bluuebunny/crossref_metadata_2025 huggingface dataset:
In Vitro-Based Production of Virus-Tested Babaco (Vasconcellea x heilbornii, syn. Carica pentagona) in Ecuador: An Integrated Approach to an Endangered Crop
Authors: Muñoz Valeria , Curillo Diana , Gómez Sebastián , Moreno-Peña Lissette , Peña Denisse , Chica Eduardo , Yánez Viviana , Sánchez-Timm Eduardo , Quito-Avila Diego F.
Published: July 2023

Babaco (Vasconcellea x heilbornii), a fruit-bearing vegetatively propagated crop native to Ecuador, is appreciated for its distinctive flavor and nutritional properties. The aim of this research was to determine a functional protocol for tissue culture propagation of virus-free babaco plants including in vitro establishment, multiplication, rooting, and acclimation. First, symptomless babaco plants from a single commercial nursery were analyzed for virus detection and cared for using different disinfection treatments in the greenhouse to reduce contamination during the in vitro establishing step, and three cytokinins, 6-(γ,γ-Dimethylallylamino) purine (2IP), 6-Benzylaminopurine (BAP), and Thidiazuron (TDZ), were used to determine the best hormone for multiplication. The best treatment for plant disinfection was the weekly application of copper sulfate at the greenhouse and a laboratory disinfection using ethanol (EtOH) (70%), Clorox (2%), and a solution of povidone iodine (2.5%), with an 80% survival during in vitro plant establishment. TDZ showed a better multiplication rate when compared with other hormones, and 70% of the rooted plants were successfully acclimated at the greenhouse. Generated plants were virus-free when tested against babaco mosaic virus (BabMV) and papaya ringspot virus (PRSV), two of the most important viruses that can affect babaco. An efficient protocol to produce virus-free babaco plants was elaborated with an integrated use of viral diagnostic tools to ensure the production of healthy start material to farmers.
Monitoring Energy Balance, Turbulent Flux Partitioning, Evapotranspiration and Biophysical Parameters of Nopalea cochenillifera (Cactaceae) in the Brazilian Semi-Arid Environment
Authors: Jardim Alexandre Maniçoba da Rosa Ferraz , Morais José Edson Florentino de , Souza Luciana Sandra Bastos de , Souza Carlos André Alves de , Araújo Júnior George do Nascimento , Alves Cléber Pereira , Silva Gabriel Ítalo Novaes da , Leite Renan Matheus Cordeiro , Moura Magna Soelma Beserra de , de Lima João L. M. P. , Silva Thieres George Freire da
Published: July 2023

The in-situ quantification of turbulent flux and evapotranspiration (ET) is necessary to monitor crop performance in stressful environments. Although cacti can withstand stressful conditions, plant responses and plant–environment interactions remain unclear. Hence, the objective of our study was to investigate the interannual and seasonal behaviour of components of the surface energy balance, environmental conditions, morphophysiological parameters, biomass yield and water relations in a crop of Nopalea cochenillifera in the semi-arid region of Brazil. The data were collected from a micrometeorological tower between 2015 and 2017. The results demonstrate that net radiation was significantly higher during the wet season. Latent heat flux was not significant between the wet season and dry season. During the dry-wet transition season in particular, sensible heat flux was higher than during the other seasons. We observed a large decline in soil heat flux during the wet season. There was no difference in ET during the wet or dry seasons; however, there was a 40% reduction during the dry-wet transition. The wet seasons and wet-dry transition showed the lowest Evaporative Stress Index. The plants showed high cladode water content and biomass during the evaluation period. In conclusion, these findings indicate high rates of growth, high biomass and a high cladode water content and explain the response of the cactus regarding energy partitioning and ET.
Local Action of Increased Pressure Induces Hyperpolarization Electrical Signals and Influences Photosynthetic Light Reactions in Wheat Plants
Authors: Yudina Lyubov , Popova Alyona , Zolin Yuriy , Sukhova Ekaterina , Sukhov Vladimir
Published: July 2023

Long-distance electrical signals caused by the local action of stressors influence numerous physiological processes in plants including photosynthesis and increase their tolerance to the action of adverse factors. Depolarization electrical signals were mainly investigated; however, we earlier showed that hyperpolarization electrical signals (HESs) can be caused by moderate stressors (e.g., local moderate heating) and induce photosynthetic inactivation. We hypothesized that HESs are related to stressor-induced increases in the hydrostatic pressure in the zone of action of the stressor and following the propagation of a hydraulic wave. In the current work, we tested this hypothesis through the direct investigation of electrical signals induced by the local action of artificially increased pressure and an analysis of the subsequent photosynthetic changes in the nonirritated parts of plants. The electrical signals and parameters of photosynthetic light reactions were investigated in wheat plants. The local action of the increased pressure was induced by the action of weights on the wheat leaf. Extracellular electrodes were used for electrical signal measurements. Pulse–amplitude–modulation fluorescent imaging was used for measurements of the quantum yield of photosystem II and nonphotochemical quenching of chlorophyll fluorescence in wheat leaves. It was shown that the local action of pressure on wheat leaf induced electrical signals near the irritated zone: HESs were caused by low pressure (10 kPa) and depolarization signals were induced by high pressure (100 kPa). The local action of moderate pressure (50 kPa) induced weak electrical signals near the irritated zone; however, HESs were observed with increasing distance from this zone. It was also shown that the local action of this moderate pressure induced the photosynthetic inactivation (decreasing the quantum yield of photosystem II and increasing the nonphotochemical quenching of chlorophyll fluorescence) in the nonirritated parts of the wheat leaves. Thus, our results show that the local action of the increased pressure and, probably, subsequent propagation of the hydraulic wave induce electrical signals (including HESs) and photosynthetic inactivation in nonirritated parts of plants that are similar to ones caused by the local action of moderate stressors (e.g., moderate heating). This means that both HESs and depolarization electrical signals can have a hydraulic mechanism of propagation.
Antiviral Activity of Chitosan Nanoparticles and Chitosan Silver Nanocomposites against Alfalfa Mosaic Virus
Authors: El-Ganainy Sherif Mohamed , Soliman Ahmed M. , Ismail Ahmed Mahmoud , Sattar Muhammad Naeem , Farroh Khaled Yehia , Shafie Radwa M.
Published: July 2023

