High Volatility Research Articles

Construction of Cyclodextrin-Based Covalent Organic Frameworks for Efficient Encapsulation of Menthol.

The application of flavoring ingredients like menthol in the food industry is hindered by their high volatility and poor thermal stability, which lead to significant losses during processing and storage. Encapsulation of flavors into porous materials to obtain inclusion complexes (ICs) has proved to be an efficient strategy. In the present study, we synthesized a series of relatively food-safe three-dimensional anionic cyclodextrin-based covalent organic frameworks (CD-COFs) with spiroborate linkages using a facile microwave-assisted method. The high surface area and newly formed cavitied of COFs significantly enhanced the encapsulation efficiency of menthol compared to native CD materials. Our findings revealed that γ-CD-COF-Li, with Li+ as the counterion, achieved superior encapsulation efficiency of 25.9%, outperforming γ-CD-COF-Na, γ-CD-COF-K and α-CD-COF-Li under the same conditions. Thermal stability measurements show that the menthol/γ-CD-COF-Li-ICs effectively stabilize menthol against heat evaporation at elevated temperatures due to the strengthened interaction between menthol and γ-CD-COF-Li. The promising results of this research suggest that rapid advancements in COF technology will provide new opportunities for enhancing the stability of flavoring ingredients in the food industry.

Economic Analysis of the Export Trends of Livestock and Livestock Products in India

Livestock provides livelihood to two-third of rural masses and employs about 8.8 per cent population of India. While dairying ensures livelihood to 70 million farm families. The study utilized export data from government sources and conducted a statistical analysis to assess trends and stability. Data revealed that the overall export value increased significantly from ₹22,092.62 crore in 2011-12 to ₹48,947.55 crore in 2021-22, reflecting a compound annual growth rate (CAGR) of 4.90%. Buffalo meat emerged as the leading export product, while dairy products exhibited a notable growth rate while dairy products and other categories showed varied growth. The instability index for the total exports was 19.02%, indicating moderate stability. State-wise analysis indicated Uttar Pradesh as the leading exporter, followed by Maharashtra and Haryana, with these three states together accounting for 85% of the total exports in 2021-22. The average growth rate of exports across states was 3.57%, demonstrating a stable growth pattern. Despite challenges posed by the COVID-19 pandemic, the sector showed resilience with a 13.98% growth in exports for 2021-22, aided by government initiatives. The analysis of dairy product exports highlighted a significant increase from ₹2,382.91 crore in 2020-21 to ₹4,742.70 crore in 2021-22, marking a 99% growth. The primary export destinations were the USA, Bangladesh, UAE, Saudi Arabia, Bahrain, and Malaysia. A transitional probability matrix revealed shifts in market shares, with notable increases in the USA and Bangladesh. Trade performance from 2011-22 showed that India's export share in dairy products improved from 0.03% to 0.135%, while import share decreased significantly. The instability index for exports was 63.53%, reflecting high volatility. Investing in advanced breeding technologies to increase productivity, and enhancing market access through trade agreements and promotional activities. Additionally, policies to manage volatility, such as price stabilization measures and export incentives, could further support industry growth.

Analysis of glycerol bound ω-oxo-fatty acids as ω-dioxane-FAME-derivatives

ω-oxo-fatty acids, also known as aldehydic fatty acids, are major products of fatty acid oxidation and pose potential health risks. When bound to glycerol, ω-oxo-fatty acids (core aldehydes) can be ingested with food. Challenges in GC–MS quantification include the absence of an appropriate internal standard. Additionally, substantial analyte losses during sample preparation, caused by the high volatility of short-chain compounds, alter their pattern based on molecular weight. In this study, among various tested derivatization methods, the formation of ω-dioxane derivatives demonstrated improved recovery rates after three evaporation cycles. For methyl 7-oxo-heptanoate, recovery increased from 43 % to 88 %, while recovery rates for different chain lengths and a novel synthesized internal standard improved from a range of 43 %–76 % to 87 %–92 %. Additionally, ω-dioxane derivatives displayed favorable GC–MS behavior, enabling clear identification and increased sensitivity. Finally, ω-oxo-fatty acids were quantified as their ω-dioxane-derivatives in thermally treated sunflower and rapeseed oil.

