Miracle-dz/UniFAR-FTUs huggingface dataset:
A Flexible Tuned Radio-Frequency Planar Resonant Loop for Noninvasive Hydration Sensing

In this work, a planar radio-frequency sensor based on a flexible polyimide substrate has been developed to monitor the water content changes noninvasively and efficiently in a human body or tissues. The sensor is based on the detection of electromagnetic resonance that is susceptible to dielectric property changes by water content variations. The planar loop resonator tuned with a metal pad features improved resonance, compact size, and flexibility to conform to a curved surface. Designs and experiments to continuously monitor human hydration processes in vivo have been demonstrated. The recorded data shows distinct trends when a person becomes hydrated from a dehydrated state. Discrete and continuous measurements are compared with the simulations conducted by using documented, generalized human skin permittivity properties. Discrepancies between the measurements and simulations are investigated and verified with directly measured skin permittivities. Measurements variations are also investigated. With the advantages of being compact, flexible, and planar, the sensor can be integrated into a wearable on the human forearm. Additionally, the sensor has been used to demonstrate its ability to detect water content changes in a phantom made of ground pork. The ex vivo results indicate the sensitivity and consequential variations in practical scenarios. The promising results show great potential for human body monitoring and also general applications in agriculture and the food processing industry from the demonstration of the sensing principle in the pork phantom.
Utility of Indices Obtained during Medical Checkups for Predicting Fatty Liver Disease in Non-obese People

Objective To predict fatty liver disease (FLD), including nonalcoholic FLD (NAFLD) and metabolic dysfunction-associated FLD (MAFLD), from blood tests and anthropometric measurements, the fatty liver index (FLI) and triglyceride glucose-body mass index (TyG-BMI) have been reported as promising indicators. We evaluated the predictive ability of several indices, including the waist circumference, BMI, FLI and TyG-BMI, that might predict FLD in non-obese individuals undergoing health checkups. Methods This retrospective observational study enrolled non-obese subjects who underwent abdominal ultrasonography between May 1, 2015, and June 30, 2022. Obesity was defined as a BMI ＜25 kg/m2. FLD was diagnosed by abdominal ultrasonography. Using a receiver operating characteristic analysis, we examined the predictive validity of indices for NAFLD and MAFLD by calculating the area under the curve (AUC). Results Of the 24,825 subjects (mean age 44.3±10.0 years old; 54% men) enrolled in this examination of the association of indices, including FLI and TyG-BMI, with NAFLD, NAFLD was diagnosed in 3,619 (27%) men and 733 (6%) women. In both men and women, the FLI and TyG-BMI had significantly higher AUC values for NAFLD prediction than the other indicators (FLI: 0.786 for men and 0.875 for women, TyG-BMI: 0.783 for men and 0.868 for women). In analyses of subjects with a BMI ＜23 kg/m2, the superiority of the FLI and TyG-BMI remained unchanged. The FLI and TyG-BMI also had significantly higher AUC values for MAFLD prediction than the other indicators. Conclusion The FLI and TyG-BMI had a particularly high predictive ability for NAFLD and MAFLD in non-obese subjects.
Wave Dispersion Behaviors of Multi-Scale CNT/Glass Fiber/Polymer Nanocomposite Laminated Plates

In this paper, wave propagation in multi-scale hybrid glass fiber (GF)/carbon nanotube (CNT)/polymer nanocomposite plates is studied for the first time by means of refined higher-order plate theory. The hybrid nanocomposite consists of CNTs and glass fibers (GF) as reinforcing components distributed within a polymeric matrix. A hierarchical micromechanical approach is used to predict the effective mechanical properties of the hybrid nanocomposite, including the three-dimensional (3D) Mori-Tanaka method and the rule of mixture. Moreover, a refined-type higher-order shear deformation theory (HSDT) is implemented to take into account the influence of the shear deformation on the motion equations of the system. Then, the governing equations are achieved on the basis of the energy-based Hamilton’s principle. Finally, the derived equations will be solved analytically for the purpose of extracting the natural frequency of the continuous system. A set of numerical examples are provided to cover the effects of various parameters on the wave dispersion characteristics of the plate. It can be declared that the hybrid nanocomposite system can achieve higher wave frequencies compared with other types of composite structures. Additionally, it is found that the selection of the lay-ups and length-to-diameter ratio plays a significant role in the determination of the sandwich plate’s acoustic response.
The origins of climate‐diversity relationships and richness patterns in Chinese plants

