Hierarchical Process Model Research Articles

Land Suitability Evaluation for Agroforestry Using Geospatial Techniques in Genale Sub-basin Oromia, Ethiopia

Agroforestry is a sustainable agricultural method that integrates trees, crops, and/or livestock within a unified land space, promoting ecological balance and resource efficiency which has been widely used for centuries due to its social, economic, and environmental advantages, despite its numerous advantages, it has not achieved substantial global acknowledgment. This research investigates the land units within the Genale sub-basin to assess their suitability for agroforestry practices, focusing on the factors that significantly impact tree and crop growth as well as productivity. Conducting a land suitability analysis is essential for designating particular areas for specific agricultural purposes. The study employs an integrated approach utilizing Geographic Information Systems (GIS), Remote Sensing (RS), and the Analytical Hierarchical Process (AHP) model, along with a weighting function, to assign suitability weights to the criteria and sub-criteria influencing plant growth, ultimately producing a predictive map of agroforestry cultivation suitability. Soil fertility parameters (soil nitrogen (N), potassium (K), organic carbon (C), phosphorus (P) and pH), Climatic (rainfall) and Topographic (Elevation and Slope) were considered in the model as a significantly determinant of agroforestry factors. Each of criteria/factor layers were classified (not suitable, less suitable, suitable and highly suitable) based on reviewed literature and expert level judgement. The Analytical Hierarchical Process indicated that the most influential variable determining agroforestry practice were, Soil nutrient availability, Slope, The Normalized Difference Water Index (NDWI), Mean annual rainfall and Elevation, respectively with 5% consistency index. The model results showed that approximately 0.6% (19,072.80 ha) of sub-basin area has optimal growth conditions, 67.83% (2,193,368 ha) suitable, 30.8% (995,382 ha) less suitable and 0.77% (24,841.60 ha) Not suitable conditions for agroforestry practice. The findings indicate that the integration of Geographic Information Systems (GIS) and Remote Sensing (RS) with the Analytic Hierarchy Process (AHP) model, incorporating a weight function, proves to be effective in identifying and assessing land units suitable for agroforestry practices aimed at optimizing production yields. This study's outcomes provide valuable insights for land-use policymakers and farmers, facilitating informed decision-making concerning agroforestry cultivation in the Genale sub-basin and similar watershed regions.

Opportunities and challenges of drones and internet of drones in healthcare supply chains under disruption

This study investigates the use of drones and the Internet of Drones (IoDs) in healthcare supply chains (HSCs), highlighting the opportunities and challenges of integrating them to respond to HSCs disruptions, an area that is currently under-examined in supply chain literature. A mixed methods approach is employed in this research. The article establishes important contributions. Firstly, it evaluates and integrates current research to provide new insights for dealing with pandemics and other similar emergencies. Secondly, the article framework reveals relevant political, economic, social, technological, environmental, and legal influencing factors to respond to disruptions. Thirdly, the analysis through the SCOR provided a hierarchical process model for HSCs enabled by drones and IoDs. Fourthly, the paper provides a framework for a shift from people-dependent HSCs to technology-dependent HSCs. Promising future research directions are indicated to advance the supply chain research on this topic.

Hierarchical Nearest Neighbor Gaussian Process models for discrete choice: Mode choice in New York City

Standard Discrete Choice Models (DCMs) assume that unobserved effects that influence decision-making are independently and identically distributed among individuals. When unobserved effects are spatially correlated, the independence assumption does not hold, leading to biased standard errors and potentially biased parameter estimates. This paper proposes an interpretable Hierarchical Nearest Neighbor Gaussian Process (HNNGP) model to account for spatially correlated unobservables in discrete choice analysis. Gaussian Processes (GPs) are often regarded as lacking interpretability due to their non-parametric nature. However, we demonstrate how to incorporate GPs directly into the latent utility specification to flexibly model spatially correlated unobserved effects without sacrificing structural economic interpretation. To empirically test our proposed HNNGP models, we analyze binary and multinomial mode choices for commuting to work in New York City. For the multinomial case, we formulate and estimate HNNGPs with and without independence from irrelevant alternatives (IIA). Building on the interpretability of our modeling strategy, we provide both point estimates and credible intervals for the value of travel time savings in NYC. Finally, we compare the results from all proposed specifications with those derived from a standard logit model and a probit model with spatially autocorrelated errors (SAE) to showcase how accounting for different sources of spatial correlation in discrete choice can significantly impact inference. We also show that the HNNGP models attain better out-of-sample prediction performance when compared to the logit and probit SAE models, especially in the multinomial case.

