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In this study we examine the spectral properties of stiffness degradation at the constitutive level and at the levels of finite elements and their assemblies. The principal objective is to assess the effects of defects on the elastic stiffness properties at different levels of observation. In particular, we are interested in quantitative damage measures, which characterize the fundamental mode of degradation in the form of elastic damage at the level of constitutive relations and at the level of finite elements and structures.

Path planning in unknown or partly unknown environment is a quite complex task, partly because it is an evolving globally optimal path affected by the motion of the robot and the changing of environmental information. The purpose of this paper is to propose an online path planning approach for a mobile robot, which aims to provide a better adaptability to the motion of the robot and the changing of environmental information.

This approach treats the globally optimal path as a changing state and estimates it online with two steps: prediction step, which predicts the globally optimal path based on the motion of the robot; and updating step, which uses the up‐to‐date environmental information to refine the prediction.

Simulations and experiments show that this approach needs less time to reach the destination than some classical algorithms, provides speedy convergence and can adapt to unexpected obstacles or very limited prior environmental information. The better performances of this approach have been proved in both field and indoor environments.

Compared with previous works, the paper's approach has three main contributions. First, it can reduce the time consumed in reaching the destination by adopting an online path planning strategy. Second, it can be applied in such environments as those with unexpected obstacles or with only limited prior environmental information. Third, both motion error of the robot and the changing of environmental information are considered, so that the global adaptability to them is improved.

EC-135P2+ helicopters operated by Polish Medical Air Rescue are highly exposed to environmental particles entering engines when performing helicopter emergency medical services. This paper aims to assess the effectiveness of inlet barrier filters installed to protect the engines, including their impact on maintenance.

The organisation adopted a comprehensive set of measures to predict and limit the impact of dust ingestion including visual inspections, health management and engine trend monitoring based on ground power checks’ (GPC) results. Three alternative particle separation solutions were considered. Finally, helicopter inlets were modified to allow the selected filter system to be installed, which reduced the number of particles ingested by the engine and prevented from premature overhauls.

The analyses carried out enabled not only the selection of the optimal filtration solution and its seamless implementation into the fleet but also confirmed its efficiency. After installing the filters, engines’ lifetime is extended from 500 to 4,500 flight hours while operating costs and the number of maintenance tasks was reduced significantly.

Lessons learned from operational experience show that a well-matched particle separation system can mitigate accelerated engine deterioration even if the platform is continuously exposed to environmental particles. The remaining useful life of engines can be predicted using performance models and data from GPC.

The purpose of this paper is to contribute to the qualitative and quantitative knowledge on ultrafine particles in air near a steel making plant located in an Italian site.

A combination of experimental methodologies was used for the online and offline monitoring and chemical characterization of particulate matter (PM) in the air near the plant. Two unfiltered twin-sampling systems were adopted, working when the plant was on/off. All condensed air samples were submitted to Ion Chromatography analysis. The same samples were submitted to acid digestion before Graphite Furnace Atomic Absorption Spectroscopy Analysis. Continuous daily PM10 samples were collected to characterize ambient air. PM10 samples were also analysed to estimate metals content. The PM size distribution was achieved by continuous online monitoring. The adopted ultrafine particulate monitor classifies particles in the range 20-200 nm. The overall size distribution was inferred from an Optical Particulate Counter able to classify particles in the range 0.3-10 µm.

The obtained results show that no causal relationship can be found between the measurements of anions and metal in the air near the plant under investigation and the presence of the steel making plant. The trend in emissions of micro-particles was found quite characteristic of similar semi-urban areas.

The paper demonstrates that a steel making plant adopting best available techniques could have a local impact compatible with the surrounding environment.

This study aims to focus on the submicron particles (with diameter of 0.2-1.0 μm) of the ambient air from a coal-fired power plant. A systematic examination of their morphology, particle size and chemical element will be analyzed, so as to provide more scientific information and theoretical basis for the formation and control method of inhalable particles, as well as data support for environmental impact and ecological effects assessments.

In this paper, the morphology, size distribution and elemental characteristics of submicron particles from ambient air of a coal-fired power plant are studied by single particle analysis.

The results show that atmospheric particles in coal-fired power plant are mainly spherical particles, and most of them are soot aggregates adhered or coated with other particles with few rectangle particles. The particles collected in the afternoon and evening are mainly of spherical particles, and small-sized particles collected in the morning are mainly spherical ones, while the overall concentration is larger than that of the spherical particles in the size range above 0.5 μm. The results indicated that the larger-sized spherical particles have a lower concentration.

Coal-fired power plants are still the main supply of electricity in China, but the inhalable particles, especially sub-micron particles (0.1-1.0 μm) cannot be effectively captured by the dust removal device from the coal-fired power plant. Thus, a large amount of inhalable particles is emitted into the atmosphere, becoming the major air pollutants in China.

This study aims to better understand the morphological characteristics of single particle and the health risk characteristics of heavy metals in PM_2.5 in different functional areas of Handan City.

