Analytic Process Model Research Articles

A hybrid SgDT framework for risk analysis of container-handling operations at automated container terminals

With the booming maritime economy and the steady expansion of port production, automated container terminals (ACTs) have rapidly developed worldwide. The safety and stability of container-handling operations at ACTs are crucial for maintaining high efficiency and performance in maritime transportation and global supply chains. However, few studies have focused on the quantitative risk analysis of container-handling operations at ACTs. This study proposes a hybrid SgDT (STPA-grey-DEMATEL-TAISM) approach to analyze risk factors and their coupling interrelationships within ACT handling operations. Initially, 22 factors influencing ACT handling operations were identified based on the system theoretic process analysis (STPA) model and literature. Subsequently, grey theory and decision-making test and evaluation laboratory (DEMATEL) were fused to visualize the cause-effect relationships of these factors. Finally, the total adversarial interpretive structural model (TAISM) was adopted to extract the hierarchical interrelationships among these factors using an adversarial hierarchical structure. The findings showed that poor supervision, communication network failures, sensor malfunctions, equipment malfunctions, lack of safety awareness, and interruptions of the power supply are the most critical factors influencing ACT handling operations. This study provides theoretical insights and practical implications for managers to enhance ACT handling risk management.

ProtoExplorer: Interpretable forensic analysis of deepfake videos using prototype exploration and refinement

In high-stakes settings, Machine Learning models that can provide predictions that are interpretable for humans are crucial. This is even more true with the advent of complex deep learning based models with a huge number of tunable parameters. Recently, prototype-based methods have emerged as a promising approach to make deep learning interpretable. We particularly focus on the analysis of deepfake videos in a forensics context. Although prototype-based methods have been introduced for the detection of deepfake videos, their use in real-world scenarios still presents major challenges, in that prototypes tend to be overly similar and interpretability varies between prototypes. This paper proposes a Visual Analytics process model for prototype learning, and, based on this, presents ProtoExplorer, a Visual Analytics system for the exploration and refinement of prototype-based deepfake detection models. ProtoExplorer offers tools for visualizing and temporally filtering prototype-based predictions when working with video data. It disentangles the complexity of working with spatio-temporal prototypes, facilitating their visualization. It further enables the refinement of models by interactively deleting and replacing prototypes with the aim to achieve more interpretable and less biased predictions while preserving detection accuracy. The system was designed with forensic experts and evaluated in a number of rounds based on both open-ended think aloud evaluation and interviews. These sessions have confirmed the strength of our prototype-based exploration of deepfake videos while they provided the feedback needed to continuously improve the system.

Delineation of groundwater prospective zones using multivariate and spatial analysis techniques in Henan Province North China Plain

Groundwater is an essential resource that meets all of humanity’s daily water demands, supports industrial development, influences agricultural output, and maintains ecological equilibrium. The objective behind this research is to find groundwater prospective zones and their sustainable development in Henan Province, located in the North China Plain, with the help of analytical hierarchical process (AHP) and frequency ratio (FR) models. Consequently, a groundwater inventory map was created, utilizing 70% of the data for training and 30% for testing purposes. The resulting maps were overlaid with groundwater levels and the locations of wells within the study area. The performance and validation of the models were assessed using the Area Under Curve (AUC) curve, which indicated an accuracy of 70% for the AHP model and 69% for the FR model. The study indicates that areas with suitable gradients and rainfall patterns generally have high groundwater prospective zonation (GWPZ), i.e., Zhumadian and Nanyang (ZN). However, AHP demonstrated a higher predictive capability in comparison to FR the broader context. Whereas GIS-RS methodology aligns with existing wells and field data that validate it in range of good and excellent. In conclusion, this study provides valuable insights for effective groundwater exploration and sustainable water supply planning in Henan Province.

Digital Twin Fundamentals of mRNA In Vitro Transcription in Variable Scale Toward Autonomous Operation.

