Miracle-dz/UniFAR-FTUs huggingface dataset:
Methylphenidate Fast Dissolving Films: Development, Optimization Using Simplex Centroid Design and In Vitro Characterization.

Objectives The focus of this study was to design and optimize methylphenidate hydrochloride mouth dissolving film (MDF) that can be beneficial in an acute condition of attention deficit hyperactivity disorder (ADHD) and narcolepsy. Materials and Methods Solvent casting method was used for the preparation of this film. Optimization of the effect of independent variables such as the number of polymers and active pharmaceutical ingredients [hydroxypropyl methyl cellulose (HPMC) E5, HPMC E15, and maltodextrin], % of drug release, disintegration time, and tensile strength of the film done using simplex centroid design. Complex formation of the film was tested using fourier-transform infrared spectroscopy and differential scanning calorimetry study. The multiple regression analysis was obtained from equations of the results that adequately describe influence of the independent variables on the selected responses. Polynomial regression analysis, contour plots, and 3-D surface plots were used to relate dependent and independent variables. Results Experimental results indicated that different polymer amounts had complex effects on % drug release from the film, disintegration time as well as the tensile strength of the film. The observed responses were in near alignment with expected values calculated from the developed regression equations as shown by percentage relative error. Final formulation showed more than 95% drug release within 2 min and was shown to disintegrate within a minute that had good tensile strength. Conclusion These findings suggest that MDF containing methylphenidate hydrochloride is likely to become a choice of methylphenidate hydrochloride preparations for treatment in ADHD and narcolepsy conditions.
Development of Detection and Volumetric Methods for the Triceps of the Lower Leg Using Magnetic Resonance Images with Deep Learning

Purpose: A deep learning technique was used to analyze the triceps surae muscle. The devised interpolation method was used to determine muscle’s volume and verify the usefulness of the method. Materials and Methods: Thirty-eight T1-weighted cross-sectional magnetic resonance images of the triceps of the lower leg were divided into three classes, i.e., gastrocnemius lateralis (GL), gastrocnemius medialis (GM), and soleus (SOL), and the regions of interest (ROIs) were manually defined. The supervised images were classified as per each patient. A total of 1199 images were prepared. Six different datasets separated patient-wise were prepared for K-fold cross-validation. A network model of the DeepLabv3+ was used for training. The images generated by the created model were divided as per each patient and classified into each muscle types. The model performance and the interpolation method were evaluated by calculating the Dice similarity coefficient (DSC) and error rates of the volume of the predicted and interpolated images, respectively. Results: The mean DSCs for the predicted images were >0.81 for GM and SOL and 0.71 for GL. The mean error rates for volume were approximately 11% for GL, SOL, and total error and 23% for GL. DSCs in the interpolated images were >0.8 for all muscles. The mean error rates of volume were <10% for GL, SOL, and total error and 18% for GM. There was no significant difference between the volumes obtained from the supervised images and interpolated images. Conclusions: Using the semantic segmentation of the deep learning technique, the triceps were detected with high accuracy and the interpolation method used in this study to find the volume was useful.
Adaptive Tracking Controller for an Alginate Artificial Cell

This paper presents an adaptive backstepping controller for the reference tracking of an alginate artificial cell. An adaptive controller was implemented to precisely manipulate a magnetic artificial cell actuated by rotating magnetic fields. The rolling motion of a small-scale robot in a fluidic environment is challenging, especially when the fluid imparts an unknown response at low Reynolds number. In order to compensate for this uncertainty, an unknown tuning parameter encapsulating these effects was added to the governing equations of motion. A controller with an update law was then designed to estimate the unknown parameter and force the artificial cell to produce the desired response. The stability of the proposed controller was established by a candidate Lyapunov function. Real-time experiments were conducted to demonstrate the effectiveness of the designed controller at guiding an artificial cell to an arbitrary target position. Alginate cells were guided through a maze using the controller and was later combined with wall constraints to allow multiple alginate cells to reach the same target location. This controller can be applied to both surface motion and swimming-based small-scale robots in future applications for micro-assembly and targeted drug delivery.
Accurate computation of wide‐band response of electromganetic scattering problems via Maehly approximation

The best uniform rational approximation, performed by the Maehly approximation, is applied to the method of moments (MOM) to obtain the radar cross section (RCS) over a broad frequency band. Numerical results for three dimensional arbitrarily shaped perfectly electrically conducting (PEC) bodies are considered. Compared with the asymptotic waveform evaluation (AWE) technique, the presented technique is found to be efficient in much broader frequency band with lower memory required. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 1144–1146, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI.10. 1002/mop.22367
Registros de mamíferos medianos y grandes en la Reserva Natural de la Sociedad Civil Jaime Duque, Cundinamarca, Colombia

