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The cyber-physical data of equipment whole life cycle are interrelated and play an important role in decision-making for business activities. However, multi-stage cyber-physical data are numerous, heterogeneous, and massive; non-real-time information data and real-time physical data are challenging to correspond to each other because of the inconsistency in space-time and granularity. As a result, equipment multi-stage heterogeneous cyber-physical data are difficult to effectively integrate and apply. Therefore, multi-stage cyber-physical data fusion of equipment is a key problem to be solved in manufacturing. To the best of our knowledge, a practicable framework and method to effectively fuse and apply equipment multi-stage data are still missing. To overcome this gap, this study proposes a multidimensional and multilevel cyber-physical fusion method for equipment. First, a four-dimensional cyber-physical fusion framework based on time, data, model, and structure is put forward. Then, the four kinds of fusion, including data fusion, model fusion, knowledge fusion, and digital twin (DT) application fusion, are discussed detailedly. Besides, the five types of algorithms set to support the fusion process are given. Finally, equipment life cycle activity is regarded as a DT application process using dynamic association of data and models. Through cyber-physical fusion, the data of equipment multi- stages could be applied for different activities to solve problems by means of DT. As a primary verification of the feasibility of the proposed approach, a case study of metro vehicle performance evaluation has been carried out, and the results have well confirmed that the multi-stage cyber-physical fusion method is feasible. [ABSTRACT FROM AUTHOR]

The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems. This paper examines baggage checked in by departing passengers at airports. The crrent state of the research on baggage flow demand is first reviewed and analyzed. Then, using examples of objective data, it is concluded that while there is a significant correlation between airport passenger flow and baggage flow, an increase in passenger flow does not necessarily result in a proportional increase in baggage flow. According to the existing research results on the influencing factors of baggage flow sorting and classification, the main influencing factors of baggage flow are divided into two categories: macro-influencing factors and micro-influencing factors. When studying the relationship between the economy and baggage flow, it is recommended to use a comprehensive analysis that includes multiple economic indicators, rather than relying solely on GDP. This paper provides a brief overview of prevalent transportation flow prediction methods, categorizing algorithmic models into three groups: based on mathematical and statistical models, intelligent algorithmic-based models, and combined algorithmic models utilizing artificial neural networks. The structures, strengths, and weaknesses of various transportation flow prediction algorithms are analyzed, as well as their application scenarios. The potential advantages of using artificial neural network-based combined prediction models for baggage flow forecasting are explained. It concludes with an outlook on research regarding the demand for baggage flow. This review may provide further research assistance to scholars in airport management and baggage handling system development. [ABSTRACT FROM AUTHOR]

Designing a complex mechatronic product involves multiple design variables, objectives, constraints, and evaluation criteria as well as their nonlinearly coupled relationships. The design space can be very big consisting of many functional design parameters, structural design parameters, and behavioral design (or running performances) parameters. Given a big design space and inexplicit relations among them, how to design a product optimally in an optimization design process is a challenging research problem. In this paper, we propose a systematic optimization design method based on design space reduction and surrogate modelling techniques. This method firstly identifies key design parameters from a very big design space to reduce the design space, secondly uses the identified key design parameters to establish a system surrogate model based on data-driven modelling principles for optimization design, and thirdly utilizes the multiobjective optimization techniques to achieve an optimal design of a product in the reduced design space. This method has been tested with a high-speed train design. With comparison to others, the research results show that this method is practical and useful for optimally designing complex mechatronic products. [ABSTRACT FROM AUTHOR]

With the increasing competition of the manufacturing industry, it is essential for manufacturers to develop a new (or next)-generation product based on their prior product design, product user experience, historical performances, etc. The digital twin (DT) is believed to be a suitable technology to support product lifecycle management with excellent data capture and analysis capabilities and product family design capabilities. However, it remains a challenge to synthesize and incorporate the data and information captured from previous generational product development and stored on their individual digital twin instances (DTIs) into next-generation product design. To address this problem, this study proposes a new nominal digital twin (NDT) concept as a collective digital representation of the current generational product digital mockups (DMUs) and all individual DTIs of the built products in services for new-generational product development. NDT is first defined here as a prototypical and synthesized digital twin (or digital representation) of multiple individual digital twins corresponding to multiple physical products in use or previously used. By analyzing the data and information on their DTIs, NDT can enable the establishment and evolution of a more precise approximated model of many related family products used previously or currently in use in various application scenarios and environments in the physical world. This paper also demonstrates how a NDT model can be first established in the product design phase from various digital mockup models and enhanced later with a stochastic forest meta-model based on Bayesian optimization connected to DTIs. With this NDT model, collaborative exploration for optimal design solutions during new-generation product design and improvement can be performed on NDT through multi-objective optimization, which in turn can make new-generation product design easier and quicker. As a primary verification of the feasibility of our proposed approach, a case study has been carried out, and the results have well confirmed that the NDT-based new-generation product design approach is feasible. [ABSTRACT FROM AUTHOR]

