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Worldwide communication bandwidth growth has largely been driven by (1) multimedia demands, (2) multicommunication-point demands and (3) multicommunication-rate demands, and has increased dramatically over the last two decades due to e-commerce, internet communication and the explosion of cell-phone use, in particular for in-flight services, all of which necessitate broadband use and low latency. In order to accommodate this huge surge in demand, next generation "mega-constellations" of satellites are being proposed combining a mix of heterogeneous unit types in LEO, MEO and GEO orbital shells, in order to provide continuous lower-latency and high-bandwidth service which exploits a wide-range of frequencies for fast internet connections (broadband, which is not possible with single satellite-type orbital shell systems). Accordingly, in this work, we develop a computationally-efficient digital-twin framework for a constellation of satellites around an arbitrary planet ("Planet-X"). The rapid speed of these simulations enables the ability to explore satellite infrastructure parameter combinations, represented by a multicomponent satellite constellation design vector Λ = def (number of satellites, satellite orbital radii, satellite orbital speeds, satellite types), that can deliver desired communication signal or camera coverage on "Planet-X", while simultaneously incorporating satellite infrastructural resource constraints. In order to cast the objective mathematically, we set up the system design as an inverse problem to minimize a cost function via a Genetic Machine Learning Algorithm (G-MLA), which is well-suited for nonconvex optimization. Numerical examples are provided to illustrate the framework. [ABSTRACT FROM AUTHOR]

The conformal cooling channel is more and more favored by the mold enterprises because of its excellent cooling performance. However, the current design of conformal cooling channel generally has the problems of complex, time-consuming, and error prone design process. The spiral cooling channel is a very important cooling channel structure in injection mold, and its application is extremely common in practical mold engineering cases. For a long time, designers have had to rely on manual construction of spiral cooling channel, which is a cumbersome and inefficient design process. Inspired by the spring structure and based on the design characteristics of spiral cooling channel, this paper puts forward an automatic generation method of conformal cooling channel based on the basic requirements of conformal cooling channel design in engineering practice. The basic idea of this method firstly is to generate the initial trajectory lines and store them in a list in the specified order. Then, based on the initial trajectory line as the basic geometry, a series of control points are generated, and all control points, including the inlet and outlet coordinate points, are sorted. Finally, generate a spline curve based on the control points and scan along the spline curve with the specified cross-section to form cooling channels. Experimental results show that this method can significantly decrease the time of design and guide users to quickly complete the design, while reducing design errors to improve working efficiency. [ABSTRACT FROM AUTHOR]

This article presents a systematic literature review on the Plug and Produce concept in advanced automated manufacturing control systems. Over recent decades, this concept has evolved significantly, with researchers focusing on enhancing its applicability and improving its conceptual, logical, and physical aspects across various sub-areas such as system design, methodologies, and supporting tools within the Industry 4.0 and Industry 5.0 frameworks. The review offers technical insights on the research domain of Plug and Produce accompanied by an analytical schematic outlining five key evolving research streams ranging from system design framework, and functionality features, up to the empirical application. Additionally, the article discusses important issues surrounding the evolution of Plug and Produce in alignment with emerging trends within Industry 5.0 automation. By analyzing the literature and current trends in industrial automation, the article highlights critical key development directions for shaping the future of manufacturing systems focusing on smart, circular, and human-centric solutions using Plug and Produce. [ABSTRACT FROM AUTHOR]

Significant evidence now supports the quality improvement seen in castings that employ naturally pressurized filling systems in their production. However, despite the documented evidence for improvement, these systems have not become ubiquitous. Gray iron, the industry's most prominent alloy, has been particularly slow to accept the shift in system design. The suggested tolerability of gray iron to turbulence, the market's price sensitivity, and the perceived economics of naturally pressurized systems are potential factors that have hindered adoption. However, if, through improved quality, the economic justification for these systems can be made in this adverse setting, there is a merit in their application to all alloys and processes. A sample gray iron part, currently being produced in a foundry environment with known porosity issues, was selected, to which a new system and several basin designs were applied. A mixed method analysis of casting simulations, production trials, and economic and environmental modeling was used to evaluate system performance. The results show a strong association between filling system design and sub-surface porosity. However, no association was found between basin design and porosity occurrence at lower porosity levels. Nevertheless, the economic and environmental modeling highlights that quality-orientated systems yield significant financial and environmental savings for the organization due to reduced scrap levels. These findings imply that quality-orientated system designs should be employed even when cost is the most critical measure. [ABSTRACT FROM AUTHOR]

The paper reports simple yet effective circuits for realizing XOR and XNOR logic operations with polarity, each employing a single current conveyor. The proposed polar logic circuits require four MOS switches and two resistors, besides a single CCII+ (second generation current conveyor), in each case. The new proposed circuits are simulated using 0.18 µm CMOS process parameters with a ± 1.8 V supply voltage and reference DC voltage of 1 V, thus enabling polar logic outputs. The polar output for logic 0 and 1 is in form of − 1 V and + 1 V respectively. The results included in support of the work are promising for future applications of the proposal in design of communication circuits. To facilitate better integration prospects, the proposed circuits are further simplified by removing one of the two used resistors and replacing CCII by CCCII (current controlled current conveyor). The simplified polar XOR gate is also verified through simulations. System design applications are expected to evolve from proposed work. The new proposed circuits are expected to significantly contribute to the advancement of circuit design. [ABSTRACT FROM AUTHOR]

In response to the burgeoning interest in the Metaverse—a virtual reality-driven immersive digital world—this study delves into the pivotal role of AI in shaping its functionalities and elevating user engagement. Focused on recent advancements, prevailing challenges, and potential future developments, our research draws from a comprehensive analysis grounded in meticulous methodology. The study, informed by credible sources including SD, Scopus, IEEE, and WoS, encompasses 846 retrieved studies. Through a rigorous selection process, 54 research papers were identified as relevant, forming the basis for a specific taxonomy of AI in the Metaverse. Our examination spans diverse dimensions of the Metaverse, encompassing augmented reality, virtual reality, mixed reality, Blockchain, Agent Systems, Intelligent NPCs, Societal and Educational Impact, HCI and Systems Design, and Technical Aspects. Emphasizing the necessity of adopting trustworthy AI in the Metaverse, our findings underscore its potential to enhance user experience, safeguard privacy, and promote responsible technology use. This paper not only sheds light on the scholarly interest in the Metaverse but also explores its impact on human behavior, education, societal norms, and community dynamics. Serving as a foundation for future development and responsible implementation of the Metaverse concept, our research identifies and addresses seven open issues, providing indispensable insights for subsequent studies on the integration of AI in the Metaverse. [ABSTRACT FROM AUTHOR]

