bluuebunny/crossref_metadata_2025 huggingface dataset:
Flow Field Characteristics of Fugitive Dust from Grab Unloading in an Open Space
Authors: Zhang Hanzhong , Meng Wenjun , Zhang Biao , Yuan Yuan , Yin Xuan , Zhao Xiaoxia , Liang Weiqiang
Published: December 2023

Aiming at addressing the problem of dust generated when grab is unloaded, the flow field characteristics of fugitive dust in an open space were studied and reflected its unstable and complex nonlinear dynamic process. Using coal, sand, and flour as research objects, an experimental model and measurement system for grab unloading were built, and the dust diffusion range, diffusion speed and direction, settling time, dust concentration, and induced wind velocity at different measurement points were compared. The computational fluid dynamics–discrete element method (CFD-DEM) coupling method was adopted, the discrete phase model (DPM) of dust was established, the interaction of the particle, dust, and airflow fields during the unloading process of the grab was further studied, and the distribution and diffusion laws of the induced airflow and dust were obtained. The acquisition of flow field characteristics is of great significance for controlling and guiding the orderly deposition of dust.
A Modified Method for the Fredlund and Xing (FX) Model of Soil-Water Retention Curves
Authors: Niu Geng , Kong Liang , Miao Yusong , Li Xue , Chen Fanxiu
Published: December 2023

The soil-water retention curve (SWRC) is fundamental in presenting the hydromechanical characteristics of soils, which are closely connected with soil deformation, permeability, and shear strength. The Fredlund and Xing (FX) model accurately fits the SWRCs of different types of soils over a wide suction range. However, experimental comparisons of the fitting showed that the obtained parameters differ from the physical meanings assigned by Fredlund and Xing. To address this issue, the traditional FX model has been improved, resulting in the proposal of a two-step FX model. Firstly, the FX model is applied without taking the correction coefficient c(ψ) into account to fit the measured SWRC. The values for α, n, and m are then determined and substituted into the FX model to refit the experimental data. Finally, the last parameter Cr can be obtained. The curves resulting from these two steps have a good agreement with the experimental results, and the obtained parameters align better with their physical meanings.
Reinforcement Learning-Based Multi-Objective of Two-Stage Blocking Hybrid Flow Shop Scheduling Problem
Authors: Xu Ke , Ye Caixia , Gong Hua , Sun Wenjuan
Published: December 2023

Consideration of upstream congestion caused by busy downstream machinery, as well as transportation time between different production stages, is critical for improving production efficiency and reducing energy consumption in process industries. A two-stage hybrid flow shop scheduling problem is studied with the objective of the makespan and the total energy consumption while taking into consideration blocking and transportation restrictions. An adaptive objective selection-based Q-learning algorithm is designed to solve the problem. Nine state characteristics are extracted from real-time information about jobs, machines, and waiting processing queues. As scheduling actions, eight heuristic rules are used, including SPT, FCFS, Johnson, and others. To address the multi-objective optimization problem, an adaptive objective selection strategy based on t-tests is designed for making action decisions. This strategy can determine the optimization objective based on the confidence of the objective function under the current job and machine state, achieving coordinated optimization for multiple objectives. The experimental results indicate that the proposed algorithm, in comparison to Q-learning and the non-dominated sorting genetic algorithm, has shown an average improvement of 4.19% and 22.7% in the makespan, as well as 5.03% and 9.8% in the total energy consumption, respectively. The generated scheduling solutions provide theoretical guidance for production scheduling in process industries such as steel manufacturing. This contributes to helping enterprises reduce blocking and transportation energy consumption between upstream and downstream.
From Religious Bubble to Interreligious Dialogue: A Personal Story of Transformation
Authors: Hulsman Cornelis
Published: December 2023

This paper argues that interreligious dialogue through study and friendships across the religious divide makes participants less susceptible to religious and cultural misinformation that is often used to maintain social bubbles, in which members draw clear boundaries between “us” and “them”. Differences between social groups can culminate in a struggle between “good” and “evil” that can escalate into tension and violence. Preventing tensions and conflicts requires respect for differences, willingness to engage in dialogue, and a sound understanding of what religion is and the historical processes that have determined its development, distinguishing between empirical facts and images to which believers adhere. Because the author is a Dutch sociologist turned journalist from a conservative Christian family involved in interreligious dialogue in the Netherlands, Israel, and Egypt, the literature review presents contemporary religious developments in all three countries. The literature review is flanked by the author’s personal narrative on the events that changed his views on truth and spirituality, making him more aware of the commonalities between peoples of different beliefs and leading him to a lifelong commitment to interreligious and intercultural dialogue.
A Moral Fine-Tuning Argument
Authors: Jakobsen Martin
Published: December 2023

This paper develops Mark D. Linville’s brief description of “a sort of moral fine-tuning argument”. I develop the argument in four ways: I unpack the argument and give it a clear formulation, I unpack the theistic explanation of why a somewhat reliable moral capacity is expected, I point to the significance of not seeking to explain a perfect moral capacity, and I put the argument up against the recent work on non-theistic moral epistemology by Derek Parfit, David Enoch, and Erik Wielenberg.
Indigenous Peoples and Land-Based Disputes: Paraguay and the Paĩ Tavyterã
Authors: Ioris Antonio Augusto Rossotto
Published: December 2023

