Miracle-dz/UniFAR-FTUs huggingface dataset:
Body and surface wave reconstruction from seismic noise correlations between arrays at Piton de la Fournaise volcano

Body wave reconstruction from ambient seismic noise correlations is an important step toward improving volcano imaging and monitoring. Here we extract body and surface waves that propagate in Piton de la Fournaise volcano on La Réunion island using ambient noise cross correlation and array‐processing techniques. Ambient noise was continuously recorded at three dense arrays, each comprising 49 geophones. To identify and enhance the Green's function from the ambient noise correlation, we apply a double beamforming (DBF) technique between the array pairs. The DBF allows us to separate surface and body waves, direct and reflected waves, and multipathing waves. Based on their azimuths and slownesses, we successfully extract body waves between all the combinations of arrays, including the wave that propagates through the active magmatic system of the volcano. Additionally, we identify the effects of uneven noise source distribution and interpret the surface wave reflections.
Applied imagination

Imagination is a fundamental human capacity, and to navigate our current global challenges, we need to define and encourage the practice of imagination, or what we term “applied imagination. ” In this study, we convened a series of focus groups or “virtual salons” to address three guiding questions: (1) How might we define imagination? (2) How might we (or should we) measure imagination? And (3) How might we foster imagination? Our efforts to define applied imagination highlight the crucial role imagination plays in human survival and thriving, the role of social forces in fostering or discouraging imagination, the connection between imagination and faith, and the “dark side” or maladaptive aspects of imagination. The discussions on measuring imagination were quite divided, with some salon participants arguing for the potential of indirect modes for measuring imaginative capacity while others argued that measuring imagination was functionally impossible and morally suspect. Finally, our results around fostering imagination suggest the importance of using play and humor, separating imaginative activities from the everyday, and employing constraints to prompt imaginative responses. We end with a discussion of possible directions for future research and a call to create a transdisciplinary field of imagination studies.
Research on Dynamic Monitoring and Early Warning of the High-Rise Building Machine during the Climbing Stage

High-rise building machines (HBMs) are commonly used for the construction of super-high skyscrapers. Monitoring and early warning are critical to ensure the safety of giant HBMs during dynamic climbing. However, no indicators or control methods directly reflect the dynamic safety state of such large structures. In this study, the key factors influencing the climbing altitude were systematically analyzed and a method for the dynamic monitoring and early warning of HBMs during climbing was proposed. This approach is innovative for monitoring 3D attitude in real time using a string of fiber grating-level sensors mounted on the main bearing surface of the HBM. Three-level early warning indicators and control methods that reflect the safety status of the HBM during dynamic climbing have been established. The method was successfully applied to a 356-m tall high-rise project, the Shenzhen Xinghe Yabao Building. Results demonstrate that the proposed method can monitor and control the safety state of the HBM climbing process more accurately than current methods in real time. In addition, it significantly reduces the impact of factors such as preclimbing differential deformation and climbing attitude recognition on the accuracy of the HBM climbing control. This guarantees the safe management of HBMs and the efficient construction of super high-rise buildings. The results of this study can also be widely applied to safety monitoring of the dynamic operation of giant construction machinery.
The Dual Functions of Andrographolide in the Epstein–Barr Virus-Positive Head-and-Neck Cancer Cells: The Inhibition of Lytic Reactivation of the Epstein–Barr Virus and the Induction of Cell Death

Andrographolide, a medicinal compound, exhibits several pharmacological activities, including antiviral and anticancer properties. Previously, we reported that andrographolide inhibits Epstein–Barr virus (EBV) lytic reactivation, which is associated with viral transmission and oncogenesis in epithelial cancers, including head-and-neck cancer (HNC) cells. However, the underlying mechanism through which andrographolide inhibits EBV lytic reactivation and affects HNC cells is poorly understood. Therefore, we investigated these mechanisms using EBV-positive HNC cells and the molecular modeling and docking simulation of protein. Based on the results, the expression of EBV lytic genes and viral production were significantly inhibited in andrographolide-treated EBV-positive HNC cells. Concurrently, there was a reduction in transcription factors (TFs), myocyte enhancer factor-2D (MEF2D), specificity protein (SP) 1, and SP3, which was significantly associated with a combination of andrographolide and sodium butyrate (NaB) treatment. Surprisingly, andrographolide treatment also significantly induced the expression of DNA Methyltransferase (DNMT) 1, DNMT3B, and histone deacetylase (HDAC) 5 in EBV-positive cells. Molecular modeling and docking simulation suggested that HDAC5 could directly interact with MEF2D, SP1, and SP3. In our in vitro study, andrographolide exhibited a stronger cytotoxic effect on EBV-positive cells than EBV-negative cells by inducing cell death. Interestingly, the proteome analysis revealed that the expression of RIPK1, RIPK3, and MLKL, the key molecules for necroptosis, was significantly greater in andrographolide-treated cells. Taken together, it seems that andrographolide exhibits concurrent activities in HNC cells; it inhibits EBV lytic reactivation by interrupting the expression of TFs and induces cell death, probably via necroptosis.
Extracellular vesicle treatment partially reverts epigenetic alterations in chronically ischemic porcine myocardium.

