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Unsupervised domain adaptation (UDA) focuses on transferring knowledge from the labeled source domain to the unlabeled target domain, reducing the costs of manual data labeling. The main challenge in UDA is bridging the substantial feature distribution gap between the source and target domains. To address this, we propose Polarized Attention Network Domain Adaptation (PANDA), a novel approach that leverages Polarized Self-Attention (PSA) to capture the intricate relationships between the source and target domains, effectively mitigating domain discrepancies. PANDA integrates both channel and spatial information, allowing it to capture detailed features and overall structures simultaneously. Our proposed method significantly outperforms current state-of-the-art unsupervised domain adaptation (UDA) techniques for semantic segmentation tasks. Specifically, it achieves a notable improvement in mean intersection over union (mIoU), with a 0.2% increase for the GTA→Cityscapes benchmark and a substantial 1.4% gain for the SYNTHIA→Cityscapes benchmark. As a result, our method attains mIoU scores of 76.1% and 68.7%, respectively, which reflect meaningful advancements in model accuracy and domain adaptation performance. Full article

(This article belongs to the Special Issue Digital Signal and Image Processing for Multimedia Technology)

10 pages, 701 KiB

Open AccessArticle

A PCR-Based Approach for Early Diagnosis of Head and Neck Aspergillosis: A Pilot Study

by Thaís Ellen Chaves Gomes, Victor Coutinho Bastos, Douglas Boniek, Mário Romañach, Fernanda Faria Rocha, Roberta Rayra Martins Chaves and Ricardo Santiago Gomez

Genes 2024, 15(11), 1428; https://doi.org/10.3390/genes15111428 (registering DOI) - 31 Oct 2024

Abstract

Abstract: Background: Aspergillosis is a fungal disease caused by the inhalation of fungal spores of the genus Aspergillus spp. This fungus mainly affects the lungs but can spread and infect the maxillofacial region through the bloodstream or inoculation of the fungus after extraction or endodontic treatment, especially in the upper posterior teeth. The disease has nonspecific clinical manifestations that hinder its early diagnosis. Although the Polymerase Chain Reaction (PCR) technique holds promise as a diagnostic tool for aspergillosis, anatomopathological analysis services do not routinely adopt this method. Objectives: Therefore, the present study aimed to evaluate the applicability of PCR and standardise the techniques of preparation of biological samples for the detection of the three species: Aspergillus niger, Aspergillus fumigatus and Aspergillus flavus. Methods: Six samples of formalin-fixed, paraffin-embedded tissue (FFPE) with a histopathological diagnosis suggestive of aspergillosis were investigated using PCR. As a positive control for the PCR reaction, morphologically and genetically characterized cultures were used, with their sequences deposited at NCBI under accession codes MW837777 (A. fumigatus) and MW837779 (A. niger). The A. flavus culture used is reference RC 2053. Results: Four of the six samples evaluated were positive for Aspergillus spp., of which one was co-infected with A. fumigatus and A. flavus species, while two others were positive only for A. flavus, and one sample was positive only for A. fumigatus. Conclusions: These findings suggest that PCR can be used as an auxiliary method for diagnosing aspergillosis. However, this was a pilot study, and expansion of the sample size and the evaluation of PCR in comparison with other diagnostic tests for aspergillosis are essential to determine the accuracy of the method. Full article

(This article belongs to the Section Human Genomics and Genetic Diseases)

14 pages, 2807 KiB

Open AccessArticle

Sensing the Changes in Stratum Corneum Using Fourier Transform Infrared Microspectroscopy and Hyperspectral Data Processing

by Krzysztof Banas, Agnieszka M. Banas, Giorgia Pastorin, Ngai Mun Hong, Shikhar Gupta, Katarzyna Dziedzic-Kocurek and Mark B. H. Breese

Sensors 2024, 24(21), 7054; https://doi.org/10.3390/s24217054 (registering DOI) - 31 Oct 2024

