bluuebunny/crossref_metadata_2025 huggingface dataset:
Porous Silica Nanomaterials as Carriers of Biologically Active Natural Polyphenols: Effect of Structure and Surface Modification
Authors: Trendafilova Ivalina , Popova Margarita
Published: July 2024

For centuries, humans have relied on natural products to prevent and treat numerous health issues. However, biologically active compounds from natural sources, such as polyphenols, face considerable challenges, due to their low solubility, rapid metabolism, and instability, which hinder their effectiveness. Advances in the nanotechnologies have provided solutions to overcoming these problems through the use of porous silica materials as polyphenol carriers. These materials possess unique properties, such as a high specific surface area, adjustable particle and pore sizes, and a surface that can be easily and selectively modified, which favor their application in delivery systems of polyphenols. In this review, we summarize and discuss findings on how the pore and particle size, structure, and surface modification of silica materials influence the preparation of efficient delivery systems for biologically active polyphenols from natural origins. The available data demonstrate how parameters such as adsorption capacity, release and antioxidant properties, bioavailability, solubility, stability, etc., of the studied delivery systems could be affected by the structural and chemical characteristics of the porous silica carriers. Results in the literature confirm that by regulating the structure and selecting the appropriate surface modifications, the health benefits of the loaded bioactive molecules can be significantly improved.
Effects of Heat Stress during Anthesis and Grain Filling Stages on Some Physiological and Agronomic Traits in Diverse Wheat Genotypes
Authors: Mirosavljević Milan , Mikić Sanja , Župunski Vesna , Abdelhakim Lamis , Trkulja Dragana , Zhou Rong , Špika Ankica Kondić , Ottosen Carl-Otto
Published: July 2024

Heat stress represents a significant environmental challenge that adversely impacts the growth, physiology, and productivity of wheat. In order to determine the response to high temperatures of the wheat varieties developed mostly in the Pannonian environmental zone, as well as varietal differences, we subjected seven varieties from Serbia, one from Australia, and one from the UK to thermal stress during anthesis and mid-grain filling and combined stress during both of these periods. The changes in chlorophyll fluorescence and index, leaf temperature, and main agronomic traits of nine winter wheat varieties were investigated under high temperatures. Heat stress negatively affected leaf temperature, chlorophyll fluorescence, and the chlorophyll index during different growth stages. Compared to the control, stress at anthesis, mid-grain filling, and combined stress resulted in yield reductions of 32%, 46%, and 59%, respectively. Single treatment at anthesis had a more severe effect on the number of grains per plant, causing a 38% reduction compared to the control. Moreover, single treatment during mid-grain filling resulted in the greatest decline in grain weight, with a 29% reduction compared to the control. There was a significant varietal variation in heat tolerance, highlighting Avangarda and NS 40s as the most tolerant varieties that should be included in regular breeding programs as valuable sources of heat tolerance. Understanding the genetic and physiological mechanisms of heat tolerance in these promising varieties should be the primary focus of future research and help develop targeted breeding strategies and agronomic practices to mitigate the adverse effects of heat stress on wheat production.
Material Compatibility in 4D Printing: Identifying the Optimal Combination for Programmable Multi-Material Structures
Authors: Pivar Matej , Vrabič-Brodnjak Urška , Leskovšek Mirjam , Gregor-Svetec Diana , Muck Deja
Published: July 2024

This study identifies the optimal combination of active and passive thermoplastic materials for producing multi-material programmable 3D structures. These structures can undergo shape changes with varying radii of curvature over time when exposed to hot water. The research focuses on examining the thermal, thermomechanical, and mechanical properties of active (PLA) and passive (PRO-PLA, ABS, and TPU) materials. It also includes the experimental determination of the radius of curvature of the programmed 3D structures. The pairing of active PLA with passive PRO-PLA was found to be the most effective for creating complex programmable 3D structures capable of two-sided transformation. This efficacy is attributed to the adequate apparent shear strength, significant differences in thermomechanical shrinkage between the two materials, identical printing parameters for both materials, and the lowest bending storage modulus of PRO-PLA among the passive materials within the activation temperature range. Multi-material 3D printing has also proven to be a suitable method for producing programmable 3D structures for practical applications such as phone stands, phone cases, door hangers, etc. It facilitates the programming of the active material and ensures the dimensional stability of the passive components of programmable 3D structures during thermal activation.
Constructing Heterostructured MWCNT-BN Hybrid Fillers in Electrospun TPU Films to Achieve Superior Thermal Conductivity and Electrical Insulation Properties
Authors: Zhang Yang , Wang Shichang , Wu Hong , Guo Shaoyun
Published: July 2024

