bluuebunny/crossref_metadata_2025 huggingface dataset:
Rapid Classification and Diagnosis of Gas Wells Driven by Production Data
Authors: Zhu Zhiyong , Han Guoqing , Liang Xingyuan , Chang Shuping , Yang Boke , Yang Dingding
Published: June 2024

Conventional gas well classification methods cannot provide effective support for gas well routine management, and suffer from poor timeliness. In order to guide the on-site operation in liquid loading gas wells and improve the timeliness of gas well classification, this paper proposes a production data-driven gas well classification method based on the LDA-DA (Linear Discriminant Analysis–Discriminant Analysis) combination model. In this method, considering the requirements of routine management, gas wells are evaluated from two aspects: liquid drainage capacity (LDC) and liquid production intensity (LPI), and are classified into six types. Domain knowledge is used to perform the feature engineering on the on-site production data, and five features are set up to quantitatively evaluate the gas well and to create classification samples. On this basis, in order to specify the optimal data processing flow to establish the gas well classification map, four linear dimensionality reduction techniques, LDA, PCA, LPP, and ICA, are used to reduce the dimensionality of original classification samples, and then, four classical classification algorithms, NB, DA, KNN, and SVM, are trained and evaluated on the low-dimensional samples, respectively. The results show that the LDA space achieves the optimal sample separation and is chosen as the decision space for gas well classification. The DA algorithm obtains the top performance, i.e., the highest Average Macro F1-score of 95.619%, in the chosen decision space, and is employed to determine the classification boundaries in the decision space. At this point, the LDA-DA combination model for sample data processing is developed. Based on this model, gas well classification maps can be established by data mining, and the rapid evaluation and diagnosis of gas wells can be achieved. This method realizes instant and efficient production data-driven gas well classification, and can provide timely decision-making support for gas well routine management. It introduces new ideas for performing gas well classification, expanding the content and scope of the classification work, and presenting valuable insights for further research in this field.
Study on Activation and Restructuring of Key Strata in Shallowly Buried Coal Seam Bearing Structure and Load Characteristics
Authors: He Yifeng , Zhang Jie , Liu Hui , Yang Tao , Sun Jianping
Published: June 2024

The mining of shallow coal seam groups triggers the activation of overlying strata, leading to increased pressure and support difficulties, thereby posing a threat to the safe extraction of underlying coal seams. Against the backdrop of Longhua Coal Mine, this study utilized physical similarity simulation experiments to obtain the activated, restructured load-bearing structure and the migration characteristics of overlying strata. Theoretical calculations were employed to establish both a rolling friction mechanics model for the activated load-bearing structure and a mechanical model for the combined load-bearing structure of key strata. The research indicates that during the initial activation phase, the load-bearing structure exhibits a V-shaped hinged arch, with directly collapsed rock masses transitioning towards spherical shapes, resulting in the sub-key strata shifting from sliding friction to rolling friction. Based on the rolling friction mechanics model of the activated load-bearing structure, we derived the rolling friction coefficient of key blocks in the sub-key strata and the instability criterion of the load-bearing structure under rolling friction conditions. Considering the migration characteristics of the activated restructured load-bearing structure, four types of combined load-bearing structures were identified, and the load calculation formulas in the mechanical model were derived, with the rationality of these formulas verified through case analysis.
Analysis of Cassini Altimetric Crossovers on Titan
Authors: Durante Daniele , Mastrogiuseppe Marco , Carli Elisa , Poggiali Valerio , Di Ruscio Andrea , Notaro Virginia , Iess Luciano
Published: June 2024

The Cassini spacecraft performed several flybys of Saturn’s largest moon, Titan, collecting valuable data. During several passes, altimetric data were acquired. Here, we focus on altimetric measurements collected by Cassini’s radar when flying over the same region at different epochs in order to correlate such measurements (crossovers) and investigate differences in altimetry. In our study, we assess altimetric errors associated with three distinct methods for extracting topography from Cassini’s radar data: the maximum likelihood estimator (MLE), the threshold method, and the first moment technique. Focusing on crossover events, during which Cassini revisited specific areas of Titan’s surface, we conduct a detailed examination of the consistency and accuracy of these three topography extraction methods. The proposed analysis involves closely examining altimetric data collected at different epochs over identical geographical regions, allowing us to investigate potential errors due to the variations in off-nadir angle, relative impact, uncertainties, and systematic errors inherent in the application of these methodologies. Our findings reveal that the correction applied for the off-nadir angle to the threshold and first moment methods significantly reduces the dispersion in the delta difference at the crossover, resulting in a dispersion of the order of 60 m, even lower than what is achieved with the MLE (~70 m). Additionally, an effort is made to assess the potential of Cassini for estimating the tidal signal on Titan. Considering the altimetric errors identified in our study and the relatively low number of crossovers performed by Cassini, our assessment indicates that it is not feasible to accurately measure the tidal signal on Titan using the currently available standard altimetry data from Cassini. Our assessment regarding the accuracy of the Cassini altimeter provides valuable insights for future planetary exploration endeavors. Our study advances the understanding of Titan’s complex landscape and contributes to refining topographical models derived from Cassini’s altimetry observations. These insights not only enhance our knowledge of Saturn’s largest moon but also open prospects for Titan surface and interior exploration using radar systems.
Accurate Quantification of 0–30 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning
Authors: Fu Peng , Clanton Christian , Demuth Kirk M. , Goodman Verena , Griffith Lauren , Khim-Young Mage , Maddalena Julia , LaMarca Kenny , Wright Logan A. , Schurman David W. , Kellner James R.
Published: June 2024

