Process Configuration Research Articles

Clinical Practice Guideline: Condylar Hyperplasia of the Mandible—Diagnosis and Treatment.

Condylar hyperplasia of the mandible is characterized by abnormal size and configuration of the condylar process. In Germany, the administrative prevalence of diagnosed and/or treated condylar hyperplasia is 2.4-9.6 cases per 100 000 persons. Misdiagnosis is common and can lead to severe esthetic and functional complications, including facial deformity that can progress into the patient's twenties, as well as dysfunctional speech and mastication. We conducted a systematic review of the literature and a structured consensus-finding process with the Delphi method. The experts recommend timely diagnosis of condylar hyperplasia so that its progression can be prevented by appropriate treatment. The basic diagnostic evaluation consists of history taking, physical examination, and imaging with orthopantomography. If condylar hyperplasia is confirmed, three-dimensional imaging is performed, usually with SPECT. The treatment is mainly surgical: partial condylectomy techniques and orthognathic surgery are the most common methods. If left untreated, the disorder causes severe dysfunction of mastication as well as disfigurement. Most studies of treatments for condylar hyperplasia have been based on low case numbers and have yielded only level 4 or level 5 evidence. Condylar hyperplasia is a common, but not widely known problem. Its early diagnosis and treatment can prevent severe jaw asymmetry and further complications such as craniomandibular dysfunction and arthrosis of the temporomandibular joints.

Increasing software deployment speed in agile environments through automated configuration management

In today's fast-paced software development landscape, the ability to deploy applications rapidly while maintaining high quality is crucial for Agile teams. This paper explores the impact of Automated Configuration Management (ACM) on enhancing software deployment speed within Agile environments. By examining the complexities of modern software systems and the challenges associated with manual configuration processes, the study underscores the necessity of automation to streamline workflows and reduce errors. Key ACM tools such as Ansible, Puppet, and Chef are discussed and their role in facilitating efficient configuration management. The integration of ACM with Continuous Integration (CI) and Continuous Deployment (CD) practices is highlighted as a pivotal strategy for achieving faster deployment cycles and improved software reliability. Additionally, the paper emphasizes the importance of fostering a DevOps culture, where collaboration between development and operations teams enhances the overall efficiency of the software delivery pipeline. Ultimately, the findings present a roadmap for Agile teams seeking to implement automated solutions, offering practical recommendations to optimize their deployment processes and adapt to the evolving demands of the market.

VALIDATION OF DYNAMIC MESSAGE VARIANT IN ADAPTIVE LOGGING METHOD

Technology, and software technology in particular, is one of the main drivers of progress in human society. It allows us to solve existing tasks more efficiently and find solutions for problems that were previously too complex to deal with. The outreach and importance of its influence on everyday life is hard to overstate. To be able to satisfy ever-growing demand software solutions become more complex and sophisticated, reaching millions users and solving numerous day to day tasks. But as a result new challenges appear, especially related to cybersecurity. And it is not only about integrity, availability and confidentiality aspects, but also – equally important – about control and observability. As the scale of software systems grows more and more, tracking their state and behavior becomes more challenging. This research takes a closer look at observability aspect of cybersecurity, in particular at adaptive logging method for software systems and its dynamic message variant which introduced the ability to have dynamic computations executed before outputting observational information about the system. The flexibility it introduces also makes possible for undesirable side effects to occur as the dynamic nature of such messages allows for execution of virtually any valid code samples. The main purpose of this work is to demonstrate a solution that would allow validation and prevent unwanted code execution. To achieve this an approach that uses analysis of abstract syntax trees with JSON-based validation logic is demonstrated. The natural tree-like structure of ASTs, as well as close resemblance between generated representation and JSON-schemas, allows for precise and easily configurable processing that reduces the possibility of unexpected behaviors. The integration into an existing formal basis of adaptive logging method is also demonstrated with provided justification for required additions and their specifics.

