bluuebunny/crossref_metadata_2025 huggingface dataset:
Comprehensive Investigation of Capabilities of the Left-Looking InSAR Observations in Coseismic Surface Deformation Mapping and Faulting Model Estimation Using Multi-Pass Measurements: An Example of the 2016 Kumamoto, Japan Earthquake
Authors: Yang Ying-Hui , Chen Qiang , Xu Qian , Zhao Jing-Jing , Hu Jyr-Ching , Li Hao-Liang , Xu Lang
Published: May 2021

We here present an example of the 2016 Kumamoto earthquake with its coseismic surface deformation mapped by the ALOS-2 satellite both in the right- and left-looking observation modes. It provides the opportunity to reveal the coseismic surface deformation and to explore the performance of the unusual left-looking data in faulting model inversion. Firstly, three tracks (ascending and descending right-looking and descending left-looking) of ALOS PALSAR-2 images are used to extract the surface deformation fields. It suggests that the displacements measured by the descending left-looking InSAR coincide well with the ascending right-looking track observations. Then, the location and strike angle of the fault are determined from the SAR pixel offset-tracking technique. A complicated four-segment fault geometry is inferred for explaining the coseismic faulting of the Kumamoto earthquake due to the interpretation of derived deformation fields. Quantitative comparisons between models constrained by the right-looking only data and by joint right- and left-looking data suggest that left-looking InSAR could provide comparable constraints for geodetic modelling to right-looking InSAR. Furthermore, the slip model suggests that the series of events are dominated by the dextral strike-slip with some normal fault motions. The fault rupture initiates on the Hinagu fault segment and propagates from southwest to northeast along the Hinagu fault, then transforms to Futagawa fault with a slip maximum of 4.96 m, and finally ends up at ~7 km NW of the Aso caldera, with a rupture length of ~55 km. The talent of left-looking InSAR in surface deformation detection and coseismic faulting inversion indicates that left-looking InSAR can be effectively utilized in the investigation of the geologic hazards in the future, same as right-looking InSAR.
Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations
Authors: Milani Lisa , Wood Norman B.
Published: May 2021

Falling snow is a key component of the Earth’s water cycle, and space-based observations provide the best current capability to evaluate it globally. The Cloud Profiling Radar (CPR) on board CloudSat is sensitive to snowfall, and other satellite missions and climatological models have used snowfall properties measured by it for evaluating and comparing against their snowfall products. Since a battery anomaly in 2011, the CPR has operated in a Daylight-Only Operations (DO-Op) mode, in which it makes measurements primarily during only the daylit portion of its orbit. This work provides estimates of biases inherent in global snowfall amounts derived from CPR measurements due to this shift to DO-Op mode. We use CloudSat’s snowfall measurements during its Full Operations (Full-Op) period prior to the battery anomaly to evaluate the impact of the DO-Op mode sampling. For multi-year global mean values, the snowfall fraction during DO-Op changes by −10.16% and the mean snowfall rate changes by −8.21% compared with Full-Op. These changes are driven by the changes in sampling in DO-Op and are very little influenced by changes in meteorology between the Full-Op and DO-Op periods. The results highlight the need to sample consistently with the CloudSat observations or to adjust snowfall estimates derived from CloudSat when using DO-Op data to evaluate other precipitation products.
Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery
Authors: Jamali Ali , Mahdianpari Masoud , Brisco Brian , Granger Jean , Mohammadimanesh Fariba , Salehi Bahram
Published: May 2021

Wetlands are important ecosystems that are linked to climate change mitigation. As 25% of global wetlands are located in Canada, accurate and up-to-date wetland classification is of high importance, nationally and internationally. The advent of deep learning techniques has revolutionized the current use of machine learning algorithms to classify complex environments, specifically in remote sensing. In this paper, we explore the potential and possible limitations to be overcome regarding the use of ensemble deep learning techniques for complex wetland classification and discusses the potential and limitation of various solo convolutional neural networks (CNNs), including DenseNet, GoogLeNet, ShuffleNet, MobileNet, Xception, Inception-ResNet, ResNet18, and ResNet101 in three different study areas located in Newfoundland and Labrador, Canada (i.e., Avalon, Gros Morne, and Grand Falls). Moreover, to improve the classification accuracies of wetland classes of bog, fen, marsh, swamp, and shallow water, the results of the three best CNNs in each study area is fused using three supervised classifiers of random forest (RF), bagged tree (BTree), Bayesian optimized tree (BOT), and one unsupervised majority voting classifier. The results suggest that the ensemble models, in particular BTree, have a valuable role to play in the classification of wetland classes of bog, fen, marsh, swamp, and shallow water. The ensemble CNNs show an improvement of 9.63–19.04% in terms of mean producer’s accuracy compared to the solo CNNs, to recognize wetland classes in three different study areas. This research indicates a promising potential for integrating ensemble-based learning and deep learning for operational large area land cover, particularly complex wetland type classification.
An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images
Authors: Yan Dongchuan , Li Guoqing , Li Xiangqiang , Zhang Hao , Lei Hua , Lu Kaixuan , Cheng Minghua , Zhu Fuxiao
Published: May 2021

