bluuebunny/crossref_metadata_2025 huggingface dataset:
Controlled Germination of Faba Beans: Drying, Thermodynamic Properties and Physical-Chemical Composition
Authors: Amadeu Lumara Tatiely Santos , Queiroz Alexandre José de Melo , Figueirêdo Rossana Maria Feitosa de , Ferreira João Paulo de Lima , Silva Wilton Pereira da , Gomes Josivanda Palmeira , Paiva Yaroslávia Ferreira , Costa Caciana Cavalcanti , Moura Henrique Valentim , Santos Dyego da Costa , Lima Ana Raquel Carmo de , Silva Hanndson Araujo
Published: July 2022

The objective of this work was to determine the drying kinetics and the thermodynamic properties of the drying process of germinated seeds from faba beans of the Olho-de-Vó Preta (OVP), Raio-de-Sol (RS) and Branca (B) varieties. Additionally, the physicochemical properties of the germinated seeds and subsequent dried flours were determined. A thin layer of seeds were dried using a convective dryer at temperatures of 50, 60, 70 and 80 °C. Mathematical models were applied to the drying experimental data. The samples were further characterized for water content, water activity, ash, pH, alcohol-soluble acidity, total and reducing sugars, proteins, and starch. Page and Midilli models revealed the best predictions of the drying kinetics for all evaluated conditions. The effective diffusion coefficient increased with increasing temperature and presented magnitude in the order of 10−9 m²/s. The activation energy presented results in the range of 19 and 27 kJ/mol, falling within the range reported for agricultural products. The entropy and enthalpy values were higher in the OVP, followed by RS, higher than in the B variety. The increase in drying temperature resulted in a reduction of enthalpy and entropy and an increase in Gibbs free energy, indicating that the drying process is endothermic and requires external energy. Samples have acidic pH and acidity decreased with drying; the RS and B varieties had higher sugar contents; the B variety had the highest protein contents, and these were obtained from the in natura germinated samples; in the B variety the highest starch content was obtained. All flours showed good characteristics, presenting themselves as an alternative for diversifying the supply of beans.
3D-QSAR, ADME-Tox In Silico Prediction and Molecular Docking Studies for Modeling the Analgesic Activity against Neuropathic Pain of Novel NR2B-Selective NMDA Receptor Antagonists
Authors: El fadili Mohamed , Er-rajy Mohammed , Imtara Hamada , Kara Mohammed , Zarougui Sara , Altwaijry Najla , Al kamaly Omkulthom , Al Sfouk Aisha , Elhallaoui Menana
Published: July 2022

A new class of selective antagonists of the N-Methyl-D-Aspartate (NMDA) receptor subunit 2B have been developed using molecular modeling techniques. The three-dimensional quantitative structure–activity relationship (3D-QSAR) study, based on comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models, indicate that steric, electrostatic and hydrogen bond acceptor fields have a key function in the analgesic activity against neuropathic pain. The predictive accuracy of the developed CoMFA model (Q2 = 0.540, R2 = 0.980, R2 pred = 0.613) and the best CoMSIA model (Q2 = 0.665, R2 = 0.916, R2 pred = 0.701) has been successfully examined through external and internal validation. Based on ADMET in silico properties, L1, L2 and L3 ligands are non-toxic inhibitors of 1A2, 2C19 and 2C9 cytochromes, predicted to passively cross the blood–brain barrier (BBB) and have the highest probability to penetrate the central nervous system (CNS). Molecular docking results indicate that the active ligands (L1, L2 and L3) interact specifically with Phe176, Glu235, Glu236, Gln110, Asp136 and Glu178 amino acids of the transport protein encoded as 3QEL. Therefore, they could be used as analgesic drugs for the treatment of neuropathic pain.
Determination of the Plastic Stress–Strain Relationship of a Rupture Disc Material with Quasi-Static and Dynamic Pneumatic Bulge Processes
Authors: Yu Xiaozhe , Yan Xingqing , Li Yue , Zeng Fangchi , Kang Shouli , Yu Jianliang
Published: July 2022

