bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating Practical Impacts of Using Single-Antenna and Dual-Antenna GNSS/INS Sensors in UAS-Lidar Applications
Authors: Brazeal Ryan G. , Wilkinson Benjamin E. , Benjamin Adam R.
Published: August 2021

Data collected from a moving lidar sensor can produce an accurate digital representation of the physical environment that is scanned, provided the time-dependent positions and orientations of the lidar sensor can be determined. The most widely used approach to determining these positions and orientations is to collect data with a GNSS/INS sensor. The use of dual-antenna GNSS/INS sensors within commercial UAS-lidar systems is uncommon due to the higher cost and more complex installation of the GNSS antennas. This study investigates the impacts of using a single-antenna and dual-antenna GNSS/INS MEMS-based sensor on the positional precision of a UAS-lidar generated point cloud, with an emphasis on the different heading determination techniques employed by each type of GNSS/INS sensor. Specifically, the impacts that sensor velocity and acceleration (single-antenna), and a GNSS compass (dual-antenna) have on heading precision are investigated. Results indicate that at the slower flying speeds often used by UAS (≤5 m/s), a dual-antenna GNSS/INS sensor can improve heading precision by up to a factor of five relative to a single-antenna GNSS/INS sensor, and that a point of diminishing returns for the improvement of heading precision exists at a flying speed of approximately 15 m/s for single-antenna GNSS/INS sensors. Additionally, a simple estimator for the expected heading precision of a single-antenna GNSS/INS sensor based on flying speed is presented. Utilizing UAS-lidar mapping systems with dual-antenna GNSS/INS sensors provides reliable, robust, and higher precision heading estimates, resulting in point clouds with higher accuracy and precision.
One- and Two-Band Sensors and Algorithms to Derive aCDOM(440) from Global Above- and In-Water Optical Observations
Authors: Hooker Stanford B. , Houskeeper Henry F. , Lind Randall N. , Suzuki Koji
Published: August 2021

The colored (or chromophoric, depending on the literature) dissolved organic matter (CDOM) spectral absorption coefficient, aCDOM(λ), is a variable of global interest that has broad application in the study of biogeochemical processes. Within the funding for scientific research, there is an overarching trend towards increasing the scale of observations both temporally and spatially, while simultaneously reducing the cost per sample, driving a systemic shift towards autonomous sensors and observations. Legacy aCDOM(λ) measurement techniques can be cost-prohibitive and do not lend themselves toward autonomous systems. Spectrally rich datasets carefully collected with advanced optical systems in diverse locations that span a global range of water bodies, in conjunction with appropriate quality assurance and processing, allow for the analysis of methods and algorithms to estimate aCDOM(440) from spectrally constrained one- and two-band subsets of the data. The resulting algorithms were evaluated with respect to established fit-for-purpose criteria as well as quality assured archival data. Existing and proposed optical sensors capable of exploiting the algorithms and intended for autonomous platforms are identified and discussed. One-band in-water algorithms and two-band above-water algorithms showed the most promise for practical use (accuracy of 3.0% and 6.5%, respectively), with the latter demonstrated for an airborne dataset.
IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet
Authors: Kundu Nidhi , Rani Geeta , Dhaka Vijaypal Singh , Gupta Kalpit , Nayak Siddaiah Chandra , Verma Sahil , Ijaz Muhammad Fazal , Woźniak Marcin
Published: August 2021

Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the ‘Automatic and Intelligent Data Collector and Classifier’ framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The ‘Custom-Net’ model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the ‘Custom-Net’. Furthermore, the impact of transfer learning on the ‘Custom-Net’ and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the ‘Custom-Net’ extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the ‘Custom-Net’ model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of ‘Custom-Net’ is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.
Ultra-High Repetition Rate Terahertz Time-Domain Spectroscopy for Micrometer Layer Thickness Measurement
Authors: Kolpatzeck Kevin , Liu Xuan , Häring Lars , Balzer Jan C. , Czylwik Andreas
Published: August 2021

Terahertz time-domain spectroscopy systems driven by monolithic mode-locked laser diodes (MLLDs) exhibit bandwidths exceeding 1 THz and a peak dynamic range that can compete with other state-of-the-art systems. Their main difference compared to fiber-laser-driven systems is their ultra-high repetition rate of typically dozens of GHz. This makes them interesting for applications where the length of the terahertz path may not be precisely known and it enables the use of a very short and potentially fast optical delay unit. However, the phase accuracy of the system is limited by the accuracy with which the delay axes of subsequent measurements are synchronized. In this work, we utilize an all-fiber approach that uses the optical signal from the MLLD in a Mach–Zehnder interferometer to generate a reference signal that we use to synchronize the detected terahertz signals. We demonstrate transmission-mode thickness measurements of stacked layers of 17μm thick low-density polyethylene (LDPE) films.
Hydrogen-Terminated Diamond Surface as a Gas Sensor: A Comparative Study of Its Sensitivities
Authors: Kočí Michal , Kromka Alexander , Bouřa Adam , Szabó Ondrej , Husák Miroslav
Published: August 2021

