Sensor placement is a vital factor affecting the quality and accuracy of virtual sensing. Modal expansion techniques are well-known methods to expand the measured displacements or accelerations to all unmeasured degrees of freedom. For this purpose, a two-phase sensor placement optimisation method is proposed for commonly used triaxial accelerometers. The method uses minimum variance criterion of an estimation error of structural responses. A measure of redundancy of information is introduced as an additional criterion for the placement of the triaxial sensors to minimise the redundancy between the sensors. This was addressed to avoid spatial correlation and clustering of the sensor locations. In addition, a proposal for modal displacement-based weighting is introduced to avoid potential selection of sensor locations with low vibration energy, which can be critical in noisy environments. The efficiency of the proposed method is verified with numerical models of different types of structures and finally with the laboratory scale experiments. The mean error of the reconstructed response in this particular experimental case study was 1.4% of the maximum measured response amplitude. This method is especially applicable to large finite element models of industrial-scale structures with fine meshes.
Cross-border mergers and acquisitions (M&As) are highly emotional events for the employees of involved organizations. The strength and directionality of emotional reactions can result in positive or negative employee outcomes contributing to success or failure of cross-border M&As. Existing studies on emotions and cross-border M&As have identified various underlying mechanisms and factors that influence employee emotions in cross-border M&A activities, leading to a fragmentation of current research on this topic. In this article, we systematically review the interdisciplinary literature on the role played by emotions in cross-border M&As by analyzing a sample of 78 articles published between 2000 and 2021. We contribute to the current literature by (1) providing a holistic and deeper understanding of the role played by emotions in cross-border M&As; (2) mapping the current state of the interdisciplinary literature on emotions and cross-border M&As; and (3) developing a multi-level framework, and identifying key theories and emerging themes to be examined in future studies.
A comprehensive understanding of the “safety” of nuclear reactors is essential for effective and efficient safety management by licensees and regulation by authorities. Nuclear reactors are designed subject to incomplete knowledge of factors that affect their safety. The idea of defence-in-depth has evolved to combat the threat of the unknown; it is implemented by means of technical artefacts, leading to a complex set of technical safety requirements to prevent accidental radioactive releases. Nuclear power plants have thus become systems of technical systems. Similarly, significant human and organizational aspects are involved in nuclear power plant construction and operation; a nuclear power plant is an organization of organizations. Earlier studies have identified the need for holistic understanding of safety and accounting for the technical and organizational aspects simultaneously (Harvey and Stanton, 2014). This paper seeks to clarify the concept of defence-in-depth using the Overall Safety Concept (ORSAC) developed at LUT (Hyvärinen et al., 2016), and the sociotechnical systems view in the nuclear power industry context, extending defence-in-depth thinking to the organizational context in one transparent framework. We show how organizational and technical aspects affect each other in the operation of nuclear power plants. This paper paves the way for systematic modelling of how technical and organizational aspects affect each other.
This paper establishes a framework for probabilistic electricity price forecasting in the electrical markets using an end-to-end and direct approach. In the proposed method, firstly, a deep Gabor filter-oriented convolutional network is designed as the strong deep structure. Then, the designed deep Gabor network is developed as a deep mixture network to predict a set of probability density functions (PDFs) in the next hours. To do so, a probabilistic loss function is formulated. The results are validated by the actual electricity market data in California independent system operator (CAISO) and show high level of accuracy and reliability in comparison with other state-of-the-arts methods
The digital transformation of businesses is no longer debatable, and the effects are visible in all sectors. What is arguable, however, is why the transformation has not been seamless—particularly given the multiple benefits of digitalization. We seek to address this question for the healthcare sector, where various reports have acknowledged end-users’ resistance to the adoption and continued usage of technology-driven innovations (e-health innovations). These accounts, though, are largely anecdotal, and the volume of academic research in the area has remained rather confined. To address this paucity of insights, particularly after the onset of the pandemic, which has brought the healthcare sector to the fore, we conducted a qualitative study among healthcare providers (doctors, nurses, and other clinical staff). The key objective of our study was to identify the perceived barriers and other inhibiting factors that impede individuals’ adoption and continued usage of e-health innovations. We conducted our study in the United Kingdom and analyzed the data using the classic approach of manual content analysis. Through these efforts, we identified barriers from the perspectives of healthcare providers (task-related, patient-care, and system barriers), healthcare organizations (threat perception and infrastructural barriers), patients (usability and resource barriers), and end-users in general (self-efficacy, tradition, and image barriers). Our study makes a noteworthy theoretical contribution by proposing a conceptual framework for resistance to e-health innovations that is grounded in innovation resistance theory (IRT). We also make some useful suggestions for practice that have the potential to accelerate the diffusion of e-health innovations.