Abstract A major goal of ecology and evolutionary biology is to explain geographic patterns of species richness. Richness is often correlated with climatic variables. However, the processes underlying these climate‐diversity relationships remain poorly understood. Two potential hypotheses to explain these relationships involve: (i) faster diversification rates (speciation minus extinction) in high‐richness climates and (ii) earlier colonization of high‐richness climates, allowing more time for speciation to build up richness. Few studies have tested these hypotheses directly, and most focused on animal clades with limited richness. In this study, we test these hypotheses in Chinese angiosperms, encompassing ~10% of Earth's plant species, using large‐scale phylogenetic, climatic, and distributional data including 26,977 species. We find that climatic zones that were colonized earlier have higher species richness. By contrast, relationships between diversification rates and richness of climatic zones are often nonsignificant or negative. Our study reveals that even when richness is strongly correlated with climate, the underlying explanation may still be rooted in phylogenetic history. Thus, climate may not be a competing explanation for richness patterns relative to colonization times and diversification rates. We also show that the timing of colonization can be crucial for explaining richness patterns. Yet, many recent studies have ignored this explanation and instead have focused solely on rates of speciation and diversification as drivers of diversity gradients.
Refinement of the convective boundedness criterion of Gaskell and Lau

Based on the normalized variable diagram, the weakness of the Gaskell and Lau's convective boundedness criterion (GL‐CBC) is revealed by numerical example. By careful consideration of the smoothness of the normalized variable variation pattern, more rigorous constraints on the interface value interpolation are found. A new CBC is thus proposed, whose feasibility and correctness are demonstrated by the inspection of ten existing bounded schemes and a numerical example.
Supersymmetry facing experiment: much ado (already) about nothing (yet)

This report emphasizes the comparison between supersymmetric models and experiments. A minimal theoretical introduction is included as a guide to the interpretation of results. The existing constraints from low energy measurements, accelerator searches (LEP, Tevatron and HERA) and non-accelerator searches for neutralinos are presented. Prospects for upgrades of these facilities and for the LHC and linear collider are summarized. Most discussions are made in the framework of the minimal supersymmetric standard model inspired by supergravity (MSUGRA). But alternatives such as gauge mediated supersymmetry breaking (GMSB), anomaly mediated supersymmetry breaking (AMSB), models with R-parity violation and even alternatives to supersymmetry are also briefly considered.
Bioequivalence and the pharmaceutical industry

Since the early 1990s, average bioequivalence (ABE) studies have served as the international regulatory standard for demonstrating that two formulations of drug product will provide the same therapeutic benefit and safety profile when used in the marketplace. Population (PBE) and individual (IBE) bioequivalence have been the subject of intense international debate since methods for their assessment were proposed in the late 1980s and since their use was proposed in United States Food and Drug Administration guidance in 1997.
Five approaches to producing actionable science in conservation

