bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Solar Powered Biodiesel Reactor for Kuwait Sheep Tallow
Authors: Alajmi Fnyees , Alajmi Amer , Alrashidi Ahmed , Alrashidi Naser , Adam Nor Mariah , Hairuddin Abdul Aziz
Published: September 2021

Biodiesel is one of the more recent green fuel products in the world. It can be produced from several raw materials such as straight vegetable oils, animal fats, tallow, and waste cooking oils, and blended with diesel. Properties of biodiesel are different compared to fossil diesel in terms of production methods and emission levels released after combustion in an internal combustion engine. Kuwait consumes a huge amount of energy which is almost 8% to meet the increasing demand for electricity and water. Moreover, the use of electricity in the production of biodiesel increases energy use and cost of production. Kuwait is receiving an amount of solar irradiation ranging from 2050 kWh/m2 to 2100 kWh/m2. The present study is concerned with the evaluation of the potential to use solar energy to produce biodiesel from sheep fat waste as a raw material. An experimental test rig was set up for a single cylinder diesel engine in the laboratory, where a solar power system was used to assist the production process of biodiesel from tallow waste. The biodiesel is then blended with diesel at different volume percentages, such as graded as B20, B50, B75 and B100, respectively. The exhaust gases such as oxygen, carbon monoxide, carbon dioxide, nitric oxide and nitric dioxide where also analyzed. An optimum decrease in values of nitric oxide levels was observed at the load of 51%, 68%, 85% and 93% during the operation at blend of biodiesel B20, B50, B75 and B100, respectively. Nitric dioxide was decreased at the load of 51%, 68% and 85% during the operation using B20, B50 and B75, respectively. Optimum SFC was achieved at B20, B50 and B75 during high loads of 85% and 93%. It can be concluded that sheep tallow biodiesel shows a promising result in terms of fuel consumption and environmental emissions of greenhouse gases.
Thermal Analysis and Energy Efficiency Improvements in Tunnel Kiln for Sustainable Environment
Authors: Hussnain Syed Ali , Farooq Muhammad , Amjad Muhammad , Riaz Fahid , Tahir Zia Ur Rehman , Sultan Muhammad , Hussain Ijaz , Shakir Muhammad Ali , Qyyum Muhammad Abdul , Han Ning , Bokhari Awais
Published: September 2021

Kiln is a prime need in the ceramics industry, where energy loss is a major part which consumes about 60% production cost through thermal energy for different applications. Higher density of fired and tunnel kiln refractory material lowers the thermal diffusivity and the proper selection of fired material minimizes the energy loss along the kiln. In particular, this research analysed the results of a heat recovery system comprised of a metallic recuperator which gives around 8% energy savings in natural gas consumption. In this work, detailed power quality analysis of low-power factor motors of a tunnel kiln was carried out and a power factor improvement solution was suggested to save electrical energy with payback period of 0.8 y. The motor operating at a low-power factor consumes more reactive power which does not produce beneficial work. A low-power factor around 0.4 causes network power loss, increases in transformer loss and voltage drops. The solution with accumulative capacitance power of 148.05 uF was installed to achieve the power factor to 0.9. Flu gas analyzer was installed to monitor the range of O2 in pre-heating, oxidation, and firing zones of the kiln which should be ≥8% and 3%, respectively. Regression analysis for thermal energy consumption of a tunnel kiln is done to find the forecast thermal energy consumption. This analysis can be used to find operational efficiency, supporting decisions regarding dependent variable of thermal energy consumption and independent variable of production. This research is very helpful for the ceramics industry to mitigate the energy loss at SMEs as well as in mass production level.
Case Study on a Potential Application of Failure Mode and Effects Analysis in Assessing Compliance Risks
Authors: Bognár Ferenc , Benedek Petra
Published: September 2021

Assessing and reducing compliance risks can now be considered one of the core criteria for business success. While failure mode and effect analysis (FMEA) is widely used in engineering, its application in the financial sector is quite novel, primarily related to compliance risk assessment. This paper presents the results of exploratory research based on the potential application of FMEA in a focus group of compliance experts at one of the largest Central and Eastern European commercial banks. This study aims to establish a process for assessing compliance risks that builds on the strengths of both the qualitative and quantitative assessment methods. Applying FMEA based on a nominal group technique and further statistical analysis provides an opportunity to compare expert assessments and the consensus level of the participants. As a result, the similarity or difference of the assessment patterns can be quantified, providing objective feedback on the evaluation. Finally, this paper proposes lifting the detectability of failures as an evaluation dimension to the same level of importance as the probability and impact of non-compliance and using agreement testing statistical methods.
Efficient Transformer for Remote Sensing Image Segmentation
Authors: Xu Zhiyong , Zhang Weicun , Zhang Tianxiang , Yang Zhifang , Li Jiangyun
Published: September 2021

