Abstract

Semiconductor industry plays a critical role for the global economy. Semiconductor industry provides various necessary technologies such as IoT, AI, modern fabrication technologies and so on to various industries including automotive industry, electronic and communication industry, healthcare industry, construction and building industry, space industry, and so on. However, semiconductor supply chain experiences various supply chain related risks and challenges because of its procedural complexities, global supply chain integrations, government policy and regulations, competitiveness, technological complexities, and so on. Not many studies available which investigated the risk, resilience, and complexities regarding green supply chain adoption by semiconductor industry. In this context, the objective of this study is to examine the risks, resilience, and complexities for managing the green supply chain adoption for higher sustainability in the semiconductor industry. Utilizing the TOE framework (Technology-Organization-Environment) and DCV (Dynamic Capability View), we developed a research model to achieve this purpose. Subsequently, this model was validated through structural equation modelling, involving 356 respondents affiliated with the semiconductor industry. This study highlights that technological risk aspects comprising of technological turbulence and risk, compatibility and complexity, organizational dynamic capabilities, and resilience along with appropriate policy and regulations could help successful adoption of green supply chain management in the semiconductor industry.

Introduction

Semiconductors are vital components of modern electronics. They are used in many diverse products, such as phones and computers to cars and health devices. The devices mentioned above, and others use microchips, memory units, and chipsets. The semiconductor supply chain is a complex network that involves companies that design, make, test, pack, and distribute semiconductors ( Li et al., 2011 ; Oliveira et al., 2019 ). It is the interconnected framework of entities responsible for the various stages in the lifecycle of semiconductor products. The supply chain is complicated, requiring the alignment of several different steps, from getting parts and materials to the delivery to the ultimate customer ( Browning et al., 1995 ; Lai et al., 2022 ). A complicated web of companies, organizations, and people make up the semiconductor supply chain. They work on the creation, production, quality control, packaging, and delivery of semiconductors. The semiconductor supply chain usually has several steps involving complex supply chain flow ( Oliveira et al., 2019 ). It is to note that for ensuring green innovation to establish a healthier society, adoption of green supply chain management (GSCM) is needed for semiconductor industry. Besides, semiconductor chips manufactured in specialized plants also release carbon content gas polluting the atmosphere ( Awa and Ojiabo, 2016 ). To address these issues, it is essential to use the GSCM process that could help to mitigate such environmental hazards. Supply Chain Management (SCM), which supports the success of many leading companies, is a key element of operations management ( Teece, 2014a ; Stekelorum et al., 2021 ; Vrontis et al., 2022a ). The competitive landscape is not shaped by individual organizations, but by supply chains that have multiple workflows across collaborating partners ( Hwang et al., 2016 ). It is worth mentioning that GSCM practices are important for creating ecofriendly products ( Tseng et al., 2019 ). In the supply chain flow followed in the semiconductor industry, it is needed to resist disruptions of flow of supply of semiconductor chips. This needs the supply chain flow to be more resilient to ensure better performance of manufacturing plants of semiconductor chips which necessitates use of GSCM practices ( Li et al., 2011 ). The strategic SCM is a process that involves multiple interactions among various factors such as strategic sourcing orientation for lasting partnerships, communication between firms, teams across organizations and integration of buyers and suppliers ( Chen & Paulraj, 2004 ). As many businesses source, sell, or compete globally or with global competitors ( Oliveira et al., 2019 ), they use strategic management theories to find ways to cooperate and gain an edge in a global setting. Therefore, many firms and business schools have focused their attention on Global SCM ( Browning et al., 1995 ; Chaudhuri et al., 2022 ; Lai et al., 2022 ). In the semiconductor industry, the global production scenario prevails, characterized by a supply chain network spanning the globe ( Lee et al., 2010 ). Dominated by leading technology-based firms, this industry demands cost efficiency, mass production, and operational flexibility. The semiconductor industry is characterized by its high demand for capital, as it involves sophisticated R&D processes, with few companies that can compete in the market. Management models in this global industry rely heavily on outsourcing and offshoring in SCM processes ( Pan et al., 2015 ). Stekelorum et al. (2021) contend that implementing green supply chain management (GSCM) would enhance the global semiconductor industry’s supply chain network in terms of effectiveness, sustainability, and environmental friendliness. It is important to mention here that the regulatory bodies of all the industries strive to motivate the firms to be environmentally conscious and restrict their activities binding them with some salient regulations to be followed ( Ratusny et al., 2022 ). In the dynamic business environment, semiconductor industry manufacturing firms need to develop their dynamic capabilities to successfully address the volatile situations for survival ( Mousavi et al., 2019 ; Song & Dong, 2024 ).

