bluuebunny/crossref_metadata_2025 huggingface dataset:
NDFTC: A New Detection Framework of Tropical Cyclones from Meteorological Satellite Images with Deep Transfer Learning
Authors: Pang Shanchen , Xie Pengfei , Xu Danya , Meng Fan , Tao Xixi , Li Bowen , Li Ying , Song Tao
Published: May 2021

Accurate detection of tropical cyclones (TCs) is important to prevent and mitigate natural disasters associated with TCs. Deep transfer learning methods have advantages in detection tasks, because they can further improve the stability and accuracy of the detection model. Therefore, on the basis of deep transfer learning, we propose a new detection framework of tropical cyclones (NDFTC) from meteorological satellite images by combining the deep convolutional generative adversarial networks (DCGAN) and You Only Look Once (YOLO) v3 model. The algorithm process of NDFTC consists of three major steps: data augmentation, a pre-training phase, and transfer learning. First, to improve the utilization of finite data, DCGAN is used as the data augmentation method to generate images simulated to TCs. Second, to extract the salient characteristics of TCs, the generated images obtained from DCGAN are inputted into the detection model YOLOv3 in the pre-training phase. Furthermore, based on the network-based deep transfer learning method, we train the detection model with real images of TCs and its initial weights are transferred from the YOLOv3 trained with generated images. Training with real images helps to extract universal characteristics of TCs and using transferred weights as initial weights can improve the stability and accuracy of the model. The experimental results show that the NDFTC has a better performance, with an accuracy (ACC) of 97.78% and average precision (AP) of 81.39%, in comparison to the YOLOv3, with an ACC of 93.96% and AP of 80.64%.
Analysis of Activity in an Open-Pit Mine by Using InSAR Coherence-Based Normalized Difference Activity Index
Authors: Moon Jihyun , Lee Hoonyol
Published: May 2021

In this study, time-series of Sentinel-1A/B Interferometric Synthetic Aperture Radar (InSAR) coherence images were used to monitor the mining activity of Musan open-pit mine, the largest iron mine in North Korea. First, the subtraction of SRTM DEM (2000) from TanDEM-X DEM (2010–2015) has identified two major accumulation areas, one in the east (+112.33 m) and the other in the west (+84.03 m), and a major excavation area (−42.54 m) at the center of the mine. A total of 89 high-quality coherence images with a 12-day baseline from 2015 to 2020 were converted to the normalized difference activity index (NDAI), a newly developed activity indicator robust to spatial and temporal decorrelation. An RGB composite of annually averaged NDAI maps (red for 2019, green for 2018, and blue for 2017) showed that overall activity has diminished since 2018. Dumping slopes were categorized into shrinking, expanding, or transitional, according to the color pattern. Migration and expansion of excavation sites were also found on the pit floor. Time series of 12-day NDAI graphs revealed the date of activities with monthly accuracy. It is believed that NDAI with continuous acquisition of Sentinel-1A/B data can provide detailed monitoring of various types of activities in open-pit mines especially with limited in situ data.
Comparison of Different Technologies for Soft Robotics Grippers
Authors: Terrile Silvia , Argüelles Miguel , Barrientos Antonio
Published: May 2021

Soft grippers have experienced a growing interest due to their considerable flexibility that allows them to grasp a variety of objects, in contrast to hard grippers, which are designed for a specific item. One of their most remarkable characteristics is the ability to manipulate soft objects without damaging them. This, together with their wide range of applications and the use of novels materials and technologies, renders them a very robust device. In this paper, we present a comparison of different technologies for soft robotics grippers. We fabricated and tested four grippers. Two use pneumatic actuation (the gripper with chambered fingers and the jamming gripper), while the other two employ electromechanical actuation (the tendon driver gripper and the gripper with passive structure). For the experiments, a group of twelve objects with different mechanical and geometrical properties have been selected. Furthermore, we analyzed the effect of the environmental conditions on the grippers, by testing each object in three different environments: normal, humid, and dusty. The aim of this comparative study is to show the different performances of different grippers tested under the same conditions. Our findings indicate that we can highlight that the mechanical gripper with a passive structure shows greater robustness.
Autonomous Fingerprinting and Large Experimental Data Set for Visible Light Positioning
Authors: Glass Tyrel , Alam Fakhrul , Legg Mathew , Noble Frazer
Published: May 2021

This paper presents an autonomous method of collecting data for Visible Light Positioning (VLP) and a comprehensive investigation of VLP using a large set of experimental data. Received Signal Strength (RSS) data are efficiently collected using a novel method that utilizes consumer grade Virtual Reality (VR) tracking for accurate ground truth recording. An investigation into the accuracy of the ground truth system showed median and 90th percentile errors of 4.24 and 7.35 mm, respectively. Co-locating a VR tracker with a photodiode-equipped VLP receiver on a mobile robotic platform allows fingerprinting on a scale and accuracy that has not been possible with traditional manual collection methods. RSS data at 7344 locations within a 6.3 × 6.9 m test space fitted with 11 VLP luminaires is collected and has been made available for researchers. The quality and the volume of the data allow for a robust study of Machine Learning (ML)- and channel model-based positioning utilizing visible light. Among the ML-based techniques, ridge regression is found to be the most accurate, outperforming Weighted k Nearest Neighbor, Multilayer Perceptron, and random forest, among others. Model-based positioning is more accurate than ML techniques when a small data set is available for calibration and training. However, if a large data set is available for training, ML-based positioning outperforms its model-based counterparts in terms of localization accuracy.
Digital Biomarker Representing Frailty Phenotypes: The Use of Machine Learning and Sensor-Based Sit-to-Stand Test
Authors: Park Catherine , Mishra Ramkinker , Sharafkhaneh Amir , Bryant Mon S. , Nguyen Christina , Torres Ilse , Naik Aanand D. , Najafi Bijan
Published: May 2021

