Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and Usability Assessment of a Mobile App (Demensia KITA) to Support Dementia Caregivers in Malaysia: A Study Protocol

The impact of dementia on caregivers is complex and multi-dimensional. In low- and middle-income settings, caregivers are often left without adequate support, despite their multiple needs. These include health information, caregiving skills, social and emotional support, and access to local resources—all of which can be partially fulfilled by technology. In recent years, mobile apps have emerged and proven useful for caregivers. We found a few existing apps suitable for Malaysian users in terms of affordability and cultural and linguistic compatibility. Our study aims to design a mobile app that suits dementia caregivers in Malaysia and consists of three phases. Phase I is content development that employs Focus Group Discussion (FGD) and Nominal Group Technique (NGT) involving field experts. Phase II comprises a mobile app (Demensia KITA) designed in collaboration with a software developer specializing in mobile health apps. Phase III entails testing the usability of the app using the Malay version of the mHealth App Usability Questionnaire (M-MAUQ). This study protocol elaborates on the rigorous steps of designing a mobile app and testing its usability, along with anticipated challenges. Our protocol will provide insight for future researchers, healthcare providers, and policymakers and pave the way for better use of digital technology in the field of aging and caregiving.
Mental distress, quality of life and physical symptoms in Chinese women with ovarian cancer receiving olaparib treatment during the COVID-19 pandemic

Objectives Women with ovarian cancer (OC) have experienced unprecedented challenges since the novel coronavirus disease-2019 (COVID-19) outbreak in China. We aim to evaluate the experience of psychological status, physical symptoms and quality of life (QoL) and investigate the impact of COVID-19 pandemic on OC patients receiving olaparib. Methods The survey was conducted online from April 22 to May 12 in 2020. Demographic and clinical questions were listed to collect general information. The degree of insomnia, depression, anxiety, stress symptoms and QoL were assessed by the Chinese versions of the Insomnia Severity Index, the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7, the Impact of Event Scale-Revised, and the General Functional Assessment of Cancer Therapy, respectively. Multivariate logistic regression analysis was conducted to analyze the risk factors for mental distress and QoL. Results A total of 56 respondents coming from 15 various provinces in China participated in the survey. The prevalence of insomnia, depressive, anxiety, stress symptoms and reduced QoL were 37.5, 51.8, 37.5, 30.4, and 51.8%, respectively. Unfavorable disease status, shorter period of olaparib administration, adverse events of olaparib and delay in cancer care were correlated with mental health problems. Reduced QoL was also significantly associated with psychological distress. Conclusions This study emphasized that mental health problems and reduced QoL should gain more attention in women with OC who are receiving oral olaparib at home. Appropriate psychological healthcare strategies are necessary for OC patients during the COVID-19 pandemic.
A path to post-trauma resilience: a mediation model of the flexibility sequence

ABSTRACT Background: Psychological resilience has grown in popularity as a topic of study in psychotraumatology research; however, this concept remains poorly understood and there are several competing theories of resilience. Objective: This study sought to assess the support for one proposed theory of resilience: the flexibility sequence. Method: This study use secondary data analysis of panel survey data (N = 563). Participants were aged 18 years or over and based in the UK. A series of sequential mediation models was used to test the flexibility sequence theory as a proposed pathway of resilience on mental health outcomes (post-traumatic stress disorder, anxiety, and depression) among a trauma-exposed sample from the UK. Results: The ‘feedback’ component of the proposed flexibility sequence components was associated with reduced symptom severity with all outcomes, whereas ‘context sensitivity’ and ‘repertoire’ were significantly associated only with depression as an outcome. When indirect mediation pathways were modelled via the flexibility sequence, statistically significant effects were observed for all outcomes under investigation. Conclusions: These findings support the theorized flexibility sequence pathway of resilience, suggesting that the combination of these skills/processes performs more favourably as a framework of resilience than any in isolation. Further research into more elaborate associations and feedback loops associated with this pathway is warranted. HIGHLIGHTS The components of the flexibility sequence were not uniformly associated with reduced psychopathology. The path model of following the proposed flexibility sequence was associated with reduced psychopathology, and therefore is more than the sum of its parts in predicting resilience. The flexibility sequence is a promising framework for understanding and promoting resilience in a trauma-exposed sample.
The Role of Vitamin D in Basal Ganglia Diseases.

