huwi , Yang Yung-Yi , Hsu Jian-Han , Chen Yen-Ting , Hung Yu-Chen , Sun Yung-Nien , Tsai Yu-Hsuan
Published: May 2021

High-strength low-alloy steels (HSLAs) are widely used in the structural body components of many domestic motor vehicles owing to their better mechanical properties and greater resistance. The real production process of HSLA steelmaking can be regarded as a model that builds on the relationship between process parameters and product quality attributes. A surrogate modeling method is used, and the resulting production process model can be applied to predict the optimal manufacturing process parameters. We used different methods in this paper, including linear regression, random forests, support vector regression, multilayer perception, and a simplified VGG model to build such a surrogate model. We then applied three bio-inspired search algorithms, namely particle swarm optimization, the artificial bee colony algorithm, and the firefly algorithm, to search for the optimal controllable manufacturing process parameters. Through experiments on 9000 test samples used for building the surrogate model and 299 test samples for making the optimal process parameter selection, we found that the combination of a simplified VGG model and the firefly algorithm was the most successful at reaching a success rate of 100%—in other words, when the product quality attributes of all test samples satisfy the mechanical requirements of the end products.
Performance of CRTS-II Ballastless Track–Bridge Structural System Rebars under Fatigue Loading Test
Authors: Zhou Lingyu , Zou Lifan , Zhao Lei , Yuan Yahui , Mahunon Akim D. , Gong Yongzhi
Published: May 2021

To study the evolution of mechanical properties of steel rebars in the China Railway Track System Type II (CRTS II) ballastless track–bridge structural system under repeated train loads, a 1/4 scale three-span ballastless slab track simple-supported bridge structural system specimen was manufactured and subjected to a multistage fatigue test with 18 million cycles. The experimental results show that the strain amplitude of the steel bar changes proportionally to the fatigue stress amplitude, and there is an obvious strain increase in the loading stage 4, where the fatigue stress amplitude is the largest. During the test, the cumulative strain–amplitude ratio first decreases then increases. At the end of the test, the cumulative strain–amplitude ratio increases by 5.46% and 5.32%, respectively, at L/2 and L/4 sections. The load–strain curve of the steel rebar keeps the shape of an oblique straight line. The slope increases first and then decreases with a degradation at the end of the test of 5.15% and 4.81%, respectively, at L/2 and L/4 sections. The mechanical properties of the rebar are enhanced under the first three million fatigue loading cycles: this is the fatigue strengthening stage. The mechanical properties of reinforcement gradually degrade from the three millionth cycle to the end of the test: this is the fatigue damage stage. Finally, based on the material fatigue damage model and the multistage cumulative damage criterion, the change rule of the load–strain curve slope of steel rebars in the fatigue damage stage is obtained by finite element simulation. The simulation results agree well with the experimental data, proving the validity of the calculation method proposed in this paper.
Reconstructing Human-Centered Interaction Networks of the Swifterbant Culture in the Dutch Wetlands: An Example from the ArchaeoEcology Project
Authors: Verhagen Philip , Crabtree Stefani A. , Peeters Hans , Raemaekers Daan
Published: May 2021

In archaeology, palaeo-ecological studies are frequently used to support archaeological investigations, but linking and synthesizing datasets and concepts from ecology, ethnography, earth sciences, and archaeology has historically been rare. While advances in computational approaches and standards of data collection have enabled more collaborative approaches to understanding the past, these endeavors are only now beginning to pick up pace. Here, we propose a method to collect data of these assorted types, synthesize ecological and archaeological understanding, and move beyond subsistence-focused studies to those that incorporate multifaceted economies. We advocate for the use of ‘human-centered interaction networks’ as a tool to synthesize and better understand the role of culture, ecology, and environment in the long-term evolution of socio-ecological systems. We advance the study of human-centered interaction networks by presenting an archaeoecological (archaeological-ecological) perspective on the Neolithic transition of the Swifterbant culture in the northwestern Netherlands (approximately 4700–4000 BCE). We employed network science to better understand the relationships of animal and plant species to the uses that people made of them. The analysis of the Swifterbant system reveals a highly connected set of interactions among people, plants, and animals, as could be expected on the basis of the hypothesis of an ‘extended broad-spectrum economy’. Importantly, this broad spectrum extends beyond the subsistence sphere.
Fabricating TiNiCu Ternary Shape Memory Alloy by Directed Energy Deposition via Elemental Metal Powders
Authors: Chen Yitao , Zhang Xinchang , Parvez Mohammad Masud , Newkirk Joseph W. , Liou Frank
Published: May 2021

