bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis of Aluminium Nitride-Based Coatings on Mild Steel Substrates Utilising an Integrated Laser/Sol–Gel Method
Authors: Eren Ogulcan , Kamara Alhaji M. , Sezer Huseyin Kursad , Marimuthu Sundar
Published: April 2024

The field of protective coatings for industrial applications is continuously evolving, driven by a need for materials that offer exceptional hardness, enhanced wear resistance, and low friction coefficients. Conventional methods of coating development, such as physical vapour deposition (PVD) and chemical vapour deposition (CVD), often face challenges like the necessity of vacuum conditions, slow growth rates, and weak substrate adhesion, leading to inadequate interface bonding. This study introduces a novel approach utilising an integrated laser/sol–gel method for synthesising aluminium nitride (AlN) coatings on EN43 mild steel substrates which overcomes these limitations. The technique employs a high-intensity diode laser with optimal power and translation speeds to consolidate a pre-applied thin layer of sol–gel slurry consisting of aluminium hydroxide, graphite, and urea on the substrate. Chemical thermodynamic calculations were used to predict the slurry composition, along with identifying the critical temperature range and the essential enthalpy needed for the synthesis of aluminium nitride. A three-dimensional heat transfer model was developed to predict the important process parameters, such as scanning speed and laser power density, required to achieve the temperature ranges necessary for a successful deposition process. Optical and scanning electron microscopy techniques were used to examine the surface morphology and microstructure of the coating. Elemental energy-dispersive X-ray spectroscopy and an X-ray diffraction analysis confirmed the synthesis of an aluminium nitride coating with a thickness ranging from 4 to 5 µm. Furthermore, the detection of sub-micron crystalline aluminium nitride structures yielding a metal matrix composite interlayer was indicative of strong metallurgical bonding. Microhardness testing indicated a hardness value of approximately 876 HV. The coated samples with the highest quality exhibited a surface roughness, Ra, ranging from 1.8 to 2.1 µm. Additionally, the coatings demonstrated an exceptionally low coefficient of friction, recorded at less than 0.1. These results represent a significant step forward in this field, offering a cost-effective, efficient, and scalable solution for producing high-quality coatings with superior performance characteristics.
Coherent Raman Scattering Spectral Shapes in a Strong Excitation Regime (Model Calculations)
Authors: Hadjichristov Georgi B.
Published: April 2024

The influence of the interference between coherent processes in third-order nonlinear Raman scattering on the spectral shapes of Raman-scattered light waves is numerically modeled and discussed in the cases of commonly used coherent Raman spectroscopy techniques. The effects on the lineshapes depending on the laser intensity are analyzed for the coherent Raman spectroscopy performed via the excitation of molecular systems with focused laser pulses at high intensities. In this case, the interplay between the coherent processes in nonlinear Raman scattering, as well as laser power-induced resonance effects, may be significant and should be taken into account in the spectral lineshape analysis in coherent Raman spectroscopy and its related applications, since the coherent Raman spectra may be considerably modified.
Permeable Properties of Hygienic Nonwovens Bonded Using Mechanical, Chemical, and Thermal Techniques
Authors: Šajn Gorjanc Dunja , Kostajnšek Klara
Published: April 2024

The demand for hygienic nonwovens has increased, especially since 2020. As expected, the market for nonwovens showed an increase during the COVID-19 outbreak, especially in the medical and hygienic nonwovens sector. The aim of this study is to analyse the influence of the permeability properties of hygienic nonwovens that have been mechanically, chemically, and thermally bonded. Hygienic nonwovens are lightweight (from 20 to 120 g/m2), produced by carding (roller carding), and are bonded using three different bonding processes (mechanical, thermal, and chemical). Hygienic nonwovens are intended for protective clothing in hospitals. For the experimental part, the seven different nonwovens used for hygienic purposes were produced using the dry laying process. The samples were produced in Tosama, a factory for sanitary supplies. The research results show that the nonwoven bonding processes have a significant influence on the structural, mechanical, and permeability properties.
Influence of Manufacturing Process on the Conductivity of Material Extrusion Components: A Comparison between Filament- and Granule-Based Processes
Authors: Nowka Maximilian , Hilbig Karl , Schulze Lukas , Heller Timo , Goutier Marijn , Vietor Thomas
Published: April 2024

The additive manufacturing of components using material extrusion (MEX) enables the integration of several materials into one component, including functional structures such as electrically conductive structures. This study investigated the influence of the selected additive MEX process on the resistivity of MEX structures. Specimens were produced from filaments and granules of an electrically conductive PLA and filled with carbon nanotubes and carbon black. Specimens were produced with a full-factorial variation of the input variables: extrusion temperature, deposition speed, and production process. The resistivity of the specimens was determined by four-wire measurement. Analysis of the obtained data showed that only the extrusion temperature had a significant influence on the resistivity of the MEX specimens. Furthermore, the impact of the nozzle diameter was evaluated by comparing the results of this study with those of a previous study, with an otherwise equal experimental setup. The nozzle diameter had a significant influence on resistivity and a larger nozzle diameter reduced the mean variance by an order of magnitude. The resistivity was lower for most process parameter sets. As the manufacturing process had no significant influence on the resistivity of MEX structures, it can be selected based on other criteria, e.g., the cost of feedstock.
FDM 3D Printing and Properties of PBAT/PLA Blends
Authors: Yu Wangwang , Li Mengya , Lei Wen , Chen Yong
Published: April 2024

