Quality Model Research Articles

Emulating Wildfire Plume Injection Using Machine Learning Trained by Large Eddy Simulation (LES).

Wildfires have a major influence on the Earth system, with costly impacts on society. Despite decades of research, wildfires are still challenging to represent in air quality and chemistry-climate models. Wildfire plume rise (injection) is one of those poorly resolved processes and is also a major source of uncertainty in evaluating the wildfire impacts on air quality. Studies have shown that current plume rise models are subject to large uncertainties, including the Freitas Scheme, a widely used 1-dimensional, cloud-resolving subgrid model. In this work, a new machine learning-based plume rise emulator is presented, trained using a high-resolution, turbulence-resolving large eddy simulation (LES) model coupled with microphysics. The preliminary results show that this machine learning emulator outperforms the benchmark model, the Freitas scheme, in both accuracy and computational efficiency. Furthermore, a bagging ensemble is built to further increase the robustness and to battle internal variability. Efforts have been made to ensure that the machine learning emulator is robust, transparent, and not overtrained, and the results are interpretable and physically sound. Overall, this Plume Rise Emulating System using Machine Learning (PRESML) is a promising solution for regional and global air quality and chemistry-climate models.

Modeling of the Stress-Strain Quality of Hydroentangled Nonwoven Fabrics

Hydroentanglement is a mechanical bonding process designed to produce nonwoven fabrics with appearances and textures that resemble woven and knitted fabrics. Eleven samples of hydroentangled nonwoven fabrics with different compositions and weights were subjected to a series of uniaxial stress-strain tests. Models, ranging from the simple Kelvin to the more complicated Kelvin–Vangheluwe, were fitted to the experimental data to find a generalized and universal model. In this model, a nonwoven fabric was considered a nonlinear viscoelastic material. The combination of Kelvin and Vangheluwe models resulted in an excellent fit to the uniaxial stress-strain curves. The model-predicted results almost overlapped with the experimental data, an indication of its excellent accuracy in predicting the mechanical behavior of nonwoven fabrics.

Service Quality Standards and Organisation Performance of Private Hospitals in Eldoret City, Kenya

The private health sector has received much attention in recent times and is crucial in the expansion of access to quality healthcare. The majority of private hospitals in the country are undergoing performance challenges due to the inability to retain and satisfy their customers. Because of the improvements in health provision by county governments, private hospitals are facing increasing performance challenges due shortage of human resource personnel, shortage of medical services and inadequate medical infrastructure. Therefore, this study sought to find how the level of implementation of service quality standards affected the organisation performance of private hospitals. This study is supported by the Service Quality (SERVQUAL) model. The study is quantitative in nature guided by correlational research design. The study was conducted in Eldoret town covering major hospitals classified from Level 3B and above. The target population involved 1875 employees from 25 private hospitals in Eldoret town. A sample size of 319 respondents was chosen through the Cochran sample size formula to be representative of the whole population. The respondents (319) out of 1875 were selected using stratified and random sampling techniques. The research instrument used in data collection was a questionnaire. Data analysis was undertaken through the use of descriptive statistics; frequencies, percentages, means and standard deviation inferential statistics; correlation and linear regression with the help of Statistical Product and Service Solutions computer software. Findings showed service quality standards (β=0.240) had a significant positive effect on the performance of private hospitals in Eldoret town. It is concluded that the performance of hospitals is dependent on the extent to which service quality standards were implemented by the management of private hospitals in Eldoret. The study recommends that private hospitals regularly collect customers’ feedback to improve the standards of services/ The study findings provide solutions to private hospitals on the way to improve their performance through the adoption of quality healthcare service strategies in the delivery of services to patients

Service Quality of Express Terminal Logistics in Dongguan China Basis for Developing a New Model for Service Quality

