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As the world is becoming more volatile and uncertain, organizations face much complexity in their daily operations. Further, there is a much ambiguity in business operations to achieve the effective utilization of resources. The work optimizes a novel constraint programming model approach of the utilization of shuttle services vehicle while considering cost savings, employee wellbeing and other real an Information Technology enabled service (ITES) industry constraints.

The present work considers a novel extension of the vehicle routing problem related to the shuttle service operation in an ITES industry in VUCA context. Additionally, the model considers the women safety aspects, which engages the company to provide a security guard for women employees in the night shift.

Numerical experiments were conducted on real instances data of ITES industrial partner. The results show that the vehicle utilization increased from 75% up to 96% while ensuring in parallel the wellbeing of employees and women safety during the night shift. Finally, the proposed model is converted to a decision support application allowing ITES partner to plan employees shuttle service operations efficiently.

Study has evaluated the shuttle services optimization for ITES industry using data from industrial which makes it a unique contribution to literature in shuttle operations. Further, the study used constraint programming to evaluate the vehicle utilization and security allocation, thereby introducing new parameter on security allocation in open VRP problem.

The purpose of this paper is to enhance the limited fluorescence of mica titania (MT) effect pigments by coating them with peripherally substituted zinc phthalocyanines (ZnPc).

The effects of deposition medium, temperature, time, initial organic pigment/MT ratio on deposition behaviour and efficiencies were investigated separately for ZnPc, nitro (TNZnPc) and amino (TAZnPc) substituted ZnPc’s.

TNZnPc could be deposited in the form of well-defined crystals on MT with per cent 64 ± 5 efficiency in chloroform at 50°C within 5 h and the amount deposited was linearly dependent on the initial TNZnPc concentration. TNZnPc fluorescence emissions, which appear mainly at 460 and 685 nm in pure pigments, could be observed in combination with MT (MT-TNZnPc) as well. A spectral analysis on the non-overlapping region of the spectrum with two different excitations (385 and 630 nm) revealed that, respectively, up to threefold to sixfold increase is possible to attain, depending on the excitation wavelengths.

Efficiency of deposition could not be taken above per cent 11 ± 4 with TAZnPc. Although ZnPc could be deposited with per cent 57 ± 3 efficiency, the pigment was not effective in imposing its fluorescence characteristics over MT.

Combining inorganic effect pigments with organic dye molecules is an idea that has been elaborated mainly for producing different colour effects and stabilization of dye molecules against agglomeration. Here, for the first time, it is used to enhance the fluorescence of the effect pigments.

The purpose of this paper is to summarize the scientific information of various qualities of bael fruit juice used in traditional system of medicine for variety of purposes. Utilization of bael fruit juice in day-to-day life has great nutritional, therapeutic, and commercial importance. Bael fruit contains nutrients like vitamins (riboflavin), minerals, trace elements, energy and phytochemicals, including flavonoids, polyphenols and antioxidants, that have been shown to have varied health benefits. In past few decades, bael has been extensively studied for its medicinal properties by advanced scientific techniques, and a variety of bioactive compounds like marmelosin, tannins, alkaloids, coumarins, steroids, rutacine, y-sitosterol, psoralin, xanthotoxin, scopolotein, aegelemine, aegeline, marmeline, fragrine, dictamine, cinnamide and different derivatives of cinnamide have been isolated from its fruit juice.

The medicinal value of bael fruit is very high when the harvests just begin to ripen. As a result, it has a high demand as alternative medicine for curing the diseases like diabetes, high cholesterol, peptic ulcer, inflammation, diarrhea and dysentery, constipation, respiratory infection. Furthermore, the bael fruit juice has anticancer, cardio protective, antibacterial, antifungal, radio protective, antipyretic, analgesic, antioxidant, antiviral, anthelmintic and anti-inflammatory, hepatoprotective, wound healing properties. The ripe fruit juice is aromatic, has cooling and laxative effects, and arrests secretion or bleeding.

The unripe or half-ripe fruit juice is good for digestion, useful in preventing or curing scurvy, and it strengthens the stomach action. It helps in the healing of ulcerated intestinal surfaces and has appreciable activity against intestinal pathogenic organisms. The present review summarizes the scientific information of various qualities of bael fruit juice used in traditional system of medicine for a variety of purposes.

