bluuebunny/crossref_metadata_2025 huggingface dataset:
Biosand Reactors for Municipal and Industrial Wastewater Treatment: Status Quo, Challenges and Opportunities
Authors: Welz Pamela Jean
Published: March 2024

Biosand reactors (BSRs), alternatively known as slow or biological sand filters, are passive systems that are used to remove contaminants from domestic wastewater, industrial wastewater and drinking water. This review focuses specifically on their application for remediation of industrial effluent and sewage-containing municipal and household effluent. The relationships between the physicochemical characteristics of the sand grains (size, size distribution, shape, chemical composition) and the hydraulic conductivity of the sand employed in BSFs are critically discussed in relationship to the achievable loading rates and hydraulic retention times. The modes of operation and influence of the functional microbial biomass as well as biodegradable and recalcitrant particulates on these parameters is comprehensively reviewed. Finally, the bioremediation of sewage-based and industrial wastewater is examined. This includes an account of the biotic and abiotic removal mechanisms and the limitations of BSRs for removal of pollutants such as phosphorus/phosphate and nitrates/nitrites. The removal mechanisms and removal efficiencies of macronutrients, micropollutants, fecal indicators and other microorganisms such as antibiotic-resistant bacteria in BSRs are discussed.
Sensitive and Cost-Effective TLC-Densitometric Method for Determination of Metronidazole and Tinidazole in Tablets
Authors: Pyka-Pająk Alina
Published: March 2024

A sensitive, easy-to-use, fast, and cost-effective TLC-densitometric method was developed for the separation of metronidazole, secnidazole, ornidazole, tinidazole, and 2-methyl-5-nitroimidazole and for the determination of metronidazole and tinidazole in Metronidazole Polpharma and Tinidazolum Polpharma tablets. Analyses were performed on chromatographic plates precoated with silica gel 60F254 using chloroform + methanol + diethylamine in a volume ratio of 9:1:1 as the optimal mobile phase. The method has been validated. The intraday and interday precision values for the three different concentrations ranged from 0.99% to 1.48% and 0.89% to 1.76%, and the precision values ranged from 1.13% to 2.48% and 0.95% to 2.49% for metronidazole and tinidazole, respectively. The limit of quantification (LOQ) was 0.036 and 0.066 µg/spot for metronidazole and tinidazole, respectively. The mean recovery was 103.1% and 100.6% for metronidazole and tinidazole, respectively. The content of metronidazole and tinidazole in tablets in relation to the content declared by the manufacturer was 101.3% and 99.8%, respectively. The obtained results were verified using the pharmacopeial method. The presented method is fast, sensitive, precise, selective, accurate, and robust. It allows for the analysis of several samples on one chromatography plate at the same time.
Integrated Design and Control of a Sustainable Stormwater Treatment System
Authors: Balcazar Ricardo , Rubio José de Jesús , Hernandez Mario Alberto , Pacheco Jaime , Retes-Mantilla Rogel , Rosas Francisco Javier , Zacarías Alejandro , Torres-Mancera María Teresa , Orozco Eduardo , Saavedra González Gabriela , Zermeño Caballero Israel
Published: March 2024

In this work, issues of water separation and purification are addressed, where, in order to achieve the desired results, it is necessary to use several disciplines such as classical physics, biotechnology, automatic control, automation, and applications of industry 4.0. Further, the need for comprehensive and automated solutions for rainwater treatment in the agricultural sector is addressed. This research focuses on designing and implementing a system adapted to these needs using Siemens technologies. The methodology ranges from the design of the Piping and Instrumentation Diagram (P&ID) to the implementation of the interface, incorporating Siemens technologies for data acquisition, electrical connections, treatment programming, and PID controller design. The results show significant advances in the development of the system, highlighting the effectiveness of automation and the HMI-PLC human–machine interface in process monitoring and control. These findings support the viability of a comprehensive rainwater treatment system for the agricultural sector, with important implications for water efficiency, environmental preservation, and increased productivity in agricultural and livestock activities. The contribution of this work is the relationship between engineering and research focused on industrial processes. The scientific contribution is to obtain the dynamic models and apply two strategies to obtain the gains of the PID controller. The first method is performed through the proposal of a Hurwitz polynomial, and the second is performed through genetic algorithms (GA), where they are implemented in a controller that is commonly used in the industry. The technological part includes the integration of work (schemes, programming, and communications) so that the result is as close to what was expected.
Low-Carbon-Oriented Capacity Optimization Method for Electric–Thermal Integrated Energy System Considering Construction Time Sequence and Uncertainty
Authors: Wang Yubo , Zhao Xingang , Huang Yujing
Published: March 2024

