Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Degree of Persistence, Trend and the Best Time Series Forecasting Models for Particulate Matter (PM10) Pollutant Across Malaysia

Particulate matter is the most common atmospheric pollutant with some negative consequences on human health, environment, and the ambient air quality. In this study, the concentration of particulate matter in sixty-five air quality monitoring stations across Malaysia during January to December 2018 is analyzed. We investigated the degree of persistence and trend of the particulate matter series and developed a forecasting model using both the autoregressive integrated moving average (ARIMA) and the autoregressive fractionally integrated moving average (ARFIMA) time series methods for each monitoring station separately. Mean absolute deviation (MAD), mean absolute percentage error (MAPE) and root mean square error (RMSE) are used to determine the best fitted model for forecasting each monitoring station. Ljung-Box test of uncorrelated residuals confirmed the adequacy of each of the model. The results confirmed the evidence of transitory form of persistence in the level of particulate matter pollutant at sixty-four monitoring stations while trend increases in seventeen monitoring stations. Forecast error analysis indicates that ARFIMA models performed better than ARIMA models by producing smaller RMSE values in forty-two of the sixty-five monitoring stations. However, the overall result indicates that none of the model could be regarded as universal in forecasting particulate matter concentration, and their performance is independent of the category or location of a given monitoring station.
Mangrove swamp foraminifera, indicators of sea level or paleoclimate ?

Recent foraminiferal samples were collected at four stations from mangrove environments of French Guiana, at the end of two rainy and two dry seasons. At the same stations, foraminiferal assemblages changed completely from the rainy season, when they were dominated by agglutinated species, to the dry season, when they were dominated by calcareous species. If one refers to the intertidal vertical zonation concept, these assemblages, collected at the same place, should indicate sea level changes depending on the season. This shows that the intertidal vertical zonation concept is not supported in environments subject to contrasting seasonal conditions, such as the inter-tropical mangrove swamps of French Guiana. Therefore, foraminiferal assemblages cannot be used as indicators of sea-level change in such environments. However, they should be of great interest as indicators of paleoclimate, responsible for changes from dominant oceanic to dominant freshwater influences.
Heat waves in Florida and their future from high‐resolution regional climate model integrations

This study examines the simulation of the heatwave events over Florida from the integration of two regional climate models (RCMs) at comparatively high resolution (at 10 km grid spacing) for two decades of the current climate (1986–2005) followed by their projection in the mid‐21st century (2041–2060). The two RCMs are coupled ocean–atmosphere (CRSM) and atmosphere only (URSM), which are forced at the lateral boundaries by the corresponding simulation of the Community Climate System Model v4 (CCSM4) for both the atmosphere and the ocean. The analysis reveals a reasonable simulation of the frequency, intensity and characteristic kinematic and thermodynamic features of the heatwave events over Florida from both the RCMs with the differences between URSM and CRSM being insignificant. The future projections (2041–2060) are presented only for URSM since the projected differences from CRSM were insignificant. We found the projected change of higher frequency ranging between 20 to over 50% increase relative to the late 20th‐century frequency of heatwave events. Similarly, the projections suggest a broadening of the season of their occurrence by over 2 months. These projected changes are associated with a projected increase in the mean surface temperature, a slight increase of surface humidity but with a mean reduction of surface relative humidity, and a westward dislocation of the North Atlantic Subtropical High.
INFLAMMATION INTENSITY-DEPENDENT EXPRESSION OF OSTEOINDUCTIVE WNT PROTEINS IS CRITICAL FOR ECTOPIC NEW BONE FORMATION IN ANKYLOSING SPONDYLITIS

Conclusions: Several studies reported the potential of BIO withdrawing, spacing or reducing in the patients with RA who reached remission 3), 5) . All cases in spacing and withdrawing group have shown DAS28 – 4ESR<2.6 in this study, however, 50% (5/10 cases) in withdrawing group have re-flare and shown the radiological joint destruction. Spacing or reducing of BIO may have potential to maintain the remission of RA and prevent the joint destruction. Withdrawing of BIO after even sustaineddeeperremissionmay bedifficult tokeepreal remission
A Triple-Stage Robust Ellipse Fitting Algorithm Based on Outlier Removal