The COVID-19 pandemic caused the rapid development of mRNA (messenger ribonucleic acid) vaccines and new RNA-based therapeutic methods. However, the approval rate for candidates has the potential to be increased, with a significant number failing so far due to efficacy, safety, and manufacturing deficiencies, hindering equitable vaccine distribution during pandemics. This study focuses on optimizing the production of mRNA, a critical component of mRNA-based vaccines, using a scalable machine by investigating the key mechanisms of mRNA in vitro transcription. First, kinetic parameters for the mRNA production process were determined. The validity of the determination and the robustness of the model are demonstrated by predicting different reactions with and without substrate limitations as well as different transcripts. The optimized reaction conditions, including temperature, urea concentration, and concentration of reaction-enhancing additives, resulted in a 55% increase in mRNA yield with a 33% reduction in truncated mRNA. Additionally, the feasibility of a segmented flow approach allowed for high-throughput screening (HTS), enabling the production of 20 vaccine candidates within a short time frame, representing a 10-fold increase in productivity, compared to nonsegmented reactions limited by the residence time in the plug flow reactor. The findings presented for the first time here contribute to the development of a fully continuous and efficient manufacturing process for mRNA and other cell and gene therapy drugs/vaccine candidates as presented in our previous work, which discussed the integration of process analytical technologies and predictive process models in a Biopharma 4.0 facility to enable the production of clinical and large-scale doses, ensuring a rapid and resilient supply of critical therapeutics. The results in this study especially highlight that the same machine and equipment can be used for screening and manufacturing different drug candidates in continuous operation. By streamlining production and adhering to quality standards, this approach enhances the industry's ability to respond swiftly to pandemics and public health emergencies, addressing the urgent need for accessible and effective vaccines.

Using the Conditional Process Analysis Model to Characterize the Evolution of Carbon Structure in Taxodium ascendens Biochar with Varied Pyrolysis Temperature and Holding Time

Temperature determines biochar structure during pyrolysis. However, differences in holding time and feedstock types may affect this relationship. The conditional process analysis model was used in this paper to investigate the potential to affect this mechanism. The branch and leaf parts of Taxodium ascendens were separately pyrolyzed at 350, 450, 650, and 750 °C, and kept for 0.5, 1, and 2 h at each target temperature. We measured the fixed carbon and ash contents and the elemental composition (C, H, O and N) of the raw materials and their char samples. After plotting a Van Krevelen (VK) diagram to determine the aromatization of chars, the changes in the functional groups were analyzed using Fourier transform infrared (FTIR), Raman, and X-ray photoelectron spectroscopy (XPS). The results revealed that pyrolysis at temperatures between 450 and 750 °C accounted for the aromatization of biochar because the atomic H/C ratio of branch-based chars (BC) decreased from 0.53–0.59 to 0.15–0.18, and the ratio of leaf-based chars (LC) decreased from 0.56–0.68 to 0.20–0.22; the atomic O/C ratio of BC decreased from 0.22–0.27 to 0.08–0.11, while that of LC decreased from 0.26–0.28 to 0.18–0.21. Moreover, the average contents of N (1.89%) and ash (13%) in LC were evidently greater than that in BC (N:0.62%; Ash: 4%). Therefore, BC was superior to LC in terms of the stability of biochar. In addition, the increasing ID/IG and ID/I(DR+GL) ratios in BC and LC indicated an increasing amount of the amorphous aromatic carbon structure with medium-sized (2~6 rings) fused benzene rings. According to the CPA analysis, an extension of the holding time significantly enhanced the increase in aromatic structures of LC with temperature. But this extension slightly reduced the growth in aromatic structures of BC. All indicate that holding time and feedstock types (branch or leaf feedstock) could significantly affect the variation in biochar aromatic structure with respect to temperature.

Analysis of the Rotation Bending Test Method and Characterization of Unidirectional Carbon Fiber-Reinforced Polycarbonate Tapes at Processing Temperatures.

Bending is one of the dominant material deformation mechanisms that occurs during the forming process of unidirectional (UD) thermoplastic tapes. Experimental characterization of the bending behavior at processing temperatures is crucial to obtaining close-to-reality data sets for process analysis or material modeling for process simulation. The main purpose of this study is to characterize to a high degree of accuracy the temperature-dependent bending behavior of single and multi-ply specimens of carbon fiber-reinforced polycarbonate (PC/CF) UD tapes at processing temperatures, which implies a molten state of the thermoplastic matrix. The application of the rotation bending test using a customized fixture may come with systematic deviations in the measured moment that result from a pivot offset or an effective clearance that is unknown under realistic test conditions. The present research analyzes these effects with analytical methods, experimental investigations, and simulations using a finite element model. In this context, a compensation method for the toe-in effect is evaluated. With this approach, we were able to obtain reliable data and characterize the bending resistance within the desired processing window. The data reveal a major drop in bending resistance between 200 °C and 250 °C and a less significant decrease between 250 °C and 300 °C. Analysis of the thickness-normalized bending resistances indicates a non-linear relationship between specimen thickness and measured moment but an increasing shear-dominated characteristic at higher temperatures.