The development of thermally conductive polymer/boron nitride (BN) composites with excellent electrically insulating properties is urgently demanded for electronic devices. However, the method of constructing an efficient thermally conductive network is still challenging. In the present work, heterostructured multi-walled carbon nanotube-boron nitride (MWCNT-BN) hybrids were easily prepared using an electrostatic self-assembly method. The thermally conductive network of the MWCNT-BN in the thermoplastic polyurethane (TPU) matrix was achieved by the electrospinning and stack-molding process. As a result, the in-plane thermal conductivity of TPU composite films reached 7.28 W m−1 K−1, an increase of 959.4% compared to pure TPU films. In addition, the Foygel model showed that the MWCNT-BN hybrid filler could largely decrease thermal resistance compared to that of BN filler and further reduce phonon scattering. Finally, the excellent electrically insulating properties (about 1012 Ω·cm) and superior flexibility of composite film make it a promising material in electronic equipment. This work offers a new idea for designing BN-based hybrids, which have broad prospects in preparing thermally conductive composites for further practical thermal management fields.
Frequency-Dependent Fatigue Properties of Additively Manufactured PLA
Authors: Česnik Martin , Slavič Janko
Published: July 2024

Vibration-fatigue failure occurs when a structure is dynamically excited within its natural frequency range. Unlike metals, which have constant fatigue parameters, polymers can exhibit frequency-dependent fatigue parameters, significantly affecting the vibration resilience of 3D-printed polymer structures. This manuscript presents a study utilizing a novel vibration-fatigue testing methodology to characterize the frequency dependence of polymer material fatigue parameters under constant temperature conditions. In this investigation, 3D-printed PLA samples with frequency-tunable geometry were experimentally tested on an electro-dynamical shaker with a random vibration profile. Using the validated numerical models, the estimation of vibration-fatigue life was obtained and compared to the experimental results. Performing the numerical minimization of estimated and actual fatigue lives, the frequency-dependent fatigue parameters were assessed. In particular, the results indicate that the tested samples exhibit varying fatigue parameters within the loading frequency range of 250–750 Hz. Specifically, as the loading frequency increases, the fatigue exponent increases and fatigue strength decreases. These findings confirm the frequency dependence of fatigue parameters for 3D-printed polymer structures, underscoring the necessity of experimental characterization to reliably estimate the vibration-fatigue life of 3D-printed polymer structures. The utilization of the introduced approach therefore enhances the vibration resilience of the 3D-printed polymer mechanical component.
Horse Meat Subjected to Sous-Vide Cooking: Texture Changes and Sensory Acceptability
Authors: Stanisławczyk Renata , Żurek Jagoda , Rudy Mariusz , Gil Marian , Krajewska Anna , Dziki Dariusz
Published: July 2024

Meat preservation is necessary to prevent harmful changes caused by microbiological, chemical, and physical processes in order, among other things, to extend storage life. In this study, we investigated how the duration (4, 8, 12, and 24 h) and temperature (50 °C, 55 °C, 60 °C) of SV (sous-vide) treatment for horse meat influence the sensory properties and quality of m. longissimus thoracis specimens. The SV temperature and time of heat treatment demonstrated notable enhancements of most texture parameters. Increasing the duration of heat treatment from 4 h to 8, 12, and 24 h at all applied temperature values resulted in an improvement in the shear force, hardness, springiness, gumminess, and chewiness of horse meat (however, the differences were not statistically significant in every case). Increasing the duration of heat treatment from 4 to 24 h, but only at a temperature of 60 °C, improved the scent (intensity and desirability) and flavor desirability. Multiple regression analysis showed that many texture parameters could be effectively described by the cooking time and process temperature. The most accurate representation of the empirical data (R2 > 0.903) was attained for adhesiveness, springiness, gumminess, and chewiness, which exhibited the highest coefficients of determination.
Research for Flow Behavior of Heavy Oil by CO2 Foam Viscosity Reducer-Assisted Steam (CFVAS) Flooding: Microscopic Displacement Experiment Study
Authors: Shi Wenyang , Gong Yunpeng , Tao Lei , Bai Jiajia , Xu Zhengxiao , Zhu Qingjie , Ma Yunpeng
Published: July 2024

Steam displacement is prone to cross-flow, small swept area, large oil–water ratio, large oil–water interfacial tension, and low oil displacement efficiency. Compared with steam flooding, foam flooding can effectively reduce the residual oil in the small throat of the main flow channel and the small hole in the near flow channel and increase the overall recovery factor. Therefore, researchers carried out CO2 and chemical agent-assisted steam displacement. However, at present, there is a lack of research on the occurrence mechanism and model of residual oil. Steam flooding often encounters challenges such as cross-flow, limited sweep area, and high oil–water ratio. Foam flooding offers a promising alternative by effectively reducing residual oil in narrow throats and the near flow channel, thereby enhancing overall recovery rates compared to steam flooding alone. Therefore, chemical agent-assisted steam flooding was applied to enhance heavy oil recovery. However, the occurrence mechanism and model of residual oil after chemical agent-assisted steam is not clear. To fill this gap, the CO2 foam viscosity reducer assisted steam (CFVAS) flooding technology has been adopted and carried out in several studies. First, the foam viscosity reducer was prepared and its foam properties (viscosity reduction effect, foam volume, and half-life) were tested. Subsequently, the CFVAS displacement experiments after steam flooding were carried out, and the flow behavior of the remaining oil in multiple regions (main flow channel, near flow channel, and far flow channel) was analyzed. Finally, the shape and number of remaining oil under different displacement stages were compared, and the occurrence mode of remaining oil under CFVAS displacement was determined. The results indicate the following: (1) During steam flooding, the amount of near flow channel residual oil decreased with injected pore volumes (PV), transforming into columnar structures in small perforations and film-like formations in far flow channels. (2) CFVAS flooding, including the foam stability mechanism, flow channel adjustment mechanism, and emulsification and dispersion mechanism, can improve overall recovery rates by 55.2% by driving the remaining oil in near flow channels. (3) During CFVAS flooding stage, crude oil mobility notably improved and flooding front expanded more evenly. Residual oil primarily existed as oil-in-water (O/W) emulsions with discontinuous columns. (4) In the CFVAS flooding stage, residual oil mainly formed O/W emulsions through emulsification and dispersion, with foam-filled large and medium pores, concentrating residual oil in thick and middle throats. This work can provide important references for injecting CO2 gas into reservoirs to enhance heavy oil recovery and promote carbon sequestration.
Research on Multi-Layer Drilling Mud Reuse Technology
Authors: Huang Jian , Wang Ling , Li Fanxiu
Published: July 2024

