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Summary: Structural performance assessment is a critical stage in the health monitoring for the maintenance and risk management of engineering structures, in which the interstory drift is a key indicator. In this paper, a robust adaptive Kalman filter is proposed for an interstory drift estimation problem to show the health condition of steel structures in the case that the statistics or internal dynamics describing the signals and measurements are not known precisely. More precisely, we build an adaptive current Jerk model (ACJM) where the model parameters are updated in each time step to presuppose the statistics characterization of the steel dynamic, while the unknown measurement noise covariance is adapted based on a fixed‐lag innovation with respect to measurements. Moreover, a robust adaptive Kalman filter is designed for the modelling uncertainties in each time increment by solving a minimax game: one player tries to select an "actual" model far from the proposed ACJM with an exponential decay tolerance, while its "hostile" player, namely, the optimum filter, is designed by minimizing the estimation error according to the selected "actual" model. Finally, some simulation and experimental results show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive study on optimizing the critical artificial intelligence (AI) factors influencing cost management in civil engineering projects using a multi-criteria decision-making (MCDM) approach. The problem addressed revolves around the need to effectively manage costs in civil engineering endeavors amidst the growing complexity of projects and the increasing integration of AI technologies. The methodology employed involves the utilization of three MCDM tools, specifically Delphi, interpretive structural modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). A total of 17 AI factors, categorized into eight broad groups, were identified and analyzed. Through the application of different MCDM techniques, the relative importance and interrelationships among these factors were determined. The key findings reveal the critical role of certain AI factors, such as risk mitigation and cost components, in optimizing the cost management processes. Moreover, the hierarchical structure generated through ISM and the influential factors identified via MICMAC provide insights for prioritizing strategic interventions. The implications of this study extend to informing decision-makers in the civil engineering domain about effective strategies for leveraging AI in their cost management practices. By adopting a systematic MCDM approach, stakeholders can enhance project outcomes while optimizing resource allocation and mitigating financial risks. [ABSTRACT FROM AUTHOR]

Shaking tables are widely used across numerous engineering research and industrial sectors, including mechanical (e.g. automotive and aerospace testing), electrical (e.g. instrumentation testing) and civil (e.g. structural and geotechnical testing) engineering. It is commonly required to replicate the shake table motions accurately and precisely. Iterative learning control algorithms can be used to complement traditional proportional–integral–differential feedback control algorithms to optimize drive signals using a test payload prior to the real experiment. Historically, the design of these test payloads has focused on matching the mass of the actual payload and neglected its dynamic response. In this study, experimental results from shake table tests using multiple geotechnical containers with dry and saturated beds that exhibit a range of stiffnesses and material damping when shaken are presented. Errors between the demanded and achieved motions are explored and compared to the changing secant stiffness abstracted from the dynamic shear stress–strain loops of the payload. A clear trend emerges that demonstrates increased errors as the payload stiffness deviates from the constant stiffness test payload originally used with the open loop iterative learning control, and further the errors are not necessarily bounded by test payloads significantly softer or stiffer than the actual specimen. The findings support that in cases where repeatable, accurate and precise shake table motions are required for payloads that exhibit a complex material response that is not readily modelled mathematically, it may be necessary to reproduce the specimen's overall dynamic response during the iterative learning control process. [ABSTRACT FROM AUTHOR]

The Multiplexed Survey Telescope (MUST) project is led by Tsinghua University, which entrusted the European Industrial Engineering (EIE) GROUP with the design, manufacture, and assembly of the dome. Located on Saishiteng Mountain in Qinghai Province, MUST benefits from exceptional atmospheric seeing conditions etc. Local dome seeing may be comparable to atmospheric seeing and requires careful consideration. This research, based on numerical simulations, focuses on refining the dome structure and temperature regulation strategies to achieve optimal dome seeing. The existing simulations only consider nighttime dome seeing and overlook the impact of daytime dome heating on nighttime conditions. The Computational Fluid Dynamics (CFD) part mostly relies on a steady-state k − ε model, which cannot simulate transient processes or capture optical turbulence. In this study, a comprehensive multiphysics field coupling simulation was conducted, encompassing radiation heat transfer, fluid heat transfer, CFD, and ray tracing. Simulations include both daytime and nighttime scenarios, taking into account the daytime heating of the dome due to solar irradiation, as well as dome seeing under natural ventilation at night. The CFD utilizes the large eddy simulation model, enabling three-dimensional transient simulation and the simulation of optical turbulence. Ultimately, the broadening of the point spread function was statistically analysed after a certain integration time, facilitating a quantitative evaluation of dome seeing. This numerical simulation approach is closer to real world conditions, improving simulation accuracy and addressing the shortcomings of existing simulations. Some qualitative conclusions are consistent with practical engineering experience. In the end, the dome seeing was successfully regulated to 0.21 arcsec, meeting the observational requirements. [ABSTRACT FROM AUTHOR]

