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The rapid proliferation of electronic cigarettes (ECs) has raised significant concerns about their potential health effects on both users and bystanders. This study systematically investigates the impact of EC aerosol exposure on human alveolar epithelial cells (A549), considering variations in device parameters, nicotine concentration, and exposure type. Using a gravity-based air–liquid interface exposure system, we assessed cytotoxicity and epithelial barrier integrity by measuring cell viability and transepithelial electrical resistance (TEER). Our results indicate that EC aerosol exposure significantly reduces cell viability and disrupts monolayer integrity in a dose- and device-dependent manner. Notably, VUSE (pod-type) exposure led to a 16% decrease in viability and a 41% reduction in TEER, while VOOPOO (mod-type) exposure caused a 25% viability loss and a 61% reduction in TEER. Power settings played a critical role: at 60 W, cell viability dropped by 48% at 12 mg/mL nicotine concentration compared to 29% at 0 mg/mL. Moreover, under the same number of puffs (30 puffs), firsthand exposure resulted in a 73% viability decrease, whereas secondhand exposure showed a 47% reduction, indicating substantial bystander risks associated with EC usage. These findings underscore the importance of device specifications and exposure conditions in determining EC aerosol toxicity. The observed epithelial barrier disruption suggests increased vulnerability to respiratory diseases. Given the comparable toxicity of firsthand and secondhand aerosols, regulatory measures should extend beyond direct users to include bystander protection. This study highlights the urgent need for comprehensive toxicity assessments to inform public health policies on EC use. 

We implement a cascaded learning framework leveraging three different EDFA and fiber component models for OSNR and GSNR prediction, achieving MAEs of 0.20 and 0.14 dB over a 5-span network under dynamic channel loading. 

Optical transmission systems require accurate modeling and performance estimation for autonomous adaption and reconfiguration. We present efficient and scalable machine learning (ML) methods for modeling optical networks at component- and network-level with minimized data collection. 

This study employed high-time-resolution systems to examine the transient properties of aerosols and gases emitted from electronic cigarette (EC) puffs. Using a fast aerosol sizer, we measured particle size distributions (PSDs) across various EC brands (JUUL, VUSE, VOOPOO), revealing sizes ranging from 5 to 1000 nm at concentrations of 107 to 1010 cm–3. Most aerosols were found to be in the ultrafine range (below 100 nm), with JUUL-, VUSE-, and VOOPOO-producing aerosols with geometric mean sizes of 19.9, 47.3, and 29.4 nm, respectively. Applying the International Commission on Radiological Protection (ICRP) deposition model and assuming no further evolution of aerosols in the respiratory system, we estimated particle deposition in different respiratory regions: 45–60% in the alveolar region, 10–25% in the tracheobronchial region, and 20–35% in the extrathoracic region. The highest single-puff deposition was observed with the VOOPOO device at 60 W, depositing 180.1 ± 7.6 μg in the alveolar region. The gas emissions (CO2, NOx, CO, and total hydrocarbons) were measured at different power settings of the VOOPOO EC. Single-puff NOx and CO levels exceeded the permissible exposure limits of the Occupational Safety and Health Administration, indicating potential acute exposure risks. Higher power settings were correlated with increased gas mixing ratios, suggesting more e-liquid vaporization and possible chemical transformations at higher temperatures. These findings demonstrated significant health risks associated with ultrafine particles from high-power ECs and emphasize the need for advanced measurements to accurately assess their physicochemical properties and potential health implications. 

Scalable methods for optical transmission performance prediction using machine learning (ML) are studied in metro reconfigurable optical add-drop multiplexer (ROADM) networks. A cascaded learning framework is introduced to encompass the use of cascaded component models for end-to-end (E2E) optical path prediction augmented with different combinations of E2E performance data and models. Additional E2E optical path data and models are used to reduce the prediction error accumulation in the cascade. Off-line training (pre-trained prior to deployment) and transfer learning are used for component-level erbium-doped fiber amplifier (EDFA) gain models to ensure scalability. Considering channel power prediction, we show that the data collection process of the pre-trained EDFA model can be reduced to only 5% of the original training set using transfer learning. We evaluate the proposed method under three different topologies with field deployed fibers and achieve a mean absolute error of 0.16 dB with a single (one-shot) E2E measurement on the deployed 6-span system with 12 EDFAs. 

Electronic cigarettes (ECs) emit many toxic substances, including metals, that can pose a threat to users and the environment. The toxicity of the emitted metals depends on their oxidation states. Hence, this study examines the oxidation states of metals observed in EC aerosols. X-ray photoelectron spectroscopy analysis of the filters that collected EC aerosols identified the oxidation states of five primary metals (based on surface sample analysis), including chromium(III) (close to 100%) under low power setting while a noticeable amount of chromium(VI) (15%) at higher power settings of the EC, and copper(II) (100%), zinc(II) (100%), nickel(II) (100%), lead(II) (65%), and lead(IV) (35%) regardless of power settings. This observation indicates that the increased temperature due to higher power settings could alter the oxidation states of certain metals. We noted that many metals were in their lesser toxic states; however, inhaling these metals may still pose health risks. 

We implement a cascaded learning framework using component-level EDFA models for optical power spectrum prediction in multi-span networks, achieving a mean absolute error of 0.17 dB across 6 spans and 12 EDFAs with only one-shot measurement. 

Abstract Ultraviolet germicidal irradiation (UVGI) and ozone disinfection are crucial methods for mitigating the airborne transmission of pathogenic microorganisms in high-risk settings, particularly with the emergence of respiratory viral pathogens such as SARS-CoV-2 and avian influenza viruses. This study quantitatively investigates the influence of UVGI and ozone on the viability ofE. coliin bioaerosols, with a particular focus on howE. coliviability depends on the size of the bioaerosols, a critical factor that determines deposition patterns within the human respiratory system and the evolution of bioaerosols in indoor environments. This study used a controlled small-scale laboratory chamber whereE. colisuspensions were aerosolized and subjected to varying levels of UVGI and ozone levels throughout the exposure time (2–6 s). The normalized viability ofE. coliwas found to be significantly reduced by UVGI (60–240μW s cm⁻²) as the exposure time increased from 2 to 6 s, and the most substantial reduction ofE. colinormalized viability was observed when UVGI and ozone (65–131 ppb) were used in combination. We also found that UVGI reduced the normalized viability ofE. coliin bioaerosols more significantly with smaller sizes (0.25–0.5μm) than with larger sizes (0.5–2.5μm). However, when combining UVGI and ozone, the normalized viability was higher for smaller particle sizes than for the larger ones. The findings provide insights into the development of effective UVGI disinfection engineering methods to control the spread of pathogenic microorganisms in high-risk environments. By understanding the influence of the viability of microorganisms in various bioaerosol sizes, we can optimize UVGI and ozone techniques to reduce the potential risk of airborne transmission of pathogens.