In this paper, an operational framework is presented to improve electrical distribution network resilience based on the Mobile Energy Hubs (MEHs) concept. In fact, critical loads should be immediately islanded in a post-flood state and then recovered. Accordingly, this paper focuses on providing an effective management solution to enhance the functioning of electricity distribution systems with the objective of maximizing restoration of critical loads and minimizing their restoration time span based on MEH. To this end, MEHs are installed on trucks to deliver the required power for supplying the islanded critical loads in zones affected by a flood. Besides, in order to demonstrate a practical resilient structure, possible damage inflicted on other critical infrastructures is considered. Moreover, obstacles resulting from the destruction of the transportation infrastructure caused by a flood are overcome by using the shortest path algorithm (SPA). In this case, the optimization algorithm determines the shortest possible path for transporting the MEHs to supply critical loads in the least time aiming to improve the network resilience indicators. Finally, the proposed framework is studied in a standard test electricity distribution network. Simulations are carried out to evaluate the network resilience indicators of the proposed framework in obtaining a resilient distribution network during natural disasters.
The different social contexts and historical backgrounds of countries in which companies operate may influence how their managers understand and apply the concept of environmental commitment. Thus, the understanding of environmental commitment in the post-communist societies of Central and Eastern Europe can be expected to be different from the Western markets. This study sheds light on these issues by analyzing managerial stories about environmental commitment in Russia. It explains how managers’ sensemaking is shaped by the Soviet socio-historical context. This study contributes to the limited literature on environmental commitment in post-communist societies and provides a link between environmental commitment and sensemaking research, thus responding to recent calls for a clarification of the microfoundations of corporate social responsibility.
In this current scenario, there is a broad range of use of resins in both domestic and industrial applications. Especially the importance of resins for adhesive in the wood industry is inevitable. But most of the commercially available resins for adhesive application are prepared of formaldehyde or fossil fuel-based resources which possess serious health issues. Henceforth, development of environmentally friendly resin from renewable resources through a cost-effective technique is a challenging task. Motivated by these facts and prospects, we present a novel technique for addressing non-renewability difficulties while also improving adhesive strength of the material. In this study, lignin-based composite resin with enhanced sustainability and adhesion strength have been achieved by a simply mixing of high concentration of poly (itaconic acid)-functionalized-lignin (P(IA)-f-Lignin) and aqueous emulsion of polyvinyl acetate. The P(IA)-f-Lignin was synthesized by in-situ free radical polymerization of partly neutralized IA in the presence of aqueous lignin dispersion at 90 °C using ammonium persulfate as initiator. The formation of P(IA)-f-Lignin was confirmed by Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), nuclear magnetic resonance (NMR), field emission scanning electron microscope (FESEM) and thermogravimetric analysis (TGA). Here, we have been fabricated 20, 30, 40, 50, and 60 wt% of P(IA)-f-Lignin containing composite resin with desired amount of aqueous emulsion of polyvinyl acetate (PVAc). The changes in physico-chemical interactions were established by FTIR analysis. Various properties like viscosity, thermo-stability, and adhesive properties of all the formulated composite resin were inspected thoroughly. The composite containing 40 wt% of P(IA)-f-Lignin shows excellent improvement of adhesion strength from 3.32 ± 0.12 MPa to 7.83 ± 0.45 MPa. These fabricated composite resin with a high concentration of bio-based P(IA)-f-Lignin content and strong adhesion strength is very promising for fabrication of biobased adhesives with improved sustainability.