Increases in organic carbon within agricultural soils are widely recognized as a “negative emission” that removes CO2 from the atmosphere. Accurate quantification of soil organic carbon (SOC) to a certain depth in the spatial domain is critical for the effective implementation of improved land management practices in croplands. Currently, there is a lack of understanding regarding what depth strategy should be used to estimate SOC at 0–30 cm when sample datasets come from multiple depths. Furthermore, few studies have examined depth strategies for mapping SOC at the agricultural management level (i.e., field level), opting instead for point-based analysis. Here, three types of approaches with different depth strategies were evaluated for their ability to quantify 0–30 cm SOC content based on soil samples from 0–5 (surface), 5–30 (subsurface), and 0–30 cm (full column). These approaches involved the generalized additive model and machine learning techniques, i.e., artificial neural networks, random forest, and XGBoost. The soil samples used for the model evaluation and selection consisted of the newly collected samples in 2020–2022 and the Rapid Carbon Assessment (RaCA) legacy samples collected in 2010–2011. Environmental covariates corresponding to these SOC measurements were used in model training, including long-term physical climate, short-term weather, topographic and edaphic, and remotely sensed variables. Among the models evaluated in this study, the XGB regression model with a full column depth assignment strategy yielded the best prediction performance for 0–30 cm SOC content, with an r2 (squared Pearson correlation coefficient) of 0.48, an RMSE (root mean square error) of 0.29%, an ME (mean error) of 0.06%, an MAE of 0.25%, and an MEC (modeling efficiency coefficient) of 0.36 at the pixel level and an r2 of 0.64, an RMSE of 0.32%, an ME of −0.20%, an MAE of 0.28%, and an MEC of 0.48 at the field level. This study highlights that machine learning models with a full column depth strategy should be used to quantify 0–30 cm SOC content in agricultural soils over the continental United States (CONUS).
FCNet: Flexible Convolution Network for Infrared Small Ship Detection
Authors: Guo Feng , Ma Hongbing , Li Liangliang , Lv Ming , Jia Zhenhong
Published: June 2024

The automatic monitoring and detection of maritime targets hold paramount significance in safeguarding national sovereignty, ensuring maritime rights, and advancing national development. Among the principal means of maritime surveillance, infrared (IR) small ship detection technology stands out. However, due to their minimal pixel occupancy and lack of discernible color and texture information, IR small ships have persistently posed a formidable challenge in the realm of target detection. Additionally, the intricate maritime backgrounds often exacerbate the issue by inducing high false alarm rates. In an effort to surmount these challenges, this paper proposes a flexible convolutional network (FCNet), integrating dilated convolutions and deformable convolutions to achieve flexible variations in convolutional receptive fields. Firstly, a feature enhancement module (FEM) is devised to enhance input features by fusing standard convolutions with dilated convolutions, thereby obtaining precise feature representations. Subsequently, a context fusion module (CFM) is designed to integrate contextual information during the downsampling process, mitigating information loss. Furthermore, a semantic fusion module (SFM) is crafted to fuse shallow features with deep semantic information during the upsampling process. Additionally, squeeze-and-excitation (SE) blocks are incorporated during upsampling to bolster channel information. Experimental evaluations conducted on two datasets demonstrate that FCNet outperforms other algorithms in the detection of IR small ships on maritime surfaces. Moreover, to propel research in deep learning-based IR small ship detection on maritime surfaces, we introduce the IR small ship dataset (Maritime-SIRST).
SIM-MultiDepth: Self-Supervised Indoor Monocular Multi-Frame Depth Estimation Based on Texture-Aware Masking
Authors: Guo Xiaotong , Zhao Huijie , Shao Shuwei , Li Xudong , Zhang Baochang , Li Na
Published: June 2024

