bluuebunny/crossref_metadata_2025 huggingface dataset:
A Theoretical Study on the Thermodynamic Cycle of Concept Engine with Miller Cycle
Authors: Oh Jungmo , Noh Kichol , Lee Changhee
Published: June 2021

The Atkinson cycle, where expansion ratio is higher than the compression ratio, is one of the methods used to improve thermal efficiency of engines. Miller improved the Atkinson cycle by controlling the intake- or exhaust-valve closing timing, a technique which is called the Miller cycle. The Otto–Miller cycle can improve thermal efficiency and reduce NOx emission by reducing compression work; however, it must compensate for the compression pressure and maintain the intake air mass through an effective compression ratio or turbocharge. Hence, we performed thermodynamic cycle analysis with changes in the intake-valve closing timing for the Otto–Miller cycle and evaluated the engine performance and Miller timing through the resulting problems and solutions. When only the compression ratio was compensated, the theoretical thermal efficiency of the Otto–Miller cycle improved by approximately 18.8% compared to that of the Otto cycle. In terms of thermal efficiency, it is more advantageous to compensate only the compression ratio; however, when considering the output of the engine, it is advantageous to also compensate the boost pressure to maintain the intake air mass flow rate.
Introducing Parameter Clustering to the OED Procedure for Model Calibration of a Synthetic Inducible Promoter in S. cerevisiae
Authors: Hou Zhaozheng
Published: June 2021

In recent years, synthetic gene circuits for adding new cell features have become one of the most powerful tools in biological and pharmaceutical research and development. However, because of the inherent non-linearity and noisy experimental data, the experiment-based model calibration of these synthetic parts is perceived as a laborious and time-consuming procedure. Although the optimal experimental design (OED) based on the Fisher information matrix (FIM) has been proved to be an effective means to improve the calibration efficiency, the required calculation increases dramatically with the model size (parameter number). To reduce the OED complexity without losing the calibration accuracy, this paper proposes two OED approaches with different parameter clustering methods and validates the accuracy of calibrated models with in-silico experiments. A model of an inducible synthetic promoter in S. cerevisiae is adopted for bench-marking. The comparison with the traditional off-line OED approach suggests that the OED approaches with both of the clustering methods significantly reduce the complexity of OED problems (for at least 49.0%), while slightly improving the calibration accuracy (11.8% and 19.6% lower estimation error in average for FIM-based and sensitivity-based approaches). This study implicates that for calibrating non-linear models of biological pathways, cluster-based OED could be a beneficial approach to improve the efficiency of optimal experimental design.
Special Issue on “Thermal Safety of Chemical Processes”
Authors: Lefebvre Dimitri , Leveneur Sébastien
Published: June 2021

Chemistry plays an essential role in our modern society [...]
Fundamental Understanding of Tea Growth and Modeling of Precise Tea Shoot Picking Based on 3-D Coordinate Instrument
Authors: Wang Xiaoming , Han Chongyang , Wu Weibin , Xu Jian , Zhang Qingzhao , Chen Ming , Hu Zhibiao , Zheng Zefeng
Published: June 2021

Tea is a popular beverage worldwide and also has great medical value. A fundamental understanding of tea shoot growth and a precision picking model should be established to realize mechanized picking of tea shoots with a small product loss. Accordingly, the terminal bud length (Lbud), tea stem length (Lstem), terminal bud angle (αbud), tea stem angle (αstem), and growth time (t) were considered as the key growth parameters; the sum of the vertical lengths of the terminal bud and stem (ξ), the picking radius (r), and the vertical length of the stem (Zstem) were considered as the picking indexes of the tea shoots. The variations in growth parameters with time were investigated using a 3-D coordinate instrument, and the relationships between the growth parameters and the picking indexes were established using an artificial neural network (ANN). The results indicated that the tea growth cycles for periods P1, P2, P3, P4, P5, and P6 were 14, 7, 6, 4, 4, and 6 d, respectively. A growth cycle diagram of the tea growth was established. Moreover, a 5-2-12-3 ANN model was developed. The best prediction of ξ, r, and Zstem was found with 16 training epochs. The MSE value was 0.0923 × 10−4, and the R values for the training, test, and validation data were 0.99976, 0.99871, and 0.99857, respectively, indicating that the established ANN model demonstrates excellent performance in predicting the picking indexes of tea shoots.
Spiral Vibration Cooler for Continual Cooling of Biomass Pellets
Authors: Žurovec David , Jezerská Lucie , Nečas Jan , Hlosta Jakub , Diviš Jan , Zegzulka Jiří
Published: June 2021

