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The heterogeneity of energy ontologies hinders the interoperability between ontology-based energymanagement applications to perform a large-scale energy management. Thus, there is the need fora global ontology that provides common vocabularies to represent the energy subdomains. A globalenergy ontology must provide a balance of reusability–usability to moderate the effort required toreuse it in different applications. This paper presents DABGEO: a reusable and usable global ontologyfor the energy domain that provides a common representation of energy domains represented byexisting energy ontologies. DABGEO can be reused by ontology engineers to develop ontologies forspecific energy management applications. In contrast to previous global energy ontologies, it followsa layered structure to provide a balance of reusability–usability. In this work, we provide an overviewof the structure of DABGEO and we explain how to reuse it in a particular application case. Inaddition, the paper includes an evaluation of DABGEO to demonstrate that it provides a balance ofreusability–usability.

Nowadays, no business activity escapes the fourth industrial revolution, called industry 4.0, which is characterized by digitalization of processes. The possibility of simultaneously having systems with greater interconnection, more information and greater flexibility, allows companies to have a clearer view of their processes and consequently improve their effectiveness and efficiency. The digital transformation can no longer be based simply on making the processes more efficient, but on creating more sustainable and profitable customer relationships, continuously aligning the value of the product with the changing customer requirements. Even though managing assets over the Internet is increasingly common, much effort is needed to identify the functionality that should be provided by these platforms to enhance existing asset management practices. The effort of IT vendors is focused on the development of IoT platforms, which allow, among other functions, to create a connection between machinery and digital systems, protect all devices and data against hacking or attacks, control operations and maintenance of equipment or perform different analyses of assets or systems. The aim of this paper is to understand the functionalities of the existing IAMP platforms, providing a system that evaluates these functionalities based on the business objectives from the point of view of asset management. This methodology allows maintenance managers guiding the evolution of the life cycle of their assets according to the business value conception. This makes this methodology especially suitable for supporting new challenging scenarios of maintenance management. In this paper we first talk about the structure of an IAMP, then how they integrate the asset management model and a summary of the features and modules that have the most known IAMP platforms. Finally, an evaluation system of IAMP platforms and a case study is presented based on their content for asset management.

Cluster analysis plays a significant role regarding automating such a knowledge discovery process in spatial data mining. A good clustering algorithm supports two essential conditions, namely high intra-cluster similarity and low inter-cluster similarity. Maximized intra-cluster/within-cluster similarity produces low distances between data points inside the same cluster. However, minimized inter-cluster/between-cluster similarity increases the distance between data points in different clusters by furthering them apart from each other. We previously presented a spatial clustering algorithm, abbreviated CutESC (Cut-Edge for Spatial Clustering) with a graph-based approach. The data presented in this article is related to and supportive to the research paper entitled "CutESC: Cutting edge spatial clustering technique based on proximity graphs" (Aksac et al., 2019) [1], where interpretation research data presented here is available. In this article, we share the parametric version of our algorithm named CutESC-P, the best parameter settings for the experiments, the additional analyses and some additional information related to the proposed algorithm (CutESC) in [1]. (C) 2019 The Authors. Published by Elsevier Inc.

Cluster analysis plays a significant role regarding automating such a knowledge discovery process in spatial data mining. A good clustering algorithm supports two essential conditions, namely high intra-cluster similarity and low inter-cluster similarity. Maximized intra-cluster/within-cluster similarity produces low distances between data points inside the same cluster. However, minimized inter-cluster/between-cluster similarity increases the distance between data points in different clusters by furthering them apart from each other. We previously presented a spatial clustering algorithm, abbreviated CutESC (Cut-Edge for Spatial Clustering) with a graph-based approach. The data presented in this article is related to and supportive to the research paper entitled "CutESC: Cutting edge spatial clustering technique based on proximity graphs" (Aksac et al., 2019) [1], where interpretation research data presented here is available. In this article, we share the parametric version of our algorithm named CutESC-P, the best parameter settings for the experiments, the additional analyses and some additional information related to the proposed algorithm (CutESC) in [1]. (C) 2019 The Authors. Published by Elsevier Inc.

Nowadays, no business activity escapes the fourth industrial revolution, called industry 4.0, which is characterized by digitalization of processes. The possibility of simultaneously having systems with greater interconnection, more information and greater flexibility, allows companies to have a clearer view of their processes and consequently improve their effectiveness and efficiency. The digital transformation can no longer be based simply on making the processes more efficient, but on creating more sustainable and profitable customer relationships, continuously aligning the value of the product with the changing customer requirements. Even though managing assets over the Internet is increasingly common, much effort is needed to identify the functionality that should be provided by these platforms to enhance existing asset management practices. The effort of IT vendors is focused on the development of IoT platforms, which allow, among other functions, to create a connection between machinery and digital systems, protect all devices and data against hacking or attacks, control operations and maintenance of equipment or perform different analyses of assets or systems. The aim of this paper is to understand the functionalities of the existing IAMP platforms, providing a system that evaluates these functionalities based on the business objectives from the point of view of asset management. This methodology allows maintenance managers guiding the evolution of the life cycle of their assets according to the business value conception. This makes this methodology especially suitable for supporting new challenging scenarios of maintenance management. In this paper we first talk about the structure of an IAMP, then how they integrate the asset management model and a summary of the features and modules that have the most known IAMP platforms. Finally, an evaluation system of IAMP platforms and a case study is presented based on their content for asset management.