Self-supervised monocular depth estimation methods have become the focus of research since ground truth data are not required. Current single-image-based works only leverage appearance-based features, thus achieving a limited performance. Deep learning based multiview stereo works facilitate the research on multi-frame depth estimation methods. Some multi-frame methods build cost volumes and take multiple frames as inputs at the time of test to fully utilize geometric cues between adjacent frames. Nevertheless, low-textured regions, which are dominant in indoor scenes, tend to cause unreliable depth hypotheses in the cost volume. Few self-supervised multi-frame methods have been used to conduct research on the issue of low-texture areas in indoor scenes. To handle this issue, we propose SIM-MultiDepth, a self-supervised indoor monocular multi-frame depth estimation framework. A self-supervised single-frame depth estimation network is introduced to learn the relative poses and supervise the multi-frame depth learning. A texture-aware depth consistency loss is designed considering the calculation of the patch-based photometric loss. Only the areas where multi-frame depth prediction is considered unreliable in low-texture regions are supervised by the single-frame network. This approach helps improve the depth estimation accuracy. The experimental results on the NYU Depth V2 dataset validate the effectiveness of SIM-MultiDepth. The zero-shot generalization studies on the 7-Scenes and Campus Indoor datasets aid in the analysis of the application characteristics of SIM-MultiDepth.
Using Graphs to Perform Effective Sensor-Based Human Activity Recognition in Smart Homes
Authors: P Srivatsa , Plötz Thomas
Published: June 2024

There has been a resurgence of applications focused on human activity recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted-living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, such as variability, sparsity, and noise in sensor measurements. Although state-of-the-art HAR systems have made considerable strides in addressing some of these challenges, they suffer from a practical limitation: they require successful pre-segmentation of continuous sensor data streams prior to automated recognition, i.e., they assume that an oracle is present during deployment, and that it is capable of identifying time windows of interest across discrete sensor events. To overcome this limitation, we propose a novel graph-guided neural network approach that performs activity recognition by learning explicit co-firing relationships between sensors. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home in a data-driven manner. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms and hierarchical pooling of node embeddings. We demonstrate the effectiveness of our proposed approach by conducting several experiments on CASAS datasets, showing that the resulting graph-guided neural network outperforms the state-of-the-art method for HAR in smart homes across multiple datasets and by large margins. These results are promising because they push HAR for smart homes closer to real-world applications.
Autonomous Exploration Method of Unmanned Ground Vehicles Based on an Incremental B-Spline Probability Roadmap
Authors: Feng Xingyang , Cong Hua , Zhang Yu , Qiu Mianhao , Hu Xuesong
Published: June 2024

Autonomous exploration in unknown environments is a fundamental problem for the practical application of unmanned ground vehicles (UGVs). However, existing exploration methods face difficulties when directly applied to UGVs due to limited sensory coverage, conservative exploration strategies, inappropriate decision frequencies, and the non-holonomic constraints of wheeled vehicles. In this paper, we present IB-PRM, a hierarchical planning method that combines Incremental B-splines with a probabilistic roadmap, which can support rapid exploration by a UGV in complex unknown environments. We define a new frontier structure that includes both information-gain guidance and a B-spline curve segment with different arrival orientations to satisfy the non-holonomic constraint characteristics of UGVs. We construct and maintain local and global graphs to generate and store filtered frontiers. By jointly solving the Traveling Salesman Problem (TSP) using these frontiers, we obtain the optimal global path traversing feasible frontiers. Finally, we optimize the global path based on the Time Elastic Band (TEB) algorithm to obtain a smooth, continuous, and feasible local trajectory. We conducted comparative experiments with existing advanced exploration methods in simulation environments of different scenarios, and the experimental results demonstrate that our method can effectively improve the efficiency of UGV exploration.
Research on the RZB-Type Three-Dimensional Drilling Strain Measurement System
Authors: Chen Zheng , Li Hong , Dong Yunkai , Wang Wenbo , Wu Liheng , Zhan Weiwei
Published: June 2024

Borehole strain gauges play a crucial role in geophysical, seismological, and crustal dynamics studies. While existing borehole strain gauges are proficient in measuring horizontal strains within vertical boreholes, their effectiveness in capturing vertical and oblique strains is limited due to technical constraints arising from the cylindrical probe’s characteristics. However, the accurate measurement of three-dimensional strain is essential for a comprehensive understanding of crustal tectonics, dynamics, and geophysics, particularly considering the diverse geological structures and force sources within the crustal medium. In this study, we present a novel approach to address this challenge by enhancing an existing horizontal-component borehole strain gauge with a bellows structure and line strain measurement technology to enable vertical and borehole oblique strain measurements. Integrating these enhancements with horizontal strain measurement capabilities enables comprehensive three-dimensional borehole strain measurements within the same hole section. The system was deployed and tested at the Gongxian seismic station in Sichuan Province. Clear observations of solid tides were recorded across horizontal, oblique, and vertical measurement units, with the tidal morphology and amplitude being consistent with the theoretical calculations. The achieved measurement sensitivity of 10-10 meets the requirements for borehole strain measurement, enabling the characterization of three-dimensional strain states within boreholes through association methods.
Numerical Simulation Study on the Influence of Fracture on Borehole Wave Modes: Insights from Fracture Width, Filling Condition, and Acoustic Frequency
Authors: Gao Ziran , Wu Dong , Wu Hongliang , Liu Peng , Cai Ming , Zhang Chengguang , Tang Jun
Published: June 2024