The interdependence of various energy forms and flexible cooperative operation between different units in an integrated energy system (IES) are essential for carbon emission reduction. To address the planning problem of an electric–thermal integrated energy system under low-carbon conditions and to fully consider the low carbon and construction sequence of the integrated energy system, a low-carbon-oriented capacity optimization method for the electric–thermal integrated energy system that considers construction time sequence (CTS) and uncertainty is proposed. A calculation model for the carbon transaction cost under the ladder carbon trading mechanism was constructed, and a multi-stage planning model of the integrated energy system was established with the minimum life cycle cost, considering carbon transaction cost as the objective function, to make the optimal decision on equipment configuration in each planning stage. Finally, a case study was considered to verify the advantages of the proposed capacity optimization method in terms of economy and environmental friendliness through a comparative analysis of different planning cases. Simulation results show that, compared with the scenario of completing planning at the beginning of the life cycle at one time, the proposed low-carbon-oriented capacity optimization method that considers construction time sequence and uncertainty can not only reduce the cost of the integrated energy system, but also help to enhance renewable energy utilization and reduce the system’s carbon emissions; the total cost of phased planning is reduced by 11.91% compared to the total cost of one-time planning at the beginning of the year.
Qualified Hope and the Ethics of Planetary Boundaries
Authors: Clingerman Forrest
Published: March 2024

The present essay explores the way theologies can contribute to the discussion of the ethics of the “planetary boundaries” framework and its rhetorical proposal for a ‘safe operating space’. I first give a brief description of the ‘planetary boundaries’ framework proposed by Johan Rockström and others. The idea of a ‘safe operating space’ is not simply a neutral scientific assessment, but more importantly, a narrative framework that weaves stability, risk, and uncertainty together. This narrative needs both the humanities and the sciences to be understood. Second, I propose how theological reflection can contribute to the discussion through its interpretation of the rhetorical and ethical facets of the ‘planetary boundaries’ proposal. Specifically, a Christian theological lens is able to develop a model of a qualified sense of hope, which can be leveraged as a bridge between the dire warnings and uncertainty of the science of ‘planetary boundaries’, on one hand, and the call for transformation and action that researchers make on the other. Finally, I provide some recent examples of this theologically-inspired ‘qualified hope’ in the face of environmental change.
The Impact of Village Savings and Loan Associations as a Financial and Climate Resilience Strategy for Mitigating Food Insecurity in Northern Ghana
Authors: Pienaah Cornelius K. A. , Luginaah Isaac
Published: March 2024

In semi-arid Northern Ghana, smallholder farmers face food insecurity and financial risk due to climate change. In response, the Village Savings and Loan Association (VSLA) model, a community-led microfinance model, has emerged as a promising finance and climate resilience strategy. VSLAs offer savings, loans, and other financial services to help smallholder farmers cope with climate risks. In northern Ghana, where formal financial banking is limited, VSLAs serve as vital financial resources for smallholder farmers. Nevertheless, it remains to be seen how VSLAs can bridge financial inclusion and climate resilience strategies to address food insecurity. From a sustainable livelihoods framework (SLF) perspective, we utilized data from a cross-sectional survey of 517 smallholder farmers in northern Ghana’s Upper West Region to investigate how VSLAs relate to food insecurity. Results from an ordered logistic regression show that households with membership in a VSLA were less likely to experience severe food insecurity (OR = 0.437, p < 0.01). In addition, households that reported good resilience, owned land, had higher wealth, were female-headed, and made financial decisions jointly were less likely to experience severe food insecurity. Also, spending time accessing the market increases the risk of severe food insecurity. Despite the challenges of the VSLA model, these findings highlight VSLAs’ potential to mitigate food insecurity and serve as a financially resilient and climate-resilient strategy in resource-poor contexts like the UWR and similar areas in Sub-Saharan Africa. VSLAs could contribute to achieving SDG2, zero hunger, and SDG13, climate action. However, policy interventions are necessary to support and scale VSLAs as a sustainable development and food security strategy in vulnerable regions.
Signal Photon Extraction and Classification for ICESat-2 Photon-Counting Lidar in Coastal Areas
Authors: Song Yue , Ma Yue , Zhou Zhibiao , Yang Jian , Li Song
Published: March 2024

The highly accurate data of topography and bathymetry are fundamental to ecological studies and policy decisions for coastal zones. Currently, the automatic extraction and classification of signal photons in coastal zones is a challenging problem, especially the surface type classification without auxiliary data. The lack of classification information limits large-scale bathymetric applications of ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). In this study, we propose a photon extraction–classification method to process geolocated photons in coastal areas from the ICESat-2 ATL03 product. The basic idea is to extract the signal photons using an adaptive photon clustering algorithm, and the extracted signal photons are classified based on the accumulated histogram and triangular grid. We also generate the bottom profile using the weighted interpolation. In four typical coastal areas (artificial coast, natural coast, island, and reefs), the extraction accuracy of a signal photons exceeds 0.90, and the Kappa coefficients of four surface types exceed 0.75. This method independently extracts and classifies signal photons without relying on auxiliary data, which can greatly improve the efficiency of obtaining bathymetric points in all kinds of coastal areas and provide technical support for other coastal studies using ICESat-2 data.
Estimation of Soil-Related Parameters Using Airborne-Based Hyperspectral Imagery and Ground Data in the Fenwei Plain, China
Authors: Jiang Chenchen , Ren Huazhong , Wang Zian , Zeng Hui , Teng Yuanjian , Zhang Hongqin , Liu Xixuan , Jin Dingjian , Wang Mengran , Liu Rongyuan , Wang Baozhen , Zhu Jinshun
Published: March 2024

