Variation In Quality Research Articles

Real-world waste dispersion modelling for benthic integrated multi-trophic aquaculture.

In real-world situations, marine fish farms accommodate multiple fish species and cohorts within the farm, leading to diverse farm layouts influenced by cage dimensions, configurations, and intricate arrangements. These cage management practices are essential to meet production demands, however, farm-level complexities can impact model predictions of waste deposition and benthic impact near fish cages. This is of particular importance when the cages are used for integrated multi-trophic aquaculture (IMTA) with benthic feeders, where this waste not only affects environmental conditions but also provides a potential food source. The Cage Aquaculture Particulate Output and Transport (CAPOT) model incorporated multiple species, cohorts, and cage arrangements to estimate waste distribution from a commercial fish farm in the Mediterranean between October 2018 and July 2019. This spreadsheet model estimated dispersion for individual fish cages using a grid resolution of 5 m x 5 m. The study categorized discrete production periods for each fish cage every month, aligning with intermittent changes in biomass and food inputs due to different cage management practices throughout production. This approach facilitated the use of detailed input data and enhanced model representativeness by considering variations in cage biomass, food types, settling velocities, and configurations. Model outputs, represented in contour plots, indicated higher deposition directly below fish cages that varied monthly throughout fish production cycles. Deposition footprints reflected changes in cage biomass, food inputs, and farm-level practices reflecting this real-world scenario where aquaculture does not follow a production continuum. Moreover, cohort dynamics and cage movements associated with the cage management practices of the fish farm influenced the quantity and fate of wastes distributed around fish cages, revealing variability in deposition footprints. Clearly, these findings have important implications for the design of benthic IMTA systems, with species such as sea cucumber and polychaetes. Variability in waste deposition creates challenges in identifying where the benthic organisms should be placed to allow optimal uptake of waste to meet their food requirements and increase survivability. Evidently, models have an important role to play and this study emphasizes the need for representative input data to describe actual food inputs, cage biomass changes, and management practices for more representative farm-scale modelling and essentially to improve particulate waste management. To effectively mitigate benthic impacts through IMTA, models must quantify and resolve particulate waste distribution and impact around fish farms to maintain a balanced system with net removal of wastes. Resolving farm-level complexities provides vital information about the variability of food availability and quality for extractive organisms that helps improve recycling of organic wastes in integrated systems, demanding a more representative modelling approach.

Innovation and adaptation: The rise of a fentanyl smoking culture in San Francisco.

Illicitly manufactured fentanyls and stimulants are implicated in the escalating US mortality from drug overdose. San Francisco, California (SF) has seen declining fentanyl injection while smoking has increased. Beliefs and behaviors surrounding this development are not well understood. The study used rapid ethnography to explore fentanyl and methamphetamine use in SF. The team conducted semi-structured interviews (n = 34) with participants recruited from syringe service programs. Video-recorded smoking sequences (n = 12), photography and daily field notes supplemented interview data. Difficulty injecting and fear of overdose motivated transitions from injecting to smoking. Fentanyl was extremely cheap-$10/gram-with variability in quality. Foil was the most commonly used smoking material but glass bubbles, bongs and dabbing devices were also popular. No reliable visible methods for determining fentanyl quality existed, however, participants could gauge potency upon inhalation, and developed techniques to regulate dosage. Several participants reported at least hourly use, some reporting one or more grams of daily fentanyl consumption. Smoking was also very social, with people sharing equipment, drugs and information. Participants raised concerns about hygiene and overdose risk to others arising from shared equipment. Reportedly potent fentanyl 'residue' accumulated on smoking materials and was commonly shared/traded/stolen or consumed accidentally with diverse preferences for its use. Our data highlight fentanyl residue as a new overdose risk with potential mismatch between the potency of the residual drug and the recipient's tolerance. Further, large doses of fentanyl are being consumed (estimated at approximately 50 mg of pure fentanyl/day). Smoking fentanyl has potential health benefits over injecting and may be protective against overdose, but substantial uncertainty exists. However, SF overdose mortality hit a record high in 2023. Recommendations to reduce fentanyl smoking overdose risks through pacing, greater awareness of dosages consumed and checking tolerance of residue recipients are potentially viable interventions deserving further exploration.