Abstract

The stratum corneum (SC) forms the outermost layer of the skin, playing a critical role in preventing water loss and protecting against external biological and chemical threats. Approximately 90% of the SC consists of large, flat corneocytes, yet its barrier function primarily relies on the intercellular lipid matrix that surrounds these cells. Traditional methods for characterizing these lipids, such as Fourier transform infrared spectroscopy (FTIR), typically involve macroscopic analysis using attenuated total reflection (ATR) techniques. In this study, we introduce a novel approach for investigating SC samples at a microscopic level to gain detailed chemical insights and assess sample heterogeneity. Special emphasis is placed on advanced hyperspectral data pre-processing to ensure the accuracy and reliability of the results. We also evaluate methods for filtering out spectral data that significantly deviate from the mean and analyze the extracted mean spectra, the intensities of specific infrared peaks, and their ratios. The novelty of this work lies in its microscopic approach to analyzing the SC lipid matrix, diverging from the traditional macroscopic FTIR–ATR methods. By focusing on hyperspectral imaging and developing robust pre-processing techniques, this study provides more localized, high-resolution chemical insights. This microscopic perspective opens up the possibility of detecting subtle heterogeneities within the skin’s lipid matrix, offering deeper, previously unattainable understanding of the SC’s barrier function. Additionally, the exploration of spectral filtering methods enhances the precision of the analysis, paving the way for more refined and reliable investigations of skin structure and behavior in future research. Full article

(This article belongs to the Special Issue Feature Papers in Optical Sensors 2024)

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13 pages, 407 KiB

Open AccessArticle

Monoterpene Hydroxy Lactones Isolated from Thalassiosira sp. Microalga and Their Antibacterial and Antioxidant Activities

by Alcina M. M. B. Morais, Decha Kumla, Valter F. R. Martins, Ana Alves, Luis Gales, Artur M. S. Silva, Paulo M. Costa, Sharad Mistry, Anake Kijjoa and Rui M. S. C. Morais

Molecules 2024, 29(21), 5175; https://doi.org/10.3390/molecules29215175 (registering DOI) - 31 Oct 2024

Abstract

Two monoterpenoid lactones, loliolide (1) and epi-loliolide (2), were isolated from the crude dichloromethane extract of a microalga, Thalassiosira sp.). The structures of loliolide (1) and epi-loliolide (2) were elucidated by 1D and 2D NMR analysis, as well as a comparison of their ¹H or/and ¹³C NMR data with those reported in the literature. In the case of loliolide (1), the absolute configurations of its stereogenic carbons were confirmed by X-ray analysis, whereas those of epi-loliolide (2) were determined by NOESY correlations. Loliolide (1) and epi-loliolide (2) were tested for their growth inhibitory activity against two Gram-positive (Staphylococcus aureus ATCC 29213, Enterococcus faecalis ATCC 29212) and two Gram-negative (Escherichia coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853) bacteria, as well as one clinical isolate (E. coli SA/2, an extended-spectrum β-lactamase producer-ESBL) and two environmental isolates, S. aureus 74/24, a methicillin-resistant (MRSA), and E. faecalis B3/101, a vancomycin-resistant (VRE) isolates. The results showed that none of the tested compounds exhibited antibacterial activity at the highest concentrations tested (325 μM), and both revealed low antioxidant activity, with ORAC values of 2.786 ± 0.070 and 2.520 ± 0.319 µmol TE/100 mg for loliolide (1) and epi-loliolide (2), respectively. Full article

(This article belongs to the Special Issue Natural Products: Extraction, Analysis and Biological Activities)

11 pages, 562 KiB

Open AccessBrief Report

LCM-RNAseq Highlights Intratumor Heterogeneity and a lncRNA Signature from Archival Tissues of GH-Secreting PitNETs

by Luca Cis, Simona Nanni, Marco Gessi, Antonio Bianchi, Sara De Martino, Valeria Pecci, Davide Bonvissuto, Angela Carlino, Luciano Giacò, Guido Rindi, Claudio Sette, Claudio Grassi, Carlo Gaetano, Alfredo Pontecorvi and Antonella Farsetti

Genes 2024, 15(11), 1426; https://doi.org/10.3390/genes15111426 (registering DOI) - 31 Oct 2024

