bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Lignin Modifier on Engineering Performance of Bituminous Binder and Mixture
Authors: Xu Chi , Wang Duanyi , Zhang Shaowei , Guo Enbei , Luo Haoyang , Zhang Zeyu , Yu Huayang
Published: March 2021

Lignin accounts for approximately 30% of the weight of herbaceous biomass. Utilizing lignin in asphalt pavement industry could enhance the performance of pavement while balancing the construction cost. This study aims to evaluate the feasibility of utilizing lignin as a bitumen performance improver. For this purpose, lignin derived from aspen wood chips (labeled as KL) and corn stalk residues (labeled as CL) were selected to prepare the lignin modified bituminous binder. The properties of the lignin modified binder were investigated through rheological, mechanical and chemical tests. The multiple stress creep recovery (MSCR) test results indicated that adding lignin decreased the Jnr of based binder by a range of 8% to 23% depending on the stress and lignin type. Lignin showed a positive effect on the low temperature performance of asphalt binder, because at −18 °C, KL and CL were able to reduce the stiffness of base binder from 441 MPa to 369 MPa and 378 MPa, respectively. However, lignin was found to deteriorate the fatigue life and workability of base binder up to 30% and 126%. With bituminous mixture, application of lignin modifiers improved the Marshall Stability and moisture resistance of base mixture up to 21% and 13%, respectively. Although, adding lignin modifiers decreased the molecular weight of asphalt binder according to the gel permeation chromatography (GPC) test results. The Fourier-transform infrared spectroscopy (FTIR) test results did not report detectable changes in functional group of based binder.
Automated Cytogenetic Biodosimetry at Population-Scale
Authors: Rogan Peter K. , Mucaki Eliseos J. , Shirley Ben C. , Li Yanxin , Wilkins Ruth C. , Norton Farrah , Sevriukova Olga , Pham Ngoc-Duy , Waller Ed , Knoll Joan H. M.
Published: March 2021

The dicentric chromosome (DC) assay accurately quantifies exposure to radiation; however, manual and semi-automated assignment of DCs has limited its use for a potential large-scale radiation incident. The Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software automates unattended DC detection and determines radiation exposures, fulfilling IAEA criteria for triage biodosimetry. This study evaluates the throughput of high-performance ADCI (ADCI-HT) to stratify exposures of populations in 15 simulated population scale radiation exposures. ADCI-HT streamlines dose estimation using a supercomputer by optimal hierarchical scheduling of DC detection for varying numbers of samples and metaphase cell images in parallel on multiple processors. We evaluated processing times and accuracy of estimated exposures across census-defined populations. Image processing of 1744 samples on 16,384 CPUs required 1 h 11 min 23 s and radiation dose estimation based on DC frequencies required 32 sec. Processing of 40,000 samples at 10 exposures from five laboratories required 25 h and met IAEA criteria (dose estimates were within 0.5 Gy; median = 0.07). Geostatistically interpolated radiation exposure contours of simulated nuclear incidents were defined by samples exposed to clinically relevant exposure levels (1 and 2 Gy). Analysis of all exposed individuals with ADCI-HT required 0.6–7.4 days, depending on the population density of the simulation.
The Eco-Theology of the Bhagavad Gītā: A Multi-Layered Ethical Theory
Authors: Sen Supratik
Published: March 2021

I argue that a normative environmental ethical theory can be coherently derived out of the theological matrix of the Bhagavad Gītā. I build upon Ithamar Theodor’s articulation of the Gītā’s underlying unifying structure to depict how the Gītā conceives of three possible relationships with nature. This allows me to tease out three concurrent worldviews in the Gītā—a world-affirming worldview, a world-renouncing worldview and a bhakti worldview, which is simultaneously world-affirming and world-renouncing. I show how three distinct theories of motivation—three different reasons for acting in the world—emerge from the interconnected normative, soteriological and ontological dimensions of each of these three worldviews. More importantly, the motivation to act for the welfare of individuals in nature, such as animals and plants, can be legitimately derived from these three theories of motivation. I contextualize the Bhagavad Gītā’s environmental ethics by placing it within the larger framework of the text’s distinctive multi-layered approach to ethical theory, in which the foundational teleological mokṣa theory grounds and explains the plurality of more superficial normative foundational theories.
Detecting Desert Locust Breeding Grounds: A Satellite-Assisted Modeling Approach
Authors: Ellenburg W. Lee , Mishra Vikalp , Roberts Jason B. , Limaye Ashutosh S. , Case Jonathan L. , Blankenship Clay B. , Cressman Keith
Published: March 2021