Addressing the issues of low reuse rates and high waste content of drilling fluids commonly observed in oilfields, research on reuse technology based on utilizing the same system across different sections of the same well has been conducted. Using the F oilfield as a case study, the mechanism of wellbore destabilization was investigated through X-ray diffraction and scanning electron microscopy. Corresponding inhibitory anti-collapse drilling fluids for shallow layers were formulated, and a successful deep drilling fluid formula was developed by adding and replacing chemicals in the base fluid, thereby achieving the reuse of multilayered waste drilling fluids. Indoor evaluation results indicate that the high-temperature rheology of the modified deep drilling fluid is reasonable; the high-temperature inhibitor performs excellently, with a 16-h rolling recovery rate of ≥98%; and the settlement stability is robust, with a settlement ratio of 0.50 after 2 h of resting. These findings demonstrate that the drilling fluid possesses both excellent sand-carrying capacity and strong inhibitory effects, meeting the requirements for rapid drilling and wellbore stabilization in this stratum. This technology is straightforward and easy to implement, and it is expected to reduce treatment costs and promote efficient development within the block.
Computational Investigation of Co-Solvent Influence on CO2 Absorption and Diffusion in Water Lean Solvents
Authors: Sharif Maimoona , Ge Chunliang , Wang Tao , Zhang Wei , Fang Mengxiang , Gao Xiang
Published: July 2024

The present research explores water-lean amine-based solvents to enhance carbon capture and provide sustainable solutions for CO2 emissions challenges. A computational approach is employed to evaluate the co-solvent’s impact on CO2 capture in MDEA-based systems. The performance of the following systems is examined: MDEA-NMP, MDEA-MAE-NMP, MDEA-MeOH, MDEA-MAE-MeOH, MDEA-EG, MDEA-MAE-EG, and MDEA-MAE with varying water concentrations. The Radial Distribution Function (RDF) analysis revealed significant interactions between amine groups, CO2, and water molecules in each system. The results indicate that the MDEA-NMP (40% H2O) and MDEA-EG (40% H2O) systems had strong interactions, indicating their potential for CO2 capture. However, adding MAE decreased interaction intensities, indicating a less favorable performance. Complementing the RDF findings, the Mean Square Displacement (MSD) analysis quantified CO2 diffusivity across temperatures (313 K, 323 K, and 333 K). MDEA-NMP (40% H2O) demonstrated the highest diffusivity, indicating superior CO2 mobility and capture efficiency. MDEA-MeOH (40% H2O) also showed moderate diffusivity, further supporting its effectiveness. However, solvent systems incorporating MAE consistently displayed lower diffusivity, reinforcing the observation from the RDF analysis. The temperature effect on the diffusivity of selected blends does not follow the regular pattern in a co-solvent-based system, whereas in an aqueous system, it increases with temperature. These molecular dynamic simulations highlight the critical role of solvent composition in optimizing CO2 capture efficiency. Applying these insights can improve solvent formulations, enhance effectiveness, and reduce costs.
Hybrid Lithology Identification Method Based on Isometric Feature Mapping Manifold Learning and Particle Swarm Optimization-Optimized LightGBM
Authors: Wang Guo , Deng Song , Xu Shuguo , Li Chaowei , Wei Wan , Zhang Haolin , Li Changsheng , Gong Wenhao , Pan Haoyu
Published: July 2024

