Probabilistic Modeling Approach Research Articles

Development and validation of a biomechanically fidelic surgical training knee model.

Knee arthroplasty technique is constantly evolving and the opportunity for surgeons to practice new techniques is currently highly dependent on the availability of cadaveric specimens requiring certified facilities. The high cost, limited supply, and heterogeneity of cadaveric specimens has increased the demand for synthetic training models, which are currently limited by a lack of biomechanical fidelity. Here, we aimed to design, manufacture, and experimentally validate a synthetic knee surgical training model which reproduces the flexion dependent varus-valgus (VV) and anterior-posterior (AP) mechanics of cadaveric knees, while maintaining anatomic accuracy. A probabilistic finite element modeling approach was employed to design physical models to exhibit passive cadaveric VV and AP mechanics. Seven synthetic models were manufactured and tested in a six-degree-of-freedom hexapod robot. Overall, the synthetic models exhibited cadaver-like VV and AP mechanics across a wide range of flexion angles with little variation between models. In the extended position, two models showed increased valgus rotation (<0.5°), and three models showed increased posterior tibial translation (<1.7 mm) when compared to the 95% confidence interval (CI) of cadaveric measurements. At full flexion, all models showed VV and AP mechanics within the 95% CI of cadaveric measurements. Given the repeatable mechanics exhibited, the knee models developed in this study can be used to reduce the current reliance on cadaveric specimens in surgical training.

Enhancing medical image classification with generative AI using latent denoising diffusion probabilistic model and wiener filtering approach

In the evolving landscape of medical diagnostics, a paradigm shift is being catalysed by the advent of generative artificial intelligence. Medical X-ray, CT and MRI images are essential diagnostic tools used by healthcare professionals to assess various musculoskeletal conditions. However, obtaining a sufficient number of medical images for training deep learning models can be challenging due to limited access to labelled data. Hence, the authors propose a novel Latent diffusion process for synthesizing medical images that closely resemble real patient images, aiming to address the challenge of limited access to labelled data in medical diagnostics. Leveraging deep learning and generative modelling techniques, this method synthesizes high-fidelity images that closely mimic real patient scans. By introducing noise and subsequently training the model to denoise, the approach captures intricate patterns inherent in authentic medical images. Among the four datasets utilized, the Diabetes Retinopathy dataset demonstrates superior performance, achieving the highest Mean Structural Similarity Index (MSSIM) score of 0.57 (compared to the dataset baseline of 0.62) and an accuracy of 93.75% when passed through the proposed pipeline. The Cataract dataset, registered a MSSIM score of 0.51 (versus the dataset baseline of 0.53) and an accuracy score of 97.52%, while the Knee OA dataset follows closely with MSSIM and accuracy scores of 0.65 (in contrast to the dataset baseline of 0.63) and 68.66% respectively. The results obtained are then compared with the results generated by the other state of the art models.

A market driven identification of current and emerging skills requiring an MBA degree: a topic modelling approach

ABSTRACT The emerging field of Labour Market Intelligence focuses on extracting real-time information for professional skills. Online job posting websites provide unique opportunities to gather such intelligence, useful for job seekers and academic administrators in charge of preparing the future labour force. We propose a probabilistic topic modelling approach, incorporating occupational ontologies, to extract skillsets required for jobs needing a Master of Business Administration (MBA) degree. Analysing LinkedIn job postings in Pennsylvania, USA, we identified 28 job functions and associated skillsets confirming known demands and revealing new ones not yet identified in government and private labour market reports. By aggregating these skills, we can align MBA curricula with market needs, offering a superior alternative to conventional surveys. Our results are comparable with other popular neural models. Our approach provides improved computational efficiency with simpler methods Additionally, this methodology can be applied globally in other fields with existing occupational ontologies.

