Breathing Modes in Rotating Bose-Condensed Gas: An Exact Diagonalization Study

We present an exact diagonalization study of the breathing mode collective excitations for a rotating Bose-Einstein condensate of $N=10$ spinless bosons interacting via repulsive finite-range Gaussian potential and harmonically confined in quasi-two-dimension. The yrast state and the low-lying excited states are variationally obtained in given subspaces of the quantized total angular momentum $L$ employing the beyond lowest Landau level approximation in slowly rotating regime with $0 \le L < 2N$. For a given $L$, the low-energy eigenspectra (bands) are obtained in weakly to moderately interacting regime. Further, for a given interaction, the split in low-lying eigenenergies with increasing $L$ is the precursor to spontaneous symmetry breaking of the axisymmetry associated with the entry of the first vortex. With increase in repulsive interaction, the value of the first breathing mode increases for stable total angular momentum states L=0~\mbox{and}~N, but decreases for intermediate $0<L<N$ metastable states. The position of the observed first breathing modes in the eigenspectrum remains unchanged as the interaction is varied over several orders of magnitude.Comment: 5 pages, 3 figures, RevTex two colum

Novel phases in rotating Bose-condensed gas: vortices and quantum correlation

We present the exact diagonalization study of rotating Bose-condensed gas interacting via finite-range Gaussian potential confined in a quasi-2D harmonic trap. The system of many-body Hamiltonian matrix is diagonalized in given subspaces of quantized total angular momentum to obtain the lowest-energy eigenstate employing the beyond lowest-Landau-level approximation. In the co-rotating frame, the quantum mechanical stability of angular momentum states is discussed for the existence of phase transition between the stable states of interacting system. Thereby analyzing the von Neumann entanglement entropy and degree of condensation provide the information about quantum phase correlation in the many-body states. Calculating the conditional probability distribution, we further probe the internal structure of quantum mechanically stable and unstable states. Much emphasis is put on finding the spatial correlation of bosonic atoms in the rotating system for the formation and entry of singly quantized vortices, and then organizing into canonical polygons with and without a central vortex at the trap center. Results are summarized in the form of a movie depicting the vortex patterns having discrete p-fold rotational symmetry with $p = 2,3,4,5,6$.Comment: Updated version with 12 pages and 25 figures. This paper is dedicated to the memory of Professor M. Rafat (JMI), deceased on January 09, 2021, at the age of 6

Towards Cyber-Physical Product-Service Systems Design

University of Technology Sydney. Faculty of Design, Architecture and Building.As markets evolve, businesses recognise that customers perceive value in the utility of a product rather than in the product itself. Consequently, strategies are being reconfigured from selling products to providing solutions. These solutions combine products and services with advanced technology to form smart or cyber-physical product-service systems (CPPSSs) that provide numerous benefits to stakeholders through mutual collaboration. This research implemented the six-step design science research method to identify the opportunities in developing service-oriented CPPSS. A CPPSS design method reference model adaptable to customers’ dynamic needs through value co-creation was developed and tested. In contrast to available design methods, this study integrates the concepts of actor-network theory and service-dominant logic into a single methodological approach. This model consists of four stages that address how providers, managers, designers, and end-users (1) identify problems, (2) negotiate relationships, (3) integrate resources and (4) communicate solutions. At the same time, it contributes a new theory to PSS/CPPSS design literature with new research directions. The case studies and practitioner feedback derived suggest that this value co-creation model effectively adapts to customer needs. Further knowledge translation and improvement are suggested for the CPPSSDM through application in the industry

Effects of Filler Hybridization on the Mechanical Properties of NR/SBR/EPDM Rubber Blends

Hybridization of active fillers is one of the techniques utilized to enhance rubber properties. This study highlights the effects of filler hybridization on the mechanical properties of an industrial applied natural rubber/styrene butadiene rubber/ethylene propylene diene terpolymer (NR/SBR/EPDM) Rubber Mat compound reinforced by non-black fillers. Initially, three different rubber compounds were prepared; i) calcium carbonate (CaCO3)-filled NR [CaCO3NR] as reference sample, ii) precipitated silica (PSi)-filled NR/SBR/EPDM [PSiBR], and iii) calcined clay (ClCy)-filled NR/SBR/EPDM [ClCyBR]. From these compounds, composites of NR/SBR/EPDM were prepared. The ratio of PSi:ClCy was varied to study the effects of filler hybridization. CaCO3 was added for cost advantage industrially and its level was fixed. It was found that inclusions of ClCy and PSi individually and their hybridizations show higher tensile and tear strengths than the reference sample. Particularly, the largest improvement was found with the amount of ClCy which is higher than PSi. A ratio of PSi:ClCy in this particular range (1:2 to 2:3), seems to provide the optimum packing factor for good interaction between the fillers. SEM analysis suggests that better dispersion and packing of fillers due to size and shape of hybrid fillers play an important role in improving the composite properties

