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The paper aims to highlight the significance and place of information-seeking in finding improved solutions in the design of innovative products. The observation method was used to identify a way to increase the efficiency of searching for information needed when pursuing a creative solution to a design problem. The study was based on the experience gained in professional activities in higher education for industrial engineering and in the classes held for two academic subjects, when students' creativity was stimulated, including in the search for information. One conclusion is that an experienced designer or researcher should make sketches and notes immediately after formulating the design objective. In this way, the possible inhibitory influence exerted by the results of a deep search for information could be avoided. In the case of a novice designer, it may be necessary to have a minimum collection of general information in the design topic field to familiarize the designer more with the problem addressed. The novice designer could thus develop a few sketches and notes and then go through a deeper stage of information-seeking. Another conclusion was that a certain minimum amount of scientific and technical knowledge is necessary to use technical creativity effectively. [ABSTRACT FROM AUTHOR]

Positive Energy Districts (PEDs) are a promising approach to urban energy transformation, aiming to optimize local energy systems and deliver environmental, social and economic benefits. However, their effectiveness and justification for investment rely on understanding the additional value they provide (additionality) in comparison to current policies and planning methods. The additionality perspective is not used yet in current evaluations of PED demonstrations and pilots. Therefore, this paper introduces the concept of additionality in the evaluation of PEDs, focusing on the additional benefits they bring and the circumstances under which they are most effective. We discuss the additionality of PEDs in addressing the challenges of climate neutrality and energy system transformation in three European cities that are funded by the European Commission’s H2020 Programme. It should be noted that given the ongoing status of these projects, the assessment is mainly based on preliminary results, as monitoring is still ongoing and quantitative results are not yet available. The paper discusses the drivers and barriers specific to PEDs, and highlights the challenges posed by technical complexities, financing aspects and social and legal restrictions. Conclusions are drawn regarding the concept of additionality and its implications for the wider development of PEDs as a response to the challenges of climate neutrality and energy system transformation in cities. We conclude that the additionality perspective provides valuable insights into the impact and potential of PEDs for societal goals and recommend this approach for use in the final evaluation of R&I projects involving PEDs using actual monitored data on PEDs.

The objective of this work is to analyze and compare AC microgrid (ACMG) solutions to introduce the topic to new researchers. The methodology used to achieve this goal is a systematic literature review using five questions: (1) How have ACMGs evolved in five years? (2) What are the standards for ACMGs? (3) What are the different schemes for connecting MGs to the utility grid? (4) What are the different control schemes in ACMGs? (5) What is an appropriate way to compare results when working with ACMGs? The articles were published in Q1/Q2 journals as based on either the Scimago Journal Rank (SJR) and/or the Journal Citation Report (JCR) between 2018 and 2022 and were from three databases: (1) Web of Science (WoS), (2) Scopus, and (3) IEEE Xplore. Publications not describing pure ACMGs, review papers, publications not related to the questions, and papers describing work that did not meet a quality assessment were excluded, resulting in 34 articles being included in this review. Results show: (1) the energy sources and AC bus nature of microgrids over five years, (2) the identification and quantification of cited standards for microgrids, (3) the pros and cons of different schemes for connecting an AC microgrid to the main grid, (4) the control schemes, classified in a hierarchical control structure, and (5) the simulation tools and experimental benches used in microgrids. Most studies considered a generic energy source and a low-voltage three-phase AC bus, 16 standards were found, and the most cited standard was IEEE Standard 1547. The most common connection scheme to the utility grid was a direct connection, most of the works proposed a modification to a hierarchical control system scheme, and the most common simulation tool was MATLAB. The preferred experimental setup consisted of parallel inverters for testing a control scheme, a prototype when proposing a power electronic system, and a laboratory microgrid for testing fault detection methods.

Cyber-physical systems, cloud computing, the Internet of Things, and big data play significant roles in shaping digital and automated landscape manufacturing. However, to fully realize the potential of these technologies and achieve tangible benefits, such as reduced manufacturing lead times, improved product quality, and enhanced organizational performance, new decision support models need development. Game theory offers a promising approach to address multi-objective problems and streamline decision-making processes, thereby reducing computational time. This paper aims to provide a comprehensive and up-to-date systematic review of the literature on the application of game theory models in various areas of digital manufacturing, including production and capacity planning, scheduling, sustainable production systems, and cloud manufacturing. This review identifies key research themes that have been explored and examines the main research gaps that exist within these domains. Furthermore, this paper outlines potential future research directions to inspire both researchers and practitioners to further explore and develop game theory models that can effectively support the digital transformation of manufacturing systems.

This paper presents an experimental campaign conducted next to the Condoroma dam, in Perú, at 4075 m a.s.l. The tests carried out in this paper were conducted in a 21 m long channel located at the toe of Condoroma dam. The setup consisted of a series of standard profile spillways with a vertical upstream face of up to five different dimensionless heights (P/Hd) ranging from 0.5 to 2. The experimental results indicated that, the P/Hd ratio influences the discharge coefficients in Condoroma, and P/Hd ≥ 1 values are recommended for the design of the spillway profile. In addition, for all the P/Hd ratios studied, the discharge coefficients adjusted to the Condoroma altitude were lower than those reported by classical formulations used in conventional spillway designs. Finally, a generalized equation is proposed to estimate the discharge coefficient for standard spillways located in dams at similar elevations above sea level.

The Digital Twin is one of the major technology trends of the last decade. During the course of its rapid expansion into various fields of application, many definitions of the Digital Twin emerged, tailored to its respective applications. Taxonomies can cluster the diversity and define application-specific archetypes. This paper presents a systematic characterization of the Digital Twin in the context of structural mechanics and lightweight design. While the importance of a shared understanding and the development of holistic solutions for implementing Digital Twins in various application areas is widely recognized, a general framework for implementing Digital Twins in structural mechanics has not yet been established. In this paper, we systematically characterize Digital Twins and develop a framework for their application in structural mechanics, enabling the digital design and monitoring of structures for improved performance and maintenance strategies. The key contributions include collecting and clustering design and operational requirements and deriving two central archetypes: structure-designing and structure-monitoring Digital Twins. The primary goal is to reduce the complexity of conceptualizing Digital Twins of structures by providing a preliminary framework and reconsidering the Digital Twins of structures as a holistic system throughout the product life cycle. Overall, in this paper, we take a systematic approach to enhancing the conceptualization and implementation of Digital Twins in structural mechanics.

