Miracle-dz/UniFAR-FTUs huggingface dataset:
One year of RST based satellite thermal monitoring over two Italian seismic areas

In this study, Earth’s emitted Thermal InfraRed (TIR) radiation measured from geostationary satellite sensors has been analyzed by using an original data analysis approach in order to evaluate possible space-time correlation with earthquakes (M≥4.0) occurrence. A clear definition of SSTA (Significant Sequence of Thermal Anomaly) concept is given and correlation rules are established in order to evaluate the potential of SSTAs among the parameters to be included in a pre-operational system for timeDependent Assessment of Seismic Hazard (t-DASH). On the considered time period (July 2012 June 2013) and testing areas (Italian southern Apennines and Po Plain) a false positive rate lesser than 33% has been obtained. Notwithstanding a missing rate up to 67% (mostly because of the presence of clouds preventing the continuity of observations), as confirmed by other independent studies performed on longer period of time and different geographic areas, seems to strongly support the inclusion of SSTAs [identified by the RST (Robust Satellite Technique) methodology] among the parameters whose continuous monitoring and integration with all the other relevant information available, could strongly improve our present capabilities to dynamically assess seismic hazard in a pre-operational context.
Geological evolution of a complex basaltic stratovolcano: Mount Etna, Italy

An updated geological evolution model is presented for the composite basaltic stratovolcano of Mount Etna. It was developed on the basis of the stratigraphic setting proposed in the new geological map that was constrained by 40Ar/39Ar age determinations. Unconformity bounded stratigraphy allows highlighting four main evolutionary phases of eruptive activity in the Etna region. The Basal Tholeiitic Supersynthem corresponds to a period, from about 500 to 330 ka, of scattered fissure-type eruptions occurring initially in the foredeep basin and then in a subaerial environment. From about 220 ka, an increase in the eruptive activity built a lava-shield during the Timpe Supersynthem. The central-type activity occurred at least 110 ka ago through the Valle del Bove Supersynthem. The earliest volcanic centres recognized are Tarderia, Rocche and Trifoglietto and later Monte Cerasa, Giannicola, Salifizio and Cuvigghiuni. During the Stratovolcano Supersynthem, from about 57 ka ago, the intense eruptive activity of Ellittico volcano formed a roughly 3600 m-high stratocone that expanded laterally, filling the Alcantara and Simeto paleovalleys.Finally, effusive activity of the last 15 ka built the Mongibello volcano. Its eruptive activity is mainly concentrated in three weakness zones in which the recurrent magma intrusion generates flank eruptions down to low altitude. The four main evolutionary phases may furnish constraints to future models on the origin of Etna volcano and help unravel the geodynamic puzzle of eastern Sicily.
Assessing the Effectiveness of Digital Upskilling: Evaluation Framework for GreatNusa Online Learning Platform using UTAUT Extension Model

In the context of education, COVID-19 pandemic has posed obstacles to the traditional face-to-face delivery of education, and reliance on Online Learning Platform (OLP) has become necessary to ensure continuity. The significance of user acceptance from the perspectives of learning, lectures, and course quality on GreatNusa platform should be investigated. Therefore, this research aimed to examine user acceptance or adoption of digital upskilling through GreatNusa OLP. UTAUT framework and three extension variables were adopted to support the main framework. A questionnaire using a 5-point Likert scale was employed to survey users of GreatNusa OLP. The sample size is 542 respondents. The data analysis software is SmartPLS4. The result showed that social influence and performance expectations positively and significantly influenced users' intention to use platform. Additionally, it held direct implications for content management, particularly within the field of OLP. By enhancing the adoption approach, these platform can shape consumers' intentions and drive the actual usage of products. This research provided valuable insights into identifying critical variables that influenced consumer behavior and informed effective content management strategies.
Exploiting reliable features of the ocean channel response