A nanocrystalline diamond (NCD) layer is used as an active (sensing) part of a conductivity gas sensor. The properties of the sensor with an NCD with H-termination (response and time characteristic of resistance change) are measured by the same equipment with a similar setup and compared with commercial sensors, a conductivity sensor with a metal oxide (MOX) active material (resistance change), and an infrared pyroelectric sensor (output voltage change) in this study. The deposited layer structure is characterized and analyzed by Scanning Electron Microscopy (SEM) and Raman spectroscopy. Electrical properties (resistance change for conductivity sensors and output voltage change for the IR pyroelectric sensor) are examined for two types of gases, oxidizing (NO2) and reducing (NH3). The parameters of the tested sensors are compared and critically evaluated. Subsequently, differences in the gas sensing principles of these conductivity sensors, namely H-terminated NCD and SnO2, are described.
TaijiGNN: A New Cycle-Consistent Generative Neural Network for High-Quality Bidirectional Transformation between RGB and Multispectral Domains
Authors: Liu Xu , Gherbi Abdelouahed , Li Wubin , Wei Zhenzhou , Cheriet Mohamed
Published: August 2021

Since multispectral images (MSIs) and RGB images (RGBs) have significantly different definitions and severely imbalanced information entropies, the spectrum transformation between them, especially reconstructing MSIs from RGBs, is a big challenge. We propose a new approach, the Taiji Generative Neural Network (TaijiGNN), to address the above-mentioned problems. TaijiGNN consists of two generators, G_MSI, and G_RGB. These two generators establish two cycles by connecting one generator’s output with the other’s input. One cycle translates the RGBs into the MSIs and converts the MSIs back to the RGBs. The other cycle does the reverse. The cycles can turn the problem of comparing two different domain images into comparing the same domain images. In the same domain, there are neither different domain definition problems nor severely underconstrained challenges, such as reconstructing MSIs from RGBs. Moreover, according to several investigations and validations, we effectively designed a multilayer perceptron neural network (MLP) to substitute the convolutional neural network (CNN) when implementing the generators to make them simple and high performance. Furthermore, we cut off the two traditional CycleGAN’s identity losses to fit the spectral image translation. We also added two consistent losses of comparing paired images to improve the two generators’ training effectiveness. In addition, during the training process, similar to the ancient Chinese philosophy Taiji’s polarity Yang and polarity Yin, the two generators update their neural network parameters by interacting with and complementing each other until they all converge and the system reaches a dynamic balance. Furthermore, several qualitative and quantitative experiments were conducted on the two classical datasets, CAVE and ICVL, to evaluate the performance of our proposed approach. Promising results were obtained with a well-designed simplistic MLP requiring a minimal amount of training data. Specifically, in the CAVE dataset, to achieve comparable state-of-the-art results, we only need half of the dataset for training; for the ICVL dataset, we used only one-fifth of the dataset to train the model, but obtained state-of-the-art results.
An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions
Authors: Vargas Jorge , Alsweiss Suleiman , Toker Onur , Razdan Rahul , Santos Joshua
Published: August 2021

Autonomous vehicles (AVs) rely on various types of sensor technologies to perceive the environment and to make logical decisions based on the gathered information similar to humans. Under ideal operating conditions, the perception systems (sensors onboard AVs) provide enough information to enable autonomous transportation and mobility. In practice, there are still several challenges that can impede the AV sensors’ operability and, in turn, degrade their performance under more realistic conditions that actually occur in the physical world. This paper specifically addresses the effects of different weather conditions (precipitation, fog, lightning, etc.) on the perception systems of AVs. In this work, the most common types of AV sensors and communication modules are included, namely: RADAR, LiDAR, ultrasonic, camera, and global navigation satellite system (GNSS). A comprehensive overview of their physical fundamentals, electromagnetic spectrum, and principle of operation is used to quantify the effects of various weather conditions on the performance of the selected AV sensors. This quantification will lead to several advantages in the simulation world by creating more realistic scenarios and by properly fusing responses from AV sensors in any object identification model used in AVs in the physical world. Moreover, it will assist in selecting the appropriate fading or attenuation models to be used in any X-in-the-loop (XIL, e.g., hardware-in-the-loop, software-in-the-loop, etc.) type of experiments to test and validate the manner AVs perceive the surrounding environment under certain conditions.
Low-Bandwidth and Compute-Bound RGB-D Planar Semantic SLAM
Authors: Zhang Jincheng , Ganesh Prashant , Volle Kyle , Willis Andrew , Brink Kevin
Published: August 2021