Compilations and updates on residual feed intake in sheep.

The increasing global demand for food and the strong effect of climate change have forced animal science to advance regarding new methods of selection in search of more efficient animals in production systems. Feed consumption represents more than 70% of the costs of sheep farms, and more efficient animals can increase the farmers' profitability. One of the main measures of feed efficiency is estimated residual feed intake (RFI), created in 1963 by Robert Koch for estimation in cattle and later adapted for sheep. Animals with negative RFI values (RFI-) are more efficient than animals with positive values (RFI+), with influence on the variables of performance, carcass quality and production of enteric gases. The RFI is the most common and accepted metric of the feed efficiency trait for genetic selection, since it is independent of growth traits, unlike the feed conversion ratio. The purpose of this review article was to present updated literature information on the relationship of RFI estimates with performance measures, molecular markers, greenhouse gas production and feed efficiency, the technical aspects and physiological basis of metabolic in sheep.

Evaluating the role of heart rate variability in monitoring stress and sleep quality among nurses in the aftermath of the COVID-19 pandemic.

To assess heart rate variability (HRV) as a measure to assess job stress and sleep quality among nurses in the post-COVID-19 period. The COVID-19 pandemic significantly affected nurses, with heightened job stress and impaired sleep quality impacting their well-being and effectiveness in patient care. HRV could offer insights for supporting strategies in the pandemic aftermath. A quantitative cross-sectional study. This study involved 403 clinical nurses recruited from a teaching hospital in Taiwan. Data on job stress, work frustration, sleep quality and HRV were collected and analysed. Among the nurses surveyed during the COVID-19 pandemic, 72.7% reported poor sleep quality (PSQI = 9.369). Job stress emerged as a strong predictor of work frustration. High stress levels and poor sleep quality were correlated with significantly decreased HRV, indicating a potential physiological impact of stress on the nurses' health and well-being. HRV is a valuable and cost-effective measure for monitoring and managing nurses' well-being in the post-COVID-19 era. Targeted interventions can be implemented to support nurses' overall performance and promote their well-being by identifying those at high risk of job stress and poor sleep quality.

The Influence of Halal Labeling, Product Quality, and Price on Purchasing Decisions for Tteokbokki Products

The goal is to find out the influence of halal labeling, product quality, and price on purchasing decisions on tteokbokki products. This study used literature studies and questionnaires with data collection techniques using a Likert scale. The population of this study were students of Sharia Economics, University of Sultan Ageng Tirtayasa. This type of research uses correlational research, which is a type of research with a problem characteristic in the form of a correlation relationship between two or more variables. As a result of data processing with SPSS Version 26, the r square value at the coefficient of determination of .374 or 37.4% of the variables of halal labeling, product quality, and price affect the variables of purchase good decisions. The regression equation obtained KP = -3.293+.721 LH- .076 KPr+.679+e. The calculated F value is 7.379 and the resulting Ftable is 2.86. Fcount>Ftable (7.379>2.86), which means that Ho4 was rejected and Ha4 was accepted. It can be seen that simultaneously the variables of halal labeling, product quality, and price have a positive effect on purchasing decisions. But partially, product quality does not affect purchasing decisions. Keywords: halal labeling, product quality, price, purchase decision

Factors Influencing the Competitiveness of Coffee Growers in Puebla, Mexico, to Export to the United States

This research aims to examine whether price, productivity, quality, innovation and technology transfer (TT), exchange rate, and financing are key factors that influence the export competitiveness of the coffee industry in the state of Puebla, Mexico. Previous studies on competitiveness have explored classical and neoclassical theories and new theories of international trade. We collected information by conducting an online survey using Google Forms, which included indicators for each of the variables studied. The multivariate regression model was employed to analyze the relationship between the dependent and independent variables, and we used the Statistical Package for the Social Sciences to process the data. The results indicated that the studied variables significantly affected coffee producers’ export competitiveness in Puebla. Specifically, the model’s exchange rate, quality, and productivity variables demonstrated considerable explanatory power. This underscored the importance of product quality, production volume, and factors related to the producer’s income in the decision to export coffee rather than sell it in the domestic market. Despite the emphasis placed on variables such as innovation and TT in the literature, they had limited relevance in the used model. However, we discussed several potential explanations for this outcome.