Modelling sound propagation in shallow water is notoriously difficult. The main difficulties are, of course, ocean variability characteristic of coastal waters and the typically downward refracting profiles that make the acoustic field extremely sensitive to the bottom characteristics. Meanwhile accurate prediction of transmission loss curves at a single frequency requires that the relative phase of each surface and bottom echo is predicted with precision. It is a daunting task even for systems that will operate in carefully surveyed areas.
The influence of a system with permanent traffic lanes on physical properties of soil, soil tillage quality and surface water runoff

The system with permanent driving tracks at the module of machines working width 6 metres, practised in a 10-ha field, allowed to consistently separate the area designed for restricted traffic lanes of farm machines from the production area of the field. The aim of the study is to assess the selected indicators of the condition of topsoil, which is characterized by soil porosity, indicators of soil workability, soil ability to absorb water from rainfall and soil loss by wash after four years of controlled traffic system application in a field trial. Indicators of soil condition were evaluated in four variants with different wheel impacts of tractors and other machines on the soil. A field trial was established in the spring 2010; the measured values in the study are from 2013 and 2014. The results show an advantage, which represents concentration of passages into permanent tracks aimed at protection of most part of a plot from soil compaction. Hardness of clods after tillage in autumn 2013 was five times higher in places with random traffic (356.7 kPa) than outside traffic lanes in the system of controlled traffic (70 kPa). An important result is that the system with permanent traffic lanes made it possible to increase the soil capacity of taking up water under intensive rainfall - in comparison to a part of the land with random passes. The results of measurements with a rainfall simulator in April 2014 showed that cumulative surface runoff after sixty minutes was 7.6 l m -2 on the land with random passes while 3.9 l m -2 outside the traffic lanes (32% of the area of the field). The soil loss by wash during water surface runoff was also lower with controlled traffic compared to the variant with random passes. Therefore it is to assume that suitable application of the controlled traffic farming system may be a contribution to soil protection from water erosion.
PDFalse: Evasive Malicious PDF Machine Learning Classifier

In recent years, detecting malicious PDF has become increasingly challenging. It is crucial to continuously improve mitigation and identification measures against malicious scripts embedded in PDF, particularly evasive malware. Evasive malware is designed to resemble harmless applications, making it difficult to detect. Despite this, only two publications in the past five years have addressed the issue of classifying evasive malicious PDF. This research seeks to fill this gap by determining the best classifier model for evasive malicious PDF. The approach involves benchmarking various machine learning models, including shallow and deep learning algorithms like Gradient Boosting, XGBoost, MLP, and Neural Networks. Each model’s performance is assessed based on the F1-score metric. Through a comprehensive process of data preprocessing, and model training with the CIC-Evasive- PDFMa12022 dataset, the Gradient Boosting algorithm emerges as the best-performing model with an impressive F1-score of 99.66%. The top-performing model is subsequently implemented into a.NET-based desktop application, named PDFalse, designed to classify PDF as safe or malicious in real-time, efficiently, and accurately. The effectiveness of PDFalse is further validated through black-box functional testing, reinforcing its practical utility in real-world scenarios.
Factors Affecting Consumer Purchase Intention to Using E-Commerce in Indonesia

In restoring the economy in Indonesia since the Covid-19 pandemic, sales in e-commerce have begun to increase. Therefore, this study analyzes the variables that are accepted in influencing or increasing consumer purchase intentions to shop online through e-commerce in Indonesia. This study adopted a quantitative research method. Questionnaires were distributed using the Google Forms through communication platforms, like Line, Instagram, Discord, and WhatsApp. The Google forms were used to collect 218 responses, which were then analyzed using SmartPLS. The results show that Service Quality, System Quality, Information Quality, Subjective Norms, Perceived Risk, and Perceived Security variables positively impact e-commerce purchase intentions. Unexpectedly, two variables are rejected, namely Perceived Ease of Use and Perceived Usefulness which have no impact on e-commerce purchase intentions. This research concludes that the utilizing e-commerce since the pandemic has increased transactions and can restore the economy in Indonesia because e-commerce has improved several qualities in specific fields, which supports user interest in using e-commerce. The study's finding provides insight into e-commerce market players like Shopee, JD.ID, Lazada and others to strategies their service and business plan to sustain their business.
The Adoption of Information Thread at Social Media during Covid using Modified Information Adoption Model