Rupture discs, manufactured using a hydraulic or pneumatic bulge process, are widely used to protect vessels from over-pressuring. The stress–strain relationship of the material in the bulge process plays a major role in understanding the performance of rupture discs. Moreover, both the theoretical analyses and numerical simulations of rupture discs demand a reliable stress–strain relationship of the material in a real bulge process. In this paper, an approach for determining the plastic stress–strain relationship of a rupture disc material in the bulge process is proposed based on plastic membrane theory and force equilibrium equations. Pressures of compressed air and methane/air mixture explosions were used for the bulge pressure to accomplish the quasi-static and dynamic bulge processes of tested discs. Experimental apparatus for the quasi-static bulge test and the dynamic bulge test were built. The stress–strain relations of 316L material during bulge tests were obtained, compared, and discussed. The results indicated that the bulge height at the top of the domed disc increased linearly with an increase in bulge pressure in the quasi-static and dynamic bulge processes, and the effective strain increased exponentially. The rate of pressure rise during the bulge process has a significant effect on the deformation behavior of disc material and hence the stress–strain relationship. At the same bulge pressure, a disc tested with a larger pressure rise rate had smaller bulge height and effective strain. At the same effective stress at the top of the domed disc, discs subjected to a higher pressure rise rate had smaller effective strain, and hence they are more difficult to rupture. Hollomon’s equation is used to represent the relationship between the effective stress and strain during bulge process. For pressure rise rates in the following range of 0 (equivalent to quasi-static condition), 2–10 MPa/s, 10–50 MPa/s, and 50–100 MPa/s, the relation of stress and strain is σe = 1259.4·εe0.4487, σe = 1192.4·εe0.3261, σe = 1381.2·εe0.2910, and σe = 1368.4·εe0.1701, respectively.
State Feedback Stabilization for a Class of Upper-Triangular Stochastic Nonlinear Systems with Time-Varying Control Coefficients
Authors: Sun Xixi , Yu Haisheng , Xu Xiaoyu
Published: July 2022

The problem explored in this article concerns the stability of the state feedback control of the upper-triangular stochastic nonlinear systems whose control coefficients are time-varying. First, the state feedback control of the corresponding nominal system is carried out by utilizing the backstepping technique combined with the appropriate Lyapunov function. Then, low-gain homogeneous domination technology and the efficient coordinate transformation method are adopted to realize the state feedback control of the original system and ensure the global asymptotic stability (GAS) in probability of the system. Finally, an example is given to illustrate the feasibility and correctness of the method.
Further Optimized Design of a Nested Rotate Sextupole Permanent Magnet Lens for the Focusing of Pulsed Neutrons
Authors: Zuo Taisen , Lu Zhanjiang , Deng Changdong , Xiao Songwen , He Yongcheng , He Zhenqiang , Lin Xiong , Ma Changli , Han Zehua , Cheng He
Published: July 2022

A compact nested rotate sextupole permanent magnet (Nest-Rot-SPM) lens was designed for the focusing of pulsed neutrons. It is based on the working conditions of the Very Small Angle Neutron Scattering (VSANS) instrument at the China Spallation Neutron Source (CSNS), and is expected to focus a neutron pulse from 6 Å to 10.5 Å, without chromatic aberration. Three hurdles must be addressed, i.e., the tremendous torque, the heat deposition, and the synchronization with the neutron pulse, respectively. The bore diameter and segment length of the lens are optimized using a formula analysis of the key parameters and model simulations of the torque and heat deposition. A twin torque canceling design is used to reduce the torque to one-third of its original value, or even lower. The goal of this project is to take the device into practical use in the VSANS at the CSNS.
Euthanasia in Mental Suffering—Overview and Discussion of the Arguments of the Roman Catholic Church
Authors: Liégeois Axel
Published: July 2022

Euthanasia for mental suffering in a nonterminal situation has been decriminalized only in a few countries, including Belgium, under certain conditions. The aim of this article is to provide an overview and an ethical discussion of the arguments of the Roman Catholic Church concerning euthanasia in mental suffering. In the research material, we include documents from the Vatican’s Magisterium, with particular reference to the recent Letter Samaritanus Bonus, and from the Belgian Bishops’ Conference. We structure the many arguments around three values: the inviolability of life, the good care by the professionals and the patients’ experience of meaning. We discuss the arguments and find that they are strongly normative and do not enter into discussion with scientific positions and other worldviews. As a result, the arguments are not convincing for people with a different view, while the underlying values remain very valid. This is why we nuance and reinterpret certain arguments. Based on Pope Francis’ Exhortation Amoris Laetitia, we make a plea to place less emphasis on the norms and rules, and more on the conscience of those involved and on their ethical discernment of the underlying values.
Pricing Energy Derivatives in Markets Driven by Tempered Stable and CGMY Processes of Ornstein–Uhlenbeck Type
Authors: Sabino Piergiacomo
Published: July 2022

In this study, we consider the pricing of energy derivatives when the evolution of spot prices follows a tempered stable or a CGMY-driven Ornstein–Uhlenbeck process. To this end, we first calculate the characteristic function of the transition law of such processes in closed form. This result is instrumental for the derivation of nonarbitrage conditions such that the spot dynamics is consistent with the forward curve. Moreover, we also conceive efficient algorithms for the exact simulation of the skeleton of such processes and propose a novel procedure when they coincide with compound Poisson processes of Ornstein–Uhlenbeck type. We illustrate the applicability of the theoretical findings and the simulation algorithms in the context of pricing different contracts, namely strips of daily call options, Asian options with European style and swing options.
Ocean Wave Inversion Based on a Ku/Ka Dual-Band Airborne Interferometric Imaging Radar Altimeter
Authors: Ma Chunyong , Pan Lichao , Qiu Zhiwei , Liang Da , Chen Ge , Yu Fangjie , Sun Hanwei , Sun Daozhong , Wu Weifeng
Published: July 2022