Surface roughness and cutting force are strongly affected by machining parameters. In the past few decades, many researchers have established the relationship between the surface roughness and machining parameters, but less attention has been paid to tool shape and geometry, and the number of tool flutes which affects vibrations and machining system. Therefore, this study includes the tool flutes in addition to cutting speed, depth of cut and feed rate as independent variables. A set of machining experimental work was carried out on vertical milling machine (end milling) and AA6061 was used as the work piece material to provide original data. Response surface method is adopted to establish the relationship between machining parameters to the surface roughness and cutting force using the Taguchi technique. The findings based on analysis of variance and Minitab 16, concluded that surface roughness has only two significant parameters (tool flutes and depth of cut) which affected the surface machining, while cutting force was significantly affected by all machining parameters used in this study. Linear and non-linear models, for surface roughness and cutting force are incorporated. Verifications and optimizations of the results carried out indicated suitability of the technique used in this study. [ABSTRACT FROM PUBLISHER]

• A HH-FGP framework is proposed to solve the SRCMPSP-NPI. • The evolutionary process is divided to achieve PR sampling and generation. • A new evaluation mechanism is proposed for depth range and attributes filtering. • The search operators are improved to avoid producing PRs with illegal depth. Multi-project management and uncertain environment are very common factors, and they bring greater challenges to scheduling due to the increase of problem complexity and response efficiency requirements. In this paper, a novel hyper-heuristic based filtering genetic programming (HH-FGP) framework is proposed for evolving priority rules (PRs) to deal with a multi-project scheduling problem considering stochastic activity duration and new project insertion together, namely the Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions (SRCMPSP-NPI), within heuristic computation time. HH-FGP is designed to divide traditional evolution into sampling and filtering evolution for simultaneously filtering two kinds of parameters constituting PRs, namely depth range and attribute, to obtain more effective PRs. Based on this, the existing genetic search and local search are improved to meet the depth constraints, and a multi-objective evaluation mechanism is designed to achieve effective filtering. Under the existing benchmark, HH-FGP is compared and analysed with the existing methods to verify its effectiveness. [ABSTRACT FROM AUTHOR]

k Nearest neighbors ( kNN ) searching algorithm is widely used for finding k nearest neighbors for each point in a point cloud model for noise removal and surface curvature computation. When the number of points and their density in a point cloud model increase significantly, the efficiency of a kNN searching algorithm becomes critical to various applications, thus, a better kNN approach is needed. In order to improve the efficiency of a kNN searching algorithm, in this paper, a new strategy and the corresponding algorithm are developed for reducing the amount of target points in a given data set by extracting nearest neighbors before the search begins. The nearest neighbors of a reverse nearest neighborhood are proposed to use in extracting nearest points of a query point, avoiding repetitive Euclidean distance calculation in an extracting process for saving time and memories. For any point in the model, its initial nearest neighbors can be extracted from its reverse neighborhood using an inner product of two related vectors other than direct Euclidean distance calculations and comparisons. The initial neighbors can be its full or partial set of the all nearest neighbors. If it is a partial set, the rest can be obtained by using other fast searching algorithms, which can be integrated with the proposed approach. Experimental results show that integrating extracting algorithm proposed in this paper with other excellent algorithms provides a better performance by comparing to their performances alone. [ABSTRACT FROM AUTHOR]

