bluuebunny/crossref_metadata_2025 huggingface dataset:
PEDOT:PSS-Coated Polybenzimidazole Electroconductive Nanofibers for Biomedical Applications
Authors: Sordini Laura , Silva João C. , Garrudo Fábio F. F. , Rodrigues Carlos A. V. , Marques Ana C. , Linhardt Robert J. , Cabral Joaquim M. S. , Morgado Jorge , Ferreira Frederico Castelo
Published: August 2021

Bioelectricity drives several processes in the human body. The development of new materials that can deliver electrical stimuli is gaining increasing attention in the field of tissue engineering. In this work, novel, highly electrically conductive nanofibers made of poly [2,2′-m-(phenylene)-5,5′-bibenzimidazole] (PBI) have been manufactured by electrospinning and then coated with cross-linked poly (3,4-ethylenedioxythiophene) doped with poly (styrene sulfonic acid) (PEDOT:PSS) by spin coating or dip coating. These scaffolds have been characterized by scanning electron microscopy (SEM) imaging and attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy. The electrical conductivity was measured by the four-probe method at values of 28.3 S·m−1 for spin coated fibers and 147 S·m−1 for dip coated samples, which correspond, respectively, to an increase of about 105 and 106 times in relation to the electrical conductivity of PBI fibers. Human bone marrow-derived mesenchymal stromal cells (hBM-MSCs) cultured on the produced scaffolds for one week showed high viability, typical morphology and proliferative capacity, as demonstrated by calcein fluorescence staining, 4′,6-diamidino-2-phenylindole (DAPI)/Phalloidin staining and MTT [3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyl tetrazolium bromide] assay. Therefore, all fiber samples demonstrated biocompatibility. Overall, our findings highlight the great potential of PEDOT:PSS-coated PBI electrospun scaffolds for a wide variety of biomedical applications, including their use as reliable in vitro models to study pathologies and the development of strategies for the regeneration of electroactive tissues or in the design of new electrodes for in vivo electrical stimulation protocols.
Adenoviral Vector COVID-19 Vaccines: Process and Cost Analysis
Authors: Ferreira Rafael G. , Gordon Neal F. , Stock Rick , Petrides Demetri
Published: August 2021

The COVID-19 pandemic has motivated the rapid development of numerous vaccines that have proven effective against SARS-CoV-2. Several of these successful vaccines are based on the adenoviral vector platform. The mass manufacturing of these vaccines poses great challenges, especially in the context of a pandemic where extremely large quantities must be produced quickly at an affordable cost. In this work, two baseline processes for the production of a COVID-19 adenoviral vector vaccine, B1 and P1, were designed, simulated and economically evaluated with the aid of the software SuperPro Designer. B1 used a batch cell culture viral production step, with a viral titer of 5 × 1010 viral particles (VP)/mL in both stainless-steel and disposable equipment. P1 used a perfusion cell culture viral production step, with a viral titer of 1 × 1012 VP/mL in exclusively disposable equipment. Both processes were sized to produce 400 M/yr vaccine doses. P1 led to a smaller cost per dose than B1 ($0.15 vs. $0.23) and required a much smaller capital investment ($126 M vs. $299 M). The media and facility-dependent expenses were found to be the main contributors to the operating cost. The results indicate that adenoviral vector vaccines can be practically manufactured at large scale and low cost.
Process Behavior and Product Quality in Fertilizer Manufacturing Using Continuous Hopper Transfer Pan Granulation—Experimental Investigations
Authors: Przywara Mateusz , Dürr Robert , Otto Eric , Kienle Achim , Antos Dorota
Published: August 2021

Fertilizers are commonly used to improve the soil quality in both conventional and organic agriculture. One such fertilizer is dolomite for which soil application in granulated form is advantageous. These granules are commonly produced from ground dolomite powder in continuous pan transfer granulators. During production, the granulator’s operation parameters affect the granules’ properties and thereby also the overall performance of the fertilizer. To ensure product granules of certain specifications and an efficient overall production, process control and intensification approaches based on mathematical models can be applied. However, the latter require high-quality quantitative experimental data describing the effects of process operation parameters on the granule properties. Therefore, in this article, such data is presented for a lab-scale experimental setup. Investigations were carried out into how variations in binder spray rate, binder composition, feed powder flow rate, pan inclination angle, and angular velocity affect particle size distribution, mechanical stability, and humidity. Furthermore, in contrast to existing work samples from both, pan granules and product granules are analyzed. The influence of operation parameter variations on the differences between both, also known as trajectory separation, is described quantitatively. The results obtained indicate an increase in the average particle size with increasing binder flow rate to feed rate and increasing binder concentration and the inclination angle of the pan. Compressive strength varied significantly depending on the operating parameters. Significant differences in properties were observed for the product and the intermediate (pan) samples. In fact, for some operation parameters, e.g., binder feed rate, the magnitude of the separation effect strongly depends on the specific value of the operation parameter. The presented concise data will enable future mathematical modeling of the pan granulation process, e.g., using the framework of population balance equations.
Universal Model for the Cogeneration of Heat, Power and Char in a Decoupled Pyrolysis and Gasification Process for Techno-Economic Assessment
Authors: Klinar Dušan , Huber Marcel , Belšak Šel Nataša , Rižnar Klavdija
Published: August 2021