Analysis of the Influence of Brand Awareness and Service Quality on Customer Interest in Using Home Ownership Products (KPR) Griya Hasanah Study on: BSI KC Customer Tanjung Karang

Awareness of a brand provides expectations of customers, while service quality is a measure of the difference between consumer expectations and the service provided. Expectations and perceived service quality generate customer interest. However, the reality is that the brand awareness and service quality provided are good, but customer interest is still unstable, still experiencing increases and decreases in using Sharia KPR products. This study aims to determine whether brand awareness, service quality, and customer interest partially and simultaneously affect customer interest in using Sharia KPR at BSI KC Tanjung Karang and how Islamic economics views brand awareness and service quality. This research uses a quantitative approach method. Data were collected directly through distributing questionnaires to KPR customers. The sample used was 86 respondents using the Slovin formula taken by purposive sampling. The data obtained was processed using SPSS version 24. This analysis includes validity and reliability tests, preliminary analysis tests, multiple linear regression analysis tests, and hypothesis testing (t-test, f-test, and coefficient of determination (R2)). The results of the t-test show that brand awareness and service quality partially have a positive and significant effect on customer interest. And the results of the f test show that brand awareness and service quality simultaneously have a positive and significant effect on customer interest in using Sharia KPR products with an effect of count 82.524> ftabel 2.71 with a significance value of 0.00 < 0.05. Based on the results of the determination test (R2) of 0.657, this explains that the influence of brand awareness and service quality variables on customer interest is 65.7%. In comparison, the remaining 34.3% were influenced by other factors outside the research model. Keywords: brand awareness, service quality, customer interest

Brazilian Atmospheric Inventories – BRAIN: a comprehensive database of air quality in Brazil

Abstract. Developing air quality management systems to control the impacts of air pollution requires reliable data. However, current initiatives do not provide datasets with large spatial and temporal resolutions for developing air pollution policies in Brazil. Here, we introduce the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database of air quality and its drivers in Brazil. BRAIN encompasses hourly datasets of meteorology, emissions, and air quality. The emissions dataset includes vehicular emissions derived from the Brazilian Vehicular Emissions Inventory Software (BRAVES), industrial emissions produced with local data from the Brazilian environmental agencies, biomass burning emissions from FINN – Fire INventory from the National Center for Atmospheric Research (NCAR), and biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (https://doi.org/10.57760/sciencedb.09858, Hoinaski et al., 2023a; https://doi.org/10.57760/sciencedb.09886, Hoinaski et al., 2023b). The meteorology dataset has been derived from the Weather Research and Forecasting Model (WRF) (https://doi.org/10.57760/sciencedb.09857, Hoinaski and Will, 2023a; https://doi.org/10.57760/sciencedb.09885, Hoinaski and Will, 2023c). The air quality dataset contains the surface concentration of 216 air pollutants produced from coupling meteorological and emissions datasets with the Community Multiscale Air Quality Modeling System (CMAQ) (https://doi.org/10.57760/sciencedb.09859, Hoinaski and Will, 2023b; https://doi.org/10.57760/sciencedb.09884, Hoinaski and Will, 2023d). We provide gridded data in two domains, one covering the Brazilian territory with 20×20 km spatial resolution and another covering southern Brazil with 4×4 km spatial resolution. This paper describes how the datasets were produced, their limitations, and their spatiotemporal features. To evaluate the quality of the database, we compare the air quality dataset with 244 air quality monitoring stations, providing the model's performance for each pollutant measured by the monitoring stations. We present a sample of the spatial variability of emissions, meteorology, and air quality in Brazil from 2019, revealing the hotspots of emissions and air pollution issues. By making BRAIN publicly available, we aim to provide the required data for developing air quality policies on municipal and state scales, especially for under-developed and data-scarce municipalities. We also envision that BRAIN has the potential to create new insights into and opportunities for air pollution research in Brazil.​​​​​​​

Associations Between Multiple Dimensions of Sleep and Mood During Early Adolescence: A Longitudinal Daily Diary Study.

