GP Model Research Articles

Bayesian Earthquake Forecasting Using Gaussian Process Modeling: GP-ETAS Applications

Abstract Numerous seismicity models are known to simulate different observed characteristics of earthquake occurrence successfully. However, their ability of prospective forecasting future events is a priori not always known. The recently proposed semiparametric model, Gaussian process epidemic-type aftershock sequence (GP-ETAS) model, which combines the ETAS model with GP modeling of the background activity, has led to promising results when applied to synthetic seismicity. In this study, we focus on the ability of GP-ETAS for different forecasting experiments in two case studies: first, the Amatrice, Italy, sequence during 2016 and 2017, and second, long-term seismicity in Southern California. The results indicate that GP-ETAS performs well compared with selected benchmark models. The advantages become particularly visible in cases with sparse data, in which GP-ETAS shows in general a more robust behavior compared to other approaches.

A Data-Driven Approach for Forecasting Current Direction With a Hybrid Model of Empirical Mode Decomposition and Warped Gaussian Process

Abstract Ocean current forecasting is essential for tidal renewable energy generation and operation. However, forecasting the current direction at multiple points along the water depth are still lacking of comprehensive studies. In this study, a data-driven approach was developed to attain short-term prediction in the current direction with reasonable uncertainty quantification. The developed approach employed empirical mode decomposition (EMD) and the warped Gaussian process (WGP) in the forecasting process. The ocean current data, which were measured by a seabed-mounted acoustic Doppler current profiler in the Haitian Strait, were used to illustrate the developed approach. The measured current direction data were preprocessed with the average shifting method to obtain the principal and random components for the improvement of the forecasting accuracy. The random components were decomposed into intrinsic mode functions (IMFs) and residuals. The principal components, IMFs, and residuals of the current direction were then forecasted by the WGP approach. The forecasting performance of the developed approach was investigated through comparisons with those of single standard GP, single WGP, and EMD + GP models. The effects of the kernel function and training input on the forecasting efficiency and precision were investigated. The extrapolation performances of the proposed model for a 1-step prediction and multistep-ahead prediction were also examined.

Novel hybrid XGBoost-based soft computing models for predicting penetration resistance of buried pipelines in cohesive soils

In this paper, both the finite element limit analysis (FELA) and soft computing techniques of four hybrid XGBoost (XGB) models, namely, GA-XGB, optimized with Genetic Algorithms; SMA-XGB, optimized with Slime Mould Algorithms; PSO-XGB, optimized with Particle Swarm Optimization; ACO-XGB, Ant Colony Optimization; and one Genetic Programing model (GP), are employed to develop surrogate models for predicting the undrained penetration resistance of buried pipelines embedded in clays. The penetration resistance of pipelines is caused by internal and external pressures, resulting in a force that acts on the pipeline in the downward and inclined directions at the same time. The penetration resistance factor (N) of a buried pipeline is determined based on four dimensionless variables, namely, the buried depth ratio (H/D), the inclination angle of the applied load (β), the soil strength or overburden ratio (γH/c), and the adhesion factor at soil-pipeline interfaces (α). The findings of this study are presented and summarized in the form of charts for dimensionless penetration resistance factors (N) and failure mechanisms of pipelines. Furthermore, the results from this study are compared with the results from previous studies and agrees well with those of previous studies. According to the results of the four developed hybrid XGBoost-based models, SMA-XGB outperforms GA-XGB, PSO-XGB, and ACO-XGB with no symptoms of overfitting, while the GP model resulted in an adequately accurate model with the advantage of a closed-form equation; its determination correlation coefficient of training and testing equals 0.98 (R2 = 0.980).

Exploring sustainable solutions for soil stabilization through explainable Gaussian process-assisted multi-objective optimization