Biodegradable polylactic acid (PLA) has been widely used in fused deposition modeling (FDM) 3D printing. In order to improve its comprehensive properties in 3D printing, in this study, 0-40% content of polybutylene adipate terephthalate(PBAT) was selected to be blended with PLA in a twin-screw extruder; the resulting pellets were drawn into a homogeneous filament; then, PBAT/PLA samples were prepared by FDM 3D printing, and the effects of the dosage of PBAT on the mechanical properties, thermal behavior, surface wettability and melt flowability of the samples were investigated. The results showed that all the samples could be printed smoothly, and the ductility was slightly improved by the increase in the PBAT dosage; the thermal stability of PLA was enhanced by blending with PBAT, and the crystallinity increased monotonically with the increase in PBAT. After blending with PBAT, the surfaces of the samples were more hydrophilic and flowable. The important conclusion achieved in this work was that the PBAT/PLA blends, especially those containing 30%PBAT, showed great potential to replace petroleum-based plastics and are suitable for use in FDM 3D printing technologies for different applications.
OH End-Capped Silicone as an Effective Nucleating Agent for Polylactide—A Robotizing Method for Evaluating the Mechanical Characteristics of PLA/Silicone Blends
Authors: Przekop Robert E. , Sztorch Bogna , Głowacka Julia , Martyła Agnieszka , Romańczuk-Ruszuk Eliza , Jałbrzykowski Marek , Derpeński Łukasz
Published: April 2024

Current research on materials engineering focuses mainly on bio-based materials. One of the most frequently studied materials in this group is polylactide (PLA), which is a polymer derived from starch. PLA does not have a negative impact on the natural environment and additionally, it possesses properties comparable to those of industrial polymers. The aim of the work was to investigate the potential of organosilicon compounds as modifiers of the mechanical and rheological properties of PLA, as well as to develop a new method for conducting mechanical property tests through innovative high-throughput technologies. Precise dosing methods were utilized to create PLA/silicone polymer blends with varying mass contents, allowing for continuous characterization of the produced blends. To automate bending tests and achieve comprehensive characterization of the blends, a self-created workstation setup has been used. The tensile properties of selected blend compositions were tested, and their ability to withstand dynamic loads was studied. The blends were characterized through various methods, including rheological (MFI), X-ray (XRD), spectroscopic (FTIR), and thermal properties analysis (TG, DSC, HDT), and they were evaluated using microscopic methods (MO, SEM) to examine their structures.
“Touch” the Sun and “Touch” the Cosmic Space to Learn How to Touch the Earth: Space Sustainability as an Ethical Guide for Relations: Mystery and Humility
Authors: Mantini Alessandro
Published: April 2024

On 14 December 2021, the extraordinary event called “touching the Sun” has been heralded as a “monumental moment for solar science”, in which the Parker Solar Probe opened up a new frontier of research for Space exploration, proposing the challenge of reaching, studying, and even “touching” our Sun at close range. The consequences of this event are scientific but also metaphysical and transcendental, offering the opportunity to reflect on the complex reality and meaning of the “boundaries” as opportunities for relationships and then for ethics. In this paper, I would like to propose an attempt to develop a possible discussion for an extension of Space Sustainability as an ethical guide for humanity which, as it goes out into outer Space, is helped to rediscover new, expanded dimensions of perception along the path of mystery and humility, in order to return back to Earth enriched for new relationships in pursuit of the common good.
Comprehensive Assessment and Analysis of the Current Global Aerosol Optical Depth Products
Authors: Zhang Liping , Wang Xufeng , Huang Guanghui , Zhang Songlin
Published: April 2024

Aerosol Optical Depth (AOD) is one of the most important optical properties of aerosols that may affect the energy budgets of our Earth–atmosphere system significantly. Currently, while regional and even global AOD knowledge has been given by various satellites or models, these products are still fraught with uncertainties. In this study, one sophisticated satellite-derived AOD product from MODIS (MODerate resolution Imaging Spectral-radiometer) and two state-of-the-art model-based AOD products from CAMS (Copernicus Atmosphere Monitoring Service) and MERRA-2 (Modern-Era Retrospective analysis for Research and Application Version 2), based on AERONET measurements from 2000–2022, analyzed the spatial distribution characteristics of global AOD. Then using the Mann-Kendall (MK) trend test, the AOD changing trends revealed by the three products were also computed and analyzed. The accuracies of these products and the reliabilities of changing trends derived are discussed and concluded finally. Our study demonstrates that MODIS products have wider applicability, matching best with AERONET globally, while CAMS and MERRA-2 products are only reliable in North America, South America, and Europe. Through comparative analysis of the AOD trends, we found that MODIS, CAMS, and MERRA-2 AOD consistently exhibited decreasing trends in eastern Asia, Europe, and eastern North America. On the other hand, different products showed increasing trends in regions like West Asia, South Asia, and South Africa, suggesting their limited reliability. The reliability assessment shows that 41.45% of the areas have consistent trends among the three products, with approximately 3.2% showing significant and consistent results. When using site trend validation, the proportions of sites with consistent trends are highest at 64.56% and 46.84% respectively. The regions with the best reliability of global trend changes are mainly distributed in North America, Europe, Australia, eastern Asia, and Central South America. This study provides new insights for validating aerosol changes using remote sensing and has the potential to enhance future monitoring and evaluation methods of aerosol products.
Assessing Satellite-Derived OpenET Platform Evapotranspiration of Mature Pecan Orchard in the Mesilla Valley, New Mexico
Authors: Tawalbeh Zada M. , Bawazir A. Salim , Fernald Alexander , Sabie Robert , Heerema Richard J.
Published: April 2024