Traffic behavior analysis of the urban expressway ramp based on continuous cellular automata

Urban expressway is an essential part of the urban transportation network, and its role is to speed up traffic operation and relieve traffic congestion. To investigate the complex traffic flow phenomena at urban expressway ramps, we use the Continuous Cellular Automata (CCA) model to model and analyze the urban expressway with entrance and exit ramps in a unified manner. Considering the vehicle's merging, diverging and following behaviors on urban expressway with an entrance and exit ramp, we design the vehicle's main road merging rules, ramp inflow and outflow rules, Cooperative Car-Following for Lane Change process analysis (LC-CCF) model, and improved Nagatani lane change rules to control the overall road vehicle operation. Based on this, we propose a CCA model for urban expressway ramp traffic flow. To clarify the model, The Chengdu Second Ring Elevated Road with entrance and exit ramps as a scenario is used for experiments. The results show that: (1) The spatio-temporal diagrams show a significant metastable congestion phenomenon and distinct congestion boundaries primarily located near the merge and divergence area. Congestion tends to start at the merge and divergence area and then spread outward. (2) The ramp disturbance has an increasingly pronounced effect on average speed and density as Pin−main and Pin−ramp increases. (3) The density-lane change frequency relationship tends to increase and then decrease, with an increase in the frequency of lane changes increasing the interference between vehicles in different lanes and affecting the average traffic speed. The lane change frequency is the main reason for the differences in traffic flow between lanes. (4) The CCA model can effectively characterize congested traffic flows on urban expressway ramps.

ESG, Green Innovation and Corporate Value: A Conditional Process Analysis Moderated by External Pressure

Using 2,372 A-share listed companies from 2009 to 2020 as research samples, this paper empirically examines the effect of corporate ESG performances on corporate value using a conditional process analysis model. The paper finds that: ESG has a significant positive effect on corporate value, and green innovation can partially mediate the path; corporate external pressure, mainly media attention and analysts' attention, can positively moderate the path of ESG on green innovation, and thus positively moderate the mediation effect played by green innovation. This paper concludes that corporate ESG performance and green innovation activities help companies achieve their goals of economic value growth and sustainable development ultimately.

The Influence of Paradoxical Leader Behaviors on Employees' Core Self-Evaluation: A Conditional Process Analysis Model

Using data obtained from a survey conducted with 737 employees of organizations in China, this study demonstrated that paradoxical leadership behavior can significantly and positively predict employees' core self-evaluation and perception of insider status. At the same time, insider status cognition significantly and positively predicted the core self-evaluation of employees, and played a mediating role in the impact of paradoxical leadership behavior on employees' core self-evaluation. Improving the perceived psychological safety level can enhance the relationship between paradoxical leadership behavior and the effect of perceived insider status on core self-evaluation. Managers can improve their paradoxical leadership skills, directly and effectively improve their employees' core self-evaluation, and also enhance employees' sense of belonging, by making employees think of themselves as insiders in the organization. Managers need to create a safe atmosphere for team members; an environment of mutual respect and trust can effectively catalyze the positive effects of paradoxical leadership behaviors on employees' core self-evaluation.

Digital Twin Implementation for Manufacturing of Adjuvants

Pharmaceutical manufacturing processes are moving towards automation and real-time process monitoring with the help of process analytical technologies (PATs) and predictive process models representing the real system. In this paper, we present a digital twin developed for an adjuvant manufacturing process involving a microfluidic formation of lipid particles. The twin uses a hybrid model for estimating the current state of the process and predicting system behavior in real time. The twin is used to control the adjuvant particle size, a critical quality attribute, by varying process parameters such as the temperature and inlet flow rates. We describe steps in the design and implementation of the twin, starting from the conception of the mechanistic model, up to the generation of its surrogate model used as state estimator, PATs and the setup of the information technology—Operational technology architecture. We demonstrate the performance of the twin by introducing different disturbances in the process and comparing the effect on the product critical quality attributes with and without the control of the digital twin. Finally, we showcase the digital twin implementation for the process in good manufacturing practice, through an engineering run, which demonstrated the robustness of the process when controlled by the digital twin.

