Future Market Research Articles

Optimized psilocybin production in tryptophan catabolism-repressed fungi.

The high therapeutic potential of psilocybin, a prodrug of the psychotropic psilocin, holds great promise for the treatment of mental disorders such as therapy-refractory depression, alcohol use disorder and anorexia nervosa. Psilocybin has been designated a 'Breakthrough Therapy' by the US Food and Drug Administration, and therefore a sustainable production process must be established to meet future market demands. Here, we present the development of an invivo psilocybin production chassis based on repression of l-tryptophan catabolism. We demonstrate the proof of principle in Saccharomyces cerevisiae expressing the psilocybin biosynthetic genes. Deletion of the two aminotransferase genes ARO8/9 and the indoleamine 2,3-dioxygenase gene BNA2 yielded a fivefold increase of psilocybin titre. We transferred this knowledge to the filamentous fungus Aspergillus nidulans and identified functional ARO8/9 orthologs involved in fungal l-tryptophan catabolism by genome mining and cross-complementation. The double deletion mutant of A. nidulans resulted in a 10-fold increased psilocybin production. Process optimization based on respiratory activity measurements led to a final psilocybin titre of 267 mg/L in batch cultures with a space-time-yield of 3.7 mg/L/h. These results demonstrate the suitability of our engineered A. nidulans to serve as a production strain for psilocybin and other tryptamine-derived pharmaceuticals.

The Ascendancy of RISC-V: An In-Depth Review of Its Open-Source Potential and Future Applications

Currently, the rapid advancement of RISC-V and the consequent changes in industry and market landscapes are capturing significant attention from various stakeholders. In recent years, there has been a notable increase in the number of companies and developers dedicating their efforts to researching and developing RISC-V technologies. In light of this trend, this paper aims to examine the global development of RISC-V, providing a comprehensive analysis of its progression on an international scale. It will compare RISC-V with other prominent instruction set architectures such as x86 and ARM, evaluating the relative merits and drawbacks of each. The paper will systematically organize and discuss relevant data to highlight RISC-V’s advantages and limitations. Furthermore, it will present an overview of existing RISC-V products and their applications across different sectors. Lastly, the paper will explore the future market potential of RISC-V, assessing how it might influence the industries related to chips and related markets in the coming years.

Optimizing Strategy for Property Development Plan Changes: A Multi-Criteria Decision-Making Approach Using AHP Methods (Case Study: Project Bumi Cipta 3 Jakarta)

This study examines the optimization strategy of property development plan changes for Bumi Cipta 3 Jakarta, a project by PT Cipta Bangun Property, one of the largest property companies in Indonesia. The analysis uses the Analytic Hierarchy Process (AHP), a multi-criteria decision-making (MCDM) method. The project is motivated by significant post-pandemic shifts in office space demand within Jakarta’s Central Business District. The study identifies four alternative development options: continuing with the office tower plan, or pivoting to an apartment, data center, or hospital. By interviewing key decision-makers and using the AHP method, the research evaluates financial and non-financial criteria, such as market attractiveness and site analysis. The findings suggest that the hospital alternative offers the highest potential in both financial returns and market alignment, followed by the data center and apartment projects. Strategic recommendations are provided to prioritize the hospital project while keeping the other alternatives in consideration for phased development, thus ensuring flexibility in response to future market conditions.

Automated Machine Learning for Optimized Load Forecasting and Economic Impact in the Greek Wholesale Energy Market

This study investigates the use of automated machine learning to forecast the demand of electrical loads. A stochastic optimization algorithm minimizes the cost and risk of the traded asset across different markets using a generic framework for trading activities of load portfolios. Assuming an always overbought condition in the Day-Ahead as well as in the Futures Market, the excess energy returns without revenue to the market, and the results are compared with a standard contract in Greece, which stands as the lowest as far as the billing price is concerned. The analysis achieved a mean absolute percentage error (MAPE) of 12.89% as the best fitted model and without using any kind of pre-processing methods.

Cannabis Licensing and Municipal Revenues

Municipal license policy has shaped the early roll-out and business outcomes of Proposition 64. Several stakeholders identify license availability as a key constraint to future cannabis market growth in California. Recent studies explore local cannabis licensing policy; however, key questions remain around municipal revenues, business location, and the role of supporting cannabis business ownership. Our paper explores the impacts of cannabis licensing on business locations and municipal taxable revenues. We use publicly-available Department of Cannabis Control licensing data to examine business locations using GIS analysis, conduct a regression analysis of city-level licenses and Department of Tax and Fee Administration revenues across California, and forecast potential municipal taxable revenues in South Bay Los Angeles. We find that licensed cannabis businesses appear to develop in Los Angeles County and are located close to borders of cities not issuing licenses, in areas of favorable zoning, in densely populated locations, and areas with higher poverty and minority populations. Our regression analysis suggests that city population drives municipal taxable revenue, indicating that business follows demand.