Plant viruses are a global concern for sustainable crop production. Among the currently available antiviral approaches, nanotechnology has been overwhelmingly playing an effective role in circumventing plant viruses. Alfalfa mosaic virus (AMV) was isolated and identified from symptomatic pepper plants in Egypt using symptomatology, serological tests using the direct ELISA technique, differential hosts and electron microscopy. The virus was biologically purified from a single local lesion that developed on Chenopodium amaranticolor. The AMV infection was further confirmed using an AMV coat protein-specific primer RT-PCR. We further evaluated the antiviral potential of chitosan nanoparticles (CS-NPs) and chitosan silver nanocomposites (CS-Ag NC) in different concentrations against AMV infections in pepper plants. All tested concentrations of CS-NPs and CS-Ag NC induced the inhibition of AMV systemically infected pepper plants when applied 24 h after virus inoculation. The foliar application of 400 ppm CS-NPs or 200 ppm CS-Ag NC produced the highest AMV inhibitory effect (90 and 91%) when applied 24 h after virus inoculation. Treatment with CS-NPs and CS-Ag NC considerably increased the phenol, proline and capsaicin contents compared to the infected plants. Moreover, the agronomic metrics (plant height, fresh and dry pod weights and number of pods per plant) were also significantly improved. According to our results, the potential applications of CS-NPs and CS-Ag NC may provide an effective therapeutic measure for better AMV and other related plant virus management.
A Review of Developments in Polymer Stabilized Liquid Crystals
Authors: Ye Yong , Guo Li , Zhong Tingjun
Published: July 2023

Polymer-stabilized liquid crystals (PSLCs) are multi-functional materials consisting of polymer networks in a continuous phase of liquid crystals (LCs), of which polymer networks provide anchoring energy to align the LCs. A number of improvements are detailed, including polymer-stabilized nematic liquid crystals (PSNLCs), polymer-stabilized cholesteric liquid crystals (PSCLCs), polymer-stabilized blue phase liquid crystals (PSBPLCs), polymer-stabilized smectic liquid crystals (PSSLCs), polymer-stabilized ferroelectric liquid crystals (PSFLCs), and polymer-stabilized antiferroelectric liquid crystals (PSAFLCs) in this review. Polymer stabilization has achieved multiple functionalities for LCs; in smart windows, a sufficiently strong electric field allows the LCs to reorient and enables switching from a scattering (transparent) state to a transparent (scattering) state. For broadband reflectors, the reflection bandwidth of LCs is manually tuned by electric fields, light, magnetic fields, or temperature. PSBPLCs open a new way for next-generation displays, spatial light modulators, sensors, lasers, lenses, and photonics applications. Polymer networks in PSFLCs or PSAFLCs enhance their grayscale memories utilized in flexible displays and energy-saving smart cards. At the end, the remaining challenges and research opportunities of PSLCs are discussed.
The Influence of the Dispersion Method on the Morphological, Curing, and Mechanical Properties of NR/SBR Reinforced with Nano-Calcium Carbonate
Authors: Damircheli Mehrnoosh , MajidiRad AmirHossein
Published: July 2023

