bluuebunny/crossref_metadata_2025 huggingface dataset:
FDM Printability of PLA Based-Materials: The Key Role of the Rheological Behavior
Authors: Arrigo Rossella , Frache Alberto
Published: April 2022

Fused deposition modeling (FDM) is one of the most commonly used commercial technologies of materials extrusion-based additive manufacturing (AM), used for obtaining 3D-printed parts using thermoplastic polymers. Notwithstanding the great variety of applications for FDM-printed objects, the choice of materials suitable for processing using AM technology is still limited, likely due to the lack of rapid screening procedures allowing for an efficient selection of processable polymer-based formulations. In this work, the rheological behavior of several 3D-printable, commercially available poly(lactic acid)-based filaments was accurately characterized. In particular, each step of a typical FDM process was addressed, from the melt flowability through the printing nozzle, to the interlayer adhesion in the post-deposition stage, evaluating the ability of the considered materials to fulfill the criteria for successful 3D printing using FDM technology. Furthermore, the rheological features of the investigated materials were related to their composition and microstructure. Although an exhaustive and accurate evaluation of the 3D printability of thermoplastics must also consider their thermal behavior, the methodology proposed in this work aimed to offer a useful tool for designing thermoplastic-based formulations that are able to ensure an appropriate rheological performance in obtaining 3D-printed parts with the desired geometry and final properties.
3D-Printed Poly (P-Dioxanone) Stent for Endovascular Application: In Vitro Evaluations
Authors: Lu Junlin , Hu Xulin , Yuan Tianyu , Cao Jianfei , Zhao Yuanli , Xiong Chengdong , Li Kainan , Ye Xun , Xu Tao , Zhao Jizong
Published: April 2022

Rapid formation of innovative, inexpensive, personalized, and quickly reproducible artery bioresorbable stents (BRSs) is significantly important for treating dangerous and sometimes deadly cerebrovascular disorders. It is greatly challenging to give BRSs excellent mechanical properties, biocompatibility, and bioabsorbability. The current BRSs, which are mostly fabricated from poly-l-lactide (PLLA), are usually applied to coronary revascularization but may not be suitable for cerebrovascular revascularization. Here, novel 3D-printed BRSs for cerebrovascular disease enabling anti-stenosis and gradually disappearing after vessel endothelialization are designed and fabricated by combining biocompatible poly (p-dioxanone) (PPDO) and 3D printing technology for the first time. We can control the strut thickness and vessel coverage of BRSs by adjusting the printing parameters to make the size of BRSs suitable for small-diameter vascular use. We added bis-(2,6-diisopropylphenyl) carbodiimide (commercial name: stabaxol®-1) to PPDO to improve its hydrolytic stability without affecting its mechanical properties and biocompatibility. In vitro cell experiments confirmed that endothelial cells can be conveniently seeded and attached to the BRSs and subsequently demonstrated good proliferation ability. Owing to the excellent mechanical properties of the monofilaments fabricated by the PPDO, the 3D-printed BRSs with PPDO monofilaments support desirable flexibility, therefore offering a novel BRS application in the vascular disorders field.
Influence of Non-Rubber Components on the Properties of Unvulcanized Natural Rubber from Different Clones
Authors: Lehman Nussana , Tuljittraporn Akarapong , Songtipya Ladawan , Uthaipan Nattapon , Sengloyluan Karnda , Johns Jobish , Nakaramontri Yeampon , Kalkornsurapranee Ekwipoo
Published: April 2022

Natural rubber from different Hevea braziliensis clones, namely RRIM600, RRIT251, PB235 and BPM24, exhibit unique properties. The influences of the various fresh natural rubber latex and cream concentrated latex on the non-rubber components related properties were studied. It was found that the fresh natural rubber latex exhibited differences in their particle size, which was attributed to the non-rubber and unique signature of clones which affect various properties. Meanwhile, the cream concentrated latex showed the protein contents, surface tension, and color of creamed latex to be lower than the fresh natural latex. However, TSC, DRC, viscosity, particle size and green strength of concentrated latex were found to be higher than the fresh natural latex. This is attributed to the incorporation of HEC molecules. Also, the rubber particle size distribution in the RRIM600 clone exhibited a large particle size and uniform distribution, showing good mechanical properties when compared to the other clones. Furthermore, the increased green strength in the RRIM600 clone can be attributed to the crystallization of the chain on straining and chain entanglement. These experimental results may provide benefits for manufacturing rubber products, which can be selected from a suitable clone.
Eco-Friendly, High-Ductility Slag/Fly-Ash-Based Engineered Cementitious Composite (ECC) Reinforced with PE Fibers
Authors: Shumuye Eskinder Desta , Liu Jie , Li Weiwen , Wang Zike
Published: April 2022

