Miracle-dz/UniFAR-FTUs huggingface dataset:
Testicular torsion, time is of the essence: a case for testicular preservation

Testicular torsion around the spermatic cord causes arterial and venous occlusion, which can lead to swelling, ischemia, and eventually necrosis of the testicle. It is a urologic emergency that requires a time-dependent diagnosis to prevent testicular loss. This is a case report of an atypical presentation of a 15-year-old male with a 5-day history of testicular torsion, that resulted in preservation of testes viability immediately after detorsion and at 4-week follow-up.
Temporal and spatial pattern of wildlife attacks on human in Chitwan National Park, Nepal

Wildlife attacks on humans pose a critical challenge to wildlife conservation. Comprehensive information about the conflict cases can be vital for effective wildlife management and conservation. In this study, we assessed the temporal and spatial patterns of wildlife attacks on humans in Chitwan National Park between January 2009 to December 2020 by using the annual reports published by the park. Our analysis revealed that out of the nine-wildlife species involved in conflicts, rhinos, elephants, tigers, sloth bears, and wild boars had significantly higher involvement in conflicts with humans. The findings showed that an average of 42 annual attacks and 10 human fatalities cases were reported annually. Of these species, rhinos exhibited the most frequent involvement in conflicts (38%), while elephants were found to be the most lethal species, causing an average of 3.75 human fatalities per year. The trend line indicated a marginal increase in wildlife attacks on humans by 3.97 per year (adjusted R2=0.36). Our study further revealed that wildlife attacks were mostly concentrated in the winter season (χ2=1.088, df=3, p<0.05), with elephant, rhino, and tiger attacks predominantly occurring in the proximity of human settlements. To evaluate the risk factors of human deaths due to wildlife attacks, we utilized logistic regression analysis and found elephant attacks were associated with the highest probability of causing human fatalities (odd ratio=15.2, p<0.05), followed by tiger attacks (odd ratio=8.41, p<0.05). The study revealed an increasing trend in attacks, predominantly occurring in winter and near human settlements. Elephants and tigers are identified as the highest risk factors for human fatalities. Understanding these patterns is crucial for effective wildlife management and conservation efforts.
Skeleton Segmentation on Bone Scintigraphy for BSI Computation

Bone Scan Index (BSI) is an image biomarker for quantifying bone metastasis of cancers. To compute BSI, not only the hotspots (metastasis) but also the bones have to be segmented. Most related research focus on binary classification in bone scintigraphy: having metastasis or none. Rare studies focus on pixel-wise segmentation. This study compares three advanced convolutional neural network (CNN) based models to explore bone segmentation on a dataset in-house. The best model is Mask R-CNN, which reaches the precision, sensitivity, and F1-score: 0.93, 0.87, 0.90 for prostate cancer patients and 0.92, 0.86, and 0.88 for breast cancer patients, respectively. The results are the average of 10-fold cross-validation, which reveals the reliability of clinical use on bone segmentation.
Development of ANN-Based Algorithm to Estimate Wintertime Sea Ice Temperature Profile Over the Arctic Ocean

The thermal structure of the Arctic sea ice is a critical indicator in the atmosphere–sea ice–ocean energy budget and, thus, for understanding Arctic warming and associated climate change. Therefore, understanding this thermal structure and its monitoring should be vital. However, it is challenging to obtain a 3-D view of the thermal structure of the sea ice (such as the temperature profile) through satellite measurements because of the lack of understanding of the nonlinear relationship between sea ice emission and measured radiance at the top of the atmosphere. In this study, a model was developed to estimate the temperature profile within the Arctic sea ice during winter using satellite-borne passive microwave measurements. An artificial neural network (ANN) technique based on deep learning was introduced, and the nonlinear relationship between satellite-measured brightness temperatures and buoy-measured sea ice temperature profiles was learned. The ANN model was mapped and verified using the tenfold cross-validation technique. The developed ANN model was able to restore the sea ice temperatures at all specified levels with correlation coefficients > 0.95, absolute biases < 0.1 K, and root mean square errors < 1.6 K. The retrieved temperature results well represent expected thermal structures, in addition to the snow–sea ice interface temperature similar to that in the published literature. Besides the data for validating climate model simulations, the results also promise applications for improving the sea ice growth model performance by tightly constraining the vertical thermal structure in the sea ice growth model.
The causal effect of family income on child investment in Thailand: an instrumental variable approach using natural “natural experiments”