Cooling is an important process during the production of pellets (as post-treatment). The pellet cooling process significantly impacts the quality of the pellets produced and the systematic use of energy. However, the cooling systems currently in use sometimes encounter technical problems, such as clogging of the perforated grids (sieves), the discharge hopper, or pellet degradation may occur. Therefore, a prototype of a new pellet cooling system using a vibrating feeder was tested. The aim of the study is to present a new variation of pellet cooling system using spiral vibration cooler as a possible solution next to a counterflow cooler. The presented system was tested (critically evaluated and discussed) in two design variants. The first variant consists in cooling by chaotic movement of the pellets. The second is then in combination with the chaotic movement of the pellets together with the action of intense air flow using specially placed air hoses. All tests involved pelletization of rapeseed straw. It was found that both cooling system variants could, realistically, be used. However, the variant with an intense air flow was more energy-intensive, a factor which is, however, offset by the higher quality of the pellets. No negative impact of vibrations to pellets quality was occur. Studies provide insight into new usable technologies that do not reduce the efficiency of the process as a result of grate clogging.
Improving Retail Warehouse Activity by Using Product Delivery Data
Authors: Burinskienė Aurelija , Lerher Tone
Published: June 2021

This paper presents a research study which is dedicated to the improvement in retail warehouse activity. This study aims to improve activity by identifying an efficient order picking strategy. (1) Background: The literature review shows the application of order picking strategies, but research related to their selection lacks an integrated approach. (2) Methods: The authors use the discrete event simulation method for the analysis of order picking strategies. The application of the discrete event simulation method enables various scenario tests in retail warehouses, allowing one to benchmark order picking strategies. By using the simulation model, experiments were designed to evaluate order picking strategies that are dependent on the delivery of the product distance variable. This research uses analysis of cost components and helps to identify the best possible order picking strategy to improve the overall warehouse performance. The authors benchmarked order picking strategies and presented constraints following product delivery data concerning their applications. (3) Results: The results presented show that the application of the order sorting strategy delivers 46.6% and the order batching strategy 6.7% lower costs compared to the single picking strategy. The results of the order batching strategy could be improved by 8.34% when the product clustering action is used. (4) Conclusions: The authors provide a theoretical framework which follows the application of order picking strategies using the product delivery data approach, which is the main scientific novelty of this paper. Recommendations are provided regarding the application of the proposed framework for the future improvement in retail warehouse activity.
Review of Swift Heavy Ion Irradiation Effects in CeO2
Authors: Cureton William F. , Tracy Cameron L. , Lang Maik
Published: June 2021

Cerium dioxide (CeO2) exhibits complex behavior when irradiated with swift heavy ions. Modifications to this material originate from the production of atomic-scale defects, which accumulate and induce changes to the microstructure, chemistry, and material properties. As such, characterizing its radiation response requires a wide range of complementary characterization techniques to elucidate the defect formation and stability over multiple length scales, such as X-ray and neutron scattering, optical spectroscopy, and electron microscopy. In this article, recent experimental efforts are reviewed in order to holistically assess the current understanding and knowledge gaps regarding the underlying physical mechanisms that dictate the response of CeO2 and related materials to irradiation with swift heavy ions. The recent application of novel experimental techniques has provided additional insight into the structural and chemical behavior of irradiation-induced defects, from the local, atomic-scale arrangement to the long-range structure. However, future work must carefully account for the influence of experimental conditions, with respect to both sample properties (e.g., grain size and impurity content) and ion-beam parameters (e.g., ion mass and energy), to facilitate a more direct comparison of experimental results.
Towards Better Valorisation of Industrial Minerals and Rocks in Serbia—Case Study of Industrial Clays
Authors: Simić Vladimir , Životić Dragana , Miladinović Zoran
Published: June 2021

The improvement in valorisation of industrial minerals and rocks in Serbia is related to improvement of legislation, and to adoption of best possible techniques for geological exploration and estimation of resources/reserves. Therefore, we analyzed the actual practice in the field of geological exploration which has not changed significantly in the last 30 years, as well as legislation related to the mining industry and access to land. The improvement of legislation mostly depends on the state authorities. Contrary to that, the enhancement of geological exploration methodology should be the issue of experts working in that area of the mining industry but is also very much related to the existing legislation. Serbia still has very strictly prescribed procedures, which are generally good; however, it is an overly complicated and restrictive system for performing geological exploration and evaluation of mineral deposits. The most important improvements in legislation in the last twenty years are presented and discussed, as well as problems that still need to be solved and solutions found to fully understand the potential of industrial minerals and rocks in Serbia.
Robust Range Ambiguous Deceptive Target Suppression Based on Covariance Matrix Reconstruction
Authors: Xie Zhuang , Zhu Jiahua , Fan Chongyi , Huang Xiaotao , Wang Jian
Published: June 2021

