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For a general production network, state-of-the-art methods for constructing sparse flexible designs are heuristic in nature, typically computing a proxy for the quality of unseen networks and using that estimate in a greedy manner to modify a current design. This paper develops two machine learning-based approaches to constructing sparse flexible designs that leverage a neural network to accurately and quickly predict the performance of large numbers of candidate designs. We demonstrate that our heuristics are competitive with existing approaches and produce high-quality solutions for both balanced and unbalanced networks. Finally, we introduce a novel application of process flexibility in healthcare operations to demonstrate the effectiveness of our approach in a large numerical case study. We study the flexibility of linear accelerators that deliver radiation to treat various types of cancer. We demonstrate how clinical constraints can be easily absorbed into the machine learning subroutine and how our sparse flexible treatment networks meet or beat the performance of those designed by state-of-the-art methods.

Randomised clinical trials (RCTs) are the gold standard for providing unbiased evidence of intervention effects. Here, we provide an overview of the history of RCTs and discuss the major challenges and limitations of current critical care RCTs, including overly optimistic effect sizes; unnuanced conclusions based on dichotomization of results; limited focus on patient-centred outcomes other than mortality; lack of flexibility and ability to adapt, increasing the risk of inconclusive results and limiting knowledge gains before trial completion; and inefficiency due to lack of re-use of trial infrastructure. We discuss recent developments in critical care RCTs and novel methods that may provide solutions to some of these challenges, including a research programme approach (consecutive, complementary studies of multiple types rather than individual, independent studies), and novel design and analysis methods. These include standardization of trial protocols; alternative outcome choices and use of core outcome sets; increased acceptance of uncertainty, probabilistic interpretations and use of Bayesian statistics; novel approaches to assessing heterogeneity of treatment effects; adaptation and platform trials; and increased integration between clinical trials and clinical practice. We outline the advantages and discuss the potential methodological and practical disadvantages with these approaches. With this review, we aim to inform clinicians and researchers about conventional and novel RCTs, including the rationale for choosing one or the other methodological approach based on a thorough discussion of pros and cons. Importantly, the most central feature remains the randomisation, which provides unparalleled restriction of confounding compared to non-randomised designs by reducing confounding to chance.

In living organisms, diffusing chemicals react with soft tissue to actuate a range of mechanical actions, such as periodic pulsations or net motion. Inspired by this biological behaviour, researchers have created chemically active ‘microswimmers’ that translate chemical input into mechanical work. Because typical microswimmers are hard spheres (that is, zero-dimensional objects) and rigid or flexible rods (1D objects), their movements are constrained relative to higher dimensional, soft matter. In recent years, these limitations have motivated research on 2D active sheets that undergo shape changes or directed motion in response to chemical cues in solution; the combination of dimensionality, chemical activity and structural flexibility leads to new, rich dynamic behaviour. In one class of materials, chemical reactions generate internal stresses in the 2D sheets that trigger the shape transformations; in the second class, the release of energy from chemical reactions drives a fluid flow, which not only transports but also morphs the shape of the sheet. Consequently, these reconfigurable, self-propelled sheets can perform self-sustained operations that were unattainable with previously studied active particles, including self-morphing and self-oscillating behaviour. The latter functionalities can lead to the creation of portable, chemical machines and the discovery of new modes of dynamic self-assembly. Chemically active, flexible sheets convert chemical energy into the flow of the surrounding fluid, which, in turn, transforms the sheet’s shape. This Perspective describes how this feedback produces shape-changing 3D objects, coupled oscillators exhibiting spatiotemporal coordination and the rotation of chemically driven gears.

The future communication will be highly data-driven, enabled by unlimited wireless connectivity. The emerging technologies are expected to fulfill the expectations not achieved with 4G, also the new technologies fusing artificial intelligence inspired applications in every corner of the world, such as the Internet of Things (IoT) with pervasive wireless connectivity. As one of the core techniques in 5G, the IoT will play a leading role in upcoming telecommunications, wearable systems, and unmanned vehicles. The innovative technologies employing interdisciplinary techniques are also expected to take advantage of the recent advancement in functional materials, semiconductor technologies, and metamaterials. Metamaterials are the materials of the future. Metamaterials are engineered materials whose electromagnetic and optical properties are determined by their structural design especially the unit cells. By the combination of different microscopic unit cell elements into large-scale designs, a new structure can be designed and fabricated with engineered properties required for new technologies. The presence of metamaterials has offered tunability, flexibility in design and has resulted in the enhancement of qualitative parameters of various electromagnetic structures like Antennas, filters, and resonators. In this review article, we focus on the concept, modeling, and advanced design approaches of metamaterials in the antenna structure. A comparative study of these structures with recent advancements and a futuristic approach is discussed at the end of the article, which will be beneficial for the young researchers.

Although incremental sheet forming (ISF) is featured with greater flexibility, lower cost, and shorter leading time, there is less comprehensive sustainability assessment for ISF. In order to summarize the research progress on the sustainability evaluation of ISF and further explore its potential application scenario specifically, the research with respect to energy consumption and environmental impact of ISF are reviewed in this paper. First, the characteristics and research status of ISF including geometry accuracy improvement, forming force prediction, and heating-aided ISF are introduced. Then, study on the influence of processing parameters, equipment, and consumables on energy consumption (both deformation energy and machine tool energy) and forming efficiency are comprehensively reviewed. Current research indicates that neither deformation energy consumption nor electric energy consumption of ISF is lower than stamping process since ISF has long motion trajectory and forming time. In addition, the current methods (such as Life Cycle Assess method) to assess the environmental impact of ISF are discussed. Additionally, the environmental impact of ISF mainly reflects on sheet material, energy consumption, and lubrication oil. Moreover, the comparison of the ISF process and the other processes with regard to sustainability is analyzed, which indicates that ISF has obvious advantage and broad prospects when the cost of molds is considered and the batch size is generally less than 1000. The study provides valuable guidance on the further improvement of ISF towards more efficiency and green manufacturing process.

This paper presents the concept of a new hybrid model called spherical fuzzy N-soft expert sets, which is an extension of spherical fuzzy soft expert sets. The proposed model is highly suitable to describe the multinary data evaluation in terms of spherical fuzzy soft information considering multiple experts’ opinions. Some fundamental properties, including subset, weak complement, spherical fuzzy complement, spherical fuzzy weak complement, union, intersection, AND operation, and OR operation, are discussed. Our proposed concepts are explained with detailed examples. An efficient algorithm is developed to solve multi-attribute group decision-making (MAGDM) problems. Further, to guarantee the high applicability scope and flexibility of our initiated framework, two real-world MAGDM problems, that is, predicting local election results using survey ratings before the election and ranking credibility of the smartphones using customer feedback, are solved. Finally, to endorse the accuracy and advantages of the proposed technique, a comprehensive comparative analysis of the presented approach with existing models such as spherical fuzzy soft expert sets and N-soft sets is provided.