Introduction Research has shown epigenetic change via alternation of the methylation profile of human skeletal muscle DNA after Cardio-Pulmonary Bypass (CPB). In this study, we investigated the change in epigenome-wide DNA methylation profiles of porcine myocardium after ischemic insult in the setting of treatment with extracellular vesicle (EV) therapy in normal vs. high-fat diet (HFD) pigs. Methods Four groups of three pigs underwent ameroid constrictor placement to the left circumflex artery (LCx) and were assigned to the following groups: (1) normal diet saline injection; (2) normal diet EV injection; (3) HFD saline injection; and (4) HFD EV injection. DNA methylation was profiled via reduced-representation bisulfite sequencing (RRBS) and compared using a custom bioinformatic pipeline. Results After initial analysis, 441 loci had a nominal P value < 0.05 when examining the effect of ischemia vs. normal heart tissue on a normal diet in the absence of treatment. 426 loci at P value threshold < 0.05 were identified when comparing the ischemic vs. normal tissue from high-fat diet animals. When examining the effect of EV treatment in ischemic tissue in subjects on a normal diet, there were 574 loci with nominal P value < 0.05 with two loci Fructosamine 3 kinase related protein [(FN3KRP) (P < 0.001)] and SNTG1 (P = 0.03) significant after Bonferroni correction. When examining the effect of EV treatment in ischemic tissue in HFD, there were 511 loci with nominal P values < 0.05. After Bonferroni correction, two loci had P values less than 0.05, betacellulin [(BTC) (P = 0.008)] and [proprotein convertase subtilisin/kexin type 7 (PCSK7) (P = 0.01)]. Conclusions Alterations in DNA methylation were identified in pig myocardium after ischemic insult, change in diet, and treatment with EVs. Hundreds of differentially methylated loci were detected, but the magnitude of the effects was low. These changes represent significant alterations in DNA methylation and merit further investigation.
A Scoping Review of the Serious Game-Based Rehabilitation of People with Cerebral Palsy

In a serious context, individuals with Cerebral Palsy (CP) have limited opportunities to engage in social interaction experiences. Through a review, this study provides an explanation and improved evidence of the methods for rehabilitation in games used in serious contexts for people with CP. Articles published from 2010 to 2022 focusing on serious game-based rehabilitation for people with CP are extracted from MEDILINE, Academic Search Ultimate, CINAHL, and the Web of Science. The articles were assessed based on the McMaster critical review form. This study analyzes the frequencies of goal and assessment tools according to the components using the International Classification of Functioning, Disability and Health (ICF). The evidence of all the studies is presented according to the principles of Population, Intervention, Comparison, Outcome (PICO) to organize the evidence. A total of 19 articles were selected. Five articles involved Randomized Controlled Trials (RCTs), six articles involved non-randomized one-group designs, three articles involved single experimental study designs, and five articles were case report designs. In the selected articles, the average score on the McMaster critical review form was 11.8 points. In the game-based rehabilitation for CP, more articles reported goals and assessment tools focusing on body function than goals and assessment tools focusing on activity and participation, according to the ICF. These findings provide a record of past work and identify the evidence to support the application of game-based rehabilitation for people with CP.
Using car4ams, the Bayesian AMS Data Analysis Code

The Bayesian CAR (continuous autoregressive) model for accelerator mass spectrometry (AMS) data analysis delivers uncertainties with less scatter and bias. Better detection and estimation of the instrumental error of the AMS machine are also achieved. Presently, the main disadvantage is the several-hour duration of the analysis. The Markov chain Monte Carlo (MCMC) program for CAR model analysis, car4ams, has been made freely available under the GPL license. Included in the package is an R program that analyzes the car4ams output and summarizes the results in graphical and spreadsheet formats. We describe the main properties of the CAR analysis and the use of the 2 parts of the program package for radiocarbon AMS data analysis.
Mathematics Preservice Teachers’ Perceptions Regarding ICT Use in Teaching and Learning Practices

