Process Model Research Articles

Modeling of Liquefied Natural Gas Cold Power Generation for Access to the Distribution Grid

Cold energy generation is an important part of liquefied natural gas (LNG) cold energy cascade utilization, and existing studies lack a specific descriptive model for LNG cold energy transmission to the AC subgrid. Therefore, this paper proposes a descriptive model for the grid-connected process of cold energy generation at LNG stations. First, the expansion kinetic energy transfer of the intermediate work mass is derived and analyzed in the LNG unipolar Rankine cycle structure, the mathematical relationship between the turbine output mechanical power and the variation in the work mass flow rate and pressure is established, and the variations in the LNG heat exchanger temperature difference, seawater flow rate, and the turbine temperature difference in the cycle system are investigated. Secondly, based on the fifth-order equation of state of the synchronous generator, the expressions of its electromagnetic power, output AC frequency, and voltage were analyzed. Finally, the average equivalent models of the machine-side and grid-side converters are established using a direct-fed grid-connected structure, thus forming a descriptive model of the overall drive process. The ORC model is built in Aspen HYSIS to obtain the time series expression of the torque output of the turbine; based on the ORC output torque, the permanent magnet synchronous generator (PMGSG) as well as the direct-fed grid-connected structure are built in MATLAB/Simulink, and the active power and current outputs of the grid-following-type voltage vector control method and the grid-forming-type power-angle synchronous control method are also verified.

Robust malicious software detection and classification using global whale optimization algorithm with deep learning approach

Software malware detection and classification leverage sophisticated procedures and methods from the cybersecurity domain for identifying and categorizing malicious software, generally called malware. This procedure analyses code behaviour, file structures, and other features to distinguish between benign and malicious programs. Machine learning (ML) and artificial intelligence (AI) are vital in this domain, allowing the progress of dynamic and adaptive systems that identify novel and developing malware attacks. By training on massive datasets of benign and malicious instances, these systems learn patterns and signatures indicative of malware. This lets them correctly categorize and respond to potential attacks in real-time. This study presents a Global Whale Optimization Algorithm with Neutrosophic Logic for Software Malware Detection and Classification (GWOANL-SMDC) technique. The GWOANL-SMDC technique secures the software via the Android malware recognition process. Primarily, the GWOANL-SMDC technique employs the Neutrosophic Cognitive Maps (NCM) model for the feature selection process. The GWOANL-SMDC technique uses a convolutional long short-term memory (ConvLSTM) model for software malware detection. At last, the GWOA-based parameter tuning is performed to improve the performance of the ConvLSTM model. The simulation values of the GWOANL-SMDC technique are examined on the malware dataset. The obtained results ensured that the GWOANL-SMDC technique improved capability in detecting software malware.

External Corporate Governance and Corporate Misconduct: A Meta‐Analysis

ABSTRACTResearch Question/IssueExternal governance parties deter corporate misconduct through their monitoring. External monitoring increases the probability of corporate misconduct being detected and sanctioned. Current research on the relationship between external governance and corporate misconduct remains fragmented across these detection and sanction mechanisms of deterrence. This separation of mechanisms leaves us with an incomplete concept of external monitoring and obscures our understanding of what facilitates monitoring by investors, auditors, analysts, and the media.Research Findings/InsightsWe integrate the detection and sanction mechanisms of deterrence into a process model of external monitoring. Our meta‐analysis of 188 studies from 14 countries covering the period from 1970 to 2019 identifies proximity, credibility, and attention as common underlying factors that facilitate monitoring by external governance parties. Proximity is of particular relevance in deterring corporate misconduct. Ethical relativism weakens external governance parties' role in deterring corporate misconduct.Theoretical/Academic ImplicationsParties outside firm boundaries affect public perception of firms, thereby possessing a unique influence on corporate governance. We integrate this form of influence into the concept of external monitoring. Our meta‐analytic synthesis suggests a fundamental role for external governance in preventing corporate misconduct and informs on the relevance of societal values for corporate governance and corporate misconduct.

Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models

Abstract This paper addresses the critical need to quantify vehicle trajectory uncertainty in autonomous driving under environmental variability. We focus on predicting the posterior distribution of vehicle trajectories over a fixed horizon, given an initial state and a sequence of actions. We propose and compare three approaches: a probabilistic seq2seq model based on stochastic variational Gaussian processes, sequential Monte Carlo simulation with a single-step Gaussian process model, and a hybrid model that leverages the strengths of both methods. Each approach incorporates a baseline vehicle kinematics model to enhance stability and convergence. We evaluate these methods using a dataset generated from the CARLA simulator, assessing both point error metrics and probabilistic prediction metrics. This research introduces novel approaches to quantifying vehicle trajectory uncertainty through various uncertainty quantification techniques, with the goal of improving the safety and reliability of autonomous vehicle control systems.

Shifts in task absorption during decision-making episodes

In today’s rapidly evolving technological landscape, this study investigates the impact of new technology on organizational work dynamics. By integrating the Job Demands-Resources theory and the Episodic Process Model, we examine the cognitive mechanisms that influence task performance during decision-making episodes. Our research focuses on episodic job demands and resources, emphasizing the mediating role of task absorption in the relationship between these factors and task performance. Our findings reveal that episodic job resources positively affect task absorption and performance, while job demands moderate the relationship between job resources and task absorption. Employing the Beer Distribution Game alongside neuroscience-based eye-tracking techniques, we analyze visual attention dynamics during decision-making episodes, providing novel insights into the interplay between task absorption and task performance. Theoretically, our study highlights the significance of task absorption in understanding how episodic job resources impact decision-making performance. Practically, our results advocate for the implementation of decision-making-focused skills development through physiological measures such as neurofeedback training programs. This research underscores the importance of individual time management in enhancing task performance, contributing to a nuanced understanding of job resources, job demands, and task absorption in the context of technological transformation.

Susceptibility of hydraulic structures to internal erosion: modeling of suffusion process

Internal erosion is a primary cause of hydraulic structure failure, manifesting as two key phenomena: piping erosion and suffusion. These processes differ in their geometrical and hydraulic boundary conditions, resulting in varying levels of failure risk. Piping erosion is the more hazardous and rapid process, often leading to immediate failure if not promptly addressed. In contrast, suffusion gradually alters the permeability of the medium, potentially culminating in failure after a prolonged period of development. In this study, we propose a model for suffusion based on Darcy’s law, Papamichos' erosion law, and Einstein's viscosity evolution relation. The model describes the temporal evolution of the average porosity in the porous medium, the concentration of eroded particles in the fluid, and the mass of particles eroded, while highlighting the impact of hydraulic and mechanical parameters, such as the erosion coefficient and maximum porosity, on this evolution. The numerical solution is obtained using the finite difference method, with the sample discretized into elementary layers. For discretization, an explicit off-centered scheme was adopted. The simulation results reveal that suffusion is strongly influenced by soil hydraulic and mechanical parameters, particularly the erosion coefficient (λ) and final porosity (Ømax).

Understanding the Integration of Building Energy Modeling into the Building Design Process: Insights from Two Collaborative Construction Projects

This research investigates the integration of building energy modeling (BEM) within collaborative construction projects to inform design decisions for achieving high-energy performance goals. The study aims to understand current practices, benefits, and challenges associated with this integration. Using an ethnographic case study approach focused on two high-energy performance social housing projects with integrated project delivery and integrated design processes, data were collected through direct observations, document analysis, and interviews with project team members. Design process modeling was utilized to dissect current practices, followed by a hybrid inductive and deductive thematic analysis to find challenges related to energy performance design in collaborative projects. Findings from this research revealed that BEM experts often operate in isolation, with late integration of energy models into design decisions. Compliance-centric BEM usage and challenges related to interoperability of design and BEM tools further compound the issue of seamless collaboration. However, the study highlights that early collaboration among project stakeholders emerges as a pivotal factor in informed design decisions, bridging the gap between energy modeling and design. This research provides valuable insights for practitioners seeking to optimize BEM in their design process, and offers support to policymakers aiming to enhance the role of BEM in projects.