Hyperspectral remote sensing technology is an advanced and powerful tool that enables fine identification of the numerous soil reflectance spectrum characteristics. Heavy metal(loid)s (HMs) are the primary pollutants affecting the soil biodiversity and ecosystem services. Estimating HMs’ concentrations in soils using hyperspectral data is an effective method but is challenging due to the effects of varied soil properties and measurement-related errors inflicted by atmospheric effects. This study focused on typical mining areas in the Fenwei Plain (FWP), China. Soil-related data were collected by leveraging airborne- and ground-based integrated remote sensing observations. The concentrations of eight HMs, namely copper (Cu), lead (Pb), zinc (Zn), nickel (Ni), chromium (Cr), cadmium (Cd), arsenic (As), and mercury (Hg), were measured by laboratory analysis from 100 in situ soil samples. Soil reflectance spectra were processed based on resampling and envelope methods. The combination datasets of the concentrations and optimal soil reflectance spectra were used to build the soil-related parameter retrieval models using three machine learning (ML) methods, and the feasibility of applying the high-performance retrieval model to estimate the HM concentrations in mining areas was evaluated and explored. Spectral analysis results show that four hundred and twenty-eight bands of five wavelength ranges are of high quality and obviously demonstrate the spectral characteristics selected to build the soil-related parameter models. The evaluation results of eight combination data subsets and three methods show that the preprocessing of spectral data (ground- and airborne-based reflectance) and soil samples with the random forest (RF) method can obtain higher accuracy than support vector machine (SVM) and partial least squares (PLS) methods, denoted as the AER-ACS-RF and GER-GCS-RF models (the average RMSE values of eight HMs were 2.61 and 2.53 mg/kg, respectively). The highest R2 values were observed in Cd and As, with an equal value of 0.98, followed by that of Pb (R2 = 0.97). The relative prediction deviation (RPD) values of Cu and AS were greater than 1.9. Moreover, the airborne-based AER-ACS-RF model presents a good mapping effect about the concentrations (mg/kg) of eight HMs in mining areas, ranging from 21.65 to 31.25 (Cu), 16.38 to 30.45 (Pb), 62.02 to 109.48 (Zn), 23.33 to 32.47 (Ni), 49.81 to 66.56 (Cr), 0.09 to 0.23 (Cd), 7.31 to 12.24 (As), and 0.03 to 0.17 (Hg), respectively.
A GEO-GEO Stereo Observation of Diurnal Cloud Variations over the Eastern Pacific
Authors: Wu Dong L. , Carr James L. , Friberg Mariel D. , Summers Tyler C. , Lee Jae N. , Horváth Ákos
Published: March 2024

Fast atmospheric processes such as deep convection and severe storms are challenging to observe and understand without adequate spatiotemporal sampling. Geostationary (GEO) imaging has the advantage of tracking these fast processes continuously at a cadence of the 10 min global and 1 min mesoscale from thermal infrared (TIR) channels. More importantly, the newly-available GEO-GEO stereo observations from our 3D-Wind algorithm provide more accurate height assignment for atmospheric motion vectors (AMVs) than those from conventional TIR methods. Unlike the radiometric methods, the stereo height is insensitive to radiometric TIR calibration of satellite sensors and can assign the feature height correctly under complex situation (e.g., multi-layer clouds and atmospheric inversion). This paper shows a case study from continuous GEO-GEO stereo observations over the Eastern Pacific during 1–5 February 2023, to highlight diurnal variations of clouds and dynamics in the planetary boundary layer (PBL), altocumulus/congestus, convective outflow and tropical tropopause layer (TTL). Because of their good vertical resolution, the stereo observations often show a wind shear in these cloud layers. As an example, the stereo winds reveal the classic Ekman spiral in marine PBL dynamics with a clockwise (counterclockwise) wind direction change with height in the Northern (Southern) Hemisphere subtropics. Over the Southeastern Pacific, the stereo cloud observations show a clear diurnal variation in the closed-to-open cell transition in the PBL and evidence of precipitation at a lower level from broken stratocumulus clouds.
Object Identification in Land Parcels Using a Machine Learning Approach
Authors: Gundermann Niels , Löwe Welf , Fransson Johan E. S. , Olofsson Erika , Wehrenpfennig Andreas
Published: March 2024

This paper introduces an AI-based approach to detect human-made objects and changes in these on land parcels. To this end, we used binary image classification performed by a convolutional neural network. Binary classification requires the selection of a decision boundary, and we provided a deterministic method for this selection. Furthermore, we varied different parameters to improve the performance of our approach, leading to a true positive rate of 91.3% and a true negative rate of 63.0%. A specific application of our work supports the administration of agricultural land parcels eligible for subsidiaries. As a result of our findings, authorities could reduce the effort involved in the detection of human made changes by approximately 50%.
Sub-PPB Detection with Gas-Phase Multiphoton Electron Extraction Spectroscopy under Ambient Conditions
Authors: Filippov Tikhon , Vervitski Elena , Kofler Hila , Birkan Lea , Levy Shaked , Zimmerman Shay , Bulatov Valery , Schechter Israel , Schuetz Roman
Published: March 2024

