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Iron deficiency anaemia is widely prevalent amongst women and children in India. The aim of the paper is to study the effect of supplementation of micronutrient fortified biscuits on haemoglobin and serum iron levels of adolescent girls (n = 46, 10‐16 years) studying in a government school in Jaipur city, India.

The study was designed to be an intervention study. The intervention was with biscuits fortified with 30 mg iron, 100  μg folic acid, 600  μg vitamin A, 40 mg vitamin C and 150  μg iodine. The iron status of adolescent girls was determined through haemoglobin and serum iron levels.

The results revealed that 95.7 per cent of the adolescent girls suffered from anaemia of which 28.3 per cent had “mild” deficiency and 67.4 per cent had “moderate” deficiency. Anaemia was more prevalent in the older age groups. On supplementation, there was a significant increase in the haemoglobin levels. There was a three‐fold increase in the percentage of adolescent girls in the “normal” category of anaemia from 4.3 to 13.0 per cent and more than two‐fold decrease in the “moderate” category of anaemia from 67.4 to 28.3 per cent. Moreover, 21.7 per cent of the subjects had “normal” levels while the rest (78.3 per cent) had low levels of serum iron; the percentage of adolescent girls in the normal category increased to 93.5 per cent after intervention on the basis of serum iron levels.

Supplementation with iron and folic acid with other micronutrients improved the haemoglobin and serum iron levels of the adolescent girls significantly.

The paper recommends that the school system can be used for micronutrient supplementation to improve the nutritional status of children and adolescents as the students are more regimented here for distribution of nutrient fortified food products.

Electricity theft is a chronic issue that undermines the power quality and reduces the financial stability of power distribution companies. This study aims to empirically investigate the factors affecting electricity theft and its impact on power quality in rural and urban regions of India.

This research involves two cross-sectional surveys using self-administered questionnaires: Study R comprises 480 rural respondents, while Study U comprises 461 urban respondents to assess the causes of electricity theft. Data analysis was conducted using the statistical package for social science and the partial least squares-structural equation modelling technique.

The findings revealed that corruption, economic and government factors significantly influence electricity theft, while technology factors insignificantly influence electricity theft in both studies. However, in Study R, social factors had a statistically significant impact, but in Study U, it had an insignificant impact on electricity theft. Furthermore, the findings also reported a substantial link between electricity theft and power quality deterioration.

The study is unique and more generalisable, providing valuable insights to the government to develop a well-planned stringent law to enhance electricity service quality and reduce electricity theft cases that can benefit the entire community. Further, this study shed light on contributing factors of electricity theft to improve power quality that can assist in achieving the (sustainable development goal 7 – reliable, affordable and sustainable energy) mission of the Indian Government.

To the best of the authors’ knowledge, the present study represents the initial study to examine the notion of Indian electricity users regarding power theft and quality deterioration among India’s rural and urban regions. That would provide a base for the government and electricity board to combat power theft and provide a quality power supply. Moreover, this study offers a robust theoretical model with the new latent variable for a current and pertinent topic.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

This case study is best suited for courses in career management, stress management, work–life management or science of well-being in organization behavior (OB) or human resources management (HRM). This case study is targeted at the MBA or executive development programs. The learning outcomes of this case study are as follows: to analyze the challenges in balancing Suraj Kumar’s (the protagonist) work–life balance situation and identifying the main causes of such a conflict. What are some strategies that could be used to address these issues? To develop a plan to balance work responsibilities with family and personal life. What specific actions could he take to achieve this balance? To design a training program for employees that addresses work–life balance issues. What topics would you cover in the training, and what methods would you use to deliver the content? To create a proposal for a flexible work arrangement program that an organization could offer to its employees. What would be the benefits of this program, and how would it be implemented and managed? To develop a business case for why an organization should prioritize work–life balance for its employees. What are the potential benefits of doing so, and how can the organization measure the impact of its efforts?

This case study discusses conflicts due to competitive priorities that people face in balancing the pressures, roles and responsibilities between their professional life (careers) and their personal life, and the trade-offs that they make across these multiple aspects of their lives. This case study revolves around Suraj Kumar, a successful consultant, who was offered a promotion as the executive director for the social sector. This promotion would require extensive travel and time away from his family. Kumar was struggling to balance his work responsibilities with his family life and his involvement with the Smile and Shine Foundation, which supports the education of children from economically weaker sections of society. He was reflecting on his past and present and trying to figure out how to prioritize his commitments while also achieving his personal and professional goals.

