Miracle-dz/UniFAR-FTUs huggingface dataset:
Critical Factors Affecting the Participation in COVID-19 Vaccination Program in Residency of Surakarta: The Extended Theory of Planned Behaviour

Objective: This study evaluates the critical factors affecting COVID-19 vaccine acceptance in Indonesia using a proposed extended theory of planned behaviour (extended-TPB). Design/Methods/Approach: We conducted an online survey (N = 222 respondents) using a valid and reliable questionnaire to evaluate people's intention to take the COVID-19 vaccination. A structural equation modeling was performed to demonstrate the impact of religious acceptance (RA), vaccine safety (SF), trust (TS), perceived behavioural control (PBC), attitude towards vaccination (ATT), and subjective norms (SN), on intention (IN) to accept COVID-19 vaccine. Findings: The results show positive relationships of all hypotheses except the correlation between RA and TS. TS becomes a positive determinant of ATT, SN, PBC, and INT, and TS is influenced by SF but not by RA. TS is the key determinant factor of intention to uptake the COVID-19 vaccine. Originality: This study used a proposed extended TPB by adding trust, religious acceptance, and safety as additional components of TPB to predict the intention to participate in COVID-19 vaccination. This study is essential because the vaccine is believed to be the best strategy to deal with virus-related diseases, such as COVID-19. However, there is a lack of information on the multifactor effects of Indonesian people's acceptance of vaccination, especially among Surakarta residents. Practical/Policy implication: This research can be a recommendation for the Indonesian government to elevate public intention to take the COVID-19 vaccine, especially by implementing policies that accentuate people's trust by confirming and publishing information about the vaccine safety.
Controversial Effects of Diverse Types of Toxoplasma gondii on the Anxiety-Related Behavior and Cognitive Impairments in the Animal Model of Alzheimer's Disease

Background: Toxoplasma gondii is a neurotropic parasite with lifelong persistence in the host brain. Many researchers suggested toxoplasmosis as a risk factor for the development of Alzheimer's disease (AD); however, the link between them has not been fully elucidated. Objectives: The present study was designed to investigate the effects of chronic toxoplasmosis infection with Types I (RH), II (PRU), and III (VEG) strains alone and in combination on cognitive impairment in Alzheimer's rat model. Methods: Seven months after the inoculation of the strains, AD was induced bilaterally in rats by injecting human amyloid beta 1-42 (Aβ1-42) peptide into the brain hippocampus. Behavioral tests, including the Morris water maze (MWM) and elevated plus maze (EPM) were conducted 10 days after the AD induction. Results: Our findings showed that chronic infection with RH strain increased anxiety-like behavior in the Alzheimer's rats in the EPM. In agreement with EPM findings, rats infected with the RH strain exacerbated spatial learning disorders in the MWM test; however, it did not affect the spatial memory. Conversely, infection with the PRU strain significantly enhanced spatial learning without being able to improve memory impairment in the Alzheimer's rat model. Improvement in spatial learning and memory impairments were also observed in rats infected with PRU and VEG strains in combination. Conclusions: Taken together, our findings suggest that chronic infection with PRU strain, as well as PRU and VEG strains in combination, can significantly improve cognitive deficits induced by Aβ1-42 in Alzheimer's rats, while RH strain plays a detrimental role in AD pathogenesis.
SMART CITIES IN SHAPING THE FUTURE OF URBAN SPACE: TECHNICAL PERSPECTIVE AND UTILITARIAN ASPECTS

Urban planning is concerned with the development of future frameworks for the development of cities, and cities are generally a reflection of the social and economic situation. The concept of the city has evolved as a result of the development of human activities. The industrial development of the 18th century increased dependence on transportation, As the urban population grew and the dependence on transport increased, a number of urban problems emerged in the cities, including traffic problems such as traffic congestion, high levels of pollution, increased mobility time and energy consumption. Traditional methods, through the creation of new roads or expanding existing roads to ease congestion, which gives a solution in the short term, and adversely affect the environment, and attracts a growing movement of vehicles within cities. However, the technical development that accompanied the twentieth century in turn presents a range of opportunities to find appropriate solutions to these problems, led to the emergence of a society of a new style increasingly dependent on knowledge and digital techniques. The concept of the city has developed as a result of this social development. On technologies such as digital, electronic, virtual, cognitive, and intelligent cities. But what distinguishes a smart city from other cities is its focus on creativity and problem-solving as one of the most important features of intelligence. The objective of this paper is to shed the light on smart cities with its mail privileges that provide it to its inhabitance. Also, we will also discuss in detail the necessity of using smart cities in the digital era. It is was shown that Smart City provides basic information about various government services such as public health, business, education, employment, transportation and other general information about weather, recreation and holidays, to the public on social media and through various applications that can be loaded on smart devices. It is fast, easy and efficient in transmitting information, and it supports a sustainable and green environment through energy conservation. Experts believe social networking has become a key feature of a smooth transition to the Smart City.
A novel deep learning-based method for damage identification of smart building structures