There are several reasons for the development of nanopolymer compounds, such as improving physical, mechanical, and chemical properties, increasing lifespan, reducing costs, and decreasing negative environmental impact. The compatibility of two rubbers and mineral nanofillers in nanocomposites is a challenge that needs to be studied, and the effect of nanofillers on morphological, physical, and mechanical properties should be investigated accordingly. In this study, calcium carbonate nanoparticles were added to a polymer compound that included natural rubber (NR), styrene-butadiene rubber (SBR), vulcanization accelerators, and other additives. For mixing nanoparticles in the polymer matrix, various methods were used, including the solvent method in toluene and W410 solvents and the surface modification of calcium carbonate nanoparticles with stearic acid. The effect of dispersion nanoparticles in nanocomposite specimens on morphology, curing characteristics, and mechanical properties was studied. The morphologies of specimens were determined by X-ray diffraction (XRD) analysis and field emission scanning electron microscopy (FESEM). The particle size of the nanocomposite was approximately 34 nm, and the interlayer spacing between crystal plates increased from 2.81 nm to 3.03 nm. These results indicate a uniform dispersion of nanoparticles, specifically with an optimum content of 3.52%, in the compounds prepared through all mixing methods, with no agglomeration observed in the nanocomposites. The results of the nanocomposites’ curing characterization demonstrate that with the addition of nanoparticles, a strong bond is created in the polymer chains, and curing properties are improved. Among the dispersion methods, the highest percentage improvement in curing properties is observed with the solvent method W410. To evaluate the effect of the addition of calcium carbonate nanoparticles and the dispersion method on improving mechanical properties, tensile, tear, hardness, and rebound resilience tests were performed. In tensile tests, the surface modification method showed the highest enhancement in ultimate stress (80%), followed by the W410 method (64%) and toluene method (63.7%). Tear strength improvements were highest in the W410-solvent sample (80%), followed by the surface modification method (57%) and the solvent-toluene method (50%). The W410 method resulted in the hardest samples, while the surface-modified samples had the lowest hardness. The addition of CaCO3 nanofillers reduced rebound resilience, with the W410 method experiencing the largest reduction (10.64%), followed by the toluene method (6.38%), and with the surface-modified samples showing the lowest reduction (4.25%). The results show that in the W410 solvent method, the nanocomposite is more elastic than for other methods. Additionally, for most of the mechanical properties, the W410 method results in the most growth in improvement.
Nonlinear Adaptive Back-Stepping Optimization Control of the Hydraulic Active Suspension Actuator
Authors: Wu Lizhe , Zhao Dingxuan , Zhao Xiaolong , Qin Yalu
Published: July 2023

The displacement tracking performance of the electro-hydraulic servo actuator is critical for hydraulic active suspension control. To tackle the problem of slow time-varying parameters in the existing actuator dynamics model, a nonlinear adaptive back-stepping control (ABC) approach is adopted. Simultaneously, the parameters of the nonlinear ABC are difficult to configure, resulting in a poor control effect. An enhanced particle swarm optimization (PSO) approach integrating crazy particles (CP) and time-varying acceleration coefficients (TVAC) is suggested to optimize the controller settings. Furthermore, in order to obtain satisfactory dynamic characteristics of the transition process, the absolute value of the error time integral performance index is used as the minimum performance index function of parameter selection, and the square term of the control input is added to the performance index function to prevent excessive controller energy. Finally, it can be observed from the simulation results of the highest value emax of the displacement tracking error, the average value eμ of error, and the standard deviation eσ of error that the performance of the ABC parameters optimized by PSO+CP+ATVC is superior to the manually given ABC parameters. Therefore, this control method significantly improves the stability and speed of the control system. It provides a new research idea for the parameter optimization of controllers.
Artificial Neural Networks (ANNs) for Vapour-Liquid-Liquid Equilibrium (VLLE) Predictions in N-Octane/Water Blends
Authors: Lopez-Ramirez Esteban , Lopez-Zamora Sandra , Escobedo Salvador , de Lasa Hugo
Published: July 2023

Blends of bitumen, clay, and quartz in water are obtained from the surface mining of the Athabasca Oil Sands. To facilitate its transportation through pipelines, this mixture is usually diluted with locally produced naphtha. As a result of this, naphtha has to be recovered later, in a naphtha recovery unit (NRU). The NRU process is a complex one and requires the knowledge of Vapour-Liquid-Liquid Equilibrium (VLLE) thermodynamics. The present study uses experimental data, obtained in a CREC-VL-Cell, and Artificial Intelligence (AI) for vapour-liquid-liquid equilibrium (VLLE) calculations. The proposed Artificial Neural Networks (ANNs) do not require prior knowledge of the number of vapour-liquid phases. These ANNs involve hyperparameters that are used to obtain the best ANN model architecture. To accomplish this, this study considers (a) R2 Coefficients of Determination and (b) ANN training requirements to avoid data underfitting and overfitting. Results demonstrate that temperature has a major influence on ANN vapour pressure predictions, while the concentration of octane, the naphtha surrogate having, in contrast, a lesser effect. Furthermore, the ANN data obtained allows the calculation of octane-in-water and water-in-octane maximum solubilities.
An Efficient Method to Fabricate the Mold Cavity for a Helical Cylindrical Pinion
Authors: Wu Bo , Zhu Likuan , Zhou Zhiwen , Guo Cheng , Cheng Tao , Wu Xiaoyu
Published: July 2023

An efficient method was proposed to fabricate the mold cavity for a helical cylindrical pinion based on a plastic torsion forming concept. The structure of the spur gear cavity with the same profile as the end face of the target helical gear cavity was first fabricated by low-speed wire electrical discharge machining (LS-WEDM). Then, the structure of the helical gear cavity could be obtained by twisting the spur gear cavity plastically around the central axis. In this way, the fabrication process of a helical cylindrical gear cavity could be greatly simplified, compared to the fabrication of a multi-stage helical gear core electrode and the highly difficult and complex spiral EDM process in the current gear manufacturing method. Moreover, several experiments were conducted to verify this novel processing concept, and a theoretical model was established to show the relationship between the machine torsion angle and the helical angle of a helical gear. Based on this theoretical model, the experimental results showed that it is feasible to precisely control the shape accuracy of a helical cylindrical pinion mold cavity by adjusting the machine torsion angle.
Bi-Objective Crop Mapping from Sentinel-2 Images Based on Multiple Deep Learning Networks
Authors: Song Weicheng , Feng Aiqing , Wang Guojie , Zhang Qixia , Dai Wen , Wei Xikun , Hu Yifan , Amankwah Solomon Obiri Yeboah , Zhou Feihong , Liu Yi
Published: July 2023

