Linear Process Model Research Articles

How does Internet use impact the mental health of social assistance recipients in China? Examining the chain mediating role of social support and family resilience.

Previous studies have explored the relationship between Internet use and mental health, but there has been a lack of focus on social assistance recipients. Additionally, there has been insufficient discussion on the impact mechanisms of social support and family resilience on this relationship. This study aims to fill these gaps. Using data from social assistance recipients in China, this study analyzes the relationship between Internet use and the mental health of social assistance recipients and its mechanisms based on the Mindsponge Theory and Conservation of Resources Theory. This study used multiple linear regression models and PROCESS models to complete data analysis on 765 Chinese social assistance recipients samples to test the hypothesis. All analyzes were performed using SPSS26.0 and MPlus 8.0. The empirical analysis shows that Internet use positively correlates with the mental health of social assistance recipients, and social support and family resilience mediate between Internet use and mental health. In addition, social support and family resilience also play a chain mediating role in the relationship between Internet use and mental health. The analysis results of this study support our hypothesis. Combined with the findings of this study, we discussed the theoretical significance, practical significance, limitations, and future research directions of this study.

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.

The entropy rate of Linear Additive Markov Processes.

This work derives a theoretical value for the entropy of a Linear Additive Markov Process (LAMP), an expressive but simple model able to generate sequences with a given autocorrelation structure. Our research establishes that the theoretical entropy rate of a LAMP model is equivalent to the theoretical entropy rate of the underlying first-order Markov Chain. The LAMP model captures complex relationships and long-range dependencies in data with similar expressibility to a higher-order Markov process. While a higher-order Markov process has a polynomial parameter space, a LAMP model is characterised only by a probability distribution and the transition matrix of an underlying first-order Markov Chain. This surprising result can be explained by the information balance between the additional structure imposed by the next state distribution of the LAMP model, and the additional randomness of each new transition. Understanding the entropy of the LAMP model provides a tool to model complex dependencies in data while retaining useful theoretical results. To emphasise the practical applications, we use the LAMP model to estimate the entropy rate of the LastFM, BrightKite, Wikispeedia and Reuters-21578 datasets. We compare estimates calculated using frequency probability estimates, a first-order Markov model and the LAMP model, also considering two approaches to ensure the transition matrix is irreducible. In most cases the LAMP entropy rates are lower than those of the alternatives, suggesting that LAMP model is better at accommodating structural dependencies in the processes, achieving a more accurate estimate of the true entropy.

A Gaussian mixture multiple-model belief propagation filter for multisensor-multitarget tracking

This paper presents a novel Gaussian mixture multi-model belief propagation (GMM-BP) filter for maneuvering multitarget tracking with multiple sensors. The filter is built upon the BP-based multisensor-multitarget tracking scheme, enabling accurate estimation of target numbers and states. It assumes linear Gaussian target motion, birth process, and sensor measurement models and utilizes the Gaussian mixture model to represent the target's marginal posterior probability density function. Additionally, multiple models are incorporated for target motion, enhancing the maneuvering target tracking capability. Closed-form recursions for means, covariances, and weights of Gaussian components in the filter are derived. To enhance scalability with the number of sensors, a simplified multi-sensor data fusion process is proposed, thereby preserving a linear relationship between Gaussian components and the number of sensors. The performance of the filter was evaluated in a three-dimensional (3D) simulated environment. Compared to the particle-implemented BP filter and the labeled multi-Bernoulli (LMB) filter, the proposed GMM-BP filter exhibited a significant improvement in computational efficiency while maintaining a high level of tracking accuracy.