Present work aims to present a complete and detailed heat-mass balance data, streams elemental and species composition as crucial input data for Techno-Economic Assessment (TEA). Combined heat, power and char (CHPC) generation in decoupled pyrolysis and gasification processes ensures a highly efficient, reliable and sustainable economic use of biomass. Innovative process model based on simultaneous heat-mass balance with the elemental (C, H, O) and species (CO, H2, CO2, CH4 and H2O) composition of each stream provides reliable data with a general/average variability in a range of ±10% and ±5% for the main outputs. Thermodynamic calculations verified not only the concept but also a numerical range of the results. Comparisons with recently published, scientific and data from technology providers, prove their general applicability and consistency. TEA cases, presented in a complete and detailed table, allow selection of the relevant calculation basis providing reliable data for doubtless evaluation at investors/entrepreneurs striving for a successful business model.
Retrieval and Calculation of Vertical Aerosol Mass Fluxes by a Coherent Doppler Lidar and a Sun Photometer
Authors: Wang Xiaoye , Dai Guangyao , Wu Songhua , Sun Kangwen , Song Xiaoquan , Chen Wenzhong , Li Rongzhong , Yin Jiaping , Wang Xitao
Published: August 2021

The direct and indirect radiation forcing of aerosol particles deeply affect the energy budget and the atmospheric chemical and physical processes. To retrieve the vertical aerosol mass fluxes and to investigate the vertical transport process of aerosol by a coherent Doppler lidar (CDL), a practical method for instrumental calibration and aerosol optical properties retrieval based on CDL and sun photometer synchronization observations has been developed. A conversion of aerosol optical properties to aerosol microphysical properties is achieved by applying a well-developed algorithm. Furthermore, combining the vertical velocity measured simultaneously with a CDL, we use the eddy covariance (EC) method to retrieve the vertical turbulent aerosol mass fluxes by a CDL and sun photometer with a spatial resolution of 15 m and a temporal resolution of 1 s throughout the planetary boundary layer (PBL). In this paper, we present a measurement case of 24-h continuous fluxes observations and analyze the diurnal variation of the vertical velocity, the aerosol backscatter coefficient at 1550 nm, the mean aerosol mass concentration, and the vertical aerosol mass fluxes on 13 April 2020. Finally, the main relative errors in aerosol mass flux retrieval, including sample error σF,S, aerosol optical properties retrieval error σF,R, and error introduced from aerosol microphysical properties retrieval algorithm σF,I, are evaluated. The sample error σF,S is the dominating error which increases with height except during 12:00–13:12 LST. The aerosol optical properties retrieval error σF,R is 21% and the error introduced from the aerosol microphysical properties retrieval algorithm σF,I is less than 50%.
Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing
Authors: Liu Zhijie , Guo Pengju , Liu Heng , Fan Pan , Zeng Pengzong , Liu Xiangyang , Feng Ce , Wang Wang , Yang Fuzeng
Published: August 2021

The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been explored for LAI estimation. In this work, the leaf area indices for different types of apple trees are obtained in terms of multispectral remote-sensing data collected with an unmanned aerial vehicle (UAV), along with simultaneous measurements of apple orchards. The proposed approach was tested on apple trees of the “Fuji”, “Golden Delicious”, and “Ruixue” types, which were planted in the Apple Experimental Station of the Northwest Agriculture and Forestry University in Baishui County, Shaanxi Province, China. Five vegetation indices of strong correlation with the apple leaf area index were selected and used to train models of support vector regression (SVR) and gradient-boosting decision trees (GBDT) for predicting the leaf area index of apple trees. The best model was selected based on the metrics of the coefficient of determination (R2) and the root-mean-square error (RMSE). The experimental results showed that the gradient-boosting decision tree model achieved the best performance with an R2 of 0.846, an RMSE of 0.356, and a spatial efficiency (SPAEF) of 0.57. This demonstrates the feasibility of our approach for fast and accurate remote-sensing-based estimation of the leaf area index of apple trees.
Understanding Urban Growth in Beijing-Tianjin-Hebei Region over the Past 100 Years Using Old Maps and Landsat Data
Authors: Li Shuang , Sun Zhongqiu , Wang Yafei , Wang Yuxia
Published: August 2021

