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Finding the optimal maintenance schedules is the primitive aim of preventive maintenance scheduling (PMS) problem dealing with the objectives of reliability, risk and cost. Most of the earlier works in the literature have focused on PMS with the objectives of leveling reserves/risk/cost independently. Nevertheless, very few publications in the current literature tackle the multi-objective PMS model with simultaneous optimization of reliability, and economic perspectives. Since, the PMS problem is highly nonlinear and complex in nature, an appropriate optimization technique is necessary to solve the problem in hand. The paper aims to discuss these issues.

The complexity of the PMS problem in power systems necessitates a simple and robust optimization tool. This paper employs the modern meta-heuristic algorithm, namely, Ant Lion Optimizer (ALO) to obtain the optimal maintenance schedules for the PMS problem. In order to extract best compromise solution in the multi-objective solution space (reliability, risk and cost), a fuzzy decision-making mechanism is incorporated with ALO (FDMALO) for solving PMS.

As a first attempt, the best feasible maintenance schedules are obtained for PMS problem using FDMALO in the multi-objective solution space. The statistical measures are computed for the test systems which are compared with various meta-heuristic algorithms. The applicability of the algorithm for PMS problem is validated through statistical t-test. The statistical comparison and the t-test results reveal the superiority of ALO in achieving improved solution quality. The numerical and statistical results are encouraging and indicate the viability of the proposed ALO technique.

As a maiden attempt, FDMALO is used to solve the multi-objective PMS problem. This paper fills the gap in the literature by solving the PMS problem in the multi-objective framework, with the improved quality of the statistical indices.

The purpose of this paper is to develop a systematic approach to extract users’ feelings and emotions about their experiences in hospitals from online reviews and rank the places using multi-criteria decision making (MCDM) techniques based on the aggregated sentiment score.

The proposed model used a linguistic approach to extract the sentiment words from the free text. It used term frequency-inverse document frequency values to represent features of various places in bag-of-words format. Sentiment dictionary is used to calculate senti-scores. It used different MCDM techniques like simple additive weight and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods for ranking hospitals based on their aggregated senti-score.

Statistical correlation analysis between the rankings of places reveals that the TOPSIS method is the most suitable ranking technique among other MCDM techniques. By improving the senti-score, one can bring their enterprise to the top position.

Data set is collected from different websites like Twitter, Facebook, etc., for various services/features. Moderate amount of reviews are collected for each place. But not all users give their views on the social media websites. It would be essential to collect responses from all the customers who avail different services at different places.

The sentiment analysis model proposed in this paper enables B2C and C2C commerce. Business may take suitable measures to overcome their issues/problems raised by the consumer. Consumers can share and educate other consumers about their experiences.

The development of internet has strong influence in all types of industries like healthcare. The availability of internet has changed the way of accessing the information and sharing their experience with others. This paper recognizes the use and impact of social media on the healthcare industry by analyzing the users’ feelings expressed in the form of free text. A suitable decision-making technique is applied to rank the places, which enables the users to plan their treatment place in a better way.

The paper develops a novel approach by applying the TOPSIS method to rank the different alternative places of the healthcare industry by using the senti-score derived from the users’ feelings, emotions and experiences expressed in the form of free text.

Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems.

The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem.

The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems.

As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

This study aims to minimize operating cost, adhere to pollution norms and maintain reserve and voltage levels subject to various operational concerns, including non linear characteristics of generators and fuel limitation issues, which are useful for the current power system applications.

Improved control settings are required while considering multiple conflicting operational objectives that necessitate using the modern bio-inspired algorithm ant lion optimizer (ALO) as the main optimization tool. Fuzzy decision-making mechanism is incorporated in ALO to extract the best compromise solution (BCS) among set of non-dominated solutions.

The BCS records of IEEE-30 bus and JEAS-118 bus systems are updated in this work. Numerical simulation results comparison and comprehensive performance analysis justify the applicability of the intended algorithm to solve multi-objective dynamic optimal power flow (DOPF) problem over the state-of-art methods.