The objective of this study is to evaluate the ability of soil physical characteristics (i.e., texture and moisture conditions) to better understand the breeding conditions of desert locust (DL). Though soil moisture and texture are well-known and necessary environmental conditions for DL breeding, in this study, we highlight the ability of model-derived soil moisture estimates to contribute towards broader desert locust monitoring activities. We focus on the recent DL upsurge in East Africa from October 2019 though June 2020, utilizing known locust observations from the United Nations Food and Agriculture Organization (FAO). We compare this information to results from the current literature and combine the two datasets to create “optimal thresholds” of breeding conditions. When considering the most optimal conditions (all thresholds met), the soil texture combined with modeled soil moisture content predicted the estimated DL egg-laying period 62.5% of the time. Accounting for the data errors and uncertainties, a 3 × 3 pixel buffer increased this to 85.2%. By including soil moisture, the areas of optimal egg laying conditions decreased from 33% to less than 20% on average.
Quantitative Analysis of the Research Trends and Areas in Grassland Remote Sensing: A Scientometrics Analysis of Web of Science from 1980 to 2020
Authors: Li Tong , Cui Lizhen , Xu Zhihong , Hu Ronghai , Joshi Pawan K. , Song Xiufang , Tang Li , Xia Anquan , Wang Yanfen , Guo Da , Zhu Jiapei , Hao Yanbin , Song Lan , Cui Xiaoyong
Published: March 2021

Grassland remote sensing (GRS) is an important research topic that applies remote sensing technology to grassland ecosystems, reflects the number of grassland resources and grassland health promptly, and provides inversion information used in sustainable development management. A scientometrics analysis based on Science Citation Index-Expanded (SCI-E) was performed to understand the research trends and areas of focus in GRS research studies. A total of 2692 papers related to GRS research studies and 82,208 references published from 1980 to 2020 were selected as the research objects. A comprehensive overview of the field based on the annual documents, research areas, institutions, influential journals, core authors, and temporal trends in keywords were presented in this study. The results showed that the annual number of documents increased exponentially, and more than 100 papers were published each year since 2010. Remote sensing, environmental sciences, and ecology were the most popular Web of Science research areas. The journal Remote Sensing was one of the most popular for researchers to publish documents and shows high development and publishing potential in GRS research studies. The institution with the greatest research documents and most citations was the Chinese Academy of Sciences. Guo X.L., Hill M.J., and Zhang L. were the most productive authors across the 40-year study period in terms of the number of articles published. Seven clusters of research areas were identified that generated contributions to this topic by keyword co-occurrence analysis. We also detected 17 main future directions of GRS research studies by document co-citation analysis. Emerging or underutilized methodologies and technologies, such as unmanned aerial systems (UASs), cloud computing, and deep learning, will continue to further enhance GRS research in the process of achieving sustainable development goals. These results can help related researchers better understand the past and future of GRS research studies.
Satellite-Derived Variation in Burned Area in China from 2001 to 2018 and Its Response to Climatic Factors
Authors: Wang Xiaoxiao , Di Zhenhua , Li Mei , Yao Yunjun
Published: March 2021

Fire is one of the most widespread and destructive disasters, which causes property losses, casualties, and disruption of the balance of ecosystems. Therefore, it is highly necessary for firefighting to study the variations in fire and its climatic attributions. This study analyzed the characteristics of fire-burned area (BA) and its response to climatic factors in seven subregions of China from 2001 to 2018 using satellite remote sensing BA products. The results show that the BA in China and most of its subregions shows a decreasing trend. In general, it is negatively correlated with precipitation and positively correlated with air temperature and wind speed based on the regression and correlation analyses. Based on Pearson correlation and random forest methods, it is also found that the temperature is commonly an important factor contributing to BA in China, except for R2 (Inner Mongolia region), where wind speed is more important, and R5 (South China), where precipitation is more important, which coexists at annual and seasonal scales. Besides temperature, precipitation in spring and summer is the main driving factor, such as in R1 (Northeast China), R5, R6 (Northwest China) and R7 (Qinghai–Tibet Plateau) in spring and R4 (Central China), R5 and R7 in summer; and wind speed in autumn and winter is the main driving factor, such as in R2 and R4 in autumn and R2, R3, R5, R6 and R7 in winter. Finally, the distributions of BA with respect to each climatic factor were also analyzed to quantify the range of climatic factors with maximum BA occurrence.
Wide Sliding Window and Subsampling Network for Hyperspectral Image Classification
Authors: Xi Jiangbo , Ersoy Okan K. , Fang Jianwu , Cong Ming , Wu Tianjun , Zhao Chaoying , Li Zhenhong
Published: March 2021

Recently, deep learning methods, for example, convolutional neural networks (CNNs), have achieved high performance in hyperspectral image (HSI) classification. The limited training samples of HSI images make it hard to use deep learning methods with many layers and a large number of convolutional kernels as in large scale imagery tasks, and CNN-based methods usually need long training time. In this paper, we present a wide sliding window and subsampling network (WSWS Net) for HSI classification. It is based on layers of transform kernels with sliding windows and subsampling (WSWS). It can be extended in the wide direction to learn both spatial and spectral features more efficiently. The learned features are subsampled to reduce computational loads and to reduce memorization. Thus, layers of WSWS can learn higher level spatial and spectral features efficiently, and the proposed network can be trained easily by only computing linear weights with least squares. The experimental results show that the WSWS Net achieves excellent performance with different hyperspectral remotes sensing datasets compared with other shallow and deep learning methods. The effects of ratio of training samples, the sizes of image patches, and the visualization of features in WSWS layers are presented.
High-Precision GNSS PWV and Its Variation Characteristics in China Based on Individual Station Meteorological Data
Authors: Wu Mingliang , Jin Shuanggen , Li Zhicai , Cao Yunchang , Ping Fan , Tang Xu
Published: March 2021