Digital transformation is among the most pervasive forces disrupting business models across every industrial sector. While prior research has explored the digital transformation of business models, the effect on the value proposition as a key element of the business model has received only limited attention. Drawing on an extensive single case study in the regional media industry involving 59 interviews with one service provider and its customers, this study explores the digital transformation of the provider’s value proposition and how this process was collectively enacted by the provider and its customers. This study develops an empirically grounded framework illustrating the key value proposition transformation drivers, provider and customer sense-making practices, and value element reshaping implications. Overall, this study advances contemporary digital transformation and value proposition research by demonstrating how the process of digital transformation changes the nature and content of the value proposition and how managers can facilitate this process.
The flexibility of the power-to-gas (P2G) technologies and natural gas units (NGUs) can enhance the resilience of the electric distribution system (EDS) considering the high penetration of renewable energy resources (RERs). The decentralized collaborative operation (co-operation) of electric distribution systems and natural gas systems (EDSs&NGSs) considering information privacy preserving can enhance the resilience during an extreme hurricane. To address this issue, this paper proposes a three-level hierarchal solution to decompose the centralized co-operation of the EDS&NGS framework in which an independent decision-making strategy and information privacy preserving for both network operators are simultaneously addressed. Furthermore, in this paper, a min–max robust resilience-constrained co-optimization model is presented to enhance the resilience of the integrated EDS&NGS against worst-case N−kcontingencies and wind power generation uncertainty under extreme hurricane events. To attain the spatial dynamics of extreme hurricanes, a the multi-zone and multi-time extreme hurricanes model is considered. Then, a column-and-constraints generation (C&CG) algorithm is used to solve the proposed robust resilience-constrained co-optimization model. To verify the effectiveness of the model, we conducted experiments on a modified IEEE-33-bus-10-node/123-bus-20-node EDS&NGS. The numerical results show that the proposed robust resilience-constrained co-operation of EDS&NGS problem is an effective model for enhancing EDS resilience.
Distributional fairness concerns are key barriers to the implementation of climate policies. Emission rights allocation is the decisive distributional feature of personal carbon trading (PCT) that is intended to incentivise individuals' low-carbon choices. The aim of the current study was to identify the predictors of the perceived distributional fairness of PCT. The perceived fairness of expected income redistribution through a PCT scheme in mobility was studied by a survey conducted among citizens of a medium-sized North European city, Lahti, Finland (n = 358). The progressive distributional effects of PCT with equal-per-capita allocation were perceived as fair by the majority, while the consequent burdens on rural households and families with children were strongly perceived as unfair. Political orientation and views on climate responsibility were the main fairness predictors for redistribution through PCT. The results imply that an allocation that acknowledges vulnerable groups with higher mobility needs would be considered fairer than an equal-per-capita allocation. An allocation that is sensitive to context-specific distributional fairness concerns appears to be a crucial feature of PCT for public acceptability.