In this study, the questionnaire was used to analyze the three groups of respondents (home delivery, self-service pick up container and post office) in Dongguan China terminal logistics from five dimensions (Tangibility, Reliability, Responsiveness, Assurance, and Empathy) in terms of their customer expectations of service quality and perceived service quality by quantitative analysis such as mean, ANOVA, and Pearson's correlation coefficient. Through the analysis, it was found that the three groups of respondents in Dongguan terminal logistics expected service quality and perceived service quality have no relationship, but there was a significant difference, among them, the gap in the Empathy dimension was the largest, followed by the Assurance dimension, the Reliability dimension, the Responsiveness dimension, and finally the Tangibility dimension has the smallest gap. The analysis of the results led to the proposal of the Dongguan terminal logistic service quality model, the larger the gap, the lower the customer satisfaction, and the greater the room for improvement. Finally, the conclusion dictated relevant suggestions, in order to achieve the purpose of improving service quality and enhance the core competitiveness of Dongguan terminal logistic enterprises.

Integration of In-VEST Habitat Quality Model with Landscape Pattern Indices to Assess Habitat Fragmentation Under the Dynamic Development of Park City: Southwest China Case

With rapid urbanization, the types of land in China’s cities are continuously evolving, irreversibly impacting the habitat patches within urban areas. However, the development of park cities has reversed this trend to some extent, particularly in Chengdu, China. To investigate the influence of land use type changes on habitat quality in Chengdu Tianfu New District, the research team selected remote sensing imagery data from the Landsat satellite for three distinct periods: 2014, 2019, and 2024. By employing a comprehensive approach that includes land cover trajectory analysis, land transfer matrices, FRAG-STATS landscape pattern indices, and the habitat quality module within the In-VEST model, this study analyzes the spatial and temporal evolution of land use patterns and the dynamics of habitat quality categories. The findings reveal: (1) the coverage of trees and shrubs in the study area initially declined but later increased, primarily driven by anthropogenic construction activities. Specifically, the land use types in the built-up areas on the northern side of Tianfu New District underwent notable fluctuations, whereas those on the southern side, adjacent to the Longquan Mountain Range, remained relatively stable. (2) From 2014 to 2019, high-quality habitats were predominantly distributed in the southeast of Tianfu New District, characterized by a robust ecological foundation, high landscape integrity, and strong connectivity of ecological land. In contrast, the areas with the poorest habitat quality were situated in the northern built-up areas of Tianfu New District, exhibiting highly fragmented habitat patches, simple edge shapes, and low connectivity. However, between 2019 and 2024, the overall habitat quality within the study area improved, characterized by an increase in the number of high-quality habitats and continuous expansion of habitat areas. The research findings offer valuable insights into future urban planning, ecological restoration, and conservation efforts in Chengdu Tianfu New District, providing critical guidance for the implementation and strategic development of the park city policy.

Short-term forecasting of dissolved oxygen based on spatial-temporal attention mechanism and kernel-based loss function

Dissolved Oxygen (DO) is a fundamental indicator of the health of aquatic ecosystems. This study introduces a novel spatial-temporal attention model combined with Kernel Mean Squared Error loss function (STA-KMSE) for forecasting short-term DO. An attention-based Convolutional Neural Network (A-CNN) is used to incorporate the influence of upstream stations in the spatial module, a factor often overlooked in prior studies. The temporal module captures the influence of both previous hours and days using attention-based Long Short-Term Memory (A-LSTM), offering a more comprehensive temporal context. Our approach addresses the issue of outlier sensitivity and non-linear error patterns by utilizing the KMSE loss function, which improves the model's robustness and more effectively captures intricate variations in DO levels compared to the standard Mean Squared Error (MSE) commonly used in water quality modelling. The model is trained on an extensive dataset consisting of various water quality parameters collected from twenty locations along the Ganga River, its tributary, and nallahs spanning from 1 April 2017 to 30 April 2021. In terms of RMSE, STA-MSE outperforms A-CNN by 62.669 % and A-LSTM by 7.419 %. Compared to STA-MSE, the STA-KMSE shows an average reduction in RMSE by 3.067 % for one-step, 3.197 % for two-step, and 3.428 % for three-step forecasting. Experiments indicate better performance for river locations than for nallahs. SHapley Additive exPlanation (SHAP) analysis highlights pH and temperature as crucial factors for estimating DO in rivers, while level, temperature, conductivity, total suspended solids, pH, chlorine, and chemical oxygen demand are significant for nallahs.

