Search

It is generally conceded that, due to security and privacy concerns, enterprises and users are reluctant to embrace the cloud computing paradigm and hence benefit from the cost reductions and the increased flexibility or business agility that this paradigm brings about. These concerns stem mainly from the significantly-expanded attack surfaces that result from the heterogeneous nature of cloud services and the dynamicity inherent in cloud environments. In order to alleviate these concerns, effective and flexible access control approaches are required to consider the contextual parameters that characterise data access requests in the cloud. In this respect, this work presents PaaSword: a novel holistic access control framework-essentially a PaaS offering-that extends the popular XACML standard with semantic reasoning capabilities that support the federation of effective context-aware access control policies and their infusion into cloud applications with minimal manual intervention and effort. To determine the performance of our solution, a comparative evaluation test is presented and discussed, against a well-known reference implementation of the XACML standard, namely the open source WSO2 Balana engine.

The emerging 5G networks promise to support novel network services with different requirements in terms of Quality of Service (QoS), such as low-latency and high bandwidth. Thanks to the network slicing concept, 5G is able to fulfill these different requirements while sharing the same physical infrastructure. Although network slicing is gaining maturity, slicing the Radio Access Network (RAN) is still challenging, particularly with the emergence of new physical features added by 5G New Radio (NR), such as Bandwidth part (BWP) and physical numerology. In this paper, we introduce a new framework, namely New Radio flexibility (NRflex), which addresses the challenge of slicing the RAN in 5G. NRflex provides a solution that dynamically assigns BWP to the running slices and their associated User Equipment (UE), aiming to fulfill the slices’ required QoS. Simulation results showed the superiority of NRflex to meet network slice requirements while optimizing the 5G RAN resources, compared to other existing solutions.

The purpose of this research is to better understand the impact of supply chain management (SCM) and flexibility on firm performance, as well as the role of competitive advantage in mediating the model in Indonesian agriculture companies. Companies must apply supply chain management and supply chain flexibility (SCF) to boost industrial competitiveness, which impacts firm performance. To ensure that supply chain management supports the company's strategy, companies must evaluate supply chain concerns. From the literature search, researchers have not found any published studies or articles on SCM and SCF in their influence on firm performance through competitive advantage, specifically for corn companies in Indonesia. The population in this study includes agriculture companies in Indonesia. Sampling was carried out using probability sampling technique, the total population of 200 obtained a sample size of 133.333 which can be rounded up to 134 research samples. The inferential statistical method used in the data analysis of this study was the Partial Least Square Version 3 program. The study found that SCM influenced firm performance and SCF had a direct influence on firm performance. However, competitive advantage variable failed in being a mediator in SCM and SCF on firm performance.

New product development to enhance companies’ competitiveness and reputation is one of the leading activities in manufacturing. At present, achieving successful product design has become more difficult, even for companies with extensive capabilities in the market, because of disorganisation in the fuzzy front end (FFE) of the innovation process. Tremendous amounts of information, such as data on customers, manufacturing capability, and market trend, are considered in the FFE phase to avoid common flaws in product design. Because of the high degree of uncertainties in the FFE, multidimensional and high-volume data are added from time to time at the beginning of the formal product development process. To address the above concerns, deploying big data analytics to establish industrial intelligence is an active but still under-researched area. In this paper, an intelligent product design framework is proposed to incorporate fuzzy association rule mining (FARM) and a genetic algorithm (GA) into a recursive association-rule-based fuzzy inference system to bridge the gap between customer attributes and design parameters. Considering the current incidence of epidemics, such as the COVID-19 pandemic, communication of information in the FFE stage may be hindered. Through this study, a recursive learning scheme is established, therefore, to strengthen market performance, design performance, and sustainability on product design. It is found that the industrial big data analytics in the FFE process achieve greater flexibility and self-improvement mechanism on the evolution of product design.

The high penetration of variable renewable energy requires flexibility from both the generation and demand sides. This raises the necessity of modeling stochastic and flexible energy resources in power system operation. However, some distributed energy resources have both stochasticity and flexibility, e.g., prosumers with distributed photovoltaics and energy storage, and plug-in electric vehicles with stochastic charging behavior and demand response capability. Such partly controllable participants pose challenges to modeling the aggregate behavior of large numbers of entities in power system operation. This paper proposes a new perspective on the aggregate modeling of such energy resources in power system operation. Specifically, a unified controllability-uncontrollability-decomposed model for various energy resources is established by modeling the controllable and uncontrollable parts of energy resources separately. Such decomposition enables straightforward aggregate modeling of massive energy resources with different controllability by integrating their controllable components using linking constraints and uncontrollable components using dependent discrete convolution. Furthermore, a two-stage stochastic unit commitment model based on the proposed model for power system operation is established. The proposed model is tested using a three-bus system and real-size Qinghai provincial power grid. The result shows that this model is able to characterize with high accuracy the aggregate behavior of massive energy resources with different controllability so that their flexibility can be fully explored.