During the COVID-19 outbreak, internet usage increased in Indonesia, causing a huge surge in social media users. It should be emphasized that there is a great deal of material shared by anyone on social media that is not verified, which could lead to catastrophe. Several previous studies have been done to evaluate the Information Adoption Model, and additional research also has adapted and used the extended version of the model to investigate how customers receive online reviews and social Q&A. This study, on the other hand, takes a different approach by incorporating Pearson and Bailey’s three aspects of information usability. As a result, the modified information adoption model includes informativeness, valuableness, and helpfulness variables. As the study’s context is established, this research is carried out to comprehend how information adoption occurs in terms of information threads. The approach used to address the research question is quantitative, and the data is calculated using Structural Equation Modelling Partial Least Squares. There were 713 samples collected over time, with 69.3 percent being female and 30.7 percent being male. Following the analysis, the research found that nine out of eleven relationships are important, while the other two are not. This study could be valuable for social media marketing, while also helping to reduce the propagation and consumption of disinformation from threads.
A Deep Learning Approach for Automated Prediction of Cardiac Arrest from Vital Sign Data of Intensive Care Unit Patients

Cardiac arrest, abbreviated as CA, is a leading cause of death worldwide, emphasizing the significance of early detection for prompt treatment and improved patient outcomes. Early detection is critical as it allows for timely intervention that saves lives. However, detecting CA can be challenging due to its non-specific symptoms, and time, which is the essence. By doing so, we can implement preventive measures before such incidents occur. In this study, we propose a prediction CA in a patient method based on employing a deep learning algorithm is convolutional neural networks (CNN) method to learn the underlying patterns in the data and to generate an accurate prediction of CA in vital sign data. To evaluate the performance of our prediction methods, we utilized publicly accessible database known as the Medical Information Mart for Intensive Care III (MIMIC III) v1.4. Our proposed model has been developed to analyze 51,577 ICU stays’ worth of data, focusing on eight essential vital sign features: body temperature, heart rate, respiration rate, systolic blood pressure, diastolic blood pressure, mean blood pressure, oxygen saturation, and glucose. Through this evaluation, we demonstrate the effectiveness of our prediction techniques in enhancing the accuracy of predictive models and compared it with another state-of-the-art prediction method is Bidirectional Long Short-Term Memory (Bi-LSTM). The test results demonstrate that the accuracy of the proposed deep learning model based on CNN is 98%, whereas Bi-LSTM is 82%. Our results demonstrate that the CNN method outperforms other methods in terms of accuracy for predicting CA.
How Fashion Live Streaming Features Affect Purchase Intention by Mediating Perceived Value and Perceived Trust

During the COVID-19 consumer's habits has changed, and one of them was the concept of live shopping, where we watched the live stream while being able to shop simultaneously. The primary purpose of this study is to determine whether live streaming functionality consisting of visibility, authenticity, and interactivity can affect consumers' purchase intent, especially in fashion products, by mediating consumer's utility value, hedonic value, and trust in Indonesia. The platform used as a reference for live shopping is TT, with the TT Live feature that provides TT Shop during live streaming. This study obtained 450 respondents who analyzed the data using SmartPLS (SEM-PLS). The study's results indicated that the live-streaming feature on fashion products had positive effect on consumer's utility value, but interactivity and authenticity did not significantly impact perceived trust. Thus, perceived trust does not mediate purchase intent. Finally, this research was conducted to provide benefits to live- streaming hosts to determine what factors can increase sales levels during live-streaming.
Mountain glaciers of southeast Siberia: current state and changes since the Little Ice Age