Association between humidity and respiratory health: the 2016–2018 Korea National Health and Nutrition Examination Survey

BackgroundAmbient humidity has a significant impact on respiratory health and influences disease and symptoms. However, large-scale studies are required to clarify its specific effects on lung function and respiratory symptoms. We examined the relationship between relative humidity (RH), lung function, and respiratory symptoms using data from the Korea National Health and Nutrition Examination Survey (KNHANES).MethodsIn this cross-sectional study, we analyzed data from KNHANES participants aged ≥ 40 years, collected between 2016 and 2018. Pulmonary function tests (PFTs) and health questionnaires were used to assess lung function and respiratory symptoms. Individual environmental data, including RH, were obtained from the Community Multiscale Air Quality model and linked to the participants’ addresses. Short-term (0–14 days), mid-term (30–180 days), and long-term (1–5 years) RH exposures were examined. Linear regression models were used to evaluate the associations between RH and PFTs. Univariate and multivariable logistic regression models were applied to assess the risk of lung function abnormalities and respiratory symptoms.ResultsIn total, 10,396 participants were included (mean age: 58.3 years, male: 43.6%). In multiple regression analysis, higher RH was negatively associated with the forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) ratio across various time lags, while FVC was positively correlated with long-term RH exposure. In multiple logistic analysis adjusted for clinical and environmental covariates, long-term higher RH exposure was associated with a lower risk of restrictive lung disease (odds ratio [OR] at 4-year moving average [MA]: 0.978, 95% confidence interval [CI]: 0.959–0.997), while mid-term RH exposure decreased the risk of chronic cough (OR at 90-day MA: 0.968, 95% CI: 0.948–0.987) and sputum production (OR at 90-day MA: 0.985, 95% CI: 0.969–1.001).ConclusionsHigher RH was negatively associated with lung function and increased the risk of obstructive lung disease, whereas mid-term RH exposure reduced the risk of chronic cough and sputum production.

Dynamic Modeling Analysis of University Physical Education Teaching Quality Evaluation System Under Big Data

With the rapid development of internet technology, the importance of data in various industries has become increasingly prominent. However, in the field of domestic education, especially the research on teaching quality evaluation is relatively sparse. This paper aims to build an evaluation model of physical education teaching quality with big data and machine learning in order to comprehensively evaluate teachers' physical education teaching ability. Firstly, a multi-dimensional evaluation system is designed to measure teachers' teaching level. Secondly, through the questionnaire and the statistical analysis of students' physical education teaching evaluation, the availability of the college physical education teaching evaluation system is verified. On this basis, a teacher's ability prediction model based on a neural network is established, and the experimental results show that the prediction accuracy of the model is 90.17%. This provides strong support for improving the quality of physical education teaching.

Direct radiative effects of black carbon and brown carbon from Southeast Asia biomass burning with the WRF-CMAQ two-way coupled model

