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ABSTRACT Purpose: To describe an experimental surgical model in rats using a dual-plane technique for evaluation of biomaterials in an in-vivo silicone implant coverage. Methods: This study was developed following the ISO 10993-6 standard. In this study, 40 male Wistar rats weighing between 250 and 350 g were used, distributed into two groups: experimental, biomaterial superimposed on the minimammary prosthesis (MP); and control, MP without implantation of the biomaterial, with eight animals at each biological point: 1, 2, 4, 12, and 26 weeks. Thus, at the end of biological points (1, 2, 4, 12, and 26 weeks; n = 8 animals per week), the tissue specimens achieved were fixed in buffered formalin and stained with hematoxylin-eosin. Results: Macroscopically, throughout the study, no postoperative complications were apparent. In the histological analysis, it was possible to observe the evolution of the inflammatory response, tissue repair, and fibrous capsule during the biological points. Conclusions: The experimental model described in this study proved to be suitable for evaluating the biomaterial used in the coverage of breast silicone implants.

Seroprevalence studies assessing community exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Ghana concluded that population-level immunity remained low as of February 2021. Thus, it is important to demonstrate how increasing vaccine coverage reduces the economic and public health impacts associated with SARS-CoV-2 transmission. To that end, this study used a Susceptible-Exposed-Presymptomatic-Symptomatic-Asymptomatic-Recovered-Dead-Vaccinated compartmental model to simulate coronavirus disease 2019 (COVID-19) transmission and the role of public health interventions in Ghana. The impact of increasing vaccination rates and decline in transmission rates due to nonpharmaceutical interventions (NPIs) on cumulative infections and deaths averted was explored under different scenarios. Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC) was used to investigate the uncertainty and sensitivity of the outcomes to the parameters. Simulation results suggest that increasing the vaccination rate to achieve 50% coverage was associated with almost 60,000 deaths and 25 million infections averted. In comparison, a 50% decrease in the transmission coefficient was associated with the prevention of about 150,000 deaths and 50 million infections. The LHS-PRCC results indicated that in the context of vaccination rate, cumulative infections and deaths averted were most sensitive to vaccination rate, waning immunity rates from vaccination, and waning immunity from natural infection. This study's findings illustrate the impact of increasing vaccination coverage and/or reducing the transmission rate by NPI adherence in the prevention of COVID-19 infections and deaths in Ghana. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to assess the learning curves of peribulbar anesthesia and estimate the number of blocks needed to attain proficiency. Methods: Anonymized records of sequential peribulbar blocks performed by first-year anesthesia residents were analyzed. The block sequential number and the outcomes were extracted from each record. Success was defined as a complete sensory and motor block of the eye, and failure was defined as an incomplete block requiring supplemental local anesthetic injections or general anesthesia. Learning curves using the LC-CUSUM method were constructed, aiming for acceptable and unacceptable failure rates of 10% and 20%, and 10% probability of type I and II errors. Simulations were used to determine the proficiency limit h0. Residents whose curves reached h0 were considered proficient. The Sequential Probability Ratio Test Cumulative Sum Method (SPRT-CUSUM) was used for follow-up. Results: Thirty-nine residents performed 2076 blocks (median = 52 blocks per resident; Interquartile Range (IQR) [range] = 27–78 [4–132]). Thirty residents (77%) achieved proficiency after a median of 13 blocks (13–24 [13–24]). Conclusions: The LC-CUSUM is a robust method for detecting resident proficiency at peribulbar anesthesia, defined as success rates exceeding 90%. Accordingly, 13 to 24 supervised double-injection peribulbar blocks are needed to attain competence at peribulbar anesthesia.

Aspiration of gastric contents during induction of general anesthesia remains a significant cause of mortality and morbidity in anesthesia. Recent data show that pulmonary aspiration still accounts for many cases with implications on mortality despite technical and technological evolution. Practical, ethical, and methodological issues prevent high-quality research in the setting of aspiration and rapid sequence induction/intubation, and significant controversy is ongoing. Patients’ position, drugs choice, dosing and timing, use of cricoid force, and a reliable risk assessment are widely debated with significant questions still unanswered. We focus our discussion on three approaches to promote a better understanding of rapid sequence induction/intubation and airway management decision-making. Firstly, we review how we can use qualitative and quantitative assessment of fasting status and gastric content with the point-of-care ultrasound as an integral part of preoperative evaluation and planning. Secondly, we propose using imaging-based mathematical models to study different patient positions and aspiration mechanisms, including identifying aspiration triggers. Thirdly, we promote the development of a global data collection system aiming to obtain precise epidemiological data. Therefore, we fill the gap between evidence-based medicine and experts’ opinion through easily accessible and diffused computer-based databases. A better understanding of aspiration epidemiology obtained through focused global data gathering systems, the widespread use of ultrasound-based prandial status evaluation, and development of advanced mathematical models might potentially guide safer airway management decision making in the 21st century.

ABSTRACT Objective Gouty arthritis is characterized by painful inflammation due to the deposition of monosodium urate crystals in joint tissues. Despite available treatments, many patients experience ineffective management and adverse effects. This study evaluated a manual therapy protocol involving passive joint mobilization at the peak of inflammation in a gouty arthritis model using functional and inflammatory parameters. Methods Twenty male Wistar rats, 12 weeks old, were divided into two groups (n=10 each): Gouty Arthritis and Control Groups, which were further subdivided into treated and untreated groups (n=5 each). The Gouty Arthritis Group received intraarticular knee injection of 50µL of monosodium urate crystals, while the Control Group received 50µL of phosphate buffered saline. The treatment involved a 9-minutes session of grade III joint mobilization (according to Maitland). Nociception, grip strength, and edema were evaluated before induction (EV0), 7 hours after assessment (EV1), immediately after treatment (EV2), and 1 hour after treatment (EV3). The animals were euthanized, and synovial fluid was collected to analyze leukocyte migration. Results The model mimicked the signs of the Gouty Arthritis Group, with a decrease in the threshold of nociception and strength and an increase in edema and leukocyte count. The mobilization protocol significantly increased the nociceptive threshold and grip strength and reduced edema; however, it did not reverse the increase in leukocyte count. Conclusion Our results suggest that mobilization promotes analgesia and may modulate the inflammatory process owing to reduced edema and subtle attenuation of cell migration, which contributes to strength gain.

ABSTRACT Purpose: To evaluate the effects of the experimental subcutaneous Walker-256 tumor and L-glutamine supplementation, an antioxidant, on the glomerular morphology of rats. Methods: Twenty Wistar rats were distributed into four groups (n = 5): control (C); control treated with 2% L-glutamine (CG); rats with Walker-256 tumor (WT); and rats with Walker-256 tumor treated with 2% L-glutamine (WTG). Renal histological samples were submitted to periodic acid-Schiff and Masson’s Trichrome staining to analyze glomerular density, morphometry of glomerular components and glomerulosclerosis; and to immunohistochemistry for fibroblast growth factor-2 (FGF-2). Results: WT showed 50% reduction in body mass gain and cachexia index > 10%, while WTG demonstrated reduction in cachexia (p < 0.05). WT revealed reduction of glomerular density, increase in the glomerular tuft area, mesangial area, matrix in the glomerular tuft, decrease in the urinary space and synechia, and consequently higher glomerulosclerosis (p < 0.05). L-glutamine supplementation in the WTG improved glomerular density, and reduced glomerular tuft area, urinary space, mesangial area, and glomerulosclerosis compared to WT(p < 0.05). WT showed higher collagen area and FGF-2 expression compared to C (p < 0.05). WTG presented lower collagen fibers and FGF-2 expression compared to WT (p < 0.05). Conclusions: L-glutamine supplementation reduced cachexia and was beneficial for glomerular morphology of the rats, as well as it reduced kidney damage and improved the remaining glomeruli morphology.