Studying urban expansion from a longer-term perspective is of great significance to obtain an in-depth understanding of the process of urbanization. Remote sensing data are mostly selected to investigate the long-term expansion of cities. In this study, we selected the world-class urban agglomeration of Beijing-Tianjin-Hebei (BTH) as the study area, and then discussed how to make full use of multi-source, multi-category, and multi-temporal spatial data (old maps and remote sensing images) to study long-term urbanization. Through this study, we addressed three questions: (1) How much has the urban area in BTH expanded in the past 100 years? (2) How did the urban area expand in the past century? (3) What factors or important historical events have changed the development of cities with different functions? By comprehensively using urban spatial data, such as old maps and remote sensing images, geo-referencing them, and extracting built-up area information, a long-term series of urban built-up areas in the BTH region can be obtained. Results show the following: (1) There was clear evidence of dramatic urban expansion in this area, and the total built-up area had increased by 55.585 times, from 126.181 km2 to 7013.832 km2. (2) Continuous outward expansion has always been the main trend, while the compactness of the built-up land within the city is constantly decreasing and the complexity of the city boundary is increasing. (3) Cities in BTH were mostly formed through the construction of city walls during the Ming and Qing dynasties, and the expansion process was mostly highly related to important political events, traffic development, and other factors. In summary, the BTH area, similarly to China and most regions of the world, has experienced rapid urbanization and the history of such ancient cities should be further preserved with the combined use of old maps.
Assessment of GPS/Galileo/BDS Precise Point Positioning with Ambiguity Resolution Using Products from Different Analysis Centers
Authors: Chen Chao , Xiao Guorui , Chang Guobin , Xu Tianhe , Yang Liu
Published: August 2021

Suffering from hardware phase biases originating from satellites and the receiver, precise point positioning (PPP) requires a long convergence time to reach centimeter coordinate accuracy, which is a major drawback of this technique and limits its application in time-critical applications. Ambiguity resolution (AR) is the key to a fast convergence time and a high-precision solution for PPP technology and PPP AR products are critical to implement PPP AR. Nowadays, various institutions provide PPP AR products in different forms with different strategies, which allow to enable PPP AR for Global Positioning System (GPS) and Galileo or BeiDou Navigation System (BDS). To give a full evaluation of PPP AR performance with various products, this work comprehensively investigates the positioning performance of GPS-only and multi-GNSS (Global Navigation Satellite System) combination PPP AR with the precise products from CNES, SGG, CODE, and PRIDE Lab using our in-house software. The positioning performance in terms of positioning accuracy, convergence time and fixing rate (FR) as well as time to first fix (TTFF), was assessed by static and kinematic PPP AR models. For GPS-only, combined GPS and Galileo PPP AR with different products, the positioning performances were all comparable with each other. Concretely, the static positioning errors can be reduced by 21.0% (to 0.46 cm), 52.5% (to 0.45 cm), 10.0% (to 1.33 cm) and 21.7% (to 0.33 cm), 47.4% (to 0.34 cm), 9.5% (to 1.16 cm) for GPS-only and GE combination in north, east, up component, respectively, while the reductions are 20.8% (to 1.13 cm), 42.9% (to 1.15 cm), 19.9% (to 3.4 cm) and 20.4% (to 0.72 cm), 44.1% (to 0.66 cm), 10.1% (to 2.44 cm) for kinematic PPP AR. Overall, the positioning performance with CODE products was superior to the others. Furthermore, multi-GNSS observations had significant improvements in PPP performance with float solutions and the TTFF as well as the FR of GPS PPP AR could be improved by adding observations from other GNSS. Additionally, we have released the source code for multi-GNSS PPP AR, anyone can freely access the code and example data from GitHub.
Vegetation Dynamics and Climatological Drivers in Ethiopia at the Turn of the Century
Authors: Muir Carly , Southworth Jane , Khatami Reza , Herrero Hannah , Akyapı Berkay
Published: August 2021

Global change, particularly climate change, poses a risk of altering vegetation composition and health. The consequences manifest throughout Earth’s system as a change in ecosystem services and socioecological stability. It is therefore critical that vegetation dynamics are monitored to establish baseline conditions and detect shifts. Africa is at high risk of environmental change, yet evaluation of the link between climate and vegetation is still needed for some regions. This work expands on more frequent local and multinational scale studies of vegetation trends by quantifying directional persistence (DP) at a national scale for Ethiopia, based on the normalized difference vegetation index (NDVI) between 2000 and 2016. The DP metric determines cumulative change in vegetation greenness and has been applied to studies of ecological stability and health. Secondary analysis utilizing panel regression methodologies is carried out to measure the effect of climate on NDVI. Models are developed to consider spatial dependence by including fixed effects and spatial weights. Results indicate widespread cumulative declines in NDVI, with the greatest change during the dry season and concentrated in northern Ethiopia. Regression analyses suggest significant control from climatic variables. However, temperature has a larger effect on NDVI, which contrasts with findings of some previous studies.
Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm
Authors: Lao Ping , Liu Qi , Ding Yuhao , Wang Yu , Li Yuan , Li Meng
Published: August 2021