The knowledge produced by conservation scientists must be actionable in order to address urgent conservation challenges. To understand the process of creating actionable science, we interviewed 71 conservation scientists who had participated in 1 of 3 fellowship programs focused on training scientists to become agents of change. Using a grounded theory approach, we identified 16 activities that these researchers employed to make their scientific products more actionable. Some activities were more common than others and, arguably, more foundational. We organized these activities into 3 nested categories (motivations, strategies, and tactics). Using a co‐occurrence matrix, we found that most activities were positively correlated. These correlations allowed us to identify 5 approaches, framed as profiles, to actionable science: the discloser, focused on open access; the educator, focused on science communication; the networker, focused on user needs and building relationships; the collaborator, focused on boundary spanning; and the pluralist, focused on knowledge coproduction resulting in valuable outcomes for all parties. These profiles build on one another in a hierarchy determined by their complexity and level of engagement, their potential to support actionable science, and their proximity to ideal coproduction with knowledge users. Our results provide clear guidance for conservation scientists to generate actionable science to address the global biodiversity conservation challenge.
Interactions between the Land Surface and Mesoscale Rainfall Variability during HAPEX-Sahel

Abstract The Hydrological Atmospheric Pilot Experiment in the Sahel (HAPEX-Sahel) was designed to investigate land–atmosphere interactions in the semiarid conditions of southwest Niger. During the intensive observation period (IOP) in 1992, a pronounced mesoscale rainfall gradient developed over the Southern Super Site (SSS). Measurements from a high-resolution rain gauge network indicate that over a distance of 9 km, cumulative rainfall in the final 7 weeks of the wet season (31 July–18 September) ranged from 224 mm in the south to 508 mm in the north. The extreme rainfall gradient is not apparent in other years and evolves through persistent local intensification of convection in passing large-scale storms. This paper assesses the influence of the rainfall variability on the surface and atmosphere, and explores the possibility of a land surface feedback on rainfall at this scale. Soil moisture estimates across the SSS illustrate the importance of rainfall on the water balance and indicate that gradients.
Machine Learning in Predicting Lattice Constant of Cubic Perovskite Oxides

A sample of 3,115 data of perovskite oxides in the form of ABO3 (A and B being the cations) was taken for this study of the application of machine learning in predicting the lattice constants (a determining factor in material design). The ANN, DT, RF, KNN, and SVM models were used to predict the lattice constants of perovskites because machine learning techniques have been phenomenal in uncovering crystal structures in the field of material research in recent years. These models used properties like ionic radii, formation energy, and band gap as input features. The R2 score was used to assess the regression model’s performance. The Random Forest Regression Model outperforms all other regression models regarding dataset and features.
Similarity-independent and non-iterative algorithm for sub-pixel motion estimation

This study presents an alternative method to estimate motion parameters to the gradient-based method, which is known as Lucas-Kanade algorithm. The proposed method is a faster version of a hyperplane-intersection method. The hyperplane-intersection method estimates the motion parameters between images as an intersection position of estimated hyperplanes in a parameter space. The hyperplanes approximate the zero positions of partial derivatives of a continuous similarity measure with respect to each parameter. The method employs a straightforward computation to estimate the parameters, instead of using an iterative framework. The noniterative method is suitable for hardware implementation. The method is the region-based and intensity-based technique that is capable of using any dissimilarity or similarity measure such as sum of squared differences (SSD) or zero-mean normalized cross correlation (ZNCC), which can be selected adequately in consideration of a property of input image sequence and a required computation time. The faster version of the method is realized with pre-computed warped images of the template, which reduce the computational cost for each input frame. This study also compares the computational cost and the accuracy of the estimated parameters of the proposed algorithm with those of the gradient descent method. Experiments using synthesized-motion sequences and real image sequences are performed to confirm the comparisons. The faster version of the hyperplane-intersection method using ZNCC demonstrates robustness to a non-uniform illumination change in the image sequences.
A machine vision method for the measurement of vibration amplitude

Measurements of vibration amplitude are important in many industrial applications. In particular, motion blur is a class of important information that has almost been ignored in conventional methods for motion perception. In this paper, a novel machine vision method is proposed to measure vibration amplitude based on image motion blur. In order to reveal the relationship between vibration and image blur, blurred images are analysed in the spatial and frequency domains. A series of dark parallel ripples which appears on the spectrum of the vibration-blurred image is exactly related to the vibration amplitude. Using image processing techniques, information on these ripples and parameters of vibration amplitude are obtained. Both simulation and experiment results are provided in detail to demonstrate the feasibility and efficiency of the proposed method.
Direct numerical simulations of transitional pulsatile flow through a constriction