Abstract

Background: This study explores the potential for hidden variations within seemingly uniform regions of growth hormone-secreting pituitary neuroendocrine tumors (GH-PitNETs). We employed archived tissue samples using Laser Capture Microdissection Sequencing (LCM-RNAseq) to probe the molecular landscape of these tumors at a deeper level. Methods: A customized protocol was developed to extract, process, and sequence small amounts of RNA from formalin-fixed, paraffin-embedded (FFPE) tissues derived from five patients with GH-secreting PitNETs and long-term follow-up (≥10 years). This approach ensured precise isolation of starting material of enough quality for subsequent sequencing. Results: The LCM-RNAseq analysis revealed a surprising level of diversity within seemingly homogeneous tumor regions. Interestingly, the 30 most highly expressed genes included the well-known long noncoding RNA (lncRNA) MALAT1. We further validated the levels of MALAT1 and of other tumor-associated lncRNAs using digital droplet PCR. Conclusions: This study demonstrates the potential of LCM-RNAseq to unlock hidden molecular diversity within archived pituitary tumor samples. By focusing on specific cell populations, we identified lncRNAs expressed at different levels within the tumors, potentially offering new insights into the complex biology of GH-secreting PitNETs. This evidence prompts further research into the role of lncRNAs in pituitary neuroendocrine tumor aggressiveness and personalized treatment strategies. Full article

(This article belongs to the Section Human Genomics and Genetic Diseases)

35 pages, 4479 KiB

Open AccessArticle

Bibliometric Mapping of the Relationships Between Accounting, Professional Accountants, and Sustainability Issues

by Valeriu Brabete, Mirela Sichigea, Daniel Cîrciumaru and Daniel Goagără

Sustainability 2024, 16(21), 9508; https://doi.org/10.3390/su16219508 (registering DOI) - 31 Oct 2024

Abstract

The accounting profession plays a crucial role in serving public interest by establishing the foundation for sustainable development and taking on social responsibility. The growing focus on sustainability practices of companies and stakeholders has also had a significant impact on the role of accounting and professional accountants. This has led to increased expectations for greater involvement in integrating sustainability into corporate decision making at every level. We used a bibliometric analysis of academic literature as a research method to identify the relationships between accounting, professional accountants, and sustainability issues (APASI). Bibliometrix R-package and VOSviewer were used to achieve the proposed objectives. This study analyzes the performance of the scientific literature, establishes the conceptual, intellectual, and social structure of research, and identifies new research directions. A period of 37 years (1987–2024) is taken into consideration, with 2556 documents and 859 sources extracted from the Web of Science database analyzed. We offer, in an original manner, descriptive statistics and relevant landmarks of the sources, authors, publications, organizations, and countries that have contributed significantly to the development of research in this field. Interested researchers have the opportunity to identify scholars for potential collaborations and valuable study resources. Full article

(This article belongs to the Special Issue Sustainability, Accounting, and Business Strategies)

21 pages, 3337 KiB

Open AccessArticle

DDAM-Net: A Difference-Directed Multi-Scale Attention Mechanism Network for Cultivated Land Change Detection

by Junbiao Feng, Haikun Yu, Xiaoping Lu, Xiaoran Lv and Junli Zhou

Sensors 2024, 24(21), 7040; https://doi.org/10.3390/s24217040 (registering DOI) - 31 Oct 2024

Abstract

Declining cultivated land poses a serious threat to food security. However, existing Change Detection (CD) methods are insufficient for overcoming intra-class differences in cropland, and the accumulation of irrelevant features and loss of key features leads to poor detection results. To effectively identify changes in agricultural land, we propose a Difference-Directed Multi-scale Attention Mechanism Network (DDAM-Net). Specifically, we use a feature extraction module to effectively extract the cropland’s multi-scale features from dual-temporal images, and we introduce a Difference Enhancement Fusion Module (DEFM) and a Cross-scale Aggregation Module (CAM) to pass and fuse the multi-scale and difference features layer by layer. In addition, we introduce the Attention Refinement Module (ARM) to optimize the edge and detail features of changing objects. In the experiments, we evaluated the applicability of DDAM-Net on the HN-CLCD dataset for cropland CD and non-agricultural identification, with F1 and precision of 79.27% and 80.70%, respectively. In addition, generalization experiments using the publicly accessible PX-CLCD and SET-CLCD datasets revealed F1 and precision values of 95.12% and 95.47%, and 72.40% and 77.59%, respectively. The relevant comparative and ablation experiments suggested that DDAM-Net has greater performance and reliability in detecting cropland changes. Full article