Dam failure of tailings ponds can result in serious casualties and environmental pollution. Therefore, timely and accurate monitoring is crucial for managing tailings ponds and preventing damage from tailings pond accidents. Remote sensing technology facilitates the regular extraction and monitoring of tailings pond information. However, traditional remote sensing techniques are inefficient and have low levels of automation, which hinders the large-scale, high-frequency, and high-precision extraction of tailings pond information. Moreover, research into the automatic and intelligent extraction of tailings pond information from high-resolution remote sensing images is relatively rare. However, the deep learning end-to-end model offers a solution to this problem. This study proposes an intelligent and high-precision method for extracting tailings pond information from high-resolution images, which improves deep learning target detection model: faster region-based convolutional neural network (Faster R-CNN). A comparison study is conducted and the model input size with the highest precision is selected. The feature pyramid network (FPN) is adopted to obtain multiscale feature maps with rich context information, the attention mechanism is used to improve the FPN, and the contribution degrees of feature channels are recalibrated. The model test results based on GoogleEarth high-resolution remote sensing images indicate a significant increase in the average precision (AP) and recall of tailings pond detection from that of Faster R-CNN by 5.6% and 10.9%, reaching 85.7% and 62.9%, respectively. Considering the current rapid increase in high-resolution remote sensing images, this method will be important for large-scale, high-precision, and intelligent monitoring of tailings ponds, which will greatly improve the decision-making efficiency in tailings pond management.
Multi-Resolution Supervision Network with an Adaptive Weighted Loss for Desert Segmentation
Authors: Wang Lexuan , Weng Liguo , Xia Min , Liu Jia , Lin Haifeng
Published: May 2021

Desert segmentation of remote sensing images is the basis of analysis of desert area. Desert images are usually characterized by large image size, large-scale change, and irregular location distribution of surface objects. The multi-scale fusion method is widely used in the existing deep learning segmentation models to solve the above problems. Based on the idea of multi-scale feature extraction, this paper took the segmentation results of each scale as an independent optimization task and proposed a multi-resolution supervision network (MrsSeg) to further improve the desert segmentation result. Due to the different optimization difficulty of each branch task, we also proposed an auxiliary adaptive weighted loss function (AWL) to automatically optimize the training process. MrsSeg first used a lightweight backbone to extract different-resolution features, then adopted a multi-resolution fusion module to fuse the local information and global information, and finally, a multi-level fusion decoder was used to aggregate and merge the features at different levels to get the desert segmentation result. In this method, each branch loss was treated as an independent task, AWL was proposed to calculate and adjust the weight of each branch. By giving priority to the easy tasks, the improved loss function could effectively improve the convergence speed of the model and the desert segmentation result. The experimental results showed that MrsSeg-AWL effectively improved the learning ability of the model and has faster convergence speed, lower parameter complexity, and more accurate segmentation results.
Multi-Scale Geophysical Methodologies Applied to Image Archaeological Ruins at Various Depths in Highly Terraneous Sites
Authors: Ibrahim Amin , Gemail Khaled S. , Abdelrahman Kamal , Al-Otaibi Naif , Ibrahim Elkhedr , Saada Saada A.
Published: May 2021

Among all geophysical techniques, electrical resistivity and magnetic surveying as an integrative approach has been used widely for archaeological prospection at different scales of investigations. In this study, DC resistivity (1D vertical sounding and/2D/3D ERT) and magnetic surveys (total and gradient) as a multi-scale approach was applied in a highly terraneous archaeological site (Tell) with a case study to characterize and image the various archaeological assets at different depths with different spatial resolutions. Four critical zones of great interest within the considered Tell were surveyed. At the heart of the study area, three layers were depicted clearly from 1D resistivity sounding. A thick conductive zone of mostly clay is sandwiched between two resistive layers. The topmost layer contains construction debris (dated back to the Islamic Era), whereas the deeper layer could be related to Gezira sand on which the probable Pharaonic temple was constructed. A long 2D ERT profile using Wenner Beta (WB) and Dipole–Dipole (DD) arrays with a 5-m electrode spacing identified shallow high resistivity anomalies that could be related to construction ruins from fired bricks. Additionally, it succeeded in imaging the turtleback-shaped deeper resistive layer of mostly sand. At an elevated rim to the east and west of the Tell, total and vertical magnetic gradient maps clearly delineated different archaeological structures: the walls of the rooms of ancient Islamic settlers and the walls of water tanks from the Byzantine Era. Magnetic modeling assuming 2.5-dimensional magnetic models constrained by the 2D ERT inversion models could be used to create a realistic representation of the buried structures. Toward the northern part of the Tell, the joint application of the quasi-3D ERT inversion scheme and the magnetic survey revealed an anomaly of a well-defined geometric shape of an archaeological interest thought to be a crypt or water cistern based on nearby archaeological evidence. The overall results of the geophysical survey integrated with the image of some partially excavated parts provided the archaeologists with a comprehensive and realistic view of the subsurface antiquities at the study area.
Comparison and Ground Truthing of Different Remote and Proximal Sensing Platforms to Characterize Variability in a Hedgerow-Trained Vineyard
Authors: Squeri Cecilia , Poni Stefano , Di Gennaro Salvatore Filippo , Matese Alessandro , Gatti Matteo
Published: May 2021

