Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Machine Learning to Determine Earthquake Hypocenter Location in Earthquake Early Warning

We propose a novel hypocenter localization model based on machine learning in the hope of providing a feasible solution for earthquake early warning system (EEWs). The model predicts the hypocenter location using the time series of trigger stations. We compare the test results of three machine learning models: 1) random forest (RF); 2) eXtreme gradient boosting (XGBoost); and 3) light gradient boosting machine (LightGBM). LightGBM is found the most efficient and thus is applied for further testing. Moreover, considering the difference in station trigger between ocean and land earthquakes, ocean and land earthquake hypocenter prediction models are determined separately. When triggered by five stations, the mean absolute errors (MAEs) for predicting the hypocenter location in land and ocean earthquakes is 2.10 and 12.66 km, respectively. With fewer stations (three stations), the predicted MAEs for distance reaches 17.80 and 2.34 km for ocean and land earthquakes, respectively. In addition, a classification model for rapid discrimination of ocean and land earthquakes using information from three trigger stations is constructed. The proposed method in this letter can provide a more reliable solution to EEWs.
Selection of Ship Evacuation Area to Construct Tsunami Emergency Action Plan

ABSTRACT Jho, M.H. ; Kim, G.H. ; Yoon, S.B. , and Hyun, S.G. , 2017. Selection of ship evacuation area to construct tsunami emergency action plan. In: Lee, J.L. ; Griffiths, T. ; Lotan, A. ; Suh, K. -S. , and Lee, J. (eds.), The 2nd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 79, pp. 169–173. Coconut Creek (Florida), ISSN 0749-0208. When a tsunami occurs and starts to propagate towards coastal region, a tsunami warning is issued. Then, the captain of ship in a port is advised to make a decision in accordance with the situation. If a captain does not take his ship out of port, more damage to the infrastructures of a port, other ship and even itself is likely. To reduce additional damages, tsunami action plan is essential to lead ships out of a port. However, exact procedures to evacuate are not provided in the evacuation action plan. In this study, how to select a ship evacuation area outside of a port will be discussed based on wave height, tsunami-induced currents and the behavior of ships. To illuminate the selection criteria to locate a safe zone, numerical simulation of the 2011 East Japan Tsunami is performed using a dispersion-corrected finite difference scheme with the time-dependent fault parameters. Moreover, Masan and Jinhae bays are selected as sample cases in order to establish the base of ship evacuation action plan in Korea.
Temperature dependence characteristics and noise characteristics of InGaAs/InAlAs X-ray APDs

In this paper, the temperature dependence characteristics and noise characteristics of InGaAs/InAlAs X-ray avalanche photodiodes (APDs) with different structures have been investigated. Firstly, according to the current-voltage curve under 50 keV hard X-ray radiation and activation energy at temperatures between 298 and 358 $\mathbf{K}$. , it is found that the thermal generation in the absorber layer (i.e., generation-recombination current) dominates the dark current. Secondly, there is a linear dependence of breakdown voltage with temperature and the temperature coefficient can be extracted for each device. The lowest temperature coefficient is 16.20 mV/K, which is conducive to operating in a complex environment. Ultimately, the noise of commercial InGaAs/InP APDs has been compared. Combining experimental and theoretical analysis, our results show that homemade devices have the advantage of low noise because the InAlAs multiplication layer with lower impact ionization coefficients $(\boldsymbol{k})$ exhibits narrower gain distributions for X-ray APDs.
On-chip Pulse Self-compression to Single-cycle Level in Silicon-rich Nitride Waveguides

We propose a siliconrich nitride waveguide with slot-assisted dispersion flattening, which is particularly suitable for on-chip pulse self-compression from 200 to 5.4 fs (1.08 optical cycles, with a compression factor as high as 37), accompanied by octave-wide supercontinuum generation, from 804 to 2645 nm. The compact pulse compressor to a single-cycle level without subsequent dispersion compensation can open a door to cycle-level pulse manipulation on a chip.
Analytical Study of the Dual-Band Log-Periodic Antenna with MIMO Configuration for S-Band CubeSat Application

