Contributing to the literature on brand stereotyping, we draw on the Stereotype Content Model to investigate whether the content of the brand stereotype (in terms of warmth and competence) impacts consumers' perceptions of functional, emotional and social value. In doing so, we explicitly account for the brand's level of perceived globalness (PBG) and localness (PBL) as known influences on both stereotype content and value perceptions. Across two studies, we find that brand warmth consistently and positively impacts functional and emotional value, whereas brand competence enhances functional value. The impact of the stereotyping dimensions on value is subsequently reflected in increased purchase intentions and higher brand ownership. Surprisingly, none of the latter outcomes is affected by social value. Our findings corroborate previous research showing that PBG and PBL are important drivers of brand stereotype content, but also reveal that brand warmth has a stronger impact on behavioral outcomes than brand competence.

To tackle a specific class of engineering problems, in this paper, we propose an effectively integrated bat algorithm with simulated annealing for solving constrained optimization problems. Our proposed method (I-BASA) involves simulated annealing, Gaussian distribution, and a new mutation operator into the simple Bat algorithm to accelerate the search performance as well as to additionally improve the diversification of the whole space. The proposed method performs balancing between the grave exploitation of the Bat algorithm and global exploration of the Simulated annealing. The standard engineering benchmark problems from the literature were considered in the competition between our integrated method and the latest swarm intelligence algorithms in the area of design optimization. The simulations results show that I-BASA produces high-quality solutions as well as a low number of function evaluations.

This paper analyzes the vollatility spillover between the conventional index in Malaysia FTSE Malaysia KLCI (KLSE) and the Islamic index in Malaysia FTSE Bursa Malaysia Shariah Index (FTFBMHS). Monthly observations spanning in a period from 2002 to 2018 are obtained from investing.com database. GARCH model and Johansen cointegration test are used to investigate volatility spillover and the relationship between two indices. The results of the analysis indicate that in the short-run there is volatility spillover between FTSE Malaysia KLCI and FTSE Bursa Malaysia Shariah Index, while in the long-run there is no relationship between the two indices. The methodology of compiling Islamic indeces is based on Shariah law. Keywords: Conventional

Abstract The paper presents results of the measurements of the tropospheric ozone (O3) concentration and meteorological parameters: temperature, air pressure, relative humidity, speed and wind direction. The data were collected from January 2016 to December 2016 at station located in locality Centre (Banja Luka), Republic of Srpska, Bosnia and Herzegovina. Ozone is one of the most harmful pollutants to plants and health and highly reactive secondary pollutant. The present study covers investigation of the relationship between the concentration of ozone and meteorological parameters as well as time variations of ozone concentration (by hours, months, seasons). This topic has not been studied up to now in this region, although the recent research data indicates that there is a correlation between them and previously obtained from the world’s relevant scientific centres, as already cited above. Statistical analysis confirms string of rolls, which shows directional connection between tropospheric ozone and meteorological parameters, specially temperature (r = 0.148), air pressure (r = –0.292) and relative humidity (r = –0.292). These parameters are the most important meteorological factors influencing the variation in ozone levels during the research. The correlation ozone concentrations with speed and direction of wind is not significant, like other parameters.

In the modern telecommunication systems, mobility is one of the key advantage of wireless communications, given that it is possible to transmit/receive data, without caring of having a static position into the network. Of course, mobility poses special issues such as degradations, channel quality fluctuations, fast topology changes, and so on. Modern researches focus their attention on predicting mobile future node positions, in order to a-priori know, for example, what the evolution of the network topology will be or which level of stability each node will reach. Each prediction scheme is based on the storage and analysis of several historical mobility trajectories, in order to train the proper prediction algorithm. In this paper, we focus our attention on the optimization of the space needed to store historical mobility samples, encoding their values and evaluating the conversion error, comparing different encoding functions. Several simulation campaigns have been carried out in order to evaluate the goodness and feasibility of our proposal.