Unconventional reservoirs, such as shale and tight formations, have become increasingly vital contributors to oil and gas production. In these reservoirs, fractures serve as crucial spaces for fluid migration and storage, making their precise assessment essential. Array acoustic logging stands out as a pivotal method for evaluating fractures. To investigate the impact of fracture width, fracture-filling conditions, and acoustic frequency on compressional and shear waves, a three-dimensional variable mesh finite difference program was employed for acoustic logging numerical simulation. Firstly, numerical models representing fractured formations with varying fracture widths and distinct fluid-filling conditions were established, and array acoustic logging numerical simulations were conducted at different frequencies. Subsequently, the waveform data were processed to extract acoustic characteristic parameters, such as velocities and amplitude attenuations of compressional and shear waves. Finally, a quantitative analysis was conducted to examine the variation patterns of characteristic parameters of refracted compressional and shear waves in relation to fracture properties. The research results indicate that amplitude attenuation information derived from borehole wave modes is particularly sensitive to the changes in fracture properties. As fracture width increased, we observed a significant amplitude attenuation in both compressional and shear waves, proportional to the logarithm of the attenuation coefficients. Furthermore, when the fracture width was constant, gas-filled fractures exhibited more prominent amplitude attenuation than water-filled fractures, with shear wave attenuation being more sensitive to the filling material. Moreover, from a quantitative perspective, the analysis revealed that the attenuation coefficients of refracted compressional and shear waves exhibited an exponential variation with gas saturation. Notably, once fracture width and filling conditions were established, the amplitudes of compressional and shear waves at the dominant frequency of 40 kHz were significantly reduced compared to those at 8 kHz, accompanied by increased attenuation. Subsequent quantitative analysis revealed that, when the product of fracture width and dominant frequency remains constant, the corresponding attenuation coefficient ratios approach 1. This indicates that the attenuation process of acoustic propagation in fractured media follows the principle of acoustic similarity. The findings of this study provide reference for further research on fracture property evaluation methods based on array acoustic logging data.
Research on Foot Contact State Detection Technology of Wheel-Legged Robot
Authors: Wang Yaodong , Hong Meng , Chai Hui , Zhang Yinglong , Wang Guan , Wu Chaoqun , Guo Min
Published: June 2024

The accurate perception of external environment information through the robot foot is crucial for the mobile robot to evaluate its ability to traverse terrain. Adequate foot-end contact signals can provide robust support for robot motion control and decision-making processes. The shape and uncertain rotation of the wheel-legged robot foot end represent a significant challenge to sensing the robot foot-end contact state, which current foot-end sensing schemes cannot solve. This paper presents a sensing method for the tire stress field of wheel-legged robots. A finite element analysis was conducted to study the deformation characteristics of the foot-end tire under force. Based on this analysis, a heuristic contact position estimator was designed that utilizes symmetrical deformation characteristics. Strain sensors, arranged in an array, extract the deformation information on the inner surface of the tire at a frequency of 200 Hz. The contact position estimator reduces the dimensionality of the data and fits the eigenvalues to the estimated contact position. Using support vector regression, the force estimator utilizes the estimated contact position and sensor signal to estimate the normal reaction force, designated as FZ. The sensing system is capable of detecting the contact position on the wheel circumference (with a root mean square error of 1.150°), as well as the normal force of 160 N on the Z axis (with a root mean square error of 6.04%). To validate the efficacy of the sensor detection method, a series of randomized and repeated experiments were conducted on a self-constructed test platform. This novel approach offers a promising avenue for perceiving contact states in wheel-legged robots.
Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies
Authors: Kamtam Sidharth Bhanu , Lu Qian , Bouali Faouzi , Haas Olivier C. L. , Birrell Stewart
Published: June 2024

