bluuebunny/crossref_metadata_2025 huggingface dataset:
Large River Plumes Detection by Satellite Altimetry: Case Study of the Ob–Yenisei Plume
Authors: Frey Dmitry , Osadchiev Alexander
Published: December 2021

Satellite altimetry is an efficient instrument for detection dynamical processes in the World Ocean, including reconstruction of geostrophic currents and tracking of mesoscale eddies. Satellite altimetry has the potential to detect large river plumes, which have reduced salinity and, therefore, elevated surface level as compared to surrounding saline sea. In this study, we analyze applicability of satellite altimetry for detection of the Ob–Yenisei plume in the Kara Sea, which is among the largest river plumes in the World Ocean. Based on the extensive in situ data collected at the study area during oceanographic surveys in 2007–2019, we analyze the accuracy and efficiency of satellite altimetry in reproducing, first, the outer boundary of the plume and, second, the internal structure of the plume. We reveal that the value of positive level anomaly within the Ob–Yenisei plume strongly depends on the vertical plume structure and is prone to significant synoptic and seasonal variability due to wind forcing and mixing of the plume with subjacent sea. As a result, despite generally high statistical correlation between the ADT and surface salinity, straightforward usage of ADT for detection of the river plume is incorrect and produces misleading results. Satellite altimetry could provide correct information about spatial extents and shape of the Ob–Yenisei plume only if it is validated by synchronous in situ measurements.
Scale-Aware Neural Network for Semantic Segmentation of Multi-Resolution Remote Sensing Images
Authors: Wang Libo , Zhang Ce , Li Rui , Duan Chenxi , Meng Xiaoliang , Atkinson Peter M.
Published: December 2021

Assigning geospatial objects with specific categories at the pixel level is a fundamental task in remote sensing image analysis. Along with the rapid development of sensor technologies, remotely sensed images can be captured at multiple spatial resolutions (MSR) with information content manifested at different scales. Extracting information from these MSR images represents huge opportunities for enhanced feature representation and characterisation. However, MSR images suffer from two critical issues: (1) increased scale variation of geo-objects and (2) loss of detailed information at coarse spatial resolutions. To bridge these gaps, in this paper, we propose a novel scale-aware neural network (SaNet) for the semantic segmentation of MSR remotely sensed imagery. SaNet deploys a densely connected feature network (DCFFM) module to capture high-quality multi-scale context, such that the scale variation is handled properly and the quality of segmentation is increased for both large and small objects. A spatial feature recalibration (SFRM) module was further incorporated into the network to learn intact semantic content with enhanced spatial relationships, where the negative effects of information loss are removed. The combination of DCFFM and SFRM allows SaNet to learn scale-aware feature representation, which outperforms the existing multi-scale feature representation. Extensive experiments on three semantic segmentation datasets demonstrated the effectiveness of the proposed SaNet in cross-resolution segmentation.
Towards the Assessment of Soil-Erosion-Related C-Factor on European Scale Using Google Earth Engine and Sentinel-2 Images
Authors: Alexakis Dimitrios D. , Manoudakis Stelios , Agapiou Athos , Polykretis Christos
Published: December 2021

Soil erosion is a constant environmental threat for the entirety of Europe. Numerous studies have been published during the last years concerning assessing soil erosion utilising Remote Sensing (RS) and Geographic Information Systems (GIS). Such studies commonly employ empirical erosion models to estimate soil loss on various spatial scales. In this context, empirical models have been highlighted as major approaches to estimate soil loss on various spatial scales. Most of these models analyse environmental factors representing soil-erosion-influencing conditions such as the climate, topography, soil regime, and surface vegetation coverage. In this study, the Google Earth Engine (GEE) cloud computing platform and Sentinel-2 satellite imagery data have been combined to assess the vegetation-coverage-related factor known as cover management factor (C-factor) at a high spatial resolution (10 m) considering a total of 38 European countries. Based on the employment of the RS derivative of the Normalised Difference Vegetation Index (NDVI) for January and December 2019, a C-factor map was generated due to mean annual estimation. National values were then calculated in terms of different types of agricultural land cover classes. Furthermore, the European C-factor (CEUROPE) values concerning the island of Crete (Greece) were compared with relevant values estimated for the island (CCRETE) based on Sentinel-2 images being individually selected at a monthly time-step of 2019 to generate a series of 12 maps for the C-factor in Crete. Our results yielded identical C-factor values for the different approaches. The outcomes denote GEE’s high analytic and processing abilities to analyse massive quantities of data that can provide efficient digital products for soil-erosion-related studies.
A Lightweight Detection Model for SAR Aircraft in a Complex Environment
Authors: Li Mingwu , Wen Gongjian , Huang Xiaohong , Li Kunhong , Lin Sizhe
Published: December 2021