When the deceptive targets are in the ambiguious range bin but are received at the same range gate with the desired target by the array, the traditional multiple-input multiple-output (MIMO) radar is not able to discriminate between them. Based on the unique range-dependent beampattern of the frequency diverse array (FDA)-MIMO radar, we propose a novel robust mainlobe deceptive target suppression method based on covariance matrix reconstruction to form nulls at the frequency points of the transmit–receive domain where deceptive targets are located. First, the proposed method collects the deceptive targets and noise information in the transmit–receive frequency domain to reconstruct the jammer-noise covariance matrix (JNCM). Then, the covariance matrix of the desired target is constructed in the desired target region, which is assumed to already be known. The transmit–receive steering vector (SV) of the desired target is estimated to be the dominant eigenvector of the desired target covariance matrix. Finally, the weighting vector of the receive beamformer is calculated by combining the reconstructed JNCM and the estimated desired target SV. By implementing the weighting vector at the receiving end, the deceptive targets can be effectively suppressed. The simulation results demonstrate that the proposed method is robust to SV mismatches and provides a signal-to-jamming-plus-noise ratio (SJNR) output that is close to the optimal.
Tropical Cyclone Winds from WindSat, AMSR2, and SMAP: Comparison with the HWRF Model
Authors: Manaster Andrew , Ricciardulli Lucrezia , Meissner Thomas
Published: June 2021

A new data set of tropical cyclone winds (‘TC-winds’) through rain as observed by the WindSat and AMSR2 microwave radiometers has been developed by making use of a linear combination of C- and X-band frequency channels. These winds, along with tropical cyclone winds from the SMAP L-band radiometer, are compared with the Hurricane Weather Research and Forecasting (HWRF) model. Due to differences in spatial scales between the satellites and the high-resolution HWRF model, resampling must be performed on the model winds before comparisons are done. Various ways of spatial resampling are discussed in detail, and an optimal method is determined. Additionally, resampled model winds must be temporally interpolated to the time of the satellite before direct comparisons are made. This interpolation can occasionally result in un-physical 2D wind fields, especially for fast-moving storms. To assist users with this problem, a methodology for handling un-physical wind features is detailed. Results of overall comparisons between the satellites and HWRF for 19 storms between 2017 and 2020 displayed consistent storm features, with overall average biases less than 1 m/s and standard deviations below 4 m/s for all tropical cyclone winds between 10 and 60 m/s. Differences were seen when the comparisons were performed separately for the Atlantic and Pacific basins, with biases and standard deviations between the satellites and HWRF showing better agreement in the Atlantic. The impact of rain on the satellite wind retrievals is discussed, and no systematic bias was seen between the three sensors, despite the fact that they use different frequency channels in their tropical cyclone winds-through-rain retrieval algorithms.
The Modified Normalized Urban Area Composite Index: A Satelliate-Derived High-Resolution Index for Extracting Urban Areas
Authors: Li Feng , Liu Xiaoyang , Liao Shunbao , Jia Peng
Published: June 2021

The accurate and efficient extraction of urban areas is of great significance for better understanding of urban sprawl, built environment, economic activities, and population distribution. Night-Time Light (NTL) data have been widely used to extract urban areas. However, most of the existing NTL indexes are incapable of identifying non-luminous built-up areas. The high-resolution NTL imagery derived from the Luojia 1-01 satellite, with low saturation and the blooming effect, can be used to map urban areas at a finer scale. A new urban spectral index, named the Modified Normalized Urban Areas Composite Index (MNUACI), improved upon the existing Normalized Urban Areas Composite Index (NUACI), was proposed in this study, which integrated the Human Settlement Index (HSI) generated from Luojia 1-01 NTL data, the Normalized Difference Vegetation Index (NDVI) from Landsat 8 imagery, and the Modified Normalized Difference Water Index (MNDWI). Our results indicated that MNUACI improved the spatial variability and differentiation of urban components by eliminating the NTL blooming effect and increasing the variation of the nighttime luminosity. Compared to urban area classification from Landsat 8 data, the MNUACI yielded better accuracy than NTL, NUACI, HSI, and the EVI-Adjusted NTL Index (EANTLI) alone. Furthermore, the quadratic polynomial regression analysis showed the model based on MNUACI had the best R2 and Root-Mean Square Error (RMSE) compared with NTL, NUACI, HSI, and EANTLI in terms of estimation of impervious surface area. It is concluded that MNUACI could improve the identification of urban areas and non-luminous built-up areas with better accuracy.
Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques
Authors: Geng Liying , Che Tao , Ma Mingguo , Tan Junlei , Wang Haibo
Published: June 2021

The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale.
Multiple Data Products Reveal Long-Term Variation Characteristics of Terrestrial Water Storage and Its Dominant Factors in Data-Scarce Alpine Regions
Authors: Wang Xuanxuan , Liu Liu , Niu Qiankun , Li Hao , Xu Zongxue
Published: June 2021