A switched model predictive control with parametric weights-based mode transition strategy for a novel parallel hybrid electric vehicle

In a novel parallel hybrid electric vehicle (HEV) configuration, the transition from pure electric mode to hybrid mode encompasses critical operations such as engine startup, coordinated control of motor and engine torque, and engagement of the clutch. Addressing the intricate challenges associated with enhancing speed tracking performance during and after mode transition, mitigating jerk during mode transition, and minimizing mode transition time, this paper conducts a meticulous analysis of the vehicle configuration and mode transition process. The mode transition process is systematically delineated into four stages, with each stage characterized by the establishment of dynamic models. Subsequently, a mode transition strategy is proposed, leveraging switched model predictive control with parametric weights (SMPC-PW). This controller framework includes the design of two model predictive controllers (MPC) tailored for two pivotal stages, the formulation of a parametric weights pattern based on pre-transition acceleration, and the development of a stage switching strategy to ensure seamless switches between controllers. The efficacy of the proposed strategy is validated through co-simulations in the Simulink and GT-Power environment. The fine-tuning of MPC parameters is grounded in multiple sets of prediction horizons and sampling time simulation results. In comparison to strategies based on MPC and PID under various acceleration scenarios, the SMPC-PW strategy consistently maintains acceleration control below 10 m/s3. It not only achieves superior speed tracking during and after mode transition but also reduces mode switch time by 0.1 s-0.3 s. These compelling results unequivocally demonstrate that the proposed mode transition strategy significantly elevates the quality of mode transition for this specific parallel HEV configuration.

Cloud-based configurable data stream processing architecture in rural economic development

Cloud-based configurable data stream processing architecture in rural economic development

Machining characteristics and optimization of TC4 alloy by mixed gas atomization discharge ablation milling (MA-DAM)

A method using mixed gas atomization discharge ablation milling (MA-DAM) for machining titanium alloys was proposed to address the processing efficiency and quality issues in conventional near-dry electrical discharge milling (N-EDM). By analyzing the discharge waveform, a pulse discharge physical model was established to reveal the machining mechanisms of N-EDM and MA-DAM. It was found that the latter had a unique ablation process compared to the former, resulting in better machining performance. The results of oxygen concentration experiments showed that compared to N-EDM, MA-DAM improved the material removal rate by 55.6 % and reduced the relative electrode wear rate by 50 %, but increased the width of the overcut by approximately 10 % and the average roughness by approximately 17 %. Orthogonal experiments and variance analysis were conducted to study the effects of the current, duty cycle, gas pressure, and atomization rate on the machining performance indicators. Finally, grey relational analysis was used for the multi-objective optimization of machining parameters to obtain the optimal process configuration for MA-DAM, which was validated through experiments.

Compartmental modeling of CO2 absorption in a rotating packed bed

Capturing carbon dioxide (CO2), i.e., the primary greenhouse gas that contributes to climate change, from CO2 containing gas mixtures in various industrial sectors, e.g., natural gas processing and treating refinery off-gases, is necessary. Rotating packed beds (RPBs) have shown great efficiency in CO2 capture from flue gases with an appreciable intensification in the absorber size compared to the conventional packed bed absorbers. Successful commercialization of an RPB technology requires a comprehensive understanding of its performance under various operating conditions, process configurations, and for different solvents. In this study, we presented a mathematical model describing the performance of an RPB absorber for CO2 capture by aqueous amine solutions. We established the compartmental model based on two plug-flow reactors for gas and liquid phases. We validated this model adopting two sets of experimental data from literature. We considered reaction rates of all compounds in the system in the model. We, subsequently, employed the validated model to highlight the potential parameters in enhancing the overall CO2 recovery by an RPB absorber. The developed model takes advantage of acquired knowledge and data from literature based on years of numerical studies on RPBs. We studied the effects of operating conditions, such as inlet gas and liquid flow rates, amine fraction, rotating speed, and inlet liquid and gas temperatures on the performance of an RPB by the validated model. Although we initially developed the model for the CO2-monoethanolamine (MEA) solvent, we also studied the effect of changing the solvent to methyl diethanolamine (MDEA) and piperazine (PZ) on the overall absorption performance of an RPB absorber. We further adopted the developed model as a design tool to assist us in replacing a pilot- and an industrial-scale packed bed absorber with corresponding single RPB absorbers.

