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Abstract Unmanned Aerial Vehicles are useful tools for many applications. However, autonomous path planning for Unmanned Aerial Vehicles in unfamiliar environments is a challenging problem when facing a series of problems such as poor consistency, high influence by the native controller of the Unmanned Aerial Vehicles. In this paper, we investigate reinforcement learning-based autonomous local path planning methods for Unmanned Aerial Vehicles with high autonomous decision-making capability and locally high portability. We propose an autonomous local path planning algorithm based on the TD3 strategy to solve the problem of local obstacle avoidance and path planning in unfamiliar environments using autonomous decision-making of Unmanned Aerial Vehicles. The simulation results on Gazebo show that our method can effectively realize the autonomous local path planning task for Unmanned Aerial Vehicles, the success rate of path planning with our method can reach 93% under the interference of no obstacles, and 92% in the environment with obstacles. Finally, our method can be used for autonomous path planning of Unmanned Aerial Vehicles in unfamiliar environments.

The southern margin of the Junggar Basin (SMJB) represents the typical intra‐continental basin–orogen coupling structure of the Central Asian Orogenic Belt and is a key area to study the deformation mechanisms and the geodynamic evolution processes of the North Tianshan Orogen. Herein, we compiled data from boreholes, gravity and magnetism, seismicity‐ and magnetotellurics‐derived geological profiles and field data to recover the sedimentary history of the SMJB and discuss the intra‐continental deformation driven by the basin–orogen coupling mechanism. The results show that the sedimentary and tectonic evolution of the SMJB were both profoundly controlled by the intra‐continental orogeny of the North Tianshan Orogen and its coupling with the Junggar Basin during the Meso‐Cenozoic period. Consequently, the SMJB is dominated by thick‐skinned thrust nappes accompanied with strike‐slip faulting and thrusting. The foreland thrust belt of the SMJB is characterised by three structural belts, from south to north, including the basement‐involved thrust belt, the cover‐detached foreland thrust‐fold belt and the thrusted foreland basement uplifts, respectively. Meanwhile, as indicated by the geometry of the basement thrusts, the thickness of décollements and the structure of the foreland basement, the stress field in the SMJB is obviously stronger in the west and weaker in the east. The sedimentation and deformation migrated northwards into the basin area in a stepwise process, that corresponded to the pace of the overthrusting of the North Tianshan Orogen onto the Junggar Block. Intense regional compression induced the rapid uplifting of the North Tianshan and the significant crustal shortening of the Junggar Block, driving the three structural belts to form accordingly during the Late Jurassic to the Neogene. There are at least one or two décollements within the SMJB, representing one of the main features of basin–orogen coupling structure in most cases. The décollement of some layers represents the decoupling of the sedimentary cover with the basement, which helps to accommodate the lateral crustal shortening of the SMJB by a translation into vertical uplift. As a result, the detached foreland thrust‐fold belts in the shallow level of the upper crust overthrusted upon the basement‐involved nappes of the mountain's side, forming the opposite thrust system. Coevally, the basement of the basin, in the deep level of the upper crust keeps underthrusting beneath the North Tianshan Orogen, forming a typical crocodile mouth‐like structure. In general, both the shallow and deep deformation in the SMJB have been formed by the intense intra‐continental compression during the Meso‐Cenozoic, which were driven by the basin–orogen coupling mechanism. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to assess the predictive capabilities of a radiomics signature obtained from arterial spin labeling (ASL) imaging in forecasting and detecting stages of kidney damage in patients with diabetes mellitus (DM), as well as to analyze the correlation between texture feature parameters and biological clinical indicators. Additionally, this study seeks to identify the imaging risk factors associated with early renal injury in diabetic patients, with the ultimate goal of offering novel insights for predicting and diagnosing early renal injury and its progression in patients with DM. Materials and methods: In total, 42 healthy volunteers (Group A); 68 individuals with diabetes (Group B) who exhibited microalbuminuria, defined by a urinary albumin-to-creatinine ratio (ACR)< 30 mg/g and an estimated glomerular filtration rate (eGFR) within the range of 60–120 mL/min/1.73m²; and 53 patients with diabetic nephropathy (Group C) were included in the study. ASL using magnetic resonance imaging (MRI) at 3.0T was conducted. The radiologist manually delineated regions of interest (ROIs) on the ASL maps of both the right and left kidney cortex. Texture features from the ROIs were extracted utilizing MaZda software. Feature selection was performed utilizing a range of methods, such as the Fisher coefficient, mutual information (MI), probability of classification error, and average correlation coefficient (POE + ACC). A radiomics model was developed to detect early diabetic renal injury, extract imaging risk factors associated with early diabetic renal injury, and examine the relationship between significant texture feature parameters and biological clinical indicators. Patients with DM and kidney injury were followed prospectively. The study utilized seven machine learning algorithms to develop a detective radiomics model and a comprehensive predictive model for assessing the progression of kidney damage in patients with DM. The diagnostic efficacy of the models in detecting variations in diabetic kidney damage over time was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Empower (R) was used to establish a correlation between clinical biological indicators and texture feature metrics. Statistical analysis was conducted using R, Python, MedCalc 15.8, and GraphPad Prism 8. Results: A total of 367 texture features were extracted from the ROIs in the kidneys and refined based on selection criteria using MaZda software across groups A, B, and C. The renal blood flow (RBF) values of the renal cortex in groups A, B, and C exhibited a decreasing trend, with values of 256.458 ± 54.256 mL/100g/min, 213.846 ± 52.109 mL/100g/min, and 170.204 ± 34.992 mL/100g/min, respectively. There was a positive correlation between kidney RBF and eGFR (r = 0.439, P<0.001). The negative correlation between RBF and various clinical parameters including urinary albumin-to-creatinine ratio (UACR), body mass index (BMI), diastolic blood pressure (DBP), blood urea nitrogen (BUN), and serum creatinine (SCr) was investigated. Through the use of a least absolute shrinkage and selection operator (LASSO) regression model, the study identified the eight most significant texture features and biological indicators, namely GeoY, GeoRf, GeoRff, GeoRh, GeoW8, GeoW12, S (0, 4) Entropy, and S (5, -5) Entropy. Spearman correlation analysis revealed associations between imaging markers in early diabetic patients with kidney damage and factors such as age, systolic blood pressure (SBP), Alanine Transaminase (ALT), Aspartate Amino Transferase (AST) albumin, uric acid (UA), microalbuminuria (UMA), UACR, 24h urinary protein, fasting blood glucose (FBG), two hours postprandial blood glucose (P2BG), and HbA1c. The study utilized ASL imaging as a detection model to identify renal injury in patients with DM across different stages, achieving a sensitivity of 85.1%, specificity of 65.5%, and an AUC of 0.865. Additionally, a comprehensive prediction model combining imaging labels and biological indicators, with the naive Bayes machine learning algorithm as the best model, demonstrated an AUC of 0.734, accuracy of 0.74, and precision of 0.43. Conclusion: ASL imaging sequences demonstrated the ability to accurately detect alterations in kidney function and blood flow in patients with DM. Strong associations were observed between renal blood flow values in ASL imaging and established clinical biomarkers. These values show promise in detecting early microstructural changes in the kidneys of diabetic patients. Utilizing image markers in conjunction with clinical indicators was effective in identifying early renal dysfunction and its progression in individuals with DM. Furthermore, the integration of imaging texture feature parameters with clinical biomarkers holds significant potential for predicting early renal damage and its progression in patients with diabetes. [ABSTRACT FROM AUTHOR]

