bluuebunny/crossref_metadata_2025 huggingface dataset:
A Narrative Review about Nutritional Management and Prevention of Oral Mucositis in Haematology and Oncology Cancer Patients Undergoing Antineoplastic Treatments
Authors: García-Gozalbo Balma , Cabañas-Alite Luis
Published: November 2021

Cancer is a prevalent disease worldwide, and treatments such as radiotherapy and chemotherapy sometimes lead to adverse events. Oral mucositis is one of the most disabling adverse events, and clinical guidelines do not take into account nutritional interventions. The primary endpoint was to gather the evidence about the efficacy of nutritional interventions in the prevention and/or treatment of antineoplastic-induced oral mucositis in oncological patients. A bibliographic review was carried out in the PubMed data base by combining MeSH terms with Boolean operators. Articles were selected based on inclusion and exclusion criteria; 50 final articles were found. Although further evidence is needed, glutamine, honey, and vitamins appear to be good therapeutic options. The rest of the compounds presented controversial or insufficient results, making it difficult to draw conclusions over their utilization as prevention or treatment options. Little evidence is reported about oral mucositis nutritional interventions in spite of them being attainable and affordable compounds. Scarce evidence is shown in paediatric patients compared with adults. Developing higher quality studies and combinations with the compounds researched is necessary for creating a stronger body of evidence.
Impact of Genetic Polymorphism on Response to Therapy in Non-Alcoholic Fatty Liver Disease
Authors: Martínez-Montoro José Ignacio , Cornejo-Pareja Isabel , Gómez-Pérez Ana María , Tinahones Francisco J.
Published: November 2021

In the last decades, the global prevalence of non-alcoholic fatty liver disease (NAFLD) has reached pandemic proportions with derived major health and socioeconomic consequences; this tendency is expected to be further aggravated in the coming years. Obesity, insulin resistance/type 2 diabetes mellitus, sedentary lifestyle, increased caloric intake and genetic predisposition constitute the main risk factors associated with the development and progression of the disease. Importantly, the interaction between the inherited genetic background and some unhealthy dietary patterns has been postulated to have an essential role in the pathogenesis of NAFLD. Weight loss through lifestyle modifications is considered the cornerstone of the treatment for NAFLD and the inter-individual variability in the response to some dietary approaches may be conditioned by the presence of different single nucleotide polymorphisms. In this review, we summarize the current evidence on the influence of the association between genetic susceptibility and dietary habits in NAFLD pathophysiology, as well as the role of gene polymorphism in the response to lifestyle interventions and the potential interaction between nutritional genomics and other emerging therapies for NAFLD, such as bariatric surgery and several pharmacologic agents.
Analysis of Eating Habits and Body Composition of Young Adult Poles
Authors: Mazurek-Kusiak Anna K. , Kobyłka Agata , Korcz Natalia , Sosnowska Małgorzata
Published: November 2021

Background: Obesity and overweight affect a large proportion of the world’s population. Increasingly, this problem can be observed among young adults. The aim of the study was to identify the motivations and barriers to healthy eating habits among young Poles, the relationship between physical activity and healthy eating and the impact of healthy eating on the body composition of the young. Methods: The method used in the research was a diagnostic survey using direct personal interviews. The research was conducted in the years 2016–2019 on a group of 399 young Poles aged 18–26. Their body composition was analyzed by determining resistance and reactance using the biological impedance method, with a TANITA SC-330ST Body Composition Analyzer. Results and conclusion: The main reasons for healthy eating among young Poles are the intent to follow a doctor’s recommendations, to lose weight and to live a healthy lifestyle and to follow a trend. On the other hand, the largest barriers to proper nutrition are: lack of time to prepare healthy meals and of financial resources, inability to prepare meals and limited knowledge of the principles of healthy eating. The eating behavior varied significantly in relation to the physical activity of the respondents. Active people’s eating habits were the best, and those of sedentary people the worst. Healthy eaters also had normal body composition indicators (adipose tissue level, BMI, body type). Young adults should be educated on the principles of healthy eating and have access to healthy food in canteens and vending machines, both at work and at university.
Regenerative Effects of CDP-Choline: A Dose-Dependent Study in the Toxic Cuprizone Model of De- and Remyelination
Authors: Gudi Viktoria , Schäfer Nora , Gingele Stefan , Stangel Martin , Skripuletz Thomas
Published: November 2021

