The objective of this study was to examine the relationships among destination personality, self-congruity, tourist-destination relationship and destination loyalty. Brand relationship theory and ...attitude theory were used to conceptualize the framework of this study. A survey with a convenience sample of 356 foreign tourists visiting Shimla and Dharamsala, India was conducted. The findings suggest that tourists attribute personality traits to tourism destinations. Furthermore, structural equation modeling reveals that both destination personality and self-congruity positively influence the tourist-destination relationship which further leads to destination loyalty. Arguably, this is the first study in tourism research which investigates the collective role of destination personality and self-congruity in influencing tourist behavior through the tourist-destination relationship. The study offers multiple theoretical and practical implications for both academicians and practitioners.
•The study examines the effects of destination personality and self-congruity on tourist-destination relationship and destination loyalty.•Using convenience sampling method, a sample of 356 foreign tourists visiting Shimla and Dharamsala has been collected.•The findings suggest that tourists develop emotional bond or relationship with the tourism destinations.•The results lend support to the applicability of self-congruity theory to tourism destinations.
•Machine learning techniques enhance data-driven decision-making in agricultural supply chains.•Systematic literature review based on 93 papers on machine learning applications in agricultural supply ...chains.•Machine learning applications supports in developing sustainable agriculture supply chains.•A machine learning applications framework is proposed and related future perspectives are presented.
Agriculture plays an important role in sustaining all human activities. Major challenges such as overpopulation, competition for resources poses a threat to the food security of the planet. In order to tackle the ever-increasing complex problems in agricultural production systems, advancements in smart farming and precision agriculture offers important tools to address agricultural sustainability challenges. Data analytics hold the key to ensure future food security, food safety, and ecological sustainability. Disruptive information and communication technologies such as machine learning, big data analytics, cloud computing, and blockchain can address several problems such as productivity and yield improvement, water conservation, ensuring soil and plant health, and enhance environmental stewardship. The current study presents a systematic review of machine learning (ML) applications in agricultural supply chains (ASCs). Ninety three research papers were reviewed based on the applications of different ML algorithms in different phases of the ASCs. The study highlights how ASCs can benefit from ML techniques and lead to ASC sustainability. Based on the study findings an ML applications framework for sustainable ASC is proposed. The framework identifies the role of ML algorithms in providing real-time analytic insights for pro-active data-driven decision-making in the ASCs and provides the researchers, practitioners, and policymakers with guidelines on the successful management of ASCs for improved agricultural productivity and sustainability.
Abstract Human exposure to toxic heavy metals is a global challenge. Concurrent exposure of heavy metals, such as lead (Pb), cadmium (Cd), methylmercury (MeHg) and arsenic (As) are particularly ...important due to their long lasting effects on the brain. Although, the exact mechanism of neurotoxicity of metal mixture (Pb, Cd, As and MeHg) is unclear, but their main target region is hippocampus and they share many common pathways for causing cognitive dysfunction. The combination of metal may produce additive/synergetic effects due to their common binding affinity with NMDA receptor (Pb, As, MeHg), Na+ − K+ ATP-ase pump (Cd, MeHg), biological Ca+2 (Pb, Cd, MeHg), Glu neurotransmitter (Pb, MeHg), which can lead to imbalance between the pro-oxidant elements (ROS) and the antioxidants (reducing elements). In this process, ROS dominates the antioxidants factors such as GPx, GS, GSH, MT-III, Catalase, SOD, BDNF, and CERB, and finally leads to cognitive dysfunction. The present review illustrates an account of the current knowledge about the individual metal induced cognitive dysfunction mechanisms and analyse common Mode of Actions (MOAs) of metal mixture (Pb, Cd, As, MeHg). This review aims to help advancement in mixture toxicology and development of next generation predictive model (such as PBPK/PD) combining both kinetic and dynamic interactions of metals.
The current study is dedicated to the optical soliton solutions and modulation instability of the Cubic–quartic Fokas–Lenells equation with nonlinear perturbation and polarization-preserving fibers. ...In this direction, we take the help of Lie Symmetry analysis method. First of all, we obtained the invariant condition which plays important role in the mechanism of Lie symmetry method. After that, we developed the appropriate vector fields. Consequently, with the application of these vector fields similarity solutions are obtained in form of trigonometric functions. Further, in this work, optical solitons are obtained for Cubic–quartic Fokas–Lenells equation which are in form of hyperbolic and exponential functions. In addition at the end of this work modulation instability of the Cubic–quartic Fokas–Lenells equation will be discussed.
•In this article, we have established the traveling wave solutions of the Benjamin-Bona-Mahony-KdV system.•These solutions are expressed in terms of rational functions involving arbitrary ...parameters.•With the help of these solutions traveling waves are derived.•It has been shown that the proposed method is direct, concise, basic and effective, and easy to calculate.
Using the Lie symmetry approach, the author has examined traveling wave solutions of coupled Benjamin–Bona–Mahony-KdV equation. The coupled Benjamin–Bona–Mahony-KdV equation is reduced to nonlinear ordinary differential equations for all optimal subalgebras by using Lie classical symmetries and various solutions are obtained by the modified (G′/G)-expansion method. Further, with the aid of solutions of the nonlinear ordinary differential equations, more explicit traveling wave solutions of the coupled Benjamin–Bona–Mahony-KdV equation are found out. The traveling wave solutions are expressed by rational function.
