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The purpose of this paper is to present a new stereolithograhy (SL) process which could substantially improve the efficiency of building process through controlling the beam spot size and optimizing the process parameters dynamically.

In this process, the focus length of UV laser scanning head is changed by the dynamic focus mirror according to the building program. The beam spot in the resin surface varies with the focus length, and the laser power is adjusted accordingly to the changing spot in building process.

The study finds that the efficiency of building process has a close relationship with the beam spot size. The experiments showed that the larger the laser spot diameter was, the less the depth and the larger the width of the cured lines would be. Then the building program would adjust the UV laser power to compensate for the decreased curing depth caused by the enlarged beam spot. At last, the spot compensation was optimized for the new method that could keep the same building accuracy with the traditional SL process.

The variable beam spot process could improve the building efficiency by 30 percent without increasing the cost of system, and keep the same accuracy compared with the conventional SL process.

This chapter will look at the development, types and effectiveness of new forms of advertising in television (NFAs) and report on the current state of research in the field.

The most relevant contributions from the literature describing the practice and assessing the effectiveness of NFAs are presented and reviewed.

NFAs have emerged in response to the decreased effectiveness of conventional television advertising (spots) due to audience fragmentation, zapping, saturation and increased competition. Currently, NFAs are widely used around the world. Although the available empirical evidence indicates that NFAs are more effective than traditional spots in terms of recall, this chapter points to a need for better scientific understanding of key aspects of these new formats. Given the important role that NFAs play in how today’s television advertising market is managed, further research is needed on their effectiveness.

The literature on the practice and analysis of the effectiveness of NFAs is unfocused and varied, making it difficult to adequately determine whether the growing use of these formats can be justified on the grounds of proven arguments regarding the qualities that set them apart from traditional spots. In this regard, the summary provided in this chapter of both the state of knowledge about different types of new advertising formats on TV and their effectiveness is an important reflection of the state of the art in research on these formats.

This chapter aims to find an optimal way to hedge foreign exchange exposures on three main currency pairs being the EURUSD, EURGBP and EURJPY. Furthermore, it analyses the risk level of each portfolio together with its kurtosis level. This chapter also looks into the relationship between the EURUSD portfolios and the VIX level.

This study is based on a back-testing analysis over a period of seven years starting in January 2007 and ending in December 2014. Two main Foreign Exchange Premium-Free strategies were structured using the Bloomberg Terminal. These were the ‘At-Expiry Forward Extra’ and the ‘Window Forward Extra’. Portfolios were created using FX options strategies, FX spot and FX forwards. The EURUSD portfolios were also analysed and compared with the VIX level in order to see whether volatility has a direct effect on the outcome of the strategies. The statistical significance of the difference between returns of portfolios was analysed using a paired sample t-test. Finally, the histogram and distribution curve of each portfolio were created and plotted in order to provide a more visual analysis of returns.

It was found that the optimal strategies in all cases were the FX option strategies. The portfolios’ risk was analysed and indicated that optimal portfolios do not necessarily derive the lowest risk. It was also found that with a high VIX level, the forward contract was the most beneficial whilst the option strategy benefited from a low VIX level. When testing for statistical significance between returns of different portfolios, in most cases, the difference in returns between portfolios resulted to be statistically insignificant. Although some similarities were noticed in distribution curves, these differed from the normal distribution. When analysing the kurtosis levels, it is found that such levels differed from that of a normal distribution which has a kurtosis level of 3. Interpretation of such histograms, distribution curves and the kurtosis analysis was explained.

This chapter investigates the impact change of the composition of market agents on the timing of the arrival of information in Bursa Malaysia. The price discovery role of futures trading on the spot market is examined through three distinct sub-periods: pre-crisis, crisis and after capital controls. For this purpose, the Johansen Cointegration (1988, 1991) and VECM and Granger causality are used. The analysis shows that there is no significant long-run relationship. As for short-run, the results show futures lead spot. However, futures’ lead is shorter in pre-crisis and crisis periods where foreign institutional investors dominate. This study deduces that the significant change in the composition of market agents could contribute to the variation of lead–lag relationship.