Since conventional screening tools for assessing frailty phenotypes are resource intensive and unsuitable for routine application, efforts are underway to simplify and shorten the frailty screening protocol by using sensor-based technologies. This study explores whether machine learning combined with frailty modeling could determine the least sensor-derived features required to identify physical frailty and three key frailty phenotypes (slowness, weakness, and exhaustion). Older participants (n = 102, age = 76.54 ± 7.72 years) were fitted with five wearable sensors and completed a five times sit-to-stand test. Seventeen sensor-derived features were extracted and used for optimal feature selection based on a machine learning technique combined with frailty modeling. Mean of hip angular velocity range (indicator of slowness), mean of vertical power range (indicator of weakness), and coefficient of variation of vertical power range (indicator of exhaustion) were selected as the optimal features. A frailty model with the three optimal features had an area under the curve of 85.20%, a sensitivity of 82.70%, and a specificity of 71.09%. This study suggests that the three sensor-derived features could be used as digital biomarkers of physical frailty and phenotypes of slowness, weakness, and exhaustion. Our findings could facilitate future design of low-cost sensor-based technologies for remote physical frailty assessments via telemedicine.
Energy Conservation for Internet of Things Tracking Applications Using Deep Reinforcement Learning
Authors: Sultan Salman Md , Waleed Muhammad , Pyun Jae-Young , Um Tai-Won
Published: May 2021

The Internet of Things (IoT)-based target tracking system is required for applications such as smart farm, smart factory, and smart city where many sensor devices are jointly connected to collect the moving target positions. Each sensor device continuously runs on battery-operated power, consuming energy while perceiving target information in a particular environment. To reduce sensor device energy consumption in real-time IoT tracking applications, many traditional methods such as clustering, information-driven, and other approaches have previously been utilized to select the best sensor. However, applying machine learning methods, particularly deep reinforcement learning (Deep RL), to address the problem of sensor selection in tracking applications is quite demanding because of the limited sensor node battery lifetime. In this study, we proposed a long short-term memory deep Q-network (DQN)-based Deep RL target tracking model to overcome the problem of energy consumption in IoT target applications. The proposed method is utilized to select the energy-efficient best sensor while tracking the target. The best sensor is defined by the minimum distance function (i.e., derived as the state), which leads to lower energy consumption. The simulation results show favorable features in terms of the best sensor selection and energy consumption.
Determination of Fatty Acid Content of Rice during Storage Based on Feature Fusion of Olfactory Visualization Sensor Data and Near-Infrared Spectra
Authors: Lu Hongping , Jiang Hui , Chen Quansheng
Published: May 2021

This study innovatively proposes a feature fusion technique to determine fatty acid content during rice storage. Firstly, a self-developed olfactory visualization sensor was used to capture the odor information of rice samples at different storage periods and a portable spectroscopy system was employed to collect the near-infrared (NIR) spectra during rice storage. Then, principal component analysis (PCA) was performed on the pre-processed olfactory visualization sensor data and the NIR spectra, and the number of the best principal components (PCs) based on the single technique model was optimized during the backpropagation neural network (BPNN) modeling. Finally, the optimal PCs were fused at the feature level, and a BPNN detection model based on the fusion feature was established to achieve rapid measurement of fatty acid content during rice storage. The experimental results showed that the best BPNN model based on the fusion feature had a good predictive performance where the correlation coefficient (RP) was 0.9265, and the root mean square error (RMSEP) was 1.1005 mg/100 g. The overall results demonstrate that the detection accuracy and generalization performance of the feature fusion model are an improvement on the single-technique data model; and the results of this study can provide a new technical method for high-precision monitoring of grain storage quality.
A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations
Authors: Ahmed Naeem , Hua Boyu , Zhu Qiuming , Mao Kai , Bao Junwei
Published: May 2021

A new non-stationary (NS) geometry-based stochastic model (GBSM) is presented for developing and testing the communication systems of vehicle-to-vehicle (V2V) applications, which considers the three-dimensional (3D) scattering environments and allows 3D velocity as well. In this paper, the proposed GBSM for NS V2V channels allowed 3D velocity variations and was more suitable for actual V2V communications because it provided smoother transitions between the consecutive channel segments. The time-variant channel coefficient and the channel parameters, i.e., Doppler frequencies, path delay and power, angle of arrival (AoA), and angle of departure (AoD), were analyzed and derived. Likewise, the theoretical statistical properties as the probability density function (PDF), the auto-correlation function (ACF), and Doppler power spectral density (DPSD) were also analyzed and derived under the von Mises–Fisher (VMF) distribution. Finally, the theoretical and measured results were well coordinated alongside the implemented results, which confirmed the feasibility of the introduced model along with the theoretical expressions.
Utilization of Inertial Measurement Units for Determining the Sequential Chain of Baseball Strike Posture
Authors: Lee Yun-Ju , Lin Po-Chieh , Chen Ling-Ying , Chen Yu-Jung , Liang Jing Nong
Published: May 2021