Consumer-Oriented Marketing Strategy of Chinese Domestic Otome Games: A Case Study of Love and Deepspace

Abstract: Otome games, story-based romance video games targeting women, originated in Japan in the late 1990s and have since gained popularity all around the world. This paper investigates the rise of Chinese domestic otome games, focusing on Love and Deepspace, a burgeoning and competitive title. The study analyzes its marketing strategy using the 4P model and examines the game's reflection on consumer behavior. The findings show that Love and Deepspace has innovated marketing approaches within the otome game industry, leveraging 3D modeling as the core of its product and price strategies and employing consumer-centric promotion strategies globally. As a conclusion, the paper states that understanding target consumers preferences is vital for future market planning. The research uses Love and Deepspaces success as an example to highlight the significant business value of domestic otome games and provide insights for companies to refine their strategies in this growing market.

It’s a Woman’s World: Exploring the Role of Female Qualities in the Evolving 21st Century Workplace

As the Fourth Industrial Revolution (4IR/Industry 4.0) continues to be realised through technological advances and social shifts, it has become evident that the future job market remains unpredictable and will continuously evolve. To accommodate the inevitable changes future generations will experience, teaching and learning environments are adapting their approaches. Instead of purely focusing on hard skills, the importance of conveying soft skills is increasingly recognised. Pedagogical experts have pulled the 4 C’s into focus as important 21st-century skills: critical thinking, creativity, communication, and collaboration. The connection between these soft skills, lateral, right-brain thinking, and female qualities is often drawn. This study's methodology explored and synthesised literature across various fields to map key concepts, establish new connections, and explore whether women may inherently possess skills aligned with future workplace demands shaped by the Fourth Industrial Revolution. Digital native characteristics are examined through Generational Theory, highlighting the benefits that soft skills will have for future generations. Additionally, literature on 21st-century skills is examined, and the differences between left and right brain characteristics are analysed to assess the importance of right-brain thinking in future workplaces. Lastly, gender-related qualities are evaluated to determine whether women may have an advantage in the evolving job market. By connecting these perspectives, the study aims to evaluate whether or not women may have the upper hand in the future working world. The study finds no concrete evidence that women inherently possess superior traits for future workplace demands, though the 4IR's emphasis on soft skills and inclusivity could enhance female participation. However, challenges like the lack of role models and digital echo chambers may still impede progress.

Antibacterial Effectiveness Test of Liquid Soap from Seven-Blade Needle Leaf Extract (Pereskia Bleo) Against Staphylococcus aureus Bacteria, Faculty of Medicine, University of Prima Indonesia

This study aims to analyze the digital-based marketing strategy implemented by FranchiseGlobal.com in order to increase exposure, market penetration, and brand awareness in the franchise industry. Using qualitative descriptive methods and a case study approach, this study explores digital marketing strategies that utilize big data, digital platforms, and social media to reach potential franchisees. Data were obtained through interviews, documentation, and direct observation of FranchiseGlobal.com's marketing activities. The results of the study indicate that the digital marketing strategy implemented by FranchiseGlobal.com, both in the short, medium, and long term, has succeeded in increasing the number of website visitors and brand awareness, although there are still challenges in expanding penetration into international markets, especially ASEAN. This study concludes that digital-based marketing is a crucial component in developing a franchise business more widely and effectively, and provides recommendations for optimizing the use of big data and technological innovation in future marketing strategies.

Development of Digital-Based Marketing Strategies: A Case Study on Franchiseglobal.com

This study aims to analyze the digital-based marketing strategy implemented by FranchiseGlobal.com in order to increase exposure, market penetration, and brand awareness in the franchise industry. Using qualitative descriptive methods and a case study approach, this study explores digital marketing strategies that utilize big data, digital platforms, and social media to reach potential franchisees. Data were obtained through interviews, documentation, and direct observation of FranchiseGlobal.com's marketing activities. The results of the study indicate that the digital marketing strategy implemented by FranchiseGlobal.com, both in the short, medium, and long term, has succeeded in increasing the number of website visitors and brand awareness, although there are still challenges in expanding penetration into international markets, especially ASEAN. This study concludes that digital-based marketing is a crucial component in developing a franchise business more widely and effectively, and provides recommendations for optimizing the use of big data and technological innovation in future marketing strategies.

