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Fault diagnosis and prognosis in mechanical systems have been researched and developed in the last few decades at a very rapid rate. However, owing to the high complexity of machine centers, research on improving the accuracy and reliability of fault diagnosis and prognosis via data mining remains a prominent issue in this field. This study investigates fault diagnosis and prognosis in machine centers based on data mining approaches to formulate a systematic approach and obtain knowledge for predictive maintenance in Industry 4.0 era. We introduce a system framework based on Industry 4.0 concepts, which includes the process of fault analysis and treatment for predictive maintenance in machine centers. The framework includes five modules: sensor selection and data acquisition module, data preprocessing module, data mining module, decision support module, and maintenance implementation module. Furthermore, a case study is presented to illustrate the application of the data mining methods for fault diagnosis and prognosis in machine centers as an Industry 4.0 scenario.

Although mass customization, which utilizes modularization to simultaneously increase product variety and maintain mass production (MP) efficiency, has become a trend in recent times, there are some limitations to mass customization. Firstly, customers do not participate wholeheartedly in the design phase. Secondly, potential combinations are predetermined by designers. Thirdly, the concept of mass customization is not necessary to satisfy individual requirements and is not capable of providing personalized services and goods. Industry 4.0 is a collective term for technologies and concepts of value chain organization. Based on the technological concepts of radio frequency identification, cyber-physical system, the Internet of things, Internet of service, and data mining, Industry 4.0 will enable novel forms of personalization. Direct customer input to design will enable companies to increasingly produce customized products with shorter cycle-times and lower costs than those associated with standardization and MP. The producer and the customer will share in the new value created. To overcome the gaps between mass customization and mass personalization, this paper presents a framework for mass personalization production based on the concepts of Industry 4.0. Several industrial practices and a lab demonstration show how we can realize mass personalization.

The Taiwan Strait contains a vast potential for wind energy. However, the power grid balance is challenging due to wind energy’s uncertainty and intermittent nature. Wind speed forecasting reduces this risk, increasing the penetration rate. Machine learning (ML) models are adopted in this study for the short-term prediction of wind speed based on the complex nonlinear relationships among wind speed, terrain, air pressure, air temperature, and other weather conditions. Feature selection is crucial for ML modeling. Finding more valuable features in observations is the key to improving the accuracy of prediction models. The random forest method was selected because of its stability, interpretability, low computational cost, and immunity to noise, which helps maintain focus on investigating the essential features from vast data. In this study, several new exogenous features were found on the basis of physics and the spatiotemporal correlation of surrounding data. Apart from the conventional input features used for wind speed prediction, such as wind speed, wind direction, air pressure, and air temperature, new features were identified through the feature importance of the random forest method, including wave height, air pressure difference, air-sea temperature difference, and hours and months, representing the periodic components of time series analysis. The air–sea temperature difference is proposed to replace the wind speed difference to represent atmosphere stability due to the availability and adequate accuracy of the data. A random forest and an artificial neural network model were created to investigate the effectiveness and generality of these new features. Both models are superior to persistence models and models using only conventional features. The random forest model outperformed all models. We believe that time-consuming and tune-required sophisticated models may also benefit from these new features.

In this work, we calculate the transition form factors of Λ b decaying into proton and N ∗ ( 1535 ) ( J P = 1 / 2 + and 1 / 2 - respectively) within the framework of light-cone sum rules with the distribution amplitudes (DAs) of Λ b -baryon. In the hadronic representation of the correlation function, we have isolated both the proton and the N ∗ ( 1535 ) states so that the Λ b → p , N ∗ ( 1535 ) form factors can be evaluated simultaneously. Due to the less known properties of the baryons, we investigate three interpolating currents of the light baryons and five parametrization models for DAs of Λ b . Numerically, our predictions on the Λ b → p form factors and the branching fractions of Λ b → p ℓ ν from the Ioffe or the tensor currents are consistent with the Lattice simulation and the results from the light-baryon sum rules, as well as the experimental data of B r ( Λ b 0 → p μ - ν ¯ ) . The predictions on the form factors of Λ b → N ∗ ( 1535 ) are very sensitive to the choice of the interpolating currents so that the relevant measurement could be helpful to clarify the properties of baryons.

Existing studies on transversely isotropic rock formations, a special geology, have mainly focused on its mechanical characteristics; whereas, investigations on its fracture process and damage microscopic mechanisms are relatively limited. To remedy this deficiency, in this study, a three-dimensional numerical model is established using discrete elements (PFC3D), focusing on the effects of confining pressure (0, 5, 10, 15, and 20 MPa) and laminar inclination angle (θ0°, θ15°, θ30°, θ45°, θ60°, θ75°, and θ90°) on the failure behavior of the composite rock. To demonstrate the accuracy of the simulations, the stress–strain curves and ultimate failure modes obtained from the numerical simulations were compared with the previous laboratory mechanical test results and X-ray CT images. Numerical models using the smooth-joint contact model were shown to simulate the laboratory results reasonably well. Numerical simulation results indicate that the confining pressure and laminar angle significantly influence the internal crack evolution patterns of the specimen. The internal cracks change from a concentrated to a discrete distribution as the confining pressure increases. The internal cracks of specimens with θ0° and θ90° laminar inclination emerges from the soft rock and eventually extends to the hard rock, while the inclined specimens crack from the laminar face and finally spread to the rock matrix, which can be explained by the graph of the increasing number of cracks. In addition, the internal principal stress and tangential stress in soft and hard rocks were monitored by arranging measurement circles, and it was found that the tangential stresses are the essential cause of the difference between the mechanical behavior of the two rock types.HighlightsThe complicated three-dimensional discrete element transversely models captured the prospective mechanical behavior and cracking characteristicThe failure patterns and crack coalescence process are characterized by various confining pressure and bedding inclination anglesThe difference behavior between the soft and hard rock matrix is dependent on the confining pressure and internal tangential stress

