bluuebunny/crossref_metadata_2025 huggingface dataset:
Therapeutic Response Monitoring with 89Zr-DFO-Pertuzumab in HER2-Positive and Trastuzumab-Resistant Breast Cancer Models
Authors: Kang Minwoo , Shin Jong Il , Han Sangjin , Kim Jung Young , Park Jeonghoon , Kim Kwang Il , Kang Joo Hyun , Lee Tae Sup
Published: June 2022

Immuno-positron emission tomography (PET) has great potential to evaluate the target expression level and therapeutic response for targeted cancer therapy. Immuno-PET imaging with pertuzumab, due to specific recognition in different binding sites of HER2, could be useful for the determination of the therapeutic efficacy of HER2-targeted therapy, trastuzumab, and heat shock protein 90 (HSP90) inhibitor, in HER2-expressing breast cancer. The aim of this study is to evaluate the feasibility of monitoring therapeutic response with 89Zr-DFO-pertuzumab for the treatment of HER2-targeted therapeutics, trastuzumab, or the HSP90 inhibitor 17-DMAG, in trastuzumab-resistant JIMT-1 breast cancer models. We prepared an immuno-PET imaging agent using desferoxamine (DFO)-pertuzumab labeled with 89Zr and performed the biodistribution and PET imaging in breast cancer xenograft models for monitoring therapeutic response to HER2-targeted therapy. 89Zr-DFO-pertuzumab was successfully prepared and showed specific binding to HER2 in vitro and clearly visualized HER2 expressing JIMT-1 tumors. 89Zr-DFO-pertuzumab had prominent tumor uptake in HER2 expressing JIMT-1 tumors. JIMT-1 tumors showed trastuzumab-resistant and HSP90 inhibitor sensitive characterization. In immuno-PET imaging, isotype antibody-treated JIMT-1 tumors had similar uptake in trastuzumab-treated JIMT-1 tumors, but 17-DMAG-treated JIMT-1 tumors showed greatly reduced uptake compared to vehicle-treated tumors. Additionally, HER2 downregulation evaluated by immuno-PET imaging was verified by western blot analysis and immunofluorescence staining which resulted in a significant reduction in the tumor’s HER2 level in 17-DMAG-treated JIMT-1 tumors. 89Zr-DFO-pertuzumab immuno-PET may be clinically translated to select pertinent patients for HER2-targeted therapy and to monitor the therapeutic response in HER2-positive cancer patients under various HER2-targeted therapeutics treatments.
Continuous Affinity Purification of Adeno-Associated Virus Using Periodic Counter-Current Chromatography
Authors: Mendes João P. , Bergman Magnus , Solbrand Anita , Peixoto Cristina , Carrondo Manuel J. T. , Silva Ricardo J. S.
Published: June 2022

Replacing batch unit operations of biopharmaceuticals by continuous manufacturing is a maturing concept, with periodic counter-current chromatography (PCC) favoured to replace batch chromatography. Continuous affinity capture of adeno-associated virus (AAV) using PCC has the potential to cope with the high doses required for AAV therapies thanks to its inherent high throughput. The implementation of continuous AAV affinity capture using a four-column PCC process is described herein. First, elution buffer screening was used to optimize virus recovery. Second, breakthrough curves were generated and described using a mechanistic model, which was later used to characterize the loading zone of the PCC. The experimental runs achieved a stable cyclic steady state yielding virus recoveries in line with the optimized batch process (>82%), with almost a three-fold improvement in productivity. The PCC affinity capture process developed here can bolster further improvements to process economics and manufacturing footprint, thereby contributing to the integrated continuous manufacturing concept.
Analyzing siRNA Concentration, Complexation and Stability in Cationic Dendriplexes by Stem-Loop Reverse Transcription-qPCR
Authors: Neugebauer Maximilian , Grundmann Clara E. , Lehnert Michael , von Stetten Felix , Früh Susanna M. , Süss Regine
Published: June 2022

RNA interference (RNAi) is a powerful therapeutic approach for messenger RNA (mRNA) level regulation in human cells. RNAi can be triggered by small interfering RNAs (siRNAs) which are delivered by non-viral carriers, e.g., dendriplexes. siRNA quantification inside carriers is essential in drug delivery system development. However, current siRNA measuring methods either are not very sensitive, only semi-quantitative or not specific towards intact target siRNA sequences. We present a novel reverse transcription real-time PCR (RT-qPCR)-based application for siRNA quantification in drug formulations. It enables specific and highly sensitive quantification of released, uncomplexed target siRNA and thus also indirect assessment of siRNA stability and concentration inside dendriplexes. We show that comparison with a dilution series allows for siRNA quantification, exclusively measuring intact target sequences. The limit of detection (LOD) was 4.2 pM (±0.2 pM) and the limit of quantification (LOQ) 77.8 pM (±13.4 pM) for uncomplexed siRNA. LOD and LOQ of dendriplex samples were 31.6 pM (±0 pM) and 44.4 pM (±9.0 pM), respectively. Unspecific non-target siRNA sequences did not decrease quantification accuracy when present in samples. As an example of use, we assessed siRNA complexation inside dendriplexes with varying nitrogen-to-phosphate ratios. Further, protection of siRNA inside dendriplexes from RNase A degradation was quantitatively compared to degradation of uncomplexed siRNA. This novel application for quantification of siRNA in drug delivery systems is an important tool for the development of new siRNA-based drugs and quality checks including drug stability measurements.
Effect of Exo/Endogenous Prophylaxis Dentifrice/Drug and Cariogenic Conditions of Patient on Molecular Property of Dental Biofilm: Synchrotron FTIR Spectroscopic Study
Authors: Seredin Pavel , Goloshchapov Dmitry , Kashkarov Vladimir , Nesterov Dmitry , Ippolitov Yuri , Ippolitov Ivan , Vongsvivut Jitraporn
Published: June 2022

