Miracle-dz/UniFAR-FTUs huggingface dataset:
A scalable software package for time series reconstruction of remote sensing datasets on the Google Earth Engine platform

ABSTRACT Spatiotemporal residual noise in terrestrial earth observation products, often caused by unfavorable atmospheric conditions, impedes their broad applications. Most users prefer to use gap-filled remote sensing products with time series reconstruction (TSR) algorithms. Applying currently available implementations of TSR to large-volume datasets is time-consuming and challenging for non-professional users with limited computation or storage resources. This study introduces a new open-source software package entitled ‘HANTS-GEE’ that implements a well-known and robust TSR algorithm, i.e. Harmonic ANalysis of Time Series (HANTS), on the Google Earth Engine (GEE) platform for scalable reconstruction of terrestrial earth observation data. Reconstruction tasks can be conducted on user-defined spatiotemporal extents when raw datasets are available on GEE. According to site-based and regional-based case evaluation, the new tool can effectively eliminate cloud contamination in the time series of earth observation data. Compared with traditional PC-based HANTS implementation, the HANTS-GEE provides quite consistent reconstruction results for most terrestrial vegetated sites. The HANTS-GEE can provide scalable reconstruction services with accelerated processing speed and reduced internet data transmission volume, promoting algorithm usage by much broader user communities. To our knowledge, the software package is the first tool to support full-stack TSR processing for popular open-access satellite sensors on cloud platforms.
Utility of low-profile visualized intraluminal support (LVIS™) stent for treatment of acutely ruptured bifurcation aneurysms: A single-center study

Objective Stent-assisted coiling has been increasingly used in the treatment of intracranial aneurysms. However, its application in ruptured bifurcation aneurysms remains controversial and challenging. This study aimed to present the safety and feasibility of low-profile visualized intraluminal support (LVIS™, LVIS, and LVIS Jr.) stent for acutely ruptured bifurcation aneurysms. Methods A total of 41 patients with acutely ruptured intracranial aneurysms arising at the bifurcation were treated with LVIS™ stent-assisted coiling in our hospital between January 2017 and December 2021. The clinical data and angiographic results of the patients were analyzed. Results Among these patients, all stents were successfully implanted. According to the immediate angiographic results, 29 aneurysms (70.7%) were completely occluded. Intraoperative thrombosis and hemorrhage occurred in two and one cases, respectively. No post-operative thrombosis or rebleeding events were observed. The clinical follow-up of all patients revealed that 38 (92.7%) cases had favorable outcomes (modified Rankin scale: 0–2). The angiographic results available for the 36 patients during the follow-up period revealed complete occlusion was achieved in 30 patients (83.3%) and residual neck in six patients. Conclusion The LVIS™ stent-assistant coiling is a safe and feasible option for acutely ruptured bifurcation aneurysms. Further studies with a prospective design, a larger sample size, and long-term follow-up are needed to validate these findings.
Social media use for coping with stress and psychological adjustment: A transactional model of stress and coping perspective

Introduction The psychological well-being of individuals has become an essential issue during the global pandemic. As a pervasive activity for individuals to pull through COVID-19, social media use may play a role in psychological well-being. Drawing on the transactional model of stress and coping, the current study investigated the relationships between COVID-19-related stressors and the use of social media to facilitate specific coping strategies. We further investigated how social media coping strategies impact users’ psychological adjustment. Methods We collected the data from 641 quarantined residents through a two-wave survey that was conducted in two cities in China during government-mandated lockdowns. Results The results showed that perceived COVID-19 stress was related to the intensity of social media use for problem-focused coping, socioemotional coping, and mental disengagement. In addition, individuals’ psychological adjustment was positively associated with social media use for socioemotional coping and mental disengagement while negatively related to problem-focused coping. Age was also found to be a moderator of the relationship between socioemotional coping and psychological adjustment. Discussion To relieve pandemic-related stress, individuals can actively utilize social media to implement various coping strategies. However, coping activities with social media may not always induce psychological benefits. By revealing the different levels of psychological adjustment among social media users with specific coping strategies, the current research enriched the literature on the effects of social media use on mental health. Findings from this study suggest the need for the prudent use of social media to cope with public health crises.
Domain Adaptation with Contrastive Simultaneous Multi-Loss Training for Hand Gesture Recognition