Engineered cementitious composites (ECCs) are a special class of ultra-ductile fiber-reinforced cementitious composites containing a significant amount of short discontinuous fibers. The distinctive tensile strain-hardening behavior of ECCs is the result of a systematic design based on the micromechanics of the fiber, matrix, and fiber–matrix interface. However, ECCs require extensive cement content, which is inconsistent with the goal of sustainable and green building materials. Consequently, the objective of this study is to investigate the mechanical performance of slag/fly-ash-based engineered cementitious composites (ECCs) reinforced with polyethylene (PE) fiber under axial compressive loading, as well as direct tensile and flexural strength tests. The composites’ microstructure and mineralogical composition were analyzed using images obtained from scanning electron microscopy (SEM), X-ray energy diffraction spectroscopy (EDS), X-ray powder diffraction (XRD), and X-ray fluorescence (XRF). The experimental results reveal that a slag-containing composite mixture shows strain-hardening behavior and comparable ductility properties to those of fly-ash-based composite mixtures. A ternary system of binder materials with 5% and 15% slag can increase the compressive strength of ECC by 3.5% and 34.9%, respectively, compared to slag-free ECC composite. Moreover, the microstructural results show that the slag-based cementitious matrix has a more closely cross-linked and dense microstructure at the matrix–aggregate interface. In addition, the concentration of particles on the surface of the fibers was higher in the slag-based cementitious composites than in the fly ash-based composite. This supports the concept that there is a stronger bonding between the fibers and matrix in the slag-based cementitious matrix than in fly-ash-based matrix.
Portuguese Version of the Spiritual Well-Being Questionnaire: Validation Study in People under Assisted Reproductive Techniques
Authors: Romeiro Joana , Nogueira Paulo Jorge , Fisher John , Caldeira Sílvia
Published: April 2022

Attention has been given to the experience of individuals undergoing assisted reproductive techniques. However, only recent literature has focused on the spiritual journey triggered by such an event and highlighted the nurses and midwives’ roles in the assessment of the spiritual needs of those living with infertility. This study aimed to perform a psychometric test of the factor structure of the Portuguese version of the Spiritual Well-being Questionnaire in a sample of people undergoing assisted reproductive techniques. This descriptive cross-sectional study was conducted between September 2019 and June 2020 on a sample of 104 Portuguese adults attending fertility treatment. An online questionnaire was released on fertility-related websites. A confirmatory factor analysis was conducted on six hypothesized models of the instrument. The findings provided evidence of an adequate internal consistency of the instrument, and the validity and reliability of the oblique four-factor model was confirmed. Spiritual well-being had a significant association with the nature of infertility and a higher score was achieved by participants that perceived religion as very important. This study provides a validated tool for nurses and midwives to assess spiritual well-being and promote an integrated reproductive healthcare approach. Further longitudinal research with bigger samples would provide more evidence of the spiritual needs of people living with this condition.
Hyperspectral Image Mixed Noise Removal Using a Subspace Projection Attention and Residual Channel Attention Network
Authors: Sun Hezhi , Zheng Ke , Liu Ming , Li Chao , Yang Dong , Li Jindong
Published: April 2022

Although the existing deep-learning-based hyperspectral image (HSI) denoising methods have achieved tremendous success, recovering high-quality HSIs in complex scenes that contain mixed noise is still challenging. Besides, these methods have not fully explored the local and global spatial–spectral information of HSIs. To address the above issues, a novel HSI mixed noise removal network called subspace projection attention and residual channel attention network (SPARCA-Net) is proposed. Specifically, we propose an orthogonal subspace projection attention (OSPA) module to adaptively learn to generate bases of the signal subspace and project the input into such space to remove noise. By leveraging the local and global spatial relations, OSPA is able to reconstruct the local structure of the feature maps more precisely. We further propose a residual channel attention (RCA) module to emphasize the interdependence between feature maps and exploit the global channel correlation of them, which could enhance the channel-wise adaptive learning. In addition, multiscale joint spatial–spectral input and residual learning strategies are employed to capture multiscale spatial–spectral features and reduce the degradation problem, respectively. Synthetic and real HSI data experiments demonstrated that the proposed HSI denoising network outperforms many of the advanced methods in both quantitative and qualitative assessments.
Effects of Charged Martian Dust on Martian Atmosphere Remote Sensing
Authors: Gao Xuebang , Xie Li , Dou Xuqiang , Zhou Jun
Published: April 2022

In this paper, the extinction property and optical depth of charged Martian dust at infrared band 3 THz–300 THz are studied using the Mie scattering theory. It is found that the extinction coefficients of Martian atmospheric dust and the dust optical depth (DOD) of the Martian atmosphere can be amplified significantly as the dust particles are charged. This extinction amplification has a peak, called amplification resonance, which shifts toward the upper left of the r-q parameter plane with increasing frequency. Here, r denotes the particle radius and q denotes the particle’s total net charge. The amplification of the Martian DOD is more significant at high altitudes than at low altitudes because the particles at high altitudes are smaller. For example, at an altitude of 30–50 km, the dust optical depth at 30 THz can be increased by 60–200%. However, at 3 THz–10 THz, the DOD at the near surface altitude (0–10 km) can still be enhanced by ~80%. This implies that by treating the Martian dust as uncharged particles, the dust density constructed from the Martian DOD data might be overestimated. The estimation error of the dust density of the Martin atmosphere may be reduced by counting the enhancement of the DOD that is caused by charged dust.
Convective Entrainment Rate over the Tibetan Plateau and Its Adjacent Regions in the Boreal Summer Using SNPP-VIIRS
Authors: Li Junjun , Yue Zhiguo , Lu Chunsong , Chen Jinghua , Wu Xiaoqing , Xu Xiaoqi , Luo Shi , Zhu Lei , Wu Shiying , Wang Fan , He Xin
Published: April 2022