Many complex mechanical products can be considered as multibody systems; 3D computer-aided design (CAD), multibody dynamics, finite element-based strength and fatigue analyses, optimization, and other CAE tools, are often used to develop this kind of product. But design is difficult and challenging because of information inconsistence among different engineering domains, and isolated information model islands exist. Lots of research are attracted to design and analysis information integration, but attentions are mainly focused on integration of CAD-finite element analysis (FEA), supporting the bottom-up design principle, while multibody dynamics analysis for analyzing global performance of a mechanical system is rarely considered, which follows the top-down design principle. In this paper, a novel multilevel system representation modeling framework for supporting both bottom-up and top-down or mixed design methods is proposed. It can integrate the product design CAD models with multibody dynamics, finite element-based strength and fatigue analyses, realizing a CAD-dynamic-FEA-fatigue analysis integration. It can also support data exchange and transfer in multidomain analyses. The framework is illustrated with a case study of a load haul dump (LHD) design using currently available software tools. Furthermore, a design case study of the working unit of the LHD is given to highlight the applicability of the modeling framework for multibody mechanical systems. It has been demonstrated that the framework can describe information exchanging and integration among CAD, FEA-based strength and fatigue analyses, and multibody dynamics analysis during a multibody mechanical product design process. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an integrated geometric error modeling, identification and compensation method for machine tools. Regarding a machine tool as a rigid multi-body system (MBS), a geometric error model has been established. It supports the identification of the 21 translational geometric error parameters associated with linear-motion axes based on a laser interferometer, and 6 angular geometric error parameters for each rotation axis based on a ball-bar. Based on this model, a new identification method is proposed to recognize these geometric errors. Finally, the identified geometric errors are compensated by correcting corresponding NC codes. In order to validate our method, a prototype software system has been developed, which can be used for conducting tests on any type of CNC machine tool with not more than five axes. An experiment has been conducted on a five-axis machine center with rotary table and tilting head; the results show that the integrated geometric error modeling, identification and compensation method is effective and applicable in multi-axis machine tools. [Copyright &y& Elsevier]

The aerodynamic characteristics of a hyperloop vehicle with or without channels operating at subsonic speeds in a low-pressure tube were simulated using a scale-adaptive simulation (SAS) method based on the shear-stress transport (SST) κ-ω turbulence model. The aerodynamic behavior of the hyperloop pod, fluid flow, and heat transfer phenomena in the tube were analyzed and evaluated. The grid resolution was analyzed, and the numerical algorithm was validated using a wind tunnel test. The results show that the effects of using channels on the pressure drag of the pod are mainly noticeable at the end of the deceleration stage, and using channels has a great influence on the friction drag relative to the pressure drag, especially during deceleration. The friction drag of such a pod increased with the inclusion of an axial channel in both the acceleration and deceleration stages, whereas it was decreased using a radial gap inside the pod in conjunction with an axial channel during deceleration. The flow field around the pod tail is greatly affected when channels are used relative to other areas around the pod. Using both radial gap and axial channel can effectively reduce the abnormally high temperature in the wake of the pod when the pod surpasses the critical speed. The effects of the radial gap on the flow field in the radial gap are different during different stages of operation, especially in terms of the pressure field. The inclusion of a radial gap is not conducive to improving the surrounding flow field, especially during deceleration. It was also found that the aerodynamic profile design of the channel exit is very important, and the pod speed must be considered. • Effects of the radial gap on the flow field differ between different stages • Flow fields around the pods were affected by the channels, especially the radial gap • Use of channels has a small effect on pressure drag except during deceleration • High temperatures from shockwaves can be reduced using radial and axial channels [ABSTRACT FROM AUTHOR]

The forming grinding process is a crucial method for helical flute grinding of the end mill and screw tap, and the grinding wheel profile is the key to ensure the precision of the helical flute, which is constrained by the grinding wheel posture. However, the existing methods have some deficiencies such as the invalidity or uneven wear problems. In this paper, an optimized calculation method of the grinding wheel profile for the forming grinding of the helical flute is proposed, including the suitable grinding posture correspondingly. Firstly, the parametric models of the helical cutting edge and the helical flute surface are established. Secondly, the contact condition between the grinding wheel and the flute is deduced to calculate the grinding wheel profile by the envelop theory. Thirdly, an optimization method for grinding wheel profile is proposed, which could predict of the solution interval, avoid the profile intersection, and improve wear resistance. Finally, the method was verified by a series of simulations and experiments, and the results show that the method could meet the grinding precision requirements and expand the application range of forming grinding technology in helical flute. [ABSTRACT FROM AUTHOR]

