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Learning node representations in a network has a wide range of applications. Most of the existing work focuses on improving the performance of the learned node representations by designing advanced network embedding models. In contrast to these work, this article aims to provide some understanding of the rationale behind the existing network embedding models, e.g., why a given embedding algorithm outputs the specific node representations and how the resulting node representations relate to the structure of the input network. In particular, we propose to discern the edge influence for two widely-studied classes of network embedding models, i.e., skip-gram based models and graph neural networks. We provide algorithms to effectively and efficiently quantify the edge influence on node representations, and further identify high-influential edges by exploiting the linkage between edge influence and network structure. Experimental evaluations are conducted on real datasets showing that: 1) in terms of quantifying edge influence, the proposed method is significantly faster (up to 2,000x) than straightforward methods with little quality loss, and 2) in terms of identifying high-influential edges, the identified edges by the proposed method have a significant impact in the context of downstream prediction task and adversarial attacking.

The artificial bee colony (ABC) algorithm shows a relatively powerful exploration search capability but is constrained by the curse of dimensionality, especially on nonseparable functions, where its convergence speed slows dramatically. In this article, based on an analysis of the difference between updating mechanisms that include both all-variable and one-variable updating mechanisms, we find that when equipped with the former strategy, the algorithm rapidly converges to an optimal region, while with the latter strategy, it searches the solution space thoroughly. To utilize multivariable and one-variable updating mechanisms on nonseparable and separable functions, respectively, we embed an improved linkage identification strategy into the ABC by detecting the linkage between variables more effectively. Then, we propose three common strategies for ABC to improve its performance. First, a new approach that considers the historic experiences of the population is proposed to balance exploration and exploitation. Second, a new strategy for initializing scout bees is used to reduce the number of function evaluations. Finally, the individual with the worst performance is updated with a defined probability on multiple dimensions instead of one dimension, causing it to follow the population steps on nonseparable functions. This article is the first to propose all these concepts, which could be adopted for other ABC variants. The effectiveness of our algorithm is validated through basic, CEC2010, CEC2013, and CEC2014 functions and real-world problems.

Ethereum is one of the most popular blockchain systems that support more than half a million transactions every day and foster miscellaneous decentralized applications with its Turing-complete smart contract machine. Whereas it remains mysterious what the transaction pattern of Ethereum is and how it evolves over time. In this article, we study the evolutionary behavior of Ethereum transactions from a temporal graph point of view. We first develop a data analytic platform to collect external transactions associated with users as well as internal transactions initiated by smart contracts. Three types of temporal graphs, user-to-user, contract-to-contract, and user-contract graphs, are constructed according to trading relationships and are segmented with an appropriate time window. We observe a strong correlation between the size of the user-to-user transaction graph and the average Ether price in a time window, while no evidence of such linkage is shown at the average degree, average edge weights, and average triplet closure duration. The macroscopic and microscopic burstiness of Ethereum transactions are validated. We analyze the Gini indexes of the transaction graphs and the user wealth in which Ethereum is found to be very unfair since the very beginning, in a sense, ``the rich is already very rich.''

A defining characteristic of the Internet of Things (IoT) is pervasive collection and linkage of user data to provide personalised experiences. To enable this functionality, IoT devices and services must be connected and share data about users' interactions with multiple nodes in the network. Consistent identification of users and devices across the network is likewise necessary. These aspects of the IoT can pose risks to user privacy. Potentially invasive inferences can be drawn from linked datasets, including data generated through usage of connected devices and services. The forthcoming General Data Protection Regulation (GDPR) contains numerous provisions relevant to the risks posed by identification technologies. However, the strict legal requirements defined in the Articles of the GDPR may be insufficient to ensure a fair balance is struck between user's interests in privacy and the interests of IoT developers and data controllers. To address this gap, this paper proposes a three-step transparency model based on known privacy risks of the IoT, weaknesses in relevant legally binding provisions in the GDPR, and the GDPR's governing principles. Eleven guidelines aimed at IoT developers and data controllers are described addressing how information about the functionality of IoT devices and services should be shared with users. The guidelines describe ethically desirable standards to be adhered to in addition to the GDPR's legally binding requirements. To demonstrate how the guidelines could apply in practice and alter the design choices and practices of IoT developers and data controllers, connected cars are considered as a use case.

Coin mixing can be used to protect the identity privacy of Bitcoin owners, by engaging a set of middlepersons (i.e., ${Mix}$ ) to temporarily hold on to the transacting Bitcoins and remove the linkage between the transacting parties. However, existing schemes are generally not scalable due to limitations associated with the anonymity set, and self-credibility. In this paper, we propose an efficient coin mixing scheme (CoinLayering). To achieve strong anonymity, CoinLayering randomly selects two sets of middlepersons to respectively execute Bitcoin holding and Bitcoin trading. The seller can also select lower-loaded sets of middlepersons in the shortest time possible. We also design two coin mixing protocols (CoinLayering-PA and CoinLayering-PB) to mitigate the risk due to misbehaving middlepersons and ${Supervisor}$ . We then mathematically prove that CoinLayering achieves both strong anonymity and self-credibility, and evaluate its performance to demonstrate its scalability.