Appropriate characterization of intra-parcel variability is a key element for the effective application of precision farming techniques. Nowadays there are many platforms available to end users differing for pixel spatial resolution and the type of acquisition (remote or proximal). A challenging aspect pertaining to remote sensing image acquisition in the vineyard ecosystem is that, in a large majority of cases, vegetation is discontinuous and single rows alternate with strips of either bare or grassed soil. In this paper, four different satellite platforms (Sentinel-2, Spot-6, Pleiades, and WorldView-3) having different spatial resolution and MECS-VINE® proximity sensor were compared in terms of accuracy at describing spatial variability. Vineyard mapping was coupled with detailed ground truthing of growth, yield, and grape composition variables. The analysis was conducted based on vigor indices (Normalized Difference Vegetation Index or Canopy Index) and using the Moran Index (MI) to assess the degree of spatial auto-correlation for the different variables. The results obtained showed a large degree of intra-plot variability in the main agronomic parameters (pruning weight CV: 33.86%, yield: 32.09%). The univariate Moran index showed a log-linear function relating MI coefficients to the resolution levels. Comparison between vigor indices and agronomic data showed that the highest bivariate MI was reached by Pleiades followed by MECS-VINE® which also did not exhibit the negative effect of the border pixel owing to the proximal scanning acquisition. Despite WorldView-3′s high resolution (1.24 m pixel) allowing very detailed data imaging, the comparison with ground-truth data was not encouraging, probably due to the presence of pure ground pixels, while Sentinel-2 was affected by the oversized pixel at 10 m.
Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges
Authors: Matongera Trylee Nyasha , Mutanga Onisimo , Sibanda Mbulisi , Odindi John
Published: May 2021

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.
Onboard and External Magnetic Bias Estimation for UAS through CDGNSS/Visual Cooperative Navigation
Authors: Vitiello Federica , Causa Flavia , Opromolla Roberto , Fasano Giancarmine
Published: May 2021

This paper describes a calibration technique aimed at combined estimation of onboard and external magnetic disturbances for small Unmanned Aerial Systems (UAS). In particular, the objective is to estimate the onboard horizontal bias components and the external magnetic declination, thus improving heading estimation accuracy. This result is important to support flight autonomy, even in environments characterized by significant magnetic disturbances. Moreover, in general, more accurate attitude estimates provide benefits for georeferencing and mapping applications. The approach exploits cooperation with one or more “deputy” UAVs and combines drone-to-drone carrier phase differential GNSS and visual measurements to attain magnetic-independent attitude information. Specifically, visual and GNSS information is acquired at different heading angles, and bias estimation is modelled as a non-linear least squares problem solved by means of the Levenberg–Marquardt method. An analytical error budget is derived to predict the achievable accuracy. The method is then demonstrated in flight using two customized quadrotors. A pointing analysis based on ground and airborne control points demonstrates that the calibrated heading estimate allows obtaining an angular error below 1°, thus resulting in a substantial improvement against the use of either the non-calibrated magnetic heading or the multi-sensor-based solution of the DJI onboard navigation filter, which determine angular errors of the order of several degrees.
A Novel Versatile Approach for Underwater Conformal Volumetric Array Design
Authors: Yusuf Taofeek Ayotunde , Pyo Seonghun , Roh Yongrae
Published: May 2021