Ellipse fitting is a fundamental yet critical task in computer vision, and the development of robust and accurate algorithms is crucial for various applications. In this study, we propose a triple-stage robust ellipse fitting algorithm to address the challenges posed by noise and outliers in the input data. Specifically, to overcome the sensitivity of existing methods to outliers, we introduce an adaptive outlier removal (AOR) algorithm. This algorithm dynamically removes outliers based on the probability density of all data points, eliminating the need for manual parameter adjustment and enhancing robustness to outliers. Furthermore, we tackle the issue of multiple ellipses in the input data by projecting the filtered points into the polar coordinate system. The points are then divided into equal intervals based on the polar angle, facilitating linear clustering to identify the point sets belonging to candidate ellipses, which helps to avoid erroneous fits and improve accuracy. Finally, to avoid solving the geometric distance between the point and the quadratic curve, a simplified ellipse fitting objective function and its corresponding optimization scheme are developed, in which the ellipse parameters are iteratively solved. To verify the universality and accuracy of the algorithm, we tested it on both synthetic data and real-world images from various scenarios with state-of-the-art approaches. Additionally, experiments have been carried out on a physical spacecraft pose measurement platform. The experimental results demonstrate that the algorithm exhibits excellent performance in terms of fitting accuracy and robustness, with a position estimation error of less than 2 mm and an attitude estimation error of less than 0.1°.
Secure Consensus of Uncertain Nonlinear Second-Order Multi-Agent Systems Under DoS Attacks: Impulsive Estimator Approach

The secure consensus under denial-of-service (DoS) attacks for uncertain nonlinear second-order multi-agent systems (MASs) has been investigated in this brief. A hierarchical control strategy consisting of an impulsive estimator and a local controller is proposed to estimate the leader information and observe the uncertainty of the system in order to realize secure consensus. To realize the goal, some sufficient conditions that are easily satisfied are obtained by impulsive system theory, input-to-stable theory and step-function method. Eventually, we use a single link robotic arm system to validate the feasibility of the proposed control strategy.
Evolving Mental Health Dynamics among Medical Students amid COVID-19: A Comparative Analysis of Stress, Depression, and Alcohol Use among Medical Students

Background and Objectives: The ongoing COVID-19 pandemic has posed an array of new challenges for medical students worldwide. Amidst academic rigors, students are confronted with unique stressors, potentially affecting their mental health and substance use. This study aimed to investigate the multifaceted effects of depression, alcohol use, and stress on medical students and discern how these factors have been amplified by the pandemic’s circumstances, and to identify predictors of mental distress during the COVID-19 pandemic period. Materials and Methods: Two online anonymous and cross-sectional surveys were conducted at the University of Medicine and Pharmacy “Iuliu Hatieganu” in Romania among medical students in 2018 and in 2022. Data were gathered via online questionnaires distributed through closed student groups on social media platforms, with a total of 1061 participants, to investigate stress, depression, alcohol and drug use, and the impact of the pandemic and online education on mental health, maintaining anonymity and ethical approval. The Medical Student Stress Questionnaire (MSSQ) was employed to measure different aspects of stress, the Beck Depression Inventory (BDI) provided insights into the participants’ depressive symptoms, and for the assessment of alcohol consumption habits, the Alcohol Use Disorders Identification Test (AUDIT) was utilized. Results: Our findings showed a significant decrease in mean depression scores (13.81 vs. 11.56, with p < 0.001) from 2018 to 2022. In 2018, students scored significantly higher in the overall stress-related domains. Additionally, being female, facing financial constraints, and being in pre-clinical years emerged as predictors of heightened academic-related stress and depressive symptoms. Students who had experienced the loss of a family member due to COVID-19 exhibited a statistically significant rise in their average BDI score and current anxiety levels. Conclusions: The pandemic, despite introducing new stressors, may have indirectly fostered an increased focus on students’ mental health, leading to more refined support mechanisms. Specialized interventions, taking into account gender and financial problems, are needed to address the multifaceted challenges faced by medical students. Our study highlights the ongoing need to nurture both the academic and emotional strength of future medical practitioners.
Virtual training, real effects: a narrative review on sports performance enhancement through interventions in virtual reality