Resumen Por medio de un muestreo con cámaras trampa entre marzo y mayo de 2017, se caracterizó el ensamblaje de mamíferos medianos y grandes de la Reserva Natural de la Sociedad Civil (RNSC) Jaime Duque, ubicado en la Sabana de Bogotá, departamento de Cundinamarca, Colombia. Se registraron seis especies incluyendo Nasuella olivacea, Leopardus tigrinus, Cerdocyon thous, Neogale frenata, Didelphis pernigra y Sylvilagus apollinaris. Se resalta la presencia de estas especies en un espacio de desarrollo predominantemente industrial, en especial de N. olivacea y L. tigrinus. Se destaca el papel que tienen las RNSC en la conservación de paisajes fragmentados, convirtiéndose en potenciales refugios de biodiversidad en zonas altamente fragmentadas. Palabras clave: Carnivora, Didelphimorpia, Lagomorpha, sabana de Bogotá, reservas privadas.
Simple and Two-Level Hierarchical Bayesian Approaches for Parameter Estimation with One- and Two-Layer Evapotranspiration Models of Crop Fields

The two-layer Shuttleworth–Wallace (SW) evapotranspiration (ET) model has been widely used for predicting ET with good results. Since the SW model has a large number of specific parameters, these parameters have been estimated using a simple non-hierarchical Bayesian (SB) approach. To further improve the performance of the SW model, we aimed to assess parameter estimation using a two-level hierarchical Bayesian (HB) approach that takes into account the variation in observed conditions through the comparison with a traditional one-layer Penman–Monteith (PM) model. The difference between the SB and HB approaches were evaluated using a field-based ET dataset collected from five agricultural fields over three seasons in Myanmar. For a calibration period with large variation in environmental factors, the models with parameters calibrated by the HB approach showed better fitting to observed ET than that with parameters estimated using the SB approach, indicating the potential importance of accounting for seasonal fluctuations and variation in crop growth stages. The validation of parameter estimation showed that the ET estimation of the SW model with calibrated parameters was superior to that of the PM model, and the SW model provided acceptable estimations of ET, with little difference between the SB and HB approaches.
Coherent signal-subspace transformation beam former

A family of coherent signal-subspace transformation (CST) preprocessors for improving the performance of beam formers is presented. The proposed beam former includes a CST preprocessor followed by a frequency-independent nulling procedure. If the exact CST preprocessor is used, it is shown that the steering and nulling responses of the CST beam former become frequency independent. This CST beam former is also interpreted as a wideband minimum-variance distortionless response (MVDR) beam former for minimising the total interference and noise powers. Several classes of the CST matrix are proposed and discussed. Unlike the traditional linearly constrained beam formers, the CST beam former effectively nulls the narrowband and wideband correlated interference(s). Simulation results show that even low-order approximate CST preprocessors substantially improve the nulling and the steering bandwidth of general arrays.
An In-Line, Non-Invasive Cotton Moisture Measurement Device for Gin Ducts

The management of moisture in cotton during ginning remains one of the most important factors in determining final quality. Resistance plates are typically used to measure moisture in seed-cotton and lint as it is processed through the gin, but these are limited by inaccuracies that arise from the small proportion of cotton tested, contamination of the sensor, and by the non-linear response to low and high moisture levels. In this article, we present a new sensing device that has potential for measuring the moisture of lint while it is being processed in the gin. The device combines large-area capacitance plates with light detectors to measure the mass and moisture of material traveling quickly under pneumatic pressure or gravity through gin ducts. Industrial trials showed significant correlations between moisture values given by the device and values measured off-line. This was despite only a narrow range of moisture contents (4.9% to 6.1%) occurring during the trial period. Further trials are necessary to evaluate the device over a wider range of conditions and to examine its potential to control ginning processes.
Applications of Genetic Algorithm in Aqueduct's Optimization Design

A mathematical model for nonlinear constraints is introduced briefly in the paper. Aimed at optimum design of engineering with multivariable and nonlinear constraints, a new method of penalty function applying to GA in combination with the traditional idea of penalty function and Genetic Algorithm is put forward. Optimum design of a certain aqueduct is completed with the method, and good optimum results are given out. Study shows that the function of GA to search for the minimum and pleasing convergence rate is embodied sufficiently by employing the method. The study offers an effective tool for rational design of aqueduct and has certain referenced value for other engineering.
Using Public Datasets to Understand the Psychological Correlates of Smoking, Alcohol Consumption, and Obesity: A Country-Level Analysis

