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The purpose of this paper is to gain an in-depth understanding into the research progress, hot spots and future trends in smart gripping technology in the field of apparel smart manufacturing.

This work scrutinised the current research status of the five automatic grasping methods for garment fabrics including the pneumatic suction grasping, the electrostatic grasping, the intrusive grasping and the dexterous grasping. Specifically, the principles, characteristics, main devices and the impact on garment production were discussed.

In particular, soft finger of the dexterous grasping method has good flexibility and adaptability in the process of fabric grasping, which provides a new solution for garment production automation. Up to now, the reviewed method in general exhibit good grasping speed, high grasping stability and flat grasping process. However, in the face of complex fabric materials which are thin and flexible and do not return their original shapes when deformed in practical applications, the gripper for automatic fabric grasping need new technological breakthroughs in the positioning accuracy, grab efficiency and flexible grasping.

The outcomes offered an overview of the research status and future trends of the automatic grasping methods for garment fabrics in the field of apparel intelligent manufacturing. It could not only provide scholars with convenience in identifying research hot spots and building potential cooperation in the follow-up research but also assist beginners in searching core scholars and literature of great significance.

Mucormycosis has evolved as a post-COVID-19 complication globally, especially in India. The research on fungus has been very primitive, and many scientific publications have been discovered. The current COVID-19 pandemic needs further investigation into this unusual fungal infection. This review study aims to provide a pen-picture to researchers, science policymakers and scientists about different bibliometric indicators related to the research literature on mucormycosis.

The quantitative research was conducted using the established procedure of bibliometric investigation on data collected from Scopus from 2011 to 2020 using a validated search query. The search query consisted of keywords “Mucormycosis” or “Mucormycoses” or “Mucormycose” or “Mucorales Infection” or “Mucorales Infections” or “Black Fungus Infection” or “Black Fungus Infections” or “Zygomycosis” in the “Title-Keyword-Abstract” search option for data extraction. The analysis of data is performed using MS-Excel. Mapping was done with state-of-the-art visualization tools Biblioshiny and VOSviewer, using bibliometric indicators as units of analysis.

The analysis reveals that the first publication on this topic was reported from 1923 onwards. In total, 9,423 authors contributed 1,896 papers with 11,437 collaborated authors, documents per author are 0.201, authors per document are 4.97 and co-authors per document are 6.03. Total records were published in 779 journals in the English language from 75 countries globally. Mucormycosis literature is mostly open access, with 1,210 publications available via different open access routes. The highest number of articles (204) published in the journal “Mycoses” with 1,333 authors received 4,875 cited references, and the h-index has 24. The growth of publications is exponential, as depicted by the Price Law. The USA has recorded a maximum number of publications at both country and institutional levels compared to the other nations. There has been extensive research on mucormycosis before the outbreak as a post-COVID complication, as indicated by the highest number of publications in 2019.

The research hot spots have altered from “Mucormycosis,” “fungi,” “Zygomycosis” and “Drug efficacy”, “Drug Safety” to “Microbiology,” “Pathology,” “nucleotide sequence,” “surgical debridement” which indicates that potential area of research in the near future will be concerned with more extensive research in mucormycosis to develop standard treatment procedures to fight this infection. The quantity of scientific publications has also increased over time. The research and health community are called upon to join forces to activate existing knowledge, generate new insights and develop decision-supporting tools for health authorities in different nations to leverage vaccination in its transformational role toward successfully attaining nil cases of COVID-19.

The analysis of collaboration, findings, the research networks and visualization makes this study novel and separates from traditional metrics analysis. To the best of the authors’ knowledge, this work is original, and no similar studies have been found with the objectives included here.

The purpose of this paper is to investigate for symmetries – in sign and size – between spot and futures prices in the markets of energy commodities.

The aforementioned objective is pursued using daily observations of spot and futures prices for the commodities of crude oil, Brent, heating oil, gasoline and natural gas, along with local nonlinear regression.

Symmetry in sign and size cannot be rejected. This means that, shocks of the same absolute magnitude, but of different sign, are transmitted from futures prices to spot prices with the same intensity. In addition, larger absolute value price shocks in the futures are transmitted to the spot markets with the same intensity compared with smaller ones. The findings of symmetry in the comovements among prices reveal a lack of those commodities on diversifying the investors’ investment risk.