The investigation of 3U CubeSat-based dual band Log-periodic antenna with multi-input-multi-output (MIMO) configuration for S-band application is proposed in this paper. This work focuses on the performance of antenna with the exploration of the possibility of MIMO configuration. The MIMO antenna design utilizes the concept of using two identical radiators fed in opposite directions to maintain the isolation. The degree of isolation observed between the ports remains below −40 dB within the desired frequency range. The antenna exhibits dual-band with operating center frequency at 2.3 GHz and 2.9 GHz for lower and upper bands, respectively. The design maintains a large frequency ratio of operation to be used for different satellite applications. Additionally, it offers a directivity of 8.592 dBi and an efficiency of 62 %. Further, MIMO performance matrices like mean effective gain (MEG), envelope correlation coefficient (ECC), total active reflection coefficient (TARC), and diversity gain (DG) are investigated. In addition, a systematic investigation is carried out to perform a parametric analysis with assess the sensitivity of the optimized dimensions in order to fine tune the proposed antenna response.
Research on Underwater Collaborative Detection Method Based on Complex Marine Environment

In modern underwater warfare, detecting and locating enemy submarines is the primary task, but there are still many difficulties in detecting submarines. The acoustic signals may distort in the ocean channel due to the complexity of the ocean, which affects the detection of sonar. That is to say, the impact of environmental factors needs to be considered in submarine detection. Therefore, this article proposes an underwater collaborative detection method based on complex marine environments, constructs a sonar underwater detection capability analysis system to improve detection performance, analyzes the impact of marine environmental factors on sonar detection capability, evaluates sonar performance using sonar quality factor, calculates sonar detection distance, establishes polar coordinates, and explores sonar detection capability; Besides, the concept of collaborative detection is also introduced, and a collaborative detection probability distribution surface is proposed to fuse the detection capabilities of different platforms, presenting the collaborative detection capabilities of multiple platforms in the form of probability contour lines. The detection performance of different deployment methods is explored by changing the placement position and number of platforms. At last, the research has shown that the underwater detection capability of sonar is closely related to the marine environment. The square layout in underwater collaborative detection has better detection performance. The research results of this article can provide references for the layout of platforms in underwater detection tasks.
Filipino public-school teachers' empathy: A cross-sectional, descriptive study

This cross-sectional study describes Filipino public school teachers' empathy levels and whether these levels are affected by attributes such as age, sex at birth, educational attainment, years of service, and grade level taught. One hundred forty-six public school teachers agreed to participate in a survey conducted during in-service training. Teacher participants reported above average levels of affective, cognitive, and overall empathy. Among the attributes, years of service were found to have significant results from non-parametric analysis. Teachers teaching for 1-5 years had significantly higher affective and overall empathy than those teaching for 16-20 years. No such differences were observed for cognitive empathy. The implications of this decreasing trend in affective empathy, but not cognitive empathy, to length of service were discussed. Recommendations were also provided to address the possible varied effects of cognitive and affective empathy on teachers’ well-being and empathy as a risk or protective factor.
From a Clustering of Adverse Symptoms after Colorectal Cancer Therapy to Chronic Fatigue and Low Ability to Work: A Cohort Study Analysis with 3 Months of Follow-Up

