bluuebunny/crossref_metadata_2025 huggingface dataset:
Experiencing Religion: Post-Colonial Views for Religious Education
Authors: Baring Rito
Published: December 2021

Framed within religious historicism, the present study reviews, through historical and empirical insights, the lessons that Philippine RE can learn from the liberating function of religion and liberated religious undercurrents parallel to institutional religion in the Philippines. The liberating function of religion is often overlooked in post-colonial discourses while religious undercurrent views seem neglected due to pre-occupations with untangling power imbalances submerged in the voices of institutional religion in post-colonial analysis. Hence, in this presentation, I give particular attention to the liberating role of contemporary religion in contrast to the post-colonial thrust to rid institutional religion of power and control and secondly, the liberated religious views of young Filipino audiences from empirical findings I found from my previous studies. For religious undercurrents, I limit myself to current unorthodox religious interpretations of young Filipino audiences departing from conventional assumptions of religion and culture. My analysis of liberating religion and liberated religious views from empirical findings show epistemological shifts from the Christian interpretation in a post-colonial context. These shifts point to de-institutionalized but theocentric religious ideas inspired by moral and communal considerations, which form the basis of RE content.
Shouldering His Guru’s Legacy: Khenpo Tsultrim Lodro’s Discourse in Relation to thos-bsam-sgom after the Death of Khenpo Jigme Phuntsok
Authors: Hardie Catherine , Hobhouse Nicholas S.
Published: December 2021

This article contributes to the growing discussion of the ways that Khenpo Jigme Phuntsok’s legacy has been carried forward by his spiritual successors at the Larung Five Sciences Buddhist Academy (or Larung Gar), in contemporary Eastern Tibet, by focusing on the contribution of the Larung vice principal and leading second-generation luminary, Khenpo Tsultrim Lodro (1962–). Drawing on a range of Khenpo Tsultrim Lodro’s spoken teachings and writings over a twenty-year period, this article undertakes a contextualised analysis of how he has shouldered his guru’s legacy in the areas of Tibetan Buddhist monastic education, monastic governance, and monastic–lay relations. It explores how the threefold framework of thos-bsam-sgom (listening, reflecting, meditating), whether in its parts or its entirety, lies at the centre of his reformist discourse in these matters, articulated in relation to a wide range of Buddhist and secular concepts, as well as to concrete institutions and organisational structures. It is argued that, while Khenpo Tsultrim Lodro expounds the entire framework of thos-bsam-sgom throughout the years in question, in ‘the earlier period’ (c.2000–2012), from shortly before his guru’s death until approximately a decade afterwards, he subtly privileges the intellectual attributes associated with thos-bsam, while in ‘the later period’ (c.2013–2021) he reweights his exposition to give a balanced emphasis to the practice of sgom.
Hospitalization Due to Fire-Induced Pollution in the Brazilian Legal Amazon from 2005 to 2018
Authors: Campanharo Wesley Augusto , Morello Thiago , Christofoletti Maria A. M. , Anderson Liana O.
Published: December 2021

Fire is widely used in the Amazon as a ubiquitous driver of land management and land cover change. Regardless of their purpose, fires release a considerable amount of pollutants into the atmosphere, with severe consequences for human health. This paper adds to the extant literature by measuring the causal effect of fires on hospitalizations, using the approach of instrumental variables, whose validity is assessed with multiple statistical tests. A wide range of confounders are added as covariates, seizing on the accuracy enhancement potential of a broad and fine-grained dataset that covers 14 years of the whole Amazon territory at a municipal–monthly level. The results reveal a positive effect of fire on hospitalizations due to respiratory illnesses in general, and particularly in those due to asthma. A 1% increase in pollution concentration would increase hospitalizations by 0.14% at a municipality–monthly level. A total of 5% of respiratory hospitalizations were estimated to be attributable to fire-induced pollution, corresponding to 822 cases per month. The analysis demonstrates that the coupling of econometrics and remote sensing data is a promising avenue towards the assessment of impacts caused by fires, which may be applied to other regions of the world subjected to anthropogenic fires.
Evaluation of Seasonal, Drought, and Wet Condition Effects on Performance of Satellite-Based Precipitation Data over Different Climatic Conditions in Iran
Authors: Qureshi Salman , Koohpayma Javad , Firozjaei Mohammad Karimi , Kakroodi Ata Abdollahi
Published: December 2021

The Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) are the most important and widely used data sources in several applications—e.g., forecasting drought and flood, and managing water resources—especially in the areas with sparse or no other robust sources. This study explored the accuracy and precision of satellite data products over a span of 18 years (2000–2017) using synoptic ground station data for three regions in Iran with different climates, namely (a) humid and high rainfall, (b) semi-arid, and (c) arid. The results show that the monthly precipitation products of GPM and TRMM overestimate the rainfall. On average, they overestimated the precipitation amount by 11% in humid, by 50% in semi-arid, and by 43% in arid climate conditions compared to the ground-based data. This study also evaluated the satellite data accuracy in drought and wet conditions based on the standardized precipitation index (SPI) and different seasons. The results showed that the accuracy of satellite data varies significantly under drought, wet, and normal conditions and different timescales, being lowest under drought conditions, especially in arid regions. The highest accuracy was obtained on the 12-month timescale and the lowest on the 3-month timescale. Although the accuracy of the data is dependent on the season, the seasonal effects depend on climatic conditions.
Multi-Sensors Remote Sensing Applications for Assessing, Monitoring, and Mapping NPK Content in Soil and Crops in African Agricultural Land
Authors: Misbah Khalil , Laamrani Ahmed , Khechba Keltoum , Dhiba Driss , Chehbouni Abdelghani
Published: December 2021

Demand for agricultural products is increasing as population continues to grow in Africa. To attain a higher crop yield while preserving the environment, appropriate management of macronutrients (i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops are of critical prominence. This paper aims to review the state of art of the use of remote sensing in soil agricultural applications, especially in monitoring NPK availability for widely grown crops in Africa. In this study, we conducted a substantial literature review of the use of airborne imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing spectral information, and advances of these applications in farming practices by the African scientific community. Here we aimed to identify knowledge gaps in this field and challenges related to the acquisition, processing, and analysis of hyperspectral imagery for soil agriculture investigations. To do so, publications over the past 10 years (i.e., 2008–2021) in hyperspectral imaging technology and applications in monitoring macronutrients status for crops were reviewed. In this study, the imaging platforms and sensors, as well as the different methods of processing encountered across the literature, were investigated and their benefit for NPK assessment were highlighted. Furthermore, we identified and selected particular spectral regions, bands, or features that are most sensitive to describe NPK content (both in crop and soil) that allowed to characterize NPK. In this review, we proposed a hyperspectral data-based research protocol to quantify variability of NPK in soil and crop at the field scale for the sake of optimizing fertilizers application. We believe that this review will contribute promoting the adoption of hyperspectral technology (i.e., imaging and spectroscopy) for the optimization of soil NPK investigation, mapping, and monitoring in many African countries.
A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle
Authors: Jiao Quanjun , Sun Qi , Zhang Bing , Huang Wenjiang , Ye Huichun , Zhang Zhaoming , Zhang Xiao , Qian Binxiang
Published: December 2021

Canopy chlorophyll content (CCC) is an important indicator for crop-growth monitoring and crop productivity estimation. The hybrid method, involving the PROSAIL radiative transfer model and machine learning algorithms, has been widely applied for crop CCC retrieval. However, PROSAIL’s homogeneous canopy hypothesis limits the ability to use the PROSAIL-based CCC estimation across different crops with a row structure. In addition to leaf area index (LAI), average leaf angle (ALA) is the most important canopy structure factor in the PROSAIL model. Under the same LAI, adjustment of the ALA can make a PROSAIL simulation obtain the same canopy gap as the heterogeneous canopy at a specific observation angle. Therefore, parameterization of an adjusted ALA (ALAadj) is an optimal choice to make the PROSAIL model suitable for specific row-planted crops. This paper attempted to improve PROSAIL-based CCC retrieval for different crops, using a random forest algorithm, by introducing the prior knowledge of crop-specific ALAadj. Based on the field reflectance spectrum at nadir, leaf area index, and leaf chlorophyll content, parameterization of the ALAadj in the PROSAIL model for wheat and soybean was carried out. An algorithm integrating the random forest and PROSAIL simulations with prior ALAadj information was developed for wheat and soybean CCC retrieval. Ground-measured CCC measurements were used to validate the CCC retrieved from canopy spectra. The results showed that the ALAadj values (62 degrees for wheat; 45 degrees for soybean) that were parameterized for the PROSAIL model demonstrated good discrimination between the two crops. The proposed algorithm improved the CCC retrieval accuracy for wheat and soybean, regardless of whether continuous visible to near-infrared spectra with 50 bands (RMSE from 39.9 to 32.9 μg cm−2; R2 from 0.67 to 0.76) or discrete spectra with 13 bands (RMSE from 43.9 to 33.7 μg cm−2; R2 from 0.63 to 0.74) and nine bands (RMSE from 45.1 to 37.0 μg cm−2; R2 from 0.61 to 0.71) were used. The proposed hybrid algorithm, based on PROSAIL simulations with ALAadj, has the potential for satellite-based CCC estimation across different crop types, and it also has a good reference value for the retrieval of other crop parameters.
Attentively Learning Edge Distributions for Semantic Segmentation of Remote Sensing Imagery
Authors: Li Xin , Li Tao , Chen Ziqi , Zhang Kaiwen , Xia Runliang
Published: December 2021

