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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]

Developing real-time automatic intuitive devices based on electroencephalography (EEG) to improve hazard recognition performance (HRP) is essential in construction engineering management. However, existing studies generally developed devices based on unimodal data, failing to distinguish the role played by brain activities in different visual areas and ignoring the possible existence of other psychological mechanisms. Therefore, this study aims to use multimodal data based on brain-eye synergy to predict HRP and reveal the cognitive mechanism of hazard recognition to develop intuitive hazard recognition devices. Accordingly, we designed experiments to investigate this problem: (1) 53 construction workers completed a 120-trial hazard recognition task in a laboratory environment (N=6,360), (2) an EEG recorder collected the participants' EEG activities and an eye-tracking device recorded the data of eye movement, and (3) the power spectral density (PSD) of EEG was calculated and then imported into Matlab2018a in combination with EEGLAB for analysis. The results showed that the prediction results of hazard recognition have a time-variant effect. For fall-related hazards, EEG signals within 0–320 ms after the onset were significant in predicting HRP; for electric-related and fire-related hazards, EEG signals within 560–640 ms after the starting point were the most valuable. Also, compared with fall-related hazards, it took a significantly longer time to recognize electric-related and fire-related hazards, which consumed more attentional resources. Finally, the frontal lobe requires more attentional resources to finish cognitive activities than the occipital lobe to finish visual information processing activities, and the occipital lobe needs to process visual information first, and then transmit it to the frontal lobe for analysis and judgment. For the body of knowledge, this study, finding the time-variant effect of HRP, improves the theory of construction hazard recognition from an eye-brain synergy perspective and provides guidance for developing intuitive hazard recognition devices and improving management performance. This study combined eye-movement data and EEG data to predict the performance of hazard recognition using multimodal data. Not only did this study reveal the mechanism of hazard recognition, but it also proposed suggestions for project management and device development. Managers are suggested to implement refined management and regularly monitor the physiological signals of employees. Some construction companies started to use smart devices to measure workers' arousal, emotional valence, emotional regulation ability, fatigue, workload, or safety attention level before work every day. And it is recommended to provide safety reminders to employees based on whether there are any abnormalities in their physiological indicators. In addition to project management, the results of this study can serve the development or optimization of hazard recognition equipment. Developing devices cannot only consider EEG information, but also combine EEG information with eye-movement information. Meanwhile, developers can also consider different types of hazards and different brain regions to improve device performance. [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]

The rise of extreme precipitation events has attracted wide attention throughout the world. However, the analysis of precipitation extremes is not sufficient in the short period of instrumental observation and it is complicated by their non‐stationary behaviours. As a period dominated by natural forcing, the last millennium is helpful for understanding long‐term changes in precipitation extremes. Therefore, this study aimed to (1) investigate changes in daily and subdaily precipitation extremes in China from the last millennium to the end of the twenty‐first century, (2) detect the non‐stationarity of precipitation extremes and (3) compare design storms by using stationary and non‐stationary distributions. The annual maximum 1‐day precipitation (RX1day) and annual maximum 3‐hour precipitation (RX3hour) are used to quantify variations of precipitation extremes. The results show that the trend of RX1day was insignificant for the Medieval Climate Anomaly (MCA, 950–1250) and the Little Ice Age (LIA, 1500–1800). For the historical period (1850–2014), the trend of RX1day and RX3hour was insignificant in most subregions. In addition, the differences for RX1day between the historical period and the MCA/LIA were mostly in the range of −5% to 5%. For the future period (2015–2100), RX1day and RX3hour are projected to increase in almost all land grids compared with the historical period, and they show greater increases in western China than eastern China. The non‐stationarity of RX1day and RX3hour is mainly found under the high‐emission scenario. Moreover, stationary generalized extreme value (GEV) distribution underestimates 50‐year return levels for RX1day and RX3hour in most areas compared with non‐stationary GEV distribution under the high‐emission scenario. Overall, this study suggests that daily and subdaily extreme precipitation will intensify over China in the future. For design storms, non‐stationary methods need to be used for risk management and engineering design under the high‐emission scenario. [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]

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]

Markowitz's approach could not deal with the overall optimization of both minimizing variance of return rate and maximizing return rate simultaneously due to its short of rational method for handling multi-objective optimization. In this article, a hybrid of the probability-based multi-objective optimization with the uniform design for experiments of mixtures is performed to solve the portfolio investment problem of concurrent optimization of both maximizing return rate and minimizing variance of return rate. The probability-based multi-objective optimization is employed to transfer the bi-objective problem of portfolio investment into a mono-objective one with total preferable probability as the goal for the overall optimization of the system in spirit of probability theory, the uniform design for experiments of mixtures is used to perform the subsequent discretization. Project management problem is rather a multi-objective to conduct naturally. The analysis shows the rationality of the hybrid solution.

