bluuebunny/crossref_metadata_2025 huggingface dataset:
Editorial for Special Issue “Advancements in Wireless Communications, Networks, and Signal Processing”
Authors: Zhang Runzhou , Zhang Lin , Yue Yang , Feng Hao , Li Zheda , Ying Dawei
Published: July 2024

Due to dramatic increases in data traffic over the last decade, there has been growing interest in enhancing system performance levels in data communications and networking [...]
Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
Authors: Luo Dawei , Yang Ilhwan , Bae Joonsoo , Woo Yoonhyuck
Published: July 2024

Lung nodule classification is crucial for the diagnosis and treatment of lung diseases. However, selecting appropriate metrics to evaluate classifier performance is challenging, due to the prevalence of negative samples over positive ones, resulting in imbalanced datasets. This imbalance often necessitates the augmentation of positive samples to train powerful models effectively. Furthermore, specific medical tasks require tailored augmentation methods, the effectiveness of which merits further exploration based on task objectives. This study conducted a detailed analysis of commonly used metrics in lung nodule detection, examining their characteristics and selecting suitable metrics based on this analysis and our experimental findings. The selected metrics were then applied to assessing different combinations of image augmentation techniques for nodule classification. Ultimately, the most effective metric was identified, leading to the determination of the most advantageous augmentation method combinations.
Fuzzy Relationship between Kansei Images: A Grey Decision-Making Method for Product Form
Authors: Wang Shijie , Zhang Shutao , Su Jianning , Yang Zhiqiang , Liu Shifeng , Zhou Aimin , Yang Wenjin , Qiu Kai
Published: July 2024

Current design decision-making methods ignore the fuzzy relationship between Kansei images, and the use of constant weights reduces the accuracy of cognitive evaluation results. To solve these problems, this paper proposes a grey decision-making method for product form driven by the fuzzy relationship between Kansei images. First, according to the initial weight of the Kansei images, variable weight theory is used to determine the Kansei image variable weights of the samples, and the variable weight comprehensive evaluation results for each sample are obtained. Then, based on the correlation and angle of the Kansei images, a cobweb diagram is drawn to represent the fuzzy relationship between the Kansei images of each sample. Combined with the cobweb grey target decision-making model (CGTDM) for multiple Kansei images, decision coefficients are calculated. The decision coefficients are compared and ranked to determine the relatively optimal design reference sample. Finally, the constructed model is compared with the CGTDM for multiple Kansei images and TOPSIS. The results show that the difference coefficient of the proposed method is the largest, and it can reflect the decision-making thinking of the designers and improve the discrimination among the decision-making results to a certain extent.
Development of a Prototype Solution for Reducing Soup Waste in an Institutional Canteen
Authors: Correia Ana , Aidos Clara , Caldeira João M. L. P. , Soares Vasco N. G. J.
Published: July 2024

Food waste has gained increasing attention and debate, given its economic, environmental, social, and nutritional implications. One-third of food intended for human consumption is wasted. Although it is present at all stages of the food supply chain, it is in the final stages of consumption, such as households and food services, that the problem becomes most evident. This work builds on a previous study by the same authors, which identified computer vision as a suitable technology for identifying and quantifying food waste in institutional canteens. Based on this result, this paper describes the proposal and implementation process of a prototype demonstration. It is based on a Raspberry Pi 4 platform, a ResNet-50 model adapted with the Faster Region-Convolutional Neural Network (Faster R-CNN) model, and an algorithm for feature extracting. A specially built dataset was used to meet the challenge of detecting soup bowls and classifying waste in their consumption. A web application was developed to visualize the data collected, supporting decision making for more efficient food waste management. The prototype was subjected to validation and functional tests, and proved to be a viable, low-cost solution.
Named Entity Recognition for Chinese Texts on Marine Coral Reef Ecosystems Based on the BERT-BiGRU-Att-CRF Model
Authors: Zhao Danfeng , Chen Xiaolian , Chen Yan
Published: July 2024

In addressing the challenges of non-standardization and limited annotation resources in Chinese marine domain texts, particularly with complex entities like long and nested entities in coral reef ecosystem-related texts, existing Named Entity Recognition (NER) methods often fail to capture deep semantic features, leading to inefficiencies and inaccuracies. This study introduces a deep learning model that integrates Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Gated Recurrent Units (BiGRU), and Conditional Random Fields (CRF), enhanced by an attention mechanism, to improve the recognition of complex entity structures. The model utilizes BERT to capture context-relevant character vectors, employs BiGRU to extract global semantic features, incorporates an attention mechanism to focus on key information, and uses CRF to produce optimized label sequences. We constructed a specialized coral reef ecosystem corpus to evaluate the model’s performance through a series of experiments. The results demonstrated that our model achieved an F1 score of 86.54%, significantly outperforming existing methods. The contributions of this research are threefold: (1) We designed an efficient named entity recognition framework for marine domain texts, improving the recognition of long and nested entities. (2) By introducing the attention mechanism, we enhanced the model’s ability to recognize complex entity structures in coral reef ecosystem texts. (3) This work offers new tools and perspectives for marine domain knowledge graph construction and study, laying a foundation for future research. These advancements propel the development of marine domain text analysis technology and provide valuable references for related research fields.
Modeling and Experimental Validation on Current Uniformity Characteristics of Parallel Spiral Structure Surge Arrester in ±550 kV DC GIS
Authors: Liu Siyuan , Wang Liudan , Liu Shiyu , Liu Zhiyuan , Geng Yingsan
Published: July 2024