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Background/Aim. The Structured Inventory of Malingered Symptomatology (SIMS) is a self-report measure to be used with adults, which may be utilized to assess the potential malingering of psychosis, neurologic impairment, amnesia, low intelligence, or affective disorder. The aim of the study was to examine the discriminant validity of SIMS under conditions of simulating symptoms of neurological and memory disorders, in response to a hypothetical situation, after watching a recording of an actual car accident in which a motorcycle rider sustained head injuries. Methods. The study involved 94 students (35 men and 59 women) from the University of Criminal Investigation and Police Studies in Belgrade and the Faculty of Medicine – Special Education and Rehabilitation in Novi Sad, aged 20–26 [arithmetic mean = 20.69; standard deviation (SD) = 0.80], divided into two groups (n = 4 7), malingerer and control. The malingerer group was instructed to identify with the motorcycle rider hit by the car and malinger symptoms related to neurological difficulties and amnesia in order to obtain greater reimbursement from the insurance company. The control group had instructions to honestly assess the probability of occurrence of the symptoms. Results. The results of the multivariate one-way analysis of variance suggested that the effect of experimental manipulation was statistically significant [F (88, 5) = 91.21, p < 0.001; η2p = 0.838]. Univariate effects were also statistically significant for all five scales. Participants in the malingerer group scored higher on all five scales than participants in the control group. The magnitudes of the effects support the largest differences between the malingerer and control groups on the scales of Memory Disorders and Neuro-logical Disorders, which was also the basic instruction for simulating symptoms given to the participants in the malingerer group. Conclusion. The obtained results support the discriminant validity of the SIMS questionnaire in the situation of simulating symptoms of neurological disorders and memory disorders.

Objective. To summarize the results of research conducted in Costa Rica in which mathematical and statistical methods were implemented to study the transmission dynamics of mosquito-borne diseases. Methods. Three articles with mathematical and statistical analysis on vector-borne diseases in Costa Rica were selected and reviewed. These papers show the value and relevance of using different quantitative methods to understand disease dynamics and support decision-making. Results. The results of these investigations: 1) show the impact on dengue case reports when a second pathogen emerges, such as chikungunya; 2) recover key parameters in Zika dynamics using Bayesian inference; and 3) show the use of machine learning algorithms and climatic variables to forecast the dengue relative risk in five different locations. Conclusions. Mathematical and statistical modeling enables the description of mosquito-borne disease transmission dynamics, providing quantitative information to support prevention/control methods and resource allocation planning.

Objective: Gouty arthritis is characterized by painful inflammation due to the deposition of monosodium urate crystals in joint tissues. Despite available treatments, many patients experience ineffective management and adverse effects. This study evaluated a manual therapy protocol involving passive joint mobilization at the peak of inflammation in a gouty arthritis model using functional and inflammatory parameters. Methods: Twenty male Wistar rats, 12 weeks old, were divided into two groups (n=10 each): Gouty Arthritis and Control Groups, which were further subdivided into treated and untreated groups (n=5 each). The Gouty Arthritis Group received intraarticular knee injection of 50μL of monosodium urate crystals, while the Control Group received 50μL of phosphate buffered saline. The treatment involved a 9-minutes session of grade III joint mobilization (according to Maitland). Nociception, grip strength, and edema were evaluated before induction (EV0), 7 hours after assessment (EV1), immediately after treatment (EV2), and 1 hour after treatment (EV3). The animals were euthanized, and synovial fluid was collected to analyze leukocyte migration. Results: The model mimicked the signs of the Gouty Arthritis Group, with a decrease in the threshold of nociception and strength and an increase in edema and leukocyte count. The mobilization protocol significantly increased the nociceptive threshold and grip strength and reduced edema; however, it did not reverse the increase in leukocyte count. Conclusion: Our results suggest that mobilization promotes analgesia and may modulate the inflammatory process owing to reduced edema and subtle attenuation of cell migration, which contributes to strength gain. [ABSTRACT FROM AUTHOR]

Background: The Theory of Planned Behavior (TPB) has guided the investigation of breastfeeding since the 1980's, incorporating the major constructs of attitudes, subjective norms/normative beliefs, perceived behavioral control, and intentions. The purpose of this research study was to define a TPB-based structural latent variable model so as to explain variance in breastfeeding intentions and behaviors among a cohort of Midwest breastfeeding mothers.Methods: The longitudinal descriptive study utilized questionnaire data collected from a convenience sample of 100 women with low-risk pregnancies with the intention to breastfeed at three separate time points (> 30 weeks antepartum, 10 and 60 days postpartum). Data were coded and analyzed using IBM SPSS, SAS and the lavaan package in R.Results: Participants were predominantly White (94%, n = 94), married (95%, n = 95), college-educated (96%, n = 96), and had previous breastfeeding experience (75%, n = 75). The majority gave birth vaginally (79%, n = 75). Varimax analysis revealed a plurality of factors within each domain. Attempts to fit a structural model, including both hierarchical and bi-factor latent variables, failed, revealing a lack of statistical significance and poor fit statistics.Conclusion(s): These findings illustrate the importance of using methods that fit the phenomena explained. Contributors to poor model fit may include outdated tools lacking cultural relevance, a change in social norms, or a failure to capture the possible influence of social media and formula marketing on breastfeeding behaviors. The null finding is a significant finding, indicating the need to revisit and refine the operationalization and conceptual underpinnings of the TPB through qualitative methods such as exploring the lived experiences of breastfeeding women in the Midwest region. [ABSTRACT FROM AUTHOR]

Abstract Background The Theory of Planned Behavior (TPB) has guided the investigation of breastfeeding since the 1980’s, incorporating the major constructs of attitudes, subjective norms/normative beliefs, perceived behavioral control, and intentions. The purpose of this research study was to define a TPB-based structural latent variable model so as to explain variance in breastfeeding intentions and behaviors among a cohort of Midwest breastfeeding mothers. Methods The longitudinal descriptive study utilized questionnaire data collected from a convenience sample of 100 women with low-risk pregnancies with the intention to breastfeed at three separate time points (> 30 weeks antepartum, 10 and 60 days postpartum). Data were coded and analyzed using IBM SPSS, SAS and the lavaan package in R. Results Participants were predominantly White (94%, n = 94), married (95%, n = 95), college-educated (96%, n = 96), and had previous breastfeeding experience (75%, n = 75). The majority gave birth vaginally (79%, n = 75). Varimax analysis revealed a plurality of factors within each domain. Attempts to fit a structural model, including both hierarchical and bi-factor latent variables, failed, revealing a lack of statistical significance and poor fit statistics. Conclusion(s) These findings illustrate the importance of using methods that fit the phenomena explained. Contributors to poor model fit may include outdated tools lacking cultural relevance, a change in social norms, or a failure to capture the possible influence of social media and formula marketing on breastfeeding behaviors. The null finding is a significant finding, indicating the need to revisit and refine the operationalization and conceptual underpinnings of the TPB through qualitative methods such as exploring the lived experiences of breastfeeding women in the Midwest region.