This article discusses some of the long-term tendencies of the Paraguayan political economy, focusing, in particular, on impacts on indigenous peoples and, because of the ongoing agribusiness expansion in the northeast of the country, on the Paĩ Tavyterã nation. This analysis is warranted because of the growing recognition of the importance of land-related disputes affecting indigenous peoples, particularly in countries such as Paraguay that rely heavily on agribusiness exports and on the exploitation of natural resources. It is based on more than six years of research dedicated to the land struggles of the Paĩ Tavyterã (and members of the same ethnic group in Brazil, called Guarani-Kaiowa). Instead of a comparative study, this is a relational storytelling text that draws insights from various actors, communities and situations that were obtained through a qualitative and participative methodology, involving indigenous communities as co-participants and co-investigators in the study. Empirical results demonstrate that, despite the fact that Paraguay is a major exporter of agribusiness commodities the accumulation of multiple forms of subtraction is a prevailing geographical force. The reaction of indigenous peoples is in the form of anti-subtraction. The main implication of this research is that the process of decolonisation is, first and foremost, an anti-subtraction movement that aims to revert the deficit caused by the systemic subtraction of socio-economic and socio-ecological opportunities.
Physics-Informed Deep Learning Inversion with Application to Noisy Magnetotelluric Measurements
Authors: Liu Wei , Wang He , Xi Zhenzhu , Wang Liang
Published: December 2023

Despite demonstrating exceptional inversion production for synthetic data, the application of deep learning (DL) inversion methods to invert realistic magnetotelluric (MT) measurements, which are inevitably contaminated by noise in acquisition, poses a significant challenge. Hence, to facilitate DL inversion for realistic MT measurements, this work explores developing a noise-robust MT DL inversion method by generating targeted noisy training datasets and constructing a physics-informed neural network. Different from most previous works that only considered the noise of one fixed distribution and level, we propose three noise injection strategies and compare their combinations to mitigate the adverse effect of measurement noise on MT DL inversion results: (1) add synthetic relative noise obeying Gaussian distribution; (2) propose a multiwindow Savitzky–Golay (MWSG) filtering scheme to extract potential and possible noise from the target field data and then introduce them into training data; (3) create an augmented training dataset based on the former two strategies. Moreover, we employ the powerful Swin Transformer as the backbone network to construct a U-shaped DL model (SwinTUNet), based on which a physics-informed SwinTUNet (PISwinTUNet) is implemented to further enhance its generalization ability. In synthetic examples, the proposed noise injection strategies demonstrate impressive inversion effects, regardless of whether they are contaminated by familiar or unfamiliar noise. In a field example, the combination of three strategies drives PISwinTUNet to produce considerably faithful reconstructions for subsurface resistivity structures and outperform the classical deterministic Occam inversions. The experimental results show that the proposed noise-robust DL inversion method based on the noise injection strategies and physics-informed DL architecture holds great promise in processing MT field data.
Glacier Surface Velocity Variations in the West Kunlun Mts. with Sentinel-1A Image Feature-Tracking (2014–2023)
Authors: Wang Zhenfeng , Gao Tanguang , Kang Yulong , Guo Wanqin , Jiang Zongli
Published: December 2023

Glacier velocity is a crucial parameter in understanding glacier dynamics and mass balance, especially in response to climate change. Despite numerous studies on glaciers in the West Kunlun Mts., there is still insufficient knowledge about the details of inter- and intra-annual velocity changes under global warming. This study analyzed the glacier velocity changes in the West Kunlun Mts. using Sentinel-1A satellite data. Our results revealed that: (1) The velocity of glaciers across the region shows an increasing trend from 2014 to 2023. (2) Five glaciers were found to have been surged during the study period, among which two of them were not reported before. (3) The surges in the study region were potentially controlled through a combination of hydrological and thermal mechanisms. (4) The glacier N2, Duofeng Glacier, and b2 of Kunlun Glacier exhibit higher annual velocities (32.82 m a−1) compared to surging glaciers in quiescent phases (13.22 m a−1), and were speculated as advancing or fast-flowing glaciers.
High-Temporal-Resolution Rock Slope Monitoring Using Terrestrial Structure-from-Motion Photogrammetry in an Application with Spatial Resolution Limitations
Authors: Butcher Bradford , Walton Gabriel , Kromer Ryan , Gonzales Edgard , Ticona Javier , Minaya Armando
Published: December 2023