Semantic segmentation for remote sensing images (RSIs) is widely applied in geological surveys, urban resources management, and disaster monitoring. Recent solutions on remote sensing segmentation tasks are generally addressed by CNN-based models and transformer-based models. In particular, transformer-based architecture generally struggles with two main problems: a high computation load and inaccurate edge classification. Therefore, to overcome these problems, we propose a novel transformer model to realize lightweight edge classification. First, based on a Swin transformer backbone, a pure Efficient transformer with mlphead is proposed to accelerate the inference speed. Moreover, explicit and implicit edge enhancement methods are proposed to cope with object edge problems. The experimental results evaluated on the Potsdam and Vaihingen datasets present that the proposed approach significantly improved the final accuracy, achieving a trade-off between computational complexity (Flops) and accuracy (Efficient-L obtaining 3.23% mIoU improvement on Vaihingen and 2.46% mIoU improvement on Potsdam compared with HRCNet_W48). As a result, it is believed that the proposed Efficient transformer will have an advantage in dealing with remote sensing image segmentation problems.
Assessing the Impacts of Rising Sea Level on Coastal Morpho-Dynamics with Automated High-Frequency Shoreline Mapping Using Multi-Sensor Optical Satellites
Authors: Adebisi Naheem , Balogun Abdul-Lateef , Mahdianpari Masoud , Min Teh Hee
Published: September 2021

Rising sea level is generally assumed and widely reported to be the significant driver of coastal erosion of most low-lying sandy beaches globally. However, there is limited data-driven evidence of this relationship due to the challenges in quantifying shoreline dynamics at the same temporal scale as sea-level records. Using a Google Earth Engine (GEE)-enabled Python toolkit, this study conducted shoreline dynamic analysis using high-frequency data sampling to analyze the impact of sea-level rise on the Malaysian coastline between 1993 and 2019. Instantaneous shorelines were extracted from a test site on Teluk Nipah Island and 21 tide gauge sites from the combined Landsat 5–8 and Sentinel 2 images using an automated shoreline-detection method, which was based on supervised image classification and sub-pixel border segmentation. The results indicated that rising sea level is contributing to shoreline erosion in the study area, but is not the only driver of shoreline displacement. The impacts of high population density, anthropogenic activities, and longshore sediment transportation on shoreline displacement were observed in some of the beaches. The conclusions of this study highlight that the synergistic use of multi-sensor remote-sensing data improves temporal resolution of shoreline detection, removes short-term variability, and reduces uncertainties in satellite-derived shoreline analysis compared to the low-frequency sampling approach.
The Potential of Satellite Remote Sensing Time Series to Uncover Wetland Phenology under Unique Challenges of Tidal Setting
Authors: Miller Gwen Joelle , Dronova Iryna , Oikawa Patricia Y. , Knox Sara Helen , Windham-Myers Lisamarie , Shahan Julie , Stuart-Haëntjens Ellen
Published: September 2021

While growth history of vegetation within upland systems is well studied, plant phenology within coastal tidal systems is less understood. Landscape-scale, satellite-derived indicators of plant greenness may not adequately represent seasonality of vegetation biomass and productivity within tidal wetlands due to limitations of cloud cover, satellite temporal frequency, and attenuation of plant signals by tidal flooding. However, understanding plant phenology is necessary to gain insight into aboveground biomass, photosynthetic activity, and carbon sequestration. In this study, we use a modeling approach to estimate plant greenness throughout a year in tidal wetlands located within the San Francisco Bay Area, USA. We used variables such as EVI history, temperature, and elevation to predict plant greenness on a 14-day timestep. We found this approach accurately estimated plant greenness, with larger error observed within more dynamic restored wetlands, particularly at early post-restoration stages. We also found modeled EVI can be used as an input variable into greenhouse gas models, allowing for an estimate of carbon sequestration and gross primary production. Our strategy can be further developed in future research by assessing restoration and management effects on wetland phenological dynamics and through incorporating the entire Sentinel-2 time series once it becomes available within Google Earth Engine.
A Spectral Spatial Attention Fusion with Deformable Convolutional Residual Network for Hyperspectral Image Classification
Authors: Zhang Tianyu , Shi Cuiping , Liao Diling , Wang Liguo
Published: September 2021

Convolutional neural networks (CNNs) have exhibited excellent performance in hyperspectral image classification. However, due to the lack of labeled hyperspectral data, it is difficult to achieve high classification accuracy of hyperspectral images with fewer training samples. In addition, although some deep learning techniques have been used in hyperspectral image classification, due to the abundant information of hyperspectral images, the problem of insufficient spatial spectral feature extraction still exists. To address the aforementioned issues, a spectral–spatial attention fusion with a deformable convolution residual network (SSAF-DCR) is proposed for hyperspectral image classification. The proposed network is composed of three parts, and each part is connected sequentially to extract features. In the first part, a dense spectral block is utilized to reuse spectral features as much as possible, and a spectral attention block that can refine and optimize the spectral features follows. In the second part, spatial features are extracted and selected by a dense spatial block and attention block, respectively. Then, the results of the first two parts are fused and sent to the third part, and deep spatial features are extracted by the DCR block. The above three parts realize the effective extraction of spectral–spatial features, and the experimental results for four commonly used hyperspectral datasets demonstrate that the proposed SSAF-DCR method is superior to some state-of-the-art methods with very few training samples.
Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings
Authors: Pandey Piyush , Payn Kitt G. , Lu Yuzhen , Heine Austin J. , Walker Trevor D. , Acosta Juan J. , Young Sierra
Published: September 2021