Ocean wave parameters (OWPs), including wave propagation direction (WPD), significant wave height (SWH), and main wave wavelength (MWW) can be typically retrieved using an interferometric imaging radar altimeter (InIRA). However, the inversion accuracy of ocean waves in Ku (15.8 GHz) and Ka (35.8 GHz) bands has not yet been evaluated due to the lack of field observation data. In this paper, to assess the inversion accuracy of OWPs in Ku and Ka bands, an airborne observation experiment using simultaneous Ku and Ka bands was carried out for the first time in Rizhao, Shandong Province, China. A dual-band InIRA (DInIRA) was configured with small incidence angles (4°–18°) and a Global Navigation Satellite System (GNSS) buoy; a mobile weather station was placed at the intersection of the plane routes for validation. Afterward, the WPD, SWH, and MWW were retrieved based on the imaging of sea surface height. As compared with the field in situ data, the WPD inversion results of main wind wave were found to be consistent with the measurement environmental wind direction. The SWH inversion biases, retrieved by the Ku and Ka bands, were 0.38 m and 0.27 m; the MWW inversion biases for the swells were equal to 16.75 m and 3.67 m; and the MWW inversion biases about the wind waves were 2.32 m and 0.57 m. Ultimately, it was established that the OWPs could be effectively retrieved by the DInIRA, and the inversion accuracy of the SWH and the MWW in the Ka band outperformed that in the Ku band.
Linking Land Cover Change with Landscape Pattern Dynamics Induced by Damming in a Small Watershed
Authors: Xie Zheyu , Liu Jihui , Huang Jinliang , Chen Zilong , Lu Xixi
Published: July 2022

Cascade damming can shape land surfaces; however, little is known about the specific impacts of dam construction on watershed land cover changes. Therefore, we developed a framework in which remote sensing, transition patterns, and landscape metrics were coupled to measure the impact of dam construction on watershed land cover changes and landscape patterns in the Longmen–Su (L–S) Creek, a small headwater watershed in Southeast China. During the transition and post-impact periods of dam construction, the land cover in the L–S Creek watershed underwent dynamic changes within the affected area. Changes in land cover were dominated by a surge in water and buildup and a decrease in woodland and cropland areas; bareland also increased steadily during construction. Woodlands and croplands were mainly flooded into water areas, although some were converted to bareland and built-up areas owing to the combined impact of dam construction and urbanization. By linking land cover changes with landscape patterns, we found that land use changes in water were significantly associated with landscape fragmentation and heterogeneity in the impacted zone. Our research demonstrates how damming can change land cover locally and may provide a basis for sustainable land management within the context of the extensive development of cascade hydropower dams.
Evaluating the Performance of High Spatial Resolution UAV-Photogrammetry and UAV-LiDAR for Salt Marshes: The Cádiz Bay Study Case
Authors: Curcio Andrea Celeste , Peralta Gloria , Aranda María , Barbero Luis
Published: July 2022

Salt marshes are very valuable and threatened ecosystems, and are challenging to study due to their difficulty of access and the alterable nature of their soft soil. Remote sensing methods in unmanned aerial vehicles (UAVs) offer a great opportunity to improve our knowledge in this type of complex habitat. However, further analysis of UAV technology performance is still required to standardize the application of these methods in salt marshes. This work evaluates and tunes UAV-photogrammetry and UAV-LiDAR techniques for high-resolution applications in salt marsh habitats, and also analyzes the best sensor configuration to collect reliable data and generate the best results. The performance is evaluated through the accuracy assessment of the corresponding generated products. UAV-photogrammetry yields the highest spatial resolution (1.25 cm/pixel) orthomosaics and digital models, but at the cost of large files that require long processing times, making it applicable only for small areas. On the other hand, UAV-LiDAR has proven to be a promising tool for coastal research, providing high-resolution orthomosaics (2.7 cm/pixel) and high-accuracy digital elevation models from lighter datasets, with less time required to process them. One issue with UAV-LiDAR application in salt marshes is the limited effectiveness of the autoclassification of bare ground and vegetated surfaces, since the scattering of the LiDAR point clouds for both salt marsh surfaces is similar. Fortunately, when LiDAR and multispectral data are combined, the efficiency of this step improves significantly. The correlation between LiDAR measurements and field values improves from R2 values of 0.79 to 0.94 when stable reference points (i.e., a few additional GCPs in rigid infrastructures) are also included as control points. According to our results, the most reliable LiDAR sensor configuration for salt marsh applications is the nadir non-repetitive combination. This configuration has the best balance between dataset size, spatial resolution, and processing time. Nevertheless, further research is still needed to develop accurate canopy height models. The present work demonstrates that UAV-LiDAR technology offers a suitable solution for coastal research applications where high spatial and temporal resolutions are required.
Terraced Landscapes as NBSs for Geo-Hydrological Hazard Mitigation: Towards a Methodology for Debris and Soil Volume Estimations through a LiDAR Survey
Authors: Paliaga Guido , Luino Fabio , Turconi Laura , Profeta Mario , Vojinovic Zoran , Cucchiaro Sara , Faccini Francesco
Published: July 2022