Competitive intelligence in tourism: charting future directions through bibliometric analysis

PurposeThis study explores the crucial role of competitive intelligence (CI) in the tourism sector’s strategic decision-making. CI has significantly transformed the tourism sector through new insights and sophistication in data analysis and strategic planning. The rise in tourism-related competition, due to new destinations, varied tourist preferences and sustainability emphasis, makes competitive intelligence essential for understanding future market trends and making informed strategic choices.Design/methodology/approachUtilising PRISMA techniques for bibliometric analysis, the study examines literature from 1998 to 2023 (WoS), focusing on service innovation, customer experience management and sustainable strategies. It presents an analysis of the evolution of CI in tourism, its impact, influential works and future research directions.FindingsFindings show that the multidisciplinary nature of CI in tourism is further evidenced by studies on quality cues, travellers’ information needs and the utilisation of big data. Future studies need to understand both global trends and regional specifics, as shown in investigations of spatial-temporal tourism dynamics.Originality/valueThis study represents a novel contribution to the field of tourism research by offering a comprehensive bibliometric analysis of CI literature from 1998 to 2023. It uniquely integrates service innovation, customer experience management and sustainable strategies within the context of CI, highlighting its multidisciplinary impacts and evolution. These insights collectively emphasise the need for future innovation and a comprehensive understanding of the global-local nexus to inform future tourism research and practice.

Revolutionizing Computer Science Education: Integrating Blockchain for Enhanced Learning and Future Readiness

Abstract: This research investigates the transformative potential of incorporating blockchain technology into computer science education. In light of the rapid evolution of the digital landscape, traditional educational frameworks often fail to meet the demands of the industry, resulting in a significant skills gap among graduates. This paper analyzes how the integration of blockchain can revolutionize computer science curricula by enhancing learning experiences and equipping students to navigate future technological challenges. The study highlights the advantages of blockchain in educational contexts, including increased security and transparency of academic records, streamlined credentialing processes, and the establishment of decentralized learning platforms that promote collaboration and innovation. It presents case studies of institutions that have successfully implemented blockchain, along with strategies for educators to effectively integrate this technology into their pedagogical approaches. Additionally, the research addresses the challenges and limitations associated with blockchain integration, such as the requisite learning curve and infrastructure demands. The findings indicate that the incorporation of blockchain in computer science education can significantly boost student engagement, provide verifiable skill sets, and align academic outcomes more closely with industry requirements. This study contributes to the ongoing discourse on educational innovation and offers a strategic framework for institutions aiming to utilize blockchain technology in preparing students for the future job market.

Artificial intelligence in healthcare forecasting: Enhancing market strategy with predictive analytics

This research examines the transformative role of artificial intelligence (AI) and predictive analytics in healthcare market forecasting, with a specific focus on their application at Eli Lilly. AI-driven insights are becoming critical tools in anticipating market trends, assessing the impact of regulatory changes, and optimizing product positioning in the competitive global healthcare landscape. By leveraging machine learning algorithms and vast datasets, AI can identify patterns and correlations that traditional forecasting methods may overlook, offering companies like Eli Lilly a more comprehensive understanding of future market dynamics. The study explores how AI enhances decision-making by predicting healthcare market trends, particularly in response to regulatory shifts such as the U.S. Inflation Reduction Act. This legislative change, aimed at reducing drug prices and increasing market access, presents significant challenges and opportunities for pharmaceutical companies. AI allows firms to model the potential outcomes of such regulatory changes and adjust their market strategies accordingly. For example, predictive analytics can forecast the pricing pressures on specific drugs, helping companies develop competitive pricing models while maintaining profitability. Moreover, the research highlights the use of AI in mitigating risks associated with market volatility. By continuously analyzing real-time data, AI can detect early signals of potential disruptions—whether from economic downturns, competitor actions, or public health crises—allowing companies to take proactive measures. Additionally, AI-driven forecasting helps identify growth opportunities in emerging healthcare markets by analyzing demographic trends, disease prevalence, and healthcare infrastructure development. In conclusion, AI and predictive analytics are revolutionizing how healthcare companies like Eli Lilly approach market forecasting and strategy development. By offering deeper insights into future market conditions, these technologies not only reduce risks but also optimize product positioning and unlock growth opportunities in the global healthcare market. This research underscores the critical importance of AI in shaping the future of healthcare market forecasting. Keywords: Artificial Intelligence, Predictive Analytics, Market Forecasting, Regulatory Impact, Healthcare Strategy