As the “Water Tower of Asia” and “The Third Pole” of the world, the Qinghai–Tibet Plateau (QTP) shows great sensitivity to global climate change, and the change in its terrestrial water storage has become a focus of attention globally. Differences in multi-source data and different calculation methods have caused great uncertainty in the accurate estimation of terrestrial water storage. In this study, the Yarlung Zangbo River Basin (YZRB), located in the southeast of the QTP, was selected as the study area, with the aim of investigating the spatio-temporal variation characteristics of terrestrial water storage change (TWSC). Gravity Recovery and Climate Experiment (GRACE) data from 2003 to 2017, combined with the fifth-generation reanalysis product of the European Centre for Medium-Range Weather Forecasts (ERA5) data and Global Land Data Assimilation System (GLDAS) data, were adopted for the performance evaluation of TWSC estimation. Based on ERA5 and GLDAS, the terrestrial water balance method (PER) and the summation method (SS) were used to estimate terrestrial water storage, obtaining four sets of TWSC, which were compared with TWSC derived from GRACE. The results show that the TWSC estimated by the SS method based on GLDAS is most consistent with the results of GRACE. The time-lag effect was identified in the TWSC estimated by the PER method based on ERA5 and GLDAS, respectively, with 2-month and 3-month lags. Therefore, based on the GLDAS, the SS method was used to further explore the long-term temporal and spatial evolution of TWSC in the YZRB. During the period of 1948–2017, TWSC showed a significantly increasing trend; however, an abrupt change in TWSC was detected around 2002. That is, TWSC showed a significantly increasing trend before 2002 (slope = 0.0236 mm/month, p < 0.01) but a significantly decreasing trend (slope = −0.397 mm/month, p < 0.01) after 2002. Additional attribution analysis on the abrupt change in TWSC before and after 2002 was conducted, indicating that, compared with the snow water equivalent, the soil moisture dominated the long-term variation of TWSC. In terms of spatial distribution, TWSC showed a large spatial heterogeneity, mainly in the middle reaches with a high intensity of human activities and the Parlung Zangbo River Basin, distributed with great glaciers. The results obtained in this study can provide reliable data support and technical means for exploring the spatio-temporal evolution mechanism of terrestrial water storage in data-scarce alpine regions.
Quantifying the Responses of Evapotranspiration and Its Components to Vegetation Restoration and Climate Change on the Loess Plateau of China
Authors: Qiu Linjing , Wu Yiping , Shi Zhaoyang , Chen Yuting , Zhao Fubo
Published: June 2021

Quantitatively identifying the influences of vegetation restoration (VR) on water resources is crucial to ecological planning. Although vegetation coverage has improved on the Loess Plateau (LP) of China since the implementation of VR policy, the way vegetation dynamics influences regional evapotranspiration (ET) remains controversial. In this study, we first investigate long-term spatiotemporal trends of total ET (TET) components, including ground evaporation (GE) and canopy ET (CET, sum of canopy interception and canopy transpiration) based on the GLEAM-ET dataset. The ET changes are attributed to VR on the LP from 2000 to 2015 and these results are quantitatively evaluated here using the Community Land Model (CLM). Finally, the relative contributions of VR and climate change to ET are identified by combining climate scenarios and VR scenarios. The results show that the positive effect of VR on CET is offset by the negative effect of VR on GE, which results in a weak variation in TET at an annual scale and an increased TET is only shown in summer. Regardless of the representative concentration pathway (RCP4.5 or RCP8.5), differences resulted from the responses of TET to different vegetation conditions ranging from −3.7 to −1.2 mm, while climate change from RCP4.5 to RCP8.5 caused an increase in TET ranging from 0.1 to 65.3 mm. These findings imply that climate change might play a dominant role in ET variability on the LP, and this work emphasizes the importance of comprehensively considering the interactions among climate factors to assess the relative contributions of VR and climate change to ET.
Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
Authors: Schirninger Christoph , Eichelberger Hans U. , Magnes Werner , Boudjada Mohammed Y. , Schwingenschuh Konrad , Pollinger Andreas , Besser Bruno P. , Biagi Pier F. , Solovieva Maria , Wang Jindong , Cheng Bingjun , Zhou Bin , Shen Xuhui , Delva Magda , Lammegger Roland
Published: June 2021