Performance Analysis and Novel Cross-Flow Scheme of Low-Temperature Multi-Effect Distillation for Treating High-Mineralized Mine Water

Low-temperature multi-effect distillation (LT-MED) can be used to desalinate and demineralize highly mineralized mine water, facilitating the recycling and reuse of water resources. This study conducted a simulation of LT-MED technology for treating highly mineralized mine water using Aspen Plus and proposed a novel cross-flow optimization scheme. Initially, the impact of operational parameters such as process configuration, number of evaporation effects, and steam input on the gained output ratio (GOR) and scaling risk of a conventional LT-MED system was analyzed. It was found that the number of effects and heating steam flow rate had the most significant influence on GOR, while different processes exhibited limitations regarding GOR and scaling trends. To address these issues, this paper introduced a cross-flow operation process that combined forward, backward, and parallel flow. The simulation results indicated that, under conditions of eight effects, a maximum evaporation temperature of 70 °C, a temperature difference between adjacent effects of 4 °C, and a feed temperature of 45 °C, the cross-flow process—where the feed was introduced from the sixth effect—achieved the highest GOR and significantly reduced scaling risks compared to parallel and backward flow configurations. Finally, to further utilize low-pressure exhaust steam from the final effect of the cross-flow LT-MED system, mechanical vapor compression (MVC) and thermal vapor compression (TVC) were integrated into the LT-MED process. The thermodynamic performance of the coupled system was analyzed, and the simulations demonstrated that the coupled system outperformed the standalone use of either TVC or MVC, with the LT-MED-MVC-TVC system showing superior performance overall.

Perception of pointing gestures in 3D space

Pointing gestures are often used to refer to distant referents by indicating in which vertical and horizontal direction the referent is located relative to the pointer. In the present manuscript, we address whether and how both dimensions interact when people spatially interpret pointing gestures, or whether both dimensions are processed independently as reflected in many current models. We found that both dimensions interact on different levels. First, cross-dimensional effects were found on a between-gestures level. That is, the perception of the vertical position implied by a pointing gesture depended on horizontal arm and finger orientation. Conversely, the horizontal interpretation depended on vertical arm and finger orientation. Second, we found cross-dimensional interactions on the level of intra-individual biases. That is, participants’ horizontal perceptual biases in interpretations (e.g., perceiving a gesture as directed more rightward than others) were related to their vertical perceptual biases. Third, we found cross-dimensional interactions on the level of intra-individual variability. That is, the vertical and horizontal interpretations of the same pointing gestures were correlated within participants and gestures. Together, these findings indicate that human spatial pointing perception is based on configural processing of a gesture on different levels of information processing.

Pasteables: A Flexible and Smart “Stick-and-Peel” Wearable Platform for Fitness and Athletics

Wearable technologies are becoming increasingly popular and transforming our fitness and sports industry. Monitoring health parameters and body activity can help achieve fitness goals, improve sports performance, and aid in physiotherapy. However, state-of-the-art wearable devices are often not flexible, expensive, hard to set up, or have privacy concerns. They are designed and optimized for a specific application with tight hardware-software integration and it is hard or impossible to re-purpose them for diverse use cases. In this paper, we propose a flexible, re-configurable human body movement and health monitoring platform, called "Pasteables". It is a stick-and-peel device (which acts like a band-aid) that attaches to the skin or clothing and can create an on-body network of smart wearables. Given an application, Pasteables can be easily adapted to its requirements while ensuring good performance and privacy. We present the overall architecture, system design steps, and the configuration process. We have designed a set of functional prototypes and configured them for performing a variety of camera-less pose and posture detection tasks, which are important for athletes, professional dancing, physiotherapy, and fitness workouts. We note that the Pasteables can perform pose/posture detection without external stationary reference at low cost and high accuracy.