(1) Objectives: This study is the first one to investigate the molecular composition of the dental biofilm during the exogenous and endogenous prophylaxis stages (use of dentifrice/drug) of individuals with different cariogenic conditions using molecular spectroscopy methods. (2) Materials and Methods: The study involved 100 participants (50 males and 50 females), aged 18–25 years with different caries conditions. Biofilm samples were collected from the teeth surface of all participants. The molecular composition of biofilms was investigated using synchrotron infrared microspectroscopy. Changes in the molecular composition were studied through calculation and analysis of ratios between organic and mineral components of biofilm samples. (3) Results: Based on the data obtained by synchrotron FTIR, calculations of organic and mineral component ratios, and statistical analysis of the data, we were able to assess changes occurring in the molecular composition of the dental biofilm. Variations in the phosphate/protein/lipid, phosphate/mineral, and phospholipid/lipid ratios and the presence of statistically significant intra- and inter-group differences in these ratios indicate that the mechanisms of ion adsorption, compounds and complexes arriving from oral fluid into dental biofilm during exo/endogenous prophylaxis, differ for patients in norm and caries development. (4) Conclusions: The conformational environment and charge interaction in the microbiota and the electrostatic state of the biofilm protein network in patients with different cariogenic conditions play an important role. (5) Clinical Significance: Understanding the changes that occur in the molecular composition of the dental biofilm in different oral homeostasis conditions will enable successful transition to a personalised approach in dentistry and high-tech healthcare.
Novel In Silico Insights into Rv1417 and Rv2617c as Potential Protein Targets: The Importance of the Medium on the Structural Interactions with Exported Repetitive Protein (Erp) of Mycobacterium tuberculosis
Authors: Paco-Chipana Margot , Febres-Molina Camilo , Aguilar-Pineda Jorge Alberto , Gómez Badhin
Published: June 2022

Nowadays, tuberculosis is the second leading cause of death from a monopathogenic transmitted disease, only ahead of COVID-19. The role of exported repetitive protein (Erp) in the virulence of Mycobacterium tuberculosis has been extensively demonstrated. In vitro and in vivo assays have identified that Erp interacts with Rv1417 and Rv2617c proteins, forming putative transient molecular complexes prior to localization to the cell envelope. Although new insights into the interactions and functions of Erp have emerged over the years, knowledge about its structure and protein–protein interactions at the atomistic level has not been sufficiently explored. In this work, we have combined several in silico methodologies to gain new insights into the structural relationship between these proteins. Two system conditions were evaluated by MD simulations: Rv1417 and Rv2617c embedded in a lipid membrane and another with a semi-polar solvent to mimic the electrostatic conditions on the membrane surface. The Erp protein was simulated as an unanchored structure. Stabilized structures were docked, and complexes were evaluated to recognize the main residues involved in protein–protein interactions. Our results show the influence of the medium on the structural conformation of proteins. Globular conformations were favored under high polarity conditions and showed a higher energetic affinity in complex formation. Meanwhile, disordered conformations were favored under semi-polar conditions and an increase in the number of contacts between residues was observed. In addition, the electrostatic potential analysis showed remarkable changes in protein interactions due to the polarity of the medium, demonstrating the relevance of Erp protein in heterodimer formation. On the other hand, contact analysis showed that several C-terminal residues of Erp were involved in the protein interactions, which seems to contradict experimental observations; however, these complexes could be transient forms. The findings presented in this work are intended to open new perspectives in the studies of Erp protein molecular interactions and to improve the knowledge about its function and role in the virulence of Mycobacterium tuberculosis.
Effect of Electrostatic Interactions on the Interfacial Energy between Thermoplastic Polymers and Graphene Oxide: A Molecular Dynamics Study
Authors: Morita Mayu , Oya Yutaka , Kato Nobuhiko , Mori Kazuki , Koyanagi Jun
Published: June 2022

In this study, the atomistic-scale mechanisms affecting the interfacial stability of a thermoplastic polymer/graphene oxide interface are investigated using molecular dynamics simulations. Different combinations of thermoplastic polymers (polyethersulfone (PES) and polyetherimide (PEI)) and graphene oxides modified with –O–, –OH, and –COOH are prepared. PES is found to be more strongly stabilized with modified/functionalized graphene oxide in the order of –COOH, –OH, –O–, which is opposite to the stability order of PEI. Our results suggest that these orders of stability are governed by a balance between the following two factors resulting from electrostatic interactions: (1) atoms with a strong charge bias attract each other, thereby stabilizing the interface; (2) the excluded-volume effect of the functional groups on graphene oxide destabilizes the interface by preventing π-π stacking of aromatic rings.
Blended Polymer Dry Electrodes for Reliable Electrocardiogram and Electromyogram Measurements and Their Eco-Friendly Disposal Led by Degradability in Hot Water
Authors: Lee Dong Hyun , Lee Eun Kwang , Kim Chae Hyun , Yun Hyung Joong , Kim Young-Joon , Yoo Hocheon
Published: June 2022

