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Although deep learning techniques have potential in vehicle behavior prediction, it is difficult to integrate traffic rules and environmental information. Moreover, its black-box nature leads to an opaque and difficult-to-interpret prediction process, limiting its acceptance in practical applications. In contrast, ontology reasoning, which can utilize human domain knowledge and mimic human reasoning, can provide reliable explanations for the speculative results. To address the limitations of the above deep learning methods in the field of vehicle behavior prediction, this paper proposes a front vehicle behavior prediction method that combines deep learning techniques with ontology reasoning. Specifically, YOLOv5s is first selected as the base model for recognizing the brake light status of vehicles. In order to further enhance the performance of the model in complex scenes and small target recognition, the Convolutional Block Attention Module (CBAM) is introduced. In addition, so as to balance the feature information of different scales more efficiently, a weighted bi-directional feature pyramid network (BIFPN) is introduced to replace the original PANet structure in YOLOv5s. Next, using a four-lane intersection as an application scenario, multiple factors affecting vehicle behavior are analyzed. Based on these factors, an ontology model for predicting front vehicle behavior is constructed. Finally, for the purpose of validating the effectiveness of the proposed method, we make our own brake light detection dataset. The accuracy and mAP@0.5 of the improved model on the self-made dataset are 3.9% and 2.5% higher than that of the original model, respectively. Afterwards, representative validation scenarios were selected for inference experiments. The ontology model created in this paper accurately reasoned out the behavior that the target vehicle would slow down until stopping and turning left. The reasonableness and practicality of the front vehicle behavior prediction method constructed in this paper are verified. [ABSTRACT FROM AUTHOR]

A direct regioselective C-H cyanation of purines was developed through a sequential triflic anhydride activation, nucleophilic cyanation with TMSCN, followed by a process of base-mediated elimination of triflous acid (CF3SO2H). In most cases, the direct C-H cyanation occurred on the electron-rich imidazole motif of purines, affording 8-cyanated purine derivatives in moderate to excellent yields. Various functional groups, including allyl, alkynyl, ketone, ester, nitro et al. were tolerated and acted as a C8 directing group. The electron-donating 6-diethylamino, as C2-directing group substituent, can switch the regioselectivity of purine from 8- to 2-position, enabling the synthesis of 8- and 2-cyano 6-dialkylaminopurines from corresponding 6-chloropurine in different reaction order. Further functional manipulations of the cyano group allow the conversions of 8-cyanopurines to corresponding purine amides, imidates, imidothioates, imidamides, oxazolines, and isothiazoles. [ABSTRACT FROM AUTHOR]

Assembly process designers typically confront the challenge of seeking information out of large volumes of non-structured files with a view to supporting the decision-making to be made. It is a leading concern that embedding data in text documents can hardly be retrieved semantically in order to facilitate decision-making with timely support. For tackling this gap, we propose in this paper a knowledge graph-based approach used to merge and retrieve information decided to be relevant within an engineering context. The proposed approach is to establish a multidimensional integrated assembly resource knowledge graph (ARKG) based on the structure of function-structure-assembly procedure-assembly resource, and this multidimensional integrated structure can well accomplish the retrieval of related knowledge. The upper semantic framework of ARKG is established by the assembly resource ontology model, which is a semantic-type framework involving multiple domains of knowledge to create instantiated data reflecting the full profile of the assembly resource for obtaining structured data of ARKG while avoiding the data redundancy problem. The ARKG method is validated through assembly scenario of the aircraft, and the results show the effectiveness and accuracy of the ARKG used by the assembly process designer in the assembly process design phase for retrieving the target knowledge of the assembly resources. [ABSTRACT FROM AUTHOR]