• A hyper-heuristic based ensemble GP framework is proposed to solve the SRCPSP. • A sequence voting mechanism is designed to achieve PRs collaborative scheduling. • The basic components of existing hyper-heuristic scheduling are modified. • The local search, mutation and renewal are designed for improving GP evolution. In project scheduling studies, to the best of our knowledge, the hyper-heuristic collaborative scheduling is first-time applied to project scheduling with random activity durations. A hyper-heuristic based ensemble genetic programming (HH-EGP) method is proposed for solving stochastic resource constrained project scheduling problem (SRCPSP) by evolving an ensemble of priority rules (PRs). The proposed approach features with (1) integrating the critical path method into the resource-based policy class to generate schedules; (2) improving the existing single hyper-heuristic project scheduling research to construct a suitable solution space for solving SRCPSP; and (3) bettering genetic evolution of each subpopulation from a decision ensemble with three different local searches in corporation with discriminant mutation and discriminant population renewal. In addition, a sequence voting mechanism is designed to deal with collaborative decision-making in the scheduling process for SRCPSP. The benchmark PSPLIB is performed to verify the advantage of the HH-EGP over heuristics, meta-heuristics and the single hyper-heuristic approaches. [ABSTRACT FROM AUTHOR]

Crowd intelligence innovation (CII) is an open problem-solving model, typically performs on a crowdsourcing-based online innovation platform, which is highly dependent on the engagement and high-quality work of participants. On an online platform, many demanders publish tasks, and innovators submit schemes, which forms a complex CII system. To ensure such a CII system operates sustainably, it is critical for the platform operators to know the current system's health statuses before taking proper management actions. However, the large influx of participators with varied behaviors makes it extremely difficult to evaluate the health statuses of the system and capture its characteristics. Practicable method for quantitatively evaluating the health status of a CII system is still missing. Therefore, this study proposed a new quantitative health status evaluation method from an ecosystem perspective. First, the crowd intelligence innovation ecosystem (CIIE) was established by mapping the CII system onto the natural ecosystem based on ecological theory. Second, the health status of CIIE was described by its internal health status and external health status using a mixed method. The internal health status of the CIIE was defined and evaluated based on its health characteristics in terms of stability, ascendency, development capacity, and redundancy measurements using ecological network analysis. The external health status was evaluated using the ecological entropy index system. As the primary verification of the feasibility of our proposed approach, a case study of Zhubajie was conducted. These results confirmed the feasibility of our evaluation method. With this method, the health status of CIIEs could be accurately measured, which supported the management and organization the CII systems. Future work will focus on adapting suitable measures to improve the operation of CII. [ABSTRACT FROM AUTHOR]

Non-contact R test is an instrument to measure the synchronous errors of five-axis machine tools. However, there are still some deficiencies in its researches, such as the difficult and laborious calibrations. How to systematically improve the measurement accuracy with a good balance to minimum cost is a real problem in guiding practice. This paper proposes a new systematic optimization method to solve this problem based on a comprehensive understanding of the non-contact R test in terms of structure parameters and relations. Firstly, the algorithm for sphere center coordinates is established based on the self-adaptive differential evolution algorithm to obtain the definite computational accuracy and efficiency. Secondly, the parameters of the fixture structure are optimized to maximize the measurement stability, measuring space, and non-interference space. Thirdly, the on-machine calibration is performed to replace pre-calibration and re-calibration and to establish the positional relationships between sensors, the fixture, and the machine tool simultaneously. It can reduce the difficulties of manufacture, maintenance, and application. Fourthly, the measurement accuracy can be evaluated to determine whether the iterative optimization achieves the goal. The proposed method has been verified with case studies to support the setting-up of the optimized non-contact R test, leading to a cost-effective and accurate test on five-axis machine tools. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, big data, Internet of Things, and other technologies, digital twin has gained great attention and become a current research topic. Using digital twin technology, the digital twin model can be constructed in the cyber space that is fully equivalent to the physical entity. It is always consistent with the physical entity in the operation process, which greatly improves the dynamic perception and prediction ability of the real world. After the development in recent years, digital twin has gradually changed from the initial concept discussion to the study of model framework and implementation method. However, because the research objects in different industries have great differences in their own composition, service conditions, and application scenarios, they have personalized characteristics in modeling strategies and usage methods. Therefore, based on different industries, this paper reviews the current articles on digital twins and distinguishes the focus of digital twin modeling research; subsequently, the relevant supporting techniques and methods are summarized according to their different importance for digital twin modeling. Based on the review in this paper, future researchers can conduct targeted research on digital twin technology in term of the characteristics of the objects in their industry. [ABSTRACT FROM AUTHOR]

