Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptions and Intentions around Uptake of the COVID-19 Vaccination among Older People: A Mixed-Methods Study in Phuket Province, Thailand

Background: A 70% vaccination rate against COVID-19 in the general population was required for re-opening Phuket tourist industry. However, prior to this research, 39.61% of older people remained unvaccinated. This study aimed to examine perceptions and intentions around COVID-19 vaccination amongst older people and to explore the reasons and factors influencing their decisions to receive or refuse vaccination. Methods: This was a mixed-methods approach with a sequential explanatory design. We conducted an online survey and semi-structured qualitative interview with a subsample. Multinomial logistic regression was applied and thematic content analysis was conducted. Results: 92.4% of participants reported intention to receive the vaccine. Multinomial regression analysis revealed that perceived barriers (AdjOR = 0.032; 95% CI: 0.17–0.59), perceived benefit (AdjOR = 2.65; 95% CI: 1.49–4.71), good health (AdjOR = 3.51; 95% CI: 1.01–12.12) and health not good (AdjOR = 0.10; 95% CI: 0.02–0.49) were predictors of vaccine uptake. In the qualitative interviews, four key influences on up-take for the 28 vaccinated participants were: prevention and protection, convenience, fear of death from COVID-19, and trust in the vaccine. Four key influences on refusal of vaccination in the eight unvaccinated participants were: rarely leaving the house, fear of vaccine side-effects, fear of death after getting the vaccine, and not enough information for decision-making. Conclusion: Intervention and campaigns addressing COVID-19 vaccination should employ strategies, including the widespread use of social and other popular media to increase older people’s perceived benefit of vaccination on their current and future health status, while decreasing perceived barriers to receiving the vaccine.
The New Testament vs. The Quran: Americans’ Beliefs About the Content of Muslim and Christian Holy Texts

The stereotypes and biases that people have about various religions may not be applied to just the individuals who belong to those religions, but to the belief systems themselves. We hypothesized that non-Muslim and Muslim Americans would demonstrate biased estimates about the language content of the Quran and New Testament holding positive views towards their relative ingroup and negative views towards the outgroup text. We used two samples (N1 = 163, N2 = 204) of Americans and the Linguistic Inquiry Word Count Software (LIWC) to test our hypotheses. We determined the differences between the texts across language categories. Participants then rated the categories in terms of their relative frequency across the texts. We compared these ratings to the actual differences between the texts. As hypothesized, participants perceived the ingroup text as positive and the outgroup text as negative. We discuss whether biased beliefs about religious teachings may be separate from but aligned with biases against believers and further contribute to religious stereotypes.
A Case Series of Continuous Theta Burst Stimulation Treatment for the Supplementary Motor Area Twice a Day in Patients with Obsessive-Compulsive Disorder: A Real World TMS Registry Study in Japan

Obsessive-compulsive disorder (OCD) is a psychiatric disorder characterized by patterns in which unwanted thoughts and fears are evoked as obsessions and furthermore, compulsive behaviors are provoked repeatedly, with a prevalence rate of 2% of the population. These obsessive-compulsive symptoms disrupt daily life and cause great distress to the individual. At present, OCD is treated with antidepressants, mainly selective serotonin reuptake inhibitors, and psychotherapy, including the exposure and response prevention method. However, these approaches may only show a certain level of efficacy, and approximately 50% of patients with OCD show treatment resistance. This situation has led to the research and development of neuromodulation therapies, including transcranial magnetic stimulation treatment, for OCD worldwide in recent years. In this case series, we retrospectively analyzed the TMS registry data of continuous theta burst stimulation (cTBS) therapy targeting the bilateral supplementary motor cortex for six patients with OCD whose obsessive-compulsive symptoms had not improved with pharmacotherapy. The results suggest that treatment with cTBS for the bilateral supplementary motor area may reduce obsessive-compulsive symptoms in patients with OCD, despite the limitations of an open-label preliminary case series. The present findings warrant further validation with a randomized, sham-controlled trial with a larger sample size in the future.
An Image Interpolation Algorithm Based on Edge Gradient