Assessment on fast simulation of wind-driven pollutant dispersion around a street canyon with regime-switching Markov chain

High-density cities shorten commutes but also create proximity to air pollution sources. Sensors coupled with simulations can achieve real-time monitoring and risk assessment, and the recently emerging data-driven models provide opportunities that allow minimizing the computation burden of physical models. This study investigates the use of a time inhomogeneous Markov chain (CLF-MC) to fast simulation of wind-driven pollutant dispersion around a street canyon. The CLF-MC model is trained and tested using synthetic datasets obtained from large eddy simulations (LES). The LES model is validated using a benchmark wind tunnel test, which yields correlation coefficients above 0.95. The results show that the CLF-MC can faithfully reproduce the cumulative distribution of air pollution concentrations at the receptor site using the optimal model configuration identified in this study. For the hold-out test dataset, the release condition is a time-varying release strength, and the CLF-MC shows improved prediction results compared to the baseline models, i.e. the weighted average deviation is 15 % and 51 % smaller, respectively. In comparison to LES, the CLF-MC model is 4.8 × 106 times faster. These findings provide scientific references for fast and accurate wind-driven pollutant dispersion simulation using the probabilistic modelling approach.

A novel probabilistic modeling for multilateral random attacks in cyber‐physical system reliability analysis

AbstractCyber‐Physical Systems (CPS) are at the forefront of the intersection between information technology and physical operations, revolutionizing industries such as communication, automation, control, and intelligent transportation. However, evaluating the reliability of CPS, especially in the presence of multilateral random attacks, poses a complex challenge. This paper introduces an inventive probabilistic modeling approach to enhance the analysis of CPS reliability under the influence of multilateral random attacks. We provide an overview of fundamental CPS concepts and their significant role in modern society, followed by a comprehensive exploration of multilateral random attacks. Our novel probabilistic modeling approach is presented as a means to improve the accuracy and robustness of CPS reliability assessments when faced with multilateral random attacks. Numerical experiments and simulations validate the effectiveness of our innovative approach, emphasizing its potential to fortify the reliability of Cyber‐Physical Systems.

N − 1 Security Criteria Based Integrated Deterministic and Probabilistic Framework for Composite Power System Reliability

Unpredictable variations in load demand and unanticipated component failures are progressively impacting the operation of modern power systems, making system evaluation more stochastic in nature. Although deterministic approaches were formerly the norm for determining system status, probabilistic approaches have greatly improved the capacity to capture the stochastic behavior characteristic of power system operations. The presented work in the paper recommends the use of probabilistic modelling approaches with deterministic approaches, highlighting their crucial function in augmenting the reliability and security of contemporary power systems to unanticipated failures. In this paper, N − 1 security criteria based reliability of the composite power system (CPS) is proposed using an integrated deterministic and probabilistic framework (D-P) considering outage of the transmission line. For the deterministic approach (DA), line overloading on available lines is determined using the static security index (SSI). For the probabilistic approach (PA), reliability indices such as expected loss of power (ELOP), expected frequency of contingency (EFOC), expected loss of load (ELOL), probability of load curtailment (PLC), and expected duration of load curtailments (EDLC) are calculated. Further, for each contingency, a performance index is determined using both approaches to assess the severity of the contingency that occurred on the power system. Based on the N − 1 security criteria based reliability analysis using an integrated D-P framework, a credible critical set of transmission lines is obtained, which can serve as important information to system operators. The proposed techniques have been tested on IEEE 24 bus reliability test system (RTS).

Multimorbidity impacts cardiovascular disease risk following percutaneous coronary intervention: latent class analysis of the Melbourne Interventional Group (MIG) registry