The Global Navigation Satellite System (GNSS) plays an important role in retrieving high temporal–spatial resolution precipitable water vapor (PWV) and its applications. The weighted mean temperature (Tm) is a key parameter for the GNSS PWV estimation, which acts as the conversion factor from the zenith wet delay (ZWD) to the PWV. The Tm is determined by the air pressure and water vapor pressure, while it is not available nearby most GNSS stations. The empirical formular is often applied for the GNSS station surface temperature (Ts) but has a lower accuracy. In this paper, the temporal and spatial distribution characteristics of the coefficients of the linear Tm-Ts model are analyzed, and then a piecewise-linear Tm-Ts relationship is established for each GPS station using radiosonde data collected from 2011 to 2019. The Tm accuracy was increased by more than 10% and 20% for 86 and 52 radiosonde stations, respectively. The PWV time series at 377 GNSS stations from the infrastructure construction of national geodetic datum modernization and Crustal Movement Observation Network of China (CMONC) were further obtained from the GPS observations and meteorological data from 2011 to 2019. The PWV accuracy was improved when compared with the Bevis model. Furthermore, the daily and monthly average values, long-term trend, and its change characteristics of the PWV were analyzed using the high-precision inversion model. The results showed that the averaged PWV was higher in Central-Eastern China and Southern China and lower in Northwest China, Northeast China, and North China. The PWV is increasing in most parts of China, while the some PWVs in North China show a downward trend.
An Open-Source Platform for Human Pose Estimation and Tracking Using a Heterogeneous Multi-Sensor System
Authors: Patil Ashok Kumar , Balasubramanyam Adithya , Ryu Jae Yeong , Chakravarthi Bharatesh , Chai Young Ho
Published: March 2021

Human pose estimation and tracking in real-time from multi-sensor systems is essential for many applications. Combining multiple heterogeneous sensors increases opportunities to improve human motion tracking. Using only a single sensor type, e.g., inertial sensors, human pose estimation accuracy is affected by sensor drift over longer periods. This paper proposes a human motion tracking system using lidar and inertial sensors to estimate 3D human pose in real-time. Human motion tracking includes human detection and estimation of height, skeletal parameters, position, and orientation by fusing lidar and inertial sensor data. Finally, the estimated data are reconstructed on a virtual 3D avatar. The proposed human pose tracking system was developed using open-source platform APIs. Experimental results verified the proposed human position tracking accuracy in real-time and were in good agreement with current multi-sensor systems.
Loss-Modulation-Based Wavelength-Range Shifting of Tunable EDF Ring Laser with Cascaded-Chirped Long-Period Fiber Grating for Temperature Measurement
Authors: Fukushima Koken , Guterres Soares Manuel , Wada Atsushi , Tanaka Satoshi , Ito Fumihiko
Published: March 2021

A novel tunable Erbium-doped fiber ring laser (EDFRL) with a cascaded-chirped long-period fiber grating (C-CLPG) as a wavelength selection filter is proposed from the viewpoint of the sensor use, in which a variable optical attenuator (VOA) is employed as an intracavity loss modulator to change the oscillation wavelength region so that the resultant tuning wavelength range is widened. In the demonstrative experiment for temperature measurements, oscillation over the wavelength range of 12.85 nm (1557.62~1570.47 nm), which is more than three times range of the previously presented laser and is equivalent to 64 °C in terms of temperature change, was achieved, while a single-wavelength oscillation was maintained. In addition, a practical technique for realizing a temperature measurement by combining with the VOA control is also discussed.
Big Data Warehouse for Healthcare-Sensitive Data Applications
Authors: Shahid Arsalan , Nguyen Thien-An Ngoc , Kechadi M-Tahar
Published: March 2021