In today’s power system landscape, renewable energy (RE) resources play a pivotal role, particularly within the residential sector. Despite the significance of these resources, the intermittent nature of RE resources, influenced by variable weather conditions, poses challenges to their reliability as energy resources. Addressing this challenge, the integration of an energy storage system (ESS) emerges as a viable solution, enabling the storage of surplus energy during peak-generation periods and subsequent release during shortages. One of the great challenges of ESSs is how to design ESSs efficiently. This paper focuses on a distributed power-flow system within a smart home environment, comprising uncontrollable power generators, uncontrollable loads, and multiple energy storage units. To address the challenge of minimizing energy loss in ESSs, this paper proposes a novel approach, called energy-efficient storage capacity with loss reduction (SCALE) scheme, that combines multiple-load power-flow assignment with a load-shifting algorithm to minimize energy loss and determine the optimal energy storage capacity. The optimization problem for optimal energy storage capacity is formalized using linear programming techniques. To validate the proposed scheme, real experimental data from a smart home environment during winter and summer seasons are employed. The results demonstrate the efficacy of the proposed algorithm in significantly reducing energy loss, particularly under winter conditions, and determining optimal energy storage capacity, with reductions of up to 11.4% in energy loss and up to 62.1% in optimal energy storage capacity.

As a result of the Internet of Things (IoT), smart city infrastructure has been able to advance, enhancing efficiency and enabling remote management. Despite this, this interconnectivity poses significant security and privacy concerns, as cyberthreats are rapidly adapting to exploit IoT vulnerabilities. In order to safeguard privacy and ensure secure IoT operations, robust security strategies are necessary. To detect anomalies effectively, intrusion detection systems (IDSs) must employ sophisticated algorithms capable of handling complex and voluminous datasets. A novel approach to IoT security is presented in this paper, which focuses on safeguarding smart vertical networks (SVNs) integral to sector-specific IoT implementations. It is proposed that a deep learning-based method employing a stacking deep ensemble model be used, selected for its superior performance in managing large datasets and its ability to learn intricate patterns indicative of cyberattacks. Experimental results indicate that the model is exceptionally accurate in identifying cyberthreats, exceeding other models, with a 99.8% detection rate for the ToN-IoT dataset and 99.6% for the InSDN dataset. The paper aims not only to introduce a robust algorithm for IoT security, but also to demonstrate its efficacy through comprehensive testing. We selected a deep learning ensemble model due to its proven track record in similar applications and its ability to maintain the integrity of IoT systems in smart cities.

This paper proposes a robust design-optimization approach for eBike drive units that incorporates the highly variable driver-dependent load collectives and system conditions into a fatigue calculation. In an initial step, the relevant influences and loads on the investigated system are examined and reviewed in relation to the current normative requirements. From a methodical viewpoint, this paper presents a surrogate-based simulation-based approach to assess reliability across the entire geometry according to a probabilistic fatigue calculation. The probabilistic evaluation considers the several measured load collectives of different drivers and driving scenarios to enable a robust and type-oriented bike design. In addition to methods of fatigue calculation, this approach also includes common methods of order reduction and reliability-based design optimization. To avoid additional uncertainties in the calculation, this approach considers a complex critical-plane-based multiaxial-fatigue calculation to correctly evaluate the multiaxial and non-proportional stress state across the whole geometry. A data-based surrogate model that supports the fatigue calculation by predicting the load across the given uncertainties is the key to the efficient assessment of the service life of the eBike. Lastly, the identified uncertainties in the design of eBike drive units are investigated and evaluated by this method.

Due to climate change, Egypt has recently suffered from recurring electricity crises. Despite efforts made to increase electricity production in Egypt, recently, in the summer months, the energy demand has increased at unprecedented rates, especially in the housing sector. Therefore, the government and homeowners should work together to improve the energy performance of residential buildings. This paper aimed to develop a decision-making tool that helps homeowners choose optimal energy retrofit measures that suit their priorities. The study began with the data-collection and case study selection. Then, the thermal evaluation of the base case for dwellings in the case study was conducted through simulation runs using the DesignBuilder v7.1 software. Then, the optimal envelope energy retrofitting measures were determined, followed by a retrofitting-measure scenario simulation process. Then, the payback periods were calculated for all scenarios, and the tool database was developed using an Excel spreadsheet. Finally, the user interface for envelope energy retrofitting measures for gated communities (EERMGCs) tool was designed by Visual Basic for Applications. EERMGCs, the tool developed in this paper, is a simple, multi-objective and interactive tool that provides the optimal envelope retrofit measures according to user priorities, either a specific budget, the shortest payback period, the lowest possible costs, or the highest energy saving rate. The outcome of this research is developing a framework that can be considered a basis for developing decision-making tools for gated community housing in Egypt.

In the context of current societal challenges, such as climate neutrality, industry digitization, and circular economy, this paper addresses the importance of improving recycling practices for electric vehicle (EV) battery packs, with a specific focus on lithium–ion batteries (LIBs). To achieve this, the paper conducts a systematic review (using Google Scholar, Scopus, and Web of Science as search engines), considering the last 10 years, to examine existing recycling methods, robotic/collaborative disassembly cells, and associated control techniques. The aim is to provide a comprehensive and detailed review that can serve as a valuable resource for future research in the industrial domain. By analyzing the current state of the field, this review identifies emerging needs and challenges that need to be addressed for the successful implementation of automatic robotic disassembly cells for end-of-life (EOL) electronic products, such as EV LIBs. The findings presented in this paper enhance our understanding of recycling practices and lay the groundwork for more precise research directions in this important area.

Fused deposition modeling (FDM) technology is an emerging technology with promising applications, with the nozzle playing a crucial role in extrusion, heating, and material ejection. However, most current extrusion-based 3D printers handle only single-material printing, making the integration of multiple materials through a single nozzle challenging due to compromised quality and clogging risks. This paper introduces a method to design multi-material 3D printing nozzles using the Theory of Inventive Problem Solving (TRIZ) and knowledge graph (KG). By optimizing design and leveraging TRIZ’s contradiction resolution principle, this study addressed bottlenecks and complexities in multi-material nozzle design, providing insightful recommendations. A patent knowledge graph focused on spray nozzles was created, storing material properties, design elements, and constraints for enhanced knowledge sharing. Building on identified challenges and recommendations, the study utilized keyword searches and associative paths in the knowledge graph to guide designers in generating innovative solutions. Validation was achieved through two distinct nozzle design models resulting from guided innovations. The TRIZ-KG methodology presented in this paper provides designers with a systematic cognitive framework to empower designers in overcoming technical obstacles and proposing precise solutions.

