Hydraulic fracturing for oil-gas and geothermal reservoir stimulation is closely related to the generation and propagation of Mode I crack. Nonlinear deformation due to rock heterogeneity occurs at such crack tip, which causes the fracture process zone (FPZ) to form before the crack propagates unsteadily. However, the relationship between the FPZ characteristics and rock heterogeneity still remain elusive. We used three rock types common in reservoir for experimental investigation, and each of them includes two subtypes with different heterogeneity due to grain size or microstructural characteristics. Drawing on the experiment results, we calculated the FPZ size in each cracked chevron notched Brazilian disk, and we reproduced the formation process of the FPZ in marble by discrete element method. We showed that strong heterogeneity is favorable to large FPZ size, can enhance the ability of crack generation and complicate crack morphology. Coupling the Weibull distribution with fracture mechanics, the dependence of the FPZ size on heterogeneity degree can be theoretically explained, which suggests that inherent heterogeneity of rocks set physical foundation for formation of FPZ. These findings can improve our recognition to formation mechanism of the Mode I crack and provide useful guidelines for evaluating reservoir fracability.

A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as the Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we both study the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard, we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators; the multiple point Maximum A-Posteriori (MAP) estimators; an efficient class of multiple-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators; and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring.

Land suitability analysis is a basic premise for allocating specific land for specific purpose. The objective of this study was to predict the suitable sites for cultivating Moringa oleifera tree in Ethiopia using Spatial Analytic Hierarchy Process. Findings of this study will have paramount significance in supporting decision making in the agroforestry development sector. This study employs Spatial Analytic Hierarchy Process and Geographic Information System to generate valuable information in land allocation for moringa oleifera tree production. Climate, topography, soil type and land use parameters were evaluated for suitability analysis. The results of the study revealed that most of the central part of the country are categorized as moderately suitable for the production of moringa oleifera tree. Areas classified as highly suitable are distributed along the borders of southern and western part of the country. However, some of the central part was classified as not suitable for Moringa oleifera tree production. This paper tried to investigate analysis of spatial data to predict suitable site for moringa tree production at national level. At national level, highly suitable, moderately suitable, and not suitable class covers an area of 308,508.2, 1,628,930.8 and 59891.3 Square Kilometer respectively.

Improper lifting measures of long-span spatial steel structures during construction process may delay the construction period and even cause safety accidents. Few studies on long-span spatial steel structures considered time-varying mechanical characteristic during construction process. In order to achieve safe and efficient installation in a long-span spatial steel structure, this research presents a time-varying mechanical analysis of the synchronous and asynchronous integral lifting and a single and interlaced point’s asynchronous integral lifting analysis method of a long-span spatial steel structure. The results showed that in the case of asynchronous lifting of single point, the displacement influence on other members is related to the distance. The closer the distance from the lifting point, the greater the influence. In asynchronous integral lifting, the lifting point with large lifting force is first installed to the specified position, and the lifting point with small lifting force is installed to the specified position.

Electron Beam Melting (EBM) is an increasingly used Additive Manufacturing (AM) technique employed by many industrial sectors, including the medical device and aerospace industries. In-process EBM monitoring for quality assurance purposes has been a popular research area. Electronic imaging has recently been investigated as one of the in-process EBM data collection methods, alongside thermal/ optical imaging techniques. Despite certain capabilities of an electronic imaging system have been investigated, experiments are yet to be carried out to benchmark one of the most important features of any imaging systems – spatial resolution. This article addresses this knowledge gap by: (1) proposing an indicator for the estimation of spatial resolution which includes the Backscattered Electrons (BSE) information depth, (2) estimating the achievable spatial resolution when electronic imaging is carried out inside an Arcam A1 EBM machine, and (3) presenting an experimental method to conduct edge resolution evaluation with the EBM machine. Analyses of experimental results indicated that the spatial resolution was of the order of 0.3mm-0.4mm when electronic imaging was carried out at room temperature. It is believed that by disseminating an analysis and experimental method to estimate and quantify spatial resolution, this study has contributed to the on-going quality assessment research in the field of in-process monitoring of the EBM process.

The investigation of the spatiotemporal propagation characteristics of the "meteorological-agricultural-hydrological" drought system, under diverse climatic conditions, is crucial for the development of a robust drought warning system and the effective implementation of proactive drought prevention and resilience strategies. To achieve this, the current study utilizes the Soil and Water Assessment Tool (SWAT) model to simulate key components of the watershed water cycle, such as evaporation, surface water, soil water, and groundwater. Specifically, the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Soil Moisture Index (SSMI), and the Nonlinear Joint Hydrological Drought Index (NJHDI) are employed to characterize meteorological drought, agricultural drought, and hydrological drought, respectively. By analyzing the correlation between these types of drought, the propagation characteristics of the "meteorological-agricultural-hydrological" drought system are elucidated using the rigorous strongest correlation coefficient method. The Yellow River Basin (YRB) is chosen as the case study for this research. Results showed that (1) The propagation time from meteorological to agricultural drought exhibited distinct seasonal characteristics, with durations of 5-6 months in spring, 2-3 months in summer, 3-5 months in autumn, and 6-8 months in winter. Compared to 1961-1990, the propagation time increased in spring and summer but decreased in autumn and winter during 1991-2010 for most YRB regions. (2) The agricultural to hydrological drought propagation showed no clear seasonal differences but increased over time. Specifically, zone C (arid/semi-arid with moderate temperatures) had shorter propagation time of 1-5 months, while zones B (transitional plateau to mid-latitude) and E (semi-arid/semi-humid temperate continental climate) experienced longer propagation time of 7-12 months. (3) Despite the extended timescales, agricultural-hydrological drought correlation was weaker than meteorological-agricultural linkage. This is because meteorological deficits directly reduce soil moisture, rapidly inducing agricultural drought. However, groundwater sustaining baseflow during agricultural drought delays streamflow deficits, prolonging the agricultural-hydrological propagation time.

There are different audit methods for analysing operational facts and identifying deficiencies in business process management. However, financial data and transactions indirectly relate to physical objects and their attributes, resulting in distinct characteristics for their analysis and auditing methods. It is important to find new methods for finding a process extraction solution based on the financial audit data space to increase the efficiency of the auditors' work by reducing their time costs and increasing the possibility of detecting financial anomalies in large amounts of financial data. The research presents discussed different financial data space (FDS) methods, and process mining (PM) that include related classifications of financial data and external and internal factors that can influence data behavior (value changes) and the peculiarity of FDS is that it includes not only the characteristics of financial data but also other operational characteristics (events, environmental and internal changes, business location) that may be related to the change in financial objects (FO) values. The authors of the paper discuss the prototypes of specific elements in financial data analysis that were developed using the Process Mining software tool Disco and present the conclusions of the effectiveness of the financial processes cube defined to increase the analytical possibilities of the auditor's work with large amounts of data, introducing more flexible functionality for the analysis of financial activities.

Communication is indispensable for today's lifestyle, and thanks to technology, millions of people can communicate as quickly as possible. The effect of this breakthrough has transformed organizations to the degree that they generate billions of emails daily to facilitate their operations. There is implicit information behind this vast corpus of human-generated content that can be mined and used for their benefit. This paper tries to address the opportunity that email logs can bring to organizations and propose an approach to discover process models by combining supervised text classification and process mining. This framework consists of two main steps, text classification, and process mining. First, Emails will be classified with supervised machine learning, and to mine, the processes fuzzy Miner is used. To further investigate the application of this framework, we also applied this framework over a real-life dataset from a case study organization.

The Process Algebra model has been shown to provide an alternative mathematical framework for non-relativistic quantum mechanics (NRQM). It reproduces the wave functions of non-relativistic quantum mechanics to a high degree of accuracy. It posits a fundamental level of finite, discrete events upon which the usual entities of NRQM supervene. It has been suggested that the Process Algebra model provides a true completion of NRQM, free of divergences and paradoxes, with causally local information propagation, contextuality and realism. Arguments in support of these claims have been mathematical. Missing has been an ontology of this fundamental level from which the formalism naturally emerges. In this paper it is argued that information and information flow provides this ontology. Higher level constructs such as energy, momentum, mass, spacetime, are all emergent from this fundamental level.

The integration of automation and process optimization within the context of AI-powered digital transformation has emerged as a pivotal strategy for organizations aiming to enhance efficiency, foster innovation, and competitiveness. This paper is devoted to present innovative contribution by providing a comprehensive structured roadmap that outlines the foundational principles necessary for the successful integration of automation and optimizing processes within the con-text of emerging AI technologies. The paper introduce a cohesive framework consisting of es-sential key pillars: Data-Driven Insights, Seamless Automation, Adaptive Learning and Contin-uous Improvement, Human-Centric Collaboration, Ethical and Responsible AI, Strategic Align-ment, Scalability, and Innovation. These pillars function as guiding principles to navigate the in-tricate landscape of automation-driven initiatives within AI-powered digital transformation. By embracing these pillars, organizations can embark on a transformative journey that maximizes the potential of automation, fosters innovation, and positions them as leaders in the ever-evolving landscape of AI-driven business operations.

In the concept of the process signature, the relationship between a material load and the modification remaining in the workpiece is used to better understand and optimize manufacturing processes. The basic prerequisite for this is to be able to measure the loads occurring during the machining process in the form of mechanical deformations. Speckle photography is suitable for this in-process measurement task and is already used in a variety of ways for in-plane deformation measurements. The shortcoming of this fast and robust measurement technique based on image correlation techniques is that out-of-plane deformations in the direction of the measurement system cannot be detected and increases the measurement error of in-plane deformations. In this paper, we investigate a method that infers local out-of-plane motions of the workpiece surface from the decorrelation of speckle patterns and is thus able to reconstruct three-dimensional deformation fields. The implementation of the evaluation method enables a fast reconstruction of 3D deformation fields, so that the in-process capability remains given. First measurements in a deep rolling process show that dynamic deformations underneath the die can be captured and demonstrate the suitability of the speckle method for manufacturing process analysis.

Since their introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a (mathematical) tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. Translation of a process tree into a sound Workflow net is trivial; however, the reverse is not the case. Simultaneously, an algorithm that translates a WF-net into a process tree is of great interest, e.g., the explicit knowledge of the control-flow hierarchy in a WF-net allows one to reason on its behavior more easily. Hence, in this paper, we present such an algorithm, i.e., it detects whether a WF-net corresponds to a process tree, and, if so, constructs it. We prove that, when the algorithm finds a process tree, the language of the process tree is equal to the language of the original WF-net. The experiments conducted show that the algorithm’s corresponding implementation has a quadratic time complexity in the size of the WF-net. Furthermore, the experiments show strong evidence of process tree re-discoverability.

Process (or business process) management systems fulfill defining, executing, monitoring and managing process models deployed on process-aware enterprises. Accordingly, the functional formation of the systems is made up of three subsystems such as modeling subsystem, enacting subsystem and mining subsystem. In recent times, the mining subsystem has been becoming an essential subsystem. Many enterprises have successfully completed the introduction and application of the process automation technology through the modeling subsystem and the enacting subsystem. According as the time has come to the phase of redesigning and reengineering the deployed process models, from now on it is important for the mining subsystem to cooperate with the analyzing subsystem; the essential cooperation capability is to provide seamless integrations between the designing works with the modeling subsystem and the redesigning work with the mining subsystem. In other words, we need to seamlessly integrate the discovery functionality of the mining subsystem and the analyzing functionality of the modeling subsystem. This integrated approach might be suitable very well when those deployed process models discovered by the mining subsystem are complex and very large-scaled, in particular. In this paper, we propose an integrated approach for seamlessly as well as effectively providing the mining and the analyzing functionalities to the redesigning work on very large-scale and massively parallel process models that are discovered from their enactment event logs. The integrated approach especially aims at analyzing not only their structural complexity and correctness but also their animation-based behavioral properness, and becomes concretized to a sophisticated analyzer. The core function of the analyzer is to discover a very large-scale and massively parallel process model from a process log dataset and to validate the structural complexity and the syntactical and behavioral properness of the discovered process model. Finally, this paper writes up the detailed description of the system architecture with its functional integration of process mining and process analyzing. And more precisely, we excogitate a series of functional algorithms for extracting the structural constructs as well as for visualizing the behavioral properness on those discovered very large-scale and massively parallel process models. As experimental validation, we apply the proposed approach and analyzer to a couple of process enactment event log datasets available on the website of the 4TU.Centre for Research Data.