This paper aims to investigate whether the establishment of commodity futures can effectively hedge systemic risk in the spot network, given the context of financialization in the commodity futures market.

Utilizing industry association data from the Chinese commodity market, the authors identify systemically important commodities based on their importance in the production process using multiple graph analysis methods. Then the authors analyze the effect of listing futures on the systemic risk in the spot market with the staggered difference-in-differences (DID) method.

The findings suggest that futures contracts help reduce systemic risks in the underlying spot network. Systemic risk for a commodity will decrease by approximately 5.7% with the introduction of each corresponding futures contract, since the hedging function of futures reduces the timing behavior of firms in the spot market. Establishing futures contracts for upstream commodities lowers systemic risks for downstream commodities. Energy commodities, such as crude oil and coal, have higher systemic importance, with the energy sector dominating systemic importance, while some chemical commodities also have considerable systemic importance. Meanwhile, the shortest transmission path for risk propagation is composed of the energy industry, chemical industry, agriculture/metal industry and final products.

The paper provides the following policy insights: (1) The role of futures contracts is still positive, and future contracts should be established upstream and at more systemically important nodes in the spot production chain. (2) More attention should be paid to the chemical industry chain, as some chemical commodities are systemically important but do not have corresponding futures contracts. (3) The risk source of the commodity spot market network is the energy industry, and therefore, energy-related commodities should continue to be closely monitored.

This study empirically explores the intraday price discovery mechanism and volatility transmission effect between the dual-listed Indian Nifty index futures traded simultaneously on the onshore Indian exchange, National Stock Exchange (NSE) and offshore Singapore Exchange (SGX) and its spot market by using high-frequency data.

This study applies the vector error correction model to analyze the lead-lag relationship in price discovery among three markets. The contributions of individual markets in assimilating new information into prices are measured using various measures, Hasbrouck's (1995) information share, Lien and Shrestha's (2009) modified information share and Gonzalo and Granger's (1995) component share. Additionally, the Granger causality test is conducted to determine the causal relationship. Lastly, the BEKK-GARCH specification is employed to analyze the volatility transmission.

This study provides robust evidence that Nifty futures lead the spot in price discovery. The offshore SGX Nifty futures consistently ranked first in contributing to price discovery, followed by onshore NSE Nifty futures and finally by the spot. Empirical results also show unidirectional causality and volatility transmission from Nifty futures to spot, as well as bidirectional causal relationship and volatility spillovers between NSE and SGX Nifty futures. These novel findings provide fresh insights into the informational efficiency of the dual-listed Indian Nifty futures, which is distinct from previous literature.

These findings can potentially help market participants, policymakers, stock exchanges and regulators.

Unlike previous studies in this area, this is the first study that empirically examines the intraday price discovery mechanism and volatility spillover between the dual-listed futures markets and its spot market using 5-min overlapping price data and trivariate econometric models.

This paper aims to examine the cross-quantile correlation and causality-in-quantiles between green investments and energy commodities during the outbreak of COVID-19. To be specific, the authors aim to address the following questions: Is there any distributional predictability among green bonds and energy commodities during COVID-19? Is there exist any directional predictability between green investments and energy commodities during the global pandemic? Can green bonds hedge the risk of energy commodities during a period of the financial crisis.

The authors use the nonparametric causality in quantile and cross-quantilogram (CQ) correlation approaches as the estimation techniques to investigate the distributional and directional predictability between green investments and energy commodities respectively using daily spot prices from January 1, 2020, to March 26, 2021. The study uses daily closing price indices S&P Green Bond Index as a representative of the green bond market. In the case of energy commodities, the authors use S&P GSCI Natural Gas Spot, S&P GSCI Biofuel Spot, S&P GSCI Unleaded Gasoline Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI, OPEC Oil Basket Price, Crude Oil Oman, Crude Oil Dubai Cash, S&P GSCI Heating Oil Spot, S&P Global Clean Energy, US Gulf Coast Kerosene and Los Angeles Low Sulfur CARB Diesel Spot.