Recently, deep learning has been widely used in synthetic aperture radar (SAR) aircraft detection. However, the complex environment of the airport—consider the boarding bridges, for instance—greatly interferes with aircraft detection. Besides, the detection speed is also an important indicator in practical applications. To alleviate these problems, we propose a lightweight detection model (LDM), mainly including a reuse block (RB) and an information correction block (ICB) based on the Yolov3 framework. The RB module helps the neural network extract rich aircraft features by aggregating multi-layer information. While the RB module brings more effective information, there is also redundant and useless information aggregated by the reuse block, which is harmful to detection precision. Therefore, to accurately extract more aircraft features, we propose an ICB module combining scattering mechanism characteristics by extracting the gray features and enhancing spatial information, which helps suppress interference in a complex environment and redundant information. Finally, we conducted a series of experiments on the SAR aircraft detection dataset (SAR-ADD). The average precision was 0.6954, which is superior to the precision values achieved by other methods. In addition, the average detection time of LDM was only 6.38 ms, making it much faster than other methods.
Improving Snow Analyses for Hydrological Forecasting at ECCC Using Satellite-Derived Data
Authors: Garnaud Camille , Vionnet Vincent , Gaborit Étienne , Fortin Vincent , Bilodeau Bernard , Carrera Marco , Durnford Dorothy
Published: December 2021

As part of the National Hydrological Services Transformation Initiative, Environment and Climate Change Canada (ECCC) designed and implemented the National Surface and River Prediction System (NSRPS) in order to provide surface and river flow analysis and forecast products across Canada. Within NSRPS, the Canadian Land Data Assimilation System (CaLDAS) produces snow analyses that are used to initialise the land surface model, which in turn is used to force the river routing component. Originally, CaLDAS was designed to improve atmospheric forecasts with less focus on hydrological processes. When snow data assimilation occurs, the related increments remove/add water from/to the system, which can sometimes be problematic for streamflow forecasting, in particular during the snowmelt period. In this study, a new snow analysis method introduces multiple innovations that respond to the need for higher quality snow analyses for hydrological purposes, including the use of IMS snow cover extent data instead of in situ snow depth observations. The results show that the new snow assimilation methodology brings an overall improvement to snow analyses and substantially enhances water conservation, which is reflected in the generally improved streamflow simulations. This work represents a first step towards a new snow data assimilation process in CaLDAS, with the final objective of producing a reliable snow analysis to initialise and improve NWP as well as environmental predictions, including flood and drought forecasts.
At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping
Authors: Wang Cuizhen
Published: December 2021

Rapid advancement of drone technology enables small unmanned aircraft systems (sUAS) for quantitative applications in public and private sectors. The drone-mounted 5-band MicaSense RedEdge cameras, for example, have been popularly adopted in the agroindustry for assessment of crop healthiness. The camera extracts surface reflectance by referring to a pre-calibrated reflectance panel (CRP). This study tests the performance of a Matrace100/RedEdge-M camera in extracting surface reflectance orthoimages. Exploring multiple flights and field experiments, an at-sensor radiometric correction model was developed that integrated the default CRP and a Downwelling Light Sensor (DLS). Results at three vegetated sites reveal that the current CRP-only RedEdge-M correction procedure works fine except the NIR band, and the performance is less stable on cloudy days affected by sun diurnal, weather, and ground variations. The proposed radiometric correction model effectively reduces these local impacts to the extracted surface reflectance. Results also reveal that the Normalized Difference Vegetation Index (NDVI) from the RedEdge orthoimage is prone to overestimation and saturation in vegetated fields. Taking advantage of the camera’s red edge band centered at 717 nm, this study proposes a red edge NDVI (ReNDVI). The non-vegetation can be easily excluded with ReNDVI < 0.1. For vegetation, the ReNDVI provides reasonable values in a wider histogram than NDVI. It could be better applied to assess vegetation healthiness across the site.
A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities
Authors: Irfan Saad , Anjum Nadeem , Masood Nayyer , Khattak Ahmad S. , Ramzan Naeem
Published: December 2021

In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration. However, postural transitions assume a significant part in the enforcement of an activity recognition framework and cannot be neglected. This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple deep learning models simultaneously. For final classification, a dynamic decision fusion module is introduced. The experiments are performed on the publicly available datasets. The proposed approach achieved a classification accuracy of 96.11% and 98.38% for the transition and basic activities, respectively. The outcomes show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and precision.
Efficient Solution for Large-Scale IoT Applications with Proactive Edge-Cloud Publish/Subscribe Brokers Clustering
Authors: Pham Van-Nam , Lee Ga-Won , Nguyen VanDung , Huh Eui-Nam
Published: December 2021