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]

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]

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 incomplete data is a common phenomenon in traffic network because of the high measurement cost, the failure of data collection systems and unavoidable transmission loss. Recovering the whole data from incomplete data is a very important task in internet engineering and management. In this paper, we adopt the low-rank tensor completion model equipped with tensor nuclear norm to reconstruct the internet traffic data. Besides using a low rank tensor to capture the global information of internet traffic data, we also utilize spatial correlation and periodicity to characterize the local information. The resulting model is a convex and separable optimization. Then, a proximal alternating direction method of multipliers is customized to solve the optimization problem, where all subproblems have closed-form solutions. Convergence analysis of the algorithm is given without any assumptions. Numerical experiments on Abilene and GÉANT datasets with random missing and structured loss show that the proposed model and algorithm perform better than other existing algorithms. [ABSTRACT FROM AUTHOR]

Landscape engineering plays a crucial role in urban construction and the development of ecological civilization in China. It actively designs and modifies natural elements, such as water and mountains, acting as the primary living infrastructure. This field continually receives great recognition and praise. Recent academic research has prioritized the use of extended reality (XR) technology to create a real-time interactive visual environment to tackle the issues presented by the dynamic nature of landscape engineering. This paper utilizes the PRISMA method to filter out 68 research documents related to XR in landscape engineering construction and management for bibliometric analysis. A comprehensive review is conducted on the precise and efficient utilization of XR to solve various issues in the field of landscape engineering. Using Cite Space 6.2.R6 (a visual bibliometric software) to visualize knowledge structures and research topics, the analysis includes temporal and spatial examination, application scenario analysis, and technological hierarchy analysis. The paper summarizes the current challenges that XR still faces in the landscape engineering field and envisions extensible application scenarios for XR, providing a reference roadmap for the implementation of XR in landscape engineering. [ABSTRACT FROM AUTHOR]

Controlling heat flow is a key challenge for applications ranging from thermal management in electronics to energy systems, industrial processing, and thermal therapy. However, progress has generally been limited by slow response times and low tunability in thermal conductance. In this work, we demonstrate an electronically gated solid-state thermal switch using self-assembled molecular junctions to achieve excellent performance at room temperature. In this three-terminal device, heat flow is continuously and reversibly modulated by an electric field through carefully controlled chemical bonding and charge distributions within the molecular interface. The devices have ultrahigh switching speeds above 1 megahertz, have on/off ratios in thermal conductance greater than 1300%, and can be switched more than 1 million times. We anticipate that these advances will generate opportunities in molecular engineering for thermal management systems and thermal circuit design. [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]

This research navigates the application of simulation meta-modeling in understanding and controlling assembly line dynamics. Aimed at unveiling the relationship between input factors and output parameters in a production system, a precise meta-model was devised based on real system data and simulation statistics. The meta-model predicts parameteR-values within a verified validity range, simplifying complex variable relationships for enhanced decision-making in intricate systems. The accuracy of the model was notable within the tested range of CONWIP cards from 1 to 25, showcasing reliable output approximation. This study highlights the classical approach to simulation meta-modeling as cumbersome, propelling the quest for further simplification in analyzing complex production systems. The utilization of simulation meta-modeling emerged as a pivotal tool for validating complex simulation models and swiftly testing system sensitivity to input factor alterations. Noteworthy findings include the identification of an optimal number of CONWIP cards for maximizing throughput without excessive increases in Work-In-Progress or throughput time. The research also underscores the potential of 5th-degree polynomial models in approximating production performance and throughput time accurately, offering robust tools for informed decision-making. This venture marks a significant stride towards a more streamlined, accurate, and efficient analysis of complex production systems, showcasing the promising applicability of simulation meta-modeling in industrial engineering and production management. [ABSTRACT FROM AUTHOR]

