Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-Reversal of Nonlinear Waves - Applicability and Limitations

Time-reversal (TR) refocusing of waves is one of fundamental principles in wave physics. Using the TR approach, "Time-reversal mirrors" can physically create a time-reversed wave that exactly refocus back, in space and time, to its original source regardless of the complexity of the medium as if time were going backwards. Lately, laboratory experiments proved that this approach can be applied not only in acoustics and electromagnetism but also in the field of linear and nonlinear water waves. Studying the range of validity and limitations of the TR approach may determine and quantify its range of applicability in hydrodynamics. In this context, we report a numerical study of hydrodynamic TR using a uni-directional numerical wave tank, implemented by the nonlinear high-order spectral method, known to accurately model the physical processes at play, beyond physical laboratory restrictions. The applicability of the TR approach is assessed over a variety of hydrodynamic localized and pulsating structures' configurations, pointing out the importance of high-order dispersive and particularly nonlinear effects in the refocusing of hydrodynamic stationary envelope solitons and breathers. We expect that the results may motivate similar experiments in other nonlinear dispersive media and encourage several applications with particular emphasis on the field of ocean engineering.
Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning

Cervical cancer has high morbidity and mortality rates, affecting hundreds of thousands of women worldwide and requiring more accurate screening for early intervention and follow-up treatment. Cytology is the current dominant clinical screening approach, and though it has been used for decades, it has unsatisfactory sensitivity and specificity. In this work, fluorescence lifetime imaging microscopy (FLIM) was used for the imaging of exfoliated cervical cells in which an endogenous coenzyme involved in metabolism, namely, reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H], was detected to evaluate the metabolic status of cells. FLIM images from 71 participants were analyzed by the unsupervised machine learning method to build a prediction model for cervical cancer risk. The FLIM method combined with unsupervised machine learning (FLIM-ML) had a sensitivity and specificity of 90.9% and 100%, respectively, significantly higher than those of the cytology approach. One cancer recurrence case was predicted as high-risk several months earlier using this method as compared to using current clinical methods, implying that FLIM-ML may be very helpful for follow-up cancer care. This study illustrates the clinical applicability of FLIM-ML as a detection method for cervical cancer screening and a convenient tool for follow-up cancer care.
In Silico Investigation of SNR and Dermis Sensitivity for Optimum Dual-Channel Near-Infrared Glucose Sensor Designs for Different Skin Colors

Diabetes is a serious health condition that requires patients to regularly monitor their blood glucose level, making the development of practical, compact, and non-invasive techniques essential. Optical glucose sensors—and, specifically, NIR sensors—have the advantages of being non-invasive, compact, inexpensive, and user-friendly devices. However, these sensors have low accuracy and are yet to be adopted by healthcare providers. In our previous work, we introduced a non-invasive dual-channel technique for NIR sensors, in which a long channel is utilized to measure the glucose level in the inner skin (dermis) layer, while a short channel is used to measure the noise signal of the superficial skin (epidermis) layer. In this work, we investigated the use of dual-NIR channels for patients with different skin colors (i.e., having different melanin concentrations). We also adopted a Monte Carlo simulation model that takes into consideration the differences between different skin layers, in terms of blood content, water content, melanin concentration in the epidermis layer, and skin optical proprieties. On the basis of the signal-to-noise ratio, as well as the sensitivities of both the epidermis and dermis layers, we suggest the selection of wavelengths and source-to-detector separation for optimal NIR channels under different skin melanin concentrations. This work facilitates the improved design of a compact and non-invasive NIR glucose sensor that can be utilized by patients with different skin colors.
LEPTON ACCELERATION IN THE VICINITY OF THE EVENT HORIZON: HIGH-ENERGY AND VERY-HIGH-ENERGY EMISSIONS FROM ROTATING BLACK HOLES WITH VARIOUS MASSES

