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As the demand for spatial positioning continues to grow, positioning methods based on inertial measurement units (IMUs) are emerging as a promising research topic due to their low cost and robustness against environmental interference. These methods are particularly well suited for global navigation satellite system (GNSS)-denied environments and challenging visual scenarios. While existing algorithms for position estimation using IMUs have demonstrated some effectiveness, there is still significant room for improvement in terms of estimation accuracy. Current approaches primarily treat IMU data as simple time series, neglecting the frequency-domain characteristics of IMU signals. This paper emphasizes the importance of frequency-domain information in IMU signals and proposes a novel neural network, WINNet (Wavelet Inertial Neural Network), which integrates time- and frequency-domain signals using a wavelet transform for spatial positioning with inertial sensors. Additionally, we collected ground-truth data using a LiDAR setup and combined it with the TLIO dataset to form a new IMU spatial positioning dataset. The experimental results demonstrate that our proposed method outperforms the current state-of-the-art inertial neural network algorithms in terms of the ATE, RTE, and drift error metrics overall. [ABSTRACT FROM AUTHOR]

One of the fundamental parts of Geometric Function Theory is the study of analytic functions in different domains with critical geometrical interpretations. This article defines a new generalized domain obtained based on the quotient of two analytic functions. We derive various properties of the new class of normalized analytic functions X defined in the new domain, including the sharp estimates for the coefficients a 2 , a 3 , and a 4 , and for three second-order and third-order Hankel determinants, H 2 , 1 X , H 2 , 2 X , and H 3 , 1 X . The optimality of each obtained estimate is given as well. [ABSTRACT FROM AUTHOR]

This paper presents a geometric approach to the problems in differential subordination theory. The necessary conditions for a function to be in various subfamilies of the class of starlike functions and the class of Carathéodory functions are studied, respectively. Further, several consequences of the findings are derived. [ABSTRACT FROM AUTHOR]

Many researchers have defined the q-analogous of differential and integral operators for analytic functions using the concept of quantum calculus in the geometric function theory. In this study, we conduct a comprehensive investigation to identify the uses of the Sălăgean q-differential operator for meromorphic multivalent functions. Many features of functions that belong to geometrically defined classes have been extensively studied using differential operators based on q-calculus operator theory. In this research, we extended the idea of the q-analogous of the Sălăgean differential operator for meromorphic multivalent functions using the fundamental ideas of q-calculus. With the help of this operator, we extend the family of Janowski functions by adding two new subclasses of meromorphic q-starlike and meromorphic multivalent q-starlike functions. We discover significant findings for these new classes, including the radius of starlikeness, partial sums, distortion theorems, and coefficient estimates. [ABSTRACT FROM AUTHOR]

This article defines a new operator called the q-Babalola convolution operator by using quantum calculus and the convolution of normalized analytic functions in the open unit disk. We then study a new class of analytic and bi-univalent functions defined in the open unit disk associated with the q-Babalola convolution operator. The main results of the investigation include some upper bounds for the initial Taylor–Maclaurin coefficients and Fekete–Szego inequalities for the functions in the new class. Many applications of the finds are highlighted in the corollaries based on the various unique choices of the parameters, improving the existing results in Geometric Function Theory. [ABSTRACT FROM AUTHOR]

In the current work, by using the familiar q-calculus, first, we study certain generalized conic-type regions. We then introduce and study a subclass of the multivalent q-starlike functions that map the open unit disk into the generalized conic domain. Next, we study potentially effective outcomes such as sufficient restrictions and the Fekete–Szegö type inequalities. We attain lower bounds for the ratio of a good few functions related to this lately established class and sequences of the partial sums. Furthermore, we acquire a number of attributes of the corresponding class of q-starlike functions having negative Taylor–Maclaurin coefficients, including distortion theorems. Moreover, various important corollaries are carried out. The new explorations appear to be in line with a good few prior commissions and the current area of our recent investigation. [ABSTRACT FROM AUTHOR]

The word "symmetry" is a Greek word that originated from "symmetria". It means an agreement in dimensions, due proportion, and arrangement; however, in complex analysis, it means objects remaining invariant under some transformation. This idea has now been recently used in geometric function theory to modify the earlier classical q-derivative introduced by Ismail et al. due to its better convergence properties. Consequently, we introduce a new class of analytic functions by using the notion of q-symmetric derivative. The investigation in this paper obtains a number of the latest important results in q-theory, including coefficient inequalities and convolution characterization of q-symmetric starlike functions related to Janowski mappings. [ABSTRACT FROM AUTHOR]