Pecan is a major crop in the Mesilla Valley, New Mexico. Due to prolonged droughts, growers face challenges related to water shortages. Therefore, irrigation management is crucial for farmers. Advancements in satellite-derived evapotranspiration (ET) models and accessibility to data from web-based platforms like OpenET provide farmers with new tools to improve crop irrigation management. This study evaluates the evapotranspiration (ET) of a mature pecan orchard using OpenET platform data generated by six satellite-based models and their ensemble. The ET values obtained from the platform were compared with the ET values obtained from the eddy covariance (ETec) method from 2017 to 2021. The six models assessed included Google Earth Engine implementation of the Surface Energy Balance Algorithm for Land (geeSEBAL), Google Earth Engine implemonthsmentation of the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) model, Operational Simplified Surface Energy Balance (SSEBop), Satellite Irrigation Management Support (SIMS), Priestley–Taylor Jet Propulsion Laboratory (PT-JPL), and Atmosphere–Land Exchange Inverse and associated flux disaggregation technique (ALEXI/DisALEXI). The average growing season ET of mature pecan estimated from April to October of 2017 to 2021 by geeSEBAL, eeMETRIC, SSEBop, SIMS, PT-JPL, ALEXI/DisALEXI, and the ensemble were 1061, 1230, 1232, 1176, 1040, 1016, and 1130 mm, respectively, and 1108 mm by ETec. Overall, the ensemble model-based monthly ET of mature pecan during the growing season was relatively close to the ETec (R2 of 0.9477) with a 2% mean relative difference (MRD) and standard error of estimate (SEE) of 15 mm/month for the five years (N = 60 months). The high agreement of the OpenET ensemble of the six satellite-derived models’ estimates of mature pecan ET with the ETec demonstrates the utility of this promising approach to enhance the reliability of remote sensing-based ET data for agricultural and water resource management.
Radar Signal Classification with Multi-Frequency Multi-Scale Deformable Convolutional Networks and Attention Mechanisms
Authors: Liang Ruofei , Cen Yigang
Published: April 2024

In the realm of short-range radar applications, the focus on detecting “low, slow, and small” (LSS) targets has escalated, marking a pivotal aspect of critical area defense. This study pioneers the use of one-dimensional convolutional neural networks (1D-CNNs) for direct slow-time dimension radar feature extraction, sidestepping the complexity tied to frequency and wavelet domain transformations. It innovatively employs a network architecture enriched with multi-frequency multi-scale deformable convolution (MFMSDC) layers for nuanced feature extraction, integrates attention modules to foster comprehensive feature connectivity, and leverages linear operations to curtail overfitting. Through comparative evaluations and ablation studies, our methodology not only simplifies the analytic process but also demonstrates superior classification capabilities. This establishes a new benchmark for efficiently classifying low-altitude entities, such as birds and unmanned aerial vehicles (UAVs), thereby enhancing the precision and operational efficiency of radar detection systems.
Assessing Drought Stress of Sugarcane Cultivars Using Unmanned Vehicle System (UAS)-Based Vegetation Indices and Physiological Parameters
Authors: Khuimphukhieo Ittipon , Bhandari Mahendra , Enciso Juan , da Silva Jorge A.
Published: April 2024

Sugarcane breeding for drought tolerance is a sustainable strategy to cope with drought. In addition to biotechnology, high-throughput phenotyping has become an emerging tool for plant breeders. The objectives of the present study were to (1) identify drought-tolerant cultivars using vegetation indices (VIs), compared to the traditional method and (2) assess the accuracy of VIs-based prediction model estimating stomatal conductance (Gs) and chlorophyll content (Chl). A field trial was arranged in a randomized complete block design, consisting of seven cultivars of sugarcane. At the tillering and elongation stages, irrigation was withheld, and then furrow irrigation was applied to relieve sugarcane from stress. The physiological assessment measuring Gs and Chl using a handheld device and VIs were recorded under stress and recovery periods. The results showed that the same cultivars were identified as drought-tolerant cultivars when VIs and traditional methods were used for identification. Likewise, the results derived from genotype by trait biplot and heatmap were comparable, in which TCP93-4245 and CP72-1210 cultivars were classified as tolerant cultivars, while sensitive cultivars were CP06-2400 and CP89-2143 for both physiological parameters and VIs-based identification. In the prediction model, the random forest outperformed linear models in predicting the performance of cultivars in untested crops/environments for both Gs and Chl. In contrast, it underperformed linear models in the tested crops/environments. The identification of tolerant cultivars through prediction models revealed that at least two out of three cultivars had consistent rankings in both measured and predicted outcomes for both traits. This study shows the possibility of using UAS mounted with sensors to assist plant breeders in their decision-making.
Raster Scale Farmland Productivity Assessment with Multi-Source Data Fusion—A Case of Typical Black Soil Region in Northeast China
Authors: Liu Yuwen , Wang Chengyuan , Wang Enheng , Mao Xuegang , Liu Yuan , Hu Zhibo
Published: April 2024