Accurate assessment of the extent of crop distribution and mapping different crop types are essential for monitoring and managing modern agriculture. Medium and high spatial resolution remote sensing (RS) for Earth observation and deep learning (DL) constitute one of the most major and effective tools for crop mapping. In this study, we used high-resolution Sentinel-2 imagery from Google Earth Engine (GEE) to map paddy rice and winter wheat in the Bengbu city of Anhui Province, China. We compared the performance of different popular DL backbone networks with the traditional machine learning (ML) methods, including HRNet, MobileNet, Xception, and Swin Transformer, within the improved DeepLabv3+ architecture, Segformer and random forest (RF). The results showed that the Segformer based on the combination of the Transformer architecture encoder and the lightweight multilayer perceptron (MLP) decoder achieved an overall accuracy (OA) value of 91.06%, a mean F1 Score (mF1) value of 89.26% and a mean Intersection over Union (mIoU) value of 80.70%. The Segformer outperformed other DL methods by combining the results of multiple evaluation metrics. Except for Swin Transformer, which was slightly lower than RF in OA, all DL methods significantly outperformed RF methods in accuracy for the main mapping objects, with mIoU improving by about 13.5~26%. The predicted images of paddy rice and winter wheat from the Segformer were characterized by high mapping accuracy, clear field edges, distinct detail features and a low false classification rate. Consequently, DL is an efficient option for fast and accurate mapping of paddy rice and winter wheat based on RS imagery.
TSVR-Net: An End-to-End Ground-Penetrating Radar Images Registration and Location Network
Authors: Bi Beizhen , Shen Liang , Zhang Pengyu , Huang Xiaotao , Xin Qin , Jin Tian
Published: July 2023

Stable and reliable autonomous localization technology is fundamental for realizing autonomous driving. Localization systems based on global positioning system (GPS), cameras, LIDAR, etc., can be affected by building occlusion or drastic changes in the environment. These effects can degrade the localization accuracy and even cause the problem of localization failure. Localizing ground-penetrating radar (LGPR) as a new type of localization can rely only on robust subsurface information for autonomous localization. LGPR is mostly a 2D-2D registration process. This paper describes the LGPR as a slice-to-volume registration (SVR) problem and proposes an end-to-end TSVR-Net-based regression localization method. Firstly, the information of different dimensions in 3D data is used to ensure the high discriminative power of the data. Then the attention module is added to the design to make the network pay attention to important information and high discriminative regions while balancing the information weights of different dimensions. Eventually, it can directly regress to predict the current data location on the map. We designed several sets of experiments to verify the method’s effectiveness by a step-by-step analysis. The superiority of the proposed method over the current state-of-the-art LGPR method is also verified on five datasets. The experimental results show that both the deep learning method and the increase in dimensional information can improve the stability of the localization system. The proposed method exhibits excellent localization accuracy and better stability, providing a new concept to realize the stable and reliable real-time localization of ground-penetrating radar images.
Mapping Surface Features of an Alpine Glacier through Multispectral and Thermal Drone Surveys
Authors: Rossini Micol , Garzonio Roberto , Panigada Cinzia , Tagliabue Giulia , Bramati Gabriele , Vezzoli Giovanni , Cogliati Sergio , Colombo Roberto , Di Mauro Biagio
Published: July 2023

Glacier surfaces are highly heterogeneous mixtures of ice, snow, light-absorbing impurities and debris material. The spatial and temporal variability of these components affects ice surface characteristics and strongly influences glacier energy and mass balance. Remote sensing offers a unique opportunity to characterize glacier optical and thermal properties, enabling a better understanding of different processes occurring at the glacial surface. In this study, we evaluate the potential of optical and thermal data collected from field and drone platforms to map the abundances of predominant glacier surfaces (i.e., snow, clean ice, melting ice, dark ice, cryoconite, dusty snow and debris cover) on the Zebrù glacier in the Italian Alps. The drone surveys were conducted on the ablation zone of the glacier on 29 and 30 July 2020, corresponding to the middle of the ablation season. We identified very high heterogeneity of surface types dominated by melting ice (30% of the investigated area), dark ice (24%), clean ice (19%) and debris cover (17%). The surface temperature of debris cover was inversely related to debris-cover thickness. This relation is influenced by the petrology of debris cover, suggesting the importance of lithology when considering the role of debris over glaciers. Multispectral and thermal drone surveys can thus provide accurate high-resolution maps of different snow and ice types and their temperature, which are critical elements to better understand the glacier’s energy budget and melt rates.
Temporal Changes in Mediterranean Pine Forest Biomass Using Synergy Models of ALOS PALSAR-Sentinel 1-Landsat 8 Sensors
Authors: Velasco Pereira Edward A. , Varo Martínez María A. , Ruiz Gómez Francisco J. , Navarro-Cerrillo Rafael M.
Published: July 2023