Effectiveness of Essential Oil Component Cocrystals Against Food Spoilage Bacteria

AbstractImproving food preservation technologies is a key aspect in the struggle to reduce global food waste, and natural antimicrobial substances, such as essential oil (EO) components represent very promising food preserving agent. However, their intrinsic chemico‐physical properties, such as the low melting point, low water solubility and high volatility, pose some practical difficulties in exploiting them for practical applications. Cocrystallization is used to stabilize liquid or volatile EO components providing them whit a crystalline environment, thus improving their potential application as antibacterial agents. Five EO active ingredients (THY = thymol, CAR = carvacrol, EUG = eugenol, CAD = trans‐cinnamaldehyde, and VAN = o‐vanillin) and two coformers (INA = Isonicotinamide, and HBA = 4‐hydroxybenzoic acid) have been combined and the corresponding cocrystals have been studied for their potential inhibiting effect against four food spoilage bacteria (Bacillus thuringiensis, Enterobacter cloacae, Pseudomonas fluorescens, and Serratia marcescens). The structures of the five cocrystals have been used to derive structure‐activity relationships in terms of release energy of the active ingredients form the crystalline environment, and a correlation has been derived with the Intermolecular Interaction Energies of the EO molecules.

Triadic Halobenzene Processing Additive Combined Advantages of Both Solvent and Solid Types for Efficient and Stable Organic Solar Cells.

Solvent additives with a high boiling point (BP) and low vapor pressure (VP) have formed a key handle for improving the performance of organic solar cells (OSCs). However, it is not always clear whether they remain in the active-layer film after deposition, which can negatively affect the reproducibility and stability of OSCs. In this study, an easily removable solvent additive (4-chloro-2-fluoroiodobenzene (CFIB)) with a low BP and high VP is introduced, behaving like volatile solid additives that can be completely removed during the device fabrication process. In-depth studies of CFIB addition into the D18-Cl donor and N3 acceptor validate its dominant non-covalent intermolecular interactions with N3 through effective electrostatic interactions. Such phenomena improve charge dynamics and kinetics by optimizing the morphology, leading to enhanced performance of D18-Cl:N3-based devices with a power conversion efficiency of 18.54%. The CFIB-treated device exhibits exceptional thermal stability (T80 lifetime = 120h) at 85°C compared with the CFIB-free device, because of its morphological robustness by evolving no residual CFIB in the film. The CFIB features a combination of advantages of solvent (easy application) and solid (high volatility) additives, demonstrating its great potential use in the commercial mass production of OSCs.

Kangaroo Market Phenomenon: Identification and Impact on Indonesia’s Economic Growth

The stock market holds a crucial role in a country's economy, including Indonesia. As an indicator, the Jakarta Composite Index (JCI) experiences volatility alongside changes in the Indonesian economy. The presence of a kangaroo market in JCI returns can pose a risk of future economic slowdown, making it crucial for monetary authorities to intervene to stabilize the stock market. Therefore, this study identifies the kangaroo market phenomenon, structural economic changes, and the impact of the kangaroo market phenomenon on Indonesia's economic growth. The study focuses on many variables, including JCI returns, kangaroo market, and economic growth from Q3 1995 to Q4 2023. By using the Hodrick-Prescott Filter to define a kangaroo market as JCI returns that lack a deterministic trend, have high volatility, and deviate by one standard deviation from their volatility trend, the results show that most kangaroo phenomena occur during undervaluation. Following the kangaroo market phenomenon, Indonesia's economy underwent five structural changes based on the multiple breakpoint test. Using dynamic linear regression, the overvaluation kangaroo phenomenon negatively affects Indonesia's economic growth, while the undervaluation kangaroo phenomenon has no impact but shows a positive direction.

