Search results

The aim of this paper is to solve the toxic and harmful problems caused by traditional volatile corrosion inhibitor (VCI) and to analyze the effect of the layered structure on the enhancement of the volatile corrosion inhibition prevention performance of amino acids.

The carbon dots-montmorillonite (DMT) hybrid material is prepared via hydrothermal process. The effect of the DMT-modified alanine as VCI for mild steel is investigated by volatile inhibition sieve test, volatile corrosion inhibition ability test, electrochemical measurement and surface analysis technology. It demonstrates that the DMT hybrid materials can improve the ability of alanine to protect mild steel against atmospheric corrosion effectively. The presence of carbon dots enlarges the interlamellar spacing of montmorillonite and allows better dispersion of alanine. The DMT-modified alanine has higher volatilization ability and an excellent corrosion inhibition of 85.3% for mild steel.

The DMT hybrid material provides a good template for the distribution of VCI, which can effectively improve the vapor-phase antirust property of VCI.

The increased volatilization rate also means increased VCI consumption and higher costs.

Provides a new way of thinking to replace the traditional toxic and harmful VCI.

For the first time, amino acids are combined with nano laminar structures, which are used to solve the problem of difficult volatilization of amino acids.

The increasing accumulation of synthetic plastic waste in oceans and landfills, along with the depletion of non-renewable fossil-based resources, has sparked environmental concerns and prompted the search for environmentally friendly alternatives. Biodegradable plastics derived from lignocellulosic materials are emerging as substitutes for synthetic plastics, offering significant potential to reduce landfill stress and minimise environmental impacts. This study highlights a sustainable and cost-effective solution by utilising agricultural residues and invasive plant materials as carbon substrates for the production of biopolymers, particularly polyhydroxybutyrate (PHB), through microbiological processes. Locally sourced residual materials were preferred to reduce transportation costs and ensure accessibility. The selection of suitable residue streams was based on various criteria, including strength properties, cellulose content, low ash and lignin content, affordability, non-toxicity, biocompatibility, shelf-life, mechanical and physical properties, short maturation period, antibacterial properties and compatibility with global food security. Life cycle assessments confirm that PHB dramatically lowers CO₂ emissions compared to traditional plastics, while the growing use of lignocellulosic biomass in biopolymeric applications offers renewable and readily available resources. Governments worldwide are increasingly inclined to develop comprehensive bioeconomy policies and specialised bioplastics initiatives, driven by customer acceptability and the rising demand for environmentally friendly solutions. The implications of climate change, price volatility in fossil materials, and the imperative to reduce dependence on fossil resources further contribute to the desirability of biopolymers. The study involves fermentation, turbidity measurements, extraction and purification of PHB, and the manufacturing and testing of composite biopolymers using various physical, mechanical and chemical tests.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

This paper's purpose was to examine the impact of geomagnetic activity (GMA) on the timing and valuation of earnings information disclosed by firms every quarter.

The authors start the analyses with a sample of 112,669 client firms from 1989 to 2018. To analyze the impact of GMA on the earnings response coefficient (ERC), the authors use the three-day cumulative abnormal returns and cumulative abnormal returns for the extended post-earnings announcement window [2, 75] as the dependent variables. The authors interact unexpected earnings (UE) with the C9 Index, an index commonly used to measure GMA and study how GMA affects the pricing of new public information. To examine the effect of GMA on the timing of disclosure of earnings news, the authors regress a variant of the GMA index on the propensity to disclose bad earnings news.

The authors find significantly lower earnings response coefficients during periods of high GMA. This effect is permanent and stock prices do not correctly incorporate the implications of earnings information over time. The authors also show that managerial behavior is affected by GMA as well and the managers are more (less) likely to release bad (good) news during periods of higher activity. Finally, the authors also find that in situations where stakeholders are likely to rely on modern technology that depends minimally on humans, the adverse impact of GMA on the pricing of earnings information is mitigated.

The literature on the effect of GMA on the capital market is very limited and focuses primarily on stock returns, while the behavioral finance literature focuses on circumstances like weather, temperature and sporting outcome to study how the investors' mood affects their capital market behavior. The authors add to both the literature by investigating how GMA influences investors' and managers' behaviors in the capital market.

The purpose of this paper is to study the effect of vapor assembly sequence and assembly temperature on the corrosion protection of the complex silane films Al alloy. The performance and application range of silane films are enhanced.