With remarkable advancements in the development of connected and autonomous vehicles (CAVs), the integration of teleoperation has become crucial for improving safety and operational efficiency. However, teleoperation faces substantial challenges, with network latency being a critical factor influencing its performance. This survey paper explores the impact of network latency along with state-of-the-art mitigation/compensation approaches. It examines cascading effects on teleoperation communication links (i.e., uplink and downlink) and how delays in data transmission affect the real-time perception and decision-making of operators. By elucidating the challenges and available mitigation strategies, the paper offers valuable insights for researchers, engineers, and practitioners working towards the seamless integration of teleoperation in the evolving landscape of CAVs.
Real-Time Visual Feedback Based on MIMUs Technology Reduces Bowing Errors in Beginner Violin Students
Authors: Provenzale Cecilia , Di Tommaso Francesco , Di Stefano Nicola , Formica Domenico , Taffoni Fabrizio
Published: June 2024

Violin is one of the most complex musical instruments to learn. The learning process requires constant training and many hours of exercise and is primarily based on a student–teacher interaction where the latter guides the beginner through verbal instructions, visual demonstrations, and physical guidance. The teacher’s instruction and practice allow the student to learn gradually how to perform the correct gesture autonomously. Unfortunately, these traditional teaching methods require the constant supervision of a teacher and the interpretation of non-real-time feedback provided after the performance. To address these limitations, this work presents a novel interface (Visual Interface for Bowing Evaluation—VIBE) to facilitate student’s progression throughout the learning process, even in the absence of direct teacher intervention. The proposed interface allows two key parameters of bowing movements to be monitored, namely, the angle between the bow and the string (i.e., α angle) and the bow tilt (i.e., β angle), providing real-time visual feedback on how to correctly move the bow. Results collected on 24 beginners (12 exposed to visual feedback, 12 in a control group) showed a positive effect of the real-time visual feedback on the improvement of bow control. Moreover, the subjects exposed to visual feedback judged the latter as useful to correct their movement and clear in terms of the presentation of data. Although the task was rated as harder when performed with the additional feedback, the subjects did not perceive the presence of a violin teacher as essential to interpret the feedback.
BiLSTM-MLAM: A Multi-Scale Time Series Prediction Model for Sensor Data Based on Bi-LSTM and Local Attention Mechanisms
Authors: Fan Yongxin , Tang Qian , Guo Yangming , Wei Yifei
Published: June 2024

This paper introduces BiLSTM-MLAM, a novel multi-scale time series prediction model. Initially, the approach utilizes bidirectional long short-term memory to capture information from both forward and backward directions in time series data. Subsequently, a multi-scale patch segmentation module generates various long sequences composed of equal-length segments, enabling the model to capture data patterns across multiple time scales by adjusting segment lengths. Finally, the local attention mechanism enhances feature extraction by accurately identifying and weighting important time segments, thereby strengthening the model’s understanding of the local features of the time series, followed by feature fusion. The model demonstrates outstanding performance in time series prediction tasks by effectively capturing sequence information across various time scales. Experimental validation illustrates the superior performance of BiLSTM-MLAM compared to six baseline methods across multiple datasets. When predicting the remaining life of aircraft engines, BiLSTM-MLAM outperforms the best baseline model by 6.66% in RMSE and 11.50% in MAE. In the LTE dataset, it achieves RMSE improvements of 12.77% and MAE enhancements of 3.06%, while in the load dataset, it demonstrates RMSE enhancements of 17.96% and MAE improvements of 30.39%. Additionally, ablation experiments confirm the positive impact of each module on prediction accuracy. Through segment length parameter tuning experiments, combining different segment lengths has resulted in lower prediction errors, affirming the effectiveness of the multi-scale fusion strategy in enhancing prediction accuracy by integrating information from multiple time scales.
Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures
Authors: Mwata-Velu Tat’y , Zamora Erik , Vasquez-Gomez Juan Irving , Ruiz-Pinales Jose , Sossa Humberto
Published: June 2024

This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain–computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, using multiclass classification based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and minimum-norm estimate algorithms were implemented to select discriminant channels and enhance the EEG data. Hence, deep EEGNet and convolutional recurrent neural networks were separately implemented to classify the EEG data for image visualization into 40 labels. Using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures. The satisfactory results obtained with this method offer a new implementation opportunity for multitask embedded BCI applications utilizing a reduced number of both channels (<50%) and network parameters (<110 K).
Hand Gesture Recognition Based on High-Density Myoelectricity in Forearm Flexors in Humans
Authors: Chen Xiaoling , Yang Huaigang , Zhang Dong , Hu Xinfeng , Xie Ping
Published: June 2024