This study addresses the imperative of enhancing design flexibility in large-scale infrastructure projects to effectively navigate emerging uncertainties. Current appraisal methods often prioritize risk mitigation over uncertainty management, which hinders the implementation of flexible designs. Instead, this research explores the integration of design flexibility through real options reasoning (ROR) to create flexible infrastructures. As a result, a conceptual framework is proposed that draws from contemporary industrial practices such as modularization and project safeguards. Modularization, grounded in the alignment of functions and components, bolsters flexibility. Project safeguards, operationalized as passive or active measures, embed options in project outcomes. A proposed method emerges that synthesizes real options, modularization, and project safeguards into four guiding steps and nine helpful heuristics. These steps, enriched by heuristics, offer a structured approach to grasp, strategize, and implement design flexibility, transforming it from theory to impactful project management. Case-based and numerical validation was conducted to validate the proposed method. The proposed approach is illustrated through a case study of Heathrow Airport's £14 billion expansion. The research implications include the exploration of improved appraisal methods for large infrastructure projects, the advancement of ROR and application of heuristics in engineering management, and additional investigation into the integration of modularization and safeguards to enhance design flexibility in uncertain environments. This study presents a new methodological approach that offers a structured framework for effectively communicating the advantages of design flexibility in managing uncertainty during the appraisal process of large-scale infrastructure projects. The approach empowers project leaders to tackle uncertainties intrinsic to large-scale projects. It fosters design flexibility's benefits, guides project selection aligned with growth goals, and manages uncertainties by conceptualizing projects as sequenced steps. This method comprises four sequential steps, each pivotal for enhancing design flexibility. It begins by translating strategic needs into tangible options, enhancing adaptive decision-making. The subsequent step involves selecting optimal product types and modularization options to fortify flexibility. Operationalizing risk, modularization, and safeguards in the third step cements flexibility into project architecture. The final step involves cost–benefit analysis, aiding informed investment decisions. These steps, enriched by simple heuristics, offer a structured approach to grasp, strategize, and implement design flexibility, transforming it to impactful project management. Ultimately, this approach guides projects toward a more adaptive future. The proposed approach is ideal for intuitive executive decision-making through practical shortcuts, particularly when analytical modeling faces limitations. [ABSTRACT FROM AUTHOR]

This article investigates the Oldroyd fluid, which is widely used in industrial and engineering environments. When the Oldroyd fluid passes through a three-dimensional semi-infinite cylinder, the asymptotic properties of the solutions are established. On this basis, we also studied the continuous dependence of the viscosity coefficient. [ABSTRACT FROM AUTHOR]

The recent research in hybrid nanomaterials brings multidisciplinary applications in enhancing the heat transfer phenomenon, engineering applications, cooling systems, energy generation, etc. The literature survey claims that various models for hybrid nanofluids are suggested with interaction of viscous materials. However, the decomposition of non-Newtonian hybrid nanofluids is more interesting and valuable in industrial and engineering processes. Based on such dynamical applications of non-Newtonian hybrid nanomaterials, current model endorsed the thermal assessment of decomposition of Williamson (non-Newtonian) hybrid nanofluid in complex wavy channel with assessment of various shape factors. The properties of base materials are discussed by glycerin liquid subject to the copper and aluminum oxide hybrid particles. The joule heating and magnetic force interferences are encountered. Furthermore, detection for interesting shape features like blades, platelets, cylinders and bricks are discussed. For creeping flow phenomenon, the modeling of problem is summarized by using the long wavelength assumptions. The shooting scheme is utilized for simulating the governing flow system. Physical properties of presented model are discussed with heat transport phenomenon. The contour plots and stream functions are presented. It is observed that the pumping and heat transfer coefficient reduces due to magnetic parameter. An enhancement thermal phenomenon has been predicted due to solid volume fraction. Furthermore, for heat transfer assessment is comparatively slower for bricks-shape nanoparticles as compared to blades nanomaterial. The obtained results present applications in the blood flow, heat pumping systems, medical images, angiography, pulse pumping, etc. [ABSTRACT FROM AUTHOR]

The current digital transformation in manufacturing has a strong impact on the competencies needed by manufacturing companies. This leads to evolving requirements for digital training in industrial engineering courses. The concept of virtual labs in academic environments can be instrumental in teaching new digital skills through practical experience. This paper aims to examine the requirements and essential factors involved in designing virtual labs, proposing a framework to meet the requirements of virtual lab activities, integrating didactic and research purposes while examining the requirements and essential factors involved in designing virtual labs digital twin as an enabling technology. The framework is the result of the analysis of the technical aspects related to immersive technologies, such as extended reality, together with insights gathered from interviews and pilot testing conducted in workshops involving students, teachers, and lab managers from three institutions implementing virtual labs. The outcomes of this study include the digital model of four manufacturing labs as virtual labs that are openly available for academic purposes. This showcases a commitment towards offering quality and inclusive engineering education through cuttingedge virtual technologies. [ABSTRACT FROM AUTHOR]