A combined experimental and numerical study of transitional pulsatile flow through a planar constriction is presented. The parametric space that we adopt is similar to the one reported in a variety of past experiments relevant to the flow through stenosed arteries. In general, the flow just downstream of the constriction is dominated by the dynamic of the accelerating/decelerating jet that forms during each pulsatile cycle. We found a switch in the shedding frequency and roll-up dynamics, just after the flow rate approaches its maximum value in the cycle. The flow in the reattached area further downstream is also affected by the jet dynamics. A ‘synthetic’ turbulent-like wall-layer develops, an is constantly supported by streamwise vortices that originate from the spanwise instabilities of the large coherent structures generated by the jet. The relation of these structures to the phase-averaged turbulent statistics and the turbulent kinetic energy budgets is discussed.
A Low-Complexity and Adaptive Distributed Source Coding Design for Model Aggregation in Distributed Learning

A major bottleneck in distributed learning is the communication overhead of exchanging intermediate model update parameters between the worker nodes and the parameter server. Recently, it is found that local gradients among different worker nodes are correlated. Therefore, distributed source coding (DSC) can be applied to increase communication efficiency by exploiting such correlation. However, it is highly non-trivial to exploite the gradient correlations in distributed learning due to the unknown and time-varying gradient correlation. In this paper, we first propose a DSC framework, named successive Wyner-Ziv coding, for distributed learning based on quantization and Slepian-Wolf (SW) coding. We prove that the proposed framework can achieve the theoretically minimum communication cost from an information theory perspective. We also propose a low-complexity and adaptive DSC for distributed learning, including a gradient statistics estimator, rate controller, and a log-likelihood ratio (LLR) computer. The gradient statistics estimator estimates the gradient statistics online based only on the quantized gradients at previous iterations, hence it does not introduce extra communication cost. The computation complexity of the rate controller and the LLR computer is reduced to a linear growth in the number of worker nodes by introducing a semi-analytical Monte Carlo simulation. Finally, we design a DSC-based distributed learning process and find that the extra delay introduced by DSC does not scale with the number of worker nodes.
Compressive Strength Prediction for Industrial Waste-Based SCC Using Artificial Neural Network

Concrete is the most used construction material in the world. Sustainable construction practice demands durable material. A particular type of concrete that flows and consolidates under its weight is proposed to reduce labor dependency during construction, called self-compacting concrete. It is installed without vibration due to its excellent deformability and flowability while remaining cohesive enough to be treated without difficulty. Evaluating its compressive strength is essential as it is used in important construction projects. An artificial neural network (ANN) is a predicting tool that can predict output in various sectors. This study evaluated the compressive strength of industrial waste such as fly ash and silica fume incorporated in self-compacting concrete at various ages. A non-linear relationship was used to develop the model relating mix composition and SCC compressive strength using an Artificial Neural Network (ANN). The experimental and expected outcomes were compared with the model prediction to evaluate the predictive capacity, generalize the generated model, and observe suitable matches. The developed ANN network can predict the desired output, i.e., compressive strength incorporating industrial waste. Furthermore, the influence of individual parameters viz. cement, silica fume, and fly ash, w/b were also evaluated using parametric analysis, which shows the sensitivity of various materials on the compressive strength of Self-compacting concrete. As a result, a higher correlation coefficient of 0.9835 with a smaller value of MAPE (0.0347) and RMSE (2.4503) is obtained. Finally, a process of creating tools for practical engineers and field users is proposed, which would be very handy and fast for predicting the strength of SCC.
Microbial bioprocess performance in nanoparticle-mediated composting

