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The purpose of this study is to examine the impact of multiple brand celebrity endorsement strategies on firms’ performance and different attributes associated with celebrities on firms’ performance. In this regard, the present study specifically explores the role of celebrity reputation and experience, as well as social media as a promotion platform in influencing the economic effectiveness of multiple brand endorsement strategies, i.e. proportion of brands endorsed in a firms’ brand portfolio.

Study is based on instrumental variable regression analysis approach and is conducted in one of the emerging markets, i.e. India.

The findings indicate that firms’ market valuations increase as its proportion of brands endorsed by celebrities increases. Furthermore, popularity reputation of celebrity also influences market valuation, and relationship is positively moderated by celebrity’s experience.

Extant studies have considered one endorsement news of a firm at a time. However, how total proportion of firms’ brand endorsed by celebrities impacts its performance has not been investigated. Furthermore, impact of celebrity traits has been examined only in consumer behavior studies and has been rarely investigated in context of firms’ economic performance.

Planar periodic metallic arrays behave as artificial magnetic conductor (AMC) surfaces when placed on a grounded dielectric substrate, and they introduce a zero-degree reflection phase shift to incident waves. The antenna designers have new challenges while designing the AMC structure. The steps followed in designing the structure are as follows: 1) Designing the antenna, aimed to operate at millimetric wave frequencies, (2) Designing the AMC at desired frequencies, (3) Integrating the antenna design and AMC to resonate at millimetric wave frequencies and (4) Validate the output parameters of the antenna to be suitable for Internet of Things (IoT) applications.

The antenna is integrated with artificial material known as high impedance surface (HIS) for performance enhancement. A miniaturized, multiband, enhanced gain, AMC-integrated CPW-fed antenna is proposed and aimed to operate at millimetric wave frequencies, which is most suitable for IoT applications. The developed antenna operates at an extremely high range (30–300 GHz), i.e. from 40 to 60 GHz with the return loss values at lesser than −20 dB, and gain is greater than 10. The antenna is developed and simulated by using HFSS software.

An extensive research study has been carried out to develop a low profile, high gain and optimized antenna. The first two steps are separately designing the antenna and the AMC unit cell at the desired frequencies. The third step is finding the antenna or AMC radiating parts responsible for each resonant frequency by analysing the surface current distribution. CPW fed along with AMC integration has made the antenna feasible to achieve the extremely high frequency (EHF) range, i.e. 40–60 GHz, which is highly adoptable in IoT applications.

The result represented that the developed antenna is resonating at EHF rank with high gain and good imped matching when it is being compared with the previous models and has only CPW fed without having AMC structure integration. It is evident that the antenna which has only CPW fed has resonated at lower frequency than EHF range and justified output characteristics. But when it is embedded with the AMC structure, it resonates at the EHF range, which makes the antenna highly suitable for IoT applications, with more accuracy and high data rate possibility.

Millimeter wave spectrum represents new opportunities to add capacity and faster speeds for next-generation services as fifth generation (5G) applications. In its Spectrum Frontiers proceeding, the Federal Communications Commision decided to focus on spectrum bands where the most spectrums are potentially available. A low profile antenna array with new decoupling structure is proposed and expected to resonate at higher frequency bands, i.e. millimeter wave frequencies, which are suitable for 5G applications.

The presented antenna contains artificial magnetic conductor (AMC) surface as decoupling structure. The proposed antenna array with novel AMC surface is operating at 29.1GHz and proven to be decoupling structure and capable of enhancing the isolation by reducing mutual coupling as 8.7dB between the array elements. It is evident that, and overall gain is improved as 10.1% by incorporating 1x2 Array with AMC Method. Mutual coupling between the elements of 1 × 2 antenna array is decreased by 39.12%.

The proposed structure is designed and simulated using HFSS software and the results are obtained in terms of return loss, gain, voltage standing wave ratio (VSWR) and mutual coupling. The S-Parameters of each stage of design is tabulated and compared with each other to prove the decoupling capability of AMC surface in antenna arrays.

The proposed structure is designed and simulated using HFSS software, and the results are obtained in terms of return loss, gain, VSWR and mutual coupling. The S-Parameters of each stage of design is tabulated and compared with each other to prove the decoupling capability of AMC surface in antenna arrays.

