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The construction industry is tasked with creating sustainable, efficient and cost-effective buildings. This study aims to develop a building information modeling (BIM)-based multiobjective optimization (MOO) model integrating the nondominated sorting genetic algorithm III (NSGA-III) to enhance sustainability. The goal is to reduce embodied energy and cost in the design process.

Through a case study research method, this study uses BIM, NSGA-III and real-world data in five phases: literature review, identification of factors, BIM model development, MOO model creation and validation in the architecture, engineering and construction sectors.

The innovative BIM-based MOO model optimizes embodied energy and cost to achieve sustainable construction. A commercial building case study validation showed a reduction of 30% in embodied energy and 21% in cost. This study validates the model’s effectiveness in integrating sustainability goals, enhancing decision-making, collaboration, efficiency and providing superior assessment.

This model delivers a unified approach to sustainable design, cutting carbon footprint and strengthening the industry’s ability to attain sustainable solutions. It holds potential for broader application and future integration of social and economic factors.

The research presents a novel BIM-based MOO model, uniquely focusing on sustainable construction with embodied energy and cost considerations. This holistic and innovative framework extends existing methodologies applicable to various buildings and paves the way for additional research in this area.

Dealers positioned at the downstream of the supply chains are those who can directly influence demand scenario for the manufacturer. However, manufacturer has to ensure that the influence is favorable by competitively fulfilling the anticipations of the dealers. In this regard, the purpose of this paper is focused on developing sustainable strategic relationships between manufacturer and dealer.

In this paper, the basic requirements of the dealers expected to be fulfilled by a manufacturer are analyzed by applying fuzzy Analytic Hierarchy Process and fuzzy Quality Function Deployment. The said approach is implemented in a case situation of a paint manufacturing company in India, and the results are analyzed to provide directions for the company to strategically align manufacturer–dealer relationships.

It was found that the case company has to focus mainly on promotions and advertisements, painters meet, and recognition offered to the dealers. Although pricing was also considered as one of the prominent aspects yet through competitive assessment the case company was found to be better off compared to its competitors.

As the results obtained in the current study are specific to a manufacturing environment, they cannot be generalized for all the companies. However, similar approach can be adopted for analyzing the customer requirements in order to achieve dealers’ attractiveness and satisfaction.

The application of proposed approach assists a manufacturer to pursue right strategy to practice for achieving dealer attractiveness and satisfaction. This will definitely help the supply chain managers to have the right requirements of the dealers’ are fulfilled on priority.

Earthquake tremors not only increase the chances of fire ignition but also hinder the fire-fighting efforts due to the damage to the lifelines of a city. Most of the international codes have independent recommendations for structural safety against earthquake and fire. However, the possibility of a multi-hazard event, such as fire following an earthquake is seldom addressed.

This paper presents an experimental study of Reinforced Concrete (RC) columns in post-earthquake fire (PEF) conditions. An experimental approach is proposed that allows the testing of a column instead of a full structural frame. This approach allows us to control the loading and boundary conditions individually and facilitates the testing under a variety of these conditions. Also, it allows the structure to be tested until failure. The role of parameters, such as earthquake intensity, axial load ratio and the ductile detailing of the column on the earthquake damage and subsequently the fire performance of the structure, is studied in this research. Six RC column specimens are tested under a sequence of quasi-static earthquake loading, followed by combined fire and axial compression loading conditions.

The experiment results indicate that ductile detailed columns subjected to 4% or less lateral drift did not lose significant load-carrying capacity in fire conditions. A lateral drift of 6% caused significant damage to the columns and reduced the load-carrying capacity in fire conditions. The level of the axial load acting on the column at the time of earthquake loading was found to have a very significant effect on the extent of damage and reduction in column load capacity in fire conditions. The columns that were not detailed for a ductile behavior observed a more significant reduction in axial load carrying capacity in fire conditions.

This study is limited to columns of 230 mm size due to the limitations of the test setup. The applicability of these findings to larger column sections needs to be verified by developing a numerical analysis methodology and simulating other post-earthquake-fire tests available in the literature.

