Process Model Research Articles

Does the use of community childcare centres contribute to greater economic empowerment of rural women? A case study from Burkina-Faso

ABSTRACT Women devote much of their time to unpaid care work in West Africa. This situation is even more pronounced in rural areas and thus contributes to further weakening women’s economic empowerment in these areas. Setting up community childcare centres is one way of creating time for these women. This article therefore analyses the impact of the use of these centres on the economic empowerment of rural women in Burkina Faso, using the Conditional Mixed Process model. Data collected from 1546 rural households in 2022 as part of the implementation of a research project funded by the International Development Research Centre were used for the analyses. The investigations showed that the use of community childcare centres offers more opportunities for rural women to participate in economic activities or the labour market through the time saved as a result of the reduction in time spent on domestic chores. Revitalising and scaling up community childcare centres in Burkina Faso are, therefore, the main way of empowering rural women.

Leveraging consumer chronic time pressure and time management to improve retail venue outcomes

Purpose Consumer choices are influenced by available resources, and time is increasingly recognised as a valuable and scarce resource. This study aims to assess the effect of chronic time pressure (CTP) on the desire to stay in retail venues (DTS), considering the mediating role of utilitarian and hedonic shopping motivations and the moderating role of time management (TM); and examine the role of DTS in enhancing the shopping outcomes of satisfaction, patronage intentions and positive word-of-mouth (WOM). Design/methodology/approach Two survey-based studies were conducted, employing structural equation modelling (SEM) and Hayes’ PROCESS Model No. 1 to test the proposed model. Findings The findings indicate that CTP does not have a direct impact on shopping utilitarian motivations or DTS. However, it has a direct effect on hedonic motivations that mediate the path from CTP to DTS. In addition, TM positively moderates both paths from CTP and hedonic motivations to DTS. Finally, the results demonstrate that reinforcing shopper DTS leads to increased satisfaction, patronage intentions and positive WOM. Research limitations/implications Based on the findings, theoretical contributions and actionable managerial implications regarding how to leverage CTP and TM to enhance shopper engagement and retail venue outcomes are discussed. Originality/value While prior research has often focused on situational time pressure, this study concentrates on CTP and examines the impact of perceived time constraints and feeling rushed on utilitarian and hedonic shopping motivations and DTS. Moreover, the study tests the moderating role of TM and provides evidence that DTS leads to other desirable shopping outcomes.

Two-Dimensional Laplace NMR Reconstruction through Deep Learning Enhancement.

Laplace NMR is a powerful tool for studying molecular dynamics and spin interactions, providing diffusion and relaxation information that complements Fourier NMR used for composition determination and structure elucidation. However, Laplace NMR demands sophisticated signal processing algorithms such as inverse Laplace transform (ILT). Due to the inherently ill-posed nature of ILT problems, it is generally challenging to perform satisfactory Laplace NMR processing and reconstruction, particularly for two-dimensional Laplace NMR. Herein, we propose a proof-of-concept approach that blends a physics-informed strategy with data-driven deep learning for two-dimensional Laplace NMR reconstruction. This approach integrates prior knowledge of mathematical and physical laws governing multidimensional decay signals by constructing a forward process model to simulate relationships among different decay factors. Benefiting from a noniterative neural network algorithm that automatically acquires prior information from synthetic data during training, this approach avoids tedious parameter tuning and enhances user friendliness. Experimental results demonstrate the practical effectiveness of this approach. As an advanced and impactful technique, this approach brings a fresh perspective to multidimensional Laplace NMR inversion.