Loblolly pine is an economically important timber species in the United States, with almost 1 billion seedlings produced annually. The most significant disease affecting this species is fusiform rust, caused by Cronartium quercuum f. sp. fusiforme. Testing for disease resistance in the greenhouse involves artificial inoculation of seedlings followed by visual inspection for disease incidence. An automated, high-throughput phenotyping method could improve both the efficiency and accuracy of the disease screening process. This study investigates the use of hyperspectral imaging for the detection of diseased seedlings. A nursery trial comprising families with known in-field rust resistance data was conducted, and the seedlings were artificially inoculated with fungal spores. Hyperspectral images in the visible and near-infrared region (400–1000 nm) were collected six months after inoculation. The disease incidence was scored with traditional methods based on the presence or absence of visible stem galls. The seedlings were segmented from the background by thresholding normalized difference vegetation index (NDVI) images, and the delineation of individual seedlings was achieved through object detection using the Faster RCNN model. Plant parts were subsequently segmented using the DeepLabv3+ model. The trained DeepLabv3+ model for semantic segmentation achieved a pixel accuracy of 0.76 and a mean Intersection over Union (mIoU) of 0.62. Crown pixels were segmented using geometric features. Support vector machine discrimination models were built for classifying the plants into diseased and non-diseased classes based on spectral data, and balanced accuracy values were calculated for the comparison of model performance. Averaged spectra from the whole plant (balanced accuracy = 61%), the crown (61%), the top half of the stem (77%), and the bottom half of the stem (62%) were used. A classification model built using the spectral data from the top half of the stem was found to be the most accurate, and resulted in an area under the receiver operating characteristic curve (AUC) of 0.83.
Laboratory Validation of Instrumented Mouthguard for Use in Sport
Authors: Stitt Danyon , Draper Nick , Alexander Keith , Kabaliuk Natalia
Published: September 2021

Concussion is an inherent risk of participating in contact, combat, or collision sports, within which head impacts are numerous. Kinematic parameters such as peak linear and rotational acceleration represent primary measures of concussive head impacts. The ability to accurately measure and categorise such impact parameters in real time is important in health and sports performance contexts. The purpose of this study was to assess the accuracy of the latest HitIQ Nexus A9 instrumented mouthguard (HitIQ Pty. Ltd. Melbourne Australia) against reference sensors in an aluminium headform. The headform underwent drop testing at various impact intensities across the NOCSAE-defined impact locations, comparing the peak linear and rotational acceleration (PLA and PRA) as well as the shapes of the acceleration time-series traces for each impact. Mouthguard PLA and PRA measurements strongly correlated with (R2 = 0.996 and 0.994 respectively), and strongly agreed with (LCCC = 0.997) the reference sensors. The root mean square error between the measurement devices was 1 ± 0.6g for linear acceleration and 47.4 ± 35 rad/s2 for rotational acceleration. A Bland–Altman analysis found a systematic bias of 1% for PRA, with no significant bias for PLA. The instrumented mouthguard displayed high accuracy when measuring head impact kinematics in a laboratory setting.
An Efficient and Precise Remote Sensing Optical Image Matching Technique Using Binary-Based Feature Points
Authors: Cheng Min-Lung , Matsuoka Masashi
Published: September 2021

Matching local feature points is an important but crucial step for various optical image processing applications, such as image registration, image mosaicking, and structure-from-motion (SfM). Three significant issues associated with this subject have been the focus for years, including the robustness of the image features detected, the number of matches obtained, and the efficiency of the data processing. This paper proposes a systematic algorithm that incorporates the synthetic-colored enhanced accelerated binary robust invariant scalar keypoints (SC-EABRISK) method and the affine transformation with bounding box (ATBB) procedure to address these three issues. The SC-EABRISK approach selects the most representative feature points from an image and rearranges their descriptors by adding color information for more precise image matching. The ATBB procedure, meanwhile, is an outreach that implements geometric mapping to retrieve more matches from the feature points ignored during SC-EABRISK processing. The experimental results obtained using benchmark imagery datasets, close-range photos (CRPs), and aerial and satellite images indicate that the developed algorithm can perform up to 20 times faster than the previous EABRISK method, achieve thousands of matches, and improve the matching precision by more than 90%. Consequently, SC-EABRISK with the ATBB algorithm can address image matching efficiently and precisely.
A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning
Authors: Bouchabou Damien , Nguyen Sao Mai , Lohr Christophe , LeDuc Benoit , Kanellos Ioannis
Published: September 2021

Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy, and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. However, new challenges are emerging every day. In this paper, we present recent algorithms, works, challenges, and taxonomy of the field of human activity recognition in a smart home through ambient sensors. Moreover, since activity recognition in smart homes is a young field, we raise specific problems, as well as missing and needed contributions. However, we also propose directions, research opportunities, and solutions to accelerate advances in this field.
Textile-Based Sensors for Biosignal Detection and Monitoring
Authors: Blachowicz Tomasz , Ehrmann Guido , Ehrmann Andrea
Published: September 2021