Currently, climate change requires the quantification of carbon stored in forest biomass. Synthetic aperture radar (SAR) data offers a significant advantage over other remote detection measurement methods in providing structural and biomass-related information about ecosystems. This study aimed to develop non-parametric Random Forest regression models to assess the changes in the aboveground forest biomass (AGB), basal area (G), and tree density (N) of Mediterranean pine forests by integrating ALOS-PALSAR, Sentinel 1, and Landsat 8 data. Variables selected from the Random Forest models were related to NDVI and optical textural variables. For 2015, the biomass models with the highest performance integrated ALS-ALOS2-Sentinel 1-Landsat 8 data (R2 = 0.59) by following the model using ALS data (R2 = 0.56), and ALOS2-Sentinel 1-Landsat 8 (R2 = 0.50). The validation set showed that R2 values vary from 0.55 (ALOS2-Sentinel 1-Landsat 8) to 0.60 (ALS-ALOS2-Sentinel 1-Landsat 8 model) with RMSE below 20 Mg ha−1. It is noteworthy that the individual Sentinel 1 (R2 = 0.49). and Landsat 8 (R2 = 0.47) models yielded equivalent results. For 2020, the AGB model ALOS2-Sentinel 1-Landsat 8 had a performance of R2 = 0.55 (validation R2 = 0.70) and a RMSE of 9.93 Mg ha−1. For the 2015 forest structural variables, Random Forest models, including ALOS PAL-SAR 2-Sentinel 1 Landsat 8 explained between 30% and 55% of the total variance, and for the 2020 models, they explained between 25% and 55%. Maps of the forests’ structural variables were generated for 2015 and 2020 to assess the changes during this period using the ALOS PALSAR 2-Sentinel 1-Landsat 8 model. Aboveground biomass (AGB), diameter at breast height (dbh), and dominant height (Ho) maps were consistent throughout the entire study area. However, the Random Forest models underestimated higher biomass levels (>100 Mg ha−1) and overestimated moderate biomass levels (30–45 Mg ha−1). The AGB change map showed values ranging from gains of 43.3 Mg ha−1 to losses of −68.8 Mg ha−1 during the study period. The integration of open-access satellite optical and SAR data can significantly enhance AGB estimates to achieve consistent and long-term monitoring of forest carbon dynamics.
Analysis of Water Yield Changes in the Johor River Basin, Peninsular Malaysia Using Remote Sensing Satellite Imagery
Authors: Hashim Mazlan , Baiya Babangida , Mahmud Mohd Rizaludin , Sani Dalhatu Aliyu , Chindo Musa Muhammad , Leong Tan Mou , Pour Amin Beiranvand
Published: July 2023

Changes in land-use–land-cover (LULC) affect the water balance of a region by influencing the water yield (WY) along with variations in rainfall and evapotranspiration (ET). Remote sensing satellite imagery offers a comprehensive spatiotemporal distribution of LULC to analyse changes in WY over a large area. Hence, this study mapped and analyse successive changes in LULC and WY between 2000 and 2015 in the Johor River Basin (JRB) by specifically comparing satellite-based and in-situ-derived WY and characterising changes in WY in relation to LULC change magnitudes within watersheds. The WY was calculated using the water balance equation, which determines the WY from the equilibrium of precipitation minus ET. The precipitation and ET information were derived from the Tropical Rainfall Measuring Mission (TRMM) and moderate-resolution imaging spectroradiometer (MODIS) satellite data, respectively. The LULC maps were extracted from Landsat-Enhanced Thematic Mapper Plus (ETM+) and Landsat Operational Land Imager (OLI). The results demonstrate a good agreement between satellite-based derived quantities and in situ measurements, with an average bias of ±20.04 mm and ±43 mm for precipitation and ET, respectively. LULC changes between 2000 and 2015 indicated an increase in agriculture land other than oil palm to 11.07%, reduction in forest to 32.15%, increase in oil palm to 11.88%, and increase in urban land to 9.82%, resulting in an increase of 15.76% WY. The finding can serve as a critical initiative for satellite-based WY and LULC changes to achieve targets 6.1 and 6.2 of the United Nations Sustainable Development Goal (UNSDG) 6.
Monitoring Mining-Induced Geo-Hazards in a Contaminated Mountainous Region of Indonesia Using Satellite Imagery
Authors: Kimijima Satomi , Nagai Masahiko
Published: July 2023