Machine learning-based predictive control of an electrically-heated steam methane reforming process

Hydrogen plays a crucial role in improving sustainability and offering a clean and efficient energy carrier that significantly reduces greenhouse gas emissions. However, the primary method of industrial hydrogen production, steam methane reforming (SMR), relies on the combustion of hydrocarbons as the heating source for the reforming reactions, resulting in significant carbon emissions. To address this issue, an experimental setup of an electrically-heated steam methane reformer (e-SMR) has been constructed at UCLA, and a lumped first-principle dynamic process model was built based on parameters estimated from the experimental data in a previous study. Subsequently, the first-principle dynamic process model was implemented into the computational model predictive control (MPC) scheme, successfully driving the hydrogen production rate to the desired setpoint. While these works are important and pave the way for developing MPC for large-scale e-SMR processes, the first-principle process model may not accurately reflect the actual process behavior, particularly as the process behavior changes with time. Therefore, the development and establishment of an adaptive data-driven approach for implementing model predictive control in the e-SMR process is necessary. To address this need, the present work investigates the construction of recurrent neural network (RNN) models for an e-SMR process in-depth, utilizing data from an experimentally-validated first-principle model. Specifically, a long short-term memory (LSTM) layer was utilized in the RNN model to effectively capture the complex correlations present in long-term sequential data. Subsequently, this LSTM-based RNN process model was employed to design an MPC, and its performance was evaluated through comparison with proportional-integral (PI) control. To address potential disturbances and variability in a typical e-SMR process, three distinct approaches were developed: MPC with an integrator, MPC with real-time online retraining (transfer learning), and offset-free MPC. These approaches effectively eliminated the offset caused by disturbances. Overall, this study underscores the effectiveness of utilizing RNN models to capture process dynamics in an experimental e-SMR process. It also outlines strategies for employing RNN-based control and multiple approaches to address disturbances in general processes with partially infrequent and delayed measurement feedback. This approach is particularly valuable in scenarios where developing first-principle models for a new process may be challenging.

Impact of hydrothermal treatment on functional properties of pearl millet flour: process modelling and optimisation

Impact of hydrothermal treatment on functional properties of pearl millet flour: process modelling and optimisation

A new financial risk prediction model based on deep learning and quasi-oppositional coot algorithm

Incorporating ground-breaking technologies such as deep learning (DL) has revolutionized predictive modelling in the rapidly evolving landscape of the finance sector. DL approaches, capable of extracting complex patterns from vast data collections, become an efficient approach for predicting financial trends. By integrating the complex neural network architecture with comprehensive datasets, including investor sentiment, market indicators, and economic variables, finance experts have introduced prediction models well known for their ability to capture the nuanced dynamics of financial markets with remarkable performance. Incorporating DL approaches within the finance sector provided the basis for more informed decision-making, enabling institutions, investors, and analysts to capitalize on emerging opportunities with greater confidence and precision and navigate market volatility. This study develops a novel quasi-oppositional coot algorithm with a deep learning-based predictive method on the financial sector (QOCODL-PMFC) technique. The QOCODL-PMFC technique aims to perform a prediction process on the financial sector. The QOCODL-PMFC method applies min-max normalization to measure the input dataset into a meaningful format to achieve this. Next, the QOCODL-PMFC method designs the QOCO technique for selecting an optimal set of features. The QOCODL-PMFC technique applies the attention bidirectional gated recurrent unit (ABiGRU) model for the prediction process. The Harris Hawks Optimization (HHO) model is utilized to boost the performance of the ABiGRU network. The simulation evaluation of the QOCODL-PMFC technique is tested under a benchmark finance dataset. The experimental values of the QOCODL-PMFC technique exhibit a minimal MSE of 0.7452 over other models.

Educational approach for public health ethics in nursing: Focusing on COVID-19.

Background: With the increasing ethical challenges and dilemmas faced by nurses due to various disasters such as COVID-19 worldwide, there is a need for a new public health ethics education curriculum to strengthen competencies for ethical responses in the nursing field. Objectives: This study was aimed to identify the impact of a teaching method utilizing news articles and panel discussion material in the public health ethics education program on nursing students' thinking regarding ethical issues. Design: This was an exploratory study to identify the thinking styles inherent in ethical reflection by analyzing the reflection contents written by nursing students using text mining techniques. Participants: 73 among the students taking a nursing ethics course at a university in Seoul, South Korea, voluntarily participated in this study after providing informed consent. Methods: The public health ethics program was conducted with sessions held once a week for a total of 7 weeks, and reflections written by nursing students were collected as text files during session 5 to 7. In this study, data preprocessing process, keyword analysis, and LDA topic modeling were sequentially conducted utilizing the R program according to the data analysis procedure of text mining techniques. Ethical considerations: This study was conducted under ethics approval from the institution where participants were recruited. Findings and discussion: The results of this study show that the teaching method utilizing news articles enhanced rational ethical deliberation from the cognitive aspect, whereas the teaching method utilizing panel discussion material strengthened the response to emotions on a more internal level. Conclusions: The teaching method utilizing news articles and panel discussion materials in public health ethics education is expected to be mutually complementary and effective, so further studies are recommended.