Prior research has observed reciprocal associations between sleep and mood. However, these findings are primarily based on the examination of one or two aspects of sleep behaviors (e.g., duration, quality), neglecting how multiple dimensions of sleep (particularly indicators pertinent to adolescence, e.g., sleep variability) are linked to adolescent mood both daily and longitudinally. Drawing on a multidimensional framework for sleep, this study addressed the knowledge gap by examining the directionality of and differential effects for associations between multiple dimensions of sleep and mood during early adolescence. Participants were 273 Chinese early adolescents (34.39% girls; Mage = 11.57, SD = 1.31), who filled out a pre-survey on demographics (T1) and 7-day diaries on sleep (i.e., duration, quality, disturbance, and latency) and mood (i.e., positive and negative mood). Adolescents completed another wave of diary reports 1 year later (T2). Findings revealed both bidirectional and unidirectional, within-person effects depending on specific sleep parameters, suggesting differential associations between multiple dimensions of sleep and mood. Specifically, on days when adolescents had longer sleep latency and greater disturbance than usual, they reported higher negative mood the next day, whereas higher negative mood was linked to poorer sleep quality the next day. The longitudinal investigation found that greater variability in sleep quality at T1 was associated with higher negative mood at T2. These findings underscore the importance of understanding the complex interplay between sleep and mood by examining the directionality of and differential effects for the daily and longer-term associations between multiple dimensions of sleep and mood among early adolescents.

A Transformer and LSTM-Based Approach for Blind Well Lithology Prediction

Petrographic prediction is crucial in identifying target areas and understanding reservoir lithology in oil and gas exploration. Traditional logging methods often rely on manual interpretation and experiential judgment, which can introduce subjectivity and constraints due to data quality and geological variability. To enhance the precision and efficacy of lithology prediction, this study employed a Savitzky–Golay filter with a symmetric window for anomaly data processing, coupled with a residual temporal convolutional network (ResTCN) model tasked with completing missing logging data segments. A comparative analysis against the support vector regression and random forest regression model revealed that the ResTCN achieves the smallest MAE, at 0.030, and the highest coefficient of determination, at 0.716, which are indicative of its proximity to the ground truth. These methodologies significantly enhance the quality of the training data. Subsequently, a Transformer–long short-term memory (T-LS) model was applied to identify and classify the lithology of unexplored wells. The input layer of the Transformer model follows an embedding-like principle for data preprocessing, while the encoding block encompasses multi-head attention, Add & Norm, and feedforward components, integrating the multi-head attention mechanism. The output layer interfaces with the LSTM layer through dropout. A performance evaluation of the T-LS model against established rocky prediction techniques such as logistic regression, k-nearest neighbor, and random forest demonstrated its superior identification and classification capabilities. Specifically, the T-LS model achieved a precision of 0.88 and a recall of 0.89 across nine distinct lithology features. A Shapley analysis of the T-LS model underscored the utility of amalgamating multiple logging data sources for lithology classification predictions. This advancement partially addresses the challenges associated with imprecise predictions and limited generalization abilities inherent in traditional machine learning and deep learning models applied to lithology identification, and it also helps to optimize oil and gas exploration and development strategies and improve the efficiency of resource extraction.

Adaptive Temporal-Spatial Pyramid Variational Autoencoder Model for Multirate Dynamic Chemical Process Soft Sensing Application.

Data-driven soft sensors play an important role in practical processes and have been widely applied. They provide real-time prediction of quality variables and then guide production and improve product quality. In practical chemical production processes, nonlinear dynamic multirate data is widespread and challenging to model. This paper innovatively proposes a temporal-spatial pyramid variational autoencoder (TS-PVAE) model for the nonlinear temporal-spatial feature pyramid extraction from multirate data. This structure not only selectively utilizes multirate data but also handles complex nonlinear time-series data. Based on this, integrated with just-in-time (JIT) learning, an adaptive TS-PVAE (ATS-PVAE) model is developed. In this model, historical data are used for real-time fine-tuning of the model, leading to the development of an adaptive model. Finally, the proposed models are validated by an industrial case of a methanation furnace, demonstrating a superior estimation performance.

