An evaluation model for city-scale construction and demolition waste management effectiveness: A case study in China.

The growing generation of construction and demolition waste (CDW) has emerged as a prominent challenge on global environmental agendas. However, the effectiveness of CDW management (CDWM) strategies varies among cities. Existing literature predominantly evaluates the effectiveness of CDWM at the project level, offering a localized perspective that fails to capture a city's comprehensive CDWM profile. This localized focus has certain limitations. To fill this gap in city-scale evaluations, this study introduces a novel model for assessing CDWM effectiveness at the municipal level. An empirical investigation was conducted across 11 cities within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) to operationalize this model. The model defines five distinct levels of CDWM effectiveness. Findings indicate that Hong Kong consistently achieves the highest level (level I), while the majority of cities fall within levels III and IV. This pattern suggests that CDWM effectiveness in the GBA is moderately developed, with uneven progress in CDW management outcomes and supporting systems. Essentially, there is a lack of synchronous development of CDWM results and guarantee systems. The proposed evaluation model enriches existing CDWM research field and offers a framework that may inform future studies in other countries.

Generation mechanism and empirical model of eddy current force and torque in drum-type eddy current separation.

Enhancing the recovery efficiency of non-ferrous metals in eddy current separation is of great significance. In this study, the accuracy of the simulation model was verified by comparing the eddy current force. The transformation mechanism of the Lorentz forces into the eddy current force and torque in non-ferrous metal particles was revealed by analyzing various physical fields. Then, the influence of magnetic field parameters on eddy current, eddy current force, and torque was studied. It shows that the eddy current force and torque are affected by the vector gradient of the magnetic field and the magnetic flux density, respectively. Additionally, the time derivative of the magnetic field impacts the magnitude of the eddy current force and torque by controlling the eddy current. On this basis, the empirical models of eddy current force and torque were established by similarity theory. The results obtained can improve and expand the application of eddy current separation.

The impact of mobility costs on cooperation and welfare in spatial social dilemmas.

From over-exploitation of resources to urban pollution, sustaining well-being requires solving social dilemmas of cooperation. Often such dilemmas are studied assuming that individuals occupy fixed positions in a network or lattice. In spatial settings, however, agents can move, and such movements involve costs. Here we investigate how mobility costs impact cooperation dynamics. To this end, we study cooperation dilemmas where individuals are located in a two-dimensional space and can be of two types: cooperators-or cleaners, who pay an individual cost to have a positive impact on their neighbours-and defectors-or polluters, free-riding on others' effort to sustain a clean environment. Importantly, agents can pay a cost to move to a cleaner site. Both analytically and through agent-based simulations we find that, in general, introducing mobility costs increases pollution felt in the limit of fast movement (equivalently slow strategy revision). The effect on cooperation of increasing mobility costs is non-monotonic when mobility co-occurs with strategy revision. In such scenarios, low (yet non-zero) mobility costs minimise cooperation in low density environments; whereas high costs can promote cooperation even when a minority of agents initially defect. Finally, we find that heterogeneity in mobility cost affects the final distribution of strategies, leading to differences in who supports the burden of having a clean environment.

Influence of different position modal parameters on milling chatter stability of orthopedic surgery robots.

This research is dedicated to exploring the dynamics of milling chatter stability in orthopedic surgery robots, focusing on the impact of position modal parameters on chatter stability. Initially, we develop a dynamic milling force model for the robotic milling process that integrates both modal coupling and regenerative effects. We then employ the zero-order frequency domain method to derive a chatter stability domain model, visually represented through stability lobe diagrams (SLDs). Through conducting hammer test experiments, we ascertain the robot's modal parameters at varying positions, enabling the precise generation of SLDs. This study also includes experimental validation of the chatter SLD analysis method, laying the groundwork for further examination of chatter stability across different positional modal parameters. Finally, our analysis of the variations in modal parameters on the stability of robot milling chatter yields a theoretical framework for optimizing cutting parameters and developing control strategies within the context of orthopedic surgery robots.

How do host population dynamics impact Lyme disease risk dynamics in theoretical models?