Abstract The observed association between household income and children’s education cannot be interpreted as causal in the existence of potential income endogeneity. The non-natural factors often infringe on the orthogonality condition, even if they satisfy the relevance condition. Utilising a farm household survey, saline soil, and rainfall data from Thailand, this paper exploits natural experiments using actual natural factors as an instrumental variable to find valid instruments and check the robustness of the results using many estimators. The results show that, among the nine instrumental variables, rainfall amount and rainfall deviation are valid instruments for establishing the causal effect of income on a child’s education.
Mixing and fluid dynamics under location uncertainty

This thesis develops, analyzes and demonstrates several valuable applications of randomized fluid dynamics models referred to as under location uncertainty. The velocity is decomposed between large-scale components and random time-uncorrelated small-scale components. This assumption leads to a modification of the material derivative and hence of every fluid dynamics models. Through the thesis, the mixing induced by deterministic low-resolution flows is also investigated. We first applied that decomposition to reduced order models (ROM). The fluid velocity is expressed on a finite-dimensional basis and its evolution law is projected onto each of these modes. We derive two types of ROMs of Navier-Stokes equations. A deterministic LES-like model is able to stabilize ROMs and to better analyze the influence of the residual velocity on the resolved component. The random one additionally maintains the variability of stable modes and quantifies the model errors. We derive random versions of several geophysical models. We numerically study the transport under location uncertainty through a simplified one. A single realization of our model better retrieves the small-scale tracer structures than a deterministic simulation. Furthermore, a small ensemble of simulations accurately predicts and describes the extreme events, the bifurcations as well as the amplitude and the position of the ensemble errors. Another of our derived simplified model quantifies the frontolysis and the frontogenesis in the upper ocean. This thesis also studied the mixing of tracers generated by smooth fluid flows, after a finite time. We propose a simple model to describe the stretching as well as the spatial and spectral structures of advected tracers. With a toy flow but also with satellite images, we apply our model to locally and globally describe the mixing, specify the advection time and the filter width of the Lagrangian advection method, as well as the turbulent diffusivity in numerical simulations.
KINEMATIC AND DYNAMIC DESIGN OF FOUR-BAR LINKAGES WITH VARIABLE INPUT SPEED AND EXTERNAL APPLIED LOADS

A novel approach that integrates the kinematic synthesis, dynamic design and servo control in one design stage is presented for designing four-bar linkages with external applied loads. This approach satisfies kinematic design requirements and constraints and also attains trade-off of dynamic balance. By properly designing the speed trajectory of the input link, the balancing parameters of moving links and link dimensions of the given or desired mechanisms, the expected output motion characteristics and dynamic balancing performance are obtained. And, the input motion characteristics are designed with Bezier curves. Optimization is aided to find out optimal design parameters for achieving kinematic and dynamic design requirements and constraints. The speed trajectory of the input link could be generated by a servomotor. Examples are given to demonstrate the feasibility of this approach.
Improved Baboon Algorithm For Minimization Of Real Power Loss

This paper projects Improved Baboon Algorithm (IBA) for solving the Reactive Power dispatch problem. The key feature in this problem is reduction of real power loss and to keep voltage profiles within limits. This algorithm is inspired from the tree climbing procedures of Baboons, where the Baboons look for the highest tree by climbing up from their positions. The simulation results expose amended performance of the IBA in solving an optimal reactive power dispatch problem. In order to evaluate up the performance of the proposed algorithm, it has been tested on Standard IEEE 30 bus system and compared to other stated algorithms. Simulation results show that IBA is better than other algorithms in reducing the real power loss and voltage profiles also within the limits.
Isolation and Characterization of Poeciguamerin, a Peptide with Dual Analgesic and Anti-Thrombotic Activity from the Poecilobdella manillensis Leech

