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As a practical engineering method, earthquake response spectra play an important role in seismic hazard assessment and in seismic design of structures. However, the computing scale and the efficiency of commercial software restricted the solution of complex structures. There is a clear need of developing large-scale and highly efficient finite element procedures for response spectrum analysis.

In this paper, the kernel theories for earthquake response spectra are deduced and the corresponding parallel solution flow via the modal superposition method is presented. Based on the algorithm and the parallel data structure of JAUMIN framework, a parallel finite element (FE) solution module is established. Using the solution procedure on a supercomputer equipped with up to thousands of processors, the correctness and parallel scalability of the algorithm are evaluated via numerical experiments of typical engineering examples.

The results show that the solution module has the same precision as the commercial FE software ANSYS; the maximum solution scale achieves 154 million degrees of freedom (DOFs) with a favorable parallel computing efficiency, going far beyond the computing ability of the commercial FE software.

The solution scale in this paper is very challenging for the large-scale parallel computing of structural dynamics and will promote the dynamic analysis ability of complex facilities greatly.

This paper aims to compare the costs of deploying different wireless terrestrial broadband technologies in the Andes and Amazon Regions of Peru. These areas are representatives of different and challenging geographic regions throughout the globe that currently are severely underserved or unserved for vital broadband services necessary to bridge the “Digital Divide”.

The broadband technologies studied include Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), long term evolution (LTE), TVWS and new stratospheric platforms (super-pressure balloons). This study conducts a technical analysis (design and simulation) of wireless broadband networks, and a bottom-up engineering cost analysis to estimate and compare the deployment and operating costs of the networks over a 10-year period. The analysis also identifies potential regulatory barriers to deployment associated with spectrum allocation licenses and overbooking requirements intended to improve quality of service.

Comparison of the capital and operating expenses of these options over a 10-year period finds that LTE and Wi-Fi can be the lowest cost alternatives, though significantly, stratospheric balloons have the lowest initial costs for the first few years and can factor as a low-cost broadband catalyst early in deployment. Finally, the lowest cost technology broadband roadmap for the 10-year period is presented, which includes using stratospheric balloons (carrying micro-LTE base stations) for the first years and deploying complementary terrestrial LTE networks for the rest of the 10-year period.

This study presents detailed technical and engineering cost analysis results of wireless access network deployments, including advanced wireless technologies and new unmanned aerial systems, to expand broadband services to rural areas in mountainous (Andes Region) and rainforest (Amazon Region) geographies to reduce the digital divide in emerging countries. Results aim to aid governments, regulators, internet service providers (incumbents and competitive) and content providers to assess current alternatives to expand broadband service in these rural areas.

The purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.

Vibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.

The obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.

The proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.

In this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

The purpose of this paper is to analyze the spectral characteristics of moving average operator and to propose a novel time-frequency hybrid sequence operator.

Firstly, the complex data is converted into frequency domain data by Fourier transform. An appropriate frequency domain operator is constructed to eliminate the impact of disturbance. Then, the inverse Fourier transform transforms the frequency domain data in which the disturbance is removed, into time domain data. Finally, an appropriate moving average operator of N items is selected based on spectral characteristics to eliminate the influence of periodic factors and noise.

Through the spectrum analysis of the real-time data sensed and recorded by microwave sensors, the spectral characteristics and the ranges of information, noise and shock disturbance factors in the data can be clarified.

The real-time data analysis results for a drug component monitoring show that the hybrid sequence operator has a good effect on suppressing disturbances, periodic factors and noise implied in the data.

Firstly, the spectral analysis of moving average operator and the novel time-frequency hybrid sequence operator were presented in this paper. For complex data, the ideal effect is difficult to achieve by applying the frequency domain operator or time domain operator alone. The more satisfactory results can be obtained by time-frequency hybrid sequence operator.

Developing severe load spectrum of transport aircraft structures is crucial for enhancing the fatigue damage correlation between full-scale fatigue testing results and operational service. The lack of consensus on severe spectrum development methods for transport aircraft has prompted the current research, resulting in a proposed approach for a severe gust load spectrum based on the acceleration cumulative exceedance surface.

The measured load data were analyzed using a model based on the cumulative exceedance number surface to describe the variation in exceedance numbers. An improved sampling method based on multivariate Markov Chain Monte Carlo was employed to obtain the fleet fatigue damage distribution, enabling the determination of the severity of severe spectrum and the corresponding cumulative exceedance number surface, and a severe gust load spectrum was developed based on the surface.

The method that characterizes load spectrum variation using the cumulative exceedance surface minimizes the randomness of peak-trough pairs by incorporating the correlation of load spectrum peaks and troughs. This approach reduces the variation in fleet fatigue damage, thereby lowering the requirements for the severity of severe spectrum fatigue damage.

The proposed methodology extends from a one-dimensional curve to a two-dimensional surface, accounting for the correlation between peak and trough values to develop a severe spectrum. This approach more accurately describes the variation in acceleration cumulative exceedance numbers, directly benefiting fatigue damage calculation. This study provides valuable references for developing severe spectrum for transport aircraft.

In daily life, some special systems are difficult to handle, such as indistinct systems (the reverse of explicit systems), history systems, future systems, infinite systems, etc. It is one of the more important topics of broad-spectrum philosophy to give these systems observo-controllable and -manipulatable forms from the angle of epistemology and methodology. The paper aims to discuss these issues.

