Miracle-dz/UniFAR-FTUs huggingface dataset:
Intra-arterial chemotherapy for retinoblastoma: Experience from the pediatric ophthalmology referral center in Malaysia with literature review

PURPOSE: We aimed to analyze our 4-year experience of intra-arterial chemotherapy (IAC) for retinoblastoma (RB) and to examine the tumor response, globe salvage, mortality, and safety profile of IAC in the Malaysian profile. MATERIALS AND METHODS: This was a retrospective, interventional case series. A total of 22 eyes of 20 patients with RB who underwent IAC using melphalan and topotecan from January 2018 to December 2021 in Hospital Kuala Lumpur were retrospectively reviewed. Tumor response, globe salvage, mortality, and safety profile of IAC were compared based on the International Classification of Retinoblastoma. RESULTS: The mean patient age at IAC was 21.3 months. An overall globe salvage rate of 63.6% was observed: more specifically, 100% for Group A, 75% for Groups B and C, 66.7% for Group D, and 42.9% for Group E. Poor tumor response after IAC was significantly associated with a lesser chance of globe salvage (P = 0.045). The overall rate of good tumor response following IAC was 77.3%. Specifically, rates of good tumor response in each group were 100%, 75%, 75%, 83.3% and 71.4% in group A, B, C, D and E, respectively. The mortality rate was 5%. Complications (per-catheterization) included cerebral infarct (2.2%), oxygen desaturation (2.2%), vomiting (26.1%), periorbital edema (8.8%), ptosis (6.5%), fever, femoral hematoma, and hyperpigmentation over lid (4.4% each). CONCLUSION: Four-year experience showed that IAC is a safe and effective method for RB management. Patients with a poor response after IAC may have a lower chance of globe salvage. Careful patient selection is of utmost importance to achieve the best outcome in a setting of limited health-care resources.
Improved stability criteria for T–S fuzzy systems with time-varying delay via convex analysis approach

This study is concerned with stability analysis for Takagi–Sugeno (T–S) fuzzy systems with time-varying delay. By applying the idea of scalar function and augmented vectors, dealing with the positivity of a functional, a novel simple Lyapunov–Krasovskii functional is established. Then, fuzzy-weighting dependent matrices and the reciprocally convex approach are introduced to deal with two integral parts, convex analysis approach is used to solve the useful terms (which are ignored in previous methods), with the above methods some less conservative stability criteria is obtained. Finally, three numerical examples are provided to show the effectiveness and improvements of the proposed conditions.
Ecient econometric inference based on estimated likelihoods

Suppose we wish to carry out likelihood based inference but we solely have an unbiased simulation based estimator of the likelihood. We note that unbiasedness is enough when the estimated likelihood is used inside a Metropolis-Hastings algorithm. This result has recently been introduced in statistics literature by Andrieu, Doucet, and Holenstein (2007) and is perhaps surprising given the celebrated results on maximum simulated likelihood estimation. It can be widely applied in microeconomics, macroeconomics and nancial econometrics. One way of generating unbiased estimates of the likelihood is by the use of a particle lter. We illustrate these methods on three problems in econometrics, producing rather generic methods. Taken together, these methods imply that if we can simulate from an economic model we can carry out likelihood based inference using its simulations.
Improved Separation of Polyphonic Chamber Music Signals by Integrating Instrument Activity Labels

The separation of music signals is a very challenging task, especially in case of polyphonic chamber music signals because of the similar frequency ranges and sound characteristics of the different instruments to separate. In this work, a joint separation approach in the time domain with a U-Net architecture is extended to incorporate additional time-dependent instrument activity information for improved instrument track extractions. Different stages are investigated to integrate the additional information, but an input before the deepest encoder block achieves best separation results as well as highest robustness against randomly wrong labels. This approach outperforms a label integration by multiplication and the input of a static instrument label. Targeted data augmentation by incoherent mixtures is used for a trio example of violin, trumpet, and flute to improve separation results. Moreover, an alternative separation approach with one independent separation model for each instrument is investigated, which enables a more flexible architecture. In this case, an input after the deepest encoder block achieves best separation results, but the robustness is slightly reduced compared to the joint model. The improvements by additional information on active instruments are verified by using real instrument activity predictions for both the joint and the independent separation approaches.
Fibre Bragg gratings for high spectral and temporal resolution astronomical observations

Dynamic spectral analysis of astronomical events has the potential to deliver the information needed to clarify or complete important theoretical descriptions of astronomical phenomena. There is currently a lack of detailed sub-minute observations due to limitations in instruments and detectors. Here, we present an investigation into the feasibility of using fibre Bragg gratings (FBGs) as single-line spectral filters specifically for temporal spectral astronomy, attaining both a high spectral and fast temporal resolution simultaneously. We present the device concept and discuss it in the context of two readily available FBG profiles. We demonstrate that this instrument concept could resolve spectral shifts down to 0.02 nm (3.9 km s −1 ) with sub-second temporal resolution on a 4-m class telescope, which is far superior to existing techniques that attain resolutions of 0.05 nm over several minutes.
ML Approach to Improve the Costs and Reliability of a Wireless Sensor Network