Offering Product-Service Systems (PSS) becomes an established strategy for companies to increase the provided customer value and ensure their competitiveness. Designing PSS business models, however, remains a major challenge. One reason for this is the fact that PSS business models are characterized by a long-term nature. Decisions made in the development phase must take into account possible scenarios in the operational phase. Risks must already be anticipated in this phase and mitigated with appropriate measures. Another reason for the design phase being a major challenge is the size of the solution space for a possible business model. Developers are faced with a multitude of possible business models and have the challenge of selecting the best one. In this article, a simheuristic optimization approach is developed to test and evaluate PSS business models in the design phase in order to select the best business model configuration beforehand. For optimization, a proprietary evolutionary algorithm is developed and tested. The results validate the suitability of the approach for the design phase and the quality of the algorithm for achieving good results. This could even be transferred to already established PSS. [ABSTRACT FROM AUTHOR]

This paper presents an industrial wireless control and monitoring system for laser cutters and engravers based on computer numerical control (CNC), which aims to improve the overall performance, efficiency, and safety of the CNC machine and its peripherals as an ecosystem by allowing users to operate and maintain it remotely using Wi-Fi. The developed system is implemented through a dynamic hardware module and a multi-OS user application, thereby establishing real-time and simplified communication between the operator and the Laser Cutter/Engraver-CNC ecosystem for remote control and monitoring functions. Further, the system allows for configuring machine settings, and the start or stop of cutting or engraving processes. A Laser Cutter/Engraver-CNC ecosystem prototype was built and used in a test-validated environment to confirm the effectiveness of this system design. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal tool for exploring cellular landscapes across diverse species and tissues. Precise annotation of cell types is essential for understanding these landscapes, relying heavily on empirical knowledge and curated cell marker databases. In this study, we introduce MarkerGeneBERT, a natural language processing (NLP) system designed to extract critical information from the literature regarding species, tissues, cell types, and cell marker genes in the context of single-cell sequencing studies. Leveraging MarkerGeneBERT, we systematically parsed full-text articles from 3702 single-cell sequencing-related studies, yielding a comprehensive collection of 7901 cell markers representing 1606 cell types across 425 human tissues/subtissues, and 8223 cell markers representing 1674 cell types across 482 mouse tissues/subtissues. Comparative analysis against manually curated databases demonstrated that our approach achieved 76% completeness and 75% accuracy, while also unveiling 89 cell types and 183 marker genes absent from existing databases. Furthermore, we successfully applied the compiled brain tissue marker gene list from MarkerGeneBERT to annotate scRNA-seq data, yielding results consistent with original studies. Conclusions: Our findings underscore the efficacy of NLP-based methods in expediting and augmenting the annotation and interpretation of scRNA-seq data, providing a systematic demonstration of the transformative potential of this approach. The 27323 manual reviewed sentences for training MarkerGeneBERT and the source code are hosted at https://github.com/chengpeng1116/MarkerGeneBERT. [ABSTRACT FROM AUTHOR]

In order to address the pressing issue of detecting and identifying counterfeit agricultural products, which cause significant economic losses, this paper presents a mobile-based system designed to identify rare agricultural products for consumers. To tackle the problem of identifying fake agricultural products, we propose a novel self-monitoring deep learning model named iCZSL. This model combines the Zero-shot segmentation model and Graph embeddings with the calculation of edit distance using a candidate list obtained from a dictionary. To evaluate the effectiveness of our approach, we conduct experiments on a dataset consisting of different types of potatoes, including chinese potatoes, dalat potatoes (a rare agricultural product), and other varieties, referred to as the TLU-states dataset. The experimental results demonstrate the superiority of our proposed method over competing baselines in terms of both quantitative and qualitative performance measures. This system holds promising potential for effectively detecting and differentiating rare agricultural products, thereby mitigating economic losses caused by counterfeit items. [ABSTRACT FROM AUTHOR]

In the design process of large adaptive mirrors numerical simulations represent the first step to evaluate the system design compliance in terms of performance, stability and robustness. For the next generation of Extremely Large Telescopes increased system dimensions and bandwidths lead to the need of modeling not only the deformable mirror alone, but also all the system supporting structure or even the full telescope. The capability to perform the simulations with an acceptable amount of time and computational resources is highly dependent on finding appropriate methods to reduce the size of the resulting dynamic models. In this paper we present a framework developed together with the company Microgate to create a reduced order structural model of a large adaptive mirror as a preprocessing step to the control system simulations. The reduced dynamic model is then combined with the remaining system components allowing to simulate the full adaptive mirror in a computationally efficient way. We analyze the feasibility of our reduced models for Microgate's prototype of the adaptive mirror of the Giant Magellan Telescope. [ABSTRACT FROM AUTHOR]

Assessing pain in newborns in the NICU is crucial due to their frequent exposure to painful stimuli, yet it's challenging due to the subjective nature of current methods. This study aimed to evaluate the effectiveness of an AI system designed for automatic facial recognition by comparing its performance with the expert opinion of health care provider. This is a secondary analysis from an eye-tracking study, assessing neonatal pain evaluations by healthcare professionals. The performance of AI software, FaceReader 9, was compared to experts' evaluations using a visual-analog scale, focusing on identifying specific facial action units associated with different pain levels. The study found significant differences in AI-generated metrics—arousal and valence—across three stimulus types: non-noxious thermal, short-noxious, and prolonged-noxious, with p-values below 0.001. A strong correlation (r = 0.84, p ≤.001) was observed between AI metrics and expert ratings. Eleven facial action units were identified as relevant to describe neonatal pain. The findings highlight the AI system's potential in accurately detecting and analyzing newborn facial expressions in response to varying pain intensities, demonstrating a significant correlation with healthcare professionals' assessments. This suggests that AI technology could enhance objective pain assessment in neonates. [ABSTRACT FROM AUTHOR]