Many diverse subclasses of analytic functions, q-starlike functions, and symmetric q-starlike functions have been studied and analyzed by using q-analogous values of integral and derivative operators. In this paper, we define a q-analogous value of differential operators for harmonic functions with the help of basic concepts of quantum (q-) calculus operator theory; and introduce a new subclass of harmonic functions associated with the Janowski and q-Mittag–Leffler functions. We obtain several useful properties of the new class, such as necessary and sufficient conditions, criteria for analyticity, compactness, convexity, extreme points, radii of starlikeness, radii of convexity, distortion bounds, and integral mean inequality. Furthermore, we discuss some applications of this study in the form of some results and examples and highlight some known corollaries for verifying our main results presented in this investigation. Finally, the conclusion section summarizes the fact about the (p , q) -variations. [ABSTRACT FROM AUTHOR]

At present, a common drawback of crowd simulation models is that they are mainly simulated in (abstract) 2D environments, which limits the simulation of crowd behaviors observed in real 3D environments. Therefore, we propose a deep reinforcement learning-based model with human-like perceptron and policy for crowd evacuation in 3D environments (HDRLM3D). In HDRLM3D, we propose a vision-like ray perceptron (VLRP) and combine it with a redesigned global (or local) perceptron (GOLP) to form a human-like perception model. We propose a double-branch feature extraction and decision network (DBFED-Net) as the policy, which can extract features and make behavioral decisions. Moreover, we validate our method's ability to reproduce typical phenomena and behaviors through experiments in two different scenarios. In scenario I, we reproduce the bottleneck effect of crowds and verify the effectiveness and advantages of HDRLM3D by comparing it with real crowd experiments and classical methods in terms of density maps, fundamental diagrams, and evacuation times. In scenario II, we reproduce agents' navigation and obstacle avoidance behaviors and demonstrate the advantages of HDRLM3D for crowd simulation in unknown 3D environments by comparing it with other deep reinforcement learning-based models in terms of trajectories and numbers of collisions. [ABSTRACT FROM AUTHOR]

Virtual geographic environments (VGEs) are extensively used to explore the relationship between humans and environments. Crowd simulation provides a method for VGEs to represent crowd behaviors that are observed in the real world. The social force model (SFM) can simulate interactions among individuals, but it has not sufficiently accounted for inter-group and intra-group behaviors which are important components of crowd dynamics. We present the social group force model (SGFM), based on an extended SFM, to simulate group behaviors in VGEs with focuses on the avoiding behaviors among different social groups and the coordinate behaviors among subgroups that belong to one social group. In our model, psychological repulsions between social groups make them avoid with the whole group and group members can stick together as much as possible; while social groups are separated into several subgroups, the rear subgroups try to catch up and keep the whole group cohesive. We compare the simulation results of the SGFM with the extended SFM and the phenomena in videos. Then we discuss the function of Virtual Reality (VR) in crowd simulation visualization. The results indicate that the SGFM can enhance social group behaviors in crowd dynamics. [ABSTRACT FROM AUTHOR]

Due to their strong immersion and real-time interactivity, helmet-mounted virtual reality (VR) devices are becoming increasingly popular. Based on these devices, an immersive virtual geographic environment (VGE) provides a promising method for research into crowd behavior in an emergency. However, the current cheaper helmet-mounted VR devices are not popular enough, and will continue to coexist with personal computer (PC)-based systems for a long time. Therefore, a heterogeneous distributed virtual geographic environment (HDVGE) could be a feasible solution to the heterogeneous problems caused by various types of clients, and support the implementation of spatiotemporal crowd behavior experiments with large numbers of concurrent participants. In this study, we developed an HDVGE framework, and put forward a set of design principles to define the similarities between the real world and the VGE. We discussed the HDVGE architecture, and proposed an abstract interaction layer, a protocol-based interaction algorithm, and an adjusted dead reckoning algorithm to solve the heterogeneous distributed problems. We then implemented an HDVGE prototype system focusing on subway fire evacuation experiments. Two types of clients are considered in the system: PC, and all-in-one VR. Finally, we evaluated the performances of the prototype system and the key algorithms. The results showed that in a low-latency local area network (LAN) environment, the prototype system can smoothly support 90 concurrent users consisting of PC and all-in-one VR clients. HDVGE provides a feasible solution for studying not only spatiotemporal crowd behaviors in normal conditions, but also evacuation behaviors in emergency conditions such as fires and earthquakes. HDVGE could also serve as a new means of obtaining observational data about individual and group behavior in support of human geography research. [ABSTRACT FROM AUTHOR]

