Search Results (117)

Three-dimensional semantic segmentation plays a crucial role in advancing scene understanding in fields such as autonomous driving, drones, and robotic applications. Existing studies usually improve prediction accuracy by fusing data from vehicle-mounted cameras and vehicle-mounted LiDAR. However, current semantic segmentation methods face two main challenges: first, they often directly fuse 2D and 3D features, leading to the problem of information redundancy in the fusion process; second, there are often issues of image feature loss and missing point cloud geometric information in the feature extraction stage. From the perspective of multimodal fusion, this paper proposes a point cloud semantic segmentation method based on a multimodal gated attention mechanism. The method comprises a feature extraction network and a gated attention fusion and segmentation network. The feature extraction network utilizes a 2D image feature extraction structure and a 3D point cloud feature extraction structure to extract RGB image features and point cloud features, respectively. Through feature extraction and global feature supplementation, it effectively mitigates the issues of fine-grained image feature loss and point cloud geometric structure deficiency. The gated attention fusion and segmentation network increases the network’s attention to important categories such as vehicles and pedestrians through an attention mechanism and then uses a dynamic gated attention mechanism to control the respective weights of 2D and 3D features in the fusion process, enabling it to solve the problem of information redundancy in feature fusion. Finally, a 3D decoder is used for point cloud semantic segmentation. In this paper, tests will be conducted on the SemanticKITTI and nuScenes large-scene point cloud datasets. Full article

In this manuscript, we investigate a fractional-order conformable three-dimensional chaotic financial model with interest rate, investment demand, and price index compartments. On the application of fixed-point theorems and nonlinear analysis, we establish theoretical results regarding the existence and uniqueness of a solution and also study Ulam–Hyers criteria for the stability of the solution of the considered system. Further, we use the fractional-order Runge–Kutta (RK-4) method to approximate the solution of our problem. Also, deep neural network (DNN) techniques are applied to investigate the model from artificial intelligence (AI) perspectives. Numerical simulation shows that it reproduces accurately the qualitative dynamics and confirms the theoretical stability results of the mentioned system. Subsequently, for the DNN analysis, we follow the Levenberg–Marquardt algorithm using Matlab 2023. Different quantities like the root-mean-square error (RMSE), mean squared error (MSE), and regression coefficient and a comparison with numerical data are presented graphically. Also, absolute errors between numerical values and those predicted by DNNs corresponding to different fractional orders are presented. Full article

Material stocks (MS) serve as essential physical foundations for socio–economic systems, reflecting the accumulation, transformation, and consumption of resources over time and space. Positioned at the intersection of environmental and socio–economic systems, MS are increasingly recognized as leverage points for advancing sustainability. However, there is currently a lack of comprehensive overview, making it difficult to fully capture the latest developments and cutting–edge research. We adopt a systems perspective to conduct a comprehensive bibliometric and thematic review of 602 scholarly publications on MS research. The results showed that MS research encompasses has three development periods: preliminary exploration (before 2007), rapid development (2007–2016), and expansion and deepening (after 2016). MS research continues to deepen, gathering multiple teams and differentiating into diverse topics. MS research has evolved from simple accounting to intersection with socio–economic, resources, and environmental systems, and shifted from relying on statistical data to integrating high–spatio–temporal–resolution geographic big data. MS research is shifting from problem revelation to problem solving, constantly achieving new developments and improvements. In the future, it is still necessary to refine MS spatio–temporal distribution, reveal MS’s evolution mechanism, establish standardized databases, strengthen interaction with other systems, enhance problem–solving abilities, and provide powerful guidance for the formulation of dematerialization and decarbonization policies to achieve sustainable development. Full article

In 2014, Paul Yiu constructed two equilateral triangles inscribed in a Kiepert hyperbola associated with a reference triangle. It was asserted that each of the equilateral triangles is triply perspective with the reference triangle, and in each case, the corresponding three perspectors are collinear. In this note, we provide proof of his assertions. Furthermore, as an analogue of Lemoine’s problem, we formulated and answered the question of how to recover the reference triangle given a Kiepert hyperbola, one of the two Fermat points and one vertex of the reference triangle. Full article