Inflammatory attacks and demyelination in the central nervous system (CNS) are the key factors responsible for the damage of neurons in multiple sclerosis (MS). Remyelination is the natural regenerating process after demyelination that also provides neuroprotection but is often incomplete or fails in MS. Currently available therapeutics are affecting the immune system, but there is no substance that might enhance remyelination. Cytidine-S-diphosphate choline (CDP-choline), a precursor of the biomembrane component phospholipid phosphatidylcholine was shown to improve remyelination in two animal models of demyelination. However, the doses used in previous animal studies were high (500 mg/kg), and it is not clear if lower doses, which could be applied in human trials, might exert the same beneficial effect on remyelination. The aim of this study was to confirm previous results and to determine the potential regenerative effects of lower doses of CDP-choline (100 and 50 mg/kg). The effects of CDP-choline were investigated in the toxic cuprizone-induced mouse model of de- and remyelination. We found that even low doses of CDP-choline effectively enhanced early remyelination. The beneficial effects on myelin regeneration were accompanied by higher numbers of oligodendrocytes. In conclusion, CDP-choline could become a promising regenerative substance for patients with multiple sclerosis and should be tested in a clinical trial.
Personal Reminiscences of Reinhard Schlickeiser
Authors: Lerche Ian
Published: November 2021

In 1979, I arrived back at the University of Chicago from a two-year stint in Australia to find a very large German post-doc eagerly awaiting me, so we could work together on transport of cosmic ray electrons perpendicular to the galactic plane [...]
Public Intellectuals and Islamophobia in Greek Society: Entrenching the Discourses of Fear
Authors: Sakellariou Alexandros
Published: November 2021

The largest part of the existing literature with regard to Islamophobia in Greece focuses primarily on the policies, activities and discourse of politicians and political groups of the extreme right, Orthodox Church figures, state authorities, the media and the Internet. The purpose of this article is to cast light on an aspect which is frequently neglected in the study of Islamophobia, i.e., the role of public intellectuals, through a series of questions: Where do public intellectuals in Greece stand with regard to Islamophobia? What are the main themes in their public discourse with regard to Islam and Muslims? What is the role they play in the reproduction of Islamophobic views? Having in mind the debates over the concepts of Islamo-Fascism, Islamo-leftism, Islamophilia and Islamophobia, this article builds on the literature about the role of intellectuals in society with a special focus on their views about Islam. Analysing the discourse of three public intellectuals, the main argument is that Islamophobia in Greece is not an exclusive element of the extreme-right or the Orthodox Church. Self-proclaimed progressive or liberal intellectuals, through their public discourse, also contribute to the reproduction and entrenchment of the fear and moral panic about Islam.
Effects of Touchscreen Media Use on Toddlers’ Sleep: Insights from Longtime ECG Monitoring
Authors: Hackl-Wimmer Sigrid , Eglmaier Marina Tanja Waltraud , Eichen Lars , Rettenbacher Karoline , Macher Daniel , Walter-Laager Catherine , Lackner Helmut Karl , Papousek Ilona , Paechter Manuela
Published: November 2021

Wearable biomedical sensor technology enables reliable monitoring of physiological data, even in very young children. The purpose of the present study was to develop algorithms for gaining valid physiological indicators of sleep quality in toddlers, using data from an undisturbing and easy-to-use wearable device. The study further reports the application of this technique to the investigation of potential impacts of early touchscreen media use. Toddlers’ touchscreen media use is of strong interest for parents, educators, and researchers. Mostly, negative effects of media use are assumed, among them, disturbances of sleep and impairments of learning and development. In 55 toddlers (32 girls, 23 boys; 27.4 ± 4.9 months; range: 16–37 months), ECG monitoring was conducted for a period of 30 (±3) h. Parents were asked about their children’s touchscreen media use and they rated their children’s sleep quality. The use of touchscreen media predicted the physiologically determined quality of sleep but not parent-reported sleep quality (such as sleep onset latency). Greater heart rate differences between restless sleep phases and restful sleep indicated poorer nighttime recovery in children with more frequent use of touchscreen media. The study demonstrates that the expert analysis of the ECG during sleep is a potent tool for the estimation of sleep quality in toddlers.
Reconstruction of Velocity Curve in Long Stroke and High Dynamic Range Laser Interferometry
Authors: Feng Jinbao , Wu Jinhui , Si Yu , Gao Yubin , Liu Ji , Wang Gao
Published: November 2021