Complex practical system designs pose significant challenges from a research perspective, often resulting in high computational requirements for analysis. Model order reduction (MOR) is a valuable technique to address this issue. This study provides a comprehensive review of the literature on MOR, focusing specifically on high-dimensional complex systems. It examines the fundamental theories and limitations of established MOR methods, including the Factor division method, Pade approximation (PA) method, Stability equation (SE) method, Differentiation method, and Routh approximation (RA) method. The study also investigates the frequency domain approach for obtaining a reduced-order model (ROM). Among the MOR methods, the PA method is a widely studied and practical approach that aims to retain the crucial dynamics of a high-dimensional complex system. This survey presents a detailed discussion of the PA method for obtaining the ROM. Additionally, six test systems are analyzed to compare the step and frequency responses generated by various MOR strategies. The integral square error criterion is used to assess the effectiveness of the reduction procedures. Finally, the study proposes a new system abatement method based on Atomic Orbital Search (AOS) optimization for obtaining the ROM of large-scale linear time-invariant (LTI) systems and designing controllers based on the reduced order model. [ABSTRACT FROM AUTHOR]

X-ray imaging can be efficiently used for high-throughput in-line inspection of industrial products. However, designing a system that satisfies industrial requirements and achieves high accuracy is a challenging problem. The effect of many system settings is application-specific and difficult to predict in advance. Consequently, the system is often configured using empirical rules and visual observations. The performance of the resulting system is characterized by extensive experimental testing. We propose to use computational methods to substitute real measurements with generated images corresponding to the same experimental settings. With this approach, it is possible to observe the influence of experimental settings on a large amount of data and to make a prediction of the system performance faster than with conventional methods. We argue that a high accuracy of the image generator may be unnecessary for an accurate performance prediction. We propose a quantitative methodology to characterize the quality of the generation model using Probability of Detection curves. The proposed approach can be adapted to various applications and we demonstrate it on the poultry inspection problem. We show how a calibrated image generation model can be used to quantitatively evaluate the effect of the X-ray exposure time on the performance of the inspection system. [ABSTRACT FROM AUTHOR]

One of the major study areas in VLSI system design is the creation of an area-efficient, high-speed adder. So this paper focused on an effective adder design as an Area Effective Novel Sum Generator (AENSG) to make it suitable for real-time applications. The major operations involved in this adder are Brent Kung addition, group kill and generate (KG) signal generation, Kill Generate Propagate (KGP) signal generation, and various sum generation categories. In the sum-generating block, AENSG is utilized to minimize the space by a reduction in the number of logic gates. Xilinx ISE 13.1 is used to synthesize and validate the suggested adder design. The 64-bit AENSG adder has 37.98%, 9.09% lesser number of logic gates and 34.81%, 28.40% lesser delay when compared with PPF/CSSA_4 and hybrid adder respectively. [ABSTRACT FROM AUTHOR]

Landslides pose significant threats to communities and infrastructure worldwide, necessitating the development of effective hazard and exposure reduction strategies to mitigate the potential risks. In particular, debris flows can be highly destructive landslides. The paper deals with an integrated monitoring system designed both to further validate an existing debris flow inception and propagation methodology developed by the authors and to fine-tune the landslide inception and warning model to be used in an early warning system. The design of the integrated monitoring system, the definition of the acquisition time of instruments, and the processing of the measured data were based on studies performed in the study area of Favazzina (Italy), which is affected by debris flows that are very difficult to monitor. The proposed approach used to design the integrated monitoring system may serve as a useful methodological tool to be adopted in similar geological and geotechnical contexts within the framework of risk mitigation strategies. [ABSTRACT FROM AUTHOR]

In this study, aniline-containing wastewater was treated using the microbial flora present in activated sludge and a membrane separation system reactor. A swing-type membrane bioreactor (S-MBR) system with swinging membrane components was designed to control membrane fouling during reactor operation. Furthermore, the new system was tested and compared with the aerated membrane bioreactor (A-MBR) and the common membrane bioreactor (C-MBR). The study revealed the following findings: 1) S-MBR exhibited a high aniline removal rate, with an efficiency of 97% in removing aniline from wastewater with a concentration of 600 mg/L. 2) S-MBR demonstrated a high membrane flux, reaching a maximum of 97 L/m2 h bar, whereas C-MBR and A-MBR achieved 49 L/m2 h bar and 87 L/m2 h bar, respectively. 3) S-MBR proved to be energy-efficient, with lower energy consumption during aniline removal (0.032 kW/kg C6H7N) compared to C-MBR (0.051 kW/kg C6H7N) and A-MBR (0.039 kW/kg C6H7N). These observations suggest that the S-MBR system designed in this study is comparatively more effective, feasible, and economical. [ABSTRACT FROM AUTHOR]

Our prototype system designed for clinical data acquisition and recording of studies is a novel electronic data capture (EDC) software for simple and lightweight data capture in clinical research. Existing software tools are either costly or suffer from very limited features. To overcome these shortcomings, we designed an EDC software together with a mobile client. We aimed at making it easy to set-up, modifiable, scalable and thereby facilitating research. We wrote the software in R using a modular approach and implemented existing data standards along with a meta data driven interface and database structure. The prototype is an adaptable open-source software, which can be installed locally or in the cloud without advanced IT-knowledge. A mobile web interface and progressive web app for mobile use and desktop computers is added. We show the software's capability, by demonstrating four clinical studies with over 1600 participants and 679 variables per participant. We delineate a simple deployment approach for a server-installation and indicate further use-cases. The software is available under the MIT open-source license. Conclusively the software is versatile, easily deployable, highly modifiable, and extremely scalable for clinical studies. As an open-source R-software it is accessible, open to community-driven development and improvement in the future. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth discussion on the comprehensive requirements analysis, design implementation, algorithm optimization, and experimental evaluation of an electric power marketing information system, aiming to build a modern information system that is efficient, secure, and user-friendly. In the requirements analysis phase, the importance of business process optimization, data management analysis, security compliance, system integration and scalability is emphasized, while the diversified needs of end customers are considered. For the design and implementation part, the system architecture is based on microservices and cloud-native technologies to ensure high performance and security; and modularized development is achieved through Spring Boot, Vue.js and other technology stacks. For algorithm optimization, LSTM is used for power demand prediction and anomaly detection by combining integrated learning and self-encoder, which improves the prediction accuracy and anomaly identification capability. Experimental evaluation shows that the system demonstrates good performance, security and scalability in cloud computing environment, and the cost-effectiveness is significantly better than traditional deployment. [ABSTRACT FROM AUTHOR]