Changes and modifications to existing products, known as engineering changes (EC), are common in complex product development. They require appropriate implementation planning and supervision to mitigate the economic downsides due to complexity. These tasks, however, take a high administrative toll on the organization. In response, automation by computer tools has been suggested. Due to the underlying process complexity, the application of artificial intelligence (AI) is advised. To support research and development of new AI-artifacts for EC management (ECM), a knowledge base is required. Thus, this paper aims to gather insights from existing approaches and discover literature gaps by conducting a systematic literature review. 39 publications applying AI methods and algorithms in ECM were identified and subsequently discussed. The analysis shows that the methods vary and are mostly utilized for predicting change propagation and knowledge retrieval. The review's results suggest that AI in EC requires developing distributed AI systems to manage the ensuing complexity. Additionally, five concrete suggestions are presented as future research needs: Research on metaheuristics for optimizing EC schedules, testing of stacked machine learning methods for process outcome prediction, establishment of process supervision, development of the mentioned distributed AI systems for automation, and validation with industry partners. [ABSTRACT FROM AUTHOR]

Background: Standards for image quality evaluation in multi‐detector CT (MDCT) and cone‐beam CT (CBCT) are evolving to keep pace with technological advances. A clear need is emerging for methods that facilitate rigorous quality assurance (QA) with up‐to‐date metrology and streamlined workflow suitable to a range of MDCT and CBCT systems. Purpose: To evaluate the feasibility and workflow associated with image quality (IQ) assessment in longitudinal studies for MDCT and CBCT with a single test phantom and semiautomated analysis of objective, quantitative IQ metrology. Methods: A test phantom (CorgiTM Phantom, The Phantom Lab, Greenwich, New York, USA) was used in monthly IQ testing over the course of 1 year for three MDCT scanners (one of which presented helical and volumetric scan modes) and four CBCT scanners. Semiautomated software analyzed image uniformity, linearity, contrast, noise, contrast‐to‐noise ratio (CNR), 3D noise‐power spectrum (NPS), modulation transfer function (MTF) in axial and oblique directions, and cone‐beam artifact magnitude. The workflow was evaluated using methods adapted from systems/industrial engineering, including value stream process modeling (VSPM), standard work layout (SWL), and standard work control charts (SWCT) to quantify and optimize test methodology in routine practice. The completeness and consistency of DICOM data from each system was also evaluated. Results: Quantitative IQ metrology provided valuable insight in longitudinal quality assurance (QA), with metrics such as NPS and MTF providing insight on root cause for various forms of system failure—for example, detector calibration and geometric calibration. Monthly constancy testing showed variations in IQ test metrics owing to system performance as well as phantom setup and provided initial estimates of upper and lower control limits appropriate to QA action levels. Rigorous evaluation of QA workflow identified methods to reduce total cycle time to ∼10 min for each system—viz., use of a single phantom configuration appropriate to all scanners and Head or Body scan protocols. Numerous gaps in the completeness and consistency of DICOM data were observed for CBCT systems. Conclusion: An IQ phantom and test methodology was found to be suitable to QA of MDCT and CBCT systems with streamlined workflow appropriate to busy clinical settings. [ABSTRACT FROM AUTHOR]

Computer simulation has been in the toolkit of industrial engineers for over 50 years and its value has been enhanced by advances in research, including both modeling and analysis, and in application software, both commercial and open source. However, "advances" are different from paradigm shifts. Motivated by big data, big computing and the big consequences of model-based decisions, it is time to reboot simulation for industrial engineering. [ABSTRACT FROM AUTHOR]

Over the last 30 years, the last planner system (LPS), as a Lean-driven production planning and control tool, has consistently demonstrated its capability to improve planning reliability and overall performance in construction projects. Recent research provides evidence suggesting that the LPS ability to do good to projects has not reached its full potential yet. In fact, it has been observed that by identifying the missing social mechanisms in its implementation process and managing more effectively the complex socio-technical nature of the LPS, its implementation effectiveness is improved. However, commonly used research approaches, such as the case study method, have been criticized for lacking scientific control, replicability, and external validity. Alternatively, immersive virtual reality (IVR)-based simulation games have the methodological capability to enable the study of the social mechanisms that the LPS engenders when implemented in projects, by providing a highly controlled, reproducible, and ecologically valid experimental environment. However, how to leverage effectively IVR-based gaming technology to study the social mechanisms that the LPS engenders in construction organizations is not well understood. In order to bridge this gap, this paper established the conceptual and technical foundations to set IVR-based experimental environments and developed an IVR prototype encompassing nonlean and LPS-based rounds. The evaluation of the IVR prototype involved both quantitative and qualitative research approaches, focusing on its functionality, user-perceived presence and usability, task performance, and lean expert feedback. The results demonstrated the prototype's capability to create a controlled experimental environment while offering participants a user-friendly, immersive, and realistic LPS simulation experience. The contributions of this study are twofold: (1) the addition of a new method to the research methodology toolbox in construction engineering and management; and (2) the generation of design insights to develop IVR prototypes for research in construction engineering and management, and preliminary evidence for its ecological validity. [ABSTRACT FROM AUTHOR]