OBJECTIVE Vitamin D (VitD) has been shown to influence several cellular processes in the brain. The extent to which VitD plays a role in the pathomechanism of neuronal loss and dysfunction in basal ganglia diseases (BGDs) is still debated. There is yet to be a comprehensive study that provides an overview of all of the most relevant BGDs. METHODS PubMed, and Google Scholar were systematically searched for observational studies that investigated the association between serum VitD levels and BGDs up to March 2022. RESULTS We extracted 60 studies, but with a great variety of design and quality. VitD deficiency appears to be common in most BGDs, but only in Parkinson's disease (PD) has a causal association been fully examined. There is some evidence that low VitD serum levels influence symptom severity, most notably in restless legs syndrome (RLS), PD, and tic disorders. The effects of vitamin D supplementation were studied in three BGDs, with results mostly favorable for RLS, ambiguous for tics, and mostly unfavorable for PD. CONCLUSIONS There are still various elements of BGDs with insufficient, ambiguous, or altogether absent evidence, and further high-quality research is required. However, there appears to be sufficient scientific justification already to recommend that practitioners treating BGDs check serum VitD levels and supplement as appropriate.
Electromagnetic Scattering Feature (ESF) Module Embedded Network Based on ASC Model for Robust and Interpretable SAR ATR

Deep learning has been widely used in automatic target recognition (ATR) for synthetic aperture radar (SAR) recently. However, most of the studies are based on the network structure in optical images and lack full consideration of the inherent characteristics of SAR targets, which limits the improvement of recognition accuracy and makes poor generalization ability. In addition, due to the black-box characteristics, it is difficult to effectively interpret SAR ATR results. To conquer these problems, we propose an electromagnetic scattering feature (ESF) module embedded network based on attributed scattering center (ASC) model to incorporate the SAR targets’ characteristics into the deep learning framework. First, a novel convolutional neural network (CNN)-based algorithm for extracting ASC parameters is proposed, which makes the network focus on target features under the guidance of physical model. Then, the ESF module is designed based on a well-trained ASC parameters extractor to inject the learned target features into the classification network for more robust and interpretable results. Besides, two structures are proposed combined with the ESF module for single-view and multiview SAR target classification, which further illustrates the portability of the module. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show the validity of the proposed CNN-based ASC extractor and the ESF module embedded classification network. Compared with ordinary networks, our method can achieve higher classification accuracy under complex conditions, which reflects the better generalization performance of the algorithm. Furthermore, through visualization analysis of the classification results, we show the interpretability of the network combined with the electromagnetic scattering characteristics.
Identification of QTL Underlying Plant Height and First Branch Height of Cassava

This study sought to develop a genetic map of cassava based on 91 progenies derived from a cross between “Rayong 60” (R60) and “Piroon 1” (PR1) cultivars. A total of 4,515 simple sequence repeat (SSR) primer pairs were screened against the parental lines and six progenies resulting in 656 informative polymorphic markers, which were then used to genotype the F1 population. A total of 314 linked SSR loci were identified that were distributed across 40 linkage groups (LGs) at a logarithm of the odds (LOD) score of 4.0. The total distance of the linkage map was 2,026.68 cM with an average of 13.85 cM between markers and 7.85 loci per LG. The SSR loci on each LG were categorized into the 18 chromosomes of the cassava genome. Two major quantitative trait loci (QTL) and one minor QTL controlling first branch height (FBH) were detected with phenotypic variability explained (PVE) ranging from 10.2–18.9% and 8.2%, respectively. In addition, two major QTL for plant height (PH) with PVE ranging from 16.5–24.3% were detected in the F1 population. The QTL controlling PH and FBH detected in this study will be useful for future identification of QTL and genes underlying economically important traits to accelerate molecular breeding of cassava variety improvement via marker-assisted selection.
ADAPTIVE BRIDGE REGRESSION MODELING AND SELECTION OF THE TUNING PARAMETERS