It is essential to estimate stator inductance and rotor flux linkage online for fault diagnosis and high-performance control of surface-mounted permanent magnet synchronous machine (SPMSM). Current injection method is a popular way to estimate the two parameters. However, the dead-time influence of voltage-source-inverter (VSI) and the stator inductance variation caused by injected current could not guarantee the converge of the estimation. To solve the above two problems, a method of injecting square-wave angle for online parameter estimation is proposed in this paper. Firstly, the theory of eliminating dead-time influence with and without square-wave angle injection is analysed. Next, the least square algorithm is adopted to estimate stator inductance without injection. Then the rotor flux linkage is estimated with injecting square-wave angle. The saturation effect on estimation is also analyzed and both parameters are estimated without acquiring any nominal values. At last, the appropriate amplitude and frequency of square-wave angle are determined by the parameter error and convergence analysis. The proposed method is evaluated with simulation and extensive experiments under various speed and load conditions. It is noteworthy that not less than two parameters are estimated without the dead-time influence and the injection will not cause inductance variation, which is different from the previous work.

This study presents modular transformable wheels capable of overcoming obstacles. They can be used for indoor service robots or service platforms such as wheelchairs and baby carriages, which often move up and down on thresholds or on sidewalks. The structure of the wheel and transformation mechanism are introduced. The wheel has two built-in brushless DC (BLDC) motors. The BLDC motor is remodeled to add the gear box inside. These motors create a two-degree-of-freedom (2-DOF) transformation of the wheel. The 2-DOF mechanism is achieved by a 7-bar linkage with six rotational joints and one prismatic joint. A platform using the presented modular transformable wheels was used to experimentally verify the ability to climb up and down a staircase.

This paper reviews the safety monitoring systems, bridge safety assessment methods, and hesitant fuzzy set theories at home and abroad, and presents a novel bridge safety monitoring and evaluation method based on hesitant fuzzy set, which has greatly significance to scientific research. Through multi-scale estimation and Butter Worth (BWorth) filtering, the structural response signals were identified and extracted under car load and crowd load. Then, a mathematical model was established for bridge safety monitoring and evaluation based on hesitant fuzzy set. Drawing on hesitant fuzzy similarity theory, the authors designed damage recognition algorithms based on single index, dual-index linkage, and multi-index linkage. Finally, example analysis was conducted to confirm the feasibility of multi-index linkage, coupled with multi-period dynamic recognition, in the evaluation of bridge structural damages.

The purpose of this study is to examine the causal linkage between military expenditures and economic growth in 27 North Atlantic Treaty Organization (NATO) member countries. Different periods are studied due to the unavailability of data for the common period for all countries. Both the symmetric and the asymmetric causality between military expenditures and economic growth are investigated under cross-sectional dependence and panel heterogeneity by using the bootstrap panel Granger causality testing approach. The results indicate that there is both symmetric and asymmetric Granger- causality between military expenditures and economic growth, which vary from one country to another. The robust empirical findings support the military expenditures and economic growth nexus in 12 of the 27 NATO member countries. Moreover, the findings show that more empirical evidence between military expenditures and economic growth can be obtained when the asymmetric causality is considered, in addition to the symmetric causality. Keywords: Defense economics, Military expenditures, Economic growth, Asymmetric bootstrap Granger causality. JEL: C33, H50, H56, 047.

Clearance exists in mechanical equipment, which aggravates the friction and wear in moving pairs and reduces the use accuracy of mechanism. So as to reduce the impact of clearance on industrial equipment, lubricating oil is usually added to the clearance of moving pair. The lubricating oil can effectively reduce the collision and vibration between components and reduce noise. Previous studies mainly concentrated on the research of dry clearance joints, but less on clearance lubrication. At the same time, there is little research on nonlinear characteristic of lubrication clearance. For the sake of studying the impact of lubrication clearance with multiple revolute joints on mechanical system, the dynamic response and nonlinear characteristic of planar six-bar linkage are analyzed. The movement model of lubrication clearance is developed, and lubrication force of lubrication clearance is modeled by modified Pinkus–Sternlicht model. Dynamical model of lubrication clearance of planar six-bar linkage is established by Lagrange multiplier method. The impacts of lubrication, viscosity coefficient, clearance value and driving speed on dynamic of mechanism with lubrication clearance are explored. By drawing bifurcation diagram, phase trajectory diagram and Poincare map, the nonlinear characteristic of mechanism with lubrication clearance is studied.

The objective of this study is to investigate the relationship between supply chain linkage and business performance in the lychee supply chain in Vietnam. The study collected 395 matched samples after sample screening. Partial least squares (PLS) algorithm is used to process the data. Research results show a link between supply chain linkage and business performance. Furthermore, research shows that risk supply chain, quality management, and business strategy also impact supply chain linkages and business performance.

Recently designed passenger vehicles with heightened features are entering the market every year. One of them is the power window which enables the fast movement of window-panes. Not opening of window-pane in an emergency is responsible for the death of passengers in some cases. Deaths of children, the disabled or older person have been reported due to carbon monoxide(CO) poisoning. Simultaneously, they were allowed to stay inside a static, locked car in the absence of other assisting occupants. Fatal injuries have also been reported repeatedly due to the sinking of the vehicle in a water body. In this paper, the design of an automatic opening mechanism is discussed for a car window during such crises. In this system, the temperature sensor, Carbon-monoxide sensor and water pressure sensor are placed inside a car cabin to monitor the temperature range, CO levels and water pressure continuously. In case of high internal temperatures or raised carbon monoxide in the cabin due to incomplete fuel combustion or excessive heat or vehicle rested under the Sun for long, or if the vehicle is subjected to pressure by flowing or static water, the sensors send signals to the motor controlling power window to slide it down. It also sends a warning message to the registered mobile phone of the vehicle owner.

