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The purpose of this paper is to empirically investigate the impact of board characteristics such as size, independence, busyness and duality on the extent of discretionary disclosures of listed Indian firms.

A disclosure index with 110 items was constructed to assess the discretionary disclosures in the annual reports of listed firms. The study measured disclosure using 1,024 firm-year observations over 8 years from 2009 to 2016. Board characteristics such as size, independence, busyness and duality have been used in the study as indicators of corporate governance.

The results indicate that while the proportion of independent directors positively impacts the extent of discretionary disclosures, boards with duality and the busyness of the director have a negative impact. The size of the board does not significantly impact the extent of disclosures.

This study examines the discretionary disclosures made only in the annual reports. Future studies could examine information disclosed in other media. Moreover, this study uses an un-weighted self-constructed disclosure index, which is subject to its inherent limitations.

This study has examined the impact of the “busyness” of the director on the extent of disclosures. This variable has not been explored in prior studies. The significance of the variable indicates that the number of directorships held impacts the efficiency with which a director performs his/her role in the board. The study reiterates the need for firms and policymakers to focus on improving board independence and to move away from leadership structures with duality.

The aim of this paper is to contribute to the services marketing literature by developing a scale based on Parasuraman’s SERVQUAL scale for the measurement of distributor perceived service quality at the distributor–manufacturer interface of the pharmaceutical supply chain.

Based on a literature review and discussions with experts, a questionnaire was designed basing on the widely used service quality measurement scale (SERVQUAL). Personal survey was conducted among selected distributors spread over three major cities of the Indian pharmaceutical market. The study used the exploratory factor analysis to identify the critical factors of service quality followed by the confirmatory factor analysis (AMOS 20).

A valid scale with four dimensions – (reliability, assurance, responsiveness and communication) and 13 items for measuring the distributor perceived service quality was developed which also satisfied all the reliability and validity tests. The findings of the present study indicate that distributor perceived service quality has an effect on satisfaction.

The proposed scale is an attempt to explore the less researched area. This study will give further insights to researchers to measure service quality at different phases of the pharmaceutical supply chain. The study is limited to three cities; it can be extended to other regions of the country. This study will be helpful to the practicing managers to measure the service quality and improve the performance in the pharmaceutical supply chain.

Service quality in pharmaceutical supply chain is very important, as it directly effects the health of the people, so the proposed scale can be used to control the quality of service.

The scale developed in this study can also be used for measuring distributor perceived service quality in other manufacturing sectors. This research provides direction and scope for further research to develop new concepts and models in measuring service quality in the supply chain.

To enhance the performance transmit antenna selection (TAS) of spatial modulation (SM), systems technique needs to be essential. This TAS is an effective technique for reducing the multiple input multiple output (MIMO) systems computational difficulty, and bit error rate (BER) can increase remarkably by various TAS algorithms. But these selection methods cannot provide code gain, so it is essential to join the TAS with external code to obtain cy -ode gain advantages in BER.

In this paper, Bose–Chaudhuri–Hocquenghem (BCH)-Turbo code TC is combined with the orthogonal space time block code system.

In some existing work, the improved BER has been perceived by joining forward error correction code and space time block code (STBC) for MIMO systems provided greater code gain. The proposed work can provide increasing code gain and the effective advantages of the TAS-OSTBC system.

To perform the system analysis, Rayleigh channel is used. In the case with multiple TAS-OSTBC systems, better performance can provide by this new joint of the BCH-Turbo compared to the conventional Turbo code for the Rayleigh fading.

The Internet of Things (IoT), as one of the new digital technologies, has created wide applications in various industries, and one of the most influential industries of this technology is the transportation industry. By integrating the IoT with the transportation industry, there will be dramatic changes in the industry, and it will provide many entrepreneurial opportunities for entrepreneurs to develop new businesses. Opportunity identification is at the heart of the entrepreneurial process, and entrepreneurs identify innovative goods or services to enter a new market by identifying, evaluating, and exploiting opportunities. Despite the desire of transportation managers to invest in the IoT and the increase in research in this area, limited research has focused on IoT-based entrepreneurial opportunities in the transportation industry. Therefore, the present study aims to identify IoT-based entrepreneurial opportunities in the transportation industry and examine their importance.

To achieve the research objective, the authors applied a mixed approach. First, adapting the lens of the industry value chain theory, a comprehensive literature review, besides a qualitative approach including semi-structured interviews with experts and thematic analysis, was conducted to identify the entrepreneurial opportunities. The identified opportunities were confirmed in the second stage using a quantitative survey method, including the Student t-test and factor analysis. Finally, the identified opportunities were weighted and ranked using the best worst method (BWM).

Entrepreneurial opportunities are classified into five main categories, including “smart vehicles”, “business partners/smart transportation supply side”, “supporting services”, “infrastructures”, and “smart transport management and control”. The infrastructures group of opportunities ranked the highest amongst the identified groups.

This study adds to the digital entrepreneurship opportunity recognition literature by addressing opportunities in a smart industry propelled by digital technologies, including developing new products or new applications of the available technologies. Additionally, inspired by the industry value chain theory, this article develops a framework including various digital entrepreneurial opportunity networks which are necessary to add value to any industry and, thus, could be applied by entrepreneurs to recognize opportunities for new intermediaries to enter other digital-based industries. Finally, the present study identifies the IoT-based entrepreneurial opportunities in the smart transportation industry and prioritizes them, providing practical insights regarding the creation of entrepreneurial ecosystems in the field of smart transportation for entrepreneurs and policymakers.

