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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.

Because many cutting fluids contain hazardous chemical constituents, industries and researchers are looking for alternative methods to reduce the consumption of cutting fluids in machining operations due to growing awareness of ecological and health issues, government strict environmental regulations and economic pressures. Therefore, the purpose of this study is to raise awareness of the minimum quantity lubrication (MQL) technique as a potential substitute for environmental restricted wet (flooded) machining situations.

The methodology adopted for conducting a review in this study includes four sections: establishment of MQL technique and review of MQL machining performance comparison with dry and wet (flooded) environments; analysis of the past literature to examine MQL turning performance under mono nanofluids (M-NF); MQL turning performance evaluation under hybrid nanofluids (H-NF); and MQL milling, drilling and grinding performance assessment under M-NF and H-NF.

From the extensive review, it has been found that MQL results in lower cutting zone temperature, reduction in cutting forces, enhanced tool life and better machined surface quality compared to dry and wet cutting conditions. Also, MQL under H-NF discloses notably improved tribo-performance due to the synergistic effect caused by the physical encapsulation of spherical nanoparticles between the nanosheets of lamellar structured nanoparticles when compared with M-NF. The findings of this study recommend that MQL with nanofluids can replace dry and flood lubrication conditions for superior machining performance.

Machining under the MQL regime provides a dry, clean, healthy and pollution-free working area, thereby resulting the machining of materials green and environmentally friendly.

This paper describes the suitability of MQL for different machining operations using M-NF and H-NF.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0131/

Health-care workers (HCWs) are at increased risk of exposure to the COVID-19 virus, which necessitates implementing transmission prevention measures in health-care delivery facilities, particularly hospitals. This study aims to assess COVID-19 risk in a health-care setting and recommend managerial strategies to cope with existing risk procedures.

This cross-sectional study was conducted among HCWs working in a general hospital in Qazvin, northwest of the country. A total of 310 employees working at different clinical and non-clinical occupational levels participated in the study. The WHO COVID-19 risk assessment tool categorised HCWs in high- or low-risk groups exposed to COVID-19 infection.

Findings revealed statistically significant relationships between workplace exposure to the COVID-19 virus and variables, including job type, performing the aerosol-generating procedure, access to personal protective equipment (PPE) and being trained on Infection Prevention and Control (IPC) guidelines (p < 0.05). HCWs older than 36 years were at 8% more risk of COVID-19 virus. Being a medical doctor or delivering health-care services as a nurse were relatively 28% and 32% times more likely to be at high risk of infection than other hospital staff categories. Having inadequate access to PPE and lack of training on IPC guidelines were also key determinants of high-risk infection.

As most cases at risk of COVID-19 infection belonged to frontline health-care staff in older age groups, this study recommend limiting the exposure of vulnerable staff to COVID-19 patients, increasing protective measures for HCWs and providing essential information about infection control procedures.

This paper aims to implement and extend the You Only Live Once (YOLO) algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. Video Surveillance has many applications such as Car Tracking and tracking of people related to crime prevention. This paper provides exhaustive comparison between the existing methods and proposed method. Proposed method is found to have highest object detection accuracy.

The goal of this research is to develop a deep learning framework to automate the task of analyzing video footage through object detection in images. This framework processes video feed or image frames from CCTV, webcam or a DroidCam, which allows the camera in a mobile phone to be used as a webcam for a laptop. The object detection algorithm, with its model trained on a large data set of images, is able to load in each image given as an input, process the image and determine the categories of the matching objects that it finds. As a proof of concept, this research demonstrates the algorithm on images of several different objects. This research implements and extends the YOLO algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. For video surveillance of traffic cameras, this has many applications, such as car tracking and person tracking for crime prevention. In this research, the implemented algorithm with the proposed methodology is compared against several different prior existing methods in literature. The proposed method was found to have the highest object detection accuracy for object detection and activity recognition, better than other existing methods.

The results indicate that the proposed deep learning–based model can be implemented in real-time for object detection and activity recognition. The added features of car crash detection, fall detection and social distancing detection can be used to implement a real-time video surveillance system that can help save lives and protect people. Such a real-time video surveillance system could be installed at street and traffic cameras and in CCTV systems. When this system would detect a car crash or a fatal human or pedestrian fall with injury, it can be programmed to send automatic messages to the nearest local police, emergency and fire stations. When this system would detect a social distancing violation, it can be programmed to inform the local authorities or sound an alarm with a warning message to alert the public to maintain their distance and avoid spreading their aerosol particles that may cause the spread of viruses, including the COVID-19 virus.

This paper proposes an improved and augmented version of the YOLOv3 model that has been extended to perform activity recognition, such as car crash detection, human fall detection and social distancing detection. The proposed model is based on a deep learning convolutional neural network model used to detect objects in images. The model is trained using the widely used and publicly available Common Objects in Context data set. The proposed model, being an extension of YOLO, can be implemented for real-time object and activity recognition. The proposed model had higher accuracies for both large-scale and all-scale object detection. This proposed model also exceeded all the other previous methods that were compared in extending and augmenting the object detection to activity recognition. The proposed model resulted in the highest accuracy for car crash detection, fall detection and social distancing detection.