The employment of a multi-column parallel connection is intended to enhance the energy absorption capability and reliability of surge arresters. However, the disparity in reference voltage between each varistor column and the uneven current distribution may result in a reduction in performance or even failure of the surge arrester. The objective of this study is to investigate the spiral structure of a ±550 kV DC gas-insulated switchgear (GIS) parallel arrester and its influence on the current distribution characteristics. This research develops a model of a ±550 kV DC GIS arrester and performs an in-depth theoretical analysis using multi-physics field simulations. Subsequently, a ±66 kV miniature prototype is constructed, and the accuracy of the theoretical analysis and simulation results is validated by experiments, validating the effectiveness of the proposed method. This study calculates the self-generated inductance in the spiral structure of ZnO varistors using simulations. The influence of the self-generated inductance on the current distribution of the multi-column arrester when absorbing energy is further investigated. The results indicate that the self-generated inductance of the spiral structure can reduce the current deviation factor by 28–65%. This research provides a novel approach to improving current equalization in the parallel surge arresters of DC GISs for offshore wind power converter platforms.
Development of a Premium Tea-Picking Robot Incorporating Deep Learning and Computer Vision for Leaf Detection
Authors: Wu Luofa , Liu Helai , Ye Chun , Wu Yanqi
Published: July 2024

Premium tea holds a significant place in Chinese tea culture, enjoying immense popularity among domestic consumers and an esteemed reputation in the international market, thereby significantly impacting the Chinese economy. To tackle challenges associated with the labor-intensive and inefficient manual picking process of premium tea, and to elevate the competitiveness of the premium tea sector, our research team has developed and rigorously tested a premium tea-picking robot that harnesses deep learning and computer vision for precise leaf recognition. This innovative technology has been patented by the China National Intellectual Property Administration (ZL202111236676.7). In our study, we constructed a deep-learning model that, through comprehensive data training, enabled the robot to accurately recognize tea buds. By integrating computer vision techniques, we achieved exact positioning of the tea buds. From a hardware perspective, we employed a high-performance robotic arm to ensure stable and efficient picking operations even in complex environments. During the experimental phase, we conducted detailed validations on the practical application of the YOLOv8 algorithm in tea bud identification. When compared to the YOLOv5 algorithm, YOLOv8 exhibited superior accuracy and reliability. Furthermore, we performed comprehensive testing on the path planning for the picking robotic arm, evaluating various algorithms to determine the most effective path planning approach for the picking process. Ultimately, we conducted field tests to assess the robot’s performance. The results indicated a 62.02% success rate for the entire picking process of the premium tea-picking robot, with an average picking time of approximately 1.86 s per qualified tea bud. This study provides a solid foundation for further research, development, and deployment of premium tea-picking robots, serving as a valuable reference for the design of other crop-picking robots as well.
Improving VulRepair’s Perfect Prediction by Leveraging the LION Optimizer
Authors: Kishiyama Brian , Lee Young , Yang Jeong
Published: July 2024

In current software applications, numerous vulnerabilities may be present. Attackers attempt to exploit these vulnerabilities, leading to security breaches, unauthorized entry, data theft, or the incapacitation of computer systems. Instead of addressing software or hardware vulnerabilities at a later stage, it is better to address them immediately or during the development phase. Tools such as AIBugHunter provide solutions designed to tackle software issues by predicting, categorizing, and fixing coding vulnerabilities. Essentially, developers can see where their code is susceptible to attacks and obtain details about the nature and severity of these vulnerabilities. AIBugHunter incorporates VulRepair to detect and repair vulnerabilities. VulRepair currently predicts patches for vulnerable functions at 44%. To be truly effective, this number needs to be increased. This study examines VulRepair to see whether the 44% perfect prediction can be increased. VulRepair is based on T5 and uses both natural language and programming languages during its pretraining phase, along with byte pair encoding. T5 is a text-to-text transfer transformer model with an encoder and decoder as part of its neural network. It outperforms other models such as VRepair and CodeBERT. However, the hyperparameters may not be optimized due to the development of new optimizers. We reviewed a deep neural network (DNN) optimizer developed by Google in 2023. This optimizer, the Evolved Sign Momentum (LION), is available in PyTorch. We applied LION to VulRepair and tested its influence on the hyperparameters. After adjusting the hyperparameters, we obtained a 56% perfect prediction, which exceeds the value of the VulRepair report of 44%. This means that VulRepair can repair more vulnerabilities and avoid more attacks. As far as we know, our approach utilizing an alternative to AdamW, the standard optimizer, has not been previously applied to enhance VulRepair and similar models.
Coexistence of 5G/6G and Digital Terrestrial Television Networks
Authors: Więcek Dariusz , Mora Marcin , Michalski Igor
Published: July 2024