Processes and threats related to natural hazards play an important role in the evolution of the Earth and in human history. The purpose of this study is to investigate magnetic field variations measured at low Earth orbit (LEO) altitudes possibly associated with earthquakes, volcanic eruptions, and artificial outbursts. We focus on two missions with well equipped magnetometer packages, the China Seismo-Electromagnetic Satellite (CSES) and ESA’s three spacecraft Swarm fleet. After a natural hazards survey in the context of this satellites, and consideration of external magnetospheric and solar influences, together with spacecraft interferences, wavelet analysed spatio-temporal patterns in ionospheric magnetic field variations related to atmospheric waves are examined in detail. We provide assessment of the links between specific lithospheric or near surface sources and ionospheric magnetic field measurements. For some of the diverse events the achieved statistical results show a change in the pattern between pre- and post-event periods, we show there is an increase in the fluctuations for the higher frequency (smaller scales) components. Our results are relevant to studies which establish a link between space based magnetic field measurements and natural hazards.
Real-Time Parameter Estimation of a Dual-Pol Radar Rain Rate Estimator Using the Extended Kalman Filter
Authors: Na Wooyoung , Yoo Chulsang
Published: June 2021

The extended Kalman filter is an extended version of the Kalman filter for a non-linear problem. This study applies this extended Kalman filter to the real-time estimation of the parameters of the dual-pol radar rain rate estimator. The estimated parameters are also compared with those based on the least squares method. As an application example, this study considers four storm events observed by the Beaslesan radar in Korea. The findings derived include, first, that the parameters of the radar rain rate estimator obtained by the extended Kalman filter are totally different from those by the least squares method. In fact, the parameters obtained by the extended Kalman filter are found to be more reasonable, and are similar to those reported in previous studies. Second, the estimated rain rates based on the parameters obtained by the extended Kalman filter are found to be similar to those observed on the ground. Even though the parameters estimated by applying the least squares method are quite different from previous studies as well as those based on the extended Kalman filter, the resulting radar rain rate is found to be quite similar to that based on the extended Kalman filter. In conclusion, the extended Kalman filter can be a reliable method for real-time estimation of the parameters of the dual-pol radar rain rate estimator. The resulting rain rate is also found to be of sufficiently high quality to be applicable for other purposes, such as various flood warning systems.
Deep Neural Architectures for Contrast Enhanced Ultrasound (CEUS) Focal Liver Lesions Automated Diagnosis
Authors: Căleanu Cătălin Daniel , Sîrbu Cristina Laura , Simion Georgiana
Published: June 2021

Computer vision, biomedical image processing and deep learning are related fields with a tremendous impact on the interpretation of medical images today. Among biomedical image sensing modalities, ultrasound (US) is one of the most widely used in practice, since it is noninvasive, accessible, and cheap. Its main drawback, compared to other imaging modalities, like computed tomography (CT) or magnetic resonance imaging (MRI), consists of the increased dependence on the human operator. One important step toward reducing this dependence is the implementation of a computer-aided diagnosis (CAD) system for US imaging. The aim of the paper is to examine the application of contrast enhanced ultrasound imaging (CEUS) to the problem of automated focal liver lesion (FLL) diagnosis using deep neural networks (DNN). Custom DNN designs are compared with state-of-the-art architectures, either pre-trained or trained from scratch. Our work improves on and broadens previous work in the field in several aspects, e.g., a novel leave-one-patient-out evaluation procedure, which further enabled us to formulate a hard-voting classification scheme. We show the effectiveness of our models, i.e., 88% accuracy reported against a higher number of liver lesion types: hepatocellular carcinomas (HCC), hypervascular metastases (HYPERM), hypovascular metastases (HYPOM), hemangiomas (HEM), and focal nodular hyperplasia (FNH).
The Effectiveness of Multi-Label Classification and Multi-Output Regression in Social Trait Recognition
Authors: Farlessyost Will , Grant Kelsey-Ryan , Davis Sara R. , Feil-Seifer David , Hand Emily M.
Published: June 2021

First impressions make up an integral part of our interactions with other humans by providing an instantaneous judgment of the trustworthiness, dominance and attractiveness of an individual prior to engaging in any other form of interaction. Unfortunately, this can lead to unintentional bias in situations that have serious consequences, whether it be in judicial proceedings, career advancement, or politics. The ability to automatically recognize social traits presents a number of highly useful applications: from minimizing bias in social interactions to providing insight into how our own facial attributes are interpreted by others. However, while first impressions are well-studied in the field of psychology, automated methods for predicting social traits are largely non-existent. In this work, we demonstrate the feasibility of two automated approaches—multi-label classification (MLC) and multi-output regression (MOR)—for first impression recognition from faces. We demonstrate that both approaches are able to predict social traits with better than chance accuracy, but there is still significant room for improvement. We evaluate ethical concerns and detail application areas for future work in this direction.
Feasibility of Using Electrical Impedance Spectroscopy for Assessing Biological Cell Damage during Freezing and Thawing
Authors: Abie Sisay Mebre , Martinsen Ørjan Grøttem , Egelandsdal Bjørg , Hou Jie , Bjerke Frøydis , Mason Alex , Münch Daniel
Published: June 2021