Obesity is a major public health problem worldwide, and the prevalence of childhood obesity is of particular concern. Effective interventions for preventing and treating childhood obesity aim to change behaviour and exposure at the individual, community, and societal levels. However, monitoring and evaluating such changes is very challenging. The EU Horizon 2020 project “Big Data against Childhood Obesity (BigO)” aims at gathering large-scale data from a large number of children using different sensor technologies to create comprehensive obesity prevalence models for data-driven predictions about specific policies on a community. It further provides real-time monitoring of the population responses, supported by meaningful real-time data analysis and visualisations. Since BigO involves monitoring and storing of personal data related to the behaviours of a potentially vulnerable population, the data representation, security, and access control are crucial. In this paper, we briefly present the BigO system architecture and focus on the necessary components of the system that deals with data access control, storage, anonymisation, and the corresponding interfaces with the rest of the system. We propose a three-layered data warehouse architecture: The back-end layer consists of a database management system for data collection, de-identification, and anonymisation of the original datasets. The role-based permissions and secured views are implemented in the access control layer. Lastly, the controller layer regulates the data access protocols for any data access and data analysis. We further present the data representation methods and the storage models considering the privacy and security mechanisms. The data privacy and security plans are devised based on the types of collected personal, the types of users, data storage, data transmission, and data analysis. We discuss in detail the challenges of privacy protection in this large distributed data-driven application and implement novel privacy-aware data analysis protocols to ensure that the proposed models guarantee the privacy and security of datasets. Finally, we present the BigO system architecture and its implementation that integrates privacy-aware protocols.
A Two-Layer IP Hopping-Based Moving Target Defense Approach to Enhancing the Security of Mobile Ad-Hoc Networks
Authors: Wang Pengkun , Zhou Momiao , Ding Zhizhong
Published: March 2021

Mobile ad-hoc networks (MANETs) have great potential applications in military missions or emergency rescue due to their no-infrastructure, self-organizing and multi hop capability characteristics. Obviously, it is important to implement a low-cost and efficient mechanism of anti-invasion, anti-eavesdropping and anti-attack in MANETs, especially for military scenarios. The purpose of intruding or attacking a MANET is usually different from that of wired Internet networks whose security mechanism has been widely explored and implemented. For MANETs, moving target defense (MTD) is a suitable mechanism to enhance the network security, whose basic idea is to continuously and randomly change the system parameters or configuration to create inaccessibility for intruders and attackers. In this paper, a two-layer IP hopping-based MTD approach is proposed, in which device IP addresses or virtual IP addresses change or hop according to the network security status and requirements. The proposed MTD scheme based on the two-layer IP hopping has two major advantages in terms of network security. First, the device IP address of each device is not exposed to the wireless physical channel at all. Second, the two-layer IP hops with individual interval and rules to obtain enhanced security of MANET while maintaining relatively low computational load and communication cost for network control and synchronization. The proposed MTD scheme is implemented in our developed MANET terminals, providing three level of network security: anti-intrusion in normal environment, intrusion detection in offensive environment and anti-eavesdropping in a hostile environment by combining the data encryption technology.
Novel Bandwidth Expander Supported Power Amplifier for Wideband Ultrasound Transducer Devices
Authors: Kim Kyeongjin , Choi Hojong
Published: March 2021

Ultrasound transducer devices have their own frequency ranges, depending on the applications and specifications, due to penetration depth, sensitivity, and image resolution. For imaging applications, in particular, the transducer devices are preferable to have a wide bandwidth due to the specific information generated by the tissue or blood vessel structures. To support these ultrasound transducer devices, ultrasound power amplifier hardware with a wide bandwidth can improve the transducer performance. Therefore, we developed a new bandwidth expander circuit using specially designed switching architectures to increase the power amplifier bandwidth. The measured bandwidth of the power amplifier with the help of the bandwidth expander circuit increased by 56.9%. In addition, the measured echo bandwidths of the 15-, 20-, and 25-MHz transducer devices were increased by 8.1%, 6.0%, and 9.8%, respectively, with the help of the designed bandwidth expander circuit. Therefore, the designed architecture could help an ultrasound system hardware with a wider bandwidth, thus supporting the use of different frequency ultrasound transducer devices with a single developed ultrasound system.
An Improved Pattern Synthesis Iterative Method in Planar Arrays for Obtaining Efficient Footprints with Arbitrary Boundaries
Authors: Salas-Sánchez Aarón Ángel , López-Álvarez Cibrán , Rodríguez-González Juan Antonio , López-Martín María Elena , Ares-Pena Francisco José
Published: March 2021

In the present paper, an iterative technique devoted to reproducing efficient footprints with arbitrary boundaries for planar arrays is addressed. The methodology here depicted is based on exploiting the nature of the continuous aperture distribution by expressing it as a Fourier series of moderately high orders. In this manner, the resulting illumination boundary is defined by a target three-dimensional flat-topped pattern composed of stretching and shrinking modified circular Taylor patterns and the maximum order of the series to obtain a good reconstruction is determined by means of the iterative process. Examples and comparisons with the previous literature were conducted by analyzing square and rectangular contoured beams as test cases. Additionally, interesting potentials regarding space applications from a geostationary satellite are outlined by means of the EuTELSAT (European Telecommunications Satellite Organization) European coverage case study. In such a way, its numerical approach was analyzed by including subarray architectures and discussing improvements about dynamic range ratio of the excitations without critical power losses within the illumination region.
Assessment of Vineyard Canopy Characteristics from Vigour Maps Obtained Using UAV and Satellite Imagery
Authors: Campos Javier , García-Ruíz Francisco , Gil Emilio
Published: March 2021