The purpose of this study was to employ inertial measurement units (IMU) with an eye-tracking device to investigate different swing strategies between two levels of batters. The participants were 20 healthy males aged 20 to 30 years old, with ten professional and ten amateur batters. Eye gaze position, head, shoulder, trunk, and pelvis angular velocity, and ground reaction forces were recorded. The results showed that professional batters rotated segments more rhythmically and efficiently than the amateur group. Firstly, the professional group spent less time in the preparation stages. Secondly, the maximum angular velocity timing of each segment of the professional group was centralized in the swing cycle. Thirdly, the amateur group had significantly earlier gaze timing of the maximum angular velocity than the professional group. Moreover, the maximum angular velocity timing of the gaze was the earliest parameter among the five segments, and significantly earlier (at least 16.32% of cycle time) than the maximum angular velocity of the head, shoulder, trunk, and pelvis within the amateur group. The visual-motor coordination strategies were different between the two groups, which could successfully be determined by wearable instruments of IMU.
Deep Supervised Residual Dense Network for Underwater Image Enhancement
Authors: Han Yanling , Huang Lihua , Hong Zhonghua , Cao Shouqi , Zhang Yun , Wang Jing
Published: May 2021

Underwater images are important carriers and forms of underwater information, playing a vital role in exploring and utilizing marine resources. However, underwater images have characteristics of low contrast and blurred details because of the absorption and scattering of light. In recent years, deep learning has been widely used in underwater image enhancement and restoration because of its powerful feature learning capabilities, but there are still shortcomings in detailed enhancement. To address the problem, this paper proposes a deep supervised residual dense network (DS_RD_Net), which is used to better learn the mapping relationship between clear in-air images and synthetic underwater degraded images. DS_RD_Net first uses residual dense blocks to extract features to enhance feature utilization; then, it adds residual path blocks between the encoder and decoder to reduce the semantic differences between the low-level features and high-level features; finally, it employs a deep supervision mechanism to guide network training to improve gradient propagation. Experiments results (PSNR was 36.2, SSIM was 96.5%, and UCIQE was 0.53) demonstrated that the proposed method can fully retain the local details of the image while performing color restoration and defogging compared with other image enhancement methods, achieving good qualitative and quantitative effects.
Structure–Function Relationships of Nanocarbon/Polymer Composites for Chemiresistive Sensing: A Review
Authors: Ehsani Maryam , Rahimi Parvaneh , Joseph Yvonne
Published: May 2021

Composites of organic compounds and inorganic nanomaterials provide novel sensing platforms for high-performance sensor applications. The combination of the attractive functionalities of nanomaterials with polymers as an organic matrix offers promising materials with tunable electrical, mechanical, and chemisensitive properties. This review mainly focuses on nanocarbon/polymer composites as chemiresistors. We first describe the structure and properties of carbon nanofillers as reinforcement agents used in the manufacture of polymer composites and the sensing mechanism of developed nanocomposites as chemiresistors. Then, the design and synthesizing methods of polymer composites based on carbon nanofillers are discussed. The electrical conductivity, mechanical properties, and the applications of different nanocarbon/polymer composites for the detection of different analytes are reviewed. Lastly, challenges and the future vision for applications of such nanocomposites are described.
Enabling Reliable UAV Control by Utilizing Multiple Protocols and Paths for Transmitting Duplicated Control Packets
Authors: Lee Woonghee
Published: May 2021

In the last ten years, supported by the advances in technologies for unmanned aerial vehicles (UAVs), UAVs have developed rapidly and are utilized for a wide range of applications. To operate UAVs safely, by exchanging control packets continuously, operators should be able to monitor UAVs in real-time and deal with any problems immediately. However, due to any networking problems or unstable wireless communications, control packets can be lost or transmissions can be delayed, which causes the unstable drone control. To overcome this limitation, in this paper, we propose MuTran for enabling reliable UAV control. MuTran considers the packet type and duplicates only control packets, not data packets. After that, MuTran transmits the original and duplicate packets through multiple protocols and paths to improve the reliability of control packet transmissions. We designed MuTran and conducted a lot of theoretical analyses to demonstrate the validity of MuTran and analyze it from various aspects. We implemented MuTran on real devices and evaluated MuTran using the devices. We conducted experiments to verify the limitations of the existing systems and demonstrate that control packets can be transmitted more stably by using MuTran. Through the analysis and experimental results, we confirmed that MuTran reduces the control packet transfer delay, which improves the reliability and stability of controlling UAVs.
CIR-Based Device-Free People Counting via UWB Signals
Authors: De Sanctis Mauro , Conte Aleandro , Rossi Tommaso , Di Domenico Simone , Cianca Ernestina
Published: May 2021