Research on high-temporal-resolution rock slope monitoring has tended to focus on scenarios where spatial resolution is also high. Accordingly, there is a lack of understanding of the implications for rock slope monitoring results in cases with high temporal resolution but low spatial resolution, which is the focus of this study. This study uses automatically captured photos taken at a daily frequency by five fixed-base cameras in conjunction with multi-epoch Structure-from-Motion (SfM) photogrammetric processing techniques to evaluate changes in a rock slope in Majes, Arequipa, Peru. The results of the monitoring campaign demonstrate that there are potential issues with the common notion that higher frequency change detection is always superior. For lower spatial resolutions or when only large changes are of concern, using a high-frequency monitoring method may cause small volume changes that eventually aggrade into larger areas of change to be missed, whereas most of the total volume change would be captured with lower-frequency monitoring intervals. In this study, daily change detection and volume calculation resulted in a cumulative rockfall volume of 4300 m3 over about 14 months, while change detection and volume calculation between dates at the start and end of the 14-month period resulted in a total rockfall volume of 12,300 m3. High-frequency monitoring is still the most accurate approach for evaluating slope evolution from a rockfall frequency and size distribution perspective, and it allows for the detection of short accelerations and pre-failure deformations, but longer-term comparison intervals may be required in cases where spatial resolution is low relative to temporal resolution to more accurately reflect the total volume change of a given rock slope over a long period of time.
Structural and Geomechanical Analysis of Natural Caves and Rock Shelters: Comparison between Manual and Remote Sensing Discontinuity Data Gathering
Authors: Benrabah Abdelmadjid , Senent Domínguez Salvador , Carrera-Ramírez Fernando , Álvarez-Alonso David , de Andrés-Herrero María , Jorda Bordehore Luis
Published: December 2023

The stability of many shallow caves and rock shelters relies heavily on understanding rock discontinuities, such as stratification, faults, and joints. Analyzing these discontinuities and determining their orientations and dispersion are crucial for assessing the overall stability of the cave or shelter. Traditionally, this analysis has been conducted manually using a compass with a clinometer, but it has certain limitations, as only fractures located in accessible areas like the lower part of cave walls and entrances are visible and can be assessed. Over the past decade, remote sensing techniques like LiDAR and photogrammetry have gained popularity in characterizing rocky massifs. These techniques provide 3D point clouds and high-resolution images of the cave or shelter walls and ceilings. With these data, it becomes possible to perform a three-dimensional reconstruction of the cavity and obtain important parameters of the discontinuities, such as orientation, spacing, persistence, or roughness. This paper presents a comparison between the geomechanical data obtained using the traditional manual procedures (compass readings in accessible zones) and a photogrammetric technique called Structure from Motion (SfM). The study was conducted in two caves, namely, the Reguerillo Cave (Madrid) and the Cova dos Mouros (Lugo), along with two rock shelters named Abrigo de San Lázaro and Abrigo del Molino (Segovia). The results of the study demonstrate an excellent correlation between the geomechanical parameters obtained from both methods. Indeed, the combination of traditional manual techniques and photogrammetry (SfM) offers significant advantages in developing a more comprehensive and realistic discontinuity census.
Characterization of BDS Multipath Effect Based on AT-Conv-LSTM Network
Authors: Sun Jie , Tang Zuping , Zhou Chuang , Wei Jiaolong
Published: December 2023

Multipath effects are the most challenging error sources for the Global Navigation Satellite System receiver, affecting observation quality and positioning accuracy. Due to the non-linear and time-varying nature, multipath error is difficult to process. Previous studies used a homogeneous indicator to characterize multipath effects and only revealed the temporal or spatial correlations of the multipath, resulting in limited correction performance. In this study, we consider the code multipath to be influenced not only by the elevation and azimuth angle of certain stations to satellites but also to be related to satellite characteristics such as nadir angle. Hence, azimuth angle, elevation angle, nadir angle and carrier-to-noise power density ratio are taken as multiple indicators to characterize the multipath significantly. Then, we propose an Attention-based Convolutional Long Short-Term Memory (AT-Conv-LSTM) that fully exploits the spatiotemporal correlations of multipath derived from multiple indicators. The main processing procedures using AT-Conv-LSTM are given. Finally, the AT-Conv-LSTM is applied to a station for 16 consecutive days to verify the multipath mitigation effectiveness. Compared with sidereal filtering, multipath hemispherical map (MHM) and trend-surface analysis-based MHM, the experimental results show that using AT-Conv-LSTM can decrease the root mean square error and mean absolute error values of the multipath error more than 60% and 13%, respectively. The proposed method can correct the code multipath to centimeter level, which is one order of magnitude lower than the uncorrected code multipath. Therefore, the proposed AT-Conv-LSTM network could be used as a powerful alternative tool to realize multipath reduction and will be of wide practical value in the fields of standard and high-precision positioning services.
Improving Land Use and Land Cover Information of Wunbaik Mangrove Area in Myanmar Using U-Net Model with Multisource Remote Sensing Datasets
Authors: Maung Win Sithu , Tsuyuki Satoshi , Guo Zhiling
Published: December 2023