Background : Since we have been living in the digital age, the ways of interacting with information, data and other human beings have changed. Being so, teachers need to understand their new role in pedagogical environments, changing their perspectives, and, most of all, transforming their knowledge to be able to teach with digital Information and Communication Technologies (ICT). Concerns about this new kind of teacher knowledge led to discussions that culminated, in the beginning of the 21st century, with the development of the Technological Pedagogical Content Knowledge (TPACK) framework. Objectives : Knowing the importance of preparing teachers to work with ICT to enrich their pedagogical practices, we propose this qualitative study that aims to analyse perceptions of a group of preservice teachers about the use of ICT in mathematics teaching and learning practices. Design : This study has a qualitative approach, and it can be understood as a case study. Setting and Participants : The participants are 10 preservice teachers from a Portuguese University. Data collection and analysis : Data were collected using a survey and the responses were analysed through Discursive Textual Analysis. Results : We understand that most participants have a well-developed TPACK and they can see several educational benefits from using ICT to teach and learn mathematics. Conclusions : The results of this study highlight the valuable activity of this preservice teachers’ course, which is committed with the goal of helping them to develop their own TPACK.
Nevada Test Site 2000 Waste Management Monitoring Report Area 3 and Area 5 Radioactive Waste Management Sites

Environmental monitoring data, subsidence monitoring data, and meteorology monitoring data were collected at and around the Area 3 and Area 5 Radioactive Waste Management Sites (RWMSs) at the Nevada Test Site (NTS) (refer to Figure 1). These monitoring data include radiation exposure, air, groundwater, meteorology, vadose zone, subsidence, and biota data. Although some of these media (radiation exposure, air, and groundwater) are reported in detail in other Bechtel Nevada reports (Annual Site Environmental Report [ASER], the National Emissions Standard for Hazardous Air Pollutants [NESHAP] report, and the Annual Groundwater Monitoring Report), they are also summarized in this report to provide an overall evaluation of RWMS performance and environmental compliance. Direct radiation monitoring data indicate that exposure at and around the RWMSs is not above background levels. Air monitoring data indicate that tritium concentrations are slightly above background levels, whereas radon concentrations are not above background levels. Groundwater monitoring data indicate that the groundwater in the uppermost aquifer beneath the Area 5 RWMS has not been affected by the facility. Meteorology data indicate that 2000 was an average rainfall year: rainfall totaled 167 mm (6.6 in) at the Area 3 RWMS (annual average is 156 mm [6.5 in]) and 123 mm (4.8 in) at the Area 5 RWMS (annual average is 127 mm [5.0 in]). Vadose zone monitoring data indicate that 2000 rainfall infiltrated less than one meter (3 ft) before being returned to the atmosphere by evaporation. Soil-gas tritium monitoring data indicate slow subsurface migration, and tritium concentrations in biota were lower than in previous years. All 2000 monitoring data indicate that the Area 3 and Area 5 RWMSs are performing well at isolating buried waste.
Unknown Fault Detection of Rolling Bearing Based on Similarity Mining of Stationary and Non-stationary Features

Rolling bearings are essential and critical components in rotating machinery, and their condition is vital for ensuring the reliable operation of the equipment. Currently, numerous deep learning methods have been investigated for diagnosing the condition of bearings. However, these methods can only recognize fault categories that were included in the training of the model, and they are unable to detect unknown fault categories. In this paper, we propose a method for unknown fault detection of rolling bearings based on stationary - non-stationary feature similarity mining. This method analyzes the features of fault signals and explores the relationships between various fault categories, considering both stationary and non-stationary perspectives. In particular, we propose a framework for bearing unknown fault detection based on stationary and non-stationary feature mining. The framework comprises two stages: pre-training and the discovery of unknown fault categories. Siamese networks are employed during the pre-training stage to acquire the similarity relationship between stationary and non-stationary features. During the stage of discovering unknown fault categories, the similarity information is utilized to facilitate the identification of new fault categories. Additionally, we introduce a discriminative feature time-frequency focusing module to prioritize the model's attention towards non-stationary time-frequency fluctuations. The performance of our model is evaluated on the CWRU dataset and the PU dataset, and the experimental results demonstrate its superiority over other state-of-the-art methods, resulting in a significant enhancement in accuracy.
An Integrated, Bidirectional Pronunciation, Morphology, and Diacritics Finite-State System