We consider the problem of constructing an adaptive bridge regression modeling, which is a penalized procedure where different weights are imposed on different coefficients in a bridge penalty term. A crucial issue in the modeling process is the choice of the adjusted parameters included in the models. Here, we treat the selection of the adjusted parameters as model selection and evaluation problems. In order to select the parameters, the model selection criteria are derived from information-theoretic and Bayesian approaches. We conduct some numerical studies to investigate the effectiveness of our proposed modeling strategy.
Philosophical Devices: Proofs, Probabilities, Possibilities, and Sets

Preface Introduction PART I: SETS AND NUMBERS 1. Naive Sets and Russell's Paradox 2. Infinite Sets 3. Orders of Infinity PART II: ANALYTICITY, A PRIORICITY, AND NECESSITY 4. Kinds of Truths 5. Possible Worlds 6. Naming and Necessity PART III: THE NATURE AND USES OF PROBABILITY 7. Kinds of Probability 8. Constraints on Credence 9. Correlations and Causes PART IV: LOGICS AND THEORIES 10. Syntax and Semantics 11. Soundness and Completeness 12. Theories and Godel's Theorem
Smartphone-Tracked Digital Markers of Momentary Subjective Stress in College Students: Idiographic Machine Learning Analysis

Background Stress is an important predictor of mental health problems such as burnout and depression. Acute stress is considered adaptive, whereas chronic stress is viewed as detrimental to well-being. To aid in the early detection of chronic stress, machine learning models are increasingly trained to learn the quantitative relation from digital footprints to self-reported stress. Prior studies have investigated general principles in population-wide studies, but the extent to which the findings apply to individuals is understudied. Objective We aimed to explore to what extent machine learning models can leverage features of smartphone app use log data to recognize momentary subjective stress in individuals, which of these features are most important for predicting stress and represent potential digital markers of stress, the nature of the relations between these digital markers and stress, and the degree to which these relations differ across people. Methods Student participants (N=224) self-reported momentary subjective stress 5 times per day up to 60 days in total (44,381 observations); in parallel, dedicated smartphone software continuously logged their smartphone app use. We extracted features from the log data (eg, time spent on app categories such as messenger apps and proxies for sleep duration and onset) and trained machine learning models to predict momentary subjective stress from these features using 2 approaches: modeling general relations at the group level (nomothetic approach) and modeling relations for each person separately (idiographic approach). To identify potential digital markers of momentary subjective stress, we applied explainable artificial intelligence methodology (ie, Shapley additive explanations). We evaluated model accuracy on a person-to-person basis in out-of-sample observations. Results We identified prolonged use of messenger and social network site apps and proxies for sleep duration and onset as the most important features across modeling approaches (nomothetic vs idiographic). The relations of these digital markers with momentary subjective stress differed from person to person, as did model accuracy. Sleep proxies, messenger, and social network use were heterogeneously related to stress (ie, negative in some and positive or zero in others). Model predictions correlated positively and statistically significantly with self-reported stress in most individuals (median person-specific correlation=0.15-0.19 for nomothetic models and median person-specific correlation=0.00-0.09 for idiographic models). Conclusions Our findings indicate that smartphone log data can be used for identifying digital markers of stress and also show that the relation between specific digital markers and stress differs from person to person. These findings warrant follow-up studies in other populations (eg, professionals and clinical populations) and pave the way for similar research using physiological measures of stress.
Testing Parameters in GMM without Assuming that they are identified