To increase the human lifespan, healthcare monitoring devices that diagnose diseases and check body conditions have attracted considerable interest. Commercial AgCl-based wet electrodes with the advantages of high conductivity and strong adaptability to human skin are considered the most frequently used electrode material for healthcare monitoring. However, commercial AgCl-based wet electrodes, when exposed for a long period, cause an evaporation of organic solvents, which could reduce the signal-to-noise ratio of biosignals and stimulate human skin. In this context, we demonstrate a dry electrode for a poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS)-based blended polymer electrode using a combination of PEDOT:PSS, waterborne polyurethane (WPU) and ethylene glycol (EG) that could be reused for a long period of time to detect electrocardiography (ECG) and electromyography (EMG). Both ECG and EMG are reliably detected by the wireless real-time monitoring system. In particular, the proposed dry electrode detects biosignals without deterioration for over 2 weeks. Additionally, a double layer of a polyimide (PI) substrate and fluorinated polymer CYTOP induces the strong waterproof characteristics of external liquids for the proposed dry electrodes, having a low surface energy of 14.49 mN/m. In addition, the proposed electrode has excellent degradability in water; it dissolves in hot water at 60 °C.
Fabrication and Characterization of Effective Biochar Biosorbent Derived from Agricultural Waste to Remove Cationic Dyes from Wastewater
Authors: Moharm Asmaa Elsherbeny , El Naeem Gamal A. , Soliman Hesham M. A. , Abd-Elhamid Ahmed I. , El-Bardan Ali A. , Kassem Taher S. , Nayl AbdElAziz A. , Bräse Stefan
Published: June 2022

The main aim of this work is to treat sugarcane bagasse agricultural waste and prepare an efficient, promising, and eco-friendly adsorbent material. Biochar is an example of such a material, and it is an extremely versatile and eco-friendly biosorbent to treat wastewater. Crystal violet (CV)-dye and methylene blue (MB)-dye species are examples of serious organic pollutants. Herein, biochar was prepared firstly from sugarcane bagasse (SCB), and then a biochar biosorbent was synthesized through pyrolysis and surface activation with NaOH. SEM, TEM, FTIR, Raman, surface area, XRD, and EDX were used to characterize the investigated materials. The reuse of such waste materials is considered eco-friendly in nature. After that, the adsorption of MB and CV-species from synthetically prepared wastewater using treated biochar was investigated under various conditions. To demonstrate the study’s effectiveness, it was attempted to achieve optimum effectiveness at an optimum level by working with time, adsorbent dose, dye concentration, NaCl, pH, and temperature. The number of adsorbed dyes reduced as the dye concentrations increased and marginally decreased with NaCl but increased with the adsorbent dosage, pH, and temperature of the solution increased. Furthermore, it climbed for around 15 min before reaching equilibrium, indicating that all pores were almost full. Under the optimum condition, the removal perecentages of both MB and CV-dyes were ≥98%. The obtained equilibrium data was represented by Langmuir and Freundlich isotherm models. Additionally, the thermodynamic parameters were examined at various temperatures. The results illustrated that the Langmuir isotherm was utilized to explain the experimental adsorption processes with maximum adsorption capacities of MB and CV-dyes were 114.42 and 99.50 mgg−1, respectively. The kinetic data were estimated by pseudo-first and pseudo-second-order equations. The best correlation coefficients of the investigated adsorption processes were described by the pseudo-second-order kinetic model. Finally, the data obtained were compared with some works published during the last four years.
Tensile Properties of Curaua–Aramid Hybrid Laminated Composites for Ballistic Helmet
Authors: Meliande Natalin Michele , Silveira Pedro Henrique Poubel Mendonça da , Monteiro Sergio Neves , Nascimento Lucio Fabio Cassiano
Published: June 2022

A typical ballistic protection helmet for ground military troops has an inside laminate polymer composite reinforced with 19 layers of the aramid, which are neither recyclable or biodegradable and are relatively expensive. The hybridization of synthetic aramid with a natural lignocellulosic fiber (NLF) can provide a lower cost and desirable sustainability to the helmet. In the present work, the curaua fiber, one of the strongest NLFs, is, for the first time, considered in non-woven mat layers to partially replace the aramid woven fabric layers. To investigate the possible advantage of this replacement, the tensile and impact properties of aramid/curaua hybrid laminated composites intended for ballistic helmets, in which up to four layers of curaua were substituted for the aramid, were evaluated. Tensile strength, toughness, and elastic modulus decreased with the replacement of the aramid while the deformation of rupture was improved for the replacement of nine aramid layers by two layers of curaua. Preliminary impact tests corroborate the decreasing tendency found in the tensile properties with the replacement of the aramid by curaua. Novel proposed Reduction Maps showed that, except for the replacement of four aramid layers by one layer of curaua, the decrease percentage of any tensile property value was lower than the corresponding volume percentage of replaced aramid, which revealed advantageous hybridization for the replacement of nine or more aramid layers.
The Impact of NPV on the Spectral Parameters in the Yellow-Edge, Red-Edge and NIR Shoulder Wavelength Regions in Grasslands
Authors: Xu Dandan , Liu Yanqing , Xu Weixin , Guo Xulin
Published: June 2022