Combining different features inspired by biological systems is necessary to obtain uncommon and unique multifunctional biologically inspired conceptual designs. The Expandable Domain Integrated Design (xDID) model is proposed to facilitate the multifunctional concept generation process. The xDID model extends the previously defined Domain Integrated Design (DID) method. The xDID model classifies biological features by their feature characteristics taken from various case-based bio-inspired design examples into their respective geometric designations called domains. The classified biological features are mapped to the respective plant and animal tissues from which they originate. Furthermore, the paper proposes a representation of the functions exhibited by the biological features at the embodiment level as a combination of the integrated structure (multiscale) and the structural strategy associated with the integrated structure. The xDID model is validated using three multifunctional bio-inspired design case studies at the end of the paper.

In small apparel manufacturing, unit price determination is often based on production duration given by customers and design complexity rather than information relating to internal labor resources. However, labor expertise and skills are critical factors that outweigh the machinery and technology in small and medium apparel companies. The quality of the product greatly depends on the experience and delicacy of the tailors. Using data on labor skill and wage levels in the planning process will benefit human resource utilization, increasing productivity, and profits effectively. This paper proposes a general mathematical model for task allocation and cost optimization for small and medium apparel companies. The model handles task allocation and cost minimization problems that must ensure processing time requirements and balance workloads for operators. The developed model tests two case studies in a published paper. The results prove that although the proposed model is simple, it has high applicability and efficiency in solving allocation optimization problems. The authors then integrate the formulations into a Standalone desktop app in the MATLAB “App designer” module. With a standalone desktop app, end users can enjoy the application. This app has a user-friendly design. Users unfamiliar with computers or planners with no background in programming can use the app to tackle similar optimization problems. The proposed mathematical model can further expand to include more complex issues in apparel companies and can also be a good reference for other fields.

The concept of Positive Energy Districts (PEDs) has emerged as a promising approach to achieving sustainable urban development. PEDs aim to balance the energy demand and supply within a district while reducing the carbon footprint and promoting renewable energy sources. Urban–Industrial Symbiosis (UIS) is another approach that involves the exchange of energy and resources between industrial processes and nearby urban areas to increase efficiency and reduce waste. Combining the concepts of PED and UIS can create self-sufficient, sustainable, and resilient districts. As the analysis and implementation of such systems are barely studied in North America, this research study was structured to fill the gap by evaluating the financial and environmental advantages of this combination. This study proposes a methodology to design a heat transmission system; then, it is applied to the case of a paper-making factory and a multifunctional heritage building in Montreal, Canada. The results show that the building’s new heating system can generate sufficient heat while emitting near-zero direct emissions. Overall, this paper argues that combining the concepts of PED and UIS can lead to a more sustainable and resilient urban area, and provides a roadmap for achieving this goal.

Coupling is a key concept in the field of embodied interaction with digital products and systems, describing how digital phenomena relate to the physical world. In this paper, we present a Research through Design process in which the concept of coupling is explored and deepened. The use case that we employed to conduct our research is an industrial workplace proposed by Audi Brussels and Kuka. Our aim was to enrich this workplace with projection, or Spatial Augmented Reality, while focusing on operator interaction. We went through three successive design iterations, each of which resulted in a demonstrator. We present each of the three demonstrators, focusing on how they propelled our understanding of coupling. We establish a framework in which coupling between different events, be they physical or digital, emerges on four different aspects: time, location, direction, and expression. We bring the first three aspects together under one heading—coupling of meaning—and relate it to ease of use and pragmatic usability. We uncover the characteristics of the fourth aspect—coupling of expression—and link it to the psychological wellbeing of the operator in the workplace. We conclude this paper by highlighting its contribution to the embodied interaction research agenda.

Breast cancer poses the greatest long-term health risk to women worldwide, in both industrialized and developing nations. Early detection of breast cancer allows for treatment to begin before the disease has a chance to spread to other parts of the body. The Internet of Things (IoT) allows for automated analysis and classification of medical pictures, allowing for quicker and more effective data processing. Nevertheless, Fog computing principles should be used instead of Cloud computing concepts alone to provide rapid responses while still meeting the requirements for low latency, energy consumption, security, and privacy. In this paper, we present CanDiag, an approach to cancer diagnosis based on Transfer Deep Learning (TDL) that makes use of Fog computing. This paper details an automated, real-time approach to diagnosing breast cancer using deep learning (DL) and mammography pictures from the Mammographic Image Analysis Society (MIAS) library. To obtain better prediction results, transfer learning (TL) techniques such as GoogleNet, ResNet50, ResNet101, InceptionV3, AlexNet, VGG16, and VGG19 were combined with the well-known DL approach of the convolutional neural network (CNN). The feature reduction technique principal component analysis (PCA) and the classifier support vector machine (SVM) were also applied with these TDLs. Detailed simulations were run to assess seven performance and seven network metrics to prove the viability of the proposed approach. This study on an enormous dataset of mammography images categorized as normal and abnormal, respectively, achieved an accuracy, MCR, precision, sensitivity, specificity, f1-score, and MCC of 99.01%, 0.99%, 98.89%, 99.86%, 95.85%, 99.37%, and 97.02%, outperforming some previous studies based on mammography images. It can be shown from the trials that the inclusion of the Fog computing concepts empowers the system by reducing the load on centralized servers, increasing productivity, and maintaining the security and integrity of patient data.