The author conducted a broad-spectrum analysis of some typical mechanisms of general object systems and general cognition systems through a generalized quantification method and a dynamic structure method.

Through generalized quantification and dynamic structure methods, the author can draw some new conclusions about these special systems: for example, the indistinct system has certain relations and structures, but these relations and structures cannot be observo-controlled directly under certain conditions; further, the indistinct system and explicit system are interdependent, and therefore people can transform an indistinct system into an explicit one.

Through the study of special systems, the author may find a new perspective and research topic for broad-spectrum philosophy, which may provide a new concept and study frame for future analysis of these special systems.

The objective is the application of vibration analysis for the detection and diagnosis of low isolation between the stator coil wind and the voltage phase unbalance in induction motors with different numbers of poles. The purpose of this paper is to provide an approach for maintenance engineers for diagnosis electrical fault through the vibration analyses.

A detailed review of previous work carried out by some researchers and maintenance engineers in the area of machine fault detection is performed. By vibration analysis, the spectra were collected, which used to analyze the failure. Vibration spectra could detect particular characteristic for each fault in an initial condition, so the machine health can be preserved.

Results show the efficiency of the technique of vibration analysis and their relevance to detect and diagnose faults in different induction motors. In this way, it may be included in future predictive maintenance programs.

The paper presents a laboratory investigation carried out through an experimental set-up for the study of fault, mainly related to the stator winding inter-turn short circuit and voltage phase unbalance.

The main contribution of the paper has been the characterization of one more tool that makes the predictive maintenance process more efficient, effective and faster, increasing the reliability and availability of equipment.

This paper aims to analyze the characteristics of friction vibration signals and identify the vibration excitation source at the start and stop stage of microtextured end face of dry gas seals.

The friction pair consists of a diamond-like carbon (DLC) film microtextured seal ring and a spiral groove seal ring. Friction vibration signal feature extraction method based on harmonic wavelet packet and spectrum analysis was proposed. Signals were collected using acceleration sensor, acquisition card and LabVIEW software. Vibration acceleration signal was decomposed into 32 frequency bands using MATLAB wavelet packet transformation. The 32nd band coefficient was extracted for reconstruction, time-domain and spectral waveforms were obtained and spectra before/after denoising were compared.

The end face of the DLC film microtextured seal ring generates a good dynamic pressure effect, and the friction and vibration reduction effects are obvious. The harmonic wavelet packet can decompose the vibration signal conveniently and precisely. In the case of this experiment, the frequency of vibration of the seal ring is 7500 HZ.

The results show that the method is effective for the processing of friction vibration signal and the identification of vibration excitation source. The findings will provide ideas for the frictional vibration signal processing and basis for further research in the field of tribology of dry gas seal ring.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0084/

This work aims to study the effect of mordanting method as pre-mordanting (on-chrome), meta-mordanting (meta-chrome) and post-mordanting (after-chrome) on wool dyeing with madder natural dye without tannin and pomegranates peel, as a natural dye contains tannin.

The woolen yarn was dyed with madder and pomegranate peel natural dyes by three methods as pre-mordant, meta-mordant and pos-mordant. The color parameters and reflectance spectra of dyed samples were analyzed by using derivative spectroscopy and the principal component analysis (PCA) techniques.

The obtained results indicate that the color difference between the samples dyed with madder by pre-mordanting, and the samples dyed by other methods is more than the color difference between the samples dyed by meta-mordanting and post-mordanting. However, the color difference between samples dyed with pomegranate peel by pre-mordanting and meta-mordanting methods is less compared to other pairs. Also, analysis of reflectance spectra and color depth (K/S) values indicate that the color depth of dyed sample with madder by pre-mordanting method is more than other dyeing methods. But, the color depth of sample dyed with pomegranate peel by post-mordanting method is less compared to other methods. The analysis of first-, second-, third- and fourth-order derivatives of reflectance spectra and the study of the first, second, third and fourth PCs of reflectance spectra indicate that the reflectance of dyed samples with madder and pomegranates peel depends on the mordanting method.

Evaluation of the effect of mordanting method on color and reflectance of wool dyed with madder and pomegranates peel natural dyes using derivative spectroscopy and the PCA techniques

This study aims to identify the content in introductory business sustainability courses in the USA to determine the most frequently assigned reading material and its sustainability orientation.

In total, 81 introductory sustainable business course syllabi reading lists were analyzed from 51 US colleges and universities. The study utilized frequency counts for authors and readings and R analysis of key words to classify readings along the sustainability spectrum.

The study reveals the most frequently assigned authors and readings in US sustainable business courses (by program type) and places them along the sustainability spectrum from weak to strong. In total, 55 per cent of the top readings assigned in the sample advocate a weak sustainability paradigm, and 29 per cent of the top readings advocate a strong sustainability paradigm.

This study focused on reading lists of introductory courses in the USA; cases, videos and supplemental materials were excluded, and the study does not analyze non-US courses.

The findings of this study can inform instructors of the most commonly assigned authors and readings and identify readings that align with weak sustainability and strong sustainability. Instructors are now able to select sustainable business readings consistent with peers and which advance a weak or strong sustainability orientation.

This is the first research to identify the most commonly assigned authors and readings to aid in course planning. This is also the first research to guide instructors in identifying which readings represent weak versus strong sustainability.