Degradation of black soil areas is a serious threat to national food security and ecological safety; nevertheless, the current lack of information on the location, size, and condition of black soil farmland productivity is a major obstacle to the development of strategies for the sustainable utilization of black soil resources. We synthesized remote sensing data and geospatial thematic data to construct a farmland productivity assessment indicator system to assess the productivity of black soil cropland at the regional scale. Furthermore, we conducted research on the spatial differentiation patterns and a spatial autocorrelation analysis of the assessment results. We found that farmland productivity within this region exhibited a decline pattern from south to north, with superior productivity in the east as opposed to the west, and the distribution follows a “spindle-shaped” pattern. Notably, the Songnen and Sanjiang typical black soil subregions centrally hosted about 46.17% of high-quality farmland and 53.51% of medium-quality farmland, while the Mondong typical black soil subregion in the west predominantly consisted of relatively low-quality farmland productivity. Additionally, farmland productivity displayed a significant positive spatial correlation and spatial clustering, with more pronounced fluctuations in the northeast–southwest direction. The developed indicator system for farmland productivity can illustrate the spatial differentiation and thereby offer a valuable reference for the sustainable management of farmland resources.
Sun/Shade Separation in Optical and Thermal UAV Images for Assessing the Impact of Agricultural Practices
Authors: Marais-Sicre Claire , Queguiner Solen , Bustillo Vincent , Lesage Luka , Barcet Hugues , Pelle Nathalie , Breil Nicolas , Coudert Benoit
Published: April 2024

Unmanned aerial vehicles (UAVs) provide images at decametric spatial resolutions. Their flexibility, efficiency, and low cost make it possible to apply UAV remote sensing to multisensor data acquisition. In this frame, the present study aims at employing RGB UAV images (at a 3 cm resolution) and multispectral images (at a 16 cm resolution) with related vegetation indices (VIs) for mapping surfaces according to their illumination. The aim is to map land cover in order to access temperature distribution and compare NDVI and MTVI2 dynamics as a function of their illuminance. The method, which is based on a linear discriminant analysis, is validated at different periods during the phenological cycle of the crops in place. A model based on a given date is evaluated, as well as the use of a generic model. The method provides a good capacity of separation between four classes: vegetation, no-vegetation, shade, and sun (average kappa of 0.93). The effects of agricultural practices on two adjacent plots of maize respectively submitted to conventional and conservation farming are assessed. The transition from shade to sun increases the brightness temperature by 2.4 °C and reduces the NDVI by 26% for non-vegetated surfaces. The conservation farming plot is found to be 1.9 °C warmer on the 11th of July 2019, with no significant difference between vegetation in the sun or shade. The results also indicate that the NDVI of non-vegetated areas is increased by the presence of crop residues on the conservation agriculture plot and by the effect of shade on the conventional plot which is different for MTVI2.
Object-Based Semi-Supervised Spatial Attention Residual UNet for Urban High-Resolution Remote Sensing Image Classification
Authors: Lu Yuanbing , Li Huapeng , Zhang Ce , Zhang Shuqing
Published: April 2024

Accurate urban land cover information is crucial for effective urban planning and management. While convolutional neural networks (CNNs) demonstrate superior feature learning and prediction capabilities using image-level annotations, the inherent mixed-category nature of input image patches leads to classification errors along object boundaries. Fully convolutional neural networks (FCNs) excel at pixel-wise fine segmentation, making them less susceptible to heterogeneous content, but they require fully annotated dense image patches, which may not be readily available in real-world scenarios. This paper proposes an object-based semi-supervised spatial attention residual UNet (OS-ARU) model. First, multiscale segmentation is performed to obtain segments from a remote sensing image, and segments containing sample points are assigned the categories of the corresponding points, which are used to train the model. Then, the trained model predicts class probabilities for all segments. Each unlabeled segment’s probability distribution is compared against those of labeled segments for similarity matching under a threshold constraint. Through label propagation, pseudo-labels are assigned to unlabeled segments exhibiting high similarity to labeled ones. Finally, the model is retrained using the augmented training set incorporating the pseudo-labeled segments. Comprehensive experiments on aerial image benchmarks for Vaihingen and Potsdam demonstrate that the proposed OS-ARU achieves higher classification accuracy than state-of-the-art models, including OCNN, 2OCNN, and standard OS-U, reaching an overall accuracy (OA) of 87.83% and 86.71%, respectively. The performance improvements over the baseline methods are statistically significant according to the Wilcoxon Signed-Rank Test. Despite using significantly fewer sparse annotations, this semi-supervised approach still achieves comparable accuracy to the same model under full supervision. The proposed method thus makes a step forward in substantially alleviating the heavy sampling burden of FCNs (densely sampled deep learning models) to effectively handle the complex issue of land cover information identification and classification.
Comparison of Metabolic Power and Energy Cost of Submaximal and Sprint Running Efforts Using Different Methods in Elite Youth Soccer Players: A Novel Energetic Approach
Authors: Grassadonia Gabriele , Alcaraz Pedro E. , Freitas Tomás T.
Published: April 2024