The adoption of sustainable solutions in soil stabilization has piqued the interest of the scientific community due to the potential reduction in carbon footprint. In this regard, the research community has started looking for the alternate sustainable solutions to limit the quantity of conventionally used ecologically unfriendly soil stabilizers like lime and cement by utilizing the agricultural and industrial by-products. The production of conventional soil stabilizers (lime and cement) is extremely energy-intensive and contributes tons of greenhouse gases to the atmosphere. In general, evaluating suitability of these additives requires in-lab investigation of soil samples with varying additive concentrations and curing periods, making this approach both resource and time-intensive. Hence, this article proposes a computational framework for accelerated characterization of soil-stabilization by using the coupled experimental-Gaussian process (GP) based machine learning (ML) model. The dataset utilized for constructing the GP models consists of input features such as the stabilizer content (lime and rice husk ash (RHA)), coir fiber content, and curing period (measured in days). The target responses are the strength measures of the stabilized soil, such as unconfined compressive strength (UCS), split tensile strength (STS), and California bearing ratio (CBR). The proposed computational framework is deployed to perform multi-objective genetic algorithm (MOGA)-based optimization to achieve maximum engineering performance from stabilized soil. The presented study demonstrated that with the optimal dosage of rice husk ash (agricultural by-product), and cashew nut shell liquid (CNSL) treated coir fiber, the requirement for lime dosage may be significantly reduced while maintaining the engineering performance of the soil. The presented computational framework can be extended to any construction practices for ensuring the strategic selection of the control variables for optimizing the desired performance.

Enhancing Computational Efficiency in Porous Media Analysis: Integrating Machine Learning with Monte Carlo Ray Tracing

Abstract Monte Carlo ray tracing (MCRT) has been a widely implemented and reliable computational method for calculating light-matter interaction in porous media, the computational modeling of porous media and performing MCRT becomes significantly expensive when dealing with intricate structures and numerous dependent variables. Hence, Machine Learning (ML) models have been utilized to overcome computational burdens. In this study, we investigate two distinct frameworks for characterizing radiative properties in porous media for pack-free and packing-based methods. We employ two different regression tools for each case, namely Gaussian process regressions for pack-free MCRT and Convolutional Neural Network (CNN) models for pack-based MCRT to predict the radiative properties. Our study highlights the importance of selecting the appropriate regression method based on the physical model, which can lead to significant computational efficiency improvement. Our results show that both models can predict the radiative properties with high accuracy (>90%). Furthermore, we demonstrate that combining MCRT with ML inference not only enhances predictive accuracy but also reduces the computational cost of simulation by more than 96% using the GP model and 99% for the CNN model.

Development of an Emulator for Radiation Physics Simulator Using Stochastic Variational Gaussian Process Model

This paper describes an emulator that uses a Stochastic Variational Gaussian process (SVGP) regression model to parameterize radiation in a numerical weather prediction (NWP) model that meteorologically models the Earth's weather system. The computation of radiative processes is very large, accounting for most of the total NWP model computation. Statistical emulators are surrogate models that represent simulators and can overcome the computational limitations of very complex simulators such as radiative processes. Recently, artificial neural network-based radiative transfer emulators have been developed, and in this study, a statistical model, GP, is used to develop a radiative transfer emulator. The GP model has the advantage of calculating the uncertainty of the prediction along with the prediction, so the uncertainty of the prediction can be utilized appropriately. However, the computational complexity of the conventional GP model is very high, making it difficult to apply to large data. To solve this problem, an approximate approach, the SVGP model, was utilized. To further reduce the dimensionality of the input variables, we used a combined neural network and SVGP model. As a result, the SVGP-based radiative physics emulator improved its accuracy by about 20% compared to the artificial neural network emulator. However, the computation speed was about 3 to 9 times slower than the neural network emulator, but it was faster than the computation speed of the NWP model. This suggests that statistical emulators can be used to replace NWP model simulators.

Socio-environmental predictors of diabetes incidence disparities in Tanzania mainland: a comparison of regression models for count data

BackgroundDiabetes is one of the top four non-communicable diseases that cause death and illness to many people around the world. This study aims to use an efficient count data model to estimate socio-environmental factors associated with diabetes incidences in Tanzania mainland, addressing lack of evidence on the efficient count data model for estimating factors associated with disease incidences disparities.MethodsThis study analyzed diabetes counts in 184 Tanzania mainland councils collected in 2020. The study applied generalized Poisson, negative binomial, and Poisson count data models and evaluated their adequacy using information criteria and Pearson chi-square values.ResultsThe data were over-dispersed, as evidenced by the mean and variance values and the positively skewed histograms. The results revealed uneven distribution of diabetes incidence across geographical locations, with northern and urban councils having more cases. Factors like population, GDP, and hospital numbers were associated with diabetes counts. The GP model performed better than NB and Poisson models.ConclusionThe occurrence of diabetes can be attributed to geographical locations. To address this public health issue, environmental interventions can be implemented. Additionally, the generalized Poisson model is an effective tool for analyzing health information system count data across different population subgroups.