The outbreak of COVID-19 has resulted in many different policies being adopted across the world to reduce the spread of the virus. These policies include wearing surgical masks, hand hygiene practices, increased social distancing and full country-wide lockdown. Specifically, social distancing involves keeping a certain distance from others and avoiding gathering together in large groups. Automatic crowd density estimation is a technological solution that could help in guaranteeing social distancing by reducing the probability that two persons in a public area come in close proximity to each other while moving around. This paper proposes a novel low complexity RF sensing system for automatic people counting based on low cost UWB transceivers. The proposed system is based on an ordinary classifier that exploits features extracted from the channel impulse response of UWB communication signals. Specifically, features are extracted from the sorted list of singular values obtained from the singular value decomposition applied to the matrix of the channel impulse response vector differences. Experimental results achieved in two different environments show that the proposed system is a promising candidate for future automatic crowd density monitoring systems.
Modeling the Influence of Online Social Media Information on Post-Disaster Mobility Decisions
Authors: Yabe Takahiro , Rao P. Suresh C. , Ukkusuri Satish V.
Published: May 2021

Disaster risk management, including response and recovery, are essential elements of sustainable development. With the recent increase in natural hazards, the importance of techniques to understand, model and predict the evacuation and returning behavior of affected individuals is rising. Studies have found that influence from real world social ties affects mobility decisions during disasters. Despite the rapid spread of social media platforms, little has been quantitatively understood about the influence of social ties on online social media on such decisions. Information provided by who at what timing influences users’ decision-making process by how much during disasters? In this study, we answer these research questions by proposing a data-driven framework that can predict post-disaster mobility decisions and simultaneously unravel the influence of various information on online social media. More specifically, our method quantifies the influence of information provided by different types of social media accounts on the peoples’ decisions to return or stay displaced after evacuation. We tested our approach using real world data collected from more than 13 million unique Twitter users during Hurricane Sandy. Experiments verified that we can improve the predictive accuracy of return and displacement behavior, and also quantify the influence of online information. In contrast to popular beliefs, it was found that information posted by the crowd influenced the decisions more than information disseminated by official accounts. Improving our understanding of influence dynamics on online social media could provide policy makers with insights on how to disseminate information on social media more effectively for better disaster response and recovery, which may contribute towards building sustainable urban systems.
The Influence of the Configuration of Two Electrochemical Reactors on the Process of Removing Atrazine from Water
Authors: Nápoles-Armenta Juan , Vidales-Contreras Juan Antonio , Leyva-Soto Luis Alonso , Meza-Escalante Edna Rosalba , Díaz-Tenorio Lourdes Mariana , García-Gómez Celestino , Martínez-Orozco Edgardo , De La Mora-Orozco Celia , Gortáres-Moroyoqui Pablo , Salcedo-Gastelum Lilian Alejandra
Published: May 2021

In Mexico, atrazine is widely used in agriculture to control broadleaf weeds. The objective of this research was to compare atrazine removal in water and energy consumption between an up-flow cylinder electro-oxidation reactor (UCER) and an up-flow rectangular electro-oxidation reactor (URER) using the response surface methodology. In each reactor, two titanium (Ti) mesh electrodes (cathodes) and one Titanium-Lead Dioxide (Ti-PbO2) mesh electrode (anode). Current intensity effects, electrolysis treatment time, and recirculation flow were evaluated. Synthetic water with 5 mg/L atrazine content was used. Optimum atrazine removal values were obtained at 2 A electric current, 180 min of treatment time, and 200 mL/min recirculation rate for both reactors: in these conditions an atrazine removal of 77.45% and 76.89% for URER and UCER respectively. However, energy consumption showed a significant difference of 137.45 kWh/m3 for URER and 73.63 kWh/m3 for UCER. Regarding energy efficiency, a 60% atrazine removal was reached in both reactors using less energy for UCER at (1.5 A–135 min–150 mL/min–25.8 kWh/m3) and for URER at (0.66 A–135 min–150 mL/min–20.12 kWh/m3).
Fluorescence Probe Based on Graphene Quantum Dots for Selective, Sensitive and Visualized Detection of Formaldehyde in Food
Authors: Zhang Yanpeng , Qi Junjie , Li Mengying , Gao Dong , Xing Chengfen
Published: May 2021

Graphene quantum dots (GQDs) have been successfully used as a highly sensitive probe for the sensing of formaldehyde (HCHO) in an aqueous solution. Through static quenching, the probe utilizes the interaction between HCHO and GQDs to trigger the “turn off” fluorescence response, and has good selectivity. The probe can detect HCHO in a pure aqueous solution, and it also can still detect HCHO in a complex environment with a pH range from 4 to 10. The concentration of HCHO and the fluorescence intensity of GQDs show a good linear relationship within the range of HCHO of 0–1 μg/mL, which was much more sensitive than previous reports. The limit of HCHO detection by GQDs is about 0.0515 μg/mL. In addition, we successfully applied it to the actual food inspection. It is proved to be a selective, sensitive and visualized method to check whether the concentration of HCHO in the foods exceeds the regulatory limit, which presents a potential application in food safety testing.
Uptake Prediction of Eight Potentially Toxic Elements by Pistia stratiotes L. Grown in the Al-Sero Drain (South Nile Delta, Egypt): A Biomonitoring Approach
Authors: Eid Ebrahem M. , Dakhil Mohammed A. , Hassan Loutfy M. , Salama Shaimaa G. , Galal Tarek M.
Published: May 2021

