Miracle-dz/UniFAR-FTUs huggingface dataset:
Hope is the last thing lost: Colombian captive-bred population of the critically endangered Orinoco crocodile ( Crocodylus intermedius ) is a genetic reservoir that could help to save the species from extinction

A purpose of ex-situ populations is the preservation of genetic variation, but this is a challenging task since genetic diversity is commonly lost through each generation, and so the establishment of management guidelines should be a high priority. Fifty years ago, the National University of Colombia began a breeding program in the Roberto Franco Tropical Station (in Villavicencio, Meta) to conserve the critically endangered Orinoco crocodile Crocodylus intermedius . Despite the large number of individuals raised and kept in captivity, the Station has not been able to release individuals due to a lack of a complete genetic characterization that could determine whether the population is genetically viable. In this study we used a panel of 17 microsatellite loci to overcome this problem. We estimated from the founder animals and the live crocodiles the inbreeding, heterozygosities, the number of alleles, and their richness, and frequencies to understand the effects of managing a captive breeding program without considering genetic profiles. Our results revealed that the living population maintains much of its founder diversity with high levels of heterozygosity and low overall inbreeding, making it suitable for maintaining captive breeding and for implementing wild releases. We estimated the individual genetic diversity of the living crocodiles, as well as their relationships. This information, combined with the size, sex, and location, allowed us to propose combinations and to restructure the breeding groups. We demonstrated that molecular data could be used to improve the management of ex-situ conservation programs well beyond what could be achieved with pedigree information alone.
Trends in Behavioral and Biological Non-Communicable Diseases Risk Factors among Adults in Mongolia: Results from National Cross-Sectional Surveys in 2005, 2009, 2013 and 2019

Background: Understanding national trends in noncommunicable disease (NCD) risk factors may have health policy implications. We aimed to evaluate trends in the prevalence and correlates of eight risk factors for NCD from 2005 to 2019 in Mongolia. Methods: Cross-sectional data were analyzed from 9,356 individuals (15–64/69 yr, mean age=37.3 yr) who participated in the Mongolia STEPS 2005, 2009, 2013 or 2019 surveys and had complete measurement of the risk factor for NCD. Results: The proportion of having 3–8 NCD risk factors significantly decreased from 59.1% in 2005 to 49.4% in 2019, and the mean number of NCD risk factors significantly decreased from 2.8 in 2005 to 2.6 in 2019. In linear regression analyses by study year, older age and male sex were across all study years positively associated with eight NCD risk factors. The prevalence of low physical activity increased from 8.2% in 2005 to 28.4% in 2019, overweight/obesity from 49.6% in 2005 to 64.0% in 2019, and raised total cholesterol from 23.9% in 2005 to 27.6% in 2019, there was a significant decrease in inadequate fruit and vegetable intake from 85.1% in 2005 to 79.5% in 2019, current smoking from 26.6% in 2005 to 24.1% in 2019, hypertension from 28.2% in 2005 to 23.2% in 2019, while the prevalence of diabetes remained unchanged from 2005 to 2019. Conclusion: The prevalence of eight risk factors for NCD decreased in the last 15 years in Mongolia. Several associated variables for eight and individual NCD risk factors were identified that can help guide interventions.
Correlation Between Efficacy of Treatment with Anti-psychotics and Adherence in Schizophrenic Patients: A Cross-sectional Study in Saudi Arabia

Background: Schizophrenic patients’ adherence to medication regimens can determine clinical outcome success. This study focuses on medication adherence and its correlation with the efficacy of anti-psychotics. The study's goal is to (1) assess the efficacy of anti-psychotics and adherence using the Self-evaluation of Negative Symptoms (SNS) tool and Medication Adherence Rating Scale (MARS), and (2) examine the demographic and clinical factors that could impact both outcomes (SNS and MARS). Materials and Methods: Between November 2021 and February 2022, a cross-sectional study was carried out. Only 45 of 100 schizophrenia patients screened were included in the sample. MARS and SNS self-assessment questionnaires that took approximately 15 min to complete were administered to patients in a scheduled clinic interview. Other patients’ demographic, physical, and clinical information was gathered from their electronic medical records via a Redcap designed data collection sheet. Results: Almost 53.3% of the current schizophrenic patients had perceived mild negative symptoms (scored 1 to <20 out of 40 on SNS). Surprisingly, 64.4% were either poor adherent ( n =13 scored 5 to 6) or non-adherent ( n =16 scored <5). There was a notable negative correlation between the total MARS score and both total SNS (r = -0.325, p =0.03) and social withdrawal scores (r = -0.436, p =0.003). Non-adherence was significantly associated with lower education levels ( p =0.028) and unemployment ( p =0.008), whereas SNS total score and anhedonia were both markedly associated with different marital status ( p =0.046, and p =0.016, respectively). Conclusion: According to the study, medication schizophrenia nonadherence correlates negatively with the efficacy of anti-psychotics as perceived by patients, which reflects patient quality and perception of life. These findings strongly emphasize the importance of developing adherence interventions to enhance compliance with anti-psychotic regimens and improve patients’ perceived self-evaluation of negative symptoms.
Graph-based inference with constraints for object detection and segmentation

