Process Model Research Articles

Large deviations in the symmetric simple exclusion process with slow boundaries: A hydrodynamic perspective

We revisit the one-dimensional model of the symmetric simple exclusion process slowly coupled with two unequal reservoirs at the boundaries. In its non-equilibrium stationary state, the large deviations functions of density and current have been recently derived using exact microscopic analysis by Derrida, Hirschberg and Sadhu in [J. Stat. Phys. 182, 15 (2021)]. We present an independent derivation using the hydrodynamic approach of the macroscopic fluctuation theory (MFT). The slow coupling introduces additional boundary terms in the MFT-Action, which modifies the spatial boundary conditions for the associated variational problem. For the density large deviations, we explicitly solve the corresponding Euler-Lagrange equations using a simple local transformation of the optimal fields. For the current large deviations, our solution is obtained using the additivity principle. In addition to recovering the expression of the large deviations functions, our solution describes the most probable path for these rare fluctuations.

Feasibility, performance and carbonation mechanism of ternesite as an ultra-low lime CO2 sequestration binder

In this study, ternesite was proposed as a low-lime CO2 sequestration binder, and the ternesite slurry and compacts were carbonated in different periods. The evolution of ternesite carbonation products and properties was studied by qualitative and quantitative analysis, and the kinetics equation of ternesite carbonation was established. According to the microstructure, crystal size and phase composition of carbonated products, a conceptual model of the carbonation hardening process of ternesite was constructed. The mechanism of ternesite carbonation process was analyzed. The results showed that ternesite CO2 sequestration capacity and compressive strength of ternesite reached 15.1 % and 91.95 MPa after carbonation for 120 min. Ternesite has been shown to quickly obtain a hardened product with good properties through carbonation curing, opening up the field of CO2 sequestration binder.

Chemical weathering and CO2 consumption rates of the Koshi River Basin: modelling and quantifying

The Koshi River is a typical river originating from the Tibetan Plateau that drains into the Indian Plain. To quantify the chemical weathering and CO2 consumption rates of the upstream alpine meltwater-supplied mountainous and downstream rainfall-impacted temperate plain regions, 46 river water samples were collected at 23 sites during monsoon and post-monsoon seasons, and major ions concentrations were measured in the laboratory. The forward model was applied to calculate carbonate weathering rate (CWR), silicate weathering rate (SWR), and their CO2 consumption rates ([CO2]car and [CO2]sil). For investigating the hydrological impacts, correlation analysis was applied between the chemical weathering rates and different runoff components based on the Spatial Process in Hydrology model at each sampling site. Results show that carbonate and silicate rock weathering control the river water chemistry. The average silicate weathering rate is 7.37 tons/km2/yr, consuming 3.40 × 105 mol/km2/yr CO2 while considering sulfide-involved carbonate weathering, the carbonate weathering rate is 26.35 tons/km2/yr, consuming 3.82 × 105 mol/km2/yr CO2. Overall, chemical weathering and CO2 consumption rates are enhanced in monsoon season in both the upstream and downstream regions owing to the intense hydrological cycle. These rates are relatively higher in the alpine upstream region during the post-monsoon season due to intense physical erosion, and higher in downstream during the monsoon season due to the greater volume of water. The significant positive correlations between snowmelt runoff with CWR and [CO2]car indicated that the rising precipitation and temperature leading to enhanced snowmelt would increase the carbonate weathering rate. Hence, this study provides a valuable understanding of the spatiotemporal variations in river hydrochemistry and the role of hydrology in shaping chemical budget within the snow and glacier-fed Himalaya River Basins.

A pragmatic, stepped-wedge, hybrid type II trial of interoperable clinical decision support to improve venous thromboembolism prophylaxis for patients with traumatic brain injury