Biosignals often have to be detected in sports or for medical reasons. Typical biosignals are pulse and ECG (electrocardiogram), breathing, blood pressure, skin temperature, oxygen saturation, bioimpedance, etc. Typically, scientists attempt to measure these biosignals noninvasively, i.e., with electrodes or other sensors, detecting electric signals, measuring optical or chemical information. While short-time measurements or monitoring of patients in a hospital can be performed by systems based on common rigid electrodes, usually containing a large amount of wiring, long-term measurements on mobile patients or athletes necessitate other equipment. Here, textile-based sensors and textile-integrated data connections are preferred to avoid skin irritations and other unnecessary limitations of the monitored person. In this review, we give an overview of recent progress in textile-based electrodes for electrical measurements and new developments in textile-based chemical and other sensors for detection and monitoring of biosignals.
Exploring the Predictability of Temperatures in a Scaled Model of a Smarthome
Authors: Burns Thomas , Fichthorn Gregory , Ling Jason , Zehtabian Sharare , Bacanlı Salih S. , Bölöni Ladislau , Turgut Damla
Published: September 2021

In modern smarthomes, temperature regulation is achieved through a mix of traditional and emergent technologies including air conditioning, heating, intelligent utilization of the effects of sun, wind, and shade as well as using stored heat and cold. To achieve the desired comfort for the inhabitants while minimizing environmental impact and cost, the home controller must predict how its actions will impact the temperature and other environmental factors in various parts of the home. The question we are investigating in this paper is whether the temperature values in different rooms in a home are predictable based on readings from sensors in the home. We are also interested in whether increased accuracy can be achieved by adding sensors to capture the state of doors and windows of the given room and/or the whole home, and what type of machine learning algorithms can take advantage of the additional information. As experimentation on real-world homes is highly expensive, we use ScaledHome, a 1:12 scale, IoT-enabled model of a smart home for data acquisition. Our experiments show that while additional data can improve the accuracy of the prediction, the type of machine learning models needs to be carefully adapted to the number of data features available.
Cybersecurity in ICT Supply Chains: Key Challenges and a Relevant Architecture
Authors: Masip-Bruin Xavi , Marín-Tordera Eva , Ruiz José , Jukan Admela , Trakadas Panagiotis , Cernivec Ales , Lioy Antonio , López Diego , Santos Henrique , Gonos Antonis , Silva Ana , Soriano José , Kalogiannis Grigorios
Published: September 2021

The specific demands of supply chains built upon large and complex IoT systems, make it a must to design a coordinated framework for cyber resilience provisioning, intended to guarantee trusted supply chains of ICT systems, built upon distributed, dynamic, potentially insecure, and heterogeneous ICT infrastructures. As such, the solution proposed in this paper is envisioned to deal with the whole supply chain system components, from the IoT ecosystem to the infrastructure connecting them, addressing security and privacy functionalities related to risks and vulnerabilities management, accountability, and mitigation strategies, as well as security metrics and evidence-based security assurance. In this paper, we present FISHY as a preliminary architecture that is designed to orchestrate existing and beyond state-of-the-art security appliances in composed ICT scenarios. To this end, the FISHY architecture leverages the capabilities of programmable networks and IT infrastructure through seamless orchestration and instantiation of novel security services, both in real-time and proactively. The paper also includes a thorough business analysis to go far beyond the technical benefits of a potential FISHY adoption, as well as three real-world use cases highlighting the envisioned benefits of a potential FISHY adoption.
Research on a Task Offloading Strategy for the Internet of Vehicles Based on Reinforcement Learning
Authors: Xiao Shuo , Wang Shengzhi , Zhuang Jiayu , Wang Tianyu , Liu Jiajia
Published: September 2021

Today, vehicles are increasingly being connected to the Internet of Things, which enables them to obtain high-quality services. However, the numerous vehicular applications and time-varying network status make it challenging for onboard terminals to achieve efficient computing. Therefore, based on a three-stage model of local-edge clouds and reinforcement learning, we propose a task offloading algorithm for the Internet of Vehicles (IoV). First, we establish communication methods between vehicles and their cost functions. In addition, according to the real-time state of vehicles, we analyze their computing requirements and the price function. Finally, we propose an experience-driven offloading strategy based on multi-agent reinforcement learning. The simulation results show that the algorithm increases the probability of success for the task and achieves a balance between the task vehicle delay, expenditure, task vehicle utility and service vehicle utility under various constraints.
Rural Proofing Policies for Health: Barriers to Policy Transfer for Australia
Authors: Sutarsa I Nyoman , Campbell Lachlan , Moore Malcolm
Published: September 2021