Noise abatement is one of the key techniques for Partial Discharge (PD) on-line measurement and monitoring. However, how to enhance the efficiency of PD signal noise suppression is a challenging work. Hence, this study aims to improve the efficiency of PD signal noise abatement.

In this approach, the time–frequency characteristics of PD signal had been obtained based on fast kurtogram and S-transform time–frequency spectrum, and these characteristics were used to optimize the parameters for the signal matching over-complete dictionary. Subsequently, a self-adaptive selection of matching atoms was realized when using Matching Pursuit (MP) to analyze PD signals, which leading to seldom noise signal element was represented in sparse decomposition.

The de-noising of PD signals was achieved efficiently. Simulation and experimental results show that the proposed method has good adaptability and significant noise abatement effect compared with Empirical Mode Decomposition, Wavelet Threshold and global signal sparse decomposition of MP.

A self-adaptive noise abatement method was proposed to improve the efficiency of PD signal noise suppression based on the signal sparse representation and its MP algorithm, which is significant to on-line PD measurement.

The design phase is generally characterized with two-way multiple information exchanges/overlaps between the interdependent entities. In this paper, entity is a generic term to represent teams, components, activities or parameters. Existing approaches can either capture a single overlap or lack practical application in representing multiple overlaps. The beeline diagraming method (BDM) network is efficient in representing multiple overlaps for construction projects. However, it considers any entity as indivisible and cannot distinguish partial criticality of entities. In reality, the design phase in any construction project is driven by need basis and often has numerous interruptions. Hence, there is a need to develop an alternate network analysis for BDM for interruptible execution. The paper aims to discuss these issues.

A pilot study is conducted to formulate the hypothetical examples. Subsequently, these hypothetical BDM examples are analyzed to trace a pattern for criticality. This pattern study along with the existing precedence diagramming method network analysis enabled to derive new equations for forward pass, backward pass and float. Finally, the proposed concepts are applied to two design cases and reviewed with the design experts.

The proposed network analysis for BDM is efficient for interruptible entity execution.

The proposed BDM network is an information-intensive network that enables the design participants to view the project holistically. Application to two distinct cases emphasizes that the concept is generic and can be applied to any project that is characterized with beelines.

An alternate network analysis for BDM is investigated for interruptible entity execution. This study also clarifies the related concepts – interdependency, iteration, overlaps and multiple information exchanges/linkages.

Music sentiment analysis helps to promote the diversification of music information retrieval methods. Traditional music emotion classification tasks suffer from high manual workload and low classification accuracy caused by difficulty in feature extraction and inaccurate manual determination of hyperparameter. In this paper, the authors propose an optimized convolution neural network-random forest (CNN-RF) model for music sentiment classification which is capable of optimizing the manually selected hyperparameters to improve the accuracy of music sentiment classification and reduce labor costs and human classification errors.

A CNN-RF music sentiment classification model is designed based on quantum particle swarm optimization (QPSO). First, the audio data are transformed into a Mel spectrogram, and feature extraction is conducted by a CNN. Second, the music features extracted are processed by RF algorithm to complete a preliminary emotion classification. Finally, to select the suitable hyperparameters for a CNN, the QPSO algorithm is adopted to extract the best hyperparameters and obtain the final classification results.

The model has gone through experimental validations and achieved a classification accuracy of 97 per cent for different sentiment categories with shortened training time. The proposed method with QPSO achieved 1.2 and 1.6 per cent higher accuracy than that with particle swarm optimization and genetic algorithm, respectively. The proposed model had great potential for music sentiment classification.

The dual contribution of this work comprises the proposed model which integrated two deep learning models and the introduction of a QPSO into model optimization. With these two innovations, the efficiency and accuracy of music emotion recognition and classification have been significantly improved.

Cloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.

VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.

The proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.

User’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.

Scheduling in information-driven design phase of construction projects is challenging due to multiple entity types (teams, components, deliverables, activities or parameters) and their dependencies/linkages. Established techniques such as dependency structure matrix (DSM), beeline diagramming method (BDM), multiple domain matrix (MDM), etc. have been independently utilized in past to model information dependencies/linkages and associated iteration. However, there has not been a holistic solution yet for scheduling multiple entity types and their relationships. Hence, an integrated solution needs to be developed that schedules information-driven projects accurately.

A case study data collection approach is utilized. With data from two projects, i.e. hostel design and highway design, a BDM–MDM integrated solution was developed and applied to the same. Feedback from experts was obtained for refinements.

The proposed solution is efficient for scheduling multiple entity types and their information dependencies/linkages.

The proposed integrated solution enables the project participants to schedule information-driven projects systematically. Application to two distinct design cases emphasizes that the concept is generic and can be applied to any information-driven project with multiple entity types.

The BDM–MDM integrated solution concept is investigated for scheduling multiple entity types in any information-driven projects. This study also explored the terminologies such as multiple entity types and information-driven scheduling.