bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Salicylic Acid and Macro- and Micronutrients through Foliar and Soil Applications on the Agronomic Performance, Physiological Attributes, and Water Productivity of Wheat under Normal and Limited Irrigation in Dry Climatic Conditions
Authors: Alotaibi Majed , El-Hendawy Salah , Mohammed Nabil , Alsamin Bazel , Al-Suhaibani Nasser , Refay Yahya
Published: June 2023

Ensuring food security with severe shortages of freshwater and drastic changes in climatic conditions in arid countries requires the urgent development of feasible and user-friendly strategies. Relatively little is known regarding the impacts of the co-application (Co-A) of salicylic acid (SA), macronutrients (Mac), and micronutrients (Mic) through foliar (F) and soil (S) application strategies on field crops under arid and semiarid climatic conditions. A two-year field experiment was designed to compare the impacts of seven (Co-A) treatments of this strategy, including a control, FSA+Mic, FSA+Mac, SSA + FMic, SSA + FSA+Mic, SSA+Mic + FSA, and SSA+Mic + FMac+Mic on the agronomic performance, physiological attributes, and water productivity (WP) of wheat under normal (NI) and limited (LMI) irrigation conditions. The results reveal that the LMI treatment caused a significant reduction in various traits related to the growth (plant height, tiller and green leaf numbers, leaf area index, and shoot dry weight), physiology (relative water content and chlorophyll pigments), and yield components (spike length, grain weight and grain numbers per spike, thousand-grain weight, and harvest index) of wheat by 11.4–47.8%, 21.8–39.8%, and 16.4–42.3%, respectively, while WP increased by 13.3% compared to the NI treatment. The different Co-A treatments have shown a 0.2–23.7%, 3.6–26.7%, 2.3–21.6%, and 12.2–25.0% increase in various traits related to growth, physiology, yield, and WP, respectively, in comparison to the control treatment. The SSA+ FSA+Mic was determined as the best treatment that achieved the best results for all studied traits under both irrigation conditions, followed by FSA+Mic and SSA+Mic + FSA under LMI in addition to FSA+Mac under NI conditions. It can be concluded that the Co-A of essential plant nutrients along with SA accomplished a feasible, profitable, and easy-to-use strategy to attenuate the negative impacts of deficit irrigation stress, along with the further improvement in the growth and production of wheat under NI conditions.
Removal, Adsorption, and Cleaning of Pharmaceutical on Polyamide RO and NF Membranes
Authors: Dolar Davor , Ćurić Iva , Ašperger Danijela
Published: June 2023

Pharmaceuticals are present in various waters and can be almost completely rejected by membrane separation processes, i.e., nanofiltration (NF) and reverse osmosis (RO). Nevertheless, the adsorption of pharmaceuticals can decrease their rejection, so adsorption can be considered a very important removal mechanism. In order to increase the lifetime of the membranes, the adsorbed pharmaceuticals must be cleaned from the membrane. The used pharmaceutical (albendazole), the most common anthelmintic for threatening worms, has been shown to adsorb to the membrane (solute-membrane adsorption). In this paper, which is a novelty, commercially available cleaning reagents, NaOH/EDTA solution, and methanol (20%, 50%, and ≥99.6%) were used for pharmaceutical cleaning (desorption) of the NF/RO membranes used. The effectiveness of the cleaning was verified by Fourier-transform infrared spectra of the membranes. Of all the chemical cleaning reagents used, pure methanol was the only cleaning reagent that removed albendazole from the membranes.
Sustainable Elastomers for Actuators: “Green” Synthetic Approaches and Material Properties
Authors: Filippova Olga V. , Maksimkin Aleksey V. , Dayyoub Tarek , Larionov Dmitry I. , Telyshev Dmitry V.
Published: June 2023

Elastomeric materials have great application potential in actuator design and soft robot development. The most common elastomers used for these purposes are polyurethanes, silicones, and acrylic elastomers due to their outstanding physical, mechanical, and electrical properties. Currently, these types of polymers are produced by traditional synthetic methods, which may be harmful to the environment and hazardous to human health. The development of new synthetic routes using green chemistry principles is an important step to reduce the ecological footprint and create more sustainable biocompatible materials. Another promising trend is the synthesis of other types of elastomers from renewable bioresources, such as terpenes, lignin, chitin, various bio-oils, etc. The aim of this review is to address existing approaches to the synthesis of elastomers using “green” chemistry methods, compare the properties of sustainable elastomers with the properties of materials produced by traditional methods, and analyze the feasibility of said sustainable elastomers for the development of actuators. Finally, the advantages and challenges of existing “green” methods of elastomer synthesis will be summarized, along with an estimation of future development prospects.
A New Study on Fushi of Early Quanzhen Daoism
Authors: Chen Hongyi , Huang Yongfeng
Published: June 2023