Semantic segmentation has been a fundamental task in interpreting remote sensing imagery (RSI) for various downstream applications. Due to the high intra-class variants and inter-class similarities, inflexibly transferring natural image-specific networks to RSI is inadvisable. To enhance the distinguishability of learnt representations, attention modules were developed and applied to RSI, resulting in satisfactory improvements. However, these designs capture contextual information by equally handling all the pixels regardless of whether they around edges. Therefore, blurry boundaries are generated, rising high uncertainties in classifying vast adjacent pixels. Hereby, we propose an edge distribution attention module (EDA) to highlight the edge distributions of leant feature maps in a self-attentive fashion. In this module, we first formulate and model column-wise and row-wise edge attention maps based on covariance matrix analysis. Furthermore, a hybrid attention module (HAM) that emphasizes the edge distributions and position-wise dependencies is devised combing with non-local block. Consequently, a conceptually end-to-end neural network, termed as EDENet, is proposed to integrate HAM hierarchically for the detailed strengthening of multi-level representations. EDENet implicitly learns representative and discriminative features, providing available and reasonable cues for dense prediction. The experimental results evaluated on ISPRS Vaihingen, Potsdam and DeepGlobe datasets show the efficacy and superiority to the state-of-the-art methods on overall accuracy (OA) and mean intersection over union (mIoU). In addition, the ablation study further validates the effects of EDA.
UATNet: U-Shape Attention-Based Transformer Net for Meteorological Satellite Cloud Recognition
Authors: Wang Zhanjie , Zhao Jianghua , Zhang Ran , Li Zheng , Lin Qinghui , Wang Xuezhi
Published: December 2021

Cloud recognition is a basic task in ground meteorological observation. It is of great significance to accurately identify cloud types from long-time-series satellite cloud images for improving the reliability and accuracy of weather forecasting. However, different from ground-based cloud images with a small observation range and easy operation, satellite cloud images have a wider cloud coverage area and contain more surface features. Hence, it is difficult to effectively extract the structural shape, area size, contour shape, hue, shadow and texture of clouds through traditional deep learning methods. In order to analyze the regional cloud type characteristics effectively, we construct a China region meteorological satellite cloud image dataset named CRMSCD, which consists of nine cloud types and the clear sky (cloudless). In this paper, we propose a novel neural network model, UATNet, which can realize the pixel-level classification of meteorological satellite cloud images. Our model efficiently integrates the spatial and multi-channel information of clouds. Specifically, several transformer blocks with modified self-attention computation (swin transformer blocks) and patch merging operations are used to build a hierarchical transformer, and spatial displacement is introduced to construct long-distance cross-window connections. In addition, we introduce a Channel Cross fusion with Transformer (CCT) to guide the multi-scale channel fusion, and design an Attention-based Squeeze and Excitation (ASE) to effectively connect the fused multi-scale channel information to the decoder features. The experimental results demonstrate that the proposed model achieved 82.33% PA, 67.79% MPA, 54.51% MIoU and 70.96% FWIoU on CRMSCD. Compared with the existing models, our method produces more precise segmentation performance, which demonstrates its superiority on meteorological satellite cloud recognition tasks.
Recognition of Maize Phenology in Sentinel Images with Machine Learning
Authors: Murguia-Cozar Alvaro , Macedo-Cruz Antonia , Fernandez-Reynoso Demetrio Salvador , Salgado Transito Jorge Arturo
Published: December 2021

The scarcity of water for agricultural use is a serious problem that has increased due to intense droughts, poor management, and deficiencies in the distribution and application of the resource. The monitoring of crops through satellite image processing and the application of machine learning algorithms are technological strategies with which developed countries tend to implement better public policies regarding the efficient use of water. The purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops (Zea mays L.) in Sentinel 2 satellite images through supervised classification models. The training data were obtained by monitoring cultivated plots during an agricultural cycle. Indicators and characteristics were extracted from 41 Sentinel 2 images acquired during the monitoring dates. With these images, indicators of texture, vegetation, and colour were calculated to train three supervised classifiers: linear discriminant (LD), support vector machine (SVM), and k-nearest neighbours (kNN) models. It was found that 45 of the 86 characteristics extracted contributed to maximizing the accuracy by stage of development and the overall accuracy of the trained classification models. The characteristics of the Moran’s I local indicator of spatial association (LISA) improved the accuracy of the classifiers when applied to the L*a*b* colour model and to the near-infrared (NIR) band. The local binary pattern (LBP) increased the accuracy of the classification when applied to the red, green, blue (RGB) and NIR bands. The colour ratios, leaf area index (LAI), RGB colour model, L*a*b* colour space, LISA, and LBP extracted the most important intrinsic characteristics of maize crops with regard to classifying the phenological stages of the maize cultivation. The quadratic SVM model was the best classifier of maize crop phenology, with an overall accuracy of 82.3%.
Use of a Single Wearable Sensor to Evaluate the Effects of Gait and Pelvis Asymmetries on the Components of the Timed Up and Go Test, in Persons with Unilateral Lower Limb Amputation
Authors: Valle Maria Stella , Casabona Antonino , Sapienza Ilenia , Laudani Luca , Vagnini Alessandro , Lanza Sara , Cioni Matteo
Published: December 2021

