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This paper aims to study the influence of microparticles on the surface cavitation behavior of 2Cr3WMoV steel; microparticle suspensions of different concentration, particle size, material and shape were prepared based on ultrasonic vibration cavitation experimental device.

2Cr3WMoV steel was taken as the research object for ultrasonic cavitation experiment. The morphology, quantity and distribution of cavitation pits were observed and analyzed by metallographic microscope and scanning electron microscope.

The study findings showed that the surface cavitation process produced pinhole cavitation pits on the surface of 2Cr3WMoV steel. High temperature in the process led to oxidation and carbon precipitation on the material surface, resulting in the “rainbow ring” cavitation morphology. Both the concentration and size of microparticles affected the number of pits on the material surface. When the concentration of microparticles was 1 g/L, the number of pits reached the maximum, and when the size of microparticles was 20 µm, the number of pits reached the minimum. The microparticles of Fe3O4, Al2O3, SiC and SiO2 all increased the number of pits on the surface of 2Cr3WMoV steel. In addition, the distribution of pits of spherical microparticles was more concentrated than that of irregularly shaped microparticles in turbidity.

Most of the current studies have not systematically focused on the effect of each factor of microparticles on the cavitation behavior when they act separately, and the results of the studies are more scattered and varied. At the same time, it has not been found to carry out the study of microparticle cavitation with 2Cr3WMoV steel as the research material, and there is a lack of relevant cavitation morphology and experimental data.

The purpose of this study is to investigate the influence of varying concentrations of nano-SiO₂ particle doping on the structure and properties of the micro-arc oxidation (MAO) coating of 7075 aluminum alloy. This research aims to provide novel insights and methodologies for the surface treatment and protection of 7075 aluminum alloy.

The surface morphology of the MAO coating was characterized using scanning electron microscope. Energy spectrometer was used to characterize the elemental content and distribution on the surface and cross section of the MAO coating. The phase composition of the MAO coating was characterized using X-ray diffractometer. The corrosion resistance of the MAO coating was characterized using an electrochemical workstation.

The results showed that when the addition of nano-SiO₂ particles is 3 g/L, the corrosion resistance is optimal.

This study investigated the influence of different concentrations of nano-SiO₂ particles on the structure and properties of the MAO coating of 7075 aluminum alloy.

Three-dimensional (3D)printed skulls for neurosurgical training are increasingly being used due to the widespread access to 3D printing technology, their low cost and accuracy, as well as limitations and ethical concerns associated with using human cadavers. However, little is known about the risks of airborne particles or volatile organic compounds (VOCs) released while drilling into 3D-printed plastic models. The aim of this study is to assess the level of exposure to airborne contaminants while burr hole drilling.

3D-printed skull samples were produced using three different materials (polyethylene terephthalate glycol [PETG], white resin and Bone^STN) across three different 3D print processes (fused filament fabrication, stereolithography [SLA] and material jetting). A neurosurgeon performed extended burr hole drilling for 10 min on each sample. Spot measurements of particulate matter (PM2.5 and PM10) were recorded, and air samples were analysed for approximately 90 VOCs.

The particulate matter for PETG was found to be below the threshold value for respirable particles. However, the particulate matter for white resin and Bone^STN was found to be above the threshold value at PM10, which could be harmful for long periods of exposure without personal protective equipment (PPE). The VOC measurements for all materials were found to be below safety thresholds, and therefore not harmful.

To the best of the authors’ knowledge, this is the first study to evaluate the safety of 3D-printed materials for burr hole surgical drilling. It recommends PETG as a safe material requiring minimal respiratory control measures, whereas resin-based materials will require safety controls to deal with airborne particles.

Rare book collections are special, not only in terms of their physical appearance but also because of their historical significance and the information they contain. The purpose of this study is twofold: to identify the physical condition of rare book collections and to determine the main causes of damage to rare books collection that belongs to a museum library in Indonesia.