Mining-induced or enhanced geo-hazards (MGHs) pose significant risks in rural mountainous regions with underground mining operations by harming groundwater layers, water circulation systems, and mountain stability. MGHs occurring in naturally contaminated environments can severely amplify socio-environmental risks. A high correlation was found among undermining development, precipitation, and hazards; however, details of MGHs have yet to be adequately characterized. This study investigated multiple mining-induced/enhanced geo-hazards in a naturally contaminated mountain region in Bone Bolango Regency, Gorontalo Province, Indonesia, in 2020, where a rapidly developing coexisting mining sector was present. We utilized PlanetScope’s CubeSat constellations and Sentinel-1 dataset to assess the volume, distribution, pace, and pattern of MGHs. The findings reveal that severe landslides and floods accelerated the mobilization of potentially toxic elements (PTEs) via the river water system, thus considerably exacerbating socio-environmental risks. These results indicate potential dangers of enhanced PTE contamination for marine ecosystems and humans at a regional level. The study design and data used facilitated a comprehensive assessment of the MGHs and associated risks, providing important information for decision-makers and stakeholders. However, limitations in the methodology should be considered when interpreting the findings. The societal benefits of this study include informing policies and practices that aim to mitigate the negative impacts of mining activities on the environment and society at the local and regional levels.
Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors
Authors: Karapanagiotis Christos , Krebber Katerina
Published: July 2023

This paper presents reported machine learning approaches in the field of Brillouin distributed fiber optic sensors (DFOSs). The increasing popularity of Brillouin DFOSs stems from their capability to continuously monitor temperature and strain along kilometer-long optical fibers, rendering them attractive for industrial applications, such as the structural health monitoring of large civil infrastructures and pipelines. In recent years, machine learning has been integrated into the Brillouin DFOS signal processing, resulting in fast and enhanced temperature, strain, and humidity measurements without increasing the system’s cost. Machine learning has also contributed to enhanced spatial resolution in Brillouin optical time domain analysis (BOTDA) systems and shorter measurement times in Brillouin optical frequency domain analysis (BOFDA) systems. This paper provides an overview of the applied machine learning methodologies in Brillouin DFOSs, as well as future perspectives in this area.
An End-to-End Dynamic Posture Perception Method for Soft Actuators Based on Distributed Thin Flexible Porous Piezoresistive Sensors
Authors: Shu Jing , Wang Junming , Cheng Kenneth Chik-Chi , Yeung Ling-Fung , Li Zheng , Tong Raymond Kai-yu
Published: July 2023

This paper proposes a method for accurate 3D posture sensing of the soft actuators, which could be applied to the closed-loop control of soft robots. To achieve this, the method employs an array of miniaturized sponge resistive materials along the soft actuator, which uses long short-term memory (LSTM) neural networks to solve the end-to-end 3D posture for the soft actuators. The method takes into account the hysteresis of the soft robot and non-linear sensing signals from the flexible bending sensors. The proposed approach uses a flexible bending sensor made from a thin layer of conductive sponge material designed for posture sensing. The LSTM network is used to model the posture of the soft actuator. The effectiveness of the method has been demonstrated on a finger-size 3 degree of freedom (DOF) pneumatic bellow-shaped actuator, with nine flexible sponge resistive sensors placed on the soft actuator’s outer surface. The sensor-characterizing results show that the maximum bending torque of the sensor installed on the actuator is 4.7 Nm, which has an insignificant impact on the actuator motion based on the working space test of the actuator. Moreover, the sensors exhibit a relatively low error rate in predicting the actuator tip position, with error percentages of 0.37%, 2.38%, and 1.58% along the x-, y-, and z-axes, respectively. This work is expected to contribute to the advancement of soft robot dynamic posture perception by using thin sponge sensors and LSTM or other machine learning methods for control.
Design and Performance Analysis of a Constellation of Nanosatellites to Monitor Water Quality in the Southern Catchment of the Baltic Sea
Authors: Kwapień Karolina , Lasota Patrycja , Kędzierski Michał , Walczykowski Piotr
Published: July 2023

The quality of inland waters has a significant influence on human life and the functioning of the environment. The disasters that result from water pollution may cause major financial losses and lead to irreversible changes in the ecosystem, such as the dying out of endemic species of plants and animals. Quick detection of pollution sources may minimise those negative effects and reduce the costs of their elimination. The study presents a constellation design that provides imagery in the optic range and that might supplement the point water quality measurements that are conducted in situ. The area of interest was the southern catchment of the Baltic Sea and the main rivers in the region. The requirements for the designed mission were defined in reference to the remote sensing needs concerning the monitoring of water quality, the characteristics of the analysed area, and weather conditions. Based on these requirements, the Simera Sense MultiScape100 CIS sensor and the M6P nanosatellite manufactured by NanoAvionics were selected. The authors proposed a process for selecting the optimum orbit, taking into account the area of interest, the possibilities of the satellite platform, and of the sensor’s optics. As a result of the analyses, four concepts of creating a constellation were presented. Each constellation consisted of four nanosatellites. The designs were then subjected to performance analysis, considering the lighting limitations. Among the proposed systems, the constellation designed by the authors was distinguished; it used four orbital planes and achieved the coverage and availability of imagery in the time that was best suited to monitoring the waters. Thanks to a small number of platforms, the costs of the mission are relatively low, and it might significantly improve awareness of the current state of surface waters in the southern catchment of the Baltic Sea.
Electrochemical Rapid Detection of Methamphetamine from Confiscated Samples Using a Graphene-Based Printed Platform
Authors: Truta Florina , Drăgan Ana-Maria , Tertis Mihaela , Parrilla Marc , Slosse Amorn , Van Durme Filip , de Wael Karolien , Cristea Cecilia
Published: July 2023