An analytical hierarchical process approach to assessing local food preferences among foreign tourists

PurposeThis study evaluates the relative preference for local foods among foreign tourists. It also assessed how the sociodemographic profiles of tourists influence their local food preferences.Design/methodology/approachThis research utilized a mixed-methods approach. The initial qualitative analysis phase involved semi-structured, in-depth interviews with stakeholders in Fiji’s local food sector, providing feedback on tourists’ preferred dishes. In the second phase, 204 foreign tourists were surveyed through the location intercept approach at various local restaurants, street food joints and eateries across Fiji. The preferences of these foreign tourists for the local food were subsequently analyzed utilizing the analytical hierarchical process (AHP) model and Chi-square statistics.FindingsThis research identified 25 local food dishes most preferred by foreign tourists. A pairwise comparison matrix revealed that “Kokoda” was most preferred, while “Tuna Ika Vakalolo” was the least preferred, and the normalized matrix presented the estimated overall priorities of the local foods based on taste and flavor. Among all the factors analyzed for their potential influence on the local food preferences of foreign tourists, “education attainment” and “marital status” were found to have no significant influence.Originality/valueThis study highlights the varied local dishes preferred by tourists, assessed through the AHP model, and provides the sociodemographic factors influencing these preferences. By identifying and presenting local Fijian dishes across regions and examining sociodemographics’ impact on food preference, this research provides invaluable implications for significantly benefiting the local food vending sector.

Evaluation method of carbon-fiber-reinforced polymer material anti-radiation performance based on synergistic effect model

Aiming at the high computational costs of the current multi-source X-ray radiation numerical simulations, a multi-source X-ray evaluation method based on a synergistic effect model is studied. First, based on the advantage of the low computational cost of single-source X-ray radiation numerical simulations, a hierarchical Gaussian process model is used to construct a superimposed single-source numerical simulation data fusion model. Second, by constructing composite correlation functions, the data fusion ability of the Gaussian process model is improved. The unknown parameters in the data fusion evaluation model are solved with the help of the Markov chain Monte Carlo method and a nonlinear optimization algorithm. Based on the obtained sampling values of the unknown parameters, an approximate solution method for the estimated value of the superimposed single-source X-ray radiation numerical simulations with unknown input conditions is given. Then a synergistic effect model is constructed based on the established linear regression surrogate model. The synergistic evaluation of multi-source X-ray radiation tests based on single-source X-ray radiation numerical simulation superimposed test data is studied. Finally, carbon-fiber-reinforced polymer experiments show the proposed method in better than existing Kriging methods, for prediction of multi-source X-ray radiation data.

GPMelt: A hierarchical Gaussian process framework to explore the dark meltome of thermal proteome profiling experiments.

Thermal proteome profiling (TPP) is a proteome wide technology that enables unbiased detection of protein drug interactions as well as changes in post-translational state of proteins between different biological conditions. Statistical analysis of temperature range TPP (TPP-TR) datasets relies on comparing protein melting curves, describing the amount of non-denatured proteins as a function of temperature, between different conditions (e.g. presence or absence of a drug). However, state-of-the-art models are restricted to sigmoidal melting behaviours while unconventional melting curves, representing up to 50% of TPP-TR datasets, have recently been shown to carry important biological information. We present a novel statistical framework, based on hierarchical Gaussian process models and named GPMelt, to make TPP-TR datasets analysis unbiased with respect to the melting profiles of proteins. GPMelt scales to multiple conditions, and extension of the model to deeper hierarchies (i.e. with additional sub-levels) allows to deal with complex TPP-TR protocols. Collectively, our statistical framework extends the analysis of TPP-TR datasets for both protein and peptide level melting curves, offering access to thousands of previously excluded melting curves and thus substantially increasing the coverage and the ability of TPP to uncover new biology.

Using process model to define the legislative framework of electronic prescription in the Czech Republic.