(This article belongs to the Section Remote Sensors)

18 pages, 7260 KiB

Open AccessArticle

MPCTrans: Multi-Perspective Cue-Aware Joint Relationship Representation for 3D Hand Pose Estimation via Swin Transformer

by Xiangan Wan, Jianping Ju, Jianying Tang, Mingyu Lin, Ning Rao, Deng Chen, Tingting Liu, Jing Li, Fan Bian and Nicholas Xiong

Sensors 2024, 24(21), 7029; https://doi.org/10.3390/s24217029 (registering DOI) - 31 Oct 2024

Abstract

The objective of 3D hand pose estimation (HPE) based on depth images is to accurately locate and predict keypoints of the hand. However, this task remains challenging because of the variations in hand appearance from different viewpoints and severe occlusions. To effectively address these challenges, this study introduces a novel approach, called the multi-perspective cue-aware joint relationship representation for 3D HPE via the Swin Transformer (MPCTrans, for short). This approach is designed to learn multi-perspective cues and essential information from hand depth images. To achieve this goal, three novel modules are proposed to utilize features from multiple virtual views of the hand, namely, the adaptive virtual multi-viewpoint (AVM) , hierarchy feature estimation (HFE), and virtual viewpoint evaluation (VVE) modules. The AVM module adaptively adjusts the angles of the virtual viewpoint and learns the ideal virtual viewpoint to generate informative multiple virtual views. The HFE module estimates hand keypoints through hierarchical feature extraction. The VVE module evaluates virtual viewpoints by using chained high-level functions from the HFE module. Transformer is used as a backbone to extract the long-range semantic joint relationships in hand depth images. Extensive experiments demonstrate that the MPCTrans model achieves state-of-the-art performance on four challenging benchmark datasets. Full article

(This article belongs to the Section Intelligent Sensors)

13 pages, 380 KiB

Open AccessArticle

Evaluation of the Antimicrobial Effects of Olive Mill Wastewater Extract Against Food Spoiling/Poisoning, Fish-Pathogenic and Non-Pathogenic Microorganisms

by Dilek Kahraman Yılmaz, Fevziye Işıl Kesbiç, Ekrem Şanver Çelik, Deniz Anıl Odabaşı, Sevdan Yilmaz and Hany M. R. Abdel-Latif

Microorganisms 2024, 12(11), 2216; https://doi.org/10.3390/microorganisms12112216 (registering DOI) - 31 Oct 2024

Abstract

Although antibiotics are the main therapy for bacterial infections, the reports showed that the overuse (or misuse) of antibiotics will results in several problems such as the development of antibiotic-resistant strains, persistence of drug residues, and numerous environmental concerns. Therefore, finding antibiotic alternatives is considered of vital importance. Investigation of the antimicrobial properties of several plant substances and extracts is of great value to replace antibiotics. With this objective, this study aimed to evaluate the antimicrobial activities of an ethanolic extract prepared from olive mill wastewater (OMWW), which is a by-product of olive oil production with considerable environmental burden, against 38 bacterial strains, including fish-associated pathogens, non-pathogenic isolates, collection strains, and one yeast strain, Candida albicans. Disk diffusion, minimum inhibitory concentration (MIC), and minimum bactericidal/fungicidal concentration (MBC/MFC) tests were used to determine the antimicrobial activity of the OMWWE. According to the results, OMWWE provoked strong inhibitory effects against Shewanella baltica strain SY-S145. It also showed a moderate inhibitory effect on Plesiomonas shigelloides strain SY-PS16 and Vibrio anguillarum strain SY-L24. The MIC and MBC of OMWWE on Shewanella baltica SY-S145, Vibrio gigantis strain C24, and V. anguillarum strain SY-L24 were 500 µg/mL. The MIC and MBC on V. parahaemolyticus ATCC 17802 were 1000 µg/mL, whereas the values for Aeromonas salmonicida ATCC 33658 were 500 µg/mL and 1000 µg/mL, respectively. To put it briefly, the OMWW extract showed high antimicrobial activity and can act as an environmentally friendly additive for the control and prevention of diseases caused by A. veronii, A. hydrophila, P. shigelloides, S. baltica, V. anguillarum, and V. parahaemolyticus. Its active agents also prevented infections of both fish-associated pathogens and food spoiling bacteria, which means it can not only help in the disease control mechanism but also in improving the safety of food by reduction of the microbial contamination. Full article