A reverse nearest neighbour (RNN) query returns every object for which the query is its nearest neighbour. Recently, a new useful variant of the RNN query has emerged, called reverse nearest neighbourhood (RNNH) query, to discover the neighbourhood that find query point is the nearest facilities among all other fa- cilities. However, the existing research focuses on computing RNNH in Euclidean space and, to the best of our knowledge, there does not exist any technique to compute RNNH in road networks. The existing techniques designed for Euclidean space cannot be applied for road networks mainly because all objects and facilities of interest are connected on a road network. Thus, there is a need for techniques specifically designed for computing RNNH in road networks. In this paper, we introduce the reverse nearest neighbourhood concept for road networks (RNNH-RN) where a neighbourhood is a collection of at least m chained objects such that the maximum road network distance between any pair of objects is at most d. We demonstrate the flexibility and effectiveness of the proposed RNNH-RN query through extensive experiments based on real-world, road network data for Melbourne, Victoria. Our experimental results demonstrate that our solutions are feasible for networks with low, medium or high levels of object density.

This paper proposes a methodology for calibration of industrial robots that uses a concept of measurement sub-regions, allowing low-cost solutions and easy implementation to meet the robot accuracy requirements in industrial applications. The solutions to increasing the accuracy of robots today have high-cost implementation, making calibration throughout the workplace in industry a difficult and unlikely task. Thus, reducing the time spent and the measured workspace volume of the robot end-effector are the main benefits of the implementation of the sub-region concept, ensuring sufficient flexibility in the measurement step of robot calibration procedures. The main contribution of this article is the proposal and discussion of a methodology to calibrate robots using several small measurement sub-regions and gathering the measurement data in a way equivalent to the measurements made in large volume regions, making feasible the use of high-precision measurement systems but limited to small volumes, such as vision-based measurement systems. The robot calibration procedures were simulated according to the literature, such that results from simulation are free from errors due to experimental setups as to isolate the benefits of the measurement proposal methodology. In addition, a method to validate the analytical off-line kinematic model of industrial robots is proposed using the nominal model of the robot supplier incorporated into its controller.

Accurate and reliable feeding of small quantities of powder is crucial in many industries. It is particularly true in the case of pharmaceutical manufacturing. First, the exact dosage of highly potent active pharmaceutical ingredients (APIs) is essential. Second, due to the shift towards continuous manufacturing, processing agents must be supplied continuously. Last, with regard to personalized medicine products, small doses of API have to be processed with high flexibility. In all these applications, a few grams of powder or less per hour have to be fed, which makes processing very challenging. The common route of pre-blending causes additional effort in processing and quality control compared to direct continuous feeding, while material properties influence the performance of conventional feeders with conveying screw elements. In this study, a novel micro-feeding system based on active volumetric displacement is investigated and compared to commercial LIW feeders of various scales. In the micro-feeder, the powder is filled into a cylindrical cartridge and pushed towards the process by a piston in a controlled manner. Tests are carried out with a free-flowing grade of lactose and croscarmellose sodium as an example of cohesive powder. At feed rates of a few grams per hour, for the investigated materials, the system has at least the same performance as conventional feeders at significantly higher feed rates. Especially at low feed rates, less fluctuations are obvious and accuracy and precision of the micro-feeder are at the same level as for the established feeding technology at rates ten times higher. For the two types of investigated materials (free-flowing and cohesive), the micro-feeding system shows similar or better performance than conventional and commercially applied LIW feeders. The technology offers the potential for highly accurate, continuous feeding in the low-dose range.

In the automotive industry, short-term changes in the product mix can be executed in an attempt to better react to customers’ immediate needs and preferences. These modifications impact confirmed orders sent to suppliers via material requirement planning (MRP) and may lead to supply shortages. Supply chain stakeholders can respond to these modifications in a plethora of ways to avoid shortages. However, the process of deciding on the appropriate response is not easily standardized due to the high level of flexibility and subjectivity required to deal with each specific situation. Based on an automotive manufacturer in the State of Parana—Brazil, we propose a decision support system (DSS) to guide supply chain stakeholders during the process of deciding on how to respond to short-term material requirement modifications. Flexibility is ensured by the occurrence of “decision points” in the process model, which guide a process instance through specific paths based on a mix of simple rules as well as the Analytic Hierarchy Process (AHP). The DSS has its application demonstrated by a series of simulated scenarios. Through the standardization of a flexible process driven by subjective factors, this work aims to achieve: (i) a better understanding of the cost versus risk trade-offs that complicate standardized decision making within automotive supply processes; and (ii) an artifact capable of providing standardization without hindering the flexibility requirements for this class of processes.

AI-driven edge computing is a development trend of the Industrial Internet of Things (IIoT). However, most existing solutions ignore the limitations of flexibility, security, and real-time performance caused by the rigid architecture of industrial control systems and the “end-to-end” computing paradigm of IIoT. This paper proposes an edge computing scheme for AI-driven IIoT. Specifically, we design a novel software-defined industry control architecture to enhance the flexibility and security of IIoT edge systems. The architecture decouples the software and hardware of Industrial devices by virtualization and industrial modeling technologies, which improves the flexibility and programmability of IIoT edge systems and alleviates the privacy issue of industrial data. Moreover, we adopt a new edge computing method, dispersed computing, to AI-driven IIoT to achieves better real-time performance and resource utilization. The proposed computing method optimizes the computing and networking of AI-driven industrial applications jointly by a multiobjective optimization scheduling algorithm. We also evaluated the performance of our scheme through experiments.

To cope with water scarcity, Morocco needs to integrate the reuse of treated wastewater fully into its water management strategies. However, this option imposes several concerns about the appropriate wastewater treatment system (WWTS) whose performance is balanced by technical, environmental, financial, and societal sustainability. To deal with these challenges, the present study aims to assess the sustainability of five WWTS using the cardinal (CAR) and PROMETHEE methods. After hierarchizing the criteria and identifying the WWTS, two separate surveys were performed in order to rank criteria and alternatives by preference strength. The delivered rankings were converted, then, respectively into cardinal weights (criteria) and cardinal scores (alternatives). The PROMETHEE rankings showed that the membrane systems are the most sustainable followed by trickling filters, while the infiltration-percolation is the least sustainable WWTS. The activated sludge and lagoon systems were incomparable using PROMETHEE I partial ranking, and they were both ranked in the third position of sustainability. In contrast, PROMETHEE II complete ranking favored the activated sludge than lagoon systems due to its slightly high net outranking flow. The stability intervals indicated that the weights of all criteria could not affect the two first actions, which is mainly due to the high precision and robustness of the CAR method in eliciting weights. Finally, each criterion affected variably the sustainability of WWTS according to their characteristics, but overall, the process efficiency is the key factor (21.07% of weight) to reaching higher sustainability levels in addition to gaseous emissions (12.41% of weight), flexibility (8.32% of weight), and energy requirement (7.50% of weight).