The potential to utilise the free-floating macrophyte Pistia stratiotes L. to survey contamination of the Al-Sero Drain in the South Nile Delta, Egypt, by eight potentially toxic elements (PTEs) was investigated in this study. This study considered the absorption of eight PTEs (Cd, Co, Cu, Fe, Mn, Ni, Pb, and Zn), and the evaluated P. stratiotes were located in three sampling locations along the Al-Sero Drain, with sampling conducted in both monospecific and homogenous P. stratiotes. Samples of both types of P. stratiotes and water were collected on a monthly basis between May 2013 and April 2014 at each location, utilising three randomly chosen 0.5 × 0.5 m quadrats. Regression models were designed to predict the concentration of the PTEs within the plant’s shoot and root systems. Elevated water Fe levels were correlated with a rise in shoot system Fe concentration, whereas higher Ni concentrations in the water led to a higher Ni concentration within the root system. The latter was also true for Pb. Water Cu levels had a negative association with the Cu concentration within the P. stratiotes shoot system. Raised Fe levels were also correlated with a diminished Fe level within the roots. For all PTEs, P. stratiotes was characterised by a bioconcentration factor of more than 1.0, and for the majority by a translocation factor of less than 1.0. The goodness of fit for most of the designed models, as indicated by high R2 values and low mean averaged errors, demonstrated the associations between actual and predicted PTE concentrations. Any disparity between measured and predicted parameters failed to reach significance with Student t-tests, reinforcing the predictive abilities of the designed models. Thus, these novel models have potential value for the prediction of PTE uptake by P. stratiotes macrophytes inhabiting the Al-Sero Drain. Furthermore, the macrophyte’s constituents indicate the long-term impact of water contamination; this supports the potential future use of P. stratiotes for biomonitoring the majority of the PTEs evaluated in this study.
Impacts of Green Innovation, Institutional Constraints and Their Interactions on High-Quality Economic Development across China
Authors: Li Chenggang , Wan Jun , Xu Zhenci , Lin Tao
Published: May 2021

In 2015, China put forward five development concepts, propounded the concept of green development and green innovation, and adhered to the road of sustainable development. China also promoted the vision of high-quality economic development in 2017. It is very important to study the impacts of green innovation on high-quality development. However, scant research has been conducted on the impact of green innovation and institutional constraints on high-quality economic development simultaneously over both space and time in China. In this study, we analyze the impacts of green innovation on high-quality economic development across China from 2014 to 2018. The panel data model and the panel threshold model are developed, and the impact of green innovation and institutional constraints on high-quality economic development is empirically analyzed. Our results indicate that the comprehensive index and the level of high-quality economic development in all regions of China are increasing consistently. At the national level, addressing green innovation and institutional constraints have shown a significant positive impact on high-quality economic development. At the regional level, the interaction of green innovation and institutional constraints (i.e., mitigation of institutional constraints by green revolution) has a more positive impact on the economic quality of eastern China than that of central China. The interaction between green innovation and institutional constraints shows no positive impact on the high-quality economic development of the central and western regions because the green innovation, institutional constraints, and economic development in the central and western regions are weaker than those in the eastern regions. Therefore, it is suggested that different regions of China, especially the central and western ones, should strengthen green innovation, improve the market system, and increase government support.
The Effect of Water Rights Trading Policy on Water Resource Utilization Efficiency: Evidence from a Quasi-Natural Experiment in China
Authors: Chen Shaojian , Cao Yuanyuan , Li Jun
Published: May 2021

Water shortage has become a serious problem in the world, and low water efficiency is the key to industrial and agricultural production and sustainable economic development. Based on the data of 30 provinces (municipalities) in China from 2010 to 2017, this study builds a DEA model based on the hybrid network structure of water systems to measure water resource utilization efficiency and uses a difference-in-differences (DID) model to examine the effects of the water rights trading policy on water resource utilization efficiency. We find that the water rights trading policy can significantly promote the total water efficiency of the water resources system and the initial water use efficiency, and after a series of robustness tests, the result is still valid. Further analysis indicates that the policy effect changes with the adjustment of the industrial and agricultural water use structure. Moreover, the dynamic impact of water rights trading policy on water resource utilization efficiency is still significant. This study provides macroscopic evidence for evaluating the effects of China’s water rights trading policy and offers new ideas and experiences for improving China’s water resource utilization efficiency.
Sustainable Geotechnics—Theory, Practice, and Applications
Authors: Mickovski Slobodan B.
Published: May 2021

Today, modern Geotechnical Engineers, who in the past would have considered the phenomena occurring in the (primarily soil) environment, are faced with developments in environmental sciences that are becoming more and more detailed and sophisticated, with the natural phenomena and processes surrounding the civil engineering infrastructure being modelled, designed, monitored, and assessed in a more holistic way [...]
The Role of Corporate Ethical Management on Trade Relationship Trust and Commitment: B2B
Authors: Fei Shiruo , Kwon Chanho , Jin Changhyun
Published: May 2021

