bluuebunny/crossref_metadata_2025 huggingface dataset:
A Community Project to Supplement Social Care Services
Authors: Barker Frances H. , Roach Mollie
Published: January 2024

Solva in West Wales, UK, is a small community with about 700 people on the electoral roll. In 2013, Solva Community Council faced the fact that things were not going well for the elderly in our village. Many had to leave home and go “into care.” They didn’t want to go and we didn’t want to lose them. A community councillor at that time, author Mollie Roach, did some research and decided that the village could look after its own. A small working party including first author Frances Barker was set up to plan the way forward. The original idea was not a volunteer service. We wanted to set up a local domiciliary care service, where the carers would live locally and not have to spend their precious time travelling between wide-spread destinations. We soon found that there were several administrative and monetary barriers in the way of setting up such a scheme, especially for a small community. Registration needed money and qualified people. and the “rules” were such as to prevent rather than encourage individual response to individual circumstances. However, we could see that there was a need for a local volunteer service. It is disturbing when you discover you cannot go up a ladder and change a light bulb. It is devastating when you are told you cannot drive anymore because of an eye problem. It is worrying when you cannot take the dog for a good walk or collect your prescription because of arthritis. All these problems are under the radar of statutory services. This is a gap that can be alleviated by a local community-based volunteer scheme. Solva Care evolved with a paid co-ordinator to mediate between volunteers and those needing help. We are now getting closer to the original idea, doing our best to integrate domiciliary and social care, working with agencies, private carers, families, and individuals, as well as continuing to run the volunteer service.
Reflections on Community Development, Preventative Care, and Ageing
Authors: Verity Fiona , Barker Frances H. , Richards Jonathan , Read Simon , Llewellyn Mark
Published: January 2024

Recently there has been a chorus of demands to “re‐imagine” social care. Community and faith‐based organisations, policy, and academic communities are engaged in discussions on issues such as human rights for older populations, the future of residential care, how to better support family/community care, and strengthen local place‐based community development. Moreover, the Covid‐19 pandemic has added new urgency to this mission, galvanizing developments for change and collective action and exposing public troubles of endemic system failings, prevailing discourses of ageism, tensions with health systems, and limitations of market models of care and support. Prevention is a central social welfare principle in many countries. It is associated with policy and practices that aim to meet social care needs early and is explored in this thematic issue.
Designing Effective Models for COVID-19 Diagnosis through Transfer Learning and Interlayer Visualization
Authors: ÖZDEMİR Cüneyt
Published: January 2024

Creating a model from scratch that fits the dataset can be laborious and time-consuming. The level of difficulty in designing a new model can vary depending on factors such as the complexity of the model and the size and characteristics of the dataset. Factors such as the number of variables in the dataset, the structure of the data, class imbalance, and the size of the dataset are important in deciding which model to use. In addition, long experimental studies are required to design the most appropriate model for the dataset. In this study, we investigated how transfer learning models can be utilized to solve this problem. Experimental studies were conducted on the Covid-19 dataset with transfer learning models and the most successful transfer learning models were identified. Then, layers that did not contribute to the performance of the transfer learning models and could not extract the necessary features from the dataset were identified and removed from the model. After removing the unnecessary layers from the model, new models with fast, less complex and fewer parameters were obtained. In the studies conducted with the new models derived from the most successful transfer learning models with the inter-layer imaging method, the classes were classified with an accuracy of %98.8 and the images belonging to the Covid-19 class were classified with a precision of %99.7.
Stability of non-thin anisotropic cylindrical shells in spatial position under distributed lateral pressure
Authors:   National University of Water and Environmental Engineering, Rivne, Ukraine, Trach V. M. , Podvornyi A. V. ,   National University of Water and Environmental Engineering, Rivne, Ukraine, Zhukova N. B. ,   Institute of Mechanics of NAS of Ukraine, Kyiv, Ukraine
Published: January 2024

A three-dimensional approach to solving the problem of stability of non-thin cylindrical anisotropic layered shells under distributed lateral pressure is proposed. Based on the modified Hu-Washizu variational principle, a three-dimensional system of homogeneous differential stability equations is obtained for the calculation of shells, the anisotropy of which is characterized by a material with one plane of elastic symmetry. The solution of the three-dimensional system was carried out using the Bubnov-Galerkin methods and numerical discrete orthogonalization. The influence of an increase in the number of cross-laid layers of the same thickness on the stability of an anisotropic cylindrical shell is studied. The results of the solution are presented by graphs and their analysis is given.
The use of cubic splines in the modeling of creep processes of linear viscoelastic materials under complex stress state
Authors:   S.P. Timoshenko Institute of Mechanics of National Academy of Sciences of Ukraine, Kyiv, Ushakova V. S. , Ushakov O. V. ,   Ukrainian scientific and research Institute of special equipment and forensic expertise of the Security Service of Ukraine, Kyiv, Gorun O. Y. ,   Ukrainian scientific and research Institute of special equipment and forensic expertise of the Security Service of Ukraine, Kyiv
Published: January 2024