Fascinating 3D shapes arise when a thin planar sheet is folded without stretching, tearing or cutting. The elegance amplifies when the fold/crease is changed from a straight line to a curve, due to the association of plastic deformation via folding and elastic deformation via bending. This results in the curved crease working as a hinge support providing deployability to the surface which is of significant interest in industrial engineering and architectural design. Consequently, finding a stable form of curved crease becomes pivotal in the development of deployable structures. This work proposes a novel way to evaluate such curves by taking inspiration from biomimicry. For this purpose, growth mechanism in plants was observed and an analogous model was developed to create a discrete curve of fold. A parametric model was developed for digital construction of the folded models. Test cases were formulated to compare the behavior of different folded models under various loading conditions. A simplified way to visualize the obtained results is proposed using visual programming tools. The models were further translated into physical prototypes with the aid of 3D printing, hybrid and cured-composite systems, where different mechanisms were adopted to achieve the folds. The prototypes were further tested under constrained boundary and compressive loading conditions, with results validating the analytical model. [ABSTRACT FROM AUTHOR]

Multiphysics coupling simulations are of great importance and challenge in engineering and industrial applications. A new efficient full‐wave energy‐conservative projection method between two heterogeneous meshes is proposed in this article, for the analysis of electromagnetic‐thermal‐stress coupling in the printed circuit boards (PCBs) and antenna arrays. The projected variables are directly chosen as the electric fields instead of conventional thermal sources, and a scalable domain decomposition method is used to solve the projection problems. In addition, a bounding‐box algorithm and the spatial K‐dimensional Tree searching algorithm are developed for physical quantity projection between the two heterogeneous meshes. A series of numerical analysis of the electromagnetic‐thermal‐stress coupling process are conducted. In particular, a realistic PCB example is presented, where 144 CPU cores are used and two heterogeneous meshes of over 20 million grids are constructed. A realistic antenna array with 10 000 units is also presented, where 1200 CPU cores are used and two heterogeneous meshes of over 100 million grids are constructed. [ABSTRACT FROM AUTHOR]

Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models of enormous unlabeled data produced periodically in different disciplines. However, the literature does not fully address SSL's practicalities and workabilities necessary for industrial engineering and medicine. Accordingly, this thorough review is administered to identify these prominent possibilities for prediction, focusing on industrial and medical fields. This extensive survey, with its pivotal outcomes, could support industrial engineers and medical personnel in efficiently predicting machinery faults and patients' ailments without referring to traditional numerical models that require massive computational budgets, time, storage, and effort for data annotation. Additionally, the review's numerous addressed ideas could encourage industry and healthcare actors to take SSL principles into an agile application to achieve precise maintenance prognostics and illness diagnosis with remarkable levels of accuracy and feasibility, simulating functional human thinking and cognition without compromising prediction efficacy. [ABSTRACT FROM AUTHOR]

Some studies have shown that any part machined by an outsourcer usually has several basic quality characteristics. When the outsourcer's process capabilities are insufficient, the defective rate of various quality characteristics of the product will increase, thereby raising the rework rate and scrap rate. As a result, maintenance costs will go up, economic value will decrease, and even carbon emissions can increase during the production process. In addition, the process capability index and the radar chart are widely used in engineering management and other fields. Since process indicators often contain unknown parameters, sample data are needed for evaluation. With the rapid development of the Internet of Things and big data analysis, many companies regard rapid response as a basic requirement for timeliness and cost consideration. Therefore, companies often have to evaluate the process quality of ten small samples and decide whether to make some improvements. In order to solve the above problems, this study proposed a fuzzy radar chart evaluation model for the process quality of multi-quality characteristic parts based on the process capability index. Using this model can help all parts manufacturers continue to improve the quality of their machined parts as well as reduce their rework and scrap rates. Meanwhile, carbon emissions can be lessened during the production process, and companies can fulfill their social responsibilities. This fuzzy radar chart evaluation model is based on confidence intervals. As the company's past experience is incorporated, the evaluation accuracy can be maintained even with a smaller sample size. Furthermore, the fuzzy radar evaluation chart can simultaneously evaluate the process capabilities of all quality characteristics of the part. In addition to making it easier for manufacturers to master all quality characteristics, quality process capability can also help them seize improvement opportunities. [ABSTRACT FROM AUTHOR]

Military program management and system engineering re quire the expression of costs and delay trade‐off with respect to system architecture. If architecture frameworks (AF) such as NATO (NAF) were designed to fill this common need, their current state is essentially descriptive. As it turns out, building defense systems architectures using those frameworks in a properly anticipated cost/delay budget envelope would require to have all system engineering already solved, because the architecture frameworks are designed to provide an explicit representation of the operational domain that can be used in analysis, for articulation of issues and requirements, as support to planning, and as a means of solution design and validation, among other things. Thus Quality‐Resource‐Time optimality in a regularly evolving environment cannot be represented in acceptable delay without automated optimization assistance. Our contribution in this article explores coupling architecture framework with operation research (OR) models to enable computer assisted design and evaluation of heterogeneous views in NATO Architecture Framework (NAF). Our illustrative example is a Linear Programming based bridge between program management and system engineering to anticipate optimal trade‐offs. This article presents promising results, with which we hope to show how OR and AF will be indivisible in architecture evaluation process. [ABSTRACT FROM AUTHOR]

