Search Results (5)

From the point of view of the intelligent operation and maintenance of high-speed train tracks, this paper examines the research status of high-speed train rail damage detection technology in the field of high-speed train track operation and maintenance detection in recent years, summarizes the damage detection methods for high-speed trains, and compares and analyzes different detection technologies and application research results. The analysis results show that the detection methods for high-speed train rail damage mainly focus on the research and application of non-destructive testing technology and methods, as well as testing platform equipment. Detection platforms and equipment include a new type of vortex meter, integrated track recording vehicles, laser rangefinders, thermal sensors, laser vision systems, LiDAR, new ultrasonic detectors, rail detection vehicles, rail detection robots, laser on-board rail detection systems, track recorders, self-moving trolleys, etc. The main research and application methods include electromagnetic detection, optical detection, ultrasonic guided wave detection, acoustic emission detection, ray detection, vortex detection, and vibration detection. In recent years, the most widely studied and applied methods have been rail detection based on LiDAR detection, ultrasonic detection, eddy current detection, and optical detection. The most important optical detection method is machine vision detection. Ultrasonic detection can detect internal damage of the rail. LiDAR detection can detect dirt around the rail and the surface, but the cost of this kind of equipment is very high. And the application cost is also very high. In the future, for high-speed railway rail damage detection, the damage standards must be followed first. In terms of rail geometric parameters, the domestic standard (TB 10754-2018) requires a gauge deviation of ±1 mm, a track direction deviation of 0.3 mm/10 m, and a height deviation of 0.5 mm/10 m, and some indicators are stricter than European standard EN-13848. In terms of damage detection, domestic flaw detection vehicles have achieved millimeter-level accuracy in crack detection in rail heads, rail waists, and other parts, with a damage detection rate of over 85%. The accuracy of identifying track components by the drone detection system is 93.6%, and the identification rate of potential safety hazards is 81.8%. There is a certain gap with international standards, and standards such as EN 13848 have stricter requirements for testing cycles and data storage, especially in quantifying damage detection requirements, real-time damage data, and safety, which will be the key research and development contents and directions in the future. Full article

Oral histories of Latina domestic workers in the United States feature hybrid narratives combining accounts of illness and “toxic discourse”. We approach domestic workers’ illnesses and disabilities in a capacious, extra-medical context that registers multiple axes of precarity (economic, racial, and migratory). We are naming this context “settler maintenance”. Riffing on the specific and general valences of “maintenance” (i.e., as a synonym for cleaning work, and as a term for the practices and ideologies involved in a structure’s upkeep), this term has multiple meanings. First, it describes U.S. domestic workers’ often-compulsory use of hazardous chemical agents that promise to remove dirt speedily, yet that imperil domestic workers’ health. The use of these chemicals perpetuates two other, more abstract kinds of settler maintenance: (1) the continuation of socioeconomic hierarchies between immigrant domestic workers and settler employers, and (2) the continuation of (white) settlers’ extractive relationship to the land qua private property. To challenge this logic of settler maintenance, which is predicated on a lack of care for care workers, Latina domestic workers have developed alternative forms of care via lateral networks and political activism. Full article

Understanding how humans perceive animals is important for biodiversity conservation, however, only a few studies about this issue have been carried out in South America. We selected two Brazilian cities to assess people’s perceptions of birds: Bauru (São Paulo, Brazil) and Belo Horizonte (Minas Gerais, Brazil). From the available bird data for each city, we developed a questionnaire and applied it between September 2020 and June 2021. The data obtained were analyzed by simple counts, a Likert scale, and percentages. Also, human feelings related to birds were placed on the Free Word Cloud Generator website. Our study confirmed that most respondents were aware of the importance of birds to ecological balance and that respondents had a generally positive attitude towards most of the bird species. However, they disliked exotic species such as the Domestic Dove and the House Sparrow, which are associated with disease, dirt, and disgust. Respondents also underestimated the number of birds that can live in urban areas and the song of birds is still a sense less experienced and perceived by people. Understanding these human–biodiversity relationships can help guide public policies and environmental education activities. Full article

Cleaning is an important task that is practiced in every domain and has prime importance. The significance of cleaning has led to several newfangled technologies in the domestic and professional cleaning domain. However, strategies for auditing the cleanliness delivered by the various cleaning methods remain manual and often ignored. This work presents a novel domestic dirt image dataset for cleaning auditing application including AI-based dirt analysis and robot-assisted cleaning inspection. One of the significant challenges in an AI-based robot-aided cleaning auditing is the absence of a comprehensive dataset for dirt analysis. We bridge this gap by identifying nine classes of commonly occurring domestic dirt and a labeled dataset consisting of 3000 microscope dirt images curated from a semi-indoor environment. The dirt dataset gathered using the adhesive dirt lifting method can enhance the current dirt sensing and dirt composition estimation for cleaning auditing. The dataset’s quality is analyzed by AI-based dirt analysis and a robot-aided cleaning auditing task using six standard classification models. The models trained with the dirt dataset were capable of yielding a classification accuracy above 90% in the offline dirt analysis experiment and 82% in real-time test results. Full article

Water used for domestic purposes is ideally required to be free from contaminants. Various contaminants have frequently affected the quality of such water. Water samples were obtained from 150 sources including 72 wells, 60 streams, 17 taps, and one borehole, randomly selected from five residential areas in Jos, Nigeria. Structured questionnaires and one-to-one interview was used to obtain information on features of location and use of facilities in each area. Eighty (53.3%) water sources were contaminated, predominantly wells (70.8%). The locations (identified in code) with the highest number of contaminated sources were AGO (60.0%), GBU (56.7%) and FGD (56.7%). AGD and FGD also had the highest ratio of households to one water source (25:1). Eighty-two fungi were isolated, predominantly Candida tropicalis (23.2%), Candida lipolytica (10.9%) and Rhodotorula sp (9.7%). Candida lipolytica was the highest (42.9%) contaminant in tap water. Rhodotorula sp was found in all types of water sources sampled. Type of water source had a significant effect (P < 0.05) on the presence of some fungi in the water. The residential area (Location) had a significant effect on contamination of water sources by some yeasts. Water sources for domestic use in Jos are contaminated by yeasts and yeast-like fungi. Frequency of use, exposure of the facility to dirt, and contaminations of surroundings contribute to the occurrence of fungi in water sources and, by implication, the prevalence of fungal infections. Full article