The processes of creep of thin-walled tubular elements made of linear viscoelastic materials under complex stress conditions are considered. The discrete values of basic experimental data on uniaxial tension and pure torsion are considered. These discrete values are used for identification of hereditary kernels normally used in creep modeling processes under complex stress conditions. As an example material, plexiglass ST1 is used for calculations. The area of linearity of the model is substantiated by the value of the quantile of statistics and the hypothesis of the existence of a unified creep function in a certain, predetermined confidence interval. The creep function is invariant with respect to stresses and is built with making use of the yield curves. Constitutive equations of the model contain a set of functions and coefficients determined from basic experiments. For further calculations, the experimental data are approximated by a power function followed by the smoothing with cubic splines. Approximation analysis is carried out by evaluation of the least squared deviation of experimental data from the calculated data. The approximating function is analyzed with making use of minimum of the quadratic deviation.
Model of nonlinear deformation of granular composites
Authors:   S.P. Timoshenko Institute of Mechanics of National Academy of Sciences of Ukraine, Kyiv, Shikula E. N. , Zhukova N. B. ,   S.P. Timoshenko Institute of Mechanics of National Academy of Sciences of Ukraine, Kyiv
Published: January 2024

The model of nonlinear deformation of a granular composite material of a stochastic structure with physically nonlinear components was constructed. The basis is the stochastic differential equations of the physically nonlinear theory of elasticity by L.P. Khoroshun. The solution to the problem of the stress-strain state and effective deformable properties of the composite material is built using the averaging method. An algorithm for determining the effective properties of granular material with physically nonlinear components has been developed. The solution of nonlinear equations, taking into account their physical nonlinearity, is constructed by the iterative method. The law of the relationship between macrostresses and macrostrains in granular material and the dependence of average strains and stresses in its components on macrostrains has been established. Curves of deformation of the material were constructed for different values of the volume content of its components. The dependence of the effective deformable properties of the granular material on the volume content of the components was studied. The effect of component nonlinearity on the deformation of granular composite material was studied. It was established that the nonlinearity of the components significantly affects the effective deformable properties and the stress-strain state of granular materials.
Sensor Systems with Optical Reponse
Authors:   Taras Shevchenko National University of Kyiv, Kyiv, Ukraine, Yashchuk V. M. , Grebinyk A. G. ,   Deutsches Elektronen-Synchrotron DESY, Zeuthen, Germany, Lebedyeva I. V. ,   Taras Shevchenko National University of Kyiv, Kyiv, Ukraine, Vashchilina O. V. ,   Taras Shevchenko National University of Kyiv, Kyiv, Ukraine, Losytskyy M. Yu. ,   Taras Shevchenko National University of Kyiv, Kyiv, Ukraine, Navozenko O. M. ,   Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
Published: January 2024

This paper is a review of the scientific works of the co-authors. In the paper, the basic principles of action of sensor systems with optical response of two types are considered. In particular, two spectroscopic technologies for probing some objects or events in non-living (synthetic) or biological environments are considered. In the first part of the work, sensor technologies related to the use of probes with optical response specially introduced into the sensor system are analyzed. As a rule, these probes are specially developed systems, the spectral properties of which change significantly under interaction with the object of probing. In the first part of the work, sensors of the first type are considered. In particular, the main ideas used in this type of sensors are given and specific examples of the use of such sensors are analyzed: specially created molecules that have a low fluorescence quantum yield due to a significant number of mechanical intramolecular degrees of freedom. The intercalation of such probes to large biomolecules leads to a change in the spectral characteristics of these probes, in particular, to a significant increase in fluorescence (up to 1000 times). The case of two-photon absorption of molecular probes in the transparency window of biological tissues is examined. Semiconductor and metal nanoparticles whose absorption and fluorescence spectra depend on their sizes were also proposed as probes of the first type (as sensors of elastic vibrations of the environment).The second part of the paper is devoted to the mechanisms of autosensing (the case when the medium's own optical centers are used to detect processes occurring in biological or synthetic systems). The main ideas that underlie the operation of sensors with optical response of this type were analyzed, and examples of such use were also given. In particular, the use of the own spectral properties of biomacromolecules and some coenzymes for changes in the external environment is considered. The use of the optical response of synthetic macromolecules on the value of their molecular weight was also analyzed.
Updated DTW+K-Means approach with LSTM and ARIMA-type models for Core Inflation forecasting
Authors:   Taras Shevchenko National University of Kyiv, Kyiv, Ukraine, Krukovets D.
Published: January 2024