In the present research, we investigate whether cultural value orientations (CVOs) and aggregate personality traits (Big-5) predict actual levels of alcohol consumption, smoking, and obesity across 50 countries using averages derived from millions of data points. Aggregate traits explained variance above and beyond CVOs in obesity (particularly neuroticism and extraversion), while CVOs explained variance beyond aggregate traits in alcohol consumption (particularly harmony and hierarchy). Smoking was not linked to aggregated traits or CVOs. We conclude that an understanding of the cultural correlates of risky health behaviors may help inform important policies and interventions for meeting international sustainable development goals.
Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation

Tropical forests are subject to diverse deforestation pressures but their conservation is essential to achieve global climate goals. Predicting the location of deforestation is challenging due to the complexity of the natural and human systems involved but accurate and timely forecasts could enable effective planning and on-the-ground enforcement practices to curb deforestation rates. New computer vision technologies based on deep learning can be applied to the increasing volume of Earth observation data to generate novel insights and make predictions with unprecedented accuracy. Here, we demonstrate the ability of deep convolutional neural networks to learn spatiotemporal patterns of deforestation from a limited set of freely available global data layers, including multispectral satellite imagery, the Hansen maps of historic deforestation (2001-2020) and the ALOS JAXA digital surface model, to forecast future deforestation (2021). We designed four original deep learning model architectures, based on 2D Convolutional Neural Networks (2DCNN), 3D Convolutional Neural Networks (3DCNN), and Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year. They were trained and tested on data from two ~80,000 km2 tropical forest regions in the Southern Peruvian Amazon. We found that the networks could predict the likely location of future deforestation to a high degree of accuracy. Our best performing model – a 3DCNN – had the highest pixel-wise accuracy (80-90%) when validated on 2020 deforestation based 2014-2019 training. Visual examination of the forecasts indicated that the 3DCNN network could automatically discern the drivers of forest loss from the input data. For example, pixels around new access routes (e.g. roads) were assigned high risk whereas this was not the case for recent, concentrated natural loss events (e.g. remote landslides). CNNs can harness limited time-series data to predict near-future deforestation patterns, an important step in using the growing volume of satellite remote sensing data to curb global deforestation. The modelling framework can be readily applied to any tropical forest location and used by governments and conservation organisations to prevent deforestation and plan protected areas.
On Explainability and Sensor-Adaptability of a Robot Tactile Texture Representation Using a Two-Stage Recurrent Networks

The ability to simultaneously distinguish objects, materials, and their associated physical properties is one fundamental function of the sense of touch. Recent advances in the development of tactile sensors and machine learning techniques allow more accurate and complex modelling of robotic tactile sensations. However, many state-of-the-art (SotA) approaches focus solely on constructing black-box models to achieve ever higher classification accuracy and fail to adapt across sensors with unique spatial-temporal data formats. In this work, we propose an Explainable and Sensor-Adaptable Recurrent Networks (ExSARN) model for tactile texture representation. The ExSARN model consists of a two-stage recurrent networks fed by a sensor-specific header network. The first stage recurrent network emulates our human touch receptors and decouples sensor-specific tactile sensations into different frequency response bands, while the second stage codes the overall temporal signature as a variational recurrent autoen-coder. We infuse the latent representation with ternary labels to qualitatively represent texture properties (e.g. roughness and stiffness), which facilitates representation learning and provide explainability to the latent space. The ExSARN model is tested on texture datasets collected with two different tactile sensors. Our results show that the proposed model not only achieves higher accuracy, but also provides adaptability across sensors with different sampling frequencies and data formats. The addition of the crudely obtained qualitative property labels offers a practical approach to enhance the interpretability of the latent space, facilitate property inference on unseen materials, and improve the overall performance of the model.
Sampson Distance: A New Approach to Improving Visual-Inertial Odometry's Accuracy

In this paper, we propose a new scheme based on the Sampson distance (SD) to describe visual feature residuals for visual-inertial odometry (VIO). Unlike the epipolar-constraint-based SD for visual odometry (VO), the proposed SD is formulated based on the perspective projection constraint. We proved in theory that the proposed SD retains the good properties of those earlier SD criteria in the literature of VO and it represents a visual feature residual more accurately than the prevailing transfer distance (TD) in existing VIO methods. We formulate three distance criteria, including TD, reprojection error (RE), and SD, and compared their performances by simulation. The results show that the SD is much more accurate than the TD and it is a very accurate estimate of the gold standard criteria—RE. Based on the SD, we modified VINS-Mono by replacing its TD-based visual residuals with the SD-based residuals and study the SD's efficacy in pose estimation by experiments with several public datasets. The results reveal that the SD-based VINS-Mono has a substantial improvement over the original VINS-Mono in pose estimation accuracy. This indicates that the SD is a better distance criterion than the TD for representing visual feature residuals. The proposed SD may find its applications to broader areas in computer vision and robotics.
Design and application of a fiber Bragg grating strain sensor with enhanced sensitivity in the small-scale dam model