Even though research has shown that the spectral parameters of yellow-edge, red-edge and NIR (near-infrared) shoulder wavelength regions are able to estimate green cover and leaf area index (LAI), a large amount of dead materials in grasslands challenges the accuracy of their estimation using hyperspectral remote sensing. However, the exact impact of dead vegetation cover on these spectral parameters remains unclear. Therefore, we evaluated the influences of dead materials on the spectral parameters in the wavelength regions of yellow-edge, red-edge and NIR shoulder by comparing normalized difference vegetation indices (NDVI) including the common red valley at 670 nm and NDVI using the red valley extracted by a new statistical method. This method, based on the concept of segmented linear regression, was developed to extract the spectral parameters and calculate NDVI automatically from the hyper-spectra. To fully understand the impact of dead cover on the spectral parameters (i.e., consider full coverage combinations of green vegetation, dead materials and bare soil), both in situ measured and simulated hyper-spectra were analyzed. The impact of dead cover on LAI estimation by those spectral parameters and NDVI were also evaluated. The results show that: (i) without considering the influence of bare soil, dead materials decreases the slope of red-edge, the slope of NIR shoulder and NDVI, while dead materials increases the slope of yellow-edge; (ii) the spectral characteristics of red valley disappear when dead cover exceeds 67%; (iii) large amount of dead materials also result in a blue shift of the red-edge position; (iv) accurate extraction of the red valley position enhances LAI estimation and reduces the influences of dead materials using hyperspectral NDVI; (v) the accuracy of LAI estimation using the slope of yellow-edge, the slope of red-edge, red-edge position and NDVI significantly drops when dead cover exceeds 72.3–74.5% (variation among indices).
Investigation of the Sensitivity of Microwave Land Surface Emissivity to Soil Texture in MLEM
Authors: Wu Ying , Bao Jinwang , Liu Zhiyan , Bao Yansong , Petropoulos George P.
Published: June 2022

This study analyzes the spectral characteristics of desert surface emissivity according to soil classification and the influence of mineral materials and soil texture information using simulation results from the microwave land emissivity model (MLEM). It also aims at exploring the feasibility of reducing the simulation error in MLEM by refining the soil classification characteristic parameters (such as soil composition content, distribution of particle size, etc.). The surface emissivity of the Taklimakan Desert is derived, to our knowledge for the first time, from FY-3B/MWRI (FengYun-3B Microwave Radiation Imager), and then the spectral characteristics of the study area for different soil types are further analyzed according to soil classification. In addition, emissivity spectra of the four most widely mineral materials in the desert area are reproduced using an MLEM under different conditions. Results showed that microwave land emissivity is highly correlated with the soil type and changes are markedly affected by the soil water content, soil texture, mineral composition, and soil particle size. For the desert soil, the emissivity of horizontal/vertical polarization is affected by the frequency in those soils dominated by large-size particles. However, for those dominated by smaller particles, the surface emissivity is almost constant or appears to be somehow dependent on the frequency. Moreover, the season effect on emissivity characteristics is clear, especially for soils composed of small-size particles. The emissivity of horizontal polarization shows stronger seasonal variation than that of vertical polarization. The study findings also showed that refining soil texture information (soil component content, distribution of particle size) improves the simulation accuracy in desert areas. For example, for the soil dominated by clay and clay loam, the simulation error is reduced from 6–9% to less than 6%. The latter is evident, especially for soil types containing a large number of small particles, such as clay and clay loam, for which the simulation error is reduced. All in all, our study contributes to a better understanding of the influencing factors of soil texture and stratification of the near-surface soil, helping to improve microwave land surface emissivity prediction by the studied here model. As MLEM consists of an important part of the global meteorological data assimilation and weather forecast system, results can also help towards increasing the use of satellite data in desert areas and in improving the accuracy of numerical weather forecast models.
Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
Authors: Dong Xichao , Sui Yi , Li Yuanhao , Chen Zhiyang , Hu Cheng
Published: June 2022

Due to the short revisit time and large coverage of Geosynchronous synthetic aperture radars (GEO SARs) and the increasing number of low earth orbit synthetic aperture radar (LEO SAR) constellations, radio frequency interference (RFI) between GEO SARs and LEO SARs may occur, deteriorating the quality of SAR images. Traditional methods only simplify RFI to noise-like interference without considering the signal characteristics. In this paper, to accurately evaluate the impacts of GEO-to-LEO RFI and LEO-to-GEO RFI on imaging quantitatively, an RFI-impact quantitative analysis model is established. Taking account of the chirp signal form of SAR systems, the RFI power and image Signal-to-Interference-plus-Noise Ratio (SINR) are theoretically deduced and validated by numerical experiments. Based on the proposed method, the SAR image quality under different system parameters and bistatic configurations is estimated, and the probability of different configurations is also given. The results show that specular bistatic scattering RFI between GEO SARs and LEO SARs has serious effects on imaging, and the probability can approach 2% for certain orbital parameters and will become higher as LEO SAR constellations increase in the future, implying the necessity to suppress the RFI between the GEO SAR and the LEO SAR system.
Thermal Structure of Water Exchange at the Entrance of a Tide-Dominated Strait
Authors: Bai Peng , Wang Jia , Zhao Hui , Li Bo , Yang Jingling , Li Peiliang , Zhang Tianyu
Published: June 2022