Accurate identification of lithology in petroleum engineering is very important for oil and gas reservoir evaluation, drilling decisions, and petroleum geological exploration. Using a cross-plot to identify lithology only considers two logging parameters, causing the accuracy of lithology identification to be insufficient. With the continuous development of artificial intelligence technology, machine learning has become an important means to identify lithology. In this study, the cutting logging data of the Junggar Basin were collected as lithologic samples, and the identification of argillaceous siltstone, mudstone, gravel mudstone, silty mudstone, and siltstone was established by logging and logging parameters at corresponding depths. Aiming at the non-equilibrium problem of lithologic data, this paper proposes using equilibrium accuracy to evaluate the model. In this study, manifold learning is used to reduce logging and logging parameters to three dimensions. Based on balance accuracy, four dimensionality reductions including isometric feature mapping (ISOMAP), principal component (PCA), independent component (ICA), and non-negative matrix factorization (NMF) are compared. It is found that ISOMAP improves the balance accuracy of the LightGBM model to 0.829, which can effectively deal with unbalanced lithologic data. In addition, the particle swarm optimization (PSO) algorithm is used to automatically optimize the super-parameters of the lightweight gradient hoist (LightGBM) model, which effectively improves the balance accuracy and generalization ability of the lithology identification model and provides strong support for fast and accurate lithology identification.
Fixed-Wing UAV Pose Estimation Using a Self-Organizing Map and Deep Learning
Authors: Pessanha Santos Nuno
Published: July 2024

In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV’s position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV’s 3D position and orientation. A Red, Green, and Blue (RGB) ground-based monocular approach can be used for this purpose, allowing for more complex algorithms and higher processing power. The proposed method uses a hybrid Artificial Neural Network (ANN) model, incorporating a Kohonen Neural Network (KNN) or Self-Organizing Map (SOM) to identify feature points representing a cluster obtained from a binary image containing the UAV. A Deep Neural Network (DNN) architecture is then used to estimate the actual UAV pose based on a single frame, including translation and orientation. Utilizing the UAV Computer-Aided Design (CAD) model, the network structure can be easily trained using a synthetic dataset, and then fine-tuning can be done to perform transfer learning to deal with real data. The experimental results demonstrate that the system achieves high accuracy, characterized by low errors in UAV pose estimation. This implementation paves the way for automating operational tasks like autonomous landing, which is especially hazardous and prone to failure.
Directional Applicability Analysis of Albedo Retrieval Using Prior BRDF Knowledge
Authors: Zhang Hu , Xi Qianrui , Xie Junqin , Zhang Xiaoning , Chen Lei , Lian Yi , Cao Hongtao , Liu Yan , Cui Lei , Dong Yadong
Published: July 2024

Surface albedo measures the proportion of incoming solar radiation reflected by the Earth’s surface. Accurate albedo retrieval from remote sensing data usually requires sufficient multi-angular observations to account for the surface reflectance anisotropy. However, most middle and high-resolution remote sensing satellites lack the capability to acquire sufficient multi-angular observations. Existing algorithms for retrieving surface albedo from single-direction reflectance typically rely on land cover types and vegetation indices to extract the corresponding prior knowledge of surface anisotropic reflectance from coarse-resolution Bidirectional Reflectance Distribution Function (BRDF) products. This study introduces an algorithm for retrieving albedo from directional reflectance based on a 3 × 3 BRDF archetype database established using the 2015 global time-series Moderate Resolution Imaging Spectro-radiometer (MODIS) BRDF product. For different directions, BRDF archetypes are applied to the simulated MODIS directional reflectance to retrieve albedo. By comparing the retrieved albedos with the MODIS albedo, the BRDF archetype that yields the smallest Root Mean Squared Error (RMSE) is selected as the prior BRDF for the direction. A lookup table (LUT) that contains the optimal BRDF archetypes for albedo retrieval under various observational geometries is established. The impact of the number of BRDF archetypes on the accuracy of albedo is analyzed according to the 2020 MODIS BRDF. The LUT is applied to the MODIS BRDF within specific BRDF archetype classes to validate its applicability under different anisotropic reflectance characteristics. The applicability of the LUT across different data types is further evaluated using simulated reflectance or real multi-angular measurements. The results indicate that (1) for any direction, a specific BRDF archetype can retrieve a high-accuracy albedo from directional reflectance. The optimal BRDF archetype varies with the observation direction. (2) Compared to the prior BRDF knowledge obtained through averaging method, the BRDF archetype LUT based on the 3 × 3 BRDF archetype database can more accurately retrieve the surface albedo. (3) The BRDF archetype LUT effectively eliminates the influence of surface anisotropic reflectance characteristics in albedo retrieval across different scales and types of data.
LSTT: Long-Term Spatial–Temporal Tensor Model for Infrared Small Target Detection under Dynamic Background
Authors: Lu Deyong , An Wei , Ling Qiang , Cao Dong , Wang Haibo , Li Miao , Lin Zaiping
Published: July 2024