BackgroundVenous thromboembolism (VTE) is a preventable medical condition which has substantial impact on patient morbidity, mortality, and disability. Unfortunately, adherence to the published best practices for VTE prevention, based on patient centered outcomes research (PCOR), is highly variable across U.S. hospitals, which represents a gap between current evidence and clinical practice leading to adverse patient outcomes.This gap is especially large in the case of traumatic brain injury (TBI), where reluctance to initiate VTE prevention due to concerns for potentially increasing the rates of intracranial bleeding drives poor rates of VTE prophylaxis. This is despite research which has shown early initiation of VTE prophylaxis to be safe in TBI without increased risk of delayed neurosurgical intervention or death. Clinical decision support (CDS) is an indispensable solution to close this practice gap; however, design and implementation barriers hinder CDS adoption and successful scaling across health systems. Clinical practice guidelines (CPGs) informed by PCOR evidence can be deployed using CDS systems to improve the evidence to practice gap. In the Scaling AcceptabLE cDs (SCALED) study, we will implement a VTE prevention CPG within an interoperable CDS system and evaluate both CPG effectiveness (improved clinical outcomes) and CDS implementation.MethodsThe SCALED trial is a hybrid type 2 randomized stepped wedge effectiveness-implementation trial to scale the CDS across 4 heterogeneous healthcare systems. Trial outcomes will be assessed using the RE2-AIM planning and evaluation framework. Efforts will be made to ensure implementation consistency. Nonetheless, it is expected that CDS adoption will vary across each site. To assess these differences, we will evaluate implementation processes across trial sites using the Exploration, Preparation, Implementation, and Sustainment (EPIS) implementation framework (a determinant framework) using mixed-methods. Finally, it is critical that PCOR CPGs are maintained as evidence evolves. To date, an accepted process for evidence maintenance does not exist. We will pilot a “Living Guideline” process model for the VTE prevention CDS system.DiscussionThe stepped wedge hybrid type 2 trial will provide evidence regarding the effectiveness of CDS based on the Berne-Norwood criteria for VTE prevention in patients with TBI. Additionally, it will provide evidence regarding a successful strategy to scale interoperable CDS systems across U.S. healthcare systems, advancing both the fields of implementation science and health informatics.Trial registrationClinicaltrials.gov– NCT05628207. Prospectively registered 11/28/2022, https://classic.clinicaltrials.gov/ct2/show/NCT05628207.

Analisis Dan Pemodelan Proses Bisnis Event Pada Dinas Pemuda dan Olahraga Sumatera Utara

The North Sumatra Youth and Sports Service is one of the government agencies responsible for youth services, partnership services, sports services and supporting services as regulated by law in North Sumatra Province. Therefore, this research was conducted with the aim of modeling and mapping the business processes of the North Sumatra Youth and Sports Department in accordance with Minister of Youth and Sports Regulation. By using Business Process Model Notation (BPMN) for business process notation and flowcharts for notation in compiling SOP documents. This research begins with assessing SOP needs, planning and preparing business processes, developing business processes, then verifying the business processes created, compiling SOPs and carrying out SOP verification and SOP validation. The results of this research are in the form of an optimum and efficient event business process model, as well as recommendations for improvements that can be implemented by the Youth and Sports Department.

Opportunistic Change During a Punctuation: How and When the Front Lines Can Drive Bursts of Incremental Change

Environmental jolts can trigger more conducive conditions for driving change in organizations. However, punctuated equilibrium theories of organizational change concentrate on top managers’ implementation of de novo radical changes after jolts. Existing research has not examined frontline-driven, incremental change efforts during these periods of disrupted stasis, despite the value of frontline change ideas. We develop a process model to explain how and when those on an organization’s front lines can leverage a jolt to opportunistically implement long-desired change ideas in ways that promote their retention. We conducted a two-year qualitative field study at a hospital during the Covid-19 pandemic, examining the trajectories of 33 premeditated change ideas raised by frontline staff. By comparing ideas that persisted to become part of normal operations with those that failed to be selected or retained, we identified practices and conditions that promoted the selection and retention of frontline change ideas. Our study suggests that frontline change advocates can seed the long-term retention of “shovel-ready” ideas—as opposed to de novo ideas—after a jolt by rapidly and opportunistically deploying a novel set of practices before the brief window of opportunity created by lessened constraints and increased managerial receptivity closes. Prior theories of change largely assume frontline-driven change to be slow and continuous, proceeding in a one-off fashion; we explain how and when frontline change can instead occur in rapid, opportunistic bursts. This study advances theories of punctuated equilibrium and bottom-up change in organizations by unearthing an alternative way that change can be intentionally accomplished in organizations. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.15120 .