The paper empirically examines the impact of foreign direct investment spillovers on the performance of Vietnamese manufacturing firms from 2007 to 2015, and the role exerted by absorptive capacity on this relationship. Importantly, it is expected that the thresholds of absorptive capacity of the domestic firms exist, and the benefits which local firms enjoy can vary subject to the thresholds. This paper applies a threshold fixed effect model with panel data in Vietnam to show several findings. Firstly, the domestic firms can benefit from spillovers via the horizontal channel and backward linkage. Secondly, absorptive capacity is a vital factor in linking horizontal spillovers to local firms. Finally, the paper finds that there are thresholds of absorptive capacity in the case of Vietnam. © 2020 CEPII (Centre d'Etudes Prospectives et d'Informations Internationales), a center for research and expertise on the world economy

Application-centric networking proposes a novel approach to provision connectivity services in transport networks based on application-specific requirements. By exploiting this paradigm, an application can request a service with several requirements to a generic control and management plane, which can leverage this information to differentiate a service on the overall network layers. However, an application-centric provisioning can lead to higher blocking probability when satisfying multiple requirements at the same time, thus impacting negatively both network operators and applications. This paper proposes a framework for the negotiation of connectivity services between a transport network and the applications running on top. In particular, an application can communicate its requirements to it and, in case of resource scarcity (either in term of bandwidth, latency, availability, etc.), the network can offer alternative solutions with a degraded service quality. The effectiveness of the proposed approach is proved by the lower blocking probability experienced by the service requests against a standard approach lacking negotiation capabilities, without in turn causing significant service degradation to the applications. In addition, the paper describes the software architecture behind the negotiation framework and its implementation on top of the ONOS SDN controller.

For the manufacturing system development process to deliver the expected value, it is important that the management of the process works. However, the competence of building adequate process models is often missing. The purpose of this paper is to demonstrate that with some training, it is possible to achieve much better process models. This is studied through a workshop, where the participants map their “breakfast process”. The result shows that without guidance, the process becomes self-centred. A self-centred resource-oriented process model gives focus on time and resource utilisation, while a value objective oriented process model focuses more on process quality.

We discuss several approaches involving the reconstruction of discontinuous one-dimensional functions using parameter-dependent multiquadric radial basis function (MQ-RBF) local interpolants combined with weighted essentially non-oscillatory (WENO) techniques, both in the computation of the locally optimized shape parameter and in the combination of RBF interpolants. We examine the accuracy of the proposed reconstruction techniques in smooth regions and their ability to avoid Gibbs phenomena close to discontinuities. In this paper, we propose a true MQ-RBF–WENO method that does not revert to the classical polynomial WENO approximation near discontinuities, as opposed to what was proposed in Guo and Jung (2017) [12,13]. We present also some numerical examples that confirm the theoretical approximation orders derived in the paper.

In this paper we are concerned with the classification of the finite groups admitting a bipartite DRR and a bipartite GRR. First, we find a natural obstruction that prevents a finite group from admitting a bipartite GRR. Then we give a complete classification of the finite groups satisfying this natural obstruction and hence not admitting a bipartite GRR. Based on these results and on some extensive computer computations, we state a conjecture aiming to give a complete classification of the finite groups admitting a bipartite GRR. Next, we prove the existence of bipartite DRRs for most of the finite groups not admitting a bipartite GRR found in this paper. Actually, we prove a much stronger result: we give an asymptotic enumeration of the bipartite DRRs over these groups. Again, based on these results and on some extensive computer computations, we state a conjecture aiming to give a complete classification of the finite groups admitting a bipartite DRR.

Saliency detection methods proposed in the literature exploit different rationales, visual clues, and assumptions, but there is no single best saliency detection algorithm that is able to achieve good results on all the different benchmark datasets. In this paper we show that fusing different saliency detection algorithms together by exploiting neural network architectures makes it possible to obtain better results. Designing the best architecture for a given task is still an open problem since the existing techniques have some limits with respect to the problem formulation, to the search space, and require very high computational resources. To overcome these problems, in this paper we propose a three-step fusion approach. In the first step, genetic programming techniques are exploited to combine the outputs of existing saliency algorithms using a set of provided operations. Having a discrete search space allows us a fast generation of the candidate solutions. In the second step, the obtained solutions are converted into backbone Convolutional Neural Networks (CNNs) where operations are all implemented with differentiable functions, allowing an efficient optimization of the corresponding parameters (in a continuous space) by backpropagation. In the last step, to enrich the expressiveness of the initial architectures, the networks are further extended with additional operations on intermediate levels of the processing that are once again efficiently optimized through backpropagation. Extensive experimental evaluations show that the proposed saliency fusion approach outperforms the state-of-the-art on the MSRAB dataset and it is able to generalize to unseen data of different benchmark datasets.