Information regarding land use and land cover (LULC) is essential for regional land and forest management. The contribution of reliable LULC information remains a challenge depending on the use of remote sensing data and classification methods. This study conducted a multiclass LULC classification of an intricate mangrove ecosystem using the U-Net model with PlanetScope and Sentinel-2 imagery and compared it with an artificial neural network model. We mainly used the blue, green, red, and near-infrared bands, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) of each satellite image. The Digital Elevation Model (DEM) and Canopy Height Model (CHM) were also integrated to leverage the model performance in mixed ecosystems of mangrove and non-mangrove forest areas. Through a labeled image created from field ground truth points, the models were trained and evaluated using the metrics of overall accuracy, Intersection over Union, F1 score, precision, and recall of each class. The results demonstrated that the combination of PlanetScope bands, spectral indices, DEM, and CHM yielded superior performance for both the U-Net and ANN models, achieving a higher overall accuracy (94.05% and 92.82%), mean IoU (0.82 and 0.79), mean F1 scores (0.94 and 0.93), recall (0.94 and 0.93), and precision (0.94). In contrast, models utilizing the Sentinel-2 dataset showed lower overall accuracy (86.94% and 82.08%), mean IoU (0.71 and 0.63), mean F1 scores (0.87 and 0.81), recall (0.87 and 0.82), and precision (0.87 and 0.81). The best-classified image, which was produced by U-Net using the PlanetScope dataset, was exported to create an LULC map of the Wunbaik Mangrove Area in Myanmar.
Can the Accuracy of Fine-Resolution Precipitation Products Be Assessed from the Surrounding Water Balance and Drought Chain (WBDC) in the Qinghai–Tibetan Plateau?
Authors: Li Rui , Shi Jiancheng , Pan Jinmei , Yan Nana , Zhao Tianjie , Zhang Qingtao , Wang Yu
Published: December 2023

The Qinghai–Tibetan Plateau (QTP), which has a unique and severe environment, suffers from the absence of rainfall gauges in western arid land. Using different precipitation products in this region would easily lead to contradictory results. To evaluate nine fine-resolution precipitation products in the QTP, we propose a “down to top” methodology, based on water balance and drought chain, by forecasting two accuracy assessment indices—multi-year precipitation bias and precipitation correlation. We assessed the biases of all products in the Jinsha–Yalong, Yellow, Heihe, Yangtze, Yarlung Zangbo catchments and interior drainage areas. And we assessed gauge-based correlation of precipitation products, based on the correlations between precipitation product-based effective drought index (EDI), Soil Moisture Active Passive (SMAP)-based soil moisture anomaly, and the moderate-resolution imaging spectroradiometer (MODIS)-based normalized difference vegetation index (NDVI) anomaly (R = 0.712, R = 0.36, and R = 0.785, respectively) for cross-sectional rainfall observations on the Tibetan Plateau in 2018. The results showed that ERA5-Land and IMERG merged precipitation dataset (EIMD) can efficiently close the water budget at the catchment scale. Moreover, the EIMD-based EDI exhibited the best performance in correlation with both the SMAP-based soil moisture anomaly and MODIS-based NDVI anomaly for the three main herbaceous species areas—Kobresia pygmaea meadow, Stipa purpurea steppe, and Carex moorcroftii steppe. Overall, we find that EIMD is the most accurate among the nine products. The annual average precipitation (2001–2018) was determined to be 568.16 mm in the QTP. Our assessment methodology has a remote sensing basis with low cost and can be used for other arid lands in the future.
A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data
Authors: Fatima Rimsha , Khan Muhammad Hassan , Nisar Muhammad Adeel , Doniec Rafał , Farid Muhammad Shahid , Grzegorzek Marcin
Published: December 2023

This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research community to collect kinematic and kinetic data to analyze the gait. The most crucial step for gait analysis is to find the set of appropriate features from continuous time series data to accurately represent human locomotion. This paper presents a systematic assessment of numerous feature extraction techniques. In particular, three different feature encoding techniques are presented to encode multimodal time series sensory data. In the first technique, we utilized eighteen different handcrafted features which are extracted directly from the raw sensory data. The second technique follows the Bag-of-Visual-Words model; the raw sensory data are encoded using a pre-computed codebook and a locality-constrained linear encoding (LLC)-based feature encoding technique. We evaluated two different machine learning algorithms to assess the effectiveness of the proposed features in the encoding of raw sensory data. In the third feature encoding technique, we proposed two end-to-end deep learning models to automatically extract the features from raw sensory data. A thorough experimental evaluation is conducted on four large sensory datasets and their outcomes are compared. A comparison of the recognition results with current state-of-the-art methods demonstrates the computational efficiency and high efficacy of the proposed feature encoding method. The robustness of the proposed feature encoding technique is also evaluated to recognize human daily activities. Additionally, this paper also presents a new dataset consisting of the gait patterns of 42 individuals, gathered using IMU sensors.
Knowledge Distillation for Traversable Region Detection of LiDAR Scan in Off-Road Environments
Authors: Kim Nahyeong , An Jhonghyun
Published: December 2023