Assembly precision optimization is an important means to ensure product accuracy, including two aspects: on the one hand, the relevant deviations of out-of-tolerance key characteristics are reduced to the design tolerance range; on the other hand, the deviation fluctuation range of key characteristics with a large process capability index (Cp) can be extended to achieve the balance between accuracy, process capacity, and production cost. By virtue of the accumulated experience, a fast solution can be provided for the out-of-tolerance problem. Therefore, a semantic-based assembly precision optimization method considering process capacity is proposed in this paper. By constructing an ontology model between Cp and optimization strategy, a reasonable assembly precision optimization strategy can be pushed based on product accuracy analysis results. Firstly, an assembly precision optimization semantic model is established by association between analysis results, out-of-tolerance key characteristics, assembly process, and tolerance adjustment defined with Web Ontology Language (OWL) assertions. Furtherly, according to different Cp corresponding to different assembly success rates, Semantics Web Rule Language (SWRL) rules based on Cp are constructed to the push optimization strategy. Finally, the effectiveness of the model is illustrated by an aircraft inner flap. [ABSTRACT FROM AUTHOR]

Micromachining techniques have been applied widely to many industrial sectors, including aerospace, automotive, and precision instruments. However, due to their high-precision machining requirements, and the knowledge-intensive characteristics of miniaturized parts, complex manufacturing process problems often hinder production. To solve these problems, a systematic scheme for structured micromachining process problem solving and an innovation support system is required. This paper presents a knowledge-based holistic framework that enables process planners to achieve micromachining innovation design. By analyzing innovation design procedures and available knowledge sources, an open multi-source Machining Process Innovation Knowledge (MPIK) acquisition paradigm is presented, including knowledge units and a knowledge network. Further, a MPIK network-driven structured process problem-solving and heuristic innovation design method was explored. Subsequently, a knowledge-driven heuristic design system for machining process innovation was integrated in the Computer-Aided Process Innovation (CAPI) platform. Finally, a case study involving specific process problem-solving and innovation scheme design for micro-turbine machining was studied to validate the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates the statistical inference of inverse power Lomax distribution parameters under progressive first-failure censored samples. The maximum likelihood estimates (MLEs) and the asymptotic confidence intervals are derived based on the iterative procedure and asymptotic normality theory of MLEs, respectively. Bayesian estimates of the parameters under squared error loss and generalized entropy loss function are obtained using independent gamma priors. For Bayesian computation, Tierney–Kadane's approximation method is used. In addition, the highest posterior credible intervals of the parameters are constructed based on the importance sampling procedure. A Monte Carlo simulation study is carried out to compare the behavior of various estimates developed in this paper. Finally, a real data set is analyzed for illustration purposes. [ABSTRACT FROM AUTHOR]

In geometry and topology, a family of probability distributions can be analyzed as the points on a manifold, known as statistical manifold, with intrinsic coordinates corresponding to the parameters of the distribution. Consider the exponential family of distributions with progressive Type-II censoring as the manifold of a statistical model, we use the information geometry methods to investigate the geometric quantities such as the tangent space, the Fisher metric tensors, the affine connection and the α -connection of the manifold. As an application of the geometric quantities, the asymptotic expansions of the posterior density function and the posterior Bayesian predictive density function of the manifold are discussed. The results show that the asymptotic expansions are related to the coefficients of the α -connections and metric tensors, and the predictive density function is the estimated density function in an asymptotic sense. The main results are illustrated by considering the Rayleigh distribution. [ABSTRACT FROM AUTHOR]

Entropy measures the uncertainty associated with a random variable. It has important applications in cybernetics, probability theory, astrophysics, life sciences and other fields. Recently, many authors focused on the estimation of entropy with different life distributions. However, the estimation of entropy for the generalized Bilal (GB) distribution has not yet been involved. In this paper, we consider the estimation of the entropy and the parameters with GB distribution based on adaptive Type-II progressive hybrid censored data. Maximum likelihood estimation of the entropy and the parameters are obtained using the Newton–Raphson iteration method. Bayesian estimations under different loss functions are provided with the help of Lindley's approximation. The approximate confidence interval and the Bayesian credible interval of the parameters and entropy are obtained by using the delta and Markov chain Monte Carlo (MCMC) methods, respectively. Monte Carlo simulation studies are carried out to observe the performances of the different point and interval estimations. Finally, a real data set has been analyzed for illustrative purposes. [ABSTRACT FROM AUTHOR]