Multiphoton electron extraction spectroscopy (MEES) is an advanced analytical technique that has demonstrated exceptional sensitivity and specificity for detecting molecular traces on solid and liquid surfaces. Building upon the solid-state MEES foundations, this study introduces the first application of MEES in the gas phase (gas-phase MEES), specifically designed for quantitative detection of gas traces at sub-part per billion (sub-PPB) concentrations under ambient atmospheric conditions. Our experimental setup utilizes resonant multiphoton ionization processes using ns laser pulses under a high electrical field. The generated photoelectron charges are recorded as a function of the laser’s wavelength. This research showcases the high sensitivity of gas-phase MEES, achieving high spectral resolution with resonant peak widths less than 0.02 nm FWHM. We present results from quantitative analysis of benzene and aniline, two industrially and environmentally significant compounds, demonstrating linear responses in the sub-PPM and sub-PPB ranges. The enhanced sensitivity and resolution of gas-phase MEES offer a powerful approach to trace gas analysis, with potential applications in environmental monitoring, industrial safety, security screening, and medical diagnostics. This study confirms the advantages of gas-phase MEES over many traditional optical spectroscopic methods and demonstrates its potential in direct gas-trace sensing in ambient atmosphere.
Contrastive Multiscale Transformer for Image Dehazing
Authors: Chen Jiawei , Zhao Guanghui
Published: March 2024

Images obtained in an unfavorable environment may be affected by haze or fog, leading to fuzzy image details, low contrast, and loss of important information. Recently, significant progress has been achieved in the realm of image dehazing, largely due to the adoption of deep learning techniques. Owing to the lack of modules specifically designed to learn the unique characteristics of haze, existing deep neural network-based methods are impractical for processing images containing haze. In addition, most networks primarily focus on learning clear image information while disregarding potential features in hazy images. To address these limitations, we propose an innovative method called contrastive multiscale transformer for image dehazing (CMT-Net). This method uses the multiscale transformer to enable the network to learn global hazy features at multiple scales. Furthermore, we introduce feature combination attention and a haze-aware module to enhance the network’s ability to handle varying concentrations of haze by assigning more weight to regions containing haze. Finally, we design a multistage contrastive learning loss incorporating different positive and negative samples at various stages to guide the network’s learning process to restore real and non-hazy images. The experimental findings demonstrate that CMT-Net provides exceptional performance on established datasets and exhibits superior visual outcomes.
Damage Monitoring of Steel Bars Based on Torsional Guided Waves
Authors: Zheng Zhupeng , Zhang Zihao , Huang Cheng
Published: March 2024

Ultrasonic guided waves represent a new development in the field of non-destructive testing. Longitudinal guided waves are mostly used to monitor the damage of steel bars, but the received signal is usually degraded and noisy owing to its dispersive propagation and multimodal behavior, making its implementation and location challenging. The torsional mode of T (0, 1) is not dispersive in the propagation of a steel bar and only produces circumferential displacement. It was chosen, in this study, to conduct guided wave-based damage monitoring on steel bars to reduce the signal processing complexity. The defects of steel bars, including circular surface defects, internal defects, and uniform damage defects, were thoroughly investigated, respectively, using numerical simulation. The waves were excited and received using the pitch-and-catch technique and the collected monitoring signals were processed using Hilbert transformation to highlight the amplitude and time-of-flight values of the wave signals, which were used for defect identification. In this paper, the reflectivity of guided waves is compared between torsional waves and longitudinal waves, in each case. The impact of defect size changes on damage monitoring is studied and the sensitivity of both the wave frequency and the wave mode (L and T) is also discussed. The results show that the monitoring method based on the torsional wave T (0, 1) is more sensitive to surface defects than the conventional method based on longitudinal waves. The reflectivity of the torsional wave T (0, 1) can be twice that of the longitudinal wave L (0, 1) when the depth of the defect in the circumferential grooves is less than 50% of the diameter of the steel bar. It is more sensitive to shallow surface defects within half of the bar’s radius, and it can also effectively identify defects under the conditions of the uniform damage defects of steel bars, even when the measurements are heavily noise-polluted. This proves the superiority of the torsional guided wave T (0, 1) in defect monitoring and provides a theoretical basis for the application of the torsional guided wave T (0, 1) in actual monitoring.
High-Sensitivity Seawater Refraction Index Optical Measurement Sensor Based on a Position-Sensitive Detector
Authors: Zhou Guanlong , Li Liyan , Zhou Yan , Chen Xinyu
Published: March 2024