This study investigated whether three components of ethical management—awareness, operation, and practice—have a positive effect on corporate trust and relationship commitment in business-to-business (B2B) transactions. The study examined whether the trust formed in transactions, or relationship commitment, affects the establishment of long-term oriented relationships between companies. Further, it also aimed to determine the extent to which the authenticity of ethical management serves as a moderator that allows ethical management to influence outcome variables. The study’s sample comprised personnel (top management, management, and staff) from small- and medium-sized companies that transact with companies that mainly conduct B2B transactions. An empirical analysis was performed using data from 1323 returned questionnaires. Structural equation modeling was performed to test the hypotheses. The results showed that authenticity and the corporate ethical management components did not have moderating effects when they affected trust; however, in relationship commitment or a long-term oriented relationship, authenticity has a moderating effect when trust and commitment affect the long-term oriented relationship. This study’s empirical results contribute valuable data to the literature, as it is based on a survey conducted on actual personnel members of companies in B2B relationships.
Marinated Anchovies (Engraulis encrasicolus) Prepared with Flavored Olive Oils (Chétoui cv.): Anisakicidal Effect, Microbiological, and Sensory Evaluation
Authors: Trabelsi Najla , Nalbone Luca , Di Rosa Ambra Rita , Ed-Dra Abdelaziz , Nait-Mohamed Salma , Mhamdi Ridha , Giuffrida Alessandro , Giarratana Filippo
Published: May 2021

To meet the food demand of future generations, more sustainable food production is needed. Flavored olive oils (FOOs) have been proposed as natural additives to ensure food safety and quality through a more sustainable approach. The chemical composition and antioxidant potential of two different olive oils flavored, respectively, with cumin (Cm) and with a mixture of parsley, garlic, and lemon (Mix) were investigated. Cm-FOO and Mix-FOO were tested against Anisakis both in vitro and ex vivo through an exposure test of anchovy fillets experimentally parasitized with Anisakis larvae. Microbiological and sensory analysis were carried out on marinated anchovy fillets exposed to both FOOs to evaluate their effects on the shelf life and their sensory influence. The addition of herbs and spices did not affect the chemical composition of the olive oil (free acidity, UV absorbance, and fatty acid composition). Only Mix showed antioxidant activity, while Cm had no effect in this regard. Cm-FOO and Mix-FOO devitalized the Anisakis larvae both in vitro within 24 h and ex vivo after 8 and 10 days of exposure, respectively. The results of microbiological analyses showed that FOOs inhibited the growth of typical spoilage flora in the marinated anchovies without negatively affecting their sensory characteristics, as observed from the sensory analysis.
The Use of Hydromulching as an Alternative to Plastic Films in an Artichoke (Cynara cardunculus cv. Symphony) Crop: A Study of the Economic Viability
Authors: López-Marín Josefa , Romero Miriam , Gálvez Amparo , del Amor Francisco Moisés , Piñero Maria Carmen , Brotons-Martínez José Manuel
Published: May 2021

The use of mulching in agriculture suppresses the weeds around crop plants, enhances the nutrients status of soil, controls the soil structure and temperature, and reduces soil water evaporation. Excessive use of low-density polyethylene mulches is contributing to the accumulation of high amounts of plastic wastes, an environmental problem for agricultural ecosystems. Fragments of plastic from such wastes can be found in soils, in water resources, and in organisms, including humans. The objective of this work was to study the economic viability of the use of different hydromulches in an artichoke crop. Three blends were prepared by mixing paper pulp (recycled from used paper) and cardboard (from paper mills) with different additives: wheat straw (WS), rice hulls (RH), and substrate used for mushroom cultivation (MS). These were compared with low-density polyethylene (Pe), a treatment without mulching on bare soil where hand weeding was performed (HW), and a treatment without mulching on bare soil where herbicide was applied (H). The results indicate that the use of hydromulch in an artichoke crop represents a good alternative for reducing plastic waste in agriculture. The net profits of the hydromulch treatments (MS, WS, RH) were higher than for HW and H, and slightly lower than for Pe. The most profitable treatment was Pe (€0.69 m−3), followed by RH (€0.59 m−3), WS (€0.58 m−3), MS (€0.47 m−3), HW (€0.36 m−3), and H (€0.32 m−3). A sensitivity analysis showed a probability of negative results of 0.04 in Pe, 0.13 in SM, 0.08 in WS, and 0.07 in RH, so the probability that the grower will make a profit is greater than 0.9 with the use of mulch (except mushroom substrate) or polyethylene.
The Influence of Extruded Sugar Beet Pulp on Cookies’ Nutritional, Physical and Sensory Characteristics
Authors: Simić Sonja , Petrović Jovana , Rakić Dušan , Pajin Biljana , Lončarević Ivana , Jozinović Antun , Fišteš Aleksandar , Nikolić Sanja , Blažić Marijana , Miličević Borislav
Published: May 2021