Unveiling the web of interactions: Analyzing dynamic customer engagements across multiple websites

Online customers exhibit unique dynamic patterns during their journey. In the early phases, customers gather different information from multiple websites. Collectively, the information serves as references, helping customers to make informed decisions. Further, inferior options are eliminated until the journey converges on a single website for conversion. In this study, we examine the dynamic interactions among online customers and websites. To capture the dynamics, we propose a metric, momentum of information-building and incorporate the measure into a dynamically and mutually exciting marked point process model. We apply the proposed model to clickstream data for hotel bookings and find strong evidence of dynamic behavior. Also, we show that ignoring the dynamics may lead to bias in estimating the carryover and spillover effects and diminish the prediction accuracy of website visits and conversions. Furthermore, we offer recommendations for effective advertising strategies focusing on key aspects such as advertising density, timing, and targeting.

Social and physical environment effects on toileting disability among older adults in India

BackgroundTo examine the prevalence of toileting disability among older adults in India and its association with broad aspects of the physical and social environment.MethodsWe use data from the inaugural wave of the Longitudinal Ageing Study in India and focus on adults aged 65 and older (N = 20,789). We draw on the disablement process model and existing frameworks to identify environmental factors and other risk factors that may be associated with toileting disability. Hierarchical logistic regressions are implemented to analyze the health impacts from physical and social environment characteristics.ResultsOne in five older Indian adults had difficulties with toileting, and the prevalence rate of this functional disability varied across sub-national regions. We find that low neighborhood trust was associated with an increased likelihood of toileting disability, as was the use of assistive mobility devices. The negative effects of these social and external environment characteristics hold when we stratified the sample by rural and urban residency. Also, older adults in urban areas without access to toilets and using shared latrines had higher odds of being disabled in terms of toileting. Other factors important in explaining toileting disability among older adults included poor self-rated health, arthritis, currently working, living in the East or West region, and having functional limitations.ConclusionsPoor person-environment fit can compromise older adults’ ability to perform self-care tasks. Policymakers need to look beyond the physical environment (e.g., dedicating resources to construct toilet facilities) to adopt a more holistic, multi-faceted approach in their sanitation policies. Improving the safety of neighborhood surroundings in which shared latrines are located and the availability of accessible toilets that cater to those with mobility impairments can help improve independence in toileting among older adults.

An optimization model for determining cost-efficient maintenance policies for multi-component systems with economic and structural dependencies

In most multi-component systems, the cost-efficiency of maintenance policies depends on technical structural dependencies. Motivated by the recognition that these dependencies must be accounted for in the development of optimal maintenance policies, we develop an optimization model to determine cost-efficient maintenance schedules for multi-component systems. Our main contribution is twofold. First, we introduce directed graphs as an expressive tool to represent the economic and structural dependencies of the system, including situations in which the maintenance of a given component may require other components to be disassembled or maintained. Second, we formulate a Markov Decision Process model, which is solved through the modified policy-iteration algorithm to determine the most cost-efficient policy. This policy indicates which maintenance actions consisting of disassembly and component replacement decisions are optimal when mandatory replacements must be made whenever the system fails, or the reliability of the system falls below a predefined reliability threshold. To our knowledge, this is the first model that provides optimal maintenance policies that comply with reliability requirements in the presence of constraints arising from technical structural dependencies. We illustrate the model with a realistic case study on the development of cost-efficient maintenance policies and show that its results compare favorably with heuristic maintenance policies.