Lyme disease is the most common wildlife-to-human transmitted disease reported in North America. The study of this disease requires an understanding of the ecology of the complex communities of ticks and host species involved in harboring and transmitting this disease. Much of the ecology of this system is well understood, such as the life cycle of ticks, and how hosts are able to support tick populations and serve as disease reservoirs, but there is much to be explored about how the population dynamics of different host species and communities impact disease risk to humans. In this study, we construct a stage-structured, empirically-informed model with host dynamics to investigate how host population dynamics can affect disease risk to humans. The model describes a tick population and a simplified community of three host species, where primary nymph host populations are made to fluctuate on an annual basis, as commonly observed in host populations. We tested the model under different environmental conditions to examine the effect of environment on the interactions of host dynamics and disease risk. Results show that allowing for host dynamics in the model reduces mean nymphal infection prevalence and increases the maximum annual prevalence of nymphal infection and the density of infected nymphs. Effects of host dynamics on disease measures of nymphal infection prevalence were nonlinear and patterns in the effect of dynamics on amplitude in nymphal infection prevalence varied across environmental conditions. These results highlight the importance of further study of the effect of community dynamics on disease risk. This will involve the construction of further theoretical models and collection of robust field data to inform these models. With a more complete understanding of disease dynamics we can begin to better determine how to predict and manage disease risk using these models.

On the sensitivity of centrality metrics.

Despite the huge importance that the centrality metrics have in understanding the topology of a network, too little is known about the effects that small alterations in the topology of the input graph induce in the norm of the vector that stores the node centralities. If so, then it could be possible to avoid re-calculating the vector of centrality metrics if some minimal changes occur in the network topology, which would allow for significant computational savings. Hence, after formalising the notion of centrality, three of the most basic metrics were herein considered (i.e., Degree, Eigenvector, and Katz centrality). To perform the simulations, two probabilistic failure models were used to describe alterations in network topology: Uniform (i.e., all nodes can be independently deleted from the network with a fixed probability) and Best Connected (i.e., the probability a node is removed depends on its degree). Our analysis suggests that, in the case of degree, small variations in the topology of the input graph determine small variations in Degree centrality, independently of the topological features of the input graph; conversely, both Eigenvector and Katz centralities can be extremely sensitive to changes in the topology of the input graph. In other words, if the input graph has some specific features, even small changes in the topology of the input graph can have catastrophic effects on the Eigenvector or Katz centrality.

Reducing childhood mortality extends mothers' lives.

During the twentieth century, childhood mortality was dramatically reduced globally, falling by more than 90% in the United States and much of Europe. Total fertility also fell, with the combined result that many parents who otherwise would have experienced the loss of a child were spared the trauma and negative health consequences that accompany such a loss. Here I use mathematical modeling to argue that the reduction in the frequency of child death that occurred in the twentieth century indirectly led to a substantial reduction in female mortality, resulting in an extension of female lifespan. I estimate that the reduction in maternal bereavement in the US during the twentieth century indirectly increased mean female lifespan after age 15 by approximately 1 year. I discuss implications for our understanding of the persistence of the sex gap in longevity and approaches to improving maternal health outcomes in countries that still face high levels of childhood mortality.

Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend.

BACKGROUND: Several models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves. METHODS: We used data from the Ministry of Health, Labour and Welfare of Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data, and epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques were used to depict the regression lines for each epidemic wave, denoting the "rising trend line"; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave, and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave. RESULTS: Up to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. Although there was a slight lag in the starting and peak times in our predicted seventh wave compared with the actual epidemic, our developed prediction model had a fairly high degree of accuracy. CONCLUSION: Our newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 in which the epidemic wave has high periodicity.

Experimental research on the impact pressure of tidal bore fronts.