In the past few years, intelligent structural damage identification algorithms based on machine learning techniques have been developed and obtained considerable attentions worldwide, due to the advantages of reliable analysis and high efficiency. However, the performances of existing machine learning–based damage identification methods are heavily dependent on the selected signatures from raw signals. This will cause the fact that the damage identification method, which is the optimal solution for a specific application, may fail to provide the similar performance on other cases. Besides, the feature extraction is a time-consuming task, which may affect the real-time performance in practical applications. To address these problems, this article proposes a novel method based on deep convolutional neural networks to identify and localise damages of building structures equipped with smart control devices. The proposed deep convolutional neural network is capable of automatically extracting high-level features from raw signals or low-level features and optimally selecting the combination of extracted features via a multi-layer fusion to satisfy any damage identification objective. To evaluate the performance of the proposed deep convolutional neural network method, a five-level benchmark building equipped with adaptive smart isolators subjected to the seismic loading is investigated. The result shows that the proposed method has outstanding generalisation capacity and higher identification accuracy than other commonly used machine learning methods. Accordingly, it is deemed as an ideal and effective method for damage identification of smart structures.
Integration of renewable energy sources in dynamic economic load dispatch problem using an improved fireworks algorithm

In electrical power system, economic load dispatch is a generic operation for optimal sharing of generation units to meet the system load. With the rapid development of the renewable infrastructure and wide encouragement for green energy have emerged hybrid generating systems in power systems. However, there continuous ever-increasing production is creating challenges as well as implicating economic factor also in operation. A collective cost function is considered with the conventional thermal generators along with the consideration of renewable energy sources to envisage the economic factor. For these renewable sources, like wind and solar, the proportional cost, their uncertainty and variability by overestimation and underestimation cost are considered. To achieve this economic day-ahead scheduling, dynamic operation in time scale of 1 h interval is performed. The stochastic nature of wind and solar is modelled by Weibull and Beta distributions, respectively. Moreover, economic optimisation is obtained by a newly developed algorithm called improved fireworks algorithm with non-uniform operator (IFWA-NMO). This introduces adaptive dimension strategy, limiting mapping operator and non-uniform operator. The effectiveness of proposed IFWA-NMO is investigated on standard dynamic economic load dispatch (DELD) system and also employed to solve conventional DELD with wind-solar system.
Quantifying the contamination from nebular emission in NIRSpec spectra of massive star forming regions

The Near InfraRed Spectrograph (NIRSpec) on the James Webb Space Telescope (JWST) includes a novel micro shutter array (MSA) to perform multi object spectroscopy. While the MSA is mainly targeting galaxies across a larger field, it can also be used for studying star formation in crowded fields. Crowded star formation regions typically feature strong nebular emission, both in emission lines and continuum. In this work, nebular emission is referred to as nebular contamination. Nebular contamination can obscure the light from the stars, making it more challenging to obtain high quality spectra. The amount of the nebular contamination mainly depends on the brightness distribution of the observed ‘scene’. Here we focus on 30 Doradus in the Large Magellanic Cloud, which is part of the NIRSpec GTO program. Using spectrophotometry of 30 Doradus from the Hubble Space Telescope (HST) and the Very Large Telescope (VLT)/SINFONI, we have created a 3D model of the nebular emission of 30 Doradus. Feeding the NIRSpec Instrument Performance Simulator (IPS) with this model allows us to quantify the impact of nebular emission on target stellar spectra as a function of various parameters, such as configuration of the MSA, angle on the sky, filter band, etc. The results from these simulations show that the subtraction of nebular contamination from the emission lines of pre-main sequence stars produces a typical error of 0.8%, with a 1σ spread of 13%. The results from our simulations will eventually be compared to data obtained in space, and will be important to optimize future NIRSpec observations of massive star forming regions. The results will also be useful to apply the best calibration strategy and to quantify calibration uncertainties due to nebular contamination.
Convolution‐closed models for count time series with applications

