円形および楕円形ノズルからの不足膨張噴流に関する解析的研究

The supersonic jet issuing from circular and elliptic exit of convergent-divergent nozzle has been investigated by rainbow schlieren deflectometry and modified linearized model. In experimentally, a design Mach number of 1.5 followed by a cylindrical duct with an inner diameter of 10 mm, and length of 50 mm are considered to measure the density, internal flow features of jet combing with the computed tomography. In theoretically, a modified vortex sheet model taking viscosity into account has been proposed. The properties of Bessel’s and Mathieu functions with the first Fourier mode of eigenvalues are executed for the individual exit geometry to evaluate the explicit solution of inviscid and viscous jet separately. The embodied results of density, shock-cell spacing, and size measuring have also been examined theoretically. In comparison, the accomplished yields are shown better agreement with published data due to the aspect ratios, nozzle pressure ratios and design Mach number arbitrarily.北九州市立大

Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

Brain–computer interface (BCI) is an important alternative for disabled people that enables the innovative communication pathway among individual thoughts and different assistive appliances. In order to make an efficient BCI system, different physiological signals from the brain have been utilized for instances, steady-state visual evoked potential, motor imagery, P300, movement-related potential and error-related potential. Among these physiological signals, motor imagery is widely used in almost all BCI applications. In this paper, Electrocorticography (ECoG) based motor imagery signal has been classified using long short-term memory (LSTM). ECoG based motor imagery data has been taken from BCI competition III, dataset I. The proposed LSTM approach has achieved the classification accuracy of 99.64%, which is the utmost accuracy in comparison with other state-of-art methods that have employed the same data set

Non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence

Diabetes becomes a rapidly increasing global epidemic and getting serious health concern worldwide. There is no remedy except systematic management to keep blood glucose level under control. To achieve that regular glucose level monitoring is a routine task for a patient. This involves collection of blood physically from body with some discomfort and measuring using some device. To overcome this disadvantages and distress, non-invasive blood glucose measurement system is in demand. This article presents an ultra-wide band (UWB) microwave imaging and artificial intelligence based prospective solution to detect blood glucose concentration level non-invasively (without physical blood). The system consists of a pair of small UWB biomedical planar antenna, UWB transceiver as hardware and an artificial neural network with signal acquisition and processing interface as software module. The UWB signal with center frequency of 4.7 GHz was transmitted through ear lobe and forward scattering signals were received from other side. Characteristics features of received signal were extracted for pattern recognition and detection through deep artificial neural network. The system exhibits around 88% accuracy to detect glucose concentration in blood plasma. Besides, it is affordable, safe, user friendly and can be used with comfort in near future

Various risks and safety analysis to reduce fire in oil refinery plant

It is quite hazardous to produce the final product of oil or gas in a petrochemical refinery plant due to its flammable or combustible and explosive materials. Small mistakes can cause massive damage to life, property, pollution, injury, ecosystem, and business by fire. The entire system is challenging to manage. Therefore, fire risk assessment and forecasting are necessary to overcome personal, environmental, and refinery plants' hazard situations. There have been four main threats in any refinery facility: electrical, mechanical, civil, and chemical issues, maximum cases its result burning. This research aimed to study and assess fire risk in the refinery plant by using a multi-stage early warning system and reducing the fire. The fire hazard safety layer technique would be used for our petrochemical process. Some equipment is set in place to forecast the danger and execution before and after it happens. Geographic information systems (GIS), remote sensing (RS) are some techniques for fire incidents tracking. Flame detectors, heat detectors, and gas detectors are used to maintain good contact in the entire risk analyzer portion. Various techniques and monitoring have been proposed to operate the plant efficiently and safely, like controlling, predicting, and pre-warning strategic planning

Dysbiosis of the gut microbiota and its effect on α-synuclein and prion protein misfolding: consequences for neurodegeneration