(This article belongs to the Special Issue Waterborne Pathogen Infection and Antibiotic Resistance)

15 pages, 5223 KiB

Open AccessArticle

Analytical Model for Rate Transient Behavior of Co-Production between Coalbed Methane and Tight Gas Reservoirs

by Shi Shi, Longmei Zhao, Nan Wu, Li Huang, Yawen Du, Hanxing Cai, Wenzhuo Zhou, Yanzhong Liang and Bailu Teng

Sustainability 2024, 16(21), 9505; https://doi.org/10.3390/su16219505 (registering DOI) - 31 Oct 2024

Abstract

Due to complex geological structures and potential environmental impacts, single-well production in coal-measure gas reservoirs is not satisfactory. Field studies have shown that co-production is a promising approach, which can efficiently and economically extract multiple gas resources. However, the literature lacks a mathematical model to accurately describe and predict the production behavior during co-production. Based on the five-linear flow model, this work presents an analytical solution to evaluate the production dynamics characteristics of co-production between coalbed methane and tight gas reservoirs. In addition, the proposed model accounts for factors such as dual-porosity media, the gas slippage effect, and the matrix shrinkage effect. With the aid of the model, sensitivity analyses of the Blasingame decline curve and the layered flux contribution are conducted. The calculation results show that a higher fracture conductivity, as well as a longer fracture length, lead to larger cumulative production. Additionally, increased layer thickness significantly boosts flux contribution throughout the production period. Finally, large boundary distances extend the duration of high flux contributions in late production. This research contributes to a better understanding of the production dynamics in coal-measure gas reservoirs and offers practical guidelines for reservoir management in co-production scenarios. Full article

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13 pages, 851 KiB

Open AccessArticle

Natural Inhibitors of the Polyphenol Oxidase Activity Isolated from Shredded Stored Iceberg Lettuce (Lactuca sativa L.)

by Małgorzata Sierocka and Michał Świeca

Appl. Sci. 2024, 14(21), 9980; https://doi.org/10.3390/app14219980 (registering DOI) - 31 Oct 2024

Abstract

Polyphenol oxidase (PPO) is the key enzyme responsible for enzymatic browning. To extend the shelf life of shredded lettuce, knowledge about biochemical PPO properties is required. The characterization of the enzyme from shredded, cold-stored lettuce was performed using pyrocatechol and the endogenous substrate (ES) (lettuce phenolics). The optimum pH and temperature for PPO activity were 5 and 50 °C, respectively. Natural infusions used as the PPO inhibitors (IC₅₀) were ranked as follows: lovage (0.09%), marjoram (0.13%), orange peel (0.14%), oregano (0.15%), basil (0.22%), lemon peel (0.24%), parsley leaves (0.58%), and wheat bran (1.06%). Among well-recognized PPO inhibitors, kojic acid (0.00043%), ascorbic acid (0.00053%), and L-cysteine (0.00085%) were the most effective. Among the metal ions, MgCl₂, FeCl₂, and CaCl₂ at 0.5 mM inhibited the PPO activity most effectively (by 28%, 27%, and 21%, respectively). The substrate used (pyrocatechol/ES) significantly influenced the enzyme inhibition. Using pyrocatechol, the lovage extract acted in a mixed mode (K_mi = 27.8 mM, V_maxi = 2.03 mU), while the ES acted according to the non-competitive mode (K_mi= 0.57 mg GAE/mL, V_max = 0.0046 U). The study confirms that natural extracts are more effective than L-cysteine when the ES is used. A pre-storage treatment with an infusion may be potentially used to improve the quality of shredded lettuce. Full article

