bluuebunny/crossref_metadata_2025 huggingface dataset:
Upgrading Recycled Polypropylene from Textile Wastes in Wood Plastic Composites with Short Hemp Fiber
Authors: Burgada Francisco , Fages Eduardo , Quiles-Carrillo Luis , Lascano Diego , Ivorra-Martinez Juan , Arrieta Marina P. , Fenollar Octavio
Published: April 2021

This research reports the manufacturing and characterization of green composites made from recycled polypropylene obtained from the remnants of polypropylene non-woven fabrics used in the textile industry and further reinforced with short hemp fibers (SHFs). To improve the interaction of the reinforcing fibers with the recycled polymeric matrix, two types of compatibilizing agents (maleic anhydride grafted, PP-g-MA, and maleinized linseed oil, MLO) were added during melt-processing, the percentage of which had to remain constant concerning the amount of fiber loading to ensure complete reactivity. Standardized test specimens were obtained by injection molding. The composites were characterized by mechanical (tensile, impact, and hardness), thermal (DSC, TGA), thermomechanical, FTIR, and FESEM microscopy tests. In addition, color and water uptake properties were also analyzed. The results show that the addition of PP-g-MA to rPP was satisfactory, thus improving the fiber-matrix interaction, resulting in a marked reinforcing effect of the hemp fibers in the recycled PP matrix, which can be reflected in the increased stiffness of the samples. In parallel to the compatibilizing effect, a plasticizing effect was obtained by incorporating MLO, causing a decrease in the glass transition temperature of the composites by approximately 6 °C and an increase in ductility compared to the unfilled recycled polypropylene samples.
Comparative Analysis of Viscoelastic Properties of Open Graded Friction Course under Dynamic and Static Loads
Authors: Li Liding , Wu Chunli , Cheng Yongchun , Ai Yongming , Li He , Tan Xiaoshu
Published: April 2021

The viscoelastic properties of open graded friction course (OGFC) are closely related to anti-permanent deformation ability, noise reduction ability and durability. To study the viscoelastic parameters of OGFC under dynamic and static loads and to establish the functional relationship between them, uniaxial compression creep tests and dynamic modulus tests were performed to obtain the creep compliance and the dynamic modulus of OGFC. In addition, the Burgers model, modified Burgers model, second-order extensive Maxwell model, Scott-Blair model and modified Sigmoid model were employed to quantitatively analyze the dynamic and static viscoelastic properties of OGFC. Subsequently, the relaxation modulus of OGFC was deduced by the viscoelastic theory. Then, the dynamic modulus of OGFC was calculated according to the deduced relaxation modulus. Based on the calculated values and the measured values of dynamic modulus, the functional relationship of viscoelastic parameters of OGFC under dynamic and static loads was established. The results show that the increase in test temperature has adverse effects on the viscoelastic indexes of OGFC, such as creep compliance, relaxation modulus, and dynamic modulus; the dynamic modulus derived from static creep compliance has a good linear correlation with that obtained by dynamic modulus tests, but the correlation of the phase angle is poor.
A Novel and Highly Effective Natural Vibration Modal Analysis to Predict Nominal Strength of Open Hole Glass Fiber Reinforced Polymer Composites Structure
Authors: Abdellah Mohammed Y. , Hassan Mohamed K. , Mohamed Ahmed F. , Khalil Khalil Abdelrazek
Published: April 2021

Glass fiber reinforced polymer (GFRP) composite laminates are considered the key material in many industries such as the infrastructure industries and the aerospace sector, and in building structures due to their superior specific strength and lightweight properties. The prediction of specimens’ nominal strength with open holes is still an attractive and questionable field of study. The specimen size effect is referred to its strength degradation due to the presence of holes when specimen geometry gets scaled. The non-destructive test used to measure the nominal strength of such material is a great tool for fast selection purposes, but not secure enough for several purposes. Furthermore, the destructive tests which are more expensive and time-consuming should be avoided in such structures. The present work aims to predict the nominal strength of open-hole GFRP’s composite using modal analysis of their natural frequency as non-destructive tests. At this end, the natural frequency, which is measured using modal analysis procedures, is combined with both linear elastic fracture mechanics (LEFM) and the theory of elasticity to predict the nominal strength of open-hole composite laminates. This advanced model employs two parameters of surface release energy resulting from a simple tension test and Young’s modulus based on vibration modal analysis. It is well established that these types of materials are also subjected to a size effect in dynamic response. Inversely to the known static loading size effect, the size effect in dynamic response increases with specimen size. The novel model gives excellent and acceptable results when compared with experimental and finite element ones. Size effects curves of a nominal strength of these laminates have a very close relative value with those obtained from finite element and analytical modeling. Moreover, the received design tables and graphs would be highly applicable when selecting suitable materials for similar industrial applications.
Machine Learning-Based Energy System Model for Tissue Paper Machines
Authors: Zhang Huanhuan , Li Jigeng , Hong Mengna
Published: April 2021