Aiming at the problem of blur boundaries and low computational efficiency in traditional image interpolation algorithms,an image interpolation algorithm based on edge gradient is proposed.It uses Roberts edge detection operator to determine the color segmentation and interval boundary threshold,makes use of three space points to constructed plane,and gives the interpolation formula.Experimental results show that,this algorithm can protect the image details,improves image scaling quality and operation efficiency.
STRATEGI PENGEMBANGAN AGRO KOPERASI ALKO DALAM BISNIS KOPI (Studi Kasus Koperasi ALKO Kayu Aro)

Tulisan ini bertujuan untuk mengetahui dan menganalisis apa saja faktor-faktor yang dapat mempengaruhi pengembangan kopi dan untuk mengetahui dan menganalisis strategi manakah yang paling efektif dan tepat dalam menghadapi tantangan Internal maupun eksternal. Metode yang digunakan dalam penelitian ini adalah jenis penelitian kualitatif yang bertujuan untuk mendapatkan pemahaman yang lebih luas dan mendalam terhadap situasi sosial yang diteliti. Penelitian ini menggunakan analisis SWOT dengan pendekatan kualitatif yang bertujuan untuk memaksimalkan kekuatan, peluang, juga meminimalkan ancaman dan kelemahan. Sumber data yang digunakan dalam penelititan ini adalah dataprimer dan data sekunder. Berdasarkan hasil analisis dari penelitian terdapat beberapa faktor-faktor yang mempengaruhi perkembangan kopi pada koperasi ALKO yaitu faktor internal dan faktor eksternal. Faktor internal meliputi faktor kekuatan dan kelemahan, dan faktor eksternal meliputi peluang dan ancaman. Berdasarkan analisis SWOT strategi yang diperoleh untuk mendukung perkembangan dari Koperasi ALKO yaitu berupa Strategi (SO)
3D-Printed Microrobots: Translational Challenges

The science of microrobots is accelerating towards the creation of new functionalities for biomedical applications such as targeted delivery of agents, surgical procedures, tracking and imaging, and sensing. Using magnetic properties to control the motion of microrobots for these applications is emerging. Here, 3D printing methods are introduced for the fabrication of microrobots and their future perspectives are discussed to elucidate the path for enabling their clinical translation.
The “rebirth” of traditional musical instrument: An interactive installation based on augmented reality and somatosensory technology to empower the exhibition of chimes

Tangible cultural heritage is rich in historical, artistic and scientific value, but due to its own characteristics and the constraints of museum displays, the key issue facing us today is how to utilize and activate cultural heritage to enhance the experience and learning interest of the audience. Focusing on the Chinese cultural treasure, the chimes, this article innovatively proposes an interaction system based on digital augmented projection and somatosensory technology to empower the aesthetic expression and knowledge dissemination of chimes. On this basis, a series of evaluations were carried out and the results showed that the system effectively provides a satisfying, personalized and fun way for users to interact with chimes and learn the traditional culture, successfully bringing this ancient instrument back to life. Moreover, this article shares in detail the research, design and development process, which provides inspiration and examples for other museums to present their tangible cultural heritage through mixed reality and interactive technology.
Medial Temporal Lobe Atrophy in Predementia Alzheimer’s Disease: A Longitudinal Multi-Site Study Comparing Staging and A/T/N in a Clinical Research Cohort