Temperature-controlled closed-loop systems are vital to the transportation of produce. By maintaining specific transportation temperatures and adjusting to environmental factors, these systems delay decomposition. Wireless sensor networks (WSN) can be used to monitor the temperature levels at different locations within these transportation containers and provide feedback to these systems. However, there are a range of unique challenges in WSN implementations, such as the cost of the hardware, implementation difficulties, and the general ruggedness of the environment. This paper presents the novel results of a real-life application, where a sensor network was implemented to monitor the environmental temperatures at different locations inside commercial temperature-controlled shipping containers. The possibility of predicting one or more locations inside the container in the absence or breakdown of a logger placed in that location is explored using combinatorial input–output settings. A total of 1016 machine learning (ML) models are exhaustively trained, tested, and validated in search of the best model and the best combinations to produce a higher prediction result. The statistical correlations between different loggers and logger combinations are studied to identify a systematic approach to finding the optimal setting and placement of loggers under a cost constraint. Our findings suggest that even under different and incrementally higher cost constraints, one can use empirical approaches such as neural networks to predict temperature variations in a location with an absent or failed logger, within a margin of error comparable to the manufacturer-specified sensor accuracy. In fact, the median test accuracy is 1.02 degrees Fahrenheit when using only a single sensor to predict the remaining locations under the assumptions of critical system failure, and drops to as little as 0.8 and 0.65 degrees Fahrenheit when using one or three more sensors in the prediction algorithm. We also demonstrate that, by using correlation coefficients and time series similarity measurements, one can identify the optimal input–output pairs for the prediction algorithm reliably under most instances. For example, discrete time warping can be used to select the best location to place the sensors with a 92% match between the lowest prediction error and the highest similarity sensor with the rest of the group. The findings of this research can be used for power management in sensor batteries, especially for long transportation routes, by alternating standby modes where the temperature data for the OFF sensors are predicted by the ON sensors.
Domain-Adaptive Emotion Recognition Based on Horizontal Vertical Flow Representation of EEG Signals

With the development of cognitive science and brain science, brain-computer interface technology can use Electroencephalogram (EEG) signals to better represent the inner changes of emotions. In this paper, A video-induced emotional stimulation experimental paradigm was designed, and the EEG signals of 15 hearing-impaired subjects under three emotions (positive, neutral, and negative) were collected. Considering the flow diffusion properties of EEG signals, we used the diffusion effect based on horizontal representation and vertical representation forms to obtain the spatial domain features. After EEG preprocessing, the differential entropy feature (DE) in the frequency domain is extracted. The frequency domain features of 62 channels are delivered to two Bi-directional Long Short-Term Memory (BiLSTM) to obtain spatial domain features of horizontal and vertical representations respectively, and then two kinds of domain features are fused by the residual network. The attention mechanism is applied to effectively extract emotional representational information from the fused features. To solve the cross-subject problem of emotion recognition, the domain adaptation method is utilized, and a center alignment loss function is applied to increase the distance of inter-class and reduce the distance of intra-class. According to the experimental results, the average accuracies of 75.89% (subject- dependent) and 69.59% (cross-subject) are obtained. Moreover, the validation was also performed on the public dataset SEED, achieving average accuracies of 93.99% (subject-dependent) and 84.22% (cross-subject), respectively.
Quality Determination of a High-Pressure Processed Avocado Puree-Based Smoothie Beverage

The aim was to study the quality stability of a high-pressure (HP) processed avocado puree-based smoothie beverage and to determine its shelf life. To achieve this mathematical description of HP process parameters (pressure, temperature, and pH conditions) on polyphenoloxidase (PPO) inactivation of avocado-puree (base of the smoothie beverage), use of the appropriate kinetic models was undertaken. Inactivation rate constants were obtained for combinations of constant pressure (600, 700, 750 MPa) and temperature (25, 35, 45 °C) for pH values 4 and 5. According to the Eyring and Arrhenius equations, activation volumes and activation energies, respectively, representing pressure and temperature dependence of the inactivation rate constant, were calculated for all temperatures and pressures studied. The combined use of HP led to PPO inactivation (<10% remaining PPO activity). An increase in the temperature at pressure 600 or 750 MPa caused an increase in PPO inactivation (4.5 and 9.0%, respectively). The ultimate goal was to produce a HP processed avocado puree-based smoothie beverage (containing acid whey and other ingredients) with superior quality and increased shelf life (under refrigeration). The blended ingredients were HP processed in PET packages (600 MPa-25 °C-10 min, 600 MPa-35 °C-10 min, 750 MPa-25 °C-5 min, 750 MPa-35 °C-5 min) based on PPO inactivation as well as industrial practices. Non-processed as well as thermally (TM) processed (90 °C-5 min) samples were used as control samples. No significant differences were found in sensorial attributes between non-processed and HP samples, although the aroma and acceptability scores decreased significantly for thermally pasteurized smoothies. Based on the data obtained, 600 MPa-25/35 °C-10 min are sufficient to obtain safe smoothies (of pH 5 approximately) (up to 6 months) whose organoleptic properties are equally as acceptable to consumers as freshly made smoothies.
Vehicle-Mounted Solar Occultation Flux Fourier Transform Infrared Spectrometer and Its Remote Sensing Application