Context: Systematic Literature Reviews (SLRs) have been adopted within Software Engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective: The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. Method: To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. Results: The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Conclusions: Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE. © 2020, Open access

Context: Systematic Literature Reviews (SLRs) have been adopted within Software Engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective: The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. Method: To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. Results: The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Conclusions: Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE. © 2020, Open access

Context: Systematic Literature Reviews (SLRs) have been adopted within Software Engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective: The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. Method: To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. Results: The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Conclusions: Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE. © 2020, Open access

Context: Systematic Literature Reviews (SLRs) have been adopted within Software Engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective: The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. Method: To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. Results: The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Conclusions: Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE. © 2020, Open access

In the present paper, the interface crack between two dissimilar dielectric materials under a mechanical load is investigated with including flexoelectricity effects. Flexoelectricity is a size dependent electro-mechanical coupling phenomenon, where the electric polarization is induced by a strain gradient in dielectrics. The strain gradients may potentially break the inversion symmetry in centrosymmetric crystals and polarization is observed even in all dielectric materials. The polarization is proportional to the strain gradients in the direct flexoelectricity. Layered composite structures are frequently utilized in microelectronics. Due to a poor adhesion of protection layer and basic material, the interface crack can be created there and for the prediction of failure of these structures it becomes essential to investigate distribution of the interfacial stress and strain fields. Governing equations in the gradient theory contain higher-order derivatives than in the standard continuum mechanics. Therefore, a reliable computational tool is required to solve these boundary-value problems. The mixed finite element method (FEM) is developed, where the standard C0 continuous finite elements are utilized for independent approximations of displacements and strains. The constraints between the strain gradients and displacements are satisfied by collocation at Gaussian integration points inside elements. In numerical examples, a parametric study is performed with respect to flexoelectric and elastic coefficients for both material regions. The influence of these parameters on the crack opening displacement is discussed.

In the present paper, the interface crack between two dissimilar dielectric materials under a mechanical load is investigated with including flexoelectricity effects. Flexoelectricity is a size dependent electro-mechanical coupling phenomenon, where the electric polarization is induced by a strain gradient in dielectrics. The strain gradients may potentially break the inversion symmetry in centrosymmetric crystals and polarization is observed even in all dielectric materials. The polarization is proportional to the strain gradients in the direct flexoelectricity. Layered composite structures are frequently utilized in microelectronics. Due to a poor adhesion of protection layer and basic material, the interface crack can be created there and for the prediction of failure of these structures it becomes essential to investigate distribution of the interfacial stress and strain fields. Governing equations in the gradient theory contain higher-order derivatives than in the standard continuum mechanics. Therefore, a reliable computational tool is required to solve these boundary-value problems. The mixed finite element method (FEM) is developed, where the standard C0 continuous finite elements are utilized for independent approximations of displacements and strains. The constraints between the strain gradients and displacements are satisfied by collocation at Gaussian integration points inside elements. In numerical examples, a parametric study is performed with respect to flexoelectric and elastic coefficients for both material regions. The influence of these parameters on the crack opening displacement is discussed.

Gas production from oceanic hydrates could trigger large submarine landslides. To screen out the sites susceptible to perturbation, this paper proposes a physically-based model for assessing the susceptibility of submarine landslides in continental slopes where gas hydrates are widespread. The model does not require an inventory of historical submarine landslides that are often incomplete or unavailable. Instead, the model is derived within the framework of the limit equilibrium method of slope stability analysis, and accounts for pressure buildup and decreasing cohesion in the slope due to hydrate dissociation. The proposed model is then employed to map the landslide susceptibility of a study area conditioned to a hypothetical scenario of seafloor warming that triggers hydrate dissociation. The susceptible zones are found in the regions with shallow water and high geothermal temperature. Unlike the terrestrial landslides, the slope angle plays a secondary role in affecting the susceptibility of submarine landslides triggered by hydrate dissociation, while the hydrothermal conditions are important factors that control the susceptibility.

Numerous saturable absorber (SA) fabrication methods have been proposed and employed in the development of ultrafast laser systems, particularly for microfiber-based SAs decorated with various 2D materials. However, the study on microfiber-SA with different transition lengths is still inadequate. This paper focuses on the design and fabrication method of black phosphorus/polydimethylsiloxane composite microfiber-based SA for ultrashort pulse generation. With optimized microfiber-SA dimensions of 30 mm (up/down taper), 1 mm (waist length) and 10 µm (waist diameter), black phosphorus/polydimethylsiloxane composite was spin-coated on the microfiber at 4000 rpm for 6 min. The integration of the microfiber-SA in an erbium-doped fiber laser exhibited pulse duration of 724 fs at center wavelength of 1560.18 nm. This work provides a guideline for the design of microfiber-based SA using spin-coating technique to maximize its laser performances.

Numerous saturable absorber (SA) fabrication methods have been proposed and employed in the development of ultrafast laser systems, particularly for microfiber-based SAs decorated with various 2D materials. However, the study on microfiber-SA with different transition lengths is still inadequate. This paper focuses on the design and fabrication method of black phosphorus/polydimethylsiloxane composite microfiber-based SA for ultrashort pulse generation. With optimized microfiber-SA dimensions of 30 mm (up/down taper), 1 mm (waist length) and 10 µm (waist diameter), black phosphorus/polydimethylsiloxane composite was spin-coated on the microfiber at 4000 rpm for 6 min. The integration of the microfiber-SA in an erbium-doped fiber laser exhibited pulse duration of 724 fs at center wavelength of 1560.18 nm. This work provides a guideline for the design of microfiber-based SA using spin-coating technique to maximize its laser performances.