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.
Development of a New Sensor Module for an Enhanced Fuel Flexible Operation of Biomass Boilers
Authors: Meiller Martin , Oischinger Jürgen , Daschner Robert , Hornung Andreas
Published: April 2021

The heterogeneity of biogenic fuels, and especially biogenic residues with regard to water and ash content, particle size and particle size distribution is challenging for biomass combustion, and limits fuel flexibility. Online fuel characterization as a part of process control could help to optimize combustion processes, increase fuel flexibility and reduce emissions. In this research article, a concept for a new sensor module is presented and first tests are displayed to show its feasibility. The concept is based on the principle of hot air convective drying. The idea is to pass warm air with 90 °C through a bulk of fuel like wood chips and measure different characteristics such as moisture, temperatures and pressure drop over the bulk material as a function over time. These functions are the basis to draw conclusions and estimate relevant fuel properties. To achieve this goal, a test rig with a volume of 0.038 m3 was set up in the laboratory and a series of tests was performed with different fuels (wood chips, saw dust, wood pellets, residues from forestry, corn cobs and biochar). Further tests were carried out with conditioned fuels with defined water and fines contents. The experiments show that characteristic functions arise over time. The central task for the future will be to assign these functions to specific fuel characteristics. Based on the data, the concept for a software for an automated, data-based fuel detection system was designed.
Implications of Entrepreneurial Intentions of Romanian Secondary Education Students, over the Romanian Business Market Development
Authors: Furdui Amalia , Lupu-Dima Lucian , Edelhauser Eduard
Published: April 2021

The study investigates the Romanian entrepreneurial education training program emphasizing the secondary education student entrepreneurial intents included in technical and professional Vocational Education Training (VET) programs, in order to identify its role in increasing student intention in the process of choosing a career as an entrepreneur among graduates of the vocational and technical Romanian education system. The study research methodology was based on the interpretation of two questionnaires consisting of 23 questions, which were applied to a population of 253 and 159 respondents. The survey period was conducted between 2019 and 2020. The respondents were students from the vocational and technical education system in Romania, mostly from the Central Region of Romania, but the results of the study could be extended to the entire Romanian education system. The data were processed using SPSS software, and the results of the study revealed direct, positive, and significant links between psychological and behavioral traits and entrepreneurial intentions of the student surveyed, moderated by the entrepreneurial education acquired through the school curriculum. These results could also be the basis for developing future policies and programs to encourage entrepreneurial behavior, especially for secondary education students from the Romanian education system, specifically on pre-university education.
“Dies Irae?” The Role of Religiosity in Dealing with Psychological Problems Caused by The COVID-19 Pandemic—Studies on a Polish Sample
Authors: Szałachowski Roman , Tuszyńska-Bogucka Wioletta
Published: April 2021

Based on Huber’s centrality of religiosity concept, a non-experimental research project was designed in a group of 178 women and 72 men, voluntary participants in online studies, quarantined at home during the first weeks (the first wave) of the pandemic, to determine whether and to what extent religiosity, understood as a multidimensional construct, was a predictor of the worsening of PTSD and depression symptoms in the conditions of the COVID-19 pandemic. The study made use of CRS Huber’s scale to study the centrality of religiosity, Spitzer’s PHQ-9 to determine the severity of depression, and Weiss and Marmar’s IES-R to measure the symptoms of PTSD. Our study, which provided interesting and non-obvious insights into the relationship between the studied variables, did not fully explain the protective nature of religiosity in dealing with pandemic stress. Out of five components of religiosity understood in accordance with Huber’s concept (interest in religious issues, religious beliefs, prayer, religious experience, and cult), two turned out to contribute to modifications in the severity of psychopathological reactions of the respondents to stress caused by the pandemic during its first wave. A protective role was played by prayer, which inhibited the worsening of PTSD symptoms, whereas religious experience aggravated them. This means that in order to interpret the effect of religiosity on the mental functioning of the respondents in a time of crisis (the COVID-19 pandemic), we should not try to explain this effect in a simple and linear way, because religious life may not only bring security and solace, but also be a source of stress and an inner struggle.
Life Cycle Sustainability Analysis of Resource Recovery from Waste Management Systems in a Circular Economy Perspective Key Findings from This Special Issue
Authors: Ingrao Carlo , Arcidiacono Claudia , Siracusa Valentina , Niero Monia , Traverso Marzia
Published: April 2021