Methamphetamine (MAP) is a highly addictive and illegal stimulant drug that has a significant impact on the central nervous system. Its detection in biological and street samples is crucial for various organizations involved in forensic medicine, anti-drug efforts, and clinical diagnosis. In recent years, nanotechnology and nanomaterials have played a significant role in the development of analytical sensors for MAP detection. In this study, a fast, simple, and cost-effective electrochemical sensor is presented that is used for the sensitive detection of MAP in confiscated street samples with a complex matrix. The optimized screen-printed sensor based on a carbon working electrode modified with graphene demonstrated an excellent limit of detection, good sensitivity, and a wide dynamic range (1–500 μM) for the target illicit drug both for standard solutions and real samples (seized samples, tap water, and wastewater samples). It can detect MAP at concentrations as low as 300 nM in real samples. This limit of detection is suitable for the rapid preliminary screening of suspicious samples in customs, ports, airports, and on the street. Furthermore, the sensor exhibits a good recovery rate, indicating its reliability and repeatability. This quality is crucial for ensuring consistent and accurate results during screening processes.
SY-Net: A Rice Seed Instance Segmentation Method Based on a Six-Layer Feature Fusion Network and a Parallel Prediction Head Structure
Authors: Ye Sheng , Liu Weihua , Zeng Shan , Wu Guiju , Chen Liangyan , Lai Huaqing , Yan Zi
Published: July 2023

During the rice quality testing process, the precise segmentation and extraction of grain pixels is a key technique for accurately determining the quality of each seed. Due to the similar physical characteristics, small particles and dense distributions of rice seeds, properly analysing rice is a difficult problem in the field of target segmentation. In this paper, a network called SY-net, which consists of a feature extractor module, a feature pyramid fusion module, a prediction head module and a prototype mask generation module, is proposed for rice seed instance segmentation. In the feature extraction module, a transformer backbone is used to improve the ability of the network to learn rice seed features; in the pyramid fusion module and the prediction head module, a six-layer feature fusion network and a parallel prediction head structure are employed to enhance the utilization of feature information; and in the prototype mask generation module, a large feature map is used to generate high-quality masks. Training and testing were performed on two public datasets and one private rice seed dataset. The results showed that SY-net achieved a mean average precision (mAP) of 90.71% for the private rice seed dataset and an average precision (AP) of 16.5% with small targets in COCO2017. The network improved the efficiency of rice seed segmentation and showed excellent application prospects in performing rice seed quality testing.
Reweighted Off-Grid Sparse Spectrum Fitting for DOA Estimation in Sensor Array with Unknown Mutual Coupling
Authors: Li Liangliang , Wang Xianpeng , Lan Xiang , Xu Gang , Wan Liangtian
Published: July 2023

In the environment of unknown mutual coupling, many works on direction-of-arrival (DOA) estimation with sensor array are prone to performance degradation or even failure. Moreover, there are few literatures on off-grid direction finding using regularized sparse recovery technology. Therefore, the scenario of off-grid DOA estimation in sensor array with unknown mutual coupling is investigated, and then a reweighted off-grid Sparse Spectrum Fitting (Re-OGSpSF) approach is developed in this article. Inspired by the selection matrix, an undisturbed array output is formed to remove the unknown mutual coupling effect. Subsequently, a refined off-grid SpSF (OGSpSF) recovery model is structured by integrating the off-grid error term obtained from the first-order Taylor approximation of the higher-order term into the underlying on-grid sparse representation model. After that, a novel Re-OGSpSF framework is formulated to recover the sparse vectors, where a weighted matrix is developed by the MUSIC-like spectrum function to enhance the solution’s sparsity. Ultimately, off-grid DOA estimation can be realized with the help of the recovered sparse vectors. Thanks to the off-grid representation and reweighted strategy, the proposed method can effectively and efficiently achieve high-precision continuous DOA estimation, making it favorable for real-time direction finding. The simulation results validate the superiority of the proposed method.
Use of Lateral Flow Assays in Forensics
Authors: Bruijns Brigitte , Tiggelaar Roald , Knotter Jaap , van Dam Annemieke
Published: July 2023

Already for some decades lateral flow assays (LFAs) are ‘common use’ devices in our daily life. Also, for forensic use LFAs are developed, such as for the analysis of illicit drugs and DNA, but also for the detection of explosives and body fluid identification. Despite their advantages, including ease-of-use, LFAs are not yet frequently applied at a crime scene. This review describes (academic) developments of LFAs for forensic applications, focusing on biological and chemical applications, whereby the main advantages and disadvantages of LFAs for the different forensic applications are summarized. Additionally, a critical review is provided, discussing why LFAs are not frequently applied within the forensic field and highlighting the steps that are needed to bring LFAs to the forensic market.
Center of Pressure Behavior in Response to Unexpected Base of Support Shifting: A New Objective Tool for Dynamic Balance Assessment
Authors: Rizzato Alex , Bozzato Matteo , Zullo Giuseppe , Paoli Antonio , Marcolin Giuseppe
Published: July 2023