Hand gesture recognition from images is a critical task with various real-world applications, particularly in the field of human–robot interaction. Industrial environments, where non-verbal communication is preferred, are significant areas of application for gesture recognition. However, these environments are often unstructured and noisy, with complex and dynamic backgrounds, making accurate hand segmentation a challenging task. Currently, most solutions employ heavy preprocessing to segment the hand, followed by the application of deep learning models to classify the gestures. To address this challenge and develop a more robust and generalizable classification model, we propose a new form of domain adaptation using multi-loss training and contrastive learning. Our approach is particularly relevant in industrial collaborative scenarios, where hand segmentation is difficult and context-dependent. In this paper, we present an innovative solution that further challenges the existing approach by testing the model on an entirely unrelated dataset with different users. We use a dataset for training and validation and demonstrate that contrastive learning techniques in simultaneous multi-loss functions provide superior performance in hand gesture recognition compared to conventional approaches in similar conditions.
On the security of some variants of rsa

The RSA cryptosystem, named after its inventors, Rivest, Shamir and Adleman, is the most widely known and widely used public-key cryptosystem in the world today. Compared to other public-key cryptosystems, such as elliptic curve cryptography, RSA requires longer keylengths and is computationally more expensive. In order to address these shortcomings, many variants of RSA have been proposed over the years. While the security of RSA has been well studied since it was proposed in 1977, many of these variants have not. In this thesis, we investigate the security of five of these variants of RSA. In particular, we provide detailed analyses of the best known algebraic attacks (including some new attacks) on instances of RSA with certain special private exponents, multiple instances of RSA sharing a common small private exponent, Multi-prime RSA, Common Prime RSA and Dual RSA.
Spectroscopy of the hydrogen 1S-3S transition with chirped laser pulses

We identify a systematic present in two-photon direct frequency comb spectroscopy (DFCS) which is a result of chirped laser pulses and is amanifestation of the first-order Doppler effect. We carefully analyze this systematic and propose methods for its mitigation within the context of our measurement of the hydrogen 1S-3S transition. We also report on our determination of the absolute frequency of this transition, which is comparable to a previous measurement using continuous-wave spectroscopy [O. Arnoult et al., Eur. Phys. J. D 60, 243 (2010)], but was obtained with a different experimental method.
Role of Dietary Defatted Rice Bran in the Modulation of Gut Microbiota in AOM/DSS-Induced Colitis-Associated Colorectal Cancer Rat Model

Defatted rice bran (DRB) is a by-product of rice bran derived after the oil extraction. DRB contains several bioactive compounds, including dietary fiber and phytochemicals. The supplementation with DRB manifests chemopreventive effects in terms of anti-chronic inflammation, anti-cell proliferation, and anti-tumorigenesis in the azoxymethane (AOM) and dextran sodium sulfate (DSS)-induced colitis-associated colorectal cancer (CRC) model in rats. However, little is known about its effect on gut microbiota. Herein, we investigated the effect of DRB on gut microbiota and short chain fatty acid (SCFA) production, colonic goblet cell loss, and mucus layer thickness in the AOM/DSS-induced colitis-associated CRC rat model. The results suggested that DRB enhanced the production of beneficial bacteria (Alloprevotella, Prevotellaceae UCG-001, Ruminococcus, Roseburia, Butyricicoccus) and lessened the production of harmful bacteria (Turicibacter, Clostridium sensu stricto 1, Escherichia–Shigella, Citrobacter) present in colonic feces, mucosa, and tumors. In addition, DRB also assisted the cecal SCFAs (acetate, propionate, butyrate) production. Furthermore, DRB restored goblet cell loss and improved the thickness of the mucus layer in colonic tissue. These findings suggested that DRB could be used as a prebiotic supplement to modulate gut microbiota dysbiosis, which decreases the risks of CRC, therefore encouraging further research on the utilization of DRB in various nutritional health products to promote the health-beneficial bacteria in the colon.
Mandated Data Sharing in Hybrid Marketplaces

Considering a monopolistic hybrid platform, we investigate the effect of a mandated data sharing policy on market outcomes across different data functionalities (price discrimination and cost reduction) and different market structures (perfect and imperfect competition). We find that man-dated data sharing has no effects on welfare if data can be used to price discriminate consumers who buy homogeneous goods, while mandated sharing of cost-reducing data improves welfare by lowering the average price in the markets. When goods are horizontally differentiated, data sharing with price discrimination (cost reduction) may instead negatively (positively) affect consumers. We argue that in markets where competition is softer, mandatory data sharing may damage the very agents it is intended to protect, namely consumers and (efficient) sellers.
Adolescents’ experiences of acceptance and commitment therapy for depression: An interpretative phenomenological analysis of good-outcome cases