The refractive index of seawater is one of the essential parameters in ocean observation, so it is necessary to achieve high-precision seawater refractive index measurements. In this paper, we propose a method for measuring the refractive index of seawater, based on a position-sensitive detector (PSD). A theoretical model was established to depict the correlation between laser spot displacement and refractive index change, utilizing a combination of a position-sensitive detector and laser beam deflection principles. Based on this optical measurement method, a seawater refractive index measurement system was established. To effectively enhance the sensitivity of refractive index detection, a focusing lens was incorporated into the optical path of the measuring system, and simulations were conducted to investigate the impact of focal length on refractive index sensitivity. The calibration experiment of the measuring system was performed based on the relationship between the refractive index of seawater and underwater pressure (depth). By measuring laser spot displacement at different depths, changes in displacement, with respect to both refractive index and depth, were determined. The experimental results demonstrate that the system exhibits a sensitivity of 9.93×10−9 RIU (refractive index unit), and the refractive index deviation due to stability is calculated as ±7.54×10−9 RIU. Therefore, the feasibility of this highly sensitive measurement of seawater refractive index is verified. Since the sensitivity of the refractive index measurement of this measurement system is higher than the refractive index change caused by the wake of underwater vehicles, it can also be used in various applications for underwater vehicle wake measurement, as well as seawater refractive index measurement, such as the motion state monitoring of underwater navigation targets such as AUVs and ROVs.
Securing Infrared Communication in Nuclear Power Plants: Advanced Encryption for Infrared Sensor Networks
Authors: Park Tae-Jin , Kim Ki-il , Moon Sangook
Published: March 2024

This study enhances infrared communication security in nuclear power plants’ secondary systems, addressing the risk of mechanical and cyber failures. A novel random address generator, employing an innovative S-box, was developed to secure IoT sensor data transmissions to gateway nodes, mitigating eavesdropping, interference, and replay attacks. We introduced a structured IR communication protocol, generating unique, encrypted addresses to prevent unauthorized access. Key-dependent S-boxes, based on a compound chaotic map system, significantly improved encryption, increasing data transmission randomness and uniqueness. Entropy analysis and reduced duplicated addresses confirmed the effectiveness of our method, with the Hash-CCM algorithm showing the highest entropy and fewest duplicates. Integrating advanced cryptographic techniques into IR systems significantly enhances nuclear power plants’ security, contributing to the protection of critical infrastructure from cyber threats and ensuring operational integrity.
Error Analysis of Normal Surface Measurements Based on Multiple Laser Displacement Sensors
Authors: Meng Fantong , Yang Guolin , Yang Jiankun , Lu Haibo , Dong Zhigang , Kang Renke , Guo Dongming , Qin Yan
Published: March 2024

The robotic drilling of assembly holes is a crucial process in aerospace manufacturing, in which measuring the normal of the workpiece surface is a key step to guide the robot to the correct pose and guarantee the perpendicularity of the hole axis. Multiple laser displacement sensors can be used to satisfy the portable and in-site measurement requirements, but there is still a lack of accurate analysis and layout design. In this paper, a simplified parametric method is proposed for multi-sensor normal measurement devices with a symmetrical layout, using three parameters: the sensor number, the laser beam slant angle, and the laser spot distribution radius. A normal measurement error distribution simulation method considering the random sensor errors is proposed. The measurement error distribution laws at different sensor numbers, the laser beam slant angle, and the laser spot distribution radius are revealed as a pyramid-like region. The influential factors on normal measurement accuracy, such as sensor accuracy, quantity and installation position, are analyzed by a simulation and verified experimentally on a five-axis precision machine tool. The results show that increasing the laser beam slant angle and laser spot distribution radius significantly reduces the normal measurement errors. With the laser beam slant angle ≥15° and the laser spot distribution radius ≥19 mm, the normal measurement error falls below 0.05°, ensuring normal accuracy in robotic drilling.
Improved Double Deep Q-Network Algorithm Applied to Multi-Dimensional Environment Path Planning of Hexapod Robots
Authors: Chen Liuhongxu , Wang Qibiao , Deng Chao , Xie Bo , Tuo Xianguo , Jiang Gang
Published: March 2024

Detecting transportation pipeline leakage points within chemical plants is difficult due to complex pathways, multi-dimensional survey points, and highly dynamic scenarios. However, hexapod robots’ maneuverability and adaptability make it an ideal candidate for conducting surveys across different planes. The path-planning problem of hexapod robots in multi-dimensional environments is a significant challenge, especially when identifying suitable transition points and planning shorter paths to reach survey points while traversing multi-level environments. This study proposes a Particle Swarm Optimization (PSO)-guided Double Deep Q-Network (DDQN) approach, namely, the PSO-guided DDQN (PG-DDQN) algorithm, for solving this problem. The proposed algorithm incorporates the PSO algorithm to supplant the traditional random selection strategy, and the data obtained from this guided approach are subsequently employed to train the DDQN neural network. The multi-dimensional random environment is abstracted into localized maps comprising current and next level planes. Comparative experiments were performed with PG-DDQN, standard DQN, and standard DDQN to evaluate the algorithm’s performance by using multiple randomly generated localized maps. After testing each iteration, each algorithm obtained the total reward values and completion times. The results demonstrate that PG-DDQN exhibited faster convergence under an equivalent iteration count. Compared with standard DQN and standard DDQN, reductions in path-planning time of at least 33.94% and 42.60%, respectively, were observed, significantly improving the robot’s mobility. Finally, the PG-DDQN algorithm was integrated with sensors onto a hexapod robot, and validation was performed through Gazebo simulations and Experiment. The results show that controlling hexapod robots by applying PG-DDQN provides valuable insights for path planning to reach transportation pipeline leakage points within chemical plants.
sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control
Authors: Mora Marta C. , García-Ortiz José V. , Cerdá-Boluda Joaquín
Published: March 2024