Perceived Susceptibility to and Severity of Cardiovascular Disease Is Associated With Intent to Change Behavior Among Women 25-55 Years Old.

Risk factors for cardiovascular disease (CVD) among young and middle-aged women have increased, whereas CVD knowledge and awareness remain low. The objective of this study was to describe the relationship between the stage of behavior change and awareness, knowledge, and perceptions of CVD among women 25-55 years and identify predictors of the stage of behavior change. A cross-sectional online survey of women ages 25-55 years living in the United States was conducted. Awareness was measured with the question "What is the leading cause of death for women in the United States?" Knowledge, perceptions, and the stage of behavior change were measured with the Heart Disease Fact Questionnaire, Health Beliefs Related to CVD, and Precaution Adoption Process Model instruments, respectively. Chi-square and t tests were used to determine differences between awareness, knowledge, and perceptions based on the stage of behavior change. Multiple logistic regression was used to evaluate the relationship between the stage of behavior change and awareness, knowledge, and perceptions. A total of 149 primarily minority women (n = 105) were included (mean age = 37.15 ± 7.86 years). The perception of CVD susceptibility was associated with increased intention to change behavior (odds ratio, 1.247; 95% confidence interval, 1.101-1.414; P < .001). The perception of CVD severity was associated with reduced intention to change behavior (odds ratio, 0.809; P = .004). Women who believed they were susceptible to CVD and did not perceive CVD as severe were more likely to report intent to change behavior, suggesting perception of CVD risk is more important than awareness or knowledge. Addressing misperceptions may be a strategy for primary risk reduction.

Multivariate Cluster Point Process to Quantify and Explore Multi-Entity Configurations: Application to Biofilm Image Data.

Clusters of similar or dissimilar objects are encountered in many fields. Frequently used approaches treat each cluster's central object as latent. Yet, often objects of one or more types cluster around objects of another type. Such arrangements are common in biomedical images of cells, in which nearby cell types likely interact. Quantifying spatial relationships may elucidate biological mechanisms. Parent-offspring statistical frameworks can be usefully applied even when central objects ("parents") differ from peripheral ones ("offspring"). We propose the novel multivariate cluster point process (MCPP) to quantify multi-object (e.g., multi-cellular) arrangements. Unlike commonly used approaches, the MCPP exploits locations of the central parent object in clusters. It accounts for possibly multilayered, multivariate clustering. The model formulation requires specification of which object types function as cluster centers and which reside peripherally. If such information is unknown, the relative roles of object types may be explored by comparing fit of different models via the deviance information criterion (DIC). In simulated data, we compared a series of models' DIC; the MCPP correctly identified simulated relationships. It also produced more accurate and precise parameter estimates than the classical univariate Neyman-Scott process model. We also used the MCPP to quantify proposed configurations and explore new ones in human dental plaque biofilm image data. MCPP models quantified simultaneous clustering of Streptococcus and Porphyromonas around Corynebacterium and of Pasteurellaceae around Streptococcus and successfully captured hypothesized structures for all taxa. Further exploration suggested the presence of clustering between Fusobacterium and Leptotrichia, a previously unreported relationship.