Antecedents and Mediating Role of Green Buying Behavior

Sustainability of the environment is one of the major issues both in developed and developing countries. There is an abundance of studies on green buying behavior. However, a few have studied the mediating role of green buying behavior. Thus, we have developed a new model that has five direct and three mediating relationships. This empirical research has used a snowball sampling technique for collecting the data. We distributed 400 questionnaires and received 377 valid responses. The study has used Smart PLS software for data analysis, including reliability, validity, and generating measurement and structural models. We found that green brands, green identification, and social influence are significant predictors of green buying behavior. Further, we find that the green brand image and green buying behavior promote green satisfaction. The results also suggest that green buying behavior mediates (1) green image and green satisfaction, (2) social influence and green buying behavior, (3) self-identification, and green satisfaction. We also found that consumers have a favorable attitude towards green buying behavior. However, there is a huge gap in consumers’ attitudes and actual buying behavior. Thus, marketers and practitioners need to develop strategies that would translate a favorable attitude toward actual buying behavior

Evaluation of serum alkaline phosphatase and calcium in type 2 diabetes mellitus patients at Lucknow, India

There is an increasing demand and requirement to develop new biomarkers for the early diagnosis and detection of diabetes mellitus (DM). The present study is designed to estimate serum alkaline phosphatase (ALP) and serum calcium levels in type 2 diabetes mellitus (T2DM) patients. The serum alkaline phosphatase and calcium levels were estimated using commercially available kits on a semiautoanalyzer. The fasting blood sugar (FBS) of the subjects was measured by the glucose oxidase-peroxidase (GOD-POD) method using a commercially available kit. The level of ALP was found to be statistically significant in T2DM patients when compared with matched healthy individuals (p 0.001), whereas the association of serum calcium level was found to be non-significant (p = 0.07). Although there is a decrease in serum calcium level in T2DM patients when compared with healthy controls. It is concluded that the management of T2DM and its associated complications may require regular estimation of serum ALP and calcium levels. The estimations may also be significant in the management of osteoporosis in T2DM patients. High utilization of calcium and vitamin D especially from supplements may lower the risk of DM

Positive region: An enhancement of partitioning attribute based rough set for categorical data

Datasets containing multi-value attributes are often involved in several domains, like pattern recognition, machine learning and data mining. Data partition is required in such cases. Partitioning attributes is the clustering process for the whole data set which is specified for further processing. Recently, there are already existing prominent rough set-based approaches available for group objects and for handling uncertainty data that use indiscernibility attribute and mean roughness measure to perform attribute partitioning. Nevertheless, most of the partitioning attribute methods for selecting partitioning attribute algorithm for categorical data in clustering datasets are incapable of optimal partitioning. This indiscernibility and mean roughness measures, however, require the calculation of the lower approximation, which has less accuracy and it is an expensive task to compute. This reduces the growth of the set of attributes and neglects the data found within the boundary region. This paper presents a new concept called the "Positive Region Based Mean Dependency (PRD)”, that calculates the attribute dependency. In order to determine the mean dependency of the attributes, that is acceptable for categorical datasets, using a positive region-based mean dependency measure, PRD defines the method. By avoiding the lower approximation, PRD is an optimal substitute for the conventional dependency measure in partitioning attribute selection. Contrary to traditional RST partitioning methods, the proposed method can be employed as a measure of data output uncertainty and as a tailback for larger and multiple data clustering. The performance of the method presented is evaluated and compared with the algorithmes of Information-Theoretical Dependence Roughness (ITDR) and Maximum Indiscernible Attribute (MIA)

Improving micro-hardness of stainless steel through powder-mixed electrical discharge machining

Powder-mixed electrical discharge machining (PMEDM) is the technique of using dielectric fluid mixed with various types of powders to improve the machined surface output. The process is fast gaining prominence in electrical discharge machining (EDM) industry. The objective of this investigation is to determine the ability of tantalum carbide (TaC) powder-mixed dielectric fluid to enhance the surface properties of stainless steel material during EDM. The properties of investigated are the micro-hardness and corrosion characteristics of the EDMed surface. Machining was conducted with 25.0g/L concentration of TaC powder in kerosene dielectric fluid. The machining variables used were the peak current, pulse on time and pulse off time. The effects of variables on the micro-hardness of the EDMed surface were determined. Corrosion tests were also conducted on the samples that exhibited higher hardness. Results showed that the EDMed surface was alloyed with elements from the TaC powder. The micro-hardness obtained with PMEDM is about 1,200Hv. This is about 1.5 times obtained without TaC powder in the dielectric fluid. The loss in weight during corrosion test was found to be 0.056 ug/min for PMEDM which was lower than the lowest value of 10.56 ug/min obtained for the EDM without powder dielectric fluid