Geostatistical techniques are applied to examine the life cycle of a mineral deposit. There are two main classes of geostatistical techniques: (1) deterministic techniques that include kriging and cokriging for a single best estimate, and (2) probabilistic techniques that include simulation, which infer probability distributions and simulate realizations to transfer multivariable and multilocation uncertainty through to larger-scale resource and reserve uncertainty. Probabilistic techniques are newer and more powerful in that they provide access to quantitative measures of uncertainty and models with correct spatial variability; however, they have not seen widespread application in all aspects of the life cycle of mines. Workflows and methodologies for the appropriate use of deterministic and probabilistic techniques have been discussed. Software, engineering practices and management expectations limit some applications. Applications have been reviewed, and enhancements are required to realize the full potential of geostatistical techniques, which have been discussed with examples. [ABSTRACT FROM AUTHOR]

Semitransparent photovoltaic (STPV) solar cells offer an immense opportunity to expand the scope of photovoltaics to special applications such as windows, facades, skylights, and so on. These new opportunities have encouraged researchers to develop STPVs using traditional thin‐film solar cell technologies (amorphous‐Si, CdTe, and CIGS or emerging solar cells (organic, perovskites, and dye‐sensitized). There are considerable improvements in both power conversion efficiency (PCE) and semitransparency of these STPV devices. This review studies the device structure of state‐of‐the‐art STPV devices and thereby analyzes the different approaches toward maximizing the product of PCE and average visible transmittance. The origins of PCE losses during the opaque‐to‐semitransparent transition in the different STPV technologies are discussed. In addition, critical practical aspects relevant to all STPV devices, such as compatibility of the top transparent electrode with the device structure, buffer layer optimization, light management engineering, scale‐up, and stability, are also reported. This overview is expected to facilitate researchers across different technologies to identify and overcome the challenges toward achieving higher light utilization efficiencies in STPVs. [ABSTRACT FROM AUTHOR]

Harmful algal and cyanobacterial blooms (HABs), occurring in inland and maritime waters, pose threats to natural environments by producing toxins that affect human and animal health. In the past, HABs have been assessed mainly by the manual collection and subsequent analysis of water samples and occasionally by automatic instruments that acquire information from fixed locations. These procedures do not provide data with the desirable spatial and temporal resolution to anticipate the formation of HABs. Hence, new tools and technologies are needed to efficiently detect, characterize and respond to HABs that threaten water quality. It is essential nowadays when the world's water supply is under tremendous pressure because of climate change, overexploitation, and pollution. This paper introduces Discrete Event System Specification-BLOOM, a novel framework for real-time monitoring and management of HABs. Its purpose is to support high-performance hazard detection with model-based system engineering and cyber-physical systems infrastructure for dynamic environments. [ABSTRACT FROM AUTHOR]

In the construction industry, there are a large number of project-based organizations (PBOs), where the efficiency of communication and collaboration among organizational members greatly impacts the success of projects. For PBOs employing both centralized and decentralized communication networks, it is worth delving into the question of under what circumstances which type of network will yield better results. Based on the IMO model and organizational learning theory, this paper conducts a grouped communication experiment involving 598 engineering management personnel to explore the differences in collective effectiveness of varying communication networks from the perspective of task urgency. Beyond task performance assessments, we have included organizational member perception to form evaluation criteria for collective effectiveness. Our research results show that under conditions of weak task urgency, decentralized networks yield higher collective effectiveness. Conversely, under conditions of strong task urgency, centralized networks demonstrate superior collective effectiveness. Furthermore, this study also verifies the mediating role of knowledge sharing behavior when task urgency is strong. This research provides significant managerial insights for the establishment of appropriate communication networks for PBOs in the construction industry. [ABSTRACT FROM AUTHOR]