Efficient cooling is crucial for maintaining the reliability and performance of industrial and engineering systems that generate excess heat. Entropy generation analysis, established on the second law of thermodynamics, plays a vital role in identifying inefficiencies within these systems and improving their overall efficiency. This study focuses on a theoretical investigation of the entropy generation, considering the cumulative impact of surface slipperiness, Brownian motion, applied magnetic field, thermophoresis on the nanofluid flow toward a convective heated stretching surface. The governing model partial differential equations undergo a transformation through similarity transformation, resulting in a nonlinear ordinary differential equation. This equation is subsequently solved numerically by employing the Runge–Kutta–Fehlberg integration scheme in conjunction with the shooting method. The obtained results reveal the influence of various parameters on the temperature, velocity, Nusselt number, skin friction, Sherwood number, Bejan number, nanoparticle concentration, and entropy generation. Upon analysis, it was notable that the introduction of a magnetic field, higher Biot numbers, Eckert numbers, and elevated Brownian motion led to an increase in the entropy generation within the system. Conversely, the presence of thermophoresis and reduced surface slipperiness resulted in a decrease in entropy. These results are presented through graphical representations, tables, and quantitative discussions, providing valuable insights for optimizing the cooling and performance of industrial and engineering systems. [ABSTRACT FROM AUTHOR]

The integration of computer vision technology into construction sites poses various challenges due to the complex environment. Prior studies on computer vision related to heavy construction equipment has primarily focused on a limited range of equipment types provided in standard databases, such as the Microsoft Common Objects in Context (MS COCO) data set. The conventional approach has limitations in capturing the diverse working conditions and dynamic environments encountered in real construction sites. To overcome the challenge, this study proposes a new benchmark model for the automated detection and classification of a wide range of heavy construction equipment (i.e., nine representative types) commonly used in construction sites by using a deep convolution neural network. This study was conducted in four steps: (1) data collection and preparation, (2) data transformation, (3) model training, and (4) model validation. The proposed you only look once (YOLO)v5l (large, YOLOv5 with a larger network) model demonstrated high reliability, achieving a mean average precision (mAP)_0.5∶0.95 of 90.26%. This study makes a significant contribution to the domain of construction engineering and management by providing a more efficient and systematic management system to proactively prevent heavy equipment–related safety accidents with diverse working conditions and dynamic environments encountered at construction sites. Moreover, the proposed approach can be extended to integrate advanced techniques such as case-based reasoning, digital twin, and blockchain, allowing for the automated activity recognition in various occlusions, the carbon emissions monitoring and diagnostics of heavy equipment, and a robust real-time construction management system with enhanced security. [ABSTRACT FROM AUTHOR]

Seismic landslides are dangerous natural hazards that can cause immense damage to human lives and property. Susceptibility assessment of earthquake-triggered landslides provides the scientific basis and theoretical foundation for disaster emergency management in engineering projects. However, landslide susceptibility assessment requires a massive amount of historical landslide data. Evidence of past landslide activities may be lost due to changes in geographical conditions and human factors over time. The lack of landslide data poses difficulties in assessing landslide susceptibility. The aim of this study is to establish a generalized seismic landslide susceptibility assessment model for applying it to the Dayong highway in the Chenghai area, where earthquakes occur frequently but with a lack of landslide data. The landslide data used comes from the 2014 Ludian Ms (Surface wave magnitude) 6.5 earthquake in a region with geographical conditions similar to those in the Chenghai area. The influencing factors considered include elevation, slope, slope aspect, distance to streams, distance to faults, geology, terrain wetness index, normalized difference vegetation index, epicenter distance and peak ground acceleration. The frequency ratio method is used to eliminate influencing factors with poor statistical dispersion of landslides. Principal component analysis (PCA) is utilized to reduce the dimensionality of landslide conditioning factors and to improve the transferability of the assessment model to different regions. A support vector machine model is used to establish the susceptibility assessment model. The results show that the accuracy of the PCA–SVM model reaches 93.6%. The landslide susceptibility of the Chenghai area is classified into 5 classes, with the "Very high" landslide susceptibility class accounting for 0.63%. The 13-km section in the middle of the Dayong highway, which accounts for 8.9%, is identified as the high-risk area most obviously impacted by seismic landslides. This study provides a new approach for seismic landslide susceptibility assessment in areas lacking in landslide inventory data. [ABSTRACT FROM AUTHOR]

In response to the evolving landscape of the modern era, the requirements for engineering audit have undergone significant changes. To achieve efficient audit tasks and obtain accurate and reliable results, the integration of machine learning and wireless network technology has become essential, leading to the emergence of digital and information-based audit modes. This paper focuses on the development of a digital audit system that combines engineering audit management fusion with machine learning and wireless network technology. Such an approach reflects the dynamic shift in internal audit functions and objectives, providing clear guidelines for the future of digital audit management. By harnessing the power of machine learning and wireless networks, the digital audit system effectively addresses challenges associated with data management, sharing, exchange, and security during the audit process. Through seamless integration, it enables comprehensive electronic and digital management of internal and audit business processes. This research explores the platform's functionalities and its potential application, using actual audit data for analysis. The proposed digital audit system showcases superior real-time data querying performance, heightened accuracy in checks, and enhanced retrieval capabilities. The simulation results validate the system's efficacy, highlighting its ability to deliver true and dependable audit outcomes. By embracing digital transformation, the engineering audit field can harness the potential of cutting-edge technologies, thus paving the way for a more efficient, reliable, and future-ready approach to audit management. [ABSTRACT FROM AUTHOR]