For the demand of rapid monitoring of pollution gas disorganized emissions in industrial parks, this paper studies the solar fast tracker system of vehicle-mounted SOF-FTIR (Solar Occultation Flux Fourier Transform Infrared Spectroscopy) system, where the spectrometer directly measures the broadband absorption spectrum of solar radiation light. A fast portable solar tracking system based on PSD (position sensitive detector) is designed, the mathematical model of solar spot position on the PSD surface source is established, and the optimal optical design parameters are simulated using the model. The dead-zone integral separation PID (Proportion Integration Differentiation) control algorithm is used to track the trajectory of the solar, and the light spot position model is used to nonlinearly compensate the output of PID control so that the PID controller has the same control precision and response speed in different error areas. Experimental analysis of the solar tracking performance of the vehicle-mounted SOF-FTIR under static and dynamic conditions, as well as the spectral effects on the measurements under static vehicle, constant speed, and turning driving conditions. The remote sensing application experiment of vehicle-mounted SOF-FTIR pollution gas emission flux was carried out in a tire factory in Hefei City, Anhui Province. A vehicle-mounted SOF-FTIR system realized the qualitative and quantitative analysis of the pollution gas at the boundary of the tire plant and calculated the flux of each component pollution gas. The emission flux of pollution gas was highly consistent with the actual pollution distribution of the tire plant. The results show that the positioning accuracy of PSD in the vehicle tracking experiment can also meet SOF-FTIR requirements for solar tracking. The remote sensing system will be useful in the field of atmospheric environment monitoring, and the mobile monitoring of regional pollutant gases based on solar infrared spectroscopy has application value.
The Good News, the Bad News, and the Ugly Truth: A Review on the 3D Interaction of Light Field Displays

Light field displays offer glasses-free 3D visualization, which means that multiple individuals may observe the same content simultaneously from a virtually infinite number of perspectives without the need of viewing devices. The practical utilization of such visualization systems include various passive and active use cases. In the case of the latter, users often engage with the utilized system via human–computer interaction. Beyond conventional controls and interfaces, it is also possible to use advanced solutions such as motion tracking, which may seem seamless and highly convenient when paired with glasses-free 3D visualization. However, such solutions may not necessarily outperform conventional controls, and their true potentials may fundamentally depend on the use case in which they are deployed. In this paper, we provide a review on the 3D interaction of light field displays. Our work takes into consideration the different requirements posed by passive and active use cases, discusses the numerous challenges, limitations and potentials, and proposes research initiatives that could progress the investigated field of science.
Internal Validation of a Real-Time qPCR Kit following the UNE/EN ISO/IEC 17025:2005 for Detection of the Re-Emerging Monkeypox virus

Human mpox is caused by the Monkeypox virus, a microorganism closely related to the Variola virus, both belonging to the Orthopoxvirus genus. Mpox had been considered a rare disease until a global outbreak occurred in 2022. People infected with the virus present similar symptoms to patients suffering smallpox and other rash illnesses, hindering diagnosis. The WHO indicated that no commercial PCR or serology kits are currently widely available. In the present study, the MPXV MONODOSE dtec-qPCR kit was validated following guidelines of the UNE/EN ISO/IEC 17025:2005. The parameters evaluated for the acceptance of the assay were in silico and in vitro specificity, quantitative phase analysis, reliability, and sensitivity. The assay passed validation criteria and yielded an efficiency of 95.8%, high repeatability, reproducibility, and a Limit of Detection and Quantification of at least 10 copies. Results from the validation of the MPXV dtec-qPCR kit were satisfactory. The use of the MONODOSE format (dehydrated single PCR-tubes, ready to use) provided considerable advantages allowing the detection of the Monkeypox virus to be accurately achieved. This detection kit may be considered a reliable, fast, simple, and universally available option.
Prediction of particulate content in oil based on SPA vibration feature selection