A ‘rural proofing’ framework, which offers assessment of the potential impacts of policies on rural and remote communities, has been advocated for by state governments and interest groups throughout Australia. It is argued that rural proofing can be used to redress health inequities between urban and rural and remote communities. While implementation of rural proofing in some countries shows promising results, there are many social and spatial contexts that should be considered prior to its adoption in Australia. Rural proofing is not the best option for rural health policy in Australia. It has been imported from communities where the urban/rural divide is minimal. It is based on a rigid urban/rural binary model that targets disparity rather than accommodating the diversity of rural communities. Rural proofing concentrates on tick-the-box activities, where rural communities are not sufficiently consulted. There is no unified federal ministry in Australia with responsibility for rural and remote affairs. Considering potential shortcomings of rural proofing for health policies, it is imperative for Australia to have a specific rural health policy at both federal and state levels.
Old Wine in New Bottles: Exploiting Data from the EU’s Farm Accountancy Data Network for Pan-EU Sustainability Assessments of Agricultural Production Systems
Authors: Matthews Keith B. , Renner Ansel , Blackstock Kirsty L. , Waylen Kerry A. , Miller Dave G. , Wardell-Johnson Doug H. , Juarez-Bourke Alba , Cadillo-Benalcazar Juan , Schyns Joep F. , Giampietro Mario
Published: September 2021

The paper presents insights from carrying out a pan-EU sustainability assessment using Farm Accountancy Data Network (FADN) data (the old wine) with societal metabolism accounting (SMA) processes (the new bottles). The SMA was deployed as part of a transdisciplinary study with EU policy stakeholders of how EU policy may need to change to deliver sustainability commitments, particularly to the UN Sustainable Development Goals. The paper outlines the concepts underlying SMA and its specific implementation using the FADN data. A key focus was on the interactions between crop and livestock systems and how this determines imported feedstuffs requirements, with environmental and other footprints beyond the EU. Examples of agricultural production systems performance are presented in terms of financial/efficiency, resource use (particularly the water footprint) and quantifies potential pressures on the environment. Benefits and limitations of the FADN dataset and the SMA outputs are discussed, highlighting the challenges of linking quantified pressures with environmental impacts. The paper concludes that the complexity of agriculture’s interactions with economy and society means there is great need for conceptual frameworks, such as SMA, that can take multiple, non-equivalent, perspectives and that can be deployed with policy stakeholders despite generating uncomfortable knowledge.
The Impact of Public-Private Partnership Investment in Energy and Technological Innovation on Ecological Footprint: The Case of Pakistan
Authors: Chunling Li , Memon Javed Ahmed , Thanh Tiep Le , Ali Minhaj , Kirikkaleli Dervis
Published: September 2021

This novel research looked into the role of public-private partnership investment in energy in affecting Pakistan’s long-term environmental sustainability. Employing time series data from 1992 to 2018 and utilizing the autoregressive distributive lag model (ARDL) model, we found a long-term equilibrium association of ecological footprint with public-private partnership investment in energy, technological innovation, economic growth, and trade openness. Our outcomes showed a significant positive association between public-private partnership investment in energy and ecological footprint in the long-run and the short-run, specifying that the increase in public-private partnership investment in energy affects the environmental sustainability of Pakistan. Similarly, our study confirmed that technological innovation, economic growth, and trade openness increase the ecological footprint in Pakistan. It demonstrates that these factors are unfavorable to the sustainable environment in Pakistan. Furthermore, robustness check findings are analogous to the results of ARDL estimates, utilizing dynamic ordinary least squares and fully modified ordinary least squares. On the basis of the research conclusions, a multi-pronged sustainable development goal (SDG) model was proposed that addresses SDG 8 and SDG 13 while incorporating SDG 17 as a medium.
Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection
Authors: Ahmadpour Hamed , Bazrafshan Ommolbanin , Rafiei-Sardooi Elham , Zamani Hossein , Panagopoulos Thomas
Published: September 2021

Gully erosion susceptibility mapping is an essential land management tool to reduce soil erosion damages. This study investigates gully susceptibility based on multiple diagnostic analysis, support vector machine and random forest algorithms, and also a combination of these models, namely the ensemble model. Thus, a gully susceptibility map in the Kondoran watershed of Iran was generated by applying these models on the occurrence and non-occurrence points (as the target variable) and several predictors (slope, aspect, elevation, topographic wetness index, drainage density, plan curvature, distance to streams, lithology, soil texture and land use). The Boruta algorithm was used to select the most effective variables in modeling gully erosion susceptibility. The area under the receiver operating characteristic curve (AUC), the receiver operating characteristics, and true skill statistics (TSS) were used to assess the model performance. The results indicated that the ensemble model had the best performance (AUC = 0.982, TSS = 0.93) compared to the others. The most effective factors in gully erosion susceptibility mapping of the study region were topological, anthropogenic, and geological. The methodology and variables of this study can be used in other regions to control and mitigate the gully erosion phenomenon by applying biophilic and regenerative techniques at the locations of the most influential factors.
Research on Evaluation of Climate Comfort in Northwest China under Climate Change
Authors: Yao Xuyang , Zhang Mingjun , Zhang Yu , Xiao Hanyu , Wang Jiaxin
Published: September 2021