Visual simultaneous location and mapping (SLAM) using RGB-D cameras has been a necessary capability for intelligent mobile robots. However, when using point-cloud map representations as most RGB-D SLAM systems do, limitations in onboard compute resources, and especially communication bandwidth can significantly limit the quantity of data processed and shared. This article proposes techniques that help address these challenges by mapping point clouds to parametric models in order to reduce computation and bandwidth load on agents. This contribution is coupled with a convolutional neural network (CNN) that extracts semantic information. Semantics provide guidance in object modeling which can reduce the geometric complexity of the environment. Pairing a parametric model with a semantic label allows agents to share the knowledge of the world with much less complexity, opening a door for multi-agent systems to perform complex tasking, and human–robot cooperation. This article takes the first step towards a generalized parametric model by limiting the geometric primitives to a planar surface and providing semantic labels when appropriate. Two novel compression algorithms for depth data and a method to independently fit planes to RGB-D data are provided, so that plane data can be used for real-time odometry estimation and mapping. Additionally, we extend maps with semantic information predicted from sparse geometries (planes) by a CNN. In experiments, the advantages of our approach in terms of computational and bandwidth resources savings are demonstrated and compared with other state-of-the-art SLAM systems.
Sensors for Fire and Smoke Monitoring
Authors: Allison Robert S. , Johnston Joshua M. , Wooster Martin J.
Published: August 2021

Mastery of fire is intimately linked to advances in human civilization, culture and technology [...]
Control and Real-Time Data Acquisition of an Experimental Platform for Stored Grain Aeration Study
Authors: Liu Jingyun , Li Ping
Published: August 2021

Aeration is one of the most important methods to keep stored grain safe and maintain its quality. Experimental platforms are used for stored grain aeration study in a laboratory-scale. The purpose of this paper was to provide the real-time data acquisition and control system design of a new experimental platform with multifunction for stored grain study. Requirements of the aeration experiments were analyzed, and multi running modes were designed. The aeration inlet air conditions were designed to be adjustable and multi variables need to be controlled simultaneously, which was a key problem to be solved for the platform. An ON/OFF-PID based multivariable cooperative control method was proposed, and two control loops were formed where inlet air temperature and humidity were considered separately while could be controlled simultaneously with a logic judgement strategy. Real-time data needed to be monitored was acquired with different sensors and displayed intuitively. Experiments were carried out to test the static and dynamic characteristics of the control method and three inlet air flow rates of 0.03, 0.08 and 0.13 m·s−1were used. Performance of the data acquisition system was also tested. The results showed that, the inlet air conditions control error was within ±1 °C and 10% for temperature and relative humidity, respectively. The real-time data acquisition of multi parameters during aeration process was realized. The experimental platform can be used for studies of different aeration objectives.
Emic Views of Community Resilience and Coastal Tourism Development
Authors: Naylor Ryan S. , Hunt Carter A. , Zimmerer Karl S. , Taff B. Derrick
Published: August 2021

Coastal communities are among the most rapidly changing, institutionally complex, and culturally diverse in the world, and they are among the most vulnerable to anthropogenic change. While being a driver of anthropogenic change, tourism can also provide socio-economic alternatives to declining natural resource-based livelihoods for coastal residents. The purpose of this study is to assess the impact of small-scale cruise tourism on coastal community resiliency in Petersburg, Alaska. Exploring these impacts through resiliency theory’s lens of exposure, sensitivity, and adaptive capacity, we employed ethnographic research methods that emphasize emic viewpoints to determine how residents see this form of tourism affecting the resiliency of valued community culture, institutions, and traditional livelihoods. Findings indicate that with purposeful engagement in niche cruise tourism involving boats with 250 passengers or less, and an active rejection of the large cruise ship industry, Petersburg exhibits increased adaptive capacity to promote the resilience of valued community institutions and heritage. This work draws needed recognition to the diversity of activities that fall under the label of cruise tourism, including the distinct implications of smaller-scale, niche cruise tourism for the resilience of coastal communities. It also highlights the need to capture emic perspectives to understand the politics of community resiliency.
Teamwork, Spirit of the Game and Communication: A Review of Implications from Sociological Constructs for Research and Practice in Ultimate Frisbee Games
Authors: Amoroso José Pedro , Coakley Jay , Rebelo-Gonçalves Ricardo , Antunes Raul , Valente-dos-Santos João , Furtado Guilherme Eustáquio
Published: August 2021