Nutritional risk factors are country-specific and change over time, requiring systematic verification. Objective: The study was designed to develop a nutritional risk factors model for seniors living in a Polish community. Methods: The pilot study was conducted in 2022 and 2023 among 301 people aged 60 and older in the Lower Silesia region of Poland. The questionnaire contained 107 test items describing dietary problems rated on a five-point Likert scale. The pre-study concerned understanding of the test items, rating the reproducibility (kappa statistic) and reliability of the scale (α-Cronbach coefficient). The factor structure of the model was developed using structural equation modelling (SEM) in the program R (version 4.3.2.). An exploratory factor analysis (EFA) extracted the three-factor model. Results: The factors were described as unhealthy eating (eight test items), irregularities related to meals (four test items), and perception of body weight (four test items). The model was verified using confirmatory factor analysis (CFA). The model’s acceptability was confirmed based on data matching indexes, convergent accuracy, differential accuracy, and measurement reliability. There was variation in the identified nutritional risk factors by gender, education, social activity, and family relationships. Conclusions: Focusing on irregularities in nutrition and perception of body weight as nutritional risk factors reveals a very narrow perspective in diagnosing nutritional risk, thus further testing of the model, in a representative group of older people in Poland and other countries, is necessary to confirm the results obtained. Full article

Aiming at the problems of insufficient function, cultural aphasia, and blunted perception faced by contemporary urban public space, this study explores the potential of paper-based materials in enhancing spatial quality and realizing spatial expansion effects, providing new solutions for urban renewal. Taking the sensory plasticity, visual aesthetics, cultural carrying, and ecological and environmental protection of paper materials as the entry point, we constructed a theoretical model of “paper art space expansion”. Through the design intervention strategy, we explored the application of paper art in the design of interface, space, art creation, and cultural empowerment from visual and tactile perspectives. Through course design, artist interviews, and questionnaire analysis, the study shows that (1) paper material can achieve a balance between function and aesthetics through multi-dimensional design strategies; (2) its environmental attributes and emotional healing value can effectively enhance the emotional connection between people and space; and (3) the contemporary translation of paper art provides an important path for cultural empowerment. This study forms a three-dimensional design framework of “Perception Layer-Technology Layer-Cultural Layer” and proposes a set of innovative models for the application of paper materials in contemporary art and space design, which can provide support for the expansion of space and the increase in content. Future research will focus on the transition of paper art from decoration to the design paradigm of the cultural narrative of intelligent space, deepening the value of paper material as an ecological, cultural, and technological medium, and open up a new direction for the theory and practice of spatial design. At the same time, more attention will be paid to the exploration of the possibility of sensory healing for the blind and other special populations. Full article

Proteins are biomolecules characterized by uncommon chemical and physicochemical complexities coupled with extreme responsiveness to even minor chemical modifications or environmental variations. Since the shape that proteins assume is fundamental for their function, understanding the chemical and structural bases that drive their three-dimensional structures represents the central problem for an atomic-level interpretation of biology. Not surprisingly, this question has progressively become the Holy Grail of structural biology (the folding problem). From this perspective, we initially describe and discuss the different formulations of the folding problem. In the present manuscript, the folding problem is framed from a historical perspective, effectively highlighting the progress made in the last lustrum. We chronologically summarize the major contributions that traditional methodologies provide in approaching this multifaceted problem. We then describe the recent advent and evolution of predictive approaches based on machine learning techniques that are revolutionizing the field by pointing out the potentialities and limitations of this approach. In the final part of the perspective, we illustrate the contribution that computational approaches will make in current structural biology to overcome the limitations of the reductionist approach of studying individual molecules to afford the atomic-level characterization of entire cellular compartments. Full article

In robot navigation and manipulation, accurately determining the camera’s pose relative to the environment is crucial for effective task execution. In this paper, we systematically prove that this problem corresponds to the Perspective-3-Point (P3P) formulation, where exactly three known 3D points and their corresponding 2D image projections are used to estimate the pose of a stereo camera. In image-based visual servoing (IBVS) control, the system becomes overdetermined, as the six degrees of freedom (DoF) of the stereo camera must align with nine observed 2D features in the scene. When more constraints are imposed than available DoFs, global stability cannot be guaranteed, as the camera may become trapped in a local minimum far from the desired configuration during servoing. To address this issue, we propose a novel control strategy for accurately positioning a calibrated stereo camera. Our approach integrates a feedforward controller with a Youla parameterization-based feedback controller, ensuring robust servoing performance. Through simulations, we demonstrate that our method effectively avoids local minima and enables the camera to reach the desired pose accurately and efficiently. Full article