This article discusses the plausibility of historical analogies and general theoretical schemes in the study on contemporary international processes. It explains how the complex character of contemporary international relations modifies the previous role of theoretical generalizations and lessons from the past. The uncertainty of contemporary international processes, including the waves of global economic, social and political turbulences, makes the general theoretical schemes more tentative. It reduces the role played by historical analogies which become more risky and potentially inconclusive. They change their initial explanatory functions. The novelty and past experience intertwine in the analysis of contemporary international phenomena, yet the elements of uncertainty and novelty prevail. Theories respond to these challenges with flexibility and pragmatism, including some “middle ground” positions. Their ideas are valuable, but do not change the article’s conclusion that general theoretical schemes and historical analogies offer a limited practical advice in the study on contemporary international relations.

The auto industry is critically dependent on lean and flexible manufacturing systems to sustain in today’s dynamic and price sensitive markets. In the current work, a machine learning-based predictive modeling and control strategy is proposed for the attainment of lean manufacturing (LM) through effective management of manufacturing flexibility. Firstly, lean manufacturing models were identified based on machine, labour, volume, routing, product flexibilities and material handling for forty six auto parts manufacturing companies in Pune region (India). As many as twenty three lean manufacturing models were derived based on system identification (control theory) structures: auto regressive with exogenous variables (ARX), auto regressive moving average with exogenous variables (ARMAX), output error (OE) and Box Jenkins (BJ) methods. All predictive models were compared for their relative performances based on validation indices such as the FIT%, mean squared error (MSE), final prediction error (FPE) and the number of model parameters. The ARX 750 structure attained the best predictive characteristics for LM (FIT 91.86%). This model was controlled for a set point of 0.8 LM level and corresponding levels of flexibilities were determined. The machine flexibility (MF) was identified to be the most significant contributor to lean manufacturing at a level of 0.7596. Consequently, MF was also modeled based on its seven sub parameters. The ARMAX 2120 structure obtained the best performance characteristics (FIT 99.94%) for MF modeling. Furthermore, this MF model was controlled at a level of 0.7596 (corresponding to 0.8 LM) and the required levels of MF sub parameters were determined. Thus, the current work provides definite guidelines for company managers to target quantitative attainment of specific machine flexibility parameters to ultimately attain an improved lean manufacturing level in the automotive industry.

Disposal of healthcare waste is a key issue of environmental sustainability in the world. The amount of healthcare waste is increasing every day, and it is necessary to adequately dispose of this kind of waste. There are various treatments for healthcare waste disposal, of which incineration of healthcare waste is one of the solutions. This paper suggests a model for selection of the type of incinerators that best solve the problem of healthcare waste in secondary healthcare institutions in Bosnia and Herzegovina. In the selection of incinerators, extended sustainability criteria were applied. Basic sustainability criteria: environmental, economic, and social criteria, were extended with the technical criterion. To assess which of the incinerators best meets the needs for healthcare waste collection, multi-criteria decision-making was used. For this purpose, a combination of two MCDA methods was applied in this paper, namely full consistency method (FUCOM) and compromise ranking of alternatives from distance to ideal solution (CRADIS). The FUCOM method was applied to determine the weights of the criteria, while the CRADIS method was applied to rank the alternatives. The best alternative of the six alternatives used is A2 (I8-M50), followed by alternative A1 (I8-M40), while the worst ranked alternative is A5 (I8-M100). These results were confirmed by applying the other six methods of multi-criteria analysis and the performed sensitivity analysis. The contribution of this paper is reflected through a new method of multi-criteria analysis that was used to solve decision-making problems. This method has shown simplicity and flexibility in operation and can be used in all problems when it is necessary to make a multi-criteria selection of alternatives.

Purpose of review This review illustrates the evolution and progress with standardization of fellowship education in neuroanesthesiology. It provides a structured discussion around the need for curricula and framework which individual training programs in neuroanesthesiology can use to meet defined educational standards thus meeting criteria for accreditation. Recent findings Neuroanesthesiology training has traditionally been heterogenous around the world but international efforts from the community of neuroanesthesiology have culminated in the development of an international council for perioperative training in neuroscience in anesthesiology(ICPNT). This serves not only as an accrediting body but also creates a platform through their neuroanesthesia program relations committee for collaboration and engagement between various training programs internationally, increasing the educational standards of the individual programs and collectively increasing the overall level of standards for neuroanesthesia training. Standardized curriculum and competency-based assessments and milestones would help with narrowing the focus to quality education in neuroanesthesiology. Summary Structured training around the three pillars of neuroanesthesiology with concomitant accreditation is expected to lead to higher education standards with better patient care. The SNACC created milestones for neuroanesthesiology training during residency and the ICPNT can now use this as a foundation for fellowship training. Having a council to accredit and standardize will likely become indispensable in creating a set path for training in neuroanesthesiology. Additionally, the flexibility built in due to the international nature would allow modified and variable pathways depending upon individual capabilities and interests. The path forward will include widespread adoption of standardization supporting the overarching goal of excellent patient outcomes around the world.

Composite materials owing to low density and beneficial properties such as high stiffness, low coefficient of thermal expansion, high mechanical strength, high dimensional stability, good wear resistance, and design flexibility are employed in various fields such as aeronautical, automobile, power generation, civil, and marine engineering etc. Over their course of service, damages can arise in the composite material due to aging, improper service conditions, and erroneous manufacturing and assembly such as inter-laminar voids, porosity, fibre waviness, wrinkles, de-bonding, and delamination. Techniques like structural health monitoring which utilize traditional techniques integrated with sensors to inspect the health of a structure can assist in localization and quantification of several types of damages present in composites based structural models. In this work, several monitoring methods have been reviewed for damage detection including vibration based sensing, embedded sensing, acoustic emissions, lamb wave method, and comparative vacuum monitoring. Several researchers have focused their study on the health monitoring of operational bridges, buildings, and aerospace vehicles for damage detection.