Black carbon (BC) and brown carbon (BrC) are significant light-absorbing components of particulate matter that impact weather and climate. Biomass burning (BB) and biofuel (BF) emissions in Southeast Asia are key global sources of BC and BrC. This study utilizes the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model, integrating a BrC module for the first time, alongside the Global Fire Emissions Database Version 4, to assess the direct radiative effect (DRE) of BC and BrC in March 2015 over Southeast Asia. The novel BrC module parameterizes light absorption based on the BB BC to organic aerosol (OA) ratio in each model grid and time step, aligning better with smog chamber experiments than default coefficients. Results show that BC DRE affects clear sky net radiation at the top of the atmosphere, reaching 14.6 W/m2 in Indochina and 5.1 W/m2 in southern China, while BrC DRE reaches 1.9 W/m2. Additionally, BC and BrC DRE induce a cooling effect at the Earth's surface, with the maximum reduction of the clear sky downward shortwave radiation by −36.9 W/m2 and −5.2 W/m2, respectively. BC and BrC lead to a tiny decrease (<0.1 °C) in surface temperature and an increase in upper air temperature. Surface O3 concentration tends to slightly decrease (<1.0 ppbv) due to the DRE of BC and BrC. In comparison to studies using the default coefficient, this result contributes to a more nuanced understanding of the interactions between BC, BrC, and radiation.

«Declared quality of life»: new perspectives for assessing patients’ quality of life in clinical medicine

In modern patient-oriented medical care, quality of life is an important criterion for a comprehensive assessment of the patient's health and evaluation of treatment effectiveness. This article is devoted to the current methodological issues of assessing quality of life and other patient-reported outcomes (PRO) and attempts to reveal the difficulties of interpreting PRO data obtained in real-world clinical practice. A new concept for identifying significant changes in the patient's general health condition in clinical practice is presented, and a new model of quality of life outcomes before and after treatment is considered. The concepts of “declared quality of life”, “index of achieved quality of life” and “declared clinical effect” have been declared. Assessment of the declared quality of life of the patient and determination of the index of the achieved quality of life can be used for practical purposes to implement the principle of patient-oriented care in clinical practice.

Service quality management under risk prioritization and imprecise information: a hybrid fuzzy approach

PurposeConventional risk prioritization methods rely on crisp inputs but struggle with imprecise data and hesitancy, resulting in inaccurate assessments that affect service and information quality and performance monitoring. This study proposes a fuzzy data-driven risk prioritization model for service quality under imprecise information.Design/methodology/approachEnterprise risk management is crucial for service quality management, ensuring effective identification, assessment and mitigation of risks impacting service delivery and customer satisfaction. This paper proposes a fuzzy data-driven multi-criteria model for risk prioritization involving multiple decision-makers. It introduces a hybrid method combining intuitionistic and hesitant fuzzy group decision-making to assess better and prioritize risks based on decision-maker preferences.FindingsThe proposed hybrid fuzzy model improves service quality in business operations by efficiently representing uncertain information in traditional frameworks. It helps identify potential risks in advance and enhances control over business operations, enabling organizations to benchmark service quality and identify best practices. Accordingly, organizations acquire information and background knowledge to benchmark their service quality. This, in turn, improves service quality under performance management.Research limitations/implicationsDespite the advantages of fuzzy models in risk prioritization, such as mimicking human reasoning more accurately, their complexity can hinder adoption. The intricate computational steps may deter shop-floor managers who prefer the more straightforward conventional crisp RPN approach, which is easier to understand and implement. However, while developing a hybrid fuzzy risk prioritization model may require more effort, its benefits become apparent over time. Once developed, the model can be integrated into software applications, allowing decision-makers to use it easily. This integration simplifies fuzzy computations and enhances risk prioritization, leading to more informed decision-making and improved risk management in the long term.Practical implicationsThe proposed robust fuzzy framework improves risk management by integrating uncertain information and multiple decision-makers expertise, leading to more reliable outputs that enhance strategic decisions and operational efficiency.Originality/valueWe validate the proposed approach at an integrated steel plant’s risk management process, covering broad areas of the service quality domain. To the best of our knowledge, no study exists in existing literature attempting to explore the efficacy of the proposed hybrid fuzzy approach in risk management practices at prime sectors like steel. The study’s novelty is backed by this validation experiment, which indicates that the effectiveness of the results obtained from the proposed multi-attribute hybrid fuzzy methodology is more practical. The model’s outcome substantially adds value to the current risk assessment and prioritization literature that significantly affects service quality.