We present new algorithms to classify structural ensembles of macromolecules, based on the recently proposed extended similarity measures. Molecular Dynamics provides a wealth of structural information on systems of biologically interest. As computer power increases we capture larger ensembles and larger conformational transitions between states. Typically, structural clustering provides the statistical mechanics treatment of the system to identify relevant biological states. The key advantage of our approach is that the newly introduced extended similiarity indices reduce the computational complexity of assessing the similarity of a set of structures from O(N2) to O(N). Here we take advantage of this favorable cost to develop several highly efficient techniques, including a linear-scaling algorithm to determine the medoid of a set (which we effectively use to select the most representative structure of a cluster). Moreover, we use our extended similarity indices as a linkage criterion in a novel hierarchical agglomerative clustering algorithm. We apply these new metrics to analyze the ensembles of several systems of biological interest such as folding and binding of macromolecules (peptide,protein,DNA -protein). In particular, we design a new workflow that is capable of identifying the most important conformations contributing to the protein folding process. We show excellent performance in the resulting clusters (surpassing traditional linkage criteria), along with faster performance and an efficient cost-function to identify when to merge clusters.

Healthcare institutions have been working to improve the efficiency and effectiveness of the service delivered. The literature has argued that their capabilities have a direct effect on service outcomes. Research has explained how their capabilities can be enhanced by implementing high-performance work practices (HPWP) bundles and how these bundles can impact performance through relational coordination. However, this previous research has focused primarily on single-specialty healthcare institutions in a developed country. Inherent characteristics of multispecialty healthcare institutions (e.g., inability to standardize) and emerging economy context (e.g., absence of case manager role) motivate further investigation in this setting. Therefore, in our research, we study the impact of HPWP on the overall performance, efficiency, and effectiveness of healthcare service delivered and how this linkage is moderated by relational coordination. We analyzed 605 valid responses from different healthcare institutions located in the southern Tamil Nadu state of India using structural equation modeling. In alignment with past research, our results show that HPWP improves the overall performance and effectiveness and this linkage is moderated by relational coordination. However, HPWP's impact on efficiency and its moderation by relational coordination is insignificant. We explain the results by anchoring them to the characteristics of the multispecialty and emerging economy context.

Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation. Following the previous studies, this paper aims to establish a linkage between the DEA method and machine learning (ML) algorithms, and proposes an alternative way that combines DEA with ML (ML-DEA) algorithms to measure and predict the DEA efficiency of DMUs. Four ML-DEA algorithms are discussed, namely DEA-CCR model combined with back-propagation neural network (BPNN-DEA), with genetic algorithm (GA) integrated with back-propagation neural network (GANN-DEA), with support vector machines (SVM-DEA), and with improved support vector machines (ISVM-DEA), respectively. To illustrate the applicability of above models, the performance of Chinese manufacturing listed companies in 2016 is measured, predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%, and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA, BPNN-DEA, ISVM-DEA, and SVM-DEA.

Metamodels are simpler computer models that are designed to mimic the input-output behavior of discrete-event or other complex simulation models. They are models of models, thus the name (provided by Jack P. C. Kleijnen). This introductory tutorial will highlight uses of metamodels, commonly used metamodel types, the linkage between metamodel type and the set of simulation model runs used to fit the metamodel, and basic issues in building and validating metamodels. The tutorial ends with a brief summary of recent research in metamodel types and use, and the implications for future metamodeling applications.

This paper is part of the research on the interlinkages between insurers and their contribution to systemic risk on the insurance market. Its main purpose is to present the results of the analysis of linkage dynamics and systemic risk in the European insurance sector which are obtained using correlation networks. These networks are based on dynamic dependence structures modelled using a copula. Then, we determine minimum spanning trees (MST). Finally, the linkage dynamics is described by means of selected topological network measures.

Enlarging 3D model databases by shape synthesis is a large field of research. Indeed, the use of machine learning techniques requires a huge amount of labeled CAD models, and it is therefore crucial to rely on large and varied databases. Most of existing works in shape synthesis focus on everyday life objects generation [1]. However, these methods often do not work on assemblies composed of several CAD models, and it is the aim of this paper to develop a new shape synthesis method to enlarge existing CAD assembly databases. Today, there exist lots of free databases of non-labeled CAD models (e.g. GrabCAD, 3D Warehouse, Turbosquid) often available as STEP or IGES files. Unfortunately, very few of these databases are labeled. Other databases like PartNet [2] and ShapeNet [3] are currently labeled by crowdsourcing, but they do not contain complex mechanical assemblies. Current works in shape synthesis often use auto-encoders to generate new coherent CAD assemblies [4]. Moreover, there are probabilistic models to create diversity in large 3D Database [5] or to classify 3D assemblies [6]. Those techniques often use linkage graphs [7] to classify and generate new coherent assemblies. Furthermore, information within the linkage graphs differs according to the method, and those graphs are not suitable for complex CAD assemblies like hydraulic pumps. But the main issue of all methods is still that those databases have to be labelled. The method explained in this paper consists in creating new labeled CAD assemblies from existing ones by linkage graph overlay. Here, the STEP file format has been adopted in order to be the most reproductible and to be adaptable. The linkage graphs are automatically created thanks to the identification of the linkages between the components. Indeed, linkages are not included in the STEP files and they need to be computed. Theses linkage graphs are then analyzed and components with similar linkages are detected. Finally, once the similarities detected, the corresponding components can be exchanged to created new assemblies for which the labels can be directly inherited from the source assemblies. The contribution is threefold: (i) a method to create linkage graphs from existing non-labelled CAD assemblies; (ii) a method to recognize basic components using linkage graphs; (iii) a smart overlay method to replace some components while keeping the coherence between all the components of the assembly. The algorithm has been implemented in Python on FreeCAD and it has been tested on several test cases. Figure 1 shows the overview of the method, from the graph synthesis to the components overlay, finishing with the replacement of the components. The results are presented and discussed, and a conclusion ends this extended abstract while discussing the next steps.