Additive manufacturing (AM) is a layer-by-layer material deposition process that allows for more manufacturing flexibility and design complexity than traditional manufacturing processes. However, the print quality in metal AM is hard to be predicted and controlled due to its high process variability. Numerous process parameters are correlated/intertwined and have a direct/indirect relationship with the final build quality. However, these interconnections among the process parameters and print qualities are not yet fully understood and identified. To achieve a functional part without additional post-processing to improve the surface finish/mechanical properties, the comprehensive process model, which includes the semantic and qualitative connections among the process inputs-signatures-qualities, is urgently needed. Currently, surrogate models (regression models/ physical-based models) are widely used for predicting part qualities, specifically thermal distortion. These surrogate models can provide a simulated result based on the user-defined parameter settings. However, the reverse determination of the highly correlated parameters that can manage the print qualities in the desired standard cannot be achieved through these surrogate models. Understanding this interconnectivity is the top priority in building process models that will help to make the process more predictable and controllable. An ontology-based process map that captures the complicated metal AM processes, including all input parameters, physical, thermal model, and resultant build qualities (microstructure and mechanical properties), is provided in this manuscript. The connections among these factors are used to diagnose the relevant process variables regarding the specific build qualities, which can further combine with experimental data to contribute to a “printable” zone. The ontology-based process map links variable selection to quality requirements, creating a printable zone. A printable zone indicates the allowable process parameter selections, contributing to the desired part quality. Ultimately, the selected variable process data from the ontological process map will scope the parametrical analysis. The implementations of the ontology-based process map and printable zone are presented to demonstrate this proposed method's effectiveness by demonstrating that the proposed printable zone's validity and desired build quality can be achieved.

Given the advantages in transparency, reproducibility, adaptability and computational efficiency in R, there is a growing interest in converting existing spreadsheet-based models into an R script for model re-use and upskilling training among health economic modellers. The objective of this exercise was to convert the Scottish Cardiovascular Disease (CVD) Policy Model from Excel to R and discuss the lessons learnt throughout this process. The CVD model is a competing risk state transition cohort model. Four health economists, with varied experience of R, attempted to replicate an identical model structure in R based on the model in Excel and reproduce the intermediate and final results. Replications varied in their use of specialist health economics packages in addition to standard data management packages. Two versions of the CVD model were created in R along with a Shiny app. Version 1 was developed without health economics specialist packages and produced identical results to the Excel version. Version 2 used the heemod package and did not achieve the same results, possibly due to the non-standard elements of the model and limited time to adapt the functions. The R model requires less than half the computational time than the Excel model. Conversion of the spreadsheet models to script models is feasible for health economists. A step-by-step guide for the conversion process is provided and modellers' experience is discussed. Coding without specialist packages allows full flexibility, while specialist packages may add convenience if the model structure is suitable. Whichever approach is taken, transparency and replicability remain the key criteria in model programming. Model conversions must maintain standards in these areas regardless of the choice of software.

Blockchain is an emergent technology and has the potential to replace the intermediaries in the asset transaction business. This is the reason many companies have started adopting and experimenting with blockchain. No standard practice has been established yet to implement this new transparent and distributed ledger technology which hinders its wide acceptance despite being beneficial to secure the digital assets in a verifiable and permanent way. Though it has transformed the way the business operates, still it is considered to be a disruptive technology. It has various challenges to overcome such as scalability, non-repudiation, data privacy, and flexibility which should be addressed in the early stage of implementation of blockchain infrastructure. These issues need to be dealt with specific techniques as the existing system development process doesn’t integrate many of the aspects of this relatively new blockchain technology. Before incorporating blockchain technology in the existing system, one must address all the issues in the requirement analysis phase of the system. This paper presents a case study on blockchain enabled medical supply chain and focuses on the identification of non-functional requirements for blockchain enabled systems which further facilitates the software engineers for requirements specification and system design.

The probabilistic hesitant fuzzy element (PHFE) is a worthwhile extension of hesitant fuzzy element (HFE) which is a means of allowing the decision makers more flexibility in expressing their preferences by the use of hesitant information in practical decision making process. To derive a more realistic expression of decision information, it is necessary to unify the arrangement of elements in PHFEs without imposing artificial elements. Up to now, several processes concerning the unification and arrangement of elements in PHFEs have been proposed, and while, most suffer from different drawbacks being critically discussed in the present study. The main aim of this study is to propose a PHFE unification process which does not have the shortcomings of existing processes, and does not change the inherent characteristic of PHFE probabilities. Based on the proposed unification process, the current study seeks to extend the theory of arithmetic operations on PHFEs by proposing and developing novel types of PHFS division and subtraction. Finally, the proposed PHFE unification process is applied to a number of multiple criteria decision-making (MCDM) problems for illustrating its vast range of applicability.

In industrial production, smart wearables (e.g. data glasses) are becoming more relevant to support employees. While economic aspects have so far been the focus of the introduction, changes for work design have been neglected. The aim of the Delphi study was to describe changes through the use of smart wearables and to derive implications for work design. Six description dimensions (application, implications for work design, data use, personalization, flexibility, introductory purpose) were identified and 18 scenarios were developed. The assessment of the scenarios in terms of desirability and degree of dissemination showed that scenarios for work-related and ergonomic support are particularly desirable and scenarios for changes in cooperation are considered particularly likely. Data usage is a relevant factor for the assumed degree of dissemination but not for the assessment of desirability. In operational practice, work design and economic aspects are important for assessing the scenarios.Practical Relevance: The results show possible development scenarios for production work when using smart wearables and highlight possible implications for work design. Positive and negative effects on task, knowledge, social and contextual characteristics are identified, which offer suggestions for a conscious selection and design of the use of technology in practice.

The question of balancing the competing uses of prior and late developers has long been the core issue in international water law (IWL). Yet, how IWL evolves to assure the flexibility and continuing adaptability of its norms in the context of changing circumstances of water use regimes, has not been the subject of comprehensive academic research. Based on an empirical analysis of 459 international water treaties, this research reveals that equal protection of prior use and later use is becoming an emerging trend for international treaty practices, while obligations of “due diligence” emerge to redefine the “no-harm” principle; the growing concern for environmental protection challenges the basic understanding of the concepts of “harm” and “reasonableness”. To strengthen the trend for equal protection and to ensure greater flexibility and adaptability of IWL, this paper focuses on the promotion of institutional water cooperation, the forging of common understanding over the importance of transboundary environmental protection, and the development of more up-to-date global legal frameworks.