Sprinting is a decisive action in soccer that is considerably taxing from a neuromuscular and energetic perspective. This study compared different calculation methods for the metabolic power (MP) and energy cost (EC) of sprinting using global positioning system (GPS) metrics and electromyography (EMG), with the aim of identifying potential differences in performance markers. Sixteen elite U17 male soccer players (age: 16.4 ± 0.5 years; body mass: 64.6 ± 4.4 kg; and height: 177.4 ± 4.3 cm) participated in the study and completed four different submaximal constant running efforts followed by sprinting actions while using portable GPS-IMU units and surface EMG. GPS-derived MP was determined based on GPS velocity, and the EMG-MP and EC were calculated based on individual profiles plotting the MP of the GPS and all EMG signals acquired. The goodness of fit of the linear regressions was assessed by the coefficient of determination (R2), and a repeated measures ANOVA was used to detect changes. A linear trend was found in EMG activity during submaximal speed runs (R2 = 1), but when the sprint effort was considered, the trend became exponential (R2 = 0.89). The EMG/force ratio displayed two different trends: linear up to a 30 m sprint (R2 = 0.99) and polynomial up to a 50 m sprint (R2 = 0.96). Statistically significant differences between the GPS and EMG were observed for MP splits at 0–5 m, 5–10 m, 25–30 m, 30–35 m, and 35–40 m and for EC splits at 5–10 m, 25–30 m, 30–35 m, and 35–40 m (p ≤ 0.05). Therefore, the determination of the MP and EC based on GPS technology underestimated the neuromuscular and metabolic engagement during the sprinting efforts. Thus, the EMG-derived method seems to be more accurate for calculating the MP and EC in this type of action.
A Method to Correct the Temporal Drift of Single-Photon Detectors Based on Asynchronous Quantum Ghost Imaging
Authors: Pitsch Carsten , Walter Dominik , Gasparini Leonardo , Bürsing Helge , Eichhorn Marc
Published: April 2024

Single-photon detection and timing has attracted increasing interest in recent years due to their necessity in the field of quantum sensing and the advantages of single-quanta detection in the field of low-level light imaging. While simple bucket detectors are mature enough for commercial applications, more complex imaging detectors are still a field of research comprising mostly prototype-level detectors. A major problem in these detectors is the implementation of in-pixel timing circuitry, especially for two-dimensional imagers. One of the most promising approaches is the use of voltage-controlled ring resonators in every pixel. Each of these runs independently based on a voltage supplied by a global reference. However, this yields the problem that the supply voltage can change across the chip which, in turn, changes the period of the ring resonator. Due to additional parasitic effects, this problem can worsen with increasing measurement time, leading to drift in the timing information. We present here a method to identify and correct such temporal drifts in single-photon detectors based on asynchronous quantum ghost imaging. We also show the effect of this correction on recent quantum ghost imaging (QGI) measurement from our group.
Adaptive Dataset Management Scheme for Lightweight Federated Learning in Mobile Edge Computing
Authors: Kim Jingyeom , Bang Juneseok , Lee Joohyung
Published: April 2024

Federated learning (FL) in mobile edge computing has emerged as a promising machine-learning paradigm in the Internet of Things, enabling distributed training without exposing private data. It allows multiple mobile devices (MDs) to collaboratively create a global model. FL not only addresses the issue of private data exposure but also alleviates the burden on a centralized server, which is common in conventional centralized learning. However, a critical issue in FL is the imposed computing for local training on multiple MDs, which often have limited computing capabilities. This limitation poses a challenge for MDs to actively contribute to the training process. To tackle this problem, this paper proposes an adaptive dataset management (ADM) scheme, aiming to reduce the burden of local training on MDs. Through an empirical study on the influence of dataset size on accuracy improvement over communication rounds, we confirm that the amount of dataset has a reduced impact on accuracy gain. Based on this finding, we introduce a discount factor that represents the reduced impact of the size of the dataset on the accuracy gain over communication rounds. To address the ADM problem, which involves determining how much the dataset should be reduced over classes while considering both the proposed discounting factor and Kullback–Leibler divergence (KLD), a theoretical framework is presented. The ADM problem is a non-convex optimization problem. To solve it, we propose a greedy-based heuristic algorithm that determines a suboptimal solution with low complexity. Simulation results demonstrate that our proposed scheme effectively alleviates the training burden on MDs while maintaining acceptable training accuracy.
Development of a Two-Finger Haptic Robotic Hand with Novel Stiffness Detection and Impedance Control
Authors: Mohammadi Vahid , Shahbad Ramin , Hosseini Mojtaba , Gholampour Mohammad Hossein , Shiry Ghidary Saeed , Najafi Farshid , Behboodi Ahad
Published: April 2024