Obtaining water body images quickly and reliably is important to guide human production activities and study urban change. This paper presents a fast and accurate method to identify water bodies in complex environments based on UAV high-resolution images. First, an improved U-Net (SU-Net) model is proposed in this paper. By increasing the number of connections in the middle layer of the neural network, more image features can be retained through S-shaped circular connections. Second, aiming at the interference of mixed ground objects and dark ground objects on water detection, the fusion of a deep learning network and visual features is investigated. We analyse the influence of a wavelet transform and grey level cooccurrence matrix (GLCM) on water extraction. Using a confusion matrix to evaluate accuracy, the following conclusions are drawn: (1) Compared with existing methods, the SU-Net method achieves a significant improvement in accuracy, and the overall accuracy (OA) is 96.25%. The kappa coefficient (KC) is 0.952. (2) SU-Net combined with the GLCM has a higher accuracy (OA is 97.4%) and robustness in distinguishing mixed and dark objects. Based on this method, a distinct water boundary in urban areas, which provides data for urban water vector mapping, can be obtained. [ABSTRACT FROM AUTHOR]

The water-level fluctuation zone (WLFZ) of the Three Gorges Reservoir is a serious landslide-prone area. However, current remote sensing methods for landslide mapping and detection in the WLFZ are insufficient because of difficulties in data acquisition and lack of facade information. We proposed a novel shipborne mobile photogrammetry approach for 3D mapping and landslide detection in the WLFZ for the first time, containing a self-designed shipborne hardware platform and a data acquisition and processing workflow. To evaluate the accuracy and usability of the resultant 3D models in the WLFZ, four bundle block adjustment (BBA) control configurations were developed and adopted. In the four configurations, the raw Global Navigation Satellite System (GNSS) data, the raw GNSS data and fixed camera height, the GCPs extracted from aerial photogrammetric products, and the mobile Light Detection and Ranging (LiDAR) point cloud were used. A comprehensive accuracy assessment of the 3D models was conducted, and the comparative results indicated the BBA with GCPs extracted from the aerial photogrammetric products was the most practical configuration (RMSE 2.00 m in plane, RMSE 0.46 m in height), while the BBA with the mobile LiDAR point cloud as a control provided the highest georeferencing accuracy (RMSE 0.59 m in plane, RMSE 0.40 m in height). Subsequently, the landslide detection ability of the proposed approach was compared with multisource remote sensing images through visual interpretation, which showed that the proposed approach provided the highest landslide detection rate and unique advantages in small landslide detection as well as in steep terrains due to the more detailed features of landslides provided by the shipborne 3D models. The approach is an effective and flexible supplement to traditional remote sensing methods. [ABSTRACT FROM AUTHOR]

To overcome the difficulties of quantitatively optimizing and evaluating evacuation guidance systems, we proposed a perception model based on virtual reality (VR) experiments and the social force model (SFM). We used VR and eye tracking devices to carry out experiments. The VR experiment data was mainly used for three purposes: to determine the parameter values of the perception model, to optimize the evacuation guidance system by quantitative analysis, and to validate the perception model. Additionally, we compared the VR experimental and model simulation results before and after the optimization to quantitatively assess the improvement in the optimized evacuation guidance system. The results showed that our model can effectively simulate the perception behaviors of evacuees on the evacuation guidance system and it can quantitatively evaluate different evacuation guidance system schemes. The model simulations showed that the optimized evacuation guidance system improved the evacuation efficiency, with the average escape time and distance of the two starting positions reduced by 37% and 28%, respectively. [ABSTRACT FROM AUTHOR]