Pain Intensity, Pain-Related Anxiety, and Hazardous Drinking Among Individuals With PTSD

Objective Evidence suggests that pain intensity may be indirectly linked to hazardous drinking and PTSD symptom severity via pain-related anxiety. The goal of this analysis was to test the hypotheses in a population with PTSD symptoms that pain intensity would be positively and indirectly associated with hazardous drinking, alcohol dependence, alcohol-related problems, and PTSD symptom severity via pain-related anxiety. Methods Heavy drinkers with probable PTSD were recruited via Qualtrics panels (N = 371, 53% Female, M age = 39.68, SD = 10.86). Linear regression and conditional process models were conducted to examine indirect associations between pain intensity and primary outcomes via pain-related anxiety. Results Pain intensity was found to be indirectly associated with hazardous drinking, alcohol dependence, alcohol-related problems, and PTSD symptom severity via greater pain-related anxiety. Conclusion These initial findings suggest that pain-related anxiety may play an important role in relations between the experience of pain and hazardous patterns of alcohol consumption among individuals with probable PTSD. Future research is needed to determine the temporal nature of these associations and to examine the potential utility of treatments that address pain-related anxiety in the context of comorbid pain, PTSD, and hazardous drinking.

Real-life data-driven model predictive control for building energy systems comparing different machine learning models

By considering forecasts and exploiting storage effects, model predictive control can achieve significant energy and cost savings in the building sector. However, due to the high individual modeling effort, model predictive control lacks practical applicability. For that reason, data-driven process models, approximating the system behavior based on measurements, have become increasingly popular in recent years. Still, scientific literature lacks consent about the most promising model types and efficient workflows to integrate different machine learning models into a model predictive controller. With this work, we present a workflow to provide efficient model predictive controllers based on measurement data automatically. The main idea is to translate different machine learning models into optimization syntax to enable efficient optimization with full access to gradients. We currently consider artificial neural networks, gaussian process regression, and simple linear regression process models. We use a generic model ontology to automatize the controller generation further and test the methodology on two real-life use cases. The first use case is the application of five office rooms with smart thermostat valves. The second use case is a test hall with an air handling unit and a concrete core activation. Using only two days of initial training data, we deploy controllers based on the different model types for six weeks in the offices and apply online learning to improve the models continuously. We observe only minor differences in controller performance despite the artificial neural networks showing the highest prediction accuracy. The second use case shows that the simple linear models require less controller tuning effort. Thus, for practical applications, we recommend linear regression models.

Multi-Performance Degradation System Reliability Analysis with Varying Failure Threshold Based on Copulas

This paper investigated reliability modeling for systems subject to dependent competing risks considering that variation of the failure threshold is not considered in most studies on competing failure reliability. Firstly, the variation of degradation quantity under shocks was analyzed, and the variation of the threshold was considered on this basis. Secondly, the cumulative degradation under the influence of the random shock process was analyzed. The attractive property of Copula functions is symmetry. Then, a linear Wiener process model was applied to model performance degradation failure, and a multi-performance degradation correlated-competition model based on a Copula function was constructed, which considered the correlated competition between multi-performance degradation failures. Lastly, a micromotor system was used to analyze the applicability of the proposed model for bivariate instances, demonstrating the rationality and effectiveness of the proposed model.

Data‐driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large‐scale nonlinear processes

AbstractIn this article, we consider a state estimation problem for large‐scale nonlinear processes in the absence of first‐principles process models. By exploiting process operation data, both process modeling and state estimation design are addressed within a distributed framework. By leveraging the Koopman operator concept, a parallel subsystem modeling approach is proposed to establish interactive linear subsystem process models in higher‐dimensional subspaces, each of which correlates with the original nonlinear subspace of the corresponding process subsystem via a nonlinear mapping. The data‐driven linear subsystem models can be used to collaboratively characterize and predict the dynamical behaviors of the entire nonlinear process. Based on the established subsystem models, local state estimators that can explicitly handle process operation constraints are designed using moving horizon estimation. The local estimators are integrated via information exchange to form a distributed estimation scheme, which provides estimates of the unmeasured/unmeasurable state variables of the original nonlinear process in a linear manner. The proposed framework is applied to a chemical process and an agro‐hydrological process to illustrate its effectiveness and applicability. Good open‐loop predictability of the linear subsystem models is confirmed, and accurate estimates of the process states are obtained without requiring a first‐principles process model.