Process model discovery covers different methodologies to mine a process model from traces of process executions, and is gaining an important role in Artificial Intelligence research. Current approaches in the area, with few exceptions, focus on determining a model of the flow of actions only. However, in several contexts, (i) restricting the attention to actions is quite limitative, since the effects of such actions have to be analysed, too, and (ii) traces provide additional pieces of information, in the form of states (i.e., values of parameters possibly affected by the actions): for instance, in several medical domains traces include both actions and measurements of patients’ parameters. In this paper, we propose AS-SIM (Action-State SIM), the first approach able to mine a process model which comprehends two distinct classes of nodes, to capture both actions and states.

The contribution deals with the selection of progressive methods and technologies to improve the management of production processes in the industrial area. The first chapter of the contribution summarises the theoretical starting points for the management of production processes and their progressive concepts. Chapter 2 deals with the analysis of the current state of play in the use of methods and technologies in production management and summarise its problem areas. Chapter 3 presents the design of a system for selecting the appropriate method and technology to improve the management of the production process. The system presented is designed from data collection blocks, a benchmark for the performance of industrial enterprises' production processes, and data mining to gain knowledge of the impact of the methods and technologies used on the performance of production processes. The last block of the system allows the presentation of the obtained results, which sets out the recommendation for the choice of progressive method and technology. The system is partially verified in the last chapter of the contribution.

In the current research work, electrical resistance tomography (ERT) was employed for crystallization process monitoring and visualization. A first-of-its-kind MATLAB-based interactive GUI application “ERT-Vis” is presented. Two case studies involving varied crystallization methods were undertaken. The experiments involving calcium carbonate reactive (precipitative) crystallization for the high conductivity solution-solute media and the cooling crystallization for sucrose crystallization representing the lower conductivity solution-solute combination were designed and performed. The software successfully provided key insights regarding the process progress in both crystallization systems. It could detect and separate the crystal agglomerations in the low as well as high conductivity solutions using visual analytics tools provided. The performance and utility of the software were studied using a software evaluation case study involving domain experts. Participant feedback indicated that ERT-Vis software helps in reconstructing images instantaneously, interactively visualizing, and evaluating the output of the crystallization process monitoring data.

Companies have been increasing the amount of data that they collect from their systems and processes, considering the decrease in the cost of memory and storage technologies in recent years. The emergence of technologies such as Big Data, Cloud Computing, E-Science, and the growing complexity of information systems made evident that traceability and provenance are promising approaches. Provenance has been successfully used in complex domains, like health sciences, chemical industries, and scientific computing, considering that these areas require a comprehensive semantic traceability mechanism. Based on these, we investigate the use of provenance in the context of Software Process (SP) and introduce a novel approach based on provenance concepts to model and represent SP data. It addresses SP provenance data capturing, storing, new information inferencing and visualization. The main contribution of our approach is PROV-SwProcess, a provenance model to deal with the specificities of SP and its ability in supporting process managers to deal with vast amounts of execution data during the process analysis and data-driven decision-making. A set of analysis possibilities were derived from this model, using SP goals and questions. A case study was conducted in collaboration with a software development company to instantiate the PROV-SwProcess model (using the proposed approach) with real-word process data. This study showed that 87.5% of the analysis possibilities using real data was correct and can assist in decision-making, while 62.5% of them are not possible to be performed by the process manager using his currently dashboard or process management tool.

This study explores the application of the BPM lifecycle to optimize the Market Analysis process within the Market Intelligence department of a major energy company. The semi-structured, virtual nature of the process necessitated careful adaptation of BPM methodology, starting with process discovery through data collection, modeling, and validation. Qualitative analysis, including value-added and root-cause analysis, revealed inefficiencies. The redesign strategy focused on selective automation using Python scripts and Power BI dashboards, incorporating techniques such as linear programming and forecasting to improve process efficiency and quality while maintaining flexibility. Post-implementation, monitoring through a questionnaire showed positive results, though ongoing interviews were recommended for sustained performance evaluation. This study highlights the value of BPM methodology in enhancing decision-critical processes and offers a model for adaptable, value-driven process improvements in complex organizational environments.

Injection molding is one of the most common and effective manufacturing processes used to produce plastic products and impacts industries around the world. However, injection molding is a complex process that requires careful consideration of several key control variables. These variables and how they are utilized greatly affect the resulting parts of any molding operation. The bounds of the acceptable values of each control process variable or CPV must be analyzed and delimited to ensure manufacturing success and produce injected molded parts efficiently and effectively. One such method in which the key CPV of an injection molding operation can be delimited is through the development of a process window. Once developed, operating CPV at values inside the boundaries of the window or region will allow for the consistent production of parts that comply with the desired performance measures (PM), promoting a stable manufacturing process. This work proposes a method to experimentally develop process windows and illustrates the methodology with a specific molding operation.

In this work, the deoxygenation of organic liquid products (OLP) obtained by thermal catalytic cracking of palm oil at 450 °C, 1.0 atmosphere, with 10% (wt.) Na₂CO₃ as catalyst, in multistage countercurrent absorber columns using supercritical carbon dioxide (SC-CO₂) as solvent, with Aspen-HYSYS process simulator was systematically investigated. In a previous study, the thermodynamic data basis and EOS modeling necessary to simulate the deoxygenation of OLP has been presented [Molecules 2021, 26, 4382. https://doi.org/10.3390/molecules26144382]. This work address a new flowsheet, consisting of 0₃ absorber columns, 10 expansions valves, 10 flash drums, 08 heat exchanges, 01 pressure pump, and 0₂ make-up of CO₂, aiming to improve the deacidification of OLP. The simulation was performed at 333 K, 140 bar, and (S/F) = 17; 350 K, 140 bar, and (S/F) = 38; 333 K, 140 bar, and (S/F) = 25. The simulation shows that 81.49% of OLP could be recovered and the concentrations of hydrocarbons in the extracts of absorber-01 and absorber-02 were 96.95 and 92.78% (wt.) in solvent-free basis, while the bottom stream of absorber-03 was enriched in oxygenates compounds with concentrations up to 32.66% (wt.) in solvent-free basis, showing that organic liquid products (OLP) was deacidified and SC-CO₂ was able to deacidify OLP and to obtain fractions with lower olefins content. The best deacidifying conditions was obtained at 333 K, 140 bar, and (S/F) = 17.

During the last decades, the production systems have developed different strategies to increase their competitiveness in the global market. In a manufacturing and services systems, Lean Manufacturing has been consolidated through the correct implementation of its tools. The present paper presents a case study developed in a Food Packer company where a Simulation Model was considered as an alternative to reduce the waste time generated by the poor distribution of operations and transportation areas for a product within the factory. As a matter of fact, the company has detected problems on the layout distribution that prevents to fulfill the market demand. In addition, the principal aim was to create a simulation model to test different hypothetical scenarios and alternative designs for the layout distribution without modifying its facilities. Moreover, the implemented methodology was based on classical models of simulation projects and a compendium of the manufacturing systems optimization by simulation process used during the last ten years. Also, a mathematic model supported by the Promodel ® simulation software was developed considering the company characteristics; along with the model development, it was possible to compare the production system performance from the percentage of used locations, the percentage of resources utilization, the number of finished products, and the level of Work in Process (WIP). Finally, the verification and validation stages were performed before running the scenarios in the real production area. The results generated by the implementation of the project represent an increase of 68% in the production capacity and a reduction of 5% in the WIP. In addition, both outcomes are associated with the resources management, which were reassigned to other production areas.

The system design for Sustainable manufacturing is to consider both ecological and financial constraints. Manufacturing industry demands to advance in sustainable manufacturing by accounting in environment factors in it. All the factors that affect the environment need to be analyzed so that proper amendments or suggestions can be provided. To favour this, Computer aided life cycle inventory model has been presented for CNC milling and turning processes. Based on utilization of resources and stages, whole machining operation time can be divided into three phases named as process (milling or turning), idle and basic times. As parameters are different for evaluating the process times i.e. depth and width of cut in case of milling and initial and final diameters for turning, two different case studies has been presented one for each milling and turning process. Effect of material choice has been studied for different processes. Highly dense and hard materials takes more time in finishing the job due to low cutting speed and feed rates as compared to that of sot materials. In addition, face milling takes more time and consumes more power as compared to peripheral milling due to more retraction time caused by over travel distance and lower vertical transverse speeds than the horizontal transverse speed used in peripheral retraction process.

The importance of oxygen in the winemaking process is widely known, as it affects the chemical aspects and therefore the organoleptic characteristics of the final product. Hence, it is evident the usefulness of a continuous and real-time measurements of the levels of oxygen in the various stages of the winemaking process, both for monitoring and for control. The WOW project has focused on the design and the development an innovative device for monitoring the oxygen levels in wine. This system is based on the use of an optical fiber to measure the luminescent lifetime variation of a reference metal/porphyrin complex, which decays in presence of oxygen. The developed technology results in a high sensitivity and low cost sensor head that can be employed for measuring the dissolved oxygen levels at several points inside a wine fermentation or aging tank. This system can be complemented with dynamic modeling techniques to provide predictive behavior of the nutrient evolution in space and time given few sampled measuring points for both process monitoring and control purposes. The experimental validation of the technology has been first performed in a controlled laboratory setup to attain calibration and study sensitivity with respect to different photo-luminescent compounds and alcoholic or non-alcoholic solutions, and then in an actual case study during a measurement campaign at a renown Italian winery.

Semisolid casting of Aluminum alloys is growing. For magnesium alloys, Thixomoulding became the dominant process around the world. For aluminium processing, the situation is different as semisolid processing of aluminium is more technically challenging than for magnesium. Today three processes are leading the process implementation, The GISS method, the RheoMetal process and the SEED process. These processes have all strengths and weaknesses and will fit a particular range of applications. The current paper aims at looking at the strengths and weaknesses of the processes to identify product types and niche applications for each process based on current applications and development directions taken for these processes

In this paper, we study a generalised CIR process with externally-exciting and self-exciting jumps, and focus on the distributional properties and applications of this process and its aggregated process. The first and second moments of this jump-diffusion process are used to calculate the insurance premium based on mean-variance principle. The Laplace transform of the aggregated process is derived, and this leads to an application for pricing default-free bonds which could capture the impacts of both exogenous and endogenous shocks. Illustrative numerical examples and comparisons with other models are also provided.

Configurable process models are frequently used to represent business workflows and other discrete event systems among different branches of large organizations: they unify commonalities shared by all branches and describe their differences, at the same time. The configuration of such models is usually done manually, which is challenging. On the one hand, when the number of configurable nodes in the configurable process model grows, the size of the search space increases exponentially. On the other hand, the person performing the configuration may lack the holistic perspective to make the right choice for all configurable nodes at the same time, since choices influence each other. Nowadays, information systems that support the execution of business processes create event data reflecting how processes are performed. In this article, we propose three strategies (based on exhaustive search, genetic algorithms, and greedy heuristic) that use event data to automatically derive a process model from a configurable process model that better represents the characteristics of the process in a specific branch. These strategies have been implemented in our proposed framework, and tested in both business-like event logs as recorded in a higher educational ERP system, and a real case scenario involving a set of Dutch municipalities.