Metropolitan areas are important for engaging in fierce global competition. Cities in metropolitan areas in China are generally characterized by a core-periphery structure. The Yangtze River Delta metropolitan area (YRD) is a national strategic region in which Shanghai, as a central city, drives the economic growth of hinterland cities. Exploring the spatiotemporal characteristics and influencing factors between the central city and its hinterland cities in the YRD can provide a basis for promoting regional development. Based on the headquarters-branches and enterprise investment data from 1978 to 2019, this study analyzes the spatiotemporal characteristics and influencing factors of enterprise linkages between Shanghai and its hinterland cities in the YRD. Our results reveal the following: (1) the headquarters-branch linkages between Shanghai and its hinterland cities manifest polarization characteristics, with different polarized characteristics among the three provinces; (2) the enterprise investment linkages between Shanghai and its hinterland cities are getting closer, but the key cities for investment in each province are different; (3) economic strength is a core factor that affects whether Shanghai establishes enterprise branches in its hinterland cities; and (4) the enterprise investment linkages between Shanghai and its hinterland cities depend on whether a city has a comparative advantage.

This study is focused on analyzing the linkage between carbon dioxide (CO2) emissions, renewable energy consumption (RE), foreign direct investment (FDI), national patents (NP), exports (X), imports (M), and gross domestic product (GDP) in Tunisia by using the time series data from 1980 to 2017. A unit root test and an autoregressive distributed lag (ARDL) model were applied to avoid bias caused by data mismatch and autocorrelation of time series data. Elasticity long-run test shows that renewable energy consumption, exports, and gross domestic product have a positive impact on CO2 emissions, while foreign direct investment acts negatively on CO2 emissions. Depending on the error correction term test, there is a long-run causality: from CO2 emissions, renewable energy, foreign direct investment, exports, and gross domestic product to home patent. Findings of the short-run causality show that there is a unidirectional causality running from exports to CO2 emissions and from exports to gross domestic product. Our results also show that Tunisia should encourage foreign direct investment because it seems to be an important factor in the mitigation of CO2 emissions.

The economic structure of countries can influence economic growth, energy demand, and environmental footprints. However, the literature on economic complexity and ecological footprint (EFP) nexus is scarce. Besides, democracy is an important factor that may affect environmental policies and environmental sustainability. Hence, this paper investigates the effect of democracy, economic complexity, and renewable energy technology budgets on the EFP in G7 countries controlling income and financial development from 1985 to 2017. The findings from Westerlund (J Appl Econ 23:193-233, 2008) and other cointegration methods depict cointegration among variables. The long-run estimates from the continuously updated fully modified method unfold that economic complexity contributes to reducing the EFP. However, greater democratic accountability boosts the EFP figures rather than reducing them. On the flipside, renewable energy technology budgets and financial development are evidenced to mitigate EFP. Moreover, the study unveils a U-shaped linkage between economic growth and EFP, which indicates that an increase in income level will boost EFP. Further, the study found causality from economic complexity, democracy, and renewable energy budgets to EFP. Based on these findings, it is pertinent for the G7 countries to increase the manufacturing of sophisticated and complex products. In addition, enhancing renewable energy technology budgets is essential to ensure environmental well-being.

In industrial applications, copper wire is often used in the cable core or the motor armature. These copper wires are made by passing copper rods through the round-hole mold in a wire drawing machine. To reduce friction, it is necessary to use a lubricant mixed with oil and water for lubrication. The oil would be consumed over time, so it is required to add oil in a while to maintain lubrication. Generally, concentration sensors are used to monitor the concentration of lubricant to determine the timing of replenishing the oil. The principle of the sensor is by the intensity change of optical reflection to detect the concentration. However, copper chips scraped off from the wire during the drawing process will gradually accumulate on the surface of the sensor, thus causing sensor misdetection due to the shading of the light source. In this paper, an improved concentration sensing system is proposed to solve this problem. In the proposed system, a clean water tank is placed beside the oil tank, and the sensor is located in the clean water tank most of the time and is moved to the oil tank for measurement only at a specific time. After the measurement is completed, it will return to the clean water tank. This can greatly reduce the time the sensor is immersed in the oil tank, minimizing the chance that the sensor is shielded by copper scraps. To enable the sensor to move in this manner, a novel linkage design is proposed to meet several requirements in the movement of the mechanism. The mechanism is featured by its simple structure and only one driving device is needed. Furthermore, motion analyses of the mechanism are provided. Finally, we explain the working principle of the measurement system where the proposed linkage and the concentration sensing system are combined. To sum up the results, the proposed method can effectively expand the frequency of on-site maintenance from monthly to annual, which greatly reduces manpower requirements and effectively improves stability.