• A consistency association is established among different conditions signals. • Tool structure and process parameters are introduced to the model structure design. • The model's generalization boundary in tool wear monitoring is studied. • The proposed method is generalized and interpretable on extensive experiments. Cross-conditions tool wear monitoring has a wide application prospect in manufacturing. However, the data distribution discrepancies caused by the inconsistency of process elements restrict the generalization of models under cross-conditions or even similar conditions. The existing methods based on diversity enhancement make it difficult to effectively establish the correlation between the source domain and target domains, which limits the improvement of model generalization. Therefore, this paper details the cause of data distribution discrepancies and proposes a discontinuous physical property-constrained single-source domain generalization network for milling tool wear monitoring. Firstly, a spatial attention mechanism is introduced to weight key signal segments adaptively. Secondly, the generation module is constrained by a standard sample and is used to generate diverse samples with physical properties. Thirdly, extensive recognition experiments on an open dataset and machining experiments with three distribution discrepancy levels were conducted to verify the effectiveness of the proposed method. Finally, feature visualizations provide consistency and interpretability. [ABSTRACT FROM AUTHOR]

A product-service system (PSS) has many engineering characteristics (ECs), their design is a critical work in PSS planning, which has an important influence on the cost and quality of PSS. How to design a reasonable PSS-ECs scheme, and evaluate its life cycle cost (LCC) is a challenging task. Aiming at the PSS-ECs design for LCC, this paper proposes a new PSS design method, it first treats and models the design of PSS-ECs as a customer requirements-based constraint satisfaction problem (CSP) for finding an initial set of satisfied PSS-ECs schemes, and then it evaluates these schemes based on Bayesian network (BN)-based LCC estimation model for finding an optimal scheme as a solution. Constructing a BN describing the uncertain relationships between PSS-ECs and LCC is the core of this research. By combining existing R&D data and expert experience, Bayesian estimation and arithmetic averaging are used to estimate the conditional probability in BN. Take a subway bogie and its maintenance service in a Chinese company as an example to verify the proposed method. The results show that the proposed method can effectively solve the problem of PSS-ECs design for LCC, it also shows that this method has positive significance in realizing engineering knowledge consolidation, assisting designers in exploring design space, and improving the rationality of design decisions. [ABSTRACT FROM AUTHOR]

Objective To investigate the expressions of calcium sensitive receptor (CaSR) and tight junction protein (Claudin)-14 in renal calcium oxalate stone rat model. Methods Thirty male SD rats were randomly divided into control group (n=15) and model group (n=15). The rat model of renal calcium oxalate stone was established by gavaging 1% glycol (2 mL/d) and 2% ammonium chloride. The expression of CaSR protein was detected using immunohistochemical assay. RT-PCR was used to detect the Claudin-14 mRNA expression. The expression levels of CaSR and Claudin-14 protein were detected by Western blot assay respectively. The full automatic biochemical analyzer was used to detect rat renal function and changes of blood and urine biochemical indices. Results A large stone crystallization was observed under light microscope in model group. The serum levels of Cr, BUN, 24-h urine calcium and urine volume were significantly higher in model group than those of control group (P< 0.01). There were no significant differences in serum calcium level and urinary pH value between two groups. The expression levels of Claudin- 14 mRNA and CaSR protein were significantly higher in model group than those of control group (P<0.01). The Claudin-14 protein expression was specifically higher in renal tissues of model group. There was no Claudin- 14 protein expression in control group. There was a positive correlation between CaSR and Claudin-14 protein expression in renal tissues of model group. Also, CaSR and Claudin-14 protein expressions were positively correlated with the 24-h urine volume. Conclusion The increased expressions of CaSR and Claudin-14 involved in the formation of kidney stone by increasing the urinary calcium excretion. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive control method of surface waviness for 5-axis flank milling, reducing cutting force variation, and improving unstable rotation state of rotary axis to suppress low-frequency milling chatter. We propose a cutting force-sweep area model and define an average cutting force term, so that the control of cutting force turns into sweep area rate (SAR) control, realized by feed rate. The feed rate might be dynamic, adjusted to make the corresponding SAR constant. In addition, the method can improve the unstable rotation state, such as discontinuous or reciprocating rotation in micro angle, by means of decomposing the unstable rotation to more micro continuous rotation or tool axis orientation adjustments based on the geometric relationship of tool axis and machining tolerance. Finally, a series of comparison experiments were conducted on a 5-axis machine tool. From the comparison of cutting force and surface scan, the results showed that the control method could suppress low-frequency milling chatter and reduce surface waviness effectively. [ABSTRACT FROM AUTHOR]