A methodological framework proposal for managing risk in small-scale farming through the integration of knowledge and data analytics

IntroductionClimate change and weather variability pose significant challenges to small-scale crop production systems, increasing the frequency and intensity of extreme weather events. In this context, data modeling becomes a crucial tool for risk management and promotes producer resilience during losses caused by adverse weather events, particularly within agricultural insurance. However, data modeling requires access to available data representing production system conditions and external risk factors. One of the main problems in the agricultural sector, especially in small-scale farming, is data scarcity, which acts as a barrier to effectively addressing these issues. Data scarcity limits understanding the local-level impacts of climate change and the design of adaptation or mitigation strategies to manage adverse events, directly impacting production system productivity. Integrating knowledge into data modeling is a proposed strategy to address the issue of data scarcity. However, despite different mechanisms for knowledge representation, a methodological framework to integrate knowledge into data modeling is lacking.MethodsThis paper proposes developing a methodological framework (MF) to guide the characterization, extraction, representation, and integration of knowledge into data modeling, supporting the application of data solutions for small farmers. The development of the MF encompasses three phases. The first phase involves identifying the information underlying the MF. To achieve this, elements such as the type of knowledge managed in agriculture, data structure types, knowledge extraction methods, and knowledge representation methods were identified using the systematic review framework proposed by Kitchemhan, considering their limitations and the tools employed. In the second phase of MF construction, the gathered information was utilized to design the process modeling of the MF using the Business Process Model and Notation (BPMN).Finally, in the third phase of MF development, an evaluation was conducted using the expert weighting method.ResultsAs a result, it was possible to theoretically verify that the proposed MF facilitates the integration of knowledge into data models. The MF serves as a foundation for establishing adaptation and mitigation strategies against adverse events stemming from climate variability and change in small-scale production systems, especially under conditions of data scarcity.DiscussionThe developed MF provides a structured approach to managing data scarcity in small-scale farming by effectively integrating knowledge into data modeling processes. This integration enhances the capacity to design and implement robust adaptation and mitigation strategies, thereby improving the resilience and productivity of small-scale crop production systems in the face of climate variability and change. Future research could focus on the practical application of this MF and its impact on small-scale farming practices, further validating its effectiveness and scalability.

Effectiveness and Duration of Action of Dolomite Screening Particles Used for Reclamation of Acidic Soils. Empirical Models of the Acidification Process of SodPodzolic Light Loamy Soil (according to Field Experience)

In a long-term 10-variant microfield experiment based on a mediumacidic sod-podzolic light loamy soil, a comparative study of the reclamation properties of the screening of dolomite crumbs stored in dumps, without separation into fractions (SIF), particles of 5–7 and 7–10 mm in size and dolomite flour (DF) prepared from the screening of dolomite when sifting dolomite crumbs with the diameter of the holes is 0/25 mm. The experiment lasts for 14 experimental years. It has been revealed that dolomite particles of 5–7 and 7–10 mm in size are valuable calcareous material. Their use leads to the neutralization of soil acidity already in the year of liming. The higher the dose of application, the greater the reclamation effect. There were no significant differences in the effect of granules of 5–7 and 7–10 mm in size, applied in equivalent doses, on the pH value. The positive effect of liming with meliorant in the amount of 3 and 5 doses of hydrolytic acidity (Ah) did not end after 14 experimental years after reclamation and allowed to postpone the period of repeated (maintenance) liming. The positive effect of using a natural mixture of chalk fractions (SIF) in a scientifically justified dose was inferior to dolomite flour. Clustering of individual variants of the experiment was carried out according to their effect on the рHKCl value for the entire period of the experiment. Linear trends of the averaged dependencies of the acidification process for meliorant particles of various sizes over the entire period of the experiment have been developed. The mechanisms of interaction of dolomite particles with soil in the process of land reclamation are considered.