Sugar beet pulp (SBP) is a by-product of the sugar industry in which the dietary fiber content ranges from 73% to 80%. Compared to cereal fibers mainly used in biscuit production, sugar beet fibers are gluten free and have a perfect ratio of 2/3 insoluble fiber. In this work, sugar beet pulp was extruded with corn grits (ratios of corn grits to sugar beet pulp in extrudates were 85:15, 70:30, and 55:45), and the obtained sugar beet pulp extrudates (SBPEs) were used for improving the nutritional quality of cookies. The wheat flour in cookies was replaced with SBPEs in the amount of 5, 10, and 15%. The influence of three factors (the percentage of sugar beet pulp in the SBPEs, the size of the SBPE particles, and the percentage of wheat flour substituted with SBPEs) and their interactions on the nutritional quality of cookies, as well as their physical and sensory characteristics are examined using the Box–Behnken experimental design. The addition of extruded sugar beet pulp (SBPEs) significantly increased the amount of total dietary fiber and mineral matter of cookies. On the whole, the addition of SBPEs increased cookie hardness, but the hardness decreased with an increase in extrudate particle size. Sensory characteristics (except for the taste) were the most influenced by extrudate particle size.
Photogrammetry as a New Scientific Tool in Archaeology: Worldwide Research Trends
Authors: Marín-Buzón Carmen , Pérez-Romero Antonio , López-Castro José Luis , Ben Jerbania Imed , Manzano-Agugliaro Francisco
Published: May 2021

Archaeology has made significant advances in the last 20 years. This can be seen by the remarkable increase in specialised literature on all archaeology-related disciplines. These advances have made it a science with links to many other sciences, both in the field of experimental sciences and in the use of techniques from other disciplines such as engineering. Within this last issue it is important to highlight the great advance that the use of photogrammetry has brought for archaeology. In this research, through a systematic study with bibliometric techniques, the main institutions and countries that are carrying them out and the main interests of the scientific community in archaeology related to photogrammetry have been identified. The main increase in this field has been observed since 2010, especially the contribution of UAVs that have reduced the cost of photogrammetric flights for reduced areas. The main lines of research in photogrammetry applied to archaeology are close-range photogrammetry, aerial photogrammetry (UAV), cultural heritage, excavation, cameras, GPS, laser scan, and virtual reconstruction including 3D printing.
Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks
Authors: Ioannou Konstantinos , Myronidis Dimitrios
Published: May 2021

The number of solar photovoltaic (PV) arrays in Greece has increased rapidly during the recent years. As a result, there is an increasing need for high quality updated information regarding the status of PV farms. This information includes the number of PV farms, power capacity and the energy generated. However, access to this data is obsolete, mainly due to the fact that there is a difficulty tracking PV investment status (from licensing to investment completion and energy production). This article presents a novel approach, which uses free access high resolution satellite imagery and a deep learning algorithm (a convolutional neural network—CNN) for the automatic detection of PV farms. Furthermore, in an effort to create an algorithm capable of generalizing better, all the current locations with installed PV farms (data provided from the Greek Energy Regulator Authority) in the Greek Territory (131,957 km2) were used. According to our knowledge this is the first time such an algorithm is used in order to determine the existence of PV farms and the results showed satisfying accuracy.
Bilateral Tempered Fractional Derivatives
Authors: Ortigueira Manuel Duarte , Bengochea Gabriel
Published: May 2021

The bilateral tempered fractional derivatives are introduced generalising previous works on the one-sided tempered fractional derivatives and the two-sided fractional derivatives. An analysis of the tempered Riesz potential is done and shows that it cannot be considered as a derivative.
Elastic Origin of the Unsymmetrical Thermal Hysteresis in Spin Crossover Materials: Evidence of Symmetry Breaking
Authors: Ndiaye Mamadou , Belmouri Nour El Islam , Linares Jorge , Boukheddaden Kamel
Published: May 2021

The jungle of experimental behaviors of spin-crossover materials contains a tremendous number of unexpected behaviors, among which, the unsymmetrical hysteresis loops having different shapes on heating and cooling, that we often encounter in literature. Excluding an extra effect of crystallographic phase transitions, we study here these phenomena from the point of view of elastic modeling and we demonstrate that a simple model accounting for the bond lengths misfits between the high-spin and low-spin states is sufficient to describe the situation of unsymmetrical hysteresis showing plateaus at the transition only on cooling or on heating branches. The idea behind this effect relates to the existence of a discriminant elastic frustration in the lattice, which expresses only along the high-spin to low-spin transition or in the opposite side. The obtained two-step transitions showed characteristics of self-organization of the spin states under the form of stripes, which we explain as an emergence process of antagonist directional elastic interactions inside the lattice. The analysis of the spin state transformation inside the plateau on cooling in terms of two sublattices demonstrated that the elastic-driven self-organization of the spin states is accompanied with a symmetry breaking.
Spherically Symmetric Tensor Fields and Their Application in Nonlinear Theory of Dislocations
Authors: Goloveshkina Evgeniya V. , Zubov Leonid M.
Published: May 2021