Background: Atrophy of the medial temporal lobe (MTL) is a biological characteristic of Alzheimer’s disease (AD) and can be measured by segmentation of magnetic resonance images (MRI). Objective: To assess the clinical utility of automated volumetry in a cognitively well-defined and biomarker-classified multi-center longitudinal predementia cohort. Methods: We used Automatic Segmentation of Hippocampal Subfields (ASHS) to determine MTL morphometry from MRI. We harmonized scanner effects using the recently developed longitudinal ComBat. Subjects were classified according to the A/T/N system, and as normal controls (NC), subjective cognitive decline (SCD), or mild cognitive impairment (MCI). Positive or negative values of A, T, and N were determined by cerebrospinal fluid measurements of the Aβ42/40 ratio, phosphorylated and total tau. From 406 included subjects, longitudinal data was available for 206 subjects by stage, and 212 subjects by A/T/N. Results: Compared to A–/T–/N– at baseline, the entorhinal cortex, anterior and posterior hippocampus were smaller in A+/T+orN+. Compared to NC A– at baseline, these subregions were also smaller in MCI A+. Longitudinally, SCD A+ and MCI A+, and A+/T–/N– and A+/T+orN+, had significantly greater atrophy compared to controls in both anterior and posterior hippocampus. In the entorhinal and parahippocampal cortices, longitudinal atrophy was observed only in MCI A+ compared to NC A–, and in A+/T–/N– and A+/T+orN+ compared to A–/T–/N–. Conclusion: We found MTL neurodegeneration largely consistent with existing models, suggesting that harmonized MRI volumetry may be used under conditions that are common in clinical multi-center cohorts.
State-of-the-Art Review on Shipboard Microgrids: Architecture, Control, Management, Protection, and Future Perspectives

Shipboard microgrids (SBMGs) are becoming increasingly popular in the power industry due to their potential for reducing fossil-fuel usage and increasing power production. However, operating SBMGs poses significant challenges due to operational and environmental constraints. To address these challenges, intelligent control, management, and protection strategies are necessary to ensure safe operation under complex and uncertain conditions. This paper provides a comprehensive review of SBMGs, including their classifications, control, management, and protection, as well as the most recent research statistics in these areas. The state-of-the-art SBMG types, propulsion systems, and power system architectures are discussed, along with a comparison of recent research contributions and issues related to control, uncertainties, management, and protection in SBMGs. In addition, a bibliometric analysis is performed to examine recent trends in SBMG research. This paper concludes with a discussion of research gaps and recommendations for further investigation in the field of SBMGs, highlighting the need for more research on the optimization of SBMGs in terms of efficiency, reliability, and cost-effectiveness, as well as the development of advanced control and protection strategies to ensure safe and stable operation.
Efficient stochastic FEM for flow in heterogeneous porous media. Part 1: random Gaussian conductivity coefficients

This paper is concerned with the development of efficient iterative methods for solving the linear system of equations arising from stochastic FEMs for single‐phase fluid flow in porous media. It is assumed that the conductivity coefficient varies randomly in space according to some given correlation function and is approximated using a truncated Karhunen–Loève expansion. Distinct discretizations of the deterministic and stochastic spaces are required for implementations of the stochastic FEM. In this paper, the deterministic space is discretized using classical finite elements and the stochastic space using a polynomial chaos expansion. The highly structured linear systems which result from this discretization mean that Krylov subspace iterative solvers are extremely effective. The performance of a range of preconditioned iterative methods is investigated and evaluated in terms of robustness with respect to mesh size and variability of the conductivity coefficient. An efficient symmetric block Gauss–Seidel preconditioner is proposed for problems in which the conductivity coefficient has a large standard deviation.The companion paper, herein, referred to as Part 2, considers the situation in which Gaussian random fields are transformed into lognormal ones by projecting the truncated Karhunen–Loève expansion onto a polynomial chaos basis. This results in a stochastic nonlinear problem because the random fields are represented using polynomial chaos containing terms that are generally nonlinear in the random variables. Copyright © 2013 John Wiley & Sons, Ltd.
Influence brand awareness and brand association on brand equity: Mediation of trust and brand loyalty in Kentucky Fried Chicken in East Java, Indonesia