BackgroundMultimorbidity is strongly associated with disability or functional decline, poor quality of life and high consumption of health care services. This study aimed (1) To identify patterns of multimorbidity among patients undergoing first recorded percutaneous coronary intervention (PCI); (2) To explore the association between the identified patterns of multimorbidity on length of hospital stay, 30-day and 12- month risk of major adverse cardiac and cerebrovascular events (MACCE) after PCI.MethodsA retrospective cohort study of the Melbourne Interventional Group (MIG) registry. This study included 14,025 participants who underwent their first PCI from 2005 to 2015 in Victoria, Australia. Based on a probabilistic modelling approach, Latent class analysis was adopted to classify clusters of people who shared similar combinations and magnitude of the comorbidity of interest. Logistic regression models were used to estimate odd ratios and 95% confidence interval (CI) for the 30-day and 12-month MACCE.ResultsMore than two-thirds of patients had multimorbidity, with the most prevalent conditions being hypertension (59%) and dyslipidaemia (60%). Four distinctive multimorbidity clusters were identified each with significant associations for higher risk of 30-day and 12-month MACCE. The cluster B had the highest risk of 30-day MACCE event that was characterised by a high prevalence of reduced estimated glomerular filtration rate (92%), hypertension (73%) and reduced ejection fraction (EF) (57%). The cluster C, characterised by a high prevalence of hypertension (94%), dyslipidaemia (88%), reduced eGFR (87%), diabetes (73%) and reduced EF (65%) had the highest risk of 12-month MACCE and highest length of hospital stay.ConclusionHypertension and dyslipidaemia are prevalent in at least four in ten patients undergoing coronary angioplasty. This study showed that clusters of patients with multimorbidity had significantly different risk of 30-day and 12-month MACCE after PCI. This suggests the necessity for treatment approaches that are more personalised and customised to enhance patient outcomes and the quality of care delivered to patients in various comorbidity clusters. These results should be validated in a prospective cohort and to evaluate the potential impacts of these clusters on the prevention of MACCE after PCI.

Probabilistic-Mechanistic Evaluation of a Hybrid Drying System Using l1 Norm Regularisation: Simultaneous Ohmic Heating and Convection Drying

Ohmic-assisted drying (OAD) is a novel drying system that combines ohmic heating and convection drying simultaneously. The present study is aimed at evaluating the mechanism of OAD system behaviours against the combined impact of operational and model uncertainties. Moreover, the dynamic (time-dependent), as well as static (end-of-drying) spatial homogeneity of the model predictions, was quantitatively described for the first time in the literature using Buzas and Gibson’s evenness value (an α-diversity index). The Monte Carlo simulation approach was used to propagate the uncertainty of randomly selected input variables using the Halton sequence sampling method. Mechanistic models include input uncertainties that lead to deviations in model estimations. Both time-dependent and independent global sensitivity of moisture, sample temperature, sample’s internal pressure, their spatial homogeneity, and drying time were assessed using Lasso regression (a variable selection method that penalises the coefficients using l1 norm). The stochastic results of the mechanistic investigation showed that the effects of the input variables are almost identical both as static and time-dependent variables. Lasso regression results indicated that operational and model uncertainties cause varying changes in the magnitude and direction of the stochastic model predictions throughout the process. By contrast, the homogeneity properties of the dry product are not caused by these variations and heterogeneous distribution of the electric field. Additionally, electrical conductivity, oven temperature, applied voltage, and initial moisture were found to be the variables which have the most significant effect on all the variables which were examined in terms of operational and model uncertainties. Practical Applications. The present study investigates the stochastic behaviour of the OAD system through the mechanistic model and using a probabilistic modelling approach. To the best of our knowledge, in addition to being the first study to probabilistically evaluate the OAD system, we are introducing Buzas and Gibson’s evenness value for the first time in the food science/technology literature. This measure serves as a numerical indicator of the spatial distribution homogeneity of the physical properties of the sample. The study’s findings and proposed methodologies will have further applications not only for researchers but also for manufacturers, particularly for those involved in the design and analysis of new drying and food systems in general. Moreover, the presented methods and novel homogeneity measures are generic tools; they can be easily adapted to other process improvement practices involving input/output uncertainty/variability.

Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach.