Abstract Contemporary glaciers of southeast Siberia are located on three high-mountain ridges (east Sayan, Baikalsky and Kodar). In this study, we present an updated glacier inventory based on high- to middle-resolution satellite imagery and field investigations. The inventory includes 51 glaciers with a total area of - 15 km2. Areas of individual glaciers vary from 0.06 to 1.33 km2, lengths from 130 to 2010 m and elevations from 1796 to 3490 m. The recent ice maximum extents (Little Ice Age) have been delineated from terminal moraines. On average, debris-free surface area shrunk by 59% between 1850 and 2006/11 (0.37% a–1), by 44% between 1850 and 2001/02 (0.29% a–1) and by 27% between 2001/02 and 2006/11 (3.39% a–1). The Kodar glaciers have experienced the largest area shrinkage, while the area loss on Baikalsky ridge was more moderate. Glacier changes are mainly related to regional summer temperature increase (by 1.7-2.6C from 1970 to 2010). There are some differences in glacier response due to different spatial patterns of snow accumulation, local topography (e.g. glacier elevation, slope) and geological activity. The studied glaciers (especially of Kodar ridge) are the most sensitive in Siberia to climate change since the late 20th century.
Platelet Secretion and Hemostasis Require Syntaxin-binding Platelet Secretion and Hemostasis Require Syntaxin-binding Protein STXBP5 Protein STXBP5

STXBP5; also known as tomosyn-1) was identified by mass spectrometry, and its expression in isolated platelets was confirmed by RT-PCR analysis. Coimmunoprecipitation studies showed that STXBP5 interacts with core secretion machinery complexes, such as syntaxin-11/SNAP23 heterodimers, and fractionation studies suggested that STXBP5 also interacts with the platelet cytoskeleton. Platelets from Stxbp5 KO mice had normal expression of other key secretory components; however, stimulation-dependent secretion from each of the 3 granule types was markedly defective. Secretion defects in STXBP5-deficient platelets were confirmed via lumi-aggregometry and FACS analysis for P-selectin and LAMP-1 exposure. Interestingly, STXBP5-deficient platelets had altered granule cargo levels, despite having normal morphology and granule numbers. Consistent with secretion and cargo deficiencies, Stxbp5 KO mice showed dramatic bleeding in the tail transection model and defective hemostasis in the FeCl 3 -induced carotid injury model. Transplantation experiments indicated that these defects were due to loss of STXBP5 in BM-derived cells. Our data demonstrate that STXBP5 is required for normal arterial hemostasis, due to its contributions to platelet granule cargo packaging and secretion.
Indosinian Orogenesis of the Gangdise Terrane: Evidences from Zircon U-Pb Dating and Petrogenesis of Granitoids

This paper reports LA-ICP-MS zircon U-Pb ages, whole-rock major and trace element and zircon Hf isotopic compositions from two-mica granite and granodiorite plutons occurring in the middle part of the Gangdise terrane, Tibet. Magma zircons from the two-mica granite yielded a weighted 206Pb/238U mean age of (205±1)Ma (MSWD=0.47), which is interpreted as its magma crystallization age (Late Indosinian). The two-mica granite is strongly peraluminous, with Al index (A/CNK)=1.16-1.20 and K2O/Na2O=1.67-1.95. The two-mica granite is characterized by enrichments of Rb, Th and U etc. Rare earth element (REE) data display Eu/Eu=0.29-0.41 and (La/Yb)N=22.62-35.08. eHf(t)(205 Ma) values from the dated zircons range from -12.4 to -1.8. It is suggested that the magma for the two-mica granite was dominately derived from patial melting of argillaceous rocks in crust, induced by dehydration of mica minerals. The petrogenesis of the two-mica granite is similar to that of the Himalayan Tertiary leucogranites. Magma zircons from the granodiorite yielded a weighted 206Pb/238U age of (202±1)Ma, representing its magma crystallization age. The granodiorite is metaluminous, with Al index (A/CNK)= 0.96-0.98, K2O/Na2O=1.42-1.77. REE data show Eu/Eu=0.54-0.65 and (La/Yb)N=6.76-13.35. Dated zircon Hf isotopic compositions exhibit eHf(202 Ma) values ranging from -8.2 to -5.5. The geochemical signatures and zircon Hf isotopic compositions suggest that the magma of granodiorite formed by partial melting of basaltic rocks in crust. The Late Indosinian stronlgly peralumineous granite is the first report in the Gangdise terrane. The occurring of the stronlgly peralumineous granite reveals Gangdise crustal thickening prior to Late Indosinian, and gives an impelling evidence that the Gangdise terrane took place an Early Indosinian orogenic event.
Shifting Coronavirus Disease 2019 Testing Policy Shifting Coronavirus Disease 2019 Testing Policy and Research to Include the Full Translation Pipeline and Research to Include the Full Translation Pipeline

