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This study aims to provide a framework aligning corporate social responsibility (CSR) initiatives with sustainable development goals (SDGs) 2030, applying the triple bottom line (TBL) approach. The research examines and evaluates the reach of Maharatna Central Public Sector Enterprises’ (CPSE) CSR spending towards sustainability and maps them with SDGs focusing on economic, social and environmental aspects. In addition, state-wise spending for CSR of all eligible Indian companies has been discussed.

The study used secondary data related to CSR spending and disclosure from the annual reports and sustainability reports accessible on the official websites of CPSE, Global Reporting Initiative standards, CSR Guidelines of Department of Public Enterprises and Securities Exchange Board of India, Government of India’s National Guidelines on Responsible Business Conduct (NGRBC) (2018) research papers, financial dailies and websites. The study includes the CPSEs awarded with the status of Maharatna companies under the Guidelines of Maharatna Scheme for CPSEs.

The top CSR initiatives focused on by Maharatna companies were related to poverty, hunger, sanitation and well-being, promotion of education and contribution to the Prime Minister’s National Relief Fund. These initiatives aligned with the top SDGs related to life on land, education and health care, which proved responsible business leadership (RBL) through TBL. The alignment indicates that India is moving towards sustainable development achievements systematically.

The practical consequences can be understood through the CSR spending of Maharatna Public Sector Undertakings towards economic, social and environmental aspects. The spending demonstrates their commitment, which other public and private sector organizations can adopt.

The Government of India’s NGRBC’s guidelines towards inclusive growth and equitable development, addressing environmental concerns, and being responsive to all its stakeholders is a thorough indication of driving the business towards being more responsible. This research has developed a framework aligning CSR and SDG through the TBL approach, which other developing countries can adopt as a model.

There is dearth of research among public sector company’s contribution towards attaining SDGs and demonstrating RBL. This research fulfils this gap. Mapping CSR activities to SDG’s also has not been clearly carried out in previous research, which is a contribution of this study.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

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 diagnosis of autism in adults often involves the use of tools recommended by NICE guidance but which are validated in children. The purpose of the paper is to establish the strength of the association between the Autism Diagnostic Interview-Revised (ADI-R) scores and the final clinical outcome in an all intellectual quotients adult autism diagnostic service and to establish if this in any way relates with gender and intellectual ability.

The sample includes referrals to Leeds Autism Diagnostic Service in 2015 that received a clinical outcome. Sensitivity, specificity and positive and negative predictive values were calculated to evaluate ADI-R and final clinical outcomes. Logistic regression model was used to predict the effect of the scores in all the domains of ADI-R and the two-way interactions with gender and intellectual ability.

ADI-R has a high sensitivity and low specificity and is useful to rule out the presence of autism, but if used alone, it can over diagnose. Restricted stereotyped behaviours are the strongest predictor for autism and suggests that the threshold should be increased to enhance its specificity.

This is a single site study with small effect size, so results may not be replicable. It supports the combined use of ADI-R and Autism Diagnostic Observation Schedule and suggests increasing ADI-R cut-offs to increase the specificity.

The clinical team may consider piloting a modified ADI-R as suggested by the results.

To the authors’ knowledge this is the only study of ADI-R in an adult population of all intellectual abilities.

This study introduces three variables related to brands that have the potential to enhance university students' advocacy intentions. The research explores how university brand identification, the perceived prestige of the university brand and the social benefits associated with the university brand impact students' advocacy intentions. Additionally, the study examines the moderating role of gender in these relationships.

Cross-sectional surveys of 326 undergraduate students enrolled in a Spanish university, and structural equation modeling was used to test and validate the conceptual model.

The findings from the structural equation modeling indicate that university brand identification, perceived university brand prestige and university brand social benefits significantly influence students' advocacy intentions. Furthermore, the multigroup analysis reveals a gender difference in the factors influencing advocacy intentions. Female students demonstrate significance in all three antecedents, whereas male students only show significance in university brand identification and perceived university brand prestige.

The current study's findings provide several insights for higher education institutions in developing enduring and committed relationships with their students.

This study offers relevant insights into the body of research on university branding, explaining the students' advocacy intentions through the variables of university brand identification, perceived university brand prestige and university brand social benefits. Also, this study is a novelty in introducing empirical evidence for the importance of the moderating role of students' gender.