Role of volatile secondary char on the combustion behavior of cellulose-based hydrochars

Hydrothermal carbonization (HTC) can transform a wide range of biomass into renewable biofuels. Hydrochar, the solid fuel resulting from HTC, has a similar energy density to low-rank coals. Despite an extensive body of literature detailing the thermogravimetric analysis (TGA) of hydrochar under slow pyrolysis and oxidation, there remains a limited understanding of hydrochar's behavior in realistic combustion settings. In this work, we integrate combustion experiments and TGA to understand the combustion characteristics of a model hydrochar fuel. Cellulose-based hydrochars produced at different temperatures along with solvent-extracted chars are tested in a Hencken burner across varying temperatures and oxygen concentrations in the oxidizer gas. Simultaneous CH* chemiluminescence, particle image velocimetry, and two-color pyrometry are used to measure ignition delay time and identify homogeneous/heterogeneous ignition mechanisms and combustion phases. Incorporating TGA with combustion results shows that the ignition mode and combustion processes are strong functions of surrounding gas temperature and oxygen mole fraction. The level of carbonization of the hydrochar dictates the ignition delay time and combustion modes. Further, the presence of a tar-like secondary char on the as-carbonized hydrochars leads to more rapid ignition due to high volatility.

Overlooked Trace Molecules in Organic Aerosol Revealed by Gas Chromatography-Orbitrap Mass Spectrometry.

Molecular characterization of organic aerosol (OA) is crucial for understanding its sources and atmospheric processes. However, the chemical components of OA remain not well constrained. This study used gas chromatography-Orbitrap mass spectrometry (GC-Orbitrap MS) and GC-Quadrupole MS (GC-qMS) to investigate the organic composition in PM2.5 from Xi'an, Northwest China. GC-Orbitrap MS identified 335 organic tracers, including overlooked isomers and low-concentration molecules, approximately 1.6 times more than GC-qMS. The "molecular corridor" assessment shows the superior capability of GC-Orbitrap MS in identifying an expansive range of compounds with higher volatility and oxidation states, such as furanoses/pyranoses, di/hydroxy/ketonic acids, di/poly alcohols, aldehydes/ketones, and amines/amides. Seasonal variations in OA composition reflect diverse sources: increased di/poly alcohols in winter are derived from indoor emissions, furanoses/pyranoses and heterocyclics in spring and summer might be from biogenic emissions and secondary formation, and amides in autumn are probably from biomass burning. Integrating partial least squares discriminant analysis (PLS-DA) and potential source contribution function (PSCF) models, the source similarities and differences are further elucidated, highlighting the role of local emissions and transport from southern cities. This study offers new insights into the OA composition aided by the high mass resolution and sensitivity of GC-Orbitrap MS.

Robustness Investigation of ML Models for Stock Price Prediction During Market Volatility: A Case Study of the Japanese Stock Market Amidst the COVID-19 Pandemic

In the evolving realm of financial markets, precise stock price prediction remains a pivotal challenge. This paper investigates the efficacy of traditional machine learning models for stock price forecasting, particularly during the high volatility phase induced by the COVID-19 pandemic, using the Japanese stock market as a case study. This study utilized a dataset from the JPX Tokyo Stock Exchange Prediction competition, selecting two stocks at random to avoid bias. After standardizing the data for consistency, this study employed linear regression and decision trees to project stock prices, comparing their performance during stable periods and the economic upheaval of the pandemic. The analysis revealed that both models' predictive capabilities were compromised during the pandemic, with the decision tree model particularly underperforming. In normal times, linear regression showed improved accuracy for stock 1301 in 2021, while for stock 6758, the model's performance declined. These findings underscore the necessity for robust models that can withstand market turbulences like those observed during the COVID-19 crisis. The research contributes to both academic discussions on financial forecasting and practical strategies for market participants navigating complex stock environments. Future work will focus on developing more resilient forecasting methods to address these challenges.

BRICS Stock Markets Performances During COVID-19: Comparison with Other Economic Crises

The study is directed to assess the downside stock market risk of the BRICS during COVID-19 and compare it with other economic crises. The study further investigates the association between the stock markets among the BRICS nations. As per the results, the stock markets of India, Brazil, and South Africa suffered the worst crisis during the COVID-19. China has shocked the world by recovering very quickly compared to the rest of the world. Additionally, its been observed that there is an increased association between different markets depending on the severity of the crisis.