Canopy characterisation is a key factor for the success and efficiency of the pesticide application process in vineyards. Canopy measurements to determine the optimal volume rate are currently conducted manually, which is time-consuming and limits the adoption of precise methods for volume rate selection. Therefore, automated methods for canopy characterisation must be established using a rapid and reliable technology capable of providing precise information about crop structure. This research providedregression models for obtaining canopy characteristics of vineyards from unmanned aerial vehicle (UAV) and satellite images collected in three significant growth stages. Between 2018 and 2019, a total of 1400 vines were characterised manually and remotely using a UAV and a satellite-based technology. The information collected from the sampled vines was analysed by two different procedures. First, a linear relationship between the manual and remote sensing data was investigated considering every single vine as a data point. Second, the vines were clustered based on three vigour levels in the parcel, and regression models were fitted to the average values of the ground-based and remote sensing-estimated canopy parameters. Remote sensing could detect the changes in canopy characteristics associated with vegetation growth. The combination of normalised differential vegetation index (NDVI) and projected area extracted from the UAV images is correlated with the tree row volume (TRV) when raw point data were used. This relationship was improved and extended to canopy height, width, leaf wall area, and TRV when the data were clustered. Similarly, satellite-based NDVI yielded moderate coefficients of determination for canopy width with raw point data, and for canopy width, height, and TRV when the vines were clustered according to the vigour. The proposed approach should facilitate the estimation of canopy characteristics in each area of a field using a cost-effective, simple, and reliable technology, allowing variable rate application in vineyards.
How Geometry Affects Sensitivity of a Differential Transformer for Contactless Characterization of Liquids
Authors: Berger Marc , Zygmanowski Anne , Zimmermann Stefan
Published: March 2021

The electrical and dielectric properties of liquids can be used for sensing. Specific applications, e.g., the continuous in-line monitoring of blood conductivity as a measure of the sodium concentration during dialysis treatment, require contactless measuring methods to avoid any contamination of the medium. The differential transformer is one promising approach for such applications, since its principle is based on a contactless, magnetically induced conductivity measurement. The objective of this work is to investigate the impact of the geometric parameters of the sample or medium under test on the sensitivity and the noise of the differential transformer to derive design rules for an optimized setup. By fundamental investigations, an equation for the field penetration depth of a differential transformer is derived. Furthermore, it is found that increasing height and radius of the medium is accompanied by an enhancement in sensitivity and precision.
A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data
Authors: Amjad Fatima , Khan Muhammad Hassan , Nisar Muhammad Adeel , Farid Muhammad Shahid , Grzegorzek Marcin
Published: March 2021

Human activity recognition (HAR) aims to recognize the actions of the human body through a series of observations and environmental conditions. The analysis of human activities has drawn the attention of the research community in the last two decades due to its widespread applications, diverse nature of activities, and recording infrastructure. Lately, one of the most challenging applications in this framework is to recognize the human body actions using unobtrusive wearable motion sensors. Since the human activities of daily life (e.g., cooking, eating) comprises several repetitive and circumstantial short sequences of actions (e.g., moving arm), it is quite difficult to directly use the sensory data for recognition because the multiple sequences of the same activity data may have large diversity. However, a similarity can be observed in the temporal occurrence of the atomic actions. Therefore, this paper presents a two-level hierarchical method to recognize human activities using a set of wearable sensors. In the first step, the atomic activities are detected from the original sensory data, and their recognition scores are obtained. Secondly, the composite activities are recognized using the scores of atomic actions. We propose two different methods of feature extraction from atomic scores to recognize the composite activities, and they include handcrafted features and the features obtained using the subspace pooling technique. The proposed method is evaluated on the large publicly available CogAge dataset, which contains the instances of both atomic and composite activities. The data is recorded using three unobtrusive wearable devices: smartphone, smartwatch, and smart glasses. We also investigated the performance evaluation of different classification algorithms to recognize the composite activities. The proposed method achieved 79% and 62.8% average recognition accuracies using the handcrafted features and the features obtained using subspace pooling technique, respectively. The recognition results of the proposed technique and their comparison with the existing state-of-the-art techniques confirm its effectiveness.
An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction
Authors: Zeng Hong , Li Xiufeng , Borghini Gianluca , Zhao Yue , Aricò Pietro , Di Flumeri Gianluca , Sciaraffa Nicolina , Zakaria Wael , Kong Wanzeng , Babiloni Fabio
Published: March 2021

Fatigued driving is one of the main causes of traffic accidents. The electroencephalogram (EEG)-based mental state analysis method is an effective and objective way of detecting fatigue. However, as EEG shows significant differences across subjects, effectively “transfering” the EEG analysis model of the existing subjects to the EEG signals of other subjects is still a challenge. Domain-Adversarial Neural Network (DANN) has excellent performance in transfer learning, especially in the fields of document analysis and image recognition, but has not been applied directly in EEG-based cross-subject fatigue detection. In this paper, we present a DANN-based model, Generative-DANN (GDANN), which combines Generative Adversarial Networks (GAN) to enhance the ability by addressing the issue of different distribution of EEG across subjects. The comparative results show that in the analysis of cross-subject tasks, GDANN has a higher average accuracy of 91.63% in fatigue detection across subjects than those of traditional classification models, which is expected to have much broader application prospects in practical brain–computer interaction (BCI).
Ensemble CNN to Detect Drowsy Driving with In-Vehicle Sensor Data
Authors: Jeon Yongsu , Kim Beomjun , Baek Yunju
Published: March 2021

