Loss-of-Control (LOC) is a major factor in fatal aircraft accidents. Although denitions of LOC remain vague in analytical terms, it is generally associated with ight outside of the normal ight envelope, with nonlinear inuences, and with a signicantly diminished capability of the pilot to control the aircraft. Primary sources of nonlinearity are the intrinsic nonlinear dynamics of the aircraft and the state and control constraints within which the aircraft must operate. This paper examines how these nonlinearities aect the ability to control the aircraft and how they may contribute to loss-of-control. Specically, the ability to regulate an aircraft around stall points is considered, as is the question of how damage to control eectors impacts the capability to remain within an acceptable envelope and to maneuver within it. It is shown that even when a sucient set of steady motions exist, the ability to regulate around them or transition between them can be dicult and nonintuitive, particularly for impaired aircraft. Examples are provided using NASA's Generic Transport Model.

No abstract

No abstract

UV sensing in pure ZnO is due to oxygen adsorption/desorption process from ZnO surface.Vanadium doping improves UV sensitivity of ZnO. Enhancement in UV sensitivity in doped ZnO is attributed to trapping and de-trapping of electrons at V 4+ & V 5+ -related defect states.An extra electron in the V 4+ state is excited under UV illumination while in absence of the same a trapping happens at the V 5+ state. An insight to the mechanism is obtained by an analytic study of the response phenomenon. Introduction:Ultraviolet detection is becoming important nowadays related to various important aspects of science/technology associated with health, environment and even space research [1,2]. Sensitive silicon based UV detectors are already available in market. But these detectors require costly visible light filters as they are sensitive to visible light. Faster, more sensitive, cost-effective UV detection is therefore an important research area. GaN, SiC and diamond are promising candidates[3-5]. But all of these are expensive materials. ZnO is an abundant, inexpensive, non-toxic and environmental friendly material with good thermal/chemical stability and high photoconductivity. UV sensing and response in ZnO, mainly depend on the surface reaction and therefore, surface defects, grain size and oxygen adsorption properties[6-8]. Several morphological studies of ZnO show enhancement in UV sensing. Doping on the other hand modifies electronic, optoelectronic and photoconductive properties of ZnO. Significant modifications have been observed in optoelectronic properties with various types of doping. Vanadium doping is one of the most interesting ones exhibiting luminescence, optoelectronic and photo sensing properties. These properties arise out of electron trapping defect states formation within the bandgap. This study analyses the effect of vanadium doping on UV

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

No abstract

Vanadium incorporation in ZnO modifies the lattice structure. The valence state of V plays an important role, controlling the oxygen content and thereby dimensions of the lattice. Both V4+ and V5+ are more electropositive than Zn2+ and reduce oxygen vacancies, resulting in lattice expansion. However, the sizes of both V4+ and V5+ are smaller than Zn2+, thereby resulting in the lattice contraction. The internal competition of increasing oxygen content and reducing effective crystal radius decides the lattice expansion and contraction. This affects the lattice strain and changes electronic levels, which modify absorption and emission processes in between the valence and conduction bands. A strong green emission band not due to oxygen vacancy but due to defects contributed by vanadium is also dependent on the oxidation state of vanadium. Bandgap also increases with the increase in the V4+ content.

No abstract