Adaptive auditory assistance for stride length cadence modification in older adults and people with Parkinson's.

Gait rehabilitation using auditory cues can help older adults and people with Parkinson's improve walking performance. While auditory cues are convenient and can reliably modify gait cadence, it is not clear if auditory cues can reliably modify stride length (SL), another key gait performance metric. Existing algorithms also do not address habituation or fluctuation in motor capability, and have not been evaluated with target populations or under dual-task conditions. In this study, we develop an adaptive auditory cueing framework that aims to modulate SL and cadence. The framework monitors the gait parameters and learns a personalized cue-response model to relate the gait parameters to the input cues. The cue-response model is represented using a multi-output Gaussian Process (MOGP) and is used during optimization to select the cue to provide. The adaptive cueing approach is benchmarked against the fixed approach, where cues are provided at a fixed cadence. The two approaches are tested under single and dual-task conditions with 13 older adults (OA) and 8 people with Parkinson's (PwP). The results show that more than half of the OA and PwP in the study can change both SL and cadence using auditory cues. The fixed approach is best at changing people's gait without secondary task, however, the addition of the secondary task significantly degrades effectiveness at changing SL. The adaptive approach can maintain the same level of SL change regardless of the presence of the secondary task. A separate analysis is conducted to identify factors that influence the performance of the adaptive framework. Gait information from the previous time step, along with the previous input cue, can improve its prediction accuracy. More diversity in the initialization data can also improve the GP model. Finally, we did not find a strong correlation between stride length and cadence when the parameters are contingent upon input cues.

Optimized Proportioning Model and Efficacy Analysis of High Performance Concrete in Bridge Construction

Abstract As an important part of the raw materials for building construction, high performance concrete is well known for its many properties. This paper analyzes the prerequisites for high-performance concrete proportioning from the technical point of view and processes involved in designing high-performance concrete proportioning. A particle swarm algorithm combined with a neural network is used to establish the relationship model between concrete strength and proportion, and the constructed PSO-BP model is used for the prediction of concrete mixing ratio strength to get the best material proportion. The prediction ability of the BP neural network model, GP model, and PSO-BP model is compared. In order to test the applicability and reliability of the high-performance concrete formulated by the PSO-BP model in members, static load tests were carried out in this paper on a 20m test single girder to test the strain and deflection of concrete in the control section. The results show that the relative error of the high-performance concrete strength predictions predicted by the PSO-BP model is within 2%. It shows that the optimum high-performance concrete can be formulated using the PSO-BP model. The test beams can meet design and practical engineering requirements in terms of strength and stiffness and can be applied to real bridges. Based on the research and analysis, suggestions have been made for the application of high-performance concrete in bridge construction from four aspects.

Epoch-Evolving Gaussian Process Guided Learning for Classification.

The conventional mini-batch gradient descent algorithms are usually trapped in the local batch-level distribution information, resulting in the ``zig-zag'' effect in the learning process. To characterize the correlation information between the batch-level distribution and the global data distribution, we propose a novel learning scheme called epoch-evolving Gaussian process guided learning (GPGL) to encode the global data distribution information in a non-parametric way. Upon a set of class-aware anchor samples, our GP model is built to estimate the class distribution for each sample in mini-batch through label propagation from the anchor samples to the batch samples. The class distribution, also named the context label, is provided as a complement for the ground-truth one-hot label. Such a class distribution structure has a smooth property and usually carries a rich body of contextual information that is capable of speeding up the convergence process. With the guidance of the context label and ground-truth label, the GPGL scheme provides a more efficient optimization through updating the model parameters with a triangle consistency loss. Furthermore, our GPGL scheme can be generalized and naturally applied to the current deep models, outperforming the state-of-the-art optimization methods on six benchmark datasets.