Conclusions

The present study has taken a novel attempt to enrich the extant literature by synthesizing the factors which include the risks, resilience, and complexities facilitating and impeding the smooth adaptation of GSCM in the semiconductor industry. This study has highlighted how the semiconductor industry could comply the policies and regulations of the governments while adopting the GSCM. This study has also discussed how the business practices in the semiconductor industry which consumes huge energy and pollutes the environment could use green supply chain practices that could lead to business sustainability and fulfils existing environmental related regulations to achieve sustainability goals. This research work has successfully been able to integrate TOE framework along with DCV to develop a hybrid theoretical framework highlighting how semiconductor industry can ensure better adoption of GSCM to achieve their sustainability goals. The proposed framework acts as a tool and guideline for future researchers who intend to ensure GSCM practices in other types of industries by updating the proposed model commensurate with the context of that study. Thus, the proposed model acts as a baseline for future researchers. This study has successfully been able to identify the factors like risk, resilience, and complexity which affect the adoption of GSCM. This study has also found that improvement of dynamic capabilities of the organizations related to semiconductor industry helps to successfully adopt GSCM. Finally, this study has also demonstrated that government policies and regulations have considerable influence on organizations towards achieving sustainability goals through adoption of GSCM.

Abstract

Sustainability issues have long been a critical notion in business, while successful green human resource management (GHRM) has the potential to help organizations perform better in terms of environmental sustainability. The current paper evaluates the mechanism through which green human resource management affects green organizational commitment, green innovation practices and low-carbon behaviour among 610 employees in South Africa. The study further evaluates the mediation connection among the variables and the moderating influence of green transformational leadership through social cognitive theory. The stratified sampling approach was applied in selecting the respondents, and the structural equation model was applied to test the hypotheses. The empirical outcome of this research is as follows: (1) GHRM has a positive and substantial effect on green organizational commitment, green innovation practices and low-carbon behaviour. (2) The findings also revealed that green organizational commitment and green innovation practices affect low-carbon behaviour among staff. (3) The results suggest that green organizational commitment and green innovation practices have a substantial mediation influence on the connection between green human resource management and low-carbon behaviour. (4) Last, green transformational leadership discovered a positive moderating effect on the link between green human resource management and low-carbon behaviour. The paper makes several practical recommendations for strengthening low-carbon behaviour. Therefore, human resource management managers should outline measures that can be used to empower and engage employees in environmental issues and provide them with autonomy to address these challenges.

Introduction

Governments are pressured globally to accelerate carbon cuts and align national climate action plans with the Paris Agreement (Cai et al., 2022a; Sampene et al., 2021; Wiredu et al., 2023). Steg et al. (2017) believe these programs and interventions will achieve their target when focused on people’s behavioural concepts. Similarly, Wang et al. (2021) proposed that addressing environmental challenges requires behavioural and cognitive changes from all societal sectors and levels. Hence, it is prudent to examine employees’ low-carbon behaviour (LCB) and how it contributes to environmental performance (EP) among small and medium-sized enterprises (SMEs) in the Republic of South Africa. South Africa is the world’s 14th largest emitter of greenhouse gases (Adebayo and Odugbesan, 2021; Shikwambana et al., 2021). Therefore, South Africa plans to reduce its unemployment rate through SMEs and the manufacturing sector; however, van Staden (2022) believes this may put pressure on the energy sector, contributing significantly to the country’s higher levels of environmental pollution. This is because the country’s CO2 emissions are principally high due to a heavy dependence on non-renewable energy sources from SMEs.