The paper is dedicated to evaluating performance in forecasting tasks of the novel routine that includes adapted DTW + K-Means for aggregating series with similar dynamics. The algorithm was developed throughout the series of papers. Novel parts are designed in a way to work with periodic series, like in the investigated monthly data case. It is used over hundreds of Consumer Price Index components to find similar dynamics and aggregate them by the similarity of their dynamics. Then aggregated series are given as input to the ARIMA, SARIMA, and LSTM models, to forecast the total Core Consumer Price Index. The choice is based on the necessity to capture possible non-linear relationships between series. The dataset is quite rich and contains hundreds of Consumer Price Index components, which is a level of prices for different goods. Data suffers from multiple issues, including seasonality, so controlling them either with satellite models such as X-12 or with the architecture of the forecasting model is sufficient. The research results are important for different groups of agents. Private businesses seek to plan their pricing while government structures want to employ their administrative measures in a proactive data-driven manner. The result shows that the SARIMA currently outperforms other models. An LSTM model combined with DTW + K-Means method shows worse results yet it was able to catch non-linearities, unlike more traditional models. Further investigation of LSTM + DTW/K-Means performance and fitting is necessary.
Limitations in the existing models for predicting accurate interdependencies between wear and friction noise,
Authors: Kalifa Mohamed , Starr Andrew , Khan Muhammad
Published: January 2024

In recent years, the investigation of friction noise has gained increasing attention within the field of tribology. Wear processes pose a significant hurdle in the industrial sector as reduce the lifespan of machine components. The replacement of damaged components, including brake
systems and high-speed running machines, incurs substantial expenses. Thus, there arises a need for a method capable of identifying alterations in surface contact conditions, enabling the monitoring of the process and anticipation of the noise and rate of wear. To directly measure wear, it
would be necessary to remove one of the contact surfaces (i.e., mechanical contacts such as gears and bearings.) for visual inspection, resulting in significant downtime. Alternatively, indirect measurements of wear can be performed by assessing the emitted machine noise and establishing a
correlation between the noise and the alteration in surface topography caused by wear progress. Friction noise refers to the acoustic emissions generated during the contact between rough surfaces in motion. These emissions contain valuable information about the dynamics and characteristics
of the sliding surfaces. Therefore, the researchers have attempted to model the interdependencies in between friction, wear and noise to find out the mentioned information and mapped it with the surface health of the mechanical contacts. The objective of this research paper is to examine all
the prominent current models to discuss their approaches about how the noise can be generated from friction and later generate surface wear. This paper mainly aims to highlight their key limitations and identify the current challenges in modeling the mentioned interdependencies with accuracy.
Ultrasonic guided wave attenuation in submerged steel pipes
Authors: Ezergani Husen , Khan Mohammad , Syed Adnan U , Olisa Samuel Chukwuemeka
Published: January 2024

Pipe structures play a pivotal role in various industrial sectors, necessitating the assurance of their structural stability to ensure safe and reliable operations. Non-destructive Testing (NDT) methods are commonly employed to assess the integrity of these systems. Among the array
of NDT techniques, Ultrasonic Guided Wave (UGW) inspection has emerged as a reliable method for assessing large mechanical structures, particularly addressing challenges associated with accessibility. This study focuses on investigating the impact of water immersion on UGW attenuation in steel
pipes. To investigate the behaviour of guided waves in water-immersed scenarios, a comprehensive experimental setup was designed and implemented. The study involved testing four distinct cases: (a) an empty bare pipe as a reference case, (b) a bare pipe filled with stagnant water, (c) an empty
pipe fully submerged in plain water, and (d) a pipe with combined loads (i.e., filled with stagnant water and submerged). The amplitude variation and Time of Flight (ToF) were analysed to assess wave propagation characteristics. The experimental results revealed notable differences in amplitude
and ToF among all the four tested cases. Introduction of stagnant water inside the bare pipe resulted in decreased wave amplitudes, indicating increased wave attenuation compared to the empty pipe scenario. This research provides valuable insights for applications involving steel pipes submerged
in water, offering guidance for accurate and reliable inspection and monitoring of pipe integrity. Future investigations may focus on understanding the underlying mechanisms behind wave attenuation in water-immersed scenarios and developing techniques to mitigate or compensate for these effects,
thereby advancing inspection and monitoring capabilities in real-world applications.
Liquefied natural gas plant orbit valve risk-based failure analysis
Authors: Pereira Eloi S
Published: January 2024

This paper present the study conduted to investigate repetitive failures on orbit valves installed on the outlet of Molecular Sieve Dehydration unit in a Liquefied Natural Gas (LNG) plant using the risk analysis approach. The Orbit valve seize during operation and casues damage to the
lower trunnion bushing. The main causes of damage to the orbit valve identified was as a result of particulate contamination and the speed of operation. The valve was found to be operating at a faster rate higher than recommended by Original Equipment Manufacturer (OEM). The orbit valve seizure
incidents are critically assessed using the Root Cause Analysis (RCA) and risk based maintenance approach to estimate the failure probability, and hence determine maintenance opportunities towards reliability improvement.
Liquefied natural gas plant maintenance: a case study of gas turbine centrifugal compressor thrust bearing failure analysis
Authors: Pereira Joana Nilsa Coutinho
Published: January 2024