The entrainment rate (λ) is difficult to estimate, and its uncertainties cause a significant error in convection parameterization and precipitation simulation, especially over the Tibetan Plateau, where observations are scarce. The λ over the Tibetan Plateau, and its adjacent regions, is estimated for the first time using five-year satellite data and a reanalysis dataset. The λ and cloud base environmental relative humidity (RH) decrease with an increase in terrain height. Quantitatively, the correlation between λ and RH changes from positive at low terrain heights to negative at high terrain heights, and the underlying mechanisms are here interpreted. When the terrain height is below 1 km, large RH decreases the difference in moist static energy (MSE) between the clouds and the environment and increases λ. When the terrain height is above 1 km, the correlation between λ and RH is related to the difference between MSE turning point and cloud base, because of decreases in specific humidity near the surface with increasing terrain height. These results enhance the theoretical understanding of the factors affecting λ and pave the way for improving the parameterization of λ.
Adaptive Max-Margin One-Class Classifier for SAR Target Discrimination in Complex Scenes
Authors: Liao Leiyao , Du Lan , Zhang Wei , Chen Jian
Published: April 2022

Synthetic aperture radar (SAR) target discrimination is an important stage that distinguishes targets from clutters in the radar automatic target recognition field. However, in complex SAR scenes, the performance of some traditional discriminators will degrade. As an effective tool for one-class classification (OCC), the max-margin one-class classifier has attracted much attention for SAR target discrimination, as it can effectively reduce the impact of multiple clutters. However, the performance of the max-margin one-class classifier is very sensitive to the values of kernel parameters. To solve the problem, this paper proposes an adaptive max-margin one-class classifier for SAR target discrimination in complex scenes. In a max-margin one-class classifier with a suitable kernel parameter, the distance between a sample and classification boundary satisfies a certain geometric relationship, i.e., edge samples in input space are transformed to the region in the kernel space close to boundary, while interior samples in input space are transformed to the region in the kernel space far away from boundary. Therefore, we define the information entropy of samples in the kernel space to measure the distance between samples and classification boundary. To automatically obtain the optimal kernel parameter of the max-margin one-class classifier, the edge and interior samples in the input space are first selected, and then the parameter optimization is performed by minimizing information entropy of interior samples and simultaneously maximizing the information entropy of edge samples. Experimental results of the synthetic datasets and measured synthetic aperture radar (SAR) datasets validate the effectiveness of our method.
Comparison of CNNs and Vision Transformers-Based Hybrid Models Using Gradient Profile Loss for Classification of Oil Spills in SAR Images
Authors: Basit Abdul , Siddique Muhammad Adnan , Bhatti Muhammad Khurram , Sarfraz Muhammad Saquib
Published: April 2022

Oil spillage over a sea or ocean surface is a threat to marine and coastal ecosystems. Spaceborne synthetic aperture radar (SAR) data have been used efficiently for the detection of oil spills due to their operational capability in all-day all-weather conditions. The problem is often modeled as a semantic segmentation task. The images need to be segmented into multiple regions of interest such as sea surface, oil spill, lookalikes, ships, and land. Training of a classifier for this task is particularly challenging since there is an inherent class imbalance. In this work, we train a convolutional neural network (CNN) with multiple feature extractors for pixel-wise classification and introduce a new loss function, namely, “gradient profile” (GP) loss, which is in fact the constituent of the more generic spatial profile loss proposed for image translation problems. For the purpose of training, testing, and performance evaluation, we use a publicly available dataset with selected oil spill events verified by the European Maritime Safety Agency (EMSA). The results obtained show that the proposed CNN trained with a combination of GP, Jaccard, and focal loss functions can detect oil spills with an intersection over union (IoU) value of 63.95%. The IoU value for sea surface, lookalikes, ships, and land class is 96.00%, 60.87%, 74.61%, and 96.80%, respectively. The mean intersection over union (mIoU) value for all the classes is 78.45%, which accounts for a 13% improvement over the state of the art for this dataset. Moreover, we provide extensive ablation on different convolutional neural networks (CNNs) and vision transformers (ViTs)-based hybrid models to demonstrate the effectiveness of adding GP loss as an additional loss function for training. Results show that GP loss significantly improves the mIoU and F1 scores for CNNs as well as ViTs-based hybrid models. GP loss turns out to be a promising loss function in the context of deep learning with SAR images.
Correction Model for Metal Oxide Sensor Drift Caused by Ambient Temperature and Humidity
Authors: Abdullah Abdulnasser Nabil , Kamarudin Kamarulzaman , Kamarudin Latifah Munirah , Adom Abdul Hamid , Mamduh Syed Muhammad , Mohd Juffry Zaffry Hadi , Bennetts Victor Hernandez
Published: April 2022