This paper analyzes practical solutions for implementing 5G/6G and Digital Terrestrial Television (DTT) network coexistence in the UHF band. Two potential scenarios are explored using the case study of a nationwide DTT network: spectrum splitting and spectrum interleaving. Both scenarios are thoroughly examined based on current UHF spectrum usage and the DTT network structure based on a case study of Poland. The analysis includes potential future benefits and limitations of each scenario. The results presented in this paper can guide administrations in implementing decisions made at the International Telecommunication Union—Radiocommunication Sector, World Radiocommunication Conference 2023 (ITU-R WRC23), improving spectrum efficiency and 5G/6G networks development.
Study on the Optical Coupling Effect of Building-Integrated Photovoltaic Modules Applied with a Shingled Technology
Authors: Jee Hongsub , Ur Seungah , Kim Juwhi , Lee Jaehyeong
Published: July 2024

Building-integrated photovoltaics (BIPV) comprise the integration of a solar power generation system into the exterior design and architectural elements of a building to produce electricity, which allows the building itself to generate electricity. By integrating shingled technology into the photovoltaic module with optimization of the optical effect, the output performance of the module can be increased while securing an aesthetic appeal as an architectural exterior material for the building simultaneously. In this research, we studied enhancing the performance of BIPV modules through an analysis of the optical coupling effect for shingled technology using PSpice simulation. Compared to the efficiency of 0.2 cm string spacing, the optical coupling effect was increased by 33.33%, 46.98%, 67.01%, and 193.49% according to the string spacing of 0.5 cm, 1 cm, 2 cm and 4 cm, respectively. To analyze this increase, we focused on studying the increase in current due to the reflection and re-absorption of light in the back layer of the solar cell as the cause of this output enhancement. Additionally, the coupling effect in accord with different layers showed that using white EVA to reflect the incident light from the top layer resulted in 117.14% and 521.90% enhancements in maximum output power (Pm) loss % compared to the conventional and black backsheet applied PV modules, respectively.
Comparative Study of Natural Fibres to Improve Insulation in Wooden Beehives Using Sensor Networks
Authors: Sanz Milagros Casado , Prado-Jimeno Rubén , Fuentes-Pérez Juan Francisco
Published: July 2024

The beekeeping sector is increasingly focused on creating optimal and natural environments for honeybees to reduce dependence on external factors, especially given progressively hotter summers. Improving hive thermal conditions can enhance bee wellbeing and production. While pinewood hives are predominant, some have started using insulating materials like polystyrene. However, many synthetic materials, despite their excellent insulation properties, are incompatible with organic food production, requiring alternative solutions. This study compares the thermal insulation properties of various natural materials, including white and black agglomerated cork, wood fibres, and rock mineral wool. These materials are potentially compatible with organic food production. Additionally, the research evaluates cost-effective sensor networks to monitor bioclimatic variables in real time. Lab tests using a Langstroth-type hive with a controlled heat source were conducted, monitoring temperature and humidity inside and outside the hive. The results revealed that all selected materials provided similar thermal insulation, superior to a hive without insulation. This finding suggests that using natural materials can enhance hive thermal comfort (i.e., the material’s ability to maintain a stable internal temperature), thereby improving honeybee wellbeing and productivity in a manner compatible with organic food production.
Automatic Composition System Based on Transformer-XL
Authors: Li Ze , Huang Qing , Yang Xinhao , Chen Qing , Zhang Li
Published: July 2024

An automatic composition system that includes music generation and music assessment is designed in this paper. In terms of music generation, we modify the Transformer-XL model for generating music. The Mask mechanism based on Transformer-XL is improved to make the attention of model tend to pay on the bidirectional information, so that the generated popular piano music forms a coherent whole. In terms of music assessment, we combine objective and subjective assessment to judge the generated music in a comprehensive way. Meanwhile, we put forward a new objective assessment method, namely the piano roll classification scoring network. It converts music into pictures and uses classification models in the CV, enabling the network itself to classify and score the generated music. The assessment results from subjective and objective experiments show that by improving the Mask mechanism of Transformer-XL, the model is trained to be better and the generated music could achieve the effect of imitating the real music.
A Numerical Hydration Model to Predict the Macro and Micro Properties of Cement–Eggshell Powder Binary Blends
Authors: Zhang Li-Na , Sun Feng , Liu Yao , Lee Sihwan , Wang Xiao-Yong
Published: July 2024