The safety monitoring information of the dam is an indicator reflecting the operational status of the dam. It is a crucial source for analyzing and assessing the safety state of reservoir dams, possessing strong real-time capabilities to detect anomalies in the dam at the earliest possible time. When using neural networks for predicting and warning dam safety monitoring data, there are issues such as redundant model parameters, difficulty in tuning, and long computation times. This study addresses real-time dam safety warning issues by first employing the Empirical Mode Decomposition (EMD) method to decompose the effective time-dependent factors and construct a dam in a service state analysis model; it also establishes a multi-dimensional time series analysis equation for dam seepage monitoring. Simultaneously, by combining the Sparrow Optimization Algorithm to optimize the LSTM neural network computation process, it reduces the complexity of model parameter selection. The method is compared to other approaches such as RNN, GRU, BP neural networks, and multivariate linear regression, demonstrating high practicality. It can serve as a valuable reference for reservoir dam state prediction and engineering operation management. [ABSTRACT FROM AUTHOR]

The concept of smart contracts (SCs) is becoming more prevalent, and their application is gaining traction across many diverse scenarios. However, producing poorly constructed contracts carries significant risks, including the potential for substantial financial loss, a lack of trust in the technology, and the risk of exposure to cyber-attacks. Several tools exist to assist in developing SCs, but their limited functionality increases development complexity. Expert knowledge is required to ensure contract reliability, resilience, and scalability. To overcome these risks and challenges, tools and services based on modeling and formal techniques are required that offer a robust methodology for SC verification and life-cycle management. This study proposes an engineering framework for the generation of a robust and verifiable smart contract (GRV-SC) framework that covers the entire SC life-cycle from design to deployment stages. It adopts SC modeling and automated formal verification methodologies to detect security vulnerabilities and improve resilience, extensibility, and code optimization to mitigate risks associated with SC development. Initially, the framework includes the implementation of a formal approach, using colored Petri nets (CPNs), to model cross-platform Digital Asset Modeling Language (DAML) SCs. It also incorporates a specialized type safety dynamic verifier, which is designed to detect and address new vulnerabilities that can arise in DAML contracts, such as access control and insecure direct object reference (Idor) vulnerabilities. The proposed GRV-SC framework provides a holistic approach to SC life-cycle management and aims to enhance the security, reliability, and adoption of SCs. [ABSTRACT FROM AUTHOR]

Industrial companies are constantly confronted with a variety of challenges caused by competitive pressure, globalization, and changing customer behavior. The discipline of Industrial Engineering and Management (IEM) aims to educate the engineers for tomorrow that balance economic competitiveness, environmental protection, and social acceptance towards facing grand issues of the world, such as climate change, resource scarcity, and energy transition. However, the critical success factors of engineering education remain insufficiently studied. A realignment of engineering education and training requires, on the one hand, a deep understanding of the necessary competences and, on the other hand, an empirically based investigation of the relationship between competences and performance in the actual working environment. This paper conceptualizes a preliminary research model for measuring the impact of competence on the performance of IEM professionals. After discussing the IEM job profile and the core working areas of IEM professionals, the preliminary research model is presented, and future implications are derived. [ABSTRACT FROM AUTHOR]

In recent years, the Chinese government has recognized the important role of urban green space, and each province has formulated corresponding policies to increase the area of green space. However, China’s urban land development faces problems such as insufficient preliminary research, disorganized construction management, and chaotic organization and management. These problems are also reflected in urban garden projects. Compared with developed countries, China’s urban garden engineering technology and management in the modern sense are relatively backward. The study takes the garden engineering project management as a comprehensive research subject, and elaborates on it from two perspectives, which are project management content and quota design method. First, based on the Plan-Do-Check-Act (PDCA) cycle, a standardized management system with cost management, progress management, quality management and safety management as the main content is constructed. Then, the limit design method is developed utilizing multiple linear regression analysis. Finally, the limit design method is tested. The test results show that the limit design scheme proposed in the study can optimize the traditional limit design scheme by 53.34%. The quota design methods were evaluated, and the 10 sample items received a score range of 38.22 to 86.09 points, with an average score of 62.36 points. There are six projects with scores above 60 and 4 projects with scores below 60. This verifies that the quota design is reasonable. In terms of theory, the research combines the current laws and regulations and the administrative management system to construct a complete urban landscape engineering management system, which can provide a reference for solving the existing problems in the current urban landscape engineering management. In practice, although the proposed limit design method has a prediction error of 7.84%, it is still less than 10% of the legal ceiling. In engineering practice, it can effectively strengthen construction management and organizational management. Overall, the study proposes to build a standardized management system based on the PDCA cycle, with cost management, schedule management, quality management, and safety management as the main contents. The limited design method constructed using multiple linear regression analysis has effectiveness and practicality. [ABSTRACT FROM AUTHOR]