Defining legislation for electronic prescription systems (EPS) is inherently challenging due to conflicting interests and requirements. The study aimed to develop a comprehensive EPS within the Czech healthcare framework, integrating legislative, process, and technical aspects to ensure security, user acceptability, and compliance with health regulations. A process modeling tool based on hierarchical state machines was employed to create a detailed process architecture for the EPS. Key participants, scenarios, and state transitions were identified and incorporated into a process model using the Craft.CASE based on the BORM methodology. The final process architecture model facilitated interdisciplinary communication and consensus-building among stakeholders, including healthcare professionals, IT specialists, and legislators. The model served as a foundation for the legislative framework and was included in the explanatory memorandum for the draft amendment to the Pharmaceuticals Act. The use of hierarchical state machines and process modeling tools in developing healthcare legislation effectively reduced misunderstandings and ensured precise implementation. This method can be applied to other complex legislative and system design projects, enhancing stakeholder communication and project success.

The distinct affect regulation functions of body image flexibility and inflexibility: A prospective study in adolescents and emerging adults

Body image flexibility and inflexibility are alternative ways of responding to body image threats. Affect regulation offers a framework for understanding how these responses impact health outcomes; however, research in young people is limited. This prospective study tested two potential affect regulation functions in adolescents and emerging adults (N = 351, 66.7% female) via online surveys completed five months apart. Specifically, body image flexibility and inflexibility were tested as (a) direct predictors of adaptive (body appreciation, intuitive eating) and maladaptive (muscle-building strategies, disordered eating) outcomes, and (b) moderators of the indirect effect of a social comparison threat via body appreciation. Results from hierarchical linear and conditional process models showed that body image flexibility and inflexibility served distinct affect regulation functions. Whereas body image inflexibility directly predicted adaptive and maladaptive body- and eating-related outcomes, body image flexibility protected against the negative effects of social comparison on intuitive eating via body appreciation. Findings suggest that adolescents and emerging adults could benefit from learning body image flexibility skills and reducing inflexibility to promote positive body image and prevent body- and eating-related disturbances. Future research should investigate how body image flexibility and inflexibility relate to other established threats and outcomes in diverse demographic groups.

Delineation of groundwater prospective zones using multivariate and spatial analysis techniques in Henan Province North China Plain

Groundwater is an essential resource that meets all of humanity’s daily water demands, supports industrial development, influences agricultural output, and maintains ecological equilibrium. The objective behind this research is to find groundwater prospective zones and their sustainable development in Henan Province, located in the North China Plain, with the help of analytical hierarchical process (AHP) and frequency ratio (FR) models. Consequently, a groundwater inventory map was created, utilizing 70% of the data for training and 30% for testing purposes. The resulting maps were overlaid with groundwater levels and the locations of wells within the study area. The performance and validation of the models were assessed using the Area Under Curve (AUC) curve, which indicated an accuracy of 70% for the AHP model and 69% for the FR model. The study indicates that areas with suitable gradients and rainfall patterns generally have high groundwater prospective zonation (GWPZ), i.e., Zhumadian and Nanyang (ZN). However, AHP demonstrated a higher predictive capability in comparison to FR the broader context. Whereas GIS-RS methodology aligns with existing wells and field data that validate it in range of good and excellent. In conclusion, this study provides valuable insights for effective groundwater exploration and sustainable water supply planning in Henan Province.

A hierarchy of visual processing deficits in body dysmorphic disorder: a conceptual review and empirical investigation

ABSTRACT Objective Abnormal visual processing has been proposed as a mechanism underlying excessive focus on minor appearance flaws in body dysmorphic disorder (BDD). Existing BDD research has not differentiated the various stages of face processing (featural, first-order configural, holistic and second-order configural) that are required for higher-order processes such as emotion recognition. This study investigated a hierarchical visual processing model to examine the nature of abnormalities in face processing in BDD. Method Thirty BDD participants and 27 healthy controls completed the Navon task, a featural and configural face processing task and a facial emotion labelling task. Results BDD participants performed similarly to controls when processing global and local non-face stimuli on the Navon task, when detecting subtle changes in the features and spacing of a target face, and when labelling emotional faces. However, BDD participants displayed poorer performance when viewing inverted faces, indicating difficulties in configural processing. Conclusions The findings only partially support prior work. However, synthesis of results with previous findings indicates that heterogenous task methodologies may contribute to inconsistent findings. Recommendations are provided regarding the task parameters that appear most sensitive to abnormalities in BDD.