This study aims to propose a hydration kinetic model for the cement–eggshell powder binary system and predict the performance development of composite concrete through this model. The specific content and results of the model are as follows. First, based on the cumulative hydration heat of the cement and eggshell powder binary system in the first seven days, the parameters of the cement hydration model and the eggshell powder nucleation parameter are calibrated. These parameters remain constant regardless of the mix ratio. Secondly, the hydration heat of the cement–eggshell powder binary system over 28 days is calculated using the hydration model. The results show that at 28 days, for specimens with 0%, 7.5%, and 15% eggshell powder substitution, the cement hydration degrees are 0.832, 0.882, and 0.923, respectively. The hydration heat per gram of cement is 402.69, 426.88, and 446.73 J/g cement, respectively, while the hydration heat per gram of binder is 402.69, 394.86, and 379.72 J/g binder, respectively. Additionally, the hydration model is used to calculate the chemically bound water and calcium hydroxide content of the cement–eggshell powder binary system. At 28 days, for samples with 0%, 7.5%, and 15% eggshell powder, the chemically bound water content is 0.191, 0.188, and 0.180 g/g binder, respectively, and the calcium hydroxide content is 0.183, 0.179, and 0.173 g/g binder, respectively. Finally, a power function is used to regress the calculated hydration heat with experimentally measured compressive strength and surface electrical resistivity. The correlation coefficients for compressive strength and surface electrical resistivity are 0.8474 and 0.9714, respectively. This is because the strength weak point effect of eggshell powder has minimal impact on hydration heat and surface electrical resistivity experiments but significantly affects the strength experiment.
Detecting Offensive Language on Malay Social Media: A Zero-Shot, Cross-Language Transfer Approach Using Dual-Branch mBERT
Authors: Guo Xingyi , Adnan Hamedi Mohd , Abidin Muhammad Zaiamri Zainal
Published: July 2024

Social media serves as a platform for netizens to stay informed and express their opinions through the Internet. Currently, the social media discourse environment faces a significant security threat—offensive comments. A group of users posts comments that are provocative, discriminatory, and objectionable, intending to disrupt online discussions, provoke others, and incite intergroup conflict. These comments undermine citizens’ legitimate rights, disrupt social order, and may even lead to real-world violent incidents. However, current automatic detection of offensive language primarily focuses on a few high-resource languages, leaving low-resource languages, such as Malay, with insufficient annotated corpora for effective detection. To address this, we propose a zero-shot, cross-language unsupervised offensive language detection (OLD) method using a dual-branch mBERT transfer approach. Firstly, using the multi-language BERT (mBERT) model as the foundational language model, the first network branch automatically extracts features from both source and target domain data. Subsequently, Sinkhorn distance is employed to measure the discrepancy between the source and target language feature representations. By estimating the Sinkhorn distance between the labeled source language (e.g., English) and the unlabeled target language (e.g., Malay) feature representations, the method minimizes the Sinkhorn distance adversarially to provide more stable gradients, thereby extracting effective domain-shared features. Finally, offensive pivot words from the source and target language training sets are identified. These pivot words are then removed from the training data in a second network branch, which employs the same architecture. This process constructs an auxiliary OLD task. By concealing offensive pivot words in the training data, the model reduces overfitting and enhances robustness to the target language. In the end-to-end framework training, the combination of cross-lingual shared features and independent features culminates in unsupervised detection of offensive speech in the target language. The experimental results demonstrate that employing cross-language model transfer learning can achieve unsupervised detection of offensive content in low-resource languages. The number of labeled samples in the source language is positively correlated with transfer performance, and a greater similarity between the source and target languages leads to better transfer effects. The proposed method achieves the best performance in OLD on the Malay dataset, achieving an F1 score of 80.7%. It accurately identifies features of offensive speech, such as sarcasm, mockery, and implicit expressions, and showcases strong generalization and excellent stability across different target languages.
A Precision Livestock Farming Technique from Breeding to Slaughter: Infrared Thermography in Pig Farming
Authors: Redaelli Veronica , Zaninelli Mauro , Martino Pieranna , Luzi Fabio , Costa Leonardo Nanni
Published: July 2024

Infrared thermography is a contactless imaging technique, useful for measuring surface temperature at distance, which has been widely applied in animal production. The possible applications within the pig husbandry include sow heat detection, the reduction in the incidence of neonatal mortality, the recognition of the febrile state caused by pathogens, the study of metabolic efficiency, the evaluation of stress caused by heat, by constraints, or by aggressive interaction, and meat quality prediction. Even if this technique could be easily influenced by environmental factors, the continuous improvement in the procedures and the decrease in the cost of the equipment will allow for new and more applications in swine farming in the future.
Optimization of Green Sample Preparation for the Determination of Hydroxycinnamic Acids in Multi-Floral Honey Using Response Surface Methodology
Authors: Stavrou Konstantina , Christou Atalanti , Goulas Vlasios
Published: July 2024

