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The purpose of this study is to investigate the influence of IFRS convergence on annual report readability in an emerging market context, with an emphasis on the contents of management discussion and analysis (MD&A), notes to the accounts (Notes) and the whole annual report.

The study performs firm-fixed effect regression on a sample of 143 Indian listed companies over a period spanning from 2012 to 2021 to examine the influence of IFRS convergence on readability. This assessment primarily focuses on broader spectrums of readability dimensions, namely annual report length and complexity, wherein complexity is measured using the Gunning Fog, Flesch Reading ease and Flesch-Kincaid grade index.

As Indian firms shift to IFRS reporting, the findings suggest that annual reports have become significantly lengthier and more complex, causing deterioration in readability. The Notes section, in particular, exhibits the most significant increase in length and complexity, followed by the entire annual report and MD&A section. Furthermore, the findings also indicate that the complexity of the Notes section is instrumental in the observed complexity growth of the whole annual report in the post-IFRS period.

The current study employs readability indices rather than directly taking into consideration the opinions of actual users of annual reports to determine readability. As a result, the study does not provide direct evidence on how information in annual reports affects users' readability.

The findings provide insightful information to managers and policymakers about the difficulties stakeholders may encounter while reading IFRS-based annual reports, which ultimately impact their investment decisions. Thus, there is an important managerial implication from this, depending upon the severity of complexity corporations participate in while complying with IFRS in the post-IFRS period.

Analyzing the influence of exogenous information shock, such as IFRS convergence, on readability is critical, particularly for emerging markets like India, where a lack of financial literacy and weaker enforcement already have detrimental effects on the capital market. In light of this, the current study provides a comprehensive examination of the impact of IFRS convergence on annual report readability and contributes to the growing IFRS literature in the less explored emerging market context.

This study aims to empirically investigate the effect of International Financial Reporting Standards (IFRS) convergence on corporate risk disclosure, with a particular emphasis on the quantity and coverage of risk information. The research also conducts economic benefit and cost analysis to investigate the economic implications that may arise from the transition to IFRS reporting.

A content analysis approach is used to measure two broader dimensions of risk disclosure, namely, risk disclosure quantity and risk topic coverage. Furthermore, using firm-fixed effect regression on a sample of 143 Indian-listed companies, this study investigates the variations in these risk disclosure dimensions before (2012–2016) and subsequent to (2017–2021) the convergence with IFRS.

The empirical results of this research demonstrate that IFRS convergence has led to a significant improvement in firms’ risk disclosure across several dimensions. Particularly, during the post-IFRS period, firms’ usage of risk-related words and sentences has considerably surged in MD&A, Notes and whole annual reports. In addition, upon IFRS convergence, firms’ risk descriptions have become more extensive and evenly distributed across risk topic categories. Moreover, the in-depth benefit and cost analysis revealed that firms reporting under IFRS benefit from decreased cost of equity capital, but they also incur a higher cost of audit fees.

This study contributes to the literature in two ways. First, this is the only study, to the best of the authors’ knowledge, to conduct a broader examination of the impact of mandatory IFRS convergence on corporate risk disclosure, with a major focus on quantity and coverage of risk information. Second, by conducting economic benefit and cost analysis, this study provides novel insights into the critical role of IFRS risk disclosures toward multiple economic outcomes.

This study aims to investigate the effects of IFRS convergence on market liquidity and to analyze the firm-level heterogeneity in liquidity effects based on reporting incentive, firm size, ownership structure and firm leverage.

The empirical analysis is based on firm-fixed effect regression using several proxies of market liquidity as dependent variables. The sample consists of 337 firms listed on the National Stock Exchange (NSE) who shifted to IFRS from the financial year 2016–2017.

The empirical findings indicate that IFRS convergence has contributed to the significant increase in market liquidity in a weaker enforcement country, i.e. India. Additionally, when the study performs the heterogeneity test of IFRS impact, the results indicate the presence of significant cross-sectional differences in such liquidity effects across firms. Thus, altogether the findings suggest that both accounting convergence and firm-level factors are likely to be the mechanism underlying the observed improvement in market liquidity.

In the current literature, there is an ongoing debate about whether the observed post-IFRS effects are driven by the change in accounting standard per se or by other related factors. Therefore, by studying the liquidity effects of IFRS convergence in India, this study provides evidence regarding the sources of the documented IFRS effects. Moreover, the study indicates the significance of firm-level factors in determining the observed liquidity outcomes around IFRS adoption, which is unique to the literature.

Generation scheduling (GS) is the most prominent and hard-hitting problem in the electrical power industry especially in an integrated power system. Countless techniques have been used so far to solve this GS problem for proper functioning of the units in the power system to dispatch the load economically to consumers at once. Therefore, this work aims to study for the best possible function of integrated power plants to obtain the most favourable solution to the GS problem.