The generation and management of waste are gaining increasing attention worldwide as two main focuses of the environmental strategies and policies developed to date at the European level [...]
Induced Seismic Events—Distribution of Ground Surface Displacements Based on InSAR Methods and Mogi and Yang Models
Authors: Milczarek Wojciech , Kopeć Anna , Głąbicki Dariusz , Bugajska Natalia
Published: April 2021

In this article, we present a possible approach to use satellite radar data for a complete description of the formation process of a subsidence trough resulting from an induced seismic event—a mining tremor. Our main goal was to verify whether SAR data allow for the calculation of the basic indicators for the trough (w—subsidence, T—trough slope, K—curvature, u—horizontal displacements, ε—horizontal deformations). We verified the extent to which the Mogi and Yang models can be fitted to match the actual displacements recorded after an induced seismic tremor. The calculations were performed for the Legnica-Glogow Copper Belt (LGCB) area in southwest Poland. Due to intensive mining operations and specific geological and tectonic conditions, the area shows a high level of induced seismic activity. Our detailed analysis focused on four powerful mining tremors: the first tremor occurred on 29 November 2016 (MW3.4), the second on 7 December 2017 (MW3.3), the next on 26 December 2017 (MW3.6) and the last tremor on 29 January 2019 (MW3.7). For each analyzed event, we determined the displacements based on the Differential Interferometric Synthetic Aperture Radar (DInSAR) method and Sentinel 1 synthetic aperture radar (SAR) data from two paths (22 and 73). Additionally, for the period from November 2014 to October 2020, we calculated the displacements using the Small Baseline Subset method (SBAS) time series method. In all cases, the tremor was followed by the development of long-lasting surface deformations. The obtained results allowed us to conclude that it is possible to calculate indicators that result from a specific induced mining event. Considering the full moment tensor and nature of the tremor source, we demonstrated that the Mogi and Yang models can be employed to describe the influence of an induced tremor on the surface in an area of mining activity. We also confirmed the global character of the influence of the reduced troposphere on SAR data calculations. Our conclusions indicate that accounting for the tropospheric correction does not distort horizontal and vertical displacement values in regions influenced by mining activity/tremors.
An EEMD-BiLSTM Algorithm Integrated with Boruta Random Forest Optimiser for Significant Wave Height Forecasting along Coastal Areas of Queensland, Australia
Authors: Raj Nawin , Brown Jason
Published: April 2021

Using advanced deep learning (DL) algorithms for forecasting significant wave height of coastal sea waves over a relatively short period can generate important information on its impact and behaviour. This is vital for prior planning and decision making for events such as search and rescue and wave surges along the coastal environment. Short-term 24 h forecasting could provide adequate time for relevant groups to take precautionary action. This study uses features of ocean waves such as zero up crossing wave period (Tz), peak energy wave period (Tp), sea surface temperature (SST) and significant lags for significant wave height (Hs) forecasting. The dataset was collected from 2014 to 2019 at 30 min intervals along the coastal regions of major cities in Queensland, Australia. The novelty of this study is the development and application of a highly accurate hybrid Boruta random forest (BRF)–ensemble empirical mode decomposition (EEMD)–bidirectional long short-term memory (BiLSTM) algorithm to predict significant wave height (Hs). The EEMD–BiLSTM model outperforms all other models with a higher Pearson’s correlation (R) value of 0.9961 (BiLSTM—0.991, EEMD-support vector regression (SVR)—0.9852, SVR—0.9801) and comparatively lower relative mean square error (RMSE) of 0.0214 (BiLSTM—0.0248, EEMD-SVR—0.043, SVR—0.0507) for Cairns and similarly a higher Pearson’s correlation (R) value of 0.9965 (BiLSTM—0.9903, EEMD–SVR—0.9953, SVR—0.9935) and comparatively lower RMSE of 0.0413 (BiLSTM—0.075, EEMD-SVR—0.0481, SVR—0.057) for Gold Coast.
Drought Extent and Severity on Arable Lands in Romania Derived from Normalized Difference Drought Index (2001–2020)
Authors: Dobri Radu-Vlad , Sfîcă Lucian , Amihăesei Vlad-Alexandru , Apostol Liviu , Țîmpu Simona
Published: April 2021