The Timed Up and Go (TUG) test quantifies physical mobility by measuring the total performance time. In this study, we quantified the single TUG subcomponents and, for the first time, explored the effects of gait cycle and pelvis asymmetries on them. Transfemoral (TF) and transtibial (TT) amputees were compared with a control group. A single wearable inertial sensor, applied to the back, captured kinematic data from the body and pelvis during the 10-m walk test and the TUG test. From these data, two categories of symmetry indexes (SI) were computed: One SI captured the differences between the antero-posterior accelerations of the two sides during the gait cycle, while another set of SI quantified the symmetry over the three-dimensional pelvis motions. Moreover, the total time of the TUG test, the time of each subcomponent, and the velocity of the turning subcomponents were measured. Only the TF amputees showed significant reductions in each SI category when compared to the controls. During the TUG test, the TF group showed a longer duration and velocity reduction mainly over the turning subtasks. However, for all the amputees there were significant correlations between the level of asymmetries and the velocity during the turning tasks. Overall, gait cycle and pelvis asymmetries had a specific detrimental effect on the turning performance instead of on linear walking.
Novel Video Surveillance-Based Fire and Smoke Classification Using Attentional Feature Map in Capsule Networks
Authors: Shakhnoza Muksimova , Sabina Umirzakova , Sevara Mardieva , Cho Young-Im
Published: December 2021

A fire is an extraordinary event that can damage property and have a notable effect on people’s lives. However, the early detection of smoke and fire has been identified as a challenge in many recent studies. Therefore, different solutions have been proposed to approach the timely detection of fire events and avoid human casualties. As a solution, we used an affordable visual detection system. This method is possibly effective because early fire detection is recognized. In most developed countries, CCTV surveillance systems are installed in almost every public location to take periodic images of a specific area. Notwithstanding, cameras are used under different types of ambient light, and they experience occlusions, distortions of view, and changes in the resulting images from different camera angles and the different seasons of the year, all of which affect the accuracy of currently established models. To address these problems, we developed an approach based on an attention feature map used in a capsule network designed to classify fire and smoke locations at different distances outdoors, given only an image of a single fire and smoke as input. The proposed model was designed to solve two main limitations of the base capsule network input and the analysis of large-sized images, as well as to compensate the absence of a deep network using an attention-based approach to improve the classification of the fire and smoke results. In term of practicality, our method is comparable with prior strategies based on machine learning and deep learning methods. We trained and tested the proposed model using our datasets collected from different sources. As the results indicate, a high classification accuracy in comparison with other modern architectures was achieved. Further, the results indicate that the proposed approach is robust and stable for the classification of images from outdoor CCTV cameras with different viewpoints given the presence of smoke and fire.
A Real-Time Wearable Physiological Monitoring System for Home-Based Healthcare Applications
Authors: Jeong Jin-Woo , Lee Woochan , Kim Young-Joon
Published: December 2021

The acquisition of physiological data are essential to efficiently predict and treat cardiac patients before a heart attack occurs and effectively expedite motor recovery after a stroke. This goal can be achieved by using wearable wireless sensor network platforms for real-time healthcare monitoring. In this paper, we present a wireless physiological signal acquisition device and a smartphone-based software platform for real-time data processing and monitor and cloud server access for everyday ECG/EMG signal monitoring. The device is implemented in a compact size (diameter: 30 mm, thickness: 4.5 mm) where the biopotential is measured and wirelessly transmitted to a smartphone or a laptop for real-time monitoring, data recording and analysis. Adaptive digital filtering is applied to eliminate any interference noise that can occur during a regular at-home environment, while minimizing the data process time. The accuracy of ECG and EMG signal coverage is assessed using Bland–Altman analysis by comparing with a reference physiological signal acquisition instrument (RHS2116 Stim/Recording System, Intan). Signal coverage of R-R peak intervals showed almost identical outcome between this proposed work and the RHS2116, showing a mean difference in heart rate of 0.15 ± 4.65 bpm and a Wilcoxon’s p value of 0.133. A 24 h continuous recording session of ECG and EMG is conducted to demonstrate the robustness and stability of the device based on extended time wearability on a daily routine.
A Robust Design for Aperture-Level Simultaneous Transmit and Receive with Digital Phased Array
Authors: Xie Mingcong , Wei Xizhang , Tang Yanqun , Hu Dujuan
Published: December 2021