An appropriate method works in a proper way and assures to give the best solution to the GS problem. The finest function of incorporated power plants should be mathematically devised as a problem and via that the aim of the GS problem to minimize the total fuel cost subject to different constraints will be achieved. In this research work, the latest meta-heuristic and swarm intelligence-based technique called grey wolf optimization (GWO) technique is used as an optimization tool that will work along with the formulated problem for correct scheduling of generating units and thus achieve the objective function.

The recommended GWO technique provides the best feasible solution which is optimal in its performance for different test cases in the GS problem of integrated power plant. It is further found that the obtained solutions using GWO method are better than the former reports of other traditional methods in terms of solution excellence. The GWO method is found to be unique in its performance and having superior computational efficiency.

Decision making is significant for effective operation of integrated power plants in an electrical power system. The recommended tactic implements a modern meta-heuristic procedure that is applied to diverse test systems. The method that is proposed is efficient in providing the best solutions of solving GS problems. The suggested method surpasses the early techniques by offering the most excellent feasible solutions. Thus, it is obvious that the proposed method may be the appropriate substitute to attain the optimal operation of GS problem.

Renewable energy sources are discontinuous and infrequent in nature, and it is tough to predict them in general. Further, integrating renewable energy source-based plants with the conventional plant is extremely difficult to operate and maintain. Operation of integrated power system is full of challenges and complications. To handle those complications and challenges, the GWO algorithm is suggested for solving the GS problem and thus obtain the optimal solution in integrated power systems by considering the reserve requirement, load balance, equality and inequality constraints.

The proposed system should be further tested on diverse test systems to evaluate its performance in solving a GS problem and the results should be compared. Computation results reveal that the proposed GWO method is efficient in attaining best solution in GS problem. Further, its performance is effectively established by comparing the result obtained by GWO with other traditional methods.

This paper aims to propose Hidden Converter (H-Converter) combined with dual port 3Ø inverter for energy storage application to produce wide range of voltage. Some of the application required wide range of voltages, but problem from E-chopper is either boost or buck mode of operations, both modes are not possible. To overcome this drawback, H-Converter is combined with dual port 3Ø inverter controlled by carrier-based pulse width modulation (CB-PWM) technique is added with zero sequence injection.

Hidden converter is a bidirectional DC-DC chopper used to convert fixed DC to variable DC and vice versa in both buck and boost modes of operations. Dual port inverter is combined with hidden DC-DC converter can produce wide range of voltages.

The bidirectional DC-AC converter requires less power for processing and consumes less power losses by using modest carrier built- pulse width modulation scheme through proposed zero structure addition.

By using this proposed strategy H-Converter can produce wide range of voltage in both the sides and mostly power is processed in the 3Ø inverter with a one stage conversion with less power loss. As a result, with one stage power conversion has more efficiency because of less power loss. This proposed converter has designed by analysis, and the real time result is tested in an experiment.

The purpose of this paper is to show that a popular conception among the practitioners is that Total Quality Service (TQS) has a long‐range perspective and the organizations that have adopted TQS could be able to see the results of their quality improvement initiatives only after a long duration. Hence firms that have been practicing TQS are interested to know the time duration before which they can get back the returns on investments in quality improvement programmes.

The present study attempts to empirically investigate the impact of TQS age on quality and operational performance. In order to examine the impact of TQS age on the different dimensions of TQS, out of the 12 critical dimensions of TQS, the two outcome constructs, namely Customer Focus and Satisfaction and Employee Satisfaction, have been considered for measuring quality performance while the other ten dimensions have been considered for measuring operational performance.

The results imply that the firms can get back their returns on investments in their quality management initiatives within three years of TQS implementation. The ways, commitment and the competence with which the TQS implementation is carried out are more important rather than the duration of TQS implementation. Further, among the successful firms, it has been observed that as TQS age increases, quality and operational performance also increases, since firms get more and more experienced in effective implementation of TQS in a continuously improving environment.

The impact of TQS age on quality and operational performance has been investigated in a developing economy with respect to the automobile service industry. Primarily this is a research paper and the results and findings will be of immense use to the business executives of service firms.

Among various metal oxide nano particles, MgO NPs and ZnO nanoparticles (NPs) in particular are gaining increasing attention due to their multifunctional characteristics, low cost and compatibility with textile materials. Each type of nanoparticle excels over others in certain properties. As such, it is often crucial to carry out comparative studies of NPs to identify the one showing higher efficiency/output for particular applications of textile products.

In the investigation reported in this paper, ZnO NPs and MgO NPs were synthesised via sol-gel technique and characterised. For comparative analysis, the synthesised NPs were evaluated for multiple properties using standard procedures before and after being applied on cotton fabrics by a dip-pad-dry-cure method.