To study the law that governs the complex movements of the mechanism in the process of automatic weapon operation, the velocity tracking test technology of photon Doppler velocimetry is introduced to accurately measure velocity, displacement and acceleration, on the condition that there are long displacement and rapid velocity change. In the traditional way, out of interference signal time-frequency (TF) transformation draws TF distribution, and then by modulus maxima frequency extraction, comes to the law of velocity change. Due to the influence resulting from the change of fundamental signal as well as that of light intensity signal in the test, based on the TF distribution obtained by TF transformation, the traditional modulus maxima frequency extraction can extract frequency signals, but they show abnormal sudden changes at some moments, making the velocity discontinuous, unsmooth and unreal, which brings obvious errors to the subsequent calculation of acceleration and accurate displacement. Addressing the above-mentioned problems, this paper proposes a ridge extracting correction algorithm based on modulus maxima frequency extraction; this method, based on a large number of experiments where rodless cylinders are used to simulate the motion of a gun automatic mechanism, conducts a detailed calculation and analysis of the experimental results. A comparison of the two algorithms’ processing results, in terms of the speed, displacement and acceleration, suggests that the ridge extracting correction algorithm successfully corrects the frequency selection error, which draws a more continuous and, therefore, effective curve of the velocity change, and by so doing, the error of the displacement test (within 1.36 m displacement) is reduced from more than 3.6% to less than 0.58%, and the uncertainty dropped 97.07%. All these show that the accurate measurement of velocity, displacement and acceleration, with sudden and rapid velocity changes considered, is realized successfully.
Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
Authors: Stankiewicz Agnieszka , Marciniak Tomasz , Dabrowski Adam , Stopa Marcin , Marciniak Elzbieta , Obara Boguslaw
Published: November 2021

This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to 96.35%. The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.
A Sequential Inspection Procedure for Fault Detection in Multistage Manufacturing Processes
Authors: Moliner-Heredia Rubén , Bruscas-Bellido Gracia M. , Abellán-Nebot José V. , Peñarrocha-Alós Ignacio
Published: November 2021

Fault diagnosis in multistage manufacturing processes (MMPs) is a challenging task where most of the research presented in the literature considers a predefined inspection scheme to identify the sources of variation and make the process diagnosable. In this paper, a sequential inspection procedure to detect the process fault based on a sequential testing algorithm and a minimum monitoring system is proposed. After the monitoring system detects that the process is out of statistical control, the features to be inspected (end of line or in process measurements) are defined sequentially according to the expected information gain of each potential inspection measurement. A case study is analyzed to prove the benefits of this approach with respect to a predefined inspection scheme and a randomized sequential inspection considering both the use and non-use of fault probabilities from historical maintenance data.
Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme
Authors: Ademola Olutosin Ajibola , Leier Mairo , Petlenkov Eduard
Published: November 2021

The demand for object detection capability in edge computing systems has surged. As such, the need for lightweight Convolutional Neural Network (CNN)-based object detection models has become a focal point. Current models are large in memory and deployment in edge devices is demanding. This shows that the models need to be optimized for the hardware without performance degradation. There exist several model compression methods; however, determining the most efficient method is of major concern. Our goal was to rank the performance of these methods using our application as a case study. We aimed to develop a real-time vehicle tracking system for cargo ships. To address this, we developed a weighted score-based ranking scheme that utilizes the model performance metrics. We demonstrated the effectiveness of this method by applying it on the baseline, compressed, and micro-CNN models trained on our dataset. The result showed that quantization is the most efficient compression method for the application, having the highest rank, with an average weighted score of 9.00, followed by binarization, having an average weighted score of 8.07. Our proposed method is extendable and can be used as a framework for the selection of suitable model compression methods for edge devices in different applications.
Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition
Authors: Zhang Lei , Zhu Yanjin , Jiang Mingliang , Wu Yuchen , Deng Kailian , Ni Qin
Published: November 2021

Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the context of the normalization of COVID-19, the prevention and control of the epidemic has become a top priority. Temperature monitoring plays an important role in the preliminary screening of the population for fever. Therefore, this paper proposes a wearable device embedded with inertial and temperature sensors that is used to apply human behavior recognition (HAR) to body surface temperature detection for body temperature monitoring and adjustment by evaluating recognition algorithms. The sensing system consists of an STM 32-based microcontroller, a 6-axis (accelerometer and gyroscope) sensor, and a temperature sensor to capture the original data from 10 individual participants under 4 different daily activity scenarios. Then, the collected raw data are pre-processed by signal standardization, data stacking and resampling. For HAR, several machine learning (ML) and deep learning (DL) algorithms are implemented to classify the activities. To compare the performance of different classifiers on the seven-dimensional dataset with temperature sensing signals, evaluation metrics and the algorithm running time are considered, and random forest (RF) is found to be the best-performing classifier with 88.78% recognition accuracy, which is higher than the case of the absence of temperature data (<78%). In addition, the experimental results show that participants’ body surface temperature in dynamic activities was lower compared to sitting, which can be associated with the possible missing fever population due to temperature deviations in COVID-19 prevention. According to different individual activities, epidemic prevention workers are supposed to infer the corresponding standard normal body temperature of a patient by referring to the specific values of the mean expectation and variance in the normal distribution curve provided in this paper.
Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications
Authors: Emambocus Bibi Aamirah Shafaa , Jasser Muhammed Basheer , Mustapha Aida , Amphawan Angela
Published: November 2021

Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.
Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network
Authors: Hasan Md Mahibul , Wang Zhijie , Hussain Muhammad Ather Iqbal , Fatima Kaniz
Published: November 2021

Vehicle type classification plays an essential role in developing an intelligent transportation system (ITS). Based on the modern accomplishments of deep learning (DL) on image classification, we proposed a model based on transfer learning, incorporating data augmentation, for the recognition and classification of Bangladeshi native vehicle types. An extensive dataset of Bangladeshi native vehicles, encompassing 10,440 images, was developed. Here, the images are categorized into 13 common vehicle classes in Bangladesh. The method utilized was a residual network (ResNet-50)-based model, with extra classification blocks added to improve performance. Here, vehicle type features were automatically extracted and categorized. While conducting the analysis, a variety of metrics was used for the evaluation, including accuracy, precision, recall, and F1 − Score. In spite of the changing physical properties of the vehicles, the proposed model achieved progressive accuracy. Our proposed method surpasses the existing baseline method as well as two pre-trained DL approaches, AlexNet and VGG-16. Based on result comparisons, we have seen that, in the classification of Bangladeshi native vehicle types, our suggested ResNet-50 pre-trained model achieves an accuracy of 98.00%.
Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review
Authors: Kabade Varad , Hooda Ritika , Raj Chahat , Awan Zainab , Young Allison S. , Welgampola Miriam S. , Prasad Mukesh
Published: November 2021

Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that are often benign and sometimes serious. An individual who develops vertigo can be effectively treated only after a correct diagnosis of the underlying vestibular disorder is reached. Recent advances in artificial intelligence promise novel strategies for the diagnosis and treatment of patients with this common symptom. Human analysts may experience difficulties manually extracting patterns from large clinical datasets. Machine learning techniques can be used to visualize, understand, and classify clinical data to create a computerized, faster, and more accurate evaluation of vertiginous disorders. Practitioners can also use them as a teaching tool to gain knowledge and valuable insights from medical data. This paper provides a review of the literatures from 1999 to 2021 using various feature extraction and machine learning techniques to diagnose vertigo disorders. This paper aims to provide a better understanding of the work done thus far and to provide future directions for research into the use of machine learning in vertigo diagnosis.
Study on Reconstruction and Feature Tracking of Silicone Heart 3D Surface
Authors: Zhang Ziyan , Liu Yan , Tian Jiawei , Liu Shan , Yang Bo , Xiang Longhai , Yin Lirong , Zheng Wenfeng
Published: November 2021

At present, feature-based 3D reconstruction and tracking technology is widely applied in the medical field. In minimally invasive surgery, the surgeon can achieve three-dimensional reconstruction through the images obtained by the endoscope in the human body, restore the three-dimensional scene of the area to be operated on, and track the motion of the soft tissue surface. This enables doctors to have a clearer understanding of the location depth of the surgical area, greatly reducing the negative impact of 2D image defects and ensuring smooth operation. In this study, firstly, the 3D coordinates of each feature point are calculated by using the parameters of the parallel binocular endoscope and the spatial geometric constraints. At the same time, the discrete feature points are divided into multiple triangles using the Delaunay triangulation method. Then, the 3D coordinates of feature points and the division results of each triangle are combined to complete the 3D surface reconstruction. Combined with the feature matching method based on convolutional neural network, feature tracking is realized by calculating the three-dimensional coordinate changes of the same feature point in different frames. Finally, experiments are carried out on the endoscope image to complete the 3D surface reconstruction and feature tracking.
Development of Surface EMG Game Control Interface for Persons with Upper Limb Functional Impairments
Authors: Muguro Joseph K. , Laksono Pringgo Widyo , Rahmaniar Wahyu , Njeri Waweru , Sasatake Yuta , Suhaimi Muhammad Syaiful Amri bin , Matsushita Kojiro , Sasaki Minoru , Sulowicz Maciej , Caesarendra Wahyu
Published: November 2021