Updating strategy of safe operation control model for dense medium coal preparation process based on Bayesian network and incremental learning

AbstractThe effectiveness of control decisions provided by the safe operation control model for the dense medium coal preparation process may decline due to its inability to adapt to changing working conditions. To address this issue, this paper investigates a safe operation control model update strategy based on Bayesian network and incremental learning. This strategy can update the model structure and parameters according to different conditions, ensuring the effectiveness of the updated model. Considering that the old model has effective information to explain the new working conditions, the Bayesian network structure update learning method based on incremental learning is proposed. This method retains the components of the old model that can describe the joint probability distribution of the sampled data under the new working conditions while updating the remaining structure. This approach improves the efficiency of model updating. The simulation results show that the updated model obtained by the proposed method can effectively deal with new abnormal conditions.

Combining Semantic and Structural Features for Reasoning on Patent Knowledge Graphs

To address the limitations in capturing complex semantic features between entities and the incomplete acquisition of entity and relationship information by existing patent knowledge graph reasoning algorithms, we propose a reasoning method that integrates semantic and structural features for patent knowledge graphs, denoted as SS-DSA. Initially, to facilitate the model representation of patent information, a directed graph representation model based on the patent knowledge graph is designed. Subsequently, structural information within the knowledge graph is mined using inductive learning, which is combined with the learning of graph structural features. Finally, an attention mechanism is employed to integrate the scoring results, enhancing the accuracy of reasoning outcomes such as patent classification, latent inter-entity relationships, and new knowledge inference. Experimental results demonstrate that the improved algorithm achieves an up to approximately 30% increase in the MRR index compared to the ComplEx model in the public Dataset 1; in Dataset 2, the MRR and Hits@n indexes, respectively, saw maximal improvements of nearly 30% and 112% when compared with MLMLM and ComplEx models; in Dataset 3, the MRR and Hits@n indexes realized maximal enhancements of nearly 200% and 40% in comparison with the MLMLM model. This effectively proves the efficacy of the refined model in the reasoning process. Compared to recently widely applied reasoning algorithms, it offers a superior capability in addressing complex structures within the datasets and accomplishing the completion of existing patent knowledge graphs.

Reflecting on the quality of a methodologically pluralist evaluation of a large-scale Indigenous health research collaboration in Australia

BackgroundIndigenous communities worldwide lead calls for all evaluations of research, programmes and policies affecting their communities to reflect the values, priorities and perspectives of the Indigenous peoples and communities involved....

Photocatalyst based on composite of trimetallic metal-organic frameworks for efficient visible-light degradation in pharmaceutical pollutants abatement and process modeling using RSM

In this study, the facile synthesis of both monometallic Co- Metal-Organic Frameworks bimetallic Ni-Co-Metal-Organic Frameworks and trimetallic Ni-Co-Cu-Metal-Organic Frameworks was successfully accomplished with the chelation method. The prepared samples were characterized by using X-ray diffraction (XRD), field emission-scanning electron microscopy (FE-SEM), N2 adsorption–desorption, Fourier transform infrared spectra (FTIR), differential reflectance spectroscopy (UV–vis-DRS), and photoluminescence (PL) techniques was undertaken to examine the physicochemical attributes of the synthesized composites. The photocatalytic efficiency of the antibiotic ceftazidime(CAZ) degradation was investigated under visible light irradiation in 100 min. Kinetic studies revealed a pseudo-first-order model (50.0143, 0.009, and 0.0057 min−1 for Co-MOF, Co-Ni-MOF, and Co-Ni-Cu-MOF, respectively). Response surface methodology (RSM) demonstrated satisfactory predictability for CAZ degradation efficiency. Under optimized conditions (pH 10, 0.25 g L−1 catalyst) more than 97.38 % for the 20.0 mg L−1 of CAZ was degraded. Furthermore, the recyclability and scavenging test for the catalyst was evaluated.

Progress in Additive Manufacturing of Magnesium Alloys: A Review.