Hence, it is imperative to advance research on pollution from SMEs in this era, where there is a growing debate for SMEs to go into “green initiatives.” As Meissner (2022) revealed, green innovation practices (GIPs) and green organizational commitment (GOC) have several implications, such as reducing production costs and improving firms’ EP. In addition, GIP helps firms reduce waste generation, reduce harmful emissions, and preserve natural resources. Firms’ involvement in GIP also helps attract customers who support environmental management initiatives (Bass, 2020; Meissner, 2022). Therefore, this study identified various drivers to improve LCB among SMEs in light of this discussion. LCB has been described as human activities geared toward protecting the environment. LCB is also known in some studies as “environmentally friendly behaviour”, “green environmental behaviour”, and “pro-environmental behaviour” (Foster et al., 2022; Yuan and Li, 2023). As a result of the upsurge in climate issues caused by human behaviour, one subject matter that has attracted interest in environmental sustainability research is LCB (Ansari et al., 2021). To understand the concept of employee LCB, the researcher envisioned social cognitive theory (SCT). SCT emphasizes that individual behaviour influences the environment. This relation is reciprocal determinism; thus, people’s behaviour is defined as the interplay of environmental factors, behavioural concepts, and cognitive factors. As a result, this article extends SCT and evaluates the effect of employees’ LCB.

Conclusions and research implications

6.1. Conclusions
International organizations such as the United Nations and the recent Glasgow Agreement are championing the need for carbon emissions and preserving the environment for future generations. This call has necessitated the importance of this study, which focuses on low-carbon behaviour among employees of SMEs from an emerging country (South Africa). The research hypothesis was evaluated through the SEM-PLS methodology and the IPMA (importance-performance map analysis) analysis approach. The analysis from this research revealed that (1) GHRM has a positive and substantial effect on GIP, GOC, and LCB. (2) The findings also revealed that GOC and GIP positively influence LCB among staff. (3) The results suggest that GIP and GOC have a substantial mediation influence on the connection between GHRM and LCB. (3) Last, GTFL revealed a positive iterative effect on the link between GHRM and LCB. The following is an explanation of the theoretical and practical contributions based on the empirical results of the current analysis.

6.2. Theoretical implications
An integral theoretical contribution of this study is that while some evidence has been provided on the influence of GHRM on LCB (Shoaib et al., 2021), little environmental research has examined the intermediary effect of GOC and GIP on these concepts. More specifically, the evidence outlined in this paper demonstrated that GOC and GIP mediate the connection between GHRM and LCB. This research outcome indicates these variables’ significance in developing green innovation actions. In other words, the commitment levels and innovation practices exhibited by staff members can be influenced by GHRM policies, eventually affecting staff LCB. Furthermore, the study provided empirical evidence indicating that SMEs should invest in GIP and improve their staff’s GOC, as it has been established to be an effective tool for improving EP. Thus, GIP and GOC are future initiatives that bring about dynamic changes in institutions, technologies, and commercial strategies and help improve environmental sustainability. In addition, incorporating GTFL as a moderator provides a novel perspective on leadership’s role in promoting GHRM and LCB. Thus, the research proved that GTFL inspires workers to achieve better results. The research has revealed how vital GTFL is in improving the connection between GHRM-LCB. Therefore, managers must practice the GTFL approach to help foster the improvement of both ecological behaviour and firms’ EP.

Abstract

In this paper, we examine which of an agency selling or a wholesale contract offered by an e-commerce platform competing suppliers with typical dual-channel supply chains should adopt, where the products are not differentiated between a direct channel and an indirect platform channel, as in the case of digital goods. Specifically, we consider the situation in which each of two competing suppliers chooses its distribution strategy regarding whether it sells via a direct channel and/or an indirect channel via the platform. In addition, a supplier also chooses an agency selling contract or a wholesale contract if selling via the platform. Constructing and solving a game-theoretic model, we derive the primary result that, in equilibrium, one supplier sells products only via the direct channel, while the other supplier sells via both the direct channel and the platform channel through adopting a wholesale contract, even given the assumption of symmetric suppliers. This finding yields the managerial implication that a supplier selling products of the same quality between direct and platform channels in a competitive environment should not adopt agency selling but instead a regular wholesale contract when selling via a platform. Moreover, this finding reverses the conventional insight from existing models in previous studies that at least one competing supplier always adopts an agency contract when the suppliers are without their own direct sales channels.