Aperture-level simultaneous transmit and receive (ALSTAR) attempts to utilize adaptive digital transmit and receive beamforming and digital self-interference cancellation methods to establish isolation between the transmit and receive apertures of the single-phase array. However, the existing methods only discuss the isolation of ALSTAR and ignore the radiation efficiency of the transmitter and the sensitivity of the receiver. The ALSTAR array design lacks perfect theoretical support and simplified engineering implementation. This paper proposes an adaptive random group quantum brainstorming optimization (ARGQBSO) algorithm to simplify the array design and improve the overall performance. ARGQBSO is derived from BSO and has been ameliorated in four aspects of the ALSTAR array, including random grouping, initial value presets, dynamic probability functions, and quantum computing. The transmit and receive beamforming carried out by ARGQBSO is robust to all elevation angles, which reduces complexity and is conducive to engineering applications. The simulated results indicate that the ARGQBSO algorithm has an excellent performance, and achieves 166.8 dB of peak EII, 47.1 dBW of peak EIRP, and −94.6 dBm of peak EIS with 1000 W of transmit power in the scenario of an 8-element array.
Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks
Authors: Pantera Laurent , Stulík Petr , Vidal-Ferràndiz Antoni , Carreño Amanda , Ginestar Damián , Ioannou George , Tasakos Thanos , Alexandridis Georgios , Stafylopatis Andreas
Published: December 2021

This work outlines an approach for localizing anomalies in nuclear reactor cores during their steady state operation, employing deep, one-dimensional, convolutional neural networks. Anomalies are characterized by the application of perturbation diagnostic techniques, based on the analysis of the so-called “neutron-noise” signals: that is, fluctuations of the neutron flux around the mean value observed in a steady-state power level. The proposed methodology is comprised of three steps: initially, certain reactor core perturbations scenarios are simulated in software, creating the respective perturbation datasets, which are specific to a given reactor geometry; then, the said datasets are used to train deep learning models that learn to identify and locate the given perturbations within the nuclear reactor core; lastly, the models are tested on actual plant measurements. The overall methodology is validated on hexagonal, pre-Konvoi, pressurized water, and VVER-1000 type nuclear reactors. The simulated data are generated by the FEMFFUSION code, which is extended in order to deal with the hexagonal geometry in the time and frequency domains. The examined perturbations are absorbers of variable strength, and the trained models are tested on actual plant data acquired by the in-core detectors of the Temelín VVER-1000 Power Plant in the Czech Republic. The whole approach is realized in the framework of Euratom’s CORTEX project.
Validity and Reliability of a Smartphone App for Gait and Balance Assessment
Authors: Rashid Usman , Barbado David , Olsen Sharon , Alder Gemma , Elvira Jose L. L. , Lord Sue , Niazi Imran Khan , Taylor Denise
Published: December 2021

Advances in technology provide an opportunity to enhance the accuracy of gait and balance assessment, improving the diagnosis and rehabilitation processes for people with acute or chronic health conditions. This study investigated the validity and reliability of a smartphone-based application to measure postural stability and spatiotemporal aspects of gait during four static balance and two gait tasks. Thirty healthy participants (aged 20–69 years) performed the following tasks: (1) standing on a firm surface with eyes opened, (2) standing on a firm surface with eyes closed, (3) standing on a compliant surface with eyes open, (4) standing on a compliant surface with eyes closed, (5) walking in a straight line, and (6) walking in a straight line while turning their head from side to side. During these tasks, the app quantified the participants’ postural stability and spatiotemporal gait parameters. The concurrent validity of the smartphone app with respect to a 3D motion capture system was evaluated using partial Pearson’s correlations (rp) and limits of the agreement (LoA%). The within-session test–retest reliability over three repeated measures was assessed with the intraclass correlation coefficient (ICC) and the standard error of measurement (SEM). One-way repeated measures analyses of variance (ANOVAs) were used to evaluate responsiveness to differences across tasks and repetitions. Periodicity index, step length, step time, and walking speed during the gait tasks and postural stability outcomes during the static tasks showed moderate-to-excellent validity (0.55 ≤ rp ≤ 0.98; 3% ≤ LoA% ≤ 12%) and reliability scores (0.52 ≤ ICC ≤ 0.92; 1% ≤ SEM% ≤ 6%) when the repetition effect was removed. Conversely, step variability and asymmetry parameters during both gait tasks generally showed poor validity and reliability except step length asymmetry, which showed moderate reliability (0.53 ≤ ICC ≤ 0.62) in both tasks when the repetition effect was removed. Postural stability and spatiotemporal gait parameters were found responsive (p < 0.05) to differences across tasks and test repetitions. Along with sound clinical judgement, the app can potentially be used in clinical practice to detect gait and balance impairments and track the effectiveness of rehabilitation programs. Further evaluation and refinement of the app in people with significant gait and balance deficits is needed.
Performance Improvement of Substrate Integrated Cavity Fed Dipole Array Antenna Using ENZ Metamaterial for 5G Applications
Authors: El-Nady Shaza , Elsharkawy Rania R. , Afifi Asmaa I. , Abd El-Hameed Anwer S.
Published: December 2021