In this paper, a low-cost and room temperature flexible carbon monoxide (CO) gas sensor is presented using multi-walled carbon nanotubes coated cotton fabric. A dip and drying method is used to fabricate a lightweight, and high-performance fabric based CO gas sensor using different concentrations of multi-walled carbon nanotubes (MWCNTs). Transmission electron microscopy (TEM) is utilized for examining the deagglomeration of MWCNTs in the presence of a sufficient amount of surfactant. The field-emission scanning electron microscopy (FESEM) is used to evaluate the formation of a uniform network of MWCNTs on the cotton fabric. Fourier transform infrared (FTIR) spectroscopy is used to confirm the presence of functional groups which plays an important role in CO gas sensing. The fabricated cotton fabric coated with MWCNTs (CCM) sensors are tested with different concentrations of CO gas ranging from 25 ppm to 100 ppm at room temperature. It is found that in comparison to all other sensors, the CCM sensor coated with the higher concentration of MWCNTs (0.5 mg/ml) shows a maximum response of 9.11 % at 25 ppm and 15.2 % at 100 ppm concentration of CO gas respectively. The CCM 4 sensor shows the fastest response and recovery within 49s for 25–100 ppm of CO gas. Moreover, the fabricated CCM sensor exhibited good repeatability, reproducibility, and selectivity. These sensors are suitable for low-cost smart textile applications.

[Abstract]: Real-world point sets tend to be clustered, so using a machine word for each point is wasteful. In this paper we first show how a compact representation of quadtrees using O(1) bits per node can break this bound on clustered point sets, while offering efficient range searches. We then describe a new compact quadtree representation based on heavy-path decompositions, which supports queries faster than previous compact structures. We present experimental evidence showing that our structure is competitive in practice.

[Abstract]: Particle Swarm Optimization is an optimization algorithm that mimics the behaviour of a flock of birds, setting multiple particles that explore the search space guided by a fitness function in order to find the best possible solution. We apply the Sticky Binary Particle Swarm Optimization algorithm to perform feature selection for domain adaptation, a specific type of transfer learning in which the source and the target domain have a common feature space, a common task, but different distributions. When applying Particle Swarm Optimization, classification error is usually employed in the fitness function to evaluate the goodness of subsets of features. In this paper, we aim to compare this approach with using complexity metrics instead, under the assumption that reducing the complexity of the problem will lead to results that are independent from the classifier used for testing while being less computationally demanding. Therefore, we carried out experiments to compare the performance of both approaches in terms of classification accuracy, speed and number of features selected. We found out that our proposal, although in some cases incurs in a slight degradation of classification performance, it is indeed faster and selects fewer features, making it a feasible trade-off.

The gas–solid particle two-phase flow in a fluidized bed shows complex physics. The multi-scale algorithm composed of the coupled gas-kinetic scheme (GKS) for the gas phase and unified gas-kinetic wave-particle method (UGKWP) for the solid particle phase is developed for three-dimensional fluidized bed study. For the solid-particle phase, different from the widely-used Eulerian and Lagrangian approaches, the UGKWP unifies the wave (dense particle region) and discrete particle (dilute particle region) formulation seamlessly according to a continuous variation of particle cell's Knudsen number (Kn). The GKS-UGKWP for the coupled gas-particle evolution system can automatically become an Eulerian–Eulerian (EE) method in the high particle collision regime and Eulerian–Lagrangian (EL) formulation in the collisionless particle regime. In the transition regime, the UGKWP can achieve a smooth transition between the Eulerian and Lagrangian limiting formulation. More importantly, the weights of mass distributions from analytical wave and discrete particle are related to the local Kn by (1−exp(−1/Kn)) for wave and exp(−1/Kn) for discrete particle. The UGKWP provides an optimal modeling in capturing the particle phase in difference regimes with the full consideration of physical accuracy and numerical efficiency. In the numerical simulation, the UGKWP does not need any prior division of dilute/dense regions, which makes it suitable for the fluidized bed problem, where the dilute/transition/dense regions instantaneously coexist and are dynamically interconvertible. In this paper, based on the GKS-UGKWP formulation two lab-scale fluidization cases, i.e., one turbulent fluidized bed and one circulating fluidized bed, are simulated in 3D and the simulation results are compared with the experimental measurements. The typical heterogeneous flow features of the fluidized bed are well captured and the statistics are in good agreement with experiment data. © 2022 Elsevier B.V.

The identification of input parameters for funite element modelling of the cutting process is still a complex task as the experimental testing equipment cannot reach its combined levels of strains, strain rates and temperatures. Inverse identification using Artificial Intelligence method provides a relevant alternative. In this paper, material constitutive and friction models parameters identified with an Efficient Global Optimization algorithm and an ALE orthogonal cutting model are introduced in a CEL model. Assessment of the differences in the results due to the formulation and dependence of parameters identification to the finite element model are then performed.