In recent years, surface Electromyography (sEMG) signals have been effectively applied in various fields such as control interfaces, prosthetics, and rehabilitation. We propose a neck rotation estimation from EMG and apply the signal estimate as a game control interface that can be used by people with disabilities or patients with functional impairment of the upper limb. This paper utilizes an equation estimation and a machine learning model to translate the signals into corresponding neck rotations. For testing, we designed two custom-made game scenes, a dynamic 1D object interception and a 2D maze scenery, in Unity 3D to be controlled by sEMG signal in real-time. Twenty-two (22) test subjects (mean age 27.95, std 13.24) participated in the experiment to verify the usability of the interface. From object interception, subjects reported stable control inferred from intercepted objects more than 73% accurately. In a 2D maze, a comparison of male and female subjects reported a completion time of 98.84 s. ± 50.2 and 112.75 s. ± 44.2, respectively, without a significant difference in the mean of the one-way ANOVA (p = 0.519). The results confirmed the usefulness of neck sEMG of sternocleidomastoid (SCM) as a control interface with little or no calibration required. Control models using equations indicate intuitive direction and speed control, while machine learning schemes offer a more stable directional control. Control interfaces can be applied in several areas that involve neck activities, e.g., robot control and rehabilitation, as well as game interfaces, to enable entertainment for people with disabilities.
Viewing Gamification Design Limitations and Weaknesses through a Pandemic Lens
Authors: Klein Gene
Published: November 2021

Design challenges and limitations of gamification were examined using the COVID-19 pandemic as a lens. Online or remote environments were also examined. These environments highlight the literature gap in evidence-based design recommendations and studies that isolate gamification from other pedagogical interventions or methodologies. The literature recognizes the differences between actual games and gamification. Gamification focuses and relies on entertainment to boost academic achievement. This focus on entertainment and its implications to motivation, both intrinsic and extrinsic, are examined. This reliance on entertainment creates unrealistic expectations. In fact, gamification expectations may be conflated with game expectations—especially in an educational setting.
Implementing Industry 4.0 through Cleaner Production and Social Stakeholders: Holistic and Sustainable Model
Authors: Satyro Walter Cardoso , Contador Jose Celso , Contador Jose Luiz , Fragomeni Marco Aurélio , Monken Sonia Francisca de Paula , Ribeiro Ana Freitas , de Lima Anderson Ferreira , Gomes Jansen Anderson , do Nascimento José Roberto , de Araújo Josiane Lima , Prado Rogério Glaser , Soares Junior Gilberto Gomes , de Souza Victor Henrique Martins
Published: November 2021

Although Industry 4.0 has received much attention in recent years due to the possibility of increasing companies’ productivity, the implementation process is complex. The aim of this study is to present a holistic model for implementing Industry 4.0 based on cleaner production as a fundamental tool for the development of production systems that meet the Sustainable Development 04026-002Goals (SDGs), and social stakeholders that cooperate with this implementation process, helping to develop sustainable infrastructure, processes and technologies to increase the sustainable transformation of these companies towards Industry 4.0. The method used was literature research, and the Delphi technique was used to ask specialists to contribute with their experience to evaluate and propose improvements to the model, in the form of a consensus. The model contributes to the Sustainable Development Goals 9, 12 and 15. This holistic and sustainable model is a contribution to theory and practice, helping executives, technicians, entrepreneurs and those involved with Industry 4.0 to base the implementation process in the needs and specificities of each company, avoiding the “one fits all” models, considering the peculiarities of each company and the complexity of the implementation process in a more efficient and collaborative digital production ecosystems base, seeking to reduce inequalities, through the joint effort of social stakeholders to find ways to restore and/or improve social harmony, impacted by Industry 4.0.
Democratising Heritage Values: A Methodological Review
Authors: Pastor Pérez Ana , Barreiro Martínez David , Parga-Dans Eva , Alonso González Pablo
Published: November 2021

This paper explores the transformation of heritage values from a critical perspective. The de-authorising conceptual shift in cultural heritage has not always been accompanied by a revitalisation of the dynamics of valorisation. To achieve the integration of multivocal discourses in sustainable preservation strategies, experts and academics need to work with methods that enable this to happen. This article presents a methodological analysis articulated through three different case studies that bring new experiences regarding the decolonisation of knowledge in the field of heritage values, addressing different aspects of the social dimension of cultural heritage. The first deals with contestation processes associated with productive winemaking traditions in the Rias Baixas, Galicia, Spain. The second case addresses the rebellious Paris of the Commune and the narratives associated with the valorisation of the traces that it has left in the city’s landscape. Finally, the third case analyses the values that come into play when citizens’ participation governs the transformation of an archaeological site in Barcelona. To conclude, we reflect on the idea of sustainability as a way of listening to, sharing and co-creating knowledge connected to communities.
Condition Diagnosis of Long-Span Bridge Pile Foundations Based on the Spatial Correlation of High-Density Strain Measurement Points
Authors: Liu Feng , Xu Qianen , Liu Yang
Published: November 2021