Background/Aim. In the past two decades, we have witnessed the emergence of new radiation therapy techniques, radiotherapy treatment planning system (TPS) with calculating algorithms for the dosage calculation in a patient, units for multislice computed tomography (CT) and image-guided treatment delivery. The aim of the study was investigating the significant difference in dosimetric calculation of radiotherapy TPS in relation to the values obtained by measuring on the linear accelerator (LINAC), and the accuracy of dosimetric calculation between calculating algorithms Analytical Anisotropic Algorithm (AAA) and Acuros XB in various tissues and photon beam energies. Methods. For End-to-End test we used the hetero-geneous phantom CIRS Thorax002LFC, which anatomically represents human torso with a set of inserts known as relative electron densities (RED) for obtaining a CT calibration curve, comparable to the “reference” CIRS 062M phantom. For the AAA and Acuros XB algorithms and for 6 MV and 16 MV photon beams in the TPS Varian Eclipse 13.6, four 3D conformal (3DCRT), and one intensity modulated (IMRT) and volumetric modulated arc (VMAT) radiotherapy plans were made. Measurements of the absolute dose in Thorax phantom, by PTW-Semiflex ionization chamber, were carried out on three Varian-DHX LINACs. Results. The difference between “reference” and measured CT conversion curves in the bone area was 3%. For 476 phantom measurements, the difference between measured and TPS calculated dose of 3–6%, was found in 30 (6.3%) cases. According to regression analysis, the standardized Beta coefficient for relative errors, 6 MV vs. 16 MV, was 0.337 (33.7%, p < 0.001). Mean relative errors for AAA and Acuros XB, using Mann-Whitney test, for bones were 1.56% and 2.64%, respectively (p = 0.004). Conclusion. End-to-End test on Thorax002LFC phantom proved the accuracy of TPS dose calculation in relation to the one delivered to a patient by LINAC. There was a significant difference for photon energies relative errors (higher values are obtained for 16 MV vs. 6 MV). A statistically significant minor relative error in AAA vs. Acuros XB was found for the bone.

COVID-19 vaccines have been illustrated to lessen the growth of sickness caused by the virus effectively. In any case, inoculation has consistently been controversial, with differing opinions and viewpoints. This has compelled some individuals to decide against receiving the vaccine. These divergent viewpoints have had a trivial impact on the epidemic's dynamics and the disease's development. In response to vaccinated individuals still falling ill, many countries have implemented booster vaccines to protect further. In this specific investigation, a mathematical model composed of seven compartments is employed to examine the effectiveness of a booster dose in preventing and treating the transmission of COVID-19. The principles of mathematics are employed to analyse and investigate the dynamics of the disease. Using a qualitative prototype analysis, we acquired valuable insights into its effectiveness. One essential aspect is the basic reproduction number, a critical determinant of the disease's spread. This calculation is determined by studying the system's equilibrium and evaluating its stability. Furthermore, we examined the balance from a local and global viewpoint, considering the possibility of bifurcation and the model's reproductive number sensitivity index. Through numerical simulations, we have visually illustrated the analytical findings outlined in this research paper and presented a thorough examination of the efficacy of booster shots as a preventive and therapeutic measure in the spread dynamics of COVID-19., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Cultural evolutionary processes can often lead to a statistical association between neutral and adaptive traits during episodes of population dispersal and the introduction of a beneficial technology in a geographic region. Here, we examine such cultural hitchhiking processes using an individual-based model that portrays the cultural interaction between a migrant and an incumbent population. Our model is loosely based on the interaction between farming and foraging populations during the initial stages of the adoption and diffusion of agricultural practices. The two populations are characterized by different variants for their neutral and adaptive cultural traits, with the latter set providing a reproductive advantage for the migrant communities over the incumbent ones. We explore how the neutral traits of the migrant population spread and how this process is conditioned by the following factors: 1) the possibility of transmission of the adaptive traits; 2) the extent of the increased reproductive advantage provided by the adaptive variants of the migrant population; 3) postmarital residence rules; and 4) how and when neutral traits are transmitted. Our results reveal a diverse range of outputs, highlighting the relevance of factors such as the nature of postmarital resocialization and the specific combination of postmarital residence rules and sex-biased transmission., Competing Interests: Competing interests statement:The authors declare no competing interest.

Cultural practices perceived to be adaptive-from clearing land for food production to medical innovations-can disseminate quickly through human populations. However, these same practices often have unintended maladaptive effects. A particularly consequential effect is the emergence of diseases. In numerous instances, a cultural change is followed by the appearance of a new pathogen. Here, we develop mathematical models to analyze the population processes through which cultural evolution precipitates the emergence of a new disease. We find that when a risk-bearing cultural practice spreads, emergence can be an unavoidable cost even if a safer alternative practice eventually evolves from the original. Social learning and a fitness advantage associated with the evolving practice drive early disease emergence but the two factors have distinct effects on the time to mutation of the pathogen and significant stochastic variation is observed. For example, a disease can take longer to emerge in a population that adopts the risk-bearing practice quickly than in a population that is slow to transition. Extending the model to explore the effects of an alternative practice evolving from the original, we find a nonmonotonic relationship between relative risk of the two practices and the median time to disease emergence. Our findings contribute to understanding how cultural evolution can shape pathogen evolution and highlight the unpredictability of disease emergence., Competing Interests: Competing interests statement:The authors declare no competing interest.

Modern cultural evolution theory adopts a variety of concepts and methods developed in mathematical biology, in particular population genetics theory. In addition to forward-looking approaches such as two-locus models, backward-looking approaches such as coalescent theory, which describe ancestral states of the current population, have played an important role in population genetics. Here, we show how forward and backward approaches can be applied to two examples in cultural evolution. The first example deals with the number of cultural traits, which can be analyzed by a backward approach. Cultural coalescent theory illustrates many unique aspects of cultural processes such as information transfer (i.e., social learning) from many donors ("cultural parents"), including or excluding the biological parents. Theory predicts that many cultural traits of intermediate (or higher) frequency (popularity) can exist that are surprisingly old. Many unsolved issues remain, however, such as how to incorporate social structure as well as natural and/or cultural selection, which we believe to be an urgent agenda. The second example is the punishment of sibling incest, a problem to which cultural coalescent theory cannot currently be usefully applied. By analyzing forward recursions, we show that punishment is ineffective in suppressing incest, unless the incestuous inclination is diminished by the presence of punishers. Based on these examples, we discuss the merits and demerits of forward and backward approaches., Competing Interests: Competing interests statement:The authors declare no competing interest.

We develop a game-theoretic model of strategic interdependence and tipping in public policy choices and show that the model can be estimated by probit and logit estimators. We test its validity and applicability by using daily data on state-level COVID-19 responses in the United States. Social distancing via shelter-in-place (SIP) strategies and wearing masks emerged as the most effective nonpharmaceutical ways of combatting COVID-19. In the United States, choices about these policies are made by individual states. We develop a game-theoretic model of such choices and test it econometrically, confirming strong interdependence in the implementation of these policies. If enough states engage in social distancing or mask wearing, others will be tipped to follow suit. Policy choices are influenced mainly by the choices of other states, especially those of similar political orientation and to a lesser degree by the number of new COVID-19 cases. The choice of mask-wearing policies is more sensitive to peer choices than the choice of SIP policies, and Republican states are much less likely than Democratic to introduce mask-wearing policies. The choices of policies are influenced more by political than public health considerations. These findings emphasize strategic interdependence in policy choice and offer an analytical framework for these complex dynamics., Competing Interests: Competing interests statement:The authors declare no competing interest.