Tidal bore impact can be strong and destructive, placing estuarine infrastructures under great threat. However, there is a lack of research focusing on accurately estimating the impact pressure exerted by tidal bores. Herein new experiments were conducted to investigate the pressure of tidal bore fronts in a glass flume. Through analysis of instantaneous pressure of three forms of tidal bore, it was observed that the pressure fluctuation of weak and strong breaking bore fronts is characterized by impact pressure. The vertical distribution and maximum impact pressure of tidal bore were studied.The maximum impact pressure of breaking bore fronts appeared around 0.46 times height of it. The relationship between relative impact pressure and height of the tidal bore fronts was found to closely follow a normal probability density function. Through nonlinear regression analysis, an empirical equation was derived to calculate impact pressure, which was validated using observation data from the Qiantang River in China. This equation can be utilized to predict the impact pressure of tidal bore fronts and provide valuable support for estuarine engineering design.

Illustrating the importance of edge constraints in backbones of bipartite projections.

Bipartite projections (e.g., event co-attendance) are often used to measure unipartite networks of interest (e.g., social interaction). Backbone extraction models can be useful for reducing the noise inherent in bipartite projections. However, these models typically assume that the bipartite edges (e.g., who attended which event) are unconstrained, which may not be true in practice (e.g., a person cannot attend an event held prior to their birth). We illustrate the importance of correctly modeling such edge constraints when extracting backbones, using both synthetic data that varies the number and type of constraints, and empirical data on children's play groups. We find that failing to impose relevant constraints when the data contain constrained edges can result in the extraction of an inaccurate backbone. Therefore, we recommend that when bipartite data contain constrained edges, backbones be extracted using a model such as the Stochastic Degree Sequence Model with Edge Constraints (SDSM-EC).

A study on the influence of perceived distance on China's inbound Tourism and the interaction of non-economic distance: An analysis of dynamic extended gravity model based on 61 countries' entry data (2004-2018).

In the post-epidemic era, the restart of China's inbound tourism is imminent. However, there are gaps in our current understanding of how distance perception dynamically affects inbound tourism in China. In order to understand the past patterns of inbound tourism in China, we mapped the data of 61 countries of origin from 2004 to 2018 into a dynamic expanding gravity model to understand the effects of cultural distance, institutional distance, geographical distance, and economic distance on inbound tourism in China and revealed the dynamic interaction mechanism of non-economic distance perception on inbound tourism in China. Our research results show that cultural distance has a positive impact on China's inbound tourism, while institutional distance has a negative impact. The significant finding is that the dynamic interaction of the above two kinds of perceived distance can still have a positive impact on China's inbound tourism. Its practical significance is that it can counteract the influence of institutional distance by strengthening the cultural distance. Generally speaking, geographical distance and institutional distance restrict China's inbound tourism flow, while cultural distance, economic distance, and interactive perceptual distance promote China's inbound tourism flow.

Harmonics management and hosting capacity enhancement: Optimal double-resistor damped double-tuned power filter with artificial hummingbird optimization.

This paper introduces a novel and improved double-resistor damped double-tuned passive power filter (DR-DDTF), designed using multi-objective optimization algorithms to mitigate harmonics and increase the hosting capacity of distribution systems with distributed energy resources. Although four different topologies of single-resistor damped double-tuned filters (DDTFs) have been studied before in the literature, the effectiveness of two different DR-DDTF configurations has not been examined. This work redresses this gap by demonstrating that via comprehensive simulations on two power systems, DR-DDTF provides better harmonic suppression and resonance mitigation than single-resistor alternatives. When it comes to optimizing the DR-DDTF for maximum hosting capacity and minimum system active power losses, the multi-objective artificial hummingbird outperformed six other algorithms in the benchmark. To allow for higher penetration of distributed generation without requiring grid upgrades, this newly developed harmonic mitigation filter provides a good alternative.

Modeling distracted driving behavior considering cognitive processes.

The modeling of distracted driving behavior has been studied for many years, however, there remain many distraction phenomena that can not be fully modeled. This study proposes a new method that establishes the model using the queuing network model human processor (QN-MHP) framework. Unlike previous models that only consider distracted-driving-related human factors from a mathematical perspective, the proposed method reflects the information processing in the human brain, and simulates the distracted driver's cognitive processes based on a model structure supported by physiological and cognitive research evidence. Firstly, a cumulative activation effect model for external stimuli is adopted to mimic the phenomenon that a driver responds only to stimuli above a certain threshold. Then, dual-task queuing and switching mechanisms are modeled to reflect the cognitive resource allocation under distraction. Finally, the driver's action is modeled by the Intelligent Driver Model (IDM). The model is developed for visual distraction auditory distraction separately. 773 distracted car-following events from the Shanghai Naturalistic Driving Study data were used to calibrate and verify the model. Results show that the model parameters are more uniform and reasonable. Meanwhile, the model accuracy has improved by 57% and 66% compared to the two baseline models respectively. Moreover, the model demonstrates its ability to generate critical pre-crash scenarios and estimate the crash rate of distracted driving. The proposed model is expected to contribute to safety research regarding new vehicle technologies and traffic safety analysis.