Hamlet: Hierarchical Multi-Objective Reinforcement Learning Method for Charging Power Control of Electric Vehicles With Dynamic Quantity

Electric vehicle (EV) is emerging as an effective choice to reduce carbon emissions in the modern transportation system. However, the large access of EVs brings tremendous passive influence on the stability and performance of power grid operation. Thus, it is valuable to investigate the charging power management method to improve charging experience of EV drivers while maintaining normal bus voltage of the power system. This paper builds the charging power control problem of multiple EVs as a hierarchical Markov decision process (MDP) model and propose a hierarchical multi-objective reinforcement learning method (Hamlet) to obtain the real-time charging power control decisions. Specifically, by treating EVs with the same remaining charging time as the same agent, this paper addresses the dynamic state and action spaces issue and significantly reduce the input dimension of both state and action spaces. Besides, this paper quantifies drivers’ anxiety about the battery volume and distributes the comfort reward over the entire charging interval, which overcomes the delayed comfort reward issue. In order to alleviate the influence of multiple objectives on training stability, this paper develops a multi-objective learning method to dynamically adjust the optimization direction. Simulation results on IEEE 33 bus and 69 bus test feeders prove the validity of the proposed method in mitigating voltage fluctuation, minimizing bill payment and maximizing battery comfort.

Сonceptual basis for building a base of models for determining the value of a construction company's information assets

An actual area of research in the field of increasing the effectiveness of the functioning of modern information and communication technologies (ICT) is the principles and methods of establishing the value of information assets (IA) in order to effectively choose means of their protection.
 The object of research of this work is the decision support system (DSPR) for establishing the value of the information asset of the information and communication technologies of the construction company.
 The purpose of the study is to determine the conceptual foundations of building a base of system models, which would allow creating a flexible dynamic decision support system for the implementation of decision-making processes, having a flexible structure that provides for the possibility of implementing alternative scenarios (options) for establishing the value of information assets.
 The principles and conceptual foundations of building a base of models, a decision-making support system with a flexible structure, configured for the possibility of implementing alternative scenarios of expert evaluation, depending on the degree of certainty and availability of data, are proposed. As a conceptual basis, the structure of the model base, alternative and scenario modeling of decision-making processes, a classifier of scenario formation conditions, a database of models in the form of a class diagram, a multivariate process model of information technology for establishing the value of information assets in the form of a set of hierarchical process models of alternative network modeling are considered.
 In the proposed approach, increasing the probability of the obtained results is achieved due to the fact that the user is given the opportunity to choose the most appropriate and effective evaluation scenario, taking into account the goals and objectives of the evaluation, the availability and degree of certainty of the basic data, the existing resource, time and information limitations when making decisions.

Multiple maneuvering target tracking based on hierarchical Dirichlet process and hidden Markov model

This paper is concerned with multiple maneuvering target tracking problems. Although the interacting multiple model (IMM) algorithm can be used for maneuvering targets tracking, the performance is limited as the motion mode set of the target is fixed in advance. In this paper, we address the problem of multiple maneuvering targets with unknown maneuvering models in cluttered environments. A finite hidden Markov model (HMM) is developed with truncated Hierarchical Dirichlet process (HDP) to recognize the unknown and changing motion modes. Further, the HDP-HMM is introduced into multiple hypothesis tracking (MHT) and probabilistic multiple hypothesis tracking (PMHT) approaches for multiple maneuvering targets tracking with data association uncertainty. To joint estimate the HDP-HMM parameters and target states, the particle learning and variational inference approaches are used, and the solutions of HDP-HMM-MHT and HDP-HMM-PMHT algorithms are obtained based on Rao-Blackwellized particle filter and variational Bayesian methods, respectively. As the changes of maneuver models can be learned using HDP, the multi-target tracking accuracy of the proposed algorithm is improved. Simulation results show that the proposed algorithms outperform the traditional IMM-MHT method with fixed model sets.

A Hierarchical Expected Improvement Method for Bayesian Optimization

The Expected Improvement (EI) method, proposed by Jones, Schonlau, andWelch, is a widely used Bayesian optimization method, which makes use of a fitted Gaussian process model for efficient black-box optimization. However, one key drawback of EI is that it is overly greedy in exploiting the fitted Gaussian process model for optimization, which results in suboptimal solutions even with large sample sizes. To address this, we propose a new hierarchical EI (HEI) framework, which makes use of a hierarchical Gaussian process model. HEI preserves a closed-form acquisition function, and corrects the over-greediness of EI by encouraging exploration of the optimization space. We then introduce hyperparameter estimation methods which allow HEI to mimic a fully Bayesian optimization procedure, while avoiding expensive Markov-chain Monte Carlo sampling steps. We prove the global convergence of HEI over a broad function space, and establish near-minimax convergence rates under certain prior specifications. Numerical experiments show the improvement of HEI over existing Bayesian optimization methods, for synthetic functions and a semiconductor manufacturing optimization problem. Supplementary materials for this article are available online.