Experience from earlier nuclear accidents has clearly shown the need for maintaining and developing appropriate modelling capabilities. Dealing with complex issues such as human exposure following a nuclear accident necessitates the implementation of a set of interconnected models such as FDMT. FDMT is an integrated module within the two main European decision support systems for radiological emergency preparedness, ARGOS and JRODOS, to simulate the transfer of radionuclides along terrestrial food chains and to predict their activity concentrations in foodstuffs. In order to make the module more fit-for-purpose, FDMT has been implemented in a new modelling platform (ECOLEGO) which provides a high degree of flexibility with regard to conducting developmental work. This paper presents improvements in FDMT further through either the incorporation of new models or further elaboration of existing ones, as well as updates in default parameters. Models have also been made more fit-for-purpose through consideration of regional-specific parameters. Specific improvements include modelling developments related to dry deposition, radioactive particle weathering, radiocaesium transfer influenced by soil characteristics and, for a region-specific case, animal uptake. In addition, the paper presents new pathways and parameters (and updated values) to be considered for making FDMT more adapted for Norwegian conditions. Overall, the improvements made in the present work should significantly reduce the uncertainties associated with the outputs of the FDMT models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Solving the challenges faced during the measurement of the cross-interface transfer of perfluoroalkyl acids (PFAAs) in lakes is crucial for clarifying environmental behaviours of these chemicals and their efficient governance. This study developed a multimedia fugacity model based on the quantitative water-air-sediment interaction (QWASI) covering abiotic/biotic matrices to investigate the cross-interface transfer and fate of PFAAs in Luoma Lake, a typical PFAA-contaminated shallow lake in eastern China. The accuracy and reliability of the established model were confirmed using Percent bias and Monte Carlo simulation, respectively. Using the QWASI model, the multimedia transfer of the PFAAs and their accumulation and persistence in different sub-compartments were described and measured, and the differences among individual PFAAs were explored. The simulation results showed that the sedimentation and resuspension of PFAAs were the most intense cross-interfacial transfers, and the sediments served as a chemical sink in the long term. A significant negative correlation of N C-F (the number of CF bonds) with the relative outflow flux (T W · out-ct ) but a positive correlation with the relative net transfer across the interface between water and aquatic plants (T p-ct ) was detected, indicating that the PFAA migration capacity decreased but the bioaccumulation potential increased with the CF bond number. The persistence in water (P w ) of individual PFAAs ranged from 19.65d (PFOA) to 32.22d (PFOS), with an average of 26.15d; their persistence in sediment (P s ) ranged from 432d (PFBA) to 3216d (PFOS), with an average of 1524d, increasing linearly with an increase in N C-F . The water advection flows into and out of the lake (Q W · in and Q W · out ), the PFAA concentration of water inflow (C W · in ), and bioconcentration factor of aquatic plants (BCF p ) were the primary parameters sensitive to PFAAs in all sub-compartments, which are essential indexes for exploring promising remediation pathways for lacustrine PFAA contamination based on the fugacity model simulation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

National models of the electric sector typically consider a handful of generator operating periods per year, while pollutant fate and transport models have an hourly resolution. We bridge that scale gap by introducing a novel fundamental-based temporal downscaling method (TDM) for translating national or regional energy scenarios to hourly emissions. Optimization-based generator dispatch is used to account for variations in emissions stemming from weather-sensitive power demands and wind and solar generation. The TDM is demonstrated by downscaling emissions from the electricity market module in the National Energy Model System. As a case study, we implement the TDM in the Virginia-Carolinas region and compare its results with traditional statistical downscaling used in the Sparse Matrix Operator Kernel Emissions (SMOKE) processing model. We find that the TDM emission profiles respond to weather and that nitrogen oxide emissions are positively correlated with conditions conducive to ozone formation. In contrast, SMOKE emission time series, which are rooted in historical operating patterns, exhibit insensitivity to weather conditions and potential biases, particularly with high renewable penetration and climate change. Relying on SMOKE profiles can also obscure variations in emission patterns across different policy scenarios, potentially downplaying their impacts on power system operations and emissions.

In this paper, we extend the gap model of disability that supports digital equipment and cyber-physical situational variations. We explore an alternative approach to visualizing and understanding disability that takes into account the diversity of personal abilities, the diversity of digital equipment and equally important, the situation (environmental, psychological, and digital) in which the information system is used. The most important implications of this work are twofold. First, this work will contribute to the need for Universal Design to take into account not only the diversity of abilities and impairments of the users that are tangible and recognizable in the physical environment, but also the diversity of digital equipment and usage situations. Second, as part of the systems development cycle, in user testing of digital services, this work highlights the essential need to not only involve a diverse set of users, but also to conduct testing in diverse digital environments (smartphones, tablets, laptops and desktop PCs), and in diverse realistic usage scenarios, including strong sunlight or low light situation, as well as tired, alert, stressed or distracted, and slow network. The proposed model attempts to give a more holistic view and promote a more comprehensive understanding of the disabilities that can occur during use of digital services or mediated communication in daily life.

Reformulating the physical processes associated with the evolution of different ailments in accordance with globally shared objectives is crucial for deeper comprehension. This study aims to investigate the mechanism by which the HB virus induces harmful inflammation of the liver, with a focus on early detection and therapy using corticosteroids or chemotherapy. Based on the developed hypothesis, a new mathematical model has been created for this purpose. The recently developed system for HBV is S E I 1 I 2 R , which is examined both quantitatively and qualitatively to determine its actual effect on stability. Reliable conclusions are ensured by examining the system's boundedness, positivity, existence, uniqueness, and conducting local and global stability analysis-all crucial components of epidemic models. Global stability is tested using Lyapunov first derivative functions to assess the overall impact of asymptomatic persons and chemotherapy treatment. Additionally, the Lipschitz condition is used to confirm the unique solutions for the newly built HBV model using methods from fixed point theory, thus meeting the requirements for uniqueness and existence. Since the population must maintain this property, positivity is confirmed using global derivatives and Lipschitz criteria to calculate the rate of change in each sub-compartment. Applying the Mittag-Leffler kernel with a fractal-fractional operator to continuously monitor the HBV virus for liver cirrhosis infection yields dependable results. Furthermore, the current situation regarding the HBV outbreak pertaining to liver cirrhosis infection, along with the control measures implemented following early diagnosis through asymptomatic measures and chemotherapy treatment under constant observation, are established to prevent chronic stage infections. Simulations have been used to study the true behavior and impact of HBV in asymptomatic persons receiving chemotherapy for liver cirrhosis infection in the community. This research is essential for understanding the spread of viruses and developing control strategies based on our validated findings to mitigate the risk factors associated with liver cirrhosis., Competing Interests: Ethical Approval Not applicable. Competing interests The authors declare no conflict of interest., (© 2024. The Author(s).)