The design optimization of mechanisms is promising as it results in more energy-efficient machines without compromising performance. However, machine builders do not apply state-of-the-art methods, as these algorithms require case-specific theoretical analysis. Moreover, the design synthesis approaches in the literature predominantly utilize heuristic optimizers, leading to suboptimal local minima. This paper introduces a widely applicable workflow, guaranteeing the global optimum. The constraints describing the feasible region of the possible designs are essential to find the global optimum. Therefore, kinematic analysis of the point-to-point planar four-bar mechanism is discussed. Within the feasible design space, objective value samples were generated through the CAD multi-body software. These motion simulations determine the required torque to fulfill the movement for a combination of design parameters. This replaces the cumbersome analytic derivation of the torque. This paper introduces sparse interpolation techniques to avoid brute force sampling of the design space. The advantage of this approach is that it is easily scalable to more design parameters, as the interpolation method minimizes the number of necessary samples. This paper explains the mathematical background of our developed interpolation approach. However, a step-by-step procedure is introduced to allow the employment of the interpolation technique by machine designers without the necessity to understand the underlying mathematics. Finally, the mathematical expression, obtained from the interpolation, enables applying global optimizers. In a case study of an emergency ventilator mechanism with three design parameters, 1870 CAD motion simulations allowed reducing the RMS torque of the mechanism by 67%.

The performance of electric vehicle (EV) drivetrains depends on the power capability of individual components, including the battery pack, motor drive, and electric motor. To ensure safety, maximum power must be limited by considering the constraint of the weakest component in the drivetrain. While there exists a large body of work that discusses state-of-power (SoP) estimation for individual components, there is no work that considers all the components’ limiting factors at once. Moreover, research on how to use these limits to adjust the performance at the system level has been rare. In this paper, the SoPs of the components are used to estimate the state-of-function (SoF) of the EV drivetrain. The SoF is defined as the maximum charge/discharge power that can be sourced and/or sunk by the drivetrain without violating the safety limits of its components. The component-level SoP estimations are fulfilled using several digital algorithms based on recursive least-squares (RLS) and Kalman filters (KFs), as well as by taking into account specific limiting conditions such as high driving altitude and ambient temperatures. An EV driven by a hybrid energy storage system based on a battery/supercapacitor, and a permanent-magnet synchronous motor is considered the use case. Based on the drivetrain SoF estimation, we propose two de-rating schemes to ensure that the drivetrain safety limits will be respected: adaptive cruise control and adaptive adjustment of pedal sensitivity. The de-rating schemes are introduced to a so-called recommender system that is implemented in MATLAB/STATEFLOW. The recommender system provides advisory feedback to the driver to switch to a different driving mode to ensure safety. The simulation results over a standard drive cycle using MATLAB/SIMULINK and STATEFLOW show the effectiveness of the proposed design at both component and system levels. The paper also proposes an implementation concept for the integration of the proposed recommender system into the advanced driver assistance system (ASAS).

While many design methods are developed, tested and reported in the literature, their utilization in industry practice remains low. Design methods are receiving substantial scholarly focus and are considered central to efficiently achieving reliable outcomes in the engineering design process. They are particularly vital as industrial companies increasingly transition to integrated offerings of products and services with a lifecycle perspective, leading to additional uncertainty and complexity. Thus, the presented research aims to support method selection and development, focusing on resource-efficient offerings. This is achieved through an in-depth, practice-centric, empirical study of users' requirements of design methods and the corresponding characteristics of design methods aimed at meeting these requirements in resource-efficient offerings. Highly relevant insight supporting a broad set of stakeholders is reported. Firstly, the user requirements and method characteristics reported support practitioners seeking to identify a design method fitting their needs. Secondly, academics and practitioners aiming to enhance the usefulness and impact of a design method may benefit from considering these requirements and characteristics during method development. Lastly, the systematic approach taken in this research can be applied by both method developers and potential users to identify additional requirements and corresponding characteristics specific to their conditions. Two use cases for the results attained are reported, focusing on applying the research results for method selection and deriving overall guidelines for developing design methods directed toward resource-efficient offerings. [ABSTRACT FROM AUTHOR]

The coordination of two collaborative robots to handle and hold huge parts is the main topic of this research. This study shows how flexible systems may accommodate large-volume components while situating components with a displacement precision between robots of no more than 10 mm into the parts, with the assistance of a single operator. The robots must be able to keep the parts in place while coordinating their movements to handle the parts and reducing external stressors. This paper suggests using collaborative robots to integrate flexible tools for adaptability to various elements in order to accomplish this goal without endangering the operators. The software architecture is described in full in this paper, including machine states to choose task executions, robot referencing in the workspace, remote monitoring via the digital twin, generation paths, and distributed control using a high-level controller (HLC).

The concept of i-Energy as a new smart demand-side energy management system is proposed, which can realize the versatile and efficient control of e-power flows between distributed generators, numerous appliances, and energy storage systems in the home domain, factories, offices, and local communities. The Energy on Demand (EoD) system is proposed, which is the automatic power control and management system that supplies power to home appliances based on the power demand requests issued from the home appliances. The EoD system can guarantee the reduction in total power consumption by implementing a ceiling control while keeping the quality of life (QoL) of the home user considering the limitation of power supply. This paper proposes an adaptive battery storage management and control method based on the EoD system, which we call the “storage-supported EoD system”. In particular, the storage-supported EoD system can handle multiple power generators, including storage batteries. The overall goals of this paper are not limited to the extension of multiple power supplies only; rather, it provides additional contributions, which are (i) extend the existing power consumption control of home appliances and peak demand shift control (i.e., EoD system) by adding a second power source, i.e., a storage battery system; (ii) propose adaptive storage system design and management for the EoD system; (iii) realize minimum storage capacity for large peak power consumption; and (iv) minimize the home user’s discomfort level due to the limited power supply of one power source. The simulation results have shown the effectiveness of the proposed algorithm with a couple of experiments using real-life data in the smart apartment room. Additionally, simulation results are presented to compare and evaluate the proposed system performance with the EoD system.

This paper describes the evolution of the Assistant Personal Robot (APR) project developed at the Robotics Laboratory of the University of Lleida, Spain. This paper describes the first APR-01 prototype developed, the basic hardware improvement, the specific anthropomorphic improvements, and the preference surveys conducted with engineering students from the same university in order to maximize the perceived affinity with the final APR-02 mobile robot prototype. The anthropomorphic improvements have covered the design of the arms, the implementation of the arm and symbolic hand, the selection of a face for the mobile robot, the selection of a neutral facial expression, the selection of an animation for the mouth, the application of proximity feedback, the application of gaze feedback, the use of arm gestures, the selection of the motion planning strategy, and the selection of the nominal translational velocity. The final conclusion is that the development of preference surveys during the implementation of the APR-02 prototype has greatly influenced its evolution and has contributed to increase the perceived affinity and social acceptability of the prototype, which is now ready to develop assistance applications in dynamic workspaces.