Fushi (服食), a method for treating diseases and nourishing life to achieve longevity, is highly valued and widely used in traditional Taoism. Regarding whether Quanzhen Taoism, a new form of Taoism founded in the Jin Dynasty (1115–1234), practices Fushi, contradictory opinions have been recorded in Collected records written on Qingyan Mountain (Qingyan conglu 青巖叢錄) from the end of the Yuan Dynasty (1271–1368) and The History of the Taoist School founded by (Qiu) Changchun (Changchun daojiao yuanliu 長春道教源流) from the late Qing Dynasty (1636–1912). Today’s scholars generally believe that Quanzhen Taoism emphasizes the cultivation of heart and mind and thus has nothing to do with Fushi. This article, centered around early Quanzhen Taoism representatives Wang Chongyang 王重陽 (1112–1170) and the “Seven True Ones (Qizhen 七真)”, combines their writings, quotations, biographies, and other materials and discovers that while Wang Chongyang and others heavily criticized the traditional method of Fushi, they also carried out extensive Fushi activities and accumulated rich practical experience in areas such as taking medicine (fuyao 服藥), breathing exercises (fuqi 服氣), fasting (bigu 辟穀), dieting (yinshi 飲食), and using talismans (fufu 服符). Early Quanzhen Taoism both denied and utilized Fushi leading to a contradiction between words and deeds. The reasons for this contradiction can be attributed to two aspects: the internal alchemy thinking of the early Quanzhen Taoism that prioritized Tao over technique (shu 術), and dual cultivation of inner nature (xing 性) and life (ming 命) and prioritizing the former over the latter.
Assessing the Magnitude of the Amazonian Forest Blowdowns and Post-Disturbance Recovery Using Landsat-8 and Time Series of PlanetScope Satellite Constellation Data
Authors: Ping Dazhou , Dalagnol Ricardo , Galvão Lênio Soares , Nelson Bruce , Wagner Fabien , Schultz David M. , Bispo Polyanna da C.
Published: June 2023

Blowdown events are a major natural disturbance in the central Amazon Forest, but their impact and subsequent vegetation recovery have been poorly understood. This study aimed to track post-disturbance regeneration after blowdown events in the Amazon Forest. We analyzed 45 blowdown sites identified after September 2020 at Amazonas, Mato Grosso, and Colombia jurisdictions using Landsat-8 and PlanetScope NICFI satellite imagery. Non-photosynthetic vegetation (NPV), green vegetation (GV), and shade fractions were calculated for each image and sensor using spectral mixture analysis in Google Earth Engine. The results showed that PlanetScope NICFI data provided more regular and higher-spatial-resolution observations of blowdown areas than Landsat-8, allowing for more accurate characterization of post-disturbance vegetation recovery. Specifically, NICFI data indicated that just four months after the blowdown event, nearly half of ΔNPV, which represents the difference between the NPV after blowdown and the NPV before blowdown, had disappeared. ΔNPV and GV values recovered to pre-blowdown levels after approximately 15 months of regeneration. Our findings highlight that the precise timing of blowdown detection has huge implications on quantification of the magnitude of damage. Landsat data may miss important changes in signal due to the difficulty of obtaining regular monthly observations. These findings provide valuable insights into vegetation recovery dynamics following blowdown events.
Shoreline Analysis and Extraction Tool (SAET): A New Tool for the Automatic Extraction of Satellite-Derived Shorelines with Subpixel Accuracy
Authors: Palomar-Vázquez Jesús , Pardo-Pascual Josep E. , Almonacid-Caballer Jaime , Cabezas-Rabadán Carlos
Published: June 2023

SAET (Shoreline Analysis and Extraction Tool) is a novel open-source tool to enable the completely automatic detection of shoreline position changes using the optical imagery acquired by the Sentinel-2 and Landsat 8 and 9 satellites. SAET has been developed within the ECFAS (European Coastal Flood Awareness System) project, which is intended to be the first European service for coastal flood forecasting, management, and recovery analysis. The tool is developed to characterise the shoreline response associated with punctual events such as coastal storms as well as any other phenomenon. For a given beach segment, SAET facilitates the selection of the satellite images closest in time to the analysed events that offer an adequate cloud coverage level for analysing the shoreline change. Subsequently, the tool automatically downloads the images from their official repositories, pre-processes them and extracts the shoreline position with sub-pixel accuracy. In order to do so, an initial approximate definition of the shoreline is carried out at the pixel level using a water index thresholding, followed by an accurate extraction operating on the shortwave infrared bands to produce a sub-pixel line in vector formats (points and lines). The tool offers different settings to be adapted to the different coastal environments and beach typologies. Its main advantages refer to its autonomy, its efficiency in extracting complete satellite scenes, its flexibility in adapting to different environments and conditions, and its high subpixel accuracy. This work presents an accuracy assessment on a long Mediterranean sandy beach of SDSs extracted from L8 and S2 imagery against coincident alongshore reference lines, showing an accuracy of about 3 m RMSE. At the same time, the work shows an example of the usage of SAET for characterising the response to Storm Gloria (January 2020) on the beaches of Valencia (E Spain). SAET provides an efficient and completely automatic workflow that leads to accurate SDSs while only relying on publicly available information. The tool appears to be a useful extraction tool for beach monitoring, both for public administrations and individual users.
Region-Based Sea Ice Mapping Using Compact Polarimetric Synthetic Aperture Radar Imagery with Learned Features and Contextual Information
Authors: Taleghanidoozdoozan Saeid , Xu Linlin , Clausi David A.
Published: June 2023