In this study, we propose a knowledge distillation (KD) method for segmenting off-road environment range images. Unlike urban environments, off-road terrains are irregular and pose a higher risk to hardware. Therefore, off-road self-driving systems are required to be computationally efficient. We used LiDAR point cloud range images to address this challenge. The three-dimensional (3D) point cloud data, which are rich in detail, require substantial computational resources. To mitigate this problem, we employ a projection method to convert the image into a two-dimensional (2D) image format using depth information. Our soft label-based knowledge distillation (SLKD) effectively transfers knowledge from a large teacher network to a lightweight student network. We evaluated SLKD using the RELLIS-3D off-road environment dataset, measuring the performance with respect to the mean intersection of union (mIoU) and GPU floating point operations per second (GFLOPS). The experimental results demonstrate that SLKD achieves a favorable trade-off between mIoU and GFLOPS when comparing teacher and student networks. This approach shows promise for enabling efficient off-road autonomous systems with reduced computational costs.
A Response-Feedback-Based Strong PUF with Improved Strict Avalanche Criterion and Reliability
Authors: Zhu Baokui , Jiang Xiaowen , Huang Kai , Yu Miao
Published: December 2023

Physical Unclonable Functions (PUFs) are significant in building lightweight Internet of Things (IoT) authentication protocols. However, PUFs are susceptible to attacks such as Machine-Learning(ML) modeling and statistical attacks. Researchers have conducted extensive research on the security of PUFs; however, existing PUFs do not always possess good statistical characteristics and few of them can achieve a balance between security and reliability. This article proposes a strong response-feedback PUF based on the Linear Feedback Shift Register (LFSR) and the Arbiter PUF (APUF). This structure not only resists existing ML modeling attacks but also exhibits good Strict Avalanche Criterion (SAC) and Generalized Strict Avalanche Criterion (GSAC). Additionally, we introduce a Two-Level Reliability Improvement (TLRI) method that achieves 95% reliability with less than 35% of the voting times and single-response generation cycles compared to the traditional pure majority voting method.
A Fast Attention-Guided Hierarchical Decoding Network for Real-Time Semantic Segmentation
Authors: Hu Xuegang , Feng Jing
Published: December 2023

Semantic segmentation provides accurate scene understanding and decision support for many applications. However, many models strive for high accuracy by adopting complex structures, decreasing the inference speed, and making it challenging to meet real-time requirements. Therefore, a fast attention-guided hierarchical decoding network for real-time semantic segmentation (FAHDNet), which is an asymmetric U-shaped structure, is proposed to address this issue. In the encoder, we design a multi-scale bottleneck residual unit (MBRU), which combines the attention mechanism and decomposition convolution to design a parallel structure for aggregating multi-scale information, making the network perform better at processing information at different scales. In addition, we propose a spatial information compensation (SIC) module that effectively uses the original input to make up for the spatial texture information lost during downsampling. In the decoder, the global attention (GA) module is used to process the feature map of the encoder, enhance the feature interaction in the channel and spatial dimensions, and enhance the ability to mine feature information. At the same time, the lightweight hierarchical decoder integrates multi-scale features to better adapt to different scale targets and accurately segment objects of different sizes. Through experiments, FAHDNet performs outstandingly on two public datasets, Cityscapes and Camvid. Specifically, the network achieves 70.6% mean intersection over union (mIoU) at 135 frames per second (FPS) on Cityscapes and 67.2% mIoU at 335 FPS on Camvid. Compared to the existing networks, our model maintains accuracy while achieving faster inference speeds, thus enhancing its practical usability.
A Simple Microwave Imaging System for Food Product Inspection through a Symmetry-Based Microwave Imaging Approach
Authors: Bellizzi Gennaro , Buzzin Alessio , Crocco Lorenzo , Mastrandrea Antonio , Zeni Noemi , Zumbo Sabrina , Cavagnaro Marta
Published: December 2023

In the food industry, there is a growing demand for cost-effective methods for the inline inspection of food items able to non-invasively detect small foreign bodies that may have contaminated the product during the production process. Microwave imaging may be a valid alternative to the existing technologies, thanks to its inherently low-cost and its capability of sensing low-density contaminants. In this paper, a simple microwave imaging system specifically designed to enable the inspection of a large variety of food products is presented. The system consists of two circularly loaded antipodal Vivaldi antennas with a very large operative band, from 1 to 15 GHz, thus allowing a suitable spatial resolution for different food products, from mostly fatty to high water-content foods. The antennas are arranged in such a way as to collect a signal that can be used to exploit a recently proposed real-time microwave imaging strategy, leveraging the inherent symmetries that usually characterize food items. The system is experimentally characterized, and the achieved results compare favorably with the design specifications and numerical simulations. Relying on these positive results, the first experimental proof of the effectiveness of the entire system is presented confirming its efficacy.
A Safe Heuristic Path-Planning Method Based on a Search Strategy
Authors: Yan Xiaozhen , Zhou Xinyue , Luo Qinghua
Published: December 2023