The experimental procedure proposed in this paper offers an alternative to the testing of a complete structural frame system for PEF behavior. In addition to the ease of conducting the tests, the procedure also allows much better control over the heating, structural loading and boundary conditions.

The current natural language processing algorithms are still lacking in judgment criteria, and these approaches often require deep knowledge of political or social contexts. Seeing the damage done by the spreading of fake news in various sectors have attracted the attention of several low-level regional communities. However, such methods are widely developed for English language and low-resource languages remain unfocused. This study aims to provide analysis of Hindi fake news and develop a referral system with advanced techniques to identify fake news in Hindi.

The technique deployed in this model uses bidirectional long short-term memory (B-LSTM) as compared with other models like naïve bayes, logistic regression, random forest, support vector machine, decision tree classifier, kth nearest neighbor, gated recurrent unit and long short-term models.

The deep learning model such as B-LSTM yields an accuracy of 95.01%.

This study anticipates that this model will be a beneficial resource for building technologies to prevent the spreading of fake news and contribute to research with low resource languages.

The main goal of the present study is to investigate the friction and wear behaviors of aluminum matrix composites with an A360 matrix reinforced with SiC, B₄C and SiC/B₄C particles.

Un‐reinforced aluminum casting alloy, Al/SiC, Al/B₄C and Al/SiC/B₄C aluminum composites were prepared for the present study. Friction and wear tests of aluminum and its composites versus AISI316L stainless steel were carried out for dry sliding condition using by a pin‐on‐disc arrangement. Tests were realized at the sliding speed of 0.5, 1.0 and 1.5 ms⁻¹ and under the loads of 10, 20 and 30 N. The microstructures of the present composites were examined by scanning electron microscopy and energy dispersive spectroscopy analysis.

The coefficient of friction of the composites is approximately 25‐30 percent lower than that of the un‐reinforced aluminum. The specific wear rate of the aluminum and its composites decreases with the increase in load and increases with the increment of sliding speed. Un‐reinforced aluminum has specific wear rate value of 1.73×10⁻¹³ which is the highest specific wear rate, while Al+17%SiC has specific wear rate value of 2.25×10⁻¹³ m² N⁻¹ which is the lowest specific wear rate among the tested materials. The average specific wear rates for Al+17%B₄C, Al+17%SiC/B₄C and Al+17%SiC composites are obtained about 49, 79 and 160 percent lower than aluminum wear rate under the same test conditions, respectively.

In the present study, composites were prepared by pressured infiltration technique. The employed composites are important in industry due to their higher wear resistance, light in weight and less thermal distortion comparing to conventional composites. Also, wear behavior of Al/B₄C, Al/SiC/B₄C and Al/SiC composites produced by pressured infiltration technique were not studied very much earlier, therefore more explanation about these composites were proposed.

The purpose of this paper is to design and development of a patient-specific implant for zygomatic area of a patient suffering from mucormycosis (fungal infection). The paper describes how integration of computer-aided design (CAD) and 3D printing can be successfully used for developing custom implants for the sites for which readymade optimal solutions are not available.

The CT scan data of the patient were used for the generation of a 3D model. The healthy side of skull was mirrored and copied on the infected part, which served as a base for designing the implant. The prototype of the implant was printed using fused deposition modelling before finally printing in Ti6Al4V alloy using direct metal laser sintering process.

The custom designed implant fitted well to the patient’s skull during surgery. Proper facial aesthetics were maintained post-surgery.

The work describes the application of CAD-based image processing software and additive manufacturing in the development of a custom implant for the sites for which no readymade optimal solution is available.

This paper employs bibliometric analysis to explore transformative trends and emerging research patterns in the intersection of digital technology and tourism. The goal is to provide insights into the evolving landscape, identify key themes, influential authors and prominent journals, contributing to a nuanced understanding of this dynamic field.

This study rigorously employs bibliometric analysis from 2002 to 2023, using Scopus and Web of Science databases, yielding 2,040 Scopus and 1,389 Web of Science documents. After meticulous data selection, 575 articles were refined. Utilising VOSviewer and Biblioshiny, the analyses focus on publication trends, citations, authorship, collaborations and thematic clusters, addressing four research questions.