Maintenance is essential for preserving gas turbine engineering system functionality during equipment life cycle. Heavy-duty gas turbine (HDGT) engineering systems are often quite complex to maintain. Therefore an effective condition based maintenance (CBM) program is vital to reduce
downtime, improve equipment reliability and cost optimization. It is estimated that 50 to 75% of gas turbine maintenance cost is related to the replacement and repair of hot section parts. Furthermore, the maintenance intervals of the engine is governed by the hot section material durability,
and the replacement of these components is the largest contributor to determining maintenance related equipment availability. This paper investigate multiple thrust bearing failures on gas turbine driven centrifugal compressor application in a liquefied natural gas (LNG) plant. A root cause
failure analysis (RCFA) approach was conducted to determine the failure mode and its consequencies. Process instability is believed to be the cause of several thrust bearing failures. Downtime analysis is presented to estimate the the loss of production (LPO).
Cavitation detection based on instantaneous angular speed
Authors: Verwimp Toby , Mousmoulis Georgios , Gryllias Konstantinos , Hajnayeb Ali
Published: January 2024

The study and detection of cavitation in hydraulic turbo-machinery has gained recently particular attention from both manufacturers and end users, due to the damage caused on machines’ surfaces that may result in costly sudden shutdowns. The traditional cavitation detection approaches
exploit the noise and vibration signals that sense the vapor bubble implosion pressure waves, while advanced demodulation techniques are used in order to locate the resonances excited from these waves. This work aims to investigate an alternative methodology, where detection is achieved by
processing the speed signal of rotary encoders. The motivation is that, contrary to microphones and accelerometers, the encoder is mounted directly on the shaft, where the rotor is connected and thus, is in direct contact with the cavitation waves. More specifically, in this study a centrifugal
pump is tested under healthy and cavitating conditions, while an incremental encoder is rigidly installed on the pump’s shaft. The encoderâ–™s signal is processed in order to accurately estimate the raw speed signal. In addition, the order spectrum is used so as to
extract the diagnostic indicators based on integer order amplitudes. From the results it can be concluded that particular components can give useful insights allowing the detection of both heavy and light intensity cavities.
The application of multiple statistical techniques to predict factors affecting non-communicable diseases (NCDs) among the OECD countries
Authors: Ampon‐Wireko S
Published: January 2024

The continuous rise in NCDs exerts pressure on government budgets, health services and personal patient finance. This has led policymakers to implement reforms and predicting models to mitigate the rise of the NCDs. The purpose of this study is to investigate the best model to predict
factors influencing NCDs by using both statistical analysis and machine learning (ML) prediction methods. Statistical analysis models such as Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS) were used to extract the most important information from the dataset and
analyze the observations' structure. To improve the accuracy of the statistical model, four optimization algorithms were proposed: genetic algorithm (GA), particle swarm optimization (PSO), Differential Evolution (DE) and Ant Colony Optimization (ACO). Two statistical criteria, such as
root-mean-squared error (RMSE) and coefficient determination (R2), were used to assess the aforementioned models potential. The results revealed that PLS performed better than PCA, with low RMSE values of 0.41809 and 0.42752 and R2 values of 0.9724 and 0.9737 in both
the training and the testing data. Further, to improve the accuracy rate of PLS, hybrid models (GA-PLS, DE-PLS, PSO-PLS and ACO-PLS) is developed. Evaluating the obtained results demonstrated a higher accuracy ability to predict NCDs. The results indicated that the GA-PLS model provided the
highest performance in predicting the NCDs. The RMSE and R2 values of (1.235e-3 and 4.643e-3) and (1and 1) were obtained for training and testing data of GA-PLS model, respectively. The most important input parameters in predicting NCDs were identified. This study indicates how
statistical analysis and machine learning modeling could help stakeholders make preliminary decisions regarding NCDs.
Using audio-data for anomaly detection in the fatigue test of a composite tidal turbine blade
Authors: Munko M.J. , Valdivia Camacho M.A. , Cuthill F. , Bradaigh Conchur M.Ó , Lopez Dubon S.
Published: January 2024

FastBlade is a research facility for testing large-scale composite and metal structures. Fatigue tests run on tidal turbine blades measure the mechanical response of a blade subject to the number of loading cycles that mimic the ones it will experience over its lifetime of a subsea
deployment. To maximise its throughput by running the facility uninterruptedly, unmanned operation of the site should be possible. One of its key enablers is anomaly detection. Microphones are used as a non-specific and affordable sensing method. Using the audio data, we applied a Fast Continuous
Wavelet Transform to extract the patterns recorded during normal and abnormal operations. These outputs are used to train a neural network autoencoder (NNA). The original image is reconstructed from the compressed vector in the latent space (LS) of the NNA, and the loss is computed to detect
and quantify anomalies. The study's findings demonstrate the success of using audio data to detect short-lived anomalies despite limited information about the critical assets in the set-up and can be easily extrapolated to other systems.
Computerised maintenance management system (CMMS) role in small and medium enterprise (SME)
Authors: Aliyu Abbas , Baglee David , Dixon Derek
Published: January 2024