The Society of Environmental Toxicology and Chemistry (SETAC) convened a Pellston workshop in 2022 to examine how information on climate change could be better incorporated into the ecological risk assessment (ERA) process for chemicals as well as other environmental stressors. A major impetus for this workshop is that climate change can affect components of ecological risks in multiple direct and indirect ways, including the use patterns and environmental exposure pathways of chemical stressors such as pesticides, the toxicity of chemicals in receiving environments, and the vulnerability of species of concern related to habitat quality and use. This article explores a modeling approach for integrating climate model projections into the assessment of near- and long-term ecological risks, developed in collaboration with climate scientists. State-of-the-art global climate modeling and downscaling techniques may enable climate projections at scales appropriate for the study area. It is, however, also important to realize the limitations of individual global climate models and make use of climate model ensembles represented by statistical properties. Here, we present a probabilistic modeling approach aiming to combine projected climatic variables as well as the associated uncertainties from climate model ensembles in conjunction with ERA pathways. We draw upon three examples of ERA that utilized Bayesian networks for this purpose and that also represent methodological advancements for better prediction of future risks to ecosystems. We envision that the modeling approach developed from this international collaboration will contribute to better assessment and management of risks from chemical stressors in a changing climate. Integr Environ Assess Manag 2024;20:367-383. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Does Interactive Conditioning Help Users Better Understand the Structure of Probabilistic Models?

Despite growing interest in probabilistic modeling approaches and availability of learning tools, people are hesitant to use them. There is a need for tools to communicate probabilistic models more intuitively and help users build, validate, use effectively or trust probabilistic models. We focus on visual representations of probabilistic models and introduce the Interactive Pair Plot (IPP) for visualization of a model's uncertainty, a scatter plot matrix of a probabilistic model allowing interactive conditioning on the model's variables. We investigate whether the use of interactive conditioning in a scatter plot matrix of a model helps users better understand variables' relations. We conducted a user study and the findings suggest that improvements in the understanding of the interaction group are the most pronounced for more exotic structures, such as hierarchical models or unfamiliar parameterizations, in comparison to the understanding of the static group. As the detail of the inferred information increases, interactive conditioning does not lead to considerably longer response times. Finally, interactive conditioning improves participants' confidence about their responses.

Identification of mobile development issues using semantic topic modeling of Stack Overflow posts

Background Increasing demands for mobile apps and services have recently led to an intensification of mobile development activities. With the proliferation of mobile development, there has been a major transformation in the architectures, paradigms, knowledge domains and skills of traditional software systems towards mobile development. Therefore, mobile developers experience a wide spectrum of issues specific to development processes of mobile apps and services. Methods In this article, we conducted a semantic content analysis based on topic modeling using mobile-related questions on Stack Overflow, a popular Q&amp;A site for developers. With the aim of providing an understanding of the issues and challenges faced by mobile developers, we used a semi-automated methodology based on latent Dirichlet allocation (LDA), a probabilistic and generative approach for topic modeling. Results Our findings revealed that mobile developers’ questions focused on 36 topics in six main categories, including “Development”, “UI settings”, “Tools”, “Data Management”, “Multimedia”, and “Mobile APIs”. Besides, we investigated the temporal trends of the discovered issues and their relationships with mobile technologies. Our findings also revealed which issues are the most popular and which issues are the most difficult for mobile development. The methodology and findings of this study have valuable implications for mobile development stakeholders including tool builders, developers, researchers, and educators.

Harnessing environmental DNA to reveal biogeographical patterns of non-indigenous species for improved co-governance of the marine environment in Aotearoa New Zealand

Aotearoa New Zealand’s Northern region is a major gateway for the incursion and establishment of non-indigenous species (NIS) populations due to high numbers of recreational and commercial vessels. This region also holds a unique marine ecosystem, home to many taonga (treasured) species of cultural and economic importance. Regular surveillance, eradication plans and public information sharing are undertaken by local communities and governmental organizations to protect these ecosystems from the impact of NIS. Recently, considerable investments went into environmental DNA (eDNA) research, a promising approach for the early detection of NIS for complementing existing biosecurity systems. We applied eDNA metabarcoding for elucidating bioregional patterns of NIS distributions across a gradient from harbors (NIS hotspots) to open seas (spreading areas). Samples were collected during a research cruise sailing across three Aotearoa New Zealand harbors, Waitematā, Whangārei and Pēwhairangi (Bay of Islands), and their adjacent coastal waters. The small-ribosomal subunit (18S rRNA) and mitochondrial cytochrome c oxidase I (COI) genes were screened using the online Pest Alert Tool for automated detection of putative NIS sequences. Using a probabilistic modelling approach, location-dependent occupancies of NIS were investigated and related to the current information on species distribution from biosecurity surveillance programs. This study was collaboratively designed with Māori partners to initiate a model of co-governance within the existing science system.

