Estimation of circular-circular probability distribution

This paper aims to introduce an estimation algorithm for the joint densityof a circular-circular random variable, which is expressible in the form asdiscussed by Fernández-Durán (2007). The performance of the algorithm hasbeen checked with the help of a simulation study and it is found to performeciently even for small sample sizes. Furthermore, the performance of theproposed algorithm is compared with that of an existing method of densityestimation and is found to perform better than the existing one, which isindicated by the higher mean square error values for the estimates obtainedby the latter method. Finally, the application of the algorithm is displayedby estimating the joint density of a circular-circular random variable arisingin a real-life data set

A Counting Process with Generalized Exponential Inter-Arrival Times

This paper introduces a new counting process which is based on Generalized Exponentially distributed inter-arrival times. The advantage of this new count model over the existing Poisson count model is that the hazard function of the inter arrival time distribution is non-constant, so that the distribution is duration dependent and hence, is able to model both under dispersed and over dispersed count data, as opposed to the exponentially distributed inter arrival time of the Poisson count model, which is not duration dependent and the corresponding count model is able to model only equidispersed data. Further, some properties of this model are explored. Simulation from this new model is performed to study the behavior of count probabilities, mean and variance of the model for different values of the parameter. Use of the proposed model is illustrated with the help of real life data sets on arrival times of patients at a clinic and on arrival times of customers at a departmental store

Circular Statistical Approach to Study the Occurrence of Seasonal Diseases

In the present study, we have developed new circular descriptive statistics for Censored circular sample and attempted to analyse the occurrence of seasonal diseases, both month-wise and season-wise.  The Rayleigh Uniformity Test has also been proposed for the same, using which the presence of seasonal effect in both the cases. Finally, a regression model for predicting binary response from circular predictor has been proposed. The months being of unequal length, have been adjusted accordingly so as to make them of equal lengths. But since the seasons differ by a significant length and making them equal in length will mislead the analysis, we propose to group the cases in unequal intervals, the width of the intervals being proportional to the length of the seasons. That the season-wise analysis using circular statistical tools has not been attempted before is the main motivation behind our study. The data has been taken from the project entitled Statistical Modeling in Circular Statistics: An Application to Health Science, sponsored by the UGC, India, where diseases have been reported for the Kamrup (rural) district of Assam, India. It is revealed that the occurrence of seasonal diseases is highest in the months of March or equivalently, during the Pre-monsoon season. The distribution of occurrence of seasonal diseases both month-wise and season-wise is found to be marginally positively skewed and platykurtic.  The regression analysis suggests that seasonal diseases is least likely to occur in April as compared to December and in Winter in comparison to Post-monsoon

Nature-Inspired Cloud–Crowd Computing for Intelligent Transportation System

Nowadays, it is crucial to have effective road traffic signal timing, especially in an ideal traffic light cycle. This problem can be resolved with modern technologies such as artificial intelligence, cloud and crowd computing. We hereby present a functional model named Cloud–Crowd Computing-based Intelligent Transportation System (CCCITS). This model aims to organize traffic by changing the phase of traffic lights in real-time based on road conditions and incidental crowdsourcing sentiment. Crowd computing is responsible for fine-tuning the system with feedback. In contrast, the cloud is responsible for the computation, which can use AI to secure efficient and effective paths for users. As a result of its installation, traffic management becomes more efficient, and traffic lights change dynamically depending on the traffic volume at the junction. The cloud medium collects updates about mishaps through the crowd computing system and incorporates updates to refine the model. It is observed that nature-inspired algorithms are very useful in solving complex transportation problems and can deal with NP-hard situations efficiently. To establish the feasibility of CCCITS, the SUMO simulation environment was used with nature-inspired algorithms (NIA), namely, Particle Swarm Optimization (PSO), Ant Colony Optimization and Genetic Algorithm (GA), and found satisfactory results

A Comprehensive Review on Third-Generation Photovoltaic Technologies

The renewable energy industry has revolutionized due to photovoltaic (PV) technologies, which offer a clean and sustainable alternative to conventional energy sources. Third-generation photovoltaic technologies refer to a group of emerging PV technologies aiming to surpass the efficiency and cost-effectiveness of traditional silicon-based solar cells. Different ceramic materials have also been investigated for use in these advanced PV technologies. This review examines the science, current state, and advancements of third-generation PV systems for wide-scale implementation. The first section of this study provides an overview of the development of PV technologies from the first to the third generation, highlighting the most significant novel developments made at each step. Organic photovoltaic (OPV) cells, dye-sensitized solar cells (DSSCs), and perovskite solar cells (PSCs) are discussed here as a few new technologies that constitute the third generation, also known as the next generation of advanced PV. This review presents how these devices can be used in specialized settings, including indoor and low-light environments, thereby expanding the range of energy harvesting potential. The brief history of these emerging technologies, their current status, future developments, and key challenges are discussed in this review paper

Optimized M24B Aminopeptidase Inhibitors for CARD8 Inflammasome Activation

Inflammasomes are innate immune signaling platforms that trigger pyroptotic cell death. NLRP1 and CARD8 are related human inflammasomes that detect similar danger signals, but NLRP1 has a higher activation threshold and triggers a more inflammatory form of pyroptosis. Both sense the accumulation of intracellular peptides with Xaa-Pro N-termini, but Xaa-Pro peptides on their own without a second danger signal only activate the CARD8 inflammasome. We recently reported that a dual inhibitor of the Xaa-Pro-cleaving M24B aminopeptidases PEPD and XPNPEP1 called CQ31 selectively activates the CARD8 inflammasome by inducing the build-up of Xaa-Pro peptides. Here, we performed structure–activity relationship studies on CQ31 to develop the optimized dual PEPD/XPNPEP1 inhibitor CQ80 that more effectively induces CARD8 inflammasome activation. We anticipate that CQ80 will become a valuable tool to study the basic biology and therapeutic potential of selective CARD8 inflammasome activation

Optimized M24B Aminopeptidase Inhibitors for CARD8 Inflammasome Activation

Inflammasomes are innate immune signaling platforms that trigger pyroptotic cell death. NLRP1 and CARD8 are related human inflammasomes that detect similar danger signals, but NLRP1 has a higher activation threshold and triggers a more inflammatory form of pyroptosis. Both sense the accumulation of intracellular peptides with Xaa-Pro N-termini, but Xaa-Pro peptides on their own without a second danger signal only activate the CARD8 inflammasome. We recently reported that a dual inhibitor of the Xaa-Pro-cleaving M24B aminopeptidases PEPD and XPNPEP1 called CQ31 selectively activates the CARD8 inflammasome by inducing the build-up of Xaa-Pro peptides. Here, we performed structure–activity relationship studies on CQ31 to develop the optimized dual PEPD/XPNPEP1 inhibitor CQ80 that more effectively induces CARD8 inflammasome activation. We anticipate that CQ80 will become a valuable tool to study the basic biology and therapeutic potential of selective CARD8 inflammasome activation