Simple Summary After getting treated for colorectal cancer, many people feel fatigue, stress, and pain and face other challenges that can make daily activities and work tough. Our research explored how these issues group together and if they are connected to fatigue and difficulties in work. We gathered data from the MIRANDA study, where patients shared their experiences after colorectal cancer treatment before the start of the rehabilitation and three months after. Using data from their questionnaire responses, we identified six main problem groups: fatigue, digestive troubles, pain, feelings of stress or anxiety, urinary issues, and side effects from chemotherapy. Our findings reveal that experiencing even a single symptom from these categories can be a precursor to feeling more fatigued or having a decreased ability to work. Our findings shed light on colorectal cancer survivors’ various challenges and how they might impact their daily lives and work. Abstract In colorectal cancer (CRC) patients, apart from fatigue, psychological and physical symptoms often converge, affecting their quality of life and ability to work. Our objective was to ascertain symptom clusters within a year following CRC treatment and their longitudinal association with persistent fatigue and reduced work ability at the 3-month follow-up. We used data from MIRANDA, a multicenter cohort study enrolling adult CRC patients who are starting a 3-week in-patient rehabilitation within a year post-curative CRC treatment. Participants completed questionnaires evaluating symptoms at the start of rehabilitation (baseline) and after three months. We performed an exploratory factor analysis to analyze the clustering of symptoms at baseline. Longitudinal analysis was performed using a multivariable linear regression model with dichotomized symptoms at baseline as independent variables, and the change in fatigue and ability to work from baseline to 3-month-follow-up as separate outcomes, adjusted for covariates. We identified six symptom clusters: fatigue, gastrointestinal symptoms, pain, psychosocial symptoms, urinary symptoms, and chemotherapy side effects. At least one symptom from each factor was associated with higher fatigue or reduced ability to work at the 3-month follow-up. This study highlights the interplay of multiple symptoms in influencing fatigue and work ability among CRC patients post-rehabilitation.
A Novel Lightweight Hybrid Intrusion Detection Method Using a Combination of Data Mining Techniques

Hybrid intrusion detection systems that make use of data mining techniques, in order to improve effectiveness, have been actively pursued in the last decade. However, their complexity to build detection models has become very expensive when confronted with large-scale datasets, making them unviable for real-time retraining. In order to overcome the limitation of the conventional hybrid method, we propose a new lightweight hybrid intrusion detection method that consists of a combination of feature selection, clustering and classification. According to our hypothesis that there are different natures of attack events in each of network protocols, the proposed method examines each of network protocol data separately, but their processes are the same. First, the training dataset is divided into training subsets, depending on their type of network protocol. Next, each training subset is reduced dimensionally by eliminating the irrelevant and redundant features throughout the feature selection process; and then broken down into disjointed regions, depending on their similar feature values, by K-Means clustering. Lastly, the C4.5 decision tree is used to build multiple misuse detection models for suspicious regions, which deviate from the normal and anomaly regions. As a result, each detection model is built from high-quality data, which are less complex and consist of relevant data. For better understanding of the enhanced performance, the proposed method was evaluated through experiments using the NSL-KDD dataset. The experimental results indicate that the proposed method is better in terms of effectiveness (F-value: 0.9957, classification accuracy: 99.52%, false positive rate: 0.26%), and efficiency (the training and testing times of the proposed method are approximately 33% and 25%, respectively, of the time required for its comparison) than the conventional hybrid method using the same algorithm.
Machine learning-based classification of children affected by malnutrition using multimodal sMRI and DTI brain images

Child malnutrition is a prevalent pathological condition in developing countries, such as Mexico, with scarce studies on its anatomical cerebral effects. This study analyzed 26 infants subjects that included brain volumes from two neuroimaging modalities: DTI and sMRI. Twelve of the subjects involved children with normal nutrition in their early years of life, and 14 represented severe malnutrition cases. Segmentation was obtained by FreeSurfer from 94 and 103 regions for sMRI and DTI modalities, respectively. Each region was analyzed using several morphological and diffusion features; afterwards, seven classification models were trained to differentiate between the two groups. The results indicate notable differences that allow accurate classification, with an accuracy up to 1.0 and an AUROC of 1.0 for classification with SVM, LR, KNN, and MLP algorithms.
MODELING AND NANOTECHNOLOGY