Operational sea ice maps are usually generated manually using dual-polarization (DP) synthetic aperture radar (SAR) satellite imagery, but there is strong interest in automating this process. Recently launched satellites offer compact polarimetry (CP) imagery that provides more comprehensive polarimetric information compared to DP, which compels the use of CP for automated classification of SAR sea ice imagery. Existing sea ice scene classification algorithms using CP imagery rely on handcrafted features, while neural networks offer the potential of features that are more discriminating. We have developed a new and effective sea ice classification algorithm that leverages the nature of CP data. First, a residual-based convolutional neural network (ResCNN) is implemented to classify each pixel. In parallel, an unsupervised segmentation is performed to generate regions based on CP statistical properties. Regions are assigned a single class label by majority voting using the ResCNN output. For testing, quad-polarimetric (QP) SAR sea ice scenes from the RADARSAT Constellation Mission (RCM) are used, and QP, DP, CP, and reconstructed QP modes are compared for classification accuracy, while also comparing them to other classification approaches. Using CP achieves an overall accuracy of 96.86%, which is comparable to QP (97.16%), and higher than reconstructed QP and DP data by about 2% and 10%, respectively. The implemented algorithm using CP imagery provides an improved option for automated sea ice mapping.
GRACE Data Quantify Water Storage Changes in the Shiyang River Basin, an Inland River in the Arid Zone
Authors: Meng Gaojia , Zhu Guofeng , Liu Jiawei , Zhao Kailiang , Lu Siyu , Li Rui , Qiu Dongdong , Jiao Yinying , Chen Longhu , Sun Niu
Published: June 2023

Global changes and human activities have significantly altered water cycle processes and water resource patterns in inland river basins in arid zones. New tools are needed to conduct more comprehensive and scientific assessments of basin water cycle processes and water resource patterns. Based on GRACE satellite and Landsat data, this study investigated terrestrial water storage changes and surface water area in the Shiyang River Drainage Basin from 2002 to 2021. It explored the effects of climate change and water conservancy construction on terrestrial water storage changes in the basin. The results of the study show that, although the surface water quantity in the Shiyang River basin has increased in the past 20 years, the overall decreasing trend of terrestrial water storage in the basin of the Shiyang River has an interannual decreasing rate of 0.01 cm/a. The decreasing trend of water storage in the midstream and downstream areas is more prominent. The change in precipitation controls the change in water storage in the Shiyang River Drainage Basin. Artificial water transfer has changed the spatial distribution of water resources in the basin of the Shiyang River. However, it still has not completely reversed the trend of decreasing water storage in the middle and lower reaches of the Shiyang River.
Multi-Temporal Satellite Investigation of gas Flaring in Iraq and Iran: The DAFI Porting on Collection 2 Landsat 8/9 and Sentinel 2A/B
Authors: Faruolo Mariapia , Genzano Nicola , Marchese Francesco , Pergola Nicola
Published: June 2023

The synergic use of satellite data at moderate spatial resolution (i.e., 20–30 m) from the new Collection 2 (C2) Landsat-8/9 (L8/9) Operational Land Imager (OLI) and Sentinel-2 (S2) Multispectral Instrument (MSI) provides a new perspective in the remote sensing applications for gas flaring (GF) identification and monitoring, thanks to a significant improvement in the revisiting time (up to ~3 days). In this study, the daytime approach for gas flaring investigation (DAFI), recently developed for identifying, mapping and monitoring GF sites on a global scale using the L8 infrared radiances, has been ported on a virtual constellation (VC) (formed by C2 L8/9 + S2) to assess its capability in understanding the GF characteristics in the space-time domain. The findings achieved for the regions of Iraq and Iran, ranked at the second and third level among the top 10 gas flaring countries in 2022, demonstrate the reliability of the developed system, with improved levels of accuracy and sensitivity (+52%). As an outcome of this study, a more realistic picture of GF sites and their behavior is achieved. A new step aimed at quantifying the GFs radiative power (RP) has been added in the original DAFI configuration. The preliminary analysis of the daily OLI- and MSI-based RP, provided for all the sites by means of a modified RP formulation, revealed their good matching. An agreement of 90% and 70% between the annual RPs computed in Iraq and Iran and both their gas-flared volumes and carbon dioxide emissions were also recorded. Being that gas flaring is one of the main sources of greenhouse gases (GHG) worldwide, the RP products may concur to infer globally the GHGs GF emissions at finer spatial scales. For the presented achievements, DAFI can be seen as a powerful satellite tool able to automatically assess the gas flaring dimension on a global scale.
Optimal Control Algorithm for Stochastic Systems with Parameter Drift
Authors: Zhang Xiaoyan , Gao Song , Chen Chaobo , Huang Jiaoru
Published: June 2023