Based on the monthly observation data of major surface meteorological observatories in Northwest China from 1960 to 2019, this paper uses the temperature and humidity index (THI), wind efficiency index (WEI), and clothing index (ICL) to construct a comprehensive climate comfort index evaluation model. This model was used to quantitatively evaluate the change characteristics of climate comfort in Northwest China against the background of climate warming. The results show that the overall climate comfort index in Northwest China is on the rise. In terms of space, an increase in the climate comfort index means an increase in the comfort zone, with the largest increase in the southeastern part of Gansu Province and the southern part of Shaanxi Province. The trend of change is that the increase in the north is greater than that in the south, and the higher the latitude, the greater the change. The space range of the comfort zone and the sub-comfort zone is generally expanding, and the climate is gradually becoming more comfortable. In terms of time, an increase in the climate comfort index means an increase in the climate comfort period, and the annual comfort index shows an increasing trend. The comfort period is mostly distributed in summer, with the most suitable cities in May and September, followed by June.
Safety Evaluation of Flower Roundabout Considering Autonomous Vehicles Operation
Authors: Severino Alessandro , Pappalardo Giuseppina , Curto Salvatore , Trubia Salvatore , Olayode Isaac Oyeyemi
Published: September 2021

With the significant technological growth that affected autonomous vehicles in the last decade, several consequences occurred as: human factor exclusion, entry and exit manoeuvres precision from roundabouts, and headway reduction. In this paper, it was carried out a microsimulation approach study that aims to evaluate benefits in terms of safety obtained with flower roundabouts in a scenario where traffic is characterized by conventional vehicles “CVs” and Connected Autonomous Vehicles “CAVs”. This study focused on the evaluation of CAVs and CVs operation with the presence of the so called “weak users” or rather, pedestrians and bikes. Then, simulated scenarios were characterized by the presence of zebra-crossings in main roads, positioned at 20 m from circulatory carriageway edges. Micro simulation choice is due to the absence of survey data collection because the presence of CAVs in ordinary traffic is still minimal. The micro simulation was carried out through VISSIM, so it was operated with a specific methodological path, consisting, in the application, of O–D matrix based on real cases, in order to achieve an assessment of potential conflicts in relation with the increase in CAVs. Simulation results showed that higher safety levels were achieved for special cases of O–D distribution and with CAVs present. Finally, considering crash absence in results related to CAVs presence, safety interventions of such roundabout types have to be thorough. There were 10 O/D matrices analysed through VISSIM considering parameters as: average tail length, maximum tail length, average speed, vehicles, and number of stops quantity. As reported in the conclusion section, O/D matrices that showed minimum conflicts and maximum dynamic performances were identified.
Economic Valuation of Ecosystem Service Benefits and Welfare Impacts of Offshore Marine Protected Areas: A Study from the Baltic Sea
Authors: Pakalniete Kristīne , Ahtiainen Heini , Aigars Juris , Andersone Ingrīda , Armoškaite Aurelija , Hansen Henning Sten , Strāķe Solvita
Published: September 2021

Knowledge of ecosystem services (ES) and the benefits provided by offshore marine areas, including the welfare impacts from the establishment of marine protected areas (MPAs) is still limited. In the present study we evaluated benefits from ES, citizens’ willingness-to-pay for potential changes in the provision of ES, and welfare losses to citizens due to restrictions on economic activities from establishing new offshore MPAs in Latvian waters. The scenarios for the economic valuation were based on analysing the supply of ES from the protected marine habitats, showing changes in the ES supply in policy relevant scenarios of the MPA size. Our study evaluates a wide array of ES delivered by offshore protected habitats and reveals that citizens’ willingness-to-pay for preserving habitats and ES supply exceeds their welfare losses from restrictions in economic activities. Our approach supports the prioritisation of habitat types according to their contribution to ES supply and benefits for citizens. The analysis can be complemented with spatial data regarding distribution of habitats, providing an opportunity to identify areas with the highest ES benefits to support marine protection and spatial planning.
Alternative Use of the Insecticide Diofenolan on Musca domestica (Diptera: Muscidae): A Morphological and Ultrastructural Investigation
Authors: Pezzi Marco , Scapoli Chiara , Marchetti Maria Gabriella , Chicca Milvia , Krčmar Stjepan , Leis Marilena , Bonacci Teresa
Published: September 2021

Musca domestica Linnaeus (Diptera: Muscidae), a relevant synanthropic pest, is the most frequent dipteran species in farms and waste landfills. Insect Growth Regulators (IGR), insecticides with species-specific toxicity and low health and environmental impact, are known to act mostly on larval stages but may induce sterility in adults. We investigated the effects of diofenolan, an IGR analogue of juvenile hormone, on M. domestica, with special attention to female reproductive ability (egg-laying and hatching), and ovarian and ovariole morphology, using optical and transmission electron microscopy. We also tested the interactions between diofenolan and the activity of corpora allata, endocrine gland producing juvenile hormone required for ovarian development, by morphological and ultrastructural investigations. The results show that diofenolan negatively affects the reproductive ability of M. domestica, causing extensive morphological alterations in ovaries and ovarioles. In treated females, ovarioles showed nine different morphotypes that could be arranged into three “transformation paths”, and these alterations were able to reduce egg-laying. The effects of diofenolan on corpora allata, investigated at the optical and ultrastructural level in M. domestica, showed extensive alterations of the nuclei, cytoplasms, and mitochondria, strongly suggesting a rapid transition of the gland from inactivity to degeneration. The sterilizing effects of diofenolan in M. domestica are very interesting for integrated pest management programs within sustainable defence strategies against this relevant and annoying pest.
Symmetries and Related Physical Balances for Discontinuous Flow Phenomena within the Framework of Lagrange Formalism
Authors: Mellmann Marcel , Scholle Markus
Published: September 2021

