Search results

This study aims to examine the environmental effects of plastic waste on the atmosphere and its implications for disaster waste management. It focuses on using ammonia, pyrolyzed plastic oil and the effectiveness of alumina nanoparticles as a catalyst.

The research explores different combinations of conventional diesel and nano Al₂O₃ derived from pyrolyzed plastic oil (ranging from P10 to P40). Critical performance metrics evaluated include brake mean effective pressure (BMEP), brake specific fuel consumption, brake thermal efficiency and emissions of CO₂, CO and NOx. The study specifically investigates the impact of adding 50 ppm of Al₂O₃ nanoparticles to these blends.

The findings indicate that using blended fuels with nanoadditives significantly lowers pollution. Specifically, the P30 blend with 50 ppm of Al₂O₃ nanoparticles greatly reduced CO emissions. Additionally, the same blend reduced NOx emissions and CO₂ emissions. The P30 mix showed improved BMEP and brake thermal efficiency due to its density, calorific value and viscosity (6.3 bar). The P30 blend exhibited higher thermal efficiency due to decreased heat loss, whereas conventional diesel demonstrated the best mechanical efficiency due to its longer ignition delay.

This study highlights the potential of using Al₂O₃ nanoparticles and pyrolyzed plastic oil to reduce emissions and enhance the performance of internal combustion engines. It underscores the environmental benefits and implications for disaster waste management by converting plastic waste into useful resources and reducing air pollution.

This study aims to examine the correlation between the readability of financial statements and the likelihood of future stock price crashes in nonfinancial companies listed on the Egyptian Stock Exchange. It further explores the possible moderating effect of audit quality on this relationship.

The study uses ordinary least squares regression, generalized least squares estimation and two-stage least squares methodology to examine and validate the research hypotheses. The sample comprises 107 nonfinancial companies registered on the Egyptian Stock Exchange from 2016 to 2019.

The results reveal a significant negative association between the readability of financial statements and stock price crash risk. This suggests that companies with more complex financial statements tend to experience higher future crash risks. Additionally, the study identifies audit quality as a significant moderating factor. Higher audit quality, often indicated by engagements with Big-4 audit firms, strengthens the influence of financial statements readability on stock price crash risk. This implies that while high audit quality enhances investor confidence and market stability, it also accentuates the negative consequences of complex financial statements.

The findings of this paper have significant implications for regulators and standard-setting bodies in Egypt. They should consider refining and revising existing standards to emphasize the importance of enhancing the readability of financial reports. Additionally, auditing firms should actively engage in efforts to ensure clearer and more transparent financial reporting. These actions are vital for boosting investor confidence, strengthening Egypt’s capital market and mitigating potential risks associated with information opacity and complexity.

This study represents a pioneering endeavor within the Arab and Egyptian financial environments. To the best of the author’s knowledge, it is the first examination of the association between the readability of financial statements and stock price crash risk in these contexts. Furthermore, it explores factors such as audit quality that may influence this connection.

This paper aims to examine the theoretical underpinnings of international financial reporting standards (IFRS)-related studies and offers directions for theoretical and empirical research. Specifically, this study examines the main theories in IFRS adoption research (i.e. adoption, compliance and effects).

The sample contains 67 empirical papers that have used theories and was collected from Web of Science database. This study uses a systematic review technique.

Generally, the review shows the prevalent and pervasive use of institutional theories of isomorphism across all the three areas of IFRS adoption. Particularly, regarding IFRS adoption stream, this study finds the institutional theory as a dominant theory used to explain IFRS diffusion around the globe. For IFRS compliance, this study finds that the agency and the capital need theories are widely used. For IFRS adoption effects stream, this study finds a few studies using the contingency and neo-institutional theories. Overall, the review provides theoretical lens for IFRS adoption, IFRS compliance and IFRS adoption effects.

Given the lack of a well-defined set of theories in the domain of accounting, the findings provide further guidance on theory building within the field. Further, accounting regulators, academics and practitioners may benefit from the findings when explaining various changes in the world of accounting.

Bullying negatively affects victims’ mental health and has been shown to be associated with anxiety and depression. Moreover, many studies have reported the prevalence of mistreatment among medical students (MS), interns and junior physicians. However, there are limited studies on bullying in the Middle East, and no studies on higher education in Bahrain. The authors’ aim was to investigate and compare the prevalence of bullying between MS at a major public university and non-medical students (NMS) at a private university in Bahrain. The authors also sought to explore the associations between bullying, depression and anxiety.

The authors conducted a survey-based cross-sectional study from October 1 to December 31, 2018, using convenient sampling method. A total of 300 students (150 MS and 150 NMS) participated in the study by answering a questionnaire that explored exposure to different types of bullying. In addition, standard anxiety and depression instruments were distributed to students.

The results showed that MS were more exposed to bullying than NMS, with a significant number (41%) reporting mistreatment from multiple sources, including teachers, consultants and peers. Furthermore, the authors found that bullying was significantly correlated with psychological health, anxiety and depression.

There are limited studies on bullying in the Middle East and no studies on the topic of higher education in Bahrain. Furthermore, the learning environment of tertiary education institutions can be improved by addressing the concerns associated with bullying identified in this study.

The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.

This study aims to identify the bioactive compounds in the Onion (Allium burdickii [A.B]) bulb using Raman and Fourier transform infrared spectroscopy (FTIR) spectroscopy. It assessed the extraction conditions of bioactive compounds from A.B. while evaluating the best extraction conditions.