The aim of this study was to evaluate the frequency and severity of drought over the arable lands of Romania using the Normalized Difference Drought Index (NDDI). This index was obtained from the Moderate Resolution Imaging Spectro-Radiometer (MODIS) sensor of the Terra satellite. The interval between March and September was investigated to study the drought occurrence from the early stage of crop growth to its harvest time. The study covered a long period (2001–2020), hence it is able to provide a sound climatological image of crop vegetation conditions. Corine Land Cover 2018 (CLC) was used to extract the arable land surfaces. According to this index, the driest year was 2003 with 25.6% of arable land affected by drought. On the contrary, the wettest year was 2016, with only 10.8% of arable land affected by drought. Regarding the multiannual average of the period 2001–2020, it can be seen that drought is not a phenomenon that occurs consistently each year, therefore only 11.7% of arable land was affected constantly by severe and extreme drought. The correlation between NDDI and precipitation amount was also investigated. Although the correlations at weekly or monthly levels are more complicated, the annual regional mean NDDI is overall negatively correlated with annual rainfall. Thus, from a climatic perspective, we consider that NDDI is a reliable and valuable tool for the assessment of droughts over the arable lands in Romania.
A CNN-Based High-Accuracy Registration for Remote Sensing Images
Authors: Lee Wooju , Sim Donggyu , Oh Seoung-Jun
Published: April 2021

In this paper, a convolutional neural network-based registration framework is proposed for remote sensing to improve the registration accuracy between two remote-sensed images acquired from different times and viewpoints. The proposed framework consists of four stages. In the first stage, key-points are extracted from two input images—a reference and a sensed image. Then, a patch is constructed at each key-point. The second stage consists of three processes for patch matching—candidate patch pair list generation, one-to-one matched label selection, and geometric distortion compensation. One-to-one matched patch pairs between two images are found, and the exact matching is found by compensating for geometric distortions in the matched patch pairs. A global geometric affine parameter set is computed using the random sample consensus algorithm (RANSAC) algorithm in the third stage. Finally, a registered image is generated after warping the input sensed image using the affine parameter set. The proposed high-accuracy registration framework is evaluated using the KOMPSAT-3 dataset by comparing the conventional frameworks based on machine learning and deep-learning-based frameworks. The proposed framework obtains the least root mean square error value of 34.922 based on all control points and achieves a 68.4% increase in the matching accuracy compared with the conventional registration framework.
Prioritized Task Distribution Considering Opportunistic Fog Computing Nodes
Authors: Kyung Yeunwoong
Published: April 2021

As service latency and core network load relates to performance issues in the conventional cloud-based computing environment, the fog computing system has gained a lot of interest. However, since the load can be concentrated on specific fog computing nodes because of spatial and temporal service characteristics, performance degradation can occur, resulting in quality of service (QoS) degradation, especially for delay-sensitive services. Therefore, this paper proposes a prioritized task distribution scheme, which considers static as well as opportunistic fog computing nodes according to their mobility feature. Based on the requirements of offloaded tasks, the proposed scheme supports delay sensitive task processing at the static fog node and delay in-sensitive tasks by means of opportunistic fog nodes for task distribution. To assess the performance of the proposed scheme, we develop an analytic model for the service response delay. Extensive simulation results are given to validate the analytic model and to show the performance of the proposed scheme, compared to the conventional schemes in terms of service response delay and outage probability.
A Deep Learning Method for 3D Object Classification and Retrieval Using the Global Point Signature Plus and Deep Wide Residual Network
Authors: Hoang Long , Lee Suk-Hwan , Kwon Ki-Ryong
Published: April 2021

A vital and challenging task in computer vision is 3D Object Classification and Retrieval, with many practical applications such as an intelligent robot, autonomous driving, multimedia contents processing and retrieval, and augmented/mixed reality. Various deep learning methods were introduced for solving classification and retrieval problems of 3D objects. Almost all view-based methods use many views to handle spatial loss, although they perform the best among current techniques such as View-based, Voxelization, and Point Cloud methods. Many views make network structure more complicated due to the parallel Convolutional Neural Network (CNN). We propose a novel method that combines a Global Point Signature Plus with a Deep Wide Residual Network, namely GPSP-DWRN, in this paper. Global Point Signature Plus (GPSPlus) is a novel descriptor because it can capture more shape information of the 3D object for a single view. First, an original 3D model was converted into a colored one by applying GPSPlus. Then, a 32 × 32 × 3 matrix stored the obtained 2D projection of this color 3D model. This matrix was the input data of a Deep Residual Network, which used a single CNN structure. We evaluated the GPSP-DWRN for a retrieval task using the Shapnetcore55 dataset, while using two well-known datasets—ModelNet10 and ModelNet40 for a classification task. Based on our experimental results, our framework performed better than the state-of-the-art methods.
High-Accuracy Guide Star Catalogue Generation with a Machine Learning Classification Algorithm
Authors: Zhang Jianming , Lian Junxiang , Yi Zhaoxiang , Yang Shuwang , Shan Ying
Published: April 2021

