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Natural Resource Rich Regions (NRRRs) are ecologically and economically vital regions that support the livelihood of people through the sustained ecosystem process involving interaction among biotic and abiotic elements. Identifying NRRRs, considering spatially ecological, geo-climatic, biological, and social dimensions, would help in conservation planning and prudent management of natural resources as per the Biodiversity Act 2002, Government of India. Changes in the landscape structure would lead to alterations in the composition and health of these regions with irreversible changes in the ecosystem process, impacting the sustenance of natural resources. Landscape dynamics is assessed by classifying temporal remote sensing data using the supervised machine learning (ML) technique based on the Random Forest (RF) algorithm. Additionally, predicting likely land use changes in ecologically fragile areas would help formulate appropriate location-specific mitigation measures. Modeling likely land uses through the simulation of long-term spatial variations of complex patterns has been done through the CA–Markov model. Prioritization of NRRRs at disaggregated levels highlights that 12% of the total geographical area of the district is under NRRR 1 and NRRR 2, 54% of the total geographical area under NRRR 3, and the rest of the region under NRRR 4. The current study emphasizes the need for robust decision support systems to aid in effective policy formulation for conserving and restoring natural resources. Clinical trial number: Not applicable. Graphical Abstract

The article is available for free on the web site of the journal: https://www.tandfonline.com/doi/full/10.1080/00207543.2024.2432463

This study examines the interconnectedness between the green bond index (GRBI) and major financial indices, focusing on three key periods: Pre-Covid, During-Covid, and Russia-Ukraine war period. Using the spillover index methodology and time-varying parameter vector autoregression (TVP-VAR), the research compares two portfolios: one excluding GRBI (base portfolio) and one including GRBI (delta portfolio). The findings reveal that GRBI consistently acts as a net receiver of shocks, significantly reducing the total connectedness index (TCI) and functioning as a spillover absorber. Notably, the equity index (EQWI) emerges as the largest net transmitter of shocks, while GRBI helps reduce systemic risk, particularly during periods of market volatility. The inclusion of GRBI enhances the delta portfolio’s resilience, improving its downside risk-adjusted returns and hedging effectiveness. During crisis periods, the delta portfolio consistently outperforms the base portfolio in downside risk measures, such as lower drawdowns and improved ratios like the Sortino and modified Sharpe ratios. GRBI also acts as a natural hedge, reducing negative hedging effectiveness (HE) values in other asset classes. These results highlight the crucial role of GRBI in strengthening portfolio diversification and risk management. Investors seeking to optimize portfolio performance and minimize exposure to systemic shocks should consider including GRBI, especially during periods of heightened market uncertainty.

Space technology innovation plays a crucial role in advancing scientific knowledge and economic growth. We investigate the impact of India's Moon mission, Chandrayaan-3, on the market value of firms involved. Our event study analysis reveals that, despite initial negative market reactions during the launch, the successful landing resulted in approximately 3.7% cumulative average abnormal returns within two days. This suggests that initial investor skepticism, driven by uncertainty and perceived costs, shifts to positive sentiment following the mission's success. Our findings underscore the significant economic benefits of successful space technology innovations and contribute to the broader discussion on their value and impact.

The present study explores the impact of spirituality and environmental concerns on the purchase intention of Indian consumers through an extended version of the Theory of Planned Behaviour (TPB). Based on the data collected from 448 people, Partial Least Square Structural Equation Modeling (PLS-SEM) is used to develop and test the proposed model. The present study confirms the positive impact of attitude, subjective norms, and perceived behaviour control on the purchase intention of millet products. The study also confirms the importance of spirituality and environmental concerns on the intention to purchase millet products. Since millet is a nutrient-dense and climate-resilient crop, the findings advocate for the government and business organizations to promote millet products on these dimensions. With the increased awareness of the importance of environmental sustainability and spirituality, the present study provides a new dimension of research and managerial action.

The marketing ecosystem is experiencing a paradigmatic shift with the advent of artificial intelligence (AI). The present study aims to widen the foundations of brand strategy by integrating AI dimensions within the branding dominion. The literature lacks a cohesive perspective on AI dimensions boosting and driving brands for sustained competitive advantage in the dynamic marketplace. Marketing practitioners are gradually getting acquainted with the nuances of AI implementation, but latent AI capabilities remain potent to unfurl enduring consumer relationships. The present study develops a novel 8‐T Strategic Framework for facilitating the strategic integration and effective implementation of AI in brand decisions and initiatives. The theorization of the 8‐T framework of AI‐driven branding (Technology, Training, Teaming, Targeting, Technique, Timing, Tailoring, Trust) offers macro and micro perspectives that serve as a formidable typology. The framework enunciates a holistic set of strategic mechanisms to help practitioners build futuristic strategies with a contemporary purpose.