DEPS (design problem specification) is a new modeling language designed to pose and solve system design problems. DEPS addresses problems of sizing, configuration, resource allocation and of architecture generation for systems. Unlike system modeling languages, which are dedicated to the representation of a defined system for evaluation or analysis, we propose a problem modeling language for representing the design problem with a view to its automatic resolution. Compared with other declarative problem modeling languages, DEPS is a declarative structured and property-based language that combines structural modeling features specific to object-oriented languages with problem specification features from constraint programming. The mathematical nature of the problems is described by formal properties encapsulated in models organized according to the architecture of the studied system. The main features of the language are presented in details and are illustrated with examples in different domains. An integrated modeling and solving environment called DEPS Studio allows the designer to express its models in DEPS, to compile the models and to compute automatically the solutions. The validation of the approach is done through two case studies. Finally, we will conclude with the studies and developments in progress which will be integrated into the next version of DEPS Studio. [ABSTRACT FROM AUTHOR]

Today, there is an ever-increasing number of workloads pushed and executed on the Cloud. Data center operators and Cloud providers have embraced application co-location and multi-tenancy as first-class system design concerns to effectively serve and manage these huge computational demands. In addition, the continuous advancements in the computers' hardware technology have made it possible to seamlessly leverage heterogeneous pools of physical machines in data center environments. Even though current modern Cloud schedulers and orchestrators adopt application-aware policies to achieve automation of time-consuming management tasks at scale, e.g., resource provisioning, they still rely on coarse-grained system metrics, such as CPU and/or memory utilization to place incoming applications, thus, not considering (1) interference effects that are provoked by co-located tasks, and (2) the impact on performance caused by the diversity of heterogeneous systems' characteristics. The lack of such knowledge in existing state-of-the-art orchestration solutions results in their inability to perform efficient allocations, which negatively impacts the overall latency distribution delivered by the infrastructure. In this paper, to alleviate this inefficiency, we present a machine learning (ML) based Cloud orchestration extension that takes into account both resource interference and heterogeneity. The framework adequately schedules data-analytics applications on a pool of heterogeneous resources. We evaluate our proposed solution on different application mixes and co-location scenarios. We show that the proposed framework improves the tail latency of the distribution of the deployed applications by up to 3.6x compared to the state-of-the-art Kubernetes scheduler. [ABSTRACT FROM AUTHOR]

In this paper, we propose an integrated sensing and communication (ISAC) base station (BS) system designed for applications by multiple users in complex offshore environments. We begin by modeling the communication and detection functions of the dual-function BS in the corresponding maritime channel environment, as well as the sea clutter influence of multiple-input-multiple-output (MIMO) radar in the marine environment. To evaluate the performance of our dual-function system, we measure the communication reachable rate and sensing signal-clutter-noise ratio (SCNR) of the offshore system separately for communication and radar functions, respectively. We then develop two optimization schemes to maximize the system's performance under power constraint of radar SCNR and communication rate constraint, respectively, and solve them using convex optimization methods such as the successive convex approximation (SCA) method. We also model the beamforming and provide simulation results that demonstrate the effectiveness of our proposed maritime integrated sensing and communication (MISAC) system. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technology is a revolutionary paradigm that aims to transform physical objects into connected devices that can be accessed ubiquitously. Due to heterogeneous manufacturers and supported infrastructures, IoT technology introduces a wide range of communication protocols. Device interoperability is a significant problem when two or more IoT applications are developed using different application-layer protocols. Conspicuously, a formalization of the system design is proposed using the Model Driven Architecture (MDA) approach for IoT interoperability. Specifically, a comprehensive MDA-based approach is proposed that reduces the processing time and effort to develop IoT interoperable systems. Metamodel, UML profile, and transformation rules are developed to make heterogeneous application-layer protocols interoperable using devices as intermediates. The objective is to elucidate the extent to which various layers of the MDA framework are encompassed. Furthermore, the smart home-based power consumption analysis environment is presented as the application scenario. For validation purposes, the proposed model is simulated using a challenging dataset obtained from an online UCI repository. Results depict that the proposed technique is more effective as compared to state-of-the-art techniques in terms of Temporal Delay (109.68s), Data Classification Accuracy (92.06%), Statistical Efficiency (92.36%), Reliability (86.23%), and Stability (71.23%). [ABSTRACT FROM AUTHOR]

In this paper, we contribute to research on enterprise artificial intelligence (AI), specifically to organizations improving the customer experiences and their internal processes through using the type of AI called machine learning (ML). Many organizations are struggling to get enough value from their AI efforts, and part of this is related to the area of explainability. The need for explainability is especially high in what is called black-box ML models, where decisions are made without anyone understanding how an AI reached a particular decision. This opaqueness creates a user need for explanations. Therefore, researchers and designers create different versions of so-called eXplainable AI (XAI). However, the demands for XAI can reduce the accuracy of the predictions the AI makes, which can reduce the perceived usefulness of the AI solution, which, in turn, reduces the interest in designing the organizational task structure to benefit from the AI solution. Therefore, it is important to ensure that the need for XAI is as low as possible. In this paper, we demonstrate how to achieve this by optimizing the task structure according to sociotechnical systems design principles. Our theoretical contribution is to the underexplored field of the intersection of AI design and organizational design. We find that explainability goals can be divided into two groups, pattern goals and experience goals, and that this division is helpful when defining the design process and the task structure that the AI solution will be used in. Our practical contribution is for AI designers who include organizational designers in their teams, and for organizational designers who answer that challenge. [ABSTRACT FROM AUTHOR]

Power system state estimation (PSSE) is a control center application that comprises a collection of algorithms aimed at providing essential information about the current operating condition of the power grid. As such, PSSE plays a vital role in the real-time operation of power systems. Accuracy and reliability of the estimator are closely connected to both quality and quantity of the real-time data that feed it. Therefore, such applications can benefit from functions that pre-filter the dataset, removing obvious spurious measurements or complementing it with reliable information. Moreover, some functionalities intended to analyze the dataset are desirable, since they indicate if state estimation can be performed with the currently available measurement set and if its vulnerabilities can be identified. Such analysis tools can assist in the design of measurement sets, or strengthen an existing one. They constitute preceding steps for PSSE and have drawn growing interest in recent years, with the evolution of computational techniques and the advent of new technologies. This paper provides a review of the literature on such preceding steps, namely pre-filtering, observability analysis, redundancy/criticality analysis, and metering system design, along with a market evaluation of existing solutions, with main focus on transmission systems. [ABSTRACT FROM AUTHOR]