Integrated analysis of air quality-vegetation-health effects of near-future air pollution control strategies

To explore the effects of air pollution control strategies on air quality, vegetation, and health, we conducted an integrated modeling analysis for a representative industrial city, Zibo in the North China Plain, China. Two air pollution control scenarios for the near future (year 2026) are developed, including basic and strict control scenarios. The integrated modeling system based on Weather Research and Forecasting-Community Multiscale Air Quality Modeling (WRF-CMAQ) is utilized to analyze the effects of different scenarios on air quality improvement, vegetation, and health effects. Results indicate that under the basic (strict) control scenarios, the emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), particulate matter (PM2.5), volatile organic compounds (VOCs), ammonia (NH3) will be reduced by 16% (53%), 14% (24%), 16% (38%), 9% (28%), and 12% (12%) respectively, together with a 15% reduction in emissions of NOx, SO2, VOCs, PM2.5, along with a 5% reduction in NH3 emissions in the vicinity of Zibo in the year 2026, could meet the air quality target for 2026 (with PM2.5 and MDA8 O3_90% at 38 and 185 μg/m³, respectively). In terms of crop yield, under the basic (strict) control scenarios, the ozone-induced yield loss for wheat and corn is expected to increase by 30,000 tonnes (decrease by 80,000 tonnes) and 6000 tonnes (decrease by 4000 tonnes), respectively. In the basic control scenario, the number of deaths due to changes in PM2.5 is 1210 (95% Cl: 950, 1472) and the number of deaths due to O3-related changes is 1042 (95% Cl: 780, 1304). In the strict control scenario, the number of deaths due to PM2.5 changes was 1180 (95% Cl: 992, 1366) and the number of deaths due to O3-related changes was 768 (95% Cl: 581, 955). Our results provide a scientific basis for governments to formulate future air pollution prevention strategies.

Insights into the effects of river network topology on sudden pollution risks

The risk of sudden water pollution is one of the biggest obstacles to ensuring the safety of river basin water environments. While external pressure factors such as illegal discharge have been extensively studied with regard to their impact on the risk of sudden pollution, the influence of the river network topology has received limited attention. To fill this gap, extensive computational experiments were conducted in this study to investigate the effects of river network topology on the potential risk of sudden pollution. Based on the real-world characteristics of dendritic river networks, we used optimal channel networks to generate various river network topologies, represented by seven indicators that encompass morphology, connectivity, and structure. The water quality model simulated the spatial and temporal responses of the samples to a wide range of sudden pollution scenarios, and quantified the associated risk of sudden pollution using three proxy indicators: the maximum standard-exceeding multiple, proportion of standard-exceeding river length, and duration of exceedance. The results showed that among the seven indicators, the average outlet path (Do) and geometric fractal dimension (Dg) had a significant impact on risk. This is because network topology alters hydrological signatures such as discharge, velocity, and travel time. Moreover, multi-group analysis of structural equations revealed that the aspect ratio influences the impact of structural and connectivity indicators on risk by modulating pollution range and concentration. This study revealed the topological factors of the risk of sudden water pollution in dendritic river networks, emphasizing the regulatory role of topology in potential sudden water pollution risk and providing valuable insights for river management and planning.

Molecular Mechanisms of Aerosol Nucleation: from CLOUD Chamber Experiments to Field Observations

Atmospheric aerosol particles contribute to over four million premature deaths annually and play a critical role in modulating Earth’s climate. Most atmospheric particles and more than 50% of the cloud condensation nuclei are formed through a secondary process named new particle formation involving unique precursor vapors. This article summarizes current knowledge of how new atmospheric particles form, based on experiments at the CERN CLOUD chamber. While the role of sulfuric acid has long been known, other vapors like highly oxygenated organic molecules and iodine oxoacids are also important, along with stabilizers like ammonia, amines, and ions from cosmic rays. We explain how findings from CLOUD experiments help us understand particle formation in various atmospheric conditions and improve air quality and climate models.