A novel optimal control problem is considered for multiple input multiple output (MIMO) stochastic systems with mixed parameter drift, external disturbance and observation noise. The proposed controller can not only track and identify the drift parameters in finite time but, furthermore, drive the system to move towards the desired trajectory. However, there is a conflict between control and estimation, which makes the analytic solution unattainable in most situations. A dual control algorithm based on weight factor and innovation is, therefore, proposed. First, the innovation is added to the control goal by the appropriate weight and the Kalman filter is introduced to estimate and track the transformed drift parameters. The weight factor is used to adjust the degree of drift parameter estimation in order to achieve a balance between control and estimation. Then, the optimal control is derived by solving the modified optimization problem. In this strategy, the analytic solution of the control law can be obtained. The control law obtained in this paper is optimal because the estimation of drift parameters is integrated into the objective function rather than the suboptimal control law, which includes two parts of control and estimation in other studies. The proposed algorithm can achieve the best compromise between optimization and estatimation. Finally, the effectiveness of the algorithm is verified by numerical experiments in two different cases.
Analytical Models for Pose Estimate Variance of Planar Fiducial Markers for Mobile Robot Localisation
Authors: Adámek Roman , Brablc Martin , Vávra Patrik , Dobossy Barnabás , Formánek Martin , Radil Filip
Published: June 2023

Planar fiducial markers are commonly used to estimate a pose of a camera relative to the marker. This information can be combined with other sensor data to provide a global or local position estimate of the system in the environment using a state estimator such as the Kalman filter. To achieve accurate estimates, the observation noise covariance matrix must be properly configured to reflect the sensor output’s characteristics. However, the observation noise of the pose obtained from planar fiducial markers varies across the measurement range and this fact needs to be taken into account during the sensor fusion to provide a reliable estimate. In this work, we present experimental measurements of the fiducial markers in real and simulation scenarios for 2D pose estimation. Based on these measurements, we propose analytical functions that approximate the variances of pose estimates. We demonstrate the effectiveness of our approach in a 2D robot localisation experiment, where we present a method for estimating covariance model parameters based on user measurements and a technique for fusing pose estimates from multiple markers.
Optical Frequency-Domain Reflectometry Based Distributed Temperature Sensing Using Rayleigh Backscattering Enhanced Fiber
Authors: Lu Ziyi , Feng Ting , Li Fang , Yao Xiaotian Steve
Published: June 2023

An innovative optical frequency-domain reflectometry (OFDR)-based distributed temperature sensing method is proposed that utilizes a Rayleigh backscattering enhanced fiber (RBEF) as the sensing medium. The RBEF features randomly high backscattering points; the analysis of the fiber position shift of these points before and after the temperature change along the fiber is achieved using the sliding cross-correlation method. The fiber position and temperature variation can be accurately demodulated by calibrating the mathematical relationship between the high backscattering point position along the RBEF and the temperature variation. Experimental results reveal a linear relationship between temperature variation and the total position displacement of high backscattering points. The temperature sensing sensitivity coefficient is 7.814 μm/(m·°C), with an average relative error temperature measurement of −1.12% and positioning error as low as 0.02 m for the temperature-influenced fiber segment. In the proposed demodulation method, the spatial resolution of temperature sensing is determined by the distribution of high backscattering points. The temperature sensing resolution depends on the spatial resolution of the OFDR system and the length of the temperature-influenced fiber. With an OFDR system spatial resolution of 12.5 μm, the temperature sensing resolution reaches 0.418 °C per meter of RBEF under test.
Lane Detection Algorithm in Curves Based on Multi-Sensor Fusion
Authors: Zhang Qiang , Liu Jianze , Jiang Xuedong
Published: June 2023

Identifying lane markings is a key technology in assisted driving and autonomous driving. The traditional sliding window lane detection algorithm has good detection performance in straight lanes and curves with small curvature, but its detection and tracking performance is poor in curves with larger curvature. Large curvature curves are common scenes in traffic roads. Therefore, in response to the problem of poor lane detection performance of traditional sliding window lane detection algorithms in large curvature curves, this article improves the traditional sliding window algorithm and proposes a sliding window lane detection calculation method, which integrates steering wheel angle sensors and binocular cameras. When a vehicle first enters a bend, the curvature of the bend is not significant. Traditional sliding window algorithms can effectively detect the lane line of the bend and provide angle input to the steering wheel, enabling the vehicle to travel along the lane line. However, as the curvature of the curve increases, traditional sliding window lane detection algorithms cannot track lane lines well. Considering that the steering wheel angle of the car does not change much during the adjacent sampling time of the video, the steering wheel angle of the previous frame can be used as input for the lane detection algorithm of the next frame. By using the steering wheel angle information, the search center of each sliding window can be predicted. If the number of white pixels within the rectangular range centered around the search center is greater than the threshold, the average of the horizontal coordinate values of these white pixels will be used as the horizontal coordinate value of the sliding window center. Otherwise, the search center will be used as the center of the sliding window. A binocular camera is used to assist in locating the position of the first sliding window. The simulation and experimental results show that compared with traditional sliding window lane detection algorithms, the improved algorithm can better recognize and track lane lines with large curvature in bends.
Continuous Monitoring of Health and Mobility Indicators in Patients with Cardiovascular Disease: A Review of Recent Technologies
Authors: Shiwani Muhammad Ali , Chico Timothy J. A. , Ciravegna Fabio , Mihaylova Lyudmila
Published: June 2023