Introduction Acceptance and commitment therapy (ACT) has been shown to help treat depression. However, little is known about the patient’s experiences with ACT. This study aimed to learn how it was used in adolescents with major depressive disorder who have achieved good treatment outcomes. Methods Five adolescents with major depressive disorder with good treatment outcomes of ACT were enrolled in the semi-structured qualitative interview and analyzed using systematic textual condensation and interpretative phenomenological analysis. Results Four primary themes emerged from the investigation. “Therapist relationships and characteristics” describes the therapist’s receptiveness and respect for adolescents with depression and having a trustworthy and sincere therapist. “Spaces to explore and experience” describes the ongoing process and content of acceptance of negative emotions and mindfulness practices in the healing process. “Do important things” refers to values and committed action. The “time settings” include the frequency and duration of treatment. Conclusion Adolescents make positive changes with a receptive and respectful therapist by exploring themselves in a genuine and trusting therapeutic relationship. Improvement seems to come from being open to all thoughts and feelings and developing the ability to live in the present moment. Teenagers attach great importance to value-oriented behaviour. Therefore, treatment should target the critical areas of depressed adolescents to guide them towards recovery effectively.
ICT Leapfrogging Amidst Labour Force-Economic Growth Nexus in EAP and ECA Regions

Towards achieving the 2030 United Nations Sustainable Development Goals, this study revisits the information and communication technology (ICT) leapfrogging hypothesis of Steinmueller (2001), and Fong (2009) to expand the literature by testing its relevance in the labour force-growth dynamics in Asia. To achieve this, the study addresses four objectives: (i) test the ICT leapfrogging hypothesis; (ii) investigate the growth-enhancing impact of labour; (iii) examine whether ICT enhances or distorts the productivity of labour on economic growth; and (iv) if these effects differ by economic development. The study uses an unbalanced panel data on 81 countries located in East Asia and Pacific (EAP) and Europe and Central Asia (ECA) from 2010 to 2019. Two estimation techniques, namely panel spatial correlation consistent fixed effects (PSCC-FE) and random effects instrumental variables two-stage least squares (RE-IV2SLS), are deployed. To appraise if the impact differs by economic development, the study engages income group analysis. Among other findings: the leapfrogging hypothesis holds; labour is a significant predictor of economic growth; mobile phones usage is a more potent ICT indicator with more leapfrogging potentials relative to fixed telephones subscription; the net effect of labour on growth is mostly positive in the mobile phones’ models.
A Least Total Distance Facility Configuration Problem Involving Lattice Points

The problem considered in this paper is to find a layout of a given number of identical facilities so that the total or average rectilinear distance between facilities is minimized. The potential locations for the facilities are lattice points in the plane, which are points whose coordinates are integers. A set of geometric properties and necessary conditions for an optimal layout configuration is derived. Based on a symmetry assumption, an implicit enumeration procedure is developed that will yield all optimal configurations together with the least total distance between facilities.
Multilevel Classification of Users’ Needs in Chinese Online Medical and Health Communities: Model Development and Evaluation Based on Graph Convolutional Network

Background Online medical and health communities provide a platform for internet users to share experiences and ask questions about medical and health issues. However, there are problems in these communities, such as the low accuracy of the classification of users’ questions and the uneven health literacy of users, which affect the accuracy of user retrieval and the professionalism of the medical personnel answering the question. In this context, it is essential to study more effective classification methods of users’ information needs. Objective Most online medical and health communities tend to provide only disease-type labels, which do not give a comprehensive summary of users’ needs. The study aims to construct a multilevel classification framework based on the graph convolutional network (GCN) model for users’ needs in online medical and health communities so that users can perform more targeted information retrieval. Methods Using the Chinese online medical and health community “Qiuyi” as an example, we crawled questions posted by users in the “Cardiovascular Disease” section as the data source. First, the disease types involved in the problem data were segmented by manual coding to generate the first-level label. Second, the needs were identified by K-means clustering to generate the users’ information needs label as the second-level label. Finally, by constructing a GCN model, users’ questions were automatically classified, thus realizing the multilevel classification of users’ needs. Results Based on the empirical research of questions posted by users in the “Cardiovascular Disease” section of Qiuyi, the hierarchical classification of users’ questions (data) was realized. The classification models designed in the study achieved accuracy, precision, recall, and F1-score of 0.6265, 0.6328, 0.5788, and 0.5912, respectively. Compared with the traditional machine learning method naïve Bayes and the deep learning method hierarchical text classification convolutional neural network, our classification model showed better performance. At the same time, we also performed a single-level classification experiment on users’ needs, which in comparison with the multilevel classification model exhibited a great improvement. Conclusions A multilevel classification framework has been designed based on the GCN model. The results demonstrated that the method is effective in classifying users’ information needs in online medical and health communities. At the same time, users with different diseases have different directions for information needs, which plays an important role in providing diversified and targeted services to the online medical and health community. Our method is also applicable to other similar disease classifications.
A calculus for intuitionistic fuzzy values