Infrared small target detection is an important and core problem in infrared search and track systems. Many infrared small target detection methods work well under the premise of a static background; however, the detection effect decreases seriously when the background changes dynamically. In addition, the spatiotemporal information of the target and background of the image sequence are not fully developed and utilized, lacking long-term temporal characteristics. To solve these problems, a novel long-term spatial–temporal tensor (LSTT) model is proposed in this paper. The image registration technique is employed to realize the matching between frames. By directly superimposing the aligned images, the spatiotemporal features of the resulting tensor are not damaged or reduced. From the perspective of the horizontal slice of this tensor, it is found that the background component has similarity in the time dimension and correlation in the space dimension, which is more consistent with the prerequisite of low rank, while the target component is sparse. Therefore, we transform the problem of infrared detection of a small moving target into a low-rank sparse decomposition problem of new tensors composed of several continuous horizontal slices of the aligned image tensor. The low rank of the background is constrained by the partial tubal nuclear norm (PTNN), and the tensor decomposition problem is quickly solved using the alternating-direction method of multipliers (ADMM). Our experimental results demonstrate that the proposed LSTT method can effectively detect small moving targets against a dynamic background. Compared with other benchmark methods, the new method has better performance in terms of detection efficiency and accuracy. In particular, the new LSTT method can extract the spatiotemporal information of more frames in a longer time domain and obtain a higher detection rate.
Improving the Gross Primary Productivity Estimation by Simulating the Maximum Carboxylation Rate of Maize Using Leaf Age
Authors: Zhang Xin , Wang Shuai , Wang Weishu , Rong Yao , Zhang Chenglong , Wang Chaozi , Huo Zailin
Published: July 2024

Although the maximum carboxylation rate (Vcmax) is an important parameter to calculate the photosynthesis rate for the terrestrial biosphere models (TBMs), current models could not satisfactorily estimate the Vcmax of a crop because the Vcmax is always changing during crop growth period. In this study, the Breathing Earth System Simulator (BESS) and light response curve (LRC) were combined to invert the time-continuous Vm25 (Vcmax normalized to 25 °C) using eddy covariance measurements and remote sensing data in five maize sites. Based on the inversion results, we propose a Two-stage linear model using leaf age to estimate crop Vm25. The leaf age can be readily calculated from the date of emergence, which is usually recorded or can be readily calculated from the leaf area index (LAI), which can be readily obtained from high spatiotemporal resolution remote sensing images. The Vm25 used to calibrate and validate our model was inversely solved by combining the BESS and LRC and using eddy covariance measurements and remote sensing data in five maize sites. Our Two-stage linear model (R2 = 0.71–0.88, RMSE = 5.40–7.54 μmol m−2 s−1) performed better than the original BESS (R2 = 0.01–0.67, RMSE = 13.25–18.93 μmol m−2 s−1) at capturing the seasonal variation in the Vm25 of all of the five maize sites. Our Two-stage linear model can also significantly improve the accuracy of maize gross primary productivity (GPP) at all of the five sites. The GPP estimated using our Two-stage linear model (underestimated by 0.85% on average) is significantly better than that estimated by the original BESS model (underestimated by 12.60% on average). Overall, our main contributions are as follows: (1) by using the BESS model instead of the BEPS model coupled with the LRC, the inversion of Vm25 took into account the photosynthesis process of C4 plants; (2) the maximum value of Vm25 (i.e., PeakVm25) during the growth and development of maize was calibrated; and (3) by using leaf age as a predictor of Vm25, we proposed a Two-stage linear model to calculate Vm25, which improved the estimation accuracy of GPP.
Prediction of Ionospheric Scintillation with ConvGRU Networks Using GNSS Ground-Based Data across South America
Authors: Atabati Alireza , Jazireeyan Iraj , Alizadeh Mahdi , Langley Richard B.
Published: July 2024

This study investigates the relationship between geomagnetic activities and ionospheric scintillations, focusing on how solar and geomagnetic parameters influence ionospheric disturbances across varying time frames and latitudes. Utilizing indices such as Kp, Dst, sunspot numbers, and the F10.7 solar flux, we elucidate the dynamics influencing ionospheric conditions, which are vital for the reliability of satellite communications, particularly in low-latitude regions. Our analysis demonstrates a clear correlation between periods of high solar activity and increased geomagnetic disturbances, leading to heightened ionospheric scintillations, such as occurred during the solar maximum of 2015. In contrast, 2020—a solar minimum period—exhibited fewer disturbances, highlighting the impact of solar activity levels on ionospheric conditions. Innovatively employing ConvGRU networks, this research advances the modeling and prediction of ionospheric scintillations by integrating deep learning techniques suited to the spatiotemporal complexities of ionospheric data. The ConvGRU model effectively captures both temporal sequences and spatial distributions, offering enhanced accuracy in depicting ionospheric scintillation patterns crucial for satellite-based navigation and communication systems. Ground-based GNSS data from 121 stations across South America, collected during 2015 and 2020, provide a robust dataset for our analysis. The study highlights the influence of the solar cycle on ionospheric scintillations, with the years of maximum and minimum solar activity showing significant differences in scintillation intensity and frequency. Our evaluation of the ConvGRU models using statistical parameters demonstrated their potential for reliable ionospheric scintillation prediction. The research underscores the necessity of integrating adaptive mechanisms within models to effectively handle the dynamic nature of ionospheric disturbances influenced by external geomagnetic and solar factors. This study enhances the understanding of ionospheric scintillations and significantly advances predictive modeling capabilities using advanced machine learning techniques. The potential establishment of real-time alert systems for ionospheric disturbances could significantly benefit civilian applications, enhancing the operational efficiency of technologies reliant on accurate ionospheric information.
Surface Vector Current Retrieval by Single-Station High-Frequency Surface Wave Radar Based on Ocean Dynamics in the Taiwan Strait
Authors: Wang Li , Feng Mengyan , Ai Weihua , Wu Xiongbin , Zhao Xianbin , Hu Shensen
Published: July 2024