This paper re-examines the causality between stock returns and foreign portfolio investment (FPI) flows in the Indian context during the COVID-19 pandemic. Using the Covid-19 index constructed by Narayan et al. [19] and the Toda and Yamamoto Granger causality test, the study reveals that bi-directional causality runs from FPI flows to stock returns in the early period of the Covid-19 pandemic. Whereas after the peak of the pandemic, there is a unidirectional causality that runs from FPI flows to stock returns. • Bi-directional causality runs from FPI flows to stock return during the initial period of COVID. • In the second period, unidirectional causality runs from FPI flows to stock returns

This paper re-examines the causality between stock returns and foreign portfolio investment (FPI) flows in the Indian context during the COVID-19 pandemic. Using the Covid-19 index constructed by Narayan et al. [19] and the Toda and Yamamoto Granger causality test, the study reveals that bi-directional causality runs from FPI flows to stock returns in the early period of the Covid-19 pandemic. Whereas after the peak of the pandemic, there is a unidirectional causality that runs from FPI flows to stock returns. • Bi-directional causality runs from FPI flows to stock return during the initial period of COVID. • In the second period, unidirectional causality runs from FPI flows to stock returns

This paper re-examines the causality between stock returns and foreign portfolio investment (FPI) flows in the Indian context during the COVID-19 pandemic. Using the Covid-19 index constructed by Narayan et al. [19] and the Toda and Yamamoto Granger causality test, the study reveals that bi-directional causality runs from FPI flows to stock returns in the early period of the Covid-19 pandemic. Whereas after the peak of the pandemic, there is a unidirectional causality that runs from FPI flows to stock returns. • Bi-directional causality runs from FPI flows to stock return during the initial period of COVID. • In the second period, unidirectional causality runs from FPI flows to stock returns

Iiboshi Y., Iwata A., Hayashi T., et al. Energy trapping of circumferential shear horizontal wave in a hollow cylinder. Ultrasonics 133, 107044 (2023); https://doi.org/10.1016/j.ultras.2023.107044., This paper describes energy trapping of circumferential shear horizontal wave (C-SH wave) at a circumferential inner groove of a hollow cylinder. We first derive exact solutions of resonant frequencies of the C-SH wave from the classical theory of guided waves propagating in a hollow cylinder and approximated solutions from the relationship between wavelength of the C-SH wave and length of circumferential path of the hollow cylinder. Next, we examined the energy trapping conditions using the dispersion curves of a guided wave propagating in the longitudinal direction of a hollow cylinder, and showed that C-SH waves trap energy when the hollow cylinder has a circumferential groove on the inner surface, rather than on the outer surface. The energy trapping at an inner groove was confirmed for the C-SH wave with the circumferential order of n = 6 using eigen frequency analysis of finite element method and experiments with electromagnetic transducers. Moreover, when the energy trap mode was used to determine the change in resonance frequency for glycerin solution of different concentrations, it was confirmed that the resonance frequency decreased monotonically as the concentration increased, indicating the possibility of using the energy trap mode as a QCM-like sensor.

This paper re-examines the causality between stock returns and foreign portfolio investment (FPI) flows in the Indian context during the COVID-19 pandemic. Using the Covid-19 index constructed by Narayan et al. [19] and the Toda and Yamamoto Granger causality test, the study reveals that bi-directional causality runs from FPI flows to stock returns in the early period of the Covid-19 pandemic. Whereas after the peak of the pandemic, there is a unidirectional causality that runs from FPI flows to stock returns. • Bi-directional causality runs from FPI flows to stock return during the initial period of COVID. • In the second period, unidirectional causality runs from FPI flows to stock returns

Iiboshi Y., Iwata A., Hayashi T., et al. Energy trapping of circumferential shear horizontal wave in a hollow cylinder. Ultrasonics 133, 107044 (2023); https://doi.org/10.1016/j.ultras.2023.107044., This paper describes energy trapping of circumferential shear horizontal wave (C-SH wave) at a circumferential inner groove of a hollow cylinder. We first derive exact solutions of resonant frequencies of the C-SH wave from the classical theory of guided waves propagating in a hollow cylinder and approximated solutions from the relationship between wavelength of the C-SH wave and length of circumferential path of the hollow cylinder. Next, we examined the energy trapping conditions using the dispersion curves of a guided wave propagating in the longitudinal direction of a hollow cylinder, and showed that C-SH waves trap energy when the hollow cylinder has a circumferential groove on the inner surface, rather than on the outer surface. The energy trapping at an inner groove was confirmed for the C-SH wave with the circumferential order of n = 6 using eigen frequency analysis of finite element method and experiments with electromagnetic transducers. Moreover, when the energy trap mode was used to determine the change in resonance frequency for glycerin solution of different concentrations, it was confirmed that the resonance frequency decreased monotonically as the concentration increased, indicating the possibility of using the energy trap mode as a QCM-like sensor.

10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022 -- 22 June 2022 through 24 June 2022 -- 148818, In this paper we propose an Improved Approximate Dynamic Programming approach for the integrated fleet sizing and replenishment planning problem utilizing fix and optimize heuristic. The problem is about deciding both the composition of a fleet for distributing a single item and the replenishment planning based on a predetermined delivery frequency. This problem integrates two important logistical issues namely fleet sizing and replenishment planning. The objective is minimizing all relevant costs composed of vehicle ownership, inventory, and approximate routing costs. We show the effectiveness of the proposed solution method on a set of fairly large size randomly generated problem instances. Copyright © 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