Large-scale IoT applications with dozens of thousands of geo-distributed IoT devices creating enormous volumes of data pose a big challenge for designing communication systems that provide data delivery with low latency and high scalability. In this paper, we investigate a hierarchical Edge-Cloud publish/subscribe brokers model using an efficient two-tier routing scheme to alleviate these issues when transmitting event notifications in wide-scale IoT systems. In this model, IoT devices take advantage of proximate edge brokers strategically deployed in edge networks for data delivery services in order to reduce latency. To deliver data more efficiently, we propose a proactive mechanism that applies collaborative filtering techniques to efficiently cluster edge brokers with geographic proximity that publish and/or subscribe to similar topics. This allows brokers in the same cluster to exchange data directly with each other to further reduce data delivery latency. In addition, we devise a coordinative scheme to help brokers discover and bridge similar topic channels in the whole system, informing other brokers for data delivery in an efficient manner. Extensive simulation results prove that our model can adeptly support event notifications in terms of low latency, small amounts of relay traffic, and high scalability for large-scale, delay-sensitive IoT applications. Specifically, in comparison with other similar Edge-Cloud approaches, our proposal achieves the best in terms of relay traffic among brokers, about 7.77% on average. In addition, our model’s average delivery latency is approximately 66% of PubSubCoord-alike’s one.
Optimal Solutions for Underwater Capacitive Power Transfer
Authors: Mahdi Hussein , Hoff Bjarte , Østrem Trond
Published: December 2021

Capacitive power transfer (CPT) has attracted attention for on-road electric vehicles, autonomous underwater vehicles, and electric ships charging applications. High power transfer capability and high efficiency are the main requirements of a CPT system. This paper proposes three possible solutions to achieve maximum efficiency, maximum power, or conjugate-matching. Each solution expresses the available load power and the efficiency of the CPT system as functions of capacitive coupling parameters and derives the required admittance of the load and the source. The experimental results demonstrated that the available power and the efficiency decrease by the increasing of the frequency from 300 kHz to 1 MHz and the separation distance change from 100 to 300 mm. The maximum efficiency solution gives 83% at 300 kHz and a distance of 100 mm, while the maximum power solution gives the maximum normalized power of 0.994 at the same frequency and distance. The CPT system can provide a good solution to charge electric ships and underwater vehicles over a wide separation distance and low-frequency ranges.
A Novel Signal Design and Performance Analysis in NavCom Based on LEO Constellation
Authors: Ji Jing , Liu Yuting , Chen Wei , Wu Di , Lu Hongyang , Zhang Jiantong
Published: December 2021

The mega-launch of low Earth orbit satellites (LEOs) represents a critical opportunity to integrate navigation and communication (NavCom), but first, challenges related to signal design must be overcome. This article proposes a novel signal scheme named CE-OFDM-PM. Via research on the in-band or adjacent band, it was found that the proposed signal scheme was suitable for S-band and had a wide normalized power spectrum density (PSD), high peak-to-side lobe ratio (PSR), and multiple peaks in autocorrelation. In an analysis of the simulation performance evaluation in navigation and communication, it is found that the proposed signal scheme has the potential for high accuracy, a code tracking accuracy of up to 0.85 m, a small mutual influence between the proposed signal scheme and other schemes, excellent anti-interference properties, and a better performance at both short and long distances in terms of its anti-multipath capability. Furthermore, the proposed signal scheme shows the ability to communicate between satellites and the ground and is outstanding in terms of its bit error rate (BER), CNR, and energy per bit to noise power spectral density ratio (Eb/N0). From the technical, theoretical, and application perspectives, our proposed signal scheme has potential as an alternative scheme in future BDS, PNTs, and even 5G/B5G.
Quad-Port Multiservice Diversity Antenna for Automotive Applications
Authors: Kannappan Lekha , Palaniswamy Sandeep Kumar , Wang Lulu , Kanagasabai Malathi , Kumar Sachin , Alsath Mohammed Gulam Nabi , Rao Thipparaju Rama
Published: December 2021

A quad-element multiple-input-multiple-output (MIMO) antenna with ultra-wideband (UWB) performance is presented in this paper. The MIMO antenna consists of four orthogonally arranged microstrip line-fed hexagonal monopole radiators and a modified ground plane. In addition, E-shaped and G-shaped stubs are added to the radiator to achieve additional resonances at 1.5 GHz and 2.45 GHz. The reliability of the antenna in the automotive environment is investigated, with housing effects taken into account. The housing effects show that the antenna performs consistently even in the presence of a large metal object. The proposed MIMO antenna has potential for various automotive applications, including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-everything (V2X), intelligent transport system (ITS), automatic vehicle identifier, and RFID-based electronic toll collection.
Bendductor—Transformer Steel Magnetomechanical Force Sensor
Authors: Grenda Przemysław , Kutyła Monika , Nowicki Michał , Charubin Tomasz
Published: December 2021

In this paper, the design and investigation of an innovative force sensor, based on the Villari effect, is presented. The sensor was built from electrical steel, in a pressductor pattern, but working in bending load mode. The results of the experimental research allowed for the evaluation of transducer’s performance, mitigation of measurement hysteresis, and optimization of its functional parameters. Several issues have been examined, among them the selection of supply and measured signals, the measured values’ impact on measurement hysteresis, harmonic analysis, and the selection of proper current waveforms and frequencies. The proposed sensor is robust, made from inexpensive materials, and has high sensitivity, as compared to other magnetoelastic sensors. It has much higher stress sensitivity than other magnetoelastic sensors due to deformation mode. Based on the tests, its measuring range can be defined as 0.5–5 N with a near-linear characteristic, SNR of 46 dB, and 0.11 N uncertainty.
Accuracy Analysis of Feature-Based Automatic Modulation Classification via Deep Neural Network
Authors: Ge Zhan , Jiang Hongyu , Guo Youwei , Zhou Jie
Published: December 2021