With the rapid process of urbanization, water conflicts between different water use industries and areas are increasing. Therefore, China has implemented the three-cordons system of water resources management since 2012, when how to make more reasonable regulation of water resources became an urgent problem in most areas of China. In this study, taking the Yuanhe River Basin as an example, an integrated model for the simulation and regulation of water resources considering water quantity and quality from a river basin perspective was proposed, where the water supply was constrained by requirements of water resources management. First, the water resources system was conceptualized into a topologically hydraulic network in the form of point, line, and area elements, including 80 water use units and 79 water supply units. Then, taking the water quantity and quality as constraint conditions in the water supply for corresponding water use sectors, a management-oriented integrated model was established, which highlights the cordon control of the total water use and the pollution load limits of a basin. Finally, through a model simulation, the total water supply was controlled by regulating the water resources, while the pollutant loads into rivers depended on the discharge of water users. Based on the model, strategies for the utilization of water resources and achieving emission reductions of pollution loads were provided. The results of the proposed model in the Yuanhe River Basin showed that benchmarked against the total water demand of 1.705 billion m³, the water shortage was 212 million m³ with a rate of 13.5%, and the loads of COD (Chemical Oxygen Demand) and NH3-N (Ammonia Nitrogen) were 29,096.7 and 2587.3 tons, respectively. The model can provide support for integrated water resources regulation in other basins or regions through a simulation of the natural–social water resources systems, and help stakeholders and decision-makers establish and implement advantageous strategies for regional efficient utilization of water resources. Full article

With the increase in installed capacity of wind turbines, the stable operation of the power system has been affected. Accurate prediction of wind power is an important condition to ensure the healthy development of the wind power industry and the safe operation of the power grid. This paper first introduces the current status of wind power prediction methods under normal weather, and introduces them in detail from three aspects: physical model method, statistical prediction method and combined prediction method. Then, from the perspectives of numerical simulation analysis and statistical prediction methods, the wind power prediction method under icy conditions is introduced, and the problems faced by the existing methods are pointed out. Then, the accurate prediction of wind power under icing weather is considered, and two possible research directions for wind power prediction under icy weather are proposed: a statistical prediction method for classifying and clustering wind turbines according to microtopography, combining large-scale meteorological parameters with small-scale meteorological parameter correlation models and using machine learning for cluster power prediction, and a power prediction model converted from the power prediction model during normal operation of the wind turbine to the power prediction model during icing. Finally, the research on wind power prediction under ice-covered weather is summarized, and further research in this area is prospected. Full article

Trip faults are obviously increased by frequent lightning strikes, and increasing lightning trip-out seriously affects a system’s stability and power supply reliability. In this paper, the reasons for high lightning trip-out rates in electric power transmission lines are analyzed in detail from three perspectives, as follows: the substandard lightning resistance level, lightning complexity at a mid-point between towers, and the complexity of first and subsequent lightning stroke conditions. Experiments and simulations demonstrate that the solid-phase gas arc-extinguishing method has a strong ability to extinguish power–frequency continuous-current arcs and to protect against first and subsequent lightning strokes. Since the time taken by gas arc-extinguishing is much less than the response time of relay protection, trip accidents caused by lightning strikes can be avoided and the trip rates of lightning strikes can be reduced using this method. The case analysis and practical operation results show that the solid-phase gas arc-extinguishing lightning protection method can reduce the lightning trip-out rate by more than 90%, completely solve the problem of high lightning trip-out rates, and significantly improve the reliability of power supply. Full article

Air compressors play an important role in energy, mining, civil engineering, and transportation engineering. However, the abnormal vibration and noise of air compressors may pose a serious threat to the structural stability and smooth operation of these types of engineering equipment. To address the broadband noise and vibration problems of a new oil-free piston air compressor, we developed a hybrid optimization method that combines experimental testing, theoretical evaluation, and numerical simulation. Firstly, we conduct noise experiment testing, identify the frequency band of aerodynamic noise using a coherence analysis method, and design orthogonal experiments to further optimize pipeline noise. Then, the vibration characteristics were discussed from both theoretical and simulation perspectives. The dynamic balance has been redesigned on the spindle counterweight plate to reduce the force on the bearings, and a multi-body dynamics model has been constructed to demonstrate the effectiveness of the optimization. Subsequently, a finite element model of the compressor housing was established to analyze the radiation noise characteristics. Finally, three weak points in the structure were selected as key objects, and the structural stiffness was increased to improve vibration stability. The simulation results of radiated noise show that the proposed design scheme can effectively reduce vibration and noise, with a maximum noise reduction rate of 7.45%. Full article