In summer, westward residual flow in the Qiongzhou Strait (QS) drives a water transport through the strait into the Beibu Gulf, playing a vital role in modifying the gulf-scale circulation. By utilizing multi-source observations, realistic simulations, as well as idealized modeling, the water exchange process at the western entrance of the QS was explored from the perspective of thermal structure. High-resolution satellite sea-surface temperature images captured an interesting thermal phenomenon in the studied region, i.e., a trident-shaped warm water tongue. Validations of numerical results suggested satisfactory model performance in reproducing the trident-shaped warm water tongue. Modeling results together with cruise observations indicates extremely weak stratification in the studied region, which is a result of local intense tidal mixing. The cold upwelling water developed at the eastern side of the strait arrived at the western entrance by the westward residual flow, and then encountered and communicated with the well-mixed warm gulf water there. The alternatively deep/shallow topography (sea trough/ridge) triggers spatial variability in tidal current intensity: stronger currents over the deep area with weaker flow above the shallow region leads to alternatively warm/cold thermal distribution in the cross-isobath direction. Further investigation demonstrates that, through modifying the circulation and regulating the intensity of upwelling on the eastern side of the QS, wind also makes a significant contribution to the thermal distribution at the western entrance of the QS.
Low-Complexity 2D DOA Estimation and Self-Calibration for Uniform Rectangle Array with Gain-Phase Error
Authors: Guo Yiduo , Hu Xiaowei , Feng Weike , Gong Jian
Published: June 2022

Most subspace-based algorithms need exact array manifold for direction of arrival (DOA) estimation, while, in practical applications, the gain-phases of different array elements are usually inconsistent, degrading their estimation performance. In this paper, a novel low-complexity 2D DOA and gain-phase error estimation algorithm is proposed by adding auxiliary array elements in a uniform rectangular array (URA). Firstly, the URA is modeled as the Kronecker product of two uniform linear arrays (ULAs) to decouple the 2D DOA estimation. Then, several well-calibrated auxiliary array elements are added in the two ULAs, based on which the rotation invariant factor of the URA destroyed by the gain-phase error is reconstructed by solving constrained optimization problems. Lastly, ESPRIT is used to estimate the 2-D DOA and the gain-phase error coefficients. The closed-form expressions of the estimation CRBs are also derived, providing insight into the impact of gain-phase error on DOA estimation. Simulation results are used to validate the effectiveness of the proposed algorithm and the correctness of the theoretical analysis.
Herbage Mass, N Concentration, and N Uptake of Temperate Grasslands Can Adequately Be Estimated from UAV-Based Image Data Using Machine Learning
Authors: Lussem Ulrike , Bolten Andreas , Kleppert Ireneusz , Jasper Jörg , Gnyp Martin Leon , Schellberg Jürgen , Bareth Georg
Published: June 2022

Precise and timely information on biomass yield and nitrogen uptake in intensively managed grasslands are essential for sustainable management decisions. Imaging sensors mounted on unmanned aerial vehicles (UAVs) along with photogrammetric structure-from-motion processing can provide timely data on crop traits rapidly and non-destructively with a high spatial resolution. The aim of this multi-temporal field study is to estimate aboveground dry matter yield (DMY), nitrogen concentration (N%) and uptake (Nup) of temperate grasslands from UAV-based image data using machine learning (ML) algorithms. The study is based on a two-year dataset from an experimental grassland trial. The experimental setup regarding climate conditions, N fertilizer treatments and slope yielded substantial variations in the dataset, covering a considerable amount of naturally occurring differences in the biomass and N status of grasslands in temperate regions with similar management strategies. Linear regression models and three ML algorithms, namely, random forest (RF), support vector machine (SVM), and partial least squares (PLS) regression were compared with and without a combination of both structural (sward height; SH) and spectral (vegetation indices and single bands) features. Prediction accuracy was quantified using a 10-fold 5-repeat cross-validation (CV) procedure. The results show a significant improvement of prediction accuracy when all structural and spectral features are combined, regardless of the algorithm. The PLS models were outperformed by their respective RF and SVM counterparts. At best, DMY was predicted with a median RMSECV of 197 kg ha−1, N% with a median RMSECV of 0.32%, and Nup with a median RMSECV of 7 kg ha−1. Furthermore, computationally less expensive models incorporating, e.g., only the single multispectral camera bands and SH metrics, or selected features based on variable importance achieved comparable results to the overall best models.
Uncertainty-Aware Interpretable Deep Learning for Slum Mapping and Monitoring
Authors: Fisher Thomas , Gibson Harry , Liu Yunzhe , Abdar Moloud , Posa Marius , Salimi-Khorshidi Gholamreza , Hassaine Abdelaali , Cai Yutong , Rahimi Kazem , Mamouei Mohammad
Published: June 2022