By rigorous analysis, it is proven that from discontinuous Lagrangians, which are invariant with respect to the Galilean group, Rankine–Hugoniot conditions for propagating discontinuities can be derived via a straight forward procedure that can be considered an extension of Noether’s theorem. The use of this general procedure is demonstrated in particular for a Lagrangian for viscous flow, reproducing the well known Rankine–Hugoniot conditions for shock waves.
Adaptive Spatial-Temporal Regularization for Correlation Filters Based Visual Object Tracking
Authors: Chen Fei , Wang Xiaodong
Published: September 2021

Recently, Discriminative Correlation Filters (DCF) have shown excellent performance in visual object tracking. The correlation for a computing response map can be conducted efficiently in Fourier domain by Discrete Fourier Transform (DFT) of inputs, where the DFT of an image has symmetry on the Fourier domain. To enhance the robustness and discriminative ability of the filters, many efforts have been devoted to optimizing the learning process. Regularization methods, such as spatial regularization or temporal regularization, used in existing DCF trackers aim to enhance the capacity of the filters. Most existing methods still fail to deal with severe appearance variations—in particular, the large scale and aspect ratio changes. In this paper, we propose a novel framework that employs adaptive spatial regularization and temporal regularization to learn reliable filters in both spatial and temporal domains for tracking. To alleviate the influence of the background and distractors to the non-rigid target objects, two sub-models are combined, and multiple features are utilized for learning of robust correlation filters. In addition, most DCF trackers that applied 1-dimensional scale space search method suffered from appearance changes, such as non-rigid deformation. We proposed a 2-dimensional scale space search method to find appropriate scales to adapt to large scale and aspect ratio changes. We perform comprehensive experiments on four benchmarks: OTB-100, VOT-2016, VOT-2018, and LaSOT. The experimental results illustrate the effectiveness of our tracker, which achieved a competitive tracking performance. On OTB-100, our tracker achieved a gain of 0.8% in success, compared to the best existing DCF trackers. On VOT2018, our tracker outperformed the top DCF trackers with a gain of 1.1% in Expected Average Overlap (EAO). On LaSOT, we obtained a gain of 5.2% in success, compared to the best DCF trackers.
New Trends in the Occurrence of Yessotoxins in the Northwestern Adriatic Sea
Authors: Rubini Silva , Albonetti Sabrina , Menotta Simonetta , Cervo Antonio , Callegari Emanuele , Cangini Monica , Dall’Ara Sonia , Baldini Erika , Vertuani Silvia , Manfredini Stefano
Published: September 2021

Yessotoxins (YTXs) are polycyclic toxic ether compounds produced by phytoplanktonic dinoflagellates which accumulate in filter-feeding organisms. We know that the water temperature in our areas Northwestern Adriatic Sea is optimal for the growth of potentially toxic algae (around 20 °C). In recent years, these temperatures have remained at these levels for longer and longer periods, probably due to global warming, which has led to an excessive increase in toxin levels. The interruption of mussel harvesting caused by algae negatively affects farmers’ revenues and the availability of local fish, causing a major economic loss in Italy’s main shellfish sector. Methods: In the nine years considered, 3359 samples were examined: 1715 marine waters, 73 common clams; 732 mussels; 66 oysters; and 773 veracious clams. Bivalve molluscs were examined for the presence of marine biotoxins, including YTXs, while potentially toxic algae, including those producing YTXs, were searched for and counted in marine waters. The method adopted for the quantification of lipophilic toxins involves the use of an LC-MS/MS system. The enumeration of phytoplankton cells was performed according to the Utermhöl method. Results: Between 2012 and 2020, 706 molluscs were tested for YTXs. In total, 246 samples tested positive, i.e., 34.84%. Of the positive samples, 30 exceeded the legal limit. Conclusion: In this regard, it is essential to develop and activate, as soon as possible, an “early warning” system that allows a better control of the production areas of live bivalve molluscs, thus allowing an optimal management of the plants in these critical situations.
Topological Aspects of Dense Matter: Lattice Studies
Authors: Lombardo Maria Paola
Published: September 2021

Topological fluctuations change their nature in the different phases of strong interactions, and the interrelation of topology, chiral symmetry and confinement at high temperature has been investigated in many lattice studies. This review is devoted to the much less explored subject of topology in dense matter. After a short overview of the status at zero density, which will serve as a baseline for the discussion, we will present lattice results for baryon rich matter, which, due to technical difficulties, has been mostly studied in two-color QCD, and for matter with isospin and chiral imbalances. In some cases, a coherent pattern emerges, and in particular the topological susceptibility seems suppressed at high temperature for baryon and isospin rich matter. However, at low temperatures the topological aspects of dense matter remain not completely clear and call for further studies.
Current Progress in the Development of Zika Virus Vaccines
Authors: Zhou Kehui , Li Chaoqun , Shi Wen , Hu Xiaodan , Nandakumar Kutty Selva , Jiang Shibo , Zhang Naru
Published: September 2021