To the best of the authors’ knowledge, this is the first study to use local nonlinear regression to test for sign and size symmetry between futures and spot prices in the energy commodities markets.

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.

Integrating artificial intelligence and quantitative investment has given birth to various agricultural futures price prediction models suitable for nonlinear and non-stationary data. However, traditional models have limitations in testing the spatial transmission relationship in time series, and the actual prediction effect is restricted by the inability to obtain the prices of other variable factors in the future.

To explore the impact of spatiotemporal factors on agricultural prices and achieve the best prediction effect, the authors innovatively propose a price prediction method for China's soybean and palm oil futures prices. First, an improved Granger Causality Test was adopted to explore the spatial transmission relationship in the data; second, the Seasonal and Trend decomposition using Loess model (STL) was employed to decompose the price; then, the Apriori algorithm was applied to test the time spillover effect between data, and CRITIC was used to extract essential features; finally, the N-Beats model was selected as the prediction model for futures prices.

Using the Apriori and STL algorithms, the authors found a spillover effect in agricultural prices, and past trends and seasonal data will impact future prices. Using the improved Granger causality test method to analyze the unidirectional causality relationship between the prices, the authors obtained a spatial effect among the agricultural product prices. By comparison, the N-Beats model based on the spatiotemporal factors shows excellent prediction effects on different prices.

This paper addressed the problem that traditional models can only predict the current prices of different agricultural products on the same date, and traditional spatial models cannot test the characteristics of time series. This result is beneficial to the sustainable development of agriculture and provides necessary numerical and technical support to ensure national agricultural security.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

Foresight J's journey started in 1999, and in 2022, it marked the conclusion of its 24 years of publication. This paper aims to provide an overall overview of important research trends published in Foresight J between 1999 and 2022 by conducting a quantitative analysis of the journal’s literature. The overarching goal is to provide valuable insights into the dynamics of scholarly communication, aiding researchers, institutions and policymakers in assessing the significance and influence of academic work, guiding future research directions and academic evaluation.

The two bibliometrics methodologies that make up the methodology of this article are scientific mapping and performance analysis. Authors have explained the development and composition of the Foresight J using these methods. The SCOPUS database is being used in current research to analyse several dimensions, such as the evolution of publications by year, the most cited papers, core authors and researchers, leading countries and prolific institutions. Moreover, the conceptual structure, scope, burst detection and co-occurrence analysis of the journal are mapped using network visualization software such as VOSviewer, CiteSpace and RStudio.

With a strong track record of output over the years, Foresight J has continued to develop in terms of publications. It is determined that “Saritas” is the author with the greatest overall impact. However, according to SCOPUS bibliometric data, “Blackman” and “Richardson” are the authors with the greatest relevance in terms of the quantity of articles. In addition, it becomes apparent that the USA, Australia and the UK are very productive nations in terms of publications. The most popular fields of the journal have always been forecasting, foresight, scenario planning, strategic planning, decision-making, technology and sustainable development. These are also the author keywords that appear the most frequently. In contrast, new study themes in the Foresight J include digital technologies, innovation, sustainability, blockchain, artificial intelligence and sustainability.

Several noteworthy research implications are provided by the bibliometric study of Foresight J. “Saritas” is the author with the most overall impact, indicating that the precise contributions and influence of this researcher in the fields of forecasting, foresight and related fields. Given that “Blackman” and “Richardson” are well-known writers, it is also critical to examine the scope and complexity of their contributions to potentially identify recurring themes or patterns in their writing. The geographic productivity results, which show that the USA, Australia and the UK are the top three countries for Foresight J publications, may encourage more research into regional differences, patterns of collaboration and the worldwide distribution of research endeavours in the context of forecasting and foresight. Popular fields including scenario planning, forecasting, foresight and sustainable development are consistent, indicating persistent research interests. Examining the causes of these subjects’ ongoing relevance can reveal information about the consistency and development of scholarly interests over time.

Foresight J’s bibliometric analysis has real-world applications for many stakeholders. It helps editors and publishers make strategic decisions about outreach and content by providing insights regarding the journal’s influence. Assessing organizational and author productivity helps institutions allocate resources more effectively. Policymakers acquire an instrument to evaluate research patterns and distribute funds efficiently. In general, bibliometric study of a journal helps decisionmakers in academic publishing make well-informed choices that maximize the potential of options for authors, editors, institutions and policymakers.