A feature-based automatic modulation classification (FB-AMC) algorithm has been widely investigated because of its better performance and lower complexity. In this study, a deep learning model was designed to analyze the classification performance of FB-AMC among the most commonly used features, including higher-order cumulants (HOC), features-based fuzzy c-means clustering (FCM), grid-like constellation diagram (GCD), cumulative distribution function (CDF), and raw IQ data. A novel end-to-end modulation classifier based on deep learning, named CCT classifier, which can automatically identify unknown modulation schemes from extracted features using a general architecture, was proposed. Features except GCD are first converted into two-dimensional representations. Then, each feature is fed into the CCT classifier for modulation classification. In addition, Gaussian channel, phase offset, frequency offset, non-Gaussian channel, and flat-fading channel are also introduced to compare the performance of different features. Additionally, transfer learning is introduced to reduce training time. Experimental results showed that the features HOC, raw IQ data, and GCD obtained better classification performance than CDF and FCM under Gaussian channel, while CDF and FCM were less sensitive to the given phase offset and frequency offset. Moreover, CDF was an effective feature for AMC under non-Gaussian and flat-fading channels, and the raw IQ data can be applied to different channels’ conditions. Finally, it showed that compared with the existing CNN and K-S classifiers, the proposed CCT classifier significantly improved the classification performance for MQAM at N = 512, reaching about 3.2% and 2.1% under Gaussian channel, respectively.
Pocket Mercury-Vapour Detection System Employing a Preconcentrator Based on Au-TiO2 Nanomaterials
Authors: Zampetti Emiliano , Papa Paolo , Bearzotti Andrea , Macagnano Antonella
Published: December 2021

In environments polluted by mercury vapors that are potentially harmful to human health, there is a need to perform rapid surveys in order to promptly identify the sources of emission. With this aim, in this work, a low cost, pocket-sized portable mercury measurement system, with a fast response signal is presented. It consists of a preconcentrator, able to adsorb and subsequently release the mercury vapour detected by a quartz crystal microbalance (QCM) sensor. The preconcentrator is based on an adsorbing layer of titania/gold nanoparticles (TiO2NP/AuNPs), deposited on a micro-heater that acts as mercury thermal desorption. For the detection of the released mercury vapour, gold electrodes QCM (20 MHz) have been used. The experimental results, performed in simulated polluted mercury-vapour environments, showed a detection capability with a prompt response. In particular, frequency shifts (−118 Hz ± 2 Hz and −30 Hz ± 2 Hz) were detected at concentrations of 65 µg/m3 Hg0 and 30 µg/m3 Hg0, with sampling times of 60 min and 30 min, respectively. A system limit of detection (LOD) of 5 µg/m3 was evaluated for the 30 min sampling time.
Passive Homodyne Phase Demodulation Technique Based on LF-TIT-DCM Algorithm for Interferometric Sensors
Authors: Zhang Wanjin , Lu Ping , Qu Zhiyuan , Zhang Jiangshan , Wu Qiang , Liu Deming
Published: December 2021

A passive homodyne phase demodulation technique based on a linear-fitting trigonometric-identity-transformation differential cross-multiplication (LF-TIT-DCM) algorithm is proposed. This technique relies on two interferometric signals whose interferometric phase difference is odd times of π. It is able to demodulate phase signals with a large dynamic range and wide frequency band. An anti-phase dual wavelength demodulation system is built to prove the LF-TIT-DCM algorithm. Comparing the traditional quadrature dual wavelength demodulation system with an ellipse fitting DCM (EF-DCM) algorithm, the phase difference of two interferometric signals of the anti-phase dual wavelength demodulation system is set to be π instead of π/2. This technique overcomes the drawback of EF-DCM—that it is not able to demodulate small signals since the ellipse degenerates into a straight line and the ellipse fitting algorithm is invalidated. Experimental results show that the dynamic range of the proposed anti-phase dual wavelength demodulation system is much larger than that of the traditional quadrature dual wavelength demodulation system. Moreover, the proposed anti-phase dual wavelength demodulation system is hardly influenced by optical power, and the laser wavelength should be strictly limited to lower the reference error.
Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis
Authors: Pelicano Ana Catarina , Gonçalves Maria C. T. , Godinho Daniela M. , Castela Tiago , Orvalho M. Lurdes , Araújo Nuno A. M. , Porter Emily , Conceição Raquel C.
Published: December 2021