Abstract Epilepsy is a neurological disorder characterised by unusual brain activity widely known as seizure affecting 4-7% of the world's population. The diagnosis of this disorder is currently based on analysis of the electroencephalography (EEG) signals in the time-frequency domain. The analysis is performed applying various algorithms that yield high performance, however the challenge of effective real-time epilepsy diagnosis persists. To address this, we have developed a Field Programmable Gate Array (FPGA) based solution for the classification of generalized and focal epileptic seizure types using a feed-forward multi-layer neural network architecture (MLP ANN). The neural network algorithm is trained, validated and tested on 822 captured signals from Temple University Hospital Seizure Detection Corpus (TUH EEG Corpus) database. Inputs into the system were five main features obtained from EEG signals by time-frequency analysis followed by Continuous Wavelet Transform (CWT) and subsequent statistical analysis. Out of the total number of samples, 583 (70 %) of them were utilised during the system development in MATLAB and TensorFlow and 239 (30 %) samples were further used for subsequent testing of the model performance on the FPGA. Subsequently, the adequate parameters of the ANN model were determined by using k-Fold Cross-Validation. Finally, the best performing ANN model in terms of average validation data accuracy achieved during cross-validation was implemented on the FPGA for real-time seizure classification. The digital ANN solution was coded in Very High-Speed Integrated Circuit Hardware Description Language (VHDL) and tested on the FPGA using 30 % reaming data. The results of this research demonstrate that epilepsy diagnosis with quite high accuracy (95.14 %) can be achieved with (5-12-3) MLP ANN implemented on FPGA. Also, the results show the steps towards appropriate implementation of ANN on the FPGA. These results can be utilised as the basis for the design of an application-specific integrated circuit (ASIC) allowing large serial production.

Abstract The paper proposes a discrete-time sliding mode controller for single input linear dynamical systems, under requirements of the fast response without overshoot and strong robustness to matched disturbances. The system input saturation is imposed during the design due to inevitable limitations of most actuators. The system disturbances are compensated by employing nonlinear estimation by integrating the signum of the sliding variable. Hence, the proposed control structure may be regarded as a super-twisting-like algorithm. The designed system stability is analyzed as well as the sliding manifold convergence conditions are derived using a discrete-time model of the system in the δ-domain. The results obtained theoretically have been verified by computer simulations.

The plant Citrullus colocynthis, a member of the squash (Cucurbitaceae) family, has a long history in traditional medicine. Based on the ancient knowledge about the healing properties of herbal preparations, plant-derived small molecules, e.g., salicylic acid, or quinine, have been integral to modern drug discovery. Additionally, many plant families, such as Cucurbitaceae, are known as a rich source for cysteine-rich peptides, which are gaining importance as valuable pharmaceuticals. In this study, we characterized the C. colocynthis peptidome using chemical modification of cysteine residues, and mass shift analysis via matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry. We identified the presence of at least 23 cysteine-rich peptides in this plant, and eight novel peptides, named citcol-1 to -8, with a molecular weight between ~3650 and 4160 Da, were purified using reversed-phase high performance liquid chromatography (HPLC), and their amino acid sequences were determined by de novo assignment of b- and y-ion series of proteolytic peptide fragments. In silico analysis of citcol peptides revealed a high sequence similarity to trypsin inhibitor peptides from Cucumis sativus, Momordica cochinchinensis, Momordica macrophylla and Momordica sphaeroidea. Using genome/transcriptome mining it was possible to identify precursor sequences of this peptide family in related Cucurbitaceae species that cluster into trypsin inhibitor and antimicrobial peptides. Based on our analysis, the presence or absence of a crucial Arg/Lys residue at the putative P1 position may be used to classify these common cysteine-rich peptides by functional properties. Despite sequence homology and the common classification into the inhibitor cysteine knot family, these peptides appear to have diverse and additional bioactivities yet to be revealed.