The complex silane films were successfully prepared on the surface of aluminum alloy using via vapor phase assembly of 1,2,3-benzotriazole (BTA) and dodecyltrimethoxysilanes (DTMS). The protection of the assembly films against corrosion of Al alloy is investigated by the electrochemical measurements and the alkaline solution accelerated corrosion test. Thickness and hydrophobicity of the complex films are studied using ellipsometric spectroscopy and contact angle tests.

It shows that the anti-corrosion ability of the complex films is overall superior to that of the single-component assembled films. DTMS-BTA films have larger thickness and best anti-corrosion ability. The alkyl chains in DTMS have better compatibility with BTA molecules. The rigid BTA molecule can permeate into the long alkyl chain of DTMS as fillers and improve the barrier properties of the complex films.

In this paper, a green and efficient method of vapor phase assembly is proposed to rust prevention during manufacture of Al alloy workpiece.

Ferrofluid seals are known for their low friction torque and high tightness. However, they have some limitation due to the allowable rotational speed. The work presented here analyzes the performance of newly designed seals which are a combination of a ferrofluid and a centrifugal seal. The new seals can operate at high speeds. The purpose of this study is to theoretically predict the performance of combined seals.

Three seals were designed and selected for analysis. A version of the seals with a nonmagnetic insert is also considered, the purpose of which is to facilitate the installation and return of ferrofluid during low rotational speeds. The analyses were based on combining the results of numerical simulation of magnetic field distribution with mathematical models.

A combination of ferrofluid sealing and centrifugal sealing is possible. Analyses showed that the combined seal could hold a minimum pressure of 190 kPa in the velocity range of 0–100 m/s. The problem with this type of seal is the temperature.

New seal designs are presented. Key parameters that affect the seal operation are discussed. A methodology that can be used in the design of such seals is presented.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2023-0221/.

The global energy sector draws significant stakeholder attention due to never-ending controversies surrounding its environmental impacts. Investors’ response to such controversies causes direct financial implications for these firms. Furthermore, environmental, social and governance (ESG) sensitivity, which is likely to safeguard the energy sector firms from such controversies, is itself conditional to the development stage of a country and its regulatory environment. Therefore, this study aims to investigate if the influence of ESG on the share price volatility (SPV) of energy sector firms is subject to the development stage of the countries.

The study investigates nine years of panel data of 93 global energy sector firms from developing and developed nations. Using dynamic two-way fixed effects estimation and computing robust standard errors to obtain the econometric results.

The main finding reveals that the impact of ESG on SPV is, indeed, subject to the development stage of the nations. Similar results are observed for the effects of the social dimension of ESG on SPV. While ESG impacts the SPV negatively for firms in developing economies, the impact is the opposite for firms in developed nations. In other words, strong ESG propositions induce share price stability for developing countries while destabilizing the firms in developed nations.

The policymakers should further streamline the regulations and policies related to ESG adoption and adherence. In practice, the energy sectors should streamline their operations. Firm managers, especially in the energy sector, should devise strategies with ESG as an essential component to safeguard their firms against environmental and market volatility and adversatives. The firms in developing nations should further strengthen their social dimension of ESG to foster social equity and harmony.

The study contributes through its niche investigations on the energy sector, which is very important for the world economy. The study is relevant in the current scenario when the world faces a severe energy crisis due to global supply chain issues.

The purpose of this work is to study the connections generated between stock market indices, representing firms whose practices focus on fighting climate change and several global risk factors in accordance with the sustainability objectives defined in the 2030 Agenda. An endogenous perspective is adopted, considering the spillovers generated within the low carbon stock market sector, as well as the latter’s exposure to exogenous shocks of an economic and financial nature.

This work uses a multivariate model of dynamic correlation (GARCH-corrected dynamic conditional correlation [cDCC]), which can accompany the correlations generated over time.

Considering five low carbon indices, representing various parts of the world, and four global macro-economic and financial variables, over a period of approximately eight years, it was possible to understand that the variables studied transmit between each other a statistically significant spillover. The period of the pandemic crisis shows a sharp increase in the information transmission process. It was also possible to conclude that some global variables are risk factors, performing the role of transmission channels for the spillover effects to low carbon indices, increasing the risk of contagion and reducing the possibilities of diversifying the investment portfolio.

Firstly, this work analyses the connection and spillover effects between low carbon indices. Secondly, considers an extended sample covering different market phases, particularly that of the pandemic crisis and the Ukrainian War, creating conditions to compare connection patterns between those indices. Thirdly, it studies the variable influence over time of global risk factors in the transmission of spillover between low carbon indices.