In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best performance. The purpose of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG and Harris are used for the process of feature extraction. The authors’ proposed method will give the best results when compared with other existing methods.

The visual tracking of many real-world applications such as robotics, smart monitoring systems, independent driving and human-computer interactions are a major and current research problem in the field of computer vision. This refers to the automated trajectory prediction of an arbitrary target object, often given in the first frame in a bounding box while moving about in successive video frames. In the community of visual tracking or object tracking, DCF has gained more importance. Discriminative trackers strive to train a classifier that differentiates the target item from the background. The fundamental concept is to train a correlation filter that creates high responses around the target and low responses elsewhere. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG and Harris are used for the process of feature extraction. Through experimental analysis, the authors have evaluated several performance assessment metrics such as accuracy, precision, F-measure and specificity. The authors’ proposed method will give the best results when compared with other existing methods.

This process involved DCF which gained more importance. When it comes to speed, DCF gives the best performance. The main objective of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique for tracking the objects and these results will be used for identifying the action of the object.

The main theme exists in the process is to identify the tracking motion of the object by using convolution regression with varied features. This method proves that it will provide better results when compared to state of art methods.

In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best performance. The main objective of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG & Harris are used for the process of feature extraction. The proposed method will give the best results when compared to other existing methods.

This paper introduces the concept and research status of tracks; later the authors focus on the representative applications of deep learning in visual tracking.

Better tracking algorithms are not mentioned in the existing method.

Visual tracking is the ability to control eye movements using the oculomotor system (vision and eye muscles working together). Visual tracking plays an important role when it comes to identifying an object and matching it with the database images. In visual tracking, deep learning has achieved great success.

The authors implement the multiple tracking methods, for better tracking purpose.

The main theme of this paper is to review the state-of-the-art tracking methods depending on deep learning. First, we introduce the visual tracking that is carried out manually, and secondly, we studied different existing methods of visual tracking based on deep learning. For every paper, we explained the analysis and drawbacks of that tracking method. This paper introduces the concept and research status of tracks, later we focus on the representative applications of deep learning in visual tracking.

This paper aims to model the aerodynamic flow characteristics of NACA0010 for various angle of attacks including stall for incompressible flows using panel methods. This paper also aims to quantify the surface pressure distribution on streamlined bodies and validate the results with analytical Jukouwski method and inverse panel methods that can predict the aerodynamic flow behaviour using the geometric iteration approach.

The 2 D panel method was implemented in Qblade software v.06 which uses the fundamental panel method which rely on source strengths and influence coefficients to determine the velocity and pressure fields on the surface. The software implements the boundary layer or viscous effects to determine the influence on aerodynamic performance at various angles of attack. Jukouwski method is also evaluated for predicting aerodynamic characteristics and is based on the geometric iteration approach. Then complex aerodynamic flow potentials are determined based on the source strengths which are used to predict the pressure and velocity fields.

At low to moderate angles of attack, panel and Jukouwski methods predict similar results for surface pressure coefficients comparable to Hess and Smith inverse method. In comparison to panel method, results from the Jukouwski mapping method predicted the pressure coefficient conservatively for the same free stream conditions. With increase in Reynolds number, lift coefficient and aerodynamic performance improved significantly for un-tripped aerofoil when stall angle is approached when compared to tripped aerofoil.

This study demonstrated that panel methods have higher efficacy in terms of computational time or resources and thus can provide benefits to many real-world aircraft or aerospace design applications.

Even though panel and Jukouwski methods have been studied extensively in the past, this paper demonstrates the efficacy of both methods for modelling aerodynamic flows that range between moderate to high Reynolds number which are critical for many aircraft applications. Both methods have been validated with analytical and inverse design methods which are able to predict aerodynamic flow characteristics for simple bluff bodies, streamlined aerofoils as well as bio-inspired corrugated aerofoils.

The paper underpins an advanced domestic manufacturing that comes with some advanced employment specialization status of individual industries as the key determinant of foreign direct investment (FDI) and considers how FDI in the food processing industry in India relates to this focal point.

