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The purpose of this study is twofold. Firstly, the study has investigated the changing scenario of gender bias in households' education expenditure and the socioeconomic factors responsible for it. Secondly, the study has estimated the inequality in education expenditure for the male and female students and determined the significance of socioeconomic variables in gender discrimination.

To address the above-mentioned issues, this paper has used the unit-level data of NSSO 52nd, 64th, 71st and 75th rounds from 1995–1996 to 2017–2018. The log linear regression model is applied to estimate factor impending average education expenditure dynamics. The Oaxaca–Blinder Decomposition method has been employed to measure gender discrimination, and the Lorenz curve and Gini coefficient are used to assess inequality among girls experiencing prejudice.

The study has discovered an gender bias in education expenditure against females during the study period in India. Further, it has been found that gender discrimination against girl students is decreasing. Moreover, the factors such as age, religion, castes, MPCE (income quantile), type of institution, present enrolment and type of education are responsible for this gender differences.

This paper uses 20 years of household-level data for study and suggests that discriminatory behaviour of households and credit constraints of the underdeveloped countries prevent investment in girl's education. Therefore, the state must pay for education of girls by offering scholarships and free or heavily subsidized education. In addition to this, awareness programs for gender equality should also be implemented by the government, especially in rural areas.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2022-0537.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

Elder abuse is the matter of grave concern in recent times in India. Today, older people are facing the abusive behaviour such as maltreatment, mental and physical torture and heedless ignominy from the family and the society. The purpose of this paper is to examine the nature of elder abuse in Indian socio-cultural context and also focuses on the causes of elder abuse and abusers.

Mixed method design has been applied in this paper. The study has been conducted from October 2012-January 2013 on a sample of 220 older people living in both rural and urban areas of Lucknow, India. The data have been analysed through descriptive and narrative analysis.

The study finds that the emotional abuse is more common among the older people, which raises the feeling of insecurity, depression and isolation. The study also points out that the respect, honour, status and authority which were enjoyed by the elderly in the traditional society, have gradually started declining. Moreover, the study finds that the family members, do not provide proper food, clothing and medical facilities in rural areas.

Since the sample of older respondents was small and focused on those living in a particular area of one state, the authors cannot generalise from this study to include the great diversity of experience and difference in perceptions among the older people even within a particular state. However, widespread and diverse types of accounts of elder abuse were reported even in these settings, which can help indicate areas for future research and policies.

This is an original paper, which is based on the experiences of older people living in rural-urban areas and discussed the elder abuse in socio-cultural context.

The research on consumerism has been dramatically rising in recent decades. However, in the food industry, little research has been empirically conducted in the beverage industry. This research empirically tests the consequences of consumer perceptions: perceived price (PPR), perceived quality (PQ), perceived packaging (PPG) and perceived taste (PT) on repurchase intention (RI) particularly; it unveils the consumer attributes, e.g. gender, age and ethnicity between consumer perceptions and RI of the consumers.

The data were collected from 403 consumers of the beverage industry (e.g. Nestle, Mitchell's Fruit Farms, Murree Brewery and OMORE) in Pakistan. The researchers used online survey questionnaires followed by a cross-sectional approach because data collection physically was not possible due to COVID-19.

Data were analyzed by Smart partial least square structural equation modeling (PLS-SEM) 3.3.3, and the results supported the significant influence of consumer perceptions separately, e.g. PPR, PQ, PPG and PT on RI. Additionally, gender, age and ethnicity were found to have a moderating role between consumer perceptions and RI, so, the truth of having consumer attributes has been revealed.

The managers of beverage industries should provide ethical and operational strategies to tackle consumer's problems based on cultural norms. Furthermore, they should make sensible measures for the quality branding of the beverage products. In this way, the consumers will have a better experience of quality, price, taste and packaging, in turn, to RI.

This research targeted the beverage industry that needs facts and figures based on consumer attributes, e.g. age, gender and ethnicity. This research also disclosed the behaviors of consumers according to their gender, age and area of residence.

This paper aims to extend the previous contributions about data-driven control in aircraft control system from academy and practice, respectively, combining iteration and learning strategy. More specifically, after returning output signal to input part, and getting one error signal, three kinds of data are measured to design the unknown controller without any information about the unknown plant. Using the main essence of data-driven control, iterative learning idea is introduced together to yield iterative learning data-driven control strategy. To get the optimal data-driven controller, other factors are considered, for example, adaptation, optimization and learning. After reviewing the aircraft control system in detail, the numerical simulation results have demonstrated the efficiency of the proposed iterative learning data-driven control strategy.

First, considering one closed loop system corresponding to the aircraft control system, data-driven control strategy is used to design the unknown controller without any message about the unknown plant. Second, iterative learning idea is combined with data-driven control to yield iterative learning data-driven control strategy. The optimal data-driven controller is designed by virtue of power spectrum and mathematical optimization. Furthermore, adaptation is tried to combine them together. Third, to achieve the combination with theory and practice, our proposed iterative learning data-driven control is applied into aircraft control system, so that the considered aircraft can fly more promptly.