In medium-size, spiny striatal neurons of the direct pathway, dopamine D1- and adenosine A1-receptors are coexpressed and are mutually antagonistic. Recently, a mutation in the gene encoding the A1-receptor (A1R), A1R-G279S7.44, was identified in an Iranian family: two affected offspring suffered from early-onset l-DOPA–responsive Parkinson’s disease. The link between the mutation and the phenotype is unclear. Here, we explored the functional consequence of the G279S substitution on the activity of the A1-receptor after heterologous expression in HEK293 cells. The mutation did not affect surface expression and ligand binding but changed the susceptibility to heat denaturation: the thermodynamic stability of A1R-G279S7.44 was enhanced by about 2 and 8 K when compared with wild-type A1-receptor and A1R-Y288A7.53 (a folding-deficient variant used as a reference), respectively. In contrast, the kinetic stability was reduced, indicating a lower energy barrier for conformational transitions in A1R-G279S7.44 (73 ± 23 kJ/mol) than in wild-type A1R (135 ± 4 kJ/mol) or in A1R-Y288A7.53 (184 ± 24 kJ/mol). Consistent with this lower energy barrier, A1R-G279S7.44 was more effective in promoting guanine nucleotide exchange than wild-type A1R. We detected similar levels of complexes formed between D1-receptors and wild-type A1R or A1R-G279S7.44 by coimmunoprecipitation and bioluminescence resonance energy transfer. However, lower concentrations of agonist were required for half-maximum inhibition of dopamine-induced cAMP accumulation in cells coexpressing D1-receptor and A1R-G279S7.44 than in those coexpressing wild-type A1R. These observations predict enhanced inhibition of dopaminergic signaling by A1R-G279S7.44 in vivo, consistent with a pathogenic role in Parkinson’s disease. SIGNIFICANCE STATEMENT Parkinson’s disease is caused by a loss of dopaminergic input from the substantia nigra to the caudate nucleus and the putamen. Activation of the adenosine A1-receptor antagonizes responses elicited by dopamine D1-receptor. We show that this activity is more pronounced in a mutant version of the A1-receptor (A1R-G279S7.44), which was identified in individuals suffering from early-onset Parkinson’s disease.

Evidence on early achievements, challenges and opportunities would help low-income and middle-income countries (LMICs) accelerate implementation of health and health-related sustainable development goals (HHSDGs). A series of country-specific and multicountry consultative meetings were conducted during 2018–2019 that involved 15 countries across five regions to determine the status of implementation of HHSDGs. Almost 120 representatives from health and non-health sectors participated. The assessment relied on a multidomain analytical framework drawing on existing public health policy frameworks. During the first 5 years of the sustainable development goals (SDGs) era, participating LMICs from South and Central Asia, East Africa and Latin America demonstrated growing political commitment to HHSDGs, with augmentation of multisectoral institutional arrangements, strengthening of monitoring systems and engagement of development partners. On the other hand, there has been limited involvement of civic society representatives and academia, relatively few capacity development initiatives were in place, a well-crafted communication strategy was missing, and there is limited evidence of additional domestic financing for implementing HHSDGs. While the momentum towards universal health coverage is notable, explicit linkages with non-health SDGs and integrated multisectoral implementation strategies are lacking. The study offers messages to LMICs that would allow for a full decade of accelerated implementation of HHSDGs, and points to the need for more implementation research in each domain and for testing interventions that are likely to work before scale-up.

The main aim of this paper is to analyse correlation between adjacent images in a video sequence. Adjacent images with a slow or fast changeable scene in a video have high correlation which shows consistency in the video sequence therefore it can be the proof of normal reproduction of video service. Due to QoS (Quality of service) problems, especially over lossy network, appearance of different visual degradations in frames (images) during delivery of video service to end users can happen very often. In that case, adjacent images in the video sequence have low correlation which can be used as an indicator the problem occurred in some part of the network. In addition, the paper analyses correlation correspondent to polygons i.e. parts of adjacent images in the video sequence in order to discover a degree of influence visual degradations to user’s QoE (Quality of Experience). In order to check this aim, tested degraded and non-degraded video sequence was captured using IPTV system of one significant market power provider and processed in offline mod with Python script created especially for this purpose.