Impact of Exchange Rate Regime Change on Bangladesh RMG Exports to the USA and the EU

Abstract Bangladesh adopted floating exchange rate system in May 2003. After that regime change, the country has faced a relatively higher volatility of nominal exchange rate than previous regime. Economic theories on volatility suggests that trade flow is adversely affected in response of fluctuation of the exchange rate. Bangladesh is the second largest readymade garments (RMG) exporter in the world and it is the main export product of the country. In this research, Difference in Difference (DID) model introduced by Card and Krueger (1994) is used for the yearly data of 1983 to 2022 to discern this impact of volatility on the RMG exports of Bangladesh. The result shows that RMG exports is negatively affected due to the exchange rate volatility and the estimated figure shows that on Bangladesh‘s average RMG exports has been lessened to the USA and EU regions by US$1.04 billion and 1.02 billion, respectively, owing to the volatility incurred by the regime change. Therefore, as volatility obviously has hindered RMG exports, the central bank of Bangladesh should stay alert to avoid high volatility of the nominal exchange rate to keep the RMG exports flow uninterrupted.

Does Uncertainty Drive Herding Behavior in Indonesia Stock Exchange

Herding behavior increases along with market risk, corporate uncertainty aggregate uncertainty. This research aims to investigate herding behavior in the Indonesian capital market by considering conditions of uncertainty. There are two uncertainty indicators used in this research, namely stock market volatility and COVID-19. By using daily data from companies listed on the LQ45 index, the research found that herding behavior was not detected when using the basic herding model, but when entering transaction volume variables and lagged dependent variables, we found that there was herding behavior during the 2017-2023 period. Furthermore, by including uncertainty variables, our research found an asymmetry of herding behavior: investors tend to herd when the market experiences high volatility and during the COVID-19 pandemic. Outside of these two periods, we found no empirical evidence of herding behavior in Indonesia. These results strengthen the theoretical framework that high uncertainty triggers herding behavior.

베타의 변동성과 주식의 기대수익률

This paper presents the stocks with high volatility of beta (VolBeta) underperform those with low VolBeta in the Korean stock market. The main findings are summarized as follows: First, the portfolio with high VolBeta has lower return relative to the one with low VolBeta. The raw and Fama-French four factor risk-adjusted return on zero-cost portfolio buying the highest and selling lowest VolBeta deciles are -1.11% and -1.45%, respectively. Second, firm-level Fama-MacBeth cross-sectional regressions show negative and statistically significant relation between VolBeta in this month and the cross-section of future returns. The results are robust to controls for firm chracteristics reportedly affecting stock returns. Third, overpricing of the stocks with high VolBeta is mostly pronounced in the ones with high idiosyncratic volatility. On the contrary, overpricing disappears in the high VolBeta stocks with low idiosyncratic volatility. The results suggest stocks with higher arbitrage risk measured by idiosyncratic volatility are more likely to be mispriced, consistent with Stambaugh, Yu, Yuan(2015).