Breast cancer diagnosis using radar-based medical MicroWave Imaging (MWI) has been studied in recent years. Realistic numerical and physical models of the breast are needed for simulation and experimental testing of MWI prototypes. We aim to provide the scientific community with an online repository of multiple accurate realistic breast tissue models derived from Magnetic Resonance Imaging (MRI), including benign and malignant tumours. Such models are suitable for 3D printing, leveraging experimental MWI testing. We propose a pre-processing pipeline, which includes image registration, bias field correction, data normalisation, background subtraction, and median filtering. We segmented the fat tissue with the region growing algorithm in fat-weighted Dixon images. Skin, fibroglandular tissue, and the chest wall boundary were segmented from water-weighted Dixon images. Then, we applied a 3D region growing and Hoshen-Kopelman algorithms for tumour segmentation. The developed semi-automatic segmentation procedure is suitable to segment tissues with a varying level of heterogeneity regarding voxel intensity. Two accurate breast models with benign and malignant tumours, with dielectric properties at 3, 6, and 9 GHz frequencies have been made available to the research community. These are suitable for microwave diagnosis, i.e., imaging and classification, and can be easily adapted to other imaging modalities.
Ultra-Low Power Hand Gesture Sensor Using Electrostatic Induction
Authors: Fuketa Hiroshi
Published: December 2021

This paper presents an ultra-low power hand gesture sensor using electrostatic induction for mobile devices. Two electrodes, which consist of electret foils stacked on metal sheets, are used to recognize two gestures such as hand movements from left to right and right to left. The hand gesture recognition is realized by detecting the electrostatic induction currents induced by hand movements. However, the electrostatic induction currents are significantly small; hence, a hand gesture recognition chip is first designed in this study to amplify and detect the small electrostatic induction currents with low power. This chip is fabricated in a commercial 180 nm complementary metal oxide semiconductor (CMOS) process, and the measurement results indicate that the fabricated gesture recognition chip consumes 406 nW, which is less than 1/100th of the power dissipation of conventional gesture sensors.
Low-Operating-Temperature NO2 Sensor Based on a CeO2/ZnO Heterojunction
Authors: Sun Kai , Zhan Guanghui , Chen Hande , Lin Shiwei
Published: December 2021

CeO2/ZnO-heterojunction-nanorod-array-based chemiresistive sensors were studied for their low-operating-temperature and gas-detecting characteristics. Arrays of CeO2/ZnO heterojunction nanorods were synthesized using anodic electrodeposition coating followed by hydrothermal treatment. The sensor based on this CeO2/ZnO heterojunction demonstrated a much higher sensitivity to NO2 at a low operating temperature (120 °C) than the pure-ZnO-based sensor. Moreover, even at room temperature (RT, 25 °C) the CeO2/ZnO-heterojunction-based sensor responds linearly and rapidly to NO2. This sensor’s reaction to interfering gases was substantially less than that of NO2, suggesting exceptional selectivity. Experimental results revealed that the enhanced gas-sensing performance at the low operating temperature of the CeO2/ZnO heterojunction due to the built-in field formed after the construction of heterojunctions provides additional carriers for ZnO. Thanks to more carriers in the ZnO conduction band, more oxygen and target gases can be adsorbed. This explains the enhanced gas sensitivity of the CeO2/ZnO heterojunction at low operating temperatures.
Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles
Authors: Minea Marius , Dumitrescu Cătălin Marian , Costea Ilona Mădălina
Published: December 2021

Background: The growth of the number of vehicles in traffic has led to an exponential increase in the number of road accidents with many negative consequences, such as loss of lives and pollution. Methods: This article focuses on using a new technology in automotive electronics by equipping a semi-autonomous vehicle with a complex sensor structure that is able to provide centralized information regarding the physiological signals (Electro encephalogram—EEG, electrocardiogram—ECG) of the driver/passengers and their location along with indoor temperature changes, employing the Internet of Things (IoT) technology. Thus, transforming the vehicle into a mobile sensor connected to the internet will help highlight and create a new perspective on the cognitive and physiological conditions of passengers, which is useful for specific applications, such as health management and a more effective intervention in case of road accidents. These sensor structures mounted in vehicles will allow for a higher detection rate of potential dangers in real time. The approach uses detection, recording, and transmission of relevant health information in the event of an incident as support for e-Call or other emergency services, including telemedicine. Results: The novelty of the research is based on the design of specialized non-invasive sensors for the acquisition of EEG and ECG signals installed in the headrest and backrest of car seats, on the algorithms used for data analysis and fusion, but also on the implementation of an IoT temperature measurement system in several points that simultaneously uses sensors based on MEMS technology. The solution can also be integrated with an e-Call system for telemedicine emergency assistance. Conclusion: The research presents both positive and negative results of field experiments, with possible further developments. In this context, the solution has been developed based on state-of-the-art technical devices, methods, and technologies for monitoring vital functions of the driver/passengers (degree of fatigue, cognitive state, heart rate, blood pressure). The purpose is to reduce the risk of accidents for semi-autonomous vehicles and to also monitor the condition of passengers in the case of autonomous vehicles for providing first aid in a timely manner. Reported abnormal values of vital parameters (critical situations) will allow interveneing in a timely manner, saving the patient’s life, with the support of the e-Call system.
A Study on Comprehensive Evaluation Methods for Coordinated Development of Water Diversion Projects Based on Advanced SWOT Analysis and Coupling Coordination Model
Authors: Long Yan , Li Youming , Lei Xiaohui , Hou Yikai , Guo Shuang , Sun Jianwei
Published: December 2021