Ultimate Frisbee (UF) is a non-contact, challenging, and self-promoted team sport. Its characteristics, such as the game environment and rules, appear to influence the on-the-pitch behaviour of players. This article examines the content of qualitative studies to determine if and how the unique characteristics of UF may be related to nine sociological themes, that may be identified during gameplay. These themes include the following: (a) competition and performance; (b) enjoyment; (c) communication; (d) cooperation/friendship; (e) behaviors/welfare; (f) teamwork/social skills; (g) environment/lifestyle; (h) rules/self-refereeing and (i) spirit of the Game (SOTG). The review was conducted according to PRISMA guidelines. A comprehensive search protocol was used to identify, screen, and select published research articles under a Qualitative Systematic Review (QSR). The search was occurred from 1 June to 30 December 2020 with no limitations regarding the year of publication. Original English-language papers that contained relevant data regarding sociological themes and UF were selected. As a result, nine papers were qualified to be included in the final version of QSR. The files analyzed were structured with MAXQDA. A total of 521 references were identified and selected for analysis. After the Screening (n = 301) and Eligibility (n = 71) phases, a total of 30 potential papers were selected and classified. Nine studies were included in the final analysis. The three most cited sociological themes in these studies were: communication, teamwork/social skills, and spirit of the game. Research suggests that UF involves patterns of interaction related to communication and the spirit of the game that encourage active lifestyles. Finally, we point out that UF is an appropriate sport to include in physical education classes in which the creation of positive relationships between students is the desired outcome. This topic should be explored further through interventional studies done in different contexts although the evidence suggests that UF offers players unique opportunities to experience a combination of physical activity and enjoyment.
Resilience toward Volcanic Eruptions: Risk Perception and Disaster Microinsurance in Yogyakarta, Indonesia
Authors: Brata Aloysius Gunadi , de Groot Henri L. F. , Rietveld Piet , Resosudarmo Budy P. , Zant Wouter
Published: August 2021

Disaster microinsurance has been argued to enhance people’s resilience toward natural hazards. In developing countries, however, the uptake of this insurance scheme has been limited. This paper investigates the influence of the perception of disaster risks on the probability of local people participating in a hypothetical disaster microinsurance scheme. We use household data to assess a specific disaster risk, notably the risk of an eruption of the Mount Merapi in Yogyakarta. We find that this perception positively influences the interest to participate in disaster microinsurance. We also find that insurance literacy has a strong positive relationship with the respondent’s interest to participate in disaster microinsurance.
Assessing Life Cycle Environmental and Economic Impacts of Building Construction Solutions with BIM
Authors: Carvalho José Pedro , Villaschi Fernanda Schmitd , Bragança Luís
Published: August 2021

Worldwide authorities are increasingly concerned about construction’s efficiency and sustainability, leading to the development of high-performance buildings. However, such facts have shifted a significant percentage of the building life cycle environmental impacts from the operation to the product and construction phases. Thus, the need to evaluate and select more sustainable materials and construction solutions arises, to also minimize impacts from these stages. To evaluate those impacts, LCA and LCC analysis are usually applied to assess the building impacts and costs, through the different life cycle stages. Despite the usefulness of LCA and LCC methods during the project phase, they are usually evaluated in the project later stages. It is too complex and time-consuming to gather and process all the required data during the project early stages. With the recent deployment of BIM, the opportunity to automate and shift LCA and LCC analysis to project early stages stands out. Facing the research gap, this study aims to develop a BIM-based decision-making tool for designers to evaluate the environmental, economic, and functional performance of different building construction solutions. To do so, 18 different simulation scenarios have been created in Autodesk Revit with different combinations of external walls, roofs, and floors. Then, a framework was developed in Dynamo to automatically characterize the building elements life cycle environmental impacts and costs, as well as to automate the LCA and LCC analysis during the project early stages. The outcomes can significantly reduce the required time, errors and efforts when performing LCA and LCC analysis, providing designers with real time decision support data and making an important contribution to the use of BIM for sustainability purposes.
Airfares Data in New Zealand Domestic Aviation Market
Authors: Ho Tin , Nguyen Dat , Ngo Thanh , Le Tu
Published: August 2021

Price competition has been a growing concern of worldwide researchers and managers. In the aviation market, especially with the help from e-commerce platforms such as Expedia, TripAdvisor, and SkyScanner, airfares are now available to customers in the easiest and quickest way. It thus allows airlines to match their fares immediately and simultaneously upon any changes of their rivals, given that customer’ choices are made with regard to their incomes. This study provides a dataset on domestic airfares in New Zealand that could be useful for future studies in the fields of marketing, business and economics, transportation and aviation, or management. The dataset covers 12 trunk routes and 40 secondary routes in New Zealand from 19 September 2019 to 18 December 2019, a total of 90 days. It provides a rich dataset of more than 162,000 observations regarding the airfare, departure time, arrival time, flight duration, airline, departure airport, arrival airport, transiting airport, and so on. There are possibilities to extend the dataset (e.g., in terms of flying distance, airport characteristics, and airline characteristics) to make it be valuable for future study.
Factors Affecting the Intention of Multi-Family House Residents to Age in Place in a Potential Naturally Occurring Retirement Community of Seoul in South Korea
Authors: Park Jung-A , Choi Byungsook
Published: August 2021