This paper presents a mechanism for active power sharing among multiple dispatchable and distributed generation units within a micro grid comprising one or multiple interconnections with the main grid. Ideally, a micro grid should act as a constant load or a constant voltage source when connected to the main grid. However, to achieve ideal operation, natural load variations and the intermittency of renewable energy sources within the microgrid need to be adequately and timely compensated for by dispatchable power sources. While several control algorithms have been reported in the literature to achieve ideal microgrid operation, the majority of the proposed methods assumed a micro grid with a single interconnection to the main grid. In the real world, micro grids may have to maintain multiple live links with the main grid for several technical and operational reasons such as reliability, power-dispatch restriction, and operational limitations requirements. Therefore, a new method of active power sharing is proposed in this paper, which is equally effective for micro grids with one or multiple grid connections. The robustness of the proposed method is examined under different microgrid operating conditions. The results reveal the flexibility of the proposed method to adapt under various real-world operating conditions.

This paper aims to provide a case study related to two small and medium-sized enterprises (SMEs) of the Italian footwear supply chain, comparing sales and production data from pre-pandemic years with those affected by the COVID-19 (SARS-CoV-2) pandemic. Specifically, two Tuscany companies in the world of fashion footwear sector have been assisted in the analysis of their supply chains. In particular, the case research method has been employed for theory building to evaluate how companies reacted to the disruption caused by the COVID-19 pandemic to focus on potential resilience strategies that could be adopted to deal with a disruption, such as that caused by the COVID-19 pandemic. Specifically, in order to understand the dynamics of the supply chains, the standard production processes were initially studied and mapped, analyzing in detail the planning, programming, and control phase. After conducting a descriptive analysis of the data, possible resilience factors of SMEs’ fashion supply chains have been extracted, and strategies that SMEs could adopt to better cope with the disruption caused by the pandemic have been suggested. The outcomes of this study can be used by decision-makers to predict the operative and long-term impacts of epidemic outbreaks on the supply chains with some suggestions of potential resilience improvement strategies. The paper is concluded by summarizing the most important insights and outlining future research agenda.

The present paper is devoted to the seismic design of steel frames constituted by multistep I-shaped cross-section beam elements. The proposed design problem formulation is aimed at protecting the connections among beams and columns. In particular, reference is made to beams welded at their ends to appropriate steel plates connected by bolts to the columns. Therefore, the protection against brittle failure of the beam end sections is ensured by appropriate constraints of the optimal design problem. A useful comparison is made between the adoption of the so-called Reduced Beam Sections (RBS) and the use of multistep beam elements. In particular, the RBS approach here considered is the well-known dogbone technique consisting of reducing the width of the beam cross-sections in correspondence with suitably located beam portions, while the typical multistep beam element is constituted by a factory-made I-shaped uniform piecewise profile. To perform the necessary comparison, reference is made to a three-story, two-span plane steel frame constituted by elastic, perfectly plastic material and subjected to static and seismic loads. The load conditions and the relevant combinations have been imposed in compliance with the Italian structural code. The frame is first studied as constituted by European standard steel profiles on sale, and the related design is obtained using the optimization tool contained in SAP2000 software. A linear dynamic analysis is performed to determine the response of the frame. Later, the same frame, either equipped with dogbone and constituted by multistep beam elements, subjected to serviceability load conditions, is studied in terms of inter-story drifts and beam deflections. The geometry of the multistep beam elements is obtained by the solution to the proposed optimization problem. Furthermore, a nonlinear static analysis is performed to evaluate the capacity curves of the same frames. The results obtained for the frames equipped with the described different devices, compared with those related to the original frame, provide very interesting information on the sensitivity of the seismic response of the structure, showing the full reliability of the multistep beam element approach.

This paper introduces an innovative hybrid wireless power transfer (H-WPT) scheme tailored for IIoT networks employing multiple relay nodes. The scheme allows relay nodes to dynamically select their power source for energy harvesting based on real-time channel conditions. Our analysis evaluates outage probability within decode-and-forward (DF) relaying and adaptive power splitting (APS) frameworks, while also considering the energy used by relay nodes for ACK signaling. A notable feature of the H-WPT scheme is its decentralized operation, enabling relay nodes to independently choose the optimal relay and power source using instantaneous channel gain. This approach conserves significant energy otherwise wasted in centralized control methods, where extensive information exchange is required. This conservation is particularly beneficial for energy-constrained sensor networks, significantly extending their operational lifetime. Numerical results demonstrate that the proposed hybrid approach significantly outperforms the traditional distance-based power source selection approach, without additional energy consumption or increased system complexity. The scheme’s efficient power management capabilities underscore its potential for practical applications in IIoT environments, where resource optimization is crucial.

Laboratory tests for the estimation of the compaction parameters, namely the maximum dry density (MDD) and optimum moisture content (OMC) are time-consuming and costly. Thus, this paper employs the artificial neural network technique for the prediction of the OMC and MDD for the aggregate base course from relatively easier index properties tests. The grain size distribution, plastic limit, and liquid limits are used as the inputs for the development of the ANNs. In this study, multiple ANNs (240 ANNs) are tested to choose the optimum ANN that produces the best predictions. This paper focuses on studying the impact of three different activation functions: number of hidden layers, number of neurons per hidden layer on the predictions, and heatmaps are generated to compare the performance of every ANN with different settings. Results show that the optimum ANN hyperparameters change depending on the predicted parameter. Additionally, the hyperbolic tangent activation is the most efficient activation function as it outperforms the other two activation functions. Additionally, the simplest ANN architectures results in the best predictions, as the performance of the ANNs deteriorates with the increase in the number of hidden layers or the number of neurons per hidden layers.