This study was performed to test bioimpedance as a tool to detect the effect of different thawing methods on meat quality to aid in the eventual creation of an electric impedance-based food quality monitoring system. The electric impedance was measured for fresh pork, thawed pork, and during quick and slow thawing. A clear difference was observed between fresh and thawed samples for both impedance parameters. Impedance was different between the fresh and the frozen-thawed samples, but there were no impedance differences between frozen-thawed samples and the ones that were frozen-thawed and then stored at +3 °C for an additional 16 h after thawing. The phase angle was also different between fresh and the frozen-thawed samples. At high frequency, there were small, but clear phase angle differences between frozen-thawed samples and the samples that were frozen-thawed and subsequently stored for more than 16 h at +3 °C. Furthermore, the deep learning model LSTM-RNN (long short-term memory recurrent neural network) was found to be a promising way to classify the different methods of thawing.
A Soft Resistive Sensor with a Semicircular Cross-Sectional Channel for Soft Cardiac Catheter Ablation
Authors: Rasmussen Eric , Guo Daniel , Murthy Vybhav , Mishra Rachit , Riviere Cameron , Majidi Carmel
Published: June 2021

The field of soft robotics has attracted the interest of the medical community due to the ability of soft elastic materials to traverse the abnormal environment of the human body. However, sensing in soft robotics has been challenging due to the sensitivity of soft sensors to various loading conditions and the nonlinear signal responses that can arise under extreme loads. Ideally, soft sensors should provide a linear response under a specific loading condition and provide a different response for other loading directions. With these specifications in mind, our team created a soft elastomeric sensor designed to provide force feedback during cardiac catheter ablation surgery. Analytical and computational methods were explored to define a relationship between resistance and applied force for a semicircular, liquid metal filled channel in the soft elastomeric sensor. Pouillet’s Law is utilized to calculate the resistance based on the change in cross-sectional area resulting from various applied pressures. FEA simulations were created to simulate the deformation of the sensor under various loads. To confirm the validity of these simulations, the elastomer was modeled as a neo-Hookean material and the liquid metal was modeled as an incompressible fluid with negligible shear modulus under uniaxial compression. Results show a linearly proportional relationship between the resistance of the sensor and the application of a uniaxial force. Altering the direction of applied force results in a quadratic relationship between total resistance and the magnitude of force.
Modern Displacement Measuring Systems Used in Geotechnical Laboratories: Advantages and Disadvantages
Authors: Jastrzębska Małgorzata
Published: June 2021

The paper presents the contemporary displacement measurement systems used in geotechnical laboratories during the determination of soil precise mechanical parameters, e.g., the shear modules G: initial and in the range of small and very small strains. In the laboratory, researchers use standard sensors for measuring deformation, pressure, and force as well as modern measuring systems such as linear variable differential transformers (LVDT), proximity transducers (PT), magnetic encoder sensors with fiber Bragg grating (FBG), or methods based on laser or X-ray measurement. None of the measurements are universal and their use depends on the type of soil (cohesive, non-cohesive), its condition (loose or dense, stiff or very soft), and its characteristic properties (e.g., organic soil, swelling soil). This study points out the interesting equipment solutions and presents the guidelines for selecting appropriate methods of deformation measurement.
Laboratory Evaluation of Low-Cost Optical Particle Counters for Environmental and Occupational Exposures
Authors: Sousan Sinan , Regmi Swastika , Park Yoo Min
Published: June 2021

Low-cost optical particle counters effectively measure particulate matter (PM) mass concentrations once calibrated. Sensor calibration can be established by deriving a linear regression model by performing side-by-side measurements with a reference instrument. However, calibration differences between environmental and occupational settings have not been demonstrated. This study evaluated four commercially available, low-cost PM sensors (OPC-N3, SPS30, AirBeam2, and PMS A003) in both settings. The mass concentrations of three aerosols (salt, Arizona road dust, and Poly-alpha-olefin-4 oil) were measured and compared with a reference instrument. OPC-N3 and SPS30 were highly correlated (r = 0.99) with the reference instrument for all aerosol types in environmental settings. In occupational settings, SPS30, AirBeam2, and PMS A003 exhibited high correlation (>0.96), but the OPC-N3 correlation varied (r = 0.88–1.00). Response significantly (p < 0.001) varied between environmental and occupational settings for most particle sizes and aerosol types. Biases varied by particle size and aerosol type. SPS30 and OPC-N3 exhibited low bias for environmental settings, but all of the sensors showed a high bias for occupational settings. For intra-instrumental precision, SPS30 exhibited high precision for salt for both settings compared to the other low-cost sensors and aerosol types. These findings suggest that SPS30 and OPC-N3 can provide a reasonable estimate of PM mass concentrations if calibrated differently for environmental and occupational settings using site-specific calibration factors.
A Framework for Planning and Execution of Drone Swarm Missions in a Hostile Environment
Authors: Siemiatkowska Barbara , Stecz Wojciech
Published: June 2021