Over a billion people live in slums, with poor sanitation, education, property rights and working conditions having a direct impact on current residents and future generations. Slum mapping is one of the key problems concerning slums. Policymakers need to delineate slum settlements to make informed decisions about infrastructure development and allocation of aid. A wide variety of machine learning and deep learning methods have been applied to multispectral satellite images to map slums with outstanding performance. Since the physical and visual manifestation of slums significantly varies with geographical region and comprehensive slum maps are rare, it is important to quantify the uncertainty of predictions for reliable and confident application of models to downstream tasks. In this study, we train a U-Net model with Monte Carlo Dropout (MCD) on 13-band Sentinel-2 images, allowing us to calculate pixelwise uncertainty in the predictions. The obtained outcomes show that the proposed model outperforms the previous state-of-the-art model, having both higher AUPRC and lower uncertainty when tested on unseen geographical regions of Mumbai using the regional testing framework introduced in this study. We also use SHapley Additive exPlanations (SHAP) values to investigate how the different features contribute to our model’s predictions which indicate a certain shortwave infrared image band is a powerful feature for determining the locations of slums within images. With our results, we demonstrate the usefulness of including an uncertainty quantification approach in detecting slum area changes over time.
Detection of Standing Dead Trees after Pine Wilt Disease Outbreak with Airborne Remote Sensing Imagery by Multi-Scale Spatial Attention Deep Learning and Gaussian Kernel Approach
Authors: Han Zemin , Hu Wenjie , Peng Shoulian , Lin Haoran , Zhang Jian , Zhou Jingjing , Wang Pengcheng , Dian Yuanyong
Published: June 2022

The continuous and extensive pinewood nematode disease has seriously threatened the sustainable development of forestry in China. At present, many studies have used high-resolution remote sensing images combined with a deep semantic segmentation algorithm to identify standing dead trees in the red attack period. However, due to the complex background, closely distributed detection scenes, and unbalanced training samples, it is difficult to detect standing dead trees (SDTs) in a variety of complex scenes by using conventional segmentation models. In order to further solve the above problems and improve the recognition accuracy, we proposed a new detection method called multi-scale spatial supervision convolutional network (MSSCN) to identify SDTs in a wide range of complex scenes based on airborne remote sensing imagery. In the method, a Gaussian kernel approach was used to generate a confidence map from SDTs marked as points for training samples, and a multi-scale spatial attention block was added into fully convolutional neural networks to reduce the loss of spatial information. Further, an augmentation strategy called copy–pasting was used to overcome the lack of efficient samples in this research area. Validation at four different forest areas belonging to two forest types and two diseased outbreak intensities showed that (1) the copy–pasting method helps to augment training samples and can improve the detecting accuracy with a suitable oversampling rate, and the best oversampling rate should be carefully determined by the input training samples and image data. (2) Based on the two-dimensional spatial Gaussian kernel distribution function and the multi-scale spatial attention structure, the MSSCN model can effectively find the dead tree extent in a confidence map, and by following this with maximum location searching we can easily locate the individual dead trees. The averaged precision, recall, and F1-score across different forest types and disease-outbreak-intensity areas can achieve 0.94, 0.84, and 0.89, respectively, which is the best performance among FCN8s and U-Net. (3) In terms of forest type and outbreak intensity, the MSSCN performs best in pure pine forest type and low-outbreak-intensity areas. Compared with FCN8s and U-Net, the MSSCN can achieve the best recall accuracy in all forest types and outbreak-intensity areas. Meanwhile, the precision metric is also maintained at a high level, which means that the proposed method provides a trade-off between the precision and recall in detection accuracy.
Defense against Adversarial Swarms with Parameter Uncertainty
Authors: Walton Claire , Kaminer Isaac , Gong Qi , Clark Abram H. , Tsatsanifos Theodoros
Published: June 2022

This paper addresses the problem of optimal defense of a high-value unit (HVU) against a large-scale swarm attack. We discuss multiple models for intra-swarm cooperation strategies and provide a framework for combining these cooperative models with HVU tracking and adversarial interaction forces. We show that the problem of defending against a swarm attack can be cast in the framework of uncertain parameter optimal control. We discuss numerical solution methods, then derive a consistency result for the dual problem of this framework, providing a tool for verifying computational results. We also show that the dual conditions can be computed numerically, providing further computational utility. Finally, we apply these numerical results to derive optimal defender strategies against a 100-agent swarm attack.
Data Analysis and Filter Optimization for Pulse-Amplitude Measurement: A Case Study on High-Resolution X-ray Spectroscopy
Authors: Mannatunga Kasun Sameera , Valinoti Bruno , Florian Samayoa Werner , Crespo Maria Liz , Cicuttin Andres , Folla Kamdem Jerome , Garcia Luis Guillermo , Carrato Sergio
Published: June 2022

In this study, we present a procedure to optimize a set of finite impulse response filter (FIR) coefficients for digital pulse-amplitude measurement. Such an optimized filter is designed using an adapted digital penalized least mean square (DPLMS) method. The effectiveness of the procedure is demonstrated using a dataset from a case study on high-resolution X-ray spectroscopy based on single-photon detection and energy measurements. The energy resolutions of the Kα and Kβ lines of the Manganese energy spectrum have been improved by approximately 20%, compared to the reference values obtained by fitting individual photon pulses with the corresponding mathematical model.
Design of Cone Penetration Test Data Relay Transmission by Magnetic Communication
Authors: Yu Xiuxin , Liu Shuting , Pei Hongxing
Published: June 2022