This study provides a detailed analysis of bidirectional inductive power transfer (IPT) systems using LCC-LCC compensation topology. It aims to contribute to the field by offering a unified framework that includes mathematical models for both time and frequency domains, accounting for the resistance of all passive components. This research introduces a concise state-space model with voltage inputs and develops conductance equations related to transferred power and losses. These conductances are used to determine the inductor currents, capacitor voltages, active and reactive powers, and efficiency relative to the primary and secondary voltages. The effectiveness of the proposed models is demonstrated through a comparison with simulation results based on a 1.5-kW LCC-LCC IPT system design, showing precise agreement. [ABSTRACT FROM AUTHOR]

With the development of simulation technology, more and more manufacturers have begun to use the digital twin to design workshops and factories. For these design scenarios under real-time interaction requirements with an excessive amount of model data, if the rendering is stuck, it will reduce the work efficiency. It is a key enabling technology to simplify and switch the geometry models with different resolutions, according to the distance of the viewpoint or the motion state to reduce the computational complexity. Existing model simplification methods emphasize the universality and efficiency under various scenarios, while the simplification performance in the 3D geometry models of industrial mechanisms is poor. This paper proposes a rapid simplification approach to the 3D geometry model of mechanisms in the digital twins-driven manufacturing system design context. A novel Vertex Saliency-oriented Classified Edge Collapse (VS-CEC) algorithm is proposed to simplify the shape feature of the 3D geometry model of mechanisms. It especially emphasizes solving the sharp shape preservation issues in the mechanical design scenario rather than a universal things design scenario. A vertex saliency factor is defined and integrated with the region boundary information obtained from the processing of detailed features to ensure visual fidelity as well as shape preservation such as sharp edges. Experiments show that this approach reduces the data model complexity more reasonably to speed up the rendering. It ensures that the digital twin model interacts quickly with the physical manufacturing system, and thus realizes the low-latency visual effect of cyber-physical synchronization. [ABSTRACT FROM AUTHOR]

Grinding is a prevalent metal processing method extensively employed in industrial production. However, the high temperatures generated during the grinding process can elevate the temperature of the grinding motorized spindle, subsequently impacting its performance and lifespan. Therefore, the design and research of the grinding motorized spindle cooling system become particularly important. In order to study the thermal characteristics of the grinding motorized spindle, this paper studies the two heating paths of the grinding motorized spindle based on the heat source, including the heat loss of the motor and the friction heat of the bearing, and establishes a fluid heat transfer model. Through cooling system design and thermal characteristic simulation analysis, the U-shaped cooling grinding motorized spindle is determined. The experiment found that the spindle temperature rise results are consistent with the numerical simulation results. Although the vibration amplitude will increase with the increase of the rotational speed, it will always remain within a stable range, which verifies the rationality of the cooling system design and provides ideas and methods for the design and optimization of the cooling system of the grinding motorized spindle in the future. [ABSTRACT FROM AUTHOR]

This article introduces and formalizes a novel stochastic method that combines inverse simulation with the theory of generalized Polynomial Chaos (gPC) to solve and study inverse problems under uncertainty in energy system design applications. The method is particularly relevant to design tasks where only a deterministic forward model of a physical system is available, in which a target design quantity is an input to the model that cannot be obtained directly, but can be quantified reversely via the outputs of the model. In this scenario, the proposed method offers an analytical and direct approach to invert such system models. The method puts emphasis on user-friendliness, as it enables its users to conduct the inverse simulation under uncertainty directly in the gPC domain by redefining basic algebra operations for computations. Moreover, the method incorporates an optimization-based approach to integrate supplementary constraints on stochastic quantities. This feature enables the solution of inverse problems bounding the statistical moments of stochastic system variables. The authors exemplify the application of the proposed method with proof-of-concept tests in energy system design, specifically performing uncertainty quantification and sensitivity analysis for a Multi-Energy System (MES). The findings demonstrate the high accuracy of the method as well as clear advantages over conventional sampling-based methods when dealing with a small number of stochastic variables in a system or model. However, the case studies also highlight the current limitations of the proposed method such as slow execution speed due to the optimization-based approach and the challenges associated with, for example, the curse of dimensionality in gPC. [ABSTRACT FROM AUTHOR]

We present the development of a dual-detector system designed for investigating the spectral shape of forbidden non-unique beta decays. Two PbMoO 4 scintillating crystals were carefully prepared for heat and light detection at milli-Kelvin (mK) temperatures. Notably, one crystal was synthesized using archaeological lead, while the other was composed of natural modern lead. The significance of employing two crystals lies in their identical dimensions and proximity, resulting in similar environmental background exposure. Their distinct internal radioactivities, particularly associated with 210 Pb, introduce a distinguishing factor between the spectra measured in the two detectors. Our detection method includes achieving clear particle identification through the relative amplitudes of light and heat signals for both crystals. This report compares the electron-induced spectra within energy regions both below and above the endpoint of 210 Bi beta decay. This comparative study provides valuable insights into an exact measurement of the 210 Bi decay spectrum, forbidden non-unique beta decay. [ABSTRACT FROM AUTHOR]

Automatic seismic data interpretation is a significant method in the exploration of geophysics. Complexities of the subsurface structures and the subsurface wave propagation media, make the decision-making process difficult in seismic data interpretation. Nevertheless, the extent of related knowledge and using the expert system method in seismic data interpretation can mitigate this problem. An expert system is a knowledge-based system that applies its knowledge in a complex and specific area and acts as an expert end-user consultant. This study investigates the design of an ANFIS expert system for mud diapirs detection with seismic data analysis in Gorgan plain. This method was applied to seismic attributes from a complex geological mud diapir bearing structure from south of the Caspian Sea. The south of the Caspian Sea is one of the richest area as petroleum reserves, and the Gorgan plain has various mud diapirs, which act as indicators of hydrocarbon reservoirs. The expert system design process to identify mud diapirs on seismic sections was modeled in two approaches including manual and automatic seismic data interpretation. In the first approach, the experience of the expert was collected by manual interpretation of training data and used to create a knowledge base and inference of the expert system in the second approach. The validation verified the accuracy of this method with an average accuracy of 90.1% according to using minimum knowledge to develop a knowledge base of the designed ANFIS expert system. [ABSTRACT FROM AUTHOR]