Analysis of long-distance travel behaviour is important for the planning and operation of inter-state, inter-city, and urban–rural transportation infrastructure and congestion management. Although there is a rich body of literature in this area from North America, Europe, and other developed regions around the world, limited work exists on long-distance travel behaviour in emerging economies such as India. This paper contributes to the literature on traveller mode choice decisions for long-distance recreational trips in India. Specifically, we develop a mode choice model that recognizes the uncertainty in the level of service (LOS) attributes arising from the lack of precise information on these variables due to the unavailability of precise destination information. In addition, we examine the influence of various household sociodemographic factors and trip characteristics on travellers' mode choice decisions. Results suggest that accounting for uncertainties in the LOS variables yields a model with improved fit and more reasonable willingness to pay (WTP) measures compared to models with aggregated LOS attributes. Furthermore, this study offers new insights into the values of travel time savings context for long distance travel, previously unavailable in the Indian context, potentially informing future transportation policies.

In recent decades, the construction of large hydropower plants in the Brazilian Amazon has increased and has put the livelihoods of Indigenous Peoples and local communities (IPLC) at risk. The Belo Monte dam has been operating since 2015, causing the dewatering, i.e., partial diversion of Xingu River flow, of the “Volta Grande” region, where the Arara da Volta Grande do Xingu Indigenous Land is located. The objectives of the study were to (1) understand the biocultural connections between the Arara Indigenous People and the Xingu River before the dam’s operation; (2) analyze hydrological alterations in the dewatered stretch and describe the implications for the floodplain forest; and (3) elucidate how these changes affect the Arara and the Volta Grande social-ecological system. We utilized document analysis, semi-structured interviews, hydrological analysis based on the environmental flow concept, and a biocultural approach that considers the interconnectedness of human and natural systems. Results show a significant decrease in flow magnitude, higher frequency of reversing cycles of wetting and drying, increased uncertainty of the Xingu River’s dynamics, and degrading effects in the floodplain forest. Arara’s perceptions have shown the effects of their biocultural connection with Xingu River, such as the hindrance of traditional activities like fishing. Results emphasize the depreciation of the Volta Grande social-ecological system’s resilience, the necessity of developing participatory environmental flow recommendations, adopting an adaptive management approach, and the need to involve IPLC in decision-making. In addition, there is an urgent need to reconsider the current hydropower agenda in the Amazon.

Rainfall has been shown to be the main cause of elevated nutrient pollution in groundwater beneath landfills. However, groundwater monitoring is often based on predetermined schedules without considering rainfall patterns. This study examined how rainfall patterns affect fluctuations in groundwater quality at the Coastal Park landfill in Cape Town, South Africa, and the relevance of current groundwater sampling schedules. Boreholes upstream and downstream of two large waste cells, one lined and the other unlined, were monitored for 15 weeks during the onset of the rainy season to detect changes in the groundwater level, pH, conductivity, dissolved oxygen, ammonia, nitrate, and phosphate. Rainfall patterns strongly affected the groundwater parameters, with widely varying fluctuation patterns and lag times. Conductivity peaked downstream of the lined cell 10 weeks later than at the unlined cell, with widely different fluctuation patterns (R2 = 0.36). Ammonia peaked downstream of both the unlined and lined cells well before the early rains, with very similar fluctuation patterns (R2 = 0.97), although it peaked 6 times higher in the unlined cell. Nitrate peaked at Weeks 2 to 4 downstream of the unlined and the lined cell, with a weak correlation (R2 = 0.56). A shorter nitrate peak and a net decrease throughout the rainy season were observed downstream of the lined cell. Phosphate showed a brief, multi-fold increase at Week 3 downstream of both the unlined and lined cells, displaying pH-induced mobilisation and a very strong correlation (R2 = 0.99) between these locations. Lag times and fluctuation patterns varied depending on the presence of liners, and rainfall patterns. Therefore, the low frequency sampling required by many South African landfill waste management permits and licences cannot identify pollutant peak concentrations or describe their trends, and high frequency sampling should be considered.

The COVID-19 disease has spread very swiftly in different parts of the world. Some of the implications of this disease are loss of life, health-related issues, negative impact on the economy, and several other social issues. For the purpose of forecasting this disease’s spread, various algorithms have been employed. Also, the application of several metaheuristic optimization algorithms has been explored for choosing optimal features from a big data set. This paper addresses this issue and proposes a chaotic algorithm based on Marine Predator Algorithm (MPA). A normalized fusion of chaotic function-is first proposed. The function is based on β$\beta$ chaotic map. Based on this function, position update mechanism is developed for improving the performance of the original MPA. The developed algorithm is named as Marine Predator Chaotic Algorithm (MPCA). The COVID-19 dataset has been employed for judging the efficacy of the proposed algorithms. Different statistical analyses and graphical visualizations affirm the efficacy of the proposed algorithms.