The molecular interaction of six single-stranded DNA aptamers to cardiac troponin I revealed by docking and molecular dynamics simulation.

Cardiac troponin I (cTnI) is a cardiac biomarker for diagnosing ischemic heart disease and acute myocardial infarction. Current biochemical assays use antibodies (Abs) due to their high specificity and sensitivity. However, there are some limitations, such as the high-cost production of Abs due to complex instruments, reagents, and steps; the variability of Abs quality from batch to batch; the low stability at high temperatures; and the difficulty of chemical modification. Aptamer overcomes the limitations of antibodies, such as relatively lower cost, high reproducibility, high stability, and ease of being chemically modified. Aptamers are three-dimensional architectures of single-stranded RNA or DNA that bind to targets such as proteins. Six aptamers (Tro1-Tro6) with higher binding affinity than an antibody have been identified, but the molecular interaction has not been studied. In this study, six DNA aptamers were modeled and docked to cTnI protein. Molecular docking revealed that the interaction between all aptamer and cTnI happened in the similar cTnI region. The interaction between aptamer and cTnI involved hydrophobic interaction, hydrogen bonds, π-cation interactions, π-stack interactions, and salt-bridge formation. The calculated binding energy of all complexes was negative, which means that the complex formation was thermodynamically favorable. The electrostatic energy term was the main driving force of the interaction between all aptamer and cTnI. This study could be used to predict the behavior of further modified aptamer to improve aptamer performance.

Pengaruh Kualitas Pelayanan Terhadap Loyalitas Nasabah Pada BMT Taqwa Muhammadiyah Cabang Pasar Raya Padang

The aim of this research is to determine and analyze the influence of service quality on customer loyalty who use BMT services using five dimensions of service quality at BMT Taqwa Muhammadiyah Cabang Pasar Raya Padang. The method used is descriptive quantitative based on multiple linear regression analysis of service quality variables on customer loyalty in using the products and services of BMT Taqwa Muhammadiyah Cabang Pasar Raya Padang. The results of this research say that Tangibles has a positive and significant effect on customer loyalty of BMT Taqwa Muhammadiyah Cabang Pasar Raya Padang, Second, Empathy has a significant and positive effect on customer loyalty of BMT Taqwa Muhammadiyah Cabang Pasar Raya Padang, Reability has a positive but not significant effect on customer loyalty BMT Taqwa Muhammadiyah Cabang Pasar Raya Padang, Responsiveness has a positive but not significant influence on customer loyalty. BMT Taqwa Muhammadiyah Cabang Pasar Raya Padang and Assurance have a positive and significant influence on customer loyalty of BMT Taqwa Muhammadiyah Cabang Pasar Raya Padang.

Determining the main driver of hypoxia potential in freshwater inland lakes

This study aimed to determine the potential for hypoxia in inland freshwater lakes via examining water quality variables, mainly dissolved oxygen (DO). To this end, field studies including environmental surveying and water column sampling at six stations along two routes in Lake Zarivar, Iran, together with laboratory analysis were conducted. Then, a stepwise multivariate regression analysis was employed to identify existing patterns and effects among variables. To secure the evolved model generalizability, the range of data was adjusted using available measurements from several similar lakes/wetlands with respect to their climate and sampling season. Finally, the evolved models’ reliability was examined using validation samples as well as a comparative performance appraisal with a benchmark model attained by the least squares regression technique. The results indicated that NH4 and pH have the most significant effect on DO. The greater amount of DO on the lake surface, i.e., wind effect, and its effect on both nitrification and ammonification processes ensure the absence of hypoxia in surface layers. Therefore, in water column monitoring programs, measuring NH4 and pH would be sufficient. Our results also demonstrated that the potential of hypoxia increases if the amount of NH4 increases in the lower layers of the water column despite releasing DO with nitrate. Strong relations between DO, as the main factor of hypoxia, with NH4 and pH, gives us the opportunity to predict the hypoxia potential and its level. Using time delay between nitrification and ammonification processes, one may predict the changes in the amount of oxygen from the changes in nitrogen.

Oxidative responses in small juveniles of Colossoma macropomum anesthetized and sedated with Ocimum gratissimum L. essential oil.