Modeling crude oil pyrolysis process using advanced white-box and black-box machine learning techniques

Accurate prediction of fuel deposition during crude oil pyrolysis is pivotal for sustaining the combustion front and ensuring the effectiveness of in-situ combustion enhanced oil recovery (ISC EOR). Employing 2071 experimental TGA datasets from 13 diverse crude oil samples extracted from the literature, this study sought to precisely model crude oil pyrolysis. A suite of robust machine learning techniques, encompassing three black-box approaches (Categorical Gradient Boosting—CatBoost, Gaussian Process Regression—GPR, Extreme Gradient Boosting—XGBoost), and a white-box approach (Genetic Programming—GP), was employed to estimate crude oil residue at varying temperature intervals during TGA runs. Notably, the XGBoost model emerged as the most accurate, boasting a mean absolute percentage error (MAPE) of 0.7796% and a determination coefficient (R2) of 0.9999. Subsequently, the GPR, CatBoost, and GP models demonstrated commendable performance. The GP model, while displaying slightly higher error in comparison to the black-box models, yielded acceptable results and proved suitable for swift estimation of crude oil residue during pyrolysis. Furthermore, a sensitivity analysis was conducted to reveal the varying influence of input parameters on residual crude oil during pyrolysis. Among the inputs, temperature and asphaltenes were identified as the most influential factors in the crude oil pyrolysis process. Higher temperatures and oil °API gravity were associated with a negative impact, leading to a decrease in fuel deposition. On the other hand, increased values of asphaltenes, resins, and heating rates showed a positive impact, resulting in an increase in fuel deposition. These findings underscore the importance of precise modeling for fuel deposition during crude oil pyrolysis, offering insights that can significantly benefit ISC EOR practices.

Tracking control of uncertain nonlinear systems via adaptive Gaussian process prediction and real-time optimisation

Control of nonlinear systems in the presence of model mismatch and system constraints is quite challenging. To address the issue, this work proposes an adaptive Gaussian process-based real-time optimisation (AGP-RTO) control framework. Specifically, the control law consists of two components, a feedforward tracking control law and an uncertainty compensation control law. Because GP has high flexibility to capture complex unknown functions by using very few parameters and it inherently handles measurement noise, this work utilises the GP as an alternative to estimate the mismatch between the real plant and the approximated model. During every RTO execution, the GPs adaptively update the predictions of the model mismatch, then the predictions are embedded into a nonlinear optimisation problem for the correction of the model cost and constraint functions, which yields the uncertainty compensation control law. The proposed AGP-RTO framework ensures that the Karush-Kuhn-Tucker (KKT) conditions determined by the model match those of the plant upon convergence. Compared to many direct adaptive control methods, AGP-RTO does not rely on a high gain for fast adaptation and hence it improves the robustness of the closed-loop system. Compared to the modifier adaptation (MA) method, AGP-RTO avoids the plant-gradient estimation by using the finite difference scheme, besides it trains the GP models offline, which speeds up online evaluation and improves the applicability and efficacy of real-time control. Comparisons are carried out to illustrate the superiority of the AGP-RTO.

Machine Learning Analysis of the Impact of Silver Nitrate and Silver Nanoparticles on Wheat (Triticum aestivum L.): Callus Induction, Plant Regeneration, and DNA Methylation.

The objective of this study was to comprehend the efficiency of wheat regeneration, callus induction, and DNA methylation through the application of mathematical frameworks and artificial intelligence (AI)-based models. This research aimed to explore the impact of treatments with AgNO3 and Ag-NPs on various parameters. The study specifically concentrated on analyzing RAPD profiles and modeling regeneration parameters. The treatments and molecular findings served as input variables in the modeling process. It included the use of AgNO3 and Ag-NPs at different concentrations (0, 2, 4, 6, and 8 mg L-1). The in vitro and epigenetic characteristics were analyzed using several machine learning (ML) methods, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor classifier (KNN), and Gaussian processes classifier (GP) methods. This study's results revealed that the highest values for callus induction (CI%) and embryogenic callus induction (EC%) occurred at a concentration of 2 mg L-1 of Ag-NPs. Additionally, the regeneration efficiency (RE) parameter reached its peak at a concentration of 8 mg L-1 of AgNO3. Taking an epigenetic approach, AgNO3 at a concentration of 2 mg L-1 demonstrated the highest levels of genomic template stability (GTS), at 79.3%. There was a positive correlation seen between increased levels of AgNO3 and DNA hypermethylation. Conversely, elevated levels of Ag-NPs were associated with DNA hypomethylation. The models were used to estimate the relationships between the input elements, including treatments, concentration, GTS rates, and Msp I and Hpa II polymorphism, and the in vitro output parameters. The findings suggested that the XGBoost model exhibited superior performance scores for callus induction (CI), as evidenced by an R2 score of 51.5%, which explained the variances. Additionally, the RF model explained 71.9% of the total variance and showed superior efficacy in terms of EC%. Furthermore, the GP model, which provided the most robust statistics for RE, yielded an R2 value of 52.5%, signifying its ability to account for a substantial portion of the total variance present in the data. This study exemplifies the application of various machine learning models in the cultivation of mature wheat embryos under the influence of treatments and concentrations involving AgNO3 and Ag-NPs.