Satellite rainrate estimation is a great challenge, especially in mesoscale convective systems (MCSs), which is mainly due to the absence of a direct physical connection between observable cloud parameters and surface rainrate. The machine learning technique was employed in this study to estimate rainrate in the MCS domain via using cloud top temperature (CTT) derived from a geostationary satellite. Five kinds of machine learning models were investigated, i.e., polynomial regression, support vector machine, decision tree, random forest, and multilayer perceptron, and the precipitation of Climate Prediction Center morphing technique (CMORPH) was used as the reference. A total of 31 CTT related features were designed to be the potential inputs for training an algorithm, and they were all proved to have a positive contribution in modulating the algorithm. Random forest (RF) shows the best performance among the five kinds of models. By combining the classification and regression schemes of the RF model, an RF-based hybrid algorithm was proposed first to discriminate the rainy pixel and then estimate its rainrate. For the MCS samples considered in this study, such an algorithm generates the best estimation, and its accuracy is definitely higher than the operational precipitation product of FY-4A. These results demonstrate the promising feasibility of applying a machine learning technique to solve the satellite precipitation retrieval problem.
Integrating Multi-Sensors Data for Species Distribution Mapping Using Deep Learning and Envelope Models
Authors: Anand Akash , Pandey Manish K. , Srivastava Prashant K. , Gupta Ayushi , Khan Mohammed Latif
Published: August 2021

The integration of ecological and atmospheric characteristics for biodiversity management is fundamental for long-term ecosystem conservation and drafting forest management strategies, especially in the current era of climate change. The explicit modelling of regional ecological responses and their impact on individual species is a significant prerequisite for any adaptation strategy. The present study focuses on predicting the regional distribution of Rhododendron arboreum, a medicinal plant species found in the Himalayan region. Advanced Species Distribution Models (SDM) based on the principle of predefined hypothesis, namely BIOCLIM, was used to model the potential distribution of Rhododendron arboreum. This hypothesis tends to vary with the change in locations, and thus, robust models are required to establish nonlinear complex relations between the input parameters. To address this nonlinear relation, a class of deep neural networks, Convolutional Neural Network (CNN) architecture is proposed, designed, and tested, which eventually gave much better accuracy than the BIOCLIM model. Both of the models were given 16 input parameters, including ecological and atmospheric variables, which were statistically resampled and were then utilized in establishing the linear and nonlinear relationship to better fit the occurrence scenarios of the species. The input parameters were mostly acquired from the recent satellite missions, including MODIS, Sentinel-2, Sentinel-5p, the Shuttle Radar Topography Mission (SRTM), and ECOSTRESS. The performance across all the thresholds was evaluated using the value of the Area Under Curve (AUC) evaluation metrics. The AUC value was found to be 0.917 with CNN, whereas it was 0.68 with BIOCLIM, respectively. The performance evaluation metrics indicate the superiority of CNN for species distribution over BIOCLIM.
‘The Best of Two Worlds’—Combining Classifier Fusion and Ecological Models to Map and Explain Landscape Invasion by an Alien Shrub
Authors: Mouta Nuno , Silva Renato , Pais Silvana , Alonso Joaquim M. , Gonçalves João F. , Honrado João , Vicente Joana R.
Published: August 2021

The spread of invasive alien species promotes ecosystem structure and functioning changes, with detrimental effects on native biodiversity and ecosystem services, raising challenges for local management authorities. Predictions of invasion dynamics derived from modeling tools are often spatially coarse and therefore unsuitable for guiding local management. Accurate information on the occurrence of invasive plants and on the main factors that promote their spread is critical to define successful control strategies. For addressing this challenge, we developed a dual framework combining satellite image classification with predictive ecological modeling. By combining data from georeferenced invaded areas with multispectral imagery with 10-meter resolution from Sentinel-2 satellites, a map of areas invaded by the woody invasive Acacia longifolia in a municipality of northern Portugal was devised. Classifier fusion techniques were implemented through which eight statistical and machine-learning algorithms were ensembled to produce accurate maps of invaded areas. Through a Random Forest (RF) model, these maps were then used to explore the factors driving the landscape-level abundance of A. longifolia. RF models were based on explanatory variables describing hypothesized environmental drivers, including climate, topography/geomorphology, soil properties, fire disturbance, landscape composition, linear structures, and landscape spatial configuration. Satellite-based maps synoptically described the spatial patterns of invaded areas, with classifications attaining high accuracy values (True Skill Statistic, TSS: 0.895, Area Under the Receiver Operating Curve, ROC: 0.988, Kappa: 0.857). The predictive RF models highlighted the primary role of climate, followed by landscape composition and configuration, as the most important drivers explaining the species abundance at the landscape level. Our innovative dual framework—combining image classification and predictive ecological modeling—can guide decision-making processes regarding effective management of invasions by prioritizing the invaded areas and tackling the primary environmental and anthropogenic drivers of the species’ abundance and spread.
Multimodal Assessment of the Pulse Rate Variability Analysis Module of a Photoplethysmography-Based Telemedicine System
Authors: Antali Flóra , Kulin Dániel , Lucz Konrád István , Szabó Balázs , Szűcs László , Kulin Sándor , Miklós Zsuzsanna
Published: August 2021

