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Point cloud registration is a crucial aspect of computer vision and 3D reconstruction. Traditional registration methods often depend on global features or iterative optimisation, leading to inefficiencies and imprecise outcomes when processing complex scene point cloud data. To address these challenges, the authors introduce a principal direction‐guided local voxelisation structural feature (PDLVSF) approach for point cloud registration. This method reliably identifies feature points regardless of initial positioning. Approach begins with the 3D Harris algorithm to extract feature points, followed by determining the principal direction within the feature points' radius neighbourhood to ensure rotational invariance. For scale invariance, voxel grid normalisation is utilised to maximise the point cloud's geometric resolution and make it scale‐independent. Cosine similarity is then employed for effective feature matching, identifying corresponding feature point pairs and determining transformation parameters between point clouds. Experimental validations on various datasets, including the real terrain dataset, demonstrate the effectiveness of our method. Results indicate superior performance in root mean square error (RMSE) and registration accuracy compared to state‐of‐the‐art methods, particularly in scenarios with high noise, limited overlap, and significant initial pose rotation. The real terrain dataset is publicly available at https://github.com/black‐2000/Real‐terrain‐data. The authors introduce a principal direction‐guided local voxelisation structural feature (PDLVSF) approach for point cloud registration. This method reliably identifies feature points regardless of initial positioning. The method has rotational invariance and scale invariance，and reduces the time complexity with guaranteed accuracy. Results indicate superior performance in root mean square error (RMSE) and registration accuracy compared to state‐of‐the‐art methods.

Glaucoma, commonly known as the silent thief of sight, is the second most common cause of blindness in humans, and the number of cases is steadily increasing. Conventional diagnostic methods utilized by ophthalmologists include the assessment of intraocular pressure using tonometry, pachymetry, etc. Yet, each of these evaluations is time-consuming, requires human involvement, and is prone to subjective errors. In order to overcome these hurdles, practitioners are studying retinal pictures for glaucoma diagnosis within the field of medical imaging. In addition, computer-assisted diagnosis (CAD) systems can be created to solve these obstacles by using machine learning approaches to classify retinal pictures as \"healthy\" or \"infected.\" This work presents a reduced set of structural and nonstructural features(characteristics) to characterize pictures of the retinal fundus. The grey level co-occurrence matrix (GLCM), the grey level run length matrix (GLRM), the first order statistical matrix (FOS), the wavelet, and the structural features (like disc damage likelihood scale (DDLS) and cup to disc ratio (CDR)) are extracted. This set of features is sent to three classical soft computing algorithms (Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Binary Cuckoo Search (BCS)) and their two-layered model (PSO-ABC) to generate subset of reduced features (feature selection phase) that computes auspicious accuracy when sent to three machine learning classifiers (Random Forest (RF), Support Vector Machine (SVM), and Ensemble of RF, SVM, and Logistic Regression). According to our understanding, these four soft computing algorithms are rarely employed in this application field. For analyzing the performance of suggested strategy, the ORIGA, REFUGE, and their combinations are chosen as subject datasets. Standard statistical performance indicators, including accuracy, specificity, precision, and sensitivity, are calculated. The BCS delivers remarkable performance with a minimum of 91% accuracy and a maximum of 98.46% accuracy. PSO-ABC heavily decreases the original feature set, with minor accuracy sacrifices. The quantitative results are also compared in light of the most recent state-of-the-art published research. Owing to its exemplary performance, the suggested method will undoubtedly serve as a second opinion for ophthalmologists.

Secondary metabolites isolated from plant endophytic fungi have been getting more and more attention. Some secondary metabolites exhibit high biological activities, hence, they have potential to be used for promising lead compounds in drug discovery. In this review, a total of 134 journal articles (from 2017 to 2019) were reviewed and the chemical structures of 449 new metabolites, including polyketides, terpenoids, steroids and so on, were summarized. Besides, various biological activities and structure-activity relationship of some compounds were aslo described.

Plant-associated fungi (endophytic fungi) are a biodiversity-rich group of microorganisms that are normally found asymptomatically within plant tissues or in the intercellular spaces. Endophytic fungi promote the growth of host plants by directly producing secondary metabolites, which enhances the plant’s resistance to biotic and abiotic stresses. Additionally, they are capable of biosynthesizing medically important “phytochemicals” that were initially thought to be produced only by the host plant. In this review, we summarized some compounds from endophyte fungi with novel structures and diverse biological activities published between 2011 and 2021, with a focus on the origin of endophytic fungi, the structural and biological activity of the compounds they produce, and special attention paid to the exploration of pharmacological activities and mechanisms of action of certain compounds. This review revealed that endophytic fungi had high potential to be harnessed as an alternative source of secondary metabolites for pharmacological studies.