Amine-functionalized adsorbents offer substantial potential for CO2 capture owing to their selectivity and diverse application scenarios. However, their effectiveness is hindered by low efficiency and unstable cyclic performance. Here we introduce an amine-support system designed to achieve efficient and stable CO2 capture. Through atom-level design, each polyethyleneimine (PEI) molecule is precisely impregnated into the cage-like pore of MIL–101(Cr), forming stable composites via strong coordination with unsaturated Cr acid sites within the crystal lattice. The resulting adsorbent demonstrates a low regeneration energy (39.6 kJ/molCO2), excellent cyclic stability (0.18% decay per cycle under dry CO2 regeneration), high CO2 adsorption capacity (4.0 mmol/g), and rapid adsorption kinetics (15 min for saturation at 30 °C). These properties stem from the unique electron-level interaction between the amine and the support, effectively preventing carbamate products' dehydration. This work presents a feasible and promising cost-effective and sustainable CO2 capture strategy. An amine-support system has been introduced through atom-level design, achieving low regeneration energy, excellent cyclic stability, high capacity, and rapid kinetics, stemming from electron-level interactions preventing carbamate dehydration. [ABSTRACT FROM AUTHOR]

One-dimensional (1D) olivine iron phosphate (FePO4) is widely proposed for electrochemical lithium (Li) extraction from dilute water sources, however, significant variations in Li selectivity were observed for particles with different physical attributes. Understanding how particle features influence Li and sodium (Na) co-intercalation is crucial for system design and enhancing Li selectivity. Here, we investigate a series of FePO4 particles with various features and revealed the importance of harnessing kinetic and chemo-mechanical barrier difference between lithiation and sodiation to promote selectivity. The thermodynamic preference of FePO4 provides baseline of selectivity while the particle features are critical to induce different kinetic pathways and barriers, resulting in different Li to Na selectivity from 6.2 × 102 to 2.3 × 104. Importantly, we categorize the FePO4 particles into two groups based on their distinctly paired phase evolutions upon lithiation and sodiation, and generate quantitative correlation maps among Li preference, morphological features, and electrochemical properties. By selecting FePO4 particles with specific features, we demonstrate fast (636 mA/g) Li extraction from a high Li source (1: 100 Li to Na) with (96.6 ± 0.2)% purity, and high selectivity (2.3 × 104) from a low Li source (1: 1000 Li to Na) with (95.8 ± 0.3)% purity in a single step. Olivine iron phosphate (FePO4) is widely proposed for electrochemical lithium extraction, but particles with different physical attributes demonstrate varying Li preferences. Here, the authors characterize the electrochemical lithiation and sodiation behavior of a series of FePO4 particles with different morphology to identify critical features that enhance Li selectivity. [ABSTRACT FROM AUTHOR]

The proliferation of smart conurbations entails an efficient system design for managing all the crowds in public places. Multitude controlling procedures are carried out for controlling compact areas where more number of peoples is present at several groups. Therefore for controlling purpose the proposed method aims to design a pictorial representation using Internet of Things (IoT). The process is carried out by taking images and then organizing it using switching techniques in the presence of square boxes where entire populace is identified on real time experimentations. For processing and controlling the occurrence a separate architecture is designed with analytical equivalences where all data set is stored in cloud platform. Further the incorporation of system model is carried out using Switching Based Algorithm (SBA) which adds more number of columns even for high population cases. In order to verify the effectiveness of proposed model five scenarios are considered with performance evaluation metrics for SBA and all the test results provides best optimal results. Moreover the projected model is improved with an average percentage of 83 as compared to existing models. [ABSTRACT FROM AUTHOR]

Instrument panels of modern aircrafts are created based on the concept of glass cockpit. This new interface philosophy improves the perception of important flight information by displaying it on a single multi-function display. This paper considers problems that arise when developing a certified cockpit display visualization system designed for operation on civil aircrafts under the Russian real-time operating system JetOS. The paper presents several algorithmic solutions that make it possible to achieve acceptable rendering speed. In particular, a solution to the problem of rigid scheduling of operating system partitions is described in detail. This solution makes it possible to overcome the degradation of rendering speed. Some directions for further research are outlined. [ABSTRACT FROM AUTHOR]

Securing critical infrastructure, particularly nuclear power plants, against emerging cyber threats necessitates innovative cybersecurity approaches. This research introduces FusionGuard, a hybrid machine learning-based anomaly detection system designed for early warnings of ransomware and spyware intrusions within nuclear power plant systems. Meticulously tailored to the unique characteristics of nuclear power plant networks, FusionGuard leverages diverse datasets encompassing normal operational behavior and historical threat data. Through cutting-edge machine learning algorithms, the system dynamically adapts to the network's baseline behavior, effectively identifying deviations indicative of ransomware or spyware activities. Rigorous experimentation and validation using real-world data and simulated attack scenarios affirm FusionGuard's proficiency in detecting anomalous behavior with remarkable accuracy and minimal false positives. The research also explores the system's scalability and adaptability to evolving attack vectors, fortifying the cybersecurity posture of nuclear power plant systems in a dynamic threat landscape. In summary, FusionGuard promises to fortify the security of nuclear power plant systems against ransomware and spyware threats by capitalizing on machine learning and anomaly detection. Serving as a sentinel, the system issues timely alerts and enables proactive responses, contributing substantively to the ongoing discourse on protecting essential systems in high-stakes environments. [ABSTRACT FROM AUTHOR]

Bike sharing systems gain traction worldwide, but previous research pay less attention to the more detailed operating characteristics at station level. This study aims to fill this void in the literature by looking into the stations' performance with considering systemic intervention and user-driven usage. Methodologically, an innovative approach that captures the underlying relevance of trip records is proposed firstly to identify the bicycle-based operating states in its lifecycle, such as being redistributed, parked, or used. From bike to station, all the bicycle-based operating status information can be linked to associated stations, consequently, station vitality and station pattern are refined into stations' operating performance. In addition to rational classification and discussion of operating features, this study has explored the impact of surrounding built environment on these specific operating features instead of simple trip intensity. To test the proposed methodology, trip record data from the bike sharing system of Boston in 2019 is used. The results indicate that user-driven and manual-scheduling bike movements are all particularly relevant to keeping stations' sustainable daily operation, but vary across the stations in their ratio. In terms of station vitality and station pattern, some stations would embody the nature of high-output-scheduling, low-bike-turnover, or high-input-scheduling relative to the baseline scenario of operating performance. Heterogeneity of stations in operating is also proved to be caused by the surrounding built environment. The outcomes and methodological framework would facilitate the assessment of bike sharing system operating state at station level, as well as instilling new insights into bike sharing system design. [ABSTRACT FROM AUTHOR]