The research opted for an experimental qualitative approach. It examined the extraction conditions of A.B., namely, temperature (°C), time (min) and mass-to-liquor ratio (M:R) using ultraviolet-visible spectrophotometry. Identification of bioactive compounds present in the dye was performed using Raman spectroscopy and the validation of the results was done by FTIR spectroscopy.

The study determined the best extraction conditions (time, temperature, M:R) for A.B bulb. The study confirmed the presence of bioactive compounds.

The limitation was quantification of bioactive compounds in A.B bulb.

The findings prove that the A.B. bulb can provide a sustainable source of bioactive compounds (functionalized compounds). The study provides suitable extraction conditions for A.B. and further elaborates on the techniques for identifying bioactive compounds in A.B. bulb extracts.

The study provides A.B. as a source of bioactive compounds and a clean dye for textile coloration.

To the best of the authors’ knowledge, there is no documented study on the qualitative analysis of bioactive compounds in A.B using Raman and FTIR. Therefore, the study fulfils the identified need to ascertain alternative procedures for the analysis of bioactive compounds.

The purpose of the study is to examine the factors that influence people's intention to adopt electric vehicles (EVs). The study used an expanded theory of planned behaviour (TPB) along with the addition of perceived trust and consideration of future consequences (CFC).

The study is based on primary data collected from 282 respondents in Punjab through a questionnaire. The collected data were analysed using SPSS 26 and AMOS 24 to perform confirmatory factor analysis and structural equation modelling.

The empirical analysis indicates that attitude, subjective norms and perceived trust have a significant influence on the adoption intention (AI) of EVs.

The implications provide policymakers and marketers with better insights into marketing EVs in the trajectory of sustainable mobility. Marketers should create focussed marketing materials that highlight the environmental ease of use, lower running costs and sophisticated technology of electric automobiles. The limitations of the study provide valuable insights for future researchers.

This is the first study that uses CFC with the TPB model in the context of AI of EVs specifically for the Punjab region.

Dietary components have been mentioned as modifiable risk factors in the development of gallstone disease (GSD), but it has been less addressed. The present study aimed to investigate the potential association between different types of dietary carbohydrate and the risk of gallstone.

In this case-control study 189 patients diagnosed with GSD as a case group and 342 people as a control group were enrolled. Dietary intake of the participants was collected through a 168-item semi-quantitative food frequency questionnaire. Total intakes of calories, macronutrients and different types of carbohydrate were estimated. Crude and multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the association between carbohydrate intake and GSD.

Patients with gallstone consumed significantly more fructose and sucrose and less fiber. After fully adjustment, the logistic regression indicated significant association between GSD with dietary intake of total carbohydrate (OR = 1.65, 95% CI: 1.1–2.4, p = 0.009), sugar (OR = 1.23, 95% CI: 0.8–1.7, p = 0.014), fructose (OR = 2.5, 95% CI: 1.7–3.9, p < 0.001), glucose (OR = 1.9, 95% CI: 1.3–2.9, p = 0.002) and sucrose (OR = 1.37, 95% CI: 0.9–1.6, p = 0.042). Also, increasing intakes of lactose, galactose and maltose were associated with a decrease in the risk of GSD, but not statistically significant, although lactose was close to significance (OR = 0.71, 95% CI: 0.48–1, p = 0.051).

There is a positive and significant relationship between total carbohydrate, sugar, fructose, glucose and sucrose intake and the occurrence of gallstone.

This paper aims to examine and compare the export performance and competitiveness of Indian and Chinese textile and clothing industry in post-multifibre arrangement (MFA) era.

Balassa’s revealed comparative advantage Index is used to assess the competitiveness of Indian and Chinese textile and clothing exports.

The results indicate that China’s textiles and garments sector holds a greater proportion of the global market compared with India. India has a robust comparative advantage in silk, carpets and cotton post-MFA. Vegetable textile fibers, paper yarn and woven fabrics of paper yarn are also competitive. China had a strong comparative advantage in silk and fabrics; special woven fabrics, tafted textile fabrics, lace, tapestries, trimmings and embroidery in 2005. China also recorded comparative advantage in silk, man-made filaments: strip and the like of man-made textile materials, fabrics; special woven fabrics, tafted textile fabrics, lace, tapestries, trimmings and embroidery and fabrics; knitted or crocheted in 2021.

This study’s results and recommendations could assist the Indian and Chinese Governments develop policies to upgrade their garment industries.

Though vast literature reviews are available for textile and apparel export performance in India and China separately, there are few studies on comparisons. This study is a significant attempt to evaluate India and China’s competitiveness in the global market.

Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems required computationally efficient calibration techniques. This paper aims to improve localization accuracy by identifying obstacles in the optimization process and network scenarios.

The proposed method is used to incorporate distance estimation between nodes and packet transmission hop counts. This estimation is used in the proposed support vector machine (SVM) to find the network path using a time difference of arrival (TDoA)-based SVM. However, if the data set is noisy, SVM is prone to poor optimization, which leads to overlapping of target classes and the pathways through TDoA. The enhanced gray wolf optimization (EGWO) technique is introduced to eliminate overlapping target classes in the SVM.

The performance and efficacy of the model using existing TDoA methodologies are analyzed. The simulation results show that the proposed TDoA-EGWO achieves a higher rate of detection efficiency of 98% and control overhead of 97.8% and a better packet delivery ratio than other traditional methods.

The proposed method is successful in detecting the unknown position of the sensor node with a detection rate greater than that of other methods.