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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 study aims to use a multi-stage scale development process to identify the dimensions of philanthropic corporate social responsibility (PCSR) in India’s fast-moving consumer goods (FMCG) sector.

The authors conducted a study to develop a comprehensive, reliable and valid scale for measuring PCSR based on the customer perception of FMCG product manufacturers. This research adopted a comprehensive and detailed scale development process using multi-stage sampling for scale development. This final study was conducted on a sample of 402 respondents from the city of Jaipur, India.

The results have underlined the multi-dimensional aspect of PCSR; these dimensions are: altruism towards society, volunteering for local community development, generosity towards ecology, benevolent spirit and problem-solving charity.

This study gives valuable insights into philanthropic scale development in the FMCG sector that can immensely help domestic and international marketers to formulate CSR as a strategy. This research provides insights into a wide range of scales which can be base for future research studies that aim to explore different organizational settings.

PCSR and CSR are important for developing strategies for sustainable businesses across the globe. Dimensions of PCSR will be useful for practitioners and researchers in developing second-order constructs for future studies.

Avatars, which are used as a technology and marketing tactic, can embody consumer-facing employees and mimic their real-life roles on companies' websites, thereby playing a key role in enhancing the relationships between consumers and brands in the online environment. Academics and practitioners have increasingly acknowledged the significance of the consumer-brand relationship in both traditional and online contexts. However, the impersonal nature of the online environment is considered to be a hindrance to the development of these relationships. Despite the importance of this technology, little attention has been paid to the investigation of the avatar concept from a marketing perspective. This paper explores the nature of the avatar concept, including its main characteristics, dimensions, and conditions as well as the attitudinal and behavioural consequences of avatar users.

Adopting the qualitative design, a taxonomy was developed from interviews. In total, 42 interviews were conducted with current university students. 30 participants participated in the exploratory interviews. A total of 12 interviews were conducted during the in-depth stage based on findings in the preceding research.

Based on the qualitative data analysis, a taxonomy was developed. The idea of the taxonomy is summarized in that different dimensions of the avatar are considered the main base (first phase) of the taxonomy. There are consequential three parts: the attitudinal consequences related to the website; the attitudinal consequences related to the brand; the behaviours towards the brand. These behaviours represent the final phase of the taxonomy.

By developing a taxonomy of using avatars on brands' websites, the authors advance the understanding consumer-brands relationships. Using avatars' verbal interactions helps in shaping consumers' cognitive, affective, attitudinal and behavioural responses and add vital empirical evidence to the increasing body of research and practices involving avatar usage in the interactive marketing area.

In recent years, natural dyes have attracted significant attention globally because of growing public awareness of the environment and health hazards associated with synthetic dyes. Natural dyes can provide special aesthetic qualities as well as the ethical significance of a product which is environmentally friendly. By keeping this burning issue in mind, this study aims to explore the dyeing properties of various unexplored environmentally friendly natural dyes.

In this study, the aqueous extract of coconut leaves is used for dyeing purpose. The silk and jute fabrics were dyed with the extract alone as well as in combination with metal salts as mordants by employing pre-, meta- and postmordanting techniques. The dyeing properties of the colored samples were evaluated by measuring their color strength; CIEL*a*b* values; and color fastness to washing, light and rubbing.

A yellow shade was achieved when the fabric samples were dyed solely with the extract. However, shade variations were observed when different mordants and mordanting techniques were applied. In all the cases, metallic salts improved the color fastness properties of dyed samples to washing, light and rubbing especially for the silk fabric.

To the best of the authors’ knowledge, this is the first report on a natural dye extracted from the leaves of coconut. Leaf as the source of dye has added an extra advantage, as it is reproducible and can be collected easily without harming the plants. The reported dye could be an attractive choice for sustainable and eco-friendly dyeing.

Traditional multivariable grey prediction models define the background-value coefficients of the dependent and independent variables uniformly, ignoring the differences between their physical properties, which in turn affects the stability and reliability of the model performance.

A novel multivariable grey prediction model is constructed with different background-value coefficients of the dependent and independent variables, and a one-to-one correspondence between the variables and the background-value coefficients to improve the smoothing effect of the background-value coefficients on the sequences. Furthermore, the fractional order accumulating operator is introduced to the new model weaken the randomness of the raw sequence. The particle swarm optimization (PSO) algorithm is used to optimize the background-value coefficients and the order of the model to improve model performance.

The new model structure has good variability and compatibility, which can achieve compatibility with current mainstream grey prediction models. The performance of the new model is compared and analyzed with three typical cases, and the results show that the new model outperforms the other two similar grey prediction models.

This study has positive implications for enriching the method system of multivariable grey prediction model.

Gossip spreads like wildfire, damaging relationships, decaying trust and creating a negative work environment. This study aims to investigate the relationship between negative workplace gossip (NWG) and quiet quitting (QQ), while considering the mediating effects of workplace stress and emotional exhaustion (EE).

Drawing upon the conservation of resource theory, the study aimed to comprehend this association in the context of 267 employees from diverse sectors in India, including health care, IT, banking and education. Through a three-wave time lagged survey design, using partial least squares structural equation modeling, significant findings were uncovered.

The results revealed a positive link between NWG and QQ. There was also a positive correlation between NWG and workplace stress. In addition, workplace stress and EE were found to mediate the relationship between NWG and QQ.

The findings have implications for both theory and practice. Organizations should consider implementing strategies to mitigate the prevalence of negative gossip and foster a healthier work environment, promoting employee well-being and retention.