Discovering interpretable medical process models: A case study in trauma resuscitation

Understanding the actual work (i.e., “work-as-done”) rather than theorized work (i.e., “work-as-imagined”) during complex medical processes is critical for developing approaches that improve patient outcomes. Although process mining has been used to discover process models from medical activity logs, it often omits critical steps or produces cluttered and unreadable models. In this paper, we introduce a TraceAlignment-based ProcessDiscovery method called TAD Miner to build interpretable process models for complex medical processes. TAD Miner creates simple linear process models using a threshold metric that optimizes the consensus sequence to represent the backbone process, and then identifies both concurrent activities and uncommon-but-critical activities to represent the side branches. TAD Miner also identifies the locations of repeated activities, an essential feature for representing medical treatment steps. We conducted a study using activity logs of 308 pediatric trauma resuscitations to develop and evaluate TAD Miner. TAD Miner was used to discover process models for five resuscitation goals, including establishing intravenous (IV) access, administering non-invasive oxygenation, performing back assessment, administering blood transfusion, and performing intubation. We quantitively evaluated the process models with several complexity and accuracy metrics, and performed qualitative evaluation with four medical experts to assess the accuracy and interpretability of the discovered models. Through these evaluations, we compared the performance of our method to that of two state-of-the-art process discovery algorithms: Inductive Miner and Split Miner. The process models discovered by TAD Miner had lower complexity and better interpretability than the state-of-the-art methods, and the fitness and precision of the models were comparable. We used the TAD process models to identify (1) the errors and (2)the best locations for the tentative steps in knowledge-driven expert models. The knowledge-driven models were revised based on the modifications suggested by the discovered models. The improved modeling using TAD Miner may enhance understanding of complex medical processes.

Design and Implementation of Intrusion Detection System (IDS) for Wireless Local Area Network (WLAN) Security at SMKN 5 Bengkulu City

The network at SMK Negeri 5 Bengkulu City at this time often has complaints such as a decrease in internet network performance which in turn affects all computers connected to the network. Intrusion Detection System (IDS) whose job is to monitor networks and suspicious activities in a network system. The research method that the authors use is a linear sequential process model. The life cycle of implementing a network system is defined in a number of phases, namely: Problem Identification and Formulation, Literature Study, Testing, Implementation, System Design, System Requirements Analysis. The author uses Snort, Telegram Bot on Ubuntu based IDS sensor engine. The results of this thesis research conclude that the applied IDS system can function to detect intruders or intruders on the IDS sensor machine, which is displayed in the form of notifications to telegrams.

Bayesian Variable Selection in Double Generalized Linear Tweedie Spatial Process Models

Double generalized linear models provide a flexible framework for modeling data by allowing the mean and the dispersion to vary across observations. Common members of the exponential dispersion family including the Gaussian, Poisson, compound Poisson-gamma (CP-g), Gamma and inverse-Gaussian are known to admit such models. The lack of their use can be attributed to ambiguities that exist in model specification under a large number of covariates and complications that arise when data display complex spatial dependence. In this work we consider a hierarchical specification for the CP-g model with a spatial random effect. The spatial effect is targeted at performing uncertainty quantification by modeling dependence within the data arising from location based indexing of the response. We focus on a Gaussian process specification for the spatial effect. Simultaneously, we tackle the problem of model specification for such models using Bayesian variable selection. It is effected through a continuous spike and slab prior on the model parameters, specifically the fixed effects. The novelty of our contribution lies in the Bayesian frameworks developed for such models. We perform various synthetic experiments to showcase the accuracy of our frameworks. They are then applied to analyze automobile insurance premiums in Connecticut, for the year of 2008.

Combined control of multirate impulse processes in a cognitive map of COVID-19 morbidity

In this article, a cognitive map (CM) of COVID-19 morbidity in a given region was built. A general linear impulse process (IP) model in the CM was developed and measured, and unmeasured CM node coordinates were defined. The general IP model was decomposed into interrelated subsystems with measurable and unmeasurable node coordinates. For the subsystem with measurable node coordinates, multirate sampling of coordinates was conducted, resulting in the development of discrete dynamics models for quickly and slowly measured node coordinates. External controls were selected in IP models based on the possible variation of resources of node coordinates and CM weighting coefficients. IP control laws based on the variation of CM nodes and weight were designed. As a result, recurrent procedures for control generation in closed-loop control subsystems with multirate sampling were formulated. Experimental research on the control subsystems was carried out. It confirmed high efficiency for decreasing COVID-19 morbidity.