The study focuses on the formation of gas bubbles, a crucial phenomenon that significantly impacts the hydrodynamics in liquid–gas reactors. This phenomenon has been observed in industrial engineering, leading to the development of model equations that describe the velocity of a liquid–gas mixture, pressure, and deviation in the gas bubble's initial radius. Kudryashov–Sinelshchikov (KS) established coupled equations (CEs) to describe the dynamics of the liquid–gas mixture. Here, the study aims to investigate the behavior of velocity, deviation in the bubble radius, and pressures by deriving exact solutions of the KSCEs and representing them graphically. The findings show that the velocity can take negative or positive values, representing interfacial velocity or superficial velocity for both liquid and gas, respectively. The pressure may also be negative or positive, corresponding to the shrinking or stretchering of the liquid surface, respectively. The dynamics of the velocity and the deviation in the bubble radius exhibit similar qualitative behavior, while the pressure shows a variant behavior. The dominant parameters are found to be the gas bubble's initial radius and the polytropic exponent. The stability of the steady-state solution is also analyzed. This study contributes to the existing literature by examining the CEs, which have not been studied until now. [ABSTRACT FROM AUTHOR]

Business process modeling is a crucial aspect of domains such as Business Process Management and Software Engineering. The availability of various BPM languages in the market makes it challenging for process modelers to select the best-fit BPM language for a specific process modeling task. A decision model is necessary to systematically capture and make scattered knowledge on BPM languages available for reuse by process modelers and academics. This paper presents a decision model for the BPM language selection problem in research projects. The model contains mappings of 72 BPM features to 23 BPM languages. We validated and refined the decision model through 10 expert interviews with domain experts from various organizations. We evaluated the efficiency, validity, and generality of the decision model by conducting four case studies of academic research projects with their original researchers. The results confirmed that the decision model supports process modelers in the selection process by providing more insights into the decision process. Based on the empirical evidence from the case studies and domain expert feedback, we conclude that having the knowledge readily available in the decision model supports academics in making more informed decisions that align with their preferences and prioritized requirements. Furthermore, the captured knowledge provides a comprehensive overview of BPM languages, features, and quality characteristics that other researchers can employ to tackle future research challenges. Our observations indicate that BPMN is a commonly used modeling language for process modeling. Therefore, it is more sensible for academics to explain why they did not select BPMN than to discuss why they chose it for their research project(s). [ABSTRACT FROM AUTHOR]

Microbial epoxide hydrolases, cis-epoxysuccinate hydrolases (CESHs), have been utilized for commercial production of enantiomerically pure L(+)- and D(-)-tartaric acids for decades. However, the stereo-catalytic mechanism of CESH producing L(+)-tartaric acid (CESH[L]) remains unclear. Herein, the crystal structures of two CESH[L]s in ligand-free, product-complexed, and catalytic intermediate forms were determined. These structures revealed the unique specific binding mode for the mirror-symmetric substrate, an active catalytic triad consisting of Asp-His-Glu, and an arginine providing a proton to the oxirane oxygen to facilitate the epoxide ring-opening reaction, which has been pursued for decades. These results provide the structural basis for the rational engineering of these industrial biocatalysts. [ABSTRACT FROM AUTHOR]

Recently, Annadurai published an important paper in International journal of Management Science and Engineering Management for the optimal replenishment policy for inventory models of decaying items with shortages and salvage value. However, his solution procedure contained some questionable results. The purpose of this note is fourfold. First, we recap the inventory model proposed by Annadurai. Second, we provide our improvement. Third, we point out questionable results of Annadurai and then offer our revisions. Furth, we consider two boundary minimums that will be an improvement for the no shortage case and no stock case, respectively. [ABSTRACT FROM AUTHOR]

Purpose: Recent reports on Industry 4.0 reveal that a large part of Spanish companies have not yet made enough progress in the digitalization of processes and one of the most relevant challenges they face is training their employees to build a new culture that allows them to develop high-performance behaviors and take advantage of the potential of new technologies. This research seeks to identify the skills that could contribute to improving certain professional behaviors of employees who work mainly in the field of industrial engineering and that are considered necessary for the advancement of digitalization and Industry 4.0 processes. Design/methodology/approach: For this purpose, a Delphi study was carried out, collecting the opinions of 38 teaching and professional experts, through face-to-face meetings or by videoconference, and analysing their opinions using descriptive statistics and structural equation models. Findings: The results obtained show that the key skills for improving professional behaviours are teamwork, conduct, management, and problem-solving, which influence most of the behaviours analysed. Originality/value: In practice, these results allow companies to be guided in terms of employee training in relation to digitalization skills and assist their selection processes. Additionally, educational institutions can review their study plans, increasing the relevance of the skills that companies value the most. [ABSTRACT FROM AUTHOR]