The present article reports a narrative review of intervention (i.e., training) studies using Virtual Reality (VR) in sports contexts. It provides a qualitative overview and narrative summary of such studies to clarify the potential benefits of VR technology for sports performance enhancement, to extract the main characteristics of the existing studies, and to inform and guide future research. Our literature search and review eventually resulted in 12 intervention studies with a pre vs. post design focused on different sports, including target and precision sports (archery, bowling, curling, darts, golf), bat/racquet and ball sports (baseball, table tennis), goal sports (football/soccer, basketball), martial arts (karate), and sport-unspecific processes such as bodily sensations and balancing. The samples investigated in the primary studies included novice, amateur, and expert athletes (total aggregated sample size N = 493). Many studies found statistically significant effects in relevant target skills following interventions in VR, often outperforming training effects in passive or active control conditions (e.g., using conventional training protocols). Therefore, interventions in VR (or extended reality) have the potential to elicit real effects in sports performance enhancement through training of motor and psychological skills and capabilities in athletes, including perception-action skills, strategic, tactical and decision-making, responding to unexpected events, and enhancing psychological resilience and mental performance under pressure. The neurocognitive mechanisms (e.g., visual search behavior, imagery), methodological aspects (e.g., adaptive training difficulty), and the issues of real-world transfer and generalizability via which these potential sports-performance-related improvements may occur are discussed. Finally, limitations of the present review, the included studies, the current state of the field in general as well as an outlook and future perspectives for research designs and directions are taken into consideration.
A Systematic Review on Blended Learning Approach in Language Education

Technological developments have greatly influenced the current culture of life. The patterns of children growing, developing, playing, socializing and communicating have changed a lot in line with the times and the latest facilities. Now children and teenagers are closer to technology and even more comfortable learning and connecting using technology. As the main driver of national education, teachers have a major role to play in imparting knowledge in various methods and strategies that are deemed appropriate. Technology integration in the classroom has been a challenge for education systems to meet the demands of 21st century learning needs. A systematic literature review was conducted on blended learning approaches in education by analysis and synthesis. Multiple databases such as Springer Link, Taylor & Francis, Sage E-Journal and Wiley were used as search engines for related articles during the period 2017-2021. The results show that blended learning approach was mostly researched in higher education and initially focused on students. The majority of blended learning approach research is also coming from developing countries, leading to the need to make blended learning approach adoption easier in particular. Teachers, students, and institutions frequently face issues related to a lack of ICT infrastructure and specialization. The results of this study can be used to develop a plan towards blended learning approach adoption.
Enhanced intra-inter view correlation learning for multi-view subspace clustering

Abstract. Multi-view subspace clustering has attracted widespread attention for its superior clustering effectiveness. However, most of the current methods do not fully exploit the intra-view (sample-to-sample) and inter-view (view-to-view) information among multi-view data. Focusing on this problem, we propose a method called enhanced intra-inter view correlation learning for multi-view subspace clustering (EIVCL). EIVCL simultaneously considers the intra-view and inter-view correlations and introduces two constraint terms for each aspect to capture the data structure information. Specifically, in the intra-view space, we apply the tensor-singular value decomposition (t-SVD)-based tensor nuclear norm (TNN) on the tensor formed by stacking self-representative coefficient matrices for obtaining the high-order correlation information between samples in the specific view. Moreover, we introduce a hypergraph-induced Laplace regularization term to preserve the local geometric structure within views. In the inter-view space, we impose the t-SVD-based TNN on the rotated tensor to obtain the multiple views correlation. Furthermore, we utilize the kernel dependence metric, namely Hilbert–Schmidt independence criterion, to capture the high-order non-linear relationships between views. In addition, all above strategies are integrated into a unified clustering framework, which is solved by our proposed optimization algorithm based on the alternating direction method of multipliers. Extensive experiments on six benchmark datasets demonstrate that EIVCL outperforms several state-of-the-art multi-view algorithms.
Measuring and modeling the influence of atmospheric turbulence on a 2-µm laser beam