Drowsy driving is a major threat to the safety of drivers and road traffic. Accurate and reliable drowsy driving detection technology can reduce accidents caused by drowsy driving. In this study, we present a new method to detect drowsy driving with vehicle sensor data obtained from the steering wheel and pedal pressure. From our empirical study, we categorized drowsy driving into long-duration drowsy driving and short-duration drowsy driving. Furthermore, we propose an ensemble network model composed of convolution neural networks that can detect each type of drowsy driving. Each subnetwork is specialized to detect long- or short-duration drowsy driving using a fusion of features, obtained through time series analysis. To efficiently train the proposed network, we propose an imbalanced data-handling method that adjusts the ratio of normal driving data and drowsy driving data in the dataset by partially removing normal driving data. A dataset comprising 198.3 h of in-vehicle sensor data was acquired through a driving simulation that includes a variety of road environments such as urban environments and highways. The performance of the proposed model was evaluated with a dataset. This study achieved the detection of drowsy driving with an accuracy of up to 94.2%.
SISME, Estuarine Monitoring System Based on IOT and Machine Learning for the Detection of Salt Wedge in Aquifers: Case Study of the Magdalena River Estuary
Authors: Ariza-Colpas Paola Patricia , Ayala-Mantilla Cristian Eduardo , Shaheen Qaisar , Piñeres-Melo Marlon Alberto , Villate-Daza Diego Andrés , Morales-Ortega Roberto Cesar , De-la-Hoz-Franco Emiro , Sanchez-Moreno Hernando , Aziz Butt Shariq , Afzal Mehtab
Published: March 2021

This article contains methods, results, and analysis agreed for the development of an application based on the internet of things and making use of machine learning techniques that serves as a support for the identification of the saline wedge in the Magdalena River estuary, Colombia. As a result of this investigation, the process of identifying the most suitable telecommunications architecture to be installed in the estuary is shown, as well as the characteristics of the software developed called SISME (Estuary Monitoring System), and the results obtained after the implementation of prediction techniques based on time series. This implementation supports the maritime security of the port of Barranquilla since it can support decision-making related to the estuary. This research is the result of the project “Implementation of a Wireless System of Temperature, Conductivity and Pressure Sensors to support the identification of the saline wedge and its impact on the maritime safety of the Magdalena River estuary”.
Edge-Sensitive Left Ventricle Segmentation Using Deep Reinforcement Learning
Authors: Xiong Jingjing , Po Lai-Man , Cheung Kwok Wai , Xian Pengfei , Zhao Yuzhi , Rehman Yasar Abbas Ur , Zhang Yujia
Published: March 2021

Deep reinforcement learning (DRL) has been utilized in numerous computer vision tasks, such as object detection, autonomous driving, etc. However, relatively few DRL methods have been proposed in the area of image segmentation, particularly in left ventricle segmentation. Reinforcement learning-based methods in earlier works often rely on learning proper thresholds to perform segmentation, and the segmentation results are inaccurate due to the sensitivity of the threshold. To tackle this problem, a novel DRL agent is designed to imitate the human process to perform LV segmentation. For this purpose, we formulate the segmentation problem as a Markov decision process and innovatively optimize it through DRL. The proposed DRL agent consists of two neural networks, i.e., First-P-Net and Next-P-Net. The First-P-Net locates the initial edge point, and the Next-P-Net locates the remaining edge points successively and ultimately obtains a closed segmentation result. The experimental results show that the proposed model has outperformed the previous reinforcement learning methods and achieved comparable performances compared with deep learning baselines on two widely used LV endocardium segmentation datasets, namely Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset, and Sunnybrook 2009 dataset. Moreover, the proposed model achieves higher F-measure accuracy compared with deep learning methods when training with a very limited number of samples.
Strain Sensing Coatings for Large Composite Structures Based on 2D MXene Nanoparticles
Authors: Monastyreckis Gediminas , Stepura Anastasiia , Soyka Yaryna , Maltanava Hanna , Poznyak Sergey K. , Omastová Mária , Aniskevich Andrey , Zeleniakiene Daiva
Published: March 2021