Abstract Microbial composting is one of the most cost-effective techniques for degradation, remediation, nutrition, etc. Currently, there is faster growth and development in nanotechnology in different sectors. This development leads nanoparticles (NPs) to enter into the composts in different ways. First, unintentional entry of NPs into the composts via: waste discharge, buried solid waste, surface runoff, direct disposal into wastes (consumer goods, food, pharmaceuticals, and personal care products). Second, intentional mediation of the NPs in the composting process is a novel approach developed to enhance the degradation rate of wastes and as a nutrient for plants. The presence of NPs in the composts can cause nanotoxicity. Conversely, their presence might also be beneficial, such as soil reclamations, degradation, etc. Alternatively, metal NPs are also helpful for all living organisms, including microorganisms, in various biological processes, such as DNA replication, precursor biosynthesis, respiration, oxidative stress responses, and transcription. NPs show exemplary performance in multiple fields, whereas their role in composting process is worth studying. Consequently, this article aids the understanding of the role of NPs in the composting process and how far their presence can be beneficial. This article reviews the significance of NPs in: the composting process, microbial bioprocess performance during nano composting, basic life cycle assessment (LCA) of NP-mediated composting, and mode of action of the NPs in the soil matrix. This article also sheds insight on the notion of nanozymes and highlights their biocatalytic characterization, which will be helpful in future composting research. GRAPHICAL ABSTRACT Highlights Nanoparticles (NPs) at lower concentrations are favorable in composting process. NPs mediated composting reduces potential toxicity because of the organic matter. Organic matter protects the extracellular enzymes by adsorbing the NPs. Toxicity of NPs during composting process mainly depends upon size and concentration of NPs. NPs can mimic enzymes (nanozymes) and helps in effective degradation along with improved compost quality.
Effect of indium vacancy point defect on the structural, electronic and optical properties of double perovskite halide Cs2InSbCl6

Efforts to replace the lead-based perovskite have gain significant improvements in the last decade. Lead free double halide perovskite are being considered to replace the toxic lead-based perovskite materials for solar cell applications. Density functional theory (DFT) and linear response time-dependent density functional theory (TDDFT) are used in this study to simulate and investigate the effect of vacancy in the Indium (In) site of leadfree double halide perovskite A2BB′X6 (A = Cs; B = In, B′ = Sb; X = Cl) on the structural, electronic, and optical properties for possible solar cell application. On the structural properties, the total bond length of the material Cs2InSbCl6 was found to be 30.3848 Å and the creation of the indium vacancy raises the value to 30.594 Å. The bulk modulus of Cs2InSbCl6 was calculated to be 28.44 Gpa and 26.22 Gpa for Cs2In-xSbCl6. The calculated band structure for Cs2InSbCl6 reveals semiconducting behavior with a direct energy band gap of 0.99 eV along 𝛤 point symmetry while the created indium vacancy band structure reveals metallic behavior with an overlap of electronic states along the Γ - W – Γ symmetry. The result of the optical properties calculations shows that the material Cs2InSbCl6 has higher absorption coefficient, low refractive index, low reflectivity and higher conductivity when compared to the created indium vacancy material.
Research on Effective Teaching. A Multilevel Study for Ibero-America

This research aimed to determine the factors for Educational Effectiveness and to build an empirical model for Teaching Effectiveness in Ibero-America. A Multilevel Model with four levels of analysis was used to assess teaching effectiveness. The sample consisted of 5,722 students, (all at their third year of primary school), from 257 classrooms at 100 schools located in 9 countries of Ibero-America. The results showed the existence of ten factors of Teaching Effectiveness that impact on the cognitive, social, and emotional development of students: Involvement and commitment of the teacher; Classroom climate; High expectations and selfesteem; Structured lessons; Varied-participatory activities; Attention to diversity; Optimization of learning time; Classroom organization and management; Use of educational resources; Educational assessment, monitoring, and continuous feedback.