We introduce ⊕ calculus and ⊗ calculus for intuitionistic fuzzy values and prove some basic theorems by using multiplicative calculus which has useful tools to represent the concepts of introduced calculi. Besides, we construct some isomorphic mappings to interpret the relationships between ⊕ calculus and ⊗ calculus. This paper reveals also new calculi for fuzzy sets in particular.
Compression for Multi-Arm Bandits

The multi-armed bandit (MAB) problem is one of the most well-known active learning frameworks. The aim is to select the best among a set of actions by sequentially observing rewards that come from an unknown distribution. Recently, a number of distributed bandit applications have become popular over wireless networks, where agents geographically separated from a learner collect and communicate the observed rewards. In this paper we propose a compression scheme, that compresses the rewards collected by the distributed agents. By providing nearly matching upper and lower bounds, we tightly characterize the <italic>number of bits</italic> needed per reward for the learner to accurately learn without suffering additional regret. In particular, we establish a generic reward quantization algorithm, <inline-formula> <tex-math notation="LaTeX">$QuBan$ </tex-math></inline-formula>, that can be applied on top of any (no-regret) MAB algorithm to form a new communication-efficient counterpart. <inline-formula> <tex-math notation="LaTeX">$QuBan$ </tex-math></inline-formula> requires only a few (converging to as low as 3 bits as the number of iterations increases) bits to be sent per reward while preserving the <italic>same</italic> regret bound as uncompressed rewards. Our lower bound is established via constructing hard instances from a subGaussian distribution. Our theory is further corroborated by numerical experiments.
Correlation of serum kisspeptin levels, ovarian kisspeptin expression, and ovarian BMP15 expression in rat model of polycystic ovary syndrome

Background: Kisspeptin is a neuropeptide that has an important role in the female reproductive cycle which is indicated by its role in regulating the hypothalamic-pituitary-gonadal axis. Aims: To analyze the correlation between serum kisspeptin levels, ovarian kisspeptin expression, and ovarian Bone Morphogenic Protein-15 (BMP15) expression in polycystic ovary syndrome (PCOS) model rats. Methods: The research was accurate experimental research with a post-test design-only control group and was carried out from August to October 2022 at the Faculty of Veterinary Medicine Universitas Airlangga. 32 Rattus novergicus rats were divided into a control group and a PCOS model group. Blood serum and ovaries were obtained from all groups. In addition, blood serum was examined for kisspeptin levels by ELISA technique, and kisspeptin expression and BMP15 Ovaries were examined immunohistochemically. Results: Serum kisspeptin levels and ovarian kisspeptin expression of the PCOS model group were not significantly higher than those of the control group (p > 0.05, p > 0.05). The ovarian BMP15 expression of the PCOS model group was not significantly lower (p > 0.05) than that of the control group. Ovarian kisspeptin expression and ovarian BMP15 expression did not significantly correlate with serum kisspeptin levels (p > 0.05). In contrast, there was a significant correlation (p < 0.05) between ovarian kisspeptin expression and ovarian BMP15 expression. Conclusion: Serum kisspeptin levels and ovarian kisspeptin expression of the PCOS model group were not higher than those of the control group, and the ovarian BMP15 expression of the PCOS model group was not lower than that of the control group. There was no correlation between serum kisspeptin levels with ovarian kisspeptin expression and ovarian BMP15 expression. However, a significant correlation was found between ovarian kisspeptin expression and ovarian BMP15 expression.
The Habitability and Stability of Earth-Like Planets in Binary Star Systems

In 2011, NASA’s Kepler spacecraft observed the first planet to be detected in a known binary star system, Kepler 16-b. Its discovery has sparked new discussion on the potential of binary systems supporting habitable Earth-like planets. In this study, by using relatively simple computation models, we are able to shed new light and place new limitations on this discussion. Habitable Zone geometry in binary systems is discussed as a part of this study, in which we are able to classify binary habitable zones into two major classes of merged and unmerged, with important special cases of each class presented. We were able to learn a good deal about the behavior of planets in S-type binary orbits, and the possibility of life on those planets. The effect of the orbit of the planet on the climate as well as how the planet experiences day and night are discussed, and we also present a lower limit for the binary separation at which a binary system would fail to have a habitable S-type orbit. In addition, we explore the possibility that a planet could remain habitable in a transfer orbit between the system’s two stars or in orbit around one of the system’s Lagrange Points. In this endeavor, we derive an analytical expression for the surface temperature of an Earth-like planet at a binary system’s stable L4 and L5 Lagrange Points, which closely match simulated results. We find definite possibilities for habitable L4 and L5 Lagrange Points in binary systems over a wide range of stellar masses and mass ratios. Finally, we propose many extensions and improvements to this study that would be worthwhile to pursue.