Magnesium alloys, renowned for their lightweight yet high-strength characteristics, with exceptional mechanical properties, are highly coveted for numerous applications. The emergence of magnesium alloy additive manufacturing (Mg AM) has further propelled their popularity, offering advantages such as unparalleled precision, swift production rates, enhanced design freedom, and optimized material utilization. This technology holds immense potential in fabricating intricate geometries, complex internal structures, and performance-tailored microstructures, enabling groundbreaking applications. In this paper, we delve into the core processes and pivotal influencing factors of the current techniques employed in Mg AM, including selective laser melting (SLM), electron beam melting (EBM), wire arc additive manufacturing (WAAM), binder jetting (BJ), friction stir additive manufacturing (FSAM), and indirect additive manufacturing (I-AM). Laser powder bed fusion (LPBF) excels in precision but is limited by a low deposition rate and chamber size; WAAM offers cost-effectiveness, high efficiency, and scalability for large components; BJ enables precise material deposition for customized parts with environmental benefits; FSAM achieves fine grain sizes, low defect rates, and potential for precision products; and I-AM boasts a high build rate and industrial adaptability but is less studied recently. This paper attempts to explore the possibilities and challenges for future research in AM. Among them, two issues are how to mix different AM applications and how to use the integration of Internet technologies, machine learning, and process modeling with AM, which are innovative breakthroughs in AM.

Research and application of the flatness target curve discrete dynamic programming based on two-dimensional decision making

The flatness target curve (FTC) is a fundamental process model in cold rolling flatness control, which directly affects the quality control effect of the strip. The accurate and efficient setting of the flatness target curve is the key to ensuring continuous rolling and is a common problem faced by various production companies. This paper establishes a discrete dynamic planning (DDP) setting method for FTC with two-dimensional decision-making. To quantitatively analyze the roles of the coefficients in the FTC, the preprocessing process is established using standardized processing methods and statistical analysis methods. The flatness target curve discrete dynamic programming (FTC-DDP) is based on Bellman optimization theory with a dual structure of local and system decision-making. The decision-making functions are established based on the effect of each coefficient on FTC geometric characteristics. The concepts of “curvature factor” and “wedge factor” are innovatively proposed to obtain the decision-making value functions. The regression analysis establishes the solution equations of even number coefficients, odd number coefficients, and gain coefficients, respectively. Roll bending and tilting roll analyze the FTC’s ability to correct flatness defects. Meanwhile, utilizing a 1450 mm five-stand six-roll cold rolling mill, it is verified that FTC-DDP can quickly and accurately set the FTC, which provides an effective solution for obtaining high-precision and high-quality cold-rolled strips. Application results show that FTC-DDP can reduce the difficulty of setting up FTC by more than 70 %, reduce the setup time to milliseconds, and improve the setup accuracy by more than 50 %.

Process mining embeddings: Learning vector representations for Petri nets

Process Mining offers a powerful framework for uncovering, analyzing, and optimizing real-world business processes. Petri nets provide a versatile means of modeling process behavior. However, traditional methods often struggle to effectively compare complex Petri nets, hindering their potential for process enhancement. To address this challenge, we introduce PetriNet2Vec, an unsupervised methodology inspired by Doc2Vec. This approach converts Petri nets into embedding vectors, facilitating the comparison, clustering, and classification of process models. We validated our approach using the PDC Dataset, comprising 96 diverse Petri net models. The results demonstrate that PetriNet2Vec effectively captures the structural properties of process models, enabling accurate process classification and efficient process retrieval. Specifically, our findings highlight the utility of the learned embeddings in two key downstream tasks: process classification and process retrieval. In process classification, the embeddings allowed for accurate categorization of process models based on their structural properties. In process retrieval, the embeddings enabled efficient retrieval of similar process models using cosine distance. These results demonstrate the potential of PetriNet2Vec to significantly enhance process mining capabilities.

PC-Kriging-powered parallelizing Bayesian updating for stochastic vehicle-track dynamical system with contact force measurements and Gaussian process discrepancy model