To mitigate empty seat loss caused by random passenger no-show behavior, this study extends seat allocation to joint optimization of overbooking and seat allocation for high-speed railways (HSR). Assuming that stochastic passenger demand follows a specific distribution and considering various constraints, including train capacity, demand, and denied boarding rate constraints, a nonlinear stochastic programming model for joint optimization of overbooking and seat allocation for HSR is constructed with the aim of maximizing railway expected revenue. To solve this optimization model, a multi-level optimization algorithm is designed. Based on the sampling averaging approximation method, demand scenarios and passenger no-show scenarios are generated and the optimization problem is decomposed, including the joint optimization of overbooking and seat allocation under a single demand scenario, and the ticket adjustment under other demand scenarios. For the former, it is further divided into two sub-problems according to the stochastic nature of passenger no-show behavior, which is optimized iteratively. Finally, the effectiveness of the proposed model and algorithm is evaluated through numerical studies. The results demonstrate that the proposed joint optimization method effectively addresses the randomness of passenger demand and no-show behavior, thereby improving HSR expected revenue and making up for the empty seat loss resulting from passenger no-show behavior., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: School sports coaching is gaining recognition as a formal profession worldwide, highlighting its significant role in students' holistic development. Given the complex social and cultural dimensions of the role, this study explores a coaching competence model tailored to the school context, emphasizing the need to consider both national and local frameworks to ensure effective implementation. In China, school sport coaches are responsible for both physical education teaching and sports training across all types of schools. Within the integration of sports and education, school sport coaches can expand their impact not only on student-athletes or athletes but also on general students in practice. Compared with physical education teachers' perspectives, sports coaches' competence development has rarely been studied. This study aims to develop a sustainable Chinese school sports coaching competence model., Methods: The qualitative data were gathered mainly through semi-structured interviews. School sports coaches and related stakeholders (N = 22) were selected as participants using purposive sampling techniques. Grounded theory was employed to analyse the data via NVivo 11.0 software., Results: The school sports coaching competence model comprises three categories consisting of 15 sub-categories, 92 concepts, and 317 labels. These categories are as follows: (a) Perception of identity, encompassing role of the coach, reflection on students, and understanding of policies; (b) Code of ethics, which includes sports virtues, sports-humanistic spirit, and principles of movement science; (c) Coaching excellence, comprising lifestyle and fitness strength, local sports development, teacher-coach partnership, leadership in extracurricular activities, safety and injury protection, athlete selection and training, instruction and demonstration, coaching reflective practice, and athlete-coach rapport., Conclusion: Chinese school sport coaching competence model emphasizes the integration of sports and education for student development. It provides both local and national perspectives to enhance the professional development of school sports coaches' community worldwide., Competing Interests: Declarations Ethics approval and consent to participate (1) Ethical approval. This study has been approved by Ethics Board at East China Normal University (HR 608–2021, 5 November 2021). The researchers obtained informed consent after explaining the purposes, procedures, and voluntary nature. Confidentiality and anonymity in the collection and presentation of results were maintained. (2) Informed consent. The participants were informed during the recruitment and interview process that their participation was voluntary and that all information was treated with confidentiality. Additionally, the participants were informed that they had the right to withdraw from the interview and were asked to tick the consent box before completing the interview. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Modern urban centers have one of the most critical challenges of congestion. Traditional electronic toll collection systems attempt to mitigate this issue through pre-defined static congestion pricing methods; however, they are inadequate in addressing the dynamic fluctuations in traffic demand. Dynamic congestion pricing has been identified as a promising approach, yet its implementation is hindered by the computational complexity involved in optimizing long-term objectives and the necessity for coordination across the traffic network. To address these challenges, we propose a novel dynamic traffic congestion pricing model utilizing multi-agent reinforcement learning with a transformer architecture. This architecture capitalizes on its encoder-decoder structure to transform the multi-agent reinforcement learning problem into a sequence modeling task. Drawing on insights from research on graph transformers, our model incorporates agent structures and positional encoding to enhance adaptability to traffic flow dynamics and network coordination. We have developed a microsimulation-based environment to implement a discrete toll-rate congestion pricing scheme on actual urban roads. Our extensive experimental results across diverse traffic demand scenarios demonstrate substantial improvements in congestion metrics and reductions in travel time, thereby effectively alleviating traffic congestion., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Lu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Despite antiretroviral therapy (ART), HIV persists in latently-infected cells (the HIV reservoir) which decay slowly over time. Here, leveraging >500 longitudinal samples from 67 people living with HIV (PLWH) treated during acute infection, we developed a mathematical model to predict reservoir decay from peripheral CD4 + T cells. Nonlinear generalized additive models demonstrated rapid biphasic decay of intact DNA (week 0-5: t 1/2  ~ 2.83 weeks; week 5-24: t 1/2  ~ 15.4 weeks) that extended out to 1 year. These estimates were ~5-fold faster than prior decay estimates among chronic treated PLWH. Defective DNA had a similar biphasic pattern, but data were more variable. Predicted intact and defective decay rates were faster for PLWH with earlier timing of ART initiation, higher initial CD4 + T cell count, and lower pre-ART viral load. In this study, we advanced our limited understanding of HIV reservoir decay at the time of ART initiation, informing future curative strategies targeting this critical time., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

This study has been carried out using a novel mathematical model on the dynamics of tuberculosis (TB) transmission considering vaccination, endogenous re-activation of the dormant infection, and exogenous re-infection. We can comprehend the behavior of TB under the influence of vaccination from this article. We compute the basic reproduction number ( R 0 ) as well as the vaccination reproduction number ( R v ) using the next-generation matrix (NGM) approach. The theoretical analysis demonstrates that the disease-free equilibrium point is locally asymptotically stable, and the fractional order system is Ulam-Hyers type stable. We perform numerical simulation of our model using the Adams-Bashforth 3-step method to verify the theoretical results and to show the model outputs graphically. By performing data fitting, we observe that our formulated model produces results that closely match real-world data. Our findings indicate that vaccinating a limited segment of the population can effectively eradicate the disease. The numerical simulations also show that vaccination can reduce the number of susceptible and infectious individuals in the population. Moreover, the graphical representations illustrate that the number of infected individuals rises due to both exogenous reinfection and endogenous reactivation., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