The digital twin workshop is the basic unit for realizing smart manufacturing. Its model is the prerequisite for applying digital twins in workshops. Geometric model assembly is the primary concern in workshop-level digital twin modeling. However, current research lacks sufficient focus on the efficiency and application modes of equipment mesh model assembly features in geometric model assembly, hindering the construction of the workshop-level digital twin model. The equipment-level digital twin model typically only contains assembly information between individual parts and is generally represented by polygonal meshes; the lack of assembly information between equipment models impedes the assembly of the workshop-level digital twin model. To improve the efficiency of workshop-level digital twin modeling and the reusability of the original 3D assembly information, this paper proposes an assembly feature construction method of equipment mesh model for digital twin workshops. A 3D assembly information model is constructed to describe the information related to the equipment geometric model and the assembly features. Then, the assembly features of the equipment mesh model are gradually positioned preliminarily and precisely, achieving precise mapping of the assembly features in the 3D information model to the equipment mesh model and efficient construction of equipment mesh model assembly features. Finally, with the construction and application of equipment mesh model assembly features in an arc welding workshop and storage workshop as an example, the feasibility of the method in improving the modeling efficiency is verified through relevant comparative experiments. [ABSTRACT FROM AUTHOR]

This study explores the integration of photovoltaic (PV) shading devices and vertical farming (VF) in school buildings to optimize indoor daylight, thermal comfort, and energy performance across three different climate regions in China: Beijing, Shanghai, and Shenzhen. With rapid urbanization and increasing energy consumption in educational buildings, this research investigates the impact of innovative facade design on both energy efficiency and occupant comfort. Through parametric simulations and multi-objective optimization, various PV and VF facade prototypes were evaluated to determine the best configurations for reducing energy consumption while enhancing thermal and visual comfort. This study optimized facade systems integrating photovoltaic and vertical farming for school buildings in Shenzhen, Beijing, and Shanghai. Key findings include: In Shenzhen, Model B's UDI increased by 5.1% and Model C by 19.02%, with glare areas reduced by 5.4% and 21.40% and stable thermal comfort (PMV 0.52–0.59) throughout the year. In Beijing, Model B's UDI decreased by 0.2%, while Model C increased by 6.55%. Glare areas reduced by 2.92% and 14.35%, with improved winter comfort (PMV −0.35 to −0.1). In Shanghai, Model C's UDI increased by 6.7%, but summer thermal discomfort was notable (PMV up to 1.2). The study finds that PV shading systems combined with vertical farming can provide significant energy savings, reduce greenhouse gas emissions, and offer organic vegetable production within school environments. The findings suggest that integrating these systems into the building envelope can optimize the energy performance of school buildings while improving the comfort and well-being of students and staff. [ABSTRACT FROM AUTHOR]