This paper investigates how inward FDI inflows relate to domestic investment and revival in the industry using Auto Regressive Distributed lags (ARDL) model over the period 2000–2017. The model allows for different specifications to study whether FDI is responsible for the revival or the prior revival induces the FDI.

The results show the lack of proper advanced specialized employment status of the food processing industry. FDI in food processing is mainly guided by exports and imports opportunities and FDI plays no role in the revival of advanced growth in the industry. This finding explains why FDI in the industry is predominantly service sector oriented.

The paper underlines (1) the proper conceptualization of human capital as an important determinant of FDI; (2) reinterpretation of Kaldor's technical progress function that uncovers how employment dynamics embedded in intermediate goods specializations play a key role in supporting a higher pace of investment (and FDI); (3) labor costs' importance should involve not only the wage rate but also the advantages that a specialized employment base and (4) FDI in manufacturing demands a greater policy focus on developing domestic bases of intermediate goods specializations.

Presents a numerical study on heat transfer by natural convection in porous media in vertical enclosures with side wall heating. The model for porous media includes inertia terms and also the Brinkman extension in addition to the Darcy resistance term. A semi‐implicit finite element scheme based on operator splitting method is adopted for solving the time‐dependent system of equations. The first half of the investigations is confined to the low permeability regime where Darcy law holds good. Presents the results for annular and rectangular cavities and proposes correlations for two types of boundary conditions, namely constant wall temperature case and uniform wall heat flux case. In the second half of the investigations, the scheme is applied in a high permeability regime, where the validity of Darcy law becomes questionable. Employs plane rectangular cavities with the two types of boundary conditions mentioned earlier. Highlights the influence of Rayleigh number (Ra) and Darcy number (Da) as separate parameters and proposes correlations for a square cavity for the first time in terms of Ra and Da as separate parameters. Discusses a qualitative study of the effect of aspect ratio on heat transfer as the permeability changes.

The purpose of this paper is to study the application of entropy based optimized fuzzy logic control for a real-time non-linear system. Optimization of the fuzzy membership function (MF) is one of the most explored areas for performance improvement of the fuzzy logic controllers (FLC). Conversely, majority of previous works are motivated on choosing an optimized shape for the MF, while on the other hand the support of fuzzy set is not accounted.

The proposed investigation provides the optimal support for predefined MFs by using genetic algorithms-based optimization of fuzzy entropy-based objective function.

The experimental results obtained indicate an improvement in the performance of the controller which includes improvement in error indices, transient and steady-state parameters. The applicability of proposed algorithm has been verified through real-time control of the twin rotor multiple-input, multiple-output system (TRMS).

The proposed algorithm has been used for the optimization of triangular sets, and can also be used for the optimization of other fussy sets, such as Gaussian, s-function, etc.

The proposed optimization can be combined with other algorithms which optimize the mathematical function (shape), and a potent optimization tool for designing of the FLC can be formulated.

This paper presents the application of a new optimized FLC which is tested for control of pitch and yaw angles in a TRMS. The performance of the proposed optimized FLC shows significant improvement when compared with standard references.

This case study aims to appraise the financial benefits of green building construction in developing countries. The case study presents, green building's positive net present value (NPV) investment in real terms and potentially enhanced stock market returns at the firm level compared to competitors.

The case study examines secondary data on a green building certification and longitudinal operation costs to estimate green building investments' financial benefits. The case study also compares the stock market performance of green building portfolio company with non-green building competitors of similar size and industry.

The case study finds out that the real return rate on green building investment is higher than the weighted average cost of capital (WACC) of the company with an inflation-adjusted payback period of fewer than ten years. Findings compare favourably to the extant literature which was mostly in developed economies. The paper further highlights that stock market performance for a green building focused company shows improved returns to shareholders relative to non-green competitors.

The results are specific to the time and building researched; green buildings costs have reduced over time, and a new study may show improved case study findings. The case study results on stock market performance are indicative and may need further research for evaluation.

The case study presents a model for critical appraisal of green buildings investment. The paper further indicates that green building investment may lead to operational savings and superior stock performance compared to competitors.

The paper presents a green building investment appraisal model which might be useful for the industry and academia. Developing countries have limited literature on green buildings' financial benefits; this case study quantifies the financial benefits and compares them with the available literature related to developed economies’ green buildings.