The use of artificial intelligence in the management of investment portfolios

The ever-increasing complexity of financial markets, characterized by high market volatility, economic uncertainty, and the massive influx of various data pose a significant challenge in investment portfolio management. Traditional methods can fall short in addressing these challenges, requiring too much from an individual managing investment portfolios. The ability of AI to process a tremendous number of large datasets and to detect patterns offers a significant advantage for a manager, providing for more informed and timely investment decisions. The purpose of this annex is to encapsulate findings of the primary paper on the use of AI in investment portfolio management, highlighting the benefits and challenges, and also identifying areas for improvement and further research. The paper explores the possibility of using AI to enhance the effectiveness of an investment portfolio manager, through the improvement of analysis’ quality, help in data collection and processing, recognition of complex patterns, and faster decision-making. This could help optimize investment strategies through risk reduction, improved forecasting, and faster adaptation to market fluctuations. In addition, the paper aims to identify potential risks associated with AI integration, for example quality of data, algorithm transparency, and cybersecurity issues. Literature analysis demonstrated that application of AI can increase the efficiency of investment portfolio management. This is possible because of AI systems' ability to conduct advanced data search and analysis, providing for better decision-making. The use of AI algorithms allows investment portfolio managers to identify market trends more easily, optimize asset allocations, as well as minimize market volatility risks. А good example is BlackRock's Aladdin expert system, which provides comprehensive data insights, providing a competitive advantage. However, the paper also demonstrates, that implementation of AI systems is associated with several challenges, particularly - quality of provided data and opacity of AI algorithms, which can raise doubts regarding their effectiveness. Moreover, cybersecurity remains a significant concern. Overall, the use of AI presents opportunities for investment portfolio managers to improve results by providing competitive advantages. However, certain challenges need to be overcome. AI holds great promise for investment portfolio management, and future studies need to focus on AI integration, cybersecurity, and improvement of data quality.

Comparing the great recession and COVID‐19 using Long Short‐Term Memory: A close look into agricultural commodity prices

AbstractWe employ a neural network (NN) approach—Long Short‐Term Memory (LSTM)—to study agricultural commodity prices during the 2008 Great Recession and the COVID‐19 recession. Our analysis reveals more structural breaks and higher volatility in plant‐based commodities like corn and soybeans during recessions compared with animal‐based commodities. The price reactions varied among commodities, with corn responding first to both recessions, while milk price, which was found independent of other prices, recovered last from the financial recession and first from the disease‐induced recession. This insight into commodity behavior during recessions can aid in trend prediction and recession preparation for investors and researchers.

Firms’ cost structure and stock return volatility

PurposeThis paper proposes a framework to identify a pattern in the relationship between firms’ cost structure (i.e. fixed versus variable) and their volatility in stock returns.Design/methodology/approachOur empirical analysis is based on a panel data regression where we use an extended sample period and a time-series regression-based elasticity measure of operating leverage.FindingsWe document significantly higher systematic risk among firms with large fixed costs, a conclusion which confirms theoretical predictions of earlier studies. In new findings, we document high firm-specific risk and high stock return volatility among firms with a fixed cost structure.Originality/valueThe paper fills a gap in the literature by examining the effect of cost structure using various operating leverage measures and other control measures for firm characteristics on idiosyncratic risk. Studies that seek to explain firms’ systematic risks are numerous; conversely, there are relatively fewer studies on the determinants of firms’ specific risks.

How to Better Qualify ICT Graduates to Support Digital Education: A contribution from CALOHEE Tuning project qualifications framework

The Information and Communications Technology subject area is a broad multidisciplinary field of academic research and professional practice covering many topics ranging from electronics to system and network administration, to software development, cyber security, VR, AR, IoT and AI. Due to its nature, it cooperates closely with diverse areas, most notably eLearning and digital education. As contemporary educational institutions undergo a digital transformation, by which its processes are reengineered to take most of the possibilities offered by the emerging technologies, the contribution of the ICT field has become transversal and consequently of critical importance. The term ICT which refers to this wide multidisciplinary field differentiates from IT, which applies specifically to the management and use of computer systems to store, retrieve, transmit, and manipulate data. The continuous expansion of the field to new territories of practice such as education is requiring additional knowledge, skills and wider competences acquisition. In addition, high volatility of the job market creates the need for very adaptable professional profiles. As a result, there is a pressure from the job market for shorter and more job-oriented programmes, but also for competence frameworks which may serve as references for quality higher education and training. In this paper we present the Tuning Quality Reference Framework (QRF) for ICT which was developed as part of the CALOHEE Erasmus+ funded project. A multidisciplinary team with experts from 12 different countries (Portugal, Lithuania, Germany, Italy, Spain, Austria, Belgium, Greece, Turkey, Finland, Ireland and Romania) took part in the task. The QRF is built in a way to flexibly address all the mentioned complexity and continuously emerging new requirements including educational institutions and non-formal education organisations. The approach used also supports innovation in learning, teaching and assessment practices in ICT degrees, fostering open and digital education.

