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The purpose of this paper is to improve energy efficiency and further reduction of side lobe level the algorithm proposed is firework algorithm. In this paper, roused by the eminent swarm conduct of firecrackers, a novel multitude insight calculation called fireworks algorithm (FA) is proposed for work enhancement. The FA is introduced and actualized by mimicking the blast procedure of firecrackers. In the FA, two blast (search) forms are utilized and systems for keeping decent variety of sparkles are likewise all around planned. To approve the presentation of the proposed FA, correlation tests were led on nine benchmark test capacities among the FA, the standard PSO (SPSO) and the clonal PSO (CPSO).

The antenna arrays are used to improve the capacity and spectral efficiency of wireless communication system. The latest communication systems use the antenna array technology to improve the spectral efficiency, fill rate and the energy efficiency of the communication system can be enhanced. One of the most important properties of antenna array is beam pattern. A directional main lobe with low side lobe level (SLL) of the beam pattern will reduce the interference and enhance the quality of communication. The classical methods for reducing the side lobe level are differential evolution algorithm and PSO algorithm. In this paper, roused by the eminent swarm conduct of firecrackers, a novel multitude insight calculation called fireworks algorithm (FA) is proposed for work enhancement. The FA is introduced and actualized by mimicking the blast procedure of firecrackers. In the FA, two blast (search) forms are utilized and systems for keeping decent variety of sparkles are likewise all around planned. To approve the presentation of the proposed FA, correlation tests were led on nine benchmark test capacities among the FA, the standard PSO (SPSO) and the clonal PSO (CPSO). It is demonstrated that the FA plainly beats the SPSO and the CPSO in both enhancement exactness and combination speed. The results convey that the side lobe level is reduced to −34.78dB and fill rate is increased to 78.53.

Samples including 16-element LAAs are conducted to verify the optimization performances of the SLL reductions. Simulation results show that the SLLs can be effectively reduced by FA. Moreover, compared with other benchmark algorithms, fireworks has a better performance in terms of the accuracy, the convergence rate and the stability.

With the use of algorithms radiation is prone to noise one way or other. Even with any optimizations we cannot expect radiation to be ideal. Power dissipation or electro magnetic interference is bound to happen, but the use of optimization algorithms tries to reduce them to the extent that is possible.

16-element linear antenna array is available with latest versions of Matlab.

The latest technologies and emerging developments in the field of communication and with exponential growth in users the capacity of communication system has bottlenecks. The antenna arrays are used to improve the capacity and spectral efficiency of wireless communication system. The latest communication systems use the antenna array technology which is to improve the spectral efficiency, fill rate and the energy efficiency of the communication system can be enhanced.

By using FA, the fill rate is increased to 78.53 and the side lobe level is reduced to 35dB, when compared with the bench mark algorithms.

BotGo was started in 2007 by Ravi Panchal, an engineer, after he lost motivation to continue at a managerial role at his job. A hands-on technical person, Panchal was inspired to create an underwater tank-cleaning robot. He started BotGo by bootstrapping it with his savings and roped in his friends for key positions in the company. He also started workshops for robotics education in colleges in order to sustain the company; he called this initiative BotLearn. In 2009, BotGo was incubated and Panchal started franchises for BotLearn as part of his growth plans. This led to a crisis within the company, escalating to a point where Panchal was forced to consider options.

This case highlights the importance of a product-to-market fit and examines the decision to franchise in view of the case facts. The case also points towards the mistakes in crisis management, with particular emphasis on channel management.

Towards the end of the case, Panchal is faced with a dilemma on whether to continue with the franchises or close them down. The dilemma is further accentuated since Panchal's decision would ultimately affect the growth of BotGo as well as directly challenge his intention to franchise.

The purpose of this study is to develop the dictionary with grammar and multiword structure has to be used in conjunction with sentiment analysis to investigate the relationship between financial news and stock market volatility.

An algorithm has been developed for calculating the sentiment orientation and score of data with added information, and the results of calculation have been integrated to construct an empirical model for calculating stock market volatility.

The experimental results reveal a statistically significant relationship between financial news and stock market volatility. Moreover, positive (negative) news is found to be positively (negatively) correlated with positive stock returns, and the score of added information of the news is positively correlated with stock returns. Model verification and stock market volatility predictions are verified over four time periods (monthly, quarterly, semiannually and annually). The results show that the prediction accuracy of the models approaches 66% and stock market volatility with a particular trend-predicting effect in specific periods by using moving window evaluation.

Only one news source is used and the research period is only two years; thus, future studies should incorporate several data sources and use a longer period to conduct a more in-depth analysis.