Interest is growing in the dark side of creativity and recent research has been instrumental in improving our understanding of the phenomenon. However, such efforts have also revealed confusion reg...

Technology opportunity analysis (TOA) is of great help to technology innovation and R&D strategy of enterprises. Most previous studies of TOA only focused on scientific research or technology devel...

Perception biases documented in the literature often pertain to subject matters that are difficult to observe or measure such as one's ability. We study perception biases with respect to a concrete indicator that can be objectively measured: land use changes in a local area. We examine four hypotheses about land use change perceptions and test them with farm survey data complemented by satellite data. We discover systematic biases in farmers' perceptions about local land use changes that are consistent with motivated beliefs, and also evidence that links perceptions with intended future land conversions. Alternative explanations and policy implications are discussed.

The accurate determination of equilibrium structures for isolated molecules plays a central role in the evaluation and interpretation of stereoelectronic, thermodynamic, and spectroscopic properties. For small semi-rigid systems, state-of-the-art quantum-chemical computations can rival the most sophisticated experimental results. For larger molecules, cheaper yet accurate approaches need to be defined. The double-hybrid rev-DSD-PBEP86 functional already delivers remarkable results that can be further improved by means of a “Lego brick” model. This is based on the idea that a molecular system can be seen as formed by different fragments (the “Lego bricks”), whose accurate semi-experimental (SE) equilibrium geometries are available. The template molecule (TM) approach can be used to account for the modifications occurring when going from the isolated fragment to the molecular system under investigation, with the linear regression (LR) model employed to correct the linkage between the different fragments. The resulting TM-SE_LR approach has been tested with respect to available SE equilibrium structures and rotational constants. Indeed, the latter parameters straightforwardly depend on the equilibrium geometry of the system under consideration. The main outcome of our study is the reliability, robustness, and accuracy of this novel approach. The molecular systems considered for benchmarking the TM-SE_LR scheme are those formally issued from addition/elimination reactions of nucleophilic unsaturated radicals (e.g., CN, C2H, and phenyl) to alkenes, imines, and aldehydes, whose rotational spectra have been investigated, but accurate structural determinations are not yet available.

In line with circular economic, the role of green entrepreneurship (GE) should be useful in preserving environmental sustainability. However, so many people are less aware of the importance of sustainability and green behavior in business activities. Therefore, the goal of the study is to explore a linkage of green value, GE, and sustainable development that is perceived by entrepreneurship students in Jakarta, Indonesia. 180 students were involved as respondents resulting in significant impacts. By using Smart-PLS proves significant relationships and finds a mediating effect of GE which links green value to sustainable development at the level of 5 percent. It forms a green triangle approach in promoting sustainability education for university students so that this linkage signs a good perception of students in expressing green value toward GE and sustainability issues. It is an early stage in promoting the triple bottom line so that the learning system could collaborate with stakeholders for enhancing the sustainability system in entrepreneurial education.

PurposeWhen the coronavirus disease 2019 (COVID-19) pandemic seriously hit the USA, a lot of cities/states announced their lockdowns, in some cases forbidding employees to go to work. But workers in the so called “essential sectors” were exempt from the order, and on the contrary were required to remain on the job in order to maintain the services and functions considered vital to the community. If they have not been paid well in comparison to those in the other sectors, there would be a stronger case for granting them a special hazard pay during the pandemic. This paper aims to design a way to measure the “importance” or being “essential” of the different sectors in the economy, and then investigates whether the actual pay of the workers in these sectors is consistent with the measured importance.Design/methodology/approachAt least two policy issues emerged from such an arrangement: (1) How can one define the “essential sectors” objectively instead of the authorities preparing a list according to their administrative procedure? (2) How well have been the workers in the essential sectors paid before the pandemic strike? The concept of a revised Leontief forward linkage effect will be used in an input–output model to gauge the relative “importance” of the different sectors in the US economy. Then the measured importance will be compared with the average compensation of the employees in these sectors.FindingsIt is found that for some sectors such as agriculture, retail trade, and repair and installation of machinery and equipment the ratio of workers' compensation relative to the national average is substantially lower than the relative importance of the sectors employing them. That is, many of them have been substantially underpaid in spite of their importance.Research limitations/implicationsThe scope of this study is limited to one country, the USA, but the methodology can be applied to other countries as well.Originality/valueThis study is an original research that contributes to an improved understanding of the importance of the workers engaged in different sectors in the USA during COVID-19.