Cardiovascular diseases kill 18 million people each year. Currently, a patient’s health is assessed only during clinical visits, which are often infrequent and provide little information on the person’s health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring.
Hyperspectral Infrared Observations of Arctic Snow, Sea Ice, and Non-Frozen Ocean from the RV Polarstern during the MOSAiC Expedition October 2019 to September 2020
Authors: Nikolla Ester , Knuteson Robert , Gero Jonathan
Published: June 2023

This study highlights hyperspectral infrared observations from the Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) collected as part of the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) deployment on the icebreaker RV Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. The ARM M-AERI directly measures the infrared radiance emission spectrum between 520 cm−1 and 3000 cm−1 (19.2–3.3 μm) at 0.5 cm−1 spectral resolution. These ship-based observations provide a valuable set of radiance data for the modeling of snow/ice infrared emission as well as validation data for the assessment of satellite soundings. Remote sensing using hyperspectral infrared observations provides valuable information on sea surface properties (skin temperature and infrared emissivity), near-surface air temperature, and temperature lapse rate in the lowest kilometer. Comparison of the M-AERI observations with those from the DOE ARM meteorological tower and downlooking infrared thermometer are generally in good agreement with some notable differences. Operational satellite soundings from the NOAA-20 satellite were also assessed using ARM radiosondes launched from the RV Polarstern and measurements of the infrared snow surface emission from the M-AERI showing reasonable agreement.
A Study on the Influence of Sensors in Frequency and Time Domains on Context Recognition
Authors: de Souza Pedro , Silva Diógenes , de Andrade Isabella , Dias Júlia , Lima João Paulo , Teichrieb Veronica , Quintino Jonysberg P. , da Silva Fabio Q. B. , Santos Andre L. M.
Published: June 2023

Adaptive AI for context and activity recognition remains a relatively unexplored field due to difficulty in collecting sufficient information to develop supervised models. Additionally, building a dataset for human context activities “in the wild” demands time and human resources, which explains the lack of public datasets available. Some of the available datasets for activity recognition were collected using wearable sensors, since they are less invasive than images and precisely capture a user’s movements in time series. However, frequency series contain more information about sensors’ signals. In this paper, we investigate the use of feature engineering to improve the performance of a Deep Learning model. Thus, we propose using Fast Fourier Transform algorithms to extract features from frequency series instead of time series. We evaluated our approach on the ExtraSensory and WISDM datasets. The results show that using Fast Fourier Transform algorithms to extract features performed better than using statistics measures to extract features from temporal series. Additionally, we examined the impact of individual sensors on identifying specific labels and proved that incorporating more sensors enhances the model’s effectiveness. On the ExtraSensory dataset, the use of frequency features outperformed that of time-domain features by 8.9 p.p., 0.2 p.p., 39.5 p.p., and 0.4 p.p. in Standing, Sitting, Lying Down, and Walking activities, respectively, and on the WISDM dataset, the model performance improved by 1.7 p.p., just by using feature engineering.
Coordinate Fault Ride-Through Strategy for Connection of Offshore Wind Farms Using Voltage Source-Converter-Based High-Voltage Direct-Current Transmission under Single Polar Fault
Authors: Zhou Huiying , Ge Siyang , Qin Liang
Published: June 2023

In a system where wind farms are connected to the grid via a bipolar flexible DC transmission, the occurrence of a short-time fault at one of the poles results in the active power emitted by the wind farm being transmitted through the non-faulty pole. This condition leads to an overcurrent in the DC system, thereby causing the wind turbine to disconnect from the grid. Addressing this issue, this paper presents a novel coordinated fault ride-through strategy for flexible DC transmission systems and wind farms, which eliminates the need for additional communication equipment. The proposed strategy leverages the power characteristics of the doubly fed induction generator (DFIG) under different terminal voltage conditions. By considering the safety constraints of both the wind turbine and the DC system, as well as optimizing the active power output during wind farm faults, the strategy establishes guidelines for the wind farm bus voltage and the crowbar switch signal. Moreover, it harnesses the power regulation capability of the DFIG rotor-side crowbar circuit to enable fault ride-through in the presence of single-pole short-time faults in the DC system. Simulation results demonstrate that the proposed coordinated control strategy effectively mitigates overcurrent in the non-faulty pole of flexible DC transmission during fault conditions.
An Optical Sensory System for Assessment of Residual Cancer Burden in Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy
Authors: Momtahen Shadi , Momtahen Maryam , Ramaseshan Ramani , Golnaraghi Farid
Published: June 2023