Information flow control (IFC) can effectively resist Trojans and viruses that steal information from systems, and is usually adopted to protect the confidentiality of systems with a high security level. However, covert channel attacks can bypass IFC by exploiting its implementation defects. Thus, it is crucial to verify the system security and identify potential covert channels. Decentralized IFC (DIFC) is a key innovation that provides new flexible mechanisms, including decentralized declassification and taint tracking. However, the flexibility of DIFC systems also brings security risks. At present, there is a lack of a systematic and automatic security analysis approach for complex DIFC systems. In this paper, we propose a formal and automatic method to analyze the security of DIFC systems by using the FDR2 tool. We provide a new definition of noninterference, based on which the security analysis is performed. The analysis results indicate that our approach can both effectively detect covert channels in DIFC systems and accommodate conditional declassification information. The proposed method is more efficient and accurate than existing manual methods of covert channel detection.

This study addresses the problem of selecting a supplier bound by sustainability and resilience criteria, focusing on the steel industry as a case in point. A new framework has been presented for the evaluation of suppliers in this study where thirty-two factors are first recognized based on previous publications and fourteen appropriate factors among them are selected based on expert opinions. Then, Interpretive Structural Modelling is used to study the reciprocal effects of factors and MICMAC analysis is performed to select the critical factors among the fourteen factors. Afterward, the Best–Worst Method is performed for weighting the critical factors. Eventually, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied for ranking the suppliers. The results show that the criteria with higher driving powers and lower dependence have higher weights in the Best–Worst Method. Gilan steel company in Iran is examined as a case study in the steel industry to verify the efficiency of the proposed framework. A comparative analysis is performed showing that taking resilience criteria into account alters the supplier ranking results, making these criteria important from a managerial point of view. It is also suggested to managers to consider all four dimensions of these criteria when evaluating suppliers. It is observed that the reputation and position of the supplier in the industry, availability of a support supplier, strategic stock, sensitivity to the political situation, pollution control, social responsibility, and flexibility in service are the main factors to consider.

In decision-making problems, q-rung orthopair fuzzy sets are considered as a more effective tool than intuitionistic fuzzy sets and Pythagorean fuzzy sets. This article develops some aggregation operators based on Frank t-norm and t-conorm for fusing q-rung orthopair fuzzy (q-ROF) information. Then a multiple attribute decision-making (MADM) approach is introduced based on the proposed operators. The Frank operations of t-norm and t-conorm can have the advantage of good flexibility with the operational parameter. From that point of view, in this paper, we extend the ideas of Frank t-norm and t-conorm to the q-ROF environment and introduce some aggregation operators. Moreover, we illustrate the compatible properties of the proposed operators. Generally, the attribute weights are unknown in the MADM problems. The analytical hierarchy process and entropy methods are efficient tools to handle such MADM problems with unknown attribute weights. So, we present an MADM approach with unknown attribute weights under q-ROF environment using proposed operators. Then to elaborate the flexibility and validity of the proposed model, we discuss and solve a numerical problem concerned with a government project of choosing the best way of industrialization. Next, we show how the involvement of the parameters in our proposed model affects the decision-making results. Finally, to exhibit the superiority of our proposed methodology, the obtained results are compared with the existing ones.

CFRP lattice structures, without the skin, offer several benefits with respect to conventional sandwich or monolithic concepts in satellite applications such as mass reduction, accessibility to the interior side and superior thermo-elastic stability. Automated manufacturing process, based on placement or winding, offers opportunities to lower the overall cost of serial products. Despite the apparent simplicity of the process, there is a large variety of manufacturing solutions proposed by industry. This evidences the interest but also an inherent practical difficulty to define a single, definitive process, that is at the same time cheap and suitable for different scales of interest, as well as accounting for flat, single, or even double-curvature shapes. CIRA has developed a specific manufacturing process for grid-stiffened structures based on a combination of rubber tooling, Dry Parallel Robotic Winding (patented) and Resin Liquid Infusion. A variation of this process has been successfully applied to VEGA-C Interstage 2/3. This paper presents the extension of this approach for two applications—promoted by the “CFRP Grid Tubular Structures” ESA ITT project in the framework of Artes 5.1—highly interesting for the space market: an instrument boom segment (a part of a satellite large deployable antenna), and a satellite central tube. Both demonstrators address rather strict requirements and challenging design and manufacturing issues, ranging from a large diameter Central Tube, with stiffness and buckling constraints and thick ribs, to a slender boom segment with stiffness and thermal stability requirements and thin ribs. The two demonstrators have been successfully designed, produced and tested, confirming the efficiency and flexibility of the approach and providing new benchmarks for the space community.

The steadfast advance of the synthetic biology field has enabled scientists to use genetically engineered cells, instead of small molecules or biologics, as the basis for the development of novel therapeutics. Cells endowed with synthetic gene circuits can control the localization, timing and dosage of therapeutic activities in response to specific disease biomarkers and thus represent a powerful new weapon in the fight against disease. Here, we conceptualize how synthetic biology approaches can be applied to programme living cells with therapeutic functions and discuss the advantages that they offer over conventional therapies in terms of flexibility, specificity and predictability, as well as challenges for their development. We present notable advances in the creation of engineered cells that harbour synthetic gene circuits capable of biological sensing and computation of signals derived from intracellular or extracellular biomarkers. We categorize and describe these developments based on the cell scaffold (human or microbial) and the site at which the engineered cell exerts its therapeutic function within its human host. The design of cell-based therapeutics with synthetic biology is a rapidly growing strategy in medicine that holds great promise for the development of effective treatments for a wide variety of human diseases.

Medical Waste Management (MWM) is an important and necessary problem in the COVID-19 situation for treatment staff. When the number of infectious patients grows up and amount of MWMs increases day by day. We present Medical Waste Chain Network Design (MWMCND) that contains Health Center (HC), Waste Segregation (WS), Waste Purchase Contractor (WPC) and landfill. We propose to locate WS to decrease waste and recover them and send them to the WPC. Recovering medical waste like metal and plastic can help the environment and return to the production cycle. Therefore, we proposed a novel Viable MWCND by a novel two-stage robust stochastic programming that considers resiliency (flexibility and network complexity) and sustainable (energy and environment) requirements. Therefore, we try to consider risks by Conditional Value at Risk (CVaR) and improve robustness and agility to demand fluctuation and network. We utilize and solve it by GAMS CPLEX solver. The results show that by increasing the conservative coefficient, the confidence level of CVaR and waste recovery coefficient increases cost function and population risk. Moreover, increasing demand and scale of the problem make to increase the cost function.