In this study, we present a novel approach to the design of a conformal volumetric array composed of M × N convex subarrays in two orthogonal curvilinear directions for underwater acoustic imaging for mine detection. Our design targets require that the proposed array transducer has three-dimensional half-power beamwidths of 85° and 25° in either of its convex subarray parts, while also reaching a peak transmitting voltage response above 147 dB. The radiated sound pressure of the subarrays was independently derived as a function of their geometrical parameters. The resulting directional factors were then combined to analyze the beam profile of the entire array. The design was finally optimized to minimize the ripple level. To validate this theoretical design, the structure was modeled and analyzed using the finite element method. The comparison between the resulting beam pattern from the finite element analysis and the analytical computation showed an excellent compliance. The method advanced is a simple and systematic analytical model to facilitate the development of new conformal volumetric arrays for underwater mine detection.
Development of a Wearable Camera and AI Algorithm for Medication Behavior Recognition
Authors: Lee Hwiwon , Youm Sekyoung
Published: May 2021

As many as 40% to 50% of patients do not adhere to long-term medications for managing chronic conditions, such as diabetes or hypertension. Limited opportunity for medication monitoring is a major problem from the perspective of health professionals. The availability of prompt medication error reports can enable health professionals to provide immediate interventions for patients. Furthermore, it can enable clinical researchers to modify experiments easily and predict health levels based on medication compliance. This study proposes a method in which videos of patients taking medications are recorded using a camera image sensor integrated into a wearable device. The collected data are used as a training dataset based on applying the latest convolutional neural network (CNN) technique. As for an artificial intelligence (AI) algorithm to analyze the medication behavior, we constructed an object detection model (Model 1) using the faster region-based CNN technique and a second model that uses the combined feature values to perform action recognition (Model 2). Moreover, 50,000 image data were collected from 89 participants, and labeling was performed on different data categories to train the algorithm. The experimental combination of the object detection model (Model 1) and action recognition model (Model 2) was newly developed, and the accuracy was 92.7%, which is significantly high for medication behavior recognition. This study is expected to enable rapid intervention for providers seeking to treat patients through rapid reporting of drug errors.
Applying a ToF/IMU-Based Multi-Sensor Fusion Architecture in Pedestrian Indoor Navigation Methods
Authors: Farhangian Farzan , Sefidgar Mohammad , Landry Rene Jr.
Published: May 2021

The advancement of indoor Inertial Navigation Systems (INS) based on the low-cost Inertial Measurement Units (IMU) has been long reviewed in the field of pedestrian localization. There are various sources of error in these systems which lead to unstable and unreliable positioning results, especially in long term performances. These inaccuracies are usually caused by imperfect system modeling, inappropriate sensor fusion models, heading drift, biases of IMUs, and calibration methods. This article addresses the issues surrounding unreliability of the low-cost Micro-Electro-Mechanical System (MEMS)-based pedestrian INS. We designed a novel multi-sensor fusion method based on a Time of Flight (ToF) distance sensor and dual chest- and foot-mounted IMUs, aided by an online calibration technique. An Extended Kalman Filter (EKF) is accounted for estimating the attitude, position, and velocity errors, as well as estimation of IMU biases. A fusion architecture is derived to provide a consistent velocity measurement by operative contribution of ToF distance sensor and foot mounted IMU. In this method, the measurements of the ToF distance sensor are used for the time-steps in which the Zero Velocity Update (ZUPT) measurements are not active. In parallel, the chest mounted IMU is accounted for attitude estimation of the pedestrian’s chest. As well, by designing a novel corridor detection filter, the heading drift is restricted in each straightway. Compared to the common INS method, developed system proves promising and resilient results in two-dimensional corridor spaces for durations of up to 11 min. Finally, the results of our experiments showed the position RMS error of less than 3 m and final-point error of less than 5 m.
Magnetoimpedance Thin Film Sensor for Detecting of Stray Fields of Magnetic Particles in Blood Vessel
Authors: Melnikov Grigory Yu. , Lepalovskij Vladimir N. , Svalov Andrey V. , Safronov Alexander P. , Kurlyandskaya Galina V.
Published: May 2021

Multilayered [FeNi (100 nm)/Cu (3 nm)]5/Cu (500 nm)/[Cu (3 nm)/[FeNi (100 nm)]5 structures were used as sensitive elements of the magnetoimpedance (MI) sensor prototype for model experiments of the detection of magnetic particles in blood vessel. Non-ferromagnetic cylindrical polymer rod with a small magnetic inclusion was used as a sample mimicking thrombus in a blood vessel. The polymer rod was made of epoxy resin with an inclusion of an epoxy composite containing 30% weight fraction of commercial magnetite microparticles. The position of the magnetic inclusion mimicking thrombus in the blood vessel was detected by the measurements of the stray magnetic fields of microparticles using MI element. Changes of the MI ratio in the presence of composite can be characterized by the shift and the decrease of the maximum value of the MI. We were able to detect the position of the magnetic composite sample mimicking thrombus in blood vessels. Comsol modeling was successfully used for the analysis of the obtained experimental results and the understanding of the origin the MI sensitivity in proposed configuration. We describe possible applications of studied configuration of MI detection for biomedical applications in the field of thrombus state evaluation and therapy.
THz Sensing of Human Skin: A Review of Skin Modeling Approaches
Authors: Wang Jiarui , Lindley-Hatcher Hannah , Chen Xuequan , Pickwell-MacPherson Emma
Published: May 2021