Terraced landscapes are widely applied in many mountainous regions around the world as a result of the necessity to practice subsistence agriculture. Hence, they can be regarded as one of the most diffused anthropogenic modifications of the Earth’s surface. Different techniques have been used for their implementation leading to the artificial immobilization of debris and soil along the slopes whose surface is interrupted by a sequence of sub-horizontal and sub-vertical areas often using stone walls. In some areas of the world, such interventions are thousands of years old and their resistance to the degradation caused by the morphogenetic system can be attributed to the permeability of the stone walls as well as to their regular maintenance. In some other areas, the lack of maintenance has been the main cause for degradation processes ending with their collapse. The effects of climate change manifested through higher intensities and higher frequencies of rainfall are likely to accelerate the degradation process further by causing terraces to act as a source of debris or hyperconcentrated flow. This will in turn increase the severity of geo-hydrological hazards. The measures concerning reduction of geo-hydrological hazards are sought through identification of abandoned terraces and assessment of the potential for their sudden collapse. The present paper describes a framework for identification of abandoned terraces and estimation of the potential volume of shallow landslides that can be generated. The research conducted aims to advance the existing hazard assessment practices by combining numerical modeling with processing of high-resolution LiDAR data. A new algorithm is developed to support localization of terraces. The catchment scale approach applied to eight smaller catchments enables estimation of the total volume of soil and debris trapped along the slopes. It also generated some important quantitative data which will be used in the future risk assessment work. The work has been carried out within the EU-funded H2020 project RECONECT.
Tectono-Geomorphic Analysis in Low Relief, Low Tectonic Activity Areas: Case Study of the Temiskaming Region in the Western Quebec Seismic Zone (WQSZ), Eastern Canada
Authors: Giona Bucci Monica , Schoenbohm Lindsay M.
Published: July 2022

We designed a workflow to investigate areas of potential neotectonic deformation, making use of well-developed techniques, but applied to a site characterized by low relief and low or moderate tectonic activity. In this pilot study, we targeted the Temiskaming Graben, in Eastern Canada, where recent and ongoing geophysical and sedimentological investigations have revealed recent activity along this ancient structure. The dataset compiled for this experimental study covers an area of nearly 147 square km across the provinces of Ontario and Quebec. For efficiency in terms of computational resources, we first performed cluster analysis on knickpoint location, identifying seven areas with a high density of disruptions along river profiles. We then performed more detailed morphometric analysis at 30 m resolution, identifying knickpoints along river profiles, calculating the hypsometric integral across the landscape with a moving window, and mapping and comparing lineaments with known structural features. The results of our workflow showed that these three techniques can be efficiently combined for neotectonic analysis, and the synergistic approach strengthens the reliability and accuracy of our results. Our research extends the application of morphometric analysis, commonly used for exploring areas with intense tectonism and high topography, to areas that are characterized by low relief and low or moderate tectonic activity. The new areas identified with the workflow proposed in this research require ground-truthing through mapping and shallow geophysical investigations.
Low-Cost Metamaterial Antennas: Forward-Looking Imaging Experiment and Analysis
Authors: Ruan Feng , Han Liang , Zhang Baoheng , Li Yachao , Guo Liang
Published: July 2022

A phase error correction method is proposed to compensate for the phase error in super-resolution correlated imaging based on metamaterial antennas. The varying carrier frequencies of a metamaterial antenna can generate the random radiation field for super-resolution correlation imaging, but the variation of the signal carrier frequency leads to large phase errors in the imaging results. In this proposed method, the sampling matrix in the super-resolution correlated imaging algorithm is used to compensate for the phase errors. Each element of the matrix is multiplied by a compensation phase corresponding to the phase error, and the error is subtly removed from the algorithm. In the experiment, the antenna pattern at each frequency of the metamaterial antenna is measured and recorded. In addition, an external field experiment is also carried out, and the collected data are imaged with the improved algorithm. Experimental results show that this technology can effectively solve the effect of phase errors on imaging results caused by signal carrier frequency changes.
Enhanced Multiscale Principal Component Analysis for Improved Sensor Fault Detection and Isolation
Authors: Malluhi Byanne , Nounou Hazem , Nounou Mohamed
Published: July 2022