Analyses of spatial and temporal patterns of land use and land cover through multi-resolution remote sensing data provide valuable insights into landscape dynamics. Land use changes leading to land degradation and deforestation have been a prime mover for changes in the climate. This necessitates accurately assessing land use dynamics using a machine-learning algorithm’s temporal remote sensing data. The current study investigates land use using the temporal Landsat data from 1973 to 2021 in Chikamagaluru district, Karnataka. The land cover analysis showed 2.77% decrease in vegetation cover. The performance of three supervised learning techniques, namely Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood classifier (MLC) were assessed, and results reveal that RF has performed better with an overall accuracy of 90.22% and a kappa value of 0.85. Land use classification has been performed with supervised machine learning classifier Random Forest (RF), which showed a decrease in the forest cover (48.91%) with an increase of agriculture (6.13%), horticulture (43.14%) and built-up cover (2.10%). Forests have been shrinking due to anthropogenic forces, especially forest encroachment for agriculture and industrial development, resulting in forest fragmentation and habitat loss. The fragmentation analysis provided the structural change in the forest cover, where interior forest cover was lost by 27.67% from 1973 to 2021, which highlights intense anthropogenic pressure even in the core Western Ghats regions with dense forests. Temporal details of the extent and condition of land use form an information base for decision-makers.

Hyperparameter tuning in the area of machine learning is often achieved using naive techniques, such as random search and grid search. However, most of these methods seldom lead to an optimal set of hyperparameters and often get very expensive. The hyperparameter optimization problem is inherently a bilevel optimization task, and there exist studies that have attempted bilevel solution methodologies to solve this problem. These techniques often assume a unique set of weights that minimizes the loss on the training set. Such an assumption is violated by deep learning architectures. We propose a bilevel solution method for solving the hyperparameter optimization problem that does not suffer from the drawbacks of the earlier studies. The proposed method is general and can be easily applied to any class of machine learning algorithms that involve continuous hyperparameters. The idea is based on the approximation of the lower level optimal value function mapping that helps in reducing the bilevel problem to a single-level constrained optimization task. The single-level constrained optimization problem is then solved using the augmented Lagrangian method. We perform extensive computational study on three datasets that confirm the efficiency of the proposed method. A comparative study against grid search, random search, Tree-structured Parzen Estimator and Quasi Monte Carlo Sampler shows that the proposed algorithm is multiple times faster and leads to models that generalize better on the testing set.

The main goal of this research is to assess the Indian Monsoon Data Assimilation and Analysis (IMDAA), a recently established high‐resolution (0.12° × 0.12°) reanalysis dataset, for observing cloudburst events over the Northwest Himalaya (NWH). In addition, a high‐resolution (0.1° × 0.1°) satellite estimate, the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (GPM) version 6 (IMERG‐V06B) (Final run), is validated against the IMDAA. The following is a summary of our significant findings. (1) Reanalysis data from the IMDAA detects 11 out of 16 cloudburst incidences. In addition, 10 events captured in the IMERG‐V06B data are a subset of those captured by IMDAA. According to contingency measures, the probability of detection (POD) of IMERG‐V06B at 99.99th percentile is 33.33%–63.39% along the geographical extent of NWH. When utilizing the improved probability of detection (IPOD) to account for time, the possibility of IMERG‐V06B detecting cloudburst occurrences ranges from 41.24% to 68.25%. (2) According to the finding, the IMDAA accurately detects cloudburst events in Jammu and Kashmir (J&K), where data from the India Metrological Department (IMD) at a resolution of 0.25° × 0.25° have underperformed. As a result, we can deduce that IMDAA can be used to observe extreme events in the Himalayas. IMERG‐V06B is also relevant satellite data for monitoring cloudburst events and validating climate models.