(This article belongs to the Special Issue Food Storage, Spoilage and Shelf Life: Latest Advances and Prospects 2nd Edition)

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20 pages, 23966 KiB

Open AccessArticle

FCSwinU: Fourier Convolutions and Swin Transformer UNet for Hyperspectral and Multispectral Image Fusion

by Rumei Li, Liyan Zhang, Zun Wang and Xiaojuan Li

Sensors 2024, 24(21), 7023; https://doi.org/10.3390/s24217023 (registering DOI) - 31 Oct 2024

Abstract

The fusion of low-resolution hyperspectral images (LR-HSI) with high-resolution multispectral images (HR-MSI) provides a cost-effective approach to obtaining high-resolution hyperspectral images (HR-HSI). Existing methods primarily based on convolutional neural networks (CNNs) struggle to capture global features and do not adequately address the significant scale and spectral resolution differences between LR-HSI and HR-MSI. To tackle these challenges, our novel FCSwinU network leverages the spectral fast Fourier convolution (SFFC) module for spectral feature extraction and utilizes the Swin Transformer’s self-attention mechanism for multi-scale global feature fusion. FCSwinU employs a UNet-like encoder–decoder framework to effectively merge spatiospectral features. The encoder integrates the Swin Transformer feature abstraction module (SwinTFAM) to encode pixel correlations and perform multi-scale transformations, facilitating the adaptive fusion of hyperspectral and multispectral data. The decoder then employs the Swin Transformer feature reconstruction module (SwinTFRM) to reconstruct the fused features, restoring the original image dimensions and ensuring the precise recovery of spatial and spectral details. Experimental results from three benchmark datasets and a real-world dataset robustly validate the superior performance of our method in both visual representation and quantitative assessment compared to existing fusion methods. Full article

(This article belongs to the Section Remote Sensors)

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15 pages, 1775 KiB

Open AccessArticle

Elimination of Ethanol for the Production of Fucoidans from Brown Seaweeds: Characterization and Bioactivities

by Periaswamy Sivagnanam Saravana, Shanmugapriya Karuppusamy, Dilip K Rai, Janith Wanigasekara, James Curtin and Brijesh K. Tiwari

Mar. Drugs 2024, 22(11), 493; https://doi.org/10.3390/md22110493 - 31 Oct 2024

Abstract

Fucoidan, a sulphated polysaccharide from brown seaweed composed of several monosaccharides, has been stated to have several bioactive properties such as antioxidant, antiviral, anticancer, antithrombic, anti-inflammatory, and immunomodulatory effects. This paper provides research findings on green extraction methods, structural analysis of fucoidan, and its associated bioactivities. Fucoidans from brown seaweeds, Fucus vesiculosus and Ascophyllum nodosum, were extracted using green solvents such as citric acid (CA) followed by MWCO (molecular weight cut-off) filtration to obtain high-purity polysaccharides. The presence of functional groups typical to fucoidans, namely, fucose, sulfate, and glycosidic bonds, in the extracts were confirmed through the data obtained from FTIR (Fourier-transform infrared spectroscopy), TGA (thermogravimetric analysis), DSC (differential scanning calorimetry), and solid-state CP–MAS (cross-polarization magic angle spinning) analysis. The MWCO analysis identified that the >300 kDa fraction can have better content of fucoidan (FV-CA 79.16%, FV-HCl 63.59%, AN-CA 79.21%, AN-HCl 80.70%) than the conventional extraction process. Furthermore, the >300 kDa fraction showed significantly higher antioxidant activities compared to crude fucoidan extracts. Crude fucoidan extracts showed significant inhibition of cell viability in human lung (A459 lung carcinoma cells) and colorectal adenocarcinoma (Caco-2) cells at higher concentrations. The fucoidan extracted with green solvents and avoiding alcohol-based precipitation has substantial antioxidant/antitumor action, so, due to this activity, it can be employed as functional foods in food applications. Full article