This paper is concerned with a new variant of bin packing called priority-based bin packing with subset constraints (PBBP-SC). PBBP-SC is a variant of traditional bin packing (TBP). In a TBP instance, we are given a collection of items {t1,t2,…tn}, where each item ti has size si∈(0,1). The goal of TBP is to pack the items in as few unit-sized bins as possible. In a PBBP-SC instance, we are given a collection of n unit-size items and a collection of m bins of varying capacities. Associated with each item is a positive integer which is called its priority. The priority of an item indicates its importance in being included in a (possibly infeasible) packing. As with the traditional case, these items need to be packed in the fewest number of bins. What complicates the problem is the fact that each item can be assigned to only one of a select set of bins, i.e., the bins are not interchangeable. We investigate several problems associated with PBBP-SC. The first problem we study is the feasibility problem. This is the problem of checking if there is a feasible assignment to a given PBBP-SC instance. The second problem we study is the priority maximization problem. This is the problem of finding a maximum priority assignment of an infeasible PBBP-SC instance. The third problem we study is the bin minimization problem. This is the problem of finding an assignment with the fewest number of bins to pack all of the items in a feasible PBBP-SC instance. We derive a number of results from both the algorithmic and computational complexity perspectives for these problems. In particular, we show that both the feasibility and priority maximization problems are solvable in polynomial time. We also show that the bin minimization problem is log-APX-complete and cannot be solved in time o(1.41m) unless the Strong Exponential Time Hypothesis fails. © 2023 Elsevier B.V., Defense Advanced Research Projects Agency, DARPA: HR001123S0001-FP-004, This research was supported in part by the Defense Advanced Research Projects Agency through grant HR001123S0001-FP-004 .

The production of synthetic plastics, especially polyethylene, has reached a crucial level, making highly challenging plastic waste management. This paper aims to elucidate the role of two novel laccase-like multicopper oxidases in polyethylene oxidative degradation based on transcriptomic data from Rhodococcus opacus R7 grown on polyethylene. The purification of the recombinant proteins, LMCO2 and LMCO3 belonging respectively to the three- and two-domains laccase families, showed that they are endowed with different functional features. LMCO2 showed the highest enzyme activity at 65 °C with an optimal pH of 7.0, while LMCO3 exhibited the highest activity at 80 °C at acidic pH. LMCOs showed activity on both phenolic (i.e., 2,6-DMP) and non-phenolic (i.e., ABTS) compounds and they were thermostable. The oxidative activity of LMCO2 and LMCO3 on untreated low-density polyethylene was assessed by combining Fourier transform infrared spectroscopy and gas chromatography coupled with mass spectrometry analyses. Oxidation of PE was recorded within a short time range (24-48 h) and revealed previously not described patterns of alkyl compounds and oxygenated products including ketones, alcohols, and carboxylic acids. Structural analysis of LMCO2 and LMCO3 together with density functional theory calculations allowed to identify structural and electronic elements presumably involved in the oxidation of polyethylene.

Recent changes in security and technology environment such as Russia's invasion against Ukraine, the rapid development of technology, internet of things, remote working due to the covid-19 pandemic, has led to an increase in cyber-attacks. The emergence of quantum cryptography could give rise to breaches in quantum security in years to come with national security being affected. Delays or disruptions in the supply chain have led to an increase in the risk of supply chain theft. Personal identity theft is on the increase and as such, the role of auditors in critical infrastructures protection cannot be over- emphasized as this has become a high priority at national and EU level. This review paper examines the role of auditors in protecting critical infrastructures in Czech Republic. It adopted a study carried out by P. Lois et al. (2021), evaluated factors relating to audit and security of information systems. The results of the study concluded that advisory roles of auditors and policy- standards affect the security of information systems.

10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022 -- 22 June 2022 through 24 June 2022 -- 148818, In this paper we propose an Improved Approximate Dynamic Programming approach for the integrated fleet sizing and replenishment planning problem utilizing fix and optimize heuristic. The problem is about deciding both the composition of a fleet for distributing a single item and the replenishment planning based on a predetermined delivery frequency. This problem integrates two important logistical issues namely fleet sizing and replenishment planning. The objective is minimizing all relevant costs composed of vehicle ownership, inventory, and approximate routing costs. We show the effectiveness of the proposed solution method on a set of fairly large size randomly generated problem instances. Copyright © 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

This paper is concerned with a new variant of bin packing called priority-based bin packing with subset constraints (PBBP-SC). PBBP-SC is a variant of traditional bin packing (TBP). In a TBP instance, we are given a collection of items {t1,t2,…tn}, where each item ti has size si∈(0,1). The goal of TBP is to pack the items in as few unit-sized bins as possible. In a PBBP-SC instance, we are given a collection of n unit-size items and a collection of m bins of varying capacities. Associated with each item is a positive integer which is called its priority. The priority of an item indicates its importance in being included in a (possibly infeasible) packing. As with the traditional case, these items need to be packed in the fewest number of bins. What complicates the problem is the fact that each item can be assigned to only one of a select set of bins, i.e., the bins are not interchangeable. We investigate several problems associated with PBBP-SC. The first problem we study is the feasibility problem. This is the problem of checking if there is a feasible assignment to a given PBBP-SC instance. The second problem we study is the priority maximization problem. This is the problem of finding a maximum priority assignment of an infeasible PBBP-SC instance. The third problem we study is the bin minimization problem. This is the problem of finding an assignment with the fewest number of bins to pack all of the items in a feasible PBBP-SC instance. We derive a number of results from both the algorithmic and computational complexity perspectives for these problems. In particular, we show that both the feasibility and priority maximization problems are solvable in polynomial time. We also show that the bin minimization problem is log-APX-complete and cannot be solved in time o(1.41m) unless the Strong Exponential Time Hypothesis fails. © 2023 Elsevier B.V., Defense Advanced Research Projects Agency, DARPA: HR001123S0001-FP-004, This research was supported in part by the Defense Advanced Research Projects Agency through grant HR001123S0001-FP-004 .