The demand for satellite communication increases with digitalization and globalization in the era of 5G and beyond; therefore, research and development of high-throughput satellites (HTSs) to increase the communication capacity and improve the flexibility of satellite communication systems is underway. Additionally, digital beamforming (DBF), which can control the directivity of multiple beams through high-speed digital signal processing, has received significant attention as a technology to improve the flexibility of resource allocation with HTSs. The principal control parameters in the DBF-based HTS, including the transmission power, beam gain, and placement location of each beam, cause alterations in the positional characteristics of the total throughput in the coverage area of an HTS. However, there are no power resource allocation models with dual control of these control parameters, based on the above positional characteristic. This study clarifies the effect of such control parameters on the total throughput in the coverage area of an HTS. We demonstrate a method for improving the flexibility of resource allocation to satisfy the geographic distribution of traffic requirements by constructing a power resource allocation model with a DBF-based fusion control in the HTS communication system. We evaluated the effectiveness of our proposed method through a simulation analysis using exponential annealing.

The accelerator driven system (ADS) is a nuclear reactor that can burn spent nuclear fuel with inherent safety features and operational flexibility. China is developing an ADS research device named...

Synthetic aperture radar (SAR) can be applied to observe the sea surface and detect ship targets. Images obtained by SAR can be hard to read because of the denseness of ships, extremely unbalanced foreground-background, and small target size. The existing objective detection approaches achieve superior performance by sacrificing detection speed and flexibility on computational resource due to redundant model parameters. These features happens to be extremely crucial for SAR ship detection in real-time scenarios. To effectively tradeoff the issue on both time and space complexity, we propose a novel objective detection method tailored for SAR ship detection problem. We examine the effectiveness of our method on two open datasets: high resolution SAR images dataset (HRSID) and large-scale SAR ship detection dataset-v1.0 (LS-SSDD-V1.0). The results show better performance over the state-of-the-art You Only Look Once version 4 (YOLOv4) framework in terms of accuracy, efficiency, and model complexity.

Today, developing technology and globalization change the cultures, lifestyles, and needs of societies. This change also impacts the expectations from architectural spaces. Spaces designed to perform a predetermined function may be insufficient to meet the changing needs over time. The aim of this study is to show the flexibility that the movable structural elements bring to the space. In this context, we propose integrating movable inner and outer shell structural elements to achieve specific functionality and meet the various functional capacity needs. In this study, a sample design was visualized to demonstrate the use of integrated kinetic building elements and was discussed its applicability with existing technologies and the advantages that it can provide. As a result, it was observed that kinetic building elements practically provide spatial flexibility and functional diversity in architectural design. In addition, the visuals show that the system is practical, applicable and efficient. Kinetic and flexible space design can be evaluated in “Generative and Parametric design” by means of to obtain variable solutions and functional sustainability according to different functional expectations from design.

In this review, we evaluated health care problems, which were not common before pandemic outbreak but have been common issues after its appereance and approaches to control pandemic considering its influences on people. We revised current health care developing approaches under the light of experience obtained throughout the pandemic so far. The aim is to be prepared in advance for possible upcoming pandemics. As in Covid 19 pandemics, such long lasting and widely affecting situations, durability is also very important together with flexibility and quickness. To provide durability, we need global policies taking the health to its center as well as health system policies.

Terahertz (THz) imaging techniques have attracted significant attention and have developed rapidly in recent years. However, despite several advances, these techniques are still not mature, and their high cost and system complexity continue to limit their applications. In this article, the techniques for achieving a practical imaging system with a compact THz transceiver are addressed, while considering the limitations of the current technique. The aim is to provide a brief review of related topics, while also covering our recent progress, which can provide some general perspectives and contrasting approaches for realizing a practical THz imaging system. The continuous wave devices are mainly focused for their flexibility of balancing the imaging resolution and data acquisition time. The importance of transceiver integration is also discussed and illustrated by introducing a 600-GHz band micro-photonic interface for integrating a THz source and detector, with a single resonant tunneling diode as a transceiver. With regard to system issues, spatial sampling with mechanical beam-scanning is discussed as an intermediate approach for moving stage and array technology. The potential and limitations of this approach are evaluated, along with an elliptical reflector as an alternative to an f-theta lens owing to its low cost and simplicity. The combination of integrated devices, along with the mechanical beam-scanning, is also discussed for demonstrating our current concept of realizing a practical THz imaging system.

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.

Background: The open and accessible regions of the chromosome are more likely to be bound by transcription factors which are important for nuclear processes and biological functions. Studying the change of chromosome flexibility can help to discover and analyze disease markers and improve the efficiency of clinical diagnosis. Current methods for predicting chromosome flexibility based on Hi-C data include the Flexibility-Rigidity Index (FRI) and the Gaussian Network Model (GNM), which have been proposed to characterize chromosome flexibility. However, these methods require the chromosome structure data based on 3D biological experiments, which is time-consuming and expensive. Objective: Generally, the folding and curling of the double helix sequence of DNA have a great impact on chromosome flexibility and function. Motivated by the success of genomic sequence analysis in biomolecular function analysis, we hope to propose a method to predict chromosome flexibility only based on genomic sequence data. Methods: We propose a new method (named "DeepCFP") using deep learning models to predict chromosome flexibility based on only genomic sequence features. The model has been tested in the GM12878 cell line. Results: The maximum accuracy of our model has reached 91%. The performance of DeepCFP is close to FRI and GNM. Conclusion: The DeepCFP can achieve high performance only based on genomic sequence.

