Search results for: Seham Al Rashidi

Authors: Atin Adhikari, Aniruddha Mitra, Abbas Rashidi, Imaobong Ekpo, Jefferson Doehling, Alexis Pawlak, Shane Lewis, Jacob Schwartz

Abstract:

Building constructions in the US involve numerous wooden structures. Woods are routinely used in walls, framing floors, framing stairs, and making of landings in building constructions. Cross-laminated timbers are currently being used as construction materials for tall buildings. Numerous workers are involved in these timber based constructions, and wood dust is one of the most common occupational exposures for them. Wood dust is a complex substance composed of cellulose, polyoses and other substances. According to US OSHA, exposure to wood dust is associated with a variety of adverse health effects among workers, including dermatitis, allergic respiratory effects, mucosal and nonallergic respiratory effects, and cancers. The amount and size of particles released as wood dust differ according to the operations performed on woods. For example, shattering of wood during sanding operations produces finer particles than does chipping in sawing and milling industries. To our knowledge, how shattering, cutting and sanding of woods and wood slabs during new building construction release fine particles and nanoparticles are largely unknown. General belief is that the dust generated during timber cutting and sanding tasks are mostly large particles. Consequently, little attention has been given to the generated submicron ultrafine and nanoparticles and their exposure levels. These data are, however, critically important because recent laboratory studies have demonstrated cytotoxicity of nanoparticles on lung epithelial cells. The above-described knowledge gaps were addressed in this study by a novel newly developed nanoparticle monitor and conventional particle counters. This study was conducted in a large new building construction site in southern Georgia primarily during the framing of wooden side walls, inner partition walls, and landings. Exposure levels of nanoparticles (n = 10) were measured by a newly developed nanoparticle counter (TSI NanoScan SMPS Model 3910) at four different distances (5, 10, 15, and 30 m) from the work location. Other airborne particles (number of particles/m3) including PM2.5 and PM10 were monitored using a 6-channel (0.3, 0.5, 1.0, 2.5, 5.0 and 10 µm) particle counter at 15 m, 30 m, and 75 m distances at both upwind and downwind directions. Mass concentration of PM2.5 and PM10 (µg/m³) were measured by using a DustTrak Aerosol Monitor. Temperature and relative humidity levels were recorded. Wind velocity was measured by a hot wire anemometer. Concentration ranges of nanoparticles of 13 particle sizes were: 11.5 nm: 221 – 816/cm³; 15.4 nm: 696 – 1735/cm³; 20.5 nm: 879 – 1957/cm³; 27.4 nm: 1164 – 2903/cm³; 36.5 nm: 1138 – 2640/cm³; 48.7 nm: 938 – 1650/cm³; 64.9 nm: 759 – 1284/cm³; 86.6 nm: 705 – 1019/cm³; 115.5 nm: 494 – 1031/cm³; 154 nm: 417 – 806/cm³; 205.4 nm: 240 – 471/cm³; 273.8 nm: 45 – 92/cm³; and 365.2 nm: Keywords: wood dust, industrial hygiene, aerosol, occupational exposure

Procedia PDF Downloads 189

Authors: Kasun P. Wijayaratna, Lauren Gardner, Taha Hossein Rashidi

Abstract:

Undergraduate students entering university across the last 2 to 3 years tend to be born during the middle years of the 1990s. This generation of students has been exposed to the internet and the desire and dependency on technology since childhood. Brains develop based on environmental influences and technology has wired this generation of student to be attuned to sophisticated complex visual imagery, indicating visual forms of learning may be more effective than the traditional lecture or discussion formats. Furthermore, post-millennials perspectives on career are not focused solely on stability and income but are strongly driven by interest, entrepreneurship and innovation. Accordingly, it is important for educators to acknowledge the generational shift and tailor the delivery of learning material to meet the expectations of the students and the needs of industry. In the context of transport engineering, effectively teaching undergraduate students the basic principles of transport planning, traffic engineering and highway design is fundamental to the progression of the profession from a practice and research perspective. Recent developments in technology have transformed the discipline as practitioners and researchers move away from the traditional “pen and paper” approach to methods involving the use of computer programs and simulation. Further, enhanced accessibility of technology for students has changed the way they understand and learn material being delivered at tertiary education institutions. As a consequence, blended learning approaches, which aim to integrate face to face teaching with flexible self-paced learning resources, have become prevalent to provide scalable education that satisfies the expectations of students. This research study involved the development of a series of ‘Blended Learning’ initiatives implemented within an introductory transport planning and geometric design course, CVEN2401: Sustainable Transport and Highway Engineering, taught at the University of New South Wales, Australia. CVEN2401 was modified by conducting interactive polling exercises during lectures, including weekly online quizzes, offering a series of supplementary learning videos, and implementing a realistic design project that students needed to complete using modelling software that is widely used in practice. These activities and resources were aimed to improve the learning environment for a large class size in excess of 450 students and to ensure that practical industry valued skills were introduced. The case study compared the 2016 and 2017 student cohorts based on their performance across assessment tasks as well as their reception to the material revealed through student feedback surveys. The initiatives were well received with a number of students commenting on the ability to complete self-paced learning and an appreciation of the exposure to a realistic design project. From an educator’s perspective, blending the course made it feasible to interact and engage with students. Personalised learning opportunities were made available whilst delivering a considerable volume of complex content essential for all undergraduate Civil and Environmental Engineering students. Overall, this case study highlights the value of blended learning initiatives, especially in the context of large class size university courses.

Keywords: blended learning, highway design, teaching, transport planning

Procedia PDF Downloads 149

Authors: Subhash Nerella, Ziyuan Guan, Azra Bihorac, Parisa Rashidi

Abstract:

Intensive care units (ICUs) provide care to critically ill patients in severe and life-threatening conditions. However, patient monitoring in the ICU is limited by the time and resource constraints imposed on healthcare providers. Many critical care indices such as mobility are still manually assessed, which can be subjective, prone to human errors, and lack granularity. Other important aspects, such as environmental factors, are not monitored at all. For example, critically ill patients often experience circadian disruptions due to the absence of effective environmental “timekeepers” such as the light/dark cycle and the systemic effect of acute illness on chronobiologic markers. Although the occurrence of delirium is associated with circadian disruption risk factors, these factors are not routinely monitored in the ICU. Hence, there is a critical unmet need to develop systems for precise and real-time assessment through novel enabling technologies. We have developed the mobility and circadian disruption quantification system (Mobi-DiQ) by augmenting biomarker and clinical data with pervasive sensing data to generate mobility and circadian cues related to mobility, nightly disruptions, and light and noise exposure. We hypothesize that Mobi-DiQ can provide accurate mobility and circadian cues that correlate with bedside clinical mobility assessments and circadian biomarkers, ultimately important for delirium risk assessment and prevention. The collected multimodal dataset consists of depth images, Electromyography (EMG) data, patient extremity movement captured by accelerometers, ambient light levels, Sound Pressure Level (SPL), and indoor air quality measured by volatile organic compounds, and the equivalent CO₂ concentration. For delirium risk assessment, the system recognizes mobility cues (axial body movement features and body key points) and circadian cues, including nightly disruptions, ambient SPL, and light intensity, as well as other environmental factors such as indoor air quality. The Mobi-DiQ system consists of three major components: the pervasive sensing system, a data storage and analysis server, and a data annotation system. For data collection, six local pervasive sensing systems were deployed, including a local computer and sensors. A video recording tool with graphical user interface (GUI) developed in python was used to capture depth image frames for analyzing patient mobility. All sensor data is encrypted, then automatically uploaded to the Mobi-DiQ server through a secured VPN connection. Several data pipelines are developed to automate the data transfer, curation, and data preparation for annotation and model training. The data curation and post-processing are performed on the server. A custom secure annotation tool with GUI was developed to annotate depth activity data. The annotation tool is linked to the MongoDB database to record the data annotation and to provide summarization. Docker containers are also utilized to manage services and pipelines running on the server in an isolated manner. The processed clinical data and annotations are used to train and develop real-time pervasive sensing systems to augment clinical decision-making and promote targeted interventions. In the future, we intend to evaluate our system as a clinical implementation trial, as well as to refine and validate it by using other data sources, including neurological data obtained through continuous electroencephalography (EEG).

Keywords: deep learning, delirium, healthcare, pervasive sensing

Procedia PDF Downloads 93