Advanced Process Control Applications to Water Resources Systems: Two Industrial Case Studies

This paper presents two industrial case studies where Advanced Process Control techniques have been applied. The first case study is represented by a hydroelectric plant located in Italy. In this plant, a model predictive controller has been designed and installed, in order to optimally satisfy the production plan. The predictive control approach has been motivated by the availability of forecasts on water requests by the hydroelectric plant and by the goodness of fit performances of the obtained linear process model. The second case study is represented by a pumping station located in an Italian Water Distribution Network. Advanced control logics have been designed in order to optimally and automatically manage the scheduling of the involved pumps. The developed Advanced Process Control systems have been successfully installed on the real plants, obtaining energy efficiency and process control improvement.

Beyond the workflow: archivists' aspirations for digital curation practices.

The documentation of archival workflows plays an important role in digital curation practice. Capturing the various steps, tools, people, and software involved at different stages, workflow documentation visually represents complex activities, and at times, invisible labor. In this article, we reflect on findings from the OSSArcFlow project, a three-year, grant-funded initiative to investigate and document workflow activities of 12 cultural heritage institutions using three open-source software systems. Building on previously published research on documentation of current digital curation practices, this article reflects on the challenges the project team encountered in modeling archivists’ aspirations for their workflows. While current practices could be accurately represented in linear process models, archivists’ aspirations for how they might advance digital curation practices extended beyond adding or changing discrete workflow steps and often involved sociotechnical factors that could not be easily mapped. This article presents a taxonomy of archivists’ aspirations for their born-digital archives, grouping these goals together around major themes that emerged throughout the research. Our findings show that workflow documentation is an essential artifact in helping archivists to understand gaps and challenges in their current workflows and to imagine the further development of digital curation tools, systems, and practices. Project participants especially benefited from engaging in this reflection on workflow documentation as part of a community of practitioners, with the opportunity to compare across institutional contexts.

Power Generation Prediction of an Open Cycle Gas Turbine Using Kalman Filter

The motivation for this paper is the enhanced role of power generation prediction in power plants and power systems in the smart grid paradigm. The proposed approach addresses the impact of the ambient temperature on the performance of an open cycle gas turbine when using the Kalman Filter (KF) technique and the power-temperature (P-T) characteristic of the turbine. Several Kalman Filtering techniques are tested to obtain improved temperature forecasts, which are then used to obtain output power predictions. A typical P-T curve of an open-cycle gas turbine is used to demonstrate the applicability of the proposed method. Nonlinear and linear discrete process models are studied. Extended Kalman Filters are proposed for the nonlinear model. The Time Varying, Time Invariant, and Steady State Kalman Filters are used with the linearized model. Simulation results show that the power generation prediction obtained using the Extended Kalman Filter with the piecewise linear model yields improved forecasts. The linear formulations, though less accurate, are a promising option when a power generation forecast for a small-term and short-term time window is required.

Interval valued data driven approach for sensor fault detection of nonlinear uncertain process

In advanced industrial fields such as chemical processes, faults must absolutely be detected; we cannot afford to operate with failing operative parts. It is therefore, necessary to take into consideration the uncertainties in order to establish a guaranteed failure detection. For this propose, several approaches have been introduced in the literature based on the Principal Components Analysis (PCA) approach for interval-valued data. However, these developed approaches treated only the linear process models. To overcome this drawback, recently, the researchers proposed to extend monitoring process based on Kernel Principal Components Analysis (KPCA) approach to the nonlinear uncertain case, in which two models are developed: model based on the lower bound (LB) and upper bound (UB) and model based on midpoints and radii.Nevertheless, the number of variables that adequately represent the normal operating condition of system can be very large. To deal with both high calculation costs and the memory storage, in this paper, we suggest an improved kernel method, in order to ameliorate failure detection process for nonlinear uncertain system. The suggested method, fuse the benefit of Interval Kernel Generalized Likelihood Ratio Test (IKGLRT) index with the Exponentially Weighted Moving Average (EWMA) filter and the Interval Reduced Kernel Principal Component (IRR-KPCA).The failure detection performance of the suggested method is valued using a Tennessee Eastman Process (TEP).