This study evaluated the use of essential oil of Ocimum gratissimum (EOOG) for anesthesia and in transport of Colossoma macropomum. Experiment 1, Test 1, anesthesia induction and recovery times were determined using different EOOG concentrations (0, 20, 50, 100, 200, 300mg L-1), with two size classes: Juveniles I (0.86g) and Juveniles II (11.46g) (independent tests in a completely randomized design). Based on the results of Test 1, in Test 2 Juveniles II were exposed to EOOG concentrations: 0, 20, 100mg L-1. Tissue samples were collected immediately after induction and 1h post-recovery, to assess oxidative status variables. Experiment 2, Juveniles I (0.91g) and Juveniles II (14.76g) were submitted to transport in water with different concentrations of EOOG (0, 5, 10mg L-1) (independent tests in a completely randomized design). The effects on oxidative status variables were evaluated. Concentrations between 50 and 200mg L-1 EOOG can be indicated for Juveniles I, while concentrations between 50 and 100mg L-1 EOOG for Juveniles II. The concentration of 100mg L-1 EOOG was able to prevent oxidative damage in the liver. In Experiment 2, the concentrations of 5 and 10mg L-1 EOOG added to the transport water caused sedation for both studied size classes of juveniles and did not cause oscillations in water quality variables nor any mortality. The concentration of 10mg L-1 EOOG improved the oxidative status. It can be concluded that EOOG can be used for anesthesia and transport of C. macropomum.

Contributions of mothers' and fathers' shared book reading with infants at 9 months to language skills at 18 months in ethnically and socioeconomically diverse families

AbstractUsing a sample of 286 mothers and fathers from ethnically and socioeconomically diverse backgrounds, we tested the associations between the frequency and quality of parents' shared book reading (SBR) with infants aged 9 months, and language skills of infants aged 18 months, and whether infants' attention during SBR at 9 months mediated these associations. Frequency of SBR was parent‐report and quality of SBR (i.e., number of reading strategies) and infants' attention were coded from recorded SBR interactions at home. The majority of mothers and fathers reported reading to their 9‐month‐olds at least weekly, and mothers reported reading, on average, significantly more often than fathers. There was large variability in parents' SBR quality ranging from 0 to 15 strategies per minute, with labelling being the most common. Path analysis showed that mothers' SBR frequency at 9 months was significantly associated with infants' receptive and expressive language skills at 18 months, whereas, SBR quality by either parent was not significant. Infants' attention did not mediate these associations. These findings suggest that early SBR is beneficial for language development and programmes targeting early language development should encourage both mothers and fathers to read often to their infants during the first year.

A physical simulation-machine learning model for optimal process schemes in laser-based directed energy deposition process

The major challenge faced is the definition of optimal process variables for rich quality of fabricated parts in laser-based directed energy deposition (DED-LB) processes. However, predicting the optimal process scheme using machine learning models is still challenging owing to the need for a large amount of training experimental data with high costs in DED-LB. In view of this, a physical simulation (PS)-machine learning (ML) model using a relatively small accurate data set of 31 simulation data for optimal process schemes is proposed in DED-LB process in this study. Firstly, a powder-scale high precision phenomenon model incorporating the mass transfer, phase transformations and heat transfer and using Lagrangian particle model to add mass is developed to depict the DED-LB process. Then, a Gaussian process regression (GPR) agent model is established to rapidly and accurately predict the geometry and dilution rate of deposition tracks under different manufacturing parameters based on the high precision simulation results. Finally, a PS-ML model for process parameter optimization is developed using the particle swarm algorithm (PSO), and the optimized parameters are experimentally validated. The results show that the developed powder-scale model and GPR model results are consistent with the Inconel 718 alloy experimental results. The proposed PS-ML model can increase the accuracy of the ML models even with a small simulation data set. None of PS-ML model optimization results has a relative error of more than 3% to the experimental results, and the dilution rate is reduced by up to 61.66% compared to the experimental design parameters without optimization. The proposed physical simulation-machine learning model in this study enables inexpensive and accurate optimization of DED-LB process parameters.