In order to detect gravitational waves and characterise their sources, three laser links were constructed with three identical satellites, such that interferometric measurements for scientific experiments can be carried out. The attitude of the spacecraft in the initial phase of laser link docking is provided by a star sensor (SSR) onboard the satellite. If the attitude measurement capacity of the SSR is improved, the efficiency of establishing laser linking can be elevated. An important technology for satellite attitude determination using SSRs is star identification. At present, a guide star catalogue (GSC) is the only basis for realising this. Hence, a method for improving the GSC, in terms of storage, completeness, and uniformity, is studied in this paper. First, the relationship between star numbers in the field of view (FOV) of a staring SSR, together with the noise equivalent angle (NEA) of the SSR—which determines the accuracy of the SSR—is discussed. Then, according to the relationship between the number of stars (NOS) in the FOV, the brightness of the stars, and the size of the FOV, two constraints are used to select stars in the SAO GSC. Finally, the performance of the GSCs generated by Decision Trees (DC), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), the Magnitude Filter Method (MFM), Gradient Boosting (GB), a Neural Network (NN), Random Forest (RF), and Stochastic Gradient Descent (SGD) is assessed. The results show that the GSC generated by the KNN method is better than those of other methods, in terms of storage, uniformity, and completeness. The KNN-generated GSC is suitable for high-accuracy spacecraft applications, such as gravitational detection satellites.
The Influence of Packing Methods and Storage Time of Poultry Sausages with Liquid and Microencapsulated Fish Oil Additives on Their Physicochemical, Microbial and Sensory Properties
Authors: Kawecki Krzysztof , Stangierski Jerzy , Cegielska-Radziejewska Renata
Published: April 2021

The aim of the study was to evaluate the influence of refined fish oil additives in liquid and microencapsulated forms, packing method (VP—vacuum packing, MAP—modified atmosphere packing) and storage time (1, 7, 14, 21 days) on selected physicochemical, microbiological and sensory characteristics of minced poultry sausage. Principle component analysis (PCA) showed that the fish oil additive, packing method and storage time significantly influenced some of the physicochemical characteristics of the sausages. The pH value was negatively correlated with the type of sample and packing method. The water activity decreased along with the storage time. The sausages with microcapsules had distinguishable hardness, gumminess and chewiness than the other samples. This tendency increased in the subsequent storage periods. The packing method and storage time of the samples had a statistically significant influence on the growth of the total colony count and count of lactic acid bacteria (p < 0.05). The most aerobic bacteria were found in the control sample, and the least in the sample with microcapsules, regardless of the packing method. The use of MAP and the addition of microcapsules resulted in the lowest microbiological contamination of the sausages. The sensory analysis made by a trained panel did not show any significant differences between the samples. After 21-day storage of the sausages there was a slight decrease in some of the sensory parameters, e.g., color, smell, taste. The liquid oil and microencapsulated oil additives in the meat filling did not negatively affect the taste or any physicochemical characteristics of the meat products. From the microbiological perspective, there were better effects from the MAP method.
Wi-Fi-Based Location-Independent Human Activity Recognition via Meta Learning
Authors: Ding Xue , Jiang Ting , Zhong Yi , Huang Yan , Li Zhiwei
Published: April 2021

Wi-Fi-based device-free human activity recognition has recently become a vital underpinning for various emerging applications, ranging from the Internet of Things (IoT) to Human–Computer Interaction (HCI). Although this technology has been successfully demonstrated for location-dependent sensing, it relies on sufficient data samples for large-scale sensing, which is enormously labor-intensive and time-consuming. However, in real-world applications, location-independent sensing is crucial and indispensable. Therefore, how to alleviate adverse effects on recognition accuracy caused by location variations with the limited dataset is still an open question. To address this concern, we present a location-independent human activity recognition system based on Wi-Fi named WiLiMetaSensing. Specifically, we first leverage a Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) feature representation method to focus on location-independent characteristics. Then, in order to well transfer the model across different positions with limited data samples, a metric learning-based activity recognition method is proposed. Consequently, not only the generalization ability but also the transferable capability of the model would be significantly promoted. To fully validate the feasibility of the presented approach, extensive experiments have been conducted in an office with 24 testing locations. The evaluation results demonstrate that our method can achieve more than 90% in location-independent human activity recognition accuracy. More importantly, it can adapt well to the data samples with a small number of subcarriers and a low sampling rate.
Non-Technical Skill Assessment and Mental Load Evaluation in Robot-Assisted Minimally Invasive Surgery
Authors: Nagyné Elek Renáta , Haidegger Tamás
Published: April 2021