High-precision vehicle-track coupled dynamical models play a vital role in assessing the running safety of the vehicle, tracking fatigue behavior, and establishing a digital twin for high-speed railways. This study established a high-fidelity vehicle-track coupled dynamical model as a forward solver, which was then updated in a Bayesian inference framework based on the field tests of rail irregularities and wheel/rail normal contact forces. The prediction errors corresponding to different time steps were represented through a Gaussian process (GP) model characterized by a covariance function with the Gaussian kernel to incorporate the correlation between different time steps. To cope with the considerable expense of the likelihood function calculations with large-scale loop operations and the computational burden due to the large batch of repetitive evaluations of the high-fidelity forward model involved in the stochastic sampling from the posterior probability distribution, a metamodel-powered parallelizing stochastic sampling scheme was adopted. The posterior distribution could be formulated by replacing the expensive explicit model evaluation involved in the likelihood function with a Polynomial-Chaos-Kriging (PC-Kriging) predictor providing a surrogate mapping between the wheel/rail normal contact forces and physical parameters. Finally, a parallelizing computing scheme running across multiple CPU cores on high-performance computers was realized to sample from the vectorized formula of the emulator-powered posterior distribution. Field test data from a high-speed railway in China was adopted to demonstrate that the Bayesian inference scheme can quantify the uncertainties of the core physical parameters of the high-fidelity vehicle-track coupled dynamical system by efficiently trading off accuracy and computational efficiency.

Search for heavy neutral Higgs bosons decaying into a top quark pair in 140 fb−1 of proton-proton collision data at s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\sqrt{s} $$\\end{document} = 13 TeV with the ATLAS detector

A search for heavy pseudo-scalar (A) and scalar (H) Higgs bosons decaying into a top-quark pair (tt¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ t\\overline{t} $$\\end{document}) has been performed with 140 fb−1 of proton-proton collision data collected by the ATLAS experiment at the Large Hadron Collider at a centre-of-mass energy of s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\sqrt{s} $$\\end{document} = 13 TeV. Interference effects between the signal process and Standard Model (SM) tt¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ t\\overline{t} $$\\end{document} production are taken into account. Final states with exactly one or exactly two electrons or muons are considered. No significant deviation from the SM prediction is observed. The results of the search are interpreted in the context of a two-Higgs-doublet model (2HDM) of type II in the alignment limit with mass-degenerate pseudo-scalar and scalar Higgs bosons (mA = mH) and the hMSSM parameterisation of the minimal supersymmetric extension of the Standard Model. Ratios of the two vacuum expectation values, tan β, smaller than 3.49 (3.16) are excluded at 95% confidence level for mA = mH = 400 GeV in the 2HDM (hMSSM). Masses up to 1240 GeV are excluded for the lowest tested tan β value of 0.4 in the 2HDM. In the hMSSM, masses up to 950 GeV are excluded for tan β = 1.0. In addition, generic exclusion limits are derived separately for single scalar and pseudo-scalar states for different choices of their mass and total width.

THE IMPORTANCE OF USING MODELING WHEN TEACHING CHEMISTRY IN SECONDARY SCHOOLS

There is a great interest of both teachers and students in the use of modeling in the process of teaching chemistry in a secondary school. The correct use of modeling in the educational process facilitates the work of the teacher and increases the interest of students in this subject. With the use of models, the visibility of lessons increases, multimedia systems stimulate the quality of work, activate all the senses of students, and due to the increase in the quality of education, conditions are created for the long-term preservation of acquired knowledge.

A New Process Model for Relationship-Centred Shared Decision-Making in Physical Medicine and Rehabilitation Settings.

We present a relationship-centred shared-decision-making (RCSDM)process model to explicate factors that shape decision-making processes during physical medicine and rehabilitation (PMR) encounters among patients, their care partnersand practitioners. Existing shared decision-making (SDM) models fall short in addressing the everyday decisions routinely made regarding persons with chronic disabilities who require high levels of support, their care partnersand rehabilitation practitioners. In PMR, these everyday decisions are small scale, immediateand in service to a larger therapeutic goal. They can be thought of as micro-decisionsand involve multiple practitioners, care partnersand patients. How micro-decisions are made in this context is contingent on multiple roles and relationships among these relevant parties. Our model centres on micro-decisions among patients, their care partnersand practitioners based on our disorders of consciousness (DoC) research. To develop our model, we examined peer-reviewed literature in SDM in PMR, chronic disabilityand person-centeredness; formed collaborations and co-created our constructs with rehabilitation practitioners and with care partners who have lived experience of caring for persons with DoC; analysed emerging empirical dataand vetted early versions with expert scientific and clinical audiences. Our model builds from the core tenets of relational autonomy,and scholarship and activism of disability advocates. Our model conceptualizes four non-hierarchical levels of analysis to understand the process of micro-decision-making in chronic disability and medical rehabilitation: social forces (historical and sociological); roles and relationships (multiple and intersecting); relational dimensions (interactional and contextual)and micro-decision moments (initiation, responseand closure). Relationships among patients, their care partnersand practitioners are the intersubjective milieu within which decisions are made. Our conceptual model explicates the process of micro-decision-making in PMR. Care partners (or caregivers) and rehabilitation practitioners are active members of our team. We work together to develop research projects, collect, analyseand disseminate data. The conceptual model we present in this manuscript was co-created-input from care partners and practitioners on previously collected data became the impetus to develop the RCSDM process model and share co-authorship in this manuscript.