In order to address the issue of limited common coverage and high cost in mapping ocean surface vector current by two (or more) high-frequency surface wave radars, this paper proposes a single-station surface wave radar vector current inversion algorithm. The feasibility of this algorithm has been validated in the Taiwan Strait. Based on the ocean dynamic characteristics of the Taiwan Strait, the algorithm utilizes the radial current obtained from a high-frequency surface wave radar (HFSWR) in Fujian Province to invert the ocean surface vector current. The surface vector current can be decomposed into three primary dynamic components: tidal currents, wind-driven currents, and geostrophic currents. Firstly, tidal current forecasting models and Ekman and Stokes theories are used to calculate the tidal and wind-driven currents in the Taiwan Strait, respectively. Subsequently, the directions of geostrophic currents in the Taiwan Strait are determined with sea surface height data, and the magnitudes of the geostrophic currents are constrained using the radial current from the single HFSWR. Finally, the three components are added together to obtain the vector current. Comparative results demonstrate that the efficacy of the algorithm has been validated through field experiments (with two HFSWRs and two drifting buoys) conducted in the southwestern of the Taiwan Strait. Further research is needed on the applicability of this algorithm to other sea areas and monitoring systems.
VE-LIOM: A Versatile and Efficient LiDAR-Inertial Odometry and Mapping System
Authors: Gao Yuhang , Zhao Long
Published: July 2024

LiDAR has emerged as one of the most pivotal sensors in the field of navigation, owing to its expansive measurement range, high resolution, and adeptness in capturing intricate scene details. This significance is particularly pronounced in challenging navigation scenarios where GNSS signals encounter interference, such as within urban canyons and indoor environments. However, the copious volume of point cloud data poses a challenge, rendering traditional iterative closest point (ICP) methods inadequate in meeting real-time odometry requirements. Consequently, many algorithms have turned to feature extraction approaches. Nonetheless, with the advent of diverse scanning mode LiDARs, there arises a necessity to devise unique methods tailored to these sensors to facilitate algorithm migration. To address this challenge, we propose a weighted point-to-plane matching strategy that focuses on local details without relying on feature extraction. This improved approach mitigates the impact of imperfect plane fitting on localization accuracy. Moreover, we present a classification optimization method based on the normal vectors of planes to further refine algorithmic efficiency. Finally, we devise a tightly coupled LiDAR-inertial odometry system founded upon optimization schemes. Notably, we pioneer the derivation of an online gravity estimation method from the perspective of S2 manifold optimization, effectively minimizing the influence of gravity estimation errors introduced during the initialization phase on localization accuracy. The efficacy of the proposed method was validated through experimentation employing various LiDAR sensors. The outcomes of indoor and outdoor experiments substantiate its capability to furnish real-time and precise localization and mapping results.
Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices
Authors: Avé Thomas , De Schepper Tom , Mets Kevin
Published: July 2024

Interest in deploying deep reinforcement learning (DRL) models on low-power edge devices, such as Autonomous Mobile Robots (AMRs) and Internet of Things (IoT) devices, has seen a significant rise due to the potential of performing real-time inference by eliminating the latency and reliability issues incurred from wireless communication and the privacy benefits of processing data locally. Deploying such energy-intensive models on power-constrained devices is not always feasible, however, which has led to the development of model compression techniques that can reduce the size and computational complexity of DRL policies. Policy distillation, the most popular of these methods, can be used to first lower the number of network parameters by transferring the behavior of a large teacher network to a smaller student model before deploying these students at the edge. This works well with deterministic policies that operate using discrete actions. However, many real-world tasks that are power constrained, such as in the field of robotics, are formulated using continuous action spaces, which are not supported. In this work, we improve the policy distillation method to support the compression of DRL models designed to solve these continuous control tasks, with an emphasis on maintaining the stochastic nature of continuous DRL algorithms. Experiments show that our methods can be used effectively to compress such policies up to 750% while maintaining or even exceeding their teacher’s performance by up to 41% in solving two popular continuous control tasks.
Design and Analysis of Receiver Coils with Multiple In-Series Windings for Inductive Eddy Current Angle Position Sensors Based on Coupling of Coils on Printed Circuit Boards
Authors: Kuntz Stefan , Gerber Daniel , Gerlach Gerald , Fella Sina
Published: July 2024

We present a method for improving the amplitude and angular error of inductive position sensors, by advancing the design of receiver coil systems with multiple windings on two layers of a printed circuit board. Multiple phase-shifted windings are connected in series, resulting in an increased amplitude of the induced voltage while decreasing the angular error of the sensor. The amplitude increase for a specific number of windings can be predicted in closed form. Windings are placed electrically in series by means of a differential connection structure, without adversely affecting the signal quality while requiring a minimal amount of space in the layout. Further, we introduce a receiver coil centerline function which specifically enables dense, space-constrained designs. It allows for maximization of the number of possible coil windings while minimizing the impact on angular error. This compromise can be fine-tuned freely with a shape parameter. The application to a typical rotary encoder design for motor control applications with five periods is presented as an example and analyzed in detail by 3D finite-element simulation of 18 different variants, varying both the number of windings and the type of centerline functions. The best peak-to-peak angular error achieved in the examples is smaller than 0.1° electrically (0.02° mechanically, periodicity 5) under nominal tolerance conditions, in addition to an amplitude increase of more than 170% compared to a conventional design which exhibits more than twice the angular error. Amplitude gains of more than 270% are achieved at the expense of increased angular error.
Laser-Induced Interference to Infrared Detector Using Continuous Wave and Short-Pulse Lasers
Authors: Ma Yingjie , Zhou Weijing , Chang Hao , Jian Zhilong
Published: July 2024