Alterations of heart rate variability (HRV) are associated with various (patho)physiological conditions; therefore, HRV analysis has the potential to become a useful diagnostic module of wearable/telemedical devices to support remote cardiovascular/autonomic monitoring. Continuous pulse recordings obtained by photoplethysmography (PPG) can yield pulse rate variability (PRV) indices similar to HRV parameters; however, it is debated whether PRV/HRV parameters are interchangeable. In this study, we assessed the PRV analysis module of a digital arterial PPG-based telemedical system (SCN4ALL). We used Bland–Altman analysis to validate the SCN4ALL PRV algorithm to Kubios Premium software and to determine the agreements between PRV/HRV results calculated from 2-min long PPG and ECG captures recorded simultaneously in healthy individuals (n = 33) at rest and during the cold pressor test, and in diabetic patients (n = 12) at rest. We found an ideal agreement between SCN4ALL and Kubios outputs (bias < 2%). PRV and HRV parameters showed good agreements for interbeat intervals, SDNN, and RMSSD time-domain variables, for total spectral and low-frequency power (LF) frequency-domain variables, and for non-linear parameters in healthy subjects at rest and during cold pressor challenge. In diabetics, good agreements were observed for SDNN, LF, and SD2; and moderate agreement was observed for total power. In conclusion, the SCN4ALL PRV analysis module is a good alternative for HRV analysis for numerous conventional HRV parameters.
Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon
Authors: Younes Younes Al , Barczyk Martin
Published: August 2021

Navigating robotic systems autonomously through unknown, dynamic and GPS-denied environments is a challenging task. One requirement of this is a path planner which provides safe trajectories in real-world conditions such as nonlinear vehicle dynamics, real-time computation requirements, complex 3D environments, and moving obstacles. This paper presents a methodological motion planning approach which integrates a novel local path planning approach with a graph-based planner to enable an autonomous vehicle (here a drone) to navigate through GPS-denied subterranean environments. The local path planning approach is based on a recently proposed method by the authors called Nonlinear Model Predictive Horizon (NMPH). The NMPH formulation employs a copy of the plant dynamics model (here a nonlinear system model of the drone) plus a feedback linearization control law to generate feasible, optimal, smooth and collision-free paths while respecting the dynamics of the vehicle, supporting dynamic obstacles and operating in real time. This design is augmented with computationally efficient algorithms for global path planning and dynamic obstacle mapping and avoidance. The overall design is tested in several simulations and a preliminary real flight test in unexplored GPS-denied environments to demonstrate its capabilities and evaluate its performance.
Signal Quality Assessment of a Novel ECG Electrode for Motion Artifact Reduction
Authors: Halvaei Hesam , Sörnmo Leif , Stridh Martin
Published: August 2021

Background: The presence of noise is problematic in the analysis and interpretation of the ECG, especially in ambulatory monitoring. Restricting the analysis to high-quality signal segments only comes with the risk of excluding significant arrhythmia episodes. Therefore, the development of novel electrode technology, robust to noise, continues to be warranted. Methods: The signal quality of a novel wet ECG electrode (Piotrode) is assessed and compared to a commercially available, commonly used electrode (Ambu). The assessment involves indices of QRS detection and atrial fibrillation detection performance, as well as signal quality indices (ensemble standard deviation and time–frequency repeatability), computed from ECGs recorded simultaneously from 20 healthy subjects performing everyday activities. Results: The QRS detection performance using the Piotrode was considerably better than when using the Ambu, especially for running but also for lighter activities. The two signal quality indices demonstrated similar trends: the gap in quality became increasingly larger as the subjects became increasingly more active. Conclusions: The novel wet ECG electrode produces signals with less motion artifacts, thereby offering the potential to reduce the review burden, and accordingly the cost, associated with ambulatory monitoring.
Deep Neural Network-Based Respiratory Pathology Classification Using Cough Sounds
Authors: Balamurali B T , Hee Hwan Ing , Kapoor Saumitra , Teoh Oon Hoe , Teng Sung Shin , Lee Khai Pin , Herremans Dorien , Chen Jer Ming
Published: August 2021

Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI). To train a deep neural network model, we collected a new dataset of cough sounds, labelled with a clinician’s diagnosis. The chosen model is a bidirectional long–short-term memory network (BiLSTM) based on Mel-Frequency Cepstral Coefficients (MFCCs) features. The resulting trained model when trained for classifying two classes of coughs—healthy or pathology (in general or belonging to a specific respiratory pathology)—reaches accuracy exceeding 84% when classifying the cough to the label provided by the physicians’ diagnosis. To classify the subject’s respiratory pathology condition, results of multiple cough epochs per subject were combined. The resulting prediction accuracy exceeds 91% for all three respiratory pathologies. However, when the model is trained to classify and discriminate among four classes of coughs, overall accuracy dropped: one class of pathological coughs is often misclassified as the other. However, if one considers the healthy cough classified as healthy and pathological cough classified to have some kind of pathology, then the overall accuracy of the four-class model is above 84%. A longitudinal study of MFCC feature space when comparing pathological and recovered coughs collected from the same subjects revealed the fact that pathological coughs, irrespective of the underlying conditions, occupy the same feature space making it harder to differentiate only using MFCC features.
Characterization of a PCB Based Pressure Sensor and Its Joining Methods for the Metal Membrane
Authors: Schwenck Adrian , Grözinger Tobias , Günther Thomas , Schumacher Axel , Schuhmacher Dietmar , Werum Kai , Zimmermann André
Published: August 2021