Real-time strain monitoring of large composite structures such as wind turbine blades requires scalable, easily processable and lightweight sensors. In this study, a new type of strain-sensing coating based on 2D MXene nanoparticles was developed. A Ti3C2Tz MXene was prepared from Ti3AlC2 MAX phase using hydrochloric acid and lithium fluoride etching. Epoxy and glass fibre–reinforced composites were spray-coated using an MXene water solution. The morphology of the MXenes and the roughness of the substrate were characterised using optical microscopy and scanning electron microscopy. MXene coatings were first investigated under various ambient conditions. The coating experienced no significant change in electrical resistance due to temperature variation but was responsive to the 301–365 nm UV spectrum. In addition, the coating adhesion properties, electrical resistance stability over time and sensitivity to roughness were also analysed in this study. The electromechanical response of the MXene coating was investigated under tensile loading and cyclic loading conditions. The gauge factor at a strain of 4% was 10.88. After 21,650 loading cycles, the MXene coating experienced a 16.25% increase in permanent resistance, but the response to loading was more stable. This work provides novel findings on electrical resistance sensitivity to roughness and electromechanical behaviour under cyclic loading, necessary for further development of MXene-based nanocoatings. The advantages of MXene coatings for large composite structures are processability, scalability, lightweight and adhesion properties.
Simple Equations Pertaining to the Particle Number and Surface Area of Metallic, Polymeric, Lipidic and Vesicular Nanocarriers
Authors: Mozafari M. R. , Mazaheri E. , Dormiani K.
Published: March 2021

Introduction: Bioactive encapsulation and drug delivery systems have already found their way to the market as efficient therapeutics to combat infections, viral diseases and different types of cancer. The fields of food fortification, nutraceutical supplementation and cosmeceuticals have also been getting the benefit of encapsulation technologies. Aim: Successful formulation of such therapeutic and nutraceutical compounds requires thorough analysis and assessment of certain characteristics including particle number and surface area without the need to employ sophisticated analytical techniques. Solution: Here we present simple mathematical formulas and equations used in the research and development of drug delivery and controlled release systems employed for bioactive encapsulation and targeting the sites of infection and cancer in vitro and in vivo. Systems covered in this entry include lipidic vesicles, polymeric capsules, metallic particles as well as surfactant- and tocopherol-based micro- and nanocarriers.
Measuring Bayesian Robustness Using Rényi Divergence
Authors: Al-Labadi Luai , Asl Forough Fazeli , Wang Ce
Published: March 2021

This paper deals with measuring the Bayesian robustness of classes of contaminated priors. Two different classes of priors in the neighborhood of the elicited prior are considered. The first one is the well-known ϵ-contaminated class, while the second one is the geometric mixing class. The proposed measure of robustness is based on computing the curvature of Rényi divergence between posterior distributions. Examples are used to illustrate the results by using simulated and real data sets.
Simulating Human Visual Perception in Tunnel Portals
Authors: Liu Changjiang , Wang Qiuping
Published: March 2021

To study the characteristics of light and dark adaptation in tunnel portals, and to determine the influencing factors in light–dark vision adaptation, basic tunnel lighting and linear design data were obtained. In this study, we used a light-shielded tent to simulate the dark environment of a tunnel, observe the driver recognition time for target objects during the light–dark adaptation process, and analyze the light–dark adaptation time of human vision. Based on the experimental data, we examined the relationships between age, gender, illuminance, and light and dark adaptation times, and established a model for these relationships. The experimental results show that the dark adaptation time is generally longer than the light adaptation time. The dark adaptation time is positively related to age and exhibits a cubic relationship. There is no significant correlation between the light adaptation time and age, but the overall trend is for the light adaptation time to gradually increase with increasing age. There is no correlation between gender and light and dark adaptation times, but there is a notable correlation between light and dark adaptation times and illuminance. When the illuminance ranges from 11,000 to 13,000 lux, the light and dark adaptation times are the longest.
An Analysis on the NASDAQ’s Potential for Sustainable Investment Practices during the Financial Shock from COVID-19
Authors: Shields Rachel , Ajour El Zein Samer , Vila Brunet Neus
Published: March 2021

There is a growing demand for sustainable business practices and for sustainable and impact investment as has been signaled by the Sustainable Development Goals ratified by all the United Nations members. However, there is not that much evidence on how sustainable investments perform during crises compared to regular investments. This paper investigates if sustainable investments within the NASDAQ have a lower volatility rate when reacting to a significant global crisis such as the COVID-19 pandemic. It groups the shares of businesses with Corporate Social Responsibility (CSR) practices that are ranked 70% or higher given by CSRHub, Inc. and compares it to business shares with the lowest-ranked CSR business practices at 30% or lower. The top 30% and bottom 30% CSR stocks’ volatility will be predicted using variations of the GARCH model. The top 30% CSR stocks of the NASDAQ had a lower rate of volatility for a global crisis than the bottom 30% CSR stocks. Technology is the only sector whose top 30% showed higher volatility. However, the top 30% of companies in the Health Care and Utilities sectors show a higher increase in returns and a lower drop in returns. These results signal the higher uncertainty associated with some cutting-edge products and services offered by the top 30% of technology companies and the preference for more established companies that offer higher quality services when it comes to satisfying basic needs such as health and utilities in difficult times.
The Influence of Power and Trust on the Initiation and Duration of Modal Shift Solutions
Authors: Rogerson Sara , Santén Vendela , Sallnäs Uni
Published: March 2021