Haptic hands and grippers, designed to enable skillful object manipulation, are pivotal for high-precision interaction with environments. These technologies are particularly vital in fields such as minimally invasive surgery, where they enhance surgical accuracy and tactile feedback: in the development of advanced prosthetic limbs, offering users improved functionality and a more natural sense of touch, and within industrial automation and manufacturing, they contribute to more efficient, safe, and flexible production processes. This paper presents the development of a two-finger robotic hand that employs simple yet precise strategies to manipulate objects without damaging or dropping them. Our innovative approach fused force-sensitive resistor (FSR) sensors with the average current of servomotors to enhance both the speed and accuracy of grasping. Therefore, we aim to create a grasping mechanism that is more dexterous than grippers and less complex than robotic hands. To achieve this goal, we designed a two-finger robotic hand with two degrees of freedom on each finger; an FSR was integrated into each fingertip to enable object categorization and the detection of the initial contact. Subsequently, servomotor currents were monitored continuously to implement impedance control and maintain the grasp of objects in a wide range of stiffness. The proposed hand categorized objects’ stiffness upon initial contact and exerted accurate force by fusing FSR and the motor currents. An experimental test was conducted using a Yale–CMU–Berkeley (YCB) object set consisted of a foam ball, an empty soda can, an apple, a glass cup, a plastic cup, and a small milk packet. The robotic hand successfully picked up these objects from a table and sat them down without inflicting any damage or dropping them midway. Our results represent a significant step forward in developing haptic robotic hands with advanced object perception and manipulation capabilities.
A Laboratory Machine Verifying the Operation of a Hydraulic Rope Equalizer with Tensometric Sensors
Authors: Hrabovský Leopold , Fries Jiří , Pravda Štěpán
Published: April 2024

In mining machines with friction discs, but also in multi-rope traction elevators, it is necessary to distribute the applied tensile load, generated by the weight of the cage and counterweight, evenly in all cross-sections of the load-bearing ropes. Hydraulic devices used for this purpose can operate on the principle of Pascal’s law. This article presents a structural design, a 3D model and an implemented solution of a laboratory device capable of simulating a practical method of evenly distributing the total weight of the load into partial tensile forces of the same size acting on a selected number of load-bearing ropes. The laboratory equipment uses two pairs of three steel cables of finite length for the simulations. During the experimental measurements, tensile forces derived from the tractive force of the piston rods, pushed into the bodies of the hydraulic cylinders by the pressure of the hydraulic oil supplied through the pipeline under the pistons of the hydraulic cylinders, were detected. The resulting amount of hydraulic oil pressure in the hydraulic circuit influenced by different values of the hydraulic oil pressures in the hydraulic cylinders and by the pressure in the supply pipe was experimentally studied on the laboratory equipment. Simulations were also carried out in order to detect the hydraulic oil pressure in the hydraulic circuit caused by the change in the different magnitudes of the tensile forces in the ropes. From the experiments carried out, it follows that with the appropriate choice of hydraulic elements and the design of the hydraulic circuit, the weight of the load, acting as the total pulling force in the ropes, can be evenly distributed (with a deviation of up to 5%) to all cross-sections of the load-bearing ropes. If the exact values of the hydraulic oil volumes under the pistons of all hydraulic cylinders are not known, it is not possible to calculate the pressure values in the hydraulic circuit when the valves of the hydraulic pipes are gradually opened.
Soundscape Characterization Using Autoencoders and Unsupervised Learning
Authors: Nieto-Mora Daniel Alexis , Ferreira de Oliveira Maria Cristina , Sanchez-Giraldo Camilo , Duque-Muñoz Leonardo , Isaza-Narváez Claudia , Martínez-Vargas Juan David
Published: April 2024

Passive acoustic monitoring (PAM) through acoustic recorder units (ARUs) shows promise in detecting early landscape changes linked to functional and structural patterns, including species richness, acoustic diversity, community interactions, and human-induced threats. However, current approaches primarily rely on supervised methods, which require prior knowledge of collected datasets. This reliance poses challenges due to the large volumes of ARU data. In this work, we propose a non-supervised framework using autoencoders to extract soundscape features. We applied this framework to a dataset from Colombian landscapes captured by 31 audiomoth recorders. Our method generates clusters based on autoencoder features and represents cluster information with prototype spectrograms using centroid features and the decoder part of the neural network. Our analysis provides valuable insights into the distribution and temporal patterns of various sound compositions within the study area. By utilizing autoencoders, we identify significant soundscape patterns characterized by recurring and intense sound types across multiple frequency ranges. This comprehensive understanding of the study area’s soundscape allows us to pinpoint crucial sound sources and gain deeper insights into its acoustic environment. Our results encourage further exploration of unsupervised algorithms in soundscape analysis as a promising alternative path for understanding and monitoring environmental changes.
A Stochastic Model Based on Optimal Satellite Subset Selection Strategy for Smartphone Pseudorange Relative Positioning
Authors: Deng Jian , Wang Huayin , Wei Shuen , Zhang Aiguo
Published: April 2024