The introduction of the Industrial Internet of Things (IIoT) has led to major changes in the industry. Thanks to machine data, business process management methods and techniques could also be applied to them. However, one data source has so far remained untouched: The network data of the machines. In the business environment, process mining, for example, has already been carried out based on network data, but the IIoT, with its particular protocols such as OPC UA, has yet to be investigated. With the help of design science research and on the shoulders of CRISP-DM, we first develop a framework for process mining in the IIoT in this paper. We then apply the framework to real-world IIoT network traffic data and evaluate the outcome and performance of our approach in detail. We find tremendous potential in network traffic data but also limitations. Among other things, due to the dependence on process experts and the existence of case IDs.

The spring-in phenomenon of the composite parts can affect the assembly process. This study aims to predict the spring-in phenomenon of carbon fiber reinforced plastic (CFRP) part. Here, we predict the spring-in of the CFRP part using a coupled analysis of the forming and cooling processes during the stamping process. First, a simulation of the entire forming process, such as the transfer of the composite laminate, gravity analysis, and forming was performed to obtain the temperature distribution of the CFRP part. Subsequently, a finite-element (FE) simulation of the cooling process was conducted to predict the spring-in phenomenon of the shaped CFRP part using the temperature data obtained in the forming simulation. Finally, a CFRP part was manufactured and compared with the results of the FE simulation.

This research explores the depolymerization of waste polymethyl methacrylate (PMMAW) from dental material in fixed-bed semi-batch reactors, focusing on three production scales: laboratory, technical, and pilot. The study investigates the thermal degradation mechanism and kinetics of PMMAW through thermogravimetric (TG) and differential scanning calorimetry (DSC) analyses, revealing a two-step degradation process. The heat flow during PMMAW decomposition is measured by DSC, providing essential parameters for designing pyrolysis processes. The results demonstrate the potential of DSC for energetic analysis and process design, with attention to standardization challenges. Material balance analysis across the production scales reveals a temperature gradient across the fixed bed negatively impacting liquid yield and methyl methacrylate (MMA) concentration. Reactor load and power load variables are introduced, demonstrating decreased temperature with increased process scale. The study identifies the influence of temperature on MMA concentration in the liquid fraction, emphasizing the importance of controlling temperature for efficient depolymerization. Furthermore, the research highlights the formation of aromatic hydrocarbons from the remaining char, indicating a shift in liquid composition during the depolymerization process. The study concludes that lower temperatures below 450 °C favor liquid fractions rich in MMA, suggesting the benefits of lower temperatures and slower heating rates in semi-batch depolymerization. The findings contribute to a novel approach in analyzing pyrolysis processes, emphasizing reactor design and economic considerations for recycling viability. Future research aims to refine and standardize the analysis and design protocols for pyrolysis and similar processes.

This article describes the Analytic Hierarchy Process (AHP) method which can be calculated by several methods. The AHP method is essential for the managerial decision-making to recognize which method is efficient for the calculation and to determine the proper order of criteria. In the article there are included methods that can be used in order to calculate the matrix in the AHP process for setting criteria such as Geometric mean, Arithmetic mean, Row sum of the adjusted the Saaty matrix, Reverse sums of the Saaty matrix columns and Row sums of the Saaty matrix. The paper is focused on the accuracy of the methods used. The results show that the most accurate method is the Saaty method, and the second most accurate method is the geometric mean in order to determine the ranking. This method is easier to use when the same order is achieved as in the Saaty method, followed by Geometric mean, which is favourable for fast and easy determination of alternatives using in the AHP process. The survey carried out among managers graduated from Universities and Colleges in Slovakia showed that the respondents considered the Saaty method as the most complex and also the most difficult method and the geometric mean average method the simplest method. 44% of respondents stated that they are capable to use a program to calculate the AHP. 46% of respondents said they had experience with some method related to the managerial decision-making process. 72% of managers regarded as important to manage some method for decision-making in their managerial position.

This contribution illustrates the training evaluation system developed within the Master’s Program in Family and Community Mediation at Università Cattolica del Sacro Cuore in Milan. This is an interim evaluation, which focuses on the training process and which considers fundamental the collaboration with the subjects of the training. The peculiarity of this work concerns the possibility of inserting research within the training process, following a logic of mutual enrichment both in terms of content and learning. The contribution illustrates in detail the outcome and the process evaluation system, defining the perspective, the objectives, and the methodology of implementation. In particular, the outcome evaluation focuses on the distal and proximal outcomes of the training, while the process analysis focuses on the dynamics within the group of participants. Although further evaluations involving different training groups and other training processes are needed, this training evaluation system allows to shed light on both the topic and the context in which training is delivered. The integration between different points of view and several levels of analysis allows the researchers to deepen the individual path of each participant as well as to have feedbacks on the progress of the training group as a whole.

This paper describes the state of the art and future opportunities for process design and sustainable development. In the Introduction the main global megatrends and European Union's response to two of them, the European Green Deal, are presented. The organization of professionals in the field, their conferences and publications support the two topics. A brief analysis of the published documents in the two most popular databases shows that the environmental dimension predominates, followed by an economic one, while the social pillar of sustainable development is undervalued. The main design tools for sustainability are described. As an important practical case, the European chemical and process industries are analyzed and their achievements in sustainable development are highlighted; in particular, their strategies are presented in more detail. The conclusions cover the most urgent future development areas of the process industries, carbon capture with utilization or storage, process analysis, simulation, synthesis and optimization tools; zero waste, circular economy and resource efficiency already play an important role. However, more profound changes are needed in the coming decades, including a shift away from growth with changes in habits, lifestyles and business models. Lifelong education for sustainable development will play a very important role in the growth of democracy and happiness instead of consumerism and neoliberalism.

One way to reduce the negative impact of human activity on the natural environment is to use natural, easily available and relatively cheap to produce compounds in industrial processes. One such compound is naturally occurring calcium carbonate (CaCO3). This compound has adsorption properties so that it can be an alternative to commonly used adsorbents. The aim of this work is to determine the possibility of using CaCO3 to remove pharmaceutical substances such as sulfadiazine and tetracycline from water. The CaCO3 used in this work was synthesised using our own method, which allows the production of CaCO3 particles with nanometric size. In the conducted research, calcium carbonate was used in the form of a suspension in purified solutions and as an inorganic filling of the developed membranes. The mass of pharmaceutical substances removed from their aqueous solutions was determined in the tests carried out. Based on the results obtained, it can be concluded that CaCO3 has the ability to adsorb both tetracycline and sulfadiazine. In suspension tests, the mass of the removed substances per unit mass of adsorbent was 1.52 mg/g and 6.85 mg/g, respectively. In turn, in the case of the process using the developed membranes, the mass of the removed substances per unit mass of adsorbent was 232 mg/m2 and 524 mg/g, which is a several times greater increase in the mass of the removed substance compared to the process using an unmodified membrane.

Most petrochemical plants still maintain a Proportional-Integral-Differential controller(PID) system, which is a feedback control system. However, gradually, the PID system is being extended and introduced to the Advanced Process Controller (APC) system, which is an integrated control system of feedforward and feedback that predicts external influences in advance. In the process of conducting on-site plant tests and calculating APC model parameter for the application of APC systems, a problem arises that Model Parameter are implemented differently depending on the proficiency of APC engineers. To minimize this problem, a technique for estimating APC model parameter without a plant test is required. In order to estimate the APC model parameter, it is necessary to train on dynamic interval data. In this paper, we use statistical techniques such as PELT, Linear Kernel, and Radial Basis Function Kernel of Change Point Detection (CPD) to extract dynamic data with minimum Mean Absolute Error (MAE) from time series data of a real petrochemical plant. Then, the hyper parameter is fixed and the APC model parameter is estimated by learning the dynamic section data. By applying the estimated APC model parameter to the APC Model Tool and measuring the fitting rate, it was confirmed that it is possible to estimate the APC model parameter with excellent control performance without plant test.

The production of butanol, acetone and ethanol by Clostridium acetobutylicum is a biphasic fer-mentation process. In the first phase the carbohydrate substrate is metabolized to acetic and bu-tyric acid, in the following second phase the product spectrum is shifted towards the economi-cally interesting solvents. Here we present a cascade of six continuous stirred tank reactors (CCSTR), which allows performing the time dependent metabolic phases of an ace-tone-butanol-ethanol (ABE) batch fermentation in a spatial domain. Experimental data of steady states under four operating conditions - with variations of the pH in the first bioreactor between 4.3 and 5.6 as well as the total dilution rate between 0.042 1/h and 0.092 1/h - were used to optimize and validate a corresponding mathematical model. Beyond a residence time distribution representation and substrate, biomass and product kinetics this model also includes the differen-tiation of cells between the metabolic states. Model simulations predict a final butanol product concentration of 8.2 g/L and a butanol productivity of 0.75 g/(L h) in the CCSTR operated at a pH in bioreactor 1 of 4.3 and D = 0.092 1/h, while 31 % of the cells are differentiated to the solventogenic state. Aiming at an enrichment of solvent-producing cells, a feedback loop was introduced into the cascade - sending cells from a later state of the process (bioreactor 4) back to an early stage of the process (bioreactor 2). In agreement with the experimental observations, the model accurately predicted an increase of butanol formation rate in bioreactor stages 2 and 3, resulting in an overall butanol productivity of 0.76 g/(L h) for the feedback loop cascade. The here presented CCSTR and the validated model will serve to investigate further ABE fermentation strategies for a controlled metabolic switch.

The paper investigates into the intricacies of semiconductor manufacturing, a highly complex process entailing a wide array of subprocesses and diverse equipment. Semiconductors are miniaturized integrated circuits comprising numerous components. The semiconductor manufacturing process begins with the thin disc-shaped silicon wafers. On each wafer, up to thousands of identical chips can be prepared depending upon the diameter of the wafer to build up the circuits layer by layer in a wafer fab. The size of the semiconductors requires a high number of units to be produced, thus necessitating a large amount of data to control for improving the semiconductor manufacturing process. Therefore, the collection and analysis of the equipment data, process data, and machine history data throughout the manufacturing process are required to diagnose faults, monitor the process, and manage the manufacturing process effectively. This research is focused on improving the semiconductor manufacturing process through a rigorous analysis of collected manufacturing process data, employing statistical process control (SPC), data mining techniques, and data-driven decision models. The project's primary objective is to increase the manufacturing process stability and productivity by utilizing the latest data-driven technologies in the scientific community. A structured review was undertaken, exploring contemporary data-driven methodologies in semiconductor manufacturing process improvement, specifically pertaining to process capability, product yield rate, and process stability. This review accentuates a comprehensive evaluation of data-driven methodologies applicable to conventional semiconductor manufacturing facilities, aiming to drive substantial process improvements. It features a detailed demonstration facilitating the selection of optimal semiconductor manufacturing processes to enhance overall operational performance. This study of process improvement in the semiconductor manufacturing steps through the application of data-driven methodologies will be effective in delivering advanced, real-time, and proactive control decisions throughout the manufacturing facilities. This endeavor is expected to promptly provide critical insights for enterprise manufacturing decision-makers to reduce manufacturing cycle time, improve the product yield rate, and increase the overall efficiency of the manufacturing process.

: The objective of this work is to elucidate the role of partnerships and the number of partners necessary for lead user innovation outcomes. Lead user theory identifies these users as being ahead of market trends and experiencing significant benefits from innovating. So far, most lead user research has concentrated on firms and not individual lead users’ perspective. Data were obtained using a quantitative design during which 321 respondents participated. The SPSS software was used to analyze the data and determine significance. These findings indicate a connection between number of partners and lead user innovation success. This demonstrates that number of partners is significant in determining the success or failure of an innovation. This study provides empirical evidence that the number of partners is an essential factor that influences the success of innovation, and establishes the absolute numbers for achievement of successful innovation. Future studies must investigate the possible implications of integrating the partner variables with other characteristics of the lead user.