For decades, Metal oxide (MOX) gas sensors have been commercially available and used in various applications such as the Smart City, gas monitoring, and safety due to advantages such as high sensitivity, a high detection range, fast reaction time, and cost-effectiveness. However, several factors affect the sensing ability of MOX gas sensors. This article presents the results of a study on the cross-sensitivity of MOX gas sensors toward ambient temperature and humidity. A gas sensor array consisting of temperature and humidity sensors and four different MOX gas sensors (MiCS-5524, GM-402B, GM-502B, and MiCS-6814) was developed. The sensors were subjected to various relative gas concentrations, temperatures (from 16 °C to 30 °C), and humidity levels (from 75% to 45%), representing a typical indoor environment. The results proved that the gas sensor responses were significantly affected by the temperature and humidity. The increased temperature and humidity levels led to a decreased response for all sensors, except for MiCS-6814, which showed the opposite response. Hence, this work proposed regression models for each sensor, which can correct the gas sensor response drift caused by the ambient temperature and humidity variations. The models were validated, and the standard deviations of the corrected sensor response were found to be 1.66 kΩ, 13.17 kΩ, 29.67 kΩ, and 0.12 kΩ, respectively. These values are much smaller compared to the raw sensor response (i.e., 18.22, 24.33 kΩ, 95.18 kΩ, and 2.99 kΩ), indicating that the model provided a more stable output and minimised the drift. Overall, the results also proved that the models can be used for MOX gas sensors employed in the training process, as well as for other sets of gas sensors.
A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography
Authors: Mertes Gert , Long Yuan , Liu Zhangdaihong , Li Yuhui , Yang Yang , Clifton David A.
Published: April 2022

Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibility to non-stationary noise sources and lack of robust extraction methods, the capture of high-quality NI-FECG remains a challenge. Recording waveforms of sufficient quality for clinical use typically requires human visual inspection of each recording. A Signal Quality Index (SQI) can help to automate this task but, contrary to adult ECG, work on SQIs for NI-FECG is sparse. In this paper, a multi-channel signal quality classifier for NI-FECG waveforms is presented. The model can be used during the capture of NI-FECG to assist technicians to record high-quality waveforms, which is currently a labour-intensive task. A Convolutional Neural Network (CNN) is trained to distinguish between NI-FECG segments of high and low quality. NI-FECG recordings with one maternal channel and three abdominal channels were collected from 100 subjects during a routine hospital screening (102.6 min of data). The model achieves an average 10-fold cross-validated AUC of 0.95 ± 0.02. The results show that the model can reliably assess the FECG signal quality on our dataset. The proposed model can improve the automated capture and analysis of NI-FECG as well as reduce technician labour time.
Use of Magnetomechanical Effect for Energy Harvesting and Data Transfer
Authors: Mech Rafał , Wiewiórski Przemysław , Wachtarczyk Karol
Published: April 2022

The presented paper describes a method where, with the use of a dedicated SMART Ultrasonic Resonant Power System (SURPS) developed by the authors, a power and data transfer between two devices can be performed at the same time. The proposed solution allows power to be supplied to the sensor, located in a hardly accessible place, with simultaneous data transfer in a half-duplex way (e.g., “question–response”). The power transmission mechanism is based on the excitation of a construction with a sinusoidal wave, with an actuator transforming this wave into useful, electrical power through a harvester device. Data transfer is achieved with the use of the F2F (Frequency Double Frequency) procedure, which is a kind of frequency modulation. To receive optimized parameters for each construction, an original software is developed, which allows the selection of the proper type of actuator, modulation, and frequency.
Smartphone-Based Hearing Aid Compression and Noise Reduction
Authors: Ni Aoxin , Kehtarnavaz Nasser
Published: April 2022

This paper presents an assistive hearing smartphone app mimicking the two main functions of hearing aids, consisting of compression and noise reduction. The app is designed to run in real time on smartphones or tablets. Appropriate levels of amplification or gain are activated by selecting a filter from a filter bank for six audio environment situations covering three sound pressure levels of speech and two sound pressure levels of noise. The results of this smartphone app for real-world audio environments are provided, indicating its effectiveness as a real-time platform for studying compression and noise reduction algorithms in the field or in realistic audio environments.
User Participatory Design of a Wearable Focal Vibration Device for Home-Based Stroke Rehabilitation
Authors: Wang Hongwu , Ghazi Mustafa , Chandrashekhar Raghuveer , Rippetoe Josiah , Duginski Grace A. , Lepak Louis V. , Milhan Lisa R. , James Shirley A.
Published: April 2022