A high-precision map (HPM) is the key infrastructure to realizing the function of automated driving (AD) and ensuring its safety. However, the current laws and regulations on HPMs in China can lead to serious legal compliance problems. Thus, proper measures should be taken to remove these barriers. Starting with a complete view of the current legal obstacles to HPMs in China, this study first explains why these legal obstacles exist and the types of legal interests they are trying to protect. It then analyzes whether new technology could be used as an alternative to resolve these concerns. Factors such as national security, AD industry needs, and personal data protection, as well as the flexibility of applying technology, are discussed and analyzed hierarchically for this purpose. This study proposes that China should adhere to national security and AD industry development, pass new technical regulations that redefine the scope of national security regarding geographic information in the field of HPMs, and establish a national platform under the guidance and monitoring of the government to integrate scattered resources and promote the development of HPMs via crowdsourcing. Regarding the legal obstacles with higher technical plasticity, priority should be given to technical solutions such as “available but invisible” technology. Compared with the previous research, this study reveals the current legal barriers in China that have different levels of relevance to national security and different technical plasticity. It also proposes original measures to remove them, such as coordinating national security with the development of the AD industry, reshaping the boundary of national security and industrial interests, and giving priority to technical solutions for legal barriers that have strong technical plasticity.

We discuss several topics: the concept of conformal mapping of Riemannian and pseudo-Riemannian manifolds, conformal rigidity of higher-dimensional domains, and conformal flexibility of two-dimensional domains of Euclidian and Minkowski planes. We present an extension of the concept of conformal mapping proposed by M. Gromov and recall an open problem related to it.

Existing security approaches for safeguarding data exchange among the sensor nodes are investigated in presence of apriori information of an adversary in wire-less sensor network. The approaches that are implemented at present witnessed with various issues associated with security and still there is a higher scope. A unique modeling is presented in this manuscript that considers multiple static sink nodes spread over geographical area and mobile sensor nodes. A dynamic authentication policy is targeted to be achieved by implementing a lightweight encryption mechanism using analytical modeling approach. The presented logic is programmed using MATLAB and the obtained resultants of the model exhibits that it offers a significantly improved energy retention performance, lower memory dependency, and lower inclusion of cost in comparison to existing standard security approaches.

Accurate travel time prediction between two locations is one of the most substantial services in transport. In travel time prediction, origin–destination (OD) method is more challenging since it has no intermediate trajectory points. This paper puts forward a deep learning-based model, called Gated Spatial–Temporal Attention (GSTA), to optimize the OD travel time prediction. While many trip features are available, their relations and particular contributions to the output are usually unknown. To give our model the flexibility to select the most relevant features, we develop a feature selection module with an integration unit and a gating mechanism to pass or suppress the trip feature based on its contribution. To capture spatial–temporal dependencies and correlations in the short and long term, we propose a new pair-wise attention mechanism with spatial inference and temporal reasoning. In addition, we adapt and integrate multi-head attention to improve model performance in case of sophisticated dependencies in long term. Extensive experiments on two large taxi datasets in New York City, USA, and Chengdu, China demonstrate the superiority of our model in comparison with other models.

In this paper, we consider a joint decision-making issue of production and maintenance plan with imperfect and perfect maintenance actions for two-stage deteriorating JIT (Just-in-time) production systems with random breakdowns. The risk cost caused by random breakdowns of each stage and the perfect maintenance cost are analyzed. The expected production duration is also worked out. A (Q, N) policy is then proposed based on probability theory and an optimization model is established to find the optimal batch size Q* and optimal number of production batches, N*. Sensitivities of different system parameters are analyzed, the results indicate that the proposed policy and the model are feasible and effective, it has good flexibility in application environment. Some managerial implications for practitioners are summarized. One of the important implications is that managers should preferentially improve the production rate of downstream subsystem rather than upstream subsystem to reduce cost and increase efficiency.

Robots have been widely used in machining due to their excellent expansibility and high flexibility. However, the robot is a weak rigid system, and its machining process is unstable. So, it is easy to produce burr due to chatter. A burr suppression pattern of the weak rigid drilling system driven by the digital twin is proposed to solve the above problem, including unstable state monitoring and strategy recommendation. The machining state is mapped in the information space through the hidden Markov model in the digital twin system. The quality of each machining feature can be tracked based on the model evolution rules. Based on the machining state mapping and quality tracking process, the machining state monitoring is realized. The drilling strategy storeroom is built based on the genetic algorithm, and the abnormal monitoring information is used to search for the optimal strategy. Then, the feedback strategies are recommended for improving the machining quality. Finally, the effectiveness of the proposed method is verified by a case of weak rigid drilling.

While metal additive manufacturing (AM) offers significant benefits, such as design flexibility and lead time improvements, over traditional manufacturing approaches, the complexity of metal AM processing presents a challenge for understanding and exploiting process–structure relationships. Local variations in laser scan path and geometry can result in a wide array of different process states, which in turn influence local fluctuations in structure. To better understand this highly local process–structure relationship, we develop a suite of tools for registering and fusing multimodal representations of the metal AM process, including design intent, in situ monitoring, and post-build characterization. The result is an open, plug-and-play workflow for correlative analysis of metals AM data streams. We apply this workflow to candidate components of additive Ti-6Al-4V and showcase examples of correlative analyses made possible by data fusion.

Aerodynamic feeding systems represent one possibility to meet the challenges of part feeding for automated production in terms of feeding performance and flexibility. The aerodynamic feeding system investigated in this article is already able to adapt itself to different workpieces using a genetic algorithm. However, due to the operating principle, the system is susceptible to changes in environmental conditions such as air pressure and pollution (e.g. dust). To minimise the effect of ambient influences, the system must be enabled to detect changes in the feeding rate and react autonomously by adapting the system’s adjustment parameters. In this work, based on pre-identified factors interfering with the aerodynamic orientation process, a new approach is developed to react to changes of the ambient conditions during operation. The presented approach makes us of an alternating sequence of monitoring and corrective algorithms. The monitoring algorithm measures the ratio of correctly oriented parts to the total number of fed parts of the process and triggers the corrective algorithm if necessary. Simulated and experimental results both show that an increased feeding rate can be achieved in varying conditions. Furthermore, it is shown that integrating both known process and parameter information can reduce the time for re-parametrisation of the feeding system.

The paradigm shift toward Industry 4.0 is facilitating human capability, and at the center of the research are the workers—Operator 4.0—and their knowledge. For example, new advances in augmented reality and human–machine interfaces have facilitated the transfer of knowledge, creating an increasing need for labor flexibility. Such flexibility represents a managerial tool for achieving volume and mix flexibility and a strategic means of facing the uncertainty of markets and growing global competition. To cope with these phenomena, which are even more challenging in high-variety, low-volume contexts, production planning and control help companies set reliable due dates and shorten lead times. However, integrating labor flexibility into the most consolidated production planning and control mechanism for a high-variety, low-volume context—workload control—has been quite overlooked, even though the benefits have been largely demonstrated. This paper presents a mathematical model of workload control that integrates labor flexibility into the order review and release phase and simulates the impact on performance. The main results show that worker transfers occur when they are most needed and are minimized compared to when labor flexibility is at a lower level of control—shop-floor level—thus reducing lead time.