The aim of this study is to empirically prove how trust and loyalty mediate the effect of brand awareness and association on the brand equity of Kentucky Fried Chicken in East Java. The research sample consists of 200 customers, and data analysis was conducted using Structural Equation Modeling. The results of the study show that brand awareness and associations have an effect on trust, loyalty, and brand equity. Trust and brand loyalty are found to mediate the influence of brand awareness and associations on brand equity. These findings contribute to the development of consumer brand relationship theory, which suggests that an emotional connection between fast food brands and customers can lead to strong brand equity. In the case of KFC, brand equity is strengthened through brand awareness and association, which in turn create trust and brand loyalty. The implications of these results for KFC management in East Java are that they should focus on increasing the popularity of the KFC brand through content marketing, sponsoring various activities, and using endorsers to ensure that customers always remember KFC as a fast-food brand. KFC outlets in East Java consistently maintain the quality of their products and always maintain good relationships with customers, so that customers are willing to recommend KFC to others.
Fuzhou destination image perception study: based on machine learning LDA model and SVM model

The image shaping of tourist attractions is one of the important factors to attract tourists. This paper explores the perceived impression of Fuzhou in tourists' mind and the perceived dimensions of tourists' attention by studying the review texts of tourists when they visit Fuzhou. By collecting reviews about Fuzhou on Tongcheng.com, Ctrip.com, and Donkey Mama, the destination image of Fuzhou is obtained through LDA (Latent Dirichlet Allocation) theme modeling model and SVM (Support Vector Machine) sentiment analysis model text data mining, so as to objectively extract the perceptual dimensions of tourists when visiting Fuzhou. The study found that the perceptual dimensions that tourists focus on during their visit to Fuzhou are mainly, namely, "tourism services," "entrance fee," "human landscape," "natural landscape;" "natural scenery," "history and culture," and "activity experience". The results of the study have some reference significance for Fuzhou in construction and promotion.
SAR ATR based on displacement- and rotation-insensitive CNN

ABSTRACT Among many synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms, convolutional neural network (CNN)-based algorithms are the commonly used methods. However, most previous SAR ATR studies assume that the precise location (and heading direction) of a target is (are) known and image is not suffering from translations, which are not always true in realistic applications. In this letter, a modern CNN model is trained by samples with no rotation and displacement, and is evaluated on the dataset with rotation and displacement. The results show that the classification accuracy is very low when the target’s displacement or rotation angle is different from the pre-assumed value in the training dataset. To overcome this problem, a displacement- and rotation-insensitive deep CNN is trained by augmented dataset. The proposed method is evaluated on moving and stationary target acquisition and recognition (MSTAR) dataset. It proves that our proposed method could achieve high accuracy in all three subsets which have different displacement and rotation settings.
Discrete Adjoint of the Navier-Stokes Equations for Aerodynamic Shape Optimization

A discrete adjoint of the Navier-Stokes equations has been developed in the unstructured finite-volume solver the DLR-TAU-code. The method consists of the explicit construction of the exact Jacobian of the spatial discretization with respect to the unknown variables allowing the adjoint equations to be formulated and solved. A wide range of the spatial discretizations available in TAU have been differentiated, including the Spallart-Almaras-Edwards one-equation, and the Wilcox k-omega two-equation turbulence models. The aim of this paper is to give an overview of the capabilities of the discrete adjoint to perform aerodynamic shape optimization in viscous flow. The strategy developed is extensively validated on 2D cases. The adjoint based design method is first validated by comparing the gradients of the drag, lift and pitching moment it produces with the approximate gradients obtained by finite-differences. Then the accuracy and efficiency of the approach are demonstrated for transonic airfoil design by considering geometric as well aerodynamic constraints, single- as well as multi-point design. Finally, the flap design of a multi-element airfoil in take off configuration confirms the capability of the discrete adjoint to solve wide range of aerodynamic problems.
Characterization of Arbuscular Mycorrhizal Effector Proteins