This article presents survey results examining the design and implementation of recordkeeping systems for organizations supporting conflict-affected individuals displaced from their homes, lands, and property (HLP). The study highlights the potential of digital systems to overcome limitations of legacy HLP recordkeeping, but also addresses the risks associated with technology in vulnerable contexts. Emphasizing the connection between records and personal identity, the authors advocate for recordkeeping systems that consider the needs, rights, and dignity of displaced people. Drawing on participatory and rights-based approaches, a framework for supporting HLP claims through system design is proposed. The findings offer insights into tailoring such an approach for conflict-affected contexts, stressing the importance of technological upgrades and careful design considerations to prevent harm. The article aims to contribute to the development of effective recordkeeping systems for displaced populations, calling for further research and collaboration in this field. [ABSTRACT FROM AUTHOR]

Flexibility is a critical process competency for delivering mass customized services. Coproduction systems involve the customer as a productive resource alongside the firm's resources to co-create this customized service. This study analyses the performance and applicability of resource flexibility and service modularity for the design of such coproduction systems. Particularly, it examines the role of level of coproduction, positioning of the customer performed activities in the process flow, and the interplay of level of coproduction with other system parameters in selecting the design alternative. We employ a suite of simulation modelling tools towards this purpose. Contrary to popular belief, the results indicate that the dominance of the resource flexibility strategy is not absolute for coproductive systems, especially when costs are considered. Service modularity strategy is preferred for systems with higher levels of coproduction and when customer-performed tasks are positioned in the middle of the process flow. Our sensitivity analysis shows that not all service providers implementing a resource flexibility strategy can respond in the same way to changes in system conditions. When resource costs are considered, there exists a zone where the sensitivity demonstrates a perverse effect. Furthermore, our results show that pure strategies are superior to mixing components from the two alternatives and we characterize their preference in the form of a single threshold policy. The insights from this study will also be of interest to practitioners designing and managing coproductive systems. [ABSTRACT FROM AUTHOR]

The growing expansion of the manufacturing sector, particularly in Mexico, has revealed a spectrum of nearshoring opportunities yet is paralleled by a discernible void in educational tools for various stakeholders, such as engineers, students, and decision-makers. This paper introduces a state-of-the-art framework, incorporating virtual reality (VR) and artificial intelligence (AI) to metamorphose the pedagogy of advanced manufacturing systems. Through a case study focused on the design, production, and evaluation of a robotic platform, the framework endeavors to offer an exhaustive educational experience via an interactive VR environment, encapsulating (1) Robotic platform system design and modeling, enabling users to immerse themselves in the design and simulation of robotic platforms under varied conditions; (2) Virtual manufacturing company, presenting a detailed virtual manufacturing setup to enhance users' comprehension of manufacturing processes and systems, and problem-solving in realistic settings; and (3) Product evaluation, wherein users employ VR to meticulously assess the robotic platform, ensuring optimal functionality and customer satisfaction. This innovative framework melds theoretical acumen with practical application in advanced manufacturing, preparing entities to navigate Mexico's manufacturing sector's vibrant and competitive nearshoring landscape. It creates an immersive environment for understanding modern manufacturing challenges, fostering Mexico's manufacturing sector growth, and maximizing nearshoring opportunities for stakeholders. [ABSTRACT FROM AUTHOR]

The weather has a detrimental effect on outdoor vision systems and raises the probability of traffic crashes and road accidents. The scattering of atmospheric particles degrades outdoor images captured in poor weather conditions such as haze and fog. The reduced visibility has a significant impact on driving assistance systems designed for automatic vehicles. As a result, clear visibility is critical for outdoor computer vision systems. Image dehazing is one of the ill-posed problems because evaluating transmission depth is challenging. It is essential to estimate transmission depth with the greatest degree of accuracy. In order to estimate or optimize the transmission depth, this paper employs the adaptive Gaussian notch filter and the concept of gamma correction to recover the final scene radiance. The results of the experiments are assessed and compared both quantitatively and qualitatively with state-of-the-art techniques. The experimental results demonstrate that the proposed indicators ensure high consistency in qualitative and quantitative evaluation using six performance metrics: two blind assessment indicators (e, r), contrast gain (C gain) , visual contrast measure (VCM), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and probability. [ABSTRACT FROM AUTHOR]

The performance of a narrowband active noise control (NANC) system can be significantly degraded due to the frequency mismatch (FM). In this paper, the statistical performance of a typical FxLMS-based NANC system in the presence of FM is analyzed in detail. Difference equations governing the system dynamics and closed-form steady-state mean-square error expressions are derived and discussed. The stochastic analysis results reveal that the FM introduces small troublesome sinusoids into the NANC system. The controller has to track these sinusoids to minimize the residual noise, which leads to a serious performance deterioration. As a by-product, the optimal step sizes that minimize the effect of a relatively small FM are also derived. The findings significantly enrich our understanding of the stochastic behavior of the FxLMS algorithm in the presence of FM and also provide some useful information for NANC system design. Extensive simulations are conducted to confirm the validity of the analytical findings. [ABSTRACT FROM AUTHOR]

The adjoint method provides an efficient way to compute sensitivities for system models with a large number of inputs. However, implementing the adjoint method requires significant effort that limits its use. The effort is exacerbated in large-scale multidisciplinary design optimization. We propose the adoption of a three-stage compiler as the method for constructing computational models for large-scale multidisciplinary design optimization to enable accurate and efficient adjoint sensitivity analysis. We develop a new modeling language called the Computational System Design Language that provides an appropriate input to the compiler front end that works well with multidisciplinary models. This paper describes the three-stage compiler methodology and the Computational System Design Language. The proposed solution uses a graph representation of the numerical model to automatically generate a computational model that computes adjoint-based sensitivities for use within an optimization framework. For two engineering models, this approach reduces the amount of user code by a factor of approximately two compared to their original implementations, without a measurable increase in computation time. This paper also includes a best-case complexity analysis that is built into the compiler implementation to allow users to estimate the memory required to evaluate a computational model and its derivatives, which is independent of the compiler back end that ultimately generates the computational model. Future compiler implementations are expected to approach the theoretical best-case memory cost and improve run time performance for both model evaluation and derivative computation. [ABSTRACT FROM AUTHOR]