Abnormal behavior of α-synuclein and prion proteins is the hallmark of Parkinson’s disease (PD) and prion illnesses, respectively, being complex neurological disorders. A primary cause of protein aggregation, brain injury, and cognitive loss in prion illnesses is the misfolding of normal cellular prion proteins (PrPC) into an infectious form (PrPSc). Aggregation of α-synuclein causes disruptions in cellular processes in Parkinson’s disease (PD), leading to loss of dopamine-producing neurons and motor symptoms. Alteration in the composition or activity of gut microbes may weaken the intestinal barrier and make it possible for prions to go from the gut to the brain. The gut-brain axis is linked to neuroinflammation; the metabolites produced by the gut microbiota affect the aggregation of α-synuclein, regulate inflammation and immunological responses, and may influence the course of the disease and neurotoxicity of proteins, even if their primary targets are distinct proteins. This thorough analysis explores the complex interactions that exist between the gut microbiota and neurodegenerative illnesses, particularly Parkinson’s disease (PD) and prion disorders. The involvement of the gut microbiota, a complex collection of bacteria, archaea, fungi, viruses etc., in various neurological illnesses is becoming increasingly recognized. The gut microbiome influences neuroinflammation, neurotransmitter synthesis, mitochondrial function, and intestinal barrier integrity through the gut-brain axis, which contributes to the development and progression of disease. The review delves into the molecular mechanisms that underlie these relationships, emphasizing the effects of microbial metabolites such as bacterial lipopolysaccharides (LPS), and short-chain fatty acids (SCFAs) in regulating brain functioning. Additionally, it looks at how environmental influences and dietary decisions affect the gut microbiome and whether they could be risk factors for neurodegenerative illnesses. This study concludes by highlighting the critical role that the gut microbiota plays in the development of Parkinson’s disease (PD) and prion disease. It also provides a promising direction for future research and possible treatment approaches. People afflicted by these difficult ailments may find hope in new preventive and therapeutic approaches if the role of the gut microbiota in these diseases is better understood

Hybrid Social Grouping Algorithm-Perturb and Observe Power Tracking Scheme for Partially Shaded Photovoltaic Array

This research work emphasizes proposing a hybrid social grouping algorithm (SGA) and perturb and observe (P&O) scheme for tracking the global power peak in a partially shaded photovoltaic (PV) array. PV panels getting shaded, even partially, exhibits multiple power peaks, and hence conventional maximum power point tracking (MPPT) algorithms fail in tracking the maximum power peak as it gets deceived by local maxima. Most of the prevailing global search algorithms suffer in performance due to the stochastic search which consumes time even after nearing the global power peak. Therefore, a hybridization of the global search algorithm and the conventional algorithm will be a prudent solution. SGA, a global search algorithm based on individual and group cognizant behaviour, has been hybridized with a well-entrenched P&O algorithm that complements each other in achieving the global power peak swiftly. The hybridized algorithm achieves the global power peak in 0.4 seconds faster than the stand-alone SGA algorithm during complex shading conditions. The proposed scheme has been implemented for an 800 W PV array in a MATLAB simulation and validated experimentally in a hardware setup using a SAS1000L solar array simulator-programmable source, a DC-DC converter, and a dSPACE 1104 controller. The simulation and experimental results reveal that the proposed search scheme is very competent in converging towards the global maximum through SGA first and achieving the peak point through P&O. The proposed scheme has also been tested for a dynamic shading pattern, and it is evident that the proposed scheme outperforms its counterparts in terms of convergence time

Unsteady Viscous Incompressible Bingham Fluid Flow through a Parallel Plate

Numerical investigation for unsteady, viscous, incompressible Bingham fluid flow through parallel plates is studied. The upper plate drifts with a constant uniform velocity and the lower plate is stationary. Both plates are studied at different fixed temperatures. To obtain the dimensionless equations, the governing equations for this study have been transformed by usual transformations. The obtained dimensionless equations are solved numerically using the explicit finite difference method (FDM). The studio developer Fortran (SDF) 6.6a and MATLAB R2015a are both used for numerical simulations. The stability criteria have been established and the system is converged for Prandtl number P r ≥ 0.08 with Δ Y = 0.05 and Δ τ = 0.0001 as constants. As a key outcome, the steady-state solutions have been occurred for the dimensionless time τ   =   4.00 The influence of parameters on the flow phenomena and on shear stress, including Nusselt number, are explained graphically. Finally, qualitative and quantitative comparison are shown

Protecting PFC capacitors from overvoltage caused by harmonics and system resonance using high temperature superconducting reactors

Shunt capacitors are deployed for power factor correction (PFC) to reduce the load reactive power and to provide voltage support. Nonlinear loads, such as variable speed drives, can inject harmonics into the network. If the line impedance value produces a resonance with the PFC capacitor and the injected frequency coincides with the resonant frequency, an overvoltage is produced across the capacitor, which can lead to failure or explosion. To protect the PFC capacitor, a reactor can be connected in series with the PFC capacitor and tuned at the harmonic frequency of the system resonance. This paper proposes the use of a high temperature-superconducting reactor (HTSR) as the tuned reactor. The reactor will have an extremely high-quality factor (Q) compared to the normal reactor that can never be manufactured commercially with such a high Q. The performance of the HTSR reactor in terms of its ability to protect the capacitor from overvoltage and to reduce power losses has been investigated. The results are compared with those using the conventional (low Q) reactor and show that the HTSR can significantly improve filter performance and reduce power losses in the filter