Abstract: Administration of an excess of oligodeoxynucleotides containing immunostimulatory CpG motifs (CpG-S ODNs) may induce systemic inflammatory response syndrome (SIRS) and sepsis. Therefore, it is important to develop neutralizing CpG ODNs (CpG-N ODNs), which can be used to reduce the release of cytokines induced by the presence of CpG-S ODNs. In the present study, CpG-N ODN208 (5′-TGCCGCGGCAGA-3′), a neutralizing twelve-oligodeoxynucleotide molecule recently identified in our laboratory, inhibited TNF-α release from human peripheral blood mononuclear cells (hPBMCs) and murine RAW264.7 cells induced by CpG-S ODN exposure in a dose- and time-dependent manner. Flow cytometry revealed that CpG-N ODN208 decreased cell-surface binding and internalization of 6-FAM-CpG-S ODN. However, the decreased cell-surface binding and internalization of CpG-S ODN could not completely account for the decreased TNF-α release. RT-PCR experiments revealed that CpG-N ODN treatment could down-regulate the CpG-S ODN-induced upregulation of Toll-like receptor 9 (TLR9) mRNA expression. This finding suggested that the decreased cytokine release following CpG-N ODN treatment might be related to decreased TLR9 mRNA expression. In in vivo experiments, no protection was found when the ratio of CpG-N ODN to CpG-S ODN delivered to mice was 3:1. However, at a 5:1 ratio, CpG-N ODN208 could protect mice from an ordinarily lethal dose of CpG-S ODN. Furthermore, we found that CpG-N ODN208 treatment decreased serum TNF-α levels in mice injected with sublethal doses of CpG-S ODN whether the CpG-N ODN208 was added prior to or concurrent with the CpG-S ODN. Our results demonstrated that CpG-N ODN-mediated protection against a lethal challenge by CpG-S ODN was associated with the reduction of TNF-α release. [Copyright &y& Elsevier]

For data-driven remaining useful life (RUL) prediction of rolling bearing (RB), deep learning methods usually do not consider the difference in distribution due to different operating conditions, which adversely affects the prediction results. Recently, transfer learning has been a research hotspot, and it can mitigate the above problem effectively. However, for different bearings under the same working condition, the data distribution changes due to the location and time of failure. To solve these problems, a transfer prediction model based on the dynamic benchmark (DB) has been proposed to predict the RUL of bearings in this study. The method is divided into three processes. First, a fully convolutional variational autoencoder is used to perform unsupervised learning; next, a domain adaptation method based on the DB is employed for reconstruction; finally, extracted hidden layer features are input to the fully connected layer of the proposed model to obtain the final prediction result. The prediction and health management (PHM) Challenging 2012 and XJTU-SY RB accelerated life test datasets were used for verification. The results showed that the proposed method could significantly improve prediction accuracy and had good stability and generalizability. [ABSTRACT FROM AUTHOR]

This research paper presents an analytical mechanics model for studying the degraded failure mechanics of an adhesively bonded corrugated sandwich structure (ABCSS) subjected to three-point bending and hygrothermal ageing loads. The model incorporates a detailed stress state model that considers the stress distribution of the adhesive layer, including the adhesive fillet, to more accurately reflect the failure mechanics of ABCSS. The Fickian Diffusion model and a degradation coefficient are used to describe the characteristics of water diffusion and hygrothermal mechanical degradation for the adhesive joint in ABCSS. The hygrothermal ageing experiments are conducted by immersing the ABCSS into deionized water and 5 wt% NaCl water, followed by three-point bending tests after hygrothermal ageing. The proposed two-step finite element (FE) method is developed to simulate both moisture diffusion and three-point bending test. The presented FE method is validated through comparison with corresponding experimental results, then it is utilized to demonstrate that the proposed analytical model is accurate enough in initial failure modes and initial failure loads predictions. At last, the hygrothermal effects on degraded stress distribution and degraded failure performance are investigated through analytical method, and the degraded failure mechanics maps are constructed to further visually demonstrate failure mechanics with the aid of the analytical model. [ABSTRACT FROM AUTHOR]