Time-Dependent Effects of Altered Prebedtime Light Exposure in Enclosed Spaces on Sleep Performance Associated with Human States.

Exposure to artificial light influences human performance, which is essential for maintaining healthy work and sleep. However, existing research has not explored the intrinsic links between sleep performance and human states over time under prebedtime light exposure interventions (LEIs). To investigate the time-dependent effects of altered prebedtime light exposure, four LEI groupings (#L1 - #L4) and a Time factor (D8, D9, and D10) were chosen for sleep experiments in enclosed spaces. Forty-eight young adults recruited were available for data analysis. Subjective alertness (SA), negative affect (NA), subjective sleep, and objective sleep were measured via the Karolinska Sleepiness Scale, Positive and Negative Affect Schedule, Next-day Self-assessment Sleep Quality, and joint assessment of wrist actigraphy and sleep diaries, respectively. Statistical analysis was used for the effects of light exposure on the human states (corresponding to the SA and NA) and sleep performance, while the process model helped construct the associations between the two. The statistical effects revealed that the Time had a significant main effect on subjective sleep and changes in prebedtime alertness; the LEI had a significant main effect only on sleep onset latency (SOL). After undergoing altered prebedtime light exposure, the mean SA increased at prebedtime of D9 (p = 0.022) and D10 (p = 0.044); No significant effect on the NA was observed; Mean subjective sleep had a significant increase from D8 to D10. Moreover, five actigraphy-estimated sleep parameters were interrelated. In light of this, a chained pathway relationship was identified. The SOL played a mediating predictor between prebedtime state and objective sleep, which was linked to the awakening state through subjective sleep. Our study suggests that time-dependent effects of altered prebedtime light exposure on sleep performance are associated with human states at prebedtime and awakening, with implications for its prediction of sleep health.

Modeling of the conditions of the process of biotransformation of fish raw materials by bacterial starter cultures

Abstract In food biotechnology, special attention is given to the biotransformation by promising strains of bacterial starter cultures to obtain food products with improved quality characteristics and functional properties. Biotransformation using bacterial starter cultures may be a means to increase the shelf life of fish products, enhance their organoleptic properties, and increase their nutritional value through the formation of metabolites of their activity, which are the main factors of bioconservation and synthesis of vitamins and other biologically active substances. The probiotic properties of bacterial starter cultures will significantly influence the optimization of the indigenous microflora of the gastrointestinal tract, thereby strengthening the human body and its immune system overall to maintain a healthy lifestyle. The development of this trend is associated with the interest of the Russian population in products with a probiotic focus. In this regard, an analysis of data on the justification of the use of bacterial starter cultures for the biotransformation of fish raw materials has been conducted, and the necessary bacterial starter cultures have been selected. Based on theoretical and practical foundations, mathematical models of the biotransformation process by bacterial starter cultures have been constructed depending on the properties of fish raw materials and the biotechnological characteristics of the introduced microorganisms. The conducted research has been used to develop subsequent technological regimes for the biotransformation of fish raw materials by bacterial starter cultures as the basis for the formulations of “fast food” products containing functional components, which is justified by the accelerated pace of life and the desire of modern society to monitor their diet by consuming products that support a healthy lifestyle.

CFD Modelling of Plasma Downdraft Coal Gasification Process: Effect of Throat Diameter on the Produced Syngas Composition

CFD Modelling of Plasma Downdraft Coal Gasification Process: Effect of Throat Diameter on the Produced Syngas Composition