In this paper, we give detailed descriptions of the Zero-Modified Negative Binomial distribution for analyzing count data. In particular, we study the characterizations and properties of this distribution, whose main advantage is its flexibility which makes it suitable for modeling a wide range of overdispersed and underdispersed count data (which may or may not be caused by zero-modification, i.e., the inflation or deflation of zeroes), without requiring previous knowledge about any of these inherent data characteristics. We derive maximum likelihood estimation of the model parameters based on positive observations, and evaluate the loss of efficiency by considering this procedure. We illustrate the suitability of this distribution on real data sets with different types of zero-modification.

Additive manufacturing (AM) technologies are currently employed for the manufacturing of completely functional parts and have gained the attention of high-technology industries such as the aerospace, automotive, and biomedical fields. This is mainly due to their advantages in terms of low material waste and high productivity, particularly owing to the flexibility in the geometries that can be generated. In the tooling industry, specifically the manufacturing of dies and molds, AM technologies enable the generation of complex shapes, internal cooling channels, the repair of damaged dies and molds, and an improved performance of dies and molds employing multiple AM materials. In the present paper, a review of AM processes and materials applied in the tooling industry for the generation of dies and molds is addressed. AM technologies used for tooling applications and the characteristics of the materials employed in this industry are first presented. In addition, the most relevant state-of-the-art approaches are analyzed with respect to the process parameters and microstructural and mechanical properties in the processing of high-performance tooling materials used in AM processes. Concretely, studies on the AM of ferrous (maraging steels and H13 steel alloy) and non-ferrous (stellite alloys and WC alloys) tooling alloys are also analyzed.

FPGA-SoCs are heterogeneous embedded computing platforms consisting of reconfigurable hardware and high-performance processing units. This combination offers flexibility and good performance for the design of embedded systems. However, allowing the sharing of resources between an FPGA and an embedded CPU enables possible attacks from one system on the other. This work demonstrates that a malicious hardware block contained inside the reconfigurable logic can manipulate the memory and peripherals of the CPU. Previous works have already considered direct memory access attacks from malicious logic on platforms containing no memory isolation mechanism. In this work, such attacks are investigated on a modern platform which contains state-of-the-art memory and peripherals isolation mechanisms. We demonstrate two attacks capable of compromising a Trusted Execution Environment based on ARM TrustZone and show a new attack capable of bypassing the secure boot configuration set by a device owner via the manipulation of Battery-Backed RAM and eFuses from malicious logic.

With the rapid advancement in the mobile and computing technologies, individuals have an inclination to depend more on them in varied range of situations. For ensuring the secured communication among the devices, privacy and security are the most prominent concerns. Accordingly, sophisticated preventive measures that support both secure storage and secret transfer has begun as the need of the hour. Several conventional techniques focuses on the data encryption and key generation approaches to attain reliability. However, there were some short-comings in key aspects such as privacy, key management, and flexibility. So as to overcome existing issues the paper implements a compact, constant-time, and fast improvised ChaCha 20 stream cipher mechanism for encryption and decryption. Likewise, the generation of key is employed with the use of Diffie-Hellman key exchange protocol scheme based on the string comparison. Thus, a secured transmission occurs with a reduced rate of execution time, decryption time, and key generation time. The performance evaluation is made in terms of encryption time, decryption time and the key generation time and are compared with the existing techniques to prove the superiority of this proposed scheme.

In Europe, the two main nuclear accident response decision support systems in use are ARGOS and JRODOS, both of which make use of the FDMT (Food Chain and Dose Module for Terrestrial pathways) model to simulate the transfer of radioactivity along terrestrial food chains and to predict radionuclide activity concentrations in human foodstuffs. FDMT was originally developed in the early 1990s for Southern German agricultural conditions. Its application to other geographical settings has raised concerns regarding its fitness for purpose. Furthermore, the FDMT model in its original format lacks transparency, flexibility, and the possibility to be run probabilistically. In order to improve FDMT’s fitness for purpose and overcome its main shortcomings, it has been implemented in a new modelling platform which incorporates powerful numerical solvers and renders uncertainty and sensitivity analysis possible. The modelling structure of FDMT has been re-configured, and a library configuration has been introduced which offers flexibility in working such that model components can be tested, modified, or replaced easily. The new FDMT allows for the consideration of case/region-specific issues and to make predictions which are of more relevance and of better use with regard to decision making and management of risk. Furthermore, the default databases of FDMT have been updated and wherever possible PDFs have been assigned. In this paper, the transition of FDMT from an old to a new modelling structure is presented along with a demonstration of developments achieved.

The study discusses how the process information reserves can be used for improving production flexibility on the basis of production component variation control. Building upon the assumption that the data in question describe the functions of the production components as those experience perturbations, we submit that it may be possible to predict the productivity and economic efficiency for new, previously unused control options. We elaborate on the production components that ensure production flexibility, approaching them as a highly autonomous holon. We determine the conditions that make it possible to analyze the holon control options in isolation from controlling all other components. We also suggest a solution to the problem of predicting the employees’ impact on production. Our suggestion is based on limiting the types of impact accessible to a human operator. The study looks at the reasons behind the uncertainty of production control simulation modeling results, which stem from the peculiarities of the data collected. We propose a criterion for assessing the impact of uncertainty on the productivity indicators’ variation. This study offers a systemic overview of the aspects of a production simulation aimed at assessing the impact of uncertainty on productivity predictions. As an example, we review how uncertainty affects the variation of coke consumption and productivity of a blast furnace, which is ensured by selecting a suitable option for iron ore sinter quality control.

Researchers have studied the nurse rostering problem (NRP) for multiple decades. Initially, the formulations were rather primitive including only a few necessary restrictions, but down the road, the formulationshave become more complex. Nonetheless, a fraction of the research reaches implementation in practice, and many wards still schedule nurses manually. In this article, we introduce a flexible nurse rostering system that employs mathematical optimization to automatically schedule nurses to shifts. We have developed this system in collaboration with practitioners to fully match their needs. The system consists of a comprehensive mixed integer programming (MIP) model along with a flexible framework. In addition to common constraints from the literature, the mathematical formulation includes three new constraints that further encourage healthy work schedules for each nurse. Additionally, we have reformulated some common constraints from the literature and allow for a complex shift struc-ture that matches the needs of real hospital wards. This flexibility results in increased adaptability for different wards with different needs and is crucial to address the complex nurse rostering problem that practitioners face. We have successfully implemented this system in two wards at two Danish hospitals. We present the MIP model along with computational results for 12 real-world rostering instances. Furthermore, we discuss the practical impact of this system and provide general feedback from the practitioners using it. Overall, the results illustrate the capabilities of the system to tackle diverse nurse rostering instances and produce outstanding results.