Background The present review examines the printing properties of different animal protein-based food inks/materials used in the production of 3D printed constructs. The review examines their microstructural, colorimetric, printability, rheological and textural properties and investigates how these properties correlate to or are reflected in the characteristics of the printed constructs. These characteristics include their shape and dimensional stability, textural, nutritional, microstructural and sensorial properties. The review also examines the potential for development of animal protein-based special diets for people with special requirements such as for dysphagia patients and geriatrics. A special focus has been given to the application of transglutaminase for the development of 3D fish-based products and the possibility of developing cultured meat-based products. Scope and approach The properties of different food inks and 3D printed constructs based on animal proteins such as beef, poultry, fish, egg, seafood, milk, cheese and dairy ingredients were analysed with emphasis on the studies that were published in the last three years. Studies on the development of special animal protein-based diets and the possibility of 3D printed products based on cultured meat were critically reviewed. Key findings and conclusions 3D food printing offers numerous possibilities for development of tailored animal protein-based structures and products which includes, but is not limited to, extraordinary flexibility in geometries, textures and flavours and customised nutrition. Technology for production of 3D printed meat products necessitates the reduction of particle size of meat and dilution of meaty and savoury flavour and this is generally expected to reduce the value of premium meat products. However, the technology might be a good option for utilization of lower value and tougher cuts and trimmings. Production of 3D printed cultured meat-based products is still at a conceptual stage and a little or no research has been done in this area. Consumer acceptance of 3D printed cultured meat products seems to be less favourable in Western countries like Australia. Technology for the production of several animal protein-based 3D printed products for special dietary requirements such as for dysphagia patients has been optimized and is currently feasible with good success. Fancy shaped 3D printed cheese and egg products may evolve as a new segment of foods with a focus on children, festive occasions and the personal gift market. This review expands our understanding of how the composition of food inks affects the properties of the printed constructs and generates knowledge that will be crucial for optimizing the 3D printed animal products for maximum nutritional and sensory benefits. Extensive research is required to develop unique animal-based products with functional and nutraceutical value to attract consumers. Scientific knowledge is required to decipher the underlying mechanisms of how 3D printing process affects the food microstructure and sensory characteristics and its impact on consumer acceptance that will expand the use of the technology within the food industry.

Full-duplex millimeter wave (mmWave) communication has shown increasing promise for self-interference cancellation via hybrid precoding and combining. This paper proposes a novel mmWave multiple-input multiple-output (MIMO) design for configuring the analog and digital beamformers of a full-duplex transceiver. Our design is the first to holistically consider the key practical constraints of analog beamforming codebooks, a minimal number of radio frequency (RF) chains, limited channel knowledge, beam alignment, and a limited receive dynamic range. To prevent self-interference from saturating the receiver of a full-duplex device having limited dynamic range, our design addresses saturation on a per-antenna and per-RF chain basis. Numerical results evaluate our design in a variety of settings and validate the need to prevent receiver-side saturation. These results and the corresponding insights serve as useful design references for practical full-duplex mmWave transceivers., This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

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.

Oncogenic mutant K-Ras promotes cancer cell proliferation, migration, invasion, and survival by assembling signaling complexes. To date, the functional and structural roles of K-Ras mutations within these complexes are incompletely understood despite their mechanistic and therapeutic significance. Here, we review recent advances in understanding specific binding between K-Ras and the calcium sensor calmodulin. This interaction positively and negatively regulates diverse functions of K-Ras in cancer, suggesting flexibility in K-Ras/calmodulin complex formation. Also, structural data suggest that oncogenic K-Ras likely samples several conformational states, influencing its distinct assemblies with calmodulin and with other proteins. Understanding how K-Ras interacts with calmodulin and with other partners is essential to discovering novel inhibitors of K-Ras in cancer.

This paper proposes several data-based models for solar fields (SF) of concentrating solar power (CSP) plants. The proposed models estimate SF thermal output from solar resource data and aim to replace first-principles-based SF models typical in CSP generation schedulers. The advantages of the first models as opposed to the second ones are the ease in their development and their adaptability to changes in the plant, facilitating the forecast of SF thermal output. This information is very useful in order to take advantage of the operational flexibility of CSP plants, which is the main attribute that differentiates them from other renewable sources. The applicability of these models is studied in the optimal generation self-scheduling problem of a CSP plant participating in a day-ahead energy market. A simulation-based economic study has been performed using realistic data, where several scheduling strategies are evaluated in a 50 MW parabolic-trough plant with thermal storage. The scheduling strategies differ only in the model used for the SF. The study shows that the data-based models provide results very similar to those obtained with a first-principles-based SF model. Therefore, they can be used as a viable alternative, while significantly simplifying the design and development of the generation scheduler.

New ventures often face resource constraints when developing their capabilities to succeed in market competition. In this study, we draw on the resource orchestration perspective and propose that efficient resource management may be more important for new ventures than their existing resources when developing superior firm capabilities that are critical for new venture success. Using data from 207 new ventures in China, our study finds that entrepreneurial bricolage, as a resource orchestration process, can significantly enhance a new venture’s strategic flexibility and growth capability. Moreover, the effectiveness of this strategy depends on the availability of alternative resources, such as resources obtained through external network ties and from founding team members with diverse backgrounds. Our research findings thus provide practical information to help small and new firms get through difficult times without seeking additional resources.