Integrating Evolutionary Game-Theoretical Methods and Deep Reinforcement Learning for Adaptive Strategy Optimization in User-Side Electricity Markets: A Comprehensive Review

With the rapid development of smart grids, the strategic behavior evolution in user-side electricity market transactions has become increasingly complex. To explore the dynamic evolution mechanisms in this area, this paper systematically reviews the application of evolutionary game theory in user-side electricity markets, focusing on its unique advantages in modeling multi-agent interactions and dynamic strategy optimization. While evolutionary game theory excels in explaining the formation of long-term stable strategies, it faces limitations when dealing with real-time dynamic changes and high-dimensional state spaces. Thus, this paper further investigates the integration of deep reinforcement learning, particularly the deep Q-learning network (DQN), with evolutionary game theory, aiming to enhance its adaptability in electricity market applications. The introduction of the DQN enables market participants to perform adaptive strategy optimization in rapidly changing environments, thereby more effectively responding to supply–demand fluctuations in electricity markets. Through simulations based on a multi-agent model, this study reveals the dynamic characteristics of strategy evolution under different market conditions, highlighting the changing interaction patterns among participants in complex market environments. In summary, this comprehensive review not only demonstrates the broad applicability of evolutionary game theory in user-side electricity markets but also extends its potential in real-time decision making through the integration of modern algorithms, providing new theoretical foundations and practical insights for future market optimization and policy formulation.

Talent Management for Enterprises in Guangdong–Hong Kong–Macao Greater Bay Area: Analysis of Current Situation and Suggestions for Countermeasures

In response to the above findings, this study concludes that enterprises should strengthen the construction of talent echelon and cultivate talents adapted to the development of the enterprise through the combination of industry-university-research and emphasizing the development of innovation ability. Secondly, enterprises need to build a scientific talent evaluation system, oriented by ability and performance, and utilize talent evaluation techniques to ensure the fairness and accuracy of evaluation. In addition, enterprises should design career development channels to provide employees with opportunities for growth and promotion, as well as improve the working environment and provide competitive compensation and benefits to attract and retain talents. In terms of talent planning, enterprises should adopt a long-term perspective and develop talent planning that meets more than three years of business operation services to cope with the uncertainty of the future market. Finally, enterprises should strengthen communication with employees, establish a transparent feedback mechanism, and enhance employee job satisfaction and loyalty by empowering employees and high-level consensus. These countermeasures help enterprises build an effective talent management system and enhance competitiveness.

California carbon allowance futures

Carbon pricing is an important tool to address climate challenge, while limited attention has been given to the U.S. carbon derivatives market, i.e., California carbon allowance (CCA) futures market. To deal with various data frequencies, we use a mixed frequency vector autoregressive model. We find that changes in trading positions of hedgers and speculators can affect the CCA futures returns. Like other commodity futures markets, commercials act as contrarians, while non-commercials behave like momentum traders. We find some evidence of a feedback relationship between the CCA futures market and California's economic condition. The interaction between the energy sector and the carbon futures market is evident. The CCA futures market is more sensitive to climate policy risk than abnormal temperature. Other ETSs’ carbon futures prices and inflation expectation could affect the CCA futures returns on some trading days.

Redefining Entertainment Horizons: Epic Games and Netflix’s Quest for Future Dominance

In the rapidly evolving and ever-changing media and entertainment landscape, Epic Games, Inc. And Netflix, Inc. are seen as innovators in the industry. This paper analyzes the strategic layout of Epic Games and Netflix in the market competition, focusing on how they compete for dominance in digital entertainment. Built on its Unreal Engine and extensive gaming ecosystem, Epic Games is committed to pushing the frontiers of gaming technology and expanding into virtual and augmented reality. Netflix, by contrast, has strengthened its global leadership in streaming through continued innovation and content diversification. This article provides a thorough analysis of their business model, strategy, and required changes, and explores how these factors have helped them continue to dominate the market. Finally, the paper summarizes the trends of Epic Games and Netflix in the future digital entertainment market and evaluates their potential opportunities and risks in the global competition.