This study reveals residents’ willingness to live in their current homes and communities and analyzes the factors related to their willingness to age in place in South Korea. Using a questionnaire survey method, data of 289 residents aged 55 or older were collected in apartment areas with the potential to become naturally occurring senior communities in Seoul. Data were analyzed using descriptive statistics, correlation tests, t-test, ANOVA, factor analysis, and regression analysis by SPSS 26.0, and the findings were followed up. Housing services are very important in aging communities. Physical environment-oriented residential services promote a safe walking environment, facilities to support the elderly, a secure complex environment, a home-safety accident-prevention diagnosis service, home improvement, and home improvement support. Multiple regression analysis showed that residential services or elderly education and activity support services influence aging in place, which is a novel finding revealed in this study.
Building Envelope Prefabricated with 3D Printing Technology
Authors: Volpe Stelladriana , Sangiorgio Valentino , Petrella Andrea , Coppola Armando , Notarnicola Michele , Fiorito Francesco
Published: August 2021

The Fourth Industrial Revolution represents the beginning of a profound change for the building sector. In the last decade, the perspective of shapes, materials, and construction techniques is evolving fast due to the additive manufacturing technology. On the other hand, even if the technology is growing fast and several 3D printed buildings are being developed worldwide, the potential of concrete 3D printing in building prefabrication remains unexplored. Consequently, the application of new digital fabrication technologies in the construction industry requires a redesign of the construction process and its components. This paper proposes a novel conception, design, and prototyping of a precast building envelope to be prefabricated with extrusion-based 3D concrete printing (3DCP). The new design and conception aim to fully exploit the potential of 3D printing for prefabricated components, especially in terms of dry assembly, speed of implementation, reusability, recyclability, modularity, versatility, adaptability, and sustainability. Beyond the novel conceptual design of precast elements, the research investigated the 3D printable cementitious material based on a magnesium potassium phosphate cement (MKPC), which was devised and tested to ensure good performances of the proposed component. Finally, a prototype has been realised in scale with additive manufacturing technology in order to verify the printability and to optimize the extruder path. This study leads us to believe that the combined use of prefabricated systems, construction automation, and innovative materials can decisively improve the construction industry’s sustainability in the future.
From Waste Pickers to Producers: An Inclusive Circular Economy Solution through Development of Cooperatives in Waste Management
Authors: Buch Rajesh , Marseille Alicia , Williams Matthew , Aggarwal Rimjhim , Sharma Aparna
Published: August 2021

The world’s global plastics waste crisis demands policy coordination and technological solutions to improve waste management systems, and organizations worldwide have created momentum around the concept of a circular economy. This paper advances a holistic, inclusive circular economy framework that aims to empower waste pickers with the following basic pillars: (1) build collaborative networks of stakeholders to enable inclusion of waste pickers; (2) establish cooperative enterprise models to integrate waste pickers into the formal economy; (3) build waste pickers’ technical skills and capacity for entrepreneurship; and (4) provide access to technologies and markets that enable waste pickers to manufacture upcycled products.
Analysis of Residents’ Preparedness Protocols during Ebola Pandemic in Urban Environment
Authors: Amoo Emmanuel , Oni Gbolahan , Obayan Aize , Alao Amos , Adekeye Olujide , Samuel Gbemisola , Oyegbile Samuel , Adesina Evaristus
Published: August 2021

Background: The study provided empirical analysis of the change in hygiene behavioural practices among community in Ogun and Lagos State with respect to Ebola outbreak in Nigeria. Methods: The data were extracted from a 2015 Cross-Sectional Survey on wellness, knowledge, attitude and practices towards the control and prevention of Ebola virus disease (EVD). Out of 1093 respondents selected in the main survey through simple random sampling technique within two enumeration areas (EAs), only 933 data cases were analyzable, leaving an attrition rate of 14.6%. The EAs represents the sampling points within the local government areas selected for the interviewed. Descriptive and inferential statistical techniques were both employed in the data analysis. Results: The results revealed high level of knowledge of EVD but over 70% were not aware of centre for treatment of EVD; 60.2% believed they cannot be susceptible to EVD. There were changes in certain practices that were canvassed and earlier adhered to during the outbreak. The practice of handshaking reduced, eating of hunted animals decreased only marginally by 6.9% and washing with soap increased by 4%. Conclusion: The study provides helpful insights for public health policy on possible mitigating strategies, especially in terms of behavioral risk factors that are prone to Ebola virus infections or other communicable diseases. The study emphasises that regular hand washing with soap and the use of sanitising agents including availability of treatment centres would be helpful in preventing the occurrence or re-occurrence of pandemic. The protocols identified in this study could be relevant to both medical personnel and the community for adoption especially as the unlikelihood of Ebola (or other pandemic) re-emergence have not been established.
Governance of Urban Green Infrastructure in Informal Settlements of Windhoek, Namibia
Authors: Wijesinghe Amayaa , Thorn Jessica P. R.
Published: August 2021

