Semiclassical Hartree-Fock theory of a rotating Bose-Einstein condensation

In this paper, we investigate the thermodynamic behavior of a rotating Bose-Einstein condensation with non-zero interatomic interactions theoretically. The analysis relies on a semiclassical Hartree-Fock approximation where an integral is performed over the phase space and function of the grand canonical ensemble is derived. Subsequently, we use this result to derive several thermodynamic quantities including the condensate fraction, critical temperature, entropy and heat capacity. Thereby, we investigate the effect of the rotation rate and interactions parameter on the thermodynamic behavior. The role of finite size is discussed. Our approach can be extended to consider the rotating condensate in optical potential

When Shall Coronavirus Disease-19 Stop? Review of Literature

In December 2019, a new coronavirus, now labeled as severe acute respiratory syndrome coronavirus 2, induced an episode of acute atypical respiratory illness started in Wuhan, Province of Hubei, China. The illness triggered by this virus was called coronavirus disease-19 (COVID-19). The infection is spread within humans and has triggered a global pandemic. The amount of death tolls continues to increase and a growing number of countries have been driven to create social barriers and lock-ups. The shortage of tailored counseling remains an issue. Epidemiological researches have shown that elderly patients are more vulnerable to serious diseases, while children tend to have milder symptoms. Here, we checked the latest understanding of this disease and found a possible explanation of the potential sequel and the expectations for the future

Genetic Algorithm Optimization Model for Determining the Probability of Failure on Demand of the Safety Instrumented System

A more accurate determination for the Probability of Failure on Demand (PFD) of the Safety Instrumented System (SIS) contributes to more SIS realiability, thereby ensuring more safety and lower cost. IEC 61508 and ISA TR.84.02 provide the PFD detemination formulas. However, these formulas suffer from an uncertaity issue due to the inclusion of uncertainty sources, which, including high redundant systems architectures, cannot be assessed, have perfect proof test assumption, and are neglegted in partial stroke testing (PST) of impact on the system PFD. On the other hand, determining the values of PFD variables to achieve the target risk reduction involves daunting efforts and consumes time. This paper proposes a new approach for system PFD determination and PFD variables optimization that contributes to reduce the uncertainty problem. A higher redundant system can be assessed by generalizing the PFD formula into KooN architecture without neglecting the diagnostic coverage factor (DC) and common cause failures (CCF). In order to simulate the proof test effectiveness, the Proof Test Coverage (PTC) factor has been incorporated into the formula. Additionally, the system PFD value has been improved by incorporating PST for the final control element into the formula. The new developed formula is modelled using the Genetic Algorithm (GA) artificial technique. The GA model saves time and effort to examine system PFD and estimate near optimal values for PFD variables. The proposed model has been applicated on SIS design for crude oil test separator using MATLAB. The comparison between the proposed model and PFD formulas provided by IEC 61508 and ISA TR.84.02 showed that the proposed GA model can assess any system structure and simulate industrial reality. Furthermore, the cost and associated implementation testing activities are reduced

Levels of certain tumor markers as differential factors between bilharzial and non-biharzial bladder cancer among Egyptian patients

<p>Abstract</p> <p>Background/Objective</p> <p>Bladder cancer is the commonest type of malignant tumors as a result of schistosomaisis which is a major healthy problem in many subtropical developing countries. The aim of this study is to comparatively elucidate the underlying biochemical tumor markers in schistosomal bladder cancer versus non-schistosomal bladder cancer when compared to normal healthy ones.</p> <p>Methods</p> <p>This work was performed on tissue specimens from total 25 patients and serum samples from total 30 patients versus ten healthy individuals served as control. The investigated parameters in serum are: xanthine oxidase (XO), fructosamine, lactate dehydrogense (LDH), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total proteins, essential and non- essential amino acids profile, hydroxyproline, total immunoglobulin E (IgE) and tumor necrosis factor alpha (TNF-<it>α</it>). In addition, the current investigation also extended to study some markers in tumor bladder tissues including, pyruvate kinase enzyme (PK), lactate dehydrogenase (LDH), aspartate aminotransferase (AST) and alanine aminotransferase (ALT).</p> <p>Results</p> <p>Results showed that biharzial bladder cancer patients recored more significant elevation in serum XO, fructosamine, LDH, AST, ALT, hydroxyproline, IgE and TNF-<it>α </it>than in bladder cancer patients when compared to control ones. While, in tissues there were significant increase in PK, LDH, AST & ALT activities of schistosomal bladder cancer than in bladder cancer as compared to control healthy patients.</p> <p>Conclusions</p> <p>It could be concluded that, bilharzial and non-bilharzial bladder cancer showed distinct biochemical profile of tumor development and progression which can be taken into consideration in diagnosis of bladder cancer.</p

A reinforcement learning hyper-heuristic for water distribution network optimisation

The Water Distribution Networks (WDNs) optimisation problem focuses on finding the combination of pipes from a collection of discrete sizes available to construct a network of pipes with minimum monetary cost. It is one of the most significant problems faced by WDN engineers. This problem belongs to the class of difficult combinatorial optimisation problems, whose optimal solution is hard to find, due to its large search space. Hyper-heuristics are high-level search algorithms that explore the space of heuristics rather than the space of solutions in a given optimisation problem. In this work, different selection hyper-heuristics were proposed and empirically analysed in the WDN optimisation problem, with the goal of minimising the network’s cost. New York Tunnels network benchmark was used to test the performance of these hyper-heuristics including the Reinforcement Learning (RL) hyper-heuristic method, that succeeded in achieving improved results