From the CQ correlation results, there exists an overall negative directional predictability between green bonds and natural gas. The authors find that the directional predictability between green bonds and S&P GSCI Biofuel Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI Spot, OPEC Oil Basket Spot, Crude Oil Oman Spot, Crude Oil Dubai Cash Spot, S&P GSCI Heating Oil Spot, US Gulf Coast Kerosene-Type Jet Fuel Spot Price and Los Angeles Low Sulfur CARB Diesel Spot Price is negative during normal market conditions and positive during extreme market conditions. Results from the non-parametric causality in the quantile approach show strong evidence of asymmetry in causality across quantiles and strong variations across markets.

The quantile time-varying dependence and predictability results documented in this paper can help market participants with different investment targets and horizons adopt better hedging strategies and portfolio diversification to aid optimal policy measures during volatile market conditions.

The outcome of this study will promote awareness regarding the environment and also increase investor’s participation in the green bond market. Further, it allows corporate institutions to fulfill their social commitment through the issuance of green bonds.

This paper differs from these previous studies in several aspects. First, the authors have included a wide range of energy commodities, comprising three green bond indices and 14 energy commodity indices. Second, the authors have explored the dependency between the two markets, particularly during COVID-19 pandemic. Third, the authors have applied CQ and causality-in-quantile methods on the given data set. Since the market of green and sustainable finance is growing drastically and the world is transmitting toward environment-friendly practices, it is essential and vital to understand the impact of green bonds on other financial markets. In this regard, the study contributes to the literature by documenting an in-depth connectedness between green bonds and crude oil, natural gas, petrol, kerosene, diesel, crude, heating oil, biofuels and other energy commodities.

The purpose is to provide decision support for tourists recommending scenic spots and corresponding suggestions for the management of scenic spots.

Based on the Baidu index data generated, this paper analyzes the temporal and spatial characteristics of network attention of 5A scenic spots in Sichuan Province. The online comment data are used to build the assessment model of scenic spots based on network attention, and the comment information of tourists is mined and analyzed through statistical analysis. At the same time, the key attributes of scenic spots from the perspective of network attention are evaluated and analyzed by using the probabilistic linguistic term set. Finally, this paper further constructs a recommendation model based on the key attribute set of scenic spots.

This paper uses different types of tourism network information, integrates multi-types of data and methods, fully excavates the value information of tourism network information, constructs the research framework of “scenic spot assessment + scenic spot recommendation” from the perspective of network attention, analyzes the network attention characteristics of scenic spots, evaluates the performance of scenic spots, and implements scenic spot recommendation.

This paper integrates multi-source data and multidisciplinary theoretical methods to form a scenic spot research framework of “assessment + recommendation” from the perspective of network attention.

The purpose of this study is to explore the antecedents and their impacts on behaviors toward agro-tourism by proposing a theory of green consumption behavior.

Based on a review of the literatures and collection of 471 usable responses, the study is conducted through partial least squares structural equation modeling method using SmartPLS 3.3.3.

Findings of the study have revealed that carbon mitigation attitude, energy saving norms, perceived hygiene value and agro-tourist spot visit intentions significantly influence agro-tourist spot visit behavior. Furthermore, the study has suggested that agro-tourist spot visit intentions partially mediate the strength of the relationship between carbon mitigation attitude, energy saving norms, perceived hygiene value and agro-tourist spot visit behavior.

The study findings may be useful to encouraging agro-tourism managers of understanding antecedents of customer’s behavior and formulating business strategies influencing behaviors toward agro-tourism, coping with competitive business environment and environmental sustainability.

The study has presented a unique case discovering what antecedents are influencing customers’ green consumption behavior (agro-tourist spot visit behaviors) and how that behavior contributes to sustainable communities and cities development. Furthermore, the study has provided important insights for industry professionals by integrating carbon mitigation attitude, energy saving norms, perceived hygiene value and agro-tourist spot visit intentions in examining agro-tourist spot visit behavior.