The response of a DPbS3200 infrared detector irradiated by a nanosecond pulsed laser and CW laser has been investigated to study laser-induced interference. A laser interference experiment system was constructed to measure the time-varying response signal. A nanosecond pulsed laser and a CW laser of 10 Hz were used, with a 1064 nm wavelength and a millimeter-scale irradiation spot diameter. Firstly, the characteristics of transient interference signals induced by pulsed lasers were analyzed. Then, the characteristics of response signal interference by both CW laser and pulsed laser irradiation were further investigated. The results showed that the pulsed laser only produced transient interference. However, the CW laser led to a significant amplitude reduction of the response signal, which could continuously interfere in the operating time. For transient interferences, the amplitude of the interference signal increased linearly with the laser fluence. The relation between the pulse repetition rate of the incident laser and the operating frequency of the detector determined the numbers of transient interference signals in one response period; for the interference induced by both the CW laser and pulsed laser, CW laser interference played a leading role when CW laser power density increased to 4.1 W/cm2 or more. As the CW laser fluence reached 6.1 W/cm2, the PbS infrared detector was no longer able to detect any signal, which caused temporary blindness. In the end, a probit model was used to determine the interference threshold.
High-Intensity GPS-Derived Parameters in Semi-Professional Soccer: Home vs. Away Roles of the Team and Their Relationship with the Match’s Final Score
Authors: Carlos-Vivas Jorge , Franco-García Juan Manuel , Mendoza-Muñoz David Manuel , Gómez-Pomares Santiago , Pérez-Gómez Jorge
Published: July 2024

High-intensity activities are related to success in football. We looked at whether high-intensity activity differed between home and away matches and its impact on the final score. Thirty football players (20.3 ± 0.8 years) were recruited from a Spanish semi-professional team. Footballers wore a GPS device to monitor high-intensity parameters from competition matches. The final score of every match was also recorded. Playing at home showed greater total distance (TD) > 24 km/h, >27 km/h, >85% Vmax, and the number of sprints > 24 km/h (all p < 0.05) than playing away. Positive correlations were also found between the match score and high-speed running (HSR) distance covered by the team per minute (r = 0.401), TD > 21 km/h (r = 0.417), TD > 24 km/h (r = 0.343), number of sprints > 24 km/h (r = 0.337), and HSR per minute (r = 0.459) (all p < 0.05). The results suggest that playing at home is associated with greater high-intensity effort than playing away. Moreover, the volume of high-intensity effort influences the final score.
A Density Clustering RAPID Based on an Array-Compensated Damage Index for Quantitative Damage Diagnosis
Authors: Bao Qiao , Xie Tian , Zhuang Yan , Wang Qiang
Published: July 2024

Guided wave array-based structural health monitoring (SHM) is a promising solution for diagnosing damage in metal-connected structures. In this field, the reconstruction algorithm for probabilistic inspection (RAPID) is one of the most widely used algorithms for performing damage localization. In this paper, a density clustering RAPID based on an array-compensated damage index is proposed. A new probability distribution function was constructed based on a new damage index, which is adaptive to different elements in the sensor array to compensate for performance variation. Then, the imaging matrix of the RAPID algorithm was density-clustered to obtain the location and degree of damage. Finally, the method was verified by experiments on a stiffened aluminum plate. The experimental results demonstrate that the method achieves damage localization and enables quantitative damage diagnosis.
Advances in Therapeutic Peptides Separation and Purification
Authors: Al Musaimi Othman , Jaradat Da’san M. M.
Published: July 2024

Peptides are gaining prominence in various fields, including the pharmaceutical industry. To meet regulatory requirements, they must achieve a certain purity threshold to ensure safe administration. Numerous purification technologies have been employed to purify peptides, aiming to reduce cost and time while being sustainable and efficient. These include chromatography, magnetic nanoparticles, isoelectric focusing, and membrane filtration. The physicochemical properties of peptides are the main driving element behind these technologies. While chromatographic separation remains the gold standard for peptide separation and purification, with various models to predict the elution behaviors of peptides, other technologies have demonstrated their capability to meet the performance of established chromatographic methodologies, with better productivity and reduced cost. This opens the door for further investigational studies to assess these outcomes and potentially introduce new techniques for peptide purification. In this review, we examine these technologies in terms of their efficiency and their ability to meet sustainability requirements, concluding with remarks and an outlook on future advancements.
Associations between Body Segment Mass and Punch, Front Kick, or Countermovement Jump Performance in Military Cadets
Authors: Vagner Michal , Malecek Jan , Olah Vladan , Stastny Petr
Published: July 2024