Facing increased rural-urban migration, population growth, climate change impacts, and cascading natural, security, and health hazards, many municipalities in sub-Saharan Africa are beginning to consider the benefits of urban green infrastructure for improving the resilience and wellbeing of residents living in informal settlements. However, present governance systems are often ill-equipped to deliver the scale of planning needed. Integration of urban green infrastructure into local government mandates, spatial planning and targeted action plans remains limited, further inhibited by scarce empirical research on the topic in Africa. Taking Windhoek, Namibia, and specifically Moses ǁGaroëb, Samora Machel, and Tobias Hainyeko constituencies as a case study, we fitted key informant interview (n = 23), focus group (n = 20), and participant observation data into existing governance theory to investigate (a) benefits and trade-offs of present urban green infrastructure in Windhoek’s informal settlements; (b) urban green infrastructure governance in terms of institutional frameworks, actors and coalitions, resources, and processes; and (c) the key desirable pathways for future urban green infrastructure governance in informal settlements. To this end, we used five green infrastructure initiatives to dissect governance intricacies and found diverse opportunities for innovative governance mechanisms. The urgent need for climate resilience in Namibia offers a policy and practice window to adopt context-specific approaches for multifunctional urban green infrastructure. However, for these initiatives to succeed, collaborative governance platforms and clearly delineated mandates are necessary, with explicit integration of urban green infrastructure into strategies for in-situ informal settlements upgrading and green job growth.
Key Competencies for Sustainability in University of Iceland Curriculum
Authors: Pálsdóttir Auður , Jóhannsdóttir Lára
Published: August 2021

Sustainable development is growingly being developed in universities around the world. The United Nations presented eight Sustainability Key Competencies (SKCs) that represent cross-cutting competencies crucial to advance sustainable development and achieve Sustainable Development Goals. The aim of this research is to know to what extent the University of Iceland courses seem to include the emphasis of the SKCs, either in the course description text or the learning outcomes. Data collection took place in early 2020 and included analysing every single university’s course description text and learning outcomes using a curriculum analysis key for SKCs. Results show that proportionally, most signs of SKCs were found for SKC 3 (Normative competency) in 53% of courses in the university, and SKC 6 (Critical thinking competency) in 46% of the university’s courses. For individual schools of the university, the far highest proportion of signs of SKC was found for the School of Education (5.0 signs per course) and the relatively fewest for the School of Humanities (1.1 sign per course). The results are discussed both in relation to identified competencies needed for the pressing sustainability problems humanity faces, and in the light of a discrepancy appearing between the university’s ranking according to Times Higher Education University Ranking by citations, research, and teaching, and the proportional signs of individual SKCs within the University of Iceland.
Influence of Technostress on Academic Performance of University Medicine Students in Peru during the COVID-19 Pandemic
Authors: Alvarez-Risco Aldo , Del-Aguila-Arcentales Shyla , Yáñez Jaime A. , Rosen Marc A. , Mejia Christian R.
Published: August 2021

The current study aims to validate and apply an instrument to assess the relationship between communication overload, social overload, technostress, exhaustion and academic performance. We performed a cross-sectional, analytical study of 2286 university medical students to assess the influence of technostress as a mediator of social media overload, communication overload and mental exhaustion and its detrimental effect on the academic performance of university students in Peru during the COVID-19 pandemic. The research model was validated using partial least square structural equation modeling (PLS-SEM) to establish the influence of variables on the model. Communication and social overload were found to positively influence technostress by correlations of 0.284 and 0.557, respectively. Technostress positively influenced exhaustion by 0.898, while exhaustion negatively influenced academic performance by -0.439. Bootstrapping demonstrated that the path coefficients of the research model were statistically significant. The research outcomes may help university managers understand students’ technostress and develop strategies to improve the balanced use of technology for their daily academic activities.
Codazzi Tensors and the Quasi-Statistical Structure Associated with Affine Connections on Three-Dimensional Lorentzian Lie Groups
Authors: Wu Tong , Wang Yong
Published: August 2021