Pile foundations of long-span bridges are often deeply buried in soil, and their structural condition is difficult to accurately diagnose by conventional methods. To address this issue, a method for diagnosing the structural condition of bridge pile foundations based on the spatial correlation of high-density strain measurement points is proposed. The strain data of the high-density measurement points of a bridge pile foundation are obtained by using distributed optical fiber sensing technology based on Brillouin scattering, and then an algorithm for diagnosing the structural condition of the pile foundation based on geographically weighted regression analysis is presented. On this basis, aiming at the scour of the pile foundation of long-span bridges, an algorithm for estimating the scour depth of the pile foundation based on sliding plane clustering is proposed. Finally, the effectiveness of the proposed method is verified by numerical simulation and actual bridge data.
Agent-Based Modeling of the Formation and Prevention of Residential Diffusion on Urban Edges
Authors: Nagai Hideyuki , Kurahashi Setsuya
Published: November 2021

This paper presents an exploratory urban dynamics agent-based model (ABM) that simulates the relationship between the introduction of a hub facility open to residents, the interaction promotion around it, and transport policies on the sustainability of urban development through the autonomous actions of individual residents. By contrasting the model results with theoretical and empirical insights from actual cities, the validity of modeling the formation of residential diffusion on urban edges based on individual gain-maximizing daily travel and residential relocation is explained. The major contribution of the model is that it offers a new perspective on the bottom-up control of residential diffusion on urban edges, with benefits for productive human interactions at the microscale. Specifically, the model experimentally suggests the existence of a trade-off between increasing human interactions, through the introduction of an open hub attracting diverse activities and promotion of interaction around it, as well as the progression of residential diffusion. The model also suggests that the direction of urbanization is the result of collective action, and sustainable urbanization may be achieved through concerted efforts.
Professional Development of In-Service Teachers: Use of Eye Tracking for Language Classes, Case Study
Authors: Dagienė Valentina , Jasutė Eglė , Dolgopolovas Vladimiras
Published: November 2021

This article presents a study on in-service professional development of Lithuanian secondary school language teachers. The motivation is based on the understanding of language education as a highly interactive, complex process that requires a teacher’s digital literacy skills combined with integrated instructional approaches. This requires the implementation of a set of professional development activities. As a research methodology, we use a mixed method approach based on collecting eye tracking data in the first phase and, then, focus on comparative teacher self-reflection using data analysis and qualitative interviews. Finally, based on the collected qualitative and quantitative data, educational experts develop and present recommendations on the scope and direction of professional development. As a result of this study, a comprehensive set of the eye tracking data from the experiment involving 93 participants in total and 23 recorded lessons is presented. This includes variables such as number of visits, time to first fixation, number of fixations, and fixation duration vertically and horizontally. The discussion presents the results of the qualitative part of the study, including comprehensive teachers’ feedback. In conclusion, an integrated training program for in-service language teachers is presented, including an eye tracking experiment that provides data for extensive self-reflection and feedback.
Techno-Economic Analysis of the Hybrid Solar PV/H/Fuel Cell Based Supply Scheme for Green Mobile Communication
Authors: Hossain Md. Sanwar , Alharbi Abdullah G. , Islam Khondoker Ziaul , Islam Md. Rabiul
Published: November 2021

Hydrogen has received tremendous global attention as an energy carrier and an energy storage system. Hydrogen carrier introduces a power to hydrogen (P2H), and power to hydrogen to power (P2H2P) facility to store the excess energy in renewable energy storage systems, with the facts of large-scale storage capacity, transportability, and multiple utilities. This work examines the techno-economic feasibility of hybrid solar photovoltaic (PV)/hydrogen/fuel cell-powered cellular base stations for developing green mobile communication to decrease environmental degradation and mitigate fossil-fuel crises. Extensive simulation is carried out using a hybrid optimization model for electric rnewables (HOMER) optimization tool to evaluate the optimal size, energy production, total production cost, per unit energy production cost, and emission of carbon footprints subject to different relevant system parameters. In addition, the throughput, and energy efficiency performance of the wireless network is critically evaluated with the help of MATLAB-based Monte-Carlo simulations taking multipath fading, system bandwidth, transmission power, and inter-cell interference (ICI) into consideration. Results show that a more stable and reliable green solution for the telecommunications sector will be the macro cellular basis stations driven by the recommended hybrid supply system. The hybrid supply system has around 17% surplus electricity and 48.1 h backup capacity that increases the system reliability by maintaining a better quality of service (QoS). To end, the outcomes of the suggested system are compared with the other supply scheme and the previously published research work for justifying the validity of the proposed system.
Efficient Detection of Link-Flooding Attacks with Deep Learning
Authors: Hsieh Chih-Hsiang , Wang Wei-Kuan , Wang Cheng-Xun , Tsai Shi-Chun , Lin Yi-Bing
Published: November 2021