This paper aims to investigate whether oil volatility risks could diffuse through the linkage of industry returns and even further contribute to global crisis throughout the stock market. To highlight such sectoral financial interconnectedness which may serve as a key channel for oil volatility diffusion, we first apply the partial cross-quantilogram (PCQ) approach to detect the directional predictability between returns of 26 industries in China's stock market across different quantiles. We construct the corresponding network to provide a more comprehensive picture of such sectoral interconnection, which is shown to vary prominently under different market states and lag order specifications. Utilizing the spatial autoregressive (SAR) model for panel data, we further assess the possibility of the oil volatility risk diffusion by decomposing the aggregate effect of oil volatility shocks into direct shocks, and indirect shocks transmitting through the estimated network linkage of industries. The empirical results point to the significantly heterogeneous pattern of oil volatility risk diffusion among networks with different lag selections under various market states. Considering the financial linkage of industries within 5 traded days under extreme market states, there exist significant indirect effects contributing to larger oil volatility shocks on industry returns, which confirms risk contagion effects of monthly oil volatilities. It indicates that the network linkage of financial assets might be an important diffusion mechanism for oil volatility risks, which should not be neglected in the research of the oil-stock nexus.

The extant literature examines the impact of US uncertainty on international stock markets without paying much attention to the inherent spillovers between the US and the stock markets. This study investigates the role of US uncertainty in driving global stock market spillover from the US. To this end, we consider a wide range of stock markets around the world and three news-based uncertainties from the US, namely economic policy uncertainty (EPU), equity market uncertainty (EMU), and equity market volatility (EMV). We find that the US uncertainties significantly explain the spillovers from the US to global stock markets. This causality from US uncertainties depends upon certain country-characteristics. Specifically, the US uncertainties better explain the spillovers between the US and target countries when those countries have a higher degree of financial openness, trade linkage with the US, and vulnerable fiscal position. However, improved stock market development levels in the target countries mitigate their stock markets' vulnerability to US uncertainty shocks. The study offers potential insights and implications for investors and policymakers.

Berbagai studi telah dilakukan untuk menganalisis dampak pengganda dari perekonomian Bali. Namun, studi-studi tersebut umumnya berfokus pada keterkaitan antar sektor menggunakan model Input-Output (IO). Padahal, perekonomian antar wilayah dapat saling bergantung melalui berbagai macam eksternalitas dan jaringan rantai suplai. Studi ini menganalisis perekonomian Bali tidak hanya berdasarkan hubungan antar sektor, namun juga mempertimbangkan hubungan ekonomi Bali dengan provinsi lainnya. Model yang digunakan adalah Inter Regional Input Output (IRIO). Tabel IRIO berukuran 17 industri x 34 provinsi diperoleh dari Badan Pusat Statistik. Hasil analisis menunjukkan bahwa terdapat beberapa industri unggulan di Provinsi Bali, yaitu Listrik dan Gas, Transportasi dan Pergudangan, Informasi dan Komunikasi, dan Jasa Perusahaan. Industri Listrik dan Gas memiliki keterkaitan antar sektor dan dampak output yang paling besar diantara industri-industri di Provinsi Bali. Selanjutnya, analisis antar wilayah menunjukkan bahwa shock permintaan akhir di Provinsi Bali berdampak besar terhadap perekonomian provinsi-provinsi di Pulau Jawa, khususnya Jawa Timur. Di sisi lain, perekonomian Bali sangat dipengaruhi oleh shock permintaan akhir di Provinsi Nusa Tenggara Barat.

ZusammenfassungEinleitung In Deutschland wurde der Rettungsdienst im Jahr 2016/2017 bei insgesamt rund 7,3 Mio. Notfallereignissen aktiv 1. Informationen zu der dabei erfolgenden prähospitalen Versorgung von Patient*innen finden sich in unterschiedlichen Sekundärdatenquellen; jedoch ist die kombinierte Analyse dieser Daten auf Fall-/Einsatz- oder Patient*innen-Ebene selten der Fall. Um dies zu ändern, ist die Erforschung von Merkmalen und Methoden erforderlich, anhand derer Sekundärdaten, welche präklinische Inhalte umfassen, verknüpft werden können.Methode Es werden Rettungsdienst-Einsatzprotokolle aus 5 bayerischen Rettungsdienstbereichen mit Abrechnungsinformationen 10 gesetzlicher Krankenkassen zusammengeführt (Daten aus 2016). Demonstriert werden 2 Verknüpfungsansätze auf Ebene patient*innenindividueller Einsätze bzw. abgerechneter Leistungen. Zuerst erfolgt die Verknüpfung deterministisch anhand des Personenidentifikators Krankenversichertennummer (KVNR). Der zweite Verknüpfungsansatz beruht auf einem probabilistischen Verfahren, das neben der nur in einem Teil der Einsatzprotokolle verfügbaren KVNR indirekte Schlüsselvariablen berücksichtigt. Dabei handelt es sich um die Kassenzugehörigkeit der Patient*innen, ihr Geschlecht und Geburtsjahr sowie die zurückgelegte Wegstrecke des eingesetzten Rettungsmittels. Zur Prüfung sowohl der deterministischen als auch probabilistischen Verknüpfung werden Übereinstimmungsquoten verschiedener, in beiden Datenquellen vorhandener Variablen ermittelt.Ergebnisse Ausgangspunkt für die Datenverknüpfungen stellen 106 371 Rettungsdienst-Einsatzprotokolle (unabhängig von der Krankenkasse) sowie 432 693 bei Krankenkassen abgerechnete Leistungen (unabhängig vom Rettungsdienstanbieter) aus dem Bereich der Notfallrettung dar. Von 5921 Einsatzprotokollen, in denen eine an Inno_RD datenliefernde Krankenkasse dokumentiert ist, können 4327 Einsatzprotolle deterministisch unter Rückgriff auf die KVNR mit den Abrechnungsdaten zusammengeführt werden. Im Rahmen der probabilistischen Verknüpfung lässt sich diese Anzahl auf 5379 Einsatzprotolle steigern. Alle Prüfungen deuten sowohl für den deterministischen als auch den probabilistischen Ansatz auf eine hohe Verknüpfungsqualität hin.Schlussfolgerung Eine personen- und einsatzbezogene Verknüpfung von Einsatzprotokollen des Rettungsdienstes mit Abrechnungsdaten ist möglich. Als Alternative zur deterministischen Verknüpfung anhand der KVNR hat sich in Inno_RD ein probabilistischer Ansatz bewährt, auf dessen Grundlage Einsatzprotokolle auch dann sinnvoll verknüpft werden können, wenn KVNR nicht vorliegen sowie indirekte Schlüsselvariablen im geringen Umfang Nicht-Übereinstimmungen oder Missing-Values aufweisen.