Risk-Based River Management Case Study Of Pocong River Bangakalan Regency

Watershed (DAS) is a land area that includes rivers and their tributaries, which functions to accommodate, store, and channel rainwater to lakes or the sea naturally. In Bangkalan Regency, the Pocong River plays an important role in the local ecosystem. This river is used for various needs, such as irrigation, industry, and household consumption. The upstream part of the Pocong River, which is classified as class I, is used for drinking water and recreation, while the downstream part, in class II, is used for fish farming and crop irrigation. However, with increasing human activity, the water quality of the Pocong River has decreased due to pollution from domestic, agricultural, and industrial waste. Research using MIKE21 software will model the water quality in both parts of the river to provide an overview of the water quality conditions in the Pocong River. This study aims to model the quality of the upstream part of the Pocong River, class I and the downstream part, class II. The expected results of this study are to provide structured data and information regarding the water quality model of the Pocong River in the upstream and downstream parts.

Molecular Characterization of Organosulfur and Organonitrogen Compounds in Summer and Winter PM2.5 via UHPLC-Q-Orbitrap MS/MS.

Organosulfur and organonitrogen compounds (OrgSs and OrgNs) notably influence haze formation, reflecting the intricacies of sulfur and nitrogen chemistry in the atmospheric process. Despite this, a comprehensive understanding of OrgSs and OrgNs remains elusive. Here, we conducted molecular analyses of OrgSs and OrgNs in PM2.5 concurrently during three haze episodes in winter and summer from 2016 to 2019. OrgSs and OrgNs collectively constituted 68.8-73.8% of identified organics, with CHON (35.8%) being the most prevalent followed by CHONS (13.6%), CHN (11.5%), CHOS (5.6%), CHNS (3.2%), and CHS (0.9%). Nitrogen within CHX (CH + CHN + CHS + CHNS) compounds were predominantly present as nitriles or amines, while sulfur existed as alkaline thioethers (ESI+) or acidic mercaptans and thiophenols (ESI-). Oxygen-containing OrgSs and OrgNs exhibited greater structural complexity. Specifically, most CHON were associated with nitric esters and nitro-compounds (ESI-), or basic amino acids (ESI+). CHONS primarily comprised nitrogen heterocyclic substances containing oxygen and sulfur, with some potentially containing organic sulfates (OSs) and organic nitrates if O ≥ 4S + 3N. CHOS with (O-3S)/C ≥ 0 were identified as sulfonic acids or sulfate esters. This comprehensive spectrum of OrgSs and OrgNs enhances the understanding of the physicochemical properties of aerosols, providing insights for future laboratory and air quality model studies.

A hybrid deep learning model-based LSTM and modified genetic algorithm for air quality applications.

Over time, computing power and storage resource advancements have enabled the widespread accumulation and utilization of data across various domains. In the field of air quality, analyzing data and developing air quality models have become pivotal in safeguarding public health. Despite significant progress in modeling, the critical need for accurate pollutant predictions persists. In addressing this challenge, deep learning models have garnered substantial attention in research due to their outstanding performance across diverse applications. However, the optimization of hyperparameters and features remains a challenging task. This study seeks to leverage historical data to construct the long short-term memory-based model for forecasting multistep PM10. To refine its architecture, a modified genetic algorithm is employed for automatic design. Furthermore, we explore principal component analysis and exhaustive feature selection to identify the optimal feature set. This paper introduces a novel hybrid deep learning model named EFS-GA-LSTM, tailored for multistep hourly PM10 forecasting. To assess its performance, we compare it with other hyperparameter optimization algorithms, including particle swarm optimization, variable neighborhood search, and Bayesian optimization with Gaussian process. The input dataset comprises hourly PM10 concentrations, meteorological variables, and time variables. The results reveal that for 3-h-ahead forecasting tasks, the EFS-GA-LSTM network demonstrates improvements in root mean square error, mean absolute percentage error, correlation coefficient, and coefficient of determination.