Stroke often leads to the significant impairment of upper limb function and is associated with a decreased quality of life. Despite study results from several interventions for muscle activation and motor coordination, wide-scale adoption remains largely elusive due to under-doses and low user compliance and participation. Recent studies have shown that focal vibration has a greater potential to increase and coordinate muscle recruitment and build muscle strength and endurance. This form of treatment could widely benefit stroke survivors and therapists. Thus, this study aimed to design and develop a novel wearable focal vibration device for upper limb rehabilitation in stroke survivors. A user participatory design approach was used for the design and development. Five stroke survivors, three physical therapists, and two occupational therapists were recruited and participated. This pilot study may help to develop a novel sustainable wearable system providing vibration-based muscle activation for upper limb function rehabilitation. It may allow users to apply the prescribed vibratory stimuli in-home and/or in community settings. It may also allow therapists to monitor treatment usage and user performance and adjust the treatment doses based on progression.
Research Trends in Collaborative Drones
Authors: Barbeau Michel , Garcia-Alfaro Joaquin , Kranakis Evangelos
Published: April 2022

The last decade has seen an explosion of interest in drones—introducing new networking technologies, such as 5G wireless connectivity and cloud computing. The resulting advancements in communication capabilities are already expanding the ubiquitous role of drones as primary solution enablers, from search and rescue missions to information gathering and parcel delivery. Their numerous applications encompass all aspects of everyday life. Our focus is on networked and collaborative drones. The available research literature on this topic is vast. No single survey article could do justice to all critical issues. Our goal in this article is not to cover everything and include everybody but rather to offer a personal perspective on a few selected research topics that might lead to fruitful future investigations that could play an essential role in developing drone technologies. The topics we address include distributed computing with drones for the management of anonymity, countering threats posed by drones, target recognition, navigation under uncertainty, risk avoidance, and cellular technologies. Our approach is selective. Every topic includes an explanation of the problem, a discussion of a potential research methodology, and ideas for future research.
A Bit-Tracking Knowledge-Based Query Tree for RFID Tag Identification in IoT Systems
Authors: Lai Yuan-Cheng , Chen Shan-Yung , Hailemariam Zelalem Legese , Lin Chih-Chung
Published: April 2022

In an IoT (Internet of Things) system where each IoT device has one/many RFID tags, there might be many RFID tags. However, when multiple tags respond to the reader’s interrogation at the same time, their signals collide. Due to the collision, the reader must request the colliding tags to retransmit their IDs, resulting in higher communication overhead and longer identification time. Therefore, this paper presents a Bit-tracking Knowledge-based Query Tree (BKQT), which uses two techniques: knowledge, which stores all the tag IDs that can possibly occur, and bit tracking, which allows the reader to detect the locations of the collided bits in a collision slot. BKQT constructs a query tree for all possible tags, called a k-tree, by using knowledge while it constructs bit-collision cases and the corresponding actions for each node in this k-tree by using bit tracking. In the identification process, BKQT traverses this constructed k-tree and thus identifies the colliding tags faster by taking the actions according to the happening bit-collision cases. From the simulation results, BKQT can improve the identification time by 44.3%, 46.4%, and 25.1%, compared with the previous knowledge-based protocols, Knowledge Query Tree (KQT), Heuristic Query Tree (H-QT), Query Tree with Shortcutting and Couple Resolution (QTSC), respectively.
Advanced Characterization of FeNi-Based Films for the Development of Magnetic Field Sensors with Tailored Functional Parameters
Authors: Komogortsev Sergey V. , Vazhenina Irina G. , Kleshnina Sofya A. , Iskhakov Rauf S. , Lepalovskij Vladimir N. , Pasynkova Anna A. , Svalov Andrey V.
Published: April 2022

Magnetometry and ferromagnetic resonance are used to quantitatively study magnetic anisotropy with an easy axis both in the film plane and perpendicular to it. In the study of single-layer and multilayer permalloy films, it is demonstrated that these methods make it possible not only to investigate the average field of perpendicular and in-plane anisotropy, but also to characterize their inhomogeneity. It is shown that the quantitative data from direct integral and local measurements of magnetic anisotropy are consistent with the direct and indirect estimates based on processing of the magnetization curves. The possibility of estimating the perpendicular magnetic anisotropy constant from the width of stripe domains in a film in the transcritical state is demonstrated. The average in-plane magnetic anisotropy field of permalloy films prepared by magnetron sputtering onto a Corning glass is almost unchanged with the thickness of a single-layer film. The inhomogeneity of the perpendicular anisotropy field for a 500 nm film is greater than that for a 100 nm film, and for a multilayer film with a total permalloy thickness of 500 nm, it is greater than that for a homogeneous film of the same thickness.
Analysis of Shock Wave Interaction with an Obstacle by Coupling Pressure Measurements and Visualization
Authors: Gautier Antoine , Sochet Isabelle , Courtiaud Sébastien
Published: April 2022

Small-scale experiments are a good means of carrying out explosion and shock wave measurements. Commonly, the shock wave is tracked thanks to pressure sensors and sometimes with a high-speed camera. In the present study, these methods were used to analyze the interaction of a shock wave with an obstacle of simple geometry. The primary aim of the study was to demonstrate the need to correlate these different methods in order to analyze certain phenomena related to the three-dimensional interaction of a shock wave with an object. The correlation between the overpressure and the visualization made it possible to carry out a complex analysis. The visualization was carried out simultaneously on two planes, from the front and top views, thanks to the optical setup. Shock wave characteristics were taken at ground level downstream of the obstacle with pressure gauges. The correlation of the images obtained allows the identification of the waves on the profile and their contribution in intensity.
Cloud of Things in Crowd Engineering: A Tile-Map-Based Method for Intelligent Monitoring of Outdoor Crowd Density
Authors: Alamri Abdullah
Published: April 2022