This article presents a framework for planning a drone swarm mission in a hostile environment. Elements of the planning framework are discussed in detail, including methods of planning routes for drone swarms using mixed integer linear programming (MILP) and methods of detecting potentially dangerous objects using EO/IR camera images and synthetic aperture radar (SAR). Methods of detecting objects in the field are used in the mission planning process to re-plan the swarm’s flight paths. The route planning model is discussed using the example of drone formations managed by one UAV that communicates through another UAV with the ground control station (GCS). This article presents practical examples of using algorithms for detecting dangerous objects for re-planning of swarm routes. A novelty in the work is the development of these algorithms in such a way that they can be implemented on mobile computers used by UAVs and integrated with MILP tasks. The methods of detection and classification of objects in real time by UAVs equipped with SAR and EO/IR are presented. Different sensors require different methods to detect objects. In the case of infrared or optoelectronic sensors, a convolutional neural network is used. For SAR images, a rule-based system is applied. The experimental results confirm that the stream of images can be analyzed in real-time.
Fully Parallel Implementation of Otsu Automatic Image Thresholding Algorithm on FPGA
Authors: Barros Wysterlânya K. P. , Dias Leonardo A. , Fernandes Marcelo A. C.
Published: June 2021

This work proposes a high-throughput implementation of the Otsu automatic image thresholding algorithm on Field Programmable Gate Array (FPGA), aiming to process high-resolution images in real-time. The Otsu method is a widely used global thresholding algorithm to define an optimal threshold between two classes. However, this technique has a high computational cost, making it difficult to use in real-time applications. Thus, this paper proposes a hardware design exploiting parallelization to optimize the system’s processing time. The implementation details and an analysis of the synthesis results concerning the hardware area occupation, throughput, and dynamic power consumption, are presented. Results have shown that the proposed hardware achieved a high speedup compared to similar works in the literature.
Improved Recursive DV-Hop Localization Algorithm with RSSI Measurement for Wireless Sensor Networks
Authors: Messous Sana , Liouane Hend , Cheikhrouhou Omar , Hamam Habib
Published: June 2021

As localization represents the main backbone of several wireless sensor networks applications, several localization algorithms have been proposed in the literature. There is a growing interest in the multi-hop localization algorithms as they permit the localization of sensor nodes even if they are several hops away from anchor nodes. One of the most famous localization algorithms is the Distance Vector Hop (DV-Hop). Aiming to minimize the large localization error in the original DV-Hop algorithm, we propose an improved DV-Hop algorithm in this paper. The distance between unknown nodes and anchors is estimated using the received signal strength indication (RSSI) and the polynomial approximation. Moreover, the proposed algorithm uses a recursive computation of the localization process to improve the accuracy of position estimation. Experimental results show that the proposed localization technique minimizes the localization error and improves the localization accuracy.
A Multifunctional Smart Meter Using ANN-PSO Flux Estimation and Harmonic Active Compensation with Fuzzy Voltage Regulation
Authors: Batista Edson A. , de Brito Moacyr A. G. , Siqueira João C. , Dias Jeandro C. , Gomez Raphael C. , Catharino Maurilio F. R. , Gomes Matheus B.
Published: June 2021

This paper aims to present the analysis and development of a complete electronic smart meter that is able to perform four-quadrant measurements, act as a three-phase shunt active power filter (APF), and control three-phase induction motors by stator flux estimation. A transmission control protocol together with Internet protocol (TCP/IP) communication protocol for the remote access of measurement data is embedded into the application to securely transmit reliable information. An artificial neural network trained with particle swarm optimization is used for stator flux estimation, and a fuzzy logic controller is adopted to regulate the power converter DC bus voltage. The present work gathers knowledge from multidisciplinary fields, and all applied techniques have not been proposed altogether before. All control functions are embedded into a field-programmable gate array (FPGA) device, using VHSIC Hardware Description Language (VHDL), to enhance efficiency taking advantage of parallelism and high speed. An FPGA-in-the-loop cosimulation technique was first applied to prove the control functions’ functionality, and, later, experimental evaluations are conducted to finally prove equipment operation and reliability.
Towards Supply Chain Visibility Using Internet of Things: A Dyadic Analysis Review
Authors: Ahmed Shehzad , Kalsoom Tahera , Ramzan Naeem , Pervez Zeeshan , Azmat Muhammad , Zeb Bassam , Ur Rehman Masood
Published: June 2021