Spatiotemporal methods for estimating subsurface ocean thermal response to tropical cyclones

Abstract. Tropical cyclones (TCs), driven by heat exchange between the air and sea, pose a substantial risk to many communities around the world. Accurate characterization of the subsurface ocean thermal response to TC passage is crucial for accurate TC intensity forecasts and an understanding of the role that TCs play in the global climate system. However, that characterization is complicated by the high-noise ocean environment, correlations inherent in spatiotemporal data, relative scarcity of in situ observations, and the entanglement of the TC-induced signal with seasonal signals. We present a general methodological framework that addresses these difficulties, integrating existing techniques in seasonal mean field estimation, Gaussian process modeling, and nonparametric regression into an ANOVA decomposition model. Importantly, we improve upon past work by properly handling seasonality, providing rigorous uncertainty quantification, and treating time as a continuous variable, rather than producing estimates that are binned in time. This ANOVA model is estimated using in situ subsurface temperature profiles from the Argo fleet of autonomous floats through a multistep procedure, which (1) characterizes the upper-ocean seasonal shift during the TC season, (2) models the variability in the temperature observations, and (3) fits a thin-plate spline using the variability estimates to account for heteroskedasticity and correlation between the observations. This spline fit reveals the ocean thermal response to the TC passage. Through this framework, we obtain new scientific insights into the interaction between TCs and the ocean on a global scale, including a three-dimensional characterization of the near-surface and subsurface cooling along the TC storm track and the mixing-induced subsurface warming on the track's right side.

Building Digital Twins to Overcome Digitalization Barriers for Automating Construction Site Management

Construction sites are highly unpredictable environments involving a wide variety of stakeholders with complex information exchanges, which lead to the well-known inefficiencies and unproductivity of the construction sector. The adoption of Building Digital Twins (BDT) in the construction site is a promising solution to this issue, by automating data acquisition and knowledge extraction processes and providing what-if scenario simulation capabilities. Furthermore, the current research sets the principles to define, replicate, and scale-up the architecture of a Building Digital Twin Platform (BDTP), conceived as a scalar ecosystem, which allows to seamlessly manage on-site construction processes, integrating cross-cutting domains for the construction site optimization (Progress monitoring, Quality control, Operational Health and Safety, Equipment control, and Production planning). The starting point of the research is a comprehensive diagnosis of on-site process inefficiencies and the barriers to its digitalization leading to the user requirements, which have been underpinned by questionnaires and interviews addressed within an open innovation user-centered approach around Living Labs. The research has been conceived following the Design Science Research (DSR) methodology and based on the Plan-Do-Check-Act (PDCA) analysis for the continuous improvement of the construction process. By means of the adoption of the standard Business Process Model and Notation (BPMN), based on the BDTP architecture, the research has resulted in BPMN workflows stemmed from the Digital Twin (DT) where the DT itself is an actor in a service-oriented data-exchange workflow. Moreover, the use of a BDTP can pave the way for the transition from user-driven construction management to hybrid management, coexisting with both human and digital actors and merging expert knowledge with artificial intelligence techniques.

Compressive load analysis of mechanical setting process of test string Packer in Ultra-high-pressure Gas Wells

ABSTRACT To address the packer setting problems under complex operating conditions in ultra-high pressure gas wells, a mechanical analysis model for the mechanical setting process of packer in ultra-high pressure gas wells is established. The model takes into account factors such as wellbore trajectory, bending moment, internal and external fluid pressure, and the coupled effect of axial load, frictional resistance and buckling. The established model can be used to calculate the axial force of tubing string in ultra-high pressure gas wells, and analyze the effects of the tubing string length below packer, line weight combination, friction coefficient and the released wellhead suspended load on the packer setting force. The case calculation and parameter sensitivity analysis indicate that the original design of a high-pressure gas well cannot meet the requirements of packer setting. The setting performance of the packer can be effectively improved by adjusting the released wellhead suspended load, optimizing the tubing string length below the packer, reducing the friction coefficient and optimizing the length configuration of tubing string with small diameter. An increase in wellhead release weight by 60 kN was accompanied by a corresponding increase in packer pressure of 12.48 kN. When the lower string length was extended by 600 m, the packer weight without suspension was reduced by 65.34 kN, whereas the packer weight with release was diminished by 21.69 kN. Furthermore, an augmentation in the friction coefficient by 0.15 resulted in a decrease in the pressure weight of the packer under release suspension by 55.79 kN. Additionally, a reduction in the small pipe diameter length by 1000 m caused an increase in the packer weight with a release load of 17.98 kN. The research content not only provides theoretical guidance for the long-term safety of test string in high-pressure gas wells, but also offers practical insights for the design and operation of packers.