Electromyography-based gesture recognition has become a challenging problem in the decoding of fine hand movements. Recent research has focused on improving the accuracy of gesture recognition by increasing the complexity of network models. However, training a complex model necessitates a significant amount of data, thereby escalating both user burden and computational costs. Moreover, owing to the considerable variability of surface electromyography (sEMG) signals across different users, conventional machine learning approaches reliant on a single feature fail to meet the demand for precise gesture recognition tailored to individual users. Therefore, to solve the problems of large computational cost and poor cross-user pattern recognition performance, we propose a feature selection method that combines mutual information, principal component analysis and the Pearson correlation coefficient (MPP). This method can filter out the optimal subset of features that match a specific user while combining with an SVM classifier to accurately and efficiently recognize the user’s gesture movements. To validate the effectiveness of the above method, we designed an experiment including five gesture actions. The experimental results show that compared to the classification accuracy obtained using a single feature, we achieved an improvement of about 5% with the optimally selected feature as the input to any of the classifiers. This study provides an effective guarantee for user-specific fine hand movement decoding based on sEMG signals.
Rapid and Online Microvolume Flow-Through Dialysis Probe for Sample Preparation in Veterinary Drug Residue Analysis
Authors: Diab Hanin , Calle Alexandra , Thompson Jonathan
Published: June 2024

A rapid and online microvolume flow-through dialysis probe designed for sample preparation in the analysis of veterinary drug residues is introduced. This study addresses the need for efficient and green sample preparation methods that reduce chemical waste and reagent use. The dialysis probe integrates with liquid chromatography and mass spectrometry (LC-MS) systems, facilitating automated, high-throughput analysis. The dialysis method utilizes minimal reagent volumes per sample, significantly reducing the generation of solvent waste compared to traditional sample preparation techniques. Several veterinary drugs were spiked into tissue homogenates and analyzed to validate the probe’s efficacy. A diagnostic sensitivity of >97% and specificity of >95% were obtained for this performance evaluation. The results demonstrated the effective removal of cellular debris and particulates, ensuring sample integrity and preventing instrument clogging. The automated dialysis probe yielded recovery rates between 27 and 77% for multiple analytes, confirming its potential to streamline veterinary drug residue analysis, while adhering to green chemistry principles. The approach highlights substantial improvements in both environmental impact and operational efficiency, presenting a viable alternative to conventional sample preparation methods in regulatory and research applications.
Frequency-Oriented Transformer for Remote Sensing Image Dehazing
Authors: Zhang Yaoqing , He Xin , Zhan Chunxia , Li Junjie
Published: June 2024

Remote sensing images are inevitably affected by the degradation of haze with complex appearance and non-uniform distribution, which remarkably affects the effectiveness of downstream remote sensing visual tasks. However, most current methods principally operate in the original pixel space of the image, which hinders the exploration of the frequency characteristics of remote sensing images, resulting in these models failing to fully exploit their representation ability to produce high-quality images. This paper proposes a frequency-oriented remote sensing dehazing Transformer named FOTformer, to explore information in the frequency domain to eliminate disturbances caused by haze in remote sensing images. It contains three components. Specifically, we developed a frequency-prompt attention evaluator to estimate the self-correlation of features in the frequency domain rather than the spatial domain, improving the image restoration performance. We propose a content reconstruction feed-forward network that captures information between different scales in features and integrates and processes global frequency domain information and local multi-scale spatial information in Fourier space to reconstruct the global content under the guidance of the amplitude spectrum. We designed a spatial-frequency aggregation block to exchange and fuse features from the frequency domain and spatial domain of the encoder and decoder to facilitate the propagation of features from the encoder stream to the decoder and alleviate the problem of information loss in the network. The experimental results show that the FOTformer achieved a more competitive performance against other remote sensing dehazing methods on commonly used benchmark datasets.
A Lightweight Algorithm to Model Radiation Damage Effects in Monte Carlo Events for High-Luminosity Large Hadron Collider Experiments
Authors: Nakkalil Keerthi , Bomben Marco
Published: June 2024

Radiation damage significantly impacts the performance of silicon tracking detectors in Large Hadron Collider (LHC) experiments such as ATLAS and CMS, with signal reduction being the most critical effect; adjusting sensor bias voltage and detection thresholds can help mitigate these effects, generating simulated data that accurately mirror the performance evolution with the accumulation of luminosity, hence fluence, is crucial. The ATLAS and CMS collaborations have developed and implemented algorithms to correct simulated Monte Carlo (MC) events for radiation damage effects, achieving impressive agreement between collision data and simulated events. In preparation for the high-luminosity phase (HL-LHC), the demand for a faster ATLAS MC production algorithm becomes imperative due to escalating collision, events, tracks, and particle hit rates, imposing stringent constraints on available computing resources. This article outlines the philosophy behind the new algorithm, its implementation strategy, and the essential components involved. The results from closure tests indicate that the events simulated using the new algorithm agree with fully simulated events at the level of few %. The first tests on computing performance show that the new algorithm is as fast as it is when no radiation damage corrections are applied.
Postfilter for Dual Channel Speech Enhancement Using Coherence and Statistical Model-Based Noise Estimation
Authors: Cheong Sein , Kim Minseung , Shin Jong Won
Published: June 2024