Breast cancer patients undergoing neoadjuvant chemotherapy (NAC) require precise and accurate evaluation of treatment response. Residual cancer burden (RCB) is a prognostic tool widely used to estimate survival outcomes in breast cancer. In this study, we introduced a machine-learning-based optical biosensor called the Opti-scan probe to assess residual cancer burden in breast cancer patients undergoing NAC. The Opti-scan probe data were acquired from 15 patients (mean age: 61.8 years) before and after each cycle of NAC. Using regression analysis with k-fold cross-validation, we calculated the optical properties of healthy and unhealthy breast tissues. The ML predictive model was trained on the optical parameter values and breast cancer imaging features obtained from the Opti-scan probe data to calculate RCB values. The results show that the ML model achieved a high accuracy of 0.98 in predicting RCB number/class based on the changes in optical properties measured by the Opti-scan probe. These findings suggest that our ML-based Opti-scan probe has considerable potential as a valuable tool for the assessment of breast cancer response after NAC and to guide treatment decisions. Therefore, it could be a promising, non-invasive, and accurate method for monitoring breast cancer patient’s response to NAC.
Note on Coarse Alignment of Gyro-Free Inertial Navigation System
Authors: Kim Heyone , Son Jae-Hoon , Oh Sang-Heon , Hwang Dong-Hwan
Published: June 2023

In this note, the feasibility of initial alignment of a gyro-free inertial navigation system (GF-INS) is investigated. Initial roll and initial pitch are obtained using leveling of conventional INS since centripetal acceleration is very small. The equation for the initial heading cannot be used since the GF inertial measurement unit (IMU) cannot directly measure the Earth rate. A new equation is derived to obtain the initial heading from GF-IMU accelerometer outputs. Initial heading is expressed in the accelerometer outputs of two configurations, which satisfies a specific condition among 15 GF-IMU configurations presented in the literature. The initial heading error to arrangement and accelerometer error is quantitatively analyzed from the initial heading calculation equation of GF-INS and the initial heading error analysis of the general INS. The initial heading error is investigated when gyroscopes are used with GF-IMU. The results show that the initial heading error depends more on the performance of the gyroscope than that of the accelerometer, and the initial heading cannot be obtained within a practical error level by using only GF-IMU, even when an extremely accurate accelerometer is used. Therefore, aiding sensors have to be used in order to have a practical initial heading.
Acoustic Roughness Measurement of Railway Tracks: Running Surface Detection and Compensation of Lateral Movements for Optical Measurements on a Train
Authors: Mauz Florian , Wigger Remo , Griesbaum Loris , Wahl Tobias , Kuffa Michal , Wegener Konrad
Published: June 2023

Rolling noise is a significant contributor to railway noise. Wheel and rail roughness are decisive for the emitted noise level. An optical measurement method installed on a moving train is suitable for closer monitoring of the rail surface condition. A measurement setup based on the chord method requires the sensors to be positioned in a straight line along the direction of measurement and in a stable lateral position. Measurements should always be performed within the shiny and uncorroded running surface, even when there are lateral movements of the train. In this study, concepts for the detection of the running surface and the compensation of lateral movements are investigated in a laboratory setting. The setup consists of a vertical lathe with a ring-shaped workpiece that incorporates an implemented artificial running surface. The detection of the running surface based on laser triangulation sensors and a laser profilometer is investigated. It is shown that the running surface can be detected using a laser profilometer that measures the intensity of the reflected laser light. It is possible to detect the lateral position and the width of the running surface. A linear positioning system is proposed to adjust the lateral position of the sensors based on the running surface detection of the laser profilometer. When the lateral position of the measuring sensor is disturbed by a movement with a wavelength of 18.85 m, the linear positioning system can keep the laser triangulation sensor inside the running surface for 98.44% of the measured data points at a velocity of approximately 7.5 km h−1. The mean positioning error is 1.40 mm. By implementing the proposed system on the train, future studies can be conducted to examine the lateral position of the running surface as a function of the various operational parameters of the train.
Blockchain-Assisted Privacy-Preserving and Context-Aware Trust Management Framework for Secure Communications in VANETs
Authors: Ahmed Waheeb , Di Wu , Mukathe Daniel
Published: June 2023

