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Bisimulation in model-changing modal logics: An algorithmic study
No full textWe discuss the notion of bisimulation in various model-changing modal logics and provide an algorithmic study of the same. We provide a general algorithm which gives an overall procedure to check whether two models are bisimilar in all these logics. Through our algorithmic analyses we provide a PSPACE upper bound of the bisimulation/model comparison problem of all these modal logics. We also provide some insight into the higher complexity of the model comparison problem for these logics compared to that for the basic modal logic
Bistability in modified Holling II response model with harvesting and Allee effect: Exploring transitions in a noisy environment
No full textHere, we explore the complex dynamics of a predator–prey system with a modified Holling type II functional response and the Allee effect that accounts for a reduced hunting efficiency due to intra-predator interactions. Besides the growth due to focal prey, the predator population follows a Beverton–Holt-like reproduction due to the alternative food sources. The model also considers the impact of harvesting on the predator population, reflecting economic interests in biological resource exploitation. We systematically investigate key aspects, including solution\u27s positivity, system\u27s equilibria, stability analysis, and various type of bifurcation. The model is extended to its stochastic version; conditions for the extinction as well as persistence of species are derived. All the theoretical findings are validated with numerical examples. In the deterministic scenario, the Allee effect, harvesting intensity, and the growth in predators due to external food sources exhibit intricate dynamics, such as Hopf, saddle–node (LP) and transcritical bifurcations; we also observe bistable behavior of the system. Notably, less growth in predators due to the other food sources results in extinction, while low intensity of Allee effect leads to bistability, where initial population size matters. A higher Allee effect reduces the region of stability generated by the harvesting effort and the predators’ growth due to additional foods. The stochastic system uncovers diverse transitions in scenarios with high noise intensity, affecting bistability occurred for lower noise intensity. Overall, this study provides valuable insights into predator–prey dynamics, with practical implications for the ecological conservation and resource management
Chitinolytic microorganisms for biological control of plant pathogens: A Comprehensive review and meta-analysis
No full textLarge-scale application of chemical pesticides and insecticides over the years have led to resistance to these chemicals, along with a reduction in crop yield, increase in production cost as well as adverse effects on the environment and human health. In this scenario, there is a need to implement some other techniques to prevent crop losses due to pests and other pathogens. Biological control seems to be a plausible approach to remedy this situation and practice sustainable agriculture through integrated pest management. A feasible way to control nematodes, insects and fungal pathogens can be through the use of chitinase-producing microorganisms. Chitin makes up the exoskeleton of insects, cell wall of fungi, and eggshells of nematodes. Chitinase-producing microorganisms can extensively damage and even kill the pathogens. Therefore, chitinolytic bacteria and fungi might be potential candidates for the biocontrol of numerous plant pathogens. In this review, we aim to discuss the available literature on chitin degrading microorganisms, chitinase enzymes and their importance in biological control. A meta-analysis has been performed with data from the last 2 decades to assess the efficacy of different microbial chitinases on biocontrol of pathogens and a forest plot was produced to conclude the variations among different studies performed so far
Choice models with stochastic variables and random coefficients
No full textIn travel choice models, variables describing alternative attributes such as travel time may have to be specified as stochastic because the analyst may not have accurate measurements of the attribute values considered by the decision-maker. Such stochasticity in alternative attributes is different from unobserved heterogeneity in the coefficients representing travellers’ response to those attributes. Specifying only one of these as random while keeping the other fixed can potentially result in biased parameter estimates, inferior goodness-of-fit, and distorted information for policy analysis. Therefore, in this study, we propose an integrated choice and stochastic variable modelling framework with random coefficients (i.e., an ICSV-RC framework) that allows the analyst to accommodate stochasticity in alternative attributes and random coefficients on such attributes. In addition, we show that ignoring either source of stochasticity – stochasticity in alternative attributes or unobserved heterogeneity in response to the attributes – results in models with inferior goodness-of-fit and a systematic bias in all parameter estimates. We demonstrate this using simulation experiments for two different travel choice settings, one involving labelled mode choice alternatives and the other involving unlabelled route choice alternatives. In addition, we present an empirical analysis in the context of truck route choice to highlight the importance of accommodating both sources of variability – stochasticity in travel times and random heterogeneity in response to travel times