Managing citizen and community safety is one of the most essential services that future cities will require. Crowd analysis and monitoring are also a high priority in the current COVID-19 pandemic scenario, especially because large-scale gatherings can significantly increase the risk of infection transmission. However, crowd tracking presents several complex technical challenges, including accurate people counting and privacy preservation. In this study, using a tile-map-based method, a new intelligent method is proposed which is integrated with the cloud of things and data analytics to provide intelligent monitoring of outdoor crowd density. The proposed system can detect and intelligently analyze the pattern of crowd activity to implement contingency plans, reducing accidents, ensuring public safety, and establishing a smart city. The experimental results demonstrate the feasibility of the proposed model in detecting crowd density status in real-time. It can effectively assist with crowd management tasks such as monitoring, guiding, and managing crowds to ensure safety. In addition, the proposed algorithm provides acceptable performance.
Current Sheet Antenna Array and 5G: Challenges, Recent Trends, Developments, and Future Directions
Authors: Hussain Sajjad , Qu Shi-Wei , Sharif Abu Bakar , Abubakar Hassan Sani , Wang Xiao-Hua , Imran Muhammad Ali , Abbasi Qammer H.
Published: April 2022

Designing an ultra-wideband array antenna for fifth generation (5G) is challenging for the antenna designing community because of the highly fragmented electromagnetic spectrum. To overcome bandwidth limitations, several millimeter-wave bands for 5G and beyond applications are considered; as a result, many antenna arrays have been proposed during the past decades. This paper aims to explore recent developments and techniques regarding a specific type of phased array antenna used in 5G applications, called current sheet array (CSA). CSA consists of capacitively coupled elements placed over a ground plane, with mutual coupling intentionally introduced in a controlled manner between the elements. CSA concept evolved and led to the realization of new array antennas with multiple octaves of bandwidth. In this review article, we provide a comprehensive overview of the existing works in this line of research. We analyze and discuss various aspects of the proposed array antennas with the wideband and wide-scan operation. Additionally, we discuss the significance of the phased array antenna in 5G communication. Moreover, we describe the current research challenges and future directions for CSA-based phased array antennas.
A Comparison and Evaluation of Stereo Matching on Active Stereo Images
Authors: Jang Mingyu , Yoon Hyunse , Lee Seongmin , Kang Jiwoo , Lee Sanghoon
Published: April 2022

The relationship between the disparity and depth information of corresponding pixels is inversely proportional. Thus, in order to accurately estimate depth from stereo vision, it is important to obtain accurate disparity maps, which encode the difference between horizontal coordinates of corresponding image points. Stereo vision can be classified as either passive or active. Active stereo vision generates pattern texture, which passive stereo vision does not have, on the image to fill the textureless regions. In passive stereo vision, many surveys have discovered that disparity accuracy is heavily reliant on attributes, such as radiometric variation and color variation, and have found the best-performing conditions. However, in active stereo matching, the accuracy of the disparity map is influenced not only by those affecting the passive stereo technique, but also by the attributes of the generated pattern textures. Therefore, in this paper, we analyze and evaluate the relationship between the performance of the active stereo technique and the attributes of pattern texture. When evaluating, experiments are conducted under various settings, such as changing the pattern intensity, pattern contrast, number of pattern dots, and global gain, that may affect the overall performance of the active stereo matching technique. Through this evaluation, our discovery can act as a noteworthy reference for constructing an active stereo system.
The Hard Skills Bases in Digital Academic Entrepreneurship in Relation to Digital Transformation
Authors: Garcez Ana , Silva Ricardo , Franco Mário
Published: April 2022

Digital transformation (DT) has changed the means and mechanisms for acquiring knowledge, meaning that higher education institutions (HEI) have changed the form of student-lecturer and teaching-learning interaction. Digital transformation must have a relevant role in building the hard skills (HS) necessary for academic entrepreneurship, where HS are easily taught and measured. This study analyses the basis of HS for digital academic entrepreneurship, answering the following research hypothesis: What are the hard skills—entrepreneurial education—for digital academic entrepreneurship and how are they formed? An extensive, inclusive literature review revealed that, due to the possibility of editing, reprogramming and generating digital technology, the basis of DT can support the development of HS in higher education students. The results show that HS can be developed considering three major pillars: (i) Management Tools, (ii) Digital Process, and (iii) Digital Products. Since HS are easily taught, this study shows how the use of digital technology can transform this scalable teaching process in order to reach the maximum number of students. It is therefore suggested that those in charge of HEIs use the pillars presented here in the framework proposed to guide their institutions’ strategic planning. With these pillars in mind, the aim is to stimulate the development of innovative business proposals developed in the academic environment and promote digital academic entrepreneurship.
Exploring Lean HRM Practices in the Aerospace Industry
Authors: Benkarim Amal , Imbeau Daniel
Published: April 2022