Approaches to increase the efficiency of the use of materials that evaporate with subsequent deposition in a vacuum have been analyzed. The high efficiency and advantage of deposition from an evaporator with a directed vapor flow in a vacuum compared to a classical crucible evaporation scheme are shown. The optimal technological modes of deposition from the evaporator to achieve homogeneity of the directional steam flow were determined. The dependence between the temperature of the target, the efficiency of the evaporation scheme and the distance of the evaporator from the target was determined experimentally.
NOVA University of

There is increasing interest in shifting the focus of virtual reality (VR) in education from consumption activities to content creation where students can produce and share their own virtual environments. This paper reports on a pilot study where junior secondary science students created a 360° VR learning resource for primary (elementary) school students. The study used a mixed methodology, participatory research approach to explore learning outcomes for students. While technical set-up and time constraints affected the research, secondary students generally enjoyed the experience with the teacher observing good levels of engagement. The virtual environments produced by female students were graded higher than their male classmates. There was no increase in the content knowledge of primary school students after viewing the VR learning resource but most reported good systems usability. The pilot study indicated that having students create learning content for an authentic audience, such as younger students, is feasible with the potential to generate positive learning outcomes if organizational and time constraints can be addressed.
Computer-aided detection of wall motion abnormalities in cardiac MRI

Regional wall motion analysis is used in clinical routine to assess myocardial diseases such as infarction or hypertrophy. Physicians/radiologists can recognize abnormal cardiac motion because they have knowledge about normal heart contraction. This chapter explores the potential of Independent Component Analysis (ICA) to extract local myocardial contractility patterns and to use them for the automatic detection of regional abnormalities. A qualitative evaluation was performed using 42 healthy volunteers to train the ICA model and 6 infarct patients to test the detection and localization. By visual comparison, the experimental results show that automated detection of regional abnormal contraction correlate very well to hyperenhanced areas from delayed-enhancement MR images.
the Effects of Lemmatisation and Spell Checking on Sentiment Analysis of Online

—With many options for text preprocessing techniques, choosing the most efficient methodology is important for both accuracy and computational expense. Online text often contains non-standard English, spelling errors, colloquialisms, emojis, slang and many other variations that affect current natural language processing tools, with no clear guidelines for preprocessing this type of text. In this work we analyse text preprocessing techniques using a data set of online reviews scraped from iTunes and Google Play store. The objective is to measure the efficacy of different combinations of these techniques to maximise the amount of detected sentiment in a dataset of 438,157 reviews. Sentiment detection was performed by two state-of-the-art sentiment analysers (RoBERTa and VADER). Statistical analysis of the results suggest preprocessing strategies for maximising sentiment detected within mental health app reviews and similar text formats.
Global mapping of burned areas from European satellites: the fire_cci project

This paper presents the results of phase 1 of the fire_cci project. This project is part of the Climate Change Initiative of the European Space Agency, and aims to generate global, long-term burned area maps from European sensors (VEGETATION and MERIS). This information is intended to help modelling efforts of climate and atmospheric and carbon scientists, by adjusting to their needs in terms of product specifications. The project has done an extensive effort of validation and error characterization of the results. Intercomparison with other existing datasets has also been carried out.
A mass spectrometric study of secondary organic aerosols formed from the photooxidation of anthropogenic and biogenic precursors in a reaction chamber

M. R. Alfarra, D. Paulsen, M. Gysel, A. A. Garforth, J. Dommen, A. S. H. Prévôt, D. R. Worsnop, U. Baltensperger, and H. Coe Laboratory of Atmospheric Chemistry, Paul Scherrer Institut, 5232 Villigen PSI, Switzerland School of Earth, Atmospheric and Environmental Sciences, The University of Manchester, P.O. Box 88, Manchester M60 1QD, UK School of Chemical Engineering and Analytical Sciences, The University of Manchester, P.O. Box 88, Manchester M60 1QD, UK Aerodyne Research Inc., 45 Manning Road, Billerica, MA 01821, USA now at: Division of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Sub-micron atmospheric aerosols in the surroundings of Marseille and Athens : physical characterization and new particle formation