The evolution and disintegration of innovation narratives during scaling in science‐based ventures

AbstractScience‐based ventures (SBVs) are crucial vehicles that bring new technologies from the lab and to the mainstream market. During this journey, innovation narratives play a crucial role in coordinating innovation. Innovation narratives are linguistic representations that actors use to make sense of innovation activities, events and actions. In other words, they are stories that drive sensemaking, which is a critical element in avoiding conflicts in product innovation process and securing coordinated activities between diverse actors. However, as SBVs shift markets and undergo radical changes, they may have to update narratives to fit these changes. Yet, little is known about how innovation narratives and coordination evolve during this journey. To improve knowledge on this matter, I conducted a 24‐month study of a science‐based venture crossing over to a commercial market. I find that during this transition, innovation narratives shift from being shaped by progressive storytelling, where the benefits of becoming commercial and hiring nonacademics is highlighted, to being shaped by retrogressive storytelling, where incumbents and newcomers use their respective pasts to develop divergent narratives, and finally to appearing as disintegrated storytelling, where narratives compete and hinder coordination in innovation processes. Building on these findings, I construct a process model of how innovation narratives evolve and disintegrate as SBVs scale. This article contributes to knowledge on innovation management by illustrating how innovation narratives affect coordination in innovation processes, as well as how they may evolve during organizational change. Furthermore, this article illuminates the challenges that SBVs face to their innovation processes when scaling.

The Kinetics of Polymer Brush Growth in the Frame of the Reaction Diffusion Front Formalism

We studied the properties of a reaction front that forms in irreversible reaction–diffusion systems with concentration-dependent diffusivities during the synthesis of polymer brushes. A coarse-grained model of the polymerization process during the formation of polymer brushes was designed and investigated for this purpose. In this model, a certain amount of initiator was placed on an impenetrable surface, and the “grafted from” procedure of polymerization was carried out. The system consisted of monomer molecules and growing chains. The obtained brush consisted of linear chains embedded in nodes of a face-centered cubic lattice with excluded volume interactions only. The simulations were carried out for high rafting densities of 0.1, 0.3, and 0.6 and for reaction probabilities of 0.02, 0.002, and 0.0002. Simulations were performed by means of the Monte Carlo method while employing the Dynamic Lattice Liquid model. Some universal behavior was found, i.e., irrespective of reaction rate and grafting density, the width of the reaction front as well as the height of the front show for long times the same scaling with respect to time. During the formation of the polymer layer despite the observed difference in dispersion of chain lengths for different grafting densities and reaction rates at a given layer height, the quality of the polymer layer does not seem to depend on these parameters.

Developmental patterns of athletic performance and physical fitness in youth baseball players: A longitudinal analysis

ABSTRACT This study aimed to evaluate developmental patterns in athletic performance during youth and determine their association with changes in physical fitness. The testing included three athletic performance (i.e. ball speed in pitching, swing velocity in batting, 30-metre sprint time) and two physical fitness tests (i.e. medicine ball [MB] back throw and modified star excursion balance test [SEBT]). Data from 235 players (557 measurements) aged 6 to 14 were eligible for a longitudinal evaluation. The statistical analysis was based on the latent growth curve models of the athletic performance development process and the random-effect panel data multivariate regression analysis for ball speed, swing velocity, and 30-metre sprint time. The 30-metre sprint time showed a small curvature but an accelerated increase around 12‒13 years old. However, the ball speed, swing velocity, and modified SEBT demonstrated a slower or less accelerated change in the developmental pattern starting around 12‒13 years old. Multivariate longitudinal analysis revealed that developmental change in athletic performance was positively associated with age and MB back throw. Our findings highlight the developmental patterns of athletic performance associated with baseball exhibiting stagnation as well as acceleration, which may help develop effective age-appropriate strategies for improving performance in youth baseball players.