For many fundamental problems of computer vision, adopting a graph-based framework can be straight-forward and very effective. In this thesis, I propose several graph-based inference methods tailored for different computer vision applications. It starts from studying contour-based object detection methods. In particular, We propose a novel framework for contour based object detection, by replacing the hough-voting framework with finding dense subgraph inference. Compared to previous work, we propose a novel shape matching scheme suitable for partial matching of edge fragments. The shape descriptor has the same geometric units as shape context but our shape representation is not histogram based. The key contribution is that we formulate the grouping of partial matching hypotheses to object detection hypotheses is expressed as maximum clique inference on a weighted graph. Consequently, each detection result not only identifies the location of the target object in the image, but also provides a precise location of its contours, since we transform a complete model contour to the image. We achieve very competitive results on ETHZ dataset, obtained in a pure shape-based framework, demonstrate that our method achieves not only accurate object detection but also precise contour localization on cluttered background. Similar to the task of grouping of partial matches in the contour-based method, in many computer vision problems, we would like to discover certain pattern among a large amount of data. For instance, in the application of unsupervised video object segmentation, where we need automatically identify the primary object and segment the object out in every frame. We propose a novel formulation of selecting object region candidates simultaneously in all frames as finding a maximum weight clique in a weighted region graph. The selected regions are expected to have high objectness score (unary potential) as well as share similar appearance (binary potential). Since both unary and binary potentials are unreliable, we introduce two types of mutex (mutual exclusion) constraints on regions in the same clique: intra-frame and inter-frame constraints. Both types of constraints are expressed in a single quadratic form. An efficient algorithm is applied to compute the maximal weight cliques that satisfy the constraints. We apply our method to challenging benchmark videos and obtain very competitive results that outperform state-of-the-art methods. We also show that the same maximum weight subgraph with mutex constraints formulation can be used to solve various computer vision problems, such as points matching, solving image jigsaw puzzle, and detecting object using 3D contours.
Optimum geometric design in spacing and depth for prefabricated vertical drains (PVDs) to accelerate the consolidation rate with validation by finite element modelling (PLAXIS 3D)

One of the common practices in the field of geotechnical engineering is the use of prefabricated vertical drains (PVDs) for ground improvement work. PVDs provides shorter drainage paths to hasten the consolidation process. The performance of the PVDs depend on the spacing, length and characteristics of the PVDs, and the condition of the surrounding subsoil. Three case studies were carried out for the validation process by comparing the actual results from PLAXIS 3D with manual spreadsheet predictions. For this research, PVDs with four 4 different spacings and lengths were constructed at the sites to obtain an optimum geometric design. Based on the analytical calculations, simulation predictions and actual results from the sites, the PVDs installed with a triangular pattern and a spacing of 1.2 m and depth of 36 m achieved the highest settlement reading and consolidation rate in 6 months, with an accuracy of 86.5% for the validation process.
Variable Selection for the Prediction of C[0,1]-Valued Autoregressive Processes using Reproducing Kernel Hilbert Spaces

ABSTRACT A model for the prediction of functional time series is introduced, where observations are assumed to be continuous random functions. We model the dependence of the data with a nonstandard autoregressive structure, motivated in terms of the reproducing kernel Hilbert space (RKHS) generated by the auto-covariance function of the data. The new approach helps to find relevant points of the curves in terms of prediction accuracy. This dimension reduction technique is particularly useful for applications, since the results are usually directly interpretable in terms of the original curves. An empirical study involving real and simulated data is included, which generates competitive results. Supplementary material includes R-code, tables, and mathematical comments.
Existence theorems for nonlinear shell models of Koiter’s type

We first show that the minimization problem associated with the nonlinear shell model of W.T. Koiter becomes coercive over its natural space of admissible deformations when the third fundamental form is added to its functional. Then, under the assumption that the middle surface of the shell is a minimal surface, we approach this minimization problem by a new minimization problem that is well-posed over the same space of admissible deformations.
Fluoride glass-based surface plasmon resonance sensor in infrared region: performance evaluation