With intense global competition, many enterprises adopt platform strategies to develop diverse products with a lower corresponding increase in cost and a shorter lead time. In the past, enormous efforts have been made in investigating product platforms. Recent researches and practices show that developing Uncertainty-Oriented Product Platform (UOPP) is a promising approach to improving the adaptability of enterprises to meet uncertain markets, customer requirements, technologies, policies, and regulations, etc., which includes flexible product platform, adaptable product platform, market-driven product platform, and sustainable product platform. This paper provides a comprehensive review in this field, including the connotation of uncertainty, the concepts of emerging UOPPs, the development process of UOPP, as well as the development technologies in each step. It also highlights the future research framework, opportunities and challenges. This literature review lays the foundation for future research of product platform design under uncertainty. [ABSTRACT FROM AUTHOR]

• The RUL prognosis framework combines the wavelet transform and the ConvLSTM. • Channel self-attention highlights weak but crucial features in wavelet coefficients. • Global attention improves the representation of the learned features in multiple wavelet coefficients of different channels. • Hybrid attention-based method boosts convlstm's performance in RUL prognosis. Wavelet transform, a time-frequency analysis method for evaluating non-stationary signals, can assist in representing equipment degradation over prolonged usage. However, a single wavelet basis function is challenging to apply to all periodic transient waveforms. As a result, this research suggests a hybrid attention-based multi-wavelet coefficient fusion method for evaluating the remaining useful life (RUL) of bearings. Firstly, a two-dimensional map is created by organizing the decomposed individual frequency bands after the approach employs several wavelets to get the original signal properties. Secondly, a hybrid attention-based ConvLSTM (HA-ConvLSTM) network is designed to weight wavelet coefficient channels adaptively. The learned features are used to evaluate RULs by a multi-layer perceptron. Finally, tests were run on the PHM2012 rolling bearing dataset to validate the proposed method. Overall, the suggested scheme outperforms previous comparable methods in the performance index. This approach optionally resolves the wavelet basis function matching issue for periodic transient waveforms. [ABSTRACT FROM AUTHOR]

• Tool structure parameters, process parameters, etc. are introduced to the model structure design. • Frequency and spatial components can be identified and weighted adaptively by designed attention modules. • The contribution of each attention module of the model is analyzed in detail through ablation experiments. • By visualizing and comparing the attention weights and features, the interpretability is explored. Real-time and accurate monitoring of tool wear status is critical for optimizing product quality and production costs. Deep learning has received much attention as an emerging data-driven technique in the field of tool wear monitoring because of its powerful feature extraction and nonlinear mapping capabilities. However, the model structure and features' poor interpretability limits the application of deep learning in practical machining. Herein, a frequency-spatial hybrid attention network is proposed, driven by tool structure and process parameters (FSHAN-SPD). The structure of FSHAN-SPD considers the recognition of critical frequencies and spatial positions simultaneously, and the receptive field of its spatial attention module can effectively distinguish the cutting stage of each tooth. Attention modules-weighted features are used for interpretability analysis. The effectiveness and flexibility of this method are verified in the PHM2010 dataset. A milling experiment illustrates a more detailed procedure. The experimental results are well interpretable in both frequency and spatial. [ABSTRACT FROM AUTHOR]

Conceptual design has a decisive impact on the product development time, cost and success. This paper presents a new conceptual design method for achieving rapid and effective mapping from product design specification (PDS) to concept design. This method can guide the creation of reasonable mapping among the PDS, behaviour parameters and structure parameters and to evaluate the rationality of performance parameters and structure parameters to confirm a reasonable conceptual design scheme. In this method, we establish a PDS-behaviour-structure conceptual design model to support the conceptual design of multi-disciplinary-oriented complex product system (CoPS) and develop a vector-based mapping tool in this method to support the rapid mapping, and demonstrate its feasibility and effectiveness by a case study. This method is not only supportive to realise the automation of a conceptual design process but also helpful to evaluate the conceptual design in the field of engineering design. [ABSTRACT FROM AUTHOR]

In the face of the requirement for professional skills in modern design processes such as mass customization and digital design techniques, our teaching of engineering graphics to engineering student needs to adapt. This paper proposes a new curriculum for an engineering graphics course, which combines traditional engineering graphics with modern digital design technologies. The paper highlights the need for training in innovative design, team cooperation and graphical communication. [ABSTRACT FROM AUTHOR]