The implementation of water diversion projects will exert different influences on upstream water offering areas and the downstream water receiving areas. In order to effectively promote the coordinated development of the two regions, a comprehensive evaluation system for the coordinated development of water transfer projects has been proposed with the Middle Route of the South-to-North Water Transfer Project as the research object. The system conducts a multidimensional evaluation of social development, economic development, and ecological environmental impact, and builds a comprehensive evaluation index system with fifteen evaluation indexes at three levels, with the indexes weighted through the comprehensive weighting method based on the combination of the G1 method and the entropy weight method. Based on the degree of coordinated development among various systems, the coordinated development of the Middle Route of the South–North Water Transfer Project is graded through a comprehensive evaluation. This method is tested in the decision support system of the Middle Route Construction and Administration Bureau, China. The results show that: (1) The coupling coordination degree value of the middle route of the South-to-North Water Diversion Project is 0.8912, which shows that the regional development of the water transfer project is high coupled coordination. (2) The coordination between the economic system and the ecological environment system is weaker than the coordination between the economic system and the social service system, and the coordination between the ecological and social services is the best. Finally, based on an advanced SWOT analysis of the future development of the middle route of the South-to-North Water Diversion Project, effective suggestions for regional development are provided. It provides reference or guidance for the competent authority to manage the water diversion project and the central government to comprehensively evaluate the effectiveness of the water diversion project.
Political Divisions and Socio-Economic Disparities in Poland: A Geographical Approach
Authors: Herodowicz Tomasz , Konecka-Szydłowska Barbara , Churski Paweł , Perdał Robert
Published: December 2021

This article attempts to identify the relationship between the persistent polarisation of political opinions and diverse levels of social and economic development. The coexistence of these phenomena may indicate a barrier to inclusive economic development, which connects with the United Nations Agenda for Sustainable Development. A research aim was to test two hypotheses: Hypothesis 1 (H1)—The increase in the spatial disparity of political support in Poland is persistent; and Hypothesis 2 (H2)—The spatial distribution of support for specific political options shows significant correlations with the distribution of social and economic growth. The study involves the following research methods implementation: desk research, intensity indicators and regression analysis. The results confirmed the persistence of spatial divisions in political support, whose distribution reflects to a large extent the diverse levels of social and economic development that exist. Two axes dividing Poland are identified, one between west and east and the other between urban and rural areas. The article connects with the international debate regarding “places that don’t matter” and the reasons behind the growing popularity of populist ideas in society.
Smart City Governance Evaluation in the Era of Internet of Things: An Empirical Analysis of Jiangsu, China
Authors: Hsu Wei-Ling , Qiao Miao , Xu Haiying , Zhang Chunmei , Liu Hsin-Lung , Shiau Yan-Chyuan
Published: December 2021

With the rapid development of smart cities all over the world, the evaluation of the smart city has become a new research hotspot in the academic circles. Nevertheless, there still exist a series of common problems in current smart city evaluation, including the cognitive deprivation, lack of experience in planning, low coordination level, etc. Therefore, it is critical to establish a new hierarchy for smart city evaluation indicators, especially in the 5G era. Based on literature review, expert consensus, and the fuzzy analytic hierarchy process, this study developed an innovative smart city evaluation framework. In the framework, an index comprising three dimensions, i.e., smart economy, smart society, and smart environmental protection, as well as several attributes for these dimensions for smart city evaluation were established. Then, taking Jiangsu Province, the fastest-growing province in China, as the research area, the development level of smart city for the cities in Jiangsu was calculated. The results have verified the effectiveness of the framework, which can provide suggestions for sustainable urbanization, and help urban decision-makers to promote the efficient development of smart cities.
Developing Environmentally Sustainable and Cost-Effective Geopolymer Concrete with Improved Characteristics
Authors: Beskopylny Alexey N. , Stel’makh Sergey A. , Shcherban’ Evgenii M. , Mailyan Levon R. , Meskhi Besarion , El’shaeva Diana , Varavka Valery
Published: December 2021