Essential quality features of pressure sensors are, among other accuracy-related factors, measurement range, operating temperature, and long-term stability. In this work, these features are optimized for a capacitive pressure sensor with a measurement range of 10 bars. The sensor consists of a metal membrane, which is connected to a PCB and a digital capacitive readout. To optimize the performance, different methods for the joining process are studied. Transient liquid phase bonding (TLP bonding), reactive joining, silver sintering, and electric resistance welding are compared by measurements of the characteristic curves and long-term measurements at maximum pressure. A scanning electron microscope (SEM) with energy-dispersive X-ray spectroscopy (EDX) analysis was used to examine the quality of the joints. The evaluation of the characteristic curves shows the smallest measurement errors for TLP bonding and sintering. For welding and sintering, no statistically significant long-term drift was measured. In terms of equipment costs, reactive joining and sintering are most favorable. With low material costs and short process times, electric resistance welding offers ideal conditions for mass production.
Multi-Sensors Geophysical Monitoring for Reinforced Concrete Engineering Structures: A Laboratory Test
Authors: Capozzoli Luigi , Fornasari Giacomo , Giampaolo Valeria , De Martino Gregory , Rizzo Enzo
Published: August 2021

Non-destructive tests are strongly required in engineering applications for monitoring civil structures. The use of compared and integrated innovative approaches based on geophysical methodologies represents an effective tool for the characterization and monitoring of reinforced concrete (RC) structures. Therefore, the main aim of the work was to improve the knowledge on the potentiality and limitations of the Ground Penetrating Radar (GPR) and the Electrical Resistivity Tomography (ERT) with electrodes disposed both on the surface and in the boreholes. The work approach was adopted on an analog model of a reinforced concrete frame built ad hoc at the Hydrogeosite Laboratory (CNR-IMAA), where simulated experiments on full-size physical models are defined. Results show the ability of an accurate use of GPR to reconstruct the rebar dispositions and detect in detail possible constructive defects, both highlighting the lack of reinforcements into the nodes and providing useful information about the safety assessment of the realized structure. The results of the ERT method defined the necessity to develop ad-hoc electrical resistivity methods to support the characterization and monitoring of buried foundation structures for civil engineering applications. Finally, the paper introduces a new approach based on the use of cross-hole ERTs (CHERTs) for the engineering structure monitoring, able to reduce the uncertainties usually affecting the indirect results.
Feasibility Study of a Time-of-Flight Brain Positron Emission Tomography Employing Individual Channel Readout Electronics
Authors: Park Kuntai , Jung Jiwoong , Choi Yong , Leem Hyuntae , Kim Yeonkyeong
Published: August 2021

The purpose of this study was to investigate the feasibility of a time-of-flight (TOF) brain positron emission tomography (PET) providing high-quality images. It consisted of 30 detector blocks arranged in a ring with a diameter of 257 mm and an axial field of view of 52.2 mm. Each detector block was composed of two detector modules and two application-specific integrated circuit (ASIC) chips. The detector module was composed of an 8 × 8 array of 3 × 3 mm2 multi-pixel photon counters and an 8 × 8 array of 3.11 × 3.11 × 15 mm3 lutetium yttrium oxyorthosilicate scintillators. The 64-channel individual readout ASIC was used to acquire the position, energy, and time information of a detected gamma ray. A coincidence timing resolution of 187 ps full width at half maximum (FWHM) was achieved using a pair of channels of two detector modules. The energy resolution and spatial resolution were 6.6 ± 0.6% FWHM (without energy nonlinearity correction) and 2.5 mm FWHM, respectively. The results of this study demonstrate that the developed TOF brain PET could provide excellent performance, allowing for a reduction in radiation dose or scanning time for brain imaging due to improved sensitivity and signal-to-noise ratio.
Detection of Explosives by SERS Platform Using Metal Nanogap Substrates
Authors: Adhikari Samir , Ampadu Emmanuel K. , Kim Minjun , Noh Daegwon , Oh Eunsoon , Lee Donghan
Published: August 2021

Detecting trace amounts of explosives to ensure personal safety is important, and this is possible by using laser-based spectroscopy techniques. We performed surface-enhanced Raman scattering (SERS) using plasmonic nanogap substrates for the solution phase detection of some nitro-based compounds, taking advantage of the hot spot at the nanogap. An excitation wavelength of 785 nm with an incident power of as low as ≈0.1 mW was used to excite the nanogap substrates. Since both RDX and PETN cannot be dissolved in water, acetone was used as a solvent. TNT was dissolved in water as well as in hexane. The main SERS peaks of TNT, RDX, and PETN were clearly observed down to the order of picomolar concentration. The variations in SERS spectra observed from different explosives can be useful in distinguishing and identifying different nitro-based compounds. This result indicates that our nanogap substrates offer an effective approach for explosives identification.
Extension and Limits of Depolarization-Fringe Contrast Roughness Method in Sub-Micron Domain
Authors: Pöller Franziska , Salazar Bloise Félix , Jakobi Martin , Dong Jie , Koch Alexander W.
Published: August 2021