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis.

Progressive organ-level disorders in the human body are often correlated with diseases in other body parts. For instance, liver diseases can be linked with heart issues, while cancers can be linked with brain diseases (or psychological conditions). Defining such correlations is a complex task, and existing deep learning models that perform this task either showcase lower accuracy or are non-comprehensive when applied to real-time scenarios. To overcome these issues, this text proposes design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. The proposed model initially collects temporal and spatial data scans for different body parts and uses a multidomain feature extraction engine to convert these scans into vector sets. These vectors are processed by a Bacterial Foraging Optimizer (BFO), which assists in identification of highly variant feature sets, which are individually classified into different disease categories. A fusion of Inception Net, XCeption Net, and GoogLeNet Models is used to perform these classifications. The classified categories are linked with other disease types via temporal analysis of blood reports. The temporal analysis engine uses Modified Analytical Hierarchical Processing (MAHP) Model for calculating inter-organ disease dependency probabilities. Based on these probabilities, the model is able to generate a patient-level correlation map, which can be used by clinical experts to suggest remedial treatments, due to which the model was able to identify correlations between brain disorders and kidneys, heart diseases and lungs, heart diseases and liver, brain diseases and different types of cancers with high efficiency when evaluated under clinical scenarios. When validated on MITBIH, DEAP, CT Kidney, RIDER, and PLCO data samples, it was observed that the proposed model was capable of improving accuracy of correlation by 8.5%, while improving precision and recall by 3.2% when compared with existing correlation models under similar clinical scenarios.

Delineation of Groundwater Potential Area using an AHP, Remote Sensing, and GIS Techniques in the Ifni Basin, Western Anti-Atlas, Morocco

An assessment of potential groundwater areas in the Ifni basin, located in the western Anti-Atlas range of Morocco, was conducted based on a multicriteria analytical approach that integrated a set of geomorphological and hydroclimatic factors influencing the availability of this resource. This approach involved the use of geographic information systems (GIS) and hierarchical analytical process (AHP) models. Different factors were classified and weighted according to their contribution to and impact on groundwater reserves. Their normalized weights were evaluated using a pairwise comparison matrix. Four classes of potentiality emerged: very high, high, moderate, and low, occupying 15.22%, 20.17%, 30.96%, and 33.65%, respectively, of the basin’s area. A groundwater potential map (GWPA) was validated by comparison with data from 134 existing water points using a receiver operating characteristic (ROC) curve. The AUC was calculated at 80%, indicating the good predictive accuracy of the AHP method. These results will enable water operators to select favorable sites with a high groundwater potential.

Improving Implementation of an Electronic Prescription System for COVID-19 Vaccination in the Czech Republic: Process Modeling Approach.

It is very difficult to find a consensus that will be accepted by most players when creating health care legislation. The Czech electronic prescription system was launched in 2011 and new functions were introduced in 2018. To ensure that these functions will not conflict with any other existing law, a process modeling tool based on the patent "Method and system for automated requirements modeling" was used successfully in the Czech Republic for the first time. The aim of this project was to develop another successful application of process modeling to add COVID-19 vaccination records to the existing electronic prescription system. The method employed was based on the mathematical theory of hierarchical state diagrams and process models. In the first step, sketches that record the results of informal discussions, interviews, meetings, and workshops were prepared. Subsequently, the architecture containing the main participants and their high-level interactions was drafted. Finally, detailed process diagrams were drawn. Each semiresult was discussed with all involved team members and stakeholders to incorporate all comments. By repeating this procedure, individual topics were gradually resolved and the areas of discussion were narrowed down until reaching complete agreement. This method proved to be faster, clearer, and significantly simpler than other methods. Owing to the use of graphic tools and symbols, the risk of errors, inaccuracies, and misunderstandings was significantly reduced. The outcome was used as an annex to the bill in the legislative process. One of the main benefits of this approach is gaining a higher level of understanding for all parties involved (ie, legislators, the medical community, patient organizations, and information technology professionals). The process architecture model in a form of a graphic scheme has proven to be a valuable communication platform and facilitated negotiation between stakeholders. Moreover, this model helped to avoid several inconsistencies that appeared during workshops and discussions. Our method worked successfully even when participants were from different knowledge areas. The vaccination record process model was drafted in 3 weeks and it took a total of 2 months to pass the bill. In comparison, the initial introduction of the electronic prescription system using conventional legislative methods took over 1 year, involving immediate creation of a text with legislative intent, followed by paragraph-by-section wording of the legislation that was commented on directly. These steps are repeated over and over, as any change in any part of the text has to be checked and rechecked within the entire document. Compared with conventional methods, we have shown that using our method for the process of modification of legislation related to such a complex issue as the integration of COVID-19 vaccination into an electronic prescription model significantly simplifies the preparation of a legislative standard.