Emissions and Flame Stability Assessment of Hydrogen Addition and Air Dilution in a Micro Gas Turbine Combustor Using 0D/1D Modeling by Chemical Reactor Network

Abstract Hydrogen emerges as a promising fuel for clean and sustain- able electricity production when utilized in micro gas turbines (mGTs). However, some challenges linked to hydrogen usage must be overcome as its high reactivity can lead to increased NOx emissions and flame instabilities. Literature suggests that combustion air humidification and/or exhaust gas recirculation (EGR) may be methods to reduce this reactivity. However, these measures are limited due to the small operating range of the mGT combustor. In this context, a 20 kWth mGT combustor is investigated un- der various inlet conditions to assess the effect of EGR towards increased flame stability and emission control when operating under hydrogen-enriched methane firing, using a Chemical Re- action Network (CRN) model. The CRN modeling is a fast and low computational complexity tool to model combustion perfor- mance and emissions by representing complex reactive flow fields through idealized reactor models, leading to reduced computa- tional costs. This study examines partial load conditions, fuel compositions, and the effect of combustion air dilution through EGR. The CRN model of the combustion process is designed based on combustion zones extracted from the main flow fields with similar thermo-chemical states from CFD, while emission predictions are experimentally validated for different operating points. Predicted NOx and CO emissions by the proposed CRN model show an acceptable agreement with the experimental data at high power loads. However, its accuracy diminishes for loads below 70% due to the variability in model tuning parameters across different loads, whereas the

Социально-культурная сфера русского мира. Междисциплинарный подход

The article is a continuation of fundamental research of the Russian world. This paper presents the development of the fluctuation “core” of the socio-cultural sphere of the organizational and managerial system of the Russian world. Based on the synthesis of beatitudes, gifts and fruits from the Holy Scriptures related to the ideal world, the spiritual and moral values of the material world are compared by the method of parallelism. As a result, a conceptual model of the progressive process of personal and social development on the path to truth has been developed. In the future, the model was verified with the principles of acmeology, praxiology, compliance with goals and objectives, systems of “God-manhood” and “unity”. In this regard, the work used an interdisciplinary approach, which synthesized the ideas of Russian religious philosophy, theology and modern achievements of the science of personal and social development. Consequently, a conceptual model of the socio-cultural phenomenon of Russian civilization has emerged, asserting its distinctive identity in the context of inter-civilizational dialogue.

Statistical Mapping of PFOA and PFOS in Groundwater throughout the Contiguous United States.

Per-and polyfluoroalkyl substances (PFAS) are synthetic chemicals that are increasingly being detected in groundwater. The negative health consequences associated with human exposure to PFAS make it essential to quantify the distribution of PFAS in groundwater systems. Mapping PFAS distributions is particularly challenging because a national patchwork of testing and reporting requirements has resulted in sparse and spatially biased data. In this analysis, an inhomogeneous Poisson process (IPP) modeling approach is adopted from ecological statistics to continuously map PFAS distributions in groundwater across the contiguous United States. The model is trained on a unique data set of 8910 PFAS groundwater measurements, using combined concentrations of two PFAS analytes. The IPP model predictions are compared with results from random forest models to highlight the robustness of this statistical modeling approach on sparse data sets. This analysis provides a new approach to not only map PFAS contamination in groundwater but also prioritize future sampling efforts.

Medication recommendation for Parkinson’s disease based on dynamics of symptom progression

Parkinson’s Disease (PD) is a chronic condition, with extensive research on initial medication selection for treatment, but limited guidance on long-term medication management. This study aims to identify optimal medication adjustment strategies based on patient clusters, focusing on either maximizing time spent in favorable health states or minimizing time spent in unfavorable ones while avoiding adverse effects. To guide treatment, we developed decision models using prescription dosages converted into standardized units for various medications. Using data from the Parkinson’s Progression Markers Initiative, we employed a multivariate time-series clustering approach to capture symptom progression dynamics. This analysis identified four distinct clusters: two representing desirable and undesirable states, and two highlighting motor-focused and non-motor-focused failures across multiple domains. We developed two separate Markov Decision Process (MDP) models to address these dual objectives, which were then integrated into a comprehensive framework that suggests optimal actions and cautions against risky ones for each patient state. This model provides valuable insights for clinical decision-making by offering flexible guidance on adjusting medication intensity rather than prescribing specific medication types, enhancing its applicability in clinical practice.