The study reveals the “black box” between NWG and QQ, adding to the body of knowledge on the novel concept of QQ. Second, the study expands the literature on NWG, by examining impact path of how it leads to stress and EE, leading to QQ.

In recent years, the field of financial technology (Fintech) has garnered significant attention due to advancements in technology, evolving consumer preferences and the growing need for financial services that are more accessible and user-friendly. The exponential expansion of Fintech is presenting novel prospects and obstacles for business. This study aims to investigate the relationship between gender diversity on corporate boards and firms’ performance, with a particular focus on the moderating role of Fintech.

The study sample consisted of financial sector firms listed on the Bahrain Bourse (banks and insurance firms) during the period 2016–2022. The data were gathered primarily from annual reports and the Bahrain Bourse website. The independent variable represents the percentage of female directors on corporate boards while firms’ accounting and market-based performance were measured using return on assets and Tobin’s Q variables. The moderating variable, Fintech, was measured using a checklist developed using the Global Fintech Adoption Index. Fixed effect (FE) regression was used to analyze the study data. An alternative gender diversity measure was used to test the reliability of the main regression analysis.

The results of the study indicate a positive relationship between gender diversity on corporate boards and financial performance. Additionally, the findings of the study highlighted the positive impact of Fintech practices on firms’ performance. Nevertheless, the impact of Fintech on the relationship between board gender diversity and corporate performance was found to be insignificant.

The study sample included a particular sector in a single country, which may limit the generalizability of the findings. Also, the current study applied FE regression to analyze the data; however, other econometric approaches could be used to overcome the endogeneity issue.

The findings of this study may have implications for policymakers and society, particularly in terms of promoting gender diversity and Fintech innovation.

This study contributes to the existing body of research by examining the potential impact of the percentage of female directors and the utilization of Fintech on firms’ performance in Bahrain. Given the ongoing endeavors to provide advanced Fintech solutions in the financial sector and the increasing focus on enhancing gender diversity in Bahraini corporate boards, this research aims to provide additional evidence in this domain. Moreover, this study stands out as one of the limited number of research endeavors that use Fintech as a moderating variable in the investigation of the impact of female directors on firms’ performance.

The purpose of this study is analysis of deformation and vibrations of turbine blades produced by high electrolyte pressure during electrochemical machining.

An experimental setup was designed, experiments were conducted and the obtained results were compared with the finite element results. The deformations were measured according to various flow rates of electrolyte. In finite element calculations, the pressure distribution created by the electrolyte on the blade surface was obtained in the ANSYS® (A finite element analysis software) Fluent software and transferred to the static structural where the deformation analysis was carried out. Three different parameters were examined, namely blade thickness, blade material and electrolyte pressure on blade disk caused by mass flow rate. The deformation results were compared with the gap distances between cathode and anode.

Large deformations were obtained at the free end of the blade and the most curved part of it. The appropriate pressure values for the electrolyte to be used in the production of blisk blades were proposed numerically. It has been determined that high pressure applications are not suitable for gap distance lower than 0.5 mm.

When the literature is examined, it is required that the high speed flow of the electrolyte is desired in order to remove the parts that are separated from the anode from the machining area during electrochemical machining. However, the electrolyte flowing at high speeds causes high pressure in the blisk blades, excessive deformation and vibration of the machined part, and as a result, contact of the anode with the cathode. This study provides important findings for smooth electro chemical machining at high electrolyte flows.

Assessing project criticality in a project portfolio (PP) is of great practical significance to improve robustness from damage. While project criticality assessment has increased diversity in approaches, the understanding of vulnerable project impacts is still limited. To promote a better understanding of assessing project criticality, a vulnerability measurement model is constructed.

First, integrating the tasks, projects and corresponding relationships among them, a project portfolio network (PPN) is constructed. Second, the project's vulnerability is measured by combining the topological structure and functional attributes. Third, project criticality is assessed by the vulnerability measurement results. Lastly, the proposed model is applied in a numerical example to illustrate its suitability and effectiveness.

For academia, this study provides a novel perspective on project vulnerability measurement and expands project criticality assessment tools. For practitioners, the straightforward model provides an effective tool for assessing project criticality and contributes to enhancing project portfolio management (PPM).

The impact of the task on the project is considered in this study. Topological structure and functional attributes are also integrated for measuring project vulnerability due to the impact of random attacks in an uncertain environment, providing a new perspective on the requirements of project criticality assessment and the measurement of project vulnerability.

This study was conducted to analyse the socio-ecological problems faced by the Suhelwa Wildlife Sanctuary and understand its potential and challenges for developing ecotourism following Triple Bottom Line (TBL) principles. The study also benchmarked best ecotourism practices across the globe to create an ecotourism plan that would provide alternative livelihood and help in sustainable management of the area by reducing poverty, dependency on forests and biodiversity protection.

Suhelwa Wildlife Sanctuary was chosen because this area has several socio-ecological crises with limited livelihood options, and there is an urgent need for alternative livelihood opportunities in the form of ecotourism. The study followed an ethnographic approach through observation, participant observation, and semi-structured interviews. Content and thematic analysis was conducted through Atlas Ti9.0 software for data analysis. Subsequently, benchmarking best ecotourism practices through a literature review was done to develop an ecotourism action plan.

The First finding was related to the study area divided into three themes: problems, potential for ecotourism development, and challenges for ecotourism development. The second finding was related to benchmarking best practices and suggesting an action plan.

This work studied an area not sufficiently acknowledged by academicians and policymakers concerning ecotourism development. The work also benchmarks the best practices for ecotourism and proposes a sight-specific ecotourism action plan in accordance with TBL.