This paper exhibits a high-gain, low-profile dipole antenna array (DAA) for 5G applications. The dipole element has a semi-triangular shape to realize a simple input impedance regime. To reduce the overall antenna size, a substrate integrated cavity (SIC) is adopted as a power splitter feeding network. The transition between the SIC and the antenna element is achieved by a grounded coplanar waveguide (GCPW) to increase the degree of freedom of impedance matching. Epsilon-near-zero (ENZ) metamaterial technique is exploited for gain enhancement. The ENZ metamaterial unit cells of meander shape are placed in front of each dipole perpendicularly to guide the radiated power into the broadside direction. The prospective antenna has an overall size of 2.58 λg3 and operates from 28.5 GHz up to 30.5 GHz. The gain is improved by 5 dB compared to that of the antenna without ENZ unit cells, reaching 11 dBi at the center frequency of 29.5 GHz. Measured and simulated results show a reasonable agreement.
Measuring Patient Similarity on Multiple Diseases by Joint Learning via a Convolutional Neural Network
Authors: Oh Sang Ho , Back Seunghwa , Park Jongyoul
Published: December 2021

Patient similarity research is one of the most fundamental tasks in healthcare, helping to make decisions without incurring additional time and costs in clinical practices. Patient similarity can also apply to various medical fields, such as cohort analysis and personalized treatment recommendations. Because of this importance, patient similarity measurement studies are actively being conducted. However, medical data have complex, irregular, and sequential characteristics, making it challenging to measure similarity. Therefore, measuring accurate similarity is a significant problem. Existing similarity measurement studies use supervised learning to calculate the similarity between patients, with similarity measurement studies conducted only on one specific disease. However, it is not realistic to consider only one kind of disease, because other conditions usually accompany it; a study to measure similarity with multiple diseases is needed. This research proposes a convolution neural network-based model that jointly combines feature learning and similarity learning to define similarity in patients with multiple diseases. We used the cohort data from the National Health Insurance Sharing Service of Korea for the experiment. Experimental results verify that the proposed model has outstanding performance when compared to other existing models for measuring multiple-disease patient similarity.
Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System
Authors: Fatani Abdulaziz , Dahou Abdelghani , Al-qaness Mohammed A. A. , Lu Songfeng , Abd Elaziz Mohamed Abd
Published: December 2021

Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.
Intelligent Approach to Network Device Migration Planning towards Software-Defined IPv6 Networks
Authors: Dawadi Babu R. , Rawat Danda B. , Joshi Shashidhar R. , Manzoni Pietro
Published: December 2021

Internet and telecom service providers worldwide are facing financial sustainability issues in migrating their existing legacy IPv4 networking system due to backward compatibility issues with the latest generation networking paradigms viz. Internet protocol version 6 (IPv6) and software-defined networking (SDN). Bench marking of existing networking devices is required to identify their status whether the existing running devices are upgradable or need replacement to make them operable with SDN and IPv6 networking so that internet and telecom service providers can properly plan their network migration to optimize capital and operational expenditures for future sustainability. In this paper, we implement “adaptive neuro fuzzy inference system (ANFIS)”, a well-known intelligent approach for network device status identification to classify whether a network device is upgradable or requires replacement. Similarly, we establish a knowledge base (KB) system to store the information of device internetwork operating system (IoS)/firmware version, its SDN, and IPv6 support with end-of-life and end-of-support. For input to ANFIS, device performance metrics such as average CPU utilization, throughput, and memory capacity are retrieved and mapped with data from KB. We run the experiment with other well-known classification methods, for example, support vector machine (SVM), fine tree, and liner regression to compare performance results with ANFIS. The comparative results show that the ANFIS-based classification approach is more accurate and optimal than other methods. For service providers with a large number of network devices, this approach assists them to properly classify the device and make a decision for the smooth transitioning to SDN-enabled IPv6 networks.
An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation
Authors: Genser Alexander , Hautle Noel , Makridis Michail , Kouvelas Anastasios
Published: December 2021