The Internet of Things (IoT) and its benefits and challenges are the most emergent research topics among academics and practitioners. With supply chains (SCs) gaining rapid complexity, having high supply chain visibility (SCV) would help companies ease the processes and reduce complexity by improving inaccuracies. Extant literature has given attention to the organisation’s capability to collect and evaluate information to balance between strategy and goals. The majority of studies focus on investigating IoT’s impact on different areas such as sustainability, organisational structure, lean manufacturing, product development, and strategic management. However, research investigating the relationships and impact of IoT on SCV is minimal. This study closes this gap using a structured literature review to critically analyse existing literature to synthesise the use of IoT applications in SCs to gain visibility, and the SC. We found key IoT technologies that help SCs gain visibility, and seven benefits and three key challenges of these technologies. We also found the concept of Supply 4.0 that grasps the element of Industry 4.0 within the SC context. This paper contributes by combining IoT application synthesis, enablers, and challenges in SCV by highlighting key IoT technologies used in the SCs to gain visibility. Finally, the authors propose an empirical research agenda to address the identified gaps.
Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System
Authors: Kubicek Jan , Strycek Michal , Cerny Martin , Penhaker Marek , Prokop Ondrej , Vilimek Dominik
Published: June 2021

In the area of musculoskeletal MR images analysis, the image denoising plays an important role in enhancing the spatial image area for further processing. Recent studies have shown that non-local means (NLM) methods appear to be more effective and robust when compared with conventional local statistical filters, including median or average filters, when Rician noise is presented. A significant limitation of NLM is the fact that thy have the tendency to suppress tiny objects, which may represent clinically important information. For this reason, we provide an extensive quantitative and objective analysis of a novel NLM algorithm, taking advantage of pixel and patch similarity information with the optimization procedure for optimal filter parameters selection to demonstrate a higher robustness and effectivity, when comparing with NLM and conventional local means methods, including average and median filters. We provide extensive testing on variable noise generators with dynamical noise intensity to objectively demonstrate the robustness of the method in a noisy environment, which simulates relevant, variable and real conditions. This work also objectively evaluates the potential and benefits of the application of NLM filters in contrast to conventional local-mean filters. The final part of the analysis is focused on the segmentation performance when an NLM filter is applied. This analysis demonstrates a better performance of tissue identification with the application of smoothing procedure under worsening image conditions.
Transformation of Transient Overvoltages by Inductive Voltage Transformers
Authors: Kaczmarek Michał , Brodecki Dariusz
Published: June 2021

Overvoltage transients occur after any type of switching activity in a power network, such as breaker operation, fault occurrence/clearance and rapid load change. This distortion of voltage is transformed to the secondary circuit of a voltage transformer. The maximum values of such impulses may many times exceed the rated value of its secondary voltage. This can lead to malfunction of measuring or protection devices connected to the secondary circuit of a voltage transformer and even their damage. The paper presents the application of determined values of ratio error at harmonics of the inductive voltage of the transformer to predict the value of transformed slow-front transient overvoltage to their secondary circuits. This will help to prevent malfunction of measuring or protection devices connected to the secondary side of the voltage transformer and increase their safety of operation. The inductive voltage transformer equivalent circuit for transformation of higher frequency components of distorted voltage must be extended with internal capacitances of windings. This is caused by the fact that the resonance phenomenon of the slow-front transient overvoltage results from leakage inductance and capacitance of primary winding, not from the magnetic core. Therefore, this behaviour is independent from the value of the applied voltage.
Identification of Abnormal Proteins in Plasma from Gout Patients by LC-MS/MS
Authors: Shen Lijin , Dong Hanyang , Guo Zhenchang , Zhai Guijin , Zhang Kai
Published: June 2021

A high level of uric acid may cause hyperuricemia, which further develops into gout, eventually leading to chronic kidney disease. However, the pathogenic mechanism remains largely unknown. To investigate the cause and block the transformation of hyperuricemia to related diseases, it is important to discover the alterations in protein levels between gout patients and non-gout individuals. To date, human blood plasma is still the predominant matrices for clinical analysis. Due to the high abundance, the proteins of plasma samples have strong shielding effects on low abundance proteins, thus, the information on low abundance protein expression is always masked, while the low abundance proteins of human plasma are often of great significance for the diagnosis and treatment of diseases. Therefore, it is very important to separate and analyze the plasma proteins. High-performance liquid chromatography (LC) tandem mass spectrometry (MS)-based proteomics has been developed as a powerful tool to investigate changes in the human plasma proteome. Here, we used LC-MS/MS to detect the differential proteins in the plasmas from simple gout patients, gout with kidney damage patients, and non-gout individuals. We identified 32 obviously differential proteins between non-gout and gout subjects and 10 differential proteins between simple gout and gout with kidney damage patients. These differential proteins were further analyzed to characterize their localization and functions. Additionally, the correlation analysis showed multiple relationships between the abnormal plasma proteins and clinical biochemical indexes, particularly for the immune-inflammatory response proteins. Furthermore, inflammation factors gelsolin (GSN) were confirmed. Our results offer a view of plasma proteins for studying biomarkers of gout patients.