The construction of ecological livability plays a crucial role in achieving ecological civilization. During economic development, conflicts between urban populations, resources, and the environment have become increasingly prominent. Although China's efforts in building ecological livability have made certain achievements, they are still in the early stages, making research in this area very necessary. This study aims to establish an indicator system for the level of ecological livability construction from four aspects: ecology, society, residents' living conditions, and economy. Based on panel data from 2008 to 2021, the entropy-TOPSIS model was used to evaluate the level of ecological livability construction in Zhangjiajie City. Additionally, the coupling coordination degree model was employed to study the correlation and coordination among different indicators within the system. Furthermore, the obstacle degree model was introduced to explore the main factors affecting the improvement of ecological livability construction levels. The results show that, over the fourteen-year period, the proximity of Zhangjiajie City's ecological livability construction level decreased from 0.2712 in 2008 to 0.2547 in 2010, then rose to 0.7686 in 2021, reaching a medium coordination level, with the constraining effects among the ecological livability systems weakening. The average wage of employees, the proportion of social security and employment expenditure to fiscal expenditure, the per capita total postal and telecommunications business volume, the per capita number of public library collections, and the proportion of education expenditure to fiscal expenditure have become the main influencing factors of Zhangjiajie City's ecological livability construction level., Competing Interests: The authors have declared that no competing interests exist, (Copyright: © 2024 Deng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The persistence of measles in many countries demonstrates large immunity gaps, resulting from incomplete or ineffective immunization with measles-containing vaccines (MCVs). MCV impact is determined, in part, by vaccination age. Infants who receive dose 1 (MCV1) at older ages have a reduced risk of vaccine failure, but also an increased risk of contracting infection before vaccination. Here, we designed a new method-based on a mathematical transmission model incorporating realistic vaccination delays and age variations in MCV1 effectiveness-to capture the MCV1 age risk trade-off and estimate the optimal age for recommending MCV1. We applied this method to a range of synthetic populations representing lower- and higher-income populations. We predict a large heterogeneity in the optimal MCV1 ages (range: 6-20 months), contrasting the homogeneity of observed recommendations worldwide. Furthermore, we show that the optimal age depends on the local epidemiology of measles, with a lower optimal age predicted in populations having lower vaccination coverage or suffering higher transmission. Overall, our results suggest the scope for public health authorities to tailor the recommended schedule for better measles control., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Purpose: Blood donors are crucial in maintaining the blood supply chain. This study aims to improve the location and distribution of blood donation centers by focusing on two main objectives: minimizing costs and maximizing quality. Minimizing costs includes setting up and transporting blood efficiently while maximizing quality to ensure that blood products are delivered to hospitals promptly and in the right quantities., Methods: A multi-objective mathematical model is proposed to address the placement of both fixed and mobile blood donation centers. The first objective focuses on minimizing the costs of setting up centers and transporting blood. The second objective aims to maximize quality by ensuring timely deliveries and meeting hospitals' blood demand. The model utilizes real-world traffic and blood donation data from urban settings to simulate its effectiveness and applicability in practice. The model uses the ε constraint method to optimize both objectives simultaneously., Results: The model was tested in various scenarios, optimizing cost and quality separately. The algorithm determined the ideal locations for blood donation centers to meet demand by exploring different options. It also accounted for factors that reduce quality, such as delayed deliveries and product returns, and showed that these issues could be minimized., Conclusion: This study highlights the need to balance cost and quality when determining the locations of blood donation centers. Using the ε constraint method, the model successfully optimized both objectives, offering valuable insights for improving the efficiency and effectiveness of blood donation operations., Competing Interests: Declarations Ethics approval and consent to participate This research does not involve human subjects, human material, or human data. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Segregation refers to the separation of components in a powder mixture, resulting in potential issues related to concentration inhomogeneity. Any well-mixed blend that undergoes secondary processing is inherently susceptible to segregation which, if unmitigated, will lead to active compound concentration variance and poorer product quality. The consequences range from adverse financial impact to manufacturers with product failures to the detrimental health effects to product users. Hence, the topic of segregation is of paramount importance to the industry, requiring it to be dissected and scrutinized intensively by scientists worldwide. This review provides a well-crafted theoretical framework designed to understand the common segregation mechanisms that manufacturing facilities face, followed by the efforts to gauge the degree of segregation. To minimize segregation in blends, various approaches - mathematical modeling, empirical experiments, and empirical methods with modeling consideration - have been utilized in segregation research and are covered in this review. The past segregation studies from many fields are discussed, with particular emphasis on pharmaceuticals. The review also discusses the evolution and advances in mixing technology and storage systems implemented by the pharmaceutical industry to prevent segregation. In the conclusion, the authors articulated their perspectives on potential mitigation measures, including suggestions for improvements and future studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