Automatic activity-travel sequence generator using language, grammar, and machine theory

ABSTRACT Activity schedule results from a complex decision-making process characterized by several interrelated decisions. Different facets of an activity schedule such as activity type, timing, duration, etc. influence each other and this makes modeling activity schedules a complex task. This complexity has compelled researchers to explore different approaches for modeling activity schedules, among which two predominant approaches can be identified: the utility-maximization theory based econometric approach and the computational process modeling approach. Despite their advantages and a few successful practical applications, challenges still remain leaving avenues for exploration of new approaches. This paper contributes in this direction by reviewing the relationship between language, grammar, and machines in the context of sequence analysis for activity sequence generation. Following that, the paper presents a stochastic Finite State Machine that can generate activity sequences to match the frequency distribution of sequences from a given data set. Our results show that the proposed algorithm can not only generate activity sequences with a distribution similar to that of original data but can also efficiently generate new patterns not in the original data.

Analysis of differential scanning calorimetry data for aged plutonium.

Differential scanning calorimetry data for samples of a 52 year old plutonium alloy with 3.3 at. % Ga that were heated beyond the melting point is analyzed using transition state theory to find activation energies for the δ to ɛ and ɛ to liquid phase transitions. A Bayesian statistical method involving a Gaussian process model is used to find mean values and confidence intervals for the activation energies. The activation energy for the δ to ɛ phase transition increases by 3.3 ± 3.8% per decade, relative to the case when all age related plutonium lattice point defects have been removed through annealing. The corresponding increase in activation energy for the ɛ to liquid transition is shown to be 7.1 ± 1.8% per decade. It is postulated that the change in activation energy with age for both phase transitions is caused, in part, by the accumulation of the same type of lattice point defects associated with the observed increase in elastic bulk modulus over time.

Simulation of intestinal perforation in experiment

The current problem of neonatal and pediatric surgery remains the tactics of surgical treatment and postoperative management of patients with perforated peritonitis. Mortality with perforation of the stomach and intestines in children in the newborn period reaches about 40-80 %. The combination of the syndrome of increased intra-abdominal pressure and multiple organ failure causes the extremely serious condition of patients. In this connection, new approaches to the surgical treatment of such patients are currently being actively developed. For this, new medical devices are tested in experimental practice, but the problem of such studies, first of all, lies in the lack of an adequate model of the pathological process. The aim of the work is to analyze the methods of experimental modeling of intestinal perforations described in the public domain. For this, a study was conducted of the most significant scientific publications of such databases as Google Scholar, PubMed, Scopus, eLIBRARY. The present work provides a detailed description of existing options for modeling perforations of various parts of the gastrointestinal tract (stomach, small intestine and colon), depending on the goals of the experiment. Several rather rare techniques based on the introduction of microorganisms are also presented. In addition, the article describes the method proposed by the authors for modeling perforation using laparoscopic access.

ЧИСЛЕННАЯ МОДЕЛЬ СВОБОДНОЙ КОНВЕКЦИИ В ПРОСТРАНСТВЕ ЭКРАННО-ВАКУУМНОЙ ИЗОЛЯЦИИ ПРИ ТЕПЛОВОМ ВОЗДЕЙСТВИИ ПОЖАРА

The objective of the study is to find the patterns of the conjugate heat transfer process in a closed space of a coaxial cylinder’s pair under the influence of an external source of thermal radiation within the framework of the free convection model. It is assumed that the priority type of heat transfer in the gas filling the coaxial space is turbulent free convection, and thermal conductivity in the external thermal insulation of the pipeline. A model of the free convection process in a coaxial gas cavity in the presence of local heating in the form of a system of differential equations is created. Comparative results of a full-scale and numerical experiment on measuring the effective coefficient of thermal conductivity of screen-vacuum insulation are presented.