A multichannel speech enhancement system usually consists of spatial filters such as adaptive beamformers followed by postfilters, which suppress remaining noise. Accurate estimation of the power spectral density (PSD) of the residual noise is crucial for successful noise reduction in the postfilters. In this paper, we propose a postfilter utilizing proposed a posteriori speech presence probability (SPP) and noise PSD estimators, which are based on both the coherence and the statistical models. We model the coherence-based a posteriori SPP as a simple function of the magnitude of coherence between two microphone signals and combine it with a single-channel SPP based on statistical models. The coherence-based estimator for the PSD of the noise remaining in the beamformer output in the presence of speech is derived using the pseudo-coherence considering the effect of the beamformers, which is used to construct the coherence-based noise PSD estimator. Then, the final noise PSD estimator is obtained by combining the coherence-based and statistical model-based noise PSD estimators with the proposed SPP. The spectral gain function is also modified, incorporating the proposed SPP. Experimental results demonstrate that the proposed method led to more accurate noise PSD estimation and perceptual evaluation of speech quality scores in various diffuse noise environments, and did not degrade the speech quality under the presence of directional interference, although the proposed method utilizes the coherence information.
Probability-Based LIDAR–Camera Calibration Considering Target Positions and Parameter Evaluation Using a Data Fusion Map
Authors: Yamada Ryuhei , Yaguchi Yuichi
Published: June 2024

The data fusion of a 3-D light detection and ranging (LIDAR) point cloud and a camera image during the creation of a 3-D map is important because it enables more efficient object classification by autonomous mobile robots and facilitates the construction of a fine 3-D model. The principle behind data fusion is the accurate estimation of the LIDAR–camera’s external parameters through extrinsic calibration. Although several studies have proposed the use of multiple calibration targets or poses for precise extrinsic calibration, no study has clearly defined the relationship between the target positions and the data fusion accuracy. Here, we strictly investigated the effects of the deployment of calibration targets on data fusion and proposed the key factors to consider in the deployment of the targets in extrinsic calibration. Thereafter, we applied a probability method to perform a global and robust sampling of the camera external parameters. Subsequently, we proposed an evaluation method for the parameters, which utilizes the color ratio of the 3-D colored point cloud map. The derived probability density confirmed the good performance of the deployment method in estimating the camera external parameters. Additionally, the evaluation quantitatively confirmed the effectiveness of our deployments of the calibration targets in achieving high-accuracy data fusion compared with the results obtained using the previous methods.
Exploration of MPSO-Two-Stage Classification Optimization Model for Scene Images with Low Quality and Complex Semantics
Authors: Liu Kexin , Wang Rong , Song Xiaoou , Deng Xiaobing , Zhu Qingchao
Published: June 2024

Currently, complex scene classification strategies are limited to high-definition image scene sets, and low-quality scene sets are overlooked. Although a few studies have focused on artificially noisy images or specific image sets, none have involved actual low-resolution scene images. Therefore, designing classification models around practicality is of paramount importance. To solve the above problems, this paper proposes a two-stage classification optimization algorithm model based on MPSO, thus achieving high-precision classification of low-quality scene images. Firstly, to verify the rationality of the proposed model, three groups of internationally recognized scene datasets were used to conduct comparative experiments with the proposed model and 21 existing methods. It was found that the proposed model performs better, especially in the 15-scene dataset, with 1.54% higher accuracy than the best existing method ResNet-ELM. Secondly, to prove the necessity of the pre-reconstruction stage of the proposed model, the same classification architecture was used to conduct comparative experiments between the proposed reconstruction method and six existing preprocessing methods on the seven self-built low-quality news scene frames. The results show that the proposed model has a higher improvement rate for outdoor scenes. Finally, to test the application potential of the proposed model in outdoor environments, an adaptive test experiment was conducted on the two self-built scene sets affected by lighting and weather. The results indicate that the proposed model is suitable for weather-affected scene classification, with an average accuracy improvement of 1.42%.
Constraint Optimization Model for Dynamic Parking Space Allocation
Authors: Elfaki Abdelrahman Osman , Messoudi Wassim , Bushnag Anas , Abuzneid Shakour , Alhmiedat Tareq
Published: June 2024

Managing car parking systems is a complex process because multiple constraints must be considered; these include organizational and operational constraints. In this paper, a constraint optimization model for dynamic parking space allocation is introduced. An ad hoc algorithm is proposed, presented, and explained to achieve the goal of our proposed model. This paper makes research contributions by providing an intelligent prioritization mechanism, considering user schedule shifts and parking constraints, and assigning suitable parking slots based on a dynamic distribution. The proposed model is implemented to demonstrate the applicability of our approach. A benchmark is constructed based on well-defined metrics to validate our proposed model and the results achieved.
Non-Analog Compounds to Sialic Acid as Inhibitors of Influenza Virus Neuraminidase: An Underexplored Approach for Novel Antivirals―Systematic Review
Authors: Márquez-Domínguez Luis , Jasso-Miranda Carolina , Sedeño-Monge Virginia , Santos-López Gerardo
Published: June 2024