Comprehensive analysis of energy saving and high-quality permeate production strategies for a large-scale seawater reverse osmosis desalination plant with diverse process configurations and external resource utilization

Seawater reverse osmosis (SWRO) desalination systems require significantly more energy in actual operation compared to the theoretical minimum. By fundamentally understanding and analyzing the energy consumption of SWRO processes, the high energy consumption can be efficiently reduced by adjusting feed water properties using external resources or by optimizing plant design and operation. This study analyzes the performance of full-scale single-pass, two-pass, partial two-pass, and split partial second-pass reverse osmosis (RO) configurations using mathematical models and proposes methods to reduce energy consumption. In addition, it compares strategies to reduce energy consumption in two-pass RO configurations by optimizing the ratio of pressure vessels between stages and the recovery rate conditions between the first and second passes. Furthermore, the study evaluates the potential of utilizing external resources such as power plant thermal discharge and brackish water to alleviate the energy burden of the SWRO process. The research was conducted on a 100,000 m3/day capacity SWRO desalination system, focusing on process performance analysis and energy consumption reduction.

Legislative configuration and strength through the idea of Ends Independence

ABSTRACT Prior approaches to evaluating legislative strength cross-nationally have focused on the idea of Means Independence, defined as the procedural autonomy of a legislature. However, expanded Means Independence might not necessarily effect policy effects outside the chief executive’s preference, referred to as Ends Independence. Spatial models estimate the influence of different configurations of legislative processes on Ends Independence. Findings show that an increase in Means Independence does not simultaneously increase Ends Independence. Ends Independence can be guaranteed only when the opposition has the right to propose first, and results are minimally affected by new information updated through the exercise of Means Independence. These findings demonstrate the importance of understanding the overall structure of the legislative process to assess legislative power vis-à-vis an authoritarian executive.

Adaptively remeshed multiphysical modeling of resistance forge welding with experimental validation of residual stress fields and measurement processes

Welding processes used in the production of pressure vessels impart residual stresses in the manufactured component. Computational modeling is critical to predicting these residual stress fields and understanding how they interact with notches and flaws to impact pressure vessel durability. In this work, we present a finite element model for a resistance forge weld and validate it using laboratory measurements. Extensive microstructural changes, near-melt temperatures, and large localized deformations along the weld interface pose significant challenges to Lagrangian finite element modeling. The proposed modeling approach overcomes these roadblocks in order to provide a high-fidelity simulation that can predict the residual stress state in the manufactured pressure vessel; a rich microstructural constitutive model accounts for material recrystallization dynamics, a frictional-to-tied contact model is coordinated with the constitutive model to represent interfacial bonding, and adaptive remeshing is employed to alleviate severe mesh distortion. An interrupted-weld approach is applied to the simulation to facilitate comparison to displacement measures. Several techniques are employed for residual stress measurement in order to validate the finite element model: neutron diffraction, the contour method, and the slitting method. Model-measurement comparisons are supplemented with detailed simulations that reflect the configurations of the residual-stress measurement processes themselves. The model results show general agreement with experimental measurements, and we observe some similarities in the features around the weld region. Factors that contribute to model-measurement differences are identified. Finally, we conclude with some discussion of the model development and residual stress measurement strategies, including how to best leverage the efforts put forth here for other weld problems.

Optimization of Power to Gas system with cooled reactor for CO2 methanation: Start-up and shut-down tests with Ru-based and Ni-based kinetics

The dynamic behavior of a methanation process is analyzed in the context of Power to Gas (PtG) technology, to convert carbon dioxide from different sources and hydrogen produced via electrolysis with renewable energy to synthetic methane. The process configuration consists of a single cooled plug flow reactor (PFR) where both ruthenium and nickel-based kinetic models are tested in the Aspen Dynamics simulation environment. In particular, dynamic analyses are performed to study the start-up and shut-down phases and respect injection parameters in the existing gas network. The process consists of three main sections: conditioning, reaction and separation. After the development and optimization of the control system, the start-up and shut-down tests are carried out, to study the trend of composition for the injection in the gas network. The plant size is set according to the following values: 700 Nm3/h of hydrogen and 175 Nm3/h of carbon dioxide, which correspond to the methanation reaction stochiometric ratio. Different cases are considered in both the start-up and shut-down tests, by changing the GHSV values and the catalyst adopted. Moreover, in the shut-down test two different rates are considered to evaluate the behavior of the system.