Appraisal of spatial and temporal behavior in monsoon precipitation series of Punjab-Pakistan using hierarchical Bayesian Models

Precipitation shows that structured variation across space and time and the pattern of this variation is changing round the world due to global warming. These changes affect the water resources and hydrological cycle. A spatio-temporal dataset of precipitation records together with data on temperature, wind speed and humidity is used to analyze changes in spatiotemporal pattern of precipitation across 17 major meteorological stations of Punjab-Pakistan for monsoon period during 2004–2012. A distinguishable trend in precipitation structure along spatial and temporal domains is noticed. We consider a model-based bayesian spatio-temporal analysis using dynamic linear model and gaussian process model. The meteorological variables (covariates) temperature, wind speed and humidity show varying effect on the precipitation at different locations and time points. Additionally, we construct the spatial and temporal boxplots of all meteorological stations and time points to observe the distribution of precipitation and covariates at micro level. Further to this, the interval plots also reveal the spatially varying and temporally dynamic pattern of covariates. The prediction results through predictive contour plots show high and low precipitation zones in the study area and are helpful for policy makers to identify the homogeneous climate.

Nonlinear Model Predictive Control based on Existing Mechanistic Models of Polymerisation Reactors

Model Predictive Control (MPC) is established as the most powerful and most successful method for multivariable control in the process industries. Most industrial applications of MPC rely on linear dynamic process models that are identified from active experiments on the plant. If a rigorous mechanistic model of the respective process unit already exists, it would be attractive to use this model directly inside an MPC algorithm. The PSE software “gPROMS Nonlinear Model Predictive Controller” (gNLMPC) provides precisely this functionality, and this paper describes its application to a polymerization reactor. Properties, features and advantages of linear and nonlinear MPC are compared systematically.

DESAIN NON-PLAYER CHARACTER PERMAINAN TIC-TAC-TOE DENGAN ALGORITMA MINIMAX

Abstract : Tic-Tac-Toe is one of the board games. It is played by filling the columns on the board with X or O in such a way as to form parallel lines vertically, horizontally and diagonally. This study aims to design Non-Player Characters (NPC) in the tic-tac-toe game with the minimax algorithm. The Tic-tac-toe game will be designed with two game modes: easy and minimum random modes. While in minimax NPC mode will determine the best step. The game development process of the tic-tac-toe application is based on a linear sequence process model. In the analysis phase, the NPC will be designed based on the concept of minimax. Software modeling was designed using Unified Modeling Language (UML), and coded with Visual Basic programming. Our tests show that NPCs with the Minimax algorithm can work well.

Modeling of Linear and Non-linear Compression Processes of Sunflower Bulk Oilseeds

The present study aimed at describing the experimental and theoretical force-deformation curves of sunflower bulk oilseeds at varying initial pressing heights and vessel diameters as well as determining the theoretical pressure and energy along the screw press FL 200 pressing chambers. The design of efficient oil expression systems for industry and small-scale application remains a major challenge to engineers and researchers. In attempting to solve the problem, it is important to understand the linear compression process and to transfer the knowledge to the industry involving mechanical screw presses. The universal compression testing machine at a preset load of 200 kN and a speed of 5 mm·min−1, tangent curve model and the screw press FL 200 geometry parameters were applied. The obtained results of pressure and energy along the screw pressing chambers (1–7) ranged from 0.31 to 101.653 MPa and 12.616 to 1231.228 J. Applying the tangent model at n = 1 and n = 2, the cumulative pressure decreased with increasing vessel diameters while energy increased. The study provides useful information for the analysis of other bulk oilseeds and optimizing the processing parameters of screw press FL 200 and the design and development of new oil presses.