In this paper, a TiNiCu shape memory alloy single-wall structure was fabricated by the directed energy deposition technique with a mixture of elemental Ti, Ni, and Cu powders following the atomic percentage of Ti50Ni45Cu5 to fully utilize the material flexibility of the additive manufacturing process to develop ternary shape memory alloys. The chemical composition, phase, and material properties at multiple locations along the build direction were studied, using scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction, Vickers hardness testing, tensile testing, and differential scanning calorimetry. The location-dependent compositions of martensitic TiNi and austenitic TiNi phases, mechanical properties, and functional properties were investigated in detail. Variations were found in atomic compositions of Ti, Ni, and Cu elements along the build direction due to the complex interaction between elemental powders and laser processing. Good correlations were present among the chemical composition, phase constituent, hardness, and feature of phase transformation temperatures at various locations. The ultimate tensile strength of the as-deposited TiNiCu alloy is comparable with the previously reported additively manufactured TiNi binary alloys. By adding Cu, a much lower thermal hysteresis was achieved, which shows good feasibility of fabricating ternary TiNiCu shape memory alloys, using elemental powders in the directed energy deposition to adjust the thermal hysteresis.
An Intelligent System for Allocating Times to the Main Activities of Managers
Authors: Solares Efrain , Guerrero Liliana , Aguilera Alberto , Hernández Juana María , Rodríguez Sandra , De-León-Gómez Víctor
Published: May 2021

Correctly allocating times to the main activities of a manager is a crucial task that directly affects the possibility of success for any company. Decision support based on state-of-the-art methods can lead to better performance in this activity. However, allocating times to managerial activities is not straightforward; the decision support should provide a flexible recommendation so the manager can make a final decision while ensuring robustness. This paper describes and assesses a novel approach where a search for the best distribution of the manager’s time is performed by an intelligent decision support system. The approach consists of eliciting manager preferences to define the value of the manager’s main activities and, by using a portfolio-like optimization based on differential evolution, obtaining the best time allocation. Aiming at applicability in practical scenarios, the approach can deal with many activities, group decisions, cope with imprecision, vagueness, ill-determination, and other types of uncertainty. We present evidence of the approach’s applicability exploiting a real case study with the participation of several managers. The approach is assessed through the satisfaction level of each manager.
Unlocking the Residential Retrofitting Potential in a Three-Degree World: A Holistic Approach to Passive Design in Hot Climates
Authors: Aldabesh Abdulmajeed , Soufi Jassmen , Omer Siddig , Haredy Abdullah
Published: May 2021

The Kingdom of Saudi Arabia (KSA), as one of the largest polluters worldwide, has released its Vision 2030 that seeks sustainable development via economic diversification to transition towards lower CO2 energy systems. Due to fast population and economic growth, the Kingdom is undergoing an increasing volume of construction, which is projected to exacerbate the energy-related emissions. Strategies are needed to decarbonise the housing stock and help bridge the existing performance gap with the updated Saudi Building Code (SBC). This study proposes a holistic retrofitting approach for the Saudi building industry to facilitate the identification of energy consumption reduction optimisation solutions, covering the assessment of insulation, reflective coating surfaces, sun shading devices, efficient glazing solutions, building-integrated renewables, and green roofs. The proposed flexible approach proved how blended retrofit packages provide improved performance, with rooftop photovoltaic microgeneration and improved glazing technologies singlehandedly outperforming the remaining proposals for KSA’s Riyadh climate conditions. Only the photovoltaic system could meet the simulated SBC performance benchmark independently, positioning it as an instrumental tool in improving the overall effectiveness of the retrofit packages.
Earth-Abundant 3d Transition Metal Catalysts for Hydroalkoxylation and Hydroamination of Unactivated Alkenes
Authors: Rocard Lou , Chen Donghuang , Stadler Adrien , Zhang Hailong , Gil Richard , Bezzenine Sophie , Hannedouche Jerome
Published: May 2021