This research has shown that a case discussion and role-play can be effective for a less experienced audience. Instructors can provide multiple perspectives to stimulate reflection and debate. For executive MBA or executive development programs, a self-reflection exercise is recommended. Participants in these programs may have personal experience dealing with or may know others who have dealt with work–life balance issues, making self-reflection a valuable tool.

Teaching notes are available for educators only.

CSS 6: Human resource management.

This paper aims to propose a machine learning approach-based power theft detection using Garra Rufa Fish (GRF) optimization. Here, the analyzing of power theft is an important part to reduce the financial loss and protect the electricity from fraudulent users.

In this section, a new method is implemented to reduce the power theft in transmission lines and utility grids. The detection of power theft using smart meter with reliable manner can be achieved by the help of GRF algorithm.

The loss of power due to non-technical loss is small by using this proposed algorithm. It provides some benefits like increased predicting capacity, less complexity, high speed and high reliable output. The result is analyzed using MATLAB/Simulink platform. The result is compared with an existing method. According to the comparison result, the proposed method provides the good performance than existing method.

The proposed method gives good results of comparison than those of the other techniques and has an ability to overcome the associated problems.

Laser metal deposition (LMD) is a type of additive manufacturing process in which the laser is used to create a melt pool on a substrate to which metal powder is added. The powder is melted within the melt pool and solidified to form a deposited track. These deposited tracks may contain porosities or cracks which affect the functionality of the part. When these defects go undetected, they may cause failure of the part or below par performance in their applications. An on demand vision system is required to detect defects in the track as and when they are formed. This is especially crucial in LMD applications as the part being repaired is typically expensive. Using a defect detection system, it is possible to complete the LMD process in one run, thus minimizing cost. The purpose of this paper is to summarize the research on a low-cost vision system to study the deposition process and detect any thermal abnormalities which might signify the presence of a defect.

During the LMD process, the track of deposited material behind the laser is incandescent due to heating by the laser; also, there is radiant heat distribution and flow on the surfaces of the track. An SLR camera is used to obtain images of the deposited track behind the melt pool. Using calibrated RGB values and radiant surface temperature, it is possible to approximate the temperature of each pixel in the image. The deposited track loses heat gradually through conduction, convection and radiation. A defect-free deposit should show a gradual decrease in temperature which enables the authors to obtain a reference cooling curve using standard deposition parameters. A defect, such as a crack or porosity, leads to an increase in temperature around the defective region due to interruption of heat flow. This leads to deviation from the reference cooling curve which alerts the authors to the presence of a defect.

The temperature gradient was obtained across the deposited track during LMD. Linear least squares curve fitting was performed and residual values were calculated between experimental temperature values and line of best fit. Porosity defects and cracks were simulated on the substrate during LMD and irregularities in the temperature gradients were used to develop a defect detection model.

Previous approaches to defect detection in LMD typically concentrate on the melt pool temperature and dimensions. Due to the dynamic and violent nature of the melt pool, consistent and reliable defect detection is difficult. An alternative method of defect detection is discussed which does not involve the melt pool and therefore presents a novel method of detecting a defect in LMD.

The purpose of this paper is to adopt the multi-objective optimization technique for identifying a set of optimum abrasive water jet machining (AWJM) parameters to achieve maximum material removal rate (MRR) and minimum surface roughness.

Data of a few experiments as per the Taguchi’s orthogonal array are considered for achieving maximum MRR and minimum surface roughness (Ra) of the Inconel718. Analysis of variance is performed to understand the statistical significance of AWJM input process parameters.

Empirical relations are developed for MRR and Ra in terms of the AWJM process parameters and demonstrated their adequacy through comparison of test results.

The signal-to-noise ratio transformation should be applied to take in to account the scatter in the repetition of tests in each test run. But, many researchers have adopted this transformation on a single output response of each test run, which has no added advantage other than additional computational task. This paper explains the impact of insignificant process parameter in selection of optimal process parameters. This paper demands drawbacks and complexity in existing theories prior to use new algorithms.