Lean places people at its core, acknowledging their contribution to the company’s growth and the fundamental role human resources management (HRM) practices play in the success and sustainability of Lean transformations. However, the relationship between HRM practices and Lean remains largely unexplored in the literature. The purpose of this work is therefore to investigate the challenges and contributions of HRM practices in a Lean company, and identify those practices that are required for successful and sustainable Lean implementations. Based on a sample of thirty employees (15 production and 15 office workers) of a Canadian aerospace company who participated in our interviews, we performed a qualitative analysis to identify prominent HRM practices. We found seven HRM practices that are of major importance in the context of Lean (i.e., job security, communication, fairness, supervisor/manager support, training, occupational health and safety, and respect). Our findings show that these practices are equally relevant to both production and office workers, and suggest that managers play a decisive role in implementing these practices, and in providing the right environment to effectively promote workforce commitment.
Retreat of Major European Tree Species Distribution under Climate Change—Minor Natives to the Rescue?
Authors: Koch Olef , de Avila Angela Luciana , Heinen Henry , Albrecht Axel Tim
Published: April 2022

Climate change is projected to trigger strong declines in the potential distribution of major tree species in Europe. While minor natives have moved into the spotlight as alternatives, their ecology is often poorly understood. We use an ensemble species distribution modelling approach on a set of promising native tree species to gain insights into their distribution potential under different climate change scenarios. Moreover, we identify the urgency and potential of altered species distributions in favor of minor natives by comparing the niche dynamics of five major native tree species with the set of six minor natives in a case study. Our models project stark range contractions and range shifts among major tree species, strongly amplified under high emission scenarios. Abies alba, Picea abies and Fagus sylvatica are affected the strongest. While also experiencing range shifts, the minor European natives Castanea sativa, Sorbus torminalis, and Ulmus laevis all considerably expand their range potential across climate change scenarios. Accompanied by Carpinus betulus, with a stable range size, they hold the potential to substantially contribute to sustainably adapting European forest to climate change.
Freight Transport Cost and Urban Sprawl across EU Regions
Authors: Tsekeris Theodore
Published: April 2022

This paper investigates the impact that urban sprawl and land use patterns have on freight transport costs at the regional level in Europe. A unique dataset is employed, which distinguishes various aspects of freight transport costs across EU regions. The measurement of sprawl metrics is based on the European soil sealing (artificial land cover) data concerning the land uptake for buildings and related infrastructure, as well as land use data originating from the Land Use/Cover Area frame Survey of Eurostat. The econometric analysis indicates that both the increased scale and compact development of land for urban settlement and specific (industrial, services/residential) activities can significantly reduce average road freight transport costs. The increased land use mixture and the share of industrial activity also have a negative impact on road freight transport costs. The results highlight the importance of integrated spatial/land use planning policies to manage freight transport costs and improve the sustainable urban development of EU regions.
Impact of Epidemics on Enterprise Innovation: An Analysis of COVID-19 and SARS
Authors: Li Xin , He Xingyuan , Zhou Lu , Xie Shushu
Published: April 2022

This study analyzes the impact of SARS and COVID-19, the two most severe epidemics to occur in China since the 21st century, on corporate innovation, in order to find a path for sustained innovation growth under the epidemic. For COVID-19, the analysis used data from China’s A-share-listed companies from 2019 to 2020; a longer period (1999–2006) and a wider sample of Chinese industrial enterprises were used for the SARS epidemic. The empirical model was constructed using the difference-in-differences method. Both COVID-19 and SARS were found to have significantly reduced enterprise innovation. However, the effect of SARS disappeared after two years. For COVID-19, information asymmetry, financing constraints, and economic policy uncertainty moderated the epidemic’s effect on innovation. The results show that financing constraints and economic policy uncertainty reduce the epidemic’s negative impact. However, while most previous studies have found that an epidemic reduces the information asymmetry between investors and enterprises in the short term, thus raising enterprise innovation, we found that information asymmetry aggravated the epidemic’s negative impact. These findings can be applied to alleviate the current epidemic’s negative impact as well as improve enterprise innovation thereafter.
Elucidating the Potential of Vertical Flow-Constructed Wetlands Vegetated with Different Wetland Plant Species for the Remediation of Chromium-Contaminated Water
Authors: Younas Fazila , Bibi Irshad , Afzal Muhammad , Niazi Nabeel Khan , Aslam Zubair
Published: April 2022