The translation of the base of support represents a promising approach for the objective assessment of dynamic balance control. Therefore, this study aimed to present a servo-controlled, electrically driven movable plate and a new set of parameters based on the center-of-pressure (CoP) trajectory. Twenty subjects were assessed on a force platform screwed over a movable plate that could combine the following settings: direction (forward (FW) and backward (BW)), displacement (25 mm, 50 mm, and 100 mm), and ramp rate (100 mm/s and 200 mm/s). The subjects underwent two sets of 12 trials randomly combining the plate settings. From the CoP trajectory of the 2.5 s time window after the perturbation, the 95% confidence-interval ellipse (Area95) and the CoP mean velocity (Unit Path) were calculated. Within the same time window, the first peak (FP), the maximal oscillations (ΔCoPMax), and the standard deviation (PPV) of the CoP anterior–posterior trajectory were calculated. The plate direction (p < 0.01), ramp rate (p < 0.001), and displacement (p < 0.01) affected the Area95, FP, and ΔCoPMax, while the Unit Path and PPV were influenced only by the ramp rate (p < 0.001) and displacement (p < 0.001). The servo-controlled, electrically driven movable plate and the CoP-related parameters presented in this study represent a new promising objective tool for dynamic balance assessment.
Two Filters for Acquiring the Profiles from Images Obtained from Weak-Light Background, Fluorescence Microscope, Transmission Electron Microscope, and Near-Infrared Camera
Authors: Huang Yinghui , Yang Ruoxi , Geng Xin , Li Zongan , Wu Ye
Published: July 2023

Extracting the profiles of images is critical because it can bring simplified description and draw special attention to particular areas in the images. In our work, we designed two filters via the exponential and hypotenuse functions for profile extraction. Their ability to extract the profiles from the images obtained from weak-light conditions, fluorescence microscopes, transmission electron microscopes, and near-infrared cameras is proven. Moreover, they can be used to extract the nesting structures in the images. Furthermore, their performance in extracting images degraded by Gaussian noise is evaluated. We used Gaussian white noise with a mean value of 0.9 to create very noisy images. These filters are effective for extracting the edge morphology in the noisy images. For the purpose of a comparative study, we used several well-known filters to process these noisy images, including the filter based on Gabor wavelet, the filter based on the watershed algorithm, and the matched filter, the performances of which in profile extraction are either comparable or not effective when dealing with extensively noisy images. Our filters have shown the potential for use in the field of pattern recognition and object tracking.
Leak Detection and Localization in Multi-Grid Space Using Improved Gaussian Plume Model
Authors: Li Daquan , Liu Gaigai , Mao Zhaoyong
Published: July 2023

Leak detection and localization of liquid or gas is of great significance to avoid potential danger and reduce the waste of resources. Leak detection and localization methods are varied and uniquely suited to specific application scenarios. The existing methods are primarily applied to conventional pressurized pipelines and open areas, and there are few methods suitable for multi-grid spaces. In this paper, a gas diffusion model applied to multi-grid space is constructed, and a method for leak detection and localization using the concentration gradient of characteristic gas is proposed according to the prediction behavior. The Gaussian plume model is selected due to its advantages of simplicity and the interpretation of gas diffusion behavior is closer to reality; the expression of the improved model is also obtained. To verify the correctness of the model and the applicability of the localization method, taking the coolant leakage in the circuit system as an example, three experiments with different source strengths were repeated. The fitting correlation coefficients between the gas concentration data of the three experiments and the model are 0.995, 0.997 and 0.997, respectively. The experimental results show that the model has a strong correlation with the real plume behavior, and it is reasonable to use the gas concentration gradient for the localization of the leak source. This study provides a reference for future research on the leak detection and localization of gas- or liquid-containing volatile substances in a complex multi-grid space.
Design and Development of a Diagnostic System for a Non-Intercepting Direct Measure of the SPIDER Ion Source Beamlet Current
Authors: Patton Tommaso , Shepherd Alastair , Pouradier Duteil Basile , Rigoni Garola Andrea , Brombin Matteo , Candeloro Valeria , Manduchi Gabriele , Pavei Mauro , Pasqualotto Roberto , Pimazzoni Antonio , Siragusa Marco , Serianni Gianluigi , Sartori Emanuele , Taliercio Cesare , Barbato Paolo , Cervaro Vannino , Ghiraldelli Raffaele , Laterza Bruno , Rossetto Federico
Published: July 2023

Stable and uniform beams with low divergence are required in particle accelerators; therefore, beyond the accelerated current, measuring the beam current spatial uniformity and stability over time is necessary to assess the beam performance, since these parameters affect the perveance and thus the beam optics. For high-power beams operating with long pulses, it is convenient to directly measure these current parameters with a non-intercepting system due to the heat management requirement. Such a system needs to be capable of operating in a vacuum in the presence of strong electromagnetic fields and overvoltages, due to electrical breakdowns in the accelerator. Finally, the measure of the beam current needs to be efficiently integrated into a pulse file with the other relevant plant parameters to allow the data analyses required for beam optimization. This paper describes the development, design and commissioning of such a non-intercepting system, the so-called beamlet current monitor (BCM), aimed to directly measure the electric current of a particle beam. In particular, the layout of the system was adapted to the SPIDER experiment, the ion source (IS) prototype of the heating neutral beam injectors (HNB) for the ITER fusion reactor. The diagnostic is suitable to provide the electric current of five beamlets from DC up to 10 MHz.