Atmospheric correction is the processes of converting radiance measured at a spectral 1 sensor to the reflectance of the materials in a multispectral or hyperspectral image. This is an 2 important step for detecting or identifying the materials present in the pixel spectra. We present 3 two machine learning models for atmospheric correction trained and tested on 100,000 batches of 40 4 reflectance spectra converted to radiance using MODTRAN, so the machine learning model learns 5 the radiative transfer physics from MODTRAN. We created a theoretically interpretable Bayesian 6 Gaussian process model and a deep learning autoencoder treating the atmosphere as noise. We 7 compare both methods for estimating gain in the correction model to the well-know QUAC method 8 of assuming a constant mean endmember reflectance. Prediction of reflectance using the Gaussian 9 process model outperforms the other methods in terms of both accuracy and reliability.

There is a lack of research based on in-depth theoretical and scientific knowledge to understand the visually impaired, and there has been little effort in the application of strategies for early intervention to minimize risk these people might encounter during development.. This study used semi-structured interviews from eight persons with visual impairments who had various experiences with resiliency. Three resilience processes based on life experiences were identified: 1) Experience and Adaptation: “self-awareness of disability” and “adaptation disability and the environment”; 2) Facing the Circumstances: “the exposure to concealment and abuse,” “the suppression of potential,” “denial and abandonment by family,” “poverty and disability,” “exchange and self-regulation,” and “social integration” themes; and 3) the Positive Reinforcement: “self-disclosure and jump-starting life,” “maintenance of a positive thinking,” and “socioeconomic independence.” These findings expand the understanding of the factors common to the resilience process experienced by individuals with visual impairment and highlight the importance of psychological support, family, education, and social support.

Living processes are integral to open systems for multiple reasons. From a thermodynamic perspective, they reduce local entropy at the cost of increasing environmental entropy. Furthermore, these processes are inherently nonlinear owing to their feedback interactions with the environment. Nonlinear systems exhibit diverse behaviours: some stabilize at a constant value, whereas others display bifurcations and chaotic dynamics. This study introduces a model that describes living processes as the dynamics of nonlinear single-value diverging maps, whereas chaotic evolution is associated with the increasing entropy environment. This model enables us to describe the lifetime of different species simply by varying the model parameters. This model also simplifies explaining the transition from life to death.

Biochar is a carbonaceous material derived from the thermochemical conversion of biomass under oxygen-lean conditions. Its properties render it many applications in agriculture, environmental remediation, and catalysis. Commonly produced by pyrolysis under a nitrogen atmosphere, the biochar composition is influenced mainly by residence time, heating rate, and process temperature. The existing literature primarily assesses biochar properties, such as ultimate, proximate, calorific, porosity, and combustibility, generated under various thermo-conversion conditions. However, there is a notable lack of sufficient investigation and attention given to the practical application of biochar. This article seeks to bridge that gap by enhancing the comprehension of the necessary experimental assessment for specific biochar applications and proposing process optimization methods using Multi-Criteria Decision Analysis (MCDA). By delving into these aspects, the study offers a concise overview of biochar production and presents a comprehensive panorama based on articles and patents. Additionally, the research explores the physicochemical characterization techniques associated with biochar and its various applications in conjunction with MCDA. This comprehensive approach aims to uncover valuable insights that could further advance the practical and sustainable utilization of biochar in a wide range of fields.

To fulfil a need of limitation automotive emissions the reduction of vehicle weight is widely recommended and achieved in many ways, both by the construction of individual elements of the vehicle, and by the selection of light materials including Al alloys. Connecting these elements with each other and with elements made of iron alloys can be realized, inter alia, by welding or stir welding. However, the quality of the welds obtained varies widely and depends on many design, operational and environmental factors. The present study focused on a review of various welding technics used to joint both similar and dissimilar Al alloys utilized in automotive industry, the effect of various process parameters on welds quality, and phenomena appeared in such welds..

Handwriting is a complex perceptual-motor task that requires years of training and practice before complete mastering. Its acquisition is crucial since handwriting is the basis, together with reading, for the acquisition of higher-level skills such as spelling, grammar, syntax, and text composition. Despite correct learning and practice of handwriting, some children never master this skill to a sufficient level. These handwriting deficits, referred to as developmental dysgraphia, can seriously impact the acquisition of other skills and thus the academic success of the child if they are not diagnosed and handled early. In this review, we present a non-exhaustive listing of the tools which are the most reported in the literature for the analysis of handwriting and the diagnosis of dysgraphia. Tools focusing on both the final handwriting product and the handwriting process will be considered. We will then discuss the pros and cons of the existing tools, and the perspectives for the development of future tools.

The innate immune system is the first line of defense against pathogens. It’s composition includes barriers, mucus and other substances as well as phagocytic and other cells. The purpose of the paper is to analyze in general grounds the immune system and the body immunity to cancer. Simple ideas and the qualitative theory of differential equations are used along with general principles such as the minimization of the pathogen load and economy of resources. In the simplest linear model, the annihilation rate of pathogens in any tissue should be greater than the pathogen’s average rate of growth. When nonlinearities are added, a reference value for the number of pathogens is set, and a stability condition emerges, which relates strength of regular threats, barrier height and annihilation rate. On the other hand, in cancer immunity, the linear model leads to an expression for the lifetime risk, which accounts for both the effects of carcinogens (endogenous or external) and the immune response. The stability condition allows a comparison of immunity in different tissues. The way the tissue responds to an infection shows a correlation with the way it responds to cancer. These statements are formulated at the qualitative level, but may and deserve to be quantitatively checked.

Arguments have been made that violation of the CHSH and similar inequalities shows that reality at the quantum level must be non-local. The derivation of Bell inequality is re-examined and it is shown that violations of these inequalities merely demonstrates the existence of contextuality - they say nothing about the causal influences underlying such contextuality. It is argued that contextual systems do not possess enduring (propositional) properties, merely contingent properties. An example is presented of a classical situation, a two player co-operative game, whose random variables are consistently connected in the sense of Dzhafarov, and which is contextual, violating the CHSH inequality. In fact it also violates the Tsierl'son bound. The key is that this system is generated and its properties are disposed, not determined.

One of the biggest challenges in arm prosthesis design is to resemble normal hand functionality. Some of arm prosthesis try to mimic all hand movement, while other try to produce several main hand grip patterns. We design a Versatile Gripping Technology (VGT) from a basic whippletree mechanism to produce main hand grip patterns in arm prosthesis. VGT development is the result of abstraction, negation, and systemic variation process of existing solution. To validate VGT, we test it to produce six pattern of hand grip from Southampton Hand Assessment Procedure. VGT capable to produce lateral, power, tip, extension, and spherical grip. In other hand, VGT including the thumb movement and it only use one simple shape moving part. Thus, VGT is effective and efficient. With this result, we argue that abstraction, negation, and systemic variation process of existing solution can create a novel solution and it is applicable in another problem or domain.

Recently, the amount of waste generated has been rapidly increasing, there have been difficulties disposing of waste in Korea. As a solution to this, treating waste using a cement kiln has suggested, but the environmental and economic effects have not been specifically studied. In this study, the effects of alternative resources, and reducing the social costs(Installation and Operation) associated with waste treatment facilities were analyzed. Through a co-processing method, a reduction of approximately 53kg of CO2 can be realized during the production of one ton of cement, and cost savings of about 3,815 milion USD. Another effect is an extension of the expiration date for landfills by 7.55 years.

A hybrid smart process and material design system for nanoimprinting is proposed, which combines with a learning system based on experimental and numerical simulation results. Instead of carrying out extensive learning experiments for various condition, the simulation learning results are partially complimented where the results can be theoretically predicted by numerical simulation. In other word, the lacking data in experimental learning are complimented by simulation-based learning results. Therefore, the prediction of nanoimprint results under various conditions without experimental learning could be realized even for unknown materials. In this study, material and process design for a low-temperature nanoimprint process using glycerol-containing polyvinyl alcohol are demonstrated. Experimental results under limited conditions are learned to investigate optimum Glycerol concentrations and process temperatures. On the other hand, simulation-based learning is used to predict the dependence on press pressure and shape parameters. The prediction results for unknown Glycerol concentrations agreed well with the follow-up experiments.

Among the knowledge areas in which process mining has had an impact, the audit domain is particularly striking. Traditionally, audits seek evidence in a data sample that allows to make inferences about a population. Mistakes are usually committed when generalizing the results and anomalies, therefore, appear in unprocessed sets. However, there are some efforts to address these limitations using process mining-based approaches for fraud detection. To the best of our knowledge, no fraud audit method exists that combines process mining techniques and visual analytics to identify relevant patterns. This paper presents a fraud audit approach based on the combination of process mining techniques and visual analytics. The main advantages are: (i) a method is included that guides the use of the visual capabilities of process mining to detect fraud data patterns during an audit; (ii) the approach can be generalized to any business domain; (iii) well-known process mining techniques are used (Dotted Chart, Trace Alignment, Fuzzy Miner…). The techniques were selected by a group of experts and were extended to enable filtering for contextual analysis, to handle levels of process abstraction, and to facilitate implementation in the area of fraud audits. Based on the proposed approach, we developed a software solution that is currently being used in the financial sector as well as in the telecommunications and hospitality sector. Finally, for demonstration purposes, we present a real hotel management use case in which we detected suspected fraud behaviors, thus validating the effectiveness of the approach.

Robotic Process Automation (RPA) is a fast growing field inside Business Process Automation (BPA). RPA deals with software robots that use human application interface to do some repetitive tasks like starting programs, filling the forms, clicking on buttons etc. Some RPA software tools like Blue Prism, Rapise and UiPath became popular during last years but there are still some problems with them starting with their high price and difficulties to adapt to the real use cases. On the other hand, Optoklik is a relatively small RPA tool developed to be highly adaptable to the real cases and still easy to use. This paper presents the main functions of Optoklik and discusses its usage in automation of different actions that use human application interface.

Nowadays, inherently safer designs are considered as key priorities to prevent or mitigate serious incidents with devastating consequences. The need for process safety assessment during early design phases has motivated the development of several contributions related to computer-aided assessment methodologies in order to measure the inherent safety of chemical processes. In this work, the large-scale production of chitosan from shrimp wastes was evaluated from process safety point of view using the numerical descriptive inherent safety technique (NuDIST).To this end, simulation of the chitosan production was performed using Aspen Plus ® to obtain extended mass and energy balances. The assessment of all the chemicals involved within the process was carried out for the following safety parameters: explosivity (EXP), flammability (FL) and toxicity (TOX). The safety assessment of the process included the parameters of temperature (T), pressure (P) and heat of reaction (HR). The maximum chemical safety score was estimated in 171.01 with ethanol as main contributor to the parameter of explosivity and flammability. The score associated with operating data was calculated in 209.30 and heat of reaction reported to be the most affecting parameter. The NuDIST score was estimated in 380.30. This NuDIST value revealed the low hazards associated with the handling of substances such as shrimp wastes, chitosan and water, as well as the non-extreme temperature and pressure conditions. In general, the large-scale production of chitosan from shrimp shells showed to be an inherently safe alternative of waste valorization.

