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This paper aims to synthesise and characterise N-cyclohexylmethacrylamide (NCMA) monomer which contains thermosensitive group. The characterisation of monomer was performed both theoretically and experimentally.

The monomer was prepared by reacting cyclohexylamine with methacryloyl chloride in the presence of triethylamine at room temperature. The synthesised monomer was characterised by using not only Density Functional Theory (DFT) and Hartree–Fock (HF) with the Gaussian 09 software but also fourier transform infrared (FT–IR), ¹H and ¹³C nuclear magnetic resonance (NMR) spectroscopy.

Both the experimental and the theoretical methods demonstrated that the monomer was successfully synthesised. The vibrational frequencies, the molecular structural geometry, such as optimised geometric bond angles, bond lengths and the Mulliken atomic charges of NCMA were investigated by using DFT/B3LYP and HF methods with the 3-21G* basis set. The experimental results were compared with theoretical values. The results revealed that the calculated frequencies were in good accord with the experimental values. Besides, frontier molecular orbitals (FMOs) and molecular electrostatic potential of NCMA were investigated by theoretical calculations at the B3LYP/3–21G* basis set.

Monomer and polymer containing a thermosensitive functional group have attracted great interest from both industrial and academic fields. Their characterisation can provide great opportunities for polymer science by using DFT and HF methods.

The monomer containing a thermosensitive functional group and a various polymer may be prepared by using DFT and HF methods described in this paper. The calculated data are greatly important to provide insight into molecular analysis and then used in technological applications.

In this paper, based on the density functional theory (DFT) and finite element method (FEM), the elastic, buckling and vibrational behaviors of the monolayer bismuthene are studied.

The computed elastic properties based on DFT are used to develop a finite element (FE) model for the monolayer bismuthene in which the Bi-Bi bonds are simulated by beam elements. Furthermore, mass elements are used to model the Bi atoms. The developed FE model is used to compute Young's modulus of monolayer bismuthene. The model is then used to evaluate the buckling force and fundamental natural frequency of the monolayer bismuthene with different geometrical parameters.

Comparing the results of the FEM and DFT, it is shown that the proposed model can predict Young's modulus of the monolayer bismuthene with an acceptable accuracy. It is also shown that the influence of the vertical side length on the fundamental natural frequency of the monolayer bismuthene is not significant. However, vibrational characteristics of the bismuthene are significantly affected by the horizontal side length.

DFT and FEM are used to study the elastic, vibrational and buckling properties of the monolayer bismuthene. The developed model can be used to predict Young's modulus of the monolayer bismuthene accurately. Effect of the vertical side length on the fundamental natural frequency is negligible. However, vibrational characteristics are significantly affected by the horizontal side length.

Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.

A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.

It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.

The generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

This paper aims to deal with the problems of failure dependence and common cause failure (CCF) that arise in reliability analysis of complex systems.

Firstly, a dynamic fault tree (DFT) is used to capture the dynamic failure behaviours and converted into an equivalent generalized stochastic petri net (GSPN) for quantitative analysis. Secondly, an efficient decomposition and aggregation (EDA) theory is combined with GSPN to deal with the CCF problem, which exists in redundant systems. Finally, Birnbaum importance measure (BIM) is calculated based on the EDA approach and GSPN model, and it is used to take decisions for system improvement and fault diagnosis.

In this paper, a new reliability evaluation method for dynamic systems subject to CCF is presented based on the DFT analysis and the GSPN model. The GSPN model is easy to capture dynamic failure behaviours of complex systems, and the movement of tokens in the GSPN model represent the changes in the state of the systems. The proposed method takes advantage of the GSPN model and incorporates the EDA method into the GSPN, which simplifies the reliability analysis process. Meanwhile, simulation results under different conditions show that CCF has made a considerable impact on reliability analysis for complex systems, which indicates that the CCF should not be ignored in reliability analysis.

The proposed method combines the EDA theory with the GSPN model to improve the efficiency of the reliability analysis.

This paper aims to present two methods for calculating the steady state probability of a repairable fault tree with priority AND gates and repeated basic events when the minimal cut sets are given.

The authors consider a situation that the occurrence of an operational demand and its disappearance occur alternately. We assume that both the occurrence and the restoration of the basic event are statistically independent and exponentially distributed. Here, restoration means the disappearance of the occurring event as a result of a restoration action. First, we obtain the steady state probability of an output event of a single‐priority AND gate by Markov analysis. Then, we propose two methods of obtaining the top event probability based on an Inclusion‐Exclusion method and by considering the sum of disjoint probabilities.

The closed form expression of steady state probability of a priority AND gate is derived. The proposed methods for obtaining the top event probability are compared numerically with conventional Markov analysis and Monte Carlo simulation to verify the effectiveness. The result shows the effectiveness of the authors’ methods.

The methodology presented shows a new solution for calculating the top event probability of repairable dynamic fault trees.

Purpose – To examine the cost-effectiveness of UK government policy with respect to the mitigation of carbon emissions from the transport sector.