Hydroxycinnamic acids are an essential phenolic group in honey that are related with its diverse health effects. Furthermore, they can be considered as potential biomarkers for botanical and geographical origin. The extraction of hydroxycinnamic acids from honey requires complicated extraction techniques due to their low contents and matrix particularity. The goal of the present work was to develop a green sample preparation method for the solubilization of hydroxycinnamic acids in honey samples. Thus, a Box–Behnken design has been used to investigate the effect of four factors: (i) percentage of ethanol (60–90%, v/v), (ii) temperature (30–60 °C), (iii) solvent-honey ratio (10–30 mL g−1), and (iv) sonication time (10–40 min) on the contents of caffeic, chlorogenic, and ferulic acids. Based on the desirability index, the ultrasound-assisted mixing of honey with 60.0% (v/v) ethanol at a solvent-honey ratio of 10 mL g−1 for a period of 17.8 min at a temperature of 54.6 °C resulted in the maximum solubilization of hydroxycinnamates. Subsequently, the greenness of developed method was assessed and applied successfully on the analysis of honeys. The present study reports for first time the hydroxycinnamic acid fingerprint of multi-floral honey produced in Cyprus.
HSAW: A Half-Face Self-Attention Weighted Approach for Facial Expression Recognition
Authors: Huang Shucheng , Yang Xingpeng
Published: July 2024

Facial expression recognition plays an increasingly important role in daily life, and it is used in several areas of human–computer interaction, such as robotics, assisted driving, and intelligent tutoring systems. However, the current mainstream methods are based on the whole face, and do not consider the existence of expression asymmetry between the left and right half-face. Hence, the accuracy of facial expression recognition needs to be improved. In this paper, we propose a half-face self-attention weighted approach called HSAW. Using statistical analysis and computer vision techniques, we found that the left half-face contains richer expression features than the right half-face. Specifically, we employed a self-attention mechanism to assign different weights to the left and right halves of the face. These weights are combined with convolutional neural network features for improved facial expression recognition. Furthermore, to attack the presence of uncertain categories in the dataset, we introduce adaptive re-labeling module, which can improve the recognition accuracy. Extensive experiments conducted on the FER2013 and RAF datasets have verified the effectiveness of the proposed method, which utilizes fewer parameters.
Performance Assessment of Existing Asphalt Pavement in China’s Highway Reconstruction and Expansion Project Based on Coupling Weighting Method and Cloud Model Theory
Authors: Xu Ying , Shi Xixin , Yao Yongsheng
Published: July 2024

In China, a substantial portion of highway asphalt pavements are no longer capable of accommodating increasing traffic volumes and necessitate renovation and expansion. Prior to commencing such activities, it is crucial to evaluate the performance of the existing asphalt pavements. This study developed a novel normal cloud framework integrating a comprehensive weighted indicator system for existing asphalt pavement. Five key performance indicators including riding quality index (RQI), rutting area ratio (Rr), cracking area ratio (Cr), patching area ratio (Pr), and pavement structural strength index (PSSI) were selected to holistically represent the pavement condition in highway renovation and expansion projects. Subsequently, a method was proposed to determine the weights of these indicators by integrating the analytic hierarchy process (AHP) and entropy. A normal cloud model was constructed to address data characteristics and representation of indicator fuzziness/randomness through digital cloud modeling. The model was applied to 12 sections of the Jingjintang Expressway (Tianjin section). The results revealed only one section where the normal cloud model differed from the pavement maintenance quality assessment (PQI) model. The 3D ground-penetrating radar detection results of this different section indicated that the normal cloud model more closely aligned with the road structure condition. Compared to absolute pass/fail criteria of the traditional PQI model, the cloud model offered enhanced sensitivity to define graded condition assessments essential for reconstruction planning and decision analysis. Therefore, the normal cloud model is more suitable for assessing the performance of existing asphalt pavements in highway reconstruction and extension projects compared to the PQI model.
Analysis and Experiment of Thermal Field Distribution and Thermal Deformation of Nut Rotary Ball Screw Transmission Mechanism
Authors: Yu Hanwen , Luan Xuecheng , Zheng Guiyuan , Hao Guangchao , Liu Yan , Xing Hongyu , Liu Yandong , Fu Xiaokui , Liu Zhi
Published: July 2024

