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Oil market VAR models have become the standard tool for understanding the evolution of the real price of oil and its impact on the macro economy. As this literature has expanded at a rapid pace, it has become increasingly difficult for mainstream economists to understand the differences between alternative oil market models, let alone the basis for the sometimes divergent conclusions reached in the literature. The purpose of this survey is to provide a guide to this literature. Our focus is on the econometric foundations of the analysis of oil market models with special attention to the identifying assumptions and methods of inference.

Segmented assimilation theory predicts that contemporary non-white groups follow three patterns of assimilation: mainstream, downward, or delayed. Yet, the homogenous treatment and primacy of ethnicity resigns all group members to a similar fate. Whereas few studies of ethnic incorporation consider both the classed and gendered nature of the labor market, this study investigates the extent to which intersectional group differences within the highly stratified American economy shape segmented assimilation trajectories.

This study introduces an intersectional approach to segmented assimilation theory. Using the 2000 census, this study examines how within group differences in skill and gender condition the hourly earnings, joblessness and self-employment participation outcomes of five ethnic minority groups from the first to the second generation, compared against US-born, non-Hispanic whites.

Findings generally support the mainstream assimilation hypothesis for all groups; a downward assimilation trajectory among Chinese men only; and a delayed assimilation trajectory for low-skilled Filipinas and high-skilled Cuban men and women. This study reveals that intra-group differences in skill and gender shape divergent segmented assimilation trajectories among members of the same ethnic group.

This study challenges the emphasis on and primacy of ethnicity in predicting segmented assimilation in favor of an intersectional approach that considers how multiple, interdependent, and intersecting dimensions of identity and not only ethnicity shape the process of economic incorporation among ethnic groups.

Technology-facilitated abuse, so-called “tech abuse,” through phones, trackers, and other emerging innovations, has a substantial impact on the nature of intimate partner violence (IPV). The current chapter examines the risks and harms posed to IPV victims/survivors from the burgeoning Internet of Things (IoT) environment. IoT systems are understood as “smart” devices such as conventional household appliances that are connected to the internet. Interdependencies between different products together with the devices' enhanced functionalities offer opportunities for coercion and control. Across the chapter, we use the example of IoT to showcase how and why tech abuse is a socio-technological issue and requires not only human-centered (i.e., societal) but also cybersecurity (i.e., technical) responses. We apply the method of “threat modeling,” which is a process used to investigate potential cybersecurity attacks, to shift the conventional technical focus from the risks to systems toward risks to people. Through the analysis of a smart lock, we highlight insufficiently designed IoT privacy and security features and uncover how seemingly neutral design decisions can constrain, shape, and facilitate coercive and controlling behaviors.

Contractor selection is critical in green buildings (GBs) since the preferred contractor has the responsibility to achieve construction sustainability as well as relationship sustainability. The developer satisfaction reflecting requirements can boost the cooperative relationship among stakeholders and act as an evaluation scale for the success of GB projects, which needs to be emphasized in the selection process but little involved in the existing research. This study explores improving GB contractor (GBC) selection by integrating developer satisfaction into selection procedures.

A systematic framework of GBC selection including twenty-five criteria from literature review and experts survey is firstly constructed. Both tactical and strategic criteria are further classified into Kano categories (must-be, one-dimensional, and attractive categories) using the fuzzy Kano model (FKM), and weighted by the developer satisfaction index. The model proposed by this study combining FKM and TOPSIS divides the selection process into the filtration phase and selection phase by Kano categories. The proposed model is finally verified through performance comparison among multiple methods in a case.

Selection criteria are measured linearly and nonlinearly, showing criteria having nonlinear satisfaction change accounts for two-thirds of all. Criteria at tactical level tend to be must-be or one-dimensional categories for the developer, and most strategic criteria are classed as the attractive category, indicating that adding strategic criteria is necessary for long-term cooperation. The proposed model, using developer satisfaction to improve the selection process, ensures the selected GBC to be the most satisfactory with requirements of the developer and makes the performance of GBCs easily distinguishable.

This study contributes to the existing body of knowledge for promoting relationship sustainability by supplementing an integrated model with emphasis on developer satisfaction in GBC selection, so as to establish a good initial foundation due to the match between performances of GBCs and needs of developers. It not only helps maximize developer satisfaction in GBC selection by applying satisfaction to pre-construction management, but also instructs GBCs to prioritize performance improvements. The framework is also conducive for developers to classify selection criteria and select other participants (like green suppliers) from the satisfaction perspective in GBs.