The late-stage functionalization (LSF) of pharmaceutical and agrochemical compounds by the site-selective activation of C-H bonds provides access to diverse structural analogs and expands ...synthetically-accessible chemical space. We report a C-H functionalization LSF strategy that hinges on the use of an alkyne linchpin to assemble conjugates of sp
-rich marketed pharmaceuticals and agrochemicals with sp
-rich 3D fragments and natural products. This is accomplished through a template-assisted inverse Sonogashira reaction that displays high levels of selectivity for the
position. This protocol is also amenable to distal structural modifications of α-amino acids. The transformation of alkyne functionality to other functional groups further highlights the applicative potential. Computational and experimental mechanistic studies shed light on the detailed mechanism. Turnover-limiting 1,2-migratory insertion of the bromoalkyne coupling partner occurs after relatively fast C-H activation. While this insertion occurs unselectively, regioconvergence results from one of the adducts undergoing a 1,2-trialkylsilyl migration to form the alkynylated product. A heterobimetallic Pd-Ag transition structure is essential for product formation in the β-bromide elimination step.
•ANN models are developed for forecasting drought severity using standard precipitation index.•Knowledge in terms of rules is extracted from the trained ANN drought classification model using ...Decision Tree.•Results show that definite rules are learned by ANN models during their training to forecast a specific drought class.
Artificial neural networks (ANNs) are one of the most widely used techniques for solving a variety of problems including classification and function approximation type of problems. However, the use of ANNs in hydrology is limited to academics and research; and hydrologists/policymakers are skeptical to use the ANNs in operational hydrology, especially where a premium is placed on the comprehensibility of the systems. This is probably because of the perceived ‘black-box’ nature of the ANNs as they do not provide an insight into their learning process or elucidation of obtaining a particular result. Since a long time, researchers have been trying to improve the comprehensibility of trained ANN models by knowledge extraction for improving their reliability and acceptability among the decision makers for operation purposes. There are few such attempts in hydrology particularly for ANN models developed for function approximation problems such as flow forecasting and evaporation estimation. In this paper, a novel approach is proposed that is capable of extracting knowledge from trained ANN models for classification type of problems in hydrology. This is achieved by extracting knowledge in terms of rules by inducing a Decision Tree using input–output relation of a trained ANN model for forecasting drought classes with standardized precipitation index for Indian Meteorological sub-divisions. The reported researches on knowledge extraction in hydrology is primarily been for the function approximation type of problems, however, the present approach is fundamentally different from them and useful for knowledge extraction exclusively for classification problems in hydrology. The findings of this research indicate that definite rules are learned by ANN models during their training to forecast a specific drought class. These extracted rules are simple, easily implementable, and can be used as the drought class forecasting tool in a drought management activity. The results suggest that due to the nature of the drought index used, the rules extracted from one region may be suitable for drought monitoring of other regions.
Plant growth promoting rhizobacteria (PGPR) hold promising future for sustainable agriculture. Here, we demonstrate a carotenoid producing halotolerant PGPR Dietzia natronolimnaea STR1 protecting ...wheat plants from salt stress by modulating the transcriptional machinery responsible for salinity tolerance in plants. The expression studies confirmed the involvement of ABA-signalling cascade, as TaABARE and TaOPR1 were upregulated in PGPR inoculated plants leading to induction of TaMYB and TaWRKY expression followed by stimulation of expression of a plethora of stress related genes. Enhanced expression of TaST, a salt stress-induced gene, associated with promoting salinity tolerance was observed in PGPR inoculated plants in comparison to uninoculated control plants. Expression of SOS pathway related genes (SOS1 and SOS4) was modulated in PGPR-applied wheat shoots and root systems. Tissue-specific responses of ion transporters TaNHX1, TaHAK, and TaHKT1, were observed in PGPR-inoculated plants. The enhanced gene expression of various antioxidant enzymes such as APX, MnSOD, CAT, POD, GPX and GR and higher proline content in PGPR-inoculated wheat plants contributed to increased tolerance to salinity stress. Overall, these results indicate that halotolerant PGPR-mediated salinity tolerance is a complex phenomenon that involves modulation of ABA-signalling, SOS pathway, ion transporters and antioxidant machinery.
Cassia tora is a plant of medicinal importance. Medicinal plants from different localities are believed to differ in their therapeutic potency. In this study, six populations of C. tora with ...different eco-geographical origins were investigated genotypically (ISSR) and phytochemically (FTIR) to establish an integrated approach for population discrimination and authentication of the origin of this medicinal herb. CHS gene expression analysis and determination of flavonoid content were carried out to substantiate the study. A total of 19 population-specific authentication bands were observed in 11 ISSR fingerprints. Authentication codes were generated using six highly polymorphic bands, including three authentication bands. FTIR spectra revealed that the peaks at wavenumber 1623 cm
(carbonyl group) and 1034 cm
(>CO- group) were powerful in separating the populations. These peaks are assigned to flavonoids and carbohydrates, respectively, were more intense for Ranchi (highland) population. Variation in the transcript level of CHS gene was observed. The findings of FTIR and RT-PCR analyses were in agreement with the TFC analysis, where, the lowest amount of flavonoids observed for Lucknow (lowland) population. All the populations of C. tora have been authenticated accurately by ISSR analyses and FTIR fingerprinting, and the Ranchi site was observed to be more suitable for the potential harvesting of therapeutic bioactive compounds.
An important step in the internationalization process of emerging economy firms is the shift from exports to foreign direct investment (FDI). We integrate the resource- and institution-based views to ...suggest that firms that can use unique institutional advantages are more likely to make this shift. We test these arguments with a longitudinal sample of 28,563 firm-year observations (1989–2005). We found that firms that are affiliated with a business group, have more firm- and group-level international experience, have more technological and marketing resources, and operate in service industries are more likely to shift from exports to FDI.
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