Programa de acción para superar las brechas en salud mental: un modelo teórico de las barreras de implementación desde el personal de salud en Chocó, Colombia / Mental Health Gap Action Programme: a theoretical model of barriers to implementation by health personnel in Chocó, Colombia / Programa de Ação para Reduzir as Lacunas em Saúde Mental: modelo teórico das barreiras de implementação por profissionais de saúde de Chocó, Colômbia

[RESUMEN]. Objetivo. Los trastornos mentales, neurológicos y por consumo de sustancias psicoactivas (MNS) tienen una prevalencia alta y existe una brecha para su atención en Colombia. El Programa de acción para superar las brechas en salud mental (mhGAP, por su sigla en inglés) de la Organización Mundial de la Salud (OMS) tiene varios componentes. Uno de ellos es la Guía de intervención mhGAP 2.0, orientada a la mejora de la atención primaria en salud; se trata de una guía para la evaluación y el manejo de trastornos MNS a partir de protocolos de toma de decisiones clínicas. Se planteó como objetivo determinar las barreras que se pueden presentar en el proceso de implementación del programa. Métodos. Se realizó un estudio cualitativo con enfoque de análisis de contenido que se desarrolló en tres fases: i) preparación, organización y presentación del estudio; ii) codificación abierta, categorización y abs- tracción de los contenidos; y iii) análisis de información. El estudio incluyó la entrevista semiestructurada a 21 personas involucradas en la prestación de servicios en salud mental en Chocó (Colombia): cinco profesionales de medicina, siete de enfermería y tres de psicología, además seis profesionales que trabajaban en el área administrativa de las secretarías de salud del departamento. Se exploraron las percepciones de las partes interesadas, que eran el personal asistencial y el personal administrativo del sector salud en el departamento del Chocó. Se realizaron preguntas abiertas para explorar las experiencias con el proceso y las barreras identificadas en la práctica. Resultados. Se identificaron cuatro categorías temáticas diferentes: intersectorialidad, viejos desafíos, posibilidades e idoneidad de herramientas. Conclusiones. Se construyó un modelo teórico sobre las barreras de implementación para el Programa de mhGAP desde las percepciones de las partes interesadas. El control de las barreras se percibe como un camino posible para generar un aporte significativo para la salud poblacional.

[ABSTRACT]. Objectives. Mental, neurological, and substance use (MNS) disorders have a high prevalence in Colombia and there is a treatment gap. The World Health Organization (WHO) Mental Health Gap Action Programme (mhGAP) has various components. The mhGAP 2.0 Intervention Guide, aimed at improving primary health care, is a guide for the assessment and management of MNS disorders based on clinical decision-making protocols. The objective of this study was to determine the barriers that may hinder the program implementation process. Methods. A qualitative study with content analysis was conducted in three phases: i) study preparation, organization, and presentation; ii) open coding, categorization, and abstraction of contents; and iii) information analysis. The study included semi-structured interviews with 21 people involved in the provision of mental health services in Chocó (Colombia): five medical doctors, seven nurses, and three psychologists, as well as six professionals working in the administrative area of the department's health secretariats. The perceptions of these stakeholders were explored. Open-ended questions were asked to explore experiences with the process, as well as the barriers identified in practice. Results. Four different thematic categories were identified: intersectoral action, long-standing challenges, opportunities, and suitability of tools. Conclusions. A theoretical model of barriers to implementation of the mhGAP program was constructed, based on stakeholder perceptions. Controlling barriers is perceived as a possible way to contribute significantly to population health.