Case Study of Structural Design and Construction Process Analysis for an Aluminum Alloy Free Form Reticulated Shell of Nanjing Niushou Mountain

The aluminum alloy reticulated shell structure of Nanjing Niushou Mountain, the largest single-layer aluminum alloy reticulated shell in Asia, is 227m long from south to north, 130m spanning from east to west. In the design process, the aluminum alloy reticulated shells has been established, then the whole stability of the structure was analyzed to get the first six buckling modes and the safety factors through elastic and elastic-plastic analyses of the whole process. According to the complex construction conditions and the difficulties of construction for a super-large free form reticulated shell, a block fabrication accompanied by step-by-step launching erection process was introduced for economical and in-site safe construction. From construction economy, four different sliding systems have been prepared and compared to enhance the construction efficiency. Also four stages in each sliding system were assumed as their key construction steps through trial calculation and conceptual judgment. Thus, the construction process analysis model in each stage was analyzed and simulated respectively according to the key construction steps. The analysis results show that the safety factor of the reticulated shell structure meets the requirements of the specification, and that the selected sliding construction scheme satisfies the construction requirement on the special construction condition. This report provides an effective reference on future practical application.

Strip tracking control using novel insights into the differential rolling process

ABSTRACT Steel strips suffer from lateral shift when being rolled in a rolling mill, especially at the head and tail ends. That results in strip pinches with costly downtime and damage to the work rolls and the strip itself. The lateral shift can be reduced by levelling the gauge control cylinders in response to shift measurements and differential roll force measurements. Designing such controllers gave rise to the desire for analytical relations that describe the shifting process. Novel insights have been revealed, which include the differential material modulus, the differential mill modulus and the ratio to apportion the effect of levelling to entry and exit strip speed changes. The insights have resulted in compact analytical process models that have been used to implement strip tracking controllers safely and sensibly, and to schedule control gains based on actual process parameters. Focus has been on hot strip mills.

Dynamic Thermal Neutron Radiography for Filling Process Analysis and CFD Model Validation of Visco-Dampers

The visco-damper is a crucial engine accessory from an operation- as well as vehicle-safety point of view. The service life of this damping product is determined by the degradation of the silicone oil applied to it. The thermal and mechanical degradation of the oil starts not at the first operation of the damper, but at the manufacturing stage when the oil is filled into the damper’s gap at high pressure. Finite volume method-based computational fluid dynamic calculations provide an opportunity to optimize the filling process by minimizing the oil degradation. A three-dimensional, transient, non-Newtonian, multiphase, coupled fluid dynamic and heat transfer simulation model was developed to analyse the filling process and to investigate the effect of the slide bearing’s cut-off position on the filling process. Dynamic thermal neutron radiography was employed to visualize the filling of a test damper for model validation from viscous and fluid dynamic aspects. Distinct properties of neutrons compared to the more commonly applied X-rays were proven to be an effective tool for real-time monitoring of the silicone oil’s front propagation in the damper’s gap and for quantifying the characteristics of the filling process. Visual matching and comparison of propagation times and oil front velocity profiles were used to validate the simulation results.

Delineation of Groundwater Potential Area using an AHP, Remote Sensing, and GIS Techniques in the Ifni Basin, Western Anti-Atlas, Morocco

An assessment of potential groundwater areas in the Ifni basin, located in the western Anti-Atlas range of Morocco, was conducted based on a multicriteria analytical approach that integrated a set of geomorphological and hydroclimatic factors influencing the availability of this resource. This approach involved the use of geographic information systems (GIS) and hierarchical analytical process (AHP) models. Different factors were classified and weighted according to their contribution to and impact on groundwater reserves. Their normalized weights were evaluated using a pairwise comparison matrix. Four classes of potentiality emerged: very high, high, moderate, and low, occupying 15.22%, 20.17%, 30.96%, and 33.65%, respectively, of the basin’s area. A groundwater potential map (GWPA) was validated by comparison with data from 134 existing water points using a receiver operating characteristic (ROC) curve. The AUC was calculated at 80%, indicating the good predictive accuracy of the AHP method. These results will enable water operators to select favorable sites with a high groundwater potential.