To guarantee quality standards for the industry, surface properties, particularly those of roughness, must be considered in many areas of application. Today, several methods are available on the market, but some damage the surface to be tested as they measure it by contact. A non-contact method for the precise estimation of sub-micron roughness values is presented, which can be used as an extension of existing roughness measurement techniques to improve them further considering the depolarized light reflected by the sample. This setup is based on a Michelson interferometer, and by introducing a quarter-wave plate on a half part of the reference mirror, the surface roughness can be directly derived by measuring the fringe contrasts. This article introduces a simple model describing the intensity distortions resulting from the microscopic roughness in divided interferograms when considering depolarization. This work aimed to extend the measurement range of the technique developed in a previous work, in which depolarization effects are taken into account. For verification, the experimental results were compared with the fringe contrast technique, which does not consider the depolarization of the scattered light, especially regarding the extended wavelength interval, highlighting the limits of the technique. In addition, simulations of the experiments are presented. For comparison, the reference values of the sample roughness were also generated by measurements with a stylus profiler.
Tissue Oximeter with Selectable Measurement Depth Using Spatially Resolved Near-Infrared Spectroscopy
Authors: Niwayama Masatsugu , Unno Naoki
Published: August 2021

Tissue oxygenation sensing at a few millimeters deep is useful for surgical and postoperative management. However, the measurement sensitivity at each depth and the proper sensor combination have not been clarified. Here, the measurement characteristics of oximetry by spatially resolved near-infrared spectroscopy were analyzed using Monte Carlo simulation and phantom experiment. From summing the sensitivities of each depth, it was quantitatively found that the measurement sensitivity curve had a peak, and the measurement depth can be adjusted by combining the two distances between the light source and the detector. Furthermore, the gastric tissue was 10–20% smaller in terms of measurement depth than the skin-subcutaneous tissue. A miniaturized oximeter was prototyped so that it could be used in combination with an endoscope or laparoscope. The optical probes consisted of light emitting diodes with wavelengths of 770 nm and 830 nm and photodetectors located 3 to 30 mm from the light source. Phantom experiments using the probes demonstrated the tendency of theoretical analysis. These results suggest the possibility of measuring tissue oxygen saturation with a selectable measurement depth. This selectable method will be useful for obtaining oxygenation information at a depth of 2–5 mm, which is difficult to measure using only laparoscopic surface imaging.
Effective Point Cloud Analysis Using Multi-Scale Features
Authors: Zheng Qiang , Sun Jian
Published: August 2021

Fully exploring the correlation of local features and their spatial distribution in point clouds is essential for feature modeling. This paper, inspired by convolutional neural networks (CNNs), explores the relationship between local patterns and point coordinates from a novel perspective and proposes a lightweight structure based on multi-scale features and a two-step fusion strategy. Specifically, local features of multi-scales and their spatial distribution can be regarded as independent features corresponding to different levels of geometric significance, which are extracted by multiple parallel branches and then merged on multiple levels. In this way, the proposed model generates a shape-level representation that contains rich local characteristics and the spatial relationship between them. Moreover, with the shared multi-layer perceptrons (MLPs) as basic operators, the proposed structure is so concise that it converges rapidly, and so we introduce the snapshot ensemble to improve performance further. The model is evaluated on classification and part segmentation tasks. The experiments prove that our model achieves on-par or better performance than previous state-of-the-art (SOTA) methods.
A Fluidics-Based Double Flexural Membrane Piezoelectric Micromachined Ultrasonic Transducer (PMUT) for Wide-Bandwidth Underwater Acoustic Applications
Authors: Ahmad Khairul Azman , Rahman Mohamad Faizal Abd , Zain Khairu Anuar Mohamed , Haron Muhammad Naim , Manaf Asrulnizam Abd
Published: August 2021

In acoustic receiver design, the receiving sensitivity and bandwidth are two primary parameters that determine the performance of a device. The trade-off between sensitivity and bandwidth makes the design very challenging, meaning it needs to be fine-tuned to suit specific applications. The ability to design a PMUT with high receiving sensitivity and a wide bandwidth is crucial to allow a wide spectrum of transmitted frequencies to be efficiently received. This paper presents a novel structure involving a double flexural membrane with a fluidic backing layer based on an in-plane polarization mode to optimize both the receiving sensitivity and frequency bandwidth for medium-range underwater acoustic applications. In this structure, the membrane material and electrode configuration are optimized to produce good receiving sensitivity. Simultaneously, a fluidic backing layer is introduced into the double flexural membrane to increase the bandwidth. Several piezoelectric membrane materials and various electrode dimensions were simulated using finite element analysis (FEA) techniques to study the receiving performance of the proposed structure. The final structure was then fabricated based on the findings from the simulation work. The pulse–echo experimental method was used to characterize and verify the performance of the proposed device. The proposed structure was found to have an improved bandwidth of 56.6% with a receiving sensitivity of −1.8864 dB rel 1 V µPa. For the proposed device, the resonance frequency and center frequency were 600 and 662.5 kHz, respectively, indicating its suitability for the targeted frequency range.
Artificial Intelligence for Smart Manufacturing: Methods and Applications
Authors: Tran Kim Phuc
Published: August 2021

