ICFAI University, Sikkim is located in Gangtok in Sikkim, India.
The development of tourism destinations is traditionally interpreted using linear and cyclical models, such as the Tourism Area Life Cycle (TALC). However, growing uncertainty, environmental constraints and crisis events highlight the limitations of these deterministic approaches. This study addresses the growing need for alternative conceptual frameworks by applying evolutionary and path dependence perspectives to examine ecotourism development in Sikkim (India). This Himalayan state's tourism trajectory has been significantly influenced by geopolitical, institutional and environmental factors. The study's primary objective is to compare the TALC model's explanatory power with that of a path dependence approach in interpreting Sikkim's long-term tourism development trajectory, and to identify the pivotal historical decisions and institutional mechanisms that have shaped its current ecotourism-oriented pathway. The research also assesses future challenges related to sustainability and resilience in the context of continued growth and external shocks. The study is based on a mixed-methods approach, combining a quantitative analysis of tourist arrivals (1993–2024) - including compound annual growth rates and trend projections - with a qualitative analysis of policy documents, regulatory frameworks and key political and institutional turning points in tourism development. Sikkim's tourism development is interpreted through two analytical frameworks: the linear-cyclical Tourism Area Life Cycle (TALC) model, and a nonlinear evolutionary path dependence perspective. The results show that, although Sikkim currently corresponds to the 'development' stage of the TALC model, its tourism trajectory cannot be adequately explained as a universal life cycle. Rather, it represents a historically conditioned, path-dependent process, shaped by early access restrictions, environmental policies and the institutionalisation of ecotourism as a dominant development strategy. Key critical junctures, such as the transition to organic farming, the 2011 Ecotourism Policy, and the declaration of Sikkim as India’s first fully eco-friendly state in 2016, created a stable but increasingly rigid development path based on regulated, low-impact tourism. However, rapid post-pandemic growth reveals emerging risks of environmental pressure, infrastructure overload and potential path lock-in. This study shows that evolutionary and path-dependent approaches offer a more reliable framework for understanding the non-linear development of tourism than classical life-cycle models do.
This paper presents a structured review of the applications of Artificial Intelligence (AI) in agribusiness, emphasizing its transformative impact on farming practices. By integrating AI technologies such as machine learning, robotics, and data analytics, AI enhances productivity, sustainability, and profitability in agriculture. The paper explores key AI-driven advancements in precision agriculture, resource management, and supply chain optimization, which allow for real-time monitoring and informed decision-making. Additionally, the research discusses the ethical challenges and barriers to AI adoption, particularly in smallholder farming and developing economies. It also identifies emerging trends, such as the integration of AI with blockchain and biotechnology, to further optimize agricultural processes. The paper concludes with recommendations for advancing AI adoption, addressing data privacy concerns, and fostering inclusive, sustainable farming practices to ensure long-term resilience and food security in the agricultural sector.
In the realm of flow measurement using rectangular sharp-crested weirs, the stage-discharge relationship, primarily represented by the discharge coefficient (Cd), holds paramount significance. In order to comprehend and predict the behavior of Cd effectively, six machine learning (ML) algorithms, namely, Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Adaboost (ADB), Xgboost (XGB) and Catboost (CATB), are assessed. The performances of each of these approaches have been evaluated based on statistical criterions and using Taylor diagram. It is revealed that all six techniques can predict the discharge coefficient with excellent accuracy, with the Catboost model outperforming the other models. In the prediction of Cd, the values of the coefficient of determination and root mean square error obtained from the CATB model are 0.9782 and 0.0064, respectively. The extensive nonlinear behaviour exhibited by the ML models is also addressed using shapely additives explanation (SHAP) framework. Further, a comparison of the predictions of ML approaches with the results of existing empirical formulas has been made. The accuracy of ML approaches is found to be much higher than that of either of the existing empirical formulas, indicating the superior prediction capability of ML models.
Geopolymer is increasingly being recognized as a sustainable alternative to conventional cementitious materials for commercial applications, offering several advantages. However, the development of geopolymer faces challenges in obtaining consistent strength due to the inconsistency in the chemical composition of alumina silicate precursors. The main purpose of conducting this study is to analyse the effect of main oxide ratios like Si/Al, Al/Na, Si/Na, and Na/H2O affect the physical and mechanical properties of the geopolymer composites. Therefore, six combinations of Fly Ash (FA) based and two combinations of Ground granulated blast furnace slag (GGBS) based geopolymer mixes were prepared with 10, 12 and 14 molar Sodium silicate (NaOH) solution were prepared for this experiment. Further, the results obtained from the experimental work were utilized to develop a correlation between different oxide ratio and compressive strength of geopolymer concrete using different machine learning techniques to understand the behaviour by considering different oxide ratios on the properties of geopolymer concrete. The study utilizes a total of four different machine learning algorithms, such as Random Forest, Gradient Boosting (GBR), AdaBoost, and stacking, which have been used to forecast the hardened properties of geopolymer concrete. The prediction performance of all the models for hardened properties was compared using a testing dataset, and it was noticed that the stacking models exhibit more accurate prediction than other Algorithm models. Analysis of the study reveals that the stacking model performed well in compressive strength for 3, 7 and 28 days with a high correlation coefficient of R2 (0.97701, 0.9564, 0.9513). Additionally, the stacking model exhibits the lowest RMSE values of 1.3065, 1.8022, and 1.8727 for compressive strength, for 3, 7 and 28 days, respectively. Furthermore, a SHAP dependency analysis was performed to understand the significance of each parameter. It is observed from this study that Na/Si, followed by setting time is the most crucial parameter in predicting both 3-day and 7-day strength prediction.
The underground mining industry is recognized as one of the most hazardous industries in the world since it is characterized by a high rate of fatal and non-fatal accidents. In most cases, mine accidents result in the death of miners, and damage to machines which directly affect the production and safety. Thus, to ensure mine safety, it is necessary to predict the accidents, determine their causes, and take preventive measures within a predetermined timeframe. In this study, we examined 224 accidents which occurred in Indian underground mines from 2010 to 2023. We categorize the collected data into four layers: root causes of potential hazards, major accidents, effects of accidents, and basic information of accidents. The Bayesian Network (BN) model is used to make a probabilistic relationship between four layers containing 29 variables and establish the conditional probability table (CPT). The real-time evidence of the accident is fed into the BN model and updates the CPT. The prior, likelihood, and posterior probability used to develop the reasoning to categorize the accident into three groups namely; catastrophic, degraded and cascaded accident. The conflict analysis is used to measure the conflict between real-time sets of evidence as well as validate the model. The five objective-based sensitivity analyses are used to identify the best combination of three elements; evidence, interesting, and investigating parameter that can used to make the preventive measure and avoid the effect of accidents. The proposed research work helps to develop the modern mining intelligence system and improve the safety governance system for the mining industry.