We study how financial flexibility impacts on firm's investment decisions under uncertainty. We use a comprehensive dataset covering US firms for the period 1983–2019. We develop a baseline model of investment under uncertainty and characterize the bindingness of financial constraints. We then use this model to explore firms’ investment responses under uncertainty in the presence of financial constraints under different assumptions regarding the value they assign to financial flexibility. Our results point to a negative uncertainty-investment relationship for constrained firms, withstanding the impact of the value of financial flexibility on this relationship. This negative relationship, however, is less pronounced when the value of financial flexibility is higher (especially for younger financially constrained firms).

In this letter we consider the coordination of connected and autonomous vehicles (CAVs) with intelligent traffic control signalization to improve the performance of traffic at intersections. We propose a coordination method based on a gradient-based multi-agent system. Specifically, we consider both the traffic light and the approaching vehicles as smart connected agents and we define a suitable edge tension function for each connection between agents. The agents then run a decentralized gradient descent control policy that drives them towards a desirable sequence of intersection crossings. In the literature, the problem of coordinating CAVs at intersections has been widely explored, with different algorithms being proposed. However, the problem of considering both smart traffic lights and smart cars simultaneously is relatively unexplored, especially from a multi-agent system, gradient-based, perspective. The advantages of the edge tension approach lie in its simplicity in terms of computation and in its ability to scale to larger networks. Furthermore, the control framework is applicable under different connected vehicle penetration rates. This flexibility is a desirable advantage given the gradual nature of the implementation of autonomy in real traffic networks. The proposed strategy is evaluated in simulation and significant fuel savings and delay improvements are shown in comparison with fixed-time traffic light control.

Work-related fatigue is a multidimensional phenomenon with significant effects on operational performance. Our work focuses on how the literature of operational research measures and models fatigue and its effects on operational performance, and on how it mitigates those effects. We position the literature of fatigue relative to that of work-rest scheduling, shift scheduling, multitasking, ergonomics, deterioration scheduling, and occupational health and safety. We classify the literature of fatigue across multiple dimensions: the methods by which it is identified and measured; the operational research methodology applied for fatigue prevention or mitigation; the flexibility allowed in work-rest scheduling and in shift scheduling; applications within manufacturing, construction, transportation, hospitals, and services; and the extent to which real data is used and results are implemented. Our work shows that operational research has contributed numerous effective algorithms and heuristic solution procedures to fatigue mitigation. We also identify several important research directions for operational research, to promote its broader and more effective use to identify and mitigate the effects of fatigue on operational performance.

This paper presents a review of modular and reconfigurable space robot systems intended for use in orbital and planetary applications. Modular autonomous robotic systems promise to be efficient, versatile, and resilient compared with conventional and monolithic robots, and have the potential to outperform traditional systems with a fixed morphology when carrying out tasks that require a high level of flexibility. Based on a set of fundamental concepts in modular self-reconfiguring robotics, advances in applying modular self-organizing robotics technologies to aerospace applications and space mission concepts are summarized for the purpose of identifying relevant requirements and solutions. Based on this survey, critical guidelines for the implementation of in space assembly and operation using modular autonomous robotic systems are identified.

By softwarizing the legacy network functions, Network Function Virtualization (NFV) allows rapid development and deployment of network services as well as simplicity and flexibility in network operations and management. Monitoring the performance characteristics of Virtual Network Functions (VNFs), particularly packet processing time, is important to ensure that VNFs are operating correctly with desired performance. This is especially crucial for low-latency network services. In this paper, we present Packet Processing Time Monitoring (PPTMon), a real-time, fine-grained, and end-to-end solution for VNF packet processing time monitoring. PPTMon can provide per-hop monitoring for a single VNF as well as end-to-end monitoring for multiple VNFs in a service function chain. PPTMon allows monitoring in both sampling and continuous fashions. Continuously monitoring every packet may greatly degrade the performance of the VNFs and generate a huge amount of monitoring data. PPTMon’s event-filtering algorithm effectively filters out non-important data and reduces the performance overhead. PPTMon processes packets in-stack by embedding timestamp information directly into the packets, thus further reducing the effect on the VNF performance. PPTMon is implemented on top of extended Berkeley Packet Filter (eBPF) – a Linux framework that allows high-speed packet processing. Our experiment results shows that PPTMon can monitor VNF packet processing time with high accuracy and low impact on performance.

Current bid formats in pool-based electricity markets are ill-equipped to accommodate the broad range of non-conventional sources of flexibility, such as demand response and interconnected heating, natural gas and water infrastructure networks. To address this issue, this paper introduces the novel price-region bid format to be used in both forward electricity markets and financial right auctions. We show that price-region bids are able to accommodate a broad range of techno-economic characteristics, including complex spatial and temporal couplings, and facilitate market access to non-conventional flexibility providers. We then show that this new bid format is compatible with existing market structures, and satisfies desirable market properties under common assumptions. Three numerical studies are provided: two motivating examples based on a district heating utility and a cascaded hydro power plant, and a case study based on an integrated power and heat system. These studies illustrate the inability of existing bid formats to accommodate flexible resources, and show how price-region bids overcome this shortcoming.

