Intermediate regimes in granular Brownian motion: Superdiffusion and subdiffusion

Brownian motion in a granular gas in a homogeneous cooling state is studied theoretically and by means of molecular dynamics. We use the simplest first-principle model for the impact-velocity dependent restitution coefficient, as it follows for the model of viscoelastic spheres. We reveal that for a wide range of initial conditions the ratio of granular temperatures of Brownian and bath particles demonstrates complicated non-monotonous behavior, which results in transition between different regimes of Brownian dynamics: It starts from the ballistic motion, switches later to superballistic one and turns at still later times into subdiffusion; eventually normal diffusion is achieved. Our theory agrees very well with the MD results, although extreme computational costs prevented to detect the final diffusion regime. Qualitatively, the reported intermediate diffusion regimes are generic for granular gases with any realistic dependence of the restitution coefficient on the impact velocity

Prescription Based Recommender System for Diabetic Patients Using Efficient Map Reduce

Healthcare sector has been deprived of leveraging knowledge gained through data insights, due to manual processes and legacy record-keeping methods. Outdated methods for maintaining healthcare records have not been proven sufficient for treating chronic diseases like diabetes. Data analysis methods such as Recommendation System (RS) can serve as a boon for treating diabetes. RS leverages predictive analysis and provides clinicians with information needed to determine the treatments to patients. Prescription-based Health Recommender System (HRS) is proposed in this paper which aids in recommending treatments by learning from the treatments prescribed to other patients diagnosed with diabetes. An Advanced Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering is also proposed to cluster the data for deriving recommendations by using winnowing algorithm as a similarity measure. A parallel processing of data is applied using map-reduce to increase the efficiency & scalability of clustering process for effective treatment of diabetes. This paper provides a good picture of how the Map Reduce can benefit in increasing the efficiency and scalability of the HRS using clustering

Proposal and Validation of Usability Model for Component Based Software System

Increasing demand of rapid and cost effective development of software system has increased the demand of Component Based Software Engineering (CBSE). In CBSE, software system is developed by using existing components. These components can be in-house components or third party components. To develop a Component Based Software System (CBSS), it is important to select the suitable component in such a manner that the components of the software system do not affect each other. To increase the acceptance of the CBSS among the users and the market value of the software industries, it is important to increase the usability of the CBSS. Several usability models have been proposed for traditional and object-oriented software system (OOSS), but there is no usability model for CBSS. Existing traditional and object-oriented models can’t be perfectly suitable for CBSS because of the unique characteristics of the components. This paper presents a usability model (UMCBSS) for CBSS. The proposed usability model is based on most significant usability factors. These factors are analysed from CBSS quality models. With the help of proposed model, usability is evaluated by using two different techniques i.e., centroid method and bisector method in MATLAB. Experimental results are also validated by using Center of Gravity (COG) and Mean-Max method. With the help of the proposed model, developers of the CBSS will be able to measure the usability of CBSS and to remove the usability flaws from the software system

Pulmonary Capillary Hemangiomatosis: A Rare Cause of Pulmonary Arterial Hypertension, Presenting as Supraventricular Tachycardia

With a prevalence of less than 1/million, Pulmonary Capillary Hemangiomatosis is a rare disorder of capillary proliferation in the alveolar septae leading to pulmonary arterial hypertension and mimics pulmonary veno-occlusive disease

Comparison Study and Review on Object-Oriented Metrics

The best elucidations to software development problems are regularly touted as object-oriented processes. The popularity of object-oriented design metrics is essential in software engineering for measuring the software complexity, estimating size, quality and project efforts. There are various approaches through which we can find the software cost estimation and predicates on various kinds of deliverable items. Object-oriented metrics assures to reduce cost and the maintenance effort by serving as early predictors to estimate software faults. Such an early quantification augments the quality of the final software. This paper reviews object-oriented metrics. A comparison table is maintained via which we can analyze the difference between all the object-oriented metrics effectively

Synthesis of Reconfigurable Multiple Shaped Beams of a Concentric Circular Ring Array Antenna Using Evolutionary Algorithms, Journal of Telecommunications and Information Technology, 2023, nr 1

The approach described in this paper uses evolutionary algorithms to create multiple-beam patterns for a concentric circular ring array (CCRA) of isotropic antennas using a common set of array excitation amplitudes. The flat top, cosec2, and pencil beam patterns are examples of multiple-beam patterns. All of these designs have an upward angle of θ = 0◦. All the patterns are further created in three azimuth planes (φ = 0◦, 5◦, and 10◦). To create the necessary patterns, non-uniform excitations are used in combination with evenly spaced isotropic components. For the flat top and cosecant-squared patterns, the best combination of common components, amplitude and various phases is applied, whereas the pencil beam pattern is produced using the common amplitude only. Differential evolutionary algorithm (DE), genetic algorithm (GA), and firefly algorithm (FA) are used to generate the best 4-bit discrete magnitudes and 5-bit discrete phases. These discrete excitations aid in lowering the feed network design complexity and the dynamic range ratio (DRR). A variety of randomly selected azimuth planes are used to verify the excitations as well. With small modifications in the desired parameters, the patterns are formed using the same excitation. The results proved both the efficacy of the suggested strategy and the dominance of DE over GA as well as FA

Transfer Learning based Low Shot Classifier for Software Defect Prediction

Background: The rapid growth and increasing complexity of software applications are causing challenges in maintaining software quality within constraints of time and resources. This challenge led to the emergence of a new field of study known as Software Defect Prediction (SDP), which focuses on predicting future defect in advance, thereby reducing costs and improving productivity in software industry. Objective: This study aimed to address data distribution disparities when applying transfer learning in multi-project scenarios, and to mitigate performance issues resulting from data scarcity in SDP. Methods: The proposed approach, namely Transfer Learning based Low Shot Classifier (TLLSC), combined transfer learning and low shot learning approaches to create an SDP model. This model was designed for application in both new projects and those with minimal historical defect data. Results: Experiments were conducted using standard datasets from projects within the National Aeronautics and Space Administration (NASA) and Software Research Laboratory (SOFTLAB) repository. TLLSC showed an average increase in F1-Measure of 31.22%, 27.66%, and 27.54% for project AR3, AR4, and AR5, respectively. These results surpassed those from Transfer Component Analysis (TCA+), Canonical Correlation Analysis (CCA+), and Kernel Canonical Correlation Analysis plus (KCCA+). Conclusion: The results of the comparison between TLLSC and state-of-the-art algorithms, namely TCA+, CCA+, and KCCA+ from the existing literature consistently showed that TLLSC performed better in terms of F1-Measure. Keywords: Just-in-time, Defect Prediction, Deep Learning, Transfer Learning, Low Shot Learnin