Introduction

Currently, a wide variety of products ranging from technologically advanced industrial products to daily necessities including food and groceries are sold through online e-commerce platforms worldwide. Consequently, platform companies have experienced tremendous growth over the past decade. For example, Amazon announced that its net sales for 2020 increased 37.6% to $386 billion, compared with $281 billion in 2019. Reflecting the development of these e-commerce platforms, the US Department of Commerce has documented that online retail sales in the US were $791.7 billion in 2020, accounting for 14% of all retail sales in the US.1 Likewise, in China, where the development of e-commerce has been especially rapid, online retail sales reached 11.76 trillion yuan in 2020, accounting for some 30% of total retail sales.2 Accordingly, manufacturers increasingly regard these online e-commerce platforms as one of their essential sales channels.

While online retailers have typically played the role of simple resellers in the past, the rapid adoption of online shopping by consumers has induced major platform companies, including Amazon in the US, Taobao in China, and Flipkart in India, to introduce a sales format known as marketplace in addition to their traditional reseller format. Accordingly, when selling products or services through an online platform, a supplier needs to select between a wholesale contract or an agency contract, depending on whether the supplier wishes to sell products wholesale to the platform or use its marketplace. The essential difference between the two contracts is who has the right to determine the retail price. With the wholesale contract, while a supplier determines the wholesale price of its products, the platform company determines the retail price. Conversely, the retail price decision is delegated to the supplier in the agency contract, such that the supplier directly determines the retail price of its products based on the revenue-sharing rules as determined by the royalty rates and commission fees set by the platform. A major benefit of agency selling to the supply chain is that it resolves the problem of double marginalization, unlike a wholesale contract, and thereby prevents a reduction in the transaction quantity.

Conclusions

Currently, many manufacturing companies are not only utilizing rapidly growing e-commerce platforms as important sales channels, but also they are opening their direct channels and selling directly to end-consumers using advanced IT technologies. Given that many suppliers are now using such dual-channel supply chains, this paper investigates whether a wholesale contract or an agency contract offered by an e-commerce platform is chosen by competing suppliers, in the situation where products are undifferentiated between the direct channel and the indirect platform channel, as is typically the case of digital goods.

The key result from our model is that if there is no differentiation between channels such that consumers perceive the direct and platform channels as substitutable, then neither of the competing suppliers chooses the agency contract. This result holds regardless of how much the platform increases the suppliers’ share of profits by lowering its royalty rate and thereby making the agency contract more profitable to them. This yields the managerial implication that a supplier selling products of the same quality across channels, such as digital goods in a competitive environment, should adopt a regular wholesale contract, not an agency contract when selling through a platform. Intuitively, if a supplier adopts a wholesale contract, this appears undesirable for the supplier because the problem of double marginalization is likely to occur. However, our results lie contrary to this intuition. Moreover, it is also a notable result that even though suppliers are assumed to be symmetric regarding demand and cost conditions in our model, their distribution strategies emerging in equilibrium are asymmetric. These asymmetric distribution channel strategies allow a supplier to differentiate itself from the rival supplier if the former sells through the platform. Because we have obtained all our main results and implications clearly in analytical form solely by solving our model, we do not conduct numerical analysis in this paper.

Abstract

     The similarity measure in this paper is verified for Pythagorean fuzzy sets (PFSs). We point out that a similar measure for two PFSs is a Pythagorean fuzzy value (PFV). To calculate the similarity measure of two PFSs, it is logical that their similarity should be vague. Therefore, we introduce for the first time, as far as we know, the similarity measures for two PVFs is the same as PFV. For calculating PFV of similar measure, we first present the method for calculating Pythagorean similar measure for two PFV by using T-norm and S-norm and then extend it to calculate similar measure for two PFSs. Finally, a numerical example is provided to illustrate the validity and applicability of the presented decision-making method.

Introduction

     In many multi-criteria decision making (MCDM) problems, the decision maker has to use vague (qualitative) values to decide. In the face of qualitative values, for the first time, Bellman and his associates introduced the theory of fuzzy sets (FSs) using mathematical modeling. In making decision with fuzzy sets, the decision maker considers only the degree of correctness of the option and cannot take into account the degree of incorrectness. Continuing this trend, Atanassov in [1] introduced the intuitionistic fuzzy sets (IFSs) theory which is a generalization from FSs which included both the degrees of membership and non-membership.