We investigate the electrostatic acceleration of electrons and positrons in the vicinity of the event horizon, applying the pulsar outer-gap model to black hole (BH) magnetospheres. During a low accretion phase, the radiatively inefficient accretion flow (RIAF) cannot emit enough MeV photons that are needed to sustain the force-free magnetosphere via two-photon collisions. In such a charge-starved region (or a gap), an electric field arises along the magnetic field lines to accelerate electrons and positrons into ultra-relativistic energies. These relativistic leptons emit copious gamma rays via curvature and inverse-Compton (IC) processes. Some of such gamma rays collide with the submillimeter-IR photons emitted from the RIAF to materialize as pairs, which polarize to partially screen the original acceleration electric field. It is found that the gap gamma-ray luminosity increases with decreasing accretion rate. However, if the accretion rate decreases too much, the diminished RIAF soft photon field can no longer sustain a stationary pair production within the gap. As long as a stationary gap is formed, the magnetosphere becomes force-free outside the gap by the cascaded pairs, irrespective of the BH mass. If a nearby stellar-mass BH is in quiescence, or if a galactic intermediate-mass BH is in a very low accretion state, its curvature and IC emissions are found to be detectable with Fermi/LAT and imaging atmospheric Cherenkov telescopes (IACT). If a low-luminosity active galactic nucleus is located within about 30 Mpc, the IC emission from its supermassive BH is marginally detectable with IACT.
Duality and gauge invariance of noncommutative spacetime Podolsky electromagnetic theory

The interest in higher derivatives field theories has its origin mainly in their influence concerning the renormalization properties of physical models and to remove ultraviolet divergences. In this letter we have introduced the noncommutative (NC) version of the Podolsky theory and we investigated the effect of the noncommutativity over its original gauge invariance property. We have demonstrated precisely that the noncommutativity spoiled the gauge invariance of the original action. After that we have used the Noether dualization technique to obtain a dual and gauge invariant action. More than to obtain the NC Podolsky theory, we have another motivation in this work, which is to show that, although the introduction of noncommutativity spoils the gauge invariance, it is possible to recover this property using a standard dualization method which did not need any modification due to any NC effect in the original theory, by the way
Brain volume loss and physical and cognitive impairment in naïve multiple sclerosis patients treated with fingolimod: prospective cohort study in Buenos Aires, Argentina

Abstract Background The percentage of brain volume loss (PBVL) has been classically considered as a biomarker in multiple sclerosis (MS). Objective The objective of the present study was to analyze if the PBVL during the 1 st year after the onset of the disease predicts physical and cognitive impairment (CI). Methods Prospective study that included naïve patients without cognitive impairment who initiated MS treatment with fingolimod. Patients were followed for 3 years and relapses, expanded disability status scale (EDSS) progression (defined as worsening of 1 point on the EDSS), the annual PBVL (evaluated by structural image evaluation using normalization of atrophy [SIENA]), and the presence of CI were evaluated. Cognitive impairment was defined in patients who scored at least 2 standard deviations (SDs) below controls on at least 2 domains. The PBVL after 1 year of treatment with fingolimod was used as an independent variable, while CI and EDSS progression at the 3 rd year of follow-up as dependent variables. Results A total of 71 patients were included, with a mean age of 35.4 ± 3 years old. At the 3 rd year, 14% of the patients were classified as CI and 6.2% had EDSS progression. In the CI group, the PBVL during the 1 st year was - 0.52 (±0.07) versus -0.42 (±0.04) in the no CI group ( p < 0.01; odds ratio [OR] = 2.24; 95% confidence interval [CI]: 1.72–2.44). In the group that showed EDSS progression, the PBVL during the 1 st year was - 0.59 (±0.05) versus - 0.42 (±0.03) ( p < 0.01; OR = 2.33; 95%CI: 1.60–2.55). Conclusions A higher PBVL during the 1 st year in naïve MS patients was independently associated with a significant risk of CI and EDSS progression.
IDCS J1426.5+3508: COSMOLOGICAL IMPLICATIONS OF A MASSIVE, STRONG LENSING CLUSTER AT z = 1.75