Habitual entrepreneurs became an important group of entrepreneurs who make a large contribution to the process of wealth creation. Previous studies have indicated that habitual entrepreneurship is a widespread phenomenon. It highlights the need to focus on the habitual entrepreneurs to understand the dynamic aspect of entrepreneurship. The aim of the article is to present the results of preliminary research on the relationships of entrepreneurial behavior of novice entrepreneurs and habitual entrepreneurs, in particular their motivation and impact on the entrepreneurial process. Research were conducted on a sample of 373 small innovative enterprises in the fourth quarter of 2017. The scale of habitual entrepreneurs in the examined sample is 32.44% and is comparable with research carried out in other countries. Survey results reveal that habitual entrepreneurs have greater tendency to create opportunities (introduction new products or services) than novice entrepreneurs. Habitual entrepreneurs also have higher development rate (increase of turnover) and larger share in the international market than novice entrepreneurs. The article also contains indications for further research into the phenomenon of multiple entrepreneurship.

The purpose of this article is to analyze urban form through the mapping of morphological indices, namely impervious surface fraction, building density, verticality, height/width ratio, roughness length, and porosity, to support urban planning in the city of João Pessoa, PB, in northeastern Brazil. The application of this study identifies and calculates such significant indices for the city's urban space from a Geographic Information System (GIS) model. The spatial indices play notable roles in climate at different scales, developing guidelines to maximize environmental quality, promote improvements to thermal comfort, minimize the urban heat island in the city of João Pessoa, and provide relevant data (considering microclimate aspects), guiding decisions related to the planning process.

In this study, we evaluated the cold drawing workability of two kinds of modified 9Cr-2W steel containing different contents of boron and nitrogen, depending on the temperature and time of normalizing and tempering treatments. Using ring compression tests at room temperature, the effect of intermediate heat treatment condition on workability was investigated. It was found that the prior austenite grain size can be changed by the austenite transformation, and the grain size increases with increasing temperature during normalizing heat treatment. Alloy B and Alloy N showed different patterns after normalizing heat treatment. Alloy N had higher stress than Alloy B, and the reduction in alloy N increased, while the reduction in alloy B decreased. Alloy B showed a larger number of initially formed cracks and a larger average crack length than Alloy N. Crack length and number increased proportionally in Alloy B as the stress increased. Alloy B had lower crack resistance than Alloy N due to boron segregation.

One of the major steps toward the industrialization of microbial product(s) is to optimize the cultivation conditions at the large scale bioreactor and successfully control the microbial behavior within large scale production environment. Statistical Design of Experiment was proven to optimize a vast number of microbial processes to achieve robustness and explore possible interactions among the variables. In this research, Taguchi Orthogonal Array was applied to optimize the cultivation condition of a newly isolated Prodigiosin-producing marine bacterial strain, Serratia AM8887, at bioreactor level. Two steps fermentation process was applied; as the productivity was scaled up from shake flask level to a bench top bioreactor (5L) and subsequently to an in-situ sterilization bioreactor system (20L) leading to a yield of 7g/L compared to 100mg/L prior to optimization confirming that; applying Taguchi experimental design is a reliable and good positive option for the optimization of biotechnological processes.. The produced pigment was purified and the chemical structure was revealed by means of Spectrophotometric, Maas Spectrum (MS), Fourier transform infrared (FT-IR), and proton nuclear magnetic resonance (1H-NMR) spectroscopy analysis. The biological activity including antibacterial, antioxidants and cytotoxicity to cancer cells line of the pigment were explored. The pigment showed very characteristic features that could helpful in food, pharmaceuticals and/or textile industries.

The research reported on this paper was aimed at improving the overall efficiency of a PDO certified artisanal cheese production process. Being a PDO certified foodstuff by the EU, it is considered to have properties and qualities determined by the geographical environment in which is made, with its production taking place in a specific and determined geographical location, in this case the Serra da Estrela region. In that sense, the authors conducted a mapping according to a systemic perspective of the processes involved in the context of manufacturing and distribution of certified Serra da Estrela cheese. Numerous methods were used throughout the process, such as a systemic design analysis, and techniques derived from ethnographic methods, which led to the collection of data in the field and consequently provided the immersion of the researcher in genuine work situations. Critical points were identified and emphasized in the systemic map with the purpose of encouraging initiatives to address and overcome the gaps and inefficiencies detected. The systemic design analysis triggered the development of design work. Observations following an ethnographic approach identified ergonomic risks in cheese making during the process of cutting excess chips, fostering the emergence of musculoskeletal disorders at the wrist. A tool that fits best to the task at hand was developed. A prototype of the new tool enabled collecting feedback from use in the work context, in order to inform product development. The domains of agricultural production and microbiology, considering the specific microorganisms developed trough the ripening process of the cheese, turned out to be aspects of high importance for the issue under focus, contributing to a broader understanding of the ripening process and its risks, simultaneously improving the efficiency and success of cheese production. If it were not for the systemic analysis, which served as a link between the boundaries of distinct domains such as the risk of microorganism contamination, ergonomics, energy efficiency, legislation and regulation policies, transportation challenges and economic viability, approached throughout the research. Simultaneously creating bridges between them, the various problems might not have been detected in the first place, as they are usually addressed in specialized disciplines, predetermined by the restrictions of each specific area of knowledge. As a consequence of the development of this research, which was based on an analysis that sought to establish possible connections between various disciplines and tried to constantly maintain a holistic perspective, making new connections and observing the issues from a new angle, apart from the already established methodologies. All this allowed to lay out the seeds for the development of a plan to tackle the critical points identified by the systemic analysis reported in this paper.

This article examines the integration of artificial intelligence (AI) and robotic process automation (RPA) in financial accounting and management, underscoring their role in driving the digital transformation of corporate finance. It discusses the shortcomings of traditional financial processes and highlights the potential of AI and RPA technologies to enhance efficiency, accuracy, and cost-effectiveness. The paper also explores the limitations of RPA, such as its challenges in processing unstructured data and handling complex decision-making scenarios. Looking forward, it considers the future trends in AI and RPA, emphasizing the benefits of cloud technology in scaling automated systems and addressing associated challenges.

Chemical plants are characterized by the management and operation of various hazardous materials; thus, accident risk is inherently high. Nevertheless, the incidence of chemical accidents in Korea has effectively stabilized, with no marked increase attributable to the implementation of systematic safety management procedures based on process safety management system principles. In June 2023, the Ministry of Employment and Labor in Korea unveiled the Serious Injury and Fatality (SIF) report, meticulously cataloging 4,432 instances of grave accidents spanning six years (2016–2021). The compilation comprises 1,834 and 2,574 cases within the manufacturing and affiliated sectors and the construction sector, respectively. This study provides an overview of each accident, and elucidates the causes, and recommends preventative strategies. The objective was to galvanize companies across various industrial landscapes and fortify their intrinsic safety management protocols by using accident data delineated per sector. Sixteen specific accident cases from the SIF report, all of which occurred in facilities regulated by process safety management (PSM), were investigated. Technical analysis was conducted, focusing on explosion accidents within the organic catalyst packaging process, notable for their unique patterns of occurrence. This analysis extended to an examination of the reproducibility of such accidents. Employing two system analysis methodologies, system theoretic accident model and process and barrier-based systemic cause analysis technique, this study provides insights into the causative factors of the accidents and formulates comprehensive strategies for the prevention of future accidents. The findings will contribute significantly to reducing the risk of accidents within the catalyst packaging domain and assist in the development of robust recurrence prevention measures.

In this work, optimal consumption and investment strategies are derived for risk-averse investors under the 4/2 stochastic volatility class of models. We work under an expected utility (EUT) framework and consider a Constant Relative Risk Aversion (CRRA) investor, who might also be ambiguity-averse. The corresponding Hamilton-Jacobi-Bellman (HJB) and HJB-Isaacs (HJBI) equations are solved in closed-form for a subset of the parametric space and under some restrictions on the portfolio setting, for complete markets. Conditions for proper changes of measure and well defined solutions are provided. These are the first analytical solutions for the 4/2 stochastic volatility model and the embedded 3/2 model for the type of excess returns established in .

The sustainable development of our world has gain particular attention of a wide range of decisional factors, civil society, business sector, and scientific community, seeing that the prosperity of people and society is possible with the aid of sustained and inclusive economic growth of all countries and regions. Educational environment has a decisive impact on changes in the way that societies are coping with national, regional, and global challenges and opportunities brought by sustainable development. Looking at the implications of HE on the progress of society, the paper addressed the lack of HE institutional capacity to integrate the principles and practices of sustainable development into all aspects of education and learning. The scope of research problem was bounded on the capability of HEI as organization and school to act as entrepreneurial university by combining the scope of its responsibility within the value chain through a practical and effective mechanism needed to align the strategy with sustainable development goals (SDGs). Embarking on the path of SDGs requires HEI to design, launch, implement, and customize specific processes architectures to govern the advance of sustainability approach. The authors applied the process scoping diagram to capture and conceptualize the educational model needed to guide the HEI through the process of change to embrace sustainability into organizational culture and daily operations. It has been used the SIPOC method (Supplier, Input, Process, Output, Customer) with Visio software tool to articulate processes relationships embedded in the educational model of HEI. The benefits relied on the organized view of the work processes needed to be performed to incorporate SDGs into the strategy of any entrepreneurial HEI. Finally, the authors shared their views on the scalability of the model which may be customized and harmonized in accordance with different HE circumstances and priorities. Implementing the proposed educational model requires long-term institutional commitment, transparency, continuous performance improvement, and communicating the strategy for SDGs and its achievements to wider stakeholders.

Petrochemicals and oil refineries are industrial processes that use compounds and polymers derived directly or indirectly from natural gas or crude oil for chemical purposes. They have posed a number of short- and long-term risks to the environment and the people who live nearby. The study aims to determine how the presence of industrial areas, such as oil refineries, affects the local population's perception of their surroundings. The investigation centers on the Tehran Oil Refinery, a major refinery hub, and its perceived environmental impact. For this purpose, a Geographic Information System for Public Participation (PPGIS) covering three basic sections has been designed: socio-demographic information of the participants; self-perceived health; and landscape valuation before and after an intervention. The main observations show a complex interaction between industrial presence and the perception of landscape values. Participants' reactions to the manipulated Photographs reveal important details about the psychological impact of visual elements on emotional perception. There is also a direct relationship between the level of stress and emotional perception in the manipulated and original photos.This research adds to the larger debate about the effects of industrialization on the environment and society. It emphasizes the importance of considering public perception when planning and developing industrial projects. The goal is to strike a balance between industrial operational needs and environmental quality preservation, resulting in long-term urban growth.

While there are software tools available for assisting to conduct the Life Cycle Assessment (LCA), such as OpenLCA, these tools lack integration with process design, simulation and optimization software. As LCA has critical role in sustainable product design, it is presented in this work a platform called EMSO_OLCA, which integrates the LCA provided by OpenLCA into the Environment for Modeling, Simulation, and Optimization (EMSO). EMSO_OLCA incorporates a database of environmental impact assessment methodologies from OpenLCA and aligns to the principles of LCA outlined in ISO 14040 and ISO 14044. Validation tests were conducted to compare the results obtained by the LCA of sugarcane ethanol using OpenLCA and EMSO_OLCA, revealing a high level of agreement. The average relative error was 0.045%, indicating a negligible discrepancy between the tools. Besides, it took only 0.3 seconds for calculation, which is desirable for using with process systems engineering tools. A second case study was applied to a steam and bioelectricity production from the combustion of sugarcane bagasse and straw in a combined heat and power system. The results show the integration of LCA with simulation and sensitivity analysis tools, thus contributing for supporting sustainable decision-making processes. EMSO_OLCA bridges the gap between LCA and process engineering, enabling a holistic approach to sustainability, design and implementation of environmentally friendly solutions.