A reliable estimation of the traffic state in a network is essential, as it is the input of any traffic management strategy. The idea of using the same type of sensors along large networks is not feasible; as a result, data fusion from different sources for the same location should be performed. However, the problem of estimating the traffic state alongside combining input data from multiple sensors is complex for several reasons, such as variable specifications per sensor type, different noise levels, and heterogeneous data inputs. To assess sensor accuracy and propose a fusion methodology, we organized a video measurement campaign in an urban test area in Zurich, Switzerland. The work focuses on capturing traffic conditions regarding traffic flows and travel times. The video measurements are processed (a) manually for ground truth and (b) with an algorithm for license plate recognition. Additional processing of data from established thermal imaging cameras and the Google Distance Matrix allows for evaluating the various sensors’ accuracy and robustness. Finally, we propose an estimation baseline MLR (multiple linear regression) model (5% of ground truth) that is compared to a final MLR model that fuses the 5% sample with conventional loop detector and traffic signal data. The comparison results with the ground truth demonstrate the efficiency and robustness of the proposed assessment and estimation methodology.
Two Obstacle Factors for Technological Standardization: The Viewpoint of Technological Frame
Authors: Fukami Yoshiaki
Published: December 2021

The concept of the Internet of Things (IoT), which is an architecture in which devices supplied by various firms and services operated by distributed organizations exchange data, has been adopted in an increasing number of situations. While there are cases in which a small number of limited organizations collaborate on certain ecosystems based on proprietary specifications, the development of open standards is increasingly important for building scalable ecosystems because of the introduction of the concepts of Industry 4.0 and Society 5.0. Under these circumstances, there are two types of barriers to standardization. One barrier is the lack of shared frames for architectural design. The other barrier is the lack of awareness of the need for scalability. In this paper, we analyze the factors underlying these two barriers and discuss the path towards breakthroughs.
Ecological Vulnerability of Adult Female Marine Turtles as Indicators of Opportunities for Regional Socioecosystem Management in the Southern Gulf of Mexico
Authors: Liceaga-Correa María de los Angeles , Uribe-Martínez Abigail , Cuevas Eduardo
Published: December 2021

Planning for marine ecosystems management demands spatially explicit information about structural and functional components, in a tradeoff between including the most taxa and keeping it functional. Sentinel, umbrella, and surrogate taxa are strategic for developing indexes that account for other associated species and contribute to a sustainable administration of our seas. Marine turtles feature the former species descriptions, and the knowledge on their ecology contributes to design conservation and restoration strategies in regions they occupy, such as the Gulf of Mexico. Several administrative tools exist to govern marine territories for biodiversity conservation, and assessing how these tools interact with the ecological vulnerability of endangered species is crucial for improving public policies. We assessed the spatial interactions among ecological sensitivity, vulnerability, and the potential impacts on four marine turtle species in the southern Gulf of Mexico and northwestern Caribbean Sea with territorial management tools (natural protected areas, marine priority sites, and ecoregions). A small percentage of the most vulnerable areas is inside natural protected areas, while marine priority sites include a higher percentage. We identified spatial covering gaps that need to be addressed to ensure the conservation and recovery of these endangered species in the Gulf of Mexico and proposed key regions for maximizing territorial conservation.
Occurrence of Linear Alkylbenzene Sulfonates, Nonylphenol Ethoxylates and Di(2-ethylhexyl)phthalate in Composting Processes: Environmental Risks
Authors: Martín Julia , Mejías Carmen , Arenas Marina , Santos Juan Luis , Aparicio Irene , Alonso Esteban
Published: December 2021

Composting is an important waste management strategy, providing an economical and environment-friendly approach to sanitizing and stabilizing biosolids for land soil amendment. However, the resulting product can contain a large number of organic pollutants that may have adverse effects on the ecosystem. This paper presents the occurrence of eight widely used organic pollutants (four linear alkylbenzene sulfonates (LAS C10-C13), nonylphenol and its mono- and di-ethoxylates (NPE) and a di(2-ethylhexyl)phthalate (DEHP)) in full-scale composting processes. LAS homologues were detected at the highest concentrations (range of ∑LAS: 2068–9375 mg kg−1 dm), exceeding the limit fixed in the EU Directive draft. The concentration levels of the NPE and DEHP were significantly lower (up to 27.5 and 156.8 mg kg−1 dm, respectively) and did not exceed their fixed limits in the EU Directive draft. Ecotoxicological risk assessment for when compost is amended onto soils has also been evaluated. The concentrations measured represented a medium-low risk for most compounds, although it was not enough in the case of LAS C11 and C13 and NP.
Transition to a Sustainable Circular Plastics Economy in The Netherlands: Discourse and Policy Analysis
Authors: Calisto Friant Martin , Lakerveld Dirkjan , Vermeulen Walter , Salomone Roberta
Published: December 2021