• An autoencoder model based on a multi-domain adversarial network was proposed. • The multi-domain adversarial network is developed to improve feature transfer. • A bearing life prediction network based on multi-channel features is proposed. • A degradation occurrence time identification method was proposed. Predicting the remaining useful life (RUL) of rolling bearings under different working conditions improved significantly by transfer learning. However, existing methods have not studied the following problems thoroughly: (1) The influence of the discrepancy between features of different dimensions on the feature transfer process; (2) The feature transfer process in the degradation stage with apparent discrepancy has a significant influence on the transfer prediction of remaining useful life. In this study, a degradation occurrence time identification method based on the distribution differences in reconstructing degradation indicators has been proposed to obtain samples of degradation stages. A stack convolutional autoencoder model based on a multi-domain adversarial network is also proposed to reduce the impact of discrepancies among extracted degradation features on the feature transfer process. As per the experimental results, it was found that the proposed method can effectively improve the RUL prediction accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Gram-positive bacteria have become the most common organisms responsible for the development of sepsis. Staphylococcus aureus (S. aureus) is the major gram-positive pathogen in both community-acquired and nosocomial infections. The Mortality associated with nosocomial infections caused by S. aureus may vary but are generally high. In the present study, we found that artesunate (AS) could protect mice against a lethal challenge with heat-killed S. aureus in a dose-dependent manner, and AS in combination with ampicillin sodium–sulbactam sodium (AMPS) could further increase survival of mice challenged with live S. aureus than AMPS alone. This protection was associated with reductions of serum at TNF-α level. In in vitro experiments, AS-pretreatment strongly inhibited TNF-α release from murine peritoneal macrophage induced by heat-killed S. aureus or peptidoglycan in a dose-dependent manner. AS reduced the Toll like receptor 2 (TLR2) and nucleotide-binding oligomerization domain containing 2 (Nod2) mRNA expressions up-regulated by heat-killed S. aureus and inhibited NF-κB activation induced by heat-killed S. aureus. In conclusion, our results demonstrated that AS-mediated protection on septic mice challenged with S. aureus was associated with its reduction on TNF-α release via inhibition of TLR2 and Nod2 mRNA expressions and transcription factor NF-κB activation. [Copyright &y& Elsevier]

Abstract: Bacterial DNA (bDNA) and lipopolysaccharide (LPS) are potent activators of immune cells such as monocytes and macrophages, which contribute to systemic inflammatory response syndrome (SIRS) and sepsis. Unfortunately, many experimental inflammatory antagonist-based therapies have failed in sepsis trials, and currently there is only one adjuvant therapy in clinical use, e.g. activated protein C. Artesunate (AS), a water-soluble derivative of dihydroartemisinin, has recently been demonstrated to protect against LPS-induced human umbilical vein endothelial cell (HUVEC) activation and injury by inhibiting tumor necrosis factor-alpha (TNF-α) mRNA expression. In the present study, heat-killed Escherichia coli was used to induce sepsis in the animal models. We observed that AS could protect mice against a lethal challenge with heat-killed E. coli in a dose-dependent manner. This protection was associated with reductions in serum TNF-α and measurable endotoxin levels. In addition, the treatment of murine peritoneal macrophage cells with AS strongly inhibited the release of TNF-α and IL-6 induced by CpG oligodeoxynucleotide (CpG ODN), LPS, or heat-killed E. coli in a dose-dependent manner. Experiments using affinity sensor technology revealed that AS could not directly bind to CpG ODN or LPS. Moreover, AS could not neutralize LPS in vitro. Further, flow cytometry revealed that AS could not alter the binding of CpG ODN to cell surfaces but could promote CpG ODN accumulation within RAW264.7 cells. Furthermore, AS reduced the expressions of TLR4 and TLR9 mRNA that were stimulated by LPS, CpG ODN, or heat-killed E. coli and inhibited heat killed E. coli-induced NF-κB activation. In conclusion, our results demonstrated that AS-mediated protection against a lethal heat-killed E. coli challenge was associated with a reduction in proinflammatory cytokine release and endotoxin levels via a mechanism involving a decrease in TLR4, TLR9 mRNA expression and NF-κB activation. [Copyright &y& Elsevier]