Zika virus (ZIKV) is an arbovirus first discovered in the Americas. ZIKV infection is insidious based on its mild clinical symptoms observed after infection. In Brazil, after 2015, ZIKV infection broke out on a large scale, and many infected pregnant women gave birth to babies with microcephaly. The teratogenic effects of the virus on the fetus and its effects on nerves and the immune system have attracted great attention. Currently, no specific prophylactics or therapeutics are clinically available to treat ZIKV infection. Development of a safe and effective vaccine is essential to prevent the rise of any potential pandemic. In this review, we summarize the latest research on Zika vaccine development based on different strategies, including DNA vaccines, subunit vaccines, live-attenuated vaccines, virus-vector-based vaccines, inactivated vaccines, virus-like particles (VLPs), mRNA-based vaccines, and others. We anticipate that this review will facilitate further progress toward the development of effective and safe vaccines against ZIKV infection.
Chimeric VLPs Bearing VP60 from Two Serotypes of Rabbit Haemorrhagic Disease Virus Are Protective against Both Viruses
Authors: Dalton Kevin P. , Alvarado Carmen , Reytor Edel , del Carmen Nuñez Maria , Podadera Ana , Martínez-Alonso Diego , Alonso Jose Manuel Martin , Nicieza Ines , Gómez-Sebastián Silvia , Dalton Romy M. , Parra Francisco , Escribano José M.
Published: September 2021

The VP60 capsid protein from rabbit haemorrhagic disease virus (RHDV), the causative agent of one of the most economically important disease in rabbits worldwide, forms virus-like particles (VLPs) when expressed using heterologous protein expression systems such as recombinant baculovirus, yeasts, plants or mammalian cell cultures. To prevent RHDV dissemination, it would be beneficial to develop a bivalent vaccine including both RHDV GI.1- and RHDV GI.2-derived VLPs to achieve robust immunisation against both serotypes. In the present work, we developed a strategy of production of a dual-serving RHDV vaccine co-expressing the VP60 proteins from the two RHDV predominant serotypes using CrisBio technology, which uses Tricholusia ni insect pupae as natural bioreactors, which are programmed by recombinant baculovirus vectors. Co-infecting the insect pupae with two baculovirus vectors expressing the RHDV GI.1- and RHDV GI.2-derived VP60 proteins, we obtained chimeric VLPs incorporating both proteins as determined by using serotype-specific monoclonal antibodies. The resulting VLPs showed the typical size and shape of this calicivirus as determined by electron microscopy. Rabbits immunised with the chimeric VLPs were fully protected against a lethal challenge infection with the two RHDV serotypes. This study demonstrates that it is possible to generate a dual cost-effective vaccine against this virus using a single production and purification process, greatly simplifying vaccine manufacturing.
Establishment of a Suspension MDBK Cell Line in Serum-Free Medium for Production of Bovine Alphaherpesvirus-1
Authors: Wang Pengpeng , Huang Shulin , Hao Chengwu , Wang Zhanhui , Zhao Haoran , Liu Mengyao , Tian Xinrui , Ge Letu , Wu Wenxue , Peng Chen
Published: September 2021

The Madin–Darby bovine kidney (MDBK) cell line is currently used for the production of bovine alphaherpesvirus-1 (BoHV-1) vaccine. For the purpose of vaccine manufacturing, suspension cells are preferred over adherent ones due to simplified sub-cultivation and an easier scale-up process, both of which could significantly reduce production cost. This study aimed to establish a procedure for the culture of BoHV-1 in the suspended MDBK cell line in serum-free medium. We screened several commercially available serum-free media and chose ST503 for subsequent experiments. We successfully adapted the adherent MDBK cells to suspended growth in ST503 in the absence of serum. The maximum density of suspension-adapted MDBK cells could reach 2.5 × 107 cells/mL in ST503 medium with optimal conditions. The average size of suspension-adapted cells increased to 18 ± 1 µm from 16 ± 1 µm. Moreover, we examined tumorigenicity of the suspended cells and found no sign of tumorigenicity post adaptation. Next, we developed a protocol for the culture of BoHV-1 in the cell line described above and found that ultrasonic treatment could facilitate virus release and enhance virus yield by 11-fold, with the virus titer reaching 8.0 ± 0.2 log10TCID50/mL. Most importantly, the prototype inactivated BoHV-1 vaccine we generated using the suspension cultures of MDBK cells induced neutralizing antibodies to a titer comparable to that of the commercial inactivated BoHV-1 vaccine. Overall, we established and optimized a protocol for the production of inactivated BoHV-1 vaccine in MDBK cells adapted for suspension culture, which provides insights for future large-scale manufacturing of BoHV-1 vaccine.