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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.

The purpose of this study is to investigate environmental factors impacting Ajanta mural deterioration, assessing global tourism effects and visitor conduct on cave environment and murals. This study recognizes stakeholder roles in conservation, providing data-driven insights to guide institutions like the Archaeological Survey of India. The objective is sustainable tourism practices to balance public access with mural preservation for future generations.

Over 25 years, Ajanta Caves' visitors doubled, impacting microclimatic conditions for ancient murals. This study assesses visitor impact to establish a regime and determine carrying capacity, considering temperature, humidity and pollution. Challenges arise from quantifying capacity because of variables. This research informs global tourism and heritage conservation, offering methodologies applicable to cultural sites worldwide.

This study examines environmental impacts on mural preservation in Ajanta Caves, including humidity, microbial growth, sunlight exposure, air quality and tourist presence. Tourist influx escalates CO₂ levels, directly endangering murals. Concerns about particulate matter, especially during visits, emphasize the need for data-driven decision-making and modern technology use to protect Ajanta Caves' artwork, crucial because of its global significance and tourism-related vulnerabilities.

This study carries substantial social implications with a global resonance. The active engagement of the local community and tourism stakeholders in conserving and promoting the Ajanta Caves fosters empowerment, igniting a sense of pride, ownership and responsibility among residents and ensuring sustainable enjoyment of cultural heritage while safeguarding it for future generations. In addition, there will be socioeconomic benefits to local residents such as employment opportunities as tour operators, tour guides, hospitality staff, artisans and souvenir shopkeepers.

This study integrates art conservation, environmental science, cultural heritage preservation and social aspects to address global tourism challenges. Focusing on a site of worldwide significance, this study offers practical strategies for artwork preservation, tourism management and environmental concerns. These recommendations provide real-world solutions applicable to heritage sites globally, bridging scientific analysis with social and cultural insights.

本研究调查了影响阿詹塔壁画退化的环境因素, 评估了全球旅游业和游客行为对洞穴环境和壁画的影响。它承认利益相关者在保护中的作用, 为印度考古调查等机构提供数据支撑的见解。目的是实践可持续旅游, 平衡公众进入和供子孙后代使用的壁画保护。

25年来, 阿詹塔洞穴的游客增加了一倍, 影响了古壁画的小气候条件。该研究评估了游客的影响, 考虑温度、湿度和污染, 建立了一个制度并确定承载能力。量化承载能力的指标面临着挑战。这项研究为全球旅游业和遗产保护提供了信息, 提供了适用于世界各地文化遗址的方法。

这项研究考察了环境对阿詹塔洞穴壁画保护的影响, 包括湿度、微生物生长、阳光照射、空气质量和游客的存在。游客的涌入使二氧化碳含量上升, 直接危及壁画。对颗粒物的考虑, 尤其是在参观期间, 强调了数据驱动决策和现代技术应用对保护阿詹塔洞穴的艺术品的必要性。这一点至关重要, 因为它具有全球意义和旅游相关的脆弱性。

这项研究具有重大的社会影响并引起全球共鸣。当地社区和旅游利益相关者积极参与阿旃陀石窟的保护和推广, 可以增强居民的赋权, 激发居民的自豪感、主人翁意识和责任感, 确保可持续享受文化遗产, 同时为子孙后代保护文化遗产。此外, 还将为当地居民带来社会经济效益, 例如旅游经营者、导游、接待人员、工匠、纪念品店主等的就业机会。

这项研究综合了艺术保护、环境科学、文化遗产保护和社会方面以应对全球旅游业的挑战。它关注一个具有世界意义的遗址, 为艺术品保护、旅游管理和环境问题提供了实用的策略。这些建议提供了适用于全球遗产地的现实世界解决方案, 将科学分析与社会和文化见解联系起来。

El estudio investiga los factores medioambientales que influyen en el deterioro de los murales de Ajanta, evaluando los efectos globales del turismo y el comportamiento de los visitantes sobre el entorno de las cuevas y los murales. Se examina el papel que desempeñan las partes interesadas en la conservación y aporta datos para orientar a instituciones como la encuesta arqueológica de India. El objetivo son las prácticas turísticas sostenibles para equilibrar el acceso del público con la conservación de los murales para las generaciones futuras.

A lo largo de 25 años, los visitantes de las cuevas de Ajanta se han duplicado, con un impacto en las condiciones microclimáticas de los murales antiguos. El estudio evalúa el impacto de los visitantes para establecer una regulación y determinar la capacidad de carga, teniendo en cuenta la temperatura, la humedad y la contaminación. La cuantificación de la capacidad plantea problemas debido a las variables. La investigación aporta información al turismo mundial y a la conservación del patrimonio, ofreciendo metodologías aplicables a sitios culturales de todo el mundo.