     Atanassov and Gargov [2] presented an interval valued IFS (IVIFS) as a generalization and an intuitionist fuzzy sets (IFS) that makes use of interval value instead of a real number.

Conclusion

     We have successfully introduced a new definition for similarity measure for Pythagorean collections, which is also a PFV. We define the similarity measure of the two PFVs with a PFV, using T-norm, and S-norm and we generalized it to PFS. This method is used for decision making with Pythagoras value, using similarity measure. The advantages of this method include:

     Calculating the similarity size in the form of PFSs takes more information into decision making, and we lose a lot of information, if converted to a real number.

Abstract

     For multi-attribute decision-making problems with interval numbers whose attribute weights are completely unknown, it is difficult to compare the value of interval numbers. So this paper proposes a method based on TOPSIS and the weighted parameter to deal with it. Firstly, this paper transforms the interval number matrix into two exact number matrices which reduces sorting complexity. Secondly, a parameter is given when determining the weights with entropy weight in order to reflect all the information of the interval numbers. Then TOPSIS is used to determine the order of each scheme. In addition, the average value of the ranking number is used to reflect the actual situation better. Finally, the analysis of the car purchase case shows the proposed method is feasible and practical. And the comparative analysis with the other method based on practical application dataset demonstrates the proposed method is stable and effective.

Introduction

     Due to the complex objective world and its uncertainty, it is difficult to describe the relevant attributes in decision-making problems with exact numbers. To make the model closer to the facts, interval numbers without clear preference information are often used when processing data, thus causing interval number multi-attribute decision-making problems, such as [1].

     At present, the research on such problems has gained attention from scholars at home and abroad. The research focuses on the following four aspects. The first is to sort the interval numbers according to possibility degree, relative superiority degree. Li Z W [2] used relative superiority degree and defined a new sorting vector to rank interval numbers. Li D Q [3] found the ranking method of possibility degree may obtain contrary results to the meaning of possibility degree. So a revised ranking by Boolean matrix was discussed. But the scientificity of the new method needs to be further verified. Yao N [4] defined an interval number ranking method considering symmetry axis compensation which consider multiple attitudes of decision makers with different risk appetites. Firozja, M [5] proposed a new interval distance of two fuzzy numbers that satisfy on metric properties. The second is to determine the weight by studying the application of entropy weight method [6], [7]. Dong P Y [8] proposed a combined weight method. Yue Z L [9] developed a determining weights method for group decision-making problems which each individual decision information is interval numbers. The third is to sort the schemes with the help of projection, grey relation analysis, etc [8], [10], [11], [12], [13], [14], [15], [16], [17], [18]. The forth is to propose the methods for group decision-making problems [9], [19], [20].

Conclusion and promotion

     It is difficult to compare the value of two intervals. In this paper, a novel method is proposed to solve multi-attribute decision-making problems with interval numbers. Two types of exact numbers which are left end point and length are used to replace an interval number which facilitates the determination of attribute weights. At the same time, when determining weights, a variety of possible rankings are considered, and the information brought by interval numbers is fully utilized to ensure the accuracy of results.

     When extracting exact numbers from interval numbers, other methods can also be applied, that is, interval numbers can be replaced with the right end point and length, or the left end point and the midpoint, or the right end point and the midpoint. And the other steps are the same as the corresponding content in this paper. All of them can reduce the difficulty of the problem, and the optimal solution obtained is the same as the method presented in this paper.

     Finally, the feasibility and effectiveness of the proposed method have been demonstrated by car purchasing. The method can also be used to solve the group decision-making problems with intervals.

