Experimental realization of quantum cheque using a five-qubit quantum computer

Quantum cheques could be a forgery-free way to make transaction in a quantum networked banking system with perfect security against any no-signalling adversary. Here, we demonstrate the implementation of quantum cheque, proposed by Moulick and Panigrahi (Quantum Inf Process 15:2475–2486, 2016), using the five-qubit IBM quantum computer. Appropriate single qubit, CNOT and Fredkin gates are used in an optimized configuration. The accuracy of implementation is checked and verified through quantum state tomography by comparing results from the theoretical and experimental density matrices.     

Energy storage behaviour of some binary alkali borate glasses by thermoluminescence studies

Thermoluminescence behaviour of a series of binary alkali borate glasses has been investigated to study their energy storage mechanism. Sodium borate glasses of varying composition have been prepared and their glow curves recorded after exposing them to X-rays (CuK radiation, 30 kV, 10 mA) of different dosages at room temperature. The effect of the nature and concentration of alkali oxide and the dose of irradiation on the nature of thermoluminescent glow curves were also studied. Borate glasses containing different concentrations of Na₂O exhibit significantly different glow curves. These glow curves have been analysed and the nature of traps responsible for TL emission are tentatively identified. The broad and complex nature of the glow pattern is attributed to distribution of trap depths in these materials. The viability of borate glasses in the construction of TL dosimeters are discussed.     

Analyzing collective behavior from blogs using swarm intelligence

With the rapid growth of the availability and popularity of interpersonal and behavior-rich resources such as blogs and other social media avenues, emerging opportunities and challenges arise as people now can, and do, actively use computational intelligence to seek out and understand the opinions of others. The study of collective behavior of individuals has implications to business intelligence, predictive analytics, customer relationship management, and examining online collective action as manifested by various flash mobs, the Arab Spring (2011) and other such events. In this article, we introduce a nature-inspired theory to model collective behavior from the observed data on blogs using swarm intelligence, where the goal is to accurately model and predict the future behavior of a large population after observing their interactions during a training phase. Specifically, an ant colony optimization model is trained with behavioral trend from the blog data and is tested over real-world blogs. Promising results were obtained in trend prediction using ant colony based pheromone classier and CHI statistical measure. We provide empirical guidelines for selecting suitable parameters for the model, conclude with interesting observations, and envision future research directions.     

Growth of La2Mo2O9 films on porous Al2O3 substrates by radio frequency magnetron sputtering

Synthesis conditions of La₂Mo₂O₉ thin film by radio frequency (RF) sputtering technique on Al₂O₃ ceramic substrates are studied. It is found that the deposition temperature and oxygen partial pressure are the most important factors for obtaining pure La₂Mo₂O₉ films. Varying both parameters, Mo-rich, stoichiometric, and Mo-deficient films are obtained. With increasing the La:Mo ratio, films become denser. A crust layer is observed on top of the Mo-rich and the Mo-deficient films. The formation of the La₂Mo₂O₉ phase is discussed with respect to the sputtering mechanism.     

Microstructure and steady state creep in Ti-24Al-11Nb

The steady state creep behaviour of the two-phase Ti₃Al-based alloy, Ti-24Al-11Nb, has been examined as a function of microstructure at temperatures ranging from 798 to 998 K and stress levels ranging from 30 to 400 MPa. Three microstructural conditions corresponding to 90% equiaxed ₂, 40% equiaxed ₂, and 100% lath ₂ structures have been studied. A low-stress Coble creep regime has been identified, with the lath ₂ structure showing the greates creep resistance in this regime. The lath ₂ structure is also stronger in the dislocation creep regime. The creep strength of this ordered alloy is shown to derive from frequency factors for diffusion, which are about two to three orders of magnitude lower than those for disordered alloys. Activation energies for creep in both the diffusional and dislocation creep domains are similar to values obtained in disordered alloys.     

In situ nitridation of titanium–molybdenum alloys during laser deposition

In situ nitridation during laser deposition of titanium–molybdenum alloys from elemental powder blends has been achieved by introducing the reactive nitrogen gas during the deposition process. Thus, Ti–Mo–N alloys have been deposited using the laser engineered net shaping (LENS^TM) process and resulted in the formation of a hard α(Ti,N) phase, exhibiting a dendritic morphology, distributed within a β(Ti–Mo) matrix with fine scale transformed α precipitates. Varying the composition of the Ar + N₂ gas employed during laser deposition permits a systematic increase in the nitrogen content of the as-deposited Ti–Mo–N alloy. Interestingly, the addition of nitrogen, which stabilizes the α phase in Ti, changes the solidification pathway and the consequent sequence of phase evolution in these alloys. The nitrogen-enriched hcp α(Ti,N) phase has higher c/a ratio, exhibits an equiaxed morphology, and tends to form in clusters separated by ribs of the Mo-rich β phase. The Ti–Mo–N alloys also exhibit a substantial enhancement in microhardness due to the formation of this α(Ti,N) phase, combining it with the desirable properties of the β-Ti matrix, such as excellent ductility, toughness, and formability.     

Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers

Acute kidney injury (AKI) is a prevalent complication in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive inpatients, which is linked to an increased mortality rate compared to patients without AKI. Here we analysed the difference in kidney blood biomarkers in SARS-CoV-2 positive patients with non-fatal or fatal outcome, in order to develop a mortality prediction model for hospitalised SARS-CoV-2 positive patients. A retrospective cohort study including data from suspected SARS-CoV-2 positive patients admitted to a large National Health Service (NHS) Foundation Trust hospital in the Yorkshire and Humber regions, United Kingdom, between 1 March 2020 and 30 August 2020. Hospitalised adult patients (aged ≥ 18 years) with at least one confirmed positive RT-PCR test for SARS-CoV-2 and blood tests of kidney biomarkers within 36 h of the RT-PCR test were included. The main outcome measure was 90-day in-hospital mortality in SARS-CoV-2 infected patients. The logistic regression and random forest (RF) models incorporated six predictors including three routine kidney function tests (sodium, urea; creatinine only in RF), along with age, sex, and ethnicity. The mortality prediction performance of the logistic regression model achieved an area under receiver operating characteristic (AUROC) curve of 0.772 in the test dataset (95% CI: 0.694–0.823), while the RF model attained the AUROC of 0.820 in the same test cohort (95% CI: 0.740–0.870). The resulting validated prediction model is the first to focus on kidney biomarkers specifically on in-hospital mortality over a 90-day period.     

CX3CL1 Action on Microglia Protects from Diet-Induced Obesity by Restoring POMC Neuronal Excitability and Melanocortin System Activity Impaired by High-Fat Diet Feeding

Both hypothalamic microglial inflammation and melanocortin pathway dysfunction contribute to diet-induced obesity (DIO) pathogenesis. Previous studies involving models of altered microglial signaling demonstrate altered DIO susceptibility with corresponding POMC neuron cytological changes, suggesting a link between microglia and the melanocortin system. We addressed this hypothesis using the specific microglial silencing molecule, CX3CL1 (fractalkine), to determine whether reducing hypothalamic microglial activation can restore POMC/melanocortin signaling to protect against DIO. We performed metabolic analyses in high fat diet (HFD)-fed mice with targeted viral overexpression of CX3CL1 in the hypothalamus. Electrophysiologic recording in hypothalamic slices from POMC-MAPT-GFP mice was used to determine the effects of HFD feeding and microglial silencing via minocycline or CX3CL1 on GFP-labeled POMC neurons. Finally, mice with hypothalamic overexpression of CX3CL1 received central treatment with the melanocortin receptor antagonist SHU9119 to determine whether melanocortin signaling is required for the metabolic benefits of CX3CL1. Hypothalamic overexpression of CX3CL1 increased leptin sensitivity and POMC gene expression, while reducing weight gain in animals fed an HFD. In electrophysiological recordings from hypothalamic slice preparations, HFD feeding was associated with reduced POMC neuron excitability and increased amplitude of inhibitory postsynaptic currents. Microglial silencing using minocycline or CX3CL1 treatment reversed these HFD-induced changes in POMC neuron electrophysiologic properties. Correspondingly, blockade of melanocortin receptor signaling in vivo prevented both the acute and chronic reduction in food intake and body weight mediated by CX3CL1. Our results show that suppressing microglial activation during HFD feeding reduces DIO susceptibility via a mechanism involving increased POMC neuron excitability and melanocortin signaling.     

Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery.

This work deals with multisensor data fusion to obtain landcover classification. The role of feature-level fusion using the Dempster-Shafer rule and that of data-level fusion in the MRF context is studied in this paper to obtain an optimally segmented image. Subsequently, segments are validated and classification accuracy for the test data is evaluated. Two examples of data fusion of optical images and a synthetic aperture radar image are presented, each set having been acquired on different dates. Classification accuracies of the technique proposed are compared with those of some recent techniques in literature for the same image data.