Value Based Conduct Confirmation in BPaaS Configuration

Business Process as a Service (BPaaS) is a cloud-based delivery model that combines business process outsourcing (BPO) with the scalability and flexibility of cloud computing. In BPaaS, organizations outsource specific business processes—such as payroll, customer support, or inventory management—to a cloud service provider. These services are designed for multitenancy, meaning they serve multiple clients simultaneously. By leveraging the cloud, companies gain cost efficiency, access to cutting-edge technologies, and the ability to scale on demand. Essentially, BPaaS streamlines operations, allowing businesses to focus on their core competencies while leaving process management to experts in the cloud. The purpose behind BPaaS is to turn multiple service companies into a corporate process system that can then be provided as its own service to consumers. Naturally, BPaaS providers target common or proven business processes applicable to a broad potential market, or require multiple complex components management. This is attractive for customers, because it offers a cheap, low risk outsource option for integral business activities. Business Process as a Service (BPaaS) is an emerging sort of cloud provider that gives configurable and executable business processes to customers over the internet. As BPaaS is still in early years of research, many open troubles remain managing the configuration of BPaaS builds on regions which include software product strains and configurable commercial enterprise approaches. The problem has concerns to recollect from numerous perspectives, along with the different forms of variable features, constraints among configuration options, and gratifying the necessities supplied by the client. In this method, the system uses temporal logic templates to elicit transactional requirements from customers that the configured carrier ought to adhere to. For formalizing constraints over configuration, feature fashions are used. To manage all these issues at some stage in BPaaS configuration, the system developed a structured approach that applies formal techniques whilst directing customers via specifying transactional necessities and choosing configurable features.

Characterization of a highly integrated, natural convection-driven, condensing methanol reactor

A highly integrated, natural convection-driven, condensing methanol reactor with the catalyst bed, heat integration section and liquid product separation in a single pressure vessel without moving parts is successfully tested. Space-time yields of up to 600kgMeOHmcat.−3h−1 are demonstrated, comparable to other CO2-to-methanol processes. The internal recycle by natural convection is successfully achieved as well as significant heat integration. A further reduction of heat losses to the environment is needed for autothermal operation. The dynamic characteristics of the process, with fast start-up times, a high turn-down ratio add to the potential to deal with intermittent feed supply. Overall the LOGIC 2.0 reactor concept is a promising process configuration for green methanol production.

Integration of energy storage and determination of optimal solar system size for biomass fast-pyrolysis

This study presents a model and analysis of a heliostat field collector (HFC) system integration with fast-pyrolysis of biomass and determination of the optimal solar system size for this integrated system. Given the intermittent nature of solar energy, an auxiliary heater and a thermochemical energy storage system (TCES) are included. Four cases of HFC integration with the fast-pyrolysis process have been studied: 1) low solar radiation, 2) sufficient solar radiation, 3) high solar radiation, and 4) no solar radiation with available stored energy in TCES. The solar energy system was modeled and calculated using the Engineering Equation Solver (EES) software, while the fast-pyrolysis process and the TCES were simulated using the Aspen Plus software. A thermodynamic and economic analysis has been conducted to estimate the share of solar energy for different process configurations. Economic calculations have been conducted for three different heliostat filed areas: 4000, 8000, and 12000 m2. Solar fraction, investment and operational costs, as well as total cost were calculated for these three heliostat field areas. The results indicate that the optimum heliostat field area for the studied biomass pyrolysis plant is 8000 m2 and the average solar fraction of the required energy in summer is 0.39 and while it is 0.34 for the whole year. Simulation results considering this optimized heliostat filed area indicate that 6.27 t/h of bio-oil is produced from 10 t/h of hybrid poplar biomass. Implementing this solar-assisted system reduces CO2 emissions, increases efficiency of the system and lowers thermal energy requirement for the fast-pyrolysis process from 6 MW to 3.99 MW.