Vehicular ad hoc networks (VANETs) are used for improving traffic efficiency and road safety. However, VANETs are vulnerable to various attacks from malicious vehicles. Malicious vehicles can disrupt the normal operation of VANET applications by broadcasting bogus event messages that may cause accidents, threatening people’s lives. Therefore, the receiver node needs to evaluate the authenticity and trustworthiness of the sender vehicles and their messages before acting. Although several solutions for trust management in VANETs have been proposed to address these issues of malicious vehicles, existing trust management schemes have two main issues. Firstly, these schemes have no authentication components and assume the nodes are authenticated before communicating. Consequently, these schemes do not meet VANET security and privacy requirements. Secondly, existing trust management schemes are not designed to operate in various contexts of VANETs that occur frequently due to sudden variations in the network dynamics, making existing solutions impractical for VANETs. In this paper, we present a novel blockchain-assisted privacy-preserving and context-aware trust management framework that combines a blockchain-assisted privacy-preserving authentication scheme and a context-aware trust management scheme for securing communications in VANETs. The authentication scheme is proposed to enable anonymous and mutual authentication of vehicular nodes and their messages and meet VANET efficiency, security, and privacy requirements. The context-aware trust management scheme is proposed to evaluate the trustworthiness of the sender vehicles and their messages, and successfully detect malicious vehicles and their false/bogus messages and eliminate them from the network, thereby ensuring safe, secure, and efficient communications in VANETs. In contrast to existing trust schemes, the proposed framework can operate and adapt to various contexts/scenarios in VANETs while meeting all VANET security and privacy requirements. According to efficiency analysis and simulation results, the proposed framework outperforms the baseline schemes and demonstrates to be secure, effective, and robust for enhancing vehicular communication security.
Two-Stream Network One-Class Classification Model for Defect Inspections
Authors: Lee Seunghun , Luo Chenglong , Lee Sungkwan , Jung Hoeryong
Published: June 2023

Defect inspection is important to ensure consistent quality and efficiency in industrial manufacturing. Recently, machine vision systems integrating artificial intelligence (AI)-based inspection algorithms have exhibited promising performance in various applications, but practically, they often suffer from data imbalance. This paper proposes a defect inspection method using a one-class classification (OCC) model to deal with imbalanced datasets. A two-stream network architecture consisting of global and local feature extractor networks is presented, which can alleviate the representation collapse problem of OCC. By combining an object-oriented invariant feature vector with a training-data-oriented local feature vector, the proposed two-stream network model prevents the decision boundary from collapsing to the training dataset and obtains an appropriate decision boundary. The performance of the proposed model is demonstrated in the practical application of automotive-airbag bracket-welding defect inspection. The effects of the classification layer and two-stream network architecture on the overall inspection accuracy were clarified by using image samples collected in a controlled laboratory environment and from a production site. The results are compared with those of a previous classification model, demonstrating that the proposed model can improve the accuracy, precision, and F1 score by up to 8.19%, 10.74%, and 4.02%, respectively.
Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
Authors: Li Ming , Fan Dazhao , Dong Yang , Li Dongzi
Published: June 2023

The complex backgrounds of satellite videos and serious interference from noise and pseudo-motion targets make it difficult to detect and track moving vehicles. Recently, researchers have proposed road-based constraints to remove background interference and achieve highly accurate detection and tracking. However, existing methods for constructing road constraints suffer from poor stability, low arithmetic performance, leakage, and error detection. In response, this study proposes a method for detecting and tracking moving vehicles in satellite videos based on the constraints from spatiotemporal characteristics (DTSTC), fusing road masks from the spatial domain with motion heat maps from the temporal domain. The detection precision is enhanced by increasing the contrast in the constrained area to accurately detect moving vehicles. Vehicle tracking is achieved by completing an inter-frame vehicle association using position and historical movement information. The method was tested at various stages, and the results show that the proposed method outperformed the traditional method in constructing constraints, correct detection rate, false detection rate, and missed detection rate. The tracking phase performed well in identity retention capability and tracking accuracy. Therefore, DTSTC is robust for detecting moving vehicles in satellite videos.
Robust Stabilization of Linear Time-Delay Systems under Denial-of-Service Attacks
Authors: Saif Abdul-Wahid A. , El-Ferik Sami , Elkhider Siddig M.
Published: June 2023

This research examines new methods for stabilizing linear time-delay systems that are subject to denial-of-service (DoS) attacks. The study takes into account the different effects that a DoS attack can have on the system, specifically delay-independent and -dependent behaviour. The traditional proportional-integral-derivative (PID) acts on the error signal, which is the difference between the reference input and the measured output. The approach in this paper uses what we call the PID state feedback strategy, where the controller acts on the state signal. Our proposed strategy uses the Lyapunov–Krasovskii functional (LKF) to develop new linear matrix inequalities (LMIs). The study considers two scenarios where the time delay is either a continuous bounded function or a differentiable and time-varying function that falls within certain bounds. In both cases, new LMIs are derived to find the PID-like state feedback gains that will ensure robust stabilization. The findings are illustrated with numerical examples.
Local Adaptive Image Filtering Based on Recursive Dilation Segmentation
Authors: Zhang Jialiang , Chen Chuheng , Chen Kai , Ju Mingye , Zhang Dengyin
Published: June 2023