It is quite evident from this review that bael is an important medicinal herb and extensively used in Ayurveda, Siddha and other medicinal systems. Bael fruit juice is an excellent source of water and natural sugar and is important principally for containing vitamins, minerals, phytochemicals, antioxidants, pigments, energy, organic acids, dietary fiber and other food components, which are the key factors in the medicinal value of this plant. Moreover, mechanisms of action of a few bioactive compounds have been identified so far.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

The study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the insufficiency of training data that occurred while classifying the lesions as melanoma and non-melanoma.

In this work, a transfer learning (TL) framework Transfer Constituent Support Vector Machine (TrCSVM) is designed for melanoma classification based on feature-based domain adaptation (FBDA) leveraging the support vector machine (SVM) and Transfer AdaBoost (TrAdaBoost). The working of the framework is twofold: at first, SVM is utilized for domain adaptation for learning much transferrable representation between source and target domain. In the first phase, for homogeneous domain adaptation, it augments features by transforming the data from source and target (different but related) domains in a shared-subspace. In the second phase, for heterogeneous domain adaptation, it leverages knowledge by augmenting features from source to target (different and not related) domains to a shared-subspace. Second, TrAdaBoost is utilized to adjust the weights of wrongly classified data in the newly generated source and target datasets.

The experimental results empirically prove the superiority of TrCSVM than the state-of-the-art TL methods on less-sized datasets with an accuracy of 98.82%.

Experiments are conducted on six skin lesion datasets and performance is compared based on accuracy, precision, sensitivity, and specificity. The effectiveness of TrCSVM is evaluated on ten other datasets towards testing its generalizing behavior. Its performance is also compared with two existing TL frameworks (TrResampling, TrAdaBoost) for the classification of melanoma.

Sustainable supply chain management (SSCM) ensures integration of socially, environmentally and economically feasible practices in entire supply chain. SSCM principles can be implemented to improve efficiency and productivity of a system by different attributes of the system. The purpose of this article is to identify the most appropriate existing (SSCM) framework that can be implemented suitably in Indian smart manufacturing industries.

Validity and reliability analysis on the existing SSCM frameworks was carried out with the help of empirical data collected using questionnaire survey methodology from various Indian smart manufacturing organizations. The empirical data were gathered from various experts from top- and middle-level management in different smart manufacturing organizations across the country. Further, factor analysis was carried on the collected data to estimate the unidimensionality of each SSCM frameworks. Cronbach's alpha value was used to assess reliability of each framework. Subsequently, the frequency distribution analysis was done to obtain familiar elements in the segregated frameworks based on validity and reliability analysis.

The work observed that only five SSCM frameworks have shown unidimensionality in terms of the elements or constructs. The work further found that these segregated frameworks have not shown sufficiently high level of reliability. Additionally, this work attempted frequency distribution analysis and observed that there were very few elements which were being repeatedly used in numerous frameworks proposed by researchers. Based on the findings of this work, the work concluded that there is acute need of a new SSCM framework for Indian smart manufacturing industries.

This study gathered empirical data from 388 Indian smart manufacturing organizations. Thus, before generalizing the findings of the study across the sectors, there is a possibility of some more explication.

The main purpose of this article is to explore the feasibility of the existing SSCM frameworks in Indian smart manufacturing sector. The study also assumes that the manufacturing managers and executives may have the complete understanding on the existing sustainable manufacturing frameworks and a chance to executing proper suitable framework in the respective manufacturing organization.

This study aims to address the adoption issues of green and sustainable practices in the hotel industry. The study identifies critical performance indicators (CPIs) and utilizes Hotel Carbon Management Initiative (HCMI) framework to prioritize CPIs for achieving a robust adoption framework for green and sustainable practices.

The hotel industry is driven by changing ecological degradation, and it is necessary to achieve feasible development goals. This research article formulates the CPIs derived from HCMI and decision-making model is created using the Analytic Hierarchy Process (AHP).

In this research, CPIs of HCMI are considered and aim to formulate five major CPIs of HCMI, namely air pollution, energy efficiency, water conservation, noise pollution and waste management. The study identifies the need for better control and sustainable growth in the Indian hotel industry with minimum carbon emissions coupled with the green approach adoption.