The non-ionizing and non-invasive nature of THz radiation, combined with its high sensitivity to water, has made THz imaging and spectroscopy highly attractive for in vivo biomedical applications for many years. Among them, the skin is primarily investigated due to the short penetration depth of THz waves caused by the high attenuation by water in biological samples. However, a complete model of skin describing the THz–skin interaction is still needed. This is also fundamental to reveal the optical properties of the skin from the measured THz spectrum. It is crucial that the correct model is used, not just to ensure compatibility between different works, but more importantly to ensure the reliability of the data and conclusions. Therefore, in this review, we summarize the models applied to skin used in the THz regime, and we compare their adaptability, accuracy, and limitations. We show that most of the models attempt to extract the hydration profile inside the skin while there is also the anisotropic model that displays skin structural changes in the stratum corneum.
Absolute Displacement-Based Formulation for Peak Inter-Story Drift Identification of Shear Structures Using Only One Accelerometer
Authors: Xu Kangqian , Mita Akira
Published: May 2021

Only one accelerometer is used in this paper for estimating the maximum inter-story drifts and time histories of the relative displacements of all stories of multi-degree-of-freedom (MDOF) shear structures under seismic excitation. The calculation based on the data of one sensor using a conventional method is unstable, and when modal coordinates are used, higher modes should be included, which is different from the estimation based on the responses recorded by many accelerometers. However, the parameters of the higher modes of structures are difficult to obtain from structures under small excitations. To overcome this difficulty, the recorded absolute acceleration is converted into the absolute displacement, and a state-space equation is formulated. Numerical simulations of a nine-story structure were conducted to check the applicability, robustness against environmental noise, and optimal installation location of the accelerometer of the proposed approach. In addition, the effects of the higher modes were analyzed in terms of the number of accelerometers and type of response. Finally, the proposed approach was validated in a simple experiment. The results indicate that it can accurately estimate the time histories of the relative displacements and maximum inter-story drifts of all floors when one accelerometer is used and just the first two modal parameters are incorporated in the model. Furthermore, the approach is robust against environmental noise.
Experimental and Theoretical Research on Pressure Drop Changes in a Two-Stage Air Filter Used in Tracked Vehicle Engine
Authors: Dziubak Tadeusz , Boruta Grzegorz
Published: May 2021

The effect of mineral dust in the air sucked in by an engine on accelerated component wear and reduction in performance was presented. The necessity to use two-stage air filters (multicyclone-paper insert) for military vehicles was shown. The results showed that placing an air filter in the path of the air entering the engine causes an additional pressure drop (air filter resistance increase), which leads to engine power decrease and increased fuel consumption. An analysis of model filter beds’ pressure drop changes (depending on bed parameters, aerosol flow parameters, and dust content) was carried out. It was revealed that it is very difficult to model changes in pressure drop in filter beds for actual conditions that appear during vehicle operation. The air filter pressure drop measurement results of more than 20 tracked vehicles operating in variable air dust concentration conditions were presented. The forms of selected regression models of the “life curve” type, best suited to the actual changes in air filters pressure drop as a function of the vehicle mileage, were determined. Significant differences were found between the same model values for different units of the tested vehicles. The quality of forecasting pressure drop value by selected functions was assessed by extrapolating them to the value of the next measurement and comparing the forecast and actual value. It was found that for the performed experiment, sufficiently good results of experimental data approximation and forecasting were obtained for a simple linear model.
Revisiting Chiral Recognition Mechanism on Chicken Alpha 1-Acid Glycoprotein: Location of Chiral Binding Sites and Insight into Chiral Binding Mechanism
Authors: Haginaka Jun , Yamashita Taku , Tsujino Hirofumi , Arisawa Mitsuhiro
Published: May 2021

Chiral stationary phases based on chicken alpha 1-acid glycoprotein (cAGP) have been used for enantioseparations of various compounds. However, the chiral binding sites and mechanism have not been clarified yet. Based on chromatographic properties of native and W26-modified cAGP columns and docking simulations of studied compounds into the generated model structure of cAGP, the chiral binding sites were located on cAGP and the chiral binding mechanism was discussed. On cAGP, there existed a binding cavity lined with H25, W26, Y47, R128, T129, D161 and E168, which contribute electrostatic or hydrogen bonding interactions. Benzoin and chlorpheniramine enantiomers interacted with cAGP at almost the same sites a little away from W26, while propranolol enantiomers docked, slightly shifting toward H25 and W26. Furthermore, in addition to hydrophobic interactions, ionic interactions between amino groups of chlorpheniramine enantiomers and a carboxyl group of D161 or E168 played an important role in the chiral recognition, while hydrophobic interactions and hydrogen bonding interactions worked for the chiral recognition of benzoin and propranolol enantiomers.
“I Will Obey Whatever Orders Will Be Given to Me …”: A Critical Discourse Analysis of an Affidavit from a Slum Upgradation and Rehabilitation Project in Islamabad, Pakistan
Authors: Waheed Arslan
Published: May 2021