Excitation parameters shown by a plasmonic structure are different in the near infrared (NIR) region as compared with that of the visible region. Fluoride-based glass materials show high transparency in the infrared (IR) region, which plays an important role from an experimental point of view. In order to explore optical sensors based on a heavy metal doped fluoride substrate, performance of a surface plasmon resonance (SPR) based sensor having HBL glass as the substrate material to couple direct light in the NIR region is discussed. The proposed sensor’s structure is based on Kretschmann configuration with gold (Au) deposited on an HBL glass substrate. Experimental refractive index values of water samples containing different NaCl concentrations are taken as the sensing medium for design and modelling of the sensor. The analysis was carried out for some different samples also. An HBL-based SPR sensor is able to provide significantly enhanced sensing performance compared with other conventional glass substrates operated in different spectral regions (SF10 in visible and SiO2 in NIR). The study paves the way for designing SPR sensor probes based on fluoride optical fibers in the IR region for different biological and chemical applications.
Universally Grasping Objects with Granular—Tendon Finger: Principle and Design

Nowadays, achieving the stable grasping of objects in robotics requires an increased emphasis on soft interactions. This research introduces a novel gripper design to achieve a more universal object grasping. The key feature of this gripper design was a hybrid mechanism that leveraged the soft structure provided by multiple granular pouches attached to the finger skeletons. To evaluate the performance of the gripper, a series of experiments were conducted using fifteen distinct types of objects, including cylinders, U-shaped brackets, M3 bolts, tape, pyramids, big pyramids, oranges, cakes, coffee sachets, spheres, drink sachets, shelves, pulley gears, aluminium profiles, and flat brackets. Our experimental results demonstrated that our gripper design achieved high success rates in gripping objects weighing less than 210 g. One notable advantage of the granular-tendon gripper was its ability to generate soft interactions during the grasping process while having a skeleton support to provide strength. This characteristic enabled the gripper to adapt effectively to various objects, regardless of their shape and material properties. Consequently, this work presented a promising solution for manipulating a wide range of objects with both stability and soft interaction capabilities, regardless of their individual characteristics.
Screening of Potential Circulating Diagnostic Biomarkers and Molecular Mechanisms of Systemic Lupus Erythematosus-Related Myocardial Infarction by Integrative Analysis

Background The risk of acute myocardial infarction (AMI) is elevated in patients with systemic lupus erythematosus (SLE), and it is of great clinical value to identify potential molecular mechanisms and diagnostic markers of AMI associated with SLE by analyzing public database data and transcriptome sequencing data. Methods AMI and SLE-related sequencing datasets GSE62646, GSE60993, GSE50772 and GSE81622 were downloaded from the Gene Expression Omnibus (GEO) database and divided into prediction and validation cohorts. To identify the key genes associated with AMI related to SLE, WGCNA and DEGs analysis were performed for the prediction and validation cohorts, respectively. The related signaling pathways were identified by GO/KEGG enrichment analysis. Peripheral blood mononuclear cells (PBMCs) from patients with AMI were collected for transcriptome sequencing to validate the expression of key genes in patients with AMI. Least absolute shrinkage and selection operator (LASSO) regression analysis was applied to screen diagnostic biomarkers. The diagnostic efficacy of biomarkers was validated by ROC analysis, and the CIBERSORTx platform was used to analyze the composition of immune cells in AMI and SLE. Results A total of 108 genes closely related to AMI and SLE were identified in the prediction cohort, and GO/KEGG analysis showed significantly enriched signaling pathways. The results of differential analysis in validation cohort were consistent with them. By transcriptional sequencing of PBMCs from peripheral blood of AMI patients, combined with the results of prediction and validation cohort analysis, seven genes were finally screened out. LASSO analysis finally identifies DYSF, LRG1 and CSF3R as diagnostic biomarkers of SLE-related-AMI. CIBERSORTx analysis revealed that the biomarkers were highly correlated with neutrophils. Conclusion Neutrophil degranulation and NETs formation play important roles in SLE-related AMI, and DYSF, LRG1 and CSF3R were identified as important diagnostic markers for the development and progression of SLE-related AMI.
Wrapping DNS into HTTP(S): An Empirical Study on Name Resolution in Mobile Applications

Wrapping DNS into HTTP(S) is a promising way to mitigate the privacy and security issues of the traditional DNS. It has been standardized by IETF, i.e., DNS-over-HTTPS (DoH). This approach allows the application to choose open resolvers that it trusts, protecting its activities from potential snooping. Moreover, an application can establish a connection with its resolvers, incorporating specific handles and identifiers for customized use. How is the name resolution process performed on the client side? What are the criteria for an application to choose a resolver? These questions are still unclear. In this paper, we examine the application-level name resolution practices of 25 popular apps on Android and iOS platforms, revealing their adoption and usage patterns. We present the following findings: (i) non-standard, self-defined HTTP(S)DNS is more prevalent than DoH in practice, (ii) popular apps tend to use dispersed resolvers, some of which are self-owned, (iii) HTTP(S)DNS usage patterns differ across apps. These findings raise new issues related to the transparency and security of DNS configuration inside apps. We also explore the implications of these changes on the DNS ecosystem and analyze the potential security risks.
High Prevalence of Energy and Nutrients Inadequacy among Brazilian Older Adults