Multienergy systems (MESs), as coupling of various energy sectors, can offer appropriate solutions to the operation of energy systems. The flexibility introduced by energy diversity can leverage the operation of MESs in supplying different energy demands, e.g., electricity, heat, etc. This article presents a continuous-time optimization framework for the day-ahead operation of MESs, where a function space solution method is proposed to reduce the dimensionality of the proposed model. The proposed methodology projects the day-ahead model into a finite-dimensional function space spanned by Bernstein polynomials and converts the problem with an indefinite dimension into a finite-order mixed-integer linear programming problem that can be easily solved with commercial solvers. The main purpose of the proposed framework is to model the parameter and decision trajectories of MESs in a way to reduce the day-ahead energy load approximation errors and minimize the costs associated with net-load deviations in real-time operation. In this way, the flexibility introduced by the operation of different energy units in MESs can be efficiently captured to further improve the performance of such systems. The proposed optimization framework is studied regarding four test MESs and the results for multiple cases are presented and compared. The results demonstrate the efficiency of the proposed methodology in capturing the load approximation errors, as it gives a better solution to the cost-efficient operation of MESs at a reasonable computational time.

Deep Neural Networks (DNNs) have shown superiority in solving the problems of classification and recognition in recent years. However, DNN hardware implementation is challenging due to the high computational complexity and diverse dataflow in different DNN models. To mitigate this design challenge, a large body of research has focused on accelerating specific DNN models or layers and proposed dedicated designs. However, dedicated designs for specific DNN models or layers limit the design flexibility. In this work, we take advantage of the similarity among different DNN models and propose a novel Lego-based Deep Neural Network on a Chip (DNNoC) design methodology. We work on common neural computing units (e.g., multiply-accumulation and pooling) and create some neuron computing units called NeuLego processing elements (NeuLegoPEs). These NeuLegoPEs are then interconnected using a flexible Network-on-Chip (NoC), allowing to construct different DNN models. To support large-scale DNN models, we enhance the reusability of each NeuLegoPE by proposing a Lego placement method. The proposed design methodology allows leveraging different DNN model implementations, helping to reduce implementation cost and time-to-market. Compared with the conventional approaches, the proposed approach can improve the average throughput by 2,802% for given DNN models. Besides, the corresponding hardware is implemented to validate the proposed design methodology, showing on average 12,523% hardware efficiency improvement by considering the throughput and area overhead simultaneously.

5G and edge computing have brought great changes to video industry. Interactive video is becoming an emerging application form of multimedia service, which provides attraction beyond typical scenarios like cloud gaming and remote virtual reality (VR), and puts forward great challenges in resource capacity, response latency, and function flexibility to its service system. In this paper, we propose an elastic system architecture with low latency features to accommodate generic interactive video applications on near user edges. To increase system flexibility, we firstly design a dynamic Directed Acyclic Graph (dDAG) model for efficient task representation. Secondly, based on the model, we present the elastic architecture together with its scalable workflow pipeline. Thirdly, we propose a set of novel latency measurement metrics to analyze and optimize the performance of an interactive video system. Based on the proposed approaches, we disassemble a real world free-viewpoint synthesis application and benchmark its performance with the metrics. Extensive experimental results show the flexibility of our system to handle the stochastic human interactions during a video service session, with less than 5 ms additional scheduling latency introduced. End to end latency is kept within 43 ms for complex functions, and 28 ms for simpler scenarios, which satisfies the restrictions of most interactive video applications provided by an edge. Client of the architecture serves as a pure video player, which is also friendly to power limited terminals such as 5G phones. Efficiency and stability analyses of the system show superiorities over existing work, and also reveal potential optimization directions for future research.

An increasing number of studies employ virtual reality (VR) to evaluate interactions between autonomous vehicles (AVs) and pedestrians. VR simulators are valued for their cost-effectiveness, flexibility in developing various traffic scenarios, safe conduct of user studies, and acceptable ecological validity. Reviewing the literature between 2010 and 2020, we found 31 empirical studies using VR as a testing apparatus for both implicit and explicit communication. By performing a systematic analysis, we identified current coverage of critical use cases, obtained a comprehensive account of factors influencing pedestrian behavior in simulated traffic scenarios, and assessed evaluation measures. Based on the findings, we present a set of recommendations for implementing VR pedestrian simulators and propose directions for future research.

This paper proposes two leader-follower affine formation control algorithms for a class of second-order uncertain multi-agent systems. The model of each agent is described by the second-order dynamics with nonlinear uncertainties. Due to the high robustness, flexibility and maneuverability, the affine formation approaches based on the stress matrix are adopted to achieve a target formation, in which the formation control laws are designed for followers to keep a desired formation configuration with respect to leaders. First, considering that the velocities of leaders are constant, we design an estimator to compensate for the uncertainties and propose a PI-like affine formation control law to guarantee that the followers can achieve the target formation. Then, for the case that the velocities of leaders are time-varying, we propose another control algorithm with variable gains for followers under which a time-varying formation configuration can be maintained. Finally, the corresponding simulations of a realistic example are carried out to validate the effectiveness of the proposed affine formation control algorithms.