Europe and North America have numerous studies on 100% renewable power systems. South America, however, lacks research on zero-carbon energy systems, especially understanding South America as an interconnected region, despite its great renewable energy sources, increasing population, and economic productivity. This work extends the cost-optimization energy planning model LEELO and applies it to South America. This results in the to-date most complete model for planning South America’s power sector, with a high temporal (8760 time steps per year) and spatial (over 40 nodes) resolution, and 30 technologies involved. Besides the base case, we study how varying spatial resolution for South America impacted investment results (43, 30, 16, 1 node). Finally, we also evaluate green hydrogen export scenarios, from 0% to 20% on top of the electricity demand. Our study reveals that South America’s energy transition will rely, in decreasing order, on solar photovoltaic, wind, gas as bridging technology, and also on some concentrated solar power. Storage technologies equal to about 10% of the total installed power capacity would be required, aided by the existing hydropower fleet. Not only is the transition to renewables technically possible, but it is also the most cost-efficient solution: electricity costs are expected to reach 32 €/MWh from the year 2035 onwards without the need for further fossil fuels. Varying the spatial resolution, the most-resolved model (43 nodes) reveals 11% and 6% more costs than the one-node and one-node-per-country (16) models, respectively, with large differences in investment recommendations, especially in concentrated solar and wind power. The difference between 43 and 30 nodes is negligible in terms of total costs, energy storage, and technology mix, indicating that 30 nodes are an adequate resolution for this region. We then use the 30-node model to analyze hydrogen export scenarios. The electricity costs drop, as hydrogen is not only a load but also a flexibility provider. Most green hydrogen is produced in Chile, Argentina, and northeast Brazil. For future work, we propose to do an integrated energy plan, including transport and heat, for the region, as well as modeling local hydrogen demands. This work aims to inform policymakers of sustainable transitions, and the energy community.
Variety of ultra-high strength steels (UHSS) with different microstructural characteristics is becoming available with continuous development of the manufacturing process in the steel industries. In order to effectively design structures made of such steel grades, a detailed knowledge of the mechanical properties is vital. Fire safety design is one of the areas in which such knowledge is essential. Welding process is indispensable in construction of steels structures with inevitable welding-induced degradation of mechanical properties of UHSSs. Thus, conducting experimental research on elevated-temperature constitutive mechanical behavior of welded joints made of UHSSs is of paramount importance. This study addresses elevated-temperature mechanical properties of as-received and as-welded S960 (manufactured via direct quenching technique) and S1100 (quenched and tempered) steel grades. A fully automated gas metal arc welding (GMAW) process with low heat input value was utilized to join the steel plates. Next, steady-state uniaxial tensile tests in the temperature range between room temperature (RT) and 900 °C were carried out. Accordingly, reduction factor-temperature relations for each tested steel in both as-received and as-welded forms are discussed and compared with several design standards, as well as with previous studies in the literature. Finally, predictive equations are proposed to estimate the elevated-temperature mechanical properties reduction factors of the tested UHSSs in as-received and as-welded forms.
Substantial benefits can be achieved from electricity trade through the interconnection of electricity networks of neighboring countries. In this study, we introduce a regional electricity exchange model to obtain an optimal power trade level between electrically-interconnected countries. In addition, producing clean electricity is one of the primary goals of many countries. Moreover, electricity production can be optimized in some areas using renewable sources and exported to regions with high demand. Thus, the power exchange using the proposed method and framework is investigated. Additionally, a decision model for planning an optimal configuration of hybrid wind/solar power systems and optimal electricity trade is presented. We analyze the economic, engineering, management, and policy issues to facilitate optimization of power generation and trade at the country level and quantify the gains from the increased trade. The proposed decision model is employed to plan electricity trade considering hybrid wind and solar energy resources. Furthermore, the effectiveness of reducing the variability of hourly power supply is evaluated by integrating the wind and solar power generation technologies. The main decision variables are the power trade value and number of installed wind turbines and solar systems. In case studies, the computational results using the proposed model yielded an optimal power trade between Iran and its neighboring countries, including Turkey, Turkmenistan, Afghanistan, Pakistan, Azerbaijan, Armenia, and Iraq. Consequently, Iran can export a maximum power capacity of 5,300 MW to its neighbors, where Iraq receives the highest share of 2,000 MW. When the electricity demand in Iran is high, Turkey, Turkmenistan, Azerbaijan, and Armenia can export 1,200, 1,000, 80, and 100 MW of electricity to Iran, respectively. Finally, various configurations of a hybrid wind/solar power generation system considering various cost and power supply variations are presented to partly supply the load requirements of the traded electricity. Consequently, Iran can supply 20% of the total demand of the electricity trade by wind and solar system installed capacities of 316.5 and 267 MW, respectively.