Influenza poses a significant threat to public health worldwide, particularly among vulnerable populations such as children, the elderly, immunocompromised individuals, and those with chronic diseases. It is associated with high mortality and morbidity rates. Neuraminidase inhibitors play a crucial role in influenza treatment by mitigating the risk of complications and death. However, the genetic variability of the influenza virus enables the emergence of drug-resistant mutations. This review focuses on the search for new compounds that are not analogous to sialic acid, aiming to inhibit the activity of viral neuraminidase in vitro, viral replication in cell cultures, or animal models. Influenza virus strains that have been reported in the literature present specific mutations that generate resistance to neuraminidase inhibitors. Since these inhibitors bear structural resemblance to sialic acid, the predominant location for these mutations is the enzyme’s active site. Consequently, exploring alternative compound classes becomes imperative to circumvent this interaction pattern. These compounds will introduce diverse molecular frameworks, serving as foundational structures for further development through rational drug design, thereby engendering novel antiviral agents targeting influenza. The potential prospects for developing novel influenza antivirals based on these findings are discussed.
Study on the Resilience Measurement of the New Energy Vehicle Industry Chain
Authors: Zhou Mi , Li Xiangdong , Shi Yangyan
Published: June 2024

In the context of economic globalization, industry chain resilience helps to improve the ability of the new energy vehicle industry to cope with external risks. Therefore, based on the CSCE principle, this paper utilizes the entropy weight method to construct a comprehensive evaluation index system for the resilience of the new energy vehicle industry chain, aiming to quantify its level of resilience. It is found that resistance is the most important (33.53%), adaptive resilience is the second most important (28.66%), and renewal (or resumption) is the least important (13.97%) in this evaluation index system. Therefore, it assists enterprises and government departments in making decisions on replenishing and strengthening the chain, reducing the risk of the industry chain, and realizing the high-quality development of the industry chain.
Applying GIS in Blue-Green Infrastructure Design in Urban Areas for Better Life Quality and Climate Resilience
Authors: Czyża Szymon , Kowalczyk Anna Maria
Published: June 2024

The expansion of urban centers and peri-urban zones significantly impacts both the natural world and human well-being, leading to issues such as increased air pollution, the formation of urban heat islands, and challenges in water management. The concept of multifunctional greening serves as a cornerstone, emphasizing the interconnectedness of ecological, social, and health-related factors. This study aimed to identify potential locations for three specific types of blue-green infrastructure (BGI): bioswales, infiltration trenches, and green bus stops. Leveraging geospatial datasets, Geographic Information System (GIS) technology, and remote sensing methodologies, this study conducted a comprehensive analysis and modeling of spatial information. Initial cartographic representations were developed to identify specific locations within Olsztyn, a city in Poland, deemed appropriate for the implementation of the designated blue-green infrastructure (BGI) components. Following this, these models were combined with two additional models created by the researchers: a surface urban heat island (SUHI) model and a demographic model that outlined the age structure of the city’s population. This synergistic approach resulted in the development of a detailed map, which identified potential locations for the implementation of blue-green infrastructure. This was achieved by utilizing vector data acquired with a precision of 1 m. The high level of detail on the map allows for an extremely accurate representation of geographical features and infrastructure layouts, which are essential for precise planning and implementation. This infrastructure is identified as a key strategy for strengthening ecosystem resilience, improving urban livability, and promoting public health and well-being.
The Role of Family Responsibility, Assertiveness, and Networking in Building Female Leadership Aspirations
Authors: Tjahjana David , Dwidienawati Diena , Rohayati Teti , Kenziro Kenziro , Wijaya Ferry , Nathania Sharren , Gandasari Dyah
Published: June 2024

Female leadership plays a crucial role in organizations through improving performance and productivity and allowing businesses to achieve better results. Unfortunately, the number of women in leadership positions in business is still behind that of men, and progress is relatively slow. This study aims to determine the effect of external factors, such as family responsibility and networking, and internal factors, such as assertiveness, on female leadership aspirations. The research used quantitative research methods, with the sample being women within the reproductive age range in Indonesia. This study involved 258 female respondents. The data analysis used the partial least squares structural equation model (PLS-SEM) approach, using SmartPLS. The results of this study show that family responsibility has a significant negative effect on female leadership aspirations, while assertiveness has a significant positive effect, as does networking.