Three-dimensional point cloud recognition is a very fundamental work in fields such as autonomous driving and face recognition. However, in real industrial scenarios, input point cloud data are often accompanied by factors such as occlusion, rotation, and noise. These factors make it challenging to apply existing point cloud classification algorithms in real industrial scenarios. Currently, most studies enhance model robustness from the perspective of neural network structure. However, researchers have found that simply adjusting the neural network structure has proven insufficient in addressing the decline in accuracy caused by data corruption. In this article, we use local feature descriptors as a preprocessing method to extract features from point cloud data and propose a new neural network architecture aligned with these local features, effectively enhancing performance even in extreme cases of data corruption. In addition, we conducted data augmentation to the 10 intentionally selected categories in ModelNet40. Finally, we conducted multiple experiments, including testing the robustness of the model to occlusion and coordinate transformation and then comparing the model with existing SOTA models. Furthermore, in actual scene experiments, we used depth cameras to capture objects and input the obtained data into the established model. The experimental results show that our model outperforms existing popular algorithms when dealing with corrupted point cloud data. Even when the input point cloud data are affected by occlusion or coordinate transformation, our proposed model can maintain high accuracy. This suggests that our method can alleviate the problem of decreased model accuracy caused by the aforementioned factors. Full article

In the global ecosystem, the slow decomposition of coniferous forest litter has caused a number of ecological problems, among which is the decay of China’s Pinus massoniana litter. It has been pointed out that converting pure P. massoniana plantations into mixed forests with broadleaf species can improve ecosystem services. Therefore, the selection of mixed species is key for the success or failure of the conversion of near-natural forests. In this study, from the perspective of apoplastic decomposition, the leaf litter of P. massoniana was mixed with three common native broadleaf species, namely Choerospondias axillaries, Cinnamomum camphora, and Cyclobalanopsis glauca, using an indoor incubation method to systematically analyse the differences in the decomposition rates of apoplastic material in each mixture, and to provide a theoretical basis for the selection and mixing of tree species for the management of near-natural forests in P. massoniana forests. After 175 days of indoor incubation of the foliar litter under dark conditions at 25 °C, the residual dry matter of the mixed apoplastic litter of P. massoniana and the three broadleaf trees was lower than that of P. massoniana. It indicated that the incorporation of broadleaf apoplastic foliage promoted litter decomposition, with the most pronounced effect in the case of admixture with C. Camphora. Compared with the group of pure P. massoniana alone, the remaining mass and residual rate decreased by 0.56 g and 9.45%, respectively. The regression equation of Olson’s negative exponential decay model showed that the P. massoniana + C. Camphora mixture had the fastest decomposition rate (k) of 1.305, an increase of 0.237, a decrease in half-life of 0.11 years, and a decrease in turnover period of 0.49 year, compared to the P. massoniana alone group. Most of the measured values throughout the incubation period were significantly lower than the predicted values, suggesting that there was a non-additive and synergistic effect of litter mixing. Full article

Environmental unsustainability in coupled human–nature systems is accumulating. Yet, there is no accreditation requirement for unsustainability resolution competency in higher education. Thus, a new and complete representation of the pedagogy for unsustainability resolution competence has been induced, using what is already available and working. The nature of unsustainability problems points to collaboration and holism attitudes. Resolution requires social skills, namely participation, perspective taking, and the generation of social capital, and cognitive skills, namely project management, knowledge building, and modeling. Resolution is scaffolded in three successive steps during the collaborative process within a systems approach: (i) collapse complexity; (ii) select a path/trajectory; and (iii) operationalize a plan. The hierarchically cumulative abilities toward unsustainability resolution competence are to source data and information about the coupled human–nature system (SEARCH); simplify the dynamics of the human–nature system (SIMULATE); generate and test alternative paths and end points for the coupled human–nature system (STRATEGIZE); chose a favorable path among the available alternatives (SELECT); operationalize the favorable path into a plan (strategy–program–project) with measurable management and policy objectives (IMPLEMENT); and develop criteria/indicators to monitor and adjust when necessary the implementation of the plan toward system goals (STEER). For each one of these learning objectives, the Bloom’s taxonomy and a progression from behaviorist through cognitivist to constructivist tools apply. The development of mastery requires the comparison and contrast of many similar cases with the same unsustainability problem and project-based learning with specific cases for deep learning. In this way, it is the resolutions of unsustainability in human–nature systems that will be accumulating. Full article