Green high-performance concrete is widely adopted in the construction industry to reduce building energy consumption and enhance quality. Prompt delivery of green high-performance concrete is vital for successful project completion because the processes relating to green high-performance concrete are the critical path in most cases, namely that the project delay will occur if the works involving green high-performance concrete are delayed caused by late delivery of green high-performance concrete. This research proposes an online scheduling model, which takes into account the idle time of green high-performance concrete in manufacturing, that is the time from release until start, in order to improve prompt delivery of green high-performance concrete. Formally, there are irrelevant jobs arriving online over time and the fundamental knowledge of each job J j is not declared until it is released at time r j . Our objective is to minimize the total completion time plus the total soft penalty cost of all jobs, where "soft penalty" cost of job J j is α (S j - r j) with soft penalty coefficient α > 0 , S j and r j are starting processing time and release date of job J j , respectively. For this problem, by applying the technique "Peeling Onion", it is explicitly derived that the classic online algorithm DSPT is best possible with competitive ratio 2 + α . [ABSTRACT FROM AUTHOR]

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This study on total suspended matter (TSM) in the Pearl River Estuary established a regression analysis model using Landsat 8 reflectance and measured TSM data, crucial for environmental management and engineering projects. High coefficients of determination (>0.6) were reported for the selected models. TSM concentration was notably high in 2013, peaking at 180 mg/L during the flood season and 80 mg/L in the dry season. In contrast, 2020 saw lower concentrations. Similar spatial distribution patterns were observed during dry and flood seasons, with high nearshore and low offshore TSM concentrations. Statistical analyses revealed natural factors (precipitation and runoff) as major influencers of the TSM distribution, with human activities presenting localized, limited impacts, except under long-term and large-scale conditions. Over time, the influence of large-scale water-based construction, such as the Hong Kong–Zhuhai–Macao Bridge, on TSM distribution became significant. [ABSTRACT FROM AUTHOR]

This study addresses the critical challenge of predicting sediment behavior in a semi-enclosed estuary, where the interplay between artificial freshwater discharge and seawater significantly impacts turbidity. Such environments are characterized by complex hydrodynamic interactions that lead to cycles of sediment settling and resuspension, influenced by tidal forces. To tackle this problem, we employed machine learning, leveraging its capability to analyze and predict complex non-linear phenomena. Our approach involved extensive transect observations conducted over two years, encompassing 11 ebb tide and 9 flood tide cycles. These observations were crucial for training the machine learning model, ensuring it captured the nuanced dynamics of sediment behavior under varying hydrodynamic conditions. The necessity of this research lies in its potential to enhance our understanding of sediment dynamics in estuaries, a vital aspect for environmental management and engineering projects. The findings demonstrate a promising alignment between the machine learning model's predictions and the theoretically assumed sediment behavior, highlighting the model's effectiveness in deciphering and predicting turbidity patterns in these challenging environments. [ABSTRACT FROM AUTHOR]

The construction engineering and management sector is facing rapid technological disruption due to the extensive adoption of digital tools. The main aim of this research was to assess the accuracy and usefulness of web-based instruments used for the evaluation and mensuration of built environment characteristics. Through on-site visits and two web-based tools, a digital management system based on fuzzy logic was used to evaluate the functional service life of two heritage timber buildings in Valdivia (Chile). The assessments of 40 professional experts (4 on-site visual inspections and 36 virtual inspections) were compared to evaluate the relative accuracy and usefulness of the on-site and web-based instruments. Web-based audits, which used Google Street View and Google Maps, tended to agree strongly with on-site audits on the functional degradation of the timber structures (vulnerabilities and external hazard affections). While web-based tools do not offer a perfect substitute for on-site audits, they do allow for preliminary audits to be performed accurately from remote locations, potentially saving time and cost and increasing the effectiveness of subsequent on-site visits. The study findings may be useful to industry stakeholders in understanding the early impacts of the use of new remote digital inspection tools on the construction industry. [ABSTRACT FROM AUTHOR]