A focused crawler automatically retrieves, organizes, and extracts specific topic-related information from the internet for analysis and application. Currently, most focused crawlers assess the relevance of web pages to a given topic through methods such as keyword matching, semantic analysis, and link structures. However, these existing focused crawlers suffer from issues such as misleading directions and reduced accuracy due to the lack of semantic analysis of topic terms, as well as biased computation of topic relevance caused by the absence of effective weighting factors. To solve the above-mentioned problems, this study proposes a semantic and optimized focused crawler based on Semantic Graph and Genetic Algorithm. The proposed crawler eliminates ambiguous terms by constructing a semantic graph, optimizes the weighting factors of topic relevance with asymmetry by using a genetic algorithm, and combines both above two points to predict the priority of each unvisited hyperlink. The experiment results indicate that the proposed SG-GA Crawler improves the evaluation indicators compared with the other three focused crawlers, including VSM Crawler, SSRM Crawler, and SG Crawler. More specifically, the percentage improvement achieved by the proposed method exceeds 19%, 19%, and 13% in terms of three evaluation indicators, including the number of relevant web pages, acquisition rate, and average relevance, respectively. In conclusion, the proposed focused crawler can grab more quantity and higher quality topic-related web pages from the Internet. [ABSTRACT FROM AUTHOR]

Automated generation of geometric models from point cloud data holds significant importance in the field of computer vision and has expansive applications, such as shape modeling and object recognition. However, prevalent methods exhibit accuracy issues. In this study, we introduce a novel hierarchical neural network that utilizes recursive PointConv operations on nested subdivisions of point sets. This network effectively extracts features, segments point clouds, and accurately identifies and computes parameters of regular geometric primitives with notable resilience to noise. On fine-grained primitive detection, our approach outperforms Supervised Primitive Fitting Network (SPFN) by 18.5% and Cascaded Primitive Fitting Network (CPFN) by 11.2%. Additionally, our approach consistently maintains low absolute errors in parameter prediction across varying noise levels in the point cloud data. Our experiments validate the robustness of our proposed method and establish its superiority relative to other methodologies in the extant literature. [ABSTRACT FROM AUTHOR]

The Sn–Pb mixed perovskite, renowned for its narrow band gap and exceptional photoelectric properties, has garnered significant attention in photodetection, particularly as a potential alternative to near-infrared materials. However, the photodetector often suffers from unfavorable energy band arrangements at the heterogeneous interface between the perovskite and the electrode, which seriously hinder the further improvement of device performance. Here, a broadband, high-performance photodetector based on the Sn–Pb mixed perovskite has been achieved through fine-tuning of the energy band alignment at the perovskite/electrode interface. The resultant photodetector demonstrates an impressive broadband response spanning 367 to 1200 nm. At 395 nm, it achieves remarkable metrics: an ultra-high responsivity of 1.2 × 103 A W−1 and an outstanding detectivity of 4.4 × 1013 Jones, accompanied by a fast rise/fall time of 2.4 ms/5.4 ms. In particular, the sub-bandgap states of the Sn–Pb mixed perovskite enable the photoresponse beyond the bandgap limit. At 1200 nm, its responsivity and detectivity reach leading levels of 24.73 A W−1 and 1.01 × 1012 Jones, respectively. Moreover, it exhibits exceptional flexibility, enduring over 500 bending cycles without encapsulation while maintaining a decent photoelectric response. These findings underscore the critical role of optimizing interface contact in realizing high-performance, broadband perovskite photodetectors. [ABSTRACT FROM AUTHOR]

We introduce a novel computational paradigm for reconstructing solid computer-aided design (CAD) features from the surface of a segmented manifold triangular mesh. This paradigm addresses the challenge of capturing high-level design semantics for manifold triangular meshes and facilitates parametric and variational design capabilities. We categorize four prevalent features, namely extrusion, rotation, sweep, and loft, as generalized swept bodies driven by cross-sectional sketches and feature paths, providing a unified mathematical representation for various feature types. The numerical optimization-based approach conducts geometric processing on the segmented manifold triangular mesh patch, extracting cross-sectional sketch curves and feature paths from its surface, and then reconstructing appropriate features using the Open CASCADE kernel. We employ the personalized three-dimensional (3D) printed model as a case study. Parametric and variant designs of the 3D-printed models are achieved through feature reconstruction of the manifold triangular mesh obtained via 3D scanning. [ABSTRACT FROM AUTHOR]