Taguchi approach is quite simple and easy to handle optimization problems, which has no practical implications (if it handles properly). There is no necessity to hunt for new algorithms for obtaining solution for multi-objective optimization AWJM process.

This paper deals with a case study, which demonstrates the simplicity of the Taguchi approach in solving multi-objective optimization problems with a few number of experiments.

The Ba River catchment and delta on the island of Viti Levu, Fiji, supports a wealth of livelihoods and is populated by diverse communities who are living with an increased frequency and intensity of hydro-meteorological hazards (floods, cyclones and droughts). Participatory mapping as part of focus group discussions is a tool that can be used to elucidate communities’ understanding of the differing impacts of multiple hazards, as well as the strategies used to prepare and respond to different hazards. In this chapter, the authors present the results of qualitative research undertaken with members of three communities along the Ba River, from the Nausori highlands to the coastal mangroves, with a particular focus on recent floods (2009, 2012) and Tropical Cyclone Winston (2016). The communities draw on a wide range of livelihood strategies from fishing and agriculture to tourism and outside work. Natural hazard events vary in their impact on these livelihood strategies across the landscape and seascape, so that community members can adjust their activities accordingly. The temporal ‘signatures’ of ongoing impacts are also variable across communities and resources. The results suggest that taking a broad, landscape (and seascape) approach to understanding how communities draw livelihoods is valuable in informing effective and inclusive adaptation strategies for environmental change. Furthermore, documenting how the landscape is used in a mapped output may be a valuable tool for future social impact assessment for resource extraction activities.

The purpose of the chapter is to explain how boys learn to mask their expression of fears and emotions. The aim is to understand how boys internalize socially prescribed masculine traits, including masking of fear, certain emotional expressions and discomfort. The sample consisted of 20 parents, 30 school teachers and 50 boy students and 50 girl students between the ages of 11 and 14 from government-funded co-education schools in Delhi, India. School observation, focus group discussion, and interviews were used for data collection. The study found that gendered social norms are enforced on boys in the form of ‘boy codes’. These boy codes are so deep rooted in daily practices that they are considered as an essential ‘ideal male’ trait. Although the ‘ideal male image’ is presented as a uniform category among boys, the masking of fears and emotional expressions is not the same for all boys. Thus, many boys internalized the ideal male images in the form of hegemonic displays of masculinity, where they are focused on conforming to rigid masculine traits. However, through challenge, negotiation and renegotiation, many boys would like to conduct themselves according to their personal masculinity. There is a shift among some boys from the internalization of the traditional male image to giving meaning to personal experiences that deviate from the ideal male figure without the fear of being judged by society.

This paper aims to report the effect of titanium oxide (TiO₂) particles on the specific wear rate (SWR) of alkaline treated bamboo and flax fiber-reinforced composites (FRCs) under dry sliding condition by using a robust statistical method.

In this research, the epoxy/bamboo and epoxy/flax composites filled with 0–8 Wt.% TiO₂ particles have been fabricated using simple hand layup techniques, and wear testing of the composite was done in accordance with the ASTM G99-05 standard. The Taguchi design of experiments (DOE) was used to conduct a statistical analysis of experimental wear results. An analysis of variance (ANOVA) was conducted to identify significant control factors affecting SWR under dry sliding conditions. Taguchi prediction model is also developed to verify the correlation between the test parameters and performance output.

The research study reveals that TiO₂ filler particles in the epoxy/bamboo and epoxy/flax composite will improve the tribological properties of the developed composites. Statistical analysis of SWR concludes that normal load is the most influencing factor, followed by sliding distance, Wt.% TiO2 filler and sliding velocity. ANOVA concludes that normal load has the maximum effect of 31.92% and 35.77% and Wt.% of TiO2 filler has the effect of 17.33% and 16.98%, respectively, on the SWR of bamboo and flax FRCs. A fairly good agreement between the Taguchi predictive model and experimental results is obtained.

This research paper attempts to include both TiO₂ filler and bamboo/flax fibers to develop a novel hybrid composite material. TiO₂ micro and nanoparticles are promising filler materials, it helps to enhance the mechanical and tribological properties of the epoxy composites. Taguchi DOE and ANOVA used for statistical analysis serve as guidelines for academicians and practitioners on how to best optimize the control variable with particular reference to natural FRCs.