This paper is intended to empirically analyse the strategic role of Computerise Maintenance Management Systems in Small and Medium Enterprises and highlight the benefits that reside in the adoption of the system by keeping the company’s maintenance budget low and improving the
versatilities of the staff and increasing the availability of the assets also X-rated the hidden the potentials of the CMMS in SMEs. Also provided clear functional/operational guidelines for the implementation of the tool and discuss the barriers that may hinder the successful implementation
of CMMS. Use as a decision-making tool to assist the management in keeping up with the day-to-day running of their industry safely and healthily.
Measurement of maintenance planning and control effectiveness using the Lean approach
Authors: Aziz Ammar Wan Abd , Ompusunggu Agusmian Partogi , Charil M Ayatollah
Published: January 2024

Ineffective maintenance management can result in a backlog of maintenance work orders, inefficient activity prioritisations, and unnecessary outages. Making a strategic decision is challenging due to traditional procedures that only treated maintenance at a tactical level. Usually,
the key performance indicator (KPI) measured for maintenance management is related to work order compliance. However, there is still less work focusing on measuring the effectiveness of Maintenance Planning and Control (MPC) processes. This paper aims to showcase a measurement of the maintenance
effectiveness at offshore facilities using Lean methodology by measuring the lead time of sub-processes of MPC activities to evaluate existing practices, identify gaps and develop key recommendations. To this end, the lead time of the baseline MPC process is analysed and benchmarked with that
of the improved MPC where new parameters to monitor and verify the process efficiency are included. The results show that a lead time improvement of close to 60% was achieved with the proposed methodology.
Wind turbine bearing fault diagnosis by maximum generalised Gaussian cyclostationarity blind deconvolution
Authors: Peng Dikang
Published: January 2024

This study aims to assess the performance of IGGCS/GGS as a criterion for blind deconvolution in the detection of wind turbine bearing faults. The challenges associated with diagnosing wind turbine bearing faults include two primary issues: (1) the theoretical fault frequencies
or orders may not align with the actual fault frequencies or orders, and (2) the vibration signal deviates from a Gaussian distribution. Consequently, the application of the blind deconvolution criterion ICS2, as utilized in CYCBD, may encounter difficulties due to the aforementioned reasons.
To address this, the criterion is modified from ICS2 to IGGCS/GGS, and a tolerance band is introduced during the generation of the weighting matrix. These modifications aim to mitigate the impact of the aforementioned challenges. The effectiveness of the modified blind deconvolution approach,
referred to as CYCBDβ, is evaluated in this study by comparing its performance against that of CYCBD using two distinct datasets from 2.0 MW wind turbines.
Crack detection of powder metal and steel gears
Authors: Mohammed Omar D.
Published: January 2024

The application of the Powder Metallurgy (PM) method is confirmed as a production technology for gears and other mechanical parts. The PM method offers lightweight, repeatability with high precision, maximum material use, freedom of design, cost efficiency, and sustainability. Condition
Monitoring (CM) is an important strategy to adopt in different gear systems. The current paper studies the possibility of crack detection for steel and PM gears to investigate the effect of having different gear materials on fault detection possibility. Four different statistical indicators,
namely the RMS, kurtosis, peak value and crest factor are studied. The study is based on calculating Tooth Varying Mesh Stiffness (TVMS) using a calculation model and generating the system dynamic response using a gear dynamic model. Different fault detection indicators are applied to examine
the detection possibility with crack propagation. The paper concludes on how having different gear materials has an impact on the fault detection possibility.
An innovative hybrid approach for better detection of bearing faults in highly noisy environments
Authors: Kiakojouri Amirmasoud , Lu Zudi , Mirring Patrick , Powrie Honor , Wang Ling
Published: January 2024