Abstract

Introduction

Survey on related models

Problem statement

Findings

Managerial implications and practical insights

Conclusion and outlook

References

Author information

Additional information

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Abstract

This research develops a regression-based Robust Optimization (RO) approach to efficiently predict the number of patients with confirmed infection caused by the recent Coronavirus Disease (COVID-19). The main idea is to study the dynamics of the COVID-19 outbreak at the first stage and then provide efficient insights to estimate the necessary resources accordingly. The convex RO with Mean Absolute Deviation (MAD) objective function is utilized to project the course of COVID-19 epidemic in Iran. To validate the performance of the suggested model, a real-case study is investigated and compared to several well-known forecasting models including Simple Moving Average, Exponential Moving Average, Weighted Moving Average and Exponential Smoothing with Trend Adjustment models. Furthermore, the effect of parameter uncertainties is examined using a set of sensitivity analyses. The results demonstrate that by increasing the degree (coefficient) of regression up to 8, MAD value decreases to 1378.12, and consequently, the corresponding equation becomes more accurate. On the other hand, from the 8th degree onwards, MAD value follows an upward trend. Furthermore, by increasing the level of regression uncertainty, MAD value follows a downward trend to reach 1309.28 and the estimation accuracy of the model increases accordingly. Finally, our proposed model achieves the least MAD and the greatest correlation coefficient against the other models.

Introduction

For the first time in December 2019, a new strain of Coronavirus was identified in Wuhan, China. People were diagnosed with pneumonia for no apparent reason and existing vaccines, and treatments were ineffective. The novel Coronavirus disease took the official name of COVID-19 from World Health Organization (WHO) when the number of deaths surpassed 1000 people. On January 30, 2020, WHO issued a statement declaring the novel Coronavirus, a public health emergency, posed a threat to the entire world, not just China. Following the COVID-19 pandemic crisis in various regions of the world, the government of Iran officially confirmed the epidemic on 19 Feb 2020. Since the beginning of the Coronavirus epidemic coincided with the H1N1 flu epidemic, the health system mistakenly recognized it as flu and was slow to notice the onset of the disease (Mardani et al., 2020). One of the challenging issues in the outbreak of COVID-19 is the incubation period, which varies from 3 to 14 days (Tirkolaee, Abbasian, et al., Tirkolaee, Hadian, et al., 2020). That is why the treatments did not work on patients infected by the COVID-19.

To date, some countries product vaccine and only some of them is successful. According to WHO, COVID-19 is an acute respiratory syndrome that is transmitted through respiratory droplets and airways. The examination of this disease needs the serious attention of governments to take the essential measures to reduce the impact of this global epidemic.

From 22 Feb 2020, the COVID-19 risk status went from white to yellow in Iran. In mid-February 2020, Iran became the second focal point for spreading the Coronavirus in the world after China. In early March 2020 and after the COVID-19 epidemic in Iran, the Iranian minister of health stated that health centers throughout the country should be ready to provide services to the COVID-19 patients. Meanwhile, many public places and events, including schools, higher education institutions and universities, movie theaters, concerts and theater performances, national sports competitions and leagues in Tehran and other cities were gradually closed and government office hours were reduced in several provinces. According to official statistics published by WHO, Iran had the highest number of COVID-19 deaths after the USA, Brazil, India, Mexico, UK, Italy and France by the end of November 2020 (WHO, 2020).

Abstract

Keywords

1. Introduction

2. Derivation of the model

2.1. Original domain

2.2. Construction of the reference domain

2.3. Navier-Stokes equations

2.4. Asymptotic analysis

3. A new generalized lubrication model

4. A new thin fluid layer model

5. Conclusions

Appendix A. Change of variable

Appendix B. Coefficients definition

Appendix C. Derivation of equations to calculate

References

Abstract

In this paper we study the behavior of an incompressible viscous fluid moving between two very close surfaces also in motion. Using the asymptotic expansion method we formally justify two models, a lubrication model and a shallow water model, depending on the boundary conditions imposed. Finally, we discuss under what conditions each of the models would be applicable.

1. Introduction

The asymptotic analysis method is a mathematical tool that has been widely used to obtain and justify reduced models, both in solid and fluid mechanics, when one or two of the dimensions of the domain in which the model is formulated are much smaller than the others.

After the pioneering works of Friedrichs, Dressler and Goldenveizer (see [28] and [30]) the asymptotic development technique has been used successfully to justify beam, plate and shell theories (see, for example, [43], [16], [17], [15], [5], [54], and many others).

This same technique has also been used in fluid mechanics to justify various types of models, such as lubrication models, shallow water models, tube flow models, etc. (see, for example, [25], [24], [18], [3], [37], [55], [36], [31], [6], [2], [29], [26], [32], [33], [9], [23], [45], [46], [47], [48], [49], [50], [21], [22], [34], [35], [40], [41], [42], [10], [11], and many others).