The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due to their difficult communication with the hand. For social robots, the interpretation of human intention is key for their integration in daily life. This can be achieved with machine learning (ML) algorithms, which are barely used for grasping posture recognition. This work proposes an ML approach to recognize nine hand postures, representing 90% of the activities of daily living in real time using an sEMG human–robot interface (HRI). Data from 20 subjects wearing a Myo armband (8 sEMG signals) were gathered from the NinaPro DS5 and from experimental tests with the YCB Object Set, and they were used jointly in the development of a simple multi-layer perceptron in MATLAB, with a global percentage success of 73% using only two features. GPU-based implementations were run to select the best architecture, with generalization capabilities, robustness-versus-electrode shift, low memory expense, and real-time performance. This architecture enables the implementation of grasping posture recognition in low-cost devices, aimed at the development of affordable functional prostheses and HRI for social robots.
An Improved TransMVSNet Algorithm for Three-Dimensional Reconstruction in the Unmanned Aerial Vehicle Remote Sensing Domain
Authors: Teng Jiawei , Sun Haijiang , Liu Peixun , Jiang Shan
Published: March 2024

It is important to achieve the 3D reconstruction of UAV remote sensing images in deep learning-based multi-view stereo (MVS) vision. The lack of obvious texture features and detailed edges in UAV remote sensing images leads to inaccurate feature point matching or depth estimation. To address this problem, this study improves the TransMVSNet algorithm in the field of 3D reconstruction by optimizing its feature extraction network and costumed body depth prediction network. The improvement is mainly achieved by extracting features with the Asymptotic Pyramidal Network (AFPN) and assigning weights to different levels of features through the ASFF module to increase the importance of key levels and also using the UNet structured network combined with an attention mechanism to predict the depth information, which also extracts the key area information. It aims to improve the performance and accuracy of the TransMVSNet algorithm’s 3D reconstruction of UAV remote sensing images. In this work, we have performed comparative experiments and quantitative evaluation with other algorithms on the DTU dataset as well as on a large UAV remote sensing image dataset. After a large number of experimental studies, it is shown that our improved TransMVSNet algorithm has better performance and robustness, providing a valuable reference for research and application in the field of 3D reconstruction of UAV remote sensing images.
Goal-Guided Graph Attention Network with Interactive State Refinement for Multi-Agent Trajectory Prediction
Authors: Wu Jianghang , Qiao Senyao , Li Haocheng , Sun Boyu , Gao Fei , Hu Hongyu , Zhao Rui
Published: March 2024

The accurate prediction of the future trajectories of traffic participants is crucial for enhancing the safety and decision-making capabilities of autonomous vehicles. Modeling social interactions among agents and revealing the inherent relationships is crucial for accurate trajectory prediction. In this context, we propose a goal-guided and interaction-aware state refinement graph attention network (SRGAT) for multi-agent trajectory prediction. This model effectively integrates high-precision map data and dynamic traffic states and captures long-term temporal dependencies through the Transformer network. Based on these dependencies, it generates multiple potential goals and Points of Interest (POIs). Through its dual-branch, multimodal prediction approach, the model not only proposes various plausible future trajectories associated with these POIs, but also rigorously assesses the confidence levels of each trajectory. This goal-oriented strategy enables SRGAT to accurately predict the future movement trajectories of other vehicles in complex traffic scenarios. Tested on the Argoverse and nuScenes datasets, SRGAT surpasses existing algorithms in key performance metrics by adeptly integrating past trajectories and current context. This goal-guided approach not only enhances long-term prediction accuracy, but also ensures its reliability, demonstrating a significant advancement in trajectory forecasting.
Absolute Ranging with Time Delay Interferometry for Space-Borne Gravitational Wave Detection
Authors: Luo Dan , Xu Mingyang , Wang Panpan , Wu Hanzhong , Shao Chenggang
Published: March 2024