With sea-level rise, flooding in coastal communities is now common during the highest high tides. Floods also occur at normal tidal levels when rainfall overcomes stormwater infrastructure that is partially submerged by tides. Data describing this type of compound flooding is scarce and, therefore, it is unclear how often these floods occur and the extent to which non-tidal factors contribute to flooding. We combine measurements of flooding on roads and within storm drains with a numerical model to examine processes that contribute to flooding in Carolina Beach, NC, USA - a community that chronically floods outside of extreme storms despite flood mitigation infrastructure to combat tidal flooding. Of the 43 non-storm floods we measured during a year-long study period, one-third were unexpected based on the tidal threshold used by the community for flood monitoring. We introduce a novel model coupling between an ocean-scale hydrodynamic model (ADCIRC) and a community-scale surface water and pipe flow model (3Di) to quantify contributions from multiple flood drivers. Accounting for the compounding effects of tides, wind, and rain increases flood water levels by up to 0.4 m compared to simulations that include only tides. Setup from sustained (non-storm) regional winds causes deeper, longer, more extensive flooding during the highest high tides and can cause floods on days when flooding would not have occurred due to tides alone. Rainfall also contributes to unexpected floods; because tides submerge stormwater outfalls on a daily basis, even minor rainstorms lead to flooding as runoff has nowhere to drain. As a particularly low-lying coastal community, Carolina Beach provides a glimpse into future challenges that coastal communities worldwide will face in predicting, preparing for, and adapting to increasingly frequent flooding from compounding tidal and non-tidal drivers atop sea-level rise., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Predictive real-time control (RTC) strategies are usually more effective than reactive strategies for the intelligent management of urban stormwater storage systems. However, it remains a challenge to ensure the practicality of RTC strategies that use accessible, non-idealized predictive information while improving their efficiency for successive rainfall events instead of specific phases. This study developed a predictive fuzzy logic and rule-based control (PFL-RBC) approach to address the continuous control of individual storage systems. This approach incorporates total rainfall depth forecast information with an intra-storm fuzzy logic system to optimize peak flow control and several rule-based strategies for pre-storm water detention, reuse, and release control. Computational experiments were conducted using a storage tank case study to test the proposed approach under various rainfall conditions and storage sizes. The results showed that PFL-RBC outperformed static rule-based control in infrequent design storms and realistic continuous rainfall events, reducing flood peaks and volumes by 55 %∼87 % and 7 %∼20 %, respectively, and significantly increasing water detention time and reuse volume. Meanwhile, PFL-RBC required less predictive information to achieve a 6 %∼15 % advantage in peak flow control compared to optimized model predictive control. More importantly, PFL-RBC was reliable in the face of input uncertainty, with <25 % performance loss for water quantity control when the realistic forecast error ranged from -50 % to +50 %. These findings suggest that the proposed approach has great potential to enhance the efficiency and practicality of stormwater storage operations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Groundwater salinization is a prevalent issue in coastal regions, yet accurately predicting and understanding its causal factors remains challenging due to the complexity of the groundwater system. Therefore, this study predicted groundwater salinity in multi-layered aquifers spanning the entire Mekong Delta (MD) region using machine learning (ML) models based on an in situ dataset and using three indicators (Cl - , pH, and HCO 3 - ). We applied nine different decision tree-based models and evaluated their prediction performances. The models were trained using 13 input variables: weather (2), hydrogeological conditions (4), water levels (3), groundwater usage (2), and relative distance from water sources (2). Subsequently, by employing model interpretation techniques, we quantified the significance of factors within the model prediction. Performance evaluations of the ML models demonstrated that the Extra Trees model exhibited superior performance and demonstrated generalization capabilities in predicting Cl - concentration, whereas the Bagging and Random Forest models outperformed the other models in predicting pH and HCO 3 - concentration. The coefficients of determination were determined to be 0.94, 0.67, and 0.78 for Cl - , pH, and HCO 3 - , respectively Additionally, the model interpretation effectively identified significant factors that depended on the target variables and aquifers. In particular, salinity indicators and aquifers that were strongly influenced by the artificial usage of groundwater were identified. Therefore, our research, which provides accurate spatial predictions and interpretations of groundwater salinity in the MD, has the potential to establish a foundation for formulating effective groundwater management policies to control groundwater salinization., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Storm water systems (SWSs) are essential infrastructure providing multiple services including environmental protection and flood prevention. Typically, utility companies rely on computer simulators to properly design, operate, and manage SWSs. However, multiple applications in SWSs are highly time-consuming. Researchers have resorted to cheaper-to-run models, i.e. metamodels, as alternatives of computationally expensive models. With the recent surge in artificial intelligence applications, machine learning has become a key approach for metamodelling urban water networks. Specifically, deep learning methods, such as feed-forward neural networks, have gained importance in this context. However, these methods require generating a sufficiently large database of examples and training their internal parameters. Both processes defeat the purpose of using a metamodel, i.e., saving time. To overcome this issue, this research focuses on the application of inductive biases and transfer learning for creating SWS metamodels which require less data and retain high performance when used elsewhere. In particular, this study proposes an auto-regressive graph neural network metamodel of the Storm Water Management Model (SWMM) from the Environmental Protection Agency (EPA) for estimating hydraulic heads. The results indicate that the proposed metamodel requires a smaller number of examples to reach high accuracy and speed-up, in comparison to fully connected neural networks. Furthermore, the metamodel shows transferability as it can be used to predict hydraulic heads with high accuracy on unseen parts of the network. This work presents a novel approach that benefits both urban drainage practitioners and water network modeling researchers. The proposed metamodel can help practitioners on the planning, operation, and maintenance of their systems by offering an efficient metamodel of SWMM for computationally intensive tasks like optimization and Monte Carlo analyses. Researchers can leverage the current metamodel's structure for developing new surrogate model architectures tailored to their specific needs or start paving the way for more general foundation metamodels of urban drainage systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Given the frequent association between freshwater plankton and water quality degradation, several predictive models have been devised to understand and estimate their dynamics. However, the significance of biotic and abiotic interactions has been overlooked. In this study, we aimed to address the importance of the interaction term in predicting plankton community dynamics by applying graph convolution embedded long short-term memory networks (GC-LSTM) models, which can incorporate interaction terms as graph signals. Temporal graph series comprising plankton genera or environmental drivers as node features and their relationships for edge features from two distinct water bodies, a reservoir and a river, were utilized to develop these models. To assess the predictability, the performances of the GC-LSTM models on community dynamics were compared those of LSTM and GCN models at various lead times. Moreover, GNNExplainer was used to examine the global and local importance of the nodes and edges for all predictions and specific predictions, respectively. The GC-LSTM models outperformed the LSTM models, consistently showing higher prediction accuracy. Although all the models exhibited performance degradation at longer lead times, the GC-LSTM models consistently demonstrated better performance regarding each graph signal and plankton genus. GNNExplainer yielded interpretable explanations for important genera and interaction pairs among communities, revealing consistent importance patterns across different lead times at both global and local scales. These findings underscore the potential of the proposed modeling approach for forecasting community dynamics and emphasize the critical role of graph signals with interaction terms in plankton communities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The understanding of mixing-controlled reactive dynamics in heterogeneous porous media remains limited, presenting significant challenges for modeling subsurface contaminant transport processes and for designing cost-effective environmental remedial efforts. The complexity of accurately observing, measuring, and modeling mixing-limited reactive transport has led to inadequate exploration of these critical processes. This study investigates the mixing and reaction kinetics affected by stagnant zones, which are commonly found in alluvial aquifers-aquitards and fracture-matrix systems. By conducting experiments involving conservative and bimolecular reactive transport through porous media within translucent chambers filled with two sizes of glass beads and under varying flow rates, we explored the effects of grain size and hydrodynamic conditions. Using a high-resolution camera, we monitored the concentration changes of conservative and reactive tracers, with subsequent interpretation through three-dimensional numerical simulations. The outcomes revealed the emergence of distinct mixing interfaces within both mobile and stagnant zones, culminating in a bi-peaked plume formation. Notably, the mixing and reaction times in media containing stagnant zones were found to be approximately 10 times longer than in homogeneous media. These findings, through experimental and modeling efforts, advance our understanding of mixing-limited reactive transport phenomena within heterogeneous media, underscoring the significant role of stagnant zones-a topic previously underexplored., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

In mining regions, flotation reagents can interact with heavy metals, thereby increasing the complexity of their migration. However, most current studies solely focus on the migration of heavy metals, neglecting the influence of flotation reagents in their models concerning mining area pollution. This study developed the reactive transport model, Multisurface Speciation Model (MSM), which integrated the reaction processes of the three main soil components (iron oxides, organic matter, clay minerals) and ethyl xanthate (EX), a typical flotation reagent, with cadmium (Cd²⁺) to investigate the effects of EX on the transport and retention of Cd²⁺ in natural porous media under varying pH conditions. The study revealed that EX formed new adsorption sites for Cd²⁺, enhancing its retention and inhibiting transport with increased EX loading (0 to 2.5 mmol·L -1 ), while higher pH levels (ranging from 4 to 8) further strengthened the retention capability of Cd²⁺. The MSM further predicted the solid-phase concentration distribution of Cd²⁺ among various components. With increasing EX-loaded concentrations, xanthate became the dominant adsorbing component, accounting for 48.93 % to 95.31 % of adsorption, and competitively interacted with other components. Xanthate retention was lower under acidic conditions compared to neutral and alkaline environments. Sensitivity analysis highlighted the concentrations of iron oxide adsorption sites (SurfaOH, SurfbOH) as critical parameters in the models, underscoring the need for precise determination of soil physicochemical indicators. This study stressed the crucial role of flotation reagents and pH conditions in controlling heavy metal mobility, offering important insights for environmental management in mining regions., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Bowen Luo Kouping Chen Jichun Wu Ping Li reports financial support was provided by National Key Research and Development Program of China. Bowen Luo Kouping Chen Jichun Wu reports financial support was provided by National Natural Science Foundation of China. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

While forward osmosis (FO) and reverse osmosis (RO) processes have been proven effective in rejecting organic pollutants, the rejection rate is highly dependent on compound and membrane characteristics, as well as operating conditions. This study aims to establish machine learning (ML) models for predicting the rejection of organic pollutants by FO and RO and providing insights into the underlying rejection mechanisms. Among the 14 ML models established, the random forest model (R 2 = 0.85) and extreme gradient boosting model (R 2 = 0.92) emerged as the best-performing models for FO and RO, respectively. Shapley additive explanations (SHAP) analysis identified the length of the compound, water flux, and hydrophobicity as the top three variables contributing to the FO model. For RO, in addition to the length of the compound and operating pressure, advanced variables including four molecular descriptors (e.g., ATSC2m and Balaban J) and three fingerprints (e.g., C=C double bond and carbonyl group) significantly contributed to the prediction. Besides, the associations between these highly ranked variables and their SHAP values shed light on the rejection mechanisms, such as size exclusion, adsorption, hydrophobic interaction, and electrostatic interaction, and illustrate the role of the operating parameters, such as the FO permeate water flux and RO operating pressure, in the rejection process. These findings provide interpretable predictive models for the removal of organic pollutants and advance the mechanistic understanding of the rejection mechanisms in the FO and RO processes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Many researchers have addressed the challenge of optimal pressure sensor placement for different purposes, such as leakage detection, model calibration, state estimation, etc. However, pressure data often need to serve multiple purposes, and a method to optimize sensor locations with versatility for various objectives is still lacking. In this paper, a graph-based optimal sensor placement (GOSP) framework is proposed, which aims to provide a robust and all-purpose approach to identify critical points for pressure monitoring. By analysing the spatial variation frequencies of WDN pressures, the relationship between measurements and the global variation of original pressures is established. On this basis, the D-optimality criterion is adopted to formulate the objective of GOSP, which aims to maximize the information on the spatial distribution of pressures that can be obtained from measurements. The new-proposed objective ensures that the sensor locations are compatible with various application scenarios. The proposed method was applied to a real-life distribution network, and was compared with other optimal sensor placement methods oriented towards burst detection and pipe roughness calibration. Based on comparative studies in different scenarios including unknown pressure estimation, burst detection, and model calibration, the effectiveness and robustness of the proposed method have been proved., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Zhou Xiao reports a relationship with Tsinghua University that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