At present, most cone penetration test experiments use cables to transmit data. The cables not only make the exploration operation very complicated, but also hinder the realization of automatic exploration. An excessively long cable can also bring about additional attenuation and noise during the transmission of probe signal. In order to simplify the procedures of exploration operation and to improve the detection accuracy, a cableless cone penetration test system is proposed in this study. It improved the system by using magnetic communication and converts the electrical signal into a magnetic signal at the connection of two adjacent probe rods for relay transmission. Exploratory experiments were carried out to evaluate the feasibility and accuracy of the new system. The experimental results show that the experimental data collected by the new system is more accurate than that collected by traditional CPT equipment with cable. The new system simplifies exploration operations and enables real-time data transmission to detect abnormalities in time. This anomaly usually means that the probe is pressed against hard rock. It is more convenient and accurate to use the new system for exploration.
Non-Invasive Acoustic Monitoring of Gas Turbine Units by Fiber Optic Sensors
Authors: Stepanov Konstantin V. , Zhirnov Andrey A. , Sazonkin Stanislav G. , Pnev Alexey B. , Bobrov Alexander N. , Yagodnikov Dmitriy A.
Published: June 2022

In this article, we study the possibility of gas turbine unit (GTU) monitoring using interferometric fiber optic sensors. We used the Mach–Zehnder interferometer (MZI) scheme, which can be easily implemented and simply installed on the turbine, and also allows us to solve the problem of phase unwrapping conveniently. In this research, the following main steps were carried out: an experimental scheme based on the MZI was assembled, and its sensitive arm was fixed on the GTU under study; data on various operation modes of the GTU was collected; the data were subjected to frequency FFT analysis, based on which the main rotational speeds of the turbine were identified. With FFT analysis, we also demonstrated multiples harmonics, which appear in the case of GTU after operating time, caused by the number of blades. The possibility of GTU monitoring and analysis using a non-invasive compact fiber-optic sensor is demonstrated: spectral analysis is used to detect the rotor speed, as well as the presence or absence of high-order multiple frequencies indicating blade and bearing defects, which are determined by the number of GTU’s blades and rolling bearing used as turbines rotor supports.
Injection Barrel/Nozzle/Mold-Cavity Scientific Real-Time Sensing and Molding Quality Monitoring for Different Polymer-Material Processes
Authors: Liew Kai-Fu , Peng Hsin-Shu , Huang Po-Wei , Su Wei-Jie
Published: June 2022

Scientific injection molding technologies involve the integration and collaboration of cyber-physical systems and smart manufacturing. In order to achieve adaptive process control and production optimization, injection molding systems with real-time sensing have gradually become the development- and application-trend of smart injection molding. At the same time, this technology is a highly non-linear process in which many factors affect the product quality during long-run fabrication processes. Therefore, in order to grasp changes in the characteristics of plastic materials and product quality monitoring, the injection process has become an important research topic. We installed sensors in the molding machine (injection barrel, nozzle, and mold-cavity) to collect the melting pressure and used different materials (semi-crystalline and amorphous polymer; the melting-fill-index (MFI) is unified to 14.5 ± 0.5 g/10 min) to explore the influences of melting pressure variation and its viscosity index on the quality characteristics of molded products. The experiment reveals that a combination of barrel, nozzle, and mold-cavity sensing on the melt-pressure trend-based injection process-control incorporated with viscosity index monitoring can confirm the weight and shrinkage variation of the injection product. At the same time, the pressure and viscosity index value measured and calculated during the melt-filling of two materials with similar MI resulted in significant variations in the amorphous polymer. This study showed the possibility of mastering and controlling the rheology (barrel position) and shrinkage properties of polymers and successful application in various product-quality monitoring platforms.
GridAttackAnalyzer: A Cyber Attack Analysis Framework for Smart Grids
Authors: Le Tan Duy , Ge Mengmeng , Anwar Adnan , Loke Seng W. , Beuran Razvan , Doss Robin , Tan Yasuo
Published: June 2022

The smart grid is one of the core technologies that enable sustainable economic and social developments. In recent years, various cyber attacks have targeted smart grid systems, which have led to severe, harmful consequences. It would be challenging to build a real smart grid system for cybersecurity experimentation and validation purposes. Hence, analytical techniques, with simulations, can be considered as a practical solution to make smart grid cybersecurity experimentation possible. This paper first provides a literature review on the current state-of-the-art in smart grid attack analysis. We then apply graphical security modeling techniques to design and implement a Cyber Attack Analysis Framework for Smart Grids, named GridAttackAnalyzer. A case study with various attack scenarios involving Internet of Things (IoT) devices is conducted to validate the proposed framework and demonstrate its use. The functionality and user evaluations of GridAttackAnalyzer are also carried out, and the evaluation results show that users have a satisfying experience with the usability of GridAttackAnalyzer. Our modular and extensible framework can serve multiple purposes for research, cybersecurity training, and security evaluation in smart grids.
Energy–Accuracy Aware Finger Gesture Recognition for Wearable IoT Devices
Authors: Jung Woosoon , Lee Hyung Gyu
Published: June 2022