There has recently been an upsurge of interest in time series models for count data. Many papers focus on the model with first‐order (Markov) dependence and Poisson innovations. Our paper considers practical models that can capture higher‐order dependence based on the work of Joe (1996). In this framework we are able to model both equidispersed and overdispersed marginal distributions of data. The latter is approached using generalized Poisson innovations. Central to the models is the use of the property of closure under convolution of certain families of random variables. The models can be thought of as stationary Markov chains of finite order. Parameter estimation is undertaken by maximum likelihood, inference procedures are considered and means of assessing model adequacy employed. Applications to two new data sets are provided.
Hierarchical Subdivision of the Simple Cubic Lattice

The simple cubic lattice defines a set of points at regular distances. The volume of the Voronoi cells around each point may serve as a weight for integration over the entire space. We add interstitial points to this grid according to the rule that these have maximum distance to the existing points, or by the equivalent rule that they are placed at the vertices of the Voronoi cells of the existing lattice. Choices of that kind appear in numerical sampling with maximum independent data points if the numerical expense of computing values represented at the lattice points is high. The volumes and shapes of the Voronoi cells of these enriched/supersampled lattices are discussed in detail while this insertion is recursively executed three times in succession.
Direction estimation of coherent narrowband signals using spatial signature

A computationally efficient spatial signature (SS) based method is proposed for estimating the directions-of-arrival (DOAs) of coherent narrowband signals impinging on a uniform linear array (ULA). The normalized SS of the coherent signals is blindly estimated from the signal eigenvector of the array covariance matrix and then is used to estimate the directions with a modified Kumaresan-Prony (K-P) method, where a linear prediction (LP) model is combined with "subarray" averaging. The proposed method not only has the maximum permissible array aperture and computational simplicity, it also better resolves closely spaced coherent signals with small length of data and at lower signal-to-noise ratio (SNR).
New Application of Evanescent Mode Wave-Guide to Filter Design

Evanescent waveguide (rectangular or round) sections are used in conjunction with an admittance inverter synthesis technique incorporating frequency variable turns ratios to realize wide-band microwave (up to 1 octave) and low-frequency (60-MHz) low-loss bandpass filters. This work extends and corrects previous published information.
Target drift discriminative network based on deep learning in visual tracking

Abstract. In visual tracking, sometimes the target response value is high, but it is not the tracking result, which can result in the wrong judgment. Moreover, the threshold to decide the tracking result needs to be set artificially in the traditional discriminative methods. We propose a deep learning-based target drift discriminative network to judge whether the target is lost. We design a lightweight network without the threshold, using four convolutional layers, three full connection layers, and the Softmax function to judge the tracking results. When training the network, the established positive and negative samples are used, and we select difficult samples for further training to achieve a better target discriminative effect. Finally, a target drift discriminative network is introduced into the accurate tracking by overlap maximization. When it is judged that the target is lost, another search area is selected to quickly find the target. Numerous experiments show that our method achieves the best performance on datasets UAV123, UAV20L, and VOT2018-LT, especially on the UAV20L dataset, for which the tracking precision and tracking success rate are improved by 3.7% and 2.8%. Compared with several other classical threshold discriminative criteria, we do not need to set the threshold artificially and have better judgment performance.
Characterization of exoplanetary atmospheres with SLOPpy

Transmission spectroscopy is among the most fruitful techniques to infer the main opacity sources present in the upper atmosphere of a transiting planet and to constrain the composition of the thermosphere and of the unbound exosphere. Not having a public tool able to automatically extract a high-resolution transmission spectrum creates a problem of reproducibility for scientific results. As a consequence, it is very difficult to compare the results obtained by different research groups and to carry out a homogeneous characterization of the exoplanetary atmospheres. In this work, we present a standard, publicly available, user-friendly tool, named SLOPpy (Spectral Lines Of Planets with python), to automatically extract and analyze the optical transmission spectrum of exoplanets as accurately as possible. Several data reduction steps are first performed by SLOPpy to correct the input spectra for sky emission, atmospheric dispersion, the presence of telluric features and interstellar lines, center-to-limb variation, and Rossiter-McLaughlin effect, thus making it a state-of-the-art tool. The pipeline has successfully been applied to HARPS and HARPS-N data of ideal targets for atmospheric characterization. To first assess the code's performance and to validate its suitability, here we present a comparison with the results obtained from the previous analyses of other works on HD 189733 b, WASP-76 b, WASP-127 b, and KELT-20 b. Comparing our results with other works that have analyzed the same datasets, we conclude that this tool gives results in agreement with the published results within 1$\sigma$ most of the time, while extracting, with SLOPpy, the planetary signal with a similar or higher statistical significance.
Development of framework for just-in-time implementation in maintenance