­­­A bidirectional phonetizer, morphologizer, and dia­ critizer pipeline (FSPMD) for modern standardArabic (MSA) that integrated pronunciation, concatenative and templatic morphol­ ogy, and diacritization were developed. Grammar and segmental phonology rules were applied in the forward direction to ensure the order of the proper rules, which were supplemented with special backward direction rules. The FSPMD comprises bidirectional finite­state transducers (FSTs) consisting of an ordered compo­ sition of FSTs, unordered parallel FSTs, unioned FSTs, and for validity, finite­state acceptors. The FSPMD has unique, innovative features and can be used as an integrated pipeline or standalone phonetizer (FSAP), morphologizer (FSAM), or diacritizer (FSAD). As the system is bidirectional, it can be used in forward (generation, synthesis) and backward (analysis, decomposition) directions and can be integrated into systems such as automatic speech recog­ nition (ASR) and language learning tools. The FSPMD is rule­ based and avoids stem listings for morphology or pronunciation dictionaries, which makes it scalable and generalizable to similar languages. The FSPMD models authentic rules, including fine granularity and nuances, such as rewrite and morphophonemic rules, subcategory identification and utilization, such as irregular verbs. FSAP performance regarding text from the Tashkeela cor­ pus and Wikipedia demonstrated that the pronunciation system can accurately pronounce all text and words, with the only errors related to foreign words and misspellings, which were out of the system’s scope. FSAM and FSAD coverage and accuracy were evaluated using the Tashkeela corpus and a gold standard derived from its intersection with the UD_PADT treebank. The coverage of extraction of root and properties from words is 82%. Accuracy results are roots computed from a word (92%), words generated from a root (100%), non­root properties (97%), and diacritization (84%). FSAM non­root results matched and/or surpassed those from MADAMIRA; however, root result comparisons were not conducted because of the concatenative nature of publicly available morphologizers.
The Effectual Design Method for Internationalizing Architectural and Engineering Services: Insights from Swiss Design Entrepreneurs

This research explores the use of effectual and causal design logic in architectural and engineering design internationalization. Specifically, it aims to understand how individual design entrepreneurs act across three international opportunity design problem spaces: (a) formation, (b) evaluation, and (c) exploitation. This qualitative multiple-case study employed phenomenographic, semi-structured interviews with 17 Swiss architectural and engineering design entrepreneurs. The findings indicate that international opportunity formation is created via the dominant effectual design logic principle; international opportunity evaluation mainly applies causal design logic; and international opportunity exploitation primarily uses actions combining effectual and causal design logic principles. This research contributes to the architectural and engineering design internationalization literature, which focuses only on causal design logic and provides a practical integrative model for successful international architectural and engineering design services.
Implementation of A.I. Vehicle Detection for Traffic Analysis Using In-situ Surveillance Infrastructure

Traffic flow parameters are required for optimizing traffic operations, design of pavements, and future planning of traffic networks. Unfortunately, due to the unique characteristics and variety of vehicles in the sub-continent i.e., size and design, the accuracy of results for a vision-based system is challenged, since most thorough datasets are based on European and American traffic. This paper proposes a solution by developing a detection model ground-up using a dataset created from the local traffic surveillance footage, and creating a python pipeline for vehicle speed detection and classification. The vehicle classification model is developed using the state-of-the-art YOLO object detector which significantly reduces the computation time required to maintain the efficiency of the proposed solution. Furthermore, a computer-vision script is developed to track the movement of vehicles in the footage and record the speeds in a spreadsheet. The technique used eliminates the video calibration, including distance and angle, required for detecting accurate speeds. Finally, the realtime traffic data is analyzed to derive the fundamental traffic flow parameters and discuss the relation between flow and density. To ascertain the validity of this survey technique, the results are compared to the following renowned traffic flow models: The Modified Greenberg model, Eddie’s model, and The Two-regime model. The results are found to closely follow the models in all three cases.
Legal View on Blockchain Technologies in Healthcare: A European States Case Study

This article presents an in-depth study of the legal landscape surrounding blockchain technology in the healthcare sector, with a special focus on case studies from European countries. Analyzing the existing legal framework and regulations, the research highlights the challenges and opportunities associated with the adoption of blockchain in healthcare. The most important research areas are data protection, security, consent, liability, and compliance. Through a comparative analysis of various European countries, the article illuminates the differences in legal approaches and points out possible areas of harmonization. The results clarify the legal aspects that must be addressed to ensure the integration of blockchain technology into healthcare systems, innovation while protecting patients' rights, and compliance with regulatory requirements.
Improved Rare Species Identification Using Focal Loss Based Deep Learning Models

 Abstract— The use of deep learning for species identification in camera trap images has revolutionised our ability to study, conserve and monitor species in a highly efficient and unobtrusive manner, with state-of-the-art models achieving accuracies surpassing the accuracy of manual human classification. The high imbalance of camera trap datasets, however, results in poor accuracies for minority (rare or endangered) species due to their relative insignificance to the overall model accuracy. This paper investigates the use of Focal Loss, in comparison to the traditional Cross Entropy Loss function, to improve the identification of minority species in the “255 Bird Species” dataset from Kaggle. The results show that, although Focal Loss slightly decreased the accuracy of the majority species, it was able to increase the F1-score by 0.06 and improve the identification of the bottom two, five and ten (minority) species by 37.5%, 15.7% and 10.8%, respectively, as well as resulting in an improved overall accuracy of 2.96%.