[RESUMO]. Objetivo. Os transtornos mentais, neurológicos e por uso de substâncias psicoativas são muito prevalentes, e há uma lacuna na atenção a esses transtornos na Colômbia. O Programa de Ação para Reduzir as Lacunas em Saúde Mental (mhGAP, na sigla em inglês) da Organização Mundial da Saúde (OMS) consta de vários componentes. Um deles é o Manual de Intervenções mhGAP 2.0, elaborado para aprimorar a atenção primária à saúde. O documento contém orientações para avaliação e manejo de transtornos mentais, neurológicos e por uso de substâncias psicoativas e foi elaborado com base em protocolos para a tomada de decisões clínicas. O objetivo do estudo foi determinar as barreiras que podem surgir no processo de imple- mentação do programa. Métodos. Estudo qualitativo com enfoque de análise de conteúdo desenvolvido em três fases: i) preparação, organização e apresentação do estudo; ii) codificação aberta, categorização e abstração do conteúdo; e iii) análise das informações. O estudo incluiu entrevistas semiestruturadas com 21 pessoas envolvidas na prestação de serviços de saúde mental em Chocó (Colômbia): cinco profissionais da medicina, sete de enfermagem e três de psicologia, bem como seis profissionais que trabalham na área administrativa das secretarias de saúde do departamento. As percepções das partes interessadas, ou seja, do pessoal assisten- cial e administrativo do setor de saúde no departamento de Chocó, foram exploradas. Foram feitas perguntas abertas a fim de investigar suas experiências com o processo de atenção e as barreiras identificadas na prática. Resultados. Foram identificadas quatro categorias temáticas: intersetorialidade, desafios de longa data, possibilidades e adequação das ferramentas. Conclusões. Elaborou-se um modelo teórico sobre as barreiras de implementação do Programa mhGAP com base nas percepções das partes interessadas. O controle das barreiras é visto como uma possível forma de contribuir significativamente para a saúde da população.

Committing to the wrong artificial delegate in a collective-risk dilemma is better than directly committing mistakes.

While autonomous artificial agents are assumed to perfectly execute the strategies they are programmed with, humans who design them may make mistakes. These mistakes may lead to a misalignment between the humans' intended goals and their agents' observed behavior, a problem of value alignment. Such an alignment problem may have particularly strong consequences when these autonomous systems are used in social contexts that involve some form of collective risk. By means of an evolutionary game theoretical model, we investigate whether errors in the configuration of artificial agents change the outcome of a collective-risk dilemma, in comparison to a scenario with no delegation. Delegation is here distinguished from no-delegation simply by the moment at which a mistake occurs: either when programming/choosing the agent (in case of delegation) or when executing the actions at each round of the game (in case of no-delegation). We find that, while errors decrease success rate, it is better to delegate and commit to a somewhat flawed strategy, perfectly executed by an autonomous agent, than to commit execution errors directly. Our model also shows that in the long-term, delegation strategies should be favored over no-delegation, if given the choice.

Global evidence for joint effects of multiple natural and anthropogenic drivers on soil nitrogen cycling.

Global soil nitrogen (N) cycling remains poorly understood due to its complex driving mechanisms. Here, we present a comprehensive analysis of global soil δ15N, a stable isotopic signature indicative of the N input-output balance, using a machine-learning approach on 10,676 observations from 2670 sites. Our findings reveal prevalent joint effects of climatic conditions, plant N-use strategies, soil properties, and other natural and anthropogenic forcings on global soil δ15N. The joint effects of multiple drivers govern the latitudinal distribution of soil δ15N, with more rapid N cycling at lower latitudes than at higher latitudes. In contrast to previous climate-focused models, our data-driven model more accurately simulates spatial changes in global soil δ15N, highlighting the need to consider the joint effects of multiple drivers to estimate the Earth's N budget. These insights contribute to the reconciliation of discordances among empirical, theoretical, and modeling studies on soil N cycling, as well as sustainable N management.

Unveiling the landscape predictors of resilient vegetation in coastal wetlands to inform conservation in the face of climate extremes.