In future space-borne gravitational wave (GW) detectors, time delay interferometry (TDI) will be utilized to reduce the overwhelming noise, including the laser frequency noise and the clock noise etc., by time shifting and recombining the data streams in post-processing. The successful operation of TDI relies on absolute inter-satellite ranging with meter-level precision. In this work, we numerically and experimentally demonstrate a strategy for inter-satellite distance measurement. The distances can be coarsely determined using the technique of arm-locking ranging with a large non-ambiguity range, and subsequently TDI can be used for precise distance measurement (TDI ranging) by finding the minimum value of the power of the residual noises. The measurement principle is introduced. We carry out the numerical simulations, and the results show millimeter-level precision. Further, we perform the experimental verifications based on the fiber link, and the distances can be measured with better than 0.05 m uncertainty, which can well satisfy the requirement of time delay interferometry.
Aluminum Nitride Thin Film Piezoelectric Pressure Sensor for Respiratory Rate Detection
Authors: Signore Maria Assunta , Rescio Gabriele , Francioso Luca , Casino Flavio , Leone Alessandro
Published: March 2024

In this study, we propose a low-cost piezoelectric flexible pressure sensor fabricated on Kapton® (Kapton™ Dupont) substrate by using aluminum nitride (AlN) thin film, designed for the monitoring of the respiration rate for a fast detection of respiratory anomalies. The device was characterized in the range of 15–30 breaths per minute (bpm), to simulate moderate difficult breathing, borderline normal breathing, and normal spontaneous breathing. These three breathing typologies were artificially reproduced by setting the expiratory to inspiratory ratios (E:I) at 1:1, 2:1, 3:1. The prototype was able to accurately recognize the breath states with a low response time (~35 ms), excellent linearity (R2 = 0.997) and low hysteresis. The piezoelectric device was also characterized by placing it in an activated carbon filter mask to evaluate the pressure generated by exhaled air through breathing acts. The results indicate suitability also for the monitoring of very weak breath, exhibiting good linearity, accuracy, and reproducibility, in very low breath pressures, ranging from 0.09 to 0.16 kPa. These preliminary results are very promising for the future development of smart wearable devices able to monitor different patients breathing patterns, also related to breathing diseases, providing a suitable real-time diagnosis in a non-invasive and fast way.
Secure Data Aggregation Using Authentication and Authorization for Privacy Preservation in Wireless Sensor Networks
Authors: Erskine Samuel Kofi
Published: March 2024

Existing secure data aggregation protocols are weaker to eliminate data redundancy and protect wireless sensor networks (WSNs). Only some existing approaches have solved this singular issue when aggregating data. However, there is a need for a multi-featured protocol to handle the multiple problems of data aggregation, such as energy efficiency, authentication, authorization, and maintaining the security of the network. Looking at the significant demand for multi-featured data aggregation protocol, we propose secure data aggregation using authentication and authorization (SDAAA) protocol to detect malicious attacks, particularly cyberattacks such as sybil and sinkhole, to extend network performance. These attacks are more complex to address through existing cryptographic protocols. The proposed SDAAA protocol comprises a node authorization algorithm that permits legitimate nodes to communicate within the network. This SDAAA protocol’s methods help improve the quality of service (QoS) parameters. Furthermore, we introduce a mathematical model to improve accuracy, energy efficiency, data freshness, authorization, and authentication. Finally, our protocol is tested in an intelligent healthcare WSN patient-monitoring application scenario and verified using an OMNET++ simulator. Based upon the results, we confirm that our proposed SDAAA protocol attains a throughput of 444 kbs, representing a 98% of data/network channel capacity rate; an energy consumption of 2.6 joules, representing 99% network energy efficiency; an effected network of 2.45, representing 99.5% achieved overall performance of the network; and time complexity of 0.08 s, representing 98.5% efficiency of the proposed SDAAA approach. By contrast, contending protocols such as SD, EEHA, HAS, IIF, and RHC have throughput ranges between 415–443, representing 85–90% of the data rate/channel capacity of the network; energy consumption in the range of 3.0–3.6 joules, representing 88–95% energy efficiency of the network; effected network range of 2.98, representing 72–89% improved overall performance of the network; and time complexity in the range of 0.20 s, representing 72–89% efficiency of the proposed SDAAA approach. Therefore, our proposed SDAAA protocol outperforms other known approaches, such as SD, EEHA, HAS, IIF, and RHC, designed for secure data aggregation in a similar environment.
The Difference in the Assessment of Knee Extension/Flexion Angles during Gait between Two Calibration Methods for Wearable Goniometer Sensors
Authors: Ishida Tomoya , Samukawa Mina
Published: March 2024

Frontal and axial knee motion can affect the accuracy of the knee extension/flexion motion measurement using a wearable goniometer. The purpose of this study was to test the hypothesis that calibrating the goniometer on an individual’s body would reduce errors in knee flexion angle during gait, compared to bench calibration. Ten young adults (23.2 ± 1.3 years) were enrolled. Knee flexion angles during gait were simultaneously assessed using a wearable goniometer sensor and an optical three-dimensional motion analysis system, and the absolute error (AE) between the two methods was calculated. The mean AE across a gait cycle was 2.4° (0.5°) for the on-body calibration, and the AE was acceptable (<5°) throughout a gait cycle (range: 1.5–3.8°). The mean AE for the on-bench calibration was 4.9° (3.4°) (range: 1.9–13.6°). Statistical parametric mapping (SPM) analysis revealed that the AE of the on-body calibration was significantly smaller than that of the on-bench calibration during 67–82% of the gait cycle. The results indicated that the on-body calibration of a goniometer sensor had acceptable and better validity compared to the on-bench calibration, especially for the swing phase of gait.
Dynamic Multiple Object Segmentation with Spatio-Temporal Filtering
Authors: Yang Wenguang , Ren Kan , Wan Minjie , Kong Xiaofang , Qian Weixian
Published: March 2024