The article deals with the problem related to the service life of the coin dies. The faultless condition of tools during the long-time process of the production of the coins is a basement for their quality. It is influenced by several aspects. The most important factors can be considered the material of the coin die - especially its purity, relief production technology, heat treatment - especially the correct observance to procedures and the choice of equipment, achieved mechanical properties, method of final relief treatment - e.g., coating and the existence of residual stresses in the tool. The aim of the present research was to examine the cause of the failure and damage of the coin dies after production of a small number of coins and, accordingly, to make changes in the production process to prolong their service life. By monitoring the individual steps of the production process, it was possible to identify specific causes of premature decommissioning of coin dies. Innovations have been made at each step of the production process to extend the life of the coin. The paper presents the modifications in the heat treatment of the coin dies as one of the basic improvements in their no-failure long time operation. This, together with other identified shortcomings in the tool manufacturing process, has contributed to an increase in the number of coins minted from around 6,000 to 740,000 coins.

The current era is characterized by rapid developing in all areas of society, but the most rapid expansion is in the field of science, knowledge, and technology. Galloping accessibility of the novel materials and technologies creates requirements of complex evaluation of their deployment to obtain economic and ecological sustainability. The authors of the paper present a proposal for a methodology enabling an effective decision-making process when choosing material and technological processes suitable for the manufacturing of products. This methodology was developed to enable the efficient evaluation of a wide range of materials, manufacturing technologies, including existing additive and hybrid and with focus on future and emerging technologies, too. The evaluation focuses on sustainability approach to minimize negative social and environmental impact while ensuring the required level of safety and profitability.

In this paper influence of ventilation system availability and effectiveness is evaluated by applying standardized analytical methods for classification of areas of explosive atmospheres with the aim to obtain high dilution for non-hazardous zone or to reduce Zone 2 area within hydrogen storage room and transportation pipeline corridors in industrial applications.

This paper presents a tutorial for the germination of bean seeds (Vigna unguiculata L. Walp.) in strong electrostatic fields up to 1240 V/cm. The seeds were allowed to germinate under different electric field strengths for 48 h. Although most of such germination experiments did not show any visible effect, the field strength of 945 V/cm strongly increased the seedling’s vigor during the early growth stage. In the end, 30 % more yield was obtained from stimulated seeds when compared to the control group. This article postulates for the first time a hypothesis of the mechanism of action of the electric field during germination. In biological cells of any species, water confined between narrow surfaces can undergo a phase transition that shifts its melting point to higher temperatures when an external electric field is applied. This effect has already been known as electrofreezing, and has been confirmed by several experimental and molecular modeling studies. As a consequence, the transport kinetics of molecules across cell organelle membranes might be altered, which in turn leads to different plant properties. With emphasis on the presented method, this work reports: • An inexpensive electric circuit for the generation of strong electric fields • Instructions regarding the setup and operation of an adequate germination chamber.

[Abstract]: Time series clustering is a central machine learning task with applications in many fields. While the majority of the methods focus on real-valued time series, very few works consider series with discrete response. In this paper, the problem of clustering ordinal time series is addressed. To this aim, two novel distances between ordinal time series are introduced and used to construct fuzzy clustering procedures. Both metrics are functions of estimated cumulative probabilities, thus automatically taking advantage of the ordering inherent to the series' range. The resulting clustering algorithms are computationally efficient and able to group series generated from similar stochastic processes, reaching accurate results with series coming from a wide variety of models. Since the dynamics of the series may vary over the time, we adopt a fuzzy approach, thus enabling the procedures to locate each series into several clusters with different membership degrees. An extensive simulation study shows that the proposed methods outperform several alternative procedures. Weighted versions of the clustering algorithms are also presented and their advantages with respect to the original methods are discussed. Two specific applications involving economic time series illustrate the usefulness of the proposed approaches.

[Abstract]: Federated learning (FL) is a distributed approach to developing collaborative learning models from decentralized data. This is relevant to many real applications, such as in the field of the Internet of Things, since the models can be used in edge computing devices. FL approaches are motivated by and designed to protect privacy, a highly relevant issue given current data protection regulations. Although FL methods are privacy-preserving by design, recently published papers show that privacy leaks do occur, caused by attacks designed to extract private data from information interchanged during learning. In this work, we present an FL method based on a neural network without hidden layers that incorporates homomorphic encryption (HE) to enhance robustness against the above-mentioned attacks. Unlike traditional FL methods that require multiple rounds of training for convergence, our method obtains the collaborative global model in a single training round, yielding an effective and efficient model that simplifies management of the FL training process. In addition, since our method includes HE, it is also robust against model inversion attacks. In experiments with big data sets and a large number of clients in a federated scenario, we demonstrate that use of HE does not affect the accuracy of the model, whose results are competitive with state-of-the-art machine learning models. We also show that behavior in terms of accuracy is the same for identically and non-identically distributed data scenarios.