BACKGROUND: Sensor technologies and data collection practices are changing and improving quality metrics across various domains. Surgical skill assessment in Robot-Assisted Minimally Invasive Surgery (RAMIS) is essential for training and quality assurance. The mental workload on the surgeon (such as time criticality, task complexity, distractions) and non-technical surgical skills (including situational awareness, decision making, stress resilience, communication, leadership) may directly influence the clinical outcome of the surgery. METHODS: A literature search in PubMed, Scopus and PsycNet databases was conducted for relevant scientific publications. The standard PRISMA method was followed to filter the search results, including non-technical skill assessment and mental/cognitive load and workload estimation in RAMIS. Publications related to traditional manual Minimally Invasive Surgery were excluded, and also the usability studies on the surgical tools were not assessed. RESULTS: 50 relevant publications were identified for non-technical skill assessment and mental load and workload estimation in the domain of RAMIS. The identified assessment techniques ranged from self-rating questionnaires and expert ratings to autonomous techniques, citing their most important benefits and disadvantages. CONCLUSIONS: Despite the systematic research, only a limited number of articles was found, indicating that non-technical skill and mental load assessment in RAMIS is not a well-studied area. Workload assessment and soft skill measurement do not constitute part of the regular clinical training and practice yet. Meanwhile, the importance of the research domain is clear based on the publicly available surgical error statistics. Questionnaires and expert-rating techniques are widely employed in traditional surgical skill assessment; nevertheless, recent technological development in sensors and Internet of Things-type devices show that skill assessment approaches in RAMIS can be much more profound employing automated solutions. Measurements and especially big data type analysis may introduce more objectivity and transparency to this critical domain as well. SIGNIFICANCE: Non-technical skill assessment and mental load evaluation in Robot-Assisted Minimally Invasive Surgery is not a well-studied area yet; while the importance of this domain from the clinical outcome’s point of view is clearly indicated by the available surgical error statistics.
Efficient Transmit Antenna Subset Selection for Multiuser Space–Time Line Code Systems
Authors: Kim Sangchoon
Published: April 2021

We consider the problem of the efficient transmit antenna subset (TAS) selection for maximizing the signal-to-interference-plus-noise ratio (SINR) of multiuser space–time line code (MU–STLC) systems. The exhaustive search for optimal TAS selection is impractical since the total number of transmit antennas increases. We propose two efficient TAS selection schemes based on the Woodbury formula. The first is to incrementally select NS active transmit antennas among the available NT transmit antennas. To reduce the complexity of the incremental selection scheme, the Woodbury formula is employed in the optimization process. The second is to perform the decremental strategy in which the Woodbury formula is also applied to develop the low-complexity TAS selection procedure for the MU–STLC systems. Simulation results show that the proposed incremental and decremental TAS selection algorithms offer better alternatives than the existing greedy TAS selection algorithm for the MU–STLC systems. Furthermore, in terms of bit error rate, the proposed minimum mean square error decremental TAS selection algorithm turns out to outperform the existing greedy algorithm with significantly lower computational complexity. Finally, we analyze the detection SINR penalty experienced from TAS selection and the analytical quantity is shown to be well matched with simulation results.
Improved Direction-of-Arrival Estimation of an Acoustic Source Using Support Vector Regression and Signal Correlation
Authors: Alam Faisal , Usman Mohammed , Alkhammash Hend I. , Wajid Mohd
Published: April 2021

The direction-of-arrival (DoA) estimation of an acoustic source can be estimated with a uniform linear array using classical techniques such as generalized cross-correlation, beamforming, subspace techniques, etc. However, these methods require a search in the angular space and also have a higher angular error at the end-fire. In this paper, we propose the use of regression techniques to improve the results of DoA estimation at all angles including the end-fire. The proposed methodology employs curve-fitting on the received multi-channel microphone signals, which when applied in tandem with support vector regression (SVR) provides a better estimation of DoA as compared to the conventional techniques and other polynomial regression techniques. A multilevel regression technique is also proposed, which further improves the estimation accuracy at the end-fire. This multilevel regression technique employs the use of linear regression over the results obtained from SVR. The techniques employed here yielded an overall 63% improvement over the classical generalized cross-correlation technique.
Water Nitrate Remote Monitoring System with Self-Diagnostic Function for Ion-Selective Electrodes
Authors: Jung Dae-Hyun , Kim Hak-Jin , Kim Joon Yong , Park Soo Hyun , Cho Woo Jae
Published: April 2021