This article primarily focuses on the localization and extraction of multiple moving objects in images taken from a moving camera platform, such as image sequences captured by drones. The positions of moving objects in the images are influenced by both the camera’s motion and the movement of the objects themselves, while the background position in the images is related to the camera’s motion. The main objective of this article was to extract all moving objects from the background in an image. We first constructed a motion feature space containing motion distance and direction, to map the trajectories of feature points. Subsequently, we employed a clustering algorithm based on trajectory distinctiveness to differentiate between moving objects and the background, as well as feature points corresponding to different moving objects. The pixels between the feature points were then designated as source points. Within local regions, complete moving objects were segmented by identifying these pixels. We validated the algorithm on some sequences in the Video Verification of Identity (VIVID) program database and compared it with relevant algorithms. The experimental results demonstrated that, in the test sequences when the feature point trajectories exceed 10 frames, there was a significant difference in the feature space between the feature points on the moving objects and those on the background. Correctly classified frames with feature points accounted for 67% of the total frames.The positions of the moving objects in the images were accurately localized, with an average IOU value of 0.76 and an average contour accuracy of 0.57. This indicated that our algorithm effectively localized and segmented the moving objects in images captured by moving cameras.
Comprehensive Analysis of Phytochemical Composition, Antioxidant Potential, and Antibacterial Activity of T. polium
Authors: Timizar Zineb , Boutemak Khalida , Hadj Ziane-Zafour Amel , Touzout Nabil , Tahraoui Hichem , Jaouadi Bassem , Zamouche Meriem , Kebir Mohammed , Zhang Jie , Amrane Abdeltif
Published: March 2024

This study aims to thoroughly examine the chemicals and effects of the ethanol extract from T. polium’s upper parts. We used the Soxhlet method for extraction, resulting in an extract with a significant yield of 20.6%. Qualitative analysis identified a variety of compounds, such as tannins, saponins, reducing compounds, terpenoids, quinones, and alkaloids. In quantitative analysis using the colorimetric method, we found the extract was rich in total flavonoids (20.78 mg equivalent QE/g DW extract) and total polyphenols (227.43 mg equivalent GAE/g DW extract). To assess antioxidant potential, we used the ferric reducing antioxidant power (FRAP) method, with ascorbic acid and butylated hydroxytoluene (BHT) as standards. The extract showed moderate activity in both the 2,2-diphenyl-1-picrylhydrazyl (DPPH) and FRAP methods at concentrations of 65 µg/mL and 21 mg/mL, respectively. Additionally, we tested the ethanolic extract against various bacteria using the disk diffusion technique on agar medium. The results indicated that the T. polium extract had moderate effectiveness against Gram-negative bacteria like Pseudomonas aeruginosa ATCC 9027 and Escherichia coli ATCC 8739, as well as Gram-positive bacteria like Staphylococcus aureus ATCC 6538 and Bacillus subtilis ATCC 6633. We further investigated the composition of the ethanolic extract through LC-MS/MS analysis, establishing a detailed profile of phenolic compounds, with six flavonoids identified as the main polyphenolic constituents. This thorough evaluation provides insights into the potential therapeutic uses of T. polium.
The Extraction and Separation of Scarce Critical Metals: A Review of Gallium, Indium and Germanium Extraction and Separation from Solid Wastes
Authors: Huang Yanfang , Wang Meimei , Liu Bingbing , Su Shengpeng , Sun Hu , Yang Shuzhen , Han Guihong
Published: March 2024

Gallium (Ga), indium (In), and germanium (Ge) play an important role in the modern high-tech material field. Due to their low content and scattered distribution in the crust, and the increasing demand for these metals in recent years, their supply risks have sharply increased. Therefore, the recycling of these metals is of great significance. In this work, a systematic review was performed using the Web of Science, Scopus, MDPI, Elsevier, and Springer Link databases. The combined terms used for the search were Ga/In/Ge, extraction, separation, and recycling. After a careful evaluation of the titles, abstracts, and full texts, a total of 106 articles were included. This paper briefly describes the resource features of Ga, In, and Ge. After that, the chemical principles, technical parameters, and metal recovery in various extraction and separation methods from monometallic and polymetallic resources are systematically reviewed. Leaching followed by solvent extraction or ion exchange is the main process for Ga, In, and Ge recovery. Although many attempts have been made to separate multiple metals from leaching solutions, highly selective solvents and resins are still the research priority. This review can provide theoretical and technical guidance for the separation of Ga, In, and Ge from various resources.