Spatiotemporal variation in methylmercury and related water quality variables in a temperate river under highly dynamic hydrologic conditions

The understanding of the essential environmental factors affecting the spatiotemporal variation in methylmercury (MeHg) in river water is limited to date, despite its importance for predicting the effect of ongoing climate change on MeHg accumulation in freshwater ecosystems. This study aimed to explore the variation in MeHg concentration and related environmental factors in the downstream zone of the Yeongsan River under highly dynamic hydrologic conditions by using water quality and hydrologic data collected from 1997 to 2022, and Hg and MeHg data collected from 2017 to 2022. The mean concentration of unfiltered MeHg was 35.7 ± 13.7 pg L−1 (n = 24) in summer and 26.7 ± 7.43 pg L−1 (n = 24) in fall. Dissolved oxygen (DO), conductivity, nitrate, and dissolved organic carbon (DOC) were determined to be the most influential variables in terms of MeHg variation based on the partial least squares regression model, and their effects on the MeHg concentration were negative, except for DOC. Heatmaps representing the similarity distances between temporal trends of hydrologic and water quality variables were constructed to determine fundamental factors related to the time-based variations in DO, conductivity, nitrate, and DOC using a dynamic time warping algorithm. The heatmap cluster analysis showed that the temporal trends of these variables were closely related to rainfall variation rather than irradiance or water temperature. Overall, biogeochemical factors directly related to in situ methylation rate of Hg(II)—rather than transport of Hg(II) and MeHg from external sources—mainly control the spatiotemporal variation of MeHg in the downstream zone of the Yeongsan River.

Two-dimensional LSTM soft sensor using noisy process data

Establishing a reliable data-based soft sensor still faces a series of challenges, particularly the presence of outliers and different kinds of noise which are non-negligible in process data. To address these challenges, a correntropy-based two-dimensional long short-term memory (TLSTM) model is developed to handle noisy process data. First, the multidimensional time series samples are reconstructed into numerous two-dimensional input matrices in the feature and time directions. Then, the convolution and pooling operations are used to extract useful information in the process variables related to the quality variable. Meanwhile, a gating mechanism is employed to learn the internal representation of time series. Finally, a correntropy-based strategy is utilized to assign relatively small weights to outliers automatically, enabling reliable prediction. Two cases illustrate the reliability and advantages of TLSTM in effectively extracting quality-related features for prediction.

Association between exposure to ambient air pollution, meteorological factors and atopic dermatitis consultations in Singapore—a stratified nationwide time-series analysis

Atopic dermatitis (AD) is a chronic inflammatory skin disease affecting approximately 20% of children globally. While studies have been conducted elsewhere, air pollution and weather variability is not well studied in the tropics. This time-series study examines the association between air pollution and meteorological factors with the incidence of outpatient visits for AD obtained from the National Skin Centre (NSC) in Singapore. The total number of 1,440,844 consultation visits from the NSC from 2009 to 2019 was analysed. Using the distributed lag non-linear model and assuming a negative binomial distribution, the short-term temporal association between outpatient visits for AD and air quality and meteorological variability on a weekly time-scale were examined, while adjusting for long-term trends, seasonality and autocorrelation. The analysis was also stratified by gender and age to assess potential effect modification. The risk of AD consultation visits was 14% lower (RR10th percentile: 0.86, 95% CI 0.78–0.96) at the 10th percentile (11.9 µg/m3) of PM2.5 and 10% higher (RR90th percentile: 1.10, 95% CI 1.01–1.19) at the 90th percentile (24.4 µg/m3) compared to the median value (16.1 µg/m3). Similar results were observed for PM10 with lower risk at the 10th percentile and higher risk at the 90th percentile (RR10th percentile: 0.86, 95% CI 0.78–0.95, RR90th percentile: 1.10, 95% CI 1.01–1.19). For rainfall for values above the median, the risk of consultation visits was higher up to 7.4 mm in the PM2.5 model (RR74th percentile: 1.07, 95% CI 1.00–1.14) and up to 9 mm in the PM10 model (RR80th percentile: 1.12, 95% CI 1.00–1.25). This study found a close association between outpatient visits for AD with ambient particulate matter concentrations and rainfall. Seasonal variations in particulate matter and rainfall may be used to alert healthcare providers on the anticipated rise in AD cases and to time preventive measures to reduce the associated health burden.