XRD and FTIR analyses confirmed the successful synthesis of ZnO and MgO NPs. Homogeneous formation of desired NPs and their dense and uniform deposition on the cotton fibre surface were observed using SEM. ZnO NPs and MgO NPs coatings on cotton were observed to significantly enhance self-cleaning/stain removal properties achieving Grade 5 and Grade 4 categories, respectively. In terms of ultraviolet (UV) protection, ZnO or MgO NP coated fabrics showed UPF values of greater than 50, i.e. excellent in blocking UV rays. MgO NPs exhibited 20% cleaning efficiency in treating reactive dye wastewater against ZnO NPs which were 4% efficient in the same treatment, so MgO was more suitable for such type of treatments at low cost. Both NPs were able to impart multifunctionality to cotton fabrics as per requirement of the end products. However, ZnO NPs were better for stain removal from the fabrics while MgO NPs were appropriate for UV blocking.

It was therefore clear that multifunctional textile products could be developed by employing a single type of cost effective and efficient nano particles.

In line with computational technological advances, obtaining optimal solutions for engineering problems has become attractive research topics in various disciplines and real engineering systems having multiple objectives. Therefore, it is aimed to ensure that the multiple objectives are simultaneously optimized by considering them among the trade-offs. Furthermore, the practical means of solving those problems are principally concentrated on handling various complicated constraints. The purpose of this paper is to suggest an algorithm based on symbiotic organisms search (SOS), which mimics the symbiotic reciprocal influence scheme adopted by organisms to live on and breed within the ecosystem, for constrained multi-objective engineering design problems.

Though the general performance of SOS algorithm was previously well demonstrated for ordinary single objective optimization problems, its efficacy on multi-objective real engineering problems will be decisive about the performance. The SOS algorithm is, hence, implemented to obtain the optimal solutions of challengingly constrained multi-objective engineering design problems using the Pareto optimality concept.

Four well-known mixed constrained multi-objective engineering design problems and a real-world complex constrained multilayer dielectric filter design problem are tackled to demonstrate the precision and stability of the multi-objective SOS (MOSOS) algorithm. Also, the comparison of the obtained results with some other well-known metaheuristics illustrates the validity and robustness of the proposed algorithm.

The algorithmic performance of the MOSOS on the challengingly constrained multi-objective multidisciplinary engineering design problems with constraint-handling approach is successfully demonstrated with respect to the obtained outperforming final optimal designs.

Assembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization criteria. Applications of evolutionary algorithms have shown a lot of promise in terms of lower computational cost and time. But there remain challenges like achieving global optimum in least number of iterations with fast convergence speed, robustness/consistency in finding global optimum, etc. With the above challenges in mind, this study aims to propose an improved flower pollination algorithm (FPA) and hybrid genetic algorithm (GA)-FPA.

In view of slower convergence rate and more computational time required by the previous discrete FPA, this paper presents an improved hybrid FPA with different representation scheme, initial population generation strategy and modifications in local and global pollination rules. Different optimization objectives are considered like direction changes, tool changes, assembly stability, base component location and feasibility. The parameter settings of hybrid GA-FPA are also discussed.

The results, when compared with previous discrete FPA and GA, memetic algorithm (MA), harmony search and improved FPA (IFPA), the proposed hybrid GA-FPA gives promising results with respect to higher global best fitness and higher average fitness, faster convergence (especially from the previously developed variant of FPA) and most importantly improved robustness/consistency in generating global optimum solutions.

It is anticipated that using the proposed approach, assembly sequence planning can be accomplished efficiently and consistently with reduced lead time for process planning, making it cost-effective for industrial applications.

Different representation schemes, initial population generation strategy and modifications in local and global pollination rules are introduced in the IFPA. Moreover, hybridization with GA is proposed to improve convergence speed and robustness/consistency in finding globally optimal solutions.

The purpose of this paper is to establish a tolerance optimization method based on manufacturing difficulty computation using the genetic algorithm (GA) method. This proposal is among the authors’ perspectives of accomplished previous research work to cooperative optimal tolerance allocation approach for concurrent engineering area.

This study introduces the proposed GA modeling. The objective function of the proposed GA is to minimize total cost constrained by the equation of functional requirements tolerances considering difficulty coefficients. The manufacturing difficulty computation is based on tools for the study and analysis of reliability of the design or the process, as the failure mode, effects and criticality analysis (FMECA) and Ishikawa diagram.

The proposed approach, based on difficulty coefficient computation and GA optimization method [genetic algorithm optimization using difficulty coefficient computation (GADCC)], has been applied to mechanical assembly taken from the literature and compared to previous methods regarding tolerance values and computed total cost. The total cost is the summation of manufacturing cost and quality loss. The proposed approach is economic and efficient that leads to facilitate the manufacturing of difficult dimensions by increasing their tolerances and reducing the rate of defect parts of the assembly.

The originality of this new optimal tolerance allocation method is to make a marriage between GA and manufacturing difficulty. The computation of part dimensions difficulty is based on incorporating FMECA tool and Ishikawa diagram This comparative study highlights the benefits of the proposed GADCC optimization method. The results lead to obtain optimal tolerances that minimize the total cost and respect the functional, quality and manufacturing requirements.