Port sustainability studies are increasing rapidly and are skewed toward environmental aspects, while at the same time their results are fragmented, making it difficult to collectively assess conclusions. This study, therefore, aims at building a framework which categorises port actions, measures, and implementation schemes (policy tools to improve the uptake), utilising the critical literature review method. Additionally, linkage of port sustainability to the United Nations’ Sustainable Development Goals (UN SDGs) is highlighted. Port sustainability includes internal (port side) and external (ships and land transport) actions and measures. The study results form 16 homogeneous and interconnected sustainability categories, including a non-exhaustive list of operationalising measures, encompassing the three dimensions of sustainability (environment, economy and society) while implementation schemes are divided into four groups. Considering that ports are under scrutiny and perceiving growing pressure to improve their sustainable pathways, for example by addressing climate change and energy consumption, the identified ports’ sustainability actions and measures, including the linkage with the UN SDGs, are overarching and multidimensional and seen as a step in achieving far-reaching sustainable implementation. The study’s practical implications guide port policymakers and industry practitioners to go beyond the low hanging fruit (customary practices), and enable them to take reliable decisions for broader sustainability implementation. Additionally, the identified research implications stimulate further academic discussions.

Efficient energy is crucial in reducing CO2 emissions. The researchers are digging for a new efficient source of energy in the modern era. Therefore, this study explores the dynamic impacts of energy efficiency and renewable energy consumption on CO2 emissions from 1991 to 2019 for China. By using the non-linear ARDL approach, we found that a negative shock in energy efficiency has a positive impact on CO2 emissions in long run. Furthermore, renewable energy consumption with positive shock has a negative significant impact on CO2 emissions, but negative shock in renewable energy consumption leads to increase pollution emissions in long run. Besides, positive shocks to energy efficiency and renewable energy consumption have also a favorable negative effect on CO2 emissions in the short run. While a negative shock in energy efficiency has only unobservable negative impacts on CO2 emissions in the short run. Based on findings, some policy measures are suggested to attain environmental sustainability in China.

The article discusses the innovative concept of the over-expanded thermodynamic cycle, the author of which is the Polish engineer-designer Mirosław Szymkowiak. This cycle is realized on the basis of a new and innovative, previously unknown design, of a piston-crankshaft linkage mechanism with the aid of an additional element known as a rocker arm. Additionally, the proposed mechanism allows for a smooth change of the compression/expansion ratio of the engine during its operation. In the beginning, the earlier conceptions of the rocker engine developed by Szymkowiak were presented, and then the main construction assumptions and kinematic calculations were described. It was confirmed, that the developed linkage has big potential in improving the engine's thermal efficiency by approximately 12% relative. Additionally, it significantly reduces the exhaust gas pressure, when the exhaust valve is opened, therefore, contributes to the reduction of the noise emitted by the engine.

For decades, research on the relationship between environmental sustainability and firm performance focused on determining whether the effect of the former on the latter was positive or negative. That potentially controversial approach had driven mainstream research to ask two focal questions: Does it pay to be green? and Does it cost to be green? Recent papers have explored a curvilinear effect (i.e., U-shaped and inverted U-shaped) by tackling another question: When does it pay to be green? These efforts, however, have either oversimplified the complex linkage between these two things into a linear relationship or confined the complexity within a formatted frame. This paper overcomes these shortfalls by exploring the holistic effect of environmental sustainability on firm performance, contingent upon corporate reputation. In conjunction with direct effect analysis, this paper uniquely explores the synergistic effect of environmental sustainability and corporate reputation on performance. In addition to detecting the nonlinear synergy pattern, we found that the synergy effect is asymmetric. Specifically, the effect is complementary for low-reputation firms but substitutive for high-reputation firms. In addition to its theoretical contributions and pragmatic implications, this paper uniquely presents PROCESS-Neural network as a promising analytic paradigm.