Modal shift to more energy-efficient alternatives, such as from road to rail or sea, is one path to reduce negative environmental effects. Transport providers and shippers have crucial roles in modal choice decisions, and a better understanding of the influence of interorganisational factors on modal shift is needed. The purpose is to increase the understanding of opportunities for modal shifts by exploring the influence of power and trust at the interface between transport providers and shippers. Aspects of power (coercive, reward, legitimate, expert and referent) and trust (contractual, competence and goodwill) influencing modal shifts were identified from interviews with shippers and transport providers in Sweden. During the initiation phase of modal shift, power appears more important, while trust is shown essential for the duration. By proactively suggesting modal shift, transport providers can use expert power to create rewards and referent power, through recognition of their expertise. Building trust, particularly goodwill trust, such as time invested in understanding the other party, transparency about challenges and jointly seeking solutions, is key to establish long-term modal shifts. This paper contributes to modal shift literature with insights on power balances and trust between transport providers and shippers.
Agricultural Credit and Extension Services: Does Their Synergy Augment Farmers’ Economic Outcomes?
Authors: Moahid Masaood , Khan Ghulam Dastgir , Yoshida Yuichiro , Joshi Niraj Prakash , Maharjan Keshav Lall
Published: March 2021

Access to credit is essential for sustainable agricultural development. This paper evaluates the impact of formal and informal agricultural credit, access to extension services, and different combinations of agricultural credit and extension services on the economic outcomes of farming households in Afghanistan. This study applies a quasi-experimental approach (propensity score matching) and inverse-probability-weighted regression adjustment (IPWRA) analysis. The data comes from a survey of 277 randomly selected farming households in the three districts of rural Afghanistan. The results show that having access to formal agricultural credit has a positive and differentiated impact on the farming costs and net revenue of farming households. However, the effects increase when a farming household has access to both formal credit and extension services. The results also reveal that credit constraints affect farming costs and net revenue. The study provides some practical implications for agricultural development policymakers. First, formal agricultural credit affects farm revenue in rural Afghanistan. Second, the impact of credit bundled with agricultural extension services on farm revenue is higher than the impact of the provision of each service separately. Therefore, a more sustainable agricultural credit arrangement should be supplemented by extension services for farmers in Afghanistan.
Organizational Forms and Agri-Environmental Practices: The Case of Brazilian Dairy Farms
Authors: Siqueira Tiago Teixeira da Silva , Galliano Danielle , Nguyen Geneviève , Bánkuti Ferenc Istvan
Published: March 2021

Understanding the relationship between the organizational characteristics of a farm and its environmental performance is essential to support the agro-ecological transition of farms. This is even more important as very few studies on the subject have been undertaken and as there is a growing diversity of organizational forms of farms that differ from the traditional family model. This paper proposes a comprehensively integrated approach of dairy farms in Brazil. A case study of six archetypes of farms with contrasted organizational characteristics is developed to explore the relations between, on the one hand, farms’ organizational structure and governance, and on the other hand, the adoption of agri-environmental practices. Results show that the adoption of agri-environmental practices varies across the wide range of farm’s organizational forms—from the family to the industrial models. Farms with limited internal resources depend more specifically on external sectoral or territorial resources to implement environmental practices. If the environment is conducive to the creation of incentives and coordination mechanisms underlying learning processes, farms will adopt agri-environmental practices, regardless of they are organized. The creation of local cooperatives, farmer’s networks and universities extension programs can strengthen farmers’ absorption, adaptation and transformation capacities and boost the adoption of environmental practices. Finally, considering farms as heterogeneous organizational forms in terms of human capital, resources, market, and informational access is essential to accelerate the agroecological transition.
The Treachery of Images: Redefining the Structural System of Havana’s National Art Schools
Authors: Del Curto Davide , Celli Sofia
Published: March 2021

This paper illustrates the contribution that on-site survey and graphical documentation offer to the structural comprehension of 20th century architectural and civil engineering heritage and, therefore, to its sustainable conservation. The research herein presented has identified the true structural system of Havana’s National Art Schools, an internationally well-known architectural masterpiece that was recently investigated within the drafting of a comprehensive conservation management plan. This iconic complex was built right after the Castro’s revolution and was meant to embody Cuba’s newfound freedom. To this end, the complex was supposed to be built using Catalan vaulting, a technique loaded with significance due to its provenance, affordability, and flexibility. While most of the literature, the architectural features, and the very designers assert that no concrete nor steel were employed during construction, recent studies suggested that a reinforced concrete core might be hidden behind the masonry-like appearance of the five buildings. The structural analysis performed in order to draft a conservation and management plan for the school site thus became a hermeneutic opportunity to address this topic. Combining direct observation, documentary research, and nondestructive analyses (infrared thermography and magnetometer testing), it was possible to finally redefine the structural nature of these notorious architectures, which are indeed mostly made of reinforced concrete.