The galaxy cluster IDCS J1426.5+3508 at z = 1.75 is the most massive galaxy cluster yet discovered at z > 1.4 and the first cluster at this epoch for which the Sunyaev–Zel’Dovich effect has been observed. In this paper, we report on the discovery with Hubble Space Telescope imaging of a giant arc associated with this cluster. The curvature of the arc suggests that the lensing mass is nearly coincident with the brightest cluster galaxy, and the color is consistent with the arc being a star-forming galaxy. We compare the constraint on M200 based upon strong lensing with Sunyaev–Zel’Dovich results, finding that the two are consistent if the redshift of the arc is z ≳ 3. Finally, we explore the cosmological implications of this system, considering the likelihood of the existence of a strongly lensing galaxy cluster at this epoch in a ΛCDM universe. While the existence of the cluster itself can potentially be accommodated if one considers the entire volume covered at this redshift by all current high-redshift cluster surveys, the existence of this strongly lensed galaxy greatly exacerbates the long-standing giant arc problem. For standard ΛCDM structure formation and observed background field galaxy counts this lens system should not exist. Specifically, there should be no giant arcs in the entire sky as bright in F814W as the observed arc for clusters at z ⩾ 1.75, and only ∼0.3 as bright in F160W as the observed arc. If we relax the redshift constraint to consider all clusters at z ⩾ 1.5, the expected number of giant arcs rises to ∼15 in F160W, but the number of giant arcs of this brightness in F814W remains zero. These arc statistic results are independent of the mass of IDCS J1426.5+3508. We consider possible explanations for this discrepancy.
Characterization of Trehalose-6-Phosphate Synthase and Trehalose-6-Phosphate Phosphatase Genes of Tomato (Solanum lycopersicum L.) and Analysis of Their Differential Expression in Response to Temperature

In plants, the trehalose biosynthetic pathway plays key roles in the regulation of carbon allocation and stress adaptation. Engineering of the pathway holds great promise to increase the stress resilience of crop plants. The synthesis of trehalose proceeds by a two-step pathway in which a trehalose-phosphate synthase (TPS) uses UDP-glucose and glucose-6-phosphate to produce trehalose-6 phosphate (T6P) that is subsequently dephosphorylated by trehalose-6 phosphate phosphatase (TPP). While plants usually do not accumulate high amounts of trehalose, their genome encodes large families of putative trehalose biosynthesis genes, with many members lacking obvious enzymatic activity. Thus, the function of putative trehalose biosynthetic proteins in plants is only vaguely understood. To gain a deeper insight into the role of trehalose biosynthetic proteins in crops, we assessed the enzymatic activity of the TPS/TPP family from tomato (Solanum lycopersicum L.) and investigated their expression pattern in different tissues as well as in response to temperature shifts. From the 10 TPS isoforms tested, only the 2 proteins belonging to class I showed enzymatic activity, while all 5 TPP isoforms investigated were catalytically active. Most of the TPS/TPP family members showed the highest expression in mature leaves, and promoter–reporter gene studies suggest that the two class I TPS genes have largely overlapping expression patterns within the vasculature, with only subtle differences in expression in fruits and flowers. The majority of tomato TPS/TPP genes were induced by heat stress, and individual family members also responded to cold. This suggests that trehalose biosynthetic pathway genes could play an important role during temperature stress adaptation. In summary, our study represents a further step toward the exploitation of the TPS and TPP gene families for the improvement of tomato stress resistance.
Time-Indexed Effect Size Metric for K-12 Reading and Math Education Evaluation

Through a synthesis of test publisher norms and national longitudinal datasets, this study provides new national norms of academic growth in K-12 reading and math that can be used to reinterpret conventional effect sizes in time units. We propose d , a time-indexed effect size metric to estimate how long it would take for an “untreated” control group to reach the treatment group outcome in terms familiar to educators—years/months of schooling. It serves as a supplement to conventional effect size metrics such as Cohen’s d by taking into account different amounts of time needed for learning at different age or grade levels. Through applications to Project STAR small class effects and NAEP racial achievement gaps, we demonstrate how to interpret and use d . It is expected to provide a more developmentally appropriate context for interpreting the size of an effect, a step toward bridging the gap between educational research and practice.
Temporal and Spatial Variation Characteristics of Actual Evapotranspiration in the Yiluo River Basin Based on the Priestley–Taylor Jet Propulsion Laboratory Model