The installation of vertical mobility systems (escalators) in high rise building has increased significantly in the recent past. Escalator systems which have been part of the utilities since the early 60s, has impact not only the movement of people and goods, but businesses that needs them to operate. It has lead to the need for continuous monitoring of the systems for failure by operators and reliability engineers. Although several authors have addressed failure related issue in the escalator system in past, literature has shown however, that the research works have not fully addressed issues relating to the root cause of failure in the system. In this paper, a reliability-centered model has been proposed and used for evaluating the root cause of failure issues in the escalator system by prioritizing the failure modes in the system. Results from the evaluation show that the failure mode 'Failure of the drag chain' has the highest risk and reliability concerns, while the 'Handrail fault' has the least risk and reliability concerns. To further check the feasibility and rationality of the model for addressing flexibility related issues that have become a basic characteristic of the modern fuzzy root cause of failure model, a sensitivity analysis has been performed using two quantitative reliability parameters of the escalator system that is ‘the Mean Distance Between Failure and the Failure Rate’.

Nowadays, a laboratory operator in the areas of chemistry, biology or medicine spends considerable time performing micropipetting procedures, a common, monotonous and repetitive task which compromises the ergonomics of individuals, namely related to wrist musculoskeletal disorders. In this work, the design of a kinesthetic teaching approach for automating the micropipetting technique is presented, allowing to redirect the operator to other non-repetitive tasks, aiming to reduce the exposure to ergonomic risks. The proposed robotic solution has an innovative gripping system capable of supporting, actuating and regulating the volume of a manual micropipette. The system is able to configure the position of diverse laboratory materials, such as lab containers and plates, on the workbench through a collaborative robotic arm, providing flexibility to adapt to different procedures. A projected human-machine interface, which combines the display of information on the workbench with an infrared based interaction device was developed, providing a more intuitive interaction between the operator and the system during the configuration and operation phases. In contrast to the majority of the existing liquid handling systems, the proposed system allows the operator to place the materials freely on the workbench and the usage of different materials’ variants, facilitating the implementation of the system in any laboratory. The attained performance and ease of use of the system were very encouraging since all the defined tasks in the conducted experiments were successfully performed by users with minimum training, highlighting its potential inclusion in the laboratory routine panorama.

The experimenters have limited flexibility as far as the number of experimental units are concerned; this could be unsuitable in bioavailability/bioequivalence study. In cross-over design literature, most of the designs require an even number of subjects, few of them are available for 3, 9 or 15 subjects, but designs are not available for number of subjects like 5, 7, 11. A new class called active balanced cross-over designs is defined and constructed for carry-over models through a 5M active balanced computer search algorithm for addressing this gap in literature. The newly generated cross-over designs are more variance efficient under self and mixed carry-over model than the two treatments three periods cross-over designs. Many cross-over designs, which have been unavailable so far, are obtained for five carry-over models in this paper. A new optimal cross-over design in the class of balanced two treatments three periods cross-over designs is also generated. An exhaustive list of optimal and/or efficient cross-over designs have been provided for designs in 4 to 13 experimental units. In this list, 10 new included designs are optimal for one of the carry-over models and 7 new included designs are optimal and/or efficient when fitting to all four plausible carry-over models.

In the year 2020, the Global Journal of Flexible Systems Management (GJFSM) has celebrated its two decades of publication. This study is an attempt to commemorate the two decades of publication by presenting the overview of the GJFSM along with the trajectory of flexibility research in various journals. By using multiple bibliometric tools and indicators, the study finds that the GJFSM has grown over the years in terms of total publications and citations. The contributors are from across the globe, i.e., South America, North America, Asia, Europe, Africa, and the Pacific. The Journal publishes several flexibility areas, including information systems flexibility, financial flexibility, supply chain flexibility, technology management flexibility, marketing flexibility, organizational flexibility, strategic flexibility, and manufacturing flexibility. The GJFSM is cited by authors from various countries across the globe. It has been cited across different Scopus categorizations, including “Strategy and Management,” “Business and International Management,” “General Business, Management and Accounting,” “Industrial and Manufacturing Engineering,” and “Management Science and Operations Research.” Keyword co-occurrences analysis helps to analyze the various groups of keywords cited together in the GJFSM. Co-citation analysis of references helps to identify crucial clusters of GJFSM, i.e., strategic flexibility, manufacturing flexibility, the conceptual framework of flexibility, supply chain flexibility, modeling flexibility, and application of TISM. Overall, GJFSM has seen an increase in both publications and citations, reflecting its increasing presence among the journals publishing flexibility research. The diversity of flexibility research and its contributions to research under-one-roof make this Journal unique. The paper concludes with the gap areas of flexibility research and the way forward.

The necessity to diminish the heliostats’ cost so that central tower concentrating solar power (CSP) systems can stride to the forefront to become the technology of choice for generating renewable electricity is obliging the industry to consider innovative designs, leading to new materials being implemented into the development of heliostats. Honeycomb sandwich composites offer a lightweight but stiff structure that appear to be an ideal substitute for existing heliostat mirrors and their steel supporting trusses, avoiding large drive units and reducing energy consumption. However, realizing a honeycomb sandwich composite as a heliostat, among a multitude of possible combinations can be tailored from, that delivers the best trade-off between the panel’s weight reduction (broadly equates to cost) and structural integrity is cumbersome and challenging due to the complex nonlinear material behaviour, along with the large number of design variables and performance constraints. We herein offer a simulation–optimization model for behaviour prediction and structural optimization of lightweight honeycomb sandwich composite heliostats utilizing artificial neural network (ANN) technique and particle swarm optimization (PSO) algorithm. Considering various honeycomb core configurations and several loading conditions, a thorough investigation was carried out to optimally choose the training algorithm, number of neurons in the hidden layer, activation function in a network and the suitable swarm size that delivers the best performance for convergence and processing time. Carried out for three case scenarios, each with different design requirements, the results showed that the proposed integrated ANN-PSO approach provides a useful, flexible and time-efficient tool for heliostat designers to predict and optimize the structural performance of honeycomb sandwich composite-based heliostats as per desired requirements. Knowing that heliostats in the field are not all subjected to the same wind conditions, this method offers flexibility to tailor heliostats independently, allowing them to be made lighter depending on the local wind speed in the field. This could lead to reductions in the size of drive units used to track the heliostat, and the foundations required to support these structures. Such reductions would deliver real cost savings, which are currently an impediment to the wider spread use of CSP systems.