Solar geoengineering may be able to significantly reduce climate-change risks, but raises sharp controversy, the leading cause of which is the concern that its research, development, or use might inappropriately displace efforts to cut greenhouse-gas emissions (“mitigation”). Although well-crafted policies could in principle manage such possible mitigation displacement, describing how they could do so effectively and feasibly has proven elusive. A possible response would be to strategically link the international policies of mitigation and solar geoengineering. Here we explore and expand on this idea, including by disaggregating states based on their relevant characteristics (including willingness to increase mitigation, willingness to implement solar geoengineering contrary to any international consensus, and their desired intensity of solar geoengineering), the incentives that various policy linkages could foster, and considering the processes of developing linkages. We explore potential linkage of mitigation with the research and development of solar geoengineering, with decision-making regarding whether to deploy; and with that regarding how to deploy. These potential linkages are assessed on whether they could effectively reduce mitigation displacement (if not increase mitigation), appear feasible, and would not be inconsistent with widely-held norms. In the linkage mechanism with the greatest apparent potential, one or more states would proclaim their right to deploy solar geoengineering if they meet their own mitigation targets and the rest of the world insufficiently mitigates. We identify possible challenges, including legitimacy, credibility, optimal size, relations among nonmembers, mitigation targets, and complexity, none of which seem insurmountable.

Agent-based models (ABMs) and state-and-transition simulation models (STSMs) have proven useful for understanding processes underlying social-ecological systems and evaluating practical questions about how systems might respond to different scenarios. ABMs can simulate a variety of agents (autonomous units, such as wildlife or people); agent characteristics, decision-making, adaptive behavior, and mobility; and agent-environment interactions. STSMs are flexible and intuitive stochastic landscape models that can track scenarios and integrate diverse data. Both can be run spatially and track metrics of management success. Due to the complementarity of these approaches, we sought to couple them through a dynamic linkage and demonstrate the relevance of this advancement for modeling landscape processes and patterns. We developed analytical techniques and software tools to couple these modeling approaches using NetLogo, R, and the ST-Sim package for SyncroSim. We demonstrated the capabilities and value of this coupled approach through a proof-of-concept case study of bison-vegetation interactions in Badlands National Park. The coupled ABM-STSM: (1) streamlined handling of model inputs and outputs; (2) allowed representation of processes at multiple temporal scales; (3) minimized assumptions; and (4) generated spatial and temporal patterns that better reflected agent-environment interactions. These developments constitute a new approach for representing agent-environment feedbacks; modelers can now use output from an ABM to dictate landscape changes within an STSM that in turn influence agents. This facilitates experimentation across domains (agent and environment) and creation of more realistic and management-relevant projections, and opens new opportunities for communicating models and linking to other methods.

PurposeAccording to relational contract theory, relational governance has potential to improve public-private partnership (PPP) infrastructure project sustainability. The main purpose of this research is to investigate the association between relational governance and the sustainability of PPP infrastructure projects. Further, this study examines the mediating effect of managerial innovation and the moderating role of public involvement.Design/methodology/approachResearch data were collected from 158 valid questionnaires completed by Chinese PPP professionals. Structural equation modeling (SEM) was then employed to test five hypotheses.FindingsResults indicate a positive correlation between relational governance and PPP infrastructure project sustainability. This linkage is regulated by public involvement. In addition, managerial innovation plays a mediating role between relational governance and the sustainability of PPP infrastructure projects.Originality/valueThis study verifies the relationship between relational governance and PPP infrastructure project sustainability, as well as intermediary and regulatory factors, providing a new approach to achieving sustainability in PPP infrastructure projects.

This paper contributes to the literature on the relationship between alcohol consumption and economic growth. Despite growing attention on the topic, existing studies have demonstrated the existence of a threshold beyond which alcohol consumption leads to disease, negatively influences professions such as driving and leads to death. However, the threshold literature has not yet explored the nonlinear relationship between alcohol consumption and economic growth. The per capita alcohol consumption expenditure is used to capture the alcohol variable. The empirical evidence is based on the Pooled Mean Group (PMG) and System Generalized Method of Moments (GMM) with a 32 sub-Saharan African countries dataset over the period 2000-2016. The empirical evidence indicates that alcohol consumption fosters economic growth in sub-Saharan African countries. Moreover, the results show that an alcohol consumption threshold exists below which greater alcohol consumption has beneficial effects on economic growth and above which the alcohol consumption has a perverse effect on economic growth. This result materializes the existence of an inverted U-shape (Laffer Curve of alcohol). Therefore, Sub-Saharan African countries must control the level of alcohol consumption of their citizens in order not only to protect them against alcohol diseases but also to ensure sustainable growth.

This article presents an active ball joint mechanism (ABENICS) enhanced by interactions of spherical gears. The gear-based joint drives three rotational degrees of freedom (RDoF) without slippage. The capabilities were inspired by the unique interactions between two different innovative gears [the cross spherical gear (CS-gear) and monopole gear (MP-gear)] and the superimposition of those interactions by the CS-gear's quadrature spherical tooth structure. One MP-gear constrains two of the three RDoF of the CS-gear. The driving module which drives the MP-gear converts this “constraint” into a “drive” and drives the CS-gear with two RDoF. The CS-gear orthogonally superimposes the interactions caused by two MP-gears to achieve three RDoF driving forces. The principle was revealed by analyzing an equivalent linkage modeled on the mechanism of ABENICS. The linkage also led to the kinematics and torque equations. The theory and physical characteristics of ABENICS were verified in comprehensive and continuous positioning experiments on manufactured prototypes. The flexibility of the actuator placement was also verified in different configurations of the driving modules. The active ball joint, ABENICS can transmit high torque and reliable positioning in three RDoF without an orientation sensor, which applies to robot joints and orientation control mechanisms.