Multiscale PCA (MSPCA) is a well-established fault-detection and isolation (FDI) technique. It utilizes wavelet analysis and PCA to extract important features from process data. This study demonstrates limitations in the conventional MSPCA fault detection algorithm, thereby proposing an enhanced MSPCA (EMSPCA) FDI algorithm that uses a new wavelet thresholding criterion. As such, it improves the projection of faults in the residual space and the threshold estimation of the fault detection statistic. When tested with a synthetic model, EMSPCA resulted in a 30% improvement in detection rate with equal false alarm rates. The EMSPCA algorithm also relies on the novel application of reconstruction-based fault isolation at multiple scales. The proposed algorithm reduces fault smearing and consequently improves fault isolation performance. The paper will further investigate the use of soft vs. hard wavelet thresholding, decimated vs. undecimated wavelet transforms, the choice of wavelet decomposition depth, and their implications on FDI performance.The FDI performance of the developed EMSPCA method was illustrated for sensor faults. This undertaking considered synthetic data, the simulated data of a continuously stirred reactor (CSTR), and experimental data from a packed-bed pilot plant. The results of these examples show the advantages of EMSPCA over existing techniques.
UNetGE: A U-Net-Based Software at Automatic Grain Extraction for Image Analysis of the Grain Size and Shape Characteristics
Authors: Zeng Ling , Li Tianbin , Wang Xiekang , Chen Lei , Zeng Peng , Herrin Jason Scott
Published: July 2022

The shape and the size of grains in sediments and soils have a significant influence on their engineering properties. Image analysis of grain shape and size has been increasingly applied in geotechnical engineering to provide a quantitative statistical description for grain morphologies. The statistic robustness and the era of big data in geotechnical engineering require the quick and efficient acquirement of large data sets of grain morphologies. In the past publications, some semi-automation algorithms in extracting grains from images may cost tens of minutes. With the rapid development of deep learning networks applied to earth sciences, we develop UNetGE software that is based on the U-Net architecture—a fully convolutional network—to recognize and segregate grains from the matrix using the electron and optical microphotographs of rock and soil thin sections or the photographs of their hand specimen and outcrops. Resultantly, it shows that UNetGE can extract approximately 300~1300 grains in a few seconds to a few minutes and provide their morphologic parameters, which will ably assist with analyses on the engineering properties of sediments and soils (e.g., permeability, strength, and expansivity) and their hydraulic characteristics.
Predicting Concentrations of Mixed Sugar Solutions with a Combination of Resonant Plasmon-Enhanced SEIRA and Principal Component Analysis
Authors: Pfezer Diana , Karst Julian , Hentschel Mario , Giessen Harald
Published: July 2022

The detection and quantification of glucose concentrations in human blood or in the ocular fluid gain importance due to the increasing number of diabetes patients. A reliable determination of these low concentrations is hindered by the complex aqueous environments in which various biomolecules are present. In this study, we push the detection limit as well as the discriminative power of plasmonic nanoantenna-based sensors towards the physiological limit. We utilize plasmonic surface-enhanced infrared absorption spectroscopy (SEIRA) to study aqueous solutions of mixtures of up to five different physiologically relevant saccharides, namely the monosaccharides glucose, fructose, and galactose, as well as the disaccharides maltose and lactose. Resonantly tuned plasmonic nanoantennas in a reflection flow cell geometry allow us to enhance the specific vibrational fingerprints of the mono- and disaccharides. The obtained spectra are analyzed via principal component analysis (PCA) using a machine learning algorithm. The high performance of the sensor together with the strength of PCA allows us to detect concentrations of aqueous mono- and disaccharides solutions down to the physiological levels of 1 g/L. Furthermore, we demonstrate the reliable discrimination of the saccharide concentrations, as well as compositions in mixed solutions, which contain all five mono- and disaccharides simultaneously. These results underline the excellent discriminative power of plasmonic SEIRA spectroscopy in combination with the PCA. This unique combination and the insights gained will improve the detection of biomolecules in different complex environments.
Correction: Elsheikh et al. Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles. Sensors 2019, 19, 4896
Authors: Elsheikh Mohamed , Abdelfatah Walid , Noureldin Aboelmagd , Iqbal Umar , Korenberg Michael
Published: July 2022

The authors wish to make the following corrections in the original paper [...]
IoT Security and Computation Management on a Multi-Robot System for Rescue Operations Based on a Cloud Framework
Authors: Roy Swarnabha , Vo Tony , Hernandez Steven , Lehrmann Austin , Ali Asad , Kalafatis Stavros
Published: July 2022

There is a growing body of literature that recognizes the importance of Multi-Robot coordination and Modular Robotics. This work evaluates the secure coordination of an Unmanned Aerial Vehicle (UAV) via a drone simulation in Unity and an Unmanned Ground Vehicle (UGV) as a rover. Each robot is equipped with sensors to gather information to send to a cloud server where all computations are performed. Each vehicle is registered by blockchain ledger-based network security. In addition to these, relevant information and alerts are displayed on a website for the users. The usage of UAV–UGV cooperation allows for autonomous surveillance due to the high vantage field of view. Furthermore, the usage of cloud computation lowers the cost of microcontrollers by reducing their complexity. Lastly, blockchain technology mitigates the security issues related to adversarial or malicious robotic nodes connecting to the cluster and not agreeing to privacy rules and norms.
An Underwater Acoustic Network Positioning Method Based on Spatial-Temporal Self-Calibration
Authors: Wang Chao , Du Pengyu , Wang Zhenduo , Wang Zhongkang
Published: July 2022