Numerical simulations are performed to determine the heat transfer characteristics of slot jet impingement of air on a concave surface. The purpose of this paper is to investigate the effect of protrusions on the heat transfer by placing semi-circular protrusions on the concave surface at several positions. After identifying appropriate locations where the heat transfer is a maximum, multiple protrusions are placed at desired locations on the plate. The gap ratio, curvature ratio (d/D) and the dimensions of the plate are varied so as to obtain heat transfer data. The curvature ratio is varied first, keeping the concave diameter (D) fixed followed by a fixed slot width (d). A surrogate model based on an artificial neural network is developed to determine optimum locations of the protrusions that maximize the heat transfer from the concave surface.

The scope and objectives of the present study are two-dimensional numerical simulations of the problem by considering all the geometrical parameters (H/d, d_p, Re, θ) affecting heat transfer characteristics with the help of networking tool and numerical simulation. Development of a surrogate forward model with artificial neural networks (ANNs) with a view to explore the full parametric space. To quantitatively ascertain if protrusions hurt or help heat transfer for an impinging jet on a concave surface. Determination of the location of protrusions where higher heat transfer could be achieved by using exhaustive search with the surrogate model to replace the time consuming forward model.

A single protrusion has nearly no effect on the heat transfer. For a fixed diameter of concave surface, a smaller jet possesses high turbulence kinetic energy with greater heat transfer. ANN is a powerful tool to not only predict impingement heat transfer characteristics by considering multiple parameters but also to determine the optimum configuration from many thousands of candidate solutions. A maximum increase of 8 per cent in the heat transfer is obtained by the best configuration constituting of multiple protrusions, with respect to the baseline smooth configuration. Even this can be considered as marginal and so it can be concluded that first cut results for heat transfer for an impinging jet on a concave surface with protrusions can be obtained by geometrically modeling a much simpler plain concave surface without any significant loss of accuracy.

The heat transfer during impingement cooling depends on various geometrical parameters but, not all the pertinent parameters have been varied comprehensively in previous studies. It is known that a rough surface may improve or degrade the amount of heat transfer depending on their geometrical dimensions of the target and the rough geometry and the flow conditions. Furthermore, to the best of authors’ knowledge, scarce studies are available with inclusion of protrusions over a concave surface. The present study is devoted to development of a surrogate forward model with ANNs with a view to explore the full parametric space.

The paper aims to test the spectral line-based weighted sum of gray gases (SLW) method in axisymmetric geometries with particles and high temperature gradients.

An SLW model is coupled with Trivic’s mean wavelength approach to estimate the radiative heat fluxes at the wall of an enclosure and to the base wall of the rocket exhaust, thereby subsequently studying the effect of concentration variation of the gases and particles in these cases. Radiative transfer equation is solved using modified discrete ordinates method. Anisotropic scattering is modeled using transport approximation.

Two cases considered show the importance of particle emission and scattering in the rocket plume base heating problems. In cases involving only gases, the concentration of H₂O tends to have more impact on the flux values than any other gas.

A full model of gases with particles in an axially varying temperature field is reported. Such cases are very common in practical applications. The present methodology gives more insight and a firm handle on the problem vis-a-vis other traditional techniques.

Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

This paper aims to present development and application of the Bayesian inverse approach for retrieving parameters of non-linear diffusion coefficient based on the integral information.

The Bayes formula was used to construct posterior distribution of the unknown parameters of non-linear diffusion coefficient. The resulting aposteriori distribution of sought parameters was integrated using Markov Chain Monte Carlo method to obtain expected values of estimated diffusivity parameters as well as their confidence intervals. Unsteady non-linear diffusion equation was discretised with the Global Radial Basis Function Collocation method and solved in time using Crank–Nicholson technique.

A number of manufactured analytical solutions of the non-linear diffusion problem was used to verify accuracy of the developed inverse approach. Reasonably good agreement, even for highly correlated parameters, was obtained. Therefore, the technique was used to compute concentration dependent diffusion coefficient of water in paper.

An original inverse technique, which couples efficiently meshless solution of the diffusion problem with the Bayesian inverse methodology, is presented in the paper. This methodology was extensively verified and applied to the real-life problem.