This review summarizes the most noteworthy achievements in the field of C–O and C–N bond formation by hydroalkoxylation and hydroamination reactions on unactivated alkenes (including 1,2- and 1,3-dienes) promoted by earth-abundant 3d transition metal catalysts based on manganese, iron, cobalt, nickel, copper and zinc. The relevant literature from 2012 until early 2021 has been covered.
Anti-SARS-CoV-2 Antibodies Testing in Recipients of COVID-19 Vaccination: Why, When, and How?
Authors: Lippi Giuseppe , Henry Brandon Michael , Plebani Mario
Published: May 2021

Although universal vaccination is one of the most important healthcare strategies for limiting SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) circulation and averting the huge number of hospitalizations and deaths due to coronavirus disease 2019 (COVID-19), significant inter-individual variability of COVID-19 vaccines’ efficacies has been described, mostly due to heterogeneous immune response in recipients. This opinion paper hence aims to discuss aspects related to the opportunity of monitoring anti-SARS-CoV-2 antibodies before and after COVID-19 vaccination, highlighting the pros and cons of this strategy. In summary, the advantages of anti-SARS-CoV-2 antibodies’ testing in recipients of COVID-19 vaccination encompass an assessment of baseline seroprevalence of SARS-CoV-2 infection in non-vaccinated individuals; early identification of low or non-responders to COVID-19 vaccination; and timely detection of faster decay of anti-SARS-CoV-2 antibody levels. In contrast, potential drawbacks to date include an unproven equivalence between anti-SARS-CoV-2 antibody titer, neutralizing activity, and vaccine efficiency; the lack of cost-effective analyses of different testing strategies; the enormous volume of blood drawings and increase of laboratory workload that would be needed to support universal anti-SARS-CoV-2 antibodies testing. A potential solution entails the identification of cohorts to be prioritized for testing, including those at higher risk of being infected by variants of concern, those at higher risk of unfavorable disease progression, and subjects in whom vaccine immunogenicity may be expectedly lower and/or shorter.
Machine Learning-Based Cardiovascular Disease Prediction Model: A Cohort Study on the Korean National Health Insurance Service Health Screening Database
Authors: Kim Joung Ouk (Ryan) , Jeong Yong-Suk , Kim Jin Ho , Lee Jong-Weon , Park Dougho , Kim Hyoung-Seop
Published: May 2021

Background: This study proposes a cardiovascular diseases (CVD) prediction model using machine learning (ML) algorithms based on the National Health Insurance Service-Health Screening datasets. Methods: We extracted 4699 patients aged over 45 as the CVD group, diagnosed according to the international classification of diseases system (I20–I25). In addition, 4699 random subjects without CVD diagnosis were enrolled as a non-CVD group. Both groups were matched by age and gender. Various ML algorithms were applied to perform CVD prediction; then, the performances of all the prediction models were compared. Results: The extreme gradient boosting, gradient boosting, and random forest algorithms exhibited the best average prediction accuracy (area under receiver operating characteristic curve (AUROC): 0.812, 0.812, and 0.811, respectively) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the CVD prediction performance, compared to previously proposed prediction models. Preexisting CVD history was the most important factor contributing to the accuracy of the prediction model, followed by total cholesterol, low-density lipoprotein cholesterol, waist-height ratio, and body mass index. Conclusions: Our results indicate that the proposed health screening dataset-based CVD prediction model using ML algorithms is readily applicable, produces validated results and outperforms the previous CVD prediction models.
Her2-Positive and Microsatellite Instability Status in Gastric Cancer—Clinicopathological Implications
Authors: Bermúdez Ana , Arranz-Salas Isabel , Mercado Silvia , López-Villodres Juan A. , González Virginia , Ríus Francisca , Ortega María V. , Alba Carmen , Hierro Isabel , Bermúdez Diego
Published: May 2021