The emergence of underwater acoustic networks has greatly improved the potential capabilities of marine environment detection. In underwater acoustic network applications, node location is a basic and important task, and node location information is the guarantee for the completion of various underwater tasks. Most of the current underwater positioning models do not consider the influence of the uneven underwater medium or the uncertainty of the position of the network beacon modem, which will reduce the accuracy of the positioning results. This paper proposes an underwater acoustic network positioning method based on spatial-temporal self-calibration. This method can automatically calibrate the space position of the beacon modem using only the GPS position and depth sensor information obtained in real-time. Under the asynchronous system, the influence of the inhomogeneity of the underwater medium is analyzed, and the unscented Kalman algorithm is used to estimate the position of underwater mobile nodes. Finally, the effectiveness of this method is verified by simulation and sea trials.
Wireless Power Transfer: Systems, Circuits, Standards, and Use Cases
Authors: Van Mulders Jarne , Delabie Daan , Lecluyse Cédric , Buyle Chesney , Callebaut Gilles , Van der Perre Liesbet , De Strycker Lieven
Published: July 2022

Wireless power transfer provides a most convenient solution to charge devices remotely and without contacts. R&D has advanced the capabilities, variety, and maturity of solutions greatly in recent years. This survey provides a comprehensive overview of the state of the art on different technological concepts, including electromagnetic coupled and uncoupled systems and acoustic technologies. Solutions to transfer mW to MW of power, over distances ranging from millimeters to kilometers, and exploiting wave concepts from kHz to THz, are covered. It is an attractive charging option for many existing applications and moreover opens new opportunities. Various technologies are proposed to provide wireless power to these devices. The main challenges reside in the efficiency and range of the transfer. We highlight innovation in beamforming and UV-assisted approaches. Of particular interest for designers is the discussion of implementation and operational aspects, standards, and safety relating to regulations. A high-level catalog of potential applications maps these to adequate technological options for wireless power transfer.
Generic Chemometric Models for Metabolite Concentration Prediction Based on Raman Spectra
Authors: Yousefi-Darani Abdolrahim , Paquet-Durand Olivier , von Wrochem Almut , Classen Jens , Tränkle Jens , Mertens Mario , Snelders Jeroen , Chotteau Veronique , Mäkinen Meeri , Handl Alina , Kadisch Marvin , Lang Dietmar , Dumas Patrick , Hitzmann Bernd
Published: July 2022

Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed “generic” models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.
Technology for Position Correction of Satellite Precipitation and Contributions to Error Reduction—A Case of the ‘720’ Rainstorm in Henan, China
Authors: Tian Wenlong , Cao Xiaoqun , Peng Kecheng
Published: July 2022

In July 2021, an extreme precipitation event occurred in Henan, China, causing tremendous damage and deaths; so, it is very important to study the observation technology of extreme precipitation. Surface rain gauge precipitation observations have high accuracy but low resolution and coverage. Satellite remote sensing has high spatial resolution and wide coverage, but has large precipitation accuracy and distribution errors. Therefore, how to merge the above two kinds of precipitation observations effectively to obtain heavy precipitation products with more accurate geographic distributions has become an important but difficult scientific problem. In this paper, a new information fusion method for improving the position accuracy of satellite precipitation estimations is used based on the idea of registration and warping in image processing. The key point is constructing a loss function that includes a term for measuring two information field differences and a term for a warping field constraint. By minimizing the loss function, the purpose of position error correction of quantitative precipitation estimation from FY-4A and Integrated Multisatellite Retrievals of GPM are achieved, respectively, using observations from surface rain gauge stations. The errors of different satellite precipitation products relative to ground stations are compared and analyzed before and after position correction, using the ‘720’ extreme precipitation in Henan, China, as an example. The experimental results show that the final run has the best performance and FY-4A has the worse performance. After position corrections, the precipitation products of the three satellites are improved, among which FY-4A has the largest improvement, IMERG final run has the smallest improvement, and IMERG late run has the best performance and the smallest error. Their mean absolute errors are reduced by 23%, 14%, and 16%, respectively, and their correlation coefficients with rain gauge stations are improved by 63%, 9%, and 16%, respectively. The error decomposition model is used to examine the contributions of each error component to the total error. The results show that the new method improves the precipitation products of GPM primarily in terms of hit bias. However, it does not significantly reduce the hit bias of precipitation products of FY-4A while it reduces the total error by reducing the number of false alarms.
Development of a Smart Chair Sensors System and Classification of Sitting Postures with Deep Learning Algorithms
Authors: Aminosharieh Najafi Taraneh , Abramo Antonio , Kyamakya Kyandoghere , Affanni Antonio
Published: July 2022