The societal ramifications of bibliometrically analysing Foresight J from 1999 and 2022 are substantial. This analysis highlights, over the past 24 years, research trends, technological developments and societal priorities have changed by methodically looking through the journal’s articles. Gaining knowledge about the academic environment covered by the journal can help raise public awareness of important topics and promote critical thinking. In addition, the analysis can support evidence-based decision-making by alerting decision makers to the influential research that was published in Foresight J. This could have an impact on the course of policies pertaining to innovation, technology and societal development.

This study presents a first comprehensive article that provides a general overview of the main trends and patterns of the research over the Foresight J’s history since its inception. Also, the paper will help the scientific community to know the value and impact of Foresight J.

The purpose of this study is to unfold the existing information channel in the higher moments of currency futures for different time horizons.

The authors use a quasi-Bayesian local likelihood approach within a time-varying parameter vector autoregression (TVP-VAR) framework and a dynamic connectedness measure to study the volatility, skewness and kurtosis of most traded currency futures.

The authors’ results suggest a time-varying presence of dynamic connectedness within higher moments of currency futures. Most spillovers pertain to shorter time horizons. The authors find that in net terms, CHF, EUR and JPY are the most important contributors to the system, while the authors emphasize that the role of being a transmitter or a receiver varies for pairwise interactions and time windows.

To the best of the authors’ knowledge, this is the first study that looks upon the connectivity vis-á-vis uncertainty, asymmetry and fat tails in currency futures within a dynamic Bayesian paradigm. The authors extend the current literature by proposing new insights into asset distributions.

After completion of the case study, the students will be able to understand the different risks associated with a business, focusing on price risk and the importance of price risk management in business; understand and evaluate the products available for hedging price risk through exchange-traded derivatives in the Indian scenario; and understand and evaluate the different strategies for price risk management through exchange-traded derivatives in the Indian scenario.

The case study pertains to a small business, M/s Sethi Jewellers. The enterprise is being run by Shri Charan Jeet Sethi and his son Tejinder Sethi. The business is located in Jain Bazar, Jammu, UT, in Northern India. The business was started in 1972 by Charan Jeet’s father. They deal in a wide range of jewelry products and are well-established jewelers known for selling quality ornaments. Tejinder (MBA in marketing) was instrumental in revamping his business recently. Under his leadership, the business has experienced rapid transformation. The business has grown from a one-room shop fully managed by Tejinder’s grandfather to a multistory showroom with several artisans, sales staff and security persons. Through his e-store, Tejinder has a bulk order from a client where the client requires him to accept the order with a small token at the current price and deliver the final product three months from now. Tejinder is in a dilemma about accepting or rejecting the large order. Second, if he accepts, should he buy the entire gold now or wait to buy it later at a lower price? He is also considering hedging the price risk through exchange-traded derivatives. However, he is not entirely sure, as he has a few apprehensions regarding the same, and he is also not fully aware of the process and the instruments he has to use for hedging the price risk on the exchange.

The case study is aimed to cater to undergraduate, postgraduate and MBA students in the field of finance. This case study can be used for students interested in commodity derivatives, risk management and market microstructure.

Teaching notes are available for educators only.

CSS 1: Accounting and finance.

This study aims to investigate the role of international price volatility and inventories on domestic market price dynamics in the case of agricultural commodities.

A structural model is employed to uncover relationships among commodity price, price volatility, inventories and convenience yield. Monthly producer price data along with annual data on trade, consumption, inventories and tariffs for 71 countries and 13 commodities covering 2010–2019 are assembled to estimate the model. With a first-stage Least Absolute Shrinkage and Selection Operator (LASSO) estimator to identify the best instrument set, a nonlinear approach is used to estimate the model.

Results show that international market information plays a critical role in domestic market price dynamics. International price volatility has a stronger effect on domestic prices than that of international inventories.

Current upheaval in commodity markets requires an understanding of how prices move together and inventories affect that movement. A country's internal price is not independent of the effects of global market events.

Although hypotheses exist that global market information (volatility and inventories) helps countries manage domestic commodity prices, there have been limited studies on this relationship, especially with a structured model and cross-country data.