Collective intelligent strategy for improved segmentation of COVID-19 from CT
No full textWe propose a novel non-invasive tool, using deep learning and imaging, for delineating COVID-19 infection in lungs. The Ensembling of selective Focus-based Multi-resolution Convolution network (EFMC), employing Leave-One-Patient-Out (LOPO) training, exhibits high sensitivity and precision in outlining infected regions along with assessment of severity. The selective focus mechanism combines contextual with local information, at multiple resolutions, for accurate segmentation. Ensemble learning integrates heterogeneity of decision through different base classifiers. The superiority of EFMC, even with severe class imbalance, is established through comparison with existing state-of-the-art learning models over four publicly-available COVID-19 datasets. The results are suggestive of the relevance of deep learning in providing assistive intelligence to medical practitioners, when they are overburdened with patients as in pandemics
Commutants and complex symmetry of finite Blaschke product multiplication operator in
No full textConsider the multiplication operator MB in, where the symbol B is a finite Blaschke product. In this article, we characterize the commutant of MB in. As an application of this characterization result, we explicitly determine the class of conjugations commuting with or making complex symmetric by introducing a new class of conjugations in. Moreover, we analyse their properties while keeping the whole Hardy space, model space and Beurling-type subspaces invariant. Furthermore, we extended our study concerning conjugations in the case of finite Blaschke products
Contributions of K. R. Parthasarathy (KRP) to Mathematics of Quantum theory - Perturbations of Operators and Quantum Stochastic Calculus
No full textDecentralized Targeting of Agricultural Credit Programs: Private Versus Political Intermediaries
No full textWe conduct a field experiment in India comparing two ways of delegating selection of microcredit clients among smallholder farmers to local intermediaries: a private trader (TRAIL), versus a local-government appointee (GRAIL). Selected beneficiaries in both schemes were equally likely to take up and repay loans, and experienced similar increases in borrowing and farm output. However farm profits increased and unit costs of production decreased significantly only in TRAIL. While there is some evidence of superior selection by ability and landholding in TRAIL, the results are mainly driven by greater reduction of unit production costs for TRAIL treated farmers than GRAIL treated farmers of similar ability or landholding. We develop and test a model where the TRAIL agents\u27 role as middlemen in the agricultural supply chain enabled and motivated them to offer treated farmers business advice, which helped them lower unit costs
DeepPRMS: Advanced deep learning model to predict protein arginine methylation sites
No full textProtein methylation is a form of post-translational modifications of protein, which is crucial for various cellular processes, including transcription activity and DNA repair. Correctly predicting protein methylation sites is fundamental for research and drug discovery. Some experimental techniques, such as methyl-specific antibodies, chromatin immune precipitation and mass spectrometry, exist for predicting protein methylation sites, but these techniques are time-consuming and costly. The ability to predict methylation sites using in silico techniques may help researchers identify potential candidate sites for future examination and make it easier to carry out site-specific investigations and downstream characterizations. In this research, we proposed a novel deep learning-based predictor, named DeepPRMS, to identify protein methylation sites in primary sequences. The DeepPRMS utilizes the gated recurrent unit (GRU) and convolutional neural network (CNN) algorithms to extract the sequential and spatial information from the primary sequences. GRU is used to extract sequential information, while CNN is used for spatial information. We combined the latent representation of GRU and CNN models to have a better interaction among them. Based on the independent test data set, DeepPRMS obtained an accuracy of 85.32%, a specificity of 84.94%, Matthew\u27s correlation coefficient of 0.71 and a sensitivity of 85.80%. The results indicate that DeepPRMS can predict protein methylation sites with high accuracy and outperform the state-of-the-art models. The DeepPRMS is expected to effectively guide future research experiments for identifying potential methylated protein sites. The web server is available at http://deepprms.nitsri.ac.in/
Disparity in Asset Holdings of the Household Sector AIDIS and NSS Definitions
No full textThe definition of household adopted in National Sample Survey household surveys does not always serve well the intended purpose. The article examines its suitability for estimating the distribution of rural and urban households’ assets over economic classes and for measuring changes over time