The concept of a spherically symmetric second-rank tensor field is formulated. A general representation of such a tensor field is derived. Results related to tensor analysis of spherically symmetric fields and their geometric properties are presented. Using these results, a formulation of the spherically symmetric problem of the nonlinear theory of dislocations is given. For an isotropic nonlinear elastic material with an arbitrary spherically symmetric distribution of dislocations, this problem is reduced to a nonlinear boundary value problem for a system of ordinary differential equations. In the case of an incompressible isotropic material and a spherically symmetric distribution of screw dislocations in the radial direction, an exact analytical solution is found for the equilibrium of a hollow sphere loaded from the outside and from the inside by hydrostatic pressures. This solution is suitable for any models of an isotropic incompressible body, i.e., universal in the specified class of materials. Based on the obtained solution, numerical calculations on the effect of dislocations on the stress state of an elastic hollow sphere at large deformations are carried out.
Comparative Analysis of Hybrid Fuzzy MCGDM Methodologies for Optimal Robot Selection Process
Authors: Rashid Tabasam , Ali Asif , Guirao Juan L. G. , Valverde Adrián
Published: May 2021

The generalized interval-valued trapezoidal fuzzy best-worst method (GITrF-BWM) provides more reliable and more consistent criteria weights for multiple criteria group decision making (MCGDM) problems. In this study, GITrF-BWM is integrated with the extended TOPSIS (technique for order preference by similarity to the ideal solution) and extended VIKOR (visekriterijumska optimizacija i kompromisno resenje) methods for the selection of the optimal industrial robot using fuzzy information. For a criteria-based selection process, assigning weights play a vital role and significantly affect the decision. Assigning weights based on direct opinions of decision makers can be biased, so weight deriving models, such as GITrF-BWM, overcome this discrepancy. In previous studies, generalized interval-valued trapezoidal fuzzy weights were not derived by using any MCGDM method for the robot selection process. For this study, both subjective and objective criteria are considered. The preferences of decision makers are provided with the help of linguistic terms that are then converted into fuzzy information. The stability and reliability of the methods were tested by performing sensitivity analysis, which showed that the ranking results of both the methodologies are not symmetrical, and the integration of GITrF-BWM with the extended TOPSIS method provides stable and reliable results as compared to the integration of GITrF-BWM with the extended VIKOR method. Hence, the proposed methodology provides robust optimal industrial robot selection.
Applications of Banach Limit in Ulam Stability
Authors: Badora Roman , Brzdęk Janusz , Ciepliński Krzysztof
Published: May 2021

We show how to get new results on Ulam stability of some functional equations using the Banach limit. We do this with the examples of the linear functional equation in single variable and the Cauchy equation.
Mechanisms of Hepatocellular Injury in Hepatitis A
Authors: Wang Minghang , Feng Zongdi
Published: May 2021

Hepatitis A virus (HAV) infection is a common cause of acute viral hepatitis worldwide. Despite decades of research, the pathogenic mechanisms of hepatitis A remain incompletely understood. As the replication of HAV is noncytopathic in vitro, a widely accepted concept has been that virus-specific cytotoxic T cells are responsible for liver injury. However, accumulating evidence suggests that natural killer (NK) cells, NKT cells, and even non-HAV-specific CD8+ T cells contribute to liver damage during HAV infection. In addition, intrinsic death of virus-infected hepatocytes has been implicated as a cause of liver injury in a murine model of hepatitis A. Furthermore, genetic variations in host factors such as T cell immunoglobulin-1 (TIM1) and IL-18 binding protein (IL-18BP) have been linked to hepatitis A severity. This review summarizes the current knowledge of the mechanisms of hepatocellular injury in hepatitis A. Different mechanisms may be involved under different conditions and they are not necessarily mutually exclusive. A better understanding of these mechanisms would aid in diagnosis and treatment of diseases associated with HAV infection.
Hepatitis B Virus Pre-S Gene Deletions and Pre-S Deleted Proteins: Clinical and Molecular Implications in Hepatocellular Carcinoma
Authors: Lin Yueh-Te , Jeng Long-Bin , Chan Wen-Ling , Su Ih-Jen , Teng Chiao-Fang
Published: May 2021

Hepatocellular carcinoma (HCC) is one of the most frequent and fatal human cancers worldwide and its development and prognosis are intimately associated with chronic infection with hepatitis B virus (HBV). The identification of genetic mutations and molecular mechanisms that mediate HBV-induced tumorigenesis therefore holds promise for the development of potential biomarkers and targets for HCC prevention and therapy. The presence of HBV pre-S gene deletions in the blood and the expression of pre-S deleted proteins in the liver tissues of patients with chronic hepatitis B and HBV-related HCC have emerged as valuable biomarkers for higher incidence rates of HCC development and a higher risk of HCC recurrence after curative surgical resection, respectively. Moreover, pre-S deleted proteins are regarded as important oncoproteins that activate multiple signaling pathways to induce DNA damage and promote growth and proliferation in hepatocytes, leading to HCC development. The signaling molecules dysregulated by pre-S deleted proteins have also been validated as potential targets for the prevention of HCC development. In this review, we summarize the clinical and molecular implications of HBV pre-S gene deletions and pre-S deleted proteins in HCC development and recurrence and highlight their potential applications in HCC prevention and therapy.