A probabilistic framework for post-disaster recovery modeling of buildings and electric power networks in developing countries

Post-disaster recovery is a significant challenge, especially in developing countries. Various technical, environmental, socioeconomic, political, and cultural factors substantially influence post-disaster recovery. As a result, methodologies relevant in developed nations may not be directly applicable in Global South contexts. This study introduces a probabilistic framework for modeling the post-disaster recovery of buildings and electric power networks (EPNs) in developing countries. The proposed framework combines an asset-level assessment of individual buildings with a community-level assessment of EPNs to evaluate a building portfolio's post-event functionality state. As part of the framework, a stochastic network analysis is proposed to estimate the recovery time of damaged buildings while accounting for technical, environmental, socioeconomic, political, and cultural factors, quantified using empirical data gathered from past events. A probabilistic modeling approach is proposed to quantify the EPN's initial post-event outage levels. Empirical formulations for estimating the recovery time of an EPN as a function of its initial post-event outage levels are calibrated using post-event data from developing countries. A case study is presented to illustrate the application of the proposed framework to model the post-earthquake recovery of a synthetic low-income residential community. The analysis shows that negative technical, environmental, socioeconomic, political, and cultural factors could amplify the reconstruction time of damaged buildings by a factor of almost three. The proposed framework can support decision-makers in disaster planning and management strategies for vulnerable low-income communities.

Forecasting the long-term impact of COVID-19 on hepatitis C elimination plans in Italy: A mathematical modelling approach.

Italy has a high HCV prevalence, and despite the approval of a dedicated fund for 'Experimental screening' for 2 years, screening has not been fully implemented. We aimed to evaluate the long-term impact of the persisting delay in HCV elimination after the Coronavirus disease 2019 (COVID-19) pandemic in Italy. We used a mathematical, probabilistic modelling approach evaluating three hypothetical 'Inefficient', 'Efficient experimental' and 'WHO Target' screening scenarios differing by treatment rates over time. A Markov chain for liver disease progression evaluated the number of active infections, decompensated cirrhosis (DC), hepatocellular carcinoma (HCC) and HCV liver-related deaths up to the years 2030 and 2050. The 'WHO Target' scenario estimated 3900 patients with DC and 600 with HCC versus 4400 and 600 cases, respectively, similar for both 'Inefficient' and 'Efficient experimental' screening up to 2030. A sharp (10-fold) decrease in DC and HCC was estimated by the 'WHO Target' scenario compared with the other two scenarios in 2050; the forecasted number of DC was 420 cases versus 4200 and 3800 and of HCC <10 versus 600 and 400 HCC cases by 'WHO Target,' 'Inefficient' and 'Efficient experimental' scenarios, respectively. A significant decrease of the cumulative estimated number of liver-related deaths was observed up to 2050 by the 'WHO Target' scenario (52000) versus 'Inefficient' or 'Efficient experimental' scenarios (79 000 and 74 000 liver-related deaths, respectively). Our estimates highlight the need to extensively and efficiently address HCV screening and cure of HCV infection in order to avoid the forecasted long-term HCV adverse outcomes in Italy.

A probabilistic modeling and evolutionary optimization approach for serverless workflow configuration

AbstractServerless computing has nowadays become a mainstream paradigm to develop cloud‐native applications owing to its high scalability, ease of usage and cost‐effectiveness. Nevertheless, because of its poor infrastructure transparency, two main challenges emerge when users migrate their applications to a serverless platform: the lack of an effective analytical model for performance and billing, and the trade‐off problem between them. In this paper, we formally define a serverless workflow and introduce the concept of execution instances. Based on them, a probabilistic performance and cost evaluation model is built to obtain their expected values for an input serverless workflow. Then, we design a tailored evolutionary optimization algorithm called EASW to tackle budget‐constrained performance optimization and performance‐constrained cost optimization problems. Extensive experiments were carried out to test the proposed model and optimization algorithm on AWS Lambda. Results reveal that our model can achieve an accuracy over 98% and EASW can yield a better memory configuration solution than existing methods for constrained optimization.