Structured pruning of neural networks for constraints learning

In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity in applications such as cancer treatment, algorithmic configuration, and chemical process optimization. This integration often uses Mixed Integer Programming (MIP) formulations to represent the chosen ML model, that is often an Artificial Neural Networks (ANNs) due to their widespread use. However, ANNs frequently contain a large number of parameters, resulting in MIP formulations impractical to solve. In this paper we showcase the effectiveness of a ANN pruning, when applied to models prior to their integration into MIPs. We discuss why pruning is more suitable in this context than other ML compression techniques, and we highlight the potential of appropriate pruning strategies via experiments on MIPs used to construct adversarial examples to ANNs. Our results demonstrate that pruning offers remarkable reductions in solution times without hindering the quality of the final decision, enabling the resolution of previously unsolvable instances.

A miniaturized feedstocks-to-fuels pipeline for screening the efficiency of deconstruction and microbial conversion of lignocellulosic biomass.

Sustainably grown biomass is a promising alternative to produce fuels and chemicals and reduce the dependency on fossil energy sources. However, the efficient conversion of lignocellulosic biomass into biofuels and bioproducts often requires extensive testing of components and reaction conditions used in the pretreatment, saccharification, and bioconversion steps. This restriction can result in a significant and unwieldy number of combinations of biomass types, solvents, microbial strains, and operational parameters that need to be characterized, turning these efforts into a daunting and time-consuming task. Here we developed a high-throughput feedstocks-to-fuels screening platform to address these challenges. The result is a miniaturized semi-automated platform that leverages the capabilities of a solid handling robot, a liquid handling robot, analytical instruments, and a centralized data repository, adapted to operate as an ionic-liquid-based biomass conversion pipeline. The pipeline was tested by using sorghum as feedstock, the biocompatible ionic liquid cholinium phosphate as pretreatment solvent, a "one-pot" process configuration that does not require ionic liquid removal after pretreatment, and an engineered strain of the yeast Rhodosporidium toruloides that produces the jet-fuel precursor bisabolene as a conversion microbe. By the simultaneous processing of 48 samples, we show that this configuration and reaction conditions result in sugar yields (~70%) and bisabolene titers (~1500 mg/L) that are comparable to the efficiencies observed at larger scales but require only a fraction of the time. We expect that this Feedstocks-to-Fuels pipeline will become an effective tool to screen thousands of bioenergy crop and feedstock samples and assist process optimization efforts and the development of predictive deconstruction approaches.

Breaking the Passivation Effect for MnO2 Catalysts in Li−S Batteries by Anion‐Cation Doping

AbstractTransition metal oxides (TMOs) are recognized as high‐efficiency electrocatalyst systems for restraining the shuttle effect in lithium‐sulfur (Li−S) batteries, owing to their robust adsorption capabilities for polysulfides. However, the sluggish catalytic conversion of Li2S redox and severe passivation effect of TMOs exacerbate polysulfide shuttling and reduce the cyclability of Li−S batteries, which significantly hinders the development of TMOs electrocatalysts. Here, through the anion‐cation doping approach, dual incorporation of phosphorus and molybdenum into MnO2 (P,Mo‐MnO2) was engineered, demonstrating effective mitigation of the passivation effect and allowing for the simultaneous immobilization of polysulfides and rapid redox kinetics of Li2S. Both experimental and theoretical investigations reveal the pivotal role of dopants in fine‐tuning the d‐band center and optimizing the electronic structure of MnO2. Furthermore, this well‐designed configuration processes catalytic selectivity. Specifically, P‐doping expedites rapid Li2S nucleation kinetics by minimizing reaction‐free energy, while Mo‐doping facilitates robust Li2S dissolution kinetics by mitigating decomposition barriers. This dual‐doping approach equips P,Mo‐MnO2 with robust bi‐directional catalytic activity, effectively overcoming passivation effect and suppressing the notorious shuttle effect. Consequently, Li−S batteries incorporating P,Mo‐MnO2‐based separators demonstrate favorable performance than pristine TMOs. This design offers rational viewpoint for the development of catalytic materials with superior bi‐directional sulfur electrocatalytic in Li−S batteries.