Rolling element bearings (REBs) are critical components in rotating machinery, and timely detection of faults in these bearings can prevent catastrophic failure and costly downtime. Vibration signals collected from REBs contain important information regarding the bearing health states.
A localized defect in a REB produces periodic impulses in the vibration signals at relevant bearing characteristic frequencies (BCFs) that are commonly used for bearing condition monitoring. Envelope analysis that has been a powerful technique in detecting BCFs in signals, however, poses a
significant challenge as it requires the selection of an effective band-pass filtering region due to the variable nature of resonant frequencies in bearing systems. A novel hybrid method for bearing fault diagnosis based on Cepstrum Pre-Whitening (CPW) and high-pass filtering has been developed
to overcome this shortcoming in envelope analysis and has shown to be a powerful technique in accurately detecting early bearing faults on a range of machines under different operating conditions. In contrast to the existing signal processing methods, the new hybrid method eliminates the need
for manual parameter selection or optimization algorithms, such as filtering bands selection in enveloping analysis. Instead, it requires inputs including bearing geometries and its operating conditions such as shaft rotating speed for bearing fault diagnosis. This enhances the practicality
and accuracy of bearing fault diagnosis in real-world scenarios, especially when data is limited. This study evaluates the effectiveness of this hybrid method for bearing fault diagnosis in the highly noisy environments under which machine is often operated in real applications. To achieve
this purpose, firstly, numerical simulations of bearing vibration signals have been conducted, wherein Gaussian white noise with varying levels of signal-to-noise ratio (SNR) is DOI: 10.1784/cm2023.3f4 added to the simulated signals. Secondly, the effectiveness of the method is tested
on vibration data collected from an experimental run-to-failure bearing dataset, which contains significant amount of background noise in the early stage of the fault progression. The results from this work have shown that the new hybrid method achieves high accuracy in diagnosing fault features
in the simulated signals with a SNR of down to ‐ 13 dB for outer ring fault and ‐ 15 dB for inner ring and ball faults, much more effective than other methods in the literature. It has also shown to detect bearing faults at very early stages from a run-to-failure test.
The lubrication engineer is the useful human for the role of the modern reliability engineer
Authors: Shorten Daniel C.
Published: January 2024

The increased proliferation of reliability related contract services in UK (United Kingdom) general industry reflects the drive towards a change in the way that we maintain our critical assets. No longer do we expect to hold internally the range of skills and behavioural competencies
that are required to operate and maintain a modern industrial plant. We now actively chose to identify partner organisations who can offer highly targeted skills but do so as a function of need as opposed to being a full time employee. This paper identifies the role of the qualified lubrication
technician as the basis for developing reliability via a truly modern condition monitoring service. Within this paper we will discuss the emerging need to use external lubrication and CM specialists to assist in the formation and delivery of an optimal maintenance strategy for a given
installation and provide the reader with an insight regarding the issues and opportunities that may be found with specific reference to ensuring that the contracted service provider has goals which are fully aligned with the organisation procuring them.
Non-linear dynamic behaviour of spiral bevel gear by considering the torsional shaft stiffness
Authors: Molaie Moslem , Ebrahimnejad Razieh , Zippo Antonio , Iarriccio Giovanni , Pellicano Francesco , Samani Farhad S.
Published: January 2024

Spiral bevel gears (SBGs) play a crucial role in developing silent power transmissions for non-parallel shaft applications, offering advantages such as improved motor allocation flexibility and space reduction. While SBGs have been recognized for reducing vibration magnitude in high-speed
gearboxes compared to straight bevel gears, complete vibration suppression remains elusive, leading to potential challenges such as teeth contact loss and complex dynamic scenarios. To construct the dynamical model of SBG, time-dependent mesh stiffness and non-smooth nonlinearity caused by
backlash is considered. The employed dynamical system is a three-degree-of-freedom model, integrating rotational shaft stiffness, to investigate the dynamic behavior of SBGs. Through the utilization of various analysis tools such as bifurcation diagram, Fourier spectrum, 3D-phase diagram,
Poincaré map, and amplitude-frequency diagram are generated, revealing the presence of periodic, quasiperiodic, and chaotic responses in specific regimes. This research provides an in-deep understanding of the dynamic behavior of SBG system, contributing to the characterization and
prediction of nonlinear phenomena, which is vital for the optimization and design of gear mechanisms across various engineering applications.
Non-linear isolators for civil applications
Authors: Iarriccio Giovanni , Zippo Antonio , Molaie Moslem , Pellicano Francesco
Published: January 2024

An experimental study on the vibration of a payload isolated through a quasi-zero stiffness (QZS) isolator subject to vertical ground motion is presented. QZS isolators exploit kinematic nonlinearities to enhance the vibration attenuation of a linear mass-spring system. In this paper,
stepped-sine tests have been performed to identify the system parameters and determine the isolator transmissibility. The parameter identification has been carried out by fitting a phenomenological model to the experimental data. The QZS isolator transmissibility exhibits a natural frequency
reduction, and the system effectively suppresses the ground harmonic vibration about the fundamental frequency of the mass-spring isolator. A non-negligible presence of dry friction has been observed, leading to the stick-slip phenomenon, and affecting the suspension activation at low frequencies.
Then, the suspension response under different realistic earthquake signals is presented and critically discussed.
Free vibration analysis of FGM plates and shallow shells by the R-functions method
Authors: Zippo A. , Shmatko T. , Pellicano F. , Kurpa L.
Published: January 2024

Research of free vibration of porous functionally graded material (FGM) plates with complex shape is carried out. It is supposed that thickness of the plate changes in direction of one of the axes. Two types of porosity distributions through the thickness even and uneven are considered.
Elastic foundation is two parameters (Winkler and Pasternak). To obtain the mathematical model of the given problem the first order shear deformation theory of the plate (FSDT) is used. The effective material properties in the thickness direction is modelled by the power law. Variational Ritz’s
method joined with the R-functions theory is used for solution of the formulated problem. The developed approach is tested on a lot of problems. Comparative analysis of the obtained results for rectangular plates confirms their verification. Demonstration of the efficiency of the proposed
approach is fulfilled for FGM plate with complex shape and various boundary conditions. Effect of the different parameters on vibration characteristics such as porosity, volume exponent, elastic foundation, types of FGM, boundary conditions is studied.
Load prediction using an intraoperative joint sensor and artificial neural network
Authors: Al-Nasser Samira , Noroozi Siamak , Haratian Roya , Aslani Navid , Harvey Adrian
Published: January 2024