A probabilistic bottom-up modelling approach for synthetic load profiles within the energy efficiency management of cruise ship cabins

A reliable estimate of the electrical demand is a decisive factor in the design of electric board systems on cruise ships. This applies to all areas of the ship, including the hotel area with its up to several thousand cabins, which account for a non-negligible share of the total energy demand. In this paper, a simplified bottom-up model that can depict the electrical demand cabins with high temporal resolution and thus support marine and electrical engineers in developing future on-board power grids is presented. Key-card data will be used as a basis to easily access the behaviour of passengers on board. Another benefit is that the key data of the electrical consumers can be easily adapted to different types of ships and cabins. By including the ships route it is possible to precisely calculate the heat input through the glazing at any time of day and to determine its effect on the air-conditioning requirement. The model presented here is therefore not limited to certain regions but can be used globally. The functionality of the model is demonstrated on the example of a Caribbean cruise where the average electrical demand of a cabin is determined to 1.6 kWh per day.

Developing sustainable management strategies in construction and demolition wastes using a q-rung orthopair probabilistic hesitant fuzzy set-based decision modelling approach

Sustainable management of construction and demolition wastes (CDWs) has become a pressing global issue in social, environmental and economic contexts, and it involves complex technological, engineering, management and regulatory challenges. Recently, many CDW management strategies have been developed based on the barrier attributes of reuse distribution. However, no strategy can simultaneously address all barrier attributes of reuse distribution. Furthermore, no research has assessed and modelled the identified CDW management strategies to determine optimality. On this basis, the presence of multiple barrier attributes, varying attribute priority and a wide range of data allow for the modelling of CDW management strategies under complex multiple-attribute decision-making (MADM) problems. This study develops the fuzzy-weighted zero inconsistency (FWZIC) and fuzzy decision by opinion score method (FDOSM)-based multiplicative multiple objective optimisation by ratio analysis (MULTIMOORA) with the q-rung orthopair probabilistic hesitant fuzzy set (q-ROPHFS) to address this problem. The developed q-ROPHFS–FWZIC method prioritised and weighted the main and sub-barrier attributes of reuse distribution in CDW management strategies. The developed q-ROPHFS–FDOSM is used to score the CDW management strategies. Then, the MULTIMOORA method is used to model 51 CDW management strategies to determine the optimum one. Results showed that Strategy 46 modelled first in six q values because it had the most essential attributes (i.e. cost, market, value-for-money, experience, infrastructure, management, risk and trust). Strategy 17 and Strategy 20 are the least sustainable strategies because they had only one attribute (i.e. experience). Sensitivity analysis, systematic modelling and comparison analysis are conducted to validate and evaluate the stability and robustness of the proposed methods. The implications of this study would likely benefit various stakeholders involved in the construction industry, including construction companies, architects, engineers, policy-makers and members of the public.

Deterministic and Probabilistic Approaches to Model and Update Dynamic Systems

Updating simulation models, based on flight test or other data, or creating a model using only experimental data is a necessary process that can be costly and laborious. Simulation updates are necessary for high-fidelity simulations used in analysis, control development, handling qualities prediction, fleet training, etc. This paper presents methods to update aircraft models based on deterministic and probabilistic approaches. The deterministic approach is based on a collection of signal processing, statistical analysis, and maximum likelihood estimators embodied in the Algorithms to Update Simulation Parameters with Experimental Data tool. Mathematically sound and statistically rigorous techniques are used to calculate updates of current model parameters and suggest new terms capturing unmodeled dynamics, thereby improving correlation with observed vehicle response. The probabilistic approach is based on generalized polynomial chaos theory included in the Algorithms for Uncertainty Representation and Analysis (AURA) tool. The AURA tool allows the user to update probabilistic models based on experimental data, formulated as a Bayesian inference problem. This paper will present the methodology for both deterministic and probabilistic approaches and provide examples of their application to different aircraft designs, including tilt-rotor and rotary-wing unmanned air system vehicles. The results for both deterministic and probabilistic modeling approaches demonstrate excellent results. The probabilistic approach is novel in its expressive power to propagate and update probabilistic models and provides users with an intuitive and powerful modeling approach.