The DDoS attack is one of the most notorious attacks, and the severe impact of the DDoS attack on GitHub in 2018 raises the importance of designing effective defense methods for detecting this type of attack. Unlike the traditional network architecture that takes too long to cope with DDoS attacks, we focus on link-flooding attacks that do not directly attack the target. An effective defense mechanism is crucial since as long as a link-flooding attack is undetected, it will cause problems over the Internet. With the flexibility of software-defined networking, we design a novel framework and implement our ideas with a deep learning approach to improve the performance of the previous work. Through rerouting techniques and monitoring network traffic, our system can detect a malicious attack from the adversary. A CNN architecture is combined to assist in finding an appropriate rerouting path that can shorten the reaction time for detecting DDoS attacks. Therefore, the proposed method can efficiently distinguish the difference between benign traffic and malicious traffic and prevent attackers from carrying out link-flooding attacks through bots.
Rapid Assessment and Long-Term Monitoring of Green Stormwater Infrastructure with Citizen Scientists
Authors: Meixner Thomas , Berkowitz Alan R. , Downey Alisen E. , Pillich Jose , LeVea Reese , Smith Brianne K. , Chandler Mark , Gupta Neha , Rullman Stan , Woodroof Anna , Cherrier Jennifer
Published: November 2021

Green stormwater infrastructure (GSI) has emerged as a promising decentralized management approach to urban stormwater challenges. A lack of data about GSI performance interferes with widespread adoption of GSI. A citizen science program that benefits researchers, lay scientists, and municipalities offers a way to provide these lacking data. We have developed an open-source, transferable green infrastructure rapid assessment (GIRA) protocol for studying the performance of GSI with citizen scientists. This protocol has been tested in six North American cities (New York City, Toronto, Vancouver, Chicago, San Francisco, and Buffalo). In this research we define the performance of GSI in varying geographic, climatic, and maintenance conditions with the intent to create technological, institutional, and management solutions to urban stormwater problems. The GIRA protocol was used by citizen scientists to assess the physical properties and capabilities of bioswales, while small, affordable Green Infrastructure Sensors Boxes (GIBoxes) were used to determine longer-term function across several rain events. Our results indicate that teams of citizen scientists can be effective for collecting and archiving widespread information on the post-installation function of GSI. The effort also showed that citizen scientists had changes in understanding of urban stormwater challenges and the role that GSI can play in solving these problems. We explore the multiple benefits to knowledge, participants, and municipal partners as a result of this research.
Publication Performance and Trends in Mangrove Forests: A Bibliometric Analysis
Authors: Ho Yuh-Shan , Mukul Sharif A.
Published: November 2021

Mangroves are one the most productive ecosystems on Earth, and they are geographically located in the tropics and sub-tropics. Notwithstanding their critical role in providing a large number of environmental services and benefits as well as livelihood provisions, mangrove forests are being lost globally at an alarming rate. At the same time, they are increasingly recognized as a cost-effective nature-based climate solution for their carbon sequestration and storage capacity. Despite their enormous importance to people’s lives and the ecosystem, no bibliometric study on this topic has been published to our knowledge. Here, we provide a bibliometric analysis of the research on mangroves with research trends, most influential research based on citation count, and the origins (country and institution) of major research. Using the Science Citation Index Expanded (SCI-EXPANDED) database of the Web of Science Core Collection (Clarivate Analytics), we identified 13,918 documents published between 1990 and 2019. Nevertheless, 12,955 articles met our final criteria and were analyzed in detail. Six publications and their citations per publication (CPP2019) were applied to evaluate the publication performance of countries and institutes. When considering the top ten Web of Science subject categories, articles published on the ecology of mangroves had the highest CPP2019 of 28. Environmental sciences have been the major category since 2013. The USA dominated the total articles and single-author articles. The USA was also the most frequent partner of international collaborative publications. China published the most single-country articles, first-author articles, and corresponding-author articles. However, articles by the USA and Australia had a higher CPP2019. Sun Yat Sen University in China was the most active university. The Australian Institute of Marine Science dominated all kinds of publications with the top CPP2019. Together with the USA, Australia, China, India, Brazil, and Japan ranked both the top six on total publications and total publications in 2019. Our bibliometric study provides useful visualization of the past and current landscape of research on mangroves and emerging fields, to facilitate future research collaboration and knowledge exchange.
Clanis bilineata tsingtauica: A Sustainable Edible Insect Resource
Authors: Gao Yu , Zhao Yi-Jin , Xu Meng-Lei , Shi Shu-Sen
Published: November 2021