Compartmental models are popular in the mathematics of epidemiology for their simplicity and wide range of applications. Although they are typically solved as initial value problems for a system of ordinary differential equations, the observed data are typically akin to a boundary value-type problem: we observe some of the dependent variables at given times, but we do not know the initial conditions. In this paper, we reformulate the classical susceptible–infectious–recovered system in terms of the number of detected positive infected cases at different times to yield what we term the observational model. We then prove the existence and uniqueness of a solution to the boundary value problem associated with the observational model and present a numerical algorithm to approximate the solution. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.
In this article, we discuss the technical and business risks associated with long-lasting functional digital twins, and describe different strategies for their alleviation. Functional digital twins are based on physics-based simulation models and are operated alongside the life cycle of their physical counterparts. These simulation-based digital twins are built using a simulation software. The problems with most of the commercial modeling and simulation tools are their black box nature and storing data in protective formats, leading to poor interoperability. Since the digital twins of certain assets need to be operated for a long period, even for several decades, there is a possibility that the computing infrastructure, i.e., the computing hardware and software, may not remain the same throughout the product or system life cycle. The computer hardware and operating systems are usually third-party components with limited choices for their users, whereas the selection of simulation tools is more flexible and the designer can choose from, for example, commercial, open-source, or in-house solutions. To avoid substantial costs or business disruption, the digital twin providers must be able to reproduce the underlying simulation models with up-to-date tools and adopt alternative solutions whenever needed. The findings of the study are presented in the form of propositions throughout the article.
Personal carbon allowances have been of considerable interest in environmental research in the last decade, yet no policy implementations have been adopted, partly due to uncertainty around the political acceptability of equal allowances. We tackled this issue by surveying public perceptions of fairness in carbon allowance allocation in urban mobility. Qualitative and quantitative inquiry data of 304 respondents were analysed statistically and thematically. Three distributive principles according to equity perceptions of equality, equity by capability, and equity by responsibility, were examined. The distribution of personal carbon allowances, which is sensitive to differing needs and capabilities, was perceived fairer than the other proposed options. The allocation preference differed according to isolationist and integrationist approaches, where both equity by capability and equity by responsibility evoked talk of benefits and burdens that could be produced by emission rights, while those who alluded to equality were interested in the distribution of emission rights as such. Prioritising needs and capabilities demonstrated that questions of managing the daily life still edge ahead of climate questions in many citizens eyes. Therefore, policy propositions should focus more on the shared mobility practices of everyday life and how they could be supported and incentivised towards sustainability. We concluded that due to the lack of absolute distributive consensus, policies gain legitimacy by procedural equity, and including citizens in decision making.
Residential electric vehicle (REV) is an advanced technology with a rapid growth rate in transportation and electric grids. One key challenge in the operation of REVs is the necessity of the accurate, reliable, and practical forecasting method to provide accurate information of the charging profile in the look-ahead hours. In power system, in order to optimize the production and consumption as much as possible, in addition to accurately predicting the amount of electricity consumption, it is necessary for the stability of the grid to take into account the imminent probabilities. This paper presents the main principle of the probability density function forecasting approach in residential electric vehicle (REV) charging profile. To this end, an end-to-end deep learning structure is designed and integrated with kernel density estimation (KDE). The designed network is composed of four major blocks, i.e., convolutional layers to extract full spatial features, gated recurrent unit (GRU) to fully understand the temporal features as a time-efficient version of the gated deep network, an autoregressive (AR) to model the long patterns including battery type, REV type, and number of REVs and kernel density estimator block. Furthermore, to improve the learning ability of the designed network, an attention mechanism is integrated into the design network. The numerical results on the actual REVs (about 348 REVs) demonstrate the effectiveness and superiority of the proposed network through several cases and comparison with several well-known deep and shallow-based methods.
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