Optimal control settings are obtained for IEEE-30 and JEAS-118 bus systems with the objectives of minimizing fuel cost and emission in dynamic environment considering take-or-pay fuel contract issue. The fuzzy supported ALO (FSALO) is applied first time to solve the DOPF problem.

The purpose of this paper is to solve the realistic problem of source maintenance scheduling (SMS) based on reliability criterion. A novel effective optimization technique is proposed to solve the problem at hand.

The problem has been formulated as a combinatorial optimization task, with the goal of maximizing reliability by minimizing the sum of squares of the reserve loads while satisfying unit and system constraints. This paper employs a nature inspired algorithm known as Teaching Learning Based Optimization (TLBO) for solving the SMS problem based on reliability.

The results reveal that optimal maintenance schedules of generating units has been obtained using TLBO algorithm with minimized values of sum of squares of reserve loads while satisfying system and operational constraints. It is also found that the inclusion of resource constraints (RC) in the model have significant effects on the objective function value which provides a deep insight of the proposed methodology.

The contribution of this paper is that an efficient nature inspired algorithm has been applied to solve source maintenance scheduling problem in viewpoint of the planning for future system capacity expansion. The incorporation of exclusion and RC in the model makes the analysis about the impact of SMS on the system reliability more reasonable.

Developing nations are starting to take centre stage and are becoming more conscious of their economic potential in the tourism sector. Earlier, these countries were largely left out of the tourist sector. Today, the foreign exchange reserves of these nations are mostly derived from tourism and a nation's balance of payments derived through tourism supports international trade and stabilizes the economy. The chapter adopts a case study method to evaluate the prospects of inclusive growth through socio economic well-being. In the dynamic and constantly evolving tourism industry, it is imperative to make the growth more inclusive so that there is a balanced approach to long term sustainable development. When it comes to using traditional skills and cultural heritage to participate in this economic paradigm, rural artisans face both opportunities and challenges as the global tourism landscape undergoes evolving transformations. The study commences by closely examining the current market obstacles that rural artisans in the tourism industry face, which encompass a range of issues such as shifting consumer preferences, market saturation and the aftermath of external shocks like pandemics. The chapter indicates major obstacles impeding the integration of rural artisans into the tourism value chain by means of a thorough empirical analysis. In order to overcome the obstacles, the study investigates inclusive growth and how it might act as a spur to improve the socio-economic circumstances of rural craftsmen. The findings in the chapter distil best practices that promote inclusivity, empower local communities and support sustainable tourism development by looking at successful case studies and policy interventions.

COVID-19’s influence on the teaching profession will be felt for many years as teachers faced experiences that they have never encountered. The pandemic forced already taxed teachers to assume additional responsibilities, many of which they were not prepared to deal with. The result was an exodus of teachers from the profession, and those who remained reported challenges that impacted their personal and professional lives. The authors describe the effects on teachers and the impact that this had on them, including reasons why many departed from teaching. In closing, the authors offer recommendations to teacher preparation programs, districts, and schools.

This paper aims to propose a two-element dual fed ultra-wideband (UWB) multiple input multiple output (MIMO) antenna system with no additional decoupling structures. The antenna operates from 3.1 to 10.6 GHz. The antenna finds its usage in on-body wearable device applications.

The antenna system measures 63.80 × 29.80 × 0.7 mm. The antenna radiating element is designed by using a modified dumbbell-shaped structure. Jean cloth material is used as substrate. The isolation improvement is achieved through spacing between two elements.

The proposed antenna has a very low mutual coupling of S₂₁ < −20 dB and impedance matching of S₁₁ < −10 dB. The radiation characteristics are stable in the antenna operating region. It provides as ECC < 0.01, diversity gain >9.9 dB. The antenna offers low average specific absorption rate (SAR) of 0.169 W/kg. The simulated and measured results are found to be in reasonable match.

The MIMO antenna is proposed for on-body communication, hence, a very thin jean cloth material is used as substrate. This negates the necessity of additional material usage in antenna design and the result range indicates good diversity performance and with a low SAR of 0.169 W/kg for on-body performance. This makes it a suitable candidate for textile antenna application.