As the size of water distribution network (WDN) models continues to grow, developing and applying real-time models or digital twins to simulate hydraulic behaviors in large-scale WDNs is becoming increasingly challenging. The long response time incurred when performing multiple hydraulic simulations in large-scale WDNs can no longer meet the current requirements for the efficient and real-time application of WDN models. To address this issue, there is a rising interest in accelerating hydraulic calculations in WDN models by integrating new model structures with abundant computational resources and mature parallel computing frameworks. This paper presents a novel and efficient framework for steady-state hydraulic calculations, comprising a joint topology-calculation decomposition method that decomposes the hydraulic calculation process and a high-performance decomposed gradient algorithm that integrates with parallel computation. Tests in four WDNs of different sizes with 8 to 85,118 nodes demonstrate that the framework maintains high calculation accuracy consistent with EPANET and can reduce calculation time by up to 51.93 % compared to EPANET in the largest WDN model. Further investigation found that factors affecting the acceleration include the decomposition level, consistency of sub-model sizes and sub-model structures. The framework aims to help develop rapid-responding models for large-scale WDNs and improve their efficiency in integrating multiple application algorithms, thereby supporting the water supply industry in achieving more adaptive and intelligent management of large-scale WDNs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Microplastics have attracted considerable attention as emerging contaminants that threaten water bodies. The removal of microplastics from a mini-hydrocyclone, enhanced by air flotation, was studied numerically. The three-phase flow was modeled using the Eulerian-Eulerian model coupled with interphase interactions. The characteristics of the flow field and distribution of microplastics and microbubbles were discussed, and the mechanism of cyclonic air flotation separation was analyzed. It was found that injecting microbubbles accelerated the axial migration of microplastics and moved the enriched area upward toward the overflow. The coalescence rate of the bubbles near the axis was higher than their breakage rate, which led to the formation of an air core. The length and diameter of the air core increased with the inlet gas holdup. When the air core size closely matched the overflow, the constrained flow channel prevented the discharge of microplastics. The optimal air holdup must be determined to ensure the efficiency of the cyclonic air flotation process. The sizes of the microbubbles used for cyclonic air flotation should be comparable to those of the separated microplastics. The upper cone angle significantly promoted the migration of microplastics to the axis. This study was conducted to purify microplastic-containing wastewater using an environmentally friendly and energy-efficient technique and to provide a theoretical basis and practical reference for applying microplastic separation technology in water., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Conflicts arising from human-wildlife interactions (HWIs) pose a significant challenge in communities neighboring Nyerere National Park. To achieve long-term conservation success, it is critical to understand and resolve complex social conflicts. Currently, most attention is focused on addressing dispute-related conflicts, whereas underlying, and identity-based conflicts are understudied, resulting in inadequate information in literature regarding underlying and identity-based conflicts that drive social conflicts. Through the use of the Conservation Conflict Transformation Model (CCT), this study aimed to identify existing conflicts across three levels of conflict and assess current intervention strategies employed within the study area. Based on data collected from 324 respondents through questionnaire surveys, the study revealed that the dispute level of conflicts was lower than the underlying and identity-based levels of conflicts, emphasizing the limited scope of addressing conflicts solely at the dispute level within the context of conflicts arising from HWIs. To alleviate conflicts at the dispute level, respondents employed both lethal and non-lethal control techniques, with a preference for non-lethal methods. Additionally, socio-demographic factors including age, gender, household size, respondent's attitude towards wildlife, and residence significantly influenced the implementation of intervention strategies (P < 0.05). Furthermore, the findings revealed that respondents faced several challenges, including a lengthy incident reporting process for conflicts arising from HWIs, lack of consolation payment for damages, exclusionary practices, and lack of transparency in seeking assistance from local, wildlife, and government authorities. Overall, the study recommends adopting and implementing a holistic approach aligned with the CCT model to effectively address conflicts under HWIs. Future research should focus on thorough case studies and actual applications of the CCT model to manage conflicts under HWIs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Optimizing nitrogen removal is crucial for ensuring the efficient operation of wastewater treatment plants (WWTPs), but it is susceptible to variations in influent conditions and operational parameter constraints, and conflicts with the energy-saving and carbon emission reduction goals. To address these issues, this study proposes a hybrid framework integrating process simulation, machine learning, and multi-objective genetic algorithms for nitrogen removal diagnosis and optimization, aiming to predict the total nitrogen in effluent, diagnose nitrogen over-limit risks, and optimize the control strategies. Taking a full-scale WWTP as a case study, a process time-lag simulation-enhanced machine learning model (PTLS-ML) was developed, achieving R 2 values of 0.94 and 0.79 for the training and testing sets, respectively. The proposed model successfully identified the potential reasons of nitrogen over-limit risks under different influent conditions and operational parameters, and accordingly provided optimization suggestions. In addition, the multi-objective optimization (MOO) algorithms analysis further demonstrated that maintaining 4-6 mg/L total nitrogen concentration in effluent by adjusting process operational parameters can effectively balance multiple objectives (i.e., effluent water quality, operating costs, and greenhouse gas emissions), achieving coordinated optimization. This framework can serve as a reference for stable operation, energy-saving, and emission reduction in the nitrogen removal of WWTPs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Accurately predicting the concentration of organochlorine pesticides (OCPs) presents a challenge due to their complex sources and environmental behaviors. In this study, we introduced a novel and advanced model that combined the power of three distinct techniques: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), and a deep learning network of Long Short-Term Memory (LSTM). The objective is to characterize the variation in OCPs concentrations with high precision. Results show that the hybrid two-stage decomposition coupled models achieved an average symmetric mean absolute percentage error (SMAPE) of 23.24 % in the empirical analysis of typical surface water. It exhibited higher predictive power than the given individual benchmark models, which yielded an average SMAPE of 40.88 %, and single decomposition coupled models with an average SMAPE of 29.80 %. The proposed CEEMDAN-VMD-LSTM model, with an average SMAPE of 13.55 %, consistently outperformed the other models, yielding an average SMAPE of 33.53 %. A comparative analysis with shallow neural network methods demonstrated the advantages of the LSTM algorithm when coupled with secondary decomposition techniques for processing time series datasets. Furthermore, the interpretable analysis derived by the SHAP approach revealed that precipitation followed by the total phosphorus had strong effects on the predicted concentration of OCPs in the given water. The data presented herein shows the effectiveness of decomposition technique-based deep learning algorithms in capturing the dynamic characteristics of pollutants in surface water., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)