A novel iterative learning data-driven strategy is proposed to efficiently achieve the combination with theory and practice. First, iterative learning and data-driven control are combined with each other, being dependent of adaptation and optimization. Second, iterative learning data-driven control is proposed to design the flight controller for the aircraft system. Generally, data-driven control is more wide in our living life, so it is important to introduce other fields to improve the performance of data-driven control.

To the best of the authors’ knowledge, this new paper extends the previous contributions about data-driven control by virtue of iterative learning strategy. Specifically, iteration means that the optimal data-driven controller is solved as one recursive form, being related with one gradient descent direction. This novel iterative learning data-driven control has more advanced properties, coming from data driven and adaptive iteration. Furthermore, it is a new subject on applying data-driven control into the aircraft control system.

This paper deals with the synthesis of water soluble electrodepositable epoxy resins from maleopimaric acid and epoxy resin. Water soluble methylated urea formaldehyde resin and melamine formaldehyde resin were also synthesised for curing purposes. The coating compositions were prepared from water soluble epoxy resins, water soluble MF resin, water soluble UF resin, red oxide of iron and zinc phosphate. Thereafter the optimum conditions for electrodeposition were determined in terms of voltage, time, solid contents, pH and bath temperature. These anodic electrocoatings had good film properties such as scratch hardness, flexibility, impact resistance and resistance to water, acid, alkali and solvent.

Micro, small and medium-sized enterprises are the live wire of construction industry in developing countries. These classes of establishments are most affected by economic contraction and turmoil, thus affecting their performance and survivability. Hence, the purpose of this study is to investigate and prioritize the survival determinants of construction consulting organization during economic contraction in Nigeria using quantity surveying firms as a focal point.

The study adopted the descriptive-survey design and quantitative data were collected through questionnaire purposely administered to quantity surveying firms in the Southwestern part of Nigeria. The data obtained from 99 quantity surveying firms on survival determinants were analysed using various statistical analysis such as mean score, standard deviation, Mann–Whitney U test, Kruskal–Wallis H test, and so on. Principal component analysis was used to identify the principal components of survival determinants, while the factors were prioritized using fuzzy synthetic evaluation (FSE).

The result of the analysis reveals eight factors that significantly determines the survival of firms during the period of economic contraction. Furthermore, the eight grouped factors were prioritized accordingly namely firm's innovation and diversification, ownership structure and networking, education level and management skills, and so on.

This study investigated the survival determinants of quantity surveying firms and prioritized it with the opinions of principal partners in quantity surveying establishments. As against obtaining large survey responses from all quantity surveyors in the study area that may not have practical experience of managing firms, the limited responses received provide valid basis to broaden the horizon of professionals and other stakeholders on the key determinants for firms to survive economic turmoil.

This study contributes to the body of knowledge by providing information on prioritized factors that must be considered in an appropriate order by quantity surveying firms to survive economic contraction.

This study aims to examine the relationship between the perceptions of local residents concerning political factors and support for tourism development in the Kashmir region.

Primary data have been collected (n = 650) from the residents of the top five tourist destinations (Pahalgam, Gulmarg, Srinagar, Sonamarg and Kokernag) through a pre-tested questionnaire by multistage sampling method. In presence of non-normal data, the partial least squares structural equation model is applied for analysis. The study is based on the theoretical framework of social exchange theory (SET) and institutional theory of political trust (ITPT).

Results suggest that trust in government, the perceived economic performance of government and level of power are negative determinants of support for tourism development, which nullifies SET for politically disturbed regions.

The results of this study are useful for the local government and tourism institutions in policy formation and fill the vast gap in tourism literature with a theoretical base. This study is also an addition to the existing literature on city tourism for the politically disturbed region.

Water soluble epoxy resins were prepared from male‐opimaric acid, linseed fatty acids and epoxy resin. The methylated urea formaldehyde resin and melamine formaldehyde resin were also prepared for curing purposes. The pigmented coating compositions were prepared from water soluble epoxy resins, red oxide and iron and zinc phosphate. These coating compositions showed good water resistance, acid resistance, alkali resistance and lubricating oil resistance.

The objective of the study is to investigate the factors that differentiate long-term shareholder value (LTSV) creating firms from LTSV destroying firms.

Through the review of literature, the hypothesis for the study is developed. To test the hypothesis, the study collects data from S&P BSE 500 companies listed in Bombay Stock Exchange (BSE). Based on the average overall return to shareholders for the period from year 1991 to 2019, the study identifies top 25 LTSV creating and LTSV destroying firms. The top 50 firms form the basis of this study. The study uses descriptive statistics and independent sample t-test to test the hypothesis of the study.

Among the variables investigated such as capital management policy and effective capital management practices, business and financial strategy, intellectual capital strategy, relational capital strategy and human capital strategy, the study found effective capital management and governance as a long-term source of value for shareholders.

The study highlights the importance of inclusion of value-relevant information in the annual report of the company. The study also supports the proposition that discretionary disclosure of intangible assets is relevant for the market to enable market participants to reasonably comprehend the fair value of the firm.

Adoption of a reporting framework that ensures the availability of all value-relevant information including off-balance-sheet resources is in the interest of the investors and policymakers alike.

This is a first such study exploring the value-relevant information and the source of long-term value for listed firms.