The impact of stock index futures margin levels on market quality

We examine the impact of margin levels on market quality in the China Shanghai–Shenzhen 300 (CSI 300) Stock Index Futures market. Utilizing microstructure theory and the OPOK model, we specifically focus on volatility, liquidity, and effectiveness. Our findings reveal that lowering the margin level for stock index futures reduces capital constraints for both informational and non-informational traders. Furthermore, we show that a reduction in margin levels enhances market liquidity and effectiveness. Improvements in liquidity and effectiveness subsequently reduce market volatility, indicating that a decrease in margin levels does not necessarily increase market risk. Based on these results, we recommend setting margins for stock index futures with comprehensive consideration of their combined effects on volatility, liquidity, and effectiveness.

The Fluctuation Characteristics and Risk Investigation of China's Gold Future Market

This paper deeply explores the fluctuation characteristics and risks of China's gold futures market. Through the collection of data on macroeconomic factors, stock index interest rates, and energy futures, the paper employs multiple linear regression models and intermediary variable models to quantitatively analyze the impact of these factors on the volatility of gold futures prices. The research finds that there is a significant positive correlation between the gold futures price and the growth of the U.S. GDP and the silver futures price index, while the impact of other macroeconomic indicators such as China's gold reserves and the inflation rate is not significant. Policy support and market regulation play a key role in the healthy development of the market, and the broad participation of market participants and the innovation of financial products have driven the diversification of the market. The intermediary effect analysis shows that the impact of macroeconomic factors on the gold futures price is partially indirect through the intermediary variable of stock index interest rates.

A joint Cournot equilibrium model for the hydrogen and electricity markets

Hydrogen production through renewable energy-powered electrolysis is pivotal for fostering a sustainable future hydrogen market. In the electricity sector, hydrogen production bears an additional demand that affects electricity price, and mathematical models are needed for the joint simulation, analysis, and planning of electricity and hydrogen sectors. This study develops a Cournot and a perfect competition model to analyze the links of the electricity and hydrogen sectors. In contrast to other solving methods approaches, the Cournot model is solved by convex reformulation techniques, substantially easing the resolution. The case studies, focusing on the Iberian Peninsula, demonstrate the region's potential for competitive hydrogen production, and the advantages of perfect competition to maximize the use of renewable energies, in contrast to Cournot's oligopoly, where the exercise of market power raises electricity prices. Sensitivity analyses highlight the importance of strategic decision-making in mitigating market inefficiencies, with valuable insights for stakeholders and policymakers.

Cotton market contagion: analyzing volatility spillovers across borders

PurposeThe paper analyzes the volatility transmission within the cotton markets by utilizing commodity futures prices from the USA, China and India, encompassing important global events that have significantly influenced the global cotton markets, like the China-USA trade dispute, the COVID-19 outbreak and the Russia–Ukraine conflict.Design/methodology/approachThe authors employ a volatility spillover measure developed by Diebold and Yilmaz (2009, 2012, 2014). Additionally, the methodology proposed by Baruník and Křehlík (2018), which divides the overall volatility spillover into short, medium and long-term segments has been used. To investigate the volatility connectedness, weekly (close-to-close) returns of the cotton futures contracts that are traded on the Chicago Board of Trade Dalian Commodity Exchange National Commodity Exchange of India (NCDEX), and Multi Commodity Exchange (MCX) are considered.FindingsThe paper identifies the presence of long-term volatility transmission among the three cotton futures markets. It demonstrates that a global shock like the Russia–Ukraine conflict has a greater impact on volatility in other markets than USA–China trade disputes. It also highlights the weakening role of the US cotton futures markets as a price leader for Indian and Chinese markets.Research limitations/implicationsSince only three major markets have been studied, the future studies can explore the interconnectedness by including other important markets including Brazil, Turkey, Bangladesh, etc. Further, the moderating role of relationship between other important variables such as cotton production, harvest, inventory, exchange rate, oil price, trade policies, etc. can be examined. Furthermore, the interconnectedness with the regional spot markets in India can also be examined to study how the volatility from the futures market can affect the volatility in the spot markets and vice-versa.Practical implicationsThe understanding of domestic food price volatility and its transmission from international to domestic markets is crucial for designing effective policies to address excessive volatility and protect vulnerable groups like producers, consumers, etc.Social implicationsThe findings emphasize on the substantial market dependence with the US and the Chinese markets which have a significant impact on the Indian markets with considerable implications for hedgers, producers and exporters, particularly during periods of higher volatility.Originality/valueThis study assesses the interdependencies among three major cotton-producing countries and the influence of factors like the USA–China trade tensions in 2018, the COVID-19 crisis and the Russia–Ukraine conflict in order to gauge the degree of volatility interconnection among these key players in the cotton market.