Within the scope of NATO SET-304 research task group, a field trial was conducted in October/2022 at the ONERA campus located in Le Fauga, Toulouse, France. Part of this trial was devoted to the study of the influence of atmospheric turbulence on laser beam propagation. In this paper, measurement results obtained during the trial for a laser beam degraded by optical turbulence are presented along with the results of analytical expressions and a numerical model. The beam of a 50-Hz pulsed 2-µm laser source (owned and operated by FFI, Norway) was focused on a target screen located approximately 1 km away from the laser emitter. Throughout the trial under various atmospheric turbulence strengths (C_n^2 varying between 5×10-16 m-2/3 and 3×10-13 m-2/3), the scattered laser radiation was captured with an infrared camera (owned and operated by TÜBİTAK BİLGEM/İLTAREN, Türkiye) looking at the nearby target screen in an oblique direction. Also, at different times of the trial, a point receiver setup consisting of two photo-diodes was positioned in the propagation path to directly capture intensity fluctuations of the incident laser beam for further investigation of the turbulence effects. The infrared camera was triggered by a 100-Hz trigger signal that enabled successive recordings of the laser beam profiles and instantaneous background. Several post-processing techniques (background subtraction, 2X soft aperture etc.) are performed on the recordings to correctly evaluate the effect of atmospheric turbulence on laser beams such as beam spreading, wandering, and degradation. Calculated beam metrics such as short- and long-term beam radii and beam wander variance are compared with the results obtained from analytical expressions and it is observed that measurement results match well with theoretical expectations. Furthermore, propagation scenarios encountered in the trial are simulated using a wave-optics-based split-step phase screen model with/without considering temporal dynamics. Numerical model results also show good agreement with the measurement results.
Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning

Background: Breast cancer is one of the most common malignant tumors in women. A noninvasive ultrasound examination can identify mammary-gland-related diseases and is well tolerated by dense breast, making it a preferred method for breast cancer screening and of significant clinical value. However, the diagnosis of breast nodules or masses via ultrasound is performed by a doctor in real time, which is time-consuming and subjective. Junior doctors are prone to missed diagnoses, especially in remote areas or grass-roots hospitals, due to limited medical resources and other factors, which bring great risks to a patient’s health. Therefore, there is an urgent need to develop fast and accurate ultrasound image analysis algorithms to assist diagnoses. Methods: We propose a breast ultrasound image-based assisted-diagnosis method based on convolutional neural networks, which can effectively improve the diagnostic speed and the early screening rate of breast cancer. Our method consists of two stages: tumor recognition and tumor classification. (1) Attention-based semantic segmentation is used to identify the location and size of the tumor; (2) the identified nodules are cropped to construct a training dataset. Then, a convolutional neural network for the diagnosis of benign and malignant breast nodules is trained on this dataset. We collected 2057 images from 1131 patients as the training and validation dataset, and 100 images of the patients with accurate pathological criteria were used as the test dataset. Results: The experimental results based on this dataset show that the MIoU of tumor location recognition is 0.89 and the average accuracy of benign and malignant diagnoses is 97%. The diagnosis performance of the developed diagnostic system is basically consistent with that of senior doctors and is superior to that of junior doctors. In addition, we can provide the doctor with a preliminary diagnosis so that it can be diagnosed quickly. Conclusion: Our proposed method can effectively improve diagnostic speed and the early screening rate of breast cancer. The system provides a valuable aid for the ultrasonic diagnosis of breast cancer.
Lack of continuity of the San Andreas Fault in southern California: Three-dimensional fault models and earthquake scenarios