The circular economy (CE) has become a key sustainability discourse in the last decade. The Netherlands seeks to become fully circular by 2050 and the EU has set ambitious circularity targets in its CE Action Plan of 2015. The plastics sector, in particular, has gained a lot of attention as it is a priority area of both the EU and Dutch CE policies. However, there has been little research on the different and often contested discourses, governance processes and policy mechanisms guiding the transition to a circular economy and society. This paper aims to fill these gaps by asking what circular discourses and policies are being promoted in the Netherlands and what sustainability implications and recommendations can be drawn from it. It does so through a mix of media analysis, policy analysis, semi-structured interviews, and surveys using Q-methodology. Results indicate a dominance of technocentric imaginaries, and a general lack of discussion on holistic, and transformative visions, which integrate the full social, political, and ecological implication of a circular future. To address those challenges, this research brings key policy insights and recommendations which can help both academics and practitioners better understand and implement the transition towards a sustainable circular plastics economy.
An Attempt to Evaluate the Green Construction of Large-Scale Hydropower Projects: Taking Wudongde Hydropower Station on the Jinsha River, China as an Example
Authors: Liu Liwen , Zhang Yaling , Zhao Lu , Zhan Cun , Liang Chuan
Published: December 2021

Huge amounts of energy and resources will be consumed, and a large quantity of environmental pollutants will be produced during the construction process of large-scale hydropower projects. As a core link of green supply chain in hydropower projects, green construction is particularly critical. To objectively evaluate the green construction level of large-scale hydropower projects, an evaluation indicator system was constructed in the study. The evaluation system consisted of 30 quantitative indicators and 7 qualitative indicators from the perspectives of environmental protection, resource saving and comprehensive management on the basis of the construction characteristics of large-scale hydropower projects. The quantitative and qualitative evaluation standards were proposed by combining with relevant laws and regulations, specifications and standards, and the environmental management maturity model. Furthermore, taking the second quarter of 2018 in Wudongde Hydropower Station on the Jinsha River, China, as an example, green construction level was assessed by the analytic hierarchy process fuzzy comprehensive evaluation (AHP-FCE) method. The calculation results indicated that the evaluation value of green construction in Wudongde Hydropower Station was 3.697, at “Good” level. The evaluation values of environmental protection, resource saving, and comprehensive management were 3.681, 3.473, and 3.965, respectively, within the range of 3 to 4, so they were all evaluated to be “Good”. To further improve the green construction level, it was necessary to supervise some aspects of construction process, i.e., treatment of waste slag in construction, economical and intensive utilization of land, publicity and training, soil erosion control, and saving water resources. In particular, management of soil erosion control should be strengthened. The proposed green construction evaluation system is relatively reliable and practical for professionals in the green hydropower industry, and can provide a reference for other large-scale hydropower projects.
Toward Baggage-Free Airport Terminals: A Case Study of London City Airport
Authors: Jiang Yirui , Yang Runjin , Zang Chenxi , Wei Zhiyuan , Thompson John , Tran Trung Hieu , Encinas-Oropesa Adriana , Williams Leon
Published: December 2021

Nowadays, the aviation industry pays more attention to emission reduction toward the net-zero carbon goals. However, the volume of global passengers and baggage is exponentially increasing, which leads to challenges for sustainable airports. A baggage-free airport terminal is considered a potential solution in solving this issue. Removing the baggage operation away from the passenger terminals will reduce workload for airport operators and promote passengers to use public transport to airport terminals. As a result, it will bring a significant impact on energy and the environment, leading to a reduction of fuel consumption and mitigation of carbon emission. This paper studies a baggage collection network design problem using vehicle routing strategies and augmented reality for baggage-free airport terminals. We use a spreadsheet solver tool, based on the integration of the modified Clark and Wright savings heuristic and density-based clustering algorithm, for optimizing the location of logistic hubs and planning the vehicle routes for baggage collection. This tool is applied for the case study at London City Airport to analyze the impacts of the strategies on carbon emission quantitatively. The result indicates that the proposed baggage collection network can significantly reduce 290.10 tonnes of carbon emissions annually.
Towards Effective Environmental Sustainability Reporting in the Large Industrial Sector of Bahrain
Authors: Rashed Abdulkarim Hasan , Rashdan Suad Ahmed , Ali-Mohamed Ahmed Y.
Published: December 2021

The industrial sector plays a vital role in economic development; therefore, there is a necessity to integrate sustainability into industrial development to maintain the economy and avoid any degradation impacts on the environment, and thereafter on society. Thus, do Bahraini companies have sustainability reports and if so, are these reports based on GRI guidelines? Has the status of their sustainability reports been analyzed? This research aims to examine the sustainability reports of companies by analyzing the status of sustainability aspects in their materiality matrices to assist in identifying and prioritizing the most significant sustainability issues for advancement in their future reporting and to improve their environmental performance. This study employs a content analysis approach and analyzes 11 reports from the period 2016–2020 for three companies in Bahrain’s large industrial sector. The study reveals that the companies using materiality analysis in their reporting benefit from better monitoring and measuring of their environmental performance, and from implementing SDGs. Furthermore, the study indicates that the utilization of a materiality matrix as a reporting tool can define and improve report contents by considering stakeholders’ views, consequently, improving the quality of the sustainability reports. The study concludes by proposing a set of recommendations.