In this paper, we classify three-dimensional Lorentzian Lie groups on which Ricci tensors associated with Bott connections, canonical connections and Kobayashi–Nomizu connections are Codazzi tensors associated with these connections. We also classify three-dimensional Lorentzian Lie groups with the quasi-statistical structure associated with Bott connections, canonical connections and Kobayashi–Nomizu connections.
The Comparison Features of ECG Signal with Different Sampling Frequencies and Filter Methods for Real-Time Measurement
Authors: Bui Ngoc-Thang , Byun Gyung-su
Published: August 2021

Electrocardiogram (ECG) signals have been used to monitor and diagnose signs of cardiovascular disease and abnormal signals about the human body. ECG signals are typically characterized by the PR, QRS, QT interval, ST-segment, and heart rate (HR) parameters. ECG devices are widely used for many applications, especially for the elderly. However, ECG signals are often affected by noises from the environment. There are mainly two types of noises that affect the ECG signals: low frequencies from muscle activity and 50/60 Hz from the electrical grid. Removing these noises is important for improving the quality of the ECG signal. A clear ECG signal makes it easy to diagnose cardiovascular problems. ECG signals with high sampling frequency are more accurate. However, the noises in the signal will be more obvious and it will be difficult to remove these noises with filters. We analyzed the symmetrical correlation between the sampling frequency of the signal and the parameters of the signal such as signal to noise ratio (SNR) and signal amplitude. This study will compare characterization of ECG signals performed at different sampling frequencies before and after applying infinite impulse response (IIR) and symmetric finite impulse response (FIR) filters. Therefore, it is critical that the sampling frequency is consistent at the same frequency of the ECG signal for accurate diagnosis. Furthermore, the approach can be also important for the device to help reduce the device’s computing power and hardware resources. Our results were tested with the MIT/ BIH database at 360 Hz sampling frequency with 11-bit resolution. We also experimented with the device operating in real-time with a sampling frequency from 100 Hz to 2133 Hz and a 24-bit resolution. The test results show the advantages of the symmetric FIR filter over IIR when applied to the filtering of ECG signals. The study’s conclusions can be applied to real-world devices to improve the quality of ECG signals.
TGSL-Dependent Feature Selection for Boosting the Visual Sentiment Classification
Authors: Karuthakannan Usha Kingsly Devi , Velusamy Gomathi
Published: August 2021

The automatic recognition of the emotions in still images is inherently more challenging than other visual recognition tasks, such as scene recognition, object classification and semantic image classification, as it involves a higher level of abstraction in the human cognition perspective. Symmetry can be found in many objects in the nature and can be used for many purposes such as object detection and recognition. Furthermore, rotating and flipping of the image is employed based on symmetry for training the classifier for the most accurate classification. Hence, there is a need to handle effectively large intra-class variance, scalability and subjectivity during recognition, and it is inherently ambiguous as an image can evoke multiple emotions. To address these issues, many of the existing works focus on improving the image representations. It is motivated by the observation that both global distributions and local image regions carry massive sentiments. In this research, three different pre-trained architectural models are implemented, and the classification performance of binary sentiment classification is examined on five widely-used effective datasets. Moreover, the features from the pre-trained models are selected optimally using the proposed Teaching Gaining Sharing Learning (TGSL) algorithm, which is the major contribution of the research. Extensive experiment results on the five datasets demonstrate that the proposed Visual sentiment analysis based on the TGSL algorithm with data augmentation achieved an improved performance compared to all other conventional techniques. The proposed framework uses the pre-trained model and never utilized any hand-crafted features, boosting the mean accuracy, sensitivity, and specificity to 99.11%, 99.31%, and 99.22%, respectively, for abstract dataset.
A Fuzzy Optimization Technique for Multi-Objective Aspirational Level Fractional Transportation Problem
Authors: Sharma Mukesh Kumar , Kamini  , Dhiman Nitesh , Mishra Vishnu Narayan , Rosales Hamurabi Gamboa , Dhaka Arvind , Nandal Amita , Fernández Edgar González , Ramirez-delReal Tania A. , Mishra Lakshmi Narayan
Published: August 2021

In this research work, a soft computing optimization operating approach is developed for a multi-objective aspirational level fractional transportation problem. In the proposed technique, a mathematical model is formulated for the multi-objective aspirational level fractional transportation problem (MOFTP) based on the highest value of one and all objectives of the model. We also used the symmetry concept over our model to identify the best optimum solution based on symmetrical data. We constructed the membership grades for the set of fetched parameters having symmetry. In this work, we also used the concept of ranking function in our mathematical model to obtain the optimum solution of the fuzzy multi-objective fractional transportation. In this proposed algorithm, the aspiration levels are also associated with the objective function of MOFTP. We are also proposing a new approach for the optimization of fractional problems in which the objectives are being optimized by using the numerator function and denominator function simultaneously. Further, a methodology is also developed to find the average cost for each fractional objective of the model. After that, we will find the ranking function for each parameter by using the defuzzification method. By this methodology, we will be able to convert the MOFTP into a bi-objective transportation problem. The provided technique is elaborated with the help of numerical computations to prove the beauty and power of the proposed technique.
Lepton-Antineutrino Entanglement and Chiral Oscillations
Authors: Bittencourt Victor A. S. V. , Bernardini Alex E. , Blasone Massimo
Published: August 2021