In this work, we investigate the transition form factors for Λ b → Λ ( 1520 ) within the framework of light-cone sum rules (LCSR), using the light-cone distribution amplitudes (LCDAs) of the Λ b -baryon. In the hadronic representation of the correlation function, we carefully select the appropriate Lorentz structures and isolate the contributions from both the Λ ( 1520 ) ( J P = ( 3 / 2 ) - ) and the Λ ( 1890 ) ( J P = ( 3 / 2 ) + ) , ensuring that the form factors for Λ b → Λ ( 1520 ) can be calculated unambiguously. We also provide predictions for various physical observables in the decay Λ b → Λ ( 1520 ) l + l - , including the differential branching fraction, the lepton-side forward–backward asymmetry, the longitudinal polarization fraction, and the CP-averaged normalized angular observable. Our prediction for the differential branching fraction of Λ b → Λ ( 1520 ) μ + μ - is in good agreement with the LHCb measurement within the uncertainties.

The quality of a product is dependent on both facilities/equipment and manufacturing processes. Any error or disorder in facilities and processes can cause a catastrophic failure. To avoid such failures, a zero- defect manufacturing (ZDM) system is necessary in order to increase the reliability and safety of manufacturing systems and reach zero-defect quality of products. One of the major challenges for ZDM is the analysis of massive raw datasets. This type of analysis needs an automated and self-organized decision making system. Data mining (DM) is an effective methodology for discovering interesting knowledge within a huge datasets. It plays an important role in developing a ZDM system. The paper presents a general framework of ZDM and explains how to apply DM approaches to manufacture the products with zero-defect. This paper also discusses 3 ongoing projects demonstrating the practice of using DM approaches for reaching the goal of ZDM.

Recently, the worldwide pulsar timing array(PTA) collaborations, such as the Chinese Pulsar Timing Array (CPTA), the European PulsarTiming Array (EPTA), the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) and the Parkers Pulsar Timing Array (PPTA) published the analysis of PTA data, which is consistent with the Hellings–Downs curve, thus provides evidence for the existence of stochastic gravitational wave backgrounds (SGWB). In this paper, we will show that such SGWB signal observed by PTA can be explained by the gravitational waves (GWs) induced from double-inflection-point inflationary model in the framework of supergravity with a single chiral superfield. In this model, one of the inflection points leads to a large peak in the scalar power spectrum at small scales, and when this peak re-enters the horizon, it will induce GWs with the frequencies around nanohertz. In addition, we show that the high-density regions corresponding to the peak can collapse into planet-mass primordial black holes (PBHs), thus act as a component of dark matter (DM).

Nanographenes, finite models of graphene sheets, are endowed with intriguing optical, electronic, and spintronic features. So-called heteroatom-doping, where one or more carbon is replaced by non-carbon light atoms has been proved effective in tuning the properties of nanographenes. Here we extend the concept of heteroatom nanographene doping to include metal centers. The method employed involves the use of a dipyrromethene fragment as an auxiliary ligand that is directly linked to the bay area of the model nanographene hexa-peri-hexabenzocoronene (HBC) to give a dipyrromethene-fused nanographene-type hybrid ligand (HBCP). HBCP has a corrole-like trianionic core that is capable of coordinating group 11 metal cations, including trivalent Cu, Ag and Au. These cations are introduced into the cavity with atomic precision to give metal complexes (HBCP-M; M = Cu, Ag, Au). The electronic structure and photophysical properties of HBCP and its metal complexes are investigated by steady-state and fs-transient spectroscopies, as well as DFT calculations. The ligand and metal complexes are also characterized via single crystal X-ray diffraction analyses. This work paves the way towards the precise metal doping of nanographenes within the carbon network, as opposed to the synthetic appendage of an independent chelating group, such as a fused tetrapyrrolic moiety. Nanographenes, finite models of graphene sheets, are endowed with intriguing optical, electronic, and spintronic features which can be tuned by replacing carbon via heteroatom-doping. Here the authors extend the concept of heteroatom nanographene doping to include metal centers.

Sonchus oleraceus L., a member of the Asteraceae family native to Eurasia, is a herbaceous plant whose young stems and leaves are consumed globally as a medicinal and edible wild vegetable; it is rich in flavonoids and exhibits various pharmacological activities, including anti-inflammatory and anti-tumor effects. This study optimized the extraction process of flavonoids from Xinjiang S. oleraceus using response surface methodology and evaluated the anti-inflammatory activity of luteoloside in vitro. Based on single-factor experiments and Box–Behnken design, the effects of ethanol concentration, extraction time, solid-to-liquid ratio, and extraction temperature on flavonoid yield were investigated. The optimal extraction conditions were determined as ethanol concentration 62%, extraction time 30 min, solid-to-liquid ratio 1:91 g/mL, and extraction temperature 64 °C, with a flavonoid yield of 21.64 mg/g. After purification via polyamide column chromatography, the luteoloside content was determined by HPLC to be 44.06 μg/g. Cytotoxicity assays revealed that a luteoloside concentration of 100 μmol/L reduced the viability of Oryctolagus cuniculus colon epithelial cells to approximately 80%. ELISA results demonstrated that luteoloside significantly inhibited the release of pro-inflammatory factors, including TNF-α, while promoting the expression of the anti-inflammatory factor IL-10. These findings indicate that luteoloside effectively alleviates LPS-induced cellular inflammation.