(This article belongs to the Special Issue Green Extraction for Obtaining Marine Bioactive Products)

23 pages, 3227 KiB

Open AccessArticle

A Study of Mixed Non-Motorized Traffic Flow Characteristics and Capacity Based on Multi-Source Video Data

by Guobin Gu, Xin Sun, Benxiao Lou, Xiang Wang, Bingheng Yang, Jianqiu Chen, Dan Zhou, Shiqian Huang, Qingwei Hu and Chun Bao

Sensors 2024, 24(21), 7045; https://doi.org/10.3390/s24217045 (registering DOI) - 31 Oct 2024

Abstract

Mixed non-motorized traffic is largely unaffected by motor vehicle congestion, offering high accessibility and convenience, and thus serving as a primary mode of “last-mile” transportation in urban areas. To advance stochastic capacity estimation methods and provide reliable assessments of non-motorized roadway capacity, this study proposes a stochastic capacity estimation model based on power spectral analysis. The model treats discrete traffic flow data as a time-series signal and employs a stochastic signal parameter model to fit stochastic traffic flow patterns. Initially, UAVs and video cameras are used to capture videos of mixed non-motorized traffic flow. The video data were processed with an image detection algorithm based on the YOLO convolutional neural network and a video tracking algorithm using the DeepSORT multi-target tracking model, extracting data on traffic flow, density, speed, and rider characteristics. Then, the autocorrelation and partial autocorrelation functions of the signal are employed to distinguish among four classical stochastic signal parameter models. The model parameters are optimized by minimizing the AIC information criterion to identify the model with optimal fit. The fitted parametric models are analyzed by transforming them from the time domain to the frequency domain, and the power spectrum estimation model is then calculated. The experimental results show that the stochastic capacity model yields a pure EV capacity of 2060–3297 bikes/(h·m) and a pure bicycle capacity of 1538–2460 bikes/(h·m). The density–flow model calculates a pure EV capacity of 2349–2897 bikes/(h·m) and a pure bicycle capacity of 1753–2173 bikes/(h·m). The minimal difference between these estimates validates the effectiveness of the proposed model. These findings hold practical significance in addressing urban road congestion. Full article

(This article belongs to the Topic Smart Cities: Infrastructure, Innovation, Technology, Governance and Citizenship)

15 pages, 1212 KiB

Open AccessArticle

Research on the Identification of Rock Mass Structural Planes and Extraction of Dominant Orientations Based on 3D Point Cloud

by Jiarui Zhu, Yonghua Xia, Bin Wang, Ziliang Yang and Kaihua Yang

Appl. Sci. 2024, 14(21), 9985; https://doi.org/10.3390/app14219985 (registering DOI) - 31 Oct 2024

Abstract

The different spatial distribution forms of rock mass structural planes create weak zones in the rock mass, which is also a key factor in controlling rock mass stability. Accurately and efficiently identifying rock mass structural planes and obtaining their dominant orientations is critical for rock mass engineering design and construction. Traditional surveying methods for high and steep rock mass structural planes pose high safety risks, offer limited data, and make comprehensive statistical analysis difficult. This paper utilizes complex rock mass surface 3D point cloud data obtained through 3D laser scanning technology and uses the Hough space transform method to calculate the normal vectors of the 3D point cloud. Based on the difference in normal vectors and surface variation, region growing segmentation is applied to identify and extract rock mass structural planes. Additionally, the fast search and density peak clustering method (CFSFDP) is used for clustering analysis of the rock mass structural planes to obtain dominant orientations. This method was applied to a highway’s high and steep rock slope, successfully identifying 281 structural planes and two sets of dominant structural planes. The orientation of the dominant structural planes identified through RocScience Dips 7.0 analysis showed a deviation of no more than ±3°, complying with engineering standards. The research results offer a feasible solution for the identification of high and steep rock mass structural planes and the extraction of the orientation of dominant structural planes. Full article

(This article belongs to the Special Issue Recent Advances in Rock Mass Engineering)

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