Instead of criminalizing slums, the global discourse on slums and urban poor is changing towards integration, rehabilitation, and internationalizing cities. As pleasant as it may look, it is important to critically reflect and evaluate the policies of upgradation and rehabilitation, especially in the global south. Is the change from criminalization to rehabilitation and integration true to its spirit or is it just another policy gimmick? This article uses critical discourse analysis to analyze an affidavit produced in an official report on the upgradation and rehabilitation of katchi abadis (slums) in Islamabad. The report describes the affidavit as the most important part of the process through which the urban poor become eligible for applying to the intended benefits of the development interventions, that is, the legalization of their housing. The affidavit is to be reproduced by the urban poor on a notarized stamped paper to be attested to by a magistrate of the first class (a Civil Judge). The discursive analysis of the affidavit shows that the text attempts to naturalize inequalities, criminalization, and essentialization of the urban poor. Further, having constructed their deviant status, the text shows that the urban poor must surrender some of their rights to access their right to housing in Islamabad.
Fiducial Inference on the Right Censored Birnbaum–Saunders Data via Gibbs Sampler
Authors: Jayalath Kalanka P.
Published: May 2021

In this article, we implement a flexible Gibbs sampler to make inferences for two-parameter Birnbaum–Saunders (BS) distribution in the presence of right-censored data. The Gibbs sampler is applied on the fiducial distributions of the BS parameters derived using the maximum likelihood, methods of moments, and their bias-reduced estimates. A Monte-Carlo study is conducted to make comparisons between these estimates for Type-II right censoring with various parameter settings, sample sizes, and censoring percentages. It is concluded that the bias-reduced estimates outperform the rest with increasing precision. Higher sample sizes improve the overall accuracy of all the estimates while the amount of censoring shows a negative effect. Further comparisons are made with existing methods using two real-world examples.
Eucalyptus globulus Coppices in Portugal: Influence of Site and Percentage of Residues Collected for Energy
Authors: Malico Isabel , Gonçalves Ana Cristina
Published: May 2021

Studies that quantify forest bioenergy potentials hardly address the questions of site quality, proportion and type of residues removed from the stands, and environmental impacts of those removals. However, those factors are important for energy-potential results and forest sustainability. This study compares, in terms of residual biomass availability for energy production and of sustainability, different locations, site indices, and forest management strategies in Eucalyptus globulus stands for pulp and paper in northern and central Portugal. A growth and production simulator was used to calculate the availability of residues and the area needed to supply a biomass-fired power plant under a variety of scenarios. Regions with more rainfall generate more residues, but site index and quantity and type of residues harvested are the most important factors. Under the different scenarios analyzed, the amount of residues potentially harvested range from 0.7 to 4.3 Mg ha−1 a−1, the upper bound corresponding to a scenario where stumps are valorized. The maximization of residue removal maximizes the bioenergy produced but has to be considered prudently. Studies indicate that stump removal has limited effect on Eucalyptus globulus stand productivity, diversity, and system sustainability, but impacts of residue removals increase with a decrease in site index.
Understanding the Behavioral Consequences of Upward Social Comparison on Social Networking Sites: The Mediating Role of Emotions
Authors: Park Jaeyoung , Kim Beomsoo , Park Sunhee
Published: May 2021

With the increase in upward social comparison occurring on social networking sites (SNSs) globally, SNS researchers have examined the impact of upward social comparison. However, they focused mainly on psychological outcomes (e.g., well-being). To extend the existing studies, this study investigates the behavioral consequences of upward social comparison through the underlying mechanisms of emotions. Drawing on Smith’s typology of social comparison-based emotions, we developed a conceptual model that integrates upward social comparison on Instagram, upward comparison emotions (upward contrastive emotions and upward assimilative emotions), and the behavioral responses (SNS discontinuance, posting of comments). A structural equation modeling analysis revealed that upward social comparison in Instagram usage provoked upward contrastive emotions including anger, depression, and envy, which, in turn, induced Instagram discontinuance and the posting of malicious comments on Instagram. Additionally, upward assimilative emotions including admiration, optimism, and inspiration triggered the posting of favorable comments on Instagram. This study enhances our understanding of SNS social comparison by revealing how upward social comparison on Instagram is related to behavioral consequences.
Fuzzy TOPSIS Application to Rank Determinants of Employee Retention in Construction Companies: South Korean Case
Authors: Park Chol-Jung , Kim Soo-Yong , Nguyen Minh V.
Published: May 2021