Evapotranspiration is an important part of the hydrological cycle, affecting the terrestrial hydrological process and the relationship between water resource transformation. Analyzing and mastering the temporal and spatial variations in evapotranspiration are of great significance for the rational development, utilization, and protection of water resources in the basin. Based on MODIS remote sensing data and combined with meteorological and hydrological data, the PT-JPL model was used to estimate the actual evapotranspiration of the Yiluo River Basin, and the applicability of the PT-JPL model for estimating actual evapotranspiration in the basin was analyzed. The spatial and temporal characteristics of actual evapotranspiration in the Yiluo River Basin were analyzed, and the correlation between actual evapotranspiration in the basin and influencing factors such as precipitation, temperature, and vegetation was explored. The main conclusions are as follows: the PT-JPL model has good applicability in the Yiluo River Basin; the total actual evapotranspiration in the basin showed a decreasing trend, which was consistent with the trend of precipitation, indicating that the actual evapotranspiration may be limited by water conditions in the study area. The evapotranspiration of the basin was higher in the upstream and lower in the downstream, which may be related to the better vegetation coverage in the upper basin than in the downstream. As an important indicator of vegetation change, NDVI showed a good correlation with evapotranspiration, which also verified this. In addition, the correlation between evapotranspiration and temperature was also good.
Bulletin of Electrical Engineering and Informatics

Received Apr 7, 2022 Revised Jul 28, 2022 Accepted Aug 30, 2022 Peer to peer lending is famous for easy and fast loans from complicated traditional lending institutions. Therefore, big data and machine learning are needed for credit risk analysis, especially for potential defaulters. However, data imbalance and high computation have a terrible effect on machine learning prediction performance. This paper proposes a stacking ensemble learning with features selection based on embedded techniques (gradient boosted trees (GBDT), random forest (RF), adaptive boosting (AdaBoost), extra gradient boosting (XGBoost), light gradient boosting machine (LGBM), and decision tree (DT)) to predict the credit risk of individual borrowers on peer to peer (P2P) lending. The stacking ensemble model is created from a stack of meta-learners used in feature selection. The feature selection+ stacking model produces an average of 94.54% accuracy and 69.10 s execution time. RF meta-learner+Stacking ensemble is the best classification model, and the LGBM meta-learner+stacking ensemble is the fastest execution time. Based on experimental results, this paper showed that the credit risk prediction for P2P lending could be improved using the stacking ensemble model in addition to proper feature selection.
Development and Validation of a Prediction Model for Chronic Post-Surgical Pain After Thoracic Surgery in Elderly Patients: A Retrospective Cohort Study

Purpose Chronic post-surgical pain (CPSP) is one of the adverse outcomes after surgery, especially in thoracotomy. However, the prevalence of CPSP in elderly adults (≥65 years), is still limited. Therefore, the present study was undertaken to establish and validate the prediction model of CPSP in those patients after thoracic surgery, including thoracotomy and video-assisted thoracoscopic surgery. Patients and Methods This retrospective, observational single-center cohort study was conducted in Nanfang Hospital, Southern Medical University, which randomly and consecutively collected 577 elderly patients who underwent thoracic surgery between January 1, 2017, and December 31, 2020. According to the Akaike information criterion, the prediction model was built based on all the data and was validated by calibration with 500 bootstrap samples. Results The mean age of participants was 69.09±3.80 years old, and 63.1% were male. The prevalence of CPSP was 26.9%. Age more than 75 years, BMI, blood loss, longer length of hospital stays, and higher pre-operative neutrophil count were associated with CPSP. Except for these factors, we incorporated history of drinking to build up the prediction model. The areas under the curve (AUCs) of the prediction models were 0.66 (95% CI, 0.61–0.71) and 0.64 (95% CI, 0.59–0.69) in the observational and validation cohorts, respectively. And the calibration curve of the predictive model showed a good fit between the predicted risk of CPSP and observed outcomes in elderly patients. Conclusion The present developed model may help clinicians to find high-risk elderly patients with CPSP after thoracic surgery and take corresponding measures in advance to reduce the incidence of CPSP and improve their life quality.
Content Analysis of Al-Islam and Kemuhammadiyahan Values on Multivariable Calculus Materials