When Poecilobdella manillensis attacks its prey, the prey bleeds profusely but feels little pain. We and other research teams have identified several anticoagulant molecules in the saliva of P. manillensis, but the substance that produces the paralyzing effect in P. manillensis is not known. In this study, we successfully isolated, purified, and identified a serine protease inhibitor containing an antistasin-like domain from the salivary secretions of P. manillensis. This peptide (named poeciguamerin) significantly inhibited elastase activity and slightly inhibited FXIIa and kallikrein activity, but had no effect on FXa, trypsin, or thrombin activity. Furthermore, poeciguamerin exhibited analgesic activity in the foot-licking and tail-withdrawal mouse models and anticoagulant activity in the FeCl3-induced carotid artery thrombosis mouse model. In this study, poeciguamerin was found to be a promising elastase inhibitor with potent analgesic and antithrombotic activity for the inhibition of pain and thrombosis after surgery or in inflammatory conditions.
Sensing with periodic nanohole arrays

In this paper we review the resonance conditions of periodic indentations in metallic layers and evaluate their potential for surface sensing of analytes. A review of significant contributions of nanohole arrays for sensing is presented in a first section. It is then followed by a theoretical analysis of their optical properties using coupled mode theory and an evaluation of their potential for sensing. The sensitivity, resolution, and field distribution are presented as a function of the different parameters of the metal film (periodicity, hole size, and thickness) to determine the optimal design for sensing. The focus of this paper is made on 1-D nanoslit arrays and 2-D square nanohole arrays to identify general considerations regarding sensing experiments using these types of structure. We include a MATLAB user interface, also available as a standalone application, that plots the transmission and reflection spectrum as well as the field distribution of nanohole arrays.
A remark on extremal sets in the theory of polynomial interpolation

Let the numbers x1, x2, . , x~ (n 2) be prescribed on the interval [ ~, 1] with — I ~ x1 <x~ ~ . ~ x,~ ~ 1. Denoting the fundamental Lagrange interpolating polynomials by 4(x), we introduce the Lebesgue function )~~(x) ~ 1(x). The question arises as how to choose the nodes x1, x~,. , x~ in such a way that max %~(x) will be as small as possible. (A set of nodes with this~ property lxi is called an extremal set.) This is at famous unsolved problem in interpolation theory. interesting results in connection with it can be found in BERNsTEIN [1], ERDOs [4] and LurrMANN RIvLIN [5]; the paper by CI-IENEY—PRIcE [3] deals with problems of the same kind in a more~gencfal setting. A footnote in [his concerned with the particular cas&of three nodes. An extremal 2— 2—~. 5 set Qonsisting of the points x1 —~-~-j/2, x2 0, x3 -~-~2 is exhibited with (Actually, the nodesdx1 x2, x3 with x1 ‘x3 ~- 1~2, x2. 0 are also extremal sets, cf. CHENEY[2], p. 65.) . Assuming 1 x1 and x,, 1, BERNSTEIN ([1], p. 1026 1027) conjectured that an extremal set of nodes is characterized by the prope~f~’ that the values of the n +1 local maxima of 2~(x) on the intervals [‘ 1, x1], [x1,x2], . , [x~, 1] are all equal. This conjecture can also be found in [4]. Attention should also be drawn to a paper by LuTTMANN and RIyuN [5]. Thcy show e.g. that one can always take the ~ndpoints of [—1, 1] as nodes in an extrernal set. ’ . The object of this note is to determine all extrernal sets in case of three nodes. Furthermore, we propose a modification of Bernstein’s conjecture.
Bayesian model assessment: Use of conditional vs marginal likelihoods