Alleviating sample imbalance in water quality assessment using the VAE–WGAN–GP model

Abstract Water resources are essential for sustaining human life and promoting sustainable development. However, rapid urbanization and industrialization have resulted in a decline in freshwater availability. Effective prevention and control of water pollution are essential for ecological balance and human well-being. Water quality assessment is crucial for monitoring and managing water resources. Existing machine learning-based assessment methods tend to classify the results into the majority class, leading to inaccuracies in the outcomes due to the prevalent issue of imbalanced class sample distribution in practical scenarios. To tackle the issue, we propose a novel approach that utilizes the VAE–WGAN–GP model. The VAE–WGAN–GP model combines the encoding and decoding mechanisms of VAE with the adversarial learning of GAN. It generates synthetic samples that closely resemble real samples, effectively compensating data of the scarcity category in water quality evaluation. Our contributions include (1) introducing a deep generative model to alleviate the issue of imbalanced category samples in water quality assessment, (2) demonstrating the faster convergence speed and improved potential distribution learning ability of the proposed VAE–WGAN–GP model, (3) introducing the compensation degree concept and conducting comprehensive compensation experiments, resulting in a 9.7% increase in the accuracy of water quality assessment for multi-classification imbalance samples.

Machine learning modeling of lung mechanics: Assessing the variability and propagation of uncertainty in respiratory-system compliance and airway resistance

Background and ObjectiveTraditional assessment of patient response in mechanical ventilation relies on respiratory-system compliance and airway resistance. Clinical evidence has shown high variability in these parameters, highlighting the difficulty of predicting them before the start of ventilation therapy. This motivates the creation of computational models that can connect structural and tissue features with lung mechanics. In this work, we leverage machine learning (ML) techniques to construct predictive lung function models informed by non-linear finite element simulations, and use them to investigate the propagation of uncertainty in the lung mechanical response. MethodsWe revisit a continuum poromechanical formulation of the lungs suitable for determining patient response. Based on this framework, we create high-fidelity finite element models of human lungs from medical images. We also develop a low-fidelity model based on an idealized sphere geometry. We then use these models to train and validate three ML architectures: single-fidelity and multi-fidelity Gaussian process regression, and artificial neural networks. We use the best predictive ML model to further study the sensitivity of lung response to variations in tissue structural parameters and boundary conditions via sensitivity analysis and forward uncertainty quantification. Codes are available for download at https://github.com/comp-medicine-uc/ML-lung-mechanics-UQ ResultsThe low-fidelity model delivers a lung response very close to that predicted by high-fidelity simulations and at a fraction of the computational time. Regarding the trained ML models, the multi-fidelity GP model consistently delivers better accuracy than the single-fidelity GP and neural network models in estimating respiratory-system compliance and resistance (R2∼0.99). In terms of computational efficiency, our ML model delivers a massive speed-up of ∼970,000× with respect to high-fidelity simulations. Regarding lung function, we observed an almost matched and non-linear behavior between specific structural parameters and chest wall stiffness with compliance. Also, we observed a strong modulation of airways resistance with tissue permeability. ConclusionsOur findings unveil the relevance of specific lung tissue parameters and boundary conditions in the respiratory-system response. Furthermore, we highlight the advantages of adopting a multi-fidelity ML approach that combines data from different levels to yield accurate and efficient estimates of clinical mechanical markers. We envision that the methods presented here can open the way to the development of predictive ML models of the lung response that can inform clinical decisions.