The term Industry 4.0 has become increasingly pervasive in the context of industrial manufacturing and it has been considered the fourth industrial revolution (Henning [1]) [...]
Changes of Viscoelastic Properties of Aptamer-Based Sensing Layers Following Interaction with Listeria innocua
Authors: Tatarko Marek , Spagnolo Sandro , Oravczová Veronika , Süle Judit , Hun Milan , Hucker Attila , Hianik Tibor
Published: August 2021

A multiharmonic quartz crystal microbalance (QCM) has been applied to study the viscoelastic properties of the aptamer-based sensing layers at the surface of a QCM transducer covered by neutravidin following interaction with bacteria Listeria innocua. Addition of bacteria in the concentration range 5 × 103–106 CFU/mL resulted in a decrease of resonant frequency and in an increase of dissipation. The frequency decrease has been lower than one would expect considering the dimension of the bacteria. This can be caused by lower penetration depth of the acoustics wave (approximately 120 nm) in comparison with the thickness of the bacterial layer (approximately 500 nm). Addition of E. coli at the surface of neutravidin as well as aptamer layers did not result in significant changes in frequency and dissipation. Using the Kelvin–Voight model the analysis of the viscoelastic properties of the sensing layers was performed and several parameters such as penetration depth, Γ, viscosity coefficient, η, and shear modulus, μ, were determined following various modifications of QCM transducer. The penetration depth decreased following adsorption of the neutravidin layer, which is evidence of the formation of a rigid protein structure. This value did not change significantly following adsorption of aptamers and Listeria innocua. Viscosity coefficient was higher for the neutravidin layer in comparison with the naked QCM transducer in a buffer. However, a further increase of viscosity coefficient took place following attachment of aptamers suggesting their softer structure. The interaction of Listeria innocua with the aptamer layer resulted in slight decrease of viscosity coefficient. The shearing modulus increased for the neutravidin layer and decreased following aptamer adsorption, while a slight increase of µ was observed after the addition of Listeria innocua.
On the Optimization of Regression-Based Spectral Reconstruction
Authors: Lin Yi-Tun , Finlayson Graham D.
Published: August 2021

Spectral reconstruction (SR) algorithms attempt to recover hyperspectral information from RGB camera responses. Recently, the most common metric for evaluating the performance of SR algorithms is the Mean Relative Absolute Error (MRAE)—an ℓ1 relative error (also known as percentage error). Unsurprisingly, the leading algorithms based on Deep Neural Networks (DNN) are trained and tested using the MRAE metric. In contrast, the much simpler regression-based methods (which actually can work tolerably well) are trained to optimize a generic Root Mean Square Error (RMSE) and then tested in MRAE. Another issue with the regression methods is—because in SR the linear systems are large and ill-posed—that they are necessarily solved using regularization. However, hitherto the regularization has been applied at a spectrum level, whereas in MRAE the errors are measured per wavelength (i.e., per spectral channel) and then averaged. The two aims of this paper are, first, to reformulate the simple regressions so that they minimize a relative error metric in training—we formulate both ℓ2 and ℓ1 relative error variants where the latter is MRAE—and, second, we adopt a per-channel regularization strategy. Together, our modifications to how the regressions are formulated and solved leads to up to a 14% increment in mean performance and up to 17% in worst-case performance (measured with MRAE). Importantly, our best result narrows the gap between the regression approaches and the leading DNN model to around 8% in mean accuracy.
Modulation of Pulse Propagation and Blood Flow via Cuff Inflation—New Distal Insights
Authors: Bogatu Laura I. , Turco Simona , Mischi Massimo , Schmitt Lars , Woerlee Pierre , Bresch Erik , Noordergraaf Gerrit J. , Paulussen Igor , Bouwman Arthur , Korsten Hendrikus H. M. , Muehlsteff Jens
Published: August 2021

In standard critical care practice, cuff sphygmomanometry is widely used for intermittent blood pressure (BP) measurements. However, cuff devices offer ample possibility of modulating blood flow and pulse propagation along the artery. We explore underutilized arrangements of sensors involving cuff devices which could be of use in critical care to reveal additional information on compensatory mechanisms. In our previous work, we analyzed the response of the vasculature to occlusion perturbations by means of observations obtained non-invasively. In this study, our aim is to (1) acquire additional insights by means of invasive measurements and (2) based on these insights, further develop cuff-based measurement strategies. Invasive BP experimental data is collected downstream from the cuff in two patients monitored in the OR. It is found that highly dynamic processes occur in the distal arm during cuff inflation. Mean arterial pressure increases in the distal artery by 20 mmHg, leading to a decrease in pulse transit time by 20 ms. Previous characterizations neglected such distal vasculature effects. A model is developed to reproduce the observed behaviors and to provide a possible explanation of the factors that influence the distal arm mechanisms. We apply the new findings to further develop measurement strategies aimed at acquiring information on pulse arrival time vs. BP calibration, artery compliance, peripheral resistance, artery-vein interaction.