Unveiling spatial variation in vegetation resilience to climate extremes can inform effective conservation planning under climate change. Although many conservation efforts are implemented on landscape scales, they often remain blind to landscape variation in vegetation resilience. We explored the distribution of drought-resilient vegetation (i.e., vegetation that could withstand and quickly recover from drought) and its predictors across a heterogeneous coastal landscape under long-term wetland conversion, through a series of high-resolution satellite image interpretations, spatial analyses, and nonlinear modelling. We found that vegetation varied greatly in drought resilience across the coastal wetland landscape and that drought-resilient vegetation could be predicted with distances to coastline and tidal channel. Specifically, drought-resilient vegetation exhibited a nearly bimodal distribution and had a seaward optimum at ~2 km from coastline (corresponding to an inundation frequency of ~30%), a pattern particularly pronounced in areas further away from tidal channels. Furthermore, we found that areas with drought-resilient vegetation were more likely to be eliminated by wetland conversion. Even in protected areas where wetland conversion was slowed, drought-resilient vegetation was increasingly lost to wetland conversion at its landward optimum in combination with rapid plant invasions at its seaward optimum. Our study highlights that the distribution of drought-resilient vegetation can be predicted using landscape features but without incorporating this predictive understanding, conservation efforts may risk failing in the face of climate extremes.

Birth weight and asthma in young adults of a Brazilian birth cohort.

This article aims to evaluate the association between birth weight and asthma in adulthood, estimated by employing structural equation modeling. Cohort study with 1,958 participants aged 23-25 years from Ribeirão Preto, São Paulo, Brazil. Standardized questionnaires were applied and pulmonary function evaluated, including bronchial reactivity with methacholine. A theoretical model was proposed to explore the effects of birth weight and asthma in adulthood. Asthma, socioeconomic status at birth (Birth SES), and current socioeconomic status (Adult SES) were obtained by constructs. Maternal age, sex, skin color, body mass index (BMI), smoking, parental asthma history, history of respiratory infection before five years old, history of hospitalization for lung disease before two years old, and atopy were the studied variables. 14.1% of participants were diagnosed with asthma. Birth weight was associated with asthma (Standardized Coefficient - SCtotal=-0.110; p=0.030), and an indirect effect was also observed (SCindirect=-0.220; p=0.037), mediated by hospitalization before two years and respiratory infection before five years. Lower birth weight showed an increased risk of asthma in adulthood and the SES Birth and Adult SES variables underlie this association.

Response Surface Model of the Reduced Flexibility Matrix for Bayesian Damage Identification.

The present work is concerned with the use of a Response Surface Model of the reduced flexibility matrix for structural damage identification. A Response Surface Model (RSM) is fitted with the aim at providing a polynomial relationship between nodal cohesion parameters, used to describe the damage field within the structure, and elements of the reduced flexibility matrix. A design of experiment built on combinations of a relatively small number of nodal cohesion parameters is used to fit the RSM. The damage identification problem is formulated within the Bayesian framework and the Delayed Rejection Adaptive Metropolis method is used to sample the posterior probability density function of the uncertain cohesion parameters. Numerical simulations addressing damage identification in plates were carried out in order to assess the proposed approach, which succeeded in the identification of the different damage profiles considered. Besides, the use of a RSM, instead of a FEM of the structure, resulted in reductions of up almost 78% in the required computational cost.

Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods.

Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Compared to mean daily flows (MDFs), which represent averaged flows over a period of 24 h, information on IPFs is often missing or unavailable in instrumental records. In this study, conventional methods for estimating IPFs from MDFs are evaluated and new methods based on the nonlinear regression framework and machine learning architectures are proposed and evaluated using streamflow records from all Canadian hydrometric stations with natural and regulated flow regimes. Based on a robust model selection criterion, it was found that multiple methods are suitable for estimating IPFs from MDFs, which precludes the idea of a single universal method. The performance of machine learning-based methods was also found reasonable compared to conventional and regression-based methods. To build on the strengths of individual methods, the fusion modeling concept from the machine learning area was invoked to synthesize outputs of multiple methods. The study findings are expected to be useful to the climate change adaptation community, which currently heavily relies on MDFs simulated by hydrologic models.