Considering the health benefits of the kombucha, already widely studied, the objective of the study was to development of the beverage with the addition of infusion of special arabica coffee from the first fermentation varying the proportion of special coffee (2% a 13%) and evaluate the pH, volatile acidity, degree of alcohol, centesimal composition, sodium contents, colour parameters as like acceptability of the beverages. With respect to the pH, all the formulations conformed with the kombucha identity standard, but K3 and K4 were below the established minimum for volatile acidity. Except for K4, all the other formulations were classified as alcoholic kombuchas, although the values were very close to the limit for non-alcoholic. The formulations presented low sugar and sodium content, which corroborated the low caloric value. Therefore, coffee-flavored kombucha fermented for 18 days becomes an option for consumers looking for low-calorie, refreshing and healthy drinks. The luminosity of the beverages decreased as the proportion of coffee increased, and consequently the values for a* and b* increased, indicating a strong tendency for a yellowish red colour. Sensorially, the formulations K4 and K3 were the most well accepted in all respects and can therefore be considered formulations with high commercialization potential.

The development in the field of ceramics has been increasing over the past several decades. Among all of the dental ceramics, zirconia ceramics show very good mechanical properties and a significant improvement in mechanical function. However, it is properly established that each step of the fabrication process of zirconia ceramics must be controlled to achieve the expected mechanical properties. These steps include the block constructing, sintering process, machining and surface processing. Recent advances have focused on zirconia ceramics in forming and sintering processes, mechanical properties, combining with various materials, and on surface processing. All these items provide conceptions about the formability and machinability of the zirconia. Until now, very excellent studies have been done to specify the evolution of microstructures, phase and mechanical properties of zirconia ceramics during the sintering process. However, more studies are required in this field. This paper aims to demonstrate general views and various studies in the field of zirconia ceramics. The properties, applications and tests performed and the recent developments in the field of zirconia bioceramics are discussed.

Floods are among the most serious natural disasters worldwide; they cause enormous crop losses every year and threaten world food security. There are many studies focused on flood impact assessments for administrative districts but fewer on postdisaster impact assessments for specific crops. Therefore, this study used remote sensing data, including the normalized difference vegetation index (NDVI), elevation data, slope data, and rainfall data, combined with crop growing season data to construct a crop flood damage assessment index (CFAI). First, an analytic hierarchy process (AHP) was used to assign weights to the impact factors; then, the weighted composite score method was used to calculate the CFAI; and finally, the impact was classified as slight, moderate, or severe based on the magnitude of the CFAI . This method was used for the Missouri River floods of 2019 in the U.S. Due to the lack of measured data, the disaster vegetation damage index (DVDI) was used to validate the results. The combined approach achieved a high degree of consistency in spatial distribution. The CFAI can respond well to the degree of crop impact after flooding, providing new ideas and reference standards for agriculture-related departments.

Research on engine operation using hydrogen may enable appropriate optimization of thrust and, therefore, performance related to its potential use in aircraft. It is particularly important that the share of hydrogen in combustion affects the reduction of combustion products such as carbon dioxide, carbon monoxide, nitrogen oxide, hydrocarbons and solid matter. This is in line with the new requirements regarding the increased supply of sustainable aviation fuels (SAF) and the related changes in emissions, i.e. reducing the harmful impact of exhaust gases on the environment. The paper presents the results of measurements carried out in the GTM400 MOD turbojet engine. Based on the research performed, the impact of hydrogen and aviation kerosene combustion on selected engine parameters was presented. The paper shows changes in the rotational speed and volume flow of JET A-1 fuel as a function of engine operation time. Changes in temperature measured at the edge of the flame tube were also examined. The tests confirmed that the combustion chamber works correctly in the selected area in the range of tested fuel mixtures. After incorporating hydrogen into the combustion process, the consumption of traditional JET A-1 fuel was significantly reduced.

We discuss the issues with tentative generalisations of the process matrix formalism from finite-dimensional mechanical systems all the way to quantum field theory. We present a detailed overview of possible open problems that arise when one attempts to move from particle ontology into the realm of field ontology, i.e., when one transitions from mechanics to field theory framework. These issues need to be addressed, and problems solved, if one aims to expand the scope of applicability of the process matrix formalism, and therefore its usefulness. This is far from a trivial and straightforward endeavour, but rather a task for a whole future research programme.

Biorefineries convert biomasses in bioproducts and they contribute to transition to the circular economy. In this way it will be avoiding the penalty associated with generating a lot of waste. The first step in the obtention chain of bioproducts consists of the high value added products extraction. The following steps are aimed at the utilisation of all by-products or waste generated. The kinetic anaerobic digestion of these residues are evaluated in this research to produce biogas/methane. Pepper waste have been employed in batch and semi-continuous anaerobic digestion assays trying the optimization of the yields adding, as catalysts, absorbent nanoparticles and/or using strategies of pretreatment.

This study evaluated the microbiological quality of farm oysters, cultivated in an estuary in northeastern Pará, Brazil, before and after purification process. Water and oyster samples were collected in June, August, October, and December 2019. Oysters underwent purification for up to 36 h in a small-scale adapted debugger. Microbiological analyses were performed in oysters and water depuration collected at different times of purification (0 h pre-treatment, 6 h, 12 h, 2 4h, and 36 h), using the multiple tube technique, to determine the most probable number (MPN) of Escherichia coli, thermotolerant coliforms - TTC, and total coliforms - TC. Bacterial isolation was used to determine the presence of Salmonella spp. Before purification, oysters had E. coli counts above the limit allowed by Brazilian legislation (≤ 230 MPN/100g). High TTC, and TC counts were observed in all the months of the study, and Salmonella spp. was present in August. All water samples were contaminated by TTC beyond the level allowed (> 43 MPN/100mL) in two months. The purification process of 36 h was effective in reducing the load of E. coli, TTC, and TC and eliminating Salmonella spp. in oysters.

This paper describes some neural representations that may be helpful for realising intelligence in the human brain. The ideas come from the author's own cognitive model, where a number of algorithms have been developed over time. Through developing and trying to implement the architecture, ideas like separating the data from the function have become architecturally appropriate and there have been several opportunities to make the system more orthogonal. Similarly for the human brain, neural structures may work in-sync with the neural functions, or may be slightly separate from them. Each section discusses one of the neural assemblies with a potential functional result, that cover ideas such as timing or scheduling, inherent intelligence and neural binding. Another aspect of self-representation or expression is interesting and may help the brain to realise higher-level functionality based on these lower-level processes.

Background: Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). Methods: Using Design Science Research, this paper introduces a new method, called Cloud Pattern API – Process Mining (CPA-PM), that enables discovering and analyzing cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface (API), with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Results: Applying CPA-PM to a case with real and evolving event logs related to the trial process of a Software-as-a-Service cloud application led to useful analyses and insights, with reusable scripts. Conclusion: CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering.

In the last four years, the evolution and adoption of blockchain and, more generally, distributed ledger systems have shown the affirmation of many concepts and models with significant differences in system governance and suitable applications. This work aims to update the critical analysis of blockchain technologies carried out by our previous contribution to this journal, extending the focus to distributed ledger components and systems. Starting from the topical concept of decentralization, we introduce concepts and building blocks currently adopted in the available systems centering on their functional aspects and impact on possible applications. We present some conceptual framing tools helpful in the application context, and we will propose the concept of process authenticity, which we will discuss through two use cases: blockchain document dematerialization and e-voting.

The paper describes a model of subjective goal-oriented semantics extending standard "view-from-nowhere" approach. Generalization is achieved by using a spherical vector structure essentially supplementing the classical bit with circular dimension, organizing contexts according to their subjective causal ordering. This structure, known in quantum theory as qubit, is shown to be universal representation of contextual-situated meaning at the core of human cognition. Subjective semantic dimension, inferred from fundamental oscillation dynamics, is discretized to six process-stage prototypes expressed in common language. Predicted process-semantic map of natural language terms is confirmed by the open-source word2vec data.

This paper examines how remittances contribute to the democratisation process in Bangladesh. The endogeneity issue between remittances and democracy is tackled by employing the Structural VAR (SVAR) approach. It is found that while remittances respond to innovations in the macro-political variables, remittances also have important impact on these variables. Our results build a synergy between two opposing findings in the politics literature where on one hand remittances flows stabilise autocracies, while on the other hand they foster the prospect for democratisation. In particular, we demonstrate that a shock in remittances flows will have a negative but transitory impact on democracy. Initially there will be a bout of autocratic episodes which will be eventually eliminated and democracy will be restored to its original level in three to five years. However, using an alternative measure for democracy with the aid of principal-component analysis, we find that after the fifth year following a shock in remittances flows, a small but positive permanent effect on democracy is observable that do not revert to zero at end of the ten period horizon.

The basis of the discrete-impulse energy supply (DIES) concept is the efficient use of supplied energy. The references describe in detail the general principles of DIES, examine the energy and thermodynamic aspects and the main mechanisms of intensification that can be initiated on the basis of this principle. DIES mechanisms conveniently can be divided into hard and soft ones. The former should be used to stimulate hydromechanical processes, and the latter to accelerate the processes of phase heat and mass transfer, or for the purpose of intensive mixing of multicomponent media. The authors have studied the possibility of using DIES to intensify the hydromechanical processes, in particular emulsification of milk fat (homogenization of milk, preparation of spreads), processing of cream cheese masses. Objects of research were whole non-homogenized milk, fat emulsions, cream cheese mass. In order to evaluate the efficiency of milk homogenization the homogenization coefficient change was studied, which was determined by centrifugation method as the most affordable and accurate one. Emulsions were evaluated according to the degree of destabilization, resistance and dispersion of the fat phase. The rheological characteristics of cheese masses were evaluated by the effective viscosity change.

Although increasing knowledge about biological systems has advanced exponentially in recent decades, it is surprising to realize that the very definition of Life keeps presenting theoretical challenges. Even if several lines of reasoning seek to identify the essence of life phenomenon, most of these thoughts contain fundamental problem in their basic conceptual structure. Most concepts fail to identify necessary and sufficient features to define life. Here, we analyzed the main conceptual framework regarding theoretical aspects supporting life concepts, such as (i) the physical, (ii) the cellular and (iii) the molecular approaches. Based on ontological analysis, we propose that Life should not be positioned under the ontological category of Matter. Yet, life should be better understood under the top-level ontology of “Process”. Exercising an epistemological approach, we propose that the essential characteristic pervading each and every living being is the presence of organic codes. Therefore, we explore theories in biosemiotics in order to propose a clear concept of life as a macrocode composed by multiple inter-related coding layers. Therefore, we suggest a clear distinction between the concept of life and living beings, a distinction that is not evident in theoretical terms. From the proposed concept, we suggest that the evolutionary process is a fundamental characteristic for life’s maintenance but not to its definition. The current proposition opens a fertile field of debate in astrobiology, biosemiotics and robotics.

This paper introduces a closed-loop control strategy for maintaining consistency of ink temperature in commercial Drop on Demand (DoD) inkjet printing. No additional sensors or additional actuators are installed in the printhead while achieving the consistency in ink temperature. To this end, this paper presents a novel in situ sensing-actuation policy at every individual ink-nozzle, where the jetting mechanism has three distinct roles. It is used for jetting liquid droplet onto the print media based on the print-job. It is used as a softsensor to estimate the real-time liquid temperature of the jetting nozzle. While not jetting liquid, it is used as a heating actuator to minimize the gradient of liquid temperature among jetting nozzles. The soft-sensing based in situ controller is implemented in an experimentally validated digital twin that models the thermo-fluidic processes of the printhead. The digital twin is scalable and flexible to incorporate an arbitrary number of inknozzles, making the control strategy applicable for future designs of the printhead.