Wheat straw, a typical agricultural solid waste, was employed to clarify the effects of torrefaction on the structural features and combustion reactivity of biomass. Two typical torrefaction temperatures (543 K and 573 K), four atmospheres (argon, 6 vol.% O2, dry flue gas and raw flue gas) were selected. The elemental distribution, compositional variation, surface physicochemical structure and combustion reactivity of each sample were identified using elemental analysis, XPS, N2 adsorption, TGA and FOW methods. Oxidative torrefaction tended to optimize the fuel quality of biomass effectively, and the enhancement of torrefaction severity improved the fuel quality of wheat straw. The O2, CO2 and H2O in flue gas could synergistically enhance the desorption of hydrophilic structures during oxidative torrefaction process, especially at high temperatures. Meanwhile, the variations in microstructure of wheat straw promoted the conversion of N-A into edge nitrogen structures (N-5 and N-6), especially N-5, which is a precursor of HCN. Additionally, mild surface oxidation tended to promote the generation of some new oxygen-containing functionalities with high reactivity on the surface of wheat straw particles after undergoing oxidative torrefaction pretreatment. Due to the removal of hemicellulose and cellulose from wheat straw particles and the generation of new functional groups on the particle surfaces, the ignition temperature of each torrefied sample expressed an increasing tendency, while the Ea clearly decreased. According to the results obtained from this research, it could be concluded that torrefaction conducted in a raw flue gas atmosphere at 573 K would improve the fuel quality and reactivity of wheat straw most significantly.

Fiber materials are promising for electrocatalysis applications due to their structural features including high surface area, controllable chemical compositions, and abundant composite forms. In the past decade, considerable research efforts have been devoted to construct advanced fiber materials possessing conductive network (to facilitate efficient electron transport) and large specific surface area (to support massive catalytically active sites) to boost electrocatalysis performance. Herein, we focused on recent advances in fiber-based electrocatalyst with enhanced electrocatalytic activity. Moreover, the synthesis, structure, and properties of fiber materials and their applications in hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, carbon dioxide reduction reaction, and nitrogen reduction reaction are discussed. Finally, the research challenges and future prospects of fiber materials in electrocatalysis applications are proposed. Graphical abstract

There is an emerging environmental awareness and social concern regarding the environmental impact of the textile industry, highlighting the growing need for developing green and sustainable approaches throughout this industry’s supply chain. Upstream, due to population growth and the rise in consumption of textile fibers, new sustainable raw materials and processes must be found. Cellulose presents unique structural features, being the most important and available renewable resource for textiles. The physical and chemical modification reactions yielding fibers are of high commercial importance today. Recently developed technologies allow the production of filaments with the strongest tensile performance without dissolution or any other harmful and complex chemical processes. Fibers without solvents are thus on the verge of commercialization. In this review, the technologies for the production of cellulose-based textiles, their surface modification and the recent trends on sustainable cellulose sources, such as bacterial nanocellulose, are discussed. The life cycle assessment of several cellulose fiber production methods is also discussed.

In the beginning was the word. But there were no words for N-glycans, at least, no simple words. Next to chemical formulas, the IUPAC code can be regarded as the best, most reliable and yet immediately comprehensible annotation of oligosaccharide structures of any type from any source. When it comes to N-glycans, the venerable IUPAC code has, however, been widely supplanted by highly simplified terms for N-glycans that count the number of antennae or certain components such as galactoses, sialic acids and fucoses and give only limited room for exact structure description. The highly illustrative – and fortunately now standardized – cartoon depictions gained much ground during the last years. By their very nature, cartoons can neither be written nor spoken. The underlying machine codes (e.g., GlycoCT, WURCS) are definitely not intended for direct use in human communication. So, one might feel the need for a simple, yet intelligible and precise system for alphanumeric descriptions of the hundreds and thousands of N-glycan structures. Here, we present a system that describes N-glycans by defining their terminal elements. To minimize redundancy and length of terms, the common elements of N-glycans are taken as granted. The preset reading order facilitates definition of positional isomers. The combination with elements of the condensed IUPAC code allows to describe even rather complex structural elements. Thus, this “proglycan” coding could be the missing link between drawn structures and software-oriented representations of N-glycan structures. On top, it may greatly facilitate keyboard-based mining for glycan substructures in glycan repositories.

There are significant geometric distortion differences, nonlinear radiation differences, or weak texture differences on multimodal remote sensing images (MRSIs) of terrain undulating areas, which make traditional matching methods difficult to extract robust and effective feature correspondences. This article proposes a robust matching approach based on phase consistency structural features (PCSF). First, the phase consistency map of MRSIs is extracted to represent the structural features. Then, feature points are extracted from the phase consistency map within the nonlinear scale space. Subsequently, a logarithmic polar coordinate description framework is constructed to generate gradient histograms for describing feature points. Finally, a refinement matching strategy is employed to attain high-accuracy matching of MRSIs in topographically complex regions.

Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one of the most important topics for electric power management. However, this is very challenging over complex urban areas, where power lines are in close proximity to buildings and trees. In this paper, we presented a new, semi-automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) local neighborhood selection, (iii) spatial structural feature extraction, and (iv) SVM classification. We introduced the power line corridor direction for candidate point filtering and multi-scale slant cylindrical neighborhood for spatial structural features extraction. In a detailed evaluation involving seven scales and four types for local neighborhood selection, 26 structural features, and two datasets, we demonstrated that the use of multi-scale slant cylindrical neighborhood for individual 3D points significantly improved the power line classification. The experiments indicated that precision, recall and quality rate of power line classification is more than 98%, 98% and 97%, respectively. Additionally, we showed that our approach can reduce the whole processing time while achieving high accuracy.