This paper investigates the use of the Kriging response surface method to estimate failure values in carbon-fibre-epoxy composite flow-lines under the influence of stochastic processes. A case study of a 125 mm flow-line was investigated. The maximum stress, Tsai-Wu and Hashin failure criteria was used to assess the burst design under combined loading with axial forces, torsion and bending moments. An extensive set of measured values was generated using Monte Carlo simulation and used as the base case population to which the results from the response surfaces was compared. The response surfaces were evaluated in detail in their ability to reproduce the statistical moments, probability and cumulative distributions and failure values at low probabilities of failure. In addition, the optimisation of the response surface calculation was investigated in terms of reducing the number of input parameters and size of the response surface. Finally, a decision chart that can be used to build a response surface to calculate failures in a carbon fibre-epoxy-composite (CFEC) flow-line was proposed based on the findings obtained. The results show that the response surface method is suitable and can calculate failure values close to that calculated using a large set of measured values. The results from this paper provide an analytical framework for identifying the principal design parameters, response surface generation, and failure prediction for CFEC flow-lines.

This paper introduces the design of a biomass gasification reactor with specific constraints for its manufacturing and operation in the West African conditions. The foreseen applications are the valorisation into heat and electricity of agricultural biomass residues. Rice husk is chosen as the reference fuel for the design. Local manufacturing is a key feature and the main focus of the design, as it allows us to reduce the capital costs and facilitate the maintenance. The design methodology is based on the conceptual approach proposed by Cross. This approach leads in several steps to a rational design choice based on the evaluation of different solutions. In this study, nine reactor types have been compared leading to a prototype that best suits the defined objectives such as a local manufacturing, a secure installation and a sufficient gas quality. From this conception approach, the Semi-Batch, Fixed-bed reactor with air Aspiration appears the most suitable. Its specific characteristics for the foreseen application are a power of 44 kW based on the syngas lower heating value, an average fuel consumption of 20.38 kg/h and an average air flow of 28.8 kg/h for optimal gasification. The gasifier resulting from the design methodology has been built. It is presented in the paper.

All-electric and hybrid-electric aircraft are a future transport goal and a possible ‘green’ solution to increasing climate-related pressures for aviation. Ensuring the safety of passengers is of high importance, informed through appropriate reliability predictions to satisfy emerging flight certification requirements. This paper introduces another important consideration related to redundancy offered by multiplex electric motors, a maturing technology which could help electric aircraft manufacturers meet the high reliability targets being set. A concept design methodology is overviewed involving a symbolic representation of aircraft and block modelling of two important figures of merit, reliability, and efficiency, supported by data. This leads to a comparative study of green aircraft configurations indicating which have the most potential now, and in the future. Two main case studies are then presented: an electric tail rotor retrofitted to an existing turbine powered helicopter (hybrid) and an eVTOL aircraft (all-electric), demonstrating the impact of multiplex level and number of propulsion channels on meeting target reliabilities. The paper closes with a summary of the important contribution to be made by multiplex electric machines, well as the advancements necessary for green VTOL aircraft sub-systems, e.g., power control unit and batteries, to improve reliability predictions and safety further.

The extensive use of electric vehicles (EVs) can reduce concerns about climate change and fossil fuel shortages. One of the main obstacles to accepting EVs is the limitation of charging stations, which consists of high-charge batteries and high-energy charging infrastructure. A new transformer-less topology for boost dc-dc converters with higher power density and lower switch stress is proposed in this paper, which may be a suitable candidate for high-power fast-charging battery chargers of EVs. Throughout this paper, two operating modes of the proposed converter, continuous current mode (CCM) and discontinuous current mode (DCM), are analyzed in detail. Additionally, critical inductances and design considerations for the proposed converter are calculated. Finally, real-time verifications based on hardware-in-loop (HiL) simulation are carried out to assess the correctness of the proposed theoretical concepts.

Nonlinear state estimation problem is an important and complex topic, especially for real-time applications with a highly nonlinear environment. This scenario concerns most aerospace applications, including satellite trajectories, whose high standards demand methods with matching performances. A very well-known framework to deal with state estimation is the Kalman Filters algorithms, whose success in engineering applications is mostly due to the Extended Kalman Filter (EKF). Despite its popularity, the EKF presents several limitations, such as exhibiting poor convergence, erratic behaviors or even inadequate linearization when applied to highly nonlinear systems. To address those limitations, this paper suggests an improved Extended Kalman Filter (iEKF), where a new Jacobian matrix expansion point is recommended and a Frobenius norm of the cross-covariance matrix is suggested as a correction factor for the a priori estimates. The core idea is to maintain the EKF structure and simplicity but improve its accuracy. In this paper, two case studies are presented to endorse the proposed iEKF. In both case studies, the classic EKF and iEKF are implemented, and the obtained results are compared to show the performance improvement of the state estimation by the iEKF.

Design thinking (DT) is considered a “human-centered” or “user-needs” process that leads to business innovation. Promising practices of DT application in real life have gained popularity in business, such as IBM’s the loop, IDEO’s 3I model, Design Council’s double diamond model, and Stanford’s d.school model. However, many existing studies have targeted DT models in a small group setting, such as a startup environment or a small group in an educational institution. The primary purpose of this paper was to investigate how large corporate environments that have multiple departments with a waterfall culture define and apply DT in real use. The first part of this paper introduces the most popular DT models used in the industry and some insights into their use in the field; the second part examines the experiences of 20 professionals who have experience with DT in the large organizational environment through in-depth interviews. As a result, this paper offers three major insights regarding the use of DT in the field. The third part of this study suggests an appropriate enterprise DT model for large corporations based on the problem-solving activities already undertaken by employees. This study is important as the first step toward understanding DT implementation in large corporations.

Recent population migrations have led to numerous accidents and deaths. Little research has been done to help migrants in their journey. For this reason, a literature review of the latest research conducted in previous years is required to identify new research trends in human-swarm interaction. This article presents a review of techniques that can be used in a robots swarm to find, locate, protect and help migrants in hazardous environment such as militarized zone. The paper presents a swarm interaction taxonomy including a detailed study on the control of swarm with and without interaction. As the interaction mainly occurs in cluttered or crowded environment (with obstacles) the paper discussed the algorithms related to navigation that can be included with an interaction strategy. It focused on comparing algorithms and their advantages and disadvantages.