This research involved conducting a survey of the physical condition of the collection of rare books owned by a museum library in Indonesia. Supporting data was also obtained through interviews with one of the staff who served as the museum collection conservator. This study used random sampling to take samples from the collection, which consisted of 950 rare books, with total sample of 91.

The results obtained state that the condition of the existing rare book collection is classified as severely damaged. One of the causes of damage that can be addressed immediately is the cleaning regime: the collection and library space should be cleaned thoroughly and regularly so that dust and dirt in and around the rare book collection can be reduced.

This research was limited to physical identification, which can be done easily because it does not require various kinds of laboratory tests. It was a case study examining a single collection in a single museum library. The pool of books from which the samples were taken was therefore relatively homogenous. Therefore, it is hoped that further research can identify other factors and types of damage in more detail so that all damage to rare book collections can be identified and mitigated.

Research discussing the condition of rare book collections, especially for special libraries in museums in Indonesia, is still very limited. Detailed surveys of the physical condition of collections, especially rare book collections in museums, have rarely been discussed by previous research. The work will contribute to assessing the physical condition of rare book collections.

The purpose of this paper is to propose a new grey prediction model, GOFHGM (1,1), which combines generalised fractal derivative and particle swarm optimisation algorithms. The aim is to address the limitations of traditional grey prediction models in order selection and improve prediction accuracy.

The paper introduces the concept of generalised fractal derivative and applies it to the order optimisation of grey prediction models. The particle swarm optimisation algorithm is also adopted to find the optimal combination of orders. Three cases are empirically studied to compare the performance of GOFHGM(1,1) with traditional grey prediction models.

The study finds that the GOFHGM(1,1) model outperforms traditional grey prediction models in terms of prediction accuracy. Evaluation indexes such as mean squared error (MSE) and mean absolute error (MAE) are used to evaluate the model.

The research study may have limitations in terms of the scope and generalisability of the findings. Further research is needed to explore the applicability of GOFHGM(1,1) in different fields and to improve the model’s performance.

The study contributes to the field by introducing a new grey prediction model that combines generalised fractal derivative and particle swarm optimisation algorithms. This integration enhances the accuracy and reliability of grey predictions and strengthens their applicability in various predictive applications.

This study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.

This paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.

The findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.

The researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.

This paper aims to establish a reliable scale measuring occupants’ levels of environmental trust in their work settings’ indoor air quality and explore the relationship between occupants’ levels of environmental trust and their perceived control over the air quality in their workspace.

The authors conducted occupant surveys concerning indoor air quality in an office building, and collected corresponding indoor air quality measurements. Descriptive statistics and correlation analysis results are reported to reveal occupants’ levels of environmental trust and perceived control.

Results reveal that psychological perceptions of indoor air quality can be quite neutral, even shortly after an extreme wildfire event resulting in very poor air quality in an urban area. Occupants’ sense of trust that their office building could protect them from harmful air outside, and their belief that the building could protect them from seasonal smoky conditions, each correlated positively with employees’ sense of control over the indoor air quality in their personal workspace.

This case study adds to an interdisciplinary understanding for facility managers and organizational leaders concerning a way to measure occupants’ sense of control over the indoor air quality in their building, as well as their environmental trust in terms of how protected they feel from harmful air quality conditions.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

The purpose of this study was to improve the overall equipment effectiveness (OEE) of the production process of the shredder operation of ABC company, an industrial waste management company which supplies pre-processed industrial waste as alternative fuel to a cement plant.

This case study investigated all possible availability and performance losses that caused the shredder system’s OEE and various problem-solving techniques, such as root cause analysis and Pareto analysis, were used to find the root cause of the reduced OEE.

After analysing this case study, three significant loss factors were identified from all the availability and performance losses, which caused the shredder system’s OEE losses. Practical solutions were found for the effect of those loss factors to improve the machine’s OEE and productivity.

This case study has been concentrated on only analysing of losses and improvement of OEE in the production process and not about cost analysis between loss and improvements.

This paper shows how to improve the OEE of a production process through various problem-solving techniques by identifying its losses and how to achieve the best solutions for those losses in a practical manner.