Te current severe acute respiratory syndrome coronavirus 2 testing policy and practice limits testing as a prevention tool. Radical shifs are required to increase the scale of rapid testing strategies and improve dissemination and implementation of venue-based and self-testing approaches. Attention to the full translation pipeline is required to reach high-risk segments of the population.
Radiology and Imaging Technology

Objectives: This study aimed to investigate differences in brain structure between Alzheimer’s disease (AD) and non-dementia psychiatric disorders (SNDMD) patients using MRI imaging for accurate diagnosis. Methods: A total of 50 AD patients and 50 SNDMD patients underwent head MRI scans, and brain structures were measured and compared using SPSS software. The MRI results were interpreted by two radiologists. Results: AD patients had wider sulci and larger ventricular systems compared to SNDMD patients and normal elderly individuals. SNDMD patients had less cortical atrophy than AD patients. Conclusion: The findings suggest that MRI is a useful tool for differentiating AD from SNDMD and aiding in early diagnosis. However, it should be noted that MRI is not a definitive diagnostic tool.
Precautionary Saving over the Business Cycle

In this paper, we present a tractable model of time-varying precautionary saving behaviour due to changes in uninsured unemployment risk. In our model, agents facing incomplete markets and borrowing constraints respond to changes in labour market conditions by altering their bu¤fer stock of precautionary wealth, with a direct impact on current consumption. The model is calibrated to match the evidence on the share of " permanent income consumers " and the distribution of wealth in the U.S. economy. We …nd a large, but relatively short-lived, impact of the precautionary motive on aggregate consumption in response to a typical labour market shock. The implications of the precautionary motive for consumption Euler equation tests are also discussed. We are particularly grateful to our discussants, Paul Beaudry, Sergio Rebelo and Pontus Rendall, for their comments on an earlier version of this paper. We greatly bene…ted from discussions with Andrew Association Conference, and seminars at Ecole Polytechnique, Gains/Université du Mans and Banque de France for their feedback. Eric Mengus provided outstanding research assistance, and the French Agence Nationale pour la Recherche provided funding (ANR grant no 06JCJC0157). The usual disclaimers apply.
2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision

We present a Multimodal Interlaced Transformer (MIT) that jointly considers 2D and 3D data for weakly supervised point cloud segmentation. Research studies have shown that 2D and 3D features are complementary for point cloud segmentation. However, existing methods require extra 2D annotations to achieve 2D-3D information fusion. Considering the high annotation cost of point clouds, effective 2D and 3D feature fusion based on weakly supervised learning is in great demand. To this end, we propose a transformer model with two encoders and one decoder for weakly supervised point cloud segmentation using only scene-level class tags. Specifically, the two encoders compute the self-attended features for 3D point clouds and 2D multi-view images, respectively. The decoder implements interlaced 2D-3D cross-attention and carries out implicit 2D and 3D feature fusion. We alternately switch the roles of queries and key-value pairs in the decoder layers. It turns out that the 2D and 3D features are iteratively enriched by each other. Experiments show that it performs favorably against existing weakly supervised point cloud segmentation methods by a large margin on the S3DIS and ScanNet benchmarks. The project page will be available at https://jimmy15923.github. io/mit_web/.