To overcome the limitations of conventional solar simulators, which suffer from inadequate irradiance adjustment range, poor adjustment accuracy, serious arc displacement of the xenon lamp at the low power supply, and insufficient consideration of irradiance uniformity, we have designed an electrically controlled irradiance continuous adjustment device. Our approach involves a theoretical analysis of the radiation distribution on the attenuation diaphragm using the annular aperture method of a condenser, enabling modulation of the radiation distribution of the converging light path using a sector-shaped mesh structure with carefully calculated and simulated structural parameters.We then determined the structural scheme of the electrically controlled irradiance continuous adjustment device, which incorporates an attenuation diaphragm, a dimming dial, rolling balls, a cage, pinions, a motor fixation plate, a triangular bracket, and an electric control system. Key components were designed and optimized using finite element analysis, and the control system was also studied.In the final step, we tested the performance of the electrically controlled irradiance continuous adjustment device. Experimental results demonstrated that the irradiance of the solar simulator can be adjusted within the range of 23.7–1315.5 W/m2, with a maximum relative error of the attenuation ratio within ± 0.2% and irradiance non-uniformity within ± 3%. Our device has achieved electrically controlled irradiance continuous adjustment, thereby expanding the application range of the solar simulator and improving its overall performance. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) play a critical role in the future development of Intelligent Transportation Systems (ITS). These networks facilitate communication between vehicles and roadside infrastructure, establishing a dynamic network capable of sharing and processing traffic data. By harnessing this data, a comprehensive understanding of traffic conditions can be achieved, ultimately improving road safety and efficiency. VANETs have the potential to warn drivers about potential hazards, suggest optimal routes, and coordinate traffic signals. However, the current system design poses a vulnerability where a vehicle can acquire multiple identities, allowing it to launch a Sybil attack by impersonating multiple vehicles. In this attack, Sybil (or fake) vehicles generate and report false data, leading to fabricated congestion reports and corrupting traffic management data. To address this issue, this research proposes a novel Sybil attack detection scheme that leverages Verifiable Delay Functions (VDFs) and location data. The proposed scheme utilizes VDFs iteratively computed by vehicles throughout their journeys, forming a VDF chain where the included data is immutable. A vehicle obtains a signature on its recent VDF state from nearby Roadside Units (RSUs) and other vehicles and incorporates these signatures into its VDF chain. The inclusion of signatures in the VDF chains is time-bound and can't be altered later. Essentially, the VDF chain serves as an immutable storage mechanism for each vehicle. Interactions between vehicles involve the exchange of signatures on VDF states, and these interactions, when compiled in a VDF chain, constitute a vehicle's trajectory. By analyzing these trajectories, we can effectively detect Sybil trajectories. Unlike existing methods that solely rely on vehicle-to-RSU interactions, resulting in high false positive rates, our approach introduces vehicle-to-vehicle interactions using VDF chains, thereby increasing the detection rate. Extensive experiments and simulations are conducted to evaluate the proposed scheme's performance in detection. The results demonstrate that our approach can accurately detect Sybil attacks while achieving low rates of false negatives and false positives when compared to existing models. [ABSTRACT FROM AUTHOR]

The main objective of this research study is to improve the performance of a standalone hybrid power system (SHPS) that consists of photovoltaic modules (PVMs), wind turbines (WTs), battery system (BS), and diesel engine (DE). The emphasis is on optimizing the system's design by incorporating demand response strategies (DRSs). Incorporating these strategies into the system can enhance system performance, stability, and profitability while also reducing the capacity of SHPS components and, consequently, lowering consumers' bills. To achieve this objective, the sizing model incorporates a novel indicator called the load variation factor (LVF). This paper assesses and contrasts various scenarios, including SHPS without DRS, with DRS, and with DRS but no DE. In this article, interruptible/curtailable (I/C) as one of the DRSs is incorporated into the model used for sizing issues. A newly developed optimization algorithm called the mountain gazelle optimizer (MGO) is utilized for the multi-objective design of the proposed SHPS. The utilization of MGO will facilitate achieving the lowest possible values for each of the following: cost of energy (COE), loss of power supply probability (LPSP), and carbon dioxide (CO2) emissions. This work introduces a mathematical model for the entire system, which is subsequently simulated using MATLAB software. The results reveal that among all the scenarios analysed, scenario iii — which has an LVF of 30% — is the most cost-effective. It has the lowest COE, at 0.2334 $/kWh, hence the lowest net present cost (NPC), at 6,836,445.5 $. [ABSTRACT FROM AUTHOR]

Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, clinical medicine, and industrial applications. In this paper, we propose innovative methods to assess mental workload from EEG data that use effective brain connectivity for the purpose of extracting features, a hierarchical feature selection algorithm to select the most significant features, and finally machine learning models. We have used the Simultaneous Task EEG Workload (STEW) dataset, an open-access collection of raw EEG data from 48 subjects. We extracted brain-effective connectivities by the direct directed transfer function and then selected the top 30 connectivities for each standard frequency band. Then we applied three feature selection algorithms (forward feature selection, Relief-F, and minimum-redundancy-maximum-relevance) on the top 150 features from all frequencies. Finally, we applied sevenfold cross-validation on four machine learning models (support vector machine (SVM), linear discriminant analysis, random forest, and decision tree). The results revealed that SVM as the machine learning model and forward feature selection as the feature selection method work better than others and could classify the mental workload levels with accuracy equal to 89.53% (± 1.36). [ABSTRACT FROM AUTHOR]

Venting system is of significance in meeting the high-quality requirements of both injection mold and plastic products. However, the G-code generation for venting system still depends on its geometric features, hindering the realization of CAD/CAM integration for venting system generation. We propose a novel and effective CAD/CAM integration approach to output the G-code of venting system without generating the vent channel geometry. The approach consists of three key modules, viz. discrete vent centerline, generate tool path points, and output G-code. By using an algorithm governed by a bow height threshold, the vent centerline is discretized into a series of points. Based on these discrete points, three categories of points: critical tool path points, safety points, and starting (ending) anchor points, are generated to constitute the tool path points. The third module outputs the G-code according to the final tool path points and verifies the accuracy. A prototype system is also developed to improve the quality and efficiency of venting system design. Finally, a case study is provided to validate the proposed approach. [ABSTRACT FROM AUTHOR]