In this work, we are interested in justifying, again using the asymptotic development technique, a lubrication model in a thin domain with curved mean surface. Following the steps of [3], but with a different starting point, we devote sections 2 and 3 to this justification. During the above process we have observed that, depending on the boundary conditions, other models can be obtained, which we show in section 4. In this section we derive a shallow water model changing the boundary conditions that we had imposed in section 3: instead of assuming that we know the velocities on the upper and lower boundaries of the domain, we assume that we know the tractions on these upper and lower boundaries.

Thus, two new models are presented in sections 3 and 4 of this article. These models can not be found in the literature, as far as we know. In addition, the method used to justify them allows us to answer the question of when each of them is applicable. In section 5 we discuss the models yielded, as well as the difference between one model and another depending on the boundary conditions, reaching the conclusion that the magnitude of the pressure differences at the lateral boundary of the domain is key when deciding which of the two models best describes the fluid behavior.

1- Introduction

2- Modelling and deformation mechanism

3- Characterization of mechanical properties

4- Conclusions

References

Abstract

Artificial auxetic materials with negative Poisson’s ratio enable distinctive elastic response in the direction orthogonal to the loaded direction, i.e. shrinking when compressed and expanding when stretched, compared to conventional materials. Such distinctive mechanical characteristic makes auxetic materials unique in practice. Current studies in this aspect focus mainly on the realization of beam-dominated microstructures such as re-entrant and chiral lattices and of cellular microstructures with orthogonal elliptical hole pattern. In this study, a novel two-dimensional auxetic microstructure is designed by introducing peanut-shaped holes in solid bulk matrix. Compared to the microstructure with elliptical hole pattern, the present design can produce slightly larger negative Poisson’s ratio and achieve significantly lower stress level. The samples consisting of a number of centimeter-scale unit cells with the peanut-shaped holes are fabricated efficiently via additive manufacturing technique. Experiment and finite element simulation of tensile test are carried out on the specific sample to demonstrate the auxetic effect of the present design and simultaneously verify the computational model. Finally, effects of some parameters on Poisson’s ratio, which may control the auxetic behavior of the present microstructure, are discussed for better understanding deformation mechanism of the proposed auxetic material.

Introduction

The concept of metamaterials with specially designed artificial microstructure made by additive manufacturing technology has recently received more and more attention because of its unconventional physical properties that are previously inaccessible in conventional materials [1–4]. With these man-made metamaterials, engineering materials with various properties have been developed including negative Poisson’s ratio (auxetic properties) [5–11], zero/negative thermal expansion materials [12,13], cloaking materials [14,15], phononic materials [16–19], twistable materials [20,21], and materials with vibration absorption [22,23]. Among these examples, mechanical consistence between longitudinal and orthogonal transverse elastic deformations has been achieved in two-dimensional (2D) and three-dimensional (3D) cellular metamaterials with well-designed cellular topologies [24], i.e. re-entrant shape (Fig. 1a), chiral shape (Fig. 1b) and elliptical hole shape (Fig. 1c) [5–11]. In the static case, such mechanical consistence is conflicted to the fact that a conventional elastic solid cannot expand or shrink laterally when stretched or compressed longitudinally (Fig. 1d) [6,25,26]. It has been revealed that the auxetic behavior of a material can be regarded as a consequence of rotation of elastic cell when it is subjected to a compressive or tensile load [5]. Therefore, auxetic structures with negative Poisson’s ratio are beneficial to enhancing shear rigidity, indentation resistance, toughness, energy dissipation ability and acoustic absorption ability in various applications [27–31]. However, the current re-entrant and chiral metamaterials [9,10] with negative Poisson’s ratio are generally designed to have beamdominated microstructures with bending dominated topologies. As a result, they are known to have low macroscopic stiffness, high flexibility, and high porosity. More seriously, joints with sharp corners in the re-entrant and chiral auxetic structures could lead to high stress concentration [32]. As an alternative to the beam-dominated auxetic microstructures, the orthogonal elliptical hole pattern in solid material was revealed to be beneficial in achieving controllable auxetic response. However, the elliptical hole usually leads to high stress level too. In order to achieve the balance of stiffness and flexibility, and meanwhile possess smoothed boundary and controllable porosity, in this study, a new two-dimensional elastic cellular metamaterial with a negative Poisson’s ratio is designed, fabricated, and characterized.