This study aims to characterize the June 2020 COVID-19 outbreak at San Quentin California State Prison and to describe what made San Quentin so vulnerable to uncontrolled transmission.

Since its onset, the COVID-19 pandemic has exposed and exacerbated the profound health harms of carceral settings, such that nearly half of state prisons reported COVID-19 infection rates that were four or more times (and up to 15 times) the rate found in the state’s general population. Thus, addressing the public health crises and inequities of carceral settings during a respiratory pandemic requires analyzing the myriad factors shaping them. In this study, we reported observations and findings from environmental risk assessments during visits to San Quentin California State Prison. We complemented our assessments with analyses of administrative data.

For future respiratory pathogens that cannot be prevented with effective vaccines, this study argues that outbreaks will no doubt occur again without robust implementation of additional levels of preparedness – improved ventilation, air filtration, decarceration with emergency evacuation planning – alongside addressing the vulnerabilities of carceral settings themselves.

This study addresses two critical aspects that are insufficiently covered in the literature: how to prepare processes to safely implement emergency epidemic measures when needed, such as potential evacuation, and how to address unique challenges throughout an evolving pandemic for each carceral setting.

The complexity of atmospheric corrosion, further compounded by the effects of climate change, makes existing models inappropriate for corrosion prediction. The commonly used kinetic model and dose-response functions are restricted in their capacity to represent the non-linear behaviour of corrosion phenomena. The application of artificial intelligence (AI)-driven machine learning algorithms to corrosion data can better represent the corrosion mechanism by considering the dynamic behaviour due to changing climatic conditions. Effective use of materials, coating systems and maintenance strategies can then be made with such a corrosivity model. Accurate corrosion prediction will help to improve climate change resilience of the social, economic and energy infrastructure in line with the UN Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure) and 13 (Climate Action). This chapter discusses atmospheric corrosion prediction in relation to the SDGs and the influence of AI in overcoming the challenges.

Personal Protective Equipment plays an inevitable part in the current scenario of pandemics in the world. A novel coronavirus, Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-Cov 2), began as an outbreak of pneumonia in Wuhan, China, in late December 2019, and quickly spread worldwide. It quickly escalated into an international public health crisis. This opened up the high demand for the innovation and research of new materials in the Personal Protective Equipment industry.

PubMed, Embase and Google Scholar were searched for relevant literature regarding personal protective equipment and the information was organized in a systematic way.

There are no adequate number of studies taken up in the field of use of textiles in medical applications especially with PPEs.

This structured review will generate a sense of the significance of using PPE for controlling pandemics and also awaken need for additional research and innovations in this area.

The authorities of the management should take timely intervention in choosing the right material for their PPE in their hospitals. Hence health care professionals teams have an inevitable role in preventing the adverse environmental impact due to the inadvertent disposal of PPEs.

There is a lack of systematic way of disposing contaminated single-use face masks in a safe, environmentally acceptable manner. The dumping of single-use PPE in domestic garbage has had an adverse effect on the environment. Mismanaged plastic waste endangers the health of ecosystems by polluting marine and terrestrial environments, posing a significant risk of ingestion or injury to animals and contaminating habitats.

This review article provides an in-depth review of the use of different materials in PPE and challenges regarding its long-term use and implications on the environment.

At the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).

The method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.

The discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO₂) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.

Finally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO₂ based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.

The purpose of this paper is to assess nurse’s knowledge and practice regarding basic life support (BLS) skills while working with SARS-CoV-2 patients in Jordanian hospitals.

A cross-sectional survey was conducted among 386 nurses with direct contact with SARS-CoV-2 patients at Jordanian hospitals. A self-administered structured questionnaire was used based on the American Heart Association (AHA) guidelines.

A total of 386 participants were recruited. The mean years of experience were 7.89 (SD = 5.97). About three quarters of participants revealed they deal with SARS-CoV-2 patients directly (n = 284, 73.6%). The total mean score of nurse’s knowledge was 4.44 (SD = 1.22), while the total mean score of practice was 8.44 (SD = 2.05). Independent t-test was used, which revealed a statistically significant difference between educational level and total score of nurse’s knowledge [t(386) = 0.215 and p = 0.001] and between training to deal with SARS-CoV-2 during BLS and total score of practice [t(386) = 2.66 and p = 0.008]. Pearson correlation discloses a positive correlation between the total score of knowledge and practice (r = 0.343 and p = 0.001).

In general, nurses revealed a moderate level of knowledge and practice of BLS skills. However, assessing nurse’s knowledge and practice during the outbreak of SARS-CoV-2 plays a key role in identifying the gap in nurse’s knowledge and practice, and therefore, it will have an impact on providing high-quality BLS to save infected patients while providing maximum safety according to AHA guidelines.

This study is the first study that examined the level of knowledge and practice of BLS skills during SARS-CoV-2 pandemic in Jordan.