Improving the efficiency and quality of construction mainly depends on the cost of building materials, which is about 55–65% of total capital-construction costs. The study aimed to obtain geopolymer fine-grained concrete with improved quality characteristics that meet the construction field’s sustainable development criteria and that have environmental friendliness, economic efficiency, and advantages over competing analogues. The dependences of strength characteristics on various compositions of geopolymer concrete were obtained. It was found that the most effective activator is a composition of NaOH and Na2SiO3 with a ratio of 1:2. The increase in the indicators of the obtained geopolymer concrete from the developed composition (4A) in relation to the base control (1X) was 17% in terms of compressive strength and 24% in tensile strength in bending. Polynomial equations were obtained showing the dependence of the change in the strength characteristics of geopolymer concrete on the individual influence of each of the activators. A significant effect of the composition of the alkaline activator on the strength characteristics of geopolymer fine-grained concrete was noted. The optimal temperature range of heat treatment of geopolymer concrete samples, contributing to the positive kinetics of compressive strength gain at the age of 28 days, was determined. The main technological and recipe parameters for obtaining geopolymers with the desired properties, which meet the ecology requirements and are efficient from the point of view of economics, were determined.
Effect of Battery Electric Vehicles on Greenhouse Gas Emissions in 29 European Union Countries
Authors: Fuinhas José Alberto , Koengkan Matheus , Leitão Nuno Carlos , Nwani Chinazaekpere , Uzuner Gizem , Dehdar Fatemeh , Relva Stefania , Peyerl Drielli
Published: December 2021

This analysis explored the effect of battery electric vehicles (BEVs) on greenhouse gas emissions (GHGs) in a panel of twenty-nine countries from the European Union (EU) from 2010 to 2020. The method of moments quantile regression (MM-QR) was used, and the ordinary least squares with fixed effects (OLSfe) was used to verify the robustness of the results. The MM-QR support that in all three quantiles, economic growth causes a positive impact on GHGs. In the 50th and 75th quantiles, energy consumption causes a positive effect on GHGs. BEVs in the 25th, 50th, and 75th quantiles have a negative impact on GHGs. The OLSfe reveals that economic growth has a negative effect on GHGs, which contradicts the results from MM-QR. Energy consumption positively impacts GHGs. BEVs negatively impacts GHGs. Although the EU has supported a more sustainable transport system, accelerating the adoption of BEVs still requires effective political planning to achieve net-zero emissions. Thus, BEVs are an important technology to reduce GHGs to achieve the EU targets of decarbonising the energy sector. This research topic can open policy discussion between industry, government, and researchers, towards ensuring that BEVs provide a climate change mitigation pathway in the EU region.
Cognitive Failures: Relationship with Perceived Emotions, Stress, and Resting Vagally-Mediated Heart Rate Variability
Authors: You Min , Laborde Sylvain , Borges Uirassu , Vaughan Robert Samuel , Dosseville Fabrice
Published: December 2021

Cognitive failures represent everyday task failures that individuals are normally capable of completing. While cognitive failures measured with the Cognitive Failures Questionnaire can be considered a trait, the psychophysiological states associated with cognitive failures are yet to be fully understood. The aim of this paper was to investigate the extent to which the perception of experiencing cognitive failures in daily life is associated with both psychological (i.e., perceived emotional valence, emotional intensity, and stress), as well as physiological (i.e., vagally-mediated heart rate variability, vmHRV) variables. A total of 69 participants were involved in this study (47 male, 22 female; Mage = 22.4 years). Participants underwent a 5-min heart rate variability measurement and filled out the self-report psychological variables, before completing the Cognitive Failures Questionnaire, providing scores for Distractibility, Forgetfulness, and False Triggering. When combining the predictors together into a hierarchical regression analysis, only the model related to the Distractibility subscale was found to be significant (unique significant negative predictor: resting vmHRV). Further research should investigate whether influencing resting vmHRV, with interventions such as slow-paced breathing, may decrease the perception of cognitive failures related to distractibility.
Shocks, Resilience and Structural Transformation in Sub-Saharan Africa
Authors: Adelaja Adesoji , George Justin , Fox Louise , Fuglie Keith , Jayne Thomas
Published: December 2021

Evidence of how resilience factors mitigate the adverse effects of shocks on individuals, households and communities is clearly established. However, such evidence at the macro level is limited, especially on the pace of structural transformation. This paper explores whether the growing incidence of terrorism, armed conflicts and natural disasters in SSA impeded the pace of structural transformation. We conceptualize the notion of macro-resilience and test whether resilience factors mitigate the adverse effects of shocks on two measures of structural transformation: agriculture’s share of GDP and of national employment. We find that structural transformation is impeded by armed conflict and terrorism-related shocks but not natural disasters and that resilience factors enhance the pace of agricultural transformation. This implies that, while agriculture is often destroyed in conflict-affected areas, the broader impacts are even more negative for other sectors of the economy. However, surprisingly, we find negative or insignificant interaction terms between the shock and resilience variables, implying no mitigative role of resilience capacities. This may suggest, in the case of conflicts and terrorism, the presence of major, debilitating effects which limit the mitigative capacity of resilience factors. We further explore the implications for future research and possible strategies to address the growing threats from shocks.
Optimization of X-ray Tube Voltage to Improve the Precision of Two Phase Flow Meters Used in Petroleum Industry
Authors: Alanazi Abdullah K. , Alizadeh Seyed Mehdi , Nurgalieva Karina Shamilyevna , Grimaldo Guerrero John William , Abo-Dief Hala M. , Eftekhari-Zadeh Ehsan , Nazemi Ehsan , Narozhnyy Igor M.
Published: December 2021