In order to overcome the limitations of traditional stochastic models for smartphones, we introduce a double-difference code pseudorange residual (DDPR)-dependent stochastic model based on an optimal satellite subset, with the goal of mitigating the constraints imposed by the quality of GNSS observations in smartphones on the accuracy and reliability of phone-based GNSS positioning. In our methodology, the satellite selection process involved considering the integrated carrier-to-noise density ratio (C/N0) index of both the reference station and the smartphone, enabling us to construct a satellite subset characterized by superior observation quality. Furthermore, by leveraging the optimal subset of satellites and incorporating the C/N0-dependent stochastic model, we could determine the approximate location of the terminal through pseudorange differential positioning. Subsequently, we estimated the DDPRs for all satellites and utilized these values as prior information to build a stochastic model of the observations. Our findings indicate that in occluded environments, the DDPR-dependent stochastic model significantly enhances positioning accuracy for both the Huawei Mate40 and P40 terminals compared to the C/N0-dependent model. Numerically, the improvements in the north (N), east (E), and up (U) directions were approximately 30%, 32%, and 34% for the Mate40, and 26%, 33%, and 24% for the P40 terminal. This suggests that the proposed DDPR-dependent stochastic model effectively identifies and mitigates large gross error signals caused by multipath and non-line-of-sight (NLOS) signals, thereby assigning lower weights to these problematic observations and ultimately enhancing positioning accuracy. Moreover, the weighting method involves minimal computations and is straightforward to implement, making it particularly suitable for GNSS positioning applications on smartphones in complex urban environments.
Co3O4-Based Materials as Potential Catalysts for Methane Detection in Catalytic Gas Sensors
Authors: Yurchenko Olena , Diehle Patrick , Altmann Frank , Schmitt Katrin , Wöllenstein Jürgen
Published: April 2024

The present work deals with the development of Co3O4-based catalysts for potential application in catalytic gas sensors for methane (CH4) detection. Among the transition-metal oxide catalysts, Co3O4 exhibits the highest activity in catalytic combustion. Doping Co3O4 with another metal can further improve its catalytic performance. Despite their promising properties, Co3O4 materials have rarely been tested for use in catalytic gas sensors. In our study, the influence of catalyst morphology and Ni doping on the catalytic activity and thermal stability of Co3O4-based catalysts was analyzed by differential calorimetry by measuring the thermal response to 1% CH4. The morphology of two Co3O4 catalysts and two NixCo3−xO4 with a Ni:Co molar ratio of 1:2 and 1:5 was studied using scanning transmission electron microscopy and energy dispersive X-ray analysis. The catalysts were synthesized by (co)precipitation with KOH solution. The investigations showed that Ni doping can improve the catalytic activity of Co3O4 catalysts. The thermal response of Ni-doped catalysts was increased by more than 20% at 400 °C and 450 °C compared to one of the studied Co3O4 oxides. However, the thermal response of the other Co3O4 was even higher than that of NixCo3−xO4 catalysts (8% at 400 °C). Furthermore, the modification of Co3O4 with Ni simultaneously brings stability problems at higher operating temperatures (≥400 °C) due to the observed inhomogeneous Ni distribution in the structure of NixCo3−xO4. In particular, the NixCo3−xO4 with high Ni content (Ni:Co ratio 1:2) showed apparent NiO separation and thus a strong decrease in thermal response of 8% after 24 h of heat treatment at 400 °C. The reaction of the Co3O4 catalysts remained quite stable. Therefore, controlling the structure and morphology of Co3O4 achieved more promising results, demonstrating its applicability as a catalyst for gas sensing.
Personal Air-Quality Monitoring with Sensor-Based Wireless Internet-of-Things Electronics Embedded in Protective Face Masks
Authors: Kuglics Lajos , Géczy Attila , Dusek Karel , Busek David , Illés Balázs
Published: April 2024

In this paper, the design and research of a sensor-based personal air-quality monitoring device are presented, which is retrofitted into different personal protective face masks. Due to its small size and low power consumption, the device can be integrated into and applied in practical urban usage. We present our research and the development of the sensor node based on a BME680-type environmental sensor cluster with a wireless IoT (Internet of Things)-capable central unit and overall low power consumption. The integration of the sensor node was investigated with traditional medical masks and a professional FFP2-type mask. The filtering efficiency after embedding was validated with a head model and a particle counter. We found that the professional mask withstood the embedding without losing the protective filtering aspect. We compared the inner and outer sensor data and investigated the temperature, pressure, humidity, and AQI (Air Quality Index) relations with possible sensor data-fusion options. The novelty is increased with the dual-sensor layout (inward and outward). It was found that efficient respiration monitoring is achievable with the device. With the analysis of the recorded data, characteristic signals were identified in an urban environment, enabling urban altimetry and urban zone detection. The results promote smart city concepts and help in endeavors related to SDGs (Sustainable Development Goals) 3 and 11.
Hard- and Software Controlled Complex for Gas-Strain Monitoring of Transition Zones
Authors: Dolgikh Grigory , Bovsun Mariia , Dolgikh Stanislav , Stepochkin Igor , Chupin Vladimir , Yatsuk Andrey
Published: April 2024