Plants are colonized by various fungi with both pathogenic and beneficial lifestyles. One type of colonization strategy is through the secretion of effector proteins that alter the plant’s physiology to accommodate the fungus. The oldest plant symbionts, the arbuscular mycorrhizal fungi (AMF), may exploit effectors to their benefit. Genome analysis coupled with transcriptomic studies in different AMFs has intensified research on the effector function, evolution, and diversification of AMF. However, of the current 338 predicted effector proteins from the AM fungus Rhizophagus irregularis, only five have been characterized, of which merely two have been studied in detail to understand which plant proteins they associate with to affect the host physiology. Here, we review the most recent findings in AMF effector research and discuss the techniques used for the functional characterization of effector proteins, from their in silico prediction to their mode of action, with an emphasis on high-throughput approaches for the identification of plant targets of the effectors through which they manipulate their hosts.
Unveiling the Stereoselectivity and Regioselectivity of the [3+2] Cycloaddition Reaction between N-methyl-C-4-methylphenyl-nitrone and 2-Propynamide from a MEDT Perspective

[3+2] cycloaddition reactions play a crucial role in synthesizing complex organic molecules and have significant applications in drug discovery and materials science. In this study, the [3+2] cycloaddition (32CA) reactions of N-methyl-C-4-methyl phenyl-nitrone 1 and 2-propynamide 2, which have not been extensively studied before, were investigated using molecular electron density theory (MEDT) at the B3LYP/6–311++G(d,p) level of theory. According to an electron localization function (ELF) study, N-methyl-C-4-methyl phenyl-nitrone 1 is a zwitterionic species with no pseudoradical or carbenoid centers. Conceptual density functional theory (CDFT) indices were used to predict the global electronic flux from the strong nucleophilic N-methyl-C-4-methyl phenylnitrone 1 to the electrophilic 2-propynamide 2 functions. The 32CA reactions proceeded through two pairs of stereo- and regioisomeric reaction pathways to generate four different products: 3, 4, 5, and 6. The reaction pathways were irreversible owing to their exothermic characters: −136.48, −130.08, −130.99, and −140.81 kJ mol−1, respectively. The enthalpy of the 32CA reaction leading to the formation of cycloadduct 6 was lower compared with the other path owing to a slight increase in its polar character, observed through the global electron density transfer (GEDT) during the transition states and along the reaction path. A bonding evolution theory (BET) analysis showed that these 32CA reactions proceed through the coupling of pseudoradical centers, and the formation of new C-C and C-O covalent bonds did not begin in the transition states.
CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting

Abstract. The prediction of precipitation patterns up to 2 h ahead, also known as precipitation nowcasting, at high spatiotemporal resolutions is of great relevance in weather-dependent decision-making and early warning systems. In this study, we are aiming to provide an efficient and easy-to-understand deep neural network – CLGAN (convolutional long short-term memory generative adversarial network) – to improve the nowcasting skills of heavy precipitation events. The model constitutes a generative adversarial network (GAN) architecture, whose generator is built upon a u-shaped encoder–decoder network (U-Net) and is equipped with recurrent long short-term memory (LSTM) cells to capture spatiotemporal features. The optical flow model DenseRotation and the competitive video prediction models ConvLSTM (convolutional LSTM) and PredRNN-v2 (predictive recurrent neural network version 2) are used as the competitors. A series of evaluation metrics, including the root mean square error, the critical success index, the fractions skill score, and object-based diagnostic evaluation, are utilized for a comprehensive comparison against competing baseline models. We show that CLGAN outperforms the competitors in terms of scores for dichotomous events and object-based diagnostics. A sensitivity analysis on the weight of the GAN component indicates that the GAN-based architecture helps to capture heavy precipitation events. The results encourage future work based on the proposed CLGAN architecture to improve the precipitation nowcasting and early warning systems.