The detection of nitrate pollutants is a widely used strategy for protecting water sources. Although ion-selective electrodes (ISEs) have been considered for the determination of ion concentrations in water, the accuracy of ISE technology decreases owing to the signal drift and decreasing sensitivity over time. The objectives of the present study were: (1) to develop an online water monitoring system mainly consisting of an Arduino board-based Internet-of-Things (IoT) device and nitrate ISEs; and (2) to propose a self-diagnostic function for monitoring and reporting the condition of the ISEs. The developed system communicates with the cloud server by using the message queuing telemetry transport (MQTT) protocol and provides monitoring information through the developed cloud-based webpage. In addition, the online monitoring system provides information on the electrode status, which is determined based on a self-diagnostic index (SDI, with a range of 0–100) of the electrode drift and sensitivity. The diagnostic method for monitoring and reporting the electrode status was validated in a one-month-long laboratory test followed by a field test in a stream near an agricultural facility. Moreover, a self-diagnostic index (SDI) was applied in the final field experiments with an accuracy of 0.77.
3D Simulation-Based Acoustic Wave Resonator Analysis and Validation Using Novel Finite Element Method Software
Authors: Vidana Morales Ruth Yadira , Ortega Cisneros Susana , Camacho Perez Jose Rodrigo , Sandoval Ibarra Federico , Casas Carrillo Ricardo
Published: April 2021

This work illustrates the analysis of Film Bulk Acoustic Resonators (FBAR) using 3D Finite Element (FEM) simulations with the software OnScale in order to predict and improve resonator performance and quality before manufacturing. This kind of analysis minimizes manufacturing cycles by reducing design time with 3D simulations running on High-Performance Computing (HPC) cloud services. It also enables the identification of manufacturing effects on device performance. The simulation results are compared and validated with a manufactured FBAR device, previously reported, to further highlight the usefulness and advantages of the 3D simulations-based design process. In the 3D simulation results, some analysis challenges, like boundary condition definitions, mesh tuning, loss source tracing, and device quality estimations, were studied. Hence, it is possible to highlight that modern FEM solvers, like OnScale enable unprecedented FBAR analysis and design optimization.
Risk-Informed Sustainable Development in the Rural Tropics
Authors: Tiepolo Maurizio , Tarchiani Vieri , Pezzoli Alessandro
Published: April 2021

In the tropics, rural areas are still the place where many people live, despite ongoing urbanization [...]
Affordable and Sustainable Housing in Rwanda
Authors: Egenti Clement , Khatib Jamal
Published: April 2021

Baked clay bricks (Impunyu) is the dominant wall construction material in Rwanda. Clay deposits in the country’s lowlands are utilized for baked clay bricks. Despite the ongoing campaign, the use of wood by some local brick producers is unfriendly to the environment. Recent research has called for alternative methods in order to reduce the cost and impact on the environment. Earlier efforts with compressed earth blocks were saddled with weight and a substantial use of cement for good surface texture and adequate resistance against surface erosion. This research explored the potentials of using an appropriate dose of clay (from Muhanzi), volcanic light aggregate (Amakoro, (from Musanze)), and cement to produce unbaked shelled compressed earth blocks (SCEB). SCEB is a compressed earth block with an outer shell and inner core of different cement content or materials, compressed into a unit block. The result is a masonry unit with a higher surface resistance, durability, and desirable architectural effect produced with a 60% reduction in cement content. A weight reduction of 12% was achieved with an optimum content of 33% of the volcanic lightweight aggregate. A cost reduction of 25% was recorded over conventional compressed earth brick walls and a 54% over sand-cement block walls. Possible future trends were also identified with appreciable prospects in earthen architecture.
Sustainable Risk Assessment of Resource Industry at Provincial Level in China
Authors: Liu Mingkai , Liu Changxin , Pei Xiaodong , Zhang Shouting , Ge Xun , Zhang Hongyan , Li Yang
Published: April 2021

The development of China’s resource industry is facing great pressures from industrial structure adjustment and environmental restraints, and the sustainable risk of the provincial resource industry is different. Considering the development of the resource industry and environmental pressure, this article selects the panel data of 31 provinces from 2015 to 2019 to construct an index evaluation system with six dimensions: influence, induction, supply and demand safety, regional pollution emission, environment quality, and pollution control. The results showed that Shanxi, Anhui, Jiangsu, and Shanghai had the highest sustainable risk in the resource industry, while Heilongjiang, Jilin, Tianjin, Fujian, Jiangxi, Hunan, Guizhou, Sichuan, and Qinghai had the lowest sustainable risk. The resource industry model of all the provinces is divided into sustainable, industrial, ecological, and unsustainable. Finally, this article puts forward reasonable suggestions for the four scenarios and argues that the balanced development of the resource industry sector and environmental protection is conducive to reducing the sustainable risks of the resource industry.
I Want to Teach Sustainable Development in My English Classroom: A Case Study of Incorporating Sustainable Development Goals in English Teaching
Authors: Kwee Ching Ting Tany
Published: April 2021