This study designs a differential dual-drive micro-feed mechanism, superposing the two “macro feed motions” (“motor drive screw” and “motor drive nut”) using the same transmission of “the nut rotary ball screw pair” structure. These two motions are almost equal in terms of speed and turning direction, thus the “micro feed” can be obtained. (1) Background: Thermal deformation is the primary factor that can restrict the high-precision micro-feed mechanism and the distribution of heat sources differs from that of the conventional screw single-drive system owing to the structure and motion features of the transmission components. (2) Discussion: This study explores the thermal field distribution and thermal deformation of the differentially driven micro-feed mechanism when two driving motors are combined at different speeds. (3) Methods: Based on the theory of heat transfer, the differential dual-drive system can be used as the research object. The thermal equilibrium equations of the micro-feed transmission system are established using the thermal resistance network method, and a thermal field distribution model is obtained. (4) Results: Combined with the mechanism of thermal deformation theory, the established thermal field model is used to predict the axial thermal deformation of the differential dual-drive ball screw. (5) Conclusions: Under the dual-drive condition, the steady-state thermal error of the nut-rotating ball screw transmission mechanism increases with the increase in nut speed and composite speed and is greater than the steady-state thermal error under the single screw drive condition. After reaching the thermal steady state, the measured thermal elongation at the end of the screw in the experiment is approximately 10.5 μm and the simulation result is 11.98 μm. The experimental measurement result demonstrates the accuracy of the theoretical analysis model for thermal error at the end of the screw.
Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
Authors: Li Yiming , Wang Yize , Lu Liuwei , Guo Yiran , An Qi
Published: July 2024

Aiming at the problem that dynamic targets in indoor environments lead to low accuracy and large errors in the localization and position estimation of visual SLAM systems and the inability to build maps containing semantic information, a semantic visual SLAM algorithm based on the semantic segmentation network DeepLabV3+ and LK optical flow is proposed based on the ORB-SLAM2 system. First, the dynamic target feature points are detected and rejected based on the lightweight semantic segmentation network DeepLabV3+ and LK optical flow method. Second, the static environment occluded by the dynamic target is repaired using the time-weighted multi-frame fusion background repair technique. Lastly, the filtered static feature points are used for feature matching and position calculation. Meanwhile, the semantic labeling information of static objects obtained based on the lightweight semantic segmentation network DeepLabV3+ is fused with the static environment information after background repair to generate dense point cloud maps containing semantic information, and the semantic dense point cloud maps are transformed into semantic octree maps using the octree spatial segmentation data structure. The localization accuracy of the visual SLAM system and the construction of the semantic maps are verified using the widely used TUM RGB-D dataset and real scene data, respectively. The experimental results show that the proposed semantic visual SLAM algorithm can effectively reduce the influence of dynamic targets on the system, and compared with other advanced algorithms, such as DynaSLAM, it has the highest performance in indoor dynamic environments in terms of localization accuracy and time consumption. In addition, semantic maps can be constructed so that the robot can better understand and adapt to the indoor dynamic environment.
The Laser Scanner Technique: A Tool for Determining Shear Strength Parameters of Rock Mass Discontinuities
Authors: Zimbardo Margherita , Nocilla Alessandra , Scotto di Santolo Anna
Published: July 2024

When evaluating the shear strength of rock mass discontinuities, certain challenges arise due to the difficulty in quantifying the roughness characteristics of surfaces and the strength of asperities. Recent research has focused on enhancing techniques for assessing these characteristics and exploring the application of laser scanning to aid in evaluating discontinuity features. The analysis of reflectivity values (I) obtained through a laser scanner survey presents an efficient method for assessing mechanical characteristics, such as joint compressive strength (JCS). Reflectivity measurements demonstrate correlations with Schmidt hammer rebound values (r). The laser scanner technique would enable the measurement of JCS without the direct application of the Schmidt hammer on rocks in areas where rebound values (r) measurements are unavailable. The use of a laser scanner allows for the acquisition of high-precision geometrical information concerning the 3D roughness and anisotropy of rock surfaces. In this study, an innovative technique was introduced that utilizes laser scanner data from six previous experimental surveys conducted on rock formations in Southern Italy. This technique facilitates the evaluation of roughness profiles, considering potential variations along kinematically admissible sliding directions, allowing for the estimation of the Joint Roughness Coefficient (JRC). This new methodology aids in evaluating the parameters of Barton’s equation to determine the strength characteristics of rock mass discontinuities.
Product Development and Design Framework Based on Interactive Innovation in the Metaverse Perspective
Authors: Lin Jie , Li Qing , Wang Chao , Hu Zijuan
Published: July 2024

Based on the theory of user needs and the product development mode and framework of mobile Internet interactive innovation, a new “reality → virtual → reality” interactive innovation product development mode is constructed. It draws on the unique characteristics, systematic technical system, and comprehensive scientific and technological layout of the Metaverse. On this basis, a framework for product development and design based on interactive innovation from the Metaverse perspective is innovatively proposed. In the Metaverse scenario, interactive innovation knowledge can be easily and effectively transformed into design knowledge, and all groups of users truly participate in the whole process of product design. Moreover, the development of interactive innovative products in the Metaverse scenario can be combined with artificial intelligence (AI) technology to further automate the statistical analysis of user needs and preferences so as to meet the dynamic needs of users and accurately develop products that fit user needs and enterprise standards. In addition, users, designers, and enterprises can make joint decisions on product design solutions and development forms, and the Metaverse technology can also optimize the products with continuous iteration and obtain the optimal solutions. An automotive case study illustrates the feasibility and effectiveness of the model for product development innovation and enterprise digital transformation.
Carbonyl Sulfide (COS) in Terrestrial Ecosystem: What We Know and What We Do Not
Authors: Li Jiaxin , Shen Lidu , Zhang Yuan , Liu Yage , Wu Jiabing , Wang Anzhi
Published: July 2024