Physical-law based models are widely utilized in the aerospace industry. One such use is to provide flight dynamics models for use in flight simulators. For human-in-the-loop use, such simulators must run in real-time. Due to the complex physics of rotorcraft flight, to meet this real-time requirement, simplifications to the underlying physics sometimes have to be applied to the model, leading to model response errors in the predictions compared to the real vehicle. This study investigated whether a machine-learning technique could be employed to provide rotorcraft dynamic response predictions, with the ultimate aim of this model taking over when the physics-based model's accuracy degrades. In the current work, a machine-learning technique was employed to train a model to predict the dynamic response of a rotorcraft. Machine learning was facilitated using a Gaussian Process (GP) non-linear autoregressive model, which predicted the on-axis pitch rate, roll rate, yaw rate and heave responses of a Bo105 rotorcraft. A variational sparse GP model was then developed to reduce the computational cost of implementing the approach on large data sets. It was found that both of the GP models were able to provide accurate on-axis response predictions, particularly when the input contained all four control inceptors and one lagged on-axis response term. The predictions made showed improvement compared to a corresponding physics-based model. The reduction of training data to one-third (rotational axes) or one-half (heave axis) resulted in only minor degradation of the GP model predictions.

This paper constitutes a first advance in the paleoenvironmental study of a small group of peatland ecosystems, of reduced size, located in the interior of the Iberian Peninsula (Puebla de Don Rodrigo, Ciudad Real, Spain). It represents a singular enclave, because these ecosystems are home to the southern-most peatlands in Europe, located at the lowest altitude in Spain, and are unique to the region of Castilla-La Mancha. They form ecosystems similar to the peat bogs of northern Europe, but in a Mediterranean climate. The analysis is followed of sample collection and data collection from documentary, textual, and cartographic sources drawn up since the 16th century up until the present day. The scientific analyses that were performed and the documentation that was consulted point to the hypothesis that these peatlands are the result of a long process of historical modification of the landscape in which anthropogenic activity has played a leading role, triggering a series of processes on the hillslopes that are culminating in the exhumation of the waterlogged areas, thereby establishing a recent genesis of these ecosystems.

In this work we aim to experimentally study the nucleation and growth of CaCO3 phases precipitated from supersaturated aqueous solutions in the presence of varying concentrations of sulphate oxyanion. The experiments were conducted under pH conditions close to neutral (7.6) and considering a wide range of initial (SO42-)/(CO32-) ratios (0 to ~ 68) in the aqueous solution. We paid special attention to the evolution of the precipitates during ageing within a time framework of 14 days. The mineralogy, morphology and composition of the precipitates were studied by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy and EDX microanalysis. The concentration of sulphate ions in the reacted aqueous solution was study by ICPs. The experimental results show that the mineral composition of the precipitate recovered in each run varied with the (SO42-)/(CO32-) ratio in the parental solution, which influences the mineral evolution of the precipitates during ageing. We observe that high concentrations of sulphate in the aqueous solution stabilize the vaterite precipitates and inhibit calcite formation. Furthermore, aragonite never precipitates directly form the solution and it is only formed via a dissolution-precipitation process in solutions with high (SO42-)/(CO32-) ratio after long reaction times. Finally, gypsum only precipitates after long ageing in those aqueous solutions with the highest concentration of sulphate. The reaction pathways during ageing, the morphology of the calcite crystals and the composition of vaterite and calcite are discussed considering both, kinetic and thermodynamic factors. These results show a considerably more complex behavior of the system than that observed in experiments conducted under higher pHs and supersaturation levels and lower (SO42-)/(CO32-) ratios in the aqueous phase.

The effect of fungicides on fermentation is of paramount importance to control the quality and safety of wines. In this work, the quality (oenological parameters, color, phenolic content, antioxidant activity, and fungicide residues) of wines from Monastrell grapes fortified with iprovalicarb, mepanipyrim and tetraconazole fungicides was evaluated. Along of the winemaking process, initial residues of mepanipyrim and tetraconazole were removed in more than a 90 % while dissipation of iprovalicarb was around 73 %. Significant statistical differences were found in presence of iprovalicarb and mepanipyrim residues especially at the highest concentration assayed. For both fungicides, an increase of the volatile acidity (between 4 and 8.6 times), the lactic acid content (between 8.6 and 20.5 times), the percentage of polymeric anthocyanins (between 1.3 and 1.7 times) and also a slight increase of the total phenolic index and the total anthocyanins content determined by spectrophotometry was observed. On the contrary, the total monomeric anthocyanins content decreased about 16.3 and 28.6 % in presence of iprovalicarb and mepanipyrim, respectively. These results could be related with the addition of SO2 to the grape must and a higher development of acetic acid or lactic bacteria in presence of these fungicides. The color of the final wines was also different in comparison with the control, with a higher yellow component, color intensity, tonality and hue angle, because of pH changes in the medium. Tetraconazole fermentations had a more similar trend to the control wine, probably due to the lower concentration of this fungicide in the grape must at the initial time. No effects on the antioxidant activity was observed for anyone of the target fungicides. A multivariate statistical analysis was done to view interrelationships between different variables (color and anthocyanins profile). The obtained model allowed to separate wines according to the fungicide treatment applied.

The goal of this contribution is an application of the Linear General Model Predictive Control (LGMPC). In this paper, stability of the LGMPC is proven by means of a demonstration of a Theorem stating a sufficient and constructive condition. This condition can be applied for calculating the weight matrices of the cost function in the optimisation problem in LGMPC. Lower bounds conditions are found for one of these matrices and then a system with saturation is taken into consideration. The conditions could be interpreted and discussions through physical aspects. The obtained results were tested by means of computer simulations and an example with a recover water process is considered.

Wet coffee processing methods will produce wastewater containing organic matter. The high content of organic matter can be utilized as biogas through the anaerobic process. Anaerobic digesters construction can affect removal process of wastewater pollution and biogas quantity. The purpose of this study is to determine the effect of digester construction between conventional digester, CSTR and UASB producing biogas from coffee wastewater. The conventional digester worked without temperature control system as control, a UASB digester, and CSTR digester worked with temperature control system. Biomass volume was about 5 L with 35 days incubation time. Temperature and pH for UASB and CSTR were set within the range 30 – 35^oC and pH 6.0 – 8.0. Based on the feeding variations, UASB has a stable performance with 83.57 ml/day of average biogas production. It has also highest remediation efficiency of COD, BOD and C/N with 85.00±0.34 %, 84.40%± 5.66 and 97.78± 0.57.

Corn fiber is a co-product of commercial ethanol dry-grind plants, which is processed into distillers dried grains with solubles (DDGS) and used as animal feed, yet it holds high potential to be used as feedstock for additional ethanol production. Due to the tight structural make-up of corn fiber, a pretreatment step is necessary to make the cellulose and hemicellulose polymers in the solid fibrous matrix more accessible to the hydrolytic enzymes. A pretreatment process was developed in which whole corn kernels were soaked in aqueous solutions of 2.5, 5.0, 7.5 and 10.0 wt% ammonia at 105oC for 24 h. The pretreated corn then was subjected to a conventional mashing procedure and subsequently ethanol fermentation using a commercial strain of natural Saccharomyces cerevisiae with addition of a commercial cellulase. Pretreatment of the corn with 7.5 wt% ammonia solution plus cellulase addition gave highest ethanol production, which improved the yield in fermentation using 25 wt% solid from 334 g ethanol/kg corn obtained in the control (no pretreatment and no cellulase addition) to 379 g ethanol/kg corn (a 14% increase). The process developed can potentially be implemented in existing dry-grind ethanol facilities as a “bolt-on” process for additional ethanol production from corn fiber, and this additional ethanol can then qualify as “cellulosic ethanol” by the EPA’s Renewable Fuels Standard and thereby receive RINS (Renewable Identification Numbers).

The demand for efficient processes through a comprehensive understanding and optimization of welding conditions continues to grow in the manufacturing industry. This study involves heat-resistant 2.25 Cr-1 Mo V-groove steel welding using the square-waveform alternating cur-rent. Experiments were conducted to build the relationship between input variables—such as current, frequency, electrode negativity ratio, and welding speed—and process performance, such as penetration, bay area, deposition rate, melting efficiency, percentage dilution, flux–wire ratio, and heat input. The process was analyzed in light of the defect-free high-deposition weld groove weld, the sensitivity to process parameters, and the optimization and development of the process map. The study proposes an innovative approach to reducing the cost and time of optimizing the one-pass-each-layer V-groove welding process using bead-on-plate welds. Square waveform welding creates a metallurgical notch in the form of a bay at the fusion boundary that can be minimized by selecting appropriate welding conditions. The square waveform submerged arc welding is more sensitive towards changes in current and welding speed than the frequency and electrode negativity ratio; however, the electrode negativity ratio and frequency are minor but helpful parameters to achieve optimal results. The proximity of the planned and experimental results to within 3% confirms the validity of the proposed approach. The investigation shows that 90% of the maximum deposition rate is possible for one-pass-each-layer V-groove welds within heat input and weld width constraints.

This paper describes the specification of spatially varying coefficient (SVC) regression models using Generalized Additive Models (GAMs). The GAMs include Gaussian Process (GP) splines (smooths) for each covariate parameterised with observation location and generate SVC estimates that capture spatially varying relationships. The ability of the proposed GAM approach to estimate true spatially varying coefficients was compared with that of Multiscale Geographically Weighted Regression (MGWR) using simulated data with complex spatial heterogeneities. The geographical GP GAM (GGP-GAM) was found to out-perform MGWR across a range of fit metrics and resulted in more accurate coefficient estimates and lower residual errors. The model for one of simulated datasets was investigated in detail to illustrate GAM diagnostics, model checks, spline / smooth convergence and basis evaluations, and tuning via the number knots. A larger simulated case study was investigated to explore the trade-offs between GGP-GAM complexity, performance and computation. Finally the GGP-GAM and MGWR approaches were applied to an empirical case study. The resulting models had very similar accuracies and fits, and generated subtly different spatially varying coefficient estimates. A number of areas of further work are identified.

Anaerobic digestion is a feasible solution for the treatment of organic wastes. The process can reduce the amount of biowaste by stabilizing the organic material and producing biogas susceptible to energetic valorization. However, the digestate needs further valorization when land application is considered unfeasible. Thermal treatments, such as gasification, pyrolysis, and hydrothermal carbonization, are alternatives capable of transforming this material into valuable syngas, obtaining, in many cases, a carbonized stream known as biochar. The feasibility of the process depends on the energy demand for the drying stage and the treatments available for removing contaminants from the syngas, attaining high-quality products, and treating the process-derived water. In the present manuscript, these critical aspects were reviewed considering the characteristics of digestates based on their origin, the modifications of this material during anaerobic digestion, and the way digestate structure affects the final thermal valorization outcome. Emphasis was placed on the energy demand of the global approach and by-product treatments.

In most organizations there is an information system where data is recorded regarding events, activities, times, and variables associated with the execution of all organizational processes; this information is generally not used to its maximum potential, limited only to the registration of processes. With process mining, a field of engineering research, responsible for the design of processes through mining algorithms, it is possible to characterize bottlenecks, reprocessing, and opportunities for improvement in organizational processes. In this article, the construction of the collection and billing process is carried out in a case study of the Colombian Pharmaceutical sector supported by process mining and business intelligence. The objective of this research is oriented to the construction of the real process of a case study of the pharmaceutical sector in Colombia supported by the alpha mining algorithm, within the framework of process mining and business intelligence to facilitate decision-making at an organizational level. The result of this research is the proposal of a methodology to implement process mining in organizations in the pharmaceutical sector in Colombia supported by the Design Science Research (DSR) methodology and the construction of the real process in the billing and collection case study in the pharmaceutical sector supported by process mining, business intelligence is also used to complement the points of improvement of the accounting process in the case study. In conclusion, the artifact built in DSR shows a rigorous methodology from obtaining data, and types of documents that make up the knowledge base to proceeding to the application of the alpha mining algorithm that built the real billing and collection process of the case study, in which points of reprocessing and activities susceptible to improvements in the case study are evident. As a contribution, this article develops an alternative approach that contributes to the knowledge gap related to the actual construction of billing and collection processes in organizations, which generates an impact an alternative paradigm in the construction of organizational processes, by proposing a methodology that Builds a billing and collection process through process mining complemented by business intelligence.