Traditional multiple discrete–continuous (MDC) choice models impose tight linkages between consumers’ discrete choice and the continuous consumption decisions due to the use of a single utility parameter driving both the decision to choose and the extent of choice. Recently, Bhat (Trans Res Part B Methodol 110:261–279, 2018) proposed a flexible MDCEV model that employs a utility function with separate parameters to determine the discrete choice and continuous consumption values. However, the flexible MDCEV model assumes an independent and identically distributed (IID) error structure across the discrete and continuous baseline utilities. In this paper, we formulate a flexible non-IID multiple discrete–continuous probit (MDCP) model that employs a multivariate normal stochastic distribution to allow for a more general variance–covariance structure. In doing so, we revisit Bhat (Trans Res Part B: Methodol 109: 238-256, 2018) flexible utility functional form and highlight that the stochastic conditions he used to derive the likelihood function are not always consistent with utility maximization. We offer an alternate interpretation of the model as representing a two-step decision-making process, where the consumers first decide which goods to choose and then decide the extent of allocation to each good. We demonstrate an application of the proposed flexible MDCP model to analyze households’ expenditure patterns on their domestic tourism trips in India. Our results indicate that, if the analyst is willing to compromise on the strict utility-maximizing aspect of behavior, while also enriching the behavioral dimension through the relaxation of the tie between the discrete and continuous consumption decisions, the preferred model would be the flexible non-IID MDCP model. On the other hand, if the analyst wants the model to be strictly grounded on utility-maximizing behavior (which may also have benefits by way of welfare measure computations), and is willing to assume a very tight tie between the discrete and continuous consumption decision processes, the preferred model would be the non-IID traditional MDCP model.

With the growth of cars and car-sharing applications, commuters in many cities, particularly developing countries, are shifting away from public transport. These shifts have affected two key stakeholders: transit operators and first- and last-mile (FLM) services. Although most cities continue to invest heavily in bus and metro projects to make public transit attractive, ridership in these systems has often failed to reach targeted levels. FLM service providers also experience lower demand and revenues in the wake of shifts to other means of transport. Effective FLM options are required to prevent this phenomenon and make public transport attractive for commuters. One possible solution is to forge partnerships between public transport and FLM providers that offer competitive joint mobility options. Such solutions require prudent allocation of supply and optimised strategies for FLM operations and ride-sharing. To this end, we build an agent- and event-based simulation model which captures interactions between passengers and FLM services using statecharts, vehicle routing models, and other trip matching rules. An optimisation model for allocating FLM vehicles at different transit stations is proposed to reduce unserved requests. Using real-world metro transit demand data from Bengaluru, India, the effectiveness of our approach in improving FLM connectivity and quantifying the benefits of sharing trips is demonstrated.

Objective: Upper urinary tract urothelial carcinoma (UUT-UC) is a very aggressive disease, characterized by 22%-50% of patients suffering from subsequent bladder recurrence after radical nephroureterectomy (RNU). Although the therapy of intravesical instillation is reported to be effective in preventing bladder recurrence, no study had been reported in Northeast China. The findings relating to the clinical effectiveness of intravesical instillation after RNU are somewhat controversial, and the best efficacy and least adverse effects of instillation drugs have not been widely accepted. Here, we aimed at evaluating the efficacy of intravesical instillation for the prevention intravesical recurrence systematically. Methods: In this retrospective cohort study, from October 2006 to September 2017, 158 UUT-UC patients underwent RNU were divided into 4 groups: epirubicin (EPB) instillation group, hydroxycamptothecin (HCPT) instillation group, bacillus Calmette-Guerin (BCG) instillation group, and noninstillation group. Cox univariate and multivariate analyses were employed to identify the risk factors for intravesical recurrence-free survival (IVRFS). The nomogram model was also applied to predict patient outcomes. Subsequently, to evaluate the clinical significance of intravesical instillation comprehensively, several databases including PubMed, Ovid, and Embase were searched and data from published studies with our results were combined by direct meta-analysis. Moreover, a network meta-analysis comparing instillation therapies was conducted to evaluate the clinical efficacy of different instillation drugs. Results: In our retrospective cohort study, the Kaplan-Meier survival curve demonstrated noninstillation groups were associated with worsened IVRFS. Meanwhile, multivariate analysis indicated that intravesical instillation was independent protective factors for IVRFS (hazard ratio [HR] = 0.731). Moreover, calibration plots, receiver operating characteristic (ROC) curves, area under the curve (AUC) values, and the C-index showed the priority of nomogram's predictive accuracy. Next, direct meta-analysis including 19 studies showed that intravesical instillation could prevent the recurrence of bladder cancer with a pooled risk ratio (RR) estimate of 0.53. Subgroup analysis by study type, year of intravesical recurrence, first instillation time, and instillation times also confirmed the robustness of the results. Moreover, intraoperative instillation was associated with a decrease in the risk of bladder recurrence compared with postoperative instillation. Then, a network meta-analysis including 7 studies indicated that pirarubicin (THP) (surface under the cumulative ranking curve [SUCRA] = 89.2%) is the most effective therapy to reduce the risk of bladder recurrence, followed by BCG (SUCRA = 83.5%), mitomycin C (MMC) (SUCRA = 53.6%), EPB (SUCRA = 52.6%), and HCPT (SUCRA = 5.1%) after the analysis of the value ranking. Conclusions: A maintenance schedule of intravesical instillation prevents the recurrence of bladder cancer after RNU in UUT-UC patients effectively. Large, prospective trials are needed to further confirm its value. Compared with other chemotherapy regimens, THP may be a promising drug with favorable efficacy to prevent bladder recurrence. As included studies had moderate risk of bias, the results of network meta-analysis should be applied with caution.