Solar3D is an open-source software application designed to interactively calculate solar irradiation on three-dimensional (3D) surfaces in a virtual environment constructed with combinations of 3D-city models, digital elevation models (DEMs), digital surface models (DSMs) and feature layers. The GRASS GIS r.sun solar radiation model computes solar irradiation based on two-dimensional (2D) raster maps for a given day, latitude, surface and atmospheric conditions. With the increasing availability of 3D-city models and demand for solar energy, there is an urgent need for better tools to computes solar radiation directly with 3D-city models. Solar3D extends the GRASS GIS r.sun model from 2D to 3D by feeding the model with input, including surface slope, aspect and time-resolved shading, which is derived directly from the 3D scene using computer graphics techniques. To summarize, Solar3D offers several new features that—as a whole—distinguish this novel approach from existing 3D solar irradiation tools in the following ways. (1) Solar3D can consume massive heterogeneous 3D-city models, including massive 3D-city models such as oblique airborne photogrammetry-based 3D-city models (OAP3Ds or integrated meshes); (2) Solar3D can perform near real-time pointwise calculation for duration from daily to annual; (3) Solar3D can integrate and interactively explore large-scale heterogeneous geospatial data; (4) Solar3D can calculate solar irradiation at arbitrary surface positions including on rooftops, facades and the ground. [ABSTRACT FROM AUTHOR]

Transport emissions and street dust are important sources of summertime air pollution in urban centers. Street greening and buildings have an influence on the diffusion of air pollution from streets. For field measurements, many studies have analyzed the effect of street green space arrangement on the diffusion of air pollution, but these studies have neglected the patterns at the landscape scale. Other studies have analyzed the effects of the large scale of green space on air pollution, but the vertical distribution of street buildings and greening has rarely been considered. In this study, we analyzed the impact of the vertical distribution of urban street green space on summertime air pollution in urban centers on the urban scale for the first time by using a deep-learning method to extract the vertical distribution of street greening and buildings from street view image data. A total of 687,354 street view images were collected. The green index and building index were proposed to quantify the street greening and street buildings. The multilevel regression method was used to analyze the association between the street green index, building index and air pollution indexes. For the cases in this study, including the central urban areas of Beijing, Shanghai and Nanjing, our multilevel regressions results suggested that, in the central area of the city, the vertical distribution of street greening and buildings within a certain range of the monitoring site is association with the summertime air pollution index of the monitoring site. There was a significant negative association between the street greening and air pollution indexes (radius = 1–2 km, NO2, p = 0.042; radius = 3–4 km, AQI, p = 0.034; PM10, p = 0.028). The street length within a certain range of the monitoring site has a positive association with the air pollution indexes (radius = 1–2 km, AQI, p = 0.072; PM10, p = 0.062). With the increase of the distance between streets and the monitoring sites, the association between streets and air pollution indexes decreases. Our findings on the association between the vertical structure of street greening, street buildings and summertime air pollution in urban centers can support urban street planning. [ABSTRACT FROM AUTHOR]

With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has been widely used by local governments of China. Therefore, timely and accurate mapping of urban green plastic covered regions is of great significance to both urban environmental management and the understanding of urban growth status. However, the complex spatial patterns of the urban landscape make it challenging to accurately identify these areas of green plastic cover. To tackle this issue, we propose a deep semi-supervised learning framework for green plastic cover mapping using very high resolution (VHR) remote sensing imagery. Specifically, a multi-scale deformable convolution neural network (CNN) was exploited to learn representative and discriminative features under complex urban landscapes. Afterwards, a semi-supervised learning strategy was proposed to integrate the limited labeled data and massive unlabeled data for model co-training. Experimental results indicate that the proposed method could accurately identify green plastic-covered regions in Jinan with an overall accuracy (OA) of 91.63%. An ablation study indicated that, compared with supervised learning, the semi-supervised learning strategy in this study could increase the OA by 6.38%. Moreover, the multi-scale deformable CNN outperforms several classic CNN models in the computer vision field. The proposed method is the first attempt to map urban green plastic-covered regions based on deep learning, which could serve as a baseline and useful reference for future research. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) remains a major public health problem in China, and its incidence shows certain regional disparities. Systematic investigations of the social and environmental factors influencing TB are necessary for the prevention and control of the disease. Data on cases were obtained from the Chinese Center for Disease and Prevention. Social and environmental variables were tabulated to investigate the latent factor structure of the data using exploratory factor analysis (EFA). Partial least square path modeling (PLS-PM) was used to analyze the complex causal relationship and hysteresis effects between the factors and TB prevalence. A geographically weighted regression (GWR) model was used to explore the local association between factors and TB prevalence. EFA and PLS-PM indicated significant associations between TB prevalence and its latent factors. Altitude, longitude, climate, and education burden played an important role; primary industry employment, population density, air quality, and economic level had hysteresis with different lag time; health service and unemployment played a limited role but had limited hysteresis. Additionally, the GWR model showed that each latent factor had different effects on TB prevalence in different areas. It is necessary to formulate regional measures and strategies for TB control and prevention in China according to the local regional effects of specific factors.