Integrating PCA with deep learning models for stock market Forecasting: An analysis of Turkish stocks markets

Financial data such as stock prices are rich time series data that contain valuable information for investors and financial professionals. Analysis of such data is critical to understanding market behaviour and predicting future price movements. However, stock price predictions are complex and difficult due to the intense noise, non-linear structures, and high volatility contained in this data. While this situation increases the difficulty of making accurate predictions, it also creates an important area for investors and analysts to identify opportunities in the market. One of the effective methods used in predicting stock prices is technical analysis. Multiple indicators are used to predict stock prices with technical analysis. These indicators formulate past stock price movements in different ways and produce signals such as buy, sell, and hold. In this study, the most frequently used ten different indicators were analyzed with PCA (Principal Component Analysis. This study aims to investigate the integration of PCA and deep learning models into the Turkish stock market using indicator values and to assess the effect of this integration on market prediction performance. The most effective indicators used as input for market prediction were selected with the PCA method, and then 4 different models were created using different deep learning architectures (LSTM, CNN, BiLSTM, GRU). The performance values of the proposed models were evaluated with MSE, MAE, MAPE and R2 measurement metrics. The results obtained show that using the indicators selected by PCA together with deep learning models improves market prediction performance. In particular, it was observed that one of the proposed models, the PCA-LSTM-CNN model, produced very successful results.

Multifunctional bio-nanocomposite films integrated with essential oils@metal−phenolic network nanocapsules for durable fruit preservation

Food spoilage exacerbates global hunger and poverty, necessitating urgent advancements in food shelf life extension methodologies. However, balancing antibacterial efficacy for food preservation with human and environmental safety remains a significant challenge. Natural essential oils (EOs), known for their potent antibacterial and antioxidant properties, offer eco-friendly alternatives, yet their high volatility and instability limit practical applications. Herein, we conducted the encapsulation of EOs within biocompatible metal phenolic networks (MPNs) to create EOs@MPN nanocapsules. Subsequently, these nanocapsules were integrated into bio-nanocomposite films composed of natural soy protein isolate (SPI) and carboxymethyl cellulose (CMC). The resulting films exhibited robust mechanical properties (Tensile Strength >10 MPa) and significantly enhanced antioxidant activity (7-fold higher than pure films). Importantly, the synergistic combination of EOs and MPNs conferred enhanced antibacterial efficacy. Safety assessments confirmed the bio-nanocomposite films' high biodegradability (> 90 %) and negligible cytotoxicity, ensuring environmental sustainability and human health safety. In practical applications, the bio-nanocomposite films effectively delayed the surface browning of fresh-cut fruits for up to 48 h, demonstrating a pronounced synergistic antioxidative effect against oxidation. Moreover, tomatoes and blueberries packaged with the bio-nanocomposite films still maintained freshness for up to 12 days, offering promising strategies for extending the shelf life of perishable fruits. These findings underscore the potential of EOs@MPN-based bio-nanocomposite films as sustainable solutions for food preservation and highlight their practical viability in mitigating food spoilage and enhancing food security globally.

Analysis of Financial Performance for Tesla: Based on Comparison with General Motors, Ford, and Toyota

As a matter of fact, the emergence of electric cars and new state-of-the-art technologies is changing the face of the automobile industry. This study compares the financial performance of Tesla with that of Toyota, General as well as Ford. To be specific, the critical financial variables compared are revenue, profitability, and market share over five years: 20192023. According to the numbers, Tesla revenues increased by 18.8% yearly, from $24.58 billion in 2019 to $96.77 billion in 2023. However, also in this very regard, Tesla appears to have way higher volatility in profitability than rivals, showing net income that is highly variable. General Motors and Ford both showed slower growth but are accompanied by Toyota, which still indicates good free cash flow margins and less overall volatility in results. This report highlights top automakers' competitive dynamics and strategic positioning, which benefit industry stakeholders and investors. Indeed, with an understanding of these financial trajectories, one can make wise investment decisions and spot future market trends.