Despite the recognized influence of body mass on combat techniques, the relationship between body segment mass (BSM) and combat moves remains unexplored. This study aimed to examine the relationship between the striking arm mass (SAM), kicking leg mass (KLM), and body mass (BM) and the dynamic forces of direct punch (DP), palm strike (PS), elbow strike (ES), front kick (FK), and countermovement jump (CMJ) performance. Sixteen male military cadets (22.3 ± 1.8 years, 181.4 ± 7.0 cm, 82.1 ± 8.5 kg) performed combat techniques, with their performance measured by using a force plate and their body segment mass assessed by dual-energy X-ray absorptiometry. Spearman’s correlation analysis, the Wilcoxon test, and Cohen’s d were applied. The results indicated the relationship between the KLM or BM and the FK impulse (r = 0.64, p = 0.01; r = 0.52, p = 0.04, respectively) and CMJ impact force (r = 0.80, p ≤ 0.01; r = 0.70, p ≤ 0.01, respectively). The FK peak and impact forces were moderately correlated with the CMJ jump height (r = 0.74, p ≤ 0.01; r = 0.77, p ≤ 0.01). Moreover, the FK peak force was significantly higher than that for DP, PS, and ES (p ≤ 0.01, d = 3.32; p ≤ 0.01, d = 1.6; and p = 0.013, d = 1.3, respectively). The highest relationship was found between the KLM and the FK impulse; however, the difference in variability explained by the KLM versus the body mass was only 12%. This suggests that knowledge of the BSM did not provide a significantly better estimate of the dynamic forces of the punches and FKs than the knowledge of the BM.
Optimization of Life Cycle Cost and Environmental Impact Functions of NiZn Batteries by Using Multi-Objective Particle Swarm Optimization (MOPSO)
Authors: Malviya Ashwani Kumar , Zarehparast Malekzadeh Mehdi , Santarremigia Francisco Enrique , Molero Gemma Dolores , Villalba Sanchis Ignacio , Fernández Pablo Martínez , Yepes Víctor
Published: July 2024

This study aims to optimize the Environmental Life Cycle Assessment (LCA) and Life Cycle Cost (LCC) of NiZn batteries using Pareto Optimization (PO) and Multi-objective Particle Swarm Optimization (MOPSO), which combine Pareto optimization and genetic algorithms (GA). The optimization focuses on the raw material acquisition phase and the end-of-life phase of NiZn batteries to improve their sustainability Key Performance Indicators (KPIs). The optimization methodology, programmed in MATLAB, is based on a formulation model of LCC and the environmental LCA, using data available from the Ecoinvent database, the OpenLCA software (V1.11.0), and other public databases. Results provide insights about the best combination of countries for acquiring raw materials to manufacture NiZn and for disposing of the waste of NiZn batteries that cannot be recycled. These results were automatically linked to some sustainability KPIs, such as global warming and capital costs, being replicable in case of data updates or changes in production or recycling locations, which were initially considered at Paris (France) and Krefeld (Germany), respectively. These results provided by an AI model were validated by using a sensitivity analysis and the Analytical Hierarchy Process (AHP) through an expert panel. The sensitivity analysis ensures the robustness of mathematical parameters and future variations in the market; on the other hand, the AHP validates the Artificial Intelligence (AI) results with interactions of human factors. Further developments should also consider the manufacturing and use phases in the optimization model.
Future of Undergraduate Education for Sustainable Development Goals: Impact of Perceived Flexibility and Attitudes on Self-Regulated Online Learning
Authors: Demir Kadir
Published: July 2024

The COVID-19 pandemic accelerated the adoption of online learning, particularly in higher education institutions. This shift underscores the importance of sustainable education practices aligned with the United Nations’ Sustainable Development Goals (SDGs). SDG 4 emphasizes inclusive and equitable quality education, highlighting how online learning environments can enhance accessibility and flexibility for students worldwide. SDG 9 underscores the role of technological advancements in education. SDG 10 focuses on reducing inequality within and among countries, and online education can bridge educational disparities by offering flexible learning options to diverse socioeconomic backgrounds. SDG 17 emphasizes the importance of partnerships, which have been crucial in developing effective online learning solutions. This study investigates the relationship between undergraduate students’ self-regulated online learning, perceived flexibility, and attitudes towards the use of distance learning environments at a state university in İzmir, Türkiye. Utilizing a survey-type correlational research model, data were collected from 300 undergraduate students. The results indicate that undergraduate students exhibit high-level self-regulation, perceive moderate flexibility, and hold positive attitudes towards the use of distance learning environments. The analysis showed that self-regulated online learning is moderately correlated with perceived flexibility and strongly correlated with attitudes towards the use of distance learning environments. These findings suggest that both perceived flexibility and positive attitudes towards the use of distance learning environments play important roles in predicting self-regulated online learning. This research provides valuable insights for educators and institutions aiming to enhance the online learning experience by promoting self-regulated learning behaviors and flexible learning environments.