Dirac bispinors belong to an irreducible representation of the complete Lorentz group, which includes parity as a symmetry yielding two intrinsic discrete degrees of freedom: chirality and spin. For massive particles, chirality is not dynamically conserved, which leads to chiral oscillations. In this contribution, we describe the effects of this intrinsic structure of Dirac bispinors on the quantum entanglement encoded in a lepton-antineutrino pair. We consider that the pair is generated through weak interactions, which are intrinsically chiral, such that in the initial state the lepton and the antineutrino have definite chirality but their spins are entangled. We show that chiral oscillations induce spin entanglement oscillations and redistribute the spin entanglement to chirality-spin correlations. Such a phenomenon is prominent if the momentum of the lepton is comparable with or smaller than its mass. We further show that a Bell-like spin observable exhibits the same behavior of the spin entanglement. Such correlations do not require the knowledge of the full density matrix. Our results show novel effects of the intrinsic bispinor structure and can be used as a basis for designing experiments to probe chiral oscillations via spin correlation measurements.
The Multifaceted Role of Macrophages in Oncolytic Virotherapy
Authors: Hofman Laura , Lawler Sean E. , Lamfers Martine L. M.
Published: August 2021

One of the cancer hallmarks is immune evasion mediated by the tumour microenvironment (TME). Oncolytic virotherapy is a form of immunotherapy based on the application of oncolytic viruses (OVs) that selectively replicate in and induce the death of tumour cells. Virotherapy confers reciprocal interaction with the host’s immune system. The aim of this review is to explore the role of macrophage-mediated responses in oncolytic virotherapy efficacy. The approach was to study current scientific literature in this field in order to give a comprehensive overview of the interactions of OVs and macrophages and their effects on the TME. The innate immune system has a central influence on the TME; tumour-associated macrophages (TAMs) generally have immunosuppressive, tumour-supportive properties. In the context of oncolytic virotherapy, macrophages were initially thought to predominantly contribute to anti-viral responses, impeding viral spread. However, macrophages have now also been found to mediate transport of OV particles and, after TME infiltration, to be subjected to a phenotypic shift that renders them pro-inflammatory and tumour-suppressive. These TAMs can present tumour antigens leading to a systemic, durable, adaptive anti-tumour immune response. After phagocytosis, they can recirculate carrying tissue-derived proteins, which potentially enables the monitoring of OV replication in the TME. Their role in therapeutic efficacy is therefore multifaceted, but based on research applying relevant, immunocompetent tumour models, macrophages are considered to have a central function in anti-cancer activity. These novel insights hold important clinical implications. When optimised, oncolytic virotherapy, mediating multifactorial inhibition of cancer immune evasion, could contribute to improved patient survival.
Variable In Vivo Hepatitis D Virus (HDV) RNA Editing Rates According to the HDV Genotype
Authors: Dziri Samira , Rodriguez Christophe , Gerber Athenaïs , Brichler Ségolène , Alloui Chakib , Roulot Dominique , Dény Paul , Pawlotsky Jean Michel , Gordien Emmanuel , Le Gal Frédéric
Published: August 2021

Human hepatitis delta virus (HDV) is a small defective RNA satellite virus that requires hepatitis B virus (HBV) envelope proteins to form its own virions. The HDV genome possesses a single coding open reading frame (ORF), located on a replicative intermediate, the antigenome, encoding the small (s) and the large (L) isoforms of the delta antigen (s-HDAg and L-HDAg). The latter is produced following an editing process, changing the amber/stop codon on the s-HDAg-ORF into a tryptophan codon, allowing L-HDAg synthesis by the addition of 19 (or 20) C-terminal amino acids. The two delta proteins play different roles in the viral cell cycle: s-HDAg activates genome replication, while L-HDAg blocks replication and favors virion morphogenesis and propagation. L-HDAg has also been involved in HDV pathogenicity. Understanding the kinetics of viral editing rates in vivo is key to unravel the biology of the virus and understand its spread and natural history. We developed and validated a new assay based on next-generation sequencing and aimed at quantifying HDV RNA editing in plasma. We analyzed plasma samples from 219 patients infected with different HDV genotypes and showed that HDV editing capacity strongly depends on the genotype of the strain.