This work proposes a conceptual design for a process for recovering Polystyrene (PS) using solvents of agro-industrial origin. The literature describes the dilution of Expanded Polyestyrene (EPS) in limonene, followed by its insolubilization with alcohols for recovery. However, there is no information on the solubility limit for the PS + limonene + alcohol system, which is critical for the process design. To determine the solubility limit, we diluted the waste EPS in D-limonene, mixed it with ethanol to form a precipitate, and used a gravimetric method to measure the mass of the compounds. These results allowed for the conceptual design of an EPS recycling process using a chemical process simulator, which includes a separator, a distillation column, and auxiliary equipment such as heaters, coolers, and pumps. An empirical correlation was obtained for the solubility limit, which enabled the design of a process for the treatment of 52 kg/s of PS using 0.75 kg /s of ethanol and 2.4 kg / s of D-limonene once the stationary state had been reached. The distillation column is six-stage, with a reflux ratio of 1.5 and duties of 30 000 kW and -25 847 kW for the condenser and reboiler, respectively.

Carbon fiber reinforced plastic (CFRP) is one of the representative lightweight material. The automotive industry has focused on producing the steel/CFRP hybrid part to reduce the weight. After manufacturing, delamination can occur at the interface between the CFRP and steel owing to the hybrid part constituting dissimilar materials. However, most studies have focused only on designing the manufacturing processes for hybrid part or evaluating the adhesive used at the interface. Therefore, it is necessary to predict the behavior of the interface after demolding the hybrid part. This study aimed to predict the interface behavior of a steel/CFRP hybrid part by considering its forming and cohesive properties. First, double cantilever beam (DCB) and end-notched flexure (ENF) tests were performed to obtain cohesive parameters, such as energy release rate of modes Ⅰ and Ⅱ (GⅠ, GⅡ). The experimentally obtained properties were applied to the bonding area of the hybrid part. Subsequently, a forming simulation was performed to obtain the stress of the steel blank in the hybrid part. The stress distribution after forming was utilized as the initial condition for spring-back simulation. Finally, the interface behavior of the hybrid part was predicted by a spring-back simulation, and the results were compared with the experimental results for verification.

Salmon aquaculture generates a substantial volume of waste material, offering potential for biomaterial production. This industry produces significant waste, which can be repurposed for various biological applications, particularly in biomaterial production for tissue engineering with pertinent applications in osteo-conduction, osteo-induction, and clinical/surgical bone regeneration and repair. The process involves a standardized pre-treatment stage aimed at minimizing biological waste content. Subsequently, the treatment stage, contingent on the chosen methodology, facilitates the removal of proteins, lipids, molecules, and other compounds, leaving the mineral phase as a valuable substrate. The selection of the optimal method, whether alkaline hydrolysis, calcination, or NaOH hydrolysis, for obtaining this substrate necessitates thorough examination through chemical, physical, and biological assessments, including Raman Spectroscopy and X-Ray Diffraction. This study aims to identify the most efficient laboratory process for biofunctional nano-sized hydroxyapatite mineral production derived from Chilean salmon fish backbone waste.

This paper examines the marketing mix of rattan canes businesses, with a focus on small and medium enterprises (SMEs). Rattan cane enterprises rely on both domestic and international sources for raw materials, and the market is largely unorganized, with individual-level operations dominating the industry. The primary aim of this study is to evaluate the marketing mix in relation to the 7Ps framework, which includes price, product, place, promotion, people, process, and physical evidence. A simple random sampling method was employed to select participants from rattan processing enterprises. A questionnaire survey was then conducted among 46 rattan entrepreneurs, representing a total of 66 enterprises, including rattan processing industries, rattan traders, and Community Forest User Groups. The findings of the study indicate that, among the seven Ps, place and process are relatively stronger aspects of service delivery, while price and people are considered moderate. However, product, promotion, and physical evidence are identified as weak areas in terms of service delivery within the rattan business. It is concluded that rattan processing industries continue to rely on traditional tools and technologies, which has resulted in a lack of competitiveness in penetrating both national and international markets. Furthermore, poorly segmented distribution channels have hindered their sales efforts, while limited promotional activities have restricted awareness of their products among potential customers. In conclusion, the study highlights the need for rattan businesses to modernize their operations and adopt more effective marketing strategies in order to enhance their competitiveness and expand their market reach.

In this study, a cholera infection model with the bilinear infection rate is developed by considering the perturbation of the infection rate by the mean-reverting process. First of all, we give the existence of a globally unique positive solution for a stochastic system at arbitrary initial value. On this basis, the sufficient condition for the model to have an ergodic stationary distribution is given by constructing proper Lyapunov functions and tight sets. This indicates in a biological sense the long-term persistence of cholera infection. Furthermore, after transforming the stochastic model to an relevant linearised system, an accurate expression for the probability density function of the stochastic model around a quasi-endemic equilibrium is derived. Subsequently, the sufficient condition to make the disease extinct is also derived. Eventually, the theoretical findings are shown by numerical simulations. Numerical simulations show that the impact of regression speed and fluctuation intensity on stochastic systems.

Oracles have been suggested as a vital piece of infrastructure necessary to allow blockchain-based smart contracts to reach their full potential by interacting with real-world data. This paper begins by providing an approachable entry point to the oracle problem and the current landscape of existing solutions. From there, we provide an overview of published research to narrow down unexplored research directions and derive questions for interviews. Finally, the results of 15 unstructured expert interviews are presented and discussed. The focus here is the industry landscape of oracle projects, opportunities for innovation, and challenges needing to be overcome.

Contemporary financial systems, despite their inherent complexity and resilience, face significant challenges related to transparency, efficiency, and security. Notable deficiencies in transparency and emerging security vulnerabilities underscore the pressing need for innovative solutions in current financial practices. Hence, this paper introduces a financial distribution system based on a consortium blockchain. Given organizations’ preference for keeping sensitive data private, the partially decentralized nature of consortium blockchains plays a pivotal role in the proposed system’s architecture. Moreover, within this system, the proposed smart contract serves a dual role. It functions not only as a transactional tool but also as a specialized solution designed to improve transactional procedures. Its multifaceted capabilities include ensuring data accuracy, facilitating swift execution, and ensuring adherence to regulatory requirements within the dynamic financial distribution landscape. Through rigorous testing and empirical analysis utilizing the Hyperledger Besu platform, it has been observed that the performance of the proposed system surpasses traditional blockchain frameworks. The results unequivocally demonstrate the proposed system’s proficiency in handling high-frequency financial transactions with minimal computational burden.

Objectives: To analyse and detail surgical process improvement activities that achieve the highest economic impact. Methods: Over 4 years, a team of technicians and healthcare professionals implemented a set of Lean surgical process improvement projects at Vall d’Hebron University Hospital (VHUH), Barcelona, Spain. These projects now form part of the daily routine in the surgical process. The economic impact on the hospital's surgical activity budget was analysed. Results: Process improvements have led to annual operational savings of over € 8.5 million. These improvements include better patient flow, better management of information between healthcare professionals, and improved logistic circuits. Conclusions: The current cultural shift towards process management in large hospitals implies shifting towards results-based healthcare, patient-perceived value (VBHC) and value-added payment. A Lean project implementation process requires long-term stability. The reason a considerable number of projects fail to complete process improvement projects is the difficulty involved in establishing the project and improving management routines. Few studies in the literature have investigated the economic impact of implementing Lean management a posteriori, and even fewer have examined actual cases. In this real case study, changes to surgical block management were initiated from zero. After being carefully thought through and designed, changes were carried out and subsequently analysed.

Product Development Process is widely known among industry and automobile companies. With the advance of technology and the need to improve product efficiency, many companies have been applying Lean Thinking principles. Their benefits lead those companies to implement their concepts and tools, like Value Stream Design in Indirect Areas, Obeya, Just-in-time (JIT) which is based on the production control system developed and implemented by Toyota. This article describes the case study of the implementation of Lean Product & Process Development (LPPD) on the current Product Development Process (PDP) in an automotive company. The PDP was created by the organization and adapted from Advanced Product Quality Planning (APQP). The main findings were a Value Stream Design model of an improved PDP as a useful and successfully contribution for the company. These improvements reduced the product development lead time from 700 days to 369 days. The process efficiency increased from 68% to 85%. The communication and transparency between the cross-functional team (CFT) was improved and show promise for achieving a successful launch of new products.

Ant Colony Optimization (ACO) is a stochastic optimization algorithm inspired by the foraging behavior of ants. We investigate a simplified computational model of ACO, wherein ants sequentially engage in binary decision-making tasks, leaving pheromone trails contingent upon their choices. The quantity of pheromone left is the number of correct answers. We scrutinize the impact of a salient parameter in the ACO algorithm, specifically, the exponent $\alpha$ that governs the pheromone levels in the stochastic choice function. In the absence of pheromone evaporation, the system is accurately modeled as a multivariate nonlinear P\'{o}lya urn, undergoing a phase transition as $\alpha$ varies. The probability of selecting the correct answer for each question asymptotically approaches the stable fixed point of the nonlinear P\'{o}lya urn. The system exhibits dual stable fixed points for $\alpha\ge \alpha_c$ and a singular stable fixed point for $\alpha<\alpha_c$. When pheromone evaporates over a time scale $\tau$, the phase transition does not occur and leads to a bimodal stationary distribution of probabilities for $\alpha\ge \alpha_c$ and a monomodal distribution for $\alpha<\alpha_c$.

The treatment of petroleum refinery wastewater (PRWW) is of great interest in the field of industrial wastewater management. The wastewater contains a diverse concentration of contaminants such as oil and grease, petroleum hydrocarbons, phenol, ammonia, sulfides, as well as other organic and inorganic composites etc. The treatment of refinery wastewater has been treated through various processes including; physical, biological, chemical or hybrid methods which combine two or more techniques. Hence, the objective of this review is to summarize the recent research studies applied in the treatment of petroleum refinery wastewater using conventional, advanced as well as integrated treatment techniques. Furthermore, critically highlight the efficiencies and major limitations of each technique as well as prospects for improvement. Several conventional treatment techniques (basically, the physicochemical and biological processes) were discussed. In this context, advanced oxidation processes (AoPs) especially electrochemical oxidation and photocatalysis as well as the integrated/hybrid processes are found effective to remove the recalcitrant fraction of organic pollutants because of their various inherent mechanisms. These techniques could effectively remove COD and phenols concentration with an average removal efficiency exceeding 90%. Hence, the review also presented an elaborate discussion of the photocatalytic process as one of the advanced techniques and highlighted some basic concepts to optimize the degradation efficiency of a photocatalyst. Finally, a brief recommendation for research prospects is also presented.

In the era of Industry 4.0, the focus on optimizing manufacturing processes is crucial. This study provides a comprehensive analysis of the EnPAS system, a proprietary automated control and monitoring tool developed by KAD3, implemented in the production process of Corten steel furnishings - the "Lila" chair and "Line" planter. Designed to enhance operational efficiency, EnPAS facilitates improved workflow by streamlining production stages and reducing downtime. The research design involves a comparative analysis of the workshop's production process, both before and after the implementation of the EnPAS system. Elements such as de-greasing, oxidation, and finishing stages, among others, were examined. The implementation of the EnPAS system led to a significant reduction in the total production time for both products. Further, there was a substantial decrease in waiting time and setup time. These findings unequivocally indicate the transformative impact of the EnPAS system on the manufacturing process, showcasing its potential in driving efficiency improvements, reducing production times, and increasing output. The study underscores the importance of leveraging such advanced control and monitoring tools to optimize modern manufacturing processes and does not exaggerate conclusions not backed by the research.