Recent research on cooling photovoltaic (PV) modules through radiative cooling methods has attracted significant interest, due to the temperature reduction that enhances the power generation of PV modules. This study uses a transparent-radiative heat mirror (TRHM) for cooling silicon PV modules, utilizing a combination of an indium tin oxide (ITO) layer and polydimethylsiloxane (PDMS) to enable visible light transmission while reflecting unnecessary heating photons. Fabricated by a low-cost method, the novelty honeycomb-like surface microstructure of PDMS enhances the material’s emissivity. Combined ITO layer with honeycomb-like PDMS, the film results in a reflectivity of 0.65 in the near-infrared range (1.1-2.5 μm, NIR) and an emissivity of 0.93 in the mid-far infrared range (8-13 μm, MIR). PV modules covered with TRHM (PV-TRHM) demonstrated a significant emissivity of 0.90 in MIR, leading to a temperature of 44.2°C, which has a 7.2°C reduction compared to the 53.6°C bare PV. Additionally, a 0.81% increase in power conversion efficiency (PCE) was achieved. These approaches hold promise for efficient PV heat management in engineering projects. [ABSTRACT FROM AUTHOR]

Stochastic averaging, as an effective technique for dimension reduction, is of great significance in stochastic dynamics and control. However, its practical applications in industrial and engineering fields are severely hindered by its dependence on governing equations and the complexity of mathematical operations. Herein, a data-driven method, named data-driven stochastic averaging, is developed to automatically discover the low-dimensional stochastic differential equations using only the random state data captured from the original high-dimensional dynamical systems. This method includes two successive steps, that is, extracting all slowly varying processes hidden in fast-varying state data and identifying drift and diffusion coefficients by their mathematical definitions. It automates dimension reduction and is especially suitable for cases with unavailable governing equations and excitation data. Its application, efficacy, and comparison with theory-based stochastic averaging are illustrated through several examples, numerical or experimental, with pure Gaussian white noise excitation or combined excitations. [ABSTRACT FROM AUTHOR]

As-cast Al–Li–Mg system alloys are a good prospect for use in transport vehicles due to the ultra-low density of Li and Mg. However, low ductility and strength restricts its large-scale application. This work multi-microalloyed Sc, Zr, Er, and Ti into an as-cast Al–Li–Mg alloy, and studied the formation of primary second phases and their influence on mechanical properties. Compared with the initial as-cast Al–Li–Mg alloy with α-Al grains of ~200 μm and a net-like primary S1–Al2MgLi phase, the as-cast Al–Li–Mg–Sc–Zr–Er–Ti alloy had a much finer and more uniform microstructure containing α–Al grains of ~20 μm and the primary Al3(Sc, Zr, Ti) phase that had a discrete or isolated distribution replacing the net-like primary S1–Al2MgLi phase. There was only the primary Al3(Sc, Zr, Ti) phases and no other primary phases in the cast microstructure of Al–Li–Mg–Sc–Zr–Er–Ti alloy. The formation of the primary Al3(Sc, Zr, Ti) phase was attributed to in-sequence formation of the enriched elements in the primary particles. Compared with the initial as-cast Al–Li–Mg alloy, the yield strength (YS), ultimate tensile strength (UTS), and elongation (EL) of the as-cast Al–Li–Mg–Sc–Zr–Er–Ti alloy were increased by ~49%, ~90% and ~324%, respectively. This improvement provides a prospect to use the new alloy directly as the castings in industrial production and engineering applications. [ABSTRACT FROM AUTHOR]

The management and exploitation of knowledge can contribute to the competitive advantage of an organisation and hence can be a driver of its value. This paper examines knowledge management practices within two engineering organisations with an emphasis on barriers to its effectiveness and the influence of current and anticipated deployment of digital technologies. Two methods were used to gather research data across both organisations by combining a series of semi-structured interviews with a quantitative social network analysis. Examination of the acquired data provided insights into the relationship between the knowledge management culture in both organisations and their adoption of digital tools. Hudson's evolutionary model of safety culture has been modified to describe the culture of knowledge management in an organisation and the acquired data used to locate the two organisations on their knowledge management journey. It is proposed that social networks could be used as an indicator of the stage of evolution of knowledge management in engineering organisations more generally with low network densities and dispersed networks representing higher stages of evolution. [ABSTRACT FROM AUTHOR]

In engineering blasting, while efficiently breaking rocks with explosives, a large amount of toxic and harmful gases are generated, which not only pollutes the production environment but also easily leads to explosion smoke poisoning accidents. It must be highly valued by engineering technicians and management personnel. To effectively control the production of harmful gases during explosive blasting, an environmentally friendly and efficient harmful gas inhibitor has been developed, and its mechanism of action has been analyzed and revealed. Through model and on-site experiments, the appropriate addition ratio and charging structure scheme were determined, and good control effects were achieved. The research results indicate that the environment in which explosives are used has a significant impact on the composition of harmful gases produced during blasting. CO, NO, and NO2 are mainly produced in natural air environments, while NH3, CO, and NO are mainly produced in underground blasting environments. As the proportion of inhibitors added increases (2%, 4%, 6%), the decrease in the concentration of harmful gases during blasting first increases and then decreases. Compared with the control experiment, the total reduction rate of harmful gas concentration is 39.23%, 68.20%, and 59.69%, respectively, and the best control effect is achieved when 4% is added. When using the developed inhibitor adding device for the full hole addition scheme, the control effect of harmful gas concentration in blasting is the best, and the decrease in harmful gas concentration reaches 62.79%~84.73% at a distance of 30m~120m. The use of harmful gas inhibitors for blasting combined with other control measures can significantly improve the blasting operation environment, enhance the safety level of production operations, and have good promotion and application value. [ABSTRACT FROM AUTHOR]