Over the past six decades, carbonyl sulfide (COS) in terrestrial ecosystems has been extensively studied, with research focusing on exploring its ecological and environmental effects, estimating source–sink volume, and identifying influencing factors. The global terrestrial COS sink has been estimated to be about 1.194–1.721 Tg a−1, with the terrestrial sink induced by plants and soils 0.50–1.20 Tg a−1, accounting for 41%–69% of the total. Hence, the role of plants and soils as COS sinks has been extensively explored. Now we know that factors such as the activity of carbonic anhydrase (CA), leaf structural traits, soil microbial activity, and environmental factors play significant roles in the COS budget. Developments in observational techniques have also made important contributions to the COS budget. This paper provides an overview of the research progress made on COS based on a comprehensive review of the literature. Then, it highlights the current research hotspots and issues requiring further exploration. For instance, it has been demonstrated that there are still significant uncertainties in the estimation of COS sources and sinks, emphasizing the need for further exploration of COS measuring techniques. This review aims to provide comprehensive guidance for COS research in terrestrial ecosystems.
Investigation into Spatial and Temporal Differences in Carbon Emissions and Driving Factors in the Pearl River Delta: The Perspective of Urbanization
Authors: Gao Ziya , Wu Dafang , Wu Zhaojun , Zeng Lechun
Published: July 2024

Urbanization is a significant indicator of city progress, and as urbanization advances, carbon emissions exhibit an increasing trend that must not be disregarded. Therefore, it is imperative to thoroughly examine the spatial and temporal variations as well as the factors influencing carbon emissions during the urbanization process. In this paper, based on the 2009–2019 PRD region, carbon emissions are measured from energy consumption, industrial production process, solid waste, and wastewater according to the IPCC coefficients, and spatial and spatial differences in carbon emissions are combined with spatial analysis and the drivers analyzed using the gray correlation scale. The results show that: (1) The total carbon emissions in the PRD region have been increasing over the study period, and the distribution of total carbon emissions shows a pattern of “strong in the east and weak in the west”, with energy consumption accounting for the highest proportion of carbon emissions. (2) The global Moran Index of carbon emissions in the PRD has been decreasing, with low and low clustering concentrated in Shaoguan and Zhaoqing, high and high clustering concentrated in Dongguan and Shenzhen, and low and high clustering concentrated in Shenzhen and Guangzhou, with cold spots mainly concentrated in Zhaoqing and hot spots mainly distributed in Guangzhou, Shenzhen, and Dongguan. (3) The degree of economic growth has a substantial influence on carbon emissions in the PRD cities, and the influence of technical advancement has intensified. Guangzhou City is propelled by low-carbon regulations that have a more equitable influence on all elements. Zhuhai City has a more significant influence on energy intensity, while Foshan City has a more noticeable decrease in the effect of foreign investment. To address the issue of carbon emissions, the government should establish appropriate rules to regulate carbon emissions in areas with high emissions, foster collaborative efforts across cities, and encourage the growth of environmentally friendly enterprises.
Air Quality in the Cartagena Basin in South-Western Europe and the Impact of the COVID-19 Pandemic
Authors: Moreno-Cegarra José-Luis , Pérez Isidro A. , García M. Ángeles
Published: July 2024

The port of Cartagena, south-east Spain, is noted for its intense activity. This paper presents the influence of the COVID-19 pandemic on the recorded levels of six pollutants. Fifteen years of measurements were used, with two objectives. The first was to investigate how these pollutants evolved, together with their usual cycles. The second objective was to ascertain whether the COVID-19 pandemic had an impact on the concentrations recorded. The results showed that nitrogen oxide concentrations remained steady in the first half of the measurement period and decreased in the second half. SO2 concentrations decreased irregularly, whereas O3 and PM10 presented steady concentrations. The annual cycle was observed for nitrogen oxides and O3. Only SO2 evidenced no weekly cycle. Finally, the analysis of pre- and post-pandemic concentrations revealed a sharp decrease in nitrogen oxides, whereas the pandemic was not seen to have had any impact on the other pollutants. In addition, NO2 emerged as the best indicator of human activity—represented by car and maritime traffic—due to its response to the relaxation measures. Finally, Lamb weather types were calculated. The unclassified type was the most frequent. However, the greatest concentration changes were observed for anticyclonic and eastern flow types.
Evaluation of Nine Planetary Boundary Layer Turbulence Parameterization Schemes of the Weather Research and Forecasting Model Applied to Simulate Planetary Boundary Layer Surface Properties in the Metropolitan Region of São Paulo Megacity, Brazil
Authors: Tito Janet Valdés , Oliveira Amauri Pereira de , Sánchez Maciel Piñero , Fornaro Adalgiza
Published: July 2024