High-strength fiber is prepared by coating a small grain, small diameter polycrystalline refractory oxide fiber with a specified amount of a glass-forming oxide or its precursor, and heating the coated fiber at a temperature sufficient to decompose the precursor to the oxide and/or to vitrify the oxide coating into an adherent, optically uniform, thin layer. The coating significantly increases the tensile strength of the fiber, while the fiber retains a substantial percentage of its original modulus. The fibers are especially suitable for reinforcement uses.
Detecting Short Period Variable Stars with Gaia

We analyzed the frequency domain of time series of simulated ZZ Ceti light‐curves to investigate the detectability and period recovery performance of short period variables (periods <2 hours) for the Gaia mission. In our analysis, first we used a non‐linear ZZ Ceti light‐curves simulator code to simulate the variability of ZZ Ceti stars (we assumed stationary power spectra over five years). Second we used the Gaia nominal scanning law and the expected photometric precision of Gaia to simulate ZZ Ceti time series with Gaia’s time sampling and photometric errors. Then we performed a Fourier analysis of these simulated time series. We found that a correct period can be recovered in ∼65% of the cases if we consider Gaia per CCD time series of a G∼18 magnitude multiperiodic ZZ Ceti star with 5%–10% light‐curve variation. In the pre‐whitened power spectrum a second correct period was also recovered in ∼26% of the cases.
Atomic Line Shapes in the Presence of an External Magnetic Field

Both the theoretical basis and computational approach for extending the capabilities of a spectral line broadening code are presented. Following standard line broadening theory, the effects of an external magnetic field are incorporated into the atomic Hamiltonian and plasma average. In the presence of an external magnetic field the atomic Hamiltonian angular properties are altered — atomic energy levels are perturbed and the spectral emission line is polarized. The magnetic field introduces a preferential axis that changes the plasma average. These extensions have been incorporated into a new spectral line broadening code that is applied to several problems of importance to the understanding of tokamak edge plasmas. Applications fall into two broad categories: 1) determination of local plasma properties from distinct line shape features; and 2) consideration of global plasma phenomenon, such as radiation transport. Observable features of the Zeeman effect make Hα a good magnetic field diagnostic. Hβ does.
An Efficient Signal Processing Algorithm for Detecting Abnormalities in EEG Signal Using CNN

Electroencephalography (EEG) is crucial for epilepsy detection; however, detecting abnormalities takes experience and knowledge. The electroencephalogram (EEG) is a technology that measures brain motion and represents the brain's function. EEG is an effective instrument for deciphering the brain's complicated activity. The information contained in the EEG signal pertains to the electric functioning of the brain. Neurologists have typically used direct visual inspection to detect epileptogenic abnormalities. This method is time-consuming, restricted by technical artifacts, produces varying findings depending on the reader's level of experience, and is ineffective at detecting irregularities. As a result, developing automated algorithms for detecting anomalies in EEGs associated with epilepsy is critical. The construction of a novel class of convolutional neural networks (CNNs) for detecting aberrant waveforms and sensors in epilepsy EEGs is described in this research. In this study, EEG signals are analyzed using a convolutional neural network (CNN). For the automatic detection of abnormal and normal EEG indications, a novel deep one-dimensional convolutional neural network (1D CNN) model is suggested in this paper. The regular, pre-ictal, and seizure categories are detected using this approach. The proposed model achieves an accuracy of 85.48% and a reduced categorization error rate of 14.5%.
Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks

Driven by growing mobile traffic, millimeter wave (mmWave) communications have recently been developed to enhance wireless network capacity. Due to insufficient coverage and the lack of support for mobility, mmWave is often deployed in the ultra-dense small cells of the 5G heterogeneous network. In this article, we first summarize the characteristics of the 5G heterogeneous network from the viewpoints of devices, spectra, and networks. We then propose a triple-band network structure which incorporates licensed bands, sub-6GHz unlicensed bands, and mmWave bands to support various types of mobile users. Based on the novel network structure, we further propose a self-optimizing traffic steering strategy which can intelligently steer traffic to specific networks and spectra according to the dynamic network and traffic environments. Several use cases are also discussed to facilitate the implementation of our proposals. Finally, we present numerical results to demonstrate that the proposed network structure and strategy can effectively enhance the system throughput and energy efficiency.
Dense Feature Learning and Compact Cost Aggregation for Deep Stereo Matching

Recently, Convolutional Neural Networks (CNN) based deep models have been successfully applied to the task of stereo matching. In this paper, we propose a novel deep stereo matching network based on the strategies of dense feature learning and compact cost aggregation, namely DFL-CCA-Net. It consists of three modules: Dense Feature Learning (DFL), Compact Cost Aggregation (CCA) and the disparity regression module. In DFL module, the CNN backbone with Dense Atrous Spatial Pyramid Pooling (DenseASPP) is employed to extract multi-scale deep feature maps of the given left and right images respectively. Then an initial 4D cost volume is obtained by concatenating left feature maps with their corresponding right feature maps across each disparity level. In the following CCA module, each initial 3D cost volume component (i.e., the component across the left or right image feature channel dimension) is aggregated into a more compact one by using the atrous convolution operation with different expansion rates. These updated 3D cost volume components are then fed into the disparity regression module, which consisting of a 3D CNN network with a stacked hourglass structure, to estimate the final disparity map. Comprehensive experimental results demonstrated on the Scene Flow, KITTI 2012 and KITTI 2015 datasets show that the 3D cost volume components obtained by the proposed DFL and CCA modules generally containing more multi-scale semantic information and thus can largely improve the final disparity regression accuracies. Compared with other deep stereo matching methods, DFL-CCA-Net achieves very competitive prediction accuracies especially in the reflective regions and regions containing detail information.
Communication, culture, competency, and stakeholder that contribute to requirement elicitation effectiveness

In the context of software development, requirement engineering is one of the crucial phases that leads to software project success or failure. According to several disruptive changes in the software engineering landscape as well as the world’s challenge of virus pandemic, the provision of practical and innovative software applications is required. Therefore, issues resolution in requirement elicitation is potentially one of the key success factors resulting in enhanced quality of system requirement. The authors have striven to create new ways of requirement elicitation according to factor effects of communication, culture, competency, and stakeholder, by incorporating tools, processes, methods, and techniques to solve the problems comprehensively, and then proposed an adaptive and applicable conceptual framework. To illustrate these effects, the authors performed a literature review from the past 8 years, and then data analysis from interviews of 27 practitioners, observations and focus groups of software development in real-life projects.
A Boundary Operator for Simplices

We generalize the very well known boundary operator of the ordinary singular homology theory, defined in many books about algebraic topology. We describe a variant of this ordinary simplicial boundary operator where the usual boundary (n-1)-simplices of each n-simplex are replaced by combinations of internal (n-1)- simplices parallel to the faces. This construction may lead to an infinite class of extraordinary non-isomorphic homology theories. We show further some interesting constructions on the standard simplex.
Upper bound for the Laplacian eigenvalues of a graph

In this note we give a new upper bound for the Laplacian eigenvalues of an unweighted graph. Let $G$ be a simple graph on $n$ vertices. Let $d_{m}(G)$ and $\lambda_{m+1}(G)$ be the $m$-th smallest degree of $G$ and the $m+1$-th smallest Laplacian eigenvalue of $G$ respectively. Then $ \lambda_{m+1}(G)\leq d_{m}(G)+m-1 $ for $\bar{G} \neq K_{m}+(n-m)K_1 $. We also introduce upper and lower bound for the Laplacian eigenvalues of weighted graphs, and compare it with the special case of unweighted graphs.