Typical Bayesian methods for models with latent variables (or random effects) involve directly sampling the latent variables along with the model parameters. In high-level software code for model definitions (using, e.g., BUGS, JAGS, Stan), the likelihood is therefore specified as conditional on the latent variables. This can lead researchers to perform model comparisons via conditional likelihoods, where the latent variables are considered model parameters. In other settings, typical model comparisons involve marginal likelihoods where the latent variables are integrated out. This distinction is often overlooked despite the fact that it can have a large impact on the comparisons of interest. In this paper, we clarify and illustrate these issues, focusing on the comparison of conditional and marginal Deviance Information Criteria (DICs) and Watanabe-Akaike Information Criteria (WAICs) in psychometric modeling. The conditional/marginal distinction corresponds to whether the model should be predictive for the clusters that are in the data or for new clusters (where "clusters" typically correspond to higher-level units like people or schools). Correspondingly, we show that marginal WAIC corresponds to leave-one-cluster out (LOcO) cross-validation, whereas conditional WAIC corresponds to leave-one-unit (LOuO). These results lead to recommendations on the general application of these criteria to models with latent variables.
Development of a global aerosol model using a two‐dimensional sectional method: 1. Model design

This study develops an aerosol module, the Aerosol Two‐dimensional bin module for foRmation and Aging Simulation version 2 (ATRAS2), and implements the module into a global climate model, Community Atmosphere Model. The ATRAS2 module uses a two‐dimensional (2‐D) sectional representation with 12 size bins for particles from 1 nm to 10 μm in dry diameter and 8 black carbon (BC) mixing state bins. The module can explicitly calculate the enhancement of absorption and cloud condensation nuclei activity of BC‐containing particles by aging processes. The ATRAS2 module is an extension of a 2‐D sectional aerosol module ATRAS used in our previous studies within a framework of a regional three‐dimensional model. Compared with ATRAS, the computational cost of the aerosol module is reduced by more than a factor of 10 by simplifying the treatment of aerosol processes and 2‐D sectional representation, while maintaining good accuracy of aerosol parameters in the simulations. Aerosol processes are simplified for condensation of sulfate, ammonium, and nitrate, organic aerosol formation, coagulation, and new particle formation processes, and box model simulations show that these simplifications do not substantially change the predicted aerosol number and mass concentrations and their mixing states. The 2‐D sectional representation is simplified (the number of advected species is reduced) primarily by the treatment of chemical compositions using two interactive bin representations. The simplifications do not change the accuracy of global aerosol simulations. In part 2, comparisons with measurements and the results focused on aerosol processes such as BC aging processes are shown.
A kinematic study of the integral shaped filament: what roles do filaments play in forming young stellar clusters?

Young protoclusters (embedded stellar clusters) are responsible for the vast majority of star formation currently occurring in the Galaxy. Recent observations suggest a scenario in which filamentary structures in the interstellar medium represent the first step towards precluster clumps and eventually star formation. Whether filaments continuously fuel the star formation process when the cluster accretes material is still an open question. In this paper, we present a case study of the famous ‘integral shaped filament’ (ISF) in the Orion A molecular cloud and we seek to study the kinematics which is truly originated from the ISF. We firstly define the central ridge of the ISF with NH3, 12CO, 13CO and N2H+. Undulations are present in all the ridges. Moreover, a large scale offset is apparent in the ridges as derived by different tracers, which may be explained by the slingshot mechanism proposed by Stutz & Gould. We fit the velocity field of the ISF and find the derived velocity gradient is about 0.7 km s−1 pc−1 which may come from an overall contraction. We propose a method to check the accretion flow along the ISF by using the velocity deviations of different molecular tracers, which is better than the common method of using the velocity distribution of one tracer alone. Using the velocity deviations, we also find that OMC-1 to 5 are located close to the local extrema of the fluctuations, which may demonstrate that gas flows toward each clump along the ISF.
On the possibility of deriving the generalized entropy from generalized exergy formulation