The article describes a hard- and software controlled complex for gas-strain monitoring, consisting of stationary laser strainmeters and a laser nanobarograph, a stationary gas analyzer, and a weather station installed at Shultz Cape in the Sea of Japan; and a mobile shipboard complex, consisting of a gas analyzer and a weather station installed in a scientific research vessel. In the course of trial methodological measurements on these systems, general patterns were identified in the dynamics of greenhouse gases and deformation of the Earth’s crust in the range of diurnal and semi-diurnal tides, and also in the range of ultra-low frequencies, caused by atmospheric wave processes and, possibly, individual tones of the Earth’s eigen oscillations.
The Relation between Atmospheric Aerosol Concentration and SARS-CoV-2 Variants’ Infection and Mortality Rates in the United States: A Remote-Sensing Perspective
Authors: Elshorbany Yasin , Mixson Sarah , Marcum Laila , Salemi Jason L.
Published: April 2024

Numerous studies have highlighted the health benefits associated with reducing aerosol particles and other pollutants. Recent studies have shown a positive correlation between exposure to aerosol particles and COVID-19 cases. In this study, we investigate the relationship between aerosol particle concentration and COVID-19 variants’ infection and mortality rates. Remote-sensing data on aerosol optical depth (AOD), a surrogate for atmospheric aerosol levels, were collected and analyzed in three regions within the states of California, Illinois, and North and South Carolina. These regions were selected to reflect the variability in aerosol concentration and anomalies during the COVID-19 period (2020–2022) compared to a reference period (2010–2019). We found consistent positive linear correlations across most regions between COVID-19 mortality rates and AOD levels below 0.2. These correlations were found to be independent of the change in aerosol levels relative to the reference period. In North and South Carolina, the Delta variant was associated with not only a high mortality rate but also a steeper increase in mortality rate per AOD of 39.6 ± 1.5 (R2 = 0.87) compared to Alpha at 10.5 ± 0.3 (R2 = 0.85). However, due to the lower mortality rate of the Delta variant compared to the Alpha variant in Illinois, the Delta variant has a lower correlation slope of 28.3 ± 1 (R2 = 0.94) compared to the Alpha variant at 52.1 ± 1.8 (R2 = 0.78). No correlation between mortality rate and AOD was observed for the Omicron variants in any of the investigated regions except in California where a weak positive correlation was evident. Our findings establish a compelling link between aerosol concentrations and SARS-CoV-2 infection and mortality. Our results underscore the urgent need for further research to elucidate the underlying mechanisms and broader implications, leading to more sustainable solutions to curb the airborne transmission of COVID-19 viruses and other viral infections in general.
Vegetation–Lake–Sand Landscape of Northeast China Sandy Land between 1980 and 2022: Pattern, Evolution, and Driving Forces
Authors: Lu Weiyi , Teni Geer , Du Huishi
Published: April 2024

Northeast China’s sandy region is an arid and semi-arid zone highly susceptible to climate change. Investigating the long-term changes in the Northeast China sandy land (Northeast China sandy land, DBSL) landscape can provide an important basis for the ecological restoration of this region. This study analyzed long-term remote sensing data of the DBSL from 1980 to 2022 and explored the spatial pattern, evolution, and driving mechanisms. In 2022, vegetation was mainly distributed in the northwest, center, and southwest, covering a total area of 30,508.82 km2. Areas with high and medium vegetation cover showed strong aggregation characteristics and were mainly distributed in the southwest, whereas those with low vegetation coverage were highly dispersed and widely distributed in the central region. Lakes were widely distributed in the northwest and central regions, with a total area of 2736.43 km2. In the last 42 years, the vegetation cover decreased by 24.48%. Areas with high and medium vegetation coverage decreased in size, and those with low vegetation coverage first increased and then decreased, with overall decreases of 35.35%, 19.16%, and 6.88%, respectively. The overall area of the DBSL showed various degrees of degradation. Shrinking and dry lakes were concentrated in the sandy hinterland. The lake landscape changed significantly from 1990 to 2010, with a decrease in lake area of 27.41%. In contrast, the sandy area increased by 25.65%, indicating a high degree of desertification. However, from 2005 to 2022, desertification decelerated. The most important factors driving the evolution of the DBSL were socio-economic factors. The increase in human disturbance will have a certain impact on the landscape changes in the region in the short term. The national policy of returning farmland to fields and grasslands will affect the increase of vegetation and lake landscape area in the short term, and the sand area and excessive animal husbandry will be reduced. This study provides a scientific basis for ecological restoration and sustainable development in Northeast China.