High resolution transmission electron microscopy was used to observe the aerosol samples collected from different functional areas of Handan City. The morphology and size distribution of the particles collected on hazy and clear days were compared. The health risk evaluation model was applied to evaluate the hazardous effects of particles on human health in different functional areas on hazy days.

The results show that the particulate matter in different functional areas is dominated by spherical particles in different weather conditions. In particular, the proportion of spherical particles exceeds 70% on the haze day, and the percentage of soot aggregates increases significantly on the clear day. The percentage of each type of particle in the teaching and living areas varied less under different weather conditions. Except for the industrial area, the size distribution of each type of particle in haze samples is larger than that on the clear day. Spherical particles contribute more to the small particle size segment. Soot aggregate and other shaped particles contribute more to the large size segment. The mass concentrations of hazardous elements (HEs) in PM_2.5 in different functional areas on consecutive haze pollution days were illustrated as industrial area > traffic area > living area > teaching area. Compared with the other functional areas, the teaching area had the lowest noncarcinogenic risk of HEs. The lifetime carcinogenic risk values of Cr and As elements in each functional area have exceeded residents’ threshold levels and are at high risk of carcinogenicity. Among the four functional areas, the industrial area has the highest carcinogenic and noncarcinogenic risks. But the effects of HEs on human health in the other functional areas should also be taken seriously and continuously controlled.

The significance of the study is to further understand the morphological characteristics of single particles and the health risks of heavy metals in different functional areas of Handan City. the authors hope to provide a reference for other coal-burning industrial cities to develop plans to improve air quality and human respiratory health.

Waiting for a bus may represent a period of intense exposure to traffic particles in hot and noisy conditions in the street. To lessen the particle load and tackle heat in bus stops a shelter was equipped with an electrostatic precipitator and a three-step adiabatic cooling system capable of dynamically adjust its operation according to actual conditions. This study evaluates the effectiveness of the Airbitat Oasis Smart Bus Stop, as the shelter was called, to provide clean and cool air.

The particle exposure experienced in this innovative shelter was contrasted with that in a conventional shelter located right next to it. Mass concentrations of fine particles and black carbon, and particle number concentration (as a proxy of ultrafine particles) were simultaneously measured in both shelters. Air temperature, relative humidity and noise level were also measured.

The new shelter did not perform as expected. It only slightly reduced the abundance of fine particles (−6.5%), but not of ultrafine particles and black carbon. Similarly, it reduced air temperature (−1 °C), but increased relative humidity (3%). Its operation did not generate additional noise.

The shelter's poor performance was presumably due to design flaws induced by a lack of knowledge on traffic particles and fluid dynamics in urban environments. This is an example where harnessing technology without understanding the problem to solve does not work.

It is uncommon to come across case studies like this one in which the performance and effectiveness of urban infrastructure can be assessed under real-life service settings.

The purpose of this paper is to prepare ultra-violet (UV)-curable inkjet inks for textile printing application. The influence of both type and component ratio of monomer/oligomer on the quality of the desired viscosity range is studied. Moreover, the effect of pigment/resin ratio on the rheological behaviour of the ink has been studied.

Aqueous dispersions of nanoscale organic pigments were prepared through ball milling and ultrasonication. The dispersed pigments were encapsulated into UV-curable resin via miniemulsion technique, using different types and component ratios of monomers and oligomers.

It was found that the monomer/oligomer ratio of 2:3 and the pigment/resin ratio of 2:1 gave the most stable miniemulsion dispersions and provided the most suitable rheological range for inkjet printing inks.

As the rheology of the ink is optimised, most of the problems associated with the jetting process could be avoided.

This method of using UV-curable encapsulated inks eliminates the usage of binders, which are the principal factor for nozzle clogging of the print head. In addition, binders are responsible for the coarse handle of the printed textiles.

The UV-curable inks were viewed as a green technology by the US Environmental Protection Agency.

This method is simple and fast and requires low cost. In addition, it could find numerous applications in surface coating.

The paper aims to develop a particle matter (PM_2.5) prediction model for open-plan office space using a variety of data sources. Monitoring of PM_2.5 levels is not widely applied in indoor settings. Many reliable methods of monitoring PM_2.5 require either time-consuming or expensive equipment, thus making PM_2.5 monitoring impractical for many settings. The goal of this paper is to identify possible low-cost, low-effort data sources that building managers can use in combination with machine learning (ML) models to approximate the performance of much more costly monitoring devices.

This study identified a variety of data sources, including freely available, public data, data from low-cost sensors and data from expensive, high-quality sensors. This study examined a variety of neural network architectures, including traditional artificial neural networks, generalized recurrent neural networks and long short-term memory neural networks as candidates for the prediction model. The authors trained the selected predictive model using this data and identified data sources that can be cheaply combined to approximate more expensive data sources.

The paper identified combinations of free data sources such as building damper percentages and weather data and low-cost sensors such as Wi-Fi-based occupancy estimator or a Plantower PMS7003 sensor that perform nearly as well as predictions made based on nephelometer data.

This work demonstrates that by combining low-cost sensors and ML, indoor PM_2.5 monitoring can be performed at a drastically reduced cost with minimal error compared to more traditional approaches.