Employee retention is becoming a major concern in organizational management. To maintain business’ competitive advantages, companies need to keep employees working for their organizations. Thus, many firms are trying to find out how to retain their employees. This study aims to investigate determinants of employee retention of South Korean construction employees. From the review of the literature and discussions with industrial practitioners, eight significant determinants affecting employee retention in South Korean construction firms are identified. The fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) is employed to prioritize the identified determinants. The fuzzy TOPSIS analysis shows that personal characteristics, personal development, promotion opportunities, and work-life balance are the four most critical determinants. Construction firms are suggested to focus on these determinants to improve employee retention rates within their companies and achieve sustainable development.
Entrepreneurs Traits/Characteristics and Innovation Performance of Waste Recycling Start-Ups in Ghana: An Application of the Upper Echelons Theory among SEED Award Winners
Authors: Dorcas Kouame Dangui , Celestin Bekolo Ngoa , Yunfei Shao
Published: May 2021

Waste management has become a pressing environmental, social, and economic issue. In Ghana, the government has decentralized the waste management system to include private sector actors as key players to improve the collection, disposal, and recycling of waste. With this development, a heterogeneous population of entrepreneurs has engaged in waste recycling, achieving mixed results in terms of performance. The aim of this paper is to identify shared personality traits and characteristics of entrepreneurs that make certain firms engage in waste recycling more innovatively than others. An extensive literature review was used to identify these personality traits and characteristics, which were then modeled using upper echelon theory (UET) to investigate their impact on innovation performance. A regression analysis approach was adopted based on the data collected from 157 entrepreneurs’ founders, co-founders, and shareholders among the waste recycling firms in Ghana selected for the annual Supporting Entrepreneurs for Environment and Development (SEED) Award competition. The key contribution of this research is to better understand the relationship between entrepreneur traits and innovation performance. Given the fact that in small start-ups, the founder plays the most important role, this paper serves as a foundation for defining individual-level factors critical in sustaining sustainable innovation performance in the waste recycling sector. The results of this study will help shareholders and policymakers better understand and implement strategies for determining and selecting innovative waste recycling entrepreneurs.
Nanoparticles from the Cosmetics and Medical Industries in Legal and Environmental Aspects
Authors: Włodarczyk Renata , Kwarciak-Kozłowska Anna
Published: May 2021

This paper presents the application and role of nanomaterials, with particular emphasis on the cosmetics and medical industries. Methods of obtaining materials at the nanoscale and their characteristic structure, which determines their attractiveness and risk, especially in recent years, have been described. The subject of the work was to indicate the hazards and risks that are associated with the properties of nanomaterials; dimension, and high chemical and physical activity, thus making ways to capture and monitor them difficult. Legal and environmental aspects were taken into account, and the involvement of the European Commission in this subject and the activities carried out in a few European countries as well as in Japan, the USA and Canada were analyzed.
The Impact of an 8-Weeks At-Home Physical Activity Plan on Academic Achievement at the Time of COVID-19 Lock-Down in Italian School
Authors: Latino Francesca , Fischetti Francesco , Cataldi Stefania , Monacis Domenico , Colella Dario
Published: May 2021

The purpose of this randomized controlled study was to investigate the efficacy of an 8-week exercise programme conducted in e-learning mode on high school students’ academic performance. The aim was to examine the changes in physical fitness and learning outcomes during the enforced period of lockdown caused by outbreak of the second wave of COVID-19 pandemic and the closure of schools in Italy. Thirty high-school students (14–15 years) were randomly assigned to an experimental group (n = 15) that performed an at-home workout programme (~60 min., twice a week), or a control group (n = 15) who received only a regular programme of theoretical lessons where no practice takes place. Both groups were synchronized in real-time with the physical education teacher. In order to assess students’ starting level and significant changes reached, at baseline and after training, a battery of standardized assessment motor tests (Standing long jump test, Harvard step test, sit and reach test, and butt kicks test), and an academic achievement test (Amos 8–15) were administered. In comparison to the control group at baseline and the end of the programme, the experimental group reported considerable improvements in motivation and concentration, significant anxiety reduction, and an increase in capacity to organize studying and to be more flexible. Moreover, it was possible to observe the efficacy of the workout to improve learning ability among practicing students (p < 0.001). No significant changes were found in the control group. The results suggest that a school-based exercise programme conducted online could be a powerful approach in order to achieve the best academic outcomes and for improving students’ physical fitness as well as their cognitive health.
A Systematic Literature Review on the Quality of MOOCs
Authors: Stracke Christian M. , Trisolini Giada
Published: May 2021