The importance of skid resistance performance assessment in pavement engineering and management is crucial due to its direct influence on road safety features. This paper provides a new approach to skid resistance predictive model definition based on experimentally obtained texture roughness parameters. The originally developed methodology is based on a photogrammetry technique for pavement surface data acquisition and analysis, named the Close-Range Orthogonal Photogrammetry (CROP) method. Texture roughness features were analyzed on pavement surface profiles extracted from surface 3D models, obtained by the CROP method. Selected non-standard roughness parameters were used as predictors in the skid resistance model. The predictive model was developed by the partial least squares (PLS) method as a feature engineering procedure in the regression analysis framework. The proposed model was compared to the simple linear regression model with a traditional texture parameter Mean Profile Depth as the predictor, showing better predictive strength when multiple non-standard texture parameters were used. [ABSTRACT FROM AUTHOR]

The research aims to assess the quality of construction and construction projects in Egypt (Damanhour City) to monitor and follow up on the reality of the implementation of engineering projects and determine their percentage compared to the methods and international standards adopted for that and then identify the reasons and factors affecting the quality of project implementation. To achieve this, we evaluated the quality and identified the problems related to it in a sample of construction projects implemented in the year 2021, and this was done by measuring the percentage of conformity to quality requirements using checklists according to the standards of the quality management program BIM 360 field, and the conformity rate was 88% and indicates the existence of a real problem according to international standards, so problems and deviations related to quality were identified and the reasons leading to the occurrence of these problems were identified, which were classified into three factors, namely laws, regulations and compliance It has technical factors and administrative factors, which constitute the basis that must be based on it and try to improve the quality of projects, and summarized the research to the need to work by the concerned authorities to reformulate laws related to engineering work and develop them with the identification of systems and procedures to improve technical and administrative factors in engineering projects. [ABSTRACT FROM AUTHOR]

Purpose: The main purpose of this paper is to conceptualize and operationalize paradoxes that are significant in the engineering change management (ECM) process. The following research question was stated: What are the paradoxes that influence the ECM process, and how can they be measured? Design/methodology/approach: The study is divided into two parts: conceptualization and operationalization. Conceptualization involved a literature review, which allowed for identifying initial categories of paradoxes that were later verified during in-depth personal interviews with the study participants. The subsequent operationalization involved developing a measurement tool which is a semantic differential scale that allows for assessing the perceived current and desired approach to solving paradoxes affecting ECM. The tool was revised based on in-depth personal interviews with participants of the ECM process. For the study, a manufacturing company which implemented a change management process was selected. Findings: Four general groups of paradoxes previously identified in the paradox theory literature (i.e. learning, belonging, performing, and organizing) were used to develop a list of paradoxes specific to the ECM process. In-depth interviews allowed to obtain data from experienced participants of the process, which was used to verify the preliminary list. The result of this verification was the list of 12 paradoxes that was included in the final measurement tool. Research limitations: Even though the study involved well-informed participants who are experienced in the ECM process, they represented a single company. Additionally, the proposed research tool may require further verification in subsequent studies. Practical implications: The knowledge regarding paradoxes specific to the ECM process and the created tool can be helpful for change administrators, project managers, and other stakeholders. They allow to define the areas of most significant tensions in the ECM process that require attention and effort. Originality/value: This paper's main value pertains to two different domains- cognitive and methodic. The undertaken attempt to include paradoxes and their significance in the analysis of the ECM process broadens the current understanding of process determinants. The proposed research tool, which is based on a well-grounded theoretical approach to paradoxes and adapted to fit the ECM process, can be used in further research. [ABSTRACT FROM AUTHOR]

With the increasing number of large-scale integrated refining and chemical projects at home and abroad, the original engineering project management model can no longer meet the needs of modern management. The main problems in traditional refining engineering project management are analyzed, and the main strategies to adapt to modern engineering construction management are proposed, such as doing a good job in early planning, introducing an IPMT professional management team, clarifying project management strategies, ensuring third-party supervision responsibilities, improving management level, implementing interface management and construction procedure management. By establishing scientific management methods, implementing and improving management elements, the efficiency of large-scale refining engineering projects has been significantly improved. [ABSTRACT FROM AUTHOR]