Large share of solar energy imposes a higher system flexibility to resolve the increased demand/supply imbalance due to the inherent intermittency and variability of the resource. In this work, we demonstrate that the additional solar-induced flexibility requirement can be fully provided by a special kind of solar farms, namely flexible PV. These plants are able to provide ancillary services by proactive generation curtailment and storage power injection and they can be managed exactly as the secondary reserve currently used. At the current and future penetration levels, we sized the flexible PV fleet required to reduce the Italian imbalance by 36 % (with respect to its 2016 value) while keeping the curtailment at 6 % of the national PV generation. We show how this result can be achieved at an equal or lower dispatching cost than current cost (depending on the solar share). In addition, we found that a fleet composed of many flexible PV plants with different capacity randomly distributed throughout the country provides an optimal solar regulation performance. Finally, we showed that the effectiveness of the proposed imbalance mitigation strategy depends only slightly on the year-specific load, wind, PV and energy prices profiles used to size the capacity of the flexible fleet.

Background Cereals and cereal-based products as an important part of the daily diet provide a considerable percent of macronutrients such as carbohydrate, protein and fiber and a range of micronutrients including vitamins (E and some of the B vitamins) and minerals (Mg and Zn). However, thermal (boiling or baking) and mechanical (milling) processes applied for conversion of cereals into consumable products and also improvement of the digestibility and palatability of cereal products lead to the reduction or even removal of some bioactive compounds, consequently loss of their nutritional and health values. Scope and approach The fortification process is a suitable means for the addition of the lost components to the formulation of cereals; although, direct usage of bioactive compounds and additives in bakery products has some limitations such as sensitivity to undesirable environmental and processing conditions, adverse effects on sensory attributes of products and interaction with other food ingredients. Encapsulation not only can solve these problems, but also improves the overall quality and shelf life of bakery products by release controlling, protecting and uniform distribution of these components. Key findings and conclusions Most studies in the literature show that the use of bioactive substances instead of free compounds can be used effectively to enrich or fortify cereal-based products, without having a significant negative impact on their physicochemical and textural properties. Spray drying was the most commonly used technique for encapsulation of bioactive compounds, which can be explained by its advantages such as flexibility and low cost. This review will provide valuable information that may be useful for identifying trends in the production of new functional cereal-based products through nano/microencapsulation technology. However, it is necessary to pay attention to the commercialization and also the safety of nanoformulations used in cereal products.

Public transportation is one of the important modes of transportation and is the backbone of transportation in Indonesia. The development of public transportation is also supported by the government, this government support is evident in the national policy, namely the National Medium Term Development Plan (RPJMN). Although public transportation is an effective mode of transportation, it also has obstacles in its development, namely how to meet customer desires in choosing a mode of transportation. There are several variables that are the focus of this research, namely age, gender, income, cost, speed, comfort, safety, efficiency and flexibility. The search for influential variables will use the K-Means and DBSCAN clustering algorithms, these two algorithms are also compared to their performance to find a better algorithm. The results of the Silhouette Coefficient show that DBSCAN has a better performance with a value of 0.99 than K-Means with a value of 0.86. The variables that affect the interest in using public transportation are the most important ones related to cost, speed, comfort, safety, efficiency and flexibility.

Cyclodextrin based nanosponges are the designed nanocarriers for the projected delivery of complex drugs. They are multifunctional hypercrosslinked cyclodextrin polymers connected in a three-dimensional, mesh-like network. Their functional characteristics can be fabricated by using different crosslinkers or their different rations with polymer. They can encapsulate various hydrophilic, lipophilic, small-sized or large-sized drug molecules. They offer formulation flexibility and are primarily used for solubility, bioavailability and stability enhancement purposes. This system is also pliable for co-delivery of pharmaceutical entities, improving therapeutic efficacy and patient compliance. If the surface of nanosponge is coupled with an appropriate ligand, even a target specific drug delivery can be achieved. It has a variety of applications in the field of pharmacy for the delivery of tricky drug molecules, proteins, enzymes, natural moieties and gaseous compounds. The list of its applications further widens with the development of nanodiagnostics, nanosensors, biomimetics and scaffolds based on nanosponges. The sudden explosion of research in this working area signifies cyclodextrin nanosponge based products in the market soon.

The integration of soft open point (SOP) effectively improves the flexibility of active distribution networks (ADNs). However, in practical operation, accurate network parameters are difficult to obtain and the operation state changes rapidly with distributed generators (DGs). With the development of information technologies, massive operation data can be acquired in ADNs. How to utilize multisource data has become the key to realize the intelligent operation of ADNs. This article proposes a data-driven operation strategy of SOP based on model-free adaptive control (MFAC). First, considering the inaccurate parameters and frequent change of operation states, a data-driven framework is formulated for the real-time operation of SOP. Then, the operation strategies of multiple SOPs are further improved with interarea coordination. The results of case studies show that driven by the measurement data, the potential benefits of SOPs are explored to adaptively respond to system state changes and improve the operational performance of ADNs.