In industrial production, it is very difficult to make a robot plan a safe, collision-free, smooth path with few inflection points. Therefore, this paper presents a safe heuristic path-planning method based on a search strategy. This method first expands the scope of the search node, then calculates the node state based on the search strategy, including whether it is a normal or dangerous state, and calculates the danger coefficient of the corresponding point to select the path with a lower danger coefficient. At the same time, the optimal boundary is obtained by incorporating the environmental facilities, and the optimal path between the starting point, the optimal boundary point and the end point is obtained. Compared to the traditional A-star algorithm, this method achieved significant improvements in various aspects such as path length, execution time, and path smoothness. Specifically, it reduced path length by 2.89%, decreased execution time by 13.98%, and enhanced path smoothness by 93.17%. The resulting paths are more secure and reliable, enabling robots to complete their respective tasks with reduced power consumption, thereby mitigating the drain on robot batteries.
ScanGuard-YOLO: Enhancing X-ray Prohibited Item Detection with Significant Performance Gains
Authors: Huang Xianning , Zhang Yaping
Published: December 2023

To address the problem of low recall rate in the detection of prohibited items in X-ray images due to the severe object occlusion and complex background, an X-ray prohibited item detection network, ScanGuard-YOLO, based on the YOLOv5 architecture, is proposed to effectively improve the model’s recall rate and the comprehensive metric F1 score. Firstly, the RFB-s module was added to the end part of the backbone, and dilated convolution was used to increase the receptive field of the backbone network to better capture global features. In the neck section, the efficient RepGFPN module was employed to fuse multiscale information from the backbone output. This aimed to capture details and contextual information at various scales, thereby enhancing the model’s understanding and representation capability of the object. Secondly, a novel detection head was introduced to unify scale-awareness, spatial-awareness, and task-awareness altogether, which significantly improved the representation ability of the object detection heads. Finally, the bounding box regression loss function was defined as the WIOUv3 loss, effectively balancing the contribution of low-quality and high-quality samples to the loss. ScanGuard-YOLO was tested on OPIXray and HiXray datasets, showing significant improvements compared to the baseline model. The mean average precision (mAP@0.5) increased by 2.3% and 1.6%, the recall rate improved by 4.5% and 2%, and the F1 score increased by 2.3% and 1%, respectively. The experimental results demonstrate that ScanGuard-YOLO effectively enhances the detection capability of prohibited items in complex backgrounds and exhibits broad prospects for application.
Application of Data Fusion in Traditional Chinese Medicine: A Review
Authors: Huang Rui , Ma Shuangcheng , Dai Shengyun , Zheng Jian
Published: December 2023

Traditional Chinese medicine is characterized by numerous chemical constituents, complex components, and unpredictable interactions among constituents. Therefore, a single analytical technique is usually unable to obtain comprehensive chemical information. Data fusion is an information processing technology that can improve the accuracy of test results by fusing data from multiple devices, which has a broad application prospect by utilizing chemometrics methods, adopting low-level, mid-level, and high-level data fusion techniques, and establishing final classification or prediction models. This paper summarizes the current status of the application of data fusion strategies based on spectroscopy, mass spectrometry, chromatography, and sensor technologies in traditional Chinese medicine (TCM) in light of the latest research progress of data fusion technology at home and abroad. It also gives an outlook on the development of data fusion technology in TCM analysis to provide references for the research and development of TCM.
Investigation on the Impact of Excitation Amplitude on AFM-TM Microcantilever Beam System’s Dynamic Characteristics and Implementation of an Equivalent Circuit
Authors: Song Peijie , Li Xiaojuan , Cui Jianjun , Chen Kai , Chu Yandong
Published: December 2023

Alterations in the dynamical properties of an atomic force microscope microcantilever beam system in tapping mode can appreciably impact its measurement precision. Understanding the influence mechanism of dynamic parameter changes on the system’s motion characteristics is vital to improve the accuracy of the atomic force microscope in tapping mode (AFM-TM). In this study, we categorize the mathematical model of the AFM-TM microcantilever beam system into systems 1 and 2 based on actual working conditions. Then, we analyze the alterations in the dynamic properties of both systems due to external excitation variations using bifurcation diagrams, phase trajectories, Lyapunov indices, and attraction domains. The numerical simulation results show that when the dimensionless external excitation g < 0.183, the motion state of system 2 is period 1. When g < 0.9, the motion state of system 1 is period 1 motion. Finally, we develop the equivalent circuit model of the AFM-TM microcantilever beam and perform related software simulations, along with practical circuit experiments. Our experimental results indicate that the constructed equivalent circuit can effectively analyze the dynamic characteristics of the AFM-TM microcantilever beam system in the presence of complex external environmental factors. It is observed that the practical circuit simulation attenuates high-frequency signals, resulting in a 31.4% reduction in excitation amplitude compared to numerical simulation results. This provides an essential theoretical foundation for selecting external excitation parameters for AFM-TM cantilever beams and offers a novel method for analyzing the dynamics of micro- and nanomechanical systems, as well as other nonlinear systems.
Political Beliefs as a Moderator for Predicting Environmental Citizenship through Environmental Education and Environmental Literacy
Authors: Poškus Mykolas Simas
Published: December 2023