Visualization of computer-supported collaborative learning models in the context of multimodal data analysis

Deep learning evaluation is a new direction formed by the intersection of multiple domains, and the core issue is how to visualize collaborative learning models to motivate learners. Therefore, this paper realizes real-time knowledge sharing and facilitates learners' interaction through computer-supported collaborative learning (CSCL) technology. In this paper, we collect, label, and analyze data based on five modalities: brain, behavior, cognition, environment, and technology. In this paper, a computer-supported collaborative learning process analysis model is developed under the threshold of multimodal data analysis. The model is based on roles and CSCL for intelligent network collaboration. This paper designs and develops an interactive visualization tool to support online collaborative learning process analysis. In addition, this paper conducts a practical study in an online classroom. The results show that the model and the tool can be effectively used for online collaborative learning process analysis, and the test model results fit well. The entropy index of the test model took a value of about 0.85, and about less than 10% of the individuals were assigned to the wrong profile. During the test, the participation of participants gradually increased from 5% to about 25%, and the participation effect improved by about 80%. This indicates the strong applicability value of the computer-supported collaborative learning process analysis model under the multimodal data analysis perspective.

Circular economy supply network transition phases management dynamics

AbstractThis paper discusses transitions towards circular economy, developing a circular economy supply network transition phases management dynamics framework. We studied seven circular supply networks in Brazil through process analysis and system dynamics modeling. Primary data were collected through 35 manager's interviews and companies' plant and their suppliers' visits. We suggest that transition phases management are pre‐development, learning, expansion, leadership, stabilization and self‐renew with cooperative and competitive challenges, characterized by specific circular factors over time. The simulation and transition phases models provide strategic tools to make decision on which circular factor, project team should focus applying multi‐tier supply chain management and hybrid leadership to stimulate internal and integrate external stakeholders to adopt circularity, unveiling challenges and average time to achieve each phase. We also enrich circular economy supply network management theoretical background defining it in three dimensions: behavioral as supply ecosystem, structural as supply network and contextual as complex adaptive system.

Split-order consolidation optimization for online supermarkets: Process analysis and optimization models

Split-order consolidation optimization for online supermarkets: Process analysis and optimization models

Integrated models for prediction and global factors sensitivity analysis of ultrafiltration (UF) membrane fouling: statistics and machine learning approach

In this work, machine learning was employed to quantitatively describe nonlinear ultrafiltration membrane fouling behaviors, from existing data process modeling, process analysis and predictive modeling of unknown data prediction and feature analysis. Instead of using the secondary data calculation that the traditional model required, direct observation data is used for modeling and analysis. This simplifies the prediction process and lowers the cost of the prediction. Besides, the problem that the long-term serial data and the short-term rapid change process were difficult to quantify has been resolved. Two distinct prediction models were established: one is semi-automatic prediction of future data with existing data based on statistics (for long-term study and prediction), and the other is fully autonomous prediction based on tree model (for short-term). 2520 12-dimentional laboratory measurements were collected enabling precise modeling prediction (less than 4 % error) for 50 % of the future timeline through supervised learning of process modeling. Results revealed that the UF membrane had a strong “rejection” impact when it came into contact with a polluted environment, which caused an inconsistent self-pollution coefficient and rapid fouling at initially. For process analysis, a global variable-based weighting factor sensitivity analysis and a statistically significant likelihood estimation were conducted using random put-back samples to accurately predict membrane fouling in an uncertain environment (MSE = 0.2 to 0.26). A high-dimensional variable-specific real-time weighting analysis was derived for inform lifespan extension of the UF membrane at environmental relevant conditions. Overall, this study illustrates the feasibility and interpretability of machine learning-based data-driven approaches in quantitatively describing and understanding nonlinear complex dynamics in membrane fouling.

Flexible single-step fabrication of programmable 3D nanostructures by pulse-modulated local anodic oxidation

A flexible single-step nanofabrication approach was developed using programmable pulse modulation local anodic oxidation for the efficient generation of various 3D nanostructures. The dependence of oxidation growth on pulse parameters was derived from parametric studies, from which an analytical process model was developed for the first time to link the pulse parameters with the geometry of 3D nanostructure with precision. The nanofabrication approach was implemented on an atomic force microscope. Experimental results show that this approach can effectively create 3D nanostructures with minimum feature sizes of sub-10 nm (lateral) and sub-nm (vertical) with nm and sub-nm level precision, respectively.

Relationship between Interaction Anxiousness, Academic Resilience, Cultural Intelligence and Ego-Identity among Chinese Vocational Pathway University Students: A Conditional Process Analysis Model

Relationship between Interaction Anxiousness, Academic Resilience, Cultural Intelligence and Ego-Identity among Chinese Vocational Pathway University Students: A Conditional Process Analysis Model