Previous studies indicated that K-12 teachers generally felt reluctant to incorporate sustainable development in their teaching due to a lack of skills, knowledge and interest, particularly language teachers. This qualitative case study, grounded in the Social Cognitive Career Theory, aims to identify the significant factors influencing English teachers’ motivation of incorporating the United Nation’s Sustainable Development Goals (SDGs) into their teaching. Data were collected from multiple sources including semi-structured interviews and classroom observations. By examining how teachers’ self-efficacy develops in relation to outcome expectations and performance goals, the findings showed that teachers’ personal beliefs, attainment of teaching goals and supportive school management can positively influence their self-efficacy and boost their motivation in incorporating SDGs in their English teaching. Such findings can be useful for educators, school management, educational institutes, universities and policy-makers to develop strategies to facilitate teachers’ active roles in ESD by fostering greater collaboration across disciplines and providing relevant professional development and goal-relevant supports.
Spatio-Temporal Characteristics and Obstacle Factors of the Urban-Rural Integration of China’s Shrinking Cities in the Context of Sustainable Development
Authors: Du Bin , Wang Ying , He Jiaxin , Li Wai , Chen Xiaohong
Published: April 2021

Based on the fundamental concept of sustainable development, this study empirically analyzes the spatio-temporal characteristics, formation mechanisms and obstacle factors of the urban-rural integration of shrinking cities in China, from 2008 to 2018. The conclusions are as follows: the overall level of the urban-rural integration of shrinking cities in China is low; the internal differences of urban-rural integration are also small, and the changes are slow. Next, the space difference is high in the east and low in the west, high in the south and low in the north. Moreover, differences exist among different levels of urban agglomerations. Urban economic efficiency, urban resources and environment, urban social equity and rural economic efficiency are the main factors affecting the urban-rural integration of shrinking cities in China. Urban and rural economic efficiency are the two most prominent shortcomings that restrict the urban-rural integration of shrinking cities. The spatial resistance mode of each city is more than the two-system resistance; the main resistance of shrinking cities with a higher level of urban-rural integration also comes from the non-economic field. This study expands the research scope that up till now has ignored the discussion of urban-rural issues in the research of shrinking cities at home and abroad, and provides practical guidance for the sustainable development of shrinking cities in China.
Design and Implementation of Probabilistic Transient Stability Approach to Assess the High Penetration of Renewable Energy in Korea
Authors: Cho Young-Been , Cho Yun-Sung , Lee Jae-Gul , Oh Seung-Chan
Published: April 2021

Recently, because of the many environmental problems worldwide, Korea is moving to increase its renewable energy output due to the Renewable 3020 Policy. Renewable energy output can change depending on environmental factors. It is for this reason that institutions should consider the instability of renewables when linked to the electric system. This paper describes the methodology of renewable energy capacity calculation based on probabilistic transient stability assessment. Probabilistic transient stability assessment consists of four algorithms: first, to create probabilistic scenarios based on the effective capacity history of renewable energy; second, to evaluate probabilistic transient stability based on transient stability index, interpolation-based transient stability index estimation, reduction-based transient stability index calculation, etc.; third, to implement multiple scenarios to calculate renewable energy capacity using probabilistic evaluation index; and finally, to create a probabilistic transient stability assessment simulator based on Python. This paper calculated renewable energy capacity based on large-scale power system to validate consistency of the proposed paper.
Revisiting Natural Resources—Globalization-Environmental Quality Nexus: Fresh Insights from South Asian Countries
Authors: Xue Jian , Rasool Zeeshan , Nazar Raima , Khan Ahmad Imran , Bhatti Shaukat Hussain , Ali Sajid
Published: April 2021

Widespread interference of human activities has resulted in major environmental problems, including pollution, global warming, land degradation, and biodiversity loss, directly affecting the sustainability and quality of the environment and ecosystem. The study aims to address the impact of the extraction of natural resources and globalization on the environmental quality in the South Asian countries for the period 1991–2018. A new methodology Dynamic Common Correlated Effects is used to deal with cross-sectional dependence. Most previous studies use only carbon dioxide emissions, which is an inadequate measure of environmental quality. Besides carbon dioxide emissions, we have used other greenhouse gas emissions like nitrous oxide and methane emissions with a new indicator, “ecological footprint”. Long-run estimation results indicate a positive and significant relationship of natural resources with all greenhouse gas emissions and a negative association with the ecological footprint. Globalization shows a negative association with carbon dioxide emissions and nitrous oxide emissions and a positive relationship with the ecological footprint. Institutional performance is negatively correlated with carbon dioxide emissions, methane emissions, and ecological footprint while positively associated with nitrous oxide emissions. The overall findings highlight the pertinence of reducing greenhouse gas emissions and ecological footprint, proper utilizing of natural resources, enhancing globalization, and improving institutional performance to ensure environmental sustainability.