Promoting environmental citizenship can be a strong positive force toward addressing our current environmental issues. Informed individuals that act in pro-environmental ways both publicly and privately, as well as engage in civic action aimed at addressing environmental issues, are a substantial force for positive change. However, there is a well-known political divide between the political left and political right, where left-leaning individuals tend to engage more in environmental action that right-leaning individuals. The present study explores whether environmental education and environmental literacy might hold the key for right-wing participation in environmental citizenship. A representative sample of 700 Lithuanian emerging adults (20–39 years of age, mean age 30.6 years, 50% female) was used in the present study. Participants filled in measures of their political orientation, environmental citizenship, environmental literacy, and environmental (self-)education. The results showed that, as expected, left-leaning views were positively associated with environmental citizenship, but when interactions between political orientation and environmental literacy as well as environmental (self-)education were introduced, right-leaning views tended to increase the association between environmental citizenship and environmental literacy as well as environmental (self-)education. The results show a promising outlook toward using quality education to promote environmental citizenship among right-leaning individuals.
“What I Want versus What I Will Agree to”—An Analysis of the Views of Ukrainian Refugees toward Work
Authors: Walawender Paweł , Liszka Damian , Szczygieł Elżbieta
Published: December 2023

Russia’s armed attack on Ukraine on 24 February 2022 started one of the worst refugee crises of the 21st century. It caused involuntary migrations of Ukrainians to other countries, mainly to Europe, and caused the refugees to face the challenge of integrating with the host countries’ labor markets. The aim of this study was to analyze the views of the Ukrainian refugees who migrated to two European countries: Poland and Hungary. We took into account self-esteem, social support, good job expectations, and submissiveness in the labor market. The data were obtained via a survey method using the modified CAPI (Computer-Assisted Personal Interview) technique between 21 November and 20 December 2022 from 807 adult Ukrainian refugees. Results show that disability fosters lower self-esteem, self-assessment of resources, and job demands. Among those who have children, the mean value of self-esteem was higher than in the group without any children. No statistically significant differences were found in the area of professional work. According to the estimation of structural model results, expectations of a good job have a significant, negative effect on the tendency toward submissiveness. The assessment of personal resources and the level of self-esteem have a significant and positive effect on the expectations of a good job. Both a higher level of personal resources and a higher level of self-esteem resulted in higher good job expectations. The assessment of personal resources also has a positive effect on the level of self-esteem. We also found a direct relationship between personal resources, self-esteem, and the tendency toward submissiveness.
Jones Fracture in the National Football League
Authors: Luo Emily J. , Anastasio Albert T. , Stauffer Taylor , Grant Caitlin , Wu Christine J. , Wu Kevin A. , Kaplan Samantha , Lau Brian C.
Published: December 2023

Background: Jones fracture, or proximal fifth metatarsal fracture, is a common injury in National Football League (NFL)-caliber athletes. Combine draft performance can greatly impact the long-term success of these athletes, and substantial emphasis has been placed on early return to play (RTP) and the minimization of post-operative complications after Jones fracture in these athletes. To date, no study has specifically described the treatment and outcomes of this injury specifically in NFL-caliber players, considering factors relevant to this unique population. Thus, the purpose of this review is to delve into Jones fracture in NFL-caliber athletes, evaluating the diagnostic, treatment, and RTP considerations. Methods: We searched Medline (PubMed), Embase (Elsevier), Scopus (Elsevier), and SPORTDiscus (EBSCOhost) for the concept of Jones fractures in the NFL. Using the PRISMA guidelines, a team of three reviewers conducted abstract screenings, full-text screenings, and the extraction of studies describing Jones fractures specifically in the NFL. Results: Of the 1911 studies identified, 6 primary retrospective studies met the inclusion and exclusion criteria. The heterogeneity of the outcome reporting precluded a meta-analysis; thus, a qualitative review of manuscripts describing Jones fracture was carried out. Classification, diagnosis, and treatment considerations, RTP statistics and outcomes, and complications were discussed. Amongst the primary studies, there were 285 Jones fractures, all athletes were able to RTP, and the average time to RTP ranged from 6 weeks to 27 weeks. For complications, with operative treatment, the refracture rate ranged from 4 to 12%, and incomplete healing ranged from 7 to 50%. RTP was 15 weeks for refractures. There were no patient-reported outcomes. Conclusions: The vast majority of Zone 2/3 Jones fractures are treated with IM screw fixation with or without adjunctive orthobiologics, such as bone marrow aspirate concentrate, in NFL-caliber athletes. The six major series investigating outcomes after the operative treatment of Jones fractures in NFL players reveal very positive findings overall with regard to RTP, reoperation, and career continuation.
A Comparative Study of Stem Rot Severity in Mature Deciduous Trees in Latvia
Authors: Liepiņš Jānis , Liepiņš Kaspars , Lazdiņš Andis , Matisons Roberts , Jansons Āris
Published: December 2023