Water scarcity is one of the key global challenges affecting food safety, food security, and human health. Constructed wetlands (CWs) provide a sustainable tool to remediate wastewater. Here we explored the potential of vertical flow-CWs (VF-CWs) vegetated with ten indigenous wetland plant species to treat chromium (Cr)-contaminated water. The wetland plants were vegetated to develop VF-CWs to treat Cr-contaminated water in a batch mode. Results revealed that the Cr removal potential of VF-CWs vegetated with different wetland plants ranged from 47% to 92% at low (15 mg L−1) Cr levels and 36% to 92% at high (30 mg L−1) Cr levels, with the maximum (92%) Cr removal exhibited by VF-CWs vegetated with Leptochloa fusca. Hexavalent Cr (Cr(VI)) was reduced to trivalent Cr (Cr(III)) in treated water (96–99 %) of all VF-CWs. All the wetland plants accumulated Cr in the shoot (1.9–34 mg kg−1 dry weight (DW)), although Cr content was higher in the roots (74–698 mg kg−1 DW) than in the shoots. Brachiaria mutica showed the highest Cr accumulation in the roots and shoots (698 and 45 mg kg−1 DW, respectively), followed by Leptochloa fusca. The high Cr level significantly (p < 0.05) decreased the stress tolerance index (STI) percentage of the plant species. Our data provide strong evidence to support the application of VF-CWs vegetated with different indigenous wetland plants as a sustainable Cr-contaminated water treatment technology such as tannery wastewater.
How to Reduce Individuals’ Ecological Footprint without Harming Their Well-Being: An Application to Belgium
Authors: Defloor Bart , Bleys Brent , Verhofstadt Elsy , Van Ootegem Luc
Published: April 2022

Human activities are a key driver of many environmental problems the world is facing today, including climate change, the disruption of biogeochemical cycles, and biodiversity loss. Behavioural changes at the individual and household level are needed to reduce humanity’s environmental impact, but people also need the capacity to behave in a sustainable way. If their well-being is negatively impacted or if behaving sustainably is too time consuming or too expensive, people might be less inclined to change their behaviour. In this article, we look at the determinants of different types of pro-environmental behaviour and how these are associated with their experienced levels of well-being. More specifically, we focus on the determinants of behaviours that influence both the ecological footprint (EF) and satisfaction with life. In our analysis we include socio-demographic characteristics and a number of psychological antecedents of pro-environmental behaviour (PEB). The data we use was collected in Flanders (Belgium) and allows us to calculate the EF of each respondent individually. Our main conclusions are threefold. First, even if individuals are provided with opportunities to behave in a more sustainable way, they do not always do so (e.g., richer people on average have a higher EF). Efforts could be put in place at the collective side (e.g., public infrastructure) to stimulate people to reduce their environmental impact. Second, as we distinguish seven EF components, we are able to show differential effects of each of the determinants. Third, the association between PEB and satisfaction with life is not strong: only the type of housing is significantly associated with satisfaction with life. Related to that, the psychological antecedents of PEB are only associated with the EF, not with satisfaction with life.
Shear Strength Estimation of Reinforced Concrete Deep Beams Using a Novel Hybrid Metaheuristic Optimized SVR Models
Authors: Kaloop Mosbeh R. , Roy Bishwajit , Chaurasia Kuldeep , Kim Sean-Mi , Jang Hee-Myung , Hu Jong-Wan , Abdelwahed Basem S.
Published: April 2022

This study looks to propose a hybrid soft computing approach that can be used to accurately estimate the shear strength of reinforced concrete (RC) deep beams. Support vector regression (SVR) is integrated with three novel metaheuristic optimization algorithms: African Vultures optimization algorithm (AVOA), particle swarm optimization (PSO), and Harris Hawks optimization (HHO). The proposed models, SVR-AVOA, -PSO, and -HHO, are designed and compared to reference existing models. Multi variables are used and evaluated to model and evaluate the deep beam’s shear strength, and the sensitivity of the selected variables in modeling the shear strength is assessed. The results indicate that the SVR-AVOA outperforms other proposed and existing models for the shear strength prediction. The mean absolute error of SVR-AVOA, SVR-PSO, and SVR-HHO are 43.17 kN, 44.09 kN, and 106.95 kN, respectively. The SVR-AVOA can be used as a soft computing technique to estimate the shear strength of the RC deep beam with a maximum error of ±3.39%. Furthermore, the sensitivity analysis shows that the deep beam’s key parameters (shear span to depth ratio, web reinforcement’s yield strength, concrete compressive strength, stirrups spacing, and the main longitudinal bars reinforcement ratio) are efficiently impacted in the shear strength detection of RC deep beam.
Mobile Applications and Healthy Habits of Adolescents, a Strange Couple: Pilot Study of the Efficacy of the Healthy Jeart App
Authors: Merino-Godoy María Ángeles , Yot-Domínguez Carmen , Villaciervos-Moreno Patricia , Moreno-Sánchez Emilia
Published: April 2022

The present study aims to analyse the healthy habits of a group of adolescents before and after an educational intervention using the Healthy Jeart mobile app. This is the first pilot study on Healthy Jeart, based on a single intervention with experimental and control groups. The participants were secondary education students in the third and fourth years, from an educational centre in Huelva (Spain). They were distributed between a control group and an experimental group. Information about their habits was obtained through a questionnaire prepared from the HSBC (Health Behaviour in School-aged Children) instrument, which was administered before and after the intervention. Although the results do not indicate a substantial change in the habits of the participants, there were some intragroup differences. In the experimental group, a difference was obtained in the feeling of being fit and healthy. The global perspective on health of the app would explain this change.