The effective implementation of Industry 4.0 requires the reformulation of industrial processes in order to achieve the vertical and horizontal digitalization of the value chain. For this purpose, it is necessary to provide tools that enable their successful implementation. This paper therefore proposes a data-centric, distributed, dynamically scalable reference architecture that integrates cutting-edge technologies being aware of the existence of legacy technology typically present in these environments. In order to make its implementation easier, we have designed a metamodel that collects the description of all the elements involved in a digital platform (data, resources, applications and monitoring metrics) as well as the necessary information to configure, deploy and execute applications on it. Likewise, we provide a tool compliant to the metamodel that automates the generation of configuration, deployment and launch files and their corresponding transference and execution in the nodes of the platform. We show the flexibility, extensibility and validity of our software artefacts through their application in two case studies, one addressed to preprocess and store pollution data and the other one, more complex, which simulates the management of an electric power distribution of a smart city.

Collective self-consumption is driving the transformation of distribution networks towards independently controllable community microgrids. The objective of this work is to utilize limited heterogeneous flexibilities in the system to perform grid-support functions to evade additional investment on dedicated storage facilities. We propose a cooperative receding horizon control that allows optimal use of the collective resources over the prediction horizon. The conventional linear state-space model employed in the predictive control is replaced by a novel rate-based model that propagates state derivatives rather than the states across the prediction horizon, thus eliminating offset errors . The proposed cooperative predictive control strategy with ratebased model has been applied to the microgrid frequency control problem. The proposed control strategy has been illustrated on a power hardware-in-the loop platform by modeling a microgrid system with grid-supporting DERs. The simulations reveal the effectiveness of the proposed strategy by comparing the control solution with a dedicated storage facility and its replacement distributed flexible resources.

When a natural disaster occurs, the most critical task is to search and rescue trapped people as soon as possible. In recent years, unmanned aerial vehicles (UAVs) have been widely employed because of their high durability, low cost, ease of implementation, and flexibility. In this article, we collected a new thermal image dataset captured by drones. After that, we used several different deep convolutional neural networks to train survivor detection models on our dataset, including YOLOV3, YOLOV3-MobileNetV1, and YOLOV3- MobileNetV3. Due to the limited computing power and memory of the onboard microcomputer, to balance the inference time and accuracy, we found the optimal points to prune and fine-tune the survivor detection network based on the sensitivity of the convolutional layer. We verified it on NVIDIA’s Jetson TX2 and achieved a real-time performance of 26.60 frames/s (FPS). Moreover, we designed a real-time survivor detection system based on DJI Matrice 210 and Manifold 2-G to provide search and rescue services after the disaster.

Cooperative control for trains shows great potential in improving line utilization, passenger comfort and operation flexibility.However,the operation of train platoons faces great challenges induced by model uncertainties,unpredictable resistances and time-varying disturbances. To address these problems,a consensus-based robust cooperative control scheme is presented in this paper for both homogeneous and heterogeneous train platoons.Taking the physical connection between carriages into consideration,each train in the platoons is modeled as a negative imaginary(NI) system,and the NI property is rigorously proved in this paper.In the foundation of cooperative control theories of NI systems,robust strictly negative imaginary(SNI) controllers considering the network topology are utilized to track a predefined motion reference.The proposed controllers are robust to both mass uncertainties and external disturbances.Moreover,the line utilization for the railway system and the ride comfort for the passengers are improved using the proposed control schemes.Numerical simulations are given to showcase the effectiveness and robustness of the proposed controllers.

A key challenge for power plant carbon capture systems is operating economically under varying load conditions. Daily variations may be handled through control systems with reduced economic performance, but longer term seasonal cycles will require different solutions to enable robust and economic operation. We propose to use modular monolith adsorbent (MMA) designs whose capacity and configuration can be altered to respond to variations in flue gas flow. The configuration of the blocks is defined by the pattern of their serial and parallel connection. The ability to present a shorter, but wider, bed at different flue gas flowrates can then be exploited to improve performance and continuing to meet constraints on product purity and recovery. This additional degree of freedom afforded by the modular nature of the design allows an active response to the flowrate conditions and increases the flexibility of the system over the space of flowrate and concentration. In order to compare the proposed MMA systems and the conventional passive design method, case studies with varying flue gas amounts in a power plant are carried out. Simulation results show that the proposed MMA systems have improved optimal solutions because of their design flexibility, compared to the conventional passive design approach. Lastly, the optimized solutions are presented as a parametric map with different CO2 capture fractions and flowrate of flue gas. These parametric off-line solutions can be a guideline for the process implementation and reduce the operating complexity even if the MMA systems are used.

In many countries distributed energy resources (DER) (e.g. photovoltaics, batteries, wind turbines, electric vehicles, electric heat pumps, air-conditioning units and smart domestic appliances) are part of the ‘Green Deal’ to deliver a climate neutral society. Policy roadmaps, despite providing a framework and penetration targets for DER, often lack the network planning strategies needed to transition from passive to active distribution networks. Currently, DER's dynamic performance parameters and location identification techniques are not fully standardised. In fact, it can be very ad hoc. Standardised distributed load modelling and non-intrusive load monitoring (NILM) for equipment manufacturers, installers and network operators is critical to low and medium voltage network management in order to facilitate better balancing, flexibility and electricity trading across and within the power system for mass DER deployment. The aim of this paper is to fill this load modelling and NILM knowledge gap for DERto inform the ‘Green Deal’ transition and support standardisation. In the paper, existing load modelling techniques and NILM methodologies are critically examined to inform and guide research activity, equipment development and regulator thinking, as well as network operators. Seven key findings that need urgent attention are identified to support a smooth power system reconfiguration.