User expectations regarding new products and services are evolving rapidly, forcing innovative organizations to explore new avenues for innovation, combining products and services. This paper focuses on the integrative design of product–service systems (PSSs) and builds on the servitization and service-based innovation literature. Many tools have been proposed for designing integrated PSS, with the intent to generate economic and/or sustainable impact. In this article, we focus on tools being used for bringing the user experience and intangibles in the design process. Although the literature is rich with tools and methods to optimize the PSS design process, it does not consider the full array of methods and their impact. This lack of research attention might hinder organizations developing PSS. Using in-depth interviews, this qualitative research systematically combines the extant conceptual literature on PSS design tools and processes with expert insights, thereby contextualizing how to lower thresholds in PSS design processes and how to increase the effectiveness of PSS design tools. The paper contributes to the literature on servitization and PSS by explicitly identifying twenty-one preconditions that support the PSS design process while integrating product and service innovation in close relation to the end-user.

The purpose of this paper is to propose a novel process failure mode and effect analysis (PFMEA) approach for the reliable design of assembly activities to prevent product defects due to errors during the assembly of complex products. PFMEA is approached as an integrated method that, in addition to implementing recommended actions, supports the design of worksheets, equipment, and layout of the assembly lines of complex systems, early in the design phase of the product. As a result, the innovative design-job element sheets (D-JESs), which report work instructions to the operator for assembly cycles, are defined before the design of the production and assembly process. The modification of the PFMEA structure, the implementation of proper recommended actions, and the designs of D-JESs, equipment, and assembly layout, early in the design phase of the product, are the novel contributions of the paper. The integrated method assures to effectively design the assembly process directly during the product design to avoid errors that could promote dissatisfaction of the end-users. It is practical to use and does not require large investments, implementation of new technologies, or complex additional training. Its practical application is demonstrated using a case study concerning a manufacturer of train wagons via manual assembly lines.

The operational effectiveness of a railroad track robot that is designed for railcar undercarriage inspection is provided. Beyond describing the robot’s design details and onboard imaging system, the paper analyzes the recorded video images and offers design improvements to increase their clarity. The robot is designed to be deployed trackside, traverse over the rails, and then maneuver in between the rails beneath a stopped train in a siding or a railyard. The under-carriage conditions are documented by onboard video cameras for automated or manual postprocessing. The intent is to inspect the components that are not visible to the conductor or train inspector during a walk-along inspection of a stationary train. An assessment of the existing design, followed by modification and validation, is presented. The results from a prototype unit developed by the Railway Technologies Laboratory at Virginia Tech (RTL) indicate that with proper positioning of off-the-shelf imaging systems such as cameras manufactured by GoPro® in San Mateo, CA, USA and appropriate lighting, it is possible to capture videos that are sufficiently clear for manual (by a railroad engineer), semi-automated, or fully automated (using Artificial Intelligence or Machine Learning methods) inspections of rolling stock undercarriages. Additionally, improvements to the control, mobility, and reliability of the system are documented, although reliability throughout operation and the ability to consistently climb out of the track bed remain points of future investigation.

The tribological properties of steels used to realise railway wheels play a fundamental role in the performances of both vehicle and infrastructure. In particular, the wear process, caused by the wheel–rail interaction, modifies the shape of wheel and rail profiles, changing the performances of the vehicle. For this reason, research institutes and vehicle manufacturers have worked hard to develop predictive tools able to estimate the evolution of the wheel and rail profiles. The efficiency of these tools is strongly influenced by the tribological properties of the materials, i.e., the wear coefficients, which are used as input data. The characterisation of these properties requires specific tools and long-lasting experimental campaigns, which are usually performed under controlled operating conditions, using twin-disc test benches. These devices usually do not consider the real contact conditions in terms of normal load, contact geometry, and slip velocity, since they are equipped with small-size rollers. The paper proposes an innovative 1:5 scaled twin-disc, which allows the reproduction of the real wheel–rail contact conditions, thanks to Pascal’s scaling technique. The testing device allows the reproduction of a wide range of typical operating conditions of railway vehicles, thanks to high-power independent brushless motors, used to actuate the rollers, and an innovative loading system.

In battery systems, there are several established form factors targeting mass market applications, like D, C, AA, AAA series, lithium round cells, and coin cells. Besides these standardized batteries, in printed electronics, there are several approaches to realize flat batteries of different material systems fabricating primary and secondary battery types. For a dedicated application in agriculture, a sensor system requires a degradable primary battery. In this paper, the development of a dedicated zinc–carbon battery is described, supplying the sensor application with 4.5 Vnom. The battery has a 170 mm length and a 23 mm outer diameter. while the inner core is open for the antenna system of the application. The active area is up to 161 cm2. The design and manufacturing aspects are described. The rolled-up battery system is fully charged after manufacturing and ready to operate. It may remain inside the degradable sensor system after use in the field.

This paper deals with the design of a complete state control for input unsigned, rank deficient matrix parameters of a linear system with system dynamics defined by ostensible structures of Metzler matrices. The proposed solution is based on the principle of diagonal stabilization of positive systems and uses a stabilizing additional component over the decomposition of the Metzler matrix in solving the incomplete internal positivity of such linear system structures. The novelty of the proposed approach is the unified representation of the parametric constraints of the Metzler matrix and the structurally constrained system inputs using linear matrix inequalities, which guarantees that the closed-loop system will be asymptotically stable. Despite the complexity of the constraint conditions on this class of linear continuous systems, the design conditions are formulated using sharp linear matrix inequalities only. A detailed design process is presented using a system-linearized mathematical model to verify the superiority and practicality of the proposed method.

While digital twins (DTs) have recently gained prominence as a viable option for creating reliable asset representations, many existing frameworks and architectures in the literature involve the integration of different technologies and paradigms, including the Internet of Things (IoTs), data modeling, and machine learning (ML). This complexity requires the orchestration of these different technologies, often resulting in subsystems and composition frameworks that are difficult to seamlessly align. In this paper, we present a scalable compositional framework designed for the development of a DT-based production management system (PMS) with advanced production monitoring capabilities. The conducted approach used to design the compositional framework utilizes the Factory Design and Improvement (FDI) methodology. Furthermore, the validation of our proposed framework is illustrated through a case study conducted in a phosphate screening station within the context of the mining industry.