Insects provide a series of ecological services vital to human survival. Over 1000 insect species have been used for human consumption in Asia, Latin America, and Africa for more than 2000 years. Among them, the soybean hawkmoth, Clanis bilineata tsingtauica (CBT), is a traditional edible insect. CBT, known for its high nutritional value, is considered safe with a long consumption history in East Asia. The present review provides an overview of the rearing technology and utilization prospects of CBT. It has been extensively cultivated on live soybean plants under field and glasshouse conditions. However, an efficient rearing technology has not been reported. The mass production of CBT is still under investigation, and more advanced technology is required to develop high-quality food ingredients to meet consumer needs on a large scale. In addition, food derived from the soybean hawkmoth is prevalent in the farm product market. It is used as freeze-dried, fried, fresh meat, and canned meat. CBT-derived food, a potential dietary supplement used to retard aging in humans, would be a novel and emerging product in the food industry. The development of CBT-derived food will generate more economic and social value if the market demand can be met. This review will provide an insight into CBT mass production and its potential application in the food industry.
Simulation of Possible Fire and Explosion Hazards of Clean Fuel Vehicles in Garages
Authors: To Chi Wing , Chow Wan Ki , Cheng Fang Ming
Published: November 2021

Clean fuel is advocated to be used for sustainability. The number of liquefied petroleum gas (LPG) and hydrogen vehicles is increasing globally. Explosion hazard is a threat. On the other hand, the use of hydrogen is under consideration in Hong Kong. Explosion hazards of these clean fuel (LPG and hydrogen) vehicles were studied and are compared in this paper. The computational fluid dynamics (CFD) software Flame Acceleration Simulator (FLACS) was used. A car garage with a rolling shutter as its entrance was selected for study. Dispersion of LPG from the leakage source with ignition at a higher position was studied. The same garage was used with a typical hydrogen vehicle leaking 3.4 pounds (1.5 kg) of hydrogen in 100 s, the mass flow rate being equal to 0.015 kgs−1. The hydrogen vehicle used in the simulation has two hydrogen tanks with a combined capacity of 5 kg. The entire tank would be completely vented out in about 333 s. Two scenarios of CFD simulation were carried out. In the first scenario, the rolling shutter was completely closed and the leaked LPG or hydrogen was ignited at 300 s after leakage. The second scenario was conducted with a gap height of 0.3 m under the rolling shutter. Predicted results of explosion pressure and temperature show that appropriate active fire engineering systems are required when servicing these clean fuel vehicles in garages. An appropriate vent in an enclosed space such as the garage is important in reducing explosion hazards.
Stability Assessment and Geomorphological Evolution of Sea Natural Arches by Geophysical Measurement: The Case Study of Wied Il-Mielah Window (Gozo, Malta)
Authors: Leucci Giovanni , Persico Raffaele , De Giorgi Lara , Lazzari Maurizio , Colica Emanuele , Martino Salvatore , Iannucci Roberto , Galone Luciano , D’Amico Sebastiano
Published: November 2021

The Wied il-Mielaħ Window (Gozo–Malta) is a limestone natural arch on the north-western coast of the island of Gozo in Malta. It is located at the end of the Wied il-Mielaħ valley north of the village of Għarb. This natural arch is less well known than the Azure Window, which collapsed in March 2017 following a heavy storm, but notwithstanding, it is an imposing and important natural monument too. In the past, the Wied il-Mielah valley was responsible for discharging wastewater from the surrounding localities to the Mediterranean directly at the Wied il-Mielah Window. The sewage flag was often clearly visible underneath the archway into the open sea. The natural features of the arch provide an outstanding touristic attraction. To avoid what happened to the Azure Window, a methodology for the evaluation of the collapse hazard, combining passive seismic, ground penetrating radar (GPR), geological/geomorphological surveys and mine engineering methods, is here proposed. In this study, a methodological approach was applied, based on the following: (i) passive seismic method to study the physical–mechanical characteristics of the rock mass that constitutes the window; (ii) GPR method in order to demonstrate the conservation state (i.e., the intensity of fracturing); (iii) geological/geomorphological surveys in order to obtain a crack pattern; and (iv) scaled span empirical analysis in order to evaluate the stability of the arch. The calculation of the safety factor, with a static method, gave a value equal to 3.75 with a probability of collapse of the marine arch within 50 and 100 years.