This paper presents the findings from a systematic literature review on the quality of massive open online courses (MOOCs). The main research question was “How can the quality criteria for MOOCs identified in the analysed studies from the systematic literature review be best organised in a categorisation scheme?” The systematic literature review was conducted using the PRISMA procedures. After conducting the screening and eligibility analysis according the pre-defined criteria, 103 studies were finally selected. The analysis was done in iterative cycles for continuous improvements of the assignments and clustering of the quality criteria. The final version was validated in consensus through the categorisation and assignment of all 103 studies in a consistent way to four dimensions (pedagogical, organisational, technological, and social) and their sub-categories. This quality framework can be re-used in future MOOC research and the discussion of the analysed studies provides a current literature overview on the quality of MOOCs.
Design and Operating Mode Study of a New Concept Maglev Car Employing Permanent Magnet Electrodynamic Suspension Technology
Authors: Zhang Ze , Deng Zigang , Zhang Shuai , Zhang Jianghua , Jin Li’an , Sang Xiaochen , Gao Pengfei , Li Jing , Zheng Jun
Published: May 2021

Based on the principle of permanent magnet electrodynamic suspension (PMEDS), a new concept maglev car was designed by using rotary magnetic wheels and a conductor plate. It has the advantages of being high-speed, low-noise, environmentally friendly, safe and efficient. The PMEDS car is designed to use a permanent magnet electrodynamic wheel (EDW) to achieve the integration of levitation force and driving force. The levitation force is generated by the repulsive force of the eddy current magnetic field, and the driving force is generated by the reaction force of magnetic resistance. A simplified electromagnetic force model of the EDW and a dynamics model of the PMEDS car were established to study the operating mode. It shows that the PMEDS car can achieve suspension when the rotational speed of the EDWs reaches a certain threshold and the critical speed of the EDWs is 600 rpm. With the cooperation of four permanent magnet EDWs, the PMEDS car can achieve stable suspension and the maximum suspension height can reach 7.3 mm. The working rotational speed of EDWs is 3500 rpm. At the same time, the movement status of the PMEDS car can be controlled by adjusting the rotational speed of rear EDWs. The functions of propulsion, acceleration, deceleration, and braking are realized and the feasibility of the PMEDS car system is verified.
Motivational Factors, Job Satisfaction, and Economic Performance in Romanian Small Farms
Authors: Maican Silvia Ștefania , Muntean Andreea Cipriana , Paștiu Carmen Adina , Stępień Sebastian , Polcyn Jan , Dobra Iulian Bogdan , Dârja Mălina , Moisă Claudia Olimpia
Published: May 2021

The agricultural sector ensures food security and is a major source of employment, income, and economic activity in rural areas. The Food and Agriculture Organization of the United Nations (FAO) considers that family farms are the key to a sustainable future in Europe and Central Asia. In Romania, small farms represent the pillar on which Romanian society has been developed. Although the trend has been a reduction in the number of small farms and an increase in the number of large farms, the Government of Romania understands the importance of small farms and therefore supports them through policies involving direct payments, rural development instruments, special initiatives, and loans and outstanding obligations, among others, which focus on increasing their economic performance. The aim of our research was to determine the relationship between farmers’ motivation, their job satisfaction, and the farm economic performance in the case of small Romanian farms. The research sample consisted of 900 small farms (utilized agricultural area (UAA): under 20 ha; standard output (SO): under EUR 15,000). The data obtained after applying the questionnaires were analyzed using SPSS 20.0 and Amos 24.0. For the exploratory factor analysis, values of Bartlett’s test of sphericity, the Kaiser–Meyer–Olkin test, and Cronbach’s alpha coefficient were calculated for each dimension of the proposed model. The hypothesis that motivation, job satisfaction, and farm economic performance directly and positively influence each other was confirmed. An important finding was that the correlation coefficient between farmers’ motivation and farm economic performance was ρ = 0.78, while that for the relation between farmers’ job satisfaction and farm economic performance was ρ = 0.53, which was similar to the correlation coefficient calculated for the relationship between farmers’ motivation and farmers’ job satisfaction. This result allows us to conclude that the influence of farmers’ motivation factors on farm economic performance is stronger than the influence of job satisfaction in the case of Romanian farmers on small farms. This might explain why, although work in agriculture is considered to be worse than an office job and the people that work in agriculture are sometimes stigmatized and receive lower incomes, there are still very strong motivators for Romanian farmers to continue their work in agriculture. This is proven by the fact that Romania has the highest number of small farms in Europe, and this number is not decreasing.