In February 2022, the Finance Minister of India in the Union Budget 2022 announced that the government proposed to issue sovereign green bonds to mobilize assets for green infrastructure. These bonds are a sort of fixed-income instrument where the money raised from investors is used exclusively to finance projects having a positive environmental impact. The announcement was in sync with India's commitment to achieving net-zero carbon emissions by 2070. However, many issues come with it such as the complexity of green data, and the lack of uniform standards to measure the impact of green investments leading to allegations of “greenwashing,” among others. Its solution lies in the digital tokenization of green bonds using blockchain technology. Foreign investors scout for green bonds issued by growing markets like India, which have attractive valuations and good growth prospects. Marketing and issuing green bonds properly would have a far greater potential to bring investment to the security markets and the much-needed advancement in the sustainable sector. It is much more likely that green bonds will bring investment to the security markets and much-needed advancement to the sustainable sector if they are marketed and issued through digital tokenization. Financial regulators and policymakers can create a global framework for the application of blockchain technology in sustainable finance. This might entail tokenizing eco-friendly assets, issuing eco-friendly bonds, trading renewable energy and 2-2 carbon credits in a decentralized ecosystem, and decentralizing crowdfunding for eco-friendly enterprises.

This chapter seeks to demonstrate how blockchain technology can help issue green bonds and increase the overall efficiency of green finance in the economy. It also aims to scrutinize how such digital tokenization of green bonds would affect the security market and increase the standards of environmental, social, and governance (ESG) worldwide. While discussing how this process is shaping up and impacting the economies of various countries, it also seeks to provide suggestions to be taken into consideration while adopting the digital tokenization of green bonds.

This study aims to explore the feasibility of adding Si₃N₄ nanoparticles to Sn58Bi and provides a theoretical basis for designing and applying new lead-free solder materials for the electronic packaging industry.

In this paper, Sn58Bi-xSi₃N₄ (x = 0, 0.2, 0.4, 0.6, 0.8, 1.0 Wt.%) was prepared for bonding Cu substrate, and the changes in thermal properties, wettability, microstructure, interfacial intermetallic compound and mechanical properties of the composite solder were systematically studied.

The experiment results demonstrate that including Si₃N₄ nanoparticles does not significantly impact the melting point of Sn58Bi solder, and the undercooling degree of solder only fluctuates slightly. The molten solder spreading area reached a maximum of 96.17 mm², raised by 19.41% relative to those without Si₃N₄, and the wetting angle was the smallest at 0.6 Wt.% of Si₃N₄, with a minimum value of 8.35°. When the Si₃N₄ nanoparticles reach 0.6 Wt.%, the solder joint microstructure is significantly refined. Appropriately adding Si₃N₄ nanoparticles will slightly increase the solder alloy hardness. When the concentration of Si₃N₄ reaches 0.6 Wt.%, the joints shear strength reached 45.30 MPa, representing a 49.85% increase compared to those without additives. A thorough examination indicates that legitimately incorporating Si₃N₄ nanoparticles into Sn58Bi solder can enhance its synthetical performance, and 0.6 Wt.% is the best addition amount in our test setting.

In this paper, Si₃N₄ nanoparticles were incorporated into Sn58Bi solder, and the effects of different contents of Si₃N₄ nanoparticles on Sn58Bi solder were investigated from various aspects.

Convection is one of the main heat transfer mechanisms in both high to low temperature media. The accurate convection heat transfer coefficient (HTC) value is required for exact prediction of heat transfer. As convection HTC depends on many variables including fluid properties, flow hydrodynamics, surface geometry and operating and boundary conditions, among others, its accurate estimation is often too hard. Homogeneous dispersion of nanoparticles in a base fluid (nanofluids) that found high popularities during the past two decades has also increased the level of this complexity. Therefore, this study aims to show the application of least-square support vector machines (LS-SVM) for prediction of convection heat transfer coefficient of nanofluids through circular pipes as an accurate alternative way and draw a clear path for future researches in the field.

The proposed LS-SVM model is developed using a relatively huge databank, including 253 experimental data sets. The predictive performance of this intelligent approach is validated using both experimental data and empirical correlations in the literature.