Methodology/approach – Existing policy as set out by the Department for Transport in Low Carbon Transport: A Greener Future is examined. This document elaborates a Low Carbon Transport Strategy intended to achieve annual emissions savings of 17.7 MtCO2 by 2020. A wide range of policy areas where further action could be taken to reduce carbon emissions are examined and their cost-effectiveness considered.

Findings – Measures that influence behaviour including smarter choices, eco-driving across modes, freight best practice and modest price increases are highly cost-effective. More cost-effective routes to saving 17.7 MtCO2 are identified, as are further cost-effective savings.

Originality/value – It appears that government targets could be delivered and indeed exceeded at lower cost than the Low Carbon Transport Strategy. However, policy development is influenced by a wide range of factors which help to explain why cost-effective measures are not always fully exploited.

The discrete Fourier transformation (DFT) has been proven to be a successful method for determining whether a discrete time series is seasonal and, if so, for detecting the period. This paper deals exclusively with rare data, in which instances occur periodically at a low frequency.

Data based on real-world situations is simulated for analysis.

Cycle number detection is done with spectral analysis, period detection is completed using DFT coefficients and signal shifts in the time domain are found using the convolution theorem. Additionally, a new method for detecting anomalies in binary, rare data is presented: the sum of distances. Using this method, expected events which have not occurred and unexpected events which have occurred at various sampling frequencies can be detected. Anomalies which are not considered outliers to be found.

Aliasing can contribute to extra frequencies which point to extra periods in the time domain. This can be reduced or removed with techniques such as windowing. In future work, this will be explored.

Applications include determining seasonality and thus investigating the underlying causes of hard drive failure, power outages and other undesired events. This work will also lend itself well to finding patterns among missing desired events, such as a scheduled hard drive backup or an employee's regular login to a server.

This paper has shown how seasonality and anomalies are successfully detected in seasonal, discrete, rare and binary data. Previously, the DFT has only been used for non-rare data.

The modern missile has low uncertain and wide range vibration frequency. The conventional notch filter with the fixed notch frequency is less effective than that of the adaptive notch filter (ANF) in vibration suppression for the time-varying vibration frequency.

To overcome the drawback, a novel method is based on frequency estimators made by interpolation of three discrete Fourier transform (DFT) spectral lines. The modified frequency estimators based on the interpolation of three DFT spectral lines are presented to identify and track the vibration frequency. Then the notch frequencies of multiple ANFs are real-timely tuned according to estimators.

Finally, taking the second-order flexible missile as an example, the performance of the proposed method is verified. The verified simulation results show that multiple ANFs are effective in vibration suppression.

Cascading multiple ANFs to achieve multi-order vibration suppression is more efficient and feasible than conventional fixed-parameter notch filtering.

The frequency estimation method based on three DFT spectral lines proposed in this paper can effectively identify and track signals in the noise environment. Compared with conventional methods, the method pretended in this paper has high identification accuracy and a stronger ability to track signals. It can meet the fast frequency identification requirements of the actual flexible missile.

Changes in physiology associated with ageing mean increased concern for the safety of older drivers and the risk they may pose on other road users. The risk of older drivers is distorted by their fragility; they are more likely to be injured or die in road collisions compared to a younger person. Older drivers are, overall, safe drivers who pose similar risks to other road users as middle-aged drivers, but who are at risk themselves because of their fragility. The fragility is greater in older females than older men; females over the age of 80 are nine times more likely to die from their injuries compared to 40–49-year old females, while men are at least five times more likely. Older drivers are overrepresented in collisions at junctions that have no formal traffic control and underrepresented in crashes that involve excess speed. While it is not possible to put traffic signals in every junction, it is suggested consideration be given to mini roundabouts or three-way stop-sign junctions (as found in United States and South Africa). There is no evidence that stringent testing for licence renewal has advantages in reducing older driver risk. Assessments at specialist centres, such as mobility assessment centres, are a more effective way to pick up drivers who are no longer safe to drive.

Community rail is a grassroots movement that spans Britain, made up of hundreds of community groups and partnerships that engage people with their railways and stations and provide a bridge between the rail industry and the public at a local level. The movement has grown up from the grassroots, but it has also been increasingly supported and nurtured by the rail industry, with train operators proactively encouraging its spread and development. They, and national and devolved governments, recognise the value of community rail, and its contribution to social inclusion, sustainable development and the railway’s ability to prosper and serve passengers and communities well, now and in the future. This idea is supported by passenger data showing that railway lines with community rail partnerships – working to enhance, promote and aid access to those lines – outperform comparable lines. A swathe of qualitative evidence shows community rail partnerships and station groups having a demonstrable impact on their localities and people’s lives, and appreciation of this role by industry leaders. The many examples of community rail volunteers and practitioners bringing about positive change resonate with academic research exploring how civic engagement and local efficacy and communications can support change, particularly with regard to sustainable behaviours and development. A range of researchers argue that localised, interactive engagement and communications may be the key, when it comes to bringing about the major shifts in behaviour needed to address the global, existential threat posed by the climate crisis, which unsustainable behaviours and policies have brought about. In this way, evidence suggests that engaging communities with their railways, and local transport networks generally, is critical, both to these networks operating in a truly sustainable manner and to achieving inter- and intra-generational equity within the communities they serve.