[Abstract]: The taxonomic and functional composition of microbial communities from environmental, agricultural, and therapeutic settings is increasingly being studied using metagenomic methodologies in large-scale genomic applications. This has led to exponential growth in the field and has impacted on healthcare, pharmacology and biotechnology. However, with the current methodologies, it is sometimes difficult to obtain conclusive identification of an organism. In addition, the growth of the metagenomic field has led to the creation of large amounts of data held by different hosts, which characterize data differently and make analysis difficult. Therefore, correct data aggregation and classification improve and facilitate the discovery of repositories of interest. This paper tackles these issues by proposing a methodology for organism identification, data aggregation and content characterization, visualization and selection. We propose a three-step pipeline for organism identification that uses compression-based metrics, an aggregation mechanism for content characterization, and a web database catalogue for data exposition and visualization.

This paper investigates the economic costs of the recent United Nations sanctions on North Korea. Exploiting a novel data set on North Korean firms, we construct measures of regional exposure to export and intermediate input sanctions and show that trade sanctions cause sharp declines in local nighttime luminosity. Additional analysis of newly available product-level price data reveals that import sanctions led to significant increases in market prices. We then estimate a quantitative spatial equilibrium model using cross-region variations. The model implies that the sanctions reduced the country's manufacturing output by 12.9% and real income by 15.3%. We further quantify the potential impact of alternative sanction scenarios. © 2023 Elsevier B.V.

In this paper, by using the time-varying parameter vector autoregression model (TVP-VAR) with the asymmetric connectedness indicator and network diagrams, we investigate the dynamic and asymmetric return connectedness in the global “Carbon-Energy-Stock” system, including carbon markets and stock markets of the three largest economies, namely the United States, European Union and China, and fossil energies of crude oil and natural gas under exogenous shocks. Our study shows that (i) the risk spillover level of the global system has significantly increased after the outbreak of two exogenous events, the COVID-19 pandemic and the Russo-Ukrainian war, and the global shock from the COVID-19 pandemic has more widespread and greater impact on the system than the geopolitical shock from the Russo-Ukrainian war, (ii) the global “Carbon-Energy-Stock” system is more sensitive to negative information on price returns than positive information, and the asymmetry of the connectedness is much larger when the system is active and in the presence of exogenous shocks, (iii) risks in the global “Carbon-Energy-Stock” system usually transformed from stock markets, especially the stock markets of the United States and European Union, to the carbon markets. These findings provide valuable guidance and have economic implications for both investors and policymakers worldwide. © 2023 Elsevier B.V.

Modern additive manufacturing systems still suffer from low fabrication accuracy and speed. This issue is particularly severe for thermal reaction bonding material extrusion systems, where the motion and the deposition processes are coupled. To enhance the deposition accuracy, this work established a method to use real-time in-situ thermal images to capture and compensate for the geometrical accuracy of the printed parts and establish the spatial and temporal relationship between the inaccuracy and the deposition trajectory commands. The paper derives an analytical slender condition to process the printing regions with low curvature while establishing deposition direction allocation methods to account for the trajectory regions with high curvature. Real-time thermal image localization is performed with key printing feature detection and calibration. The localized and calibrated thermal images are aligned with the defined deposition direction to acquire the deposition inaccuracies. With the deposition dynamics considered, the deposition inaccuracy is reduced via dynamic deposition rate compensation. The proposed thermal imaging and compensation techniques have been experimentally verified with a material extrusion experimental platform to illustrate its capabilities to alleviate the deposition inaccuracy during the corner regions. © 2023 Elsevier B.V.

Antitrust investigations in the mobile app economy often require a definition of the relevant market of mobile apps and the evaluation of their market power. However, existing antitrust tools face significant challenges due to the non-price nature of mobile apps with multiple revenue sources, two competition margins at the mobile OS and mobile app levels, and the switching costs involved in the choice of mobile OSs. In this paper, we provide a description of the mobile app economy, including its essential components, players, and characteristics, and identify the challenges currently observed in antitrust investigations. We propose a model for mobile app users and advertisers that can address these issues and suggest a method for defining the relevant market of mobile apps and evaluating their market power, which is adaptable to markets with zero-priced products or multiple revenue sources beyond mobile apps. © 2023 Elsevier B.V.

In this paper, experimental work is supported by multi-scale numerical modeling to investigate nanomechanical response of pristine and ion irradiated with Fe2+ ions with energy 5 MeV high purity iron specimens by nanoindentation and Electron Backscatter Diffraction. The appearance of a sudden displacement burst that is observed during the loading process in the load–displacement curves is connected with increased shear stress in a small subsurface volume due to dislocation slip activation and mobilization of pre-existing dislocations by irradiation. The molecular dynamics (MD) and 3D-discrete dislocation dynamics (3D-DDD) simulations are applied to model geometrically necessary dislocations (GNDs) nucleation mechanisms at early stages of nanoindentation test; providing an insight to the mechanical response of the material and its plastic instability and are in a qualitative agreement with GNDs density mapping images. Finally, we noted that dislocations and defects nucleated are responsible the material hardness increase, as observed in recorded load–displacement curves and pop-ins analysis.

Presently under construction in Lund, Sweden, the European Spallation Source (ESS) will be the world's brightest neutron source. As such, it has the potential for a particle physics program with a unique reach and which is complementary to that available at other facilities. This paper describes proposed particle physics activities for the ESS. These encompass the exploitation of both the neutrons and neutrinos produced at the ESS for high precision (sensitivity) measurements (searches).