Purpose The purpose of this paper is to propose an interpretive structural modeling (ISM) model which highlights the relationships between the identified just-in-time (JIT) elements useful for the implementation of JIT in maintenance and understand mutual influences of these identified JIT elements on JIT implementation in maintenance. Further, this paper seeks to identify dependence power and driving power of identified JIT elements using an ISM and Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis. Design/methodology/approach The methodology used in the paper is ISM with a view to evolving mutual relationships among JIT elements. The identified JIT elements have been further classified, based on their dependence power and driving power using MICMAC analysis. Findings This paper has developed the relationships among 16 identified JIT elements using the ISM methodology. Further, this paper analyses the driving power and dependence power of identified JIT elements with the help of MICMAC analysis. The incorporated approach is developed here, as the ISM provides only binary correlation among identified JIT elements. The MICMAC analysis is adopted here as it is useful in specific examination related to driving and the dependence power of identified JIT elements. The ISM developed model and MICMAC analysis finding are validated with the help of industrial experts. Research limitations/implications The weightage and validation for the ISM and MICMAC analysis are obtained throughout the opinion of academics and industry experts. Further hypothesis may be conducted to examine the validity of the planned model, and structural model may also be validated statistically with the help of structural equation modeling. Practical implications The ISM model development and MICMAC analysis of identified JIT elements provide academics and maintenance managers a macro picture of the profits gained by the organizations by the implementation of JIT in maintenance of an organization. Originality/value The results will be useful for maintenance managers to understand the process of implementation of JIT in maintenance and to gain benefits after the implementation of JIT in maintenance of an organization.
New Journey Through Young Customer Experience in Omnichannel Context: The Role of Personalization

Objective: This study investigates the relationship between personalization and customer experience, with emotion and cognitive as the components through which the result repeats purchase intention. Design/Methods/Approach: This study uses purposeful sampling to enroll research participants based on the research objective. A total of 429 respondents met the criteria, and the hypotheses were tested using Smart PLS 3 software with Partial least squares (PLS) structural equation modeling. Findings: The results of this study confirm that customer experience is a significant attribute of consumer behavior in the omnichannel context. Specifically, the main findings of our research indicate that emotions and cognitive processes are influenced by personalization, a relationship that has been established in the online context. The findings also indicate that the emotional and cognitive components of the customer experience can influence purchase intention. Originality: Our framework is based on previous research and applies to a new construct with new phenomena. Practical/Policy implication: Our finding demonstrates that a better customer experience in an omnichannel context has a beneficial effect on repeat purchase intention, indicating that personalization might influence purchasing behavior. In essence, our finding enables companies to understand the linked nature of variables and consequently prioritize marketing initiatives in an omnichannel context.
Age-friendly environment, social support, sense of community, and loneliness among middle-aged and older adults in Korea

Abstract Objectives The purpose of this research is to explore the relationship between age-friendly environment, social support, sense of community, and loneliness of Korean adults aged 45 and above. Methods A total of 590 participants from a cross-sectional and secondary data from an age integration survey conducted in 2018 was used for analysis. Structural equation modelling and bootstrapping method were applied to examine the mediating role of social support and sense of community on the relationship between age-friendly environment and loneliness. Results Age-friendly environment was positively associated with social support ( ) and sense of community ( ). Social support was negatively associated with loneliness ( ). Full mediation effect of social support was observed in the pathway from age-friendly environment to loneliness (95% CI: −0.135 to −0.036). Conclusion Social support was fundamental in lowering loneliness in an age-friendly environment. There was no significant association linking age-friendly environment, sense of community, and loneliness. The results support the adoption of AFE to protect people at risk of loneliness with social support mediating this relationship.
Image recommender system based on compact convolutional transformer image style recognition

Abstract. Social network users face a large number of images; therefore, it is very important that social media finds and recommends images that satisfy the user’s preferences properly. This article investigates the effect of using image style on the performance of a social image recommender system. The effect of distinctive visual styles is evident in art, photography, film, and advertising; however, to the best of our knowledge, it has not been used in social image recommendation systems. We propose image style recognition as a component of user preferences. To do this, we first propose a method based on deep features and compact convolutional transformer to recognize the style of images; then, an image recommender system based on an image style recognition is presented. We consider 15 distinct styles from the Flickr Style dataset, consisting of five categories: atmosphere, mood, genre, composition styles, and color. The experimental results show that style has a positive effect and can significantly improve Recall@k and Precision@k by ∼5 % − 10 % (for the 100 top images recommended) for personalized image recommendation compared with not using the.