Evaluating the Impact of E-Service Quality Attributes on Customer Satisfaction and Purchase Intentions for Electronic Gadgets and Home Appliances, India

The objective of the study is to analyse the impact of e-service quality attributes on customer satisfaction and on purchase intention and purchase frequency of electronic gadgets and home appliances in Bangalore, India using the service quality (SERVQUAL) model. The e-service quality attributes chosen for the study are empathy, information, security, reliability, user interface, responsiveness, fulfillment, and personalization. This paper extends existing research by quantifying the impact of chosen attributes of e-service quality on customer satisfaction and purchase intentions, and on purchase frequency. 120 responses were collected for the analysis through an online questionnaire using convenience sampling. A diverse mix of respondents who are proficient in digital platform navigation and online purchases were chosen for the study. SPSS and R studio was applied to analyze the data. The techniques used for the study to test the hypothesis and to understand the relationship between the e-service quality attributes on customer satisfaction and purchase intention are descriptive statistics, chi-square, correlation, logistic regression, decision tree, SEM, and k-fold cross-validation. The results indicate that personalization, and fulfillment have a significant impact on customer purchase intention, the attributes fulfillment and information are key attributes for purchase frequency. From SEM analysis it is evident that all the chosen e-service quality attributes have a positive impact on customer satisfaction and it is significant, which in turn enhances purchase intention and purchase frequency. From decision tree analysis it is evident that customers aged 20-40 prefer buying products after verifying seller information on shopping websites. The results of the study provide valuable insights into the e-commerce sector to make better policies and strategies to enhance customer satisfaction and purchase intention.

A Comprehensive Review of Data-Driven Models for Patient Safety and Care Quality

The technology in patient care has changed patient safety and care quality by providing models that improve diagnosis and health service delivery and reduce risks. Recognizing the increasing use of modeling in healthcare to enhance patient outcomes, this review examines machine learning, artificial intelligence, and statistical modeling of patient outcomes. We discuss how these models conduct ponderous data computing and interpret vast amounts of electronic health records, clinical data, and patient-reported outcomes for predictive analytics, early warning for safety concerns, and identifying qualities that need enhancements. The paper also provides vital limitations like data privacy, model interpretability, and compatibility with existing health information technology systems(McCaffrey &amp; Boudreaux, 2019). A brief overview of the most recent developments is presented, along with case studies that demonstrate how these models are used in clinical practice to minimize adverse occurrences, optimize patient-care pathways, and improve the efficiency of treatment delivery. This review offers direction for the research agenda and the expanded application of data to improve patient safety and quality care across multiple healthcare contexts.

Combined Effects of Treatment and Sewer Connections to Reduce Future Microplastic Emissions in Rivers.

Global mitigation strategies are needed to reduce the amount of microplastics reaching our oceans via rivers. However, what strategies will be most effective, and when and where to implement these strategies is unclear. We applied the global water quality model MARINA-Plastics, covering 10,226 sub-basins worldwide, to assess the effects of different emission reduction strategies on microplastic inputs to rivers worldwide over the period 2010-2100, taking time steps of 10 years. We applied four scenarios: three focused on wastewater treatment technologies, ranging from high to low technology improvement levels, and one combining high technology in wastewater treatment with source-oriented measures. The results show that the combined strategy of high wastewater treatment and source-oriented measures is expected to be the most effective for reducing future microplastics in rivers on a global scale. By 2100, this combined strategy is expected to result in a 68% microplastic reduction in global rivers compared to 2010. African rivers will be the main hotspots, receiving more than five times more microplastics in 2100 than in 2010. In 2100, wear from car tires is expected to be the dominant source of microplastics globally. Our insights support the implementation of the European Green Deal and the realization of Sustainable Development Goal 6 (clean water).