Usage of Machine Learning Algorithms for Establishing an Effective Protocol for the In Vitro Micropropagation Ability of Black Chokeberry (Aronia melanocarpa (Michx.) Elliott)

The primary objective of this research was to ascertain the optimal circumstances for the successful growth of black chokeberry (Aronia melanocarpa (Michx.) Elliott) using tissue culture techniques. Additionally, the study aimed to explore the potential use of machine learning algorithms in this context. The present research investigated a range of in vitro parameters such as total number of roots (TNR), longest root length (LRL), average root length (ARL), number of main roots (NMR), number of siblings (NS), shoot length (SL), shoot diameter (SD), leaf width (LW), and leaf length (LL) for Aronia explants cultivated in different media (Murashige and Skoog (MS) and woody plant medium (WPM)) with different concentrations (0, 0.5, 1, 1.5, and 2 mg L−1) of indole-3-butyric acid (IBA). The study showed that IBA hormone levels may affect WPM properties, affecting the LRL and ARL variables. Aronia explant media treated with 2 mg L−1 IBA had the greatest TNR, NMR, NS, SL, and SD values; 31.67 pieces, 2.37 pieces, 5.25 pieces, 66.60 mm, and 2.59 mm, in that order. However, Aronia explants treated with 1 mg L−1 IBA had the highest LW (9.10 mm) and LL (14.58 mm) values. Finally, Aronia explants containing 0.5 mg L−1 IBA had the greatest LRL (89.10 mm) and ARL (57.57 mm) values. In general, the results observed (TNR, LRL, ARL, NMR, NS, SL, SD, LW, and LL) indicate that Aronia explants exhibit superior growth and development in WPM (25.68 pieces, 68.10 mm, 51.64 mm, 2.17 pieces, 4.33 pieces, 57.95 mm, 2.49 mm, 8.08 mm, and 14.26 mm, respectively) as opposed to MS medium (20.27 pieces, 59.92 mm, 47.25 mm, 1.83 pieces, 3.57 pieces, 49.34 mm, 2.13 mm, 6.99 mm, and 12.21 mm, respectively). In the context of the in vitro culturing of Aronia explants utilizing MS medium and WPM, an analysis of machine learning models revealed that the XGBoost and SVM models perform better than the RF, KNN, and GP models when it comes to making predictions about those variables. In particular, the XGBoost model stood out due to the fact that it had the greatest R-squared value, and showed higher predictive ability in terms of properly forecasting values in comparison to actual outcomes. The findings of a linear regression (LR) analysis were used in order to conduct an efficacy study of the XGBoost model. The LR results especially confirmed the findings for the SD, NS, and NMR variables, whose R-squared values were more than 0.7. This demonstrates the extraordinary accuracy that XGboost has in predicting these particular variables. As a consequence of this, it is anticipated that it will be beneficial to make use of the XGboost model in the dosage optimization and estimation of in vitro parameters in micropropagation studies of the Aronia plant for further scientific investigation.

Integration, effectiveness and costs of different models of primary health care provision for people who are homeless: an evaluation study.

There is a high prevalence of health problems among single people who are homeless. Specialist primary health care services for this population have been developed in several locations across England; however, there have been very few evaluations of these services. This study evaluated the work of different models of primary health care provision in England to determine their effectiveness in engaging people who are homeless in health care and in providing continuity of care for long-term conditions. It concerned single people (not families or couples with dependent children) staying in hostels, other temporary accommodation or on the streets. The influence on outcomes of contextual factors and mechanisms (service delivery factors), including integration with other services, were examined. Data from medical records were collated on participants' use of health care and social care services over 12 months, and costs were calculated. The evaluation involved four existing Health Service Models: (1) health centres primarily for people who are homeless (Dedicated Centres), (2) Mobile Teams providing health care in hostels and day centres, (3) Specialist GPs providing some services exclusively for patients who are homeless and (4) Usual Care GPs providing no special services for people who are homeless (as a comparison). Two Case Study Sites were recruited for each of the specialist models, and four for the Usual Care GP model. People who had been homeless during the previous 12 months were recruited as 'case study participants'; they were interviewed at baseline and at 4 and 8 months, and information was collected about their circumstances and their health and service use in the preceding 4 months. Overall, 363 participants were recruited; medical records were obtained for 349 participants. Interviews were conducted with 65 Case Study Site staff and sessional workers, and 81 service providers and stakeholders. The primary outcome was the extent of health screening for body mass index, mental health, alcohol use, tuberculosis, smoking and hepatitis A among participants, and evidence of an intervention if a problem was identified. There were no overall differences in screening between the models apart from Mobile Teams, which scored considerably lower. Dedicated Centres and Specialist GPs were more successful in providing continuity of care for participants with depression and alcohol and drug problems. Service use and costs were significantly higher for Dedicated Centre participants and lower for Usual Care GP participants. Participants and staff welcomed flexible and tailored approaches to care, and related services being available in the same building. Across all models, dental needs were unaddressed and staff reported poor availability of mental health services. There were difficulties recruiting mainstream general practices for the Usual Care GP model. Medical records could not be accessed for 14 participants of this model. Participant characteristics, contextual factors and mechanisms were influential in determining outcomes. Overall, outcomes for Dedicated Centres and for one of the Specialist GP sites were relatively favourable. They had dedicated staff for patients who were homeless, 'drop-in' services, on-site mental health and substance misuse services, and worked closely with hospitals and homelessness sector services. This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (HSDR 13/156/03) and will be published in full in Health and Social Care Delivery Research; Vol. 11, No. 16. See the NIHR Journals Library website for further project information.