Gastric cancer (GC) is one of the leading causes of cancer-related death. The combination of new molecular classifications with clinicopathological data could contribute to the individualization of patients and to the development of new therapeutic strategies. We examined the various associations in two molecular types of GC: HER2-positive (human epidermal growth factor receptor 2) and microsatellite instability (MSI), assessing their influence on treatment and prognosis. A retrospective study of 142 GC patients was performed with molecular characterization through HER2 overexpression and DNA repair protein expression for MSI. The percentage of HER2-positive tumors was 13.4%, predominantly in men. Correlations were found with intestinal type, metastases, advanced stages and chemotherapy. Almost 75% of HER2-positive patients died. MSI occurred in 16.2%, associated with advanced age, female sex, distal location and intestinal type. These patients had few metastases and low stages. The percentage of deaths was higher among MSI patients who received perioperative chemotherapy. The determination of HER2 and MSI status in GC is important for their association with specific clinicopathological features and for their prognostic and predictive value.
Menzerath’s Law in the Syntax of Languages Compared with Random Sentences
Authors: Tanaka-Ishii Kumiko
Published: May 2021

The Menzerath law is considered to show an aspect of the complexity underlying natural language. This law suggests that, for a linguistic unit, the size (y) of a linguistic construct decreases as the number (x) of constructs in the unit increases. This article investigates this property syntactically, with x as the number of constituents modifying the main predicate of a sentence and y as the size of those constituents in terms of the number of words. Following previous articles that demonstrated that the Menzerath property held for dependency corpora, such as in Czech and Ukrainian, this article first examines how well the property applies across languages by using the entire Universal Dependency dataset ver. 2.3, including 76 languages over 129 corpora and the Penn Treebank (PTB). The results show that the law holds reasonably well for x>2. Then, for comparison, the property is investigated with syntactically randomized sentences generated from the PTB. These results show that the property is almost reproducible even from simple random data. Further analysis of the property highlights more detailed characteristics of natural language.
Ambient Intelligence Based on IoT for Assisting People with Alzheimer’s Disease Through Context Histories
Authors: Machado Savanna Denega , Tavares João Elison da Rosa , Martins Márcio Garcia , Barbosa Jorge Luis Victória , González Gabriel Villarrubia , Leithardt Valderi Reis Quietinho
Published: May 2021

New Internet of Things (IoT) applications are enabling the development of projects that help with monitoring people with different diseases in their daily lives. Alzheimer’s is a disease that affects neurological functions and needs support to maintain maximum independence and security of patients during this stage of life, as the cure and reversal of symptoms have not yet been discovered. The IoT-based monitoring system provides the caregivers’ support in monitoring people with Alzheimer’s disease (AD). This paper presents an ontology-based computational model that receives physiological data from external IoT applications, allowing identification of potentially dangerous behaviors for patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer’s disease using the analysis of context histories and context prediction, which, considering the state of the art, is the only one that uses analysis of context histories to perform predictions. In this research, we also propose a simulator to generate activities of the daily life of patients, allowing the creation of data sets. These data sets were used to evaluate the contributions of the model and were generated according to the standardization of the ontology. The simulator generated 1026 scenarios applied to guide the predictions, which achieved average accurary of 97.44%. The experiments also allowed the learning of 20 relevant lessons on technological, medical, and methodological aspects that are recorded in this article.
My-Trac: System for Recommendation of Points of Interest on the Basis of Twitter Profiles
Authors: Rivas Alberto , González-Briones Alfonso , Cea-Morán Juan J. , Prat-Pérez Arnau , Corchado Juan M.
Published: May 2021

New mapping and location applications focus on offering improved usability and services based on multi-modal door to door passenger experiences. This helps citizens develop greater confidence in and adherence to multi-modal transport services. These applications adapt to the needs of the user during their journey through the data, statistics and trends extracted from their previous uses of the application. The My-Trac application is dedicated to the research and development of these user-centered services to improve the multi-modal experience using various techniques. Among these techniques are preference extraction systems, which extract user information from social networks, such as Twitter. In this article, we present a system that allows to develop a profile of the preferences of each user, on the basis of the tweets published on their Twitter account. The system extracts the tweets from the profile and analyzes them using the proposed algorithms and returns the result in a document containing the categories and the degree of affinity that the user has with each category. In this way, the My-Trac application includes a recommender system where the user receives preference-based suggestions about activities or services on the route to be taken.