To the best knowledge of the authors, in all the former studies, a fixed value of X-ray tube voltage has been used for investigating gas–liquid two-phase flow characteristics, while the energy of emitted X-ray radiations that depends on the tube voltage can significantly affect the measurement precision of the system. The purpose of present study is to find the optimum tube voltage to increase the accuracy and efficiency of an intelligent X-ray radiation-based two-phase flow meter. The detection system consists of an industrial X-ray tube and one detector located on either side of a steel pipe. Tube voltages in the range of 125–300 kV with a step of 25 kV were investigated. For each tube voltage, different gas volume percentages (GVPs) in the range of 10–90% with a step of 5% were modeled. A feature extraction method was performed on the output signals of the detector in every case, and the obtained matrixes were applied to the designed radial basis function neural networks (RBFNNs). The desired output of the networks was GVP. The precision of the networks in every voltage and every number of neurons in the hidden layer were obtained. The results showed that 225 kV tube voltage is the optimum voltage for this purpose. The obtained mean absolute error (MAE) for this case is less than 0.05, which demonstrates the very high precision of the metering system with an optimum X-ray tube voltage.
Unravelling Mickey Mouse: The Effect of Supply Chain Position and Organisational Slack on the Uneven Balance of Sustainability Dimensions
Authors: Laari Sini , Solakivi Tomi , Bask Anu , Töyli Juuso , Ojala Lauri
Published: December 2021

This paper contributes to a less-studied area on how a firm’s position in the supply chain influences triple-bottom-line considerations in strategic decision making. We also contribute to previous research on a nuanced understanding of unabsorbed organisational slack as an antecedent to the triple-bottom-line dimensions of sustainability. The research data comprises survey data and financial reporting data from 508 manufacturing and trading firms operating in Finland, divided among four supply chain tiers. The economic dimension dominates the decision making on all tiers, followed by social and environmental considerations, resembling the shape of Mickey Mouse. Unabsorbed organisational slack is negatively related to the importance of economic considerations and positively related to environmental considerations. The results help firms in evaluating their position in terms of sustainability and in their redesigning efforts accordingly. The findings will also be useful in terms of promoting sustainability practices among supply chain members and policymakers in their practical efforts towards sustainable development.
Evaluation of Regulating and Provisioning Services Provided by a Park Designed to Be Resilient to Climate Change in Bangkok, Thailand
Authors: Yarnvudhi Arerut , Leksungnoen Nisa , Tor-Ngern Pantana , Premashthira Aerwadee , Thinkampheang Sathid , Hermhuk Sutheera
Published: December 2021

Understanding the ecosystem services provided by urban green spaces, in terms of their environmental, economic, and social benefits, is essential for a better management of area. Chulalongkorn University Centenary Park (CU 100) was established to mitigate the effects of climate change, especially flood prevention. This study focused on quantifying the ecosystem services provided by the trees in the park in terms of regulating and provisioning services. A publicly available tool, the i-Tree Eco international software, was used with data obtained from a local weather station as proxies to determine the accuracy of the analysis. Services, quantified in terms of monetary value, included avoided runoff, carbon storage, carbon sequestration, pollution removal, and timber price. The total monetary benefits, obtained from 697 trees (56 species, 49 genera, and 22 families), were estimated at USD 101,400. Of the total services, provisioning services contributed 75% to the total monetary value. Among all regulating services, the avoided runoff contributed about 60%, which was considered as the goal achieved by the park design. Azadirachta indica A. Juss (USD 518.75/tree−1/year−1), Shorea roxburghii G. Don (USD 417.17/tree−1/year−1) and Millettia leucantha Kurz (USD 414.87/tree−1/year−1) provided the greatest benefit, as indicated by a high value of provisioning services in terms of a high timber quality. These results can be used when planning the composition of trees to be planted in urban areas to increase both green spaces and maximize ecosystem services to improve the vitality of human well-being.
An Improved Cellular Automaton Traffic Model Based on STCA Model Considering Variable Direction Lanes in I-VICS
Authors: Song Ziwen , Sun Feng , Zhang Rongji , Du Yingcui , Zhou Guiliang
Published: December 2021

In this paper, we propose an improved cellular automaton model for the traffic operation characteristics of variable direction lanes in an Intelligent Vehicle Infrastructure Cooperation System (I-VICS). According to the proposed flow of variable oriented lane operation in the I-VICS environment, the idea for the improved model has been determined. According to an analysis of different signal states, an improved STCA model is proposed, in combination with the speed induction method of I-VICS and the variable direction lane switching strategy. In the assumed regular simulation environment, the STCA and STCA-V models are simulated under different vehicular densities. The results indicated that traffic parameters such as traffic flow and average speed of the variable direction lanes in the I-VICS environment are better than those in the conventional environment according to the operating rules of the proposed model. Moreover, lane utilization increased for the same density.