Nowadays in modern societies, a sedentary lifestyle is almost inevitable for a majority of the population. Long hours of sitting, especially in wrong postures, may result in health complications. A smart chair with the capability to identify sitting postures can help reduce health risks induced by a modern lifestyle. This paper presents the design, realization and evaluation of a new smart chair sensors system capable of sitting postures identification. The system consists of eight pressure sensors placed on the chair’s sitting cushion and the backrest. A signal acquisition board was designed from scratch to acquire data generated by the pressure sensors and transmit them via a Wi-Fi network to a purposely developed graphical user interface which monitors and stores the acquired sensors’ data on a computer. The designed system was tested by means of an extensive sitting experiment involving 40 subjects, and from the acquired data, the classification of the respective sitting postures out of eight possible postures was performed. Hereby, the performance of seven deep-learning algorithms was assessed. The best accuracy of 91.68% was achieved by an echo memory network model. The designed smart chair sensors system is simple and versatile, low cost and accurate, and it can easily be deployed in several smart chair environments, both for public and private contexts.
Landslide Susceptibility Mapping with Integrated SBAS-InSAR Technique: A Case Study of Dongchuan District, Yunnan (China)
Authors: Zhu Zhifu , Gan Shu , Yuan Xiping , Zhang Jianming
Published: July 2022

Landslide susceptibility maps (LSM) are often used by government departments to carry out land use management and planning, which supports decision makers in urban and infrastructure planning. The accuracy of conventional landslide susceptibility maps is often affected by classification errors. Consequently, they become less reliable, which makes it difficult to meet the needs of decision-makers. Therefore, it is proposed in this paper to reduce classification errors and improve LSM reliability by integrating the Small Baseline Subsets-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique and LSM. By using the logistic regression model (LR) and the support vector machine model (SVM), experiments were conducted to generate LSM in the Dongchuan district. It was classified into five classes: very high susceptibility, high susceptibility, medium susceptibility, low susceptibility, and very low susceptibility. Then, the surface deformation rate of the Dongchuan area was obtained through the ascending and descending orbit sentinel-1A data from January 2018 to January 2021. To correct the classification errors, the SBAS-InSAR technique was integrated into LSM under the optimal model by constructing the contingency matrix. Finally, the LSMs obtained before and after correction were compared. Moreover, the correction results were validated and analyzed by combining remote sensing images, InSAR deformation results, and field surveys. According to the research results, the susceptibility class of 66,094 classification error cells (59.48 km2) was significantly improved in the LSM after the integration of the SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images are highly consistent with the trends of InSAR cumulative deformation and the results of field investigation. It is suggested that integrating SBAS-InSAR and LSM is effective in correcting classification errors and further improving the reliability of LSM for landslide prediction. The LSM obtained by using this method plays an important role in guiding local government departments on disaster prevention and mitigation, which is conducive to eliminating the risk of landslides.
A High-Order Kalman Filter Method for Fusion Estimation of Motion Trajectories of Multi-Robot Formation
Authors: Wang Miao , Liu Weifeng , Wen Chenglin
Published: July 2022

Multi-robot motion and observation generally have nonlinear characteristics; in response to the problem that the existing extended Kalman filter (EKF) algorithm used in robot position estimation only considers first-order expansion and ignores the higher-order information, this paper proposes a multi-robot formation trajectory based on the high-order Kalman filter method. The joint estimation method uses Taylor expansion of the state equation and observation equation and introduces remainder variables on this basis, which effectively improves the estimation accuracy. In addition, the truncation error and rounding error of the filtering algorithm before and after the introduction of remainder variables, respectively, are compared. Our analysis shows that the rounding error is much smaller than the truncation error, and the nonlinear estimation performance is greatly improved.
Optimisation of Deep Learning Small-Object Detectors with Novel Explainable Verification
Authors: Mohamed Elhassan , Sirlantzis Konstantinos , Howells Gareth , Hoque Sanaul
Published: July 2022

In this paper, we present a novel methodology based on machine learning for identifying the most appropriate from a set of available state-of-the-art object detectors for a given application. Our particular interest is to develop a road map for identifying verifiably optimal selections, especially for challenging applications such as detecting small objects in a mixed-size object dataset. State-of-the-art object detection systems often find the localisation of small-size objects challenging since most are usually trained on large-size objects. These contain abundant information as they occupy a large number of pixels relative to the total image size. This fact is normally exploited by the model during training and inference processes. To dissect and understand this process, our approach systematically examines detectors’ performances using two very distinct deep convolutional networks. The first is the single-stage YOLO V3 and the second is the double-stage Faster R-CNN. Specifically, our proposed method explores and visually illustrates the impact of feature extraction layers, number of anchor boxes, data augmentation, etc., utilising ideas from the field of explainable Artificial Intelligence (XAI). Our results, for example, show that multi-head YOLO V3 detectors trained using augmented data produce better performance even with a fewer number of anchor boxes. Moreover, robustness regarding the detector’s ability to explain how a specific decision was reached is investigated using different explanation techniques. Finally, two new visualisation techniques are proposed, WS-Grad and Concat-Grad, for identifying explanation cues of different detectors. These are applied to specific object detection tasks to illustrate their reliability and transparency with respect to the decision process. It is shown that the proposed techniques can result in high resolution and comprehensive heatmaps of the image areas, significantly affecting detector decisions as compared to the state-of-the-art techniques tested.
Analysis of Oxidized 1-Palmitoyl-2-Arachidonoyl-Sn-Glycero-3 Phosphocholine Products in Uremic Patients by LC-ESI/MS
Authors: Chang Chiz-Tzung , Chang I-Ting , Hsia Min-Hui , Wang Chun-Cheng , Chen Chao-Jung , Zou Hsin-Bai , Chen Yu-Qing , Chiu Wen-Chien , Lin Zhi-Ru , Liu Mine-Yine
Published: July 2022