Este estudio examina los impactos ambientales en la conservación de los murales de las cuevas de Ajanta, incluyendo la humedad, el crecimiento microbiano, la exposición a la luz solar, la calidad del aire y la presencia de turistas. La afluencia de turistas aumenta los niveles de CO2, poniendo directamente en peligro los murales. La preocupación por las partículas, especialmente durante las visitas, pone de relieve la necesidad de tomar decisiones basadas en datos y de utilizar tecnología actual para proteger las obras de arte de las cuevas de Ajanta, algo crucial debido a su importancia mundial y a las vulnerabilidades relacionadas con el turismo.

Este estudio conlleva importantes implicaciones sociales con una resonancia global. La participación activa de la comunidad local y las partes interesadas del turismo en la conservación y promoción de las Cuevas de Ajanta fomenta el empoderamiento, generando un sentido de orgullo, propiedad y responsabilidad entre los residentes y garantiza el disfrute sostenible del patrimonio cultural al mismo tiempo que lo salvaguarda para las generaciones futuras. Además, habrá beneficios socioeconómicos para los residentes locales, como oportunidades de empleo como operadores turísticos, guías turísticos, personal de hostelería, artesanos, comerciantes de souvenirs, etc.

Este estudio integra la conservación del arte, las ciencias medioambientales, la preservación del patrimonio cultural y los aspectos sociales para abordar los retos del turismo mundial. Centrándose en un sitio de importancia mundial, ofrece estrategias prácticas para la conservación de las obras de arte, la gestión del turismo y los problemas medioambientales. Estas recomendaciones aportan soluciones reales aplicables a lugares patrimoniales de todo el mundo, tendiendo puentes entre el análisis científico y las percepciones sociales y culturales.

Electric Vehicles (EVs), owing to their low carbon emissions, have gained immense importance in achieving net-zero emissions by 2070. They have also appeared as viable substitute to conventional vehicles. Aligning with global initiatives, India has created a favourable ecosystem and has implemented several policies since 2011 to achieve its target. Consequently, the market share of EVs has surged, both globally and in India, over the past decade. Taking this into account, this study aims to identify the factors that influence EVs in a developing economy using the context of India.

This study identified important determinants of EV adoption from global literature and employed a multiple linear regression model (MLRM) using the ordinary least squares (OLS) technique. Secondary data were utilised to identify determinants in the Indian context, sourced from the Ministry, NITI Aayog, AQI, the Lok Sabha Question, and the Economic Survey of India.

This study found that the number of charging stations and local pollution levels significantly influence EV adoption in India. The insignificance of the other variables may be due to the emerging state of the Indian EV market.

This study adds to the growing body of literature on EV adoption in developing economies by analysing the factors that impact its adoption using regional data. In addition, this study provides a unique perspective on a developing economy and advocates a comprehensive policy for EV adoption that reflects long-term sustainability.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2023-0479.

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 work is to optimize the monitoring of vibrations on dynamometric test rigs for railway brakes. This is a quite demanding application considering the continuous increase of performances of high-speed trains that involve higher testing specifications for brake pads and disks.

In this work, authors propose a mixed approach in which relatively simple finite element models are used to support the optimization of a diagnostic system that is used to monitor vibration levels and rotor-dynamical behavior of the machine. The model is calibrated with experimental data recorded on the same rig that must be identified and monitored. The whole process is optimized to not interfere with normal operations of the rig, using common inertial sensor and tools and are available as standard instrumentation for this kind of applications. So at the end all the calibration activities can be performed normally without interrupting the activities of the rig introducing additional costs due to system unavailability.

Proposed approach was able to identify in a very simple and fast way the vibrational behavior of the investigated rig, also giving precious information concerning the anisotropic behavior of supports and their damping. All these data are quite difficult to be found in technical literature because they are quite sensitive to assembly tolerances and to many other factors. Dynamometric test rigs are an important application widely diffused for both road and rail vehicles. Also proposed procedure can be easily extended and generalized to a wide value of machine with horizontal rotors.

Most of the studies in literature are referred to electrical motors or turbomachines operating with relatively slow transients and constant inertial properties. For investigated machines both these conditions are not verified, making the proposed application quite unusual and original with respect to current application. At the same time, there is a wide variety of special machines that are usually marginally covered by standard testing methodologies to which the proposed approach can be successfully extended.