This paper introduces a simple but effective image filtering method, namely, local adaptive image filtering (LAIF), based on an image segmentation method, i.e., recursive dilation segmentation (RDS). The algorithm is motivated by the observation that for the pixel to be smoothed, only the similar pixels nearby are utilized to obtain the filtering result. Relying on this observation, similar pixels are partitioned by RDS before applying a locally adaptive filter to smooth the image. More specifically, by directly taking the spatial information between adjacent pixels into consideration in a recursive dilation way, RDS is firstly proposed to partition the guided image into several regions, so that the pixels belonging to the same segmentation region share a similar property. Then, guided by the iterative segmented results, the input image can be easily filtered via a local adaptive filtering technique, which smooths each pixel by selectively averaging its local similar pixels. It is worth mentioning that RDS makes full use of multiple integrated information including pixel intensity, hue information, and especially spatial adjacent information, leading to more robust filtering results. In addition, the application of LAIF in the remote sensing field has achieved outstanding results, specifically in areas such as image dehazing, denoising, enhancement, and edge preservation, among others. Experimental results show that the proposed LAIF can be successfully applied to various filtering-based tasks with favorable performance against state-of-the-art methods.
Adaptive Peptide Molecule as the Promising Highly-Efficient Gas-Sensor Material: In Silico Study
Authors: Petrunin Alexander A. , Rabchinskii Maxim K. , Sysoev Victor V. , Glukhova Olga E.
Published: June 2023

Gas sensors are currently employed in various applications in fields such as medicine, ecology, and food processing, and serve as monitoring tools for the protection of human health, safety, and quality of life. Herein, we discuss a promising direction in the research and development of gas sensors based on peptides—biomolecules with high selectivity and sensitivity to various gases. Thanks to the technique developed in this work, which uses a framework based on the density-functional tight-binding theory (DFTB), the most probable adsorption centers were identified and used to describe the interaction of some analyte molecules with peptides. The DFTB method revealed that the physical adsorption of acetone, ammonium, benzene, ethanol, hexane, methanol, toluene, and trinitrotoluene had a binding energy in the range from −0.28 eV to −1.46 eV. It was found that peptides may adapt to the approaching analyte by changing their volume up to a maximum value of approx. 13%, in order to confine electron clouds around the adsorbed molecule. Based on the results obtained, the prospects for using the proposed peptide configurations in gas sensor devices are good.
Retinal Prostheses: Engineering and Clinical Perspectives for Vision Restoration
Authors: Wu Kevin Y. , Mina Mina , Sahyoun Jean-Yves , Kalevar Ananda , Tran Simon D.
Published: June 2023

A retinal prosthesis, also known as a bionic eye, is a device that can be implanted to partially restore vision in patients with retinal diseases that have resulted in the loss of photoreceptors (e.g., age-related macular degeneration and retinitis pigmentosa). Recently, there have been major breakthroughs in retinal prosthesis technology, with the creation of numerous types of implants, including epiretinal, subretinal, and suprachoroidal sensors. These devices can stimulate the remaining cells in the retina with electric signals to create a visual sensation. A literature review of the pre-clinical and clinical studies published between 2017 and 2023 is conducted. This narrative review delves into the retinal anatomy, physiology, pathology, and principles underlying electronic retinal prostheses. Engineering aspects are explored, including electrode–retina alignment, electrode size and material, charge density, resolution limits, spatial selectivity, and bidirectional closed-loop systems. This article also discusses clinical aspects, focusing on safety, adverse events, visual function, outcomes, and the importance of rehabilitation programs. Moreover, there is ongoing debate over whether implantable retinal devices still offer a promising approach for the treatment of retinal diseases, considering the recent emergence of cell-based and gene-based therapies as well as optogenetics. This review compares retinal prostheses with these alternative therapies, providing a balanced perspective on their advantages and limitations. The recent advancements in retinal prosthesis technology are also outlined, emphasizing progress in engineering and the outlook of retinal prostheses. While acknowledging the challenges and complexities of the technology, this article highlights the significant potential of retinal prostheses for vision restoration in individuals with retinal diseases and calls for continued research and development to refine and enhance their performance, ultimately improving patient outcomes and quality of life.
Analyzing the Relationship between Green Finance and Agricultural Industrial Upgrading: A Panel Data Study of 31 Provinces in China
Authors: Zhu Min , Sun Mengqi , Elahi Ehsan , Li Yajie , Khalid Zainab
Published: June 2023

This study examines the impact of green finance on optimizing China’s agricultural industrial structure. It emphasizes the importance of innovative green financial services, improved efficiency, tailored regional approaches, and enhanced foresight to foster a high-level development in the agricultural sector amid China’s economic transformation. Based on the provincial panel data of 31 provinces in China from 2012 to 2021, this study empirically tests the effect mechanism of green finance on the optimization process of the agricultural industrial structure by constructing a fixed-effects model. This study finds that green finance can effectively promote the development of the optimization of the agricultural industrial structure. Under the current trend of China’s economic structural transformation and optimization, we suggest that China should innovate green financial service products, improve the efficiency of green finance, and enhance the depth of green financial services for the optimization of the agricultural industrial structure. It is required to strengthen foresight and improve relevant laws, regulations, policies, and standards. To help green finance be better promoted, a high-level development of the agricultural industrial structure is required.