The present study uses nine machine learning (ML) methods to predict wave runup in an innovative and comprehensive methodology. Unlike previous investigations, which often limited the factors considered when applying ML methodologies to predict wave runup, this approach takes a holistic perspective. The analysis takes into account a comprehensive range of crucial coastal parameters, including the 2% exceedance value for runup, setup, total swash excursion, incident swash, infragravity swash, significant wave height, peak wave period, foreshore beach slope, and median sediment size. Model performance, interpretability, and practicality were assessed. The findings from this study showes that linear models, while valuable in many applications, proved insufficient in grasping the complexity of this dataset. On the other hand, we found that non-linear models are essential for achieving accurate wave runup predictions, underscoring their significance in the context of the research. Within the framework of this examination, it was found that wave runup is affected by median sediment size, significant wave height, and foreshore beach slope. Coastal engineers and managers can utilize these findings to design more resilient coastal structures and evaluate the risks posed by coastal hazards. To improve forecast accuracy, the research stressed feature selection and model complexity management. This research proves machine learning algorithms can predict wave runup, aiding coastal engineering and management. These models help build coastal infrastructure and predict coastal hazards. [ABSTRACT FROM AUTHOR]

The creation of blockchain-based software applications requires today considerable technical knowledge, particularly in software design and programming. This is regarded as a major barrier in adopting this technology in business and making it accessible to a wider audience. As a solution, low-code and no-code approaches have been proposed that require only little or no programming knowledge for creating full-fledged software applications. In this paper we extend a review of academic approaches from the discipline of model-driven engineering as well as industrial low-code and no-code development platforms for blockchains. This includes a content-based, computational analysis of relevant academic papers and the derivation of major topics. In addition, the topics were manually evaluated and refined. Based on these analyses we discuss the spectrum of approaches in this field and derive opportunities for further research. [ABSTRACT FROM AUTHOR]

This work considers the analytical analysis of silver-water, silver-blood base nanofluid flow over fluctuating disk with the influence of viscous dissipation over fluctuating disk. The primary goal of this study is an effort to improve the heat transfer ratio, which is a core part of the engineering and industrial sectors. Following a continuity check, the problem is modeled using the conservation rules of momentum and energy. Nonlinear PDEs are produced through modeling, which are then transformed into ODEs using a similarity transformation and thermophysical characteristics. The resultant ODEs are resolved using the Optimal Homotopy Asymptotic Method (HAM). The outcomes of this method are compared to authenticate the outcomes of the obtained results. The Mathematica software is used to run HAM methods, and graphical interpretations are given to highlight the influence of dissimilar contributing factors on the velocity profile and temperature distribution. Nusselt's number, and the skin friction are examined through graphical representation. Convergence of the problem is checked with the help of graphs and tables by using dual solution of the problem. [ABSTRACT FROM AUTHOR]

The objective of this study is to investigate the behavior of the Bayesian exponentially weighted moving average (EWMA) control chart in the presence of measurement error (ME). It explores the impact of different ranked set sampling designs and loss functions on the performance of the control chart when ME is present. The analysis incorporates a covariate model, multiple measurement methods, and a conjugate prior to account for ME. The performance evaluation of the proposed Bayesian EWMA control chart with ME includes metrics such as average run length and standard deviation of run lengths. The findings, obtained through Monte Carlo simulation and real data application, indicated that ME significantly affects the performance of the Bayesian EWMA control chart when RSS schemes are employed. Particularly noteworthy is the superior performance of the median RSS scheme compared to the other two schemes in the presence of ME. [ABSTRACT FROM AUTHOR]

Today's fast-changing environment for construction companies requires rapid responses and adaptation of their projects. Despite the multitude of tools applied for project cost management in engineering and construction companies, there is a need to form comprehensive solutions. The purpose of the study is to form a methodological approach to project cost management in the field of engineering construction based on alternative models to diagnose the development, assessment and selection of functional areas and content of cost management in the construction project, which allows one to increase adaptability and flexibility in the process of its implementation. The basis of research methodology is modeling, which allows one to adjust the economic and financial flows based on three S-curves, one for each component of the total cost of the work: direct costs, indirect costs and reserves. These curves include the direct cost curve for the main purchasing packages as well. This brings financial flows closer to reality because it is possible to adjust the S-curves according to the behavior of each subsystem. The contribution of the study is the proposed approach of integrating concepts related to the coordination and development of project design and production management (lean construction), forming a "3D model of management", in a broad and comprehensive management system. It assumes a comprehensive and complete way to manage civil engineering projects. The proposed methodological approach can make a significant contribution to the preparation of forecasts and estimates by planners and controllers in the context of construction projects. [ABSTRACT FROM AUTHOR]