[1] The 1200-km-long San Andreas Fault loses its apparent continuity in southern California near San Gorgonio Pass [Allen, 1957], which raises significant questions given the dominant role of this fault in active California tectonics. What is the fundamental three-dimensional (3-D) geometry and kinematic behavior of the San Andreas fault system in this complex region? Is a throughgoing, if complex, San Andreas rupture from the Mojave Desert to the Coachella Valley possible? We have explored the issue of 3-D continuity by mapping over 60 faults in this region to depths of 15–20 km from hypocenter locations and focal mechanisms. We were able to constrain the 3-D geometry of the San Andreas fault zone (SAF) near San Gorgonio Pass from the 3-D geometry of the fault network surrounding it. The most likely configuration is for the San Andreas Fault to merge into the shallow-dipping San Gorgonio Pass thrust northwest of Indio. We concluded that there is no direct continuity at present but rather a network of faults, and the only kind of rupture possible for the SAF in this region is a complex rupture, involving both strike-slip and reverse faulting. GPS measurements also suggest that despite the fact that large motions must have occurred in the past based on offset geologic markers, only minor motion is occurring today in this area. Applying our findings about the fault geometry, we explored several simple earthquake scenarios to determine the most favorable conditions for a throughgoing rupture of the San Andreas fault system from the Mojave Desert to the Coachella Valley.
Temperature-Dependent Cascades in Irradiated InxGa1-xN (x < 0.34) Materials

Irradiated In0.32Ga0. 68N in previous research showed temperature-dependent changes in Raman spectra, which indicates the crucial role of temperature in the evolution of cascades. To reveal the atomic-scale reason for temperature-dependent changes in Raman spectra, this article investigates the overlapping cascades in irradiated In0.25Ga0. 75N and In0.32Ga0. 68N at different temperatures with the molecular dynamics (MD) method. The presented results indicate that defects in irradiated samples at elevated temperatures experienced enhanced atomic recombination in overlapping cascades. The temperature-dependent defects in In0.32Ga0. 68N relate to the temperature-dependent shift of peaks <inline-formula> <tex-math notation="LaTeX">$\text{A}_{1}^{\text {LO}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\text{E}_{1}^{\text {LO}}$ </tex-math></inline-formula> in Raman spectra by the LO phonon–plasmon coupling. The distribution of Ga clusters, N clusters, and In clusters is expected to have additional effects on localized electron carrier concentration which further influences the wavenumbers of peaks <inline-formula> <tex-math notation="LaTeX">$\text{A}_{1}^{\text {LO}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\text{E}_{1}^{\text {LO}}$ </tex-math></inline-formula> in Raman spectra. The presented results provide academic support for reducing the defect production rate of the ion implantation doping technique at elevated temperatures.
Virus Effects on Vine Growth and Fruit Components of Cabernet Sauvignon on Six rootstocks

Materials and Methods Vine growth and fruit components of Cabernet Sauvignon clone FPS 5 with and without viruses and grafted onto six rootstocks was evaluated. Rootstocks included: Couderc 3309, Kober 5BB, Paulsen 1103, Richter 110, Millardet et de Grasset 101-14, and Rupestris St. George. Rootstocks were selected as representative of commercial rootstocks for the region with the exception of Rupestris St. George which was selected for its known virus tolerance. The trial was established at the UC Oakville Station in Napa Valley with 1.5 x 2 meter (vine x row) spacing in rows oriented North-South. Budwood was field budded onto certified rootstock in spring, 2002, in a randomized complete block design with 5 replications and 5 – 6 vines/ treatment/ replication. Vines were trained to a bilateral cordon on a vertical shoot positioned (VSP) trellis, and spur-pruned.