Efforts to enhance carbon storage in forest ecosystems through policy and management decisions rely on accurate forest biomass assessments. However, most forest inventories consider tree mortality the only form of aboveground biomass loss, overlooking other important factors, such as wood decay in living trees. In this study, using linear mixed-effects models, we delve into the sustainability of mature and over-mature deciduous forests in Latvia by conducting a comprehensive analysis of stem rot severity, identifying species for which the impact of stem rot on their carbon stock reduction was most significant. The analysis focused on determining the proportion of discolored wood, decomposed wood, and hollow spaces within the stems of 190 living deciduous trees commonly found in hemiboreal forests. The study reveals a greater extent of stem rot and more extensive decay in Populus tremula trees than in Betula spp., Alnus glutinosa, and Alnus incana. It emphasizes the influence of tree species, age, and diameter at breast height on stem rot proportions. The stump rot area significantly predicts the amount of decomposed and discolored wood within the stem. The study provides valuable insights for sustainable forestry practices and highlights challenges in estimating stem rot severity, emphasizing the need for comprehensive diagnostic methods.
Investigation of the Energy-Saving Potential of Buildings with Radiative Roofs and Low-E Windows in China
Authors: Jia Lin-Rui , Li Qing-Yun , Yang Jie , Han Jie , Lee Chi-Chung , Chen Jian-Heng
Published: December 2023

This study develops a model for buildings with a cooling roof, walls, and low-emissivity (Low-E) windows. This model is verified through experimental analysis. The cooling demands of standard buildings and cooling buildings are compared, and the energy-saving potentials of cooling buildings are analysed. It is found that compared to standard buildings, cooling buildings exhibit superior cooling performances attributable to the application of cooling materials. Considering Hong Kong’s weather data, the indoor temperature of cooling buildings can be sub-ambient. The cooling demands of cooling buildings are decreased from 75 W/m2 to 30 W/m2, indicating a 60% energy-saving potential. The nationwide cooling demand for a standard building across China is approximately 95.7 W/m2, whereas the nationwide summer average cooling demand for cooling buildings is 52.7 W/m2. Moreover, the cooling performance of a cooling roof is adversely affected by hot and humid weather conditions, resulting in lower temperature drops in southern regions compared to northern regions. However, the nationwide temperature drop across China can still be 1.6 °C, demonstrating promising cooling potentials. For the Low-E windows, the temperature can also be sub-ambient, with a nationwide average temperature drop of 1.7 °C. Therefore, the use of Low-E windows across China can also significantly contribute to energy savings for indoor cooling. Overall, the results of this study show that cooling buildings have high energy-saving potential under various climates. The proposed model can provide a reliable tool to facilitate relevant cooling evaluation by stakeholders, thereby benefiting the popularization of this technology.
The Impact of Unconditional Priority for Escorted Vehicles in Traffic Networks on Sustainable Urban Mobility
Authors: Vujić Miroslav , Gregurić Martin , Dedić Luka , Nečoska Daniela Koltovska
Published: December 2023

Efficient traffic systems control in large cities, and the complex traffic management of signalized intersections is a challenging task, particularly when dealing with high traffic volumes. The city of Zagreb faces this challenge, as all administrative and governmental institutions are in the historic part of the city, and routes for escorted vehicles have a significant impact on the traffic network. This paper addresses the issue of the impact of unconditional priority for escorted vehicles on the energy efficiency of the urban traffic network in the city of Zagreb. The traffic network model is developed using the PTV Vissim microsimulation software. The evaluation was conducted with nodes (delay, queue length, and number of stops) and network evaluation parameters (CO2 emission, NOx emission, PM10 emission, and fuel consumption). The results show that unconditional priority has minimal impact on energy consumption and exhaust emission in the observed scenario. This is a significant result considering all actions that must be undertaken to manage the passage of the escorted vehicles through the traffic network.
Water Nutrient Management in Soilless Plant Cultivation versus Sustainability
Authors: Mielcarek Artur , Kłobukowska Karolina , Rodziewicz Joanna , Janczukowicz Wojciech , Bryszewski Kamil Łukasz
Published: December 2023

Under-cover soilless cultivation is an important technique of crop production. Due to the lack of contact with soil and precipitation, the root system of crops grown must be provided with water and all necessary nutrients in the form of a solution (nutrient medium). This nutrient medium needs to be fed in excess to ensure proper plant development and the expected qualitative and quantitative parameters of the crop yield, which means that in the case of, e.g., tomato cultivation, 20–80% of the supplied medium must be removed from the root system and managed. Uncontrolled discharge of this drainage water poses a significant threat to the environment, causing contamination of surface waters and groundwaters. The article presents the latest solutions for drainage water management as well as technologies and systems that allow saving water and fertilizers, and thus recovering elements. It also characterizes methods deployed for the treatment of overflow that enable its recirculation, its re-use for fertilization of other less demanding crops (including soil crops), and its final management in the form of a discharge to the natural environment. Due to depleting resources of adequate-quality water, increase in the prices of mineral fertilizers, and depletion of natural phosphorus deposits, the future trends in water and nutrients management in this cropping system aim at closing circuits of drainage water and recovering elements before their discharge into the natural environment. These measures are expected not only to protect the natural environment but also to reduce the costs of crop production.