T. Petäjä, V. -M. Kerminen, M. Dal Maso, H. Junninen, I. K. Koponen, T. Hussein, P. P. Aalto, S. Andronopoulos, D. Robin, K. Hämeri, J. G. Bartzis, and M. Kulmala Division of Atmospheric Sciences, Dept. of Physical Sciences, University of Helsinki, Finland Finnish Meteorological Institute, Climate and Global Change, Helsinki, Finland Department of Chemistry, University of Copenhagen, Denmark Dept. of Applied Environmental Sciences (ITM), Stockholm University, Stockholm, Sweden National Centre for Scientific Research “Demokritos”, Athens, Greece AIRMARAIX, Marseille, France Finnish Institute of Occupational Health, Helsinki, Finland Department of Engineering and Management of Energy Resources, University of West Macedonia, Kozani, Greece
Comparison of CO 2 fluxes estimated using atmospheric and oceanic inversions , and role of fluxes and their interannual variability in simulating atmospheric CO 2 concentrations

Comparison of CO2 fluxes estimated using atmospheric and oceanic inversions, and role of fluxes and their interannual variability in simulating atmospheric CO2 concentrations P. K. Patra, S. E. Mikaloff Fletcher, K. Ishijima, S. Maksyutov, and T. Nakazawa Frontier Research Center for Global Change, Japan Agency for Marine-Earth Sciences and Tecnology, Yokohama 236 0001, Japan Atmospheric and Oceanic Sciences, Princeton University, Sayre Hall, Forrestal Campus, P.O. Box CN0710, Princeton, NJ, 08544-0710, USA Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba 305-8506, Japan Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, Sendai 980-8578, Japan
A Cyber Digital Twin Framework to Support Cyber-Physical Systems Security

Cyber-Physical Systems (CPSs) cybersecurity faces several challenges due to the complexity and interconnectedness of involved Information Technology (IT) and Operational Technology (OT) systems, the need to balance safety and security, and the rapid pace of technological change, requiring continuous risk assessment, monitoring, testing and mitigation to ensure their resilience against evolving cyber threats. Unfortunately, due to the criticality of involved systems, some security-related activities such as security testing or patch management may interfere with the correct system operation. Moreover, costs related to security are typically high which may complicate things further. The recent adoption of the emerging Digital Twin technology for cybersecurity purposes allows cybersecurity experts to identify vulnerabilities and develop strategies to prevent and respond to cyber threats in a safe and controlled environment, and in a cost-effective way. The so-called Cyber Digital Twin (CDT) is a cybersecurity-oriented virtual replica of a system, network, or device that can be used, for example, to simulate potential cyber attacks and test security measures.A comprehensive CDT framework for CPS systems is presented in this paper, designed to support multiple cybersecurity objectives by suitably extending the core functionalities offered by a generic Digital Twin. To show the effectiveness of our proposal, we discuss the implementation of a proof of concept leveraging the MiniCPS simulator as the core simulation engine and aimed at offering intrusion detection and security testing services.
Towards Better Structured Pruning Saliency by Reorganizing Convolution

We present SPSRC , a novel, simple and effective framework to extract enhanced S tructured P runing S aliency scores by R eorganizing C onvolution. We observe that performance of pruning methods have gradually plateaued recently and propose to make better use of the learned convolutional kernel weights simply after a few steps of transformations. We firstly re-organize the convolutional operations between layers as matrix multiplications and then use the singular values and the matrix norms of the transformed matrices as saliency scores to decide what channels to prune or keep. We show both analytically and empirically that the long-standing kernel-norm-based channel importance measurement, like L1 magnitude, is not precise enough possessing a very obvious weakness of lacking spatial saliency but can be improved with SPSRC. We conduct extensive experiments across different settings and configurations and compare with the counterparts without our SPSRC along with other popular methods, observing obvious improvements. Our code is available at: https: //github.com/AlexSunNik/SPSRC/tree/main .