Saving energy and resources has become increasingly important for industrial applications. Foremost, this requires knowledge about the energy requirement. For this purpose, this paper presents a state-based energy requirement model for mobile robots, e.g., automated guided vehicles or autonomous mobile robots, that determines the energy requirement by integrating the linearized power requirement parameters within each system state of the vehicle. The model and their respective system states were verified using a qualitative process analysis of 25 mobile robots from different manufacturers and validated by comparing simulated data with experimental data. For this purpose, power consumption measurements over 461 operating hours were performed in experiments with two different industrial mobile robots. System components of a mobile robot, which require energy, were classified and their power consumptions were measured individually. The parameters in the study consist of vehicle speed, load-handling duration, load, utilization, material flow and layout data, and charging infrastructure system frequency, yet these varied throughout the experiments. Validation of the model through real experiments shows that, in a 99% confidence interval, the relative deviation in the modeled power requirement for a small-scale vehicle is [−1.86%,−1.14%], whereas, for a mid-scale vehicle, it is [−0.73%,−0.31%]. This sets a benchmark for modeling the energy requirement of mobile robots with multiple influencing factors, allowing for an accurate estimation of the energy requirement of mobile robots.

Deploying an airbag when a vehicle occupant is too close to it can cause injury. An adaptive Airbag Control Unit (ACU) would improve the effectiveness of the safety system, provided it is aware of the actual position of the occupants once the crash is going to occur. Occupants can be monitored with vision-based and radar-based sensing in the vehicle, but the research question is whether other reliable devices exist. In this research, a real seat is equipped with four sensors in the supports from the floor, as well as an Inertial Measurement Unit (IMU) and a microcontroller. The device is capable of identifying correct position or different Out of Position (OP) conditions and inform an adaptive ACU. The paper presents the seat layout in detail and its testing in extensive driving experiments with multiple participants. Depending on the position of the driver, the identification is correct 45–100% of the time. Monitoring the occupant position by a sensorized seat is feasible and can improve the reliability of the onboard safety system when integrated with other occupant monitoring devices.

Microbubble generators are of considerable importance to a range of scientific fields from use in aquaculture and engineering to medical applications. This is due to the fact the amount of sea life in the water is proportional to the amount of oxygen in it. In this paper, experimental measurements and computational Fluid Dynamics (CFD) simulation are performed for three water flow rates and three with three different air flow rates. The experimental data presented in the paper are used to validate the CFD model. Then, the CFD model is used to study the effect of diverging angle and throat length/throat diameter ratio on the size of the microbubble produced by the Venturi-type microbubble generator. The experimental results showed that increasing water flow rate and reducing the air flow rate produces smaller microbubbles. The prediction from the CFD results indicated that throat length/throat diameter ratio and diffuser divergent angle have a small effect on bubble diameter distribution and average bubble diameter for the range of the throat water velocities used in this study.

This paper aims to present a new structure of the parallel Z-source inverters (ZSIs) for dual-input single-phase grid-connected photovoltaic (PV) systems. The ZSI is a single-stage buck-boost converter that uses an inductor-capacitor network between the inverter bridge and the PV string and follows the maximum power point by applying the shoot-through vector. Therefore, a DC/DC converter is no longer needed to track the maximum power point, and the cost and complexity of the power conditioning system (PCS) are reduced. For controlling the proposed PCS, a cascade control structure is employed in this paper. The inner current loop injects the maximum active power with unity power factor sinusoidal current to the grid. The outer capacitor voltage loop is applied to control capacitors voltages in the Z-source networks. Additionally, an enhanced dual-string maximum power point tracking (eDS-MPPT) method is proposed to find MPPs with minimum burden competitional. The eDS-MPPT does not need the PVs voltages measurements compared to other MPPT methods. The simulation results confirm the accuracy of the performance of the system.

The use of digital technologies in the systems of diagnostics and monitoring of units of a ship’s propulsion plant can significantly increase the efficiency and quality of assessing the technical condition of operated objects in the online mode as well as expand the class of practical problems to be solved. Digital processing of signals of complex configuration at a qualitatively new level is an indispensable condition for a critical improvement in the course of processing the current values of diagnosable parameters, increasing the reliability and trouble-free performance of a ship’s technical equipment during operation. A method of approximation has been discussed in the paper. Moreover, the paper provides an algorithm for analyzing the signal of the indicator diagram of a marine diesel engine, the spectrum of which contains high-frequency components and short-term pulses indicating deviations from the normal operating mode, the assessment of which is practically impossible with the traditionally applied methods of spectral analysis of signals. The approximation method is based on the use of wavelet analysis, which makes it possible to deeply explore such modes. Examples of using wavelet analysis to approximate one-dimensional signals of elements and systems of a ship’s power complexes are given.

Owing to the results of research in system science, artificial intelligence, and cognitive engineering, engineered systems are becoming more and more powered by knowledge. Complementing common-sense and scientific knowledge, system knowledge is maturing into a crucial productive asset. However, an overall theory of the knowledge of intellectualized systems does not exist. Some researchers suggest that time has come to establish a philosophically underpinned theoretical framework. This motion is seconded by the on-going intelligence revolution, in which artificial intelligence becomes a productive power, enabler of smart systems, and a strong transformer of the social life. The goal of this paper is to propose a skeleton of the needed generic theory of system knowledge (and a possible new branch of philosophical studies). The major assumption is that a significant part of the synthetic system knowledge (SSK) is “sympérasma”, that is, knowledge conjectured, inferred, constructed, or otherwise derived during the operation of systems. This part will become even more dominant in the future. Starting out from the above term, the paper suggests calling this theory “sympérasmology”. Some specific domains of “sympérasmological” studies, such as (i) manifestations of SSK, (ii) mechanisms of generating SSK, (iii) dependability of SSK, (iv) operational power of SSK, (v) composability of SSK, and (vi) advancement of SSK, are identified. It is understood that the essence and status of SSK cannot be investigated without considering the related cognitive processes and technological enablers. The paper presents a number of open questions relevant for follow-up discussions.

The goal of the trade-off design method presented in this study is to achieve newly targeted performance requirements by modifying the current values of the design variables. The trade-off design problem is formulated in the framework of Sequential Quadratic Programming. The method is computationally efficient as it is gradient-based, which, however, requires the performance functions to be differentiable. A new equation to calculate the scale factor to control the size of the design variables is introduced in this study, which can ensure the new design achieves the targeted performance objective. Three formal approaches are developed in this study for trade-off design to handle various design scenarios, which include one that can handle cases with linearly dependent constraints and with more constraints than the number of design variables. Three engineering design problems are presented as examples to validate and demonstrate the use of these trade-off approaches to find the best way to adjust the design variables to meet the revised performance requirements. [ABSTRACT FROM AUTHOR]