Land-use transformations altering the ecosystem function have impacted the sustenance of natural resources. Implementation of unplanned developmental activities in the ecologically fragile regions has contributed to frequent landslides, conversion of perennial rivers to intermittent or seasonal rivers, reduced water retention capability, etc. Addressing these challenges entails understanding the drivers of land-use change and also their role in altering land uses. Large-scale linear projects such as roads and railways, though contribute to better infrastructure and enhance employment opportunities but severely change the landscape structure affecting peoples' livelihood due to the reduction of ecosystem goods and services. Planned interventions are essential for adopting appropriate land-use trends and shift the trajectory of ecosystem service provision through prior visualization of land-use dynamics with likely impacts. The current study analyses the possible land-use changes in the ecologically fragile central Western Ghats with the proposed railway networks, namely (i) Mysore-Kushalnagar and (ii) Mysore-Thalassery (limited to Karnataka state), using an agent-based model (Fuzzy-AHP-CA-Markov) considering the linear project regions with a buffer of 5 km. The analyses reveal a reduction of forests by 2 and 5%, respectively, during 2010 and 2019. This trend would continue with a significant forest decline by 2026. Areas under built-up have increased over 5% during 2010-2019, which would increase by 7% (2019 and 2026) at the expense of cultivation lands. Major cities such as Mysore and Kushalnagara would witness concentrated urban growth with sprawl in the peripheries, while other towns have undergone leapfrog developments. The spatial distribution of fauna and flora indicates that most parts of the buffer region endow endemic species and serve as foraging grounds. Prediction of likely land uses in 2026 suggests that these regions would undergo large-scale alterations threatening fauna and flora. Implementing linear projects in the ecologically fragile Western Ghats would further destabilize the region, posing a threat with the increased hazard frequencies and the sustenance of natural resources.

The study aims to examine the effects of interactivity on users’ adoption intention via perceived consumption values in the bookkeeping application context after post-pandemic. The study also investigates the moderating effect of users’ involvement on the association between interactivity and perceived values. Data were collected using online survey from 276 SMEs in India. The results indicated that application interactivity enhances users’ perceived consumption values (functional, emotional, social, conditional, and epistemic), in turn adoption intention. Moreover, users’ involvement positively moderates that association between application interactivity and consumption values. The findings of the study suggest bookkeeping application service providers that which specific interactivity features they should emphasize for enhancing users’ adoption intention. The study enriches interactivity, consumption values, and users’ behavioural intention literature.

The availability of multi-resolution spatial data and advances in modeling techniques have given an impetus to land use land cover (LULC) change analyses. Geo-visualization of possible land uses (LU) with policy decisions is vital for formulating appropriate sustainable resource management policies. For the prudent management of natural resources, LU planning has to take environmental dimensions into account. LU dynamics helps to understand the macro background of regional population growth, economic development, social progress, and changes in the natural environment. In this study, LU transitions from 1985 to 2019 were assessed through a supervised classifier based on the Gaussian maximum likelihood estimation algorithm. Geo-visualization of landscape dynamics was implemented through a fuzzy analytical hierarchy process (AHP) with Markov cellular automata (MCA) for Karnataka state, India. It considered five policy scenarios, namely, (i) business as usual (BAU), (ii) agent-based land use transition (ALT), (iii) reserve forest protection (RFP), (iv) afforestation (AF), and (v) sustainable development plan (SDP). Prior knowledge of likely LU aids in assessing the implications of chosen policies forms a base for sustainable resource management with conservation of biological diversity. LU analyses revealed that forests in Karnataka state constituted 21% in 1985, witnessed large-scale transitions, and reduced to 15% of the geographical area in 2019. BAU depicts a likely increase in the built-up area to 11.5% from 3% (2019). The SDP scenario (with stringent policy implementation) indicates that the forest cover would remain at 11% (compared to 15% in 2019), which is the least possible loss among all considered scenarios (BAU, ALT, RFP, AF, and SDP). Modeling and visualization of landscape dynamics aids in regional LU planning as a spatial decision support system (SDSS) towards achieving sustainable development goals.