Industrial services are increasingly becoming more relational and customer-oriented, due to manufacturers’ adoption of servitisation approaches and product service system offerings. Challenges remain regarding the effective design and delivery of these new offerings, and the understanding of their actual value for both providers and customers. This work focuses on one specific type of product service systems in the context of manufacturing equipment: result-oriented or performance-based services, which aim at delivering an outcome rather than selling the equipment to the customer. A proposal of a value-driven method for their design that engages the customer in the process is presented. This new method has been applied to a real industrial life setting through an application case, involving the service provider and its customer, and targeting manufacturing equipment within customers’ plant. Results indicate the effectiveness of this prescriptive approach. Reported benefits from participants refer to its flexibility, adaptability and applicability for different types of equipment, as well as its potential to help providing a modular service portfolio adequate to equipment specific context and requirements.

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.

Smart sensors, self-configuration, operational flexibility, and automatic learning, among others, are technological attributes from industry 4.0 appliable to the essential oil extraction by the steam distillation process. These operations are recognized by their simplicity. Nevertheless, lack of automatic controls, process monitoring, and self-adjustment lead to uncontrolled extraction, poor yields, low quality of products. It occurs because of overexposure to high temperatures and overspending resources like energy and water. As far as capacity utilization is concerned, the optimized process is key to planning and managing the production activities.

Predator diet can be influenced by competition and intraguild predation, leading to resource partitioning and/or avoidance. For sympatric, endemic predators, these processes form as predator species coevolve, facilitating coexistence. However, when novel predator interactions occur, significant dietary overlap could create acute levels of competition leading to intraguild predation and population extinction, or accelerated changes in diet and/or spatial and temporal avoidance. We measured diet, intraguild predation, and spatial and temporal overlap in two predator species in a novel predator interaction: the western quoll (Dasyurus geoffroii), a small, native carnivore reintroduced to semi-arid Australia, and the domestic cat (Felis catus), a larger introduced carnivore already resident at the release site. Both species exhibited high dietary overlap and fed on mammals, reptiles, birds, and invertebrates. Cats included quolls in their diet. Quoll diet was broader (including carrion, bats, and plant material) and flexible, changing significantly with age, sex, and season. Introduced rabbit was the most common prey item recorded for both species (frequency of occurrence = 40–50%). However, quolls consumed rabbits in relation to their availability while rabbit consumption in cats was unrelated to availability suggesting a stronger dependence on rabbit prey. Quoll diet did not change over time since release and they did not spatially or temporally avoid cats. However, cats were significantly spatially associated with rabbits while quolls were not, suggesting higher predation efficacy in quolls possibly due to their smaller body size enabling them to catch rabbits inside warrens. Despite high dietary overlap and intraguild predation, the quoll’s broad and flexible diet and high predation efficacy appeared to assist in facilitating coexistence and reducing competition in this novel predator interaction. This dietary flexibility may be harnessed to improve conservation outcomes: reducing introduced rabbits in our study area could naturally reduce feral cat populations while having less impact on native quolls.

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.

PurposeSupply chain integration (SCI) and flexibility (SCF) are recognised as crucial business practices and capability in the global competitive market. However, limited research has paid attention to study the relationship between SCI, SCF and their impact on supply chain performance (SCP). Therefore, the purpose of this paper is to establish a relationship between integration, flexibility and performance.Design/methodology/approachThe structural equation modelling technique was used to analyse the 187 data collected from Indian organisations through the survey methodology.FindingsFindings indicate that external integration contributes significantly to realise SCF and SCP. Sourcing and logistics flexibility also help to improve the SCP. The mediation analysis showed that the association of customer and supplier integration with SCP is partially and fully mediated by logistics flexibility, respectively. This study suggests that integration influences the SCP when the firm has a strong association with downstream partners and enough capability for logistics flexibility.Research limitations/implicationsThe study has collected cross-sectional data to analyse the relationship between SCI, SCF and SCP. However, as integration requires an effort of the years, longitudinal data and industry-specific studies may provide comprehensive views to validate the results of this study.Originality/valueBuilding on relational view theory and dynamic capability theory, the study has proposed the SCP assessment framework based on the relationship between SCI and SCF.

This study focused on spatial analysis to identify the changes in adaptability over the last five decades. The features influencing adaptability were selected from the reference study. An appropriate method was used to analyse these features through spatial analysis. Six distinctive typologies of rural houses were selected from six regions. Unlike the traditional houses, the contemporary houses in the same area reflected a different character. Urban houses built since the early and mid-20th century were compared with contemporary houses. After analysing the openness, generality, flexibility, depth, typicality, construction technique, involvement of end-users, and the feedback from the inhabitants, the study identified a significant decrease in contemporary houses' adaptability. Spatial analysis was used to quantify the different features and compare between traditional and contemporary houses. Though the adaptability had been reduced over time, the latest houses started to achieve better flexibility in some features due to government policy and implementation of statutory building regulations. Further recommendations were provided to enhance the residential architecture's adaptability in future. The study samples were selected from different regions of Bangladesh. Still, the result and policy recommendations can be helpful for other countries, especially with high population density and a developing economy.