The results show that the LS-SVM paradigm with a radial basis kernel outperforms all other considered approaches. It presents an absolute average relative deviation of 2.47% and the regression coefficient (R²) of 0.99935 for the estimation of the experimental databank. The proposed smart paradigm expedites the procedure of estimation of convection HTC of nanofluid flow inside circular pipes.

Therefore, the focus of the current study is concentrated on the estimation of convection HTC of nanofluid flow through circular pipes using the LS-SVM. Indeed, this estimation is done using operating conditions and some simply measured characteristics of nanoparticle, base fluid and nanofluid.

This paper aims to investigate the influence of the regional business environment on local firm innovation, considering various dimensions such as administrative, financial and legal environments.

Multiple regression analysis is employed to analyze archival data for firms listed on Chinese stock markets.

We find that the optimizations of the administrative and financial environments positively affect firm innovation, whereas the legal environment does not exert a similar impact. Our analysis also reveals that the business environment’s optimization significantly influences innovation in firms that are small, non-state-owned and operating in high-tech industries. Furthermore, the business environment acts as a moderating variable in the relationship between firm innovation and firm value.

This study contributes to a more comprehensive understanding of institutional-level determinants of firm innovation, highlighting the nuances of the legal environment and the importance of context-specific analysis, especially in emerging markets like China.

Developing countries can significantly enhance firm innovation by improving the business environment, including the optimization of administrative and financial systems, reducing transaction costs and ensuring capital supply. Tailored legal frameworks and alternative institutional strategies may also be explored.

This study explicitly emphasizes the governmental role in promoting firm innovation, shedding light on policy formulation and strategic alignment with local administrative policies.

To the best of our knowledge, this paper is the first to explore the relationship between the business environment and firm innovation using World Bank indicators in an emerging market context, providing novel insights into the unique dynamics of legal, financial and administrative sub-environments.

This study aims to examine the effects of green construction site practices on the economic performance of projects executed by “class A” contractors in Nigeria. The effects of stormwater management, materials management waste management and energy management on economic performance were examined.

Data was collected through a survey of class A contractors in Nigeria, and 168 usable responses were received. The partial least-squares structural equation modelling technique was used to analyse the collected data.

It was observed that all the green construction practices studied had significant effects on the economic performance of construction projects with the greatest effect from energy management.

The results imply that green construction practices generally lead to economic performance. The study has both theoretical and practical implications, as it helps both policymakers and contractors better understand the relationships between these variables of green construction practices and economic performance to better plan for their projects and for the government to formulate appropriate policies.

The study’s findings makes significant contributions to the ongoing discussion on the relationship between green construction practices and economic performance. The relationship established between stormwater management, waste management, materials management, energy management and economic performance in this study is different when compared with those in other similar studies.

The application of ultra-high performance concrete (UHPC) in anchorage zones can significantly improve the local compression performance of structures. However, the high cost and complex preparation of UHPC make UHPC difficult to be widely used in practice. This study proposes a method to strengthen the local compression zone of structures built by normal strength concrete (NSC) by incorporating UHPC cores.

In this study, a Finite Element Model (FEM) of local compression specimens was established by ABAQUS, and the accuracy of FEM was verified by comparing the FEM calculation results with experimental results. The verified FEM was adapted to the research on the influences of affecting factors on local compression performance of structures, including NSC strength, UHPC strength, spiral steel bar strength, and UHPC core diameter.

The results show that the peak load of the strengthened specimen SC1-U + N increases by 210.2% compared to that of the SC1-NSC. Furthermore, compared to SC1, the strengthened specimen SC1-U + N can save 64.7% amount of UHPC while the peak load decreases by only 34.4%. The peak load of the strengthened specimens increases with the axial compressive strength and the diameter of UHPC cores increasing, crack load increases with increasing the compressive strength of NSC, the spiral steel bar with high strength can prevent the sharp drop of load-deflection curve and the residual bearing capacity increases accordingly. All findings indicate that increasing the diameter of UHPC cores can improve the overall performance of the specimens. Under loading, all specimens fail by following a similar pattern. The effectiveness of this new strengthen method is also verified by FEM analytical calculations.

Based on the experimental study, this study extrapolates the influence of different parameters on the local bearing capacity of the strengthened specimens by finite element simulation. This method not only ensures the accuracy of bearing capacity assessment, but also does not require many samples, which ensures the economy of the reinforcement process. The research results provide a reference for the reinforcement design of anchorage zone.