Using the deterministic approach model for project portfolio selection problem (PPSP) solutions

Selection of projects using a robust technique is rare as most of the techniques are not considered useful due to the limitation on the number of projects that can be selected as well as cost saving projects not being selected. This study investigated the validity of a hybrid model − integrated analytical hierarchy process-goal programming (AHP-GP) − to avoid project portfolio selection problems delaying community development.The proposed model includes two steps: AHP to determine the project criteria, the relative importance of weights, and priority preferences, while the GP model was formulated to select the optimal projects. An empirical study on government agencies was carried out to validate the proposed model, and the results compared against GP as a standalone to solve the same problem. The results proved that the hybrid model (AHP-GP) was better than the GP model. AHP-GP has proved to be a robust mechanism most suitable for managerial use due to its ability to handle multi-criteria decision-making (MCDM) situations. This study showed that the hybrid model can select more projects and will create more jobs in the communities concerned compared to the single model (GP). The novelty of this study is the introduction of an integrated model formed from two distinct models as a deterministic approach to solving project portfolio selection problems.

Probabilistic assessment of heavy-haul railway track using multi-gene genetic programming

This study presented a probabilistic assessment of heavy-haul railway track using a high-performance computational model called multi-gene genetic programming (MGGP). A reliability analysis (RA) method based on MGGP and the first-order second-moment method (FOSM) has been proposed in this study. First, GP was used to map the implicit performance functions; therefore, arriving at GP-based explicit performance functions. Subsequently, the developed GP model was used to perform RA of a soil slope of heavy-haul railway track under both seismic and non-seismic conditions. Using the FOSM, soil uncertainties were mapped based on the concepts of probability theory and statistics, and a ready-made expression was developed. Simulated results demonstrate that the GP-based FOSM approach can predict the probability of failure (POF) of slope with rational accuracy. The probabilistic analysis against bearing capacity failure was also investigated in this study to ensure serviceability of the soil slope. Based on the outcomes, it can be deduced that the coefficient of variation of soil properties affects the POF of slope significantly. With the aid of the developed expression, the POF of the soil slope of heavy-haul railway track can be assessed rationally and efficiently.

A new interval meta-goal programming for sustainable planning of agricultural water-land use nexus

Meta-Goal Programming (MGP) is a simultaneous cognitive evaluation of the degree of achievements for original decision goals considered in a GP model. However, in most real-world situations, environmental coefficients and related parameters are not easily available. In such a situation, the decision-maker must consider various conflicting targets in a framework of uncertain aspiration levels at the same time. On the other side, Interval Programming (IP) is a method used to increase the range of available decision-maker preference structures in GP. In the perspective of solving the conflicts between agriculture and water use towards sustainability, this paper proposes an Interval Meta-Goal Programming Model (IMGPM) dealing with imprecision in data that covers interval coefficients, target intervals, and interval bounds of meta-goals. This novel methodology has been tested in a study area in Iran to validate its added value in solving conflicting uses of natural resources by economic sectors. This integration together with its application for sustainable optimal cropping patterns (agroecosystem planning) represents a novelty in the field of ecological modeling. The management solutions of our method in terms of land allocation are different from those in Sen and Pal (2013) model. In the case of Iran, many socio-ecological-economic strategies and policies should be necessary for improving the agricultural sector. More specifically, on the basis of rainfall amounts and spatial patterns, this approach can represent a decision-support system able to define strategies for additional water storage useful to support crop production. Furthermore, the availability of water together with the sustainable use of fertilizers can mitigate the risk of land degradation, guaranteeing people employment, food security, and economic profits. Although the present methodology seems to solve the problem of multi-goals decision-making, the inclusion of spatial relationships is able to introduce dependencies between the management of land use in adjacent areas, making the present approach nearer to real-world functioning.