Evaluating the dentistry program in Iran using the context, input, process, and product (CIPP) model: a comprehensive analysis

Evaluating the dentistry program in Iran using the context, input, process, and product (CIPP) model: a comprehensive analysis

Increased ecological land and atmospheric CO2 dominate the growth of ecosystem carbon sinks under the regulation of environmental conditions in national key ecological function zones in China

Increased ecological land (IEL) such as forests and grasslands can greatly enhance ecosystem carbon sinks. Understanding the mechanisms for the magnitude of IEL-induced ecosystem carbon sinks is crucial for achieving carbon neutrality. We estimated the impact of IEL, specifically the increase in forests and grasslands, as well as global changes including atmospheric CO2 concentration, nitrogen deposition, and climate change on net ecosystem productivity (NEP) in National Key Ecological Function Zones (NKEFZs) in China using a calibrated ecological process model. The NEP in NKEFZs in China was calculated to be 119.4 Tg C yr−1, showing an increase of 42.6 Tg C yr−1 from 2001 to 2021. Compared to the slight contributions of climate change (−8.0%), nitrogen deposition (11.5%), and reduction in ecological land (−3.5%), the increase in NEP was primarily attributed to CO2 (66.5%) and IEL (33.5%). Moreover, the effect of IEL (14.8 Tg C yr−1) surpassed that of global change (13.1 Tg C yr−1) in the land use change zone. The IEL-induced NEP is significantly associated with CO2 fertilization, regulated by precipitation and nitrogen deposition. The high values of IEL-induced NEP occurred in areas with precipitation exceeding 800 mm and nitrogen deposition exceeding 25 kg N ha−1 yr−1. We recommend prioritizing the expansion of ecological land in areas with sufficient water and nutrients to enhance CO2 fertilization, while avoiding increasing ecological land in regions facing unfavorable climate change conditions. This study serves as a foundation for comprehending the NEP response to ecological restoration and global change.

A microdiscectomy surgical video annotation framework for supervised machine learning applications.

Lumbar discectomy is among the most common spine procedures in the US, with 300,000 procedures performed each year. Like other surgical procedures, this procedure is not excluded from potential complications. This paper presents a video annotation methodology for microdiscectomy including the development of a surgical workflow. In future work, this methodology could be combined with computer vision and machine learning models to predict potential adverse events. These systems would monitor the intraoperative activities and possibly anticipate the outcomes. A necessary step in supervised machine learning methods is video annotation, which involves labeling objects frame-by-frame to make them recognizable for machine learning applications. Microdiscectomy video recordings of spine surgeries were collected from a multi-center research collaborative. These videos were anonymized and stored in a cloud-based platform. Videos were uploaded to an online annotation platform. An annotation framework was developed based on literature review and surgical observations to ensure proper understanding of the instruments, anatomy, and steps. An annotated video of microdiscectomy was produced by a single surgeon. Multiple iterations allowed for the creation of an annotated video complete with labeled surgical tools, anatomy, and phases. In addition, a workflow was developed for the training of novice annotators, which provides information about the annotation software to assist in the production of standardized annotations. A standardized workflow for managing surgical video data is essential for surgical video annotation and machine learning applications. We developed a standard workflow for annotating surgical videos for microdiscectomy that may facilitate the quantitative analysis of videos using supervised machine learning applications. Future work will demonstrate the clinical relevance and impact of this workflow by developing process modeling and outcome predictors.

Improvement of mathematical model for catalytic reforming process with a stationary catalyst layer

Improvement of mathematical model for catalytic reforming process with a stationary catalyst layer

A detailed process model of biomass gasification in bubbling fluidized bed: integration of hydrodynamics into reaction modeling

A detailed process model of biomass gasification in bubbling fluidized bed: integration of hydrodynamics into reaction modeling