Formal Modeling and Discovery of Hierarchical Business Processes: A Petri Net-Based Approach

Business processes are critical for information systems to control workflows and deliver services. Although existing process discovery techniques can generate flat process models from business event logs, few of them have investigated the notion of hierarchy (i.e., subprocesses) yet. To fill the gap, this article first defines the concept of hierarchical Petri nets (HPNs), which can support the formal modeling and correctness verification of processes with subprocesses. Followed by that, we propose an approach which can effectively discover HPNs from event logs with lifecycle information. Moreover, to quantify the quality of discovered HPNs, details on how to transform an HPN to a classical Petri net are given such that existing metrics can be applied. All proposed approaches have been fully implemented in ProM, and experiments over both synthetic and real-life event logs demonstrate that our approach can effectively discover hierarchical process models. Specifically, compared to exiting approaches on processes discovery, our approach can generally perform better in terms of model quality.

Changes in both top-down and bottom-up effective connectivity drive visual hallucinations in Parkinson's disease.

Visual hallucinations are common in Parkinson's disease and are associated with a poorer quality of life and a higher risk of dementia. An important and influential model that is widely accepted as an explanation for the mechanism of visual hallucinations in Parkinson's disease and other Lewy body diseases is that these arise due to aberrant hierarchical processing, with impaired bottom-up integration of sensory information and overweighting of top-down perceptual priors within the visual system. This hypothesis has been driven by behavioural data and supported indirectly by observations derived from regional activation and correlational measures using neuroimaging. However, until now, there was no evidence from neuroimaging for differences in causal influences between brain regions measured in patients with Parkinson's hallucinations. This is in part because previous resting-state studies focused on functional connectivity, which is inherently undirected in nature and cannot test hypotheses about the directionality of connectivity. Spectral dynamic causal modelling is a Bayesian framework that allows the inference of effective connectivity-defined as the directed (causal) influence that one region exerts on another region-from resting-state functional MRI data. In the current study, we utilize spectral dynamic causal modelling to estimate effective connectivity within the resting-state visual network in our cohort of 15 Parkinson's disease visual hallucinators and 75 Parkinson's disease non-visual hallucinators. We find that visual hallucinators display decreased bottom-up effective connectivity from the lateral geniculate nucleus to primary visual cortex and increased top-down effective connectivity from the left prefrontal cortex to primary visual cortex and the medial thalamus, as compared with non-visual hallucinators. Importantly, we find that the pattern of effective connectivity is predictive of the presence of visual hallucinations and associated with their severity within the hallucinating group. This is the first study to provide evidence, using resting-state effective connectivity, to support a model of aberrant hierarchical predictive processing as the mechanism for visual hallucinations in Parkinson's disease.

Hierarchical Bayesian modeling of ocean heat content and its uncertainty

The accurate quantification of changes in the heat content of the world’s oceans is crucial for our understanding of the effects of increasing greenhouse gas concentrations. The Argo program, consisting of Lagrangian floats that measure vertical temperature profiles throughout the global ocean, has provided a wealth of data from which to estimate ocean heat content. However, creating a globally consistent statistical model for ocean heat content remains challenging due to the need for a globally valid covariance model that can capture complex nonstationarity. In this paper, we develop a hierarchical Bayesian Gaussian process model that uses kernel convolutions with cylindrical distances to allow for spatial nonstationarity in all model parameters while using a Vecchia process to remain computationally feasible for large spatial datasets. Our approach can produce valid credible intervals for globally integrated quantities that would not be possible using previous approaches. These advantages are demonstrated through the application of the model to Argo data, yielding credible intervals for the spatially varying trend in ocean heat content that accounts for both the uncertainty induced from interpolation and from estimating the mean field and other parameters. Through cross-validation, we show that our model outperforms an out-of-the-box approach as well as other simpler models. The code for performing this analysis is provided as the R package BayesianOHC.