This study evaluates nine Planetary Boundary Layer (PBL) turbulence parameterization schemes from the Weather Research and Forecasting (WRF) mesoscale meteorological model, comparing hourly values of meteorological variables observed and simulated at the surface of the Metropolitan Region of São Paulo (MRSP). The numerical results were objectively compared with high-quality observations carried out on three micrometeorological platforms representing typical urban, suburban, and rural land use areas of the MRSP, during the 2013 summer and winter field campaigns as part of the MCITY BRAZIL project. The main objective is to identify which PBL scheme best represents the diurnal evolution of conventional meteorological variables (temperature, relative and specific humidity, wind speed, and direction) and unconventional (sensible and latent heat fluxes, net radiation, and incoming downward solar radiation) on the surface. During the summer field campaign and over the suburban area of the MRSP, most PBL scheme simulations exhibited a cold and dry bias and overestimated wind speed. They also overestimated sensible heat flux, with high agreement index and correlation values. In general, the PBL scheme simulations performed well for latent heat flux, displaying low mean bias error and root square mean error values. Both incoming downward solar radiation and net radiation were also accurately simulated by most of them. The comparison of the nine PBL schemes indicated the local Mellor-Yamada-Janjic (MYJ) scheme performed best during the summer period, particularly for conventional meteorological variables for the land use suburban in the MRSP. During the winter field campaign, simulation outcomes varied significantly based on the site’s land use and the meteorological variable analyzed. The MYJ, Bougeault-Lacarrère (BouLac), and Mellor-Yamada Nakanishi-Niino (MYNN) schemes effectively simulated temperature and humidity, especially in the urban land use area. The MYNN scheme also simulated net radiation accurately. There was a tendency to overestimate sensible and latent heat fluxes, except for the rural land use area where they were consistently underestimated. However, the rural area exhibited superior correlations compared to the urban area. Overall, the MYJ scheme was deemed the most suitable for representing the convectional and nonconventional meteorological variables on the surface in all urban, suburban, and rural land use areas of the MRSP.
Influence of Particle Surface Energy and Sphericity on Filtration Performance Based on FLUENT-EDEM Coupling Simulation
Authors: Wu Qing , Xing Zhenqiang , Chen Dejun , Chen Jianwu , Yang Bin , Zhong Jianfang , Huang Hong , Ma Zhifei , Huang Shan , You Da , Li Jianlong , Wu Daishe
Published: July 2024

The adhesion of dust particles on the surface of the dust collector tends to cause great resistance to the dust collector and affects the operating efficiency. In order to visualize particles in the filtration process and to grasp the mechanism of particle viscosity and sphericity on filtration performance, a numerical simulation study was conducted to investigate the deposition behavior of particles during filtration, employing FLUENT-EDEM coupling technology. By examining the deposition process, the role of particle characteristics on dust behavior within the entire filtration system was elucidated. The effects of varying particle surface energy and particle sphericity on filtration pressure drop and cake porosity were analyzed. The findings reveal that under the force of the air, particles on the surface of the filter membrane experience compaction, leading to a reduction in the porosity of the formed cake layer. The diminution of porosity serves to impede the air, consequently augmenting the pressure drop across the filtration system and hindering the operational efficacy of the dust collector. As the surface energy of the particles increases, the adhesive forces between particles are intensified, leading to an elevation in the porosity of the cake layer and a subsequent decrease in the pressure drop. When the surface energy of the particles is increased from 0.01 J/m2 to 0.04 J/m2, the porosity experiences a modest increase of only 9.1%, yet the pressure drop is significantly reduced by half, amounting to a decrease of 1594 Pa. Under high particle surface energy, as filtration air velocity increases, particles are compressed, resulting in a decrease in cake porosity and an increase in pressure drop. Concurrently, our findings indicate that as the sphericity of particles increases, their surfaces become increasingly smooth which in turn results in a decreased porosity of the cake layer and, consequently, an elevation in the filtration pressure drop.
Center-like Subsets in Semiprime Rings with Multiplicative Derivations
Authors: Aljohani Sarah Samah , Sögütcü Emine Koç , Rehman Nadeem ur
Published: July 2024

We introduce center-like subsets Z∘*(A,d),Z∘**(A,d), where A is the ring and d is the multiplicative derivation. In the following, we take a new derivation for the center-like subsets existing in the literature and establish the relations between these sets. In addition to these new sets, the theorems are generalized as multiplicative derivations instead of the derivations found in previous studies. Additionally, different proofs are provided for different center-like sets. Finally, we enrich this article with examples demonstrating that the hypotheses we use are necessary.