Predicting load from sensors for intraoperative joint assessment using artificial neural networks (ANNs) is a relatively new and novel technique which can be useful for various other health monitoring applications. In joint replacement surgeries, balancing the load using ANN predicted
load measurements can prove useful in cases where a larger sensing area is required, and the geometry of the sensor is more complicated. Once trained ANNs can provide real-time load predictions accurately and precisely, however finding the optimal combination of parameters and hyperparameters
to yield the most accurate results requires exploration. In this research, training data was collected by loading the sensor with known weights. Then the dataset was systematically pre-processed and trained using an ANN with different combinations of parameters and hyperparameters to investigate
its performance based on different configurations of an ANN. It was found that the Levenberg-Marquardt backpropagation function, with 50 hidden layers and a learning rate of 0.1, yielded the lowest mean square error (MSE) of 0.0034±0.000141, which has also been the best performing training
function is other studies. However, the time required to perform this function was significantly longer than for any other combinations (P<0.001). Additionally, lowering the learning rate for the Levenberg-Marquardt backpropagation function and the BFGS quasi-Newton backpropagation function
had no change in the performance. In summation, this study accentuates the importance of a methodical approach in finding the optimal combination of parameters and hyperparameters for ANNs to an achieve adequate performance. It also proves that ANNs can aid in predicting the load from sensors,
especially in cases where nonlinearity is observed. Finally, translating the performance of the network to real-time data collection presents new challenges that must be addressed in the future. However, once the ANN is trained and validated, load balancing during joint replacements can be
performed with more accuracy and precision.
Digital twin for condition monitoring of actuators
Authors: Smid Radislav
Published: January 2024

Digital Twin is an individual model of the physical system with the support of enabling technologies, including multi-physics simulation, machine learning, etc. We developed Digital Twin for electromechanical actuators (EMA) containing three essential parts: real physical system (real
EMA), digital model (virtual EMA), and information interconnection (real-time data from sensors and control), which allows capturing the current state of the physical system in a digital model. The virtual EMA provides the near real-time representation of the physical EMA. It offers the potential
to optimize operations and maintenance and further accelerates the new product development process. The virtual EMA enables degradations detection, What-If simulations, RUL estimations, KPIs trending, etc. The degradation tracking performance of the digital twin was evaluated using a test
stand with real EMA and seeded faults (partial winding short). The results indicate that this approach can be used for the monitoring of EMA degradations.
Simulation of the behaviour of engines in their current state of wear
Authors: Madane Abdellah , Lacaille JLacaillerôme
Published: January 2024

Aeronautical data is increasingly available thanks to new ways of downloading and storing. In addition, the computer clusters on which we capitalize this data become effective for the implementation of machine-learning and artificial intelligence calculations. However, our companies,
which focus on aeronautical technical solutions, find it difficult to exploit this data and to take advantage of the academic skills of our university laboratories. Indeed, these same data remain the property of the airlines and are under contract, unfortunately preventing them from being
exchanged. Indeed, whenever we present the results of algorithms developed in-house, the laboratories insist that we open the data that would enable them to demonstrate the effectiveness of their methods. At Safran Aircraft Engines, we were able to demonstrate the effectiveness of using time
series downloaded from the engines after each flight to build a representative model of the engine using a conditional generative neural algorithm (CGAN). Subject to the flight conditions and controls, this model simulates the behaviour of the engine in its current state as if it had performed
the simulated flight. It is therefore possible for us to provide virtual flights performed by our engines in their actual state of wear. These simulations pave the way for sharing open datasets, which will, we hope, influence research for the discovery of new techniques for monitoring our
engines.
An Action on Gender Equality: Educational Practices for Primary School Students in Turkey
Authors: YOLCU Ece , SARI Mediha
Published: January 2024

Traditional gender roles are transferred to the children in many ways and this makes it difficult to ensure gender equality. Starting from the problem regarding the sensitivity of students for gender equality in a public school, the aim of this study was to enhance the sensitivity of fourth grade students in a primary school towards gender equality and to examine this development process through the prepared teaching process. The research design was action research and the participants were 32 fourth graders in a primary school in Adana, Turkey. The data collection tools were, Awareness For Gender Equality Form (TCE-FF), semi-structured interview forms, class observations, researcher diary, self-assessment/course evaluation forms and observer evaluation forms. In order to analyze the data, inductive and deductive content analyses, descriptive analysis and constant comparative analysis were used. Findings showed that students enhance their way of defining sexes, gender and gender equality, they put forward many indicators of inequalities and equalities in the right way, they have a better understanding of the contributions of equality and they brought about many suggestions for providing gender equality. These results imply that educational practices could be a part of the solution regarding the struggle against gender inequality.
Pandemi (COVID-19) Döneminde Okul Öncesi Öğretmenlerinin Mesleki Kimliklerine Yönelik Görüşleri
Authors: GÖLE Mehmet Oğuz , NİŞAN Mustafa , TÜFEKCİ Aysel
Published: January 2024