: The purpose of this research is to build a category of vector field material in Multivariable Calculus learning into the values of Al Islam and Muhammadiyah. The content analysis method uses MaxQDA to code the terms that emerge from the interview texts generated by the participants related to the intersection of the two subjects. The selection of participants is important in order to avoid bias in the description of the interview. Participants were selected 5 out of 31 students based on their knowledge of Multivariable Calculus material and experience in participating in organizational activities of the Muhammadiyah Student Association and the Orientation of Islamic Fundamentals. The results showed that there were three key categories, namely, death, hard work, and brotherhood. Since the vector principle is the same as the beginning of life and the final destination of a person, vector material is closely related to death. Likewise, integral material has a connection with hard work because the principle of line integral can be seen in line with the efforts made by someone. The vector field material is related to the theme of brotherhood because the principle of the vector field is in groups that can be seen as a collection of people. Metode content analysis MaxQDA yang dilakukan seseorang. Adapun vektor memiliki keterkaitan
Studies on Extremal Segments in Random Sequences

We review our main findings on the size distribution of the largest neutral segments in a sequence of N randomly charged monomers. Upon mapping to one--dimensional random walks (RWs), this corresponds to finding the probability distribution for the size L of the largest segment that returns to its starting position in an N--step RW. Using analytical, exact enumeration, and Monte Carlo methods, we reveal the complex structure of the probability distribution in the large N limit. In particular, the size of the longest neutral segment has a distribution with a square-root singularity at \ell\equiv L/N = 1, an essential singularity at \ell = 0, and a discontinuous derivative at \ell = 1/2.
International Collaboration in Higher Education: A Reflection of Student and Lecturer Experiences

The influence of international collaboration opportunities on student and lecturer experience in higher education is unmistakable. Not only do international collaboration projects provide the opportunity for internalisation, the improvement of cultural sensitivity and understanding, as well as problem-solving in culturally diverse teams, it also provides a reflection opportunity for lecturers on current lecturer teaching practices and how teaching practices can be improved. As a result, most higher education institutions are starting to include international project-based learning projects. This paper is based on one of the international teaching mobility projects of the NWU, Centre for Teaching and Learning, that focuses on transforming university teaching, learning and research, which is in line with the National Framework for Enhancing Academics as university teachers (DHET, 2018), the UCDG (Draft Ministerial Statement 2012-2023) and the NWU Annual Performance Plan (APP). The project entailed that students from different international universities, including students from Indonesia, South Africa, the Netherlands, and Canada, work together in teams on a 24-hour design thinking project. This paper outlines the international collaboration project, provides the background of the project, a reflection on the student and lecturer experiences, and concludes with an action plan for future international collaboration projects.
Universal Motion Generator: Trajectory Autocompletion by Motion Prompts

Foundation models [1], which are large neural networks trained on massive datasets, have shown impressive generalization in both the language and vision domain [2][3]. While fine-tuning foundation models for new tasks at test-time is impractical due to billions of parameters in those models, prompts have been employed to re-purpose models for test-time tasks on the fly. In the language domain, a prompt can be an incomplete sentence for a large language model to auto-complete as seen in Fig. 2 (a) [2] [4]. In the vision domain, a prompt can be some pixels added to an image for a generic large vision model to perform specific classification tasks as seen in Fig. 2 (b) [5]. In both cases, prompts re-purpose generically trained foundation models with frozen weights to generate conditional distributions that solve specific downstream tasks.