1- INTRODUCTION

2- DESCRIPTION OF MODEL

3- CONCLUSIONS

REFERENCES

Abstract

When a transformer works under the operating mode an emergency short circuit may occur. This is a dangerous emergency mode, in which the transformer currents are many times higher than their rated values. Such currents may lead to isolation overheating, reliability decreasing or even to transformer failing. In order to prevent damage during the critical mode and increase the transformer reliability by using a protector, it is necessary to determine the short circuit current value. The article considers the problem of optimal accurate design by the method of mathematical transformer modeling by determination of short circuit current value in emergency mode.

INTRODUCTION

Computer technology progress has contributed greatly to the mathematical modeling development in all areas of science. The mathematical modeling allows to research a model of interest with minimizing the utilized resources. Modeling are used when a real experiment performing is impossible, dangerous or difficult and expensive [1]. The main task in modeling is the adoption of an adequate model which properties will be sufficiently close to the real simulated properties.

Now the problem of electromechanical compatibility of electric power system facilities is particularly acute. In [8-11], the authors touched on this important topic for the nominal operation mode of an induction motor. Of particular interest are emergency modes, such as short circuits.

Introduction

Methods

Results

Discussion

Conclusions

Acknowledgement

References

Abstract

The basic reproduction number of an infectious agent is the average number of infections one case can generate over the course of the infectious period, in a naïve, uninfected population. It is well-known that the estimation of this number may vary due to several methodological issues, including different assumptions and choice of parameters, utilized models, used datasets and estimation period. With the spreading of the novel coronavirus (2019-nCoV) infection, the reproduction number has been found to vary, reflecting the dynamics of transmission of the coronavirus outbreak as well as the case reporting rate. Due to significant variations in the control strategies, which have been changing over time, and thanks to the introduction of detection technologies that have been rapidly improved, enabling to shorten the time from infection/symptoms onset to diagnosis, leading to faster confirmation of the new coronavirus cases, our previous estimations on the transmission risk of the 2019-nCoV need to be revised. By using time-dependent contact and diagnose rates, we refit our previously proposed dynamics transmission model to the data available until January 29th, 2020 and re-estimated the effective daily reproduction ratio that better quantifies the evolution of the interventions. We estimated when the effective daily reproduction ratio has fallen below 1 and when the epidemics will peak. Our updated findings suggest that the best measure is persistent and strict self-isolation. The epidemics will continue to grow, and can peak soon with the peak time depending highly on the public health interventions practically implemented.

Introduction

Coronaviruses are a group of enveloped viruses with a positive-sense, single-stranded RNA and viral particles resembling a crown e from which the name derives. They belong to the order of Nidovirales, family of Coronaviridae, and subfamily of Orthocoronavirinae (Carlos, Dela Cruz, Cao, Pasnick, & Jamil, 2020). They can affect mammals, including humans, causing generally mild infectious disorders, sporadically leading to severe outbreaks clusters, such as those generated by the “Severe Acute Respiratory Syndrome” (SARS) virus in 2003 in mainland China, and by the “Middle East Respiratory Syndrome” (MERS) virus in 2012 in the Kingdom of Saudi Arabia and in 2015 in South Korea (Gralinski & Menachery, 2020).

Currently, there exist no vaccines or anti-viral treatments officially approved for the prevention or management of the disease. Anti-retroviral drugs belonging to the class of protease inhibitors, including Lopinavir and Ritonavir, usually utilized for the treatment of HIV/AIDS patients, seem to exert anti-viral effects against coronaviruses. GS-734 (Remdesivir), a nucleotide analogue pro-drug, originally developed against the Ebola and the Marburg viruses, has been recently suggested to be effective also against coronaviruses. Other potential pharmaceuticals include nucleoside analogues, neuraminidase inhibitors, and RNA synthesis inhibitors. Also, Umifenovir (Abidol), used for treating severe influenza cases, anti-inflammatory drugs and EK1 peptide have been proposed as possible drugs against coronaviruses (Lu, 2020)