Understanding trends in stock market volatility can decrease risk and increase profit from investment. Therefore, individuals or businesses can feasibly engage in investment activities for profit by understanding volatility trends in capital markets.

The ability to exploit textual information could potentially increase the quality of the data. Few scholars have applied sentiment analysis in investigating interdisciplinary topics that cover information management technology, accounting and finance. Furthermore, few studies have provided support for structured and unstructured data. In this paper, the efficiency of providing the algorithm, the model and the trend in stock market volatility has been demonstrated.

The case describes the performance evaluation system that has been put in place by Ravi Kumar, the MD to ensure that Oystar Hassia is able to design, deliver, service, sell its packaging machines seamlessly in all parts of the world. The performance evaluation system is periodic, regular, able to take track the progress of the people within the system. The benefits accrued from performance evaluation system are also detailed in this case.

The transition from centralized thermal power plants to distributed renewable energy sources complicates the balance between power supply and load demand in electrical networks. Energy storage systems (ESS) offer a viable solution to this challenge. This research aims to analyze the factors influencing the implementation of ESS in the Indian smart grid.

To analyze the factors affecting ESS deployment in the grid, the SAP-LAP framework (situation-actor-process and learning-action-performance) integrated with e-IRP (efficient-interpretive ranking process) was used. The variables of SAP-LAP elements were selected from expert opinion and a literature review. Here, e-IRP was utilized to prioritize elements of SAP-LAP (actors in terms of processes and actions in terms of performance).

This analysis prioritized five stakeholders in the Indian power industry for energy storage implementation: government agencies/policymakers, ESS technology developers/manufacturers, private players, research and development/academic institutions, and contractors. Furthermore, the study prioritized the necessary actions that these stakeholders must take.

The study’s findings help identify actors and manage different actions in implementing grid energy storage integration. Ranking these variables would help develop a strategic roadmap for ESS deployment and decisions about adopting new concepts.

It is one of the first attempts to analyze factors influencing ESS implementation in the power grid. Here, qualitative and quantitative methodologies are used to identify and prioritize various aspects of ESS implementation. As a result, the stakeholder can grasp the concept much more quickly.

In the face of complex and challenging economic and business environments, developing and implementing approaches to predict bankruptcy has become important for firms. Bankruptcy prediction can be regarded as a grey system problem because while factors such as the liquidity, solvency and profitability of a firm influence whether it goes bankrupt, the precise manner in which these factors influence the discrimination between failed and non-failed firms is uncertain. In view of the applicability of multivariate grey prediction models (MGPMs), this paper aimed to develop a grey bankruptcy prediction model (GBPM) based on the GM (1, N) (BP-GM (1, N)).

As the traditional GM (1, N) is designed for time series forecasting, it is better to find an appropriate permutation of firms in the financial data as if the resulting sequences are time series. To solve this challenging problem, this paper proposes GBPMs by integrating genetic algorithms (GAs) into the GM (1, N).

Experimental results obtained for the financial data of Taiwanese firms in the information technology industries demonstrated that the proposed BP-GM (1, N) performs well.

Among artificial intelligence (AI)-based techniques, GBPMs are capable of explaining which of the financial ratios has a stronger impact on bankruptcy prediction by driving coefficients.

Applying MGPMs to a problem without relation to time series is challenging. This paper focused on bankruptcy prediction, a crucial issue in financial decision-making for businesses, and proposed several GBPMs.

This study aims to identify the key factors that influence household pro-environmental behaviors (HPEBs) and explore the differences caused by the same influencing factors between household waste management behavior (HWM) and household energy-saving behavior (HES).

A meta-analysis was conducted on 90 articles about HPEBs published between 2009 and 2023 to find the key factors. HPEBs were further categorized into HWM and HES to investigate the difference influenced by the above factors on two behaviors. The correlation coefficient was used as the unified effect size, and the random-effect model was adopted to conduct both main effect and moderating effect tests.

The results showed that attitude, subjective norms, and perceived behavioral control all positively influenced intention and HPEBs, but their effects were stronger on intention than on HPEBs. Intention was found to be the strongest predictor of HPEBs. Subjective norms were found to have a more positive effect on HES compared to HWM, while habits had a more positive effect on HWM. Furthermore, household size was negatively correlated with HWM but positively correlated with HES.

The same variables have different influences on HWM and HES. These results can help develop targeted incentives to increase the adoption of HPEBs, ultimately reducing household energy consumption and greenhouse gas emissions and contributing to the mitigation of global warming.