Abstract: The present study aims at investigating about the possibility of generalizing the canonical entropy-exergy relationship and at deriving the generalized entropy by defining a generalized exergy of a system interacting with a thermo-chemical-mechanical reservoir where temperature, chemical potentials and pressure remain constant. The method adopted assumes equal chemical potential and pressure as necessary conditions for the mutual stable equilibrium of the composite system-reservoir in addition to equal temperature, which constitutes the basis for deriving entropy from energy and exergy concepts. An attempt is made here to define a generalized exergy, based on the formulation of physical and chemical exergy, and to prove the derivability of chemical and mechanical entropy, as additive components contributing to the generalized entropy. The conclusions envisage the implications in exergy analysis and possible engineering applications. Future developments are outlined in the entropy generation minimization method in which the chemical entropy could be adopted to describe the self-organizing and self-assembling processes of microscopic and molecular systems.
Improved Natural Plant Electrophysiology Sensor Design for Phytosensing System

In recent years, plant electrophysiology has become a viable measurement tool for describing plants’ thinking properties. In this article, we propose a new electrophysiological sensor as an autonomous device, which can transfer a plant’s potential electrical data into self-awareness information (a.k.a, phytosensing). Also, this device holds the potential to explore the electrophysiology of plants and its utilization in plant factories, along with its integration with intelligent monitoring and artificial intelligence (AI) technology to evaluate a plant’s cognitive capabilities. Plant sensing can measure certain aspects of a plant’s natural internal state. The proposed sensor circuit design is based on two-stage adjustable processing layers in electrophysiology. The major contribution of this article is to propose an electrophysiological sensor module that provides plant thinking data concerning environmental factors with dedicated surrounding conditions. Furthermore, the collected data were analyzed through a K-nearest neighbor (KNN) algorithm, which provides a feasible and less complex way to implement the supervised machine learning algorithm. The experimental result shows that each dedicated sensing condition, such as light, heat, humidity, and CO2, with the external stimuli through the proposed plant’s electrophysiology sensor module, can evaluate a plant’s thinking behavior. The proposed sensor module has a wide range of gains in circuits with less required complexity. Furthermore, it is suitable for larger scale data measurement for multiple plant nodes up to 16 channels.
The poly‐log Weibull model applied to space‐time interpolation of temperature

In this paper, a multivariate log‐Weibull model for spatially dependent data is defined by marginalizing a conditional Pareto distribution with respect to a shared spatial random effect of alpha‐stable distributions. Some properties of this new model are derived, and procedures for the estimation and inference are discussed. An application is developed to study observed temperature data sets collected from weather stations in the Brazilian Amazon.
“Black Women are the Mules of the World”: Black Women Professors of Practice in Teacher Education Programs

At the intersection of labor and racial equity in teacher preparation, Black women professors of practice (PoPs) find that they carry an excessive workload without a similar measure of compensation or recognition. The unique experiences of these Black contingent faculty provide a view from the margins regarding the simultaneity (Collins, 2002) of oppressions that are hidden from mainstream perceptions within teacher preparation, particularly through a raced and gendered lens. Through an autoethnographic approach, we reveal how our negotiations for respect, visibility and impact on pre-service teachers are navigated within larger institutional structures situated within the academy. This reflexive process provides us, as research practitioners, a socially conscious pathway (Ellis et al., 2011) towards both impactful practice and means for substantive contributions to investigating the often-hidden labor of Black women professors of practice within post-secondary institutions. While exploring how we were positioned as ‘mules’ of teacher preparation at our previous institution, we amplify experiences that hitherto are alluded to in the literature, thematically reflecting our scarce numbers, low access to professional advancement and lack of job security. We conclude with recommendations both for Black women PoPs about the nature of this work, the reality of its weight, and considerations on behalf of the academy relative to pathways for professionalizing and honoring Black women professors of practice in teacher education programs.
Capacity estimates and rigidity of domains with corners

We prove capacity inequalities involving the total mean curvature of hypersurfaces with boundary in convex cones and the mass of asymptotically flat manifolds with non-compact boundary. We then give the analogous of P\"olia-Szeg\"o, Alexandrov-Fenchel and Penrose type inequalities in this setting. Among the techniques used in this paper are the inverse mean curvature flow for hypersurfaces with boundary.