Accurate measurement of strain variation and effective prediction of failure within models have been major objectives for strain sensors in dam model tests. In this paper, a fiber Bragg grating (FBG) strain sensor with enhanced strain sensitivity that is packaged by two gripper tubes is presented and applied in the seismic tests of a small-scale dam model. This paper discusses the principle of enhanced sensitivity of the FBG strain sensor. Calibration experiments and reliability tests were conducted to evaluate the sensor’s strain transferring characteristics on plates of different material. This paper also investigates the applicability of the FBG strain sensors in seismic tests of a dam model by conducting a comparison between the test measurements of FBG sensors and analytical predictions, monitoring the failure progress and predicting the cracking inside the dam model. Results of the dam model tests prove that the FBG strain sensor has the advantages of small size, high precision, and embeddability. It has a promising potential in the cracking and failure monitoring and identification of the dam model.
Modified enzymatic collagen digestion-mediated isolation of osteocytes

This study established a method for isolating large numbers of high-purity osteocytes from high-density bone. Bone fragments derived from mice tibia and femurs were alternately digested with type I collagenase and EDTA nine times, and the digested cells and bone chips (BC) were cultured, digested, and passaged when cells were fully grown. The types of cells obtained were identified by morphology, viable cell counts, alkaline phosphatase staining, and biochemical activity analyses, and specific osteocyte and osteoblast markers were evaluated by quantitative real-time polymerase chain reaction. Our results showed that among the cells obtained from the third digestion (fractions 7–9) of wild mice tibias and femurs and the remaining BCs, 85%–90% of the cells were osteocytes. Moreover, their morphology was approximately one-tenth to one-fifth the size of osteoblasts, star-shaped or polygonal, with a dendritic structure, negative for alkaline phosphatase staining, and showed a high expression of dmp1 and sclerostin. Ninety percent of the cells in fractions 1–3 were osteoblasts, and were fusiform or polygonal shape. The activity of osteoblast-specific alkaline phosphatase and mRNA expression were high in this fraction, while the expression of osteocyte-specific dmp1 and sclerostin was not detected. In the second portion (fractions 4–6), a large number were osteoblasts, mixed with a small number of osteocytes, and had high alkaline phosphatase activity and osteocyte mRNA levels, a specific level of the osteocyte marker dmp1, and no sclerostin was detected. Osteocytes in daβcat mice were also successfully isolated by this method, and we found that Wnt signaling increased the proliferation of these osteocytes. The proposed method can be used to culture osteocytes and osteoblasts of high purity and can be used for isolation and culture of these two kinds of cells from high-density bone, which provides an avenue for the study of osteocyte function in vitro.
Accident Count Model Based on Multiyear Cross-Sectional Roadway Data with Serial Correlation

The use of the negative multinomial model to form a predictive model of median crossover accident frequencies with a multiyear panel of crosssectional roadway data with a roadway section-specific serial correlation across time was explored. The negative multinomial model specification is compared with previous research, which used the same database but which also used negative binomial and random-effects negative binomial count models. If there is no section-specific correlation in the panel, the negative multinomial model becomes equivalent to the negative binomial. The differences in the estimation results between those models show that such a correlation exists in the data. The results show that the negative multinomial significantly outperforms the negative binomial and the random-effects negative binomial in terms of fit, with a statistically significantly higher likelihood at convergence. The signs of the coefficients were similar in all models; when the signs differed, the negative multinomial model results were more intuitive. Overall, the analysis supports the use of the negative multinomial count model to estimate median crossover accident frequency models that are based on panel data.
Emerging Molecular Dependencies of Mutant EGFR-Driven Non-Small Cell Lung Cancer

Epidermal growth factor receptor (EGFR) mutations are the molecular driver of a subset of non-small cell lung cancers (NSCLC); tumors that harbor these mutations are often dependent on sustained oncogene signaling for survival, a concept known as “oncogene addiction”. Inhibiting EGFR with tyrosine kinase inhibitors has improved clinical outcomes for patients; however, successive generations of inhibitors have failed to prevent the eventual emergence of resistance to targeted agents. Although these tumors have a well-established dependency on EGFR signaling, there remain questions about the underlying genetic mechanisms necessary for EGFR-driven oncogenesis and the factors that allow tumor cells to escape EGFR dependence. In this review, we highlight the latest findings on mutant EGFR dependencies, co-operative drivers, and molecular mechanisms that underlie sensitivity to EGFR inhibitors. Additionally, we offer perspective on how these discoveries may inform novel combination therapies tailored to EGFR mutant NSCLC.