Araştırmanın amacı okul öncesi öğretmenlerinin mesleki kimlik oluşumuna yönelik görüşlerini incelemektir. Araştırmada temel nitel araştırma deseni kullanılmıştır. Araştırmanın çalışma grubunda; 0-5, 6-15 ve 16 yıl ve üzeri mesleki deneyime sahip, dördü anasınıfında ve sekizi anaokulunda görev yapmakta olan 12 kadın okul öncesi öğretmeni yer almıştır. Araştırma verileri, yapılandırılmış görüşme tekniği kullanılarak toplanmış ve içerik analizi yapılmıştır. İçerik analizi sürecinde görüşmelerden elde edilen nitel veriler araştırmacılar tarafından incelenerek kodlar, kategoriler ve temalar oluşturulmuştur. Verilerin analizi sonucunda araştırma soruları ile ilişkili olarak (a) okul ortamı, (b) çalışma alanı ve (c) toplumsal konum olmak üzere üç ana tema elde edilmiştir. Araştırmanın bulgularına göre okul öncesi öğretmenlerin mesleki kimliklerinin oluşumunda okul ortamındaki öğretmen ve veli, yönetici ve meslektaş arasındaki ilişkilerin ve bunların okul öncesi öğretmenine karşı tutum ve davranışlarının önemli olduğu belirlenmiştir. İnsanları, çocukları ve mesleği sevme, mesleği tercih etme nedenleri ve mesleğin sağladığı ekonomik imkânların mesleki kimlik üzerinde etkisi olduğu tespit edilmiştir. Öğretmenlik mesleğine toplumun bakış açısı, mesleğin saygınlığı gibi durumların da mesleki kimlik oluşumunu etkilediği görülmüştür.
Eksistensi Single Identity Number Melalui NIK Menjadi NPWP Terhadap Pemenuhan Kewajiban Perpajakan, Efektifkah?
Authors: Novia Nella Ayu , Dhelniati Fheni , Putra M. Yoan Eka , Amelia Roaina , Suriyanti Linda Hetri
Published: January 2024

Latar Belakang: Permasalahan mengenai pemenuhan atas kewajiban perpajakan masih menjadi permasalahan yang belum mampu dipecahkan salah satunya diindikasikan dengan adanya kesenjangan antara wajib pajak yang terdaftar dengan angkatan kerja yang sedang bekerja. Tujuan: Riset ini bertujuan untuk membuktikan secara empiris pengaruh Penerapan Single Identitiy Number (SIN), pengetahuan dan pemahaman peraturan perpajakan terhadap pemenuhan kewajiban wajib pajak orang pribadi di KPP Pratama Pekanbaru Senapelan yang dimoderasi oleh preferensi risiko. Penelitian ini juga bertujuan untuk mengetahui tingkat efektivitas penerapan SIN.Metode Penelitian: Penelitian ini menggunakan metode campuran (mixed method) dengan menggunakan data primer. Data kuantitatif diperoleh dengan metode survei menggunakan instrumen kuesioner, sedangkan data kualitatif bersumber diperoleh melalui wawancara. Data kuantitatif selanjutnya dianalisis menggunakan teknik analisis SEM-PLS menggunakan software WarpPLS 7.0. Adapun data kualitatif dianalisis menggunakan trianggulasi teknik (wawancara dan observasi) dan trianggulasi sumber.Hasil Penelitian: Penelitian ini memberikan bukti secara empiris bahwa SIN dan pengetahuan dan pemahaman peraturan perpajakan adalah faktor yang dapat meningkatkan pemenuhan kewajiban perpajakan orang pribadi. Adapun preferensi risiko memiliki peran dalam menurunkan pengaruh SIN serta pengetahuan dan pemahaman peraturan perpajakan dalam meningkatkan pemenuhan kewajiban perpajakan di KPP Pratama Pekanbaru Senapelan. Berdasarkan hasil wawancara dan hasil jawaban responden, terdapat kesamaan yaitu tingkat efektivitas pemadanaan NIK menjadi NPWP dapat dikatakan cukup baik atau sebesar 41,51%.Keaslian/Kebaruan Penelitian: Penelitian ini menguji pemenuhan kewajiban perpajakan di KPP Pratama Pekanbaru Senapelan dengan menguji peran preferensi risiko sebagai pemoderasi serta penelitian menggunakan mix method untuk melihat dua unsur tersebut dalam meneliti masalah riset.