The CPIs work on minimization of risks and maximizing optimality of return on investment. The development of the hotel industry will be improved and immensely welcomed by capping the carbon emission with the green initiatives. This research is limited as urban hotels are surveyed in this study.

This work makes a valid argument to establish HCMI as a model initiative for environment quality improvement and further extension of other activities in the hospitality sector and scale-up sustainable practices for future-ready circular economies.

Low temperature co-fired ceramics (LTCC) technology-based micro-hotplates are of immense interest owing to their ruggedness, high temperature stability and reliability. The purpose of this paper is to study the role of thermal mass of LTCC-based micro-hotplates on the power consumption and temperature for gas-sensing applications.

The LTCC micro-hotplates with different thicknesses are designed and fabricated. The role of thermal mass on power consumption and temperature of these hotplates are simulated and experimentally studied. Also, a comparison study on the performance of LTCC and alumina-based hotplates of equivalent thickness is done. A thick film-sensing layer of tin oxide is coated on LTCC micro-hotplate and demonstrated for the sensing of commercial liquefied petroleum gas.

It is found from both simulation and experimental studies that the power consumption of LTCC hotplates was decreasing with the decrease in thermal mass to attain the same temperature. Also, the LTCC hotplates are less power-consuming than alumina-based one, owing to their superior thermal characteristics (low thermal conductivity, 3.3 W/ [m-K]).

This study will be beneficial for designing hotplates based on LTCC technology with low power consumption and better stability for gas-sensing applications.

Waste management is a crucial aspect of sustainable development, but is it economically sustainable for marginalized informal firms? The study tries to answer this question by revisiting the Porter–Wagner dilemma about the association between environmental management (EM) and firm performance (FP). The study looks into the various liquid waste management practices (LWMPs) adopted by them and the overall impact of LWMPs on firms' economic performance.

The study uses the latest available cross-sectional data source on Indian informal firms by the National Sample Survey Office (NSSO), 73rd survey round 2015–16. First, ordered logistic regression was used to analyse the factors that impact a firm's adoption of a particular LWMP. Subsequently, to capture the heterogeneity among the firms based on productivity and size, a quantile regression (QR) was employed to analyse the impact of LWMPs on firm productivity. Additionally, the propensity score matching technique was used to address endogeneity concerns.

The authors find that bigger, urban-located and female-owned firms adopt cleaner LWMPs that positively impact their economic performance. Furthermore, the QR analysis observed that the most productive firms could extract higher returns from adopting cleaner LWMPs, indicating the relevance of the Porter–Wagner dilemma, i.e. environmental and economic sustainability are possibly symbiotic, thus having a feedback mechanism.

To the authors’ limited knowledge, this is the first study analysing the relationship between EM and FP among the informal sector firms, which are away from any regulations or obligations. Since sustainability is a two-way process, policies should be devised that incentivise sustainable business practices.

Urbanization and globalization in India have led to the depletion of resources and degradation of the environment to meet the demands. Because of these issues, researchers and practitioners have begun to study various strategies to reduce the level consumption of resources to utilize it for present and future needs. In pursuit of finding solutions to the problems, sustainable building construction is found as the best key to avoid depletion of resources. Sustainable material selection is found as a vital strategy in construction. The paper aims to discuss this issue.

A three-phase methodology is proposed for framing the assessment model for construction industries to select materials for construction. In the first phase, a total of 23 sub-criteria of triple bottom line (TBL) and four brick materials as alternatives were identified. The second phase finds the weights and ranks of criteria and sub-criteria using the best worst methodology (BWM) the third phase involves ranking of materials concerning sub-criteria weights determined in phase II using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

The objective of study is fixed to identify the criteria list for the selection of material in construction industries from the literature review especially for Indian construction industries; to rank the criteria for selection of materials with the help of the BWM approach; and to prioritize the identified materials in the view of sustainability with the help of Fuzzy TOPSIS in construction industries perspective. This study analyzed and choosing right sustainable materials by the three pillars of sustainability which are the environment, economic and social, also called TBL, for Indian construction companies by framing a sustainable material assessment model.

The results of this study facilitate to frame an assessment model for evaluating and selecting sustainable building materials.