This bibliometric analysis reveals a significant growth in annual scientific production on digital technology and tourism, peaking at 100 articles in 2023. Key contributors include LAW R., CHUNG N. and KOO C. China leads in research output, and influential journals include “Current Issues in Tourism” and “Information Technology and Tourism.” Noteworthy trends emphasise the centrality of “smart tourism” and the interconnectedness of technology, hospitality and management in shaping future research directions.

Stakeholders should focus on integrating emerging technologies in tourism, emphasising areas with high research activity. Professionals must stay updated on evolving trends, adopt innovative digital solutions and collaborate to navigate the dynamic landscape.

This study pioneers the first bibliometric analysis on digital technology in the tourism industry, providing a unique and comprehensive exploration of the scholarly landscape from 1998 to 2023, shedding light on key contributors, influential journals and emerging trends.

The purpose of this study is to enhance the understanding of the complete process of framing and implementing operational excellence in the pharmaceutical industry to ensure higher regulatory compliance.

A literature search was conducted using preferred reporting items for systematic reviews and meta-analyses guidelines. Strategies were set with different keywords and certain assessment criteria for the inclusion and exclusion of articles. A total of 46 articles were considered for a full review.

This study describes the impact of implementing operational excellence in day-to-day operations and the driving forces to achieve the same. Seven commonly used enablers are described can be used in combination to develop and validate an assessment model. Case studies are summarized to schematize operational excellence programs for the scope of their industry.

This study is limited to Indian pharmaceutical manufacturers. It is implied toward small-scale manufacturers. It can be further extended to manufacturers from other regions.

This study guides quality assurance managers, regulatory agencies and other top management to implement operational excellence to ensure higher regulatory compliance. It guides to develop a roadmap to operational excellence in their scope. This study is applicable to any manufacturing industry bound to comply with pharmaceutical regulatory standards.

To the best of the authors’ knowledge, at the time of publication, there are regulatory guidelines and some articles on various key enablers to achieve operational excellence. There is no published systematic review on achieving regulatory compliance by using operational excellence.

Bearings and gears are major components in any rotatory machines and, thus, gained interest for condition monitoring. The failure of such critical components may cause an increase in down time and maintenance cost. Condition monitoring using the machine learning approach is a conceivable solution for the problem raised during the operation of the machinery system. The paper aims to discuss these issues.

This paper aims engine gearbox fault diagnosis based on a decision tree and artificial neural network algorithm.

The experimental result (classification accuracy 85.55 percent) validates that the proposed approach is an effective method for engine gearbox fault diagnosis.

This paper attempts to diagnose the faults in engine gearbox based on the machine learning approach with the combination of statistical features of vibration signals, decision tree and multi-layer perceptron neural network techniques.

This study aims to investigate the use of rose petals rich in bioactive components possessing several health benefits in the form of pigmented sweetened rose petals crush (P SRP C) at varying levels in the formulation of cookies to develop consumerfavored natural flavored food products.

PSRPC was used as functional ingredient in millet cookies at varying levels of 5%, 10%, 15% and 20%. Developed cookies were evaluated for physical parameters, bioactive components along with their associated antioxidant activity, texture (hardness, fracturability), surface morphology and molecular interactions. Most acceptable cookies were selected on the basis of sensory evaluation.

Cookies formulated from millet flour were significantly different from wheat flour cookies. Furthermore, the incorporation of PSRPC in millet cookies increased the total phenolic content, flavonoid content and antioxidant activity. Texture analysis results showed that cookies with higher level of PSRPC had lower hardness and fracturability. PSRPC-incorporated cookies were darker and redder due to the pigments found in rose. Surface morphology and functional groups were altered on addition of PSRPC. Cookies incorporated with 10% PSRPC were highly acceptable on the basis of sensory score.

The current study developed newer conditions for development of cookies from PSRPC. Results foresee the potential of PSRPC as a functional ingredient in value-added food products having health benefits.