This paper offers an efficient copper-catalyzed oxidative trifluoromethylation of indoles with low-cost CF3SO2Na via C–H activation. Notably, the use of a base is crucial for the trifluoromethylation of indoles. This reaction proceeds efficiently in good to excellent yields and is tolerance of a broad range of functional groups. Furthermore, melatonin, a medicine for sleep disorders, is converted to its 2-CF3 analogue in 68% yield. Studies of possible reaction pathways suggest that this reaction proceeds through a radical process. [ABSTRACT FROM AUTHOR]

Assembly precision analysis (APA) plays an important role in the whole life cycle of complex products design, manufacturing, assembly and even remanufacturing. Assembly precision information model (APIM) is usually complex since it is affected by many factors, such as design tolerance of parts, assembly process scheme, assembly sequence planning and tolerance of positioning tooling, etc. Therefore, it is of practical significance for APA to reasonably reduce the workload of assembly precision information (API) modeling. A semantic simplification approach for APIM is proposed in this paper, which mainly takes semantic relations between APIM and design tolerance of parts into consideration. Initially, ontology of structure knowledge of APIM is constructed according to a tolerance standard. Furthermore, simplification rules are respectively established by considering two semantic relations: one semantic relation between deviation change direction and deviation accumulation direction and the other semantic relation among multiple geometric characteristics on the same geometric feature. Additionally, by utilizing ontology reasoning function, the simplified semantic APIM is generated. Finally, the effectiveness of the proposed method is demonstrated by a practical example of engine front auxiliary drive equipment. It is expected that our work would lay the foundation for APA of complex products based on actual measured data. [ABSTRACT FROM AUTHOR]

Press-braking bending is widely applied in the manufacture of aircraft integral panels because of the advantages of strong adaptability to different contours, simplicity of bending tools, short manufacturing time and low process cost. However, a simulation of bending process requires long-time calculation and consumes extensive computational resources. Considering the factors that the original model (ORM) of an integral panel is large and the press-braking bending is used only for the local area of integral panels with heavy thickness in practice, an equivalent calculation method for press-braking bending analysis of integral panels is proposed. The local bending area of an integral panel is simplified to a model of plate in this method. An exponential strengthening model is used to derive the equations of stress, strain and forming radius of the ORM and its simplified model (SPM). Meanwhile, the equivalent parameters of the SPM are determined and deduced based on three principles: that the material begin to be yielded simultaneously, the ultimate stress of the ORM is the same as that of the SPM at the same punch displacement, and the forming radii of neutral surfaces of the ORM and the SPM are identical after springback. The distribution of the stress and strain determined by finite element (FE) simulations are compared, and the FE simulations indicate that the contour curve of the SPM is in fairly good agreement with the profile of the ORM under the same bending process parameters, and the maximum difference is 13.17%. The computational efficiency is increased by more than 48%. Therefore, the proposed approach is quite suitable for industrial applications to improve the bending quality and efficiency of integral panels. [ABSTRACT FROM AUTHOR]

Phellinus is a precious perennial medicinal fungus. Its polysaccharides are important bioactive components, and their chemical composition is complex. The polysaccharides are mainly extracted from the fruiting body and mycelium. The yield of the polysaccharides is dependent on the extraction method. They have many pharmacological activities, such as antitumor, immunomodulatory, antioxidant, hypoglycemic, anti-inflammatory, etc. They are also reported to show minor toxic and side effects. Many studies have reported the anticancer activity of Phellinus polysaccharides. This review paper provides a comprehensive examination of the current methodologies for the extraction and purification of Phellinus polysaccharides. Additionally, it delves into the structural characteristics, pharmacological activities, and mechanisms of action of these polysaccharides. The primary aim of this review is to offer a valuable resource for researchers, facilitating further studies on Phellinus polysaccharides and their potential applications.