A simple liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI/MS) method has been developed to analyze oxidized 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphocholine (ox-PAPC) products on the lipoproteins of uremic patients. The native PAPC standard was in vitro oxidized by the Fenton reaction, and the ox-PAPC products were analyzed by LC- ESI/MS. For LC, a C8 column and a mobile phase (acetonitrile-isopropanol containing 0.1% formic acid (70:30, v/v)) were selected. For ESI/MS, the optimal conditions included sheath gas pressure (10 psi), capillary temperature (270 °C), and injection time (1000 ms). The identification of ox-PAPC products on human lipoproteins was based on the extracted ion chromatograms (EICs) and the ESI-MS spectra of the in vitro oxidation products of PAPC standard. The EICs and ESI-MS spectra showed good repeatability and sensitivity. A total of 21 ox-PAPC products was determined. Linear analysis has been performed for the phospholipid standard, 1, 2-Di-O-hexadecyl-sn-glycero-3-phosphocholine (PC(O-16:0/O-16:0)). The linear range was 5.0–100.0 µg/mL, and the coefficient of determination (R2) was 0.989. The concentration limit of detection (LOD) was 1.50 µg/mL, and the concentration limit of quantitation (LOQ) was 4.54 µg/mL. The selected 21 ox-PAPC products have been identified and quantified in very low-density lipoprotein (VLDL), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) of uremic and healthy subjects. Interestingly, the results showed that the levels of 18 products in VLDL, one product in LDL, and 19 products in HDL were significantly higher for uremic patients than healthy controls. This simple LC-ESI/MS method might accelerate the searching for biomarkers of uremia in the future.
Pattern and Dynamics of Teacher Emotions during Teaching: A Case Study of a Senior Secondary Mathematics Teacher
Authors: Jiang Zheng , Mok Ida Ah Chee , Yang Xin , Liu Simiao , Chen Mudan
Published: July 2022

Understanding the emotional dimension of classroom practices benefits sustainable education. However, the dynamic nature of teacher emotions during teaching remains understudied. To acquire an “in-depth” description of the emotional phenomena during teaching, a case study was adopted to examine the pattern and dynamics of a senior secondary mathematics teacher’s teaching-related emotions. Seven new mathematics lessons were observed and videotaped, and three video-stimulated, post-lesson teacher interviews were conducted. Based on appraisal theories of emotions, thematic analysis and content analysis were mainly used to analyse the data. The study revealed that the teacher most frequently experienced happiness/satisfaction as positive emotions and confusion/surprise as negative emotions. The emotions followed a three-stage process: perceptions, multilevel appraisals, and responses. The teacher most often perceived her students’ cognitive accuracy and adopted goal/need conduciveness/attainment, the discrepancy from expectations, and causal attribution as core appraisal dimensions. Teacher emotions in classrooms were also often hierarchical and cumulative, shifting from more negative at the beginning of a new semester to more positive at the middle of the semester. This study shed light on the dynamic development of teacher emotions, extending the theories and process models of emotions from the psychological field to educational research and facilitating teachers’ sustainable professional development.
Effective Resources to Mobilize Women Entrepreneurship: A Success Case Study of Family Tourism Businesses in Tioman Island, Malaysia
Authors: Dan Nurzafirah , Shimizu Tetsuo
Published: July 2022

Family-owned tourism businesses have unique resources to empower women in remote island destinations. There is limited literature that postulates the resource acquisition to mobilize women entrepreneurs within the family business study. Hence, this paper provided empirical knowledge on family tourism businesses methods to mobilize women entrepreneurs in rural Tioman Island in Malaysia. This study employed a qualitative case study approach by applying in-depth interviews for data collection. A successful case study was carried out in Tioman Island with 11 women entrepreneurs who have been actively involved in family tourism business operations since the 1980s. The resulting opportunities from the family organization setting, familiness nature and tourism industry as a setting modifier have greatly mobilized women into family tourism entrepreneurship. Thus, this case study provided comprehensive information on family tourism businesses’ implications on women entrepreneurs’ competitiveness in Tioman Island.
Sustainable Wastewater Management and Treatment
Authors: Campos José Luis , Mosquera-Corral Anuska , Val del Rio Ángeles , Pedrouso Alba
Published: July 2022

This Special Issue of Sustainability aims to report the recent developments in Sustainable Wastewater Management and Treatment, mainly those focused on improving the overall performance of wastewater treatment plants (WWTPs) in terms of both reducing their environmental impact and integrating them into the urban circular economy [...]