: Aiming at the non-stationary characteristics of oil pressure vibration signals containing particulate, a method for predicting particulate content in oil was proposed based on vibration characteristic frequency extraction by vibrational mode decomposition (VMD), variable selection using successive projections algorithm (SPA) and T_S fuzzy identification combined. Firstly, the pressure vibration signal was decomposed by VMD and a series of narrow-band characteristic frequency matrices were obtained. Then, variables were selected using SPA to construct the feature vector matrix. Finally, the feature vector matrix was used as the input of T_S fuzzy identification to identify the content of particulate in oil. The results showed that the VMD reconstruction of the original oil sample pressure signals could well characterize the main variation of the original signal; the 19 variables were selected from the characteristic frequency of the vibration signal from the oil pressure using SPA, the 19 pressure vibration characteristic frequency of 11 sample sets SPA selected was taken as the input variable of T_S identification model; for each set of sample, the predicted output of the content of particulate in oil was obtained, model prediction decision coefficient is 0.8637, the root mean square error is 0.1979, a reasonable prediction effect was obtained.
Increasing Nonfossil Fuel Contributions to Atmospheric Nitrate in Urban China from Observation to Prediction.

China's nitrogen oxide (NOx) emissions have undergone significant changes over the past few decades. However, nonfossil fuel NOx emissions are not yet well constrained in urban environments, resulting in a substantial underestimation of their importance relative to the known fossil fuel NOx emissions. We developed an approach using machine learning that is accurate enough to generate a long time series of the nitrogen isotopic composition (δ15N) of atmospheric nitrate using high-level accuracies of air pollutants and meteorology data. Air temperature was found to be the critical driver of the variation of nitrate δ15N at daily resolution based on this approach, while significant reductions of aerosol and its precursor emissions played a key role in the change of nitrate δ15N on the yearly scale. Predictions from this model found a significant decrease in nitrate δ15N in Chinese megacities (Beijing and Guangzhou as representative cities in the north and south, respectively) since 2013, implying an enhanced contribution of nonfossil fuel NOx emissions to nitrate aerosols (up to 22%-26% in 2021 from 18%-22% in 2013 quantified by an isotope mixing model), as confirmed by the Weather Research and Forecasting model coupled with online chemistry (WRF-Chem) simulation. Meanwhile, the declining contribution in coal combustion (34%-39% in 2013 to 31%-34% in 2021) and increasing contribution of natural gas combustion (11%-14% in 2013 to 14%-17% in 2021) demonstrated the transformation of China's energy structure from coal to natural gas. This approach provides missing records for exploring long-term variability in the nitrogen isotope system and may contribute to the study of the global reactive nitrogen biogeochemical cycle.
High-Resolution Modeling of Size-Resolved Stratospheric Aerosol

In certain applications the, so-called, bin or sectional models are not adequate to model aerosol or cloud droplets. Therefore, the authors developed a continuous, high-resolution in size model of aerosol. The application of this model to the stratospheric Junge layer is presented. The equations with which to model the aerosol as well as the physics underlying the equations are described in detail. In the authors’ approach, the role of vertical advection is emphasized, and in particular, a realistic diffusion coefficient that eliminates vigorous, artificial mixing between vertical levels is used. The equations are solved using appropriate and accurate numerical methods. As a test, the one-dimensional version of the model is applied to the stratospheric aerosol for both quiescent and volcanic periods. The results are able to capture some characteristics of the Junge layer, which cannot be modeled with bin models with large diffusion. It is also found that the effect of a volcanic eruption on the aerosol population can be long lasting, up to five or six years.
Improving radar estimates of rainfall using an input subset of artificial neural networks

Abstract. An input subset including average radar reflectivity (Zave) and its standard deviation (SD) is proposed to improve radar estimates of rainfall based on a radial basis function (RBF) neural network. The RBF derives a relationship from a historical input subset, called a training dataset, consisting of radar measurements such as reflectivity (Z) aloft and associated rainfall observation (R) on the ground. The unknown rainfall rate can then be predicted over the derived relationship with known radar measurements. The selection of the input subset has a significant impact on the prediction performance. This study simplified the selection of input subsets and studied its improvement in rainfall estimation. The proposed subset includes: (1) the Zave of the observed Z within a given distance from the ground observation to represent the intensity of a storm system and (2) the SD of the observed Z to describe the spatial variability. Using three historical rainfall events in 1999 near Darwin, Australia, the performance evaluation is conducted using three approaches: an empirical Z−R relation, RBF with Z, and RBF with Zave and SD. The results showed that the RBF with both Zave and SD achieved better rainfall estimations than the RBF using only Z. Two performance measures were used: (1) the Pearson correlation coefficient improved from 0.15 to 0.58 and (2) the average root-mean-square error decreased from 14.14 mm to 11.43 mm. The proposed model and findings can be used for further applications involving the use of neural networks for radar estimates of rainfall.