Effects of high heels on medial tibiofemoral cartilage mechanics: an exploration using musculoskeletal simulation and a probabilistic cartilage failure model

This study examined the effects of different high-heeled footwear heights on lower extremity medial tibiofemoral cartilage mechanics and longitudinal failure probability, using musculoskeletal simulation and probabilistic failure modelling approaches. The current investigation examined 24 participants, walking in four different footwear (high heel, medium heel, low heel, and trainer). Walking kinematics were collected using an 8-camera motion capture system and kinetics via an embedded force plate. Medial tibiofemoral loading was explored using musculoskeletal simulation and cartilage probability via probabilistic modelling. Distance travelled per day was inputted into the model as 6.0 km per day. This was modelled as participants completing all 6.0 km in the trainer condition whereas in the heeled footwear, this was modelled as participants completing 3.0, 2.4, 1.8, 1.2 and 0.6 km in the heels, and the remaining distance undertaken in the trainers. Cartilage forces and strains were significantly greater in the high (force = 3.39BW & strain = 0.26), medium (force = 2.98BW & strain = 0.22) and low heels (force = 2.91BW & strain = 0.22) compared to the trainer (force = 2.52BW & strain = 0.19). Cartilage failure probability was significantly greater in the high (3.0 km = 21.26%, 2.4 km = 18.45%, 1.8 km = 18.15%, 1.2 km = 17.90% & 0.6 km = 17.52%), medium (3.0 km = 9.97%, 2.4 km = 9.86%, 1.8 km = 9.76%, 1.2 km = 9.66% & 0.6 km = 9.55%) and low heels (3.0 km = 9.13%) compared to the trainer (1.89%), and in the high heel condition decreased linearly alongside reductions in modelled walking distance. The findings from the current investigation show that high heels of increasing height have a profoundly negative influence on medial knee cartilage health and strongly advocate that walking distance in heeled footwear and the heel height itself be reduced wherever possible.

Towards scalable and actionable pedestrian outdoor thermal comfort estimation: A progressive modelling approach

The outdoor microclimate highly affects the quality of urban outdoor spaces which are essential to a city's socio-economic vitality. Due to the complexity associated with estimating outdoor thermal comfort (OTC), its study is currently only feasible at a high computational cost and time which makes it ineffective in any iterative design process. By coupling physics-based simulations and statistical modelling techniques, this paper presents a probabilistic, progressive, and accuracy-adaptive modelling approach for faster spatially-resolved OTC estimation that is scalable to large urban neighborhoods. This is achieved through three interrelated strategies: (1) Throughout the different simulation stages, spatiotemporal OTC categories are displayed with successively rising confidence levels to support instant design decision-making. (2) Confidence levels are based on probability distributions of partially known environmental variables such as wind or mean radiant temperature. (3) Wind distributions across an urban area are initially based on a spatially-informed set of rules and later replaced with explicit simulations of wind flow fields. The approach is tested against state-of-the-art computational fluid dynamic simulations for a 3 km2 sample area of San Francisco's financial district. Results show that scalable and actionable predictions are achievable at all simulation stages with the percentage of misclassified hourly OTC ranges during occupied hours falling from 36% for instant climate-based results to 8% and 7% for spatially clustered wind and building aerodynamics informed predictions which take minutes to calculate for the investigated urban area. The building aerodynamics informed simulations accurately predicts diurnal and seasonal OTC ranges for, on average, 97% of outdoor points.