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This study aims to propose and evaluate the progress in the basic-pixel (a strategy to generate continuous trajectories that fill out the entire surface) algorithm towards performance gain. The objective is also to investigate the operational efficiency and effectiveness of an enhanced version compared with conventional strategies.

For the first objective, the proposed methodology is to apply the improvements proposed in the basic-pixel strategy, test it on three demonstrative parts and statistically evaluate the performance using the distance trajectory criterion. For the second objective, the enhanced-pixel strategy is compared with conventional strategies in terms of trajectory distance, build time and the number of arcs starts and stops (operational efficiency) and targeting the nominal geometry of a part (operational effectiveness).

The results showed that the improvements proposed to the basic-pixel strategy could generate continuous trajectories with shorter distances and comparable building times (operational efficiency). Regarding operational effectiveness, the parts built by the enhanced-pixel strategy presented lower dimensional deviation than the other strategies studied. Therefore, the enhanced-pixel strategy appears to be a good candidate for building more complex printable parts and delivering operational efficiency and effectiveness.

This paper presents an evolution of the basic-pixel strategy (a space-filling strategy) with the introduction of new elements in the algorithm and proves the improvement of the strategy’s performance with this. An interesting comparison is also presented in terms of operational efficiency and effectiveness between the enhanced-pixel strategy and conventional strategies.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

The study seeks to explore why despite growing concern for the environment, consumers’ intention to purchase organic foods remains relatively low. In addition, the study also seeks to investigate the role of perceived marketplace influence (PMI) and moral norms in organic food consumption.

Data collected from 330 young consumers chosen with non-probability sampling were analysed using structural equation modelling in Amos 22.0.

The results of the parallel mediation analysis confirmed that environmental concern influences purchase intention indirectly through attitude, subjective norms, perceived behavioural control and perceived marketplace influence. In addition, moral norms were found to moderate the effect of perceived behavioural control on purchase intention. Moreover, the results also indicated that the impact of environmental concern on consumers’ attitude toward organic foods was also moderated by moral norms. Further, the results of moderated mediation showed that the indirect effect of environmental concern on purchase intention (through attitude and perceived behavioural control) was moderated by moral norms.

The study contributes to the existing literature by investigating the inconsistency between environmental concern and purchase intention. In addition, the study also investigate role of perceived marketplace influence and moral norms in stimulating organic food consumption intentions.

The emergence of perceived marketplace influence as an important determinant of organic food consumption shows that every individual needs to realise the importance of their environment friendly actions to promote organic food consumption. In addition, the study also highlights the pivotal role of moral norms in the promotion of organic food consumption. Thus, markets, policy-makers, family, friends, society all should promote and inculcate the spirit of contributing in the cause of safeguarding the environment to the young children specially by promoting consumption of organic foods.

The study examines the role of perceived marketplace influence as predictor of purchase intention towards organic foods which is rarely explored specially in the domain of organic food consumption. In addition, the results also produced some novel insights into the moderating role of moral norms.

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.

The sustainable development goals (SDGs) are introduced to guide achieving the sustainable goals and tackle the global problems. United Nations members may perform activities to achieve the predetermined goals and report on their SDG activities. The comprehension and commitment of several stakeholders are essential for the effective implementation of the SDGs. Countries encourage their stakeholders to perform and report their activities to meet the SDGs. The purpose of this study is to investigate the extent to which corporations’ annual reports address the SDGs to assess and comprehend their level of commitment to, priority of and integration of SDGs within their reporting structure. This research makes it easier to evaluate corporations’ sustainability performance and contributions to global sustainability goals by looking at the extent to which they address the SDGs.

In the study, it is revealed to what extent the reports meet the SDGs with the multilabel text classification approach. The SDG classification is carried out by examining the report with the help of a text analysis tool based on an enhanced version of gradient boosting. The implementation of a machine learning-based model allowed it to determine which SDGs are associated with the company’s operations without the requirement for the report’s authors to perform so. Therefore, instead of reading the texts to seek for “SDG” evidence as typically occurs in the literature, SDG proof was searched in relevant texts.

To show the feasibility of the study, the annual reports of the leading companies in Turkey are examined, and the results are interpreted. The study produced results including insights into the sustainable practices of businesses, priority SDG selection, benchmarking and business comparison, gaps and improvement opportunities identification and representation of the SDGs’ importance.

The findings of the analysis of annual reports indicate which SDGs they are concerned about. A gap in the literature can be noticed in the analysis of annual reports of companies that fall under a particular framework. In addition, it has sparked the idea of conducting research on a global scale and in a time series. With the aid of this research, decision-making procedures can be guided, and advancements toward the SDGs can be achieved.

The study's primary purpose is to establish the direct and indirect roles of word-of-mouth communication (WOM) in initiating and maintaining consumer loyalty in the bottom of pyramid (BOP) markets in the Indian context. In addition, the study seeks to evaluate the conditions (viz. extent of media usage, brand distribution intensity and brand social connections) under which WOM leads to the initiation and maintenance of consumer brand loyalty.

The study hypotheses were formulated following the social identity theory. Later, a questionnaire-based survey was conducted among 898 rural BOP consumers. Structural equation modelling technique was applied to test the study hypotheses.

Results suggested a positive effect of WOM on brand credibility and self-brand connections-indicative of the initiation of strong cognitive and affective relationships respectively. Brand credibility and self-brand connections also mediated the paths between WOM and brand loyalty-indicative of the maintenance and continuation of strong affect-laden relationships. These indirect relationships were moderated by the extent of media usage, brand distribution intensity and brand social connections.

This is among the first studies that holistically evaluate the role of WOM in developing customer loyalty to rural BOP consumers against the backdrop of the systemic deficiencies in these markets.

Based on the trait activation theory and social exchange theory, this study proposed a model of the impact of Machiavellian personality on organisational cynicism (OC) through the mediating effect of psychological contract breach (PCB) and the moderating role of leader-member exchange (LMX) quality in PCB and OC.

A three-time points survey involving 264 employees from China’s hotel industry was conducted using quantitative methods. Subsequently, a structural equation model was constructed.

The results revealed that Machiavellianism positively affects OC, and PCB plays a mediating role in this process. In addition, LMX quality can buffer the effect of the PCB on OC.

The study’s findings provide another insight into the relationship between Machiavellianism, PCB and OC. Managers must pay attention to the control of PCB and the establishment of LMX quality.

The study significantly contributes to hotel literature, as the Machiavellian personality subject has not been adequately investigated in the field to date.

This study aims to assess previously developed Electronic Health Records System (EHRS) implementation models and identify successful models for decision support.

A systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources used were Scopus, PubMed and Google Scholar. The review identified peer-reviewed papers published in the English Language from January 2010 to April 2023, targeting well-defined implementation of EHRS with decision-support capabilities in healthcare. To comprehensively address the research question, we ensured that all potential sources of evidence were considered, and quantitative and qualitative studies reporting primary data and systematic review studies that directly addressed the research question were included in the review. By including these studies in our analysis, we aimed to provide a more thorough and reliable evaluation of the available evidence.

The findings suggest that the success of EHRS implementation is determined by organizational and human factors rather than technical factors alone. Successful implementation is dependent on a suitable implementation framework and management of EHRS. The review identified the capabilities of Clinical Decision Support (CDS) tools as essential in the effectiveness of EHRS in supporting decision-making.

This study contributes to the existing literature on EHRS implementation models and identifies successful models for decision support. The findings can inform future implementations and guide decision-making in healthcare facilities.