Wearable Internet of Things (IoT) devices can be used efficiently for gesture recognition applications. The nature of these applications requires high recognition accuracy with low energy consumption, which is not easy to solve at the same time. In this paper, we design a finger gesture recognition system using a wearable IoT device. The proposed recognition system uses a light-weight multi-layer perceptron (MLP) classifier which can be implemented even on a low-end micro controller unit (MCU), with a 2-axes flex sensor. To achieve high recognition accuracy with low energy consumption, we first design a framework for the finger gesture recognition system including its components, followed by system-level performance and energy models. Then, we analyze system-level accuracy and energy optimization issues, and explore the numerous design choices to finally achieve energy–accuracy aware finger gesture recognition, targeting four commonly used low-end MCUs. Our extensive simulation and measurements using prototypes demonstrate that the proposed design achieves up to 95.5% recognition accuracy with energy consumption under 2.74 mJ per gesture on a low-end embedded wearable IoT device. We also provide the Pareto-optimal designs among a total of 159 design choices to achieve energy–accuracy aware design points under given energy or accuracy constraints.
Integrated Indoor Positioning System of Greenhouse Robot Based on UWB/IMU/ODOM/LIDAR
Authors: Long Zhenhuan , Xiang Yang , Lei Xiangming , Li Yajun , Hu Zhengfang , Dai Xiufeng
Published: June 2022

Conventional mobile robots employ LIDAR for indoor global positioning and navigation, thus having strict requirements for the ground environment. Under the complicated ground conditions in the greenhouse, the accumulative error of odometer (ODOM) that arises from wheel slip is easy to occur during the long-time operation of the robot, which decreases the accuracy of robot positioning and mapping. To solve the above problem, an integrated positioning system based on UWB (ultra-wideband)/IMU (inertial measurement unit)/ODOM/LIDAR is proposed. First, UWB/IMU/ODOM is integrated by the Extended Kalman Filter (EKF) algorithm to obtain the estimated positioning information. Second, LIDAR is integrated with the established two-dimensional (2D) map by the Adaptive Monte Carlo Localization (AMCL) algorithm to achieve the global positioning of the robot. As indicated by the experiments, the integrated positioning system based on UWB/IMU/ODOM/LIDAR effectively reduced the positioning accumulative error of the robot in the greenhouse environment. At the three moving speeds, including 0.3 m/s, 0.5 m/s, and 0.7 m/s, the maximum lateral error is lower than 0.1 m, and the maximum lateral root mean square error (RMSE) reaches 0.04 m. For global positioning, the RMSEs of the x-axis direction, the y-axis direction, and the overall positioning are estimated as 0.092, 0.069, and 0.079 m, respectively, and the average positioning time of the system is obtained as 72.1 ms. This was sufficient for robot operation in greenhouse situations that need precise positioning and navigation.
Development of a Multi-Layer Marking Toolkit for Layout-Printing Automation at Construction Sites
Authors: Park Eun Soo , Seo Hee Chang , Lee An Yong
Published: June 2022

In this study, the development of a multi-layer marking toolkit was investigated to improve construction quality and mitigate the problem of irregular designs in the layout-printing work performed at construction sites. The quality of conventional layout-printing work is dependent on the skill of the worker, and construction quality can suffer owing to inconsistencies in drawings resulting from human error. In this study, these problems were analyzed, and a construction-site-layout-marking toolkit apparatus and mechanical unit, with a structure that allowed for multi-layer installation for automated implementation at construction sites, were developed. The marking toolkit and mechanical unit with the multi-layer structure were developed in a modular form so that each module can operate independently. Furthermore, each module was developed in manual mode to improve the system by acquiring information on the movement of the marking toolkit and multi-layer structure. Additionally, data on the layout-printing method was developed by connecting the system via Ethernet and operating a wireless joystick. Finally, experiments were performed on a road surface covered with B4 paper and concrete panels to confirm the operational feasibility of the system, which was developed to operate manually.
Integration of Scales and Cameras in Nondisruptive Electronic Beehive Monitoring: On the Within-Day Relationship of Hive Weight and Traffic in Honeybee (Apis mellifera) Colonies in Langstroth Hives in Tucson, Arizona, USA
Authors: Kulyukin Vladimir , Tkachenko Anastasiia , Price Kristoffer , Meikle William , Weiss Milagra
Published: June 2022

The relationship between beehive weight and traffic is a fundamental open research problem for electronic beehive monitoring and digital apiculture, because weight and traffic affect many aspects of honeybee (Apis mellifera) colony dynamics. An investigation of this relationship was conducted with a nondisruptive two-sensor (scale and camera) system on the weight and video data collected on six Apis mellifera colonies in Langstroth hives at the USDA-ARS Carl Hayden Bee Research Center in Tucson, Arizona, USA, from 15 May to 15 August 2021. Three hives had positive and two hives had negative correlations between weight and traffic. In one hive, weight and traffic were uncorrelated. The strength of the correlation between weight and traffic was stronger for longer time intervals. The traffic spread and mean, when taken separately, did not affect the correlation between weight and traffic more significantly than the exact traffic counts from videos. Lateral traffic did not have a significant impact on weight.