Poor nutrition increases the risk of diseases and adverse health outcomes in older adults. We evaluated the potential inadequacy of nutrient intake among older adults in Brazil and its association with body anthropometry and composition outcomes. Dietary intake was obtained from 295 community-living older adults (>60 years old), of both genders, using a seven-day food record. Nutrient inadequacy was further identified based on the Dietary Reference Intakes and European Guidelines. Skeletal muscle mass (SM), strength and performance, and the diagnosis of sarcopenia were assessed using reference methods. Nutritional inadequacy was high, with energy, dietary fiber, and six micronutrients exhibiting the greatest inadequacy levels (>80%). Energy intake was correlated with SM strength (p = 0.000) and performance (p = 0.001). Inadequate energy, fiber, and protein intakes influenced BMI, while inadequate intake of vitamin B6 directly affected the diagnosis of sarcopenia (p ≤ 0.005). Further research is required to investigate whether these inadequacies can be associated with other clinical health outcomes.
Lactate Dehydrogenase Inhibitors Suppress Borrelia burgdorferi Growth In Vitro

Borrelia burgdorferi, the causative agent of Lyme disease, has a highly reduced genome and relies heavily on glycolysis for carbon metabolism. As such, established inhibitors of lactate dehydrogenase (LDH) were evaluated in cultures to determine the extent of their impacts on B. burgdorferi growth. Both racemic and enantiopure (AT-101) gossypol, as well as oxamate, galloflavin, and stiripentol, caused the dose-dependent suppression of B. burgdorferi growth in vitro. Racemic gossypol and AT-101 were shown to fully inhibit spirochetal growth at concentrations of 70.5 and 187.5 μM, respectively. Differences between racemic gossypol and AT-101 efficacy may indicate that the dextrorotatory enantiomer of gossypol is a more effective inhibitor of B. burgdorferi growth than the levorotatory enantiomer. As a whole, LDH inhibition appears to be a promising mechanism for suppressing Borrelia growth, particularly with bulky LDH inhibitors like gossypol.
Contribution of Madrasah Head: Building Quality School Culture at MAN 2 Cirebon

This paper describes one of the contributions of the madrasah head at MAN 2 Cirebon as an effort to create competitive education by building a quality school culture. A qualitative approach was used, by collecting primary data sources from observations and interviews; while secondary data was obtained by digging from books, articles related to the discussion, from internet searches. The finding is that there is a new culture that is now built by the madrasah head as a form of his contribution by integrating technological innovation in learning as well as promoting institutions and student robotic extracurricular activities. The novelty of this research is the technological innovation program implemented at MAN 2 Cirebon. The limitations of this research are a review of other similar studies.
Electrical characteristics and hot carrier effects in quantum well solar cells

We report on the opto-electrical characterization of quantum-well solar cells designed for generation of hot carriers. Short-circuit current is proportional to laser power in the entire range. Population density, temperature and quasi-Fermi level splitting of photo-generated confined carriers are investigated by fitting the full luminescence spectra using generalized Planck’s law. The energy-dependent absorptivity is identified to obtain good fit accuracy and takes into account the absorption of excitons and free carriers in the quantum well. Furthermore, electrical injection and extraction across the barriers modify the temperature of the quantum well carrier population linearly, hinting at the role of barriers as semi-selective high-energy contact.
Formally verified compositional algorithms for factored transition systems

Artificial Intelligence (AI) planning and model checking are two disciplines that found wide practical applications. It is often the case that a problem in those two fields concerns a transition system whose behaviour can be encoded in a digraph that models the system’s state space. However, due to the very large size of state spaces of realistic systems, they are compactly represented as propositionally factored transition systems. These representations have the advantage of being exponentially smaller than the state space of the represented system. Many problems in AI planning and model checking involve questions about state spaces, which correspond to graph theoretic questions on digraphs modelling the state spaces. However, existing techniques to answer those graph theoretic questions effectively require, in the worst case, constructing the digraph that models the state space, by expanding the propositionally factored representation of the system. This is not practical, if not impossible, in many cases because of the state space size compared to the factored representation. One common approach that is used to avoid constructing the state space is the compositional approach, where only smaller abstractions of the system at hand are processed and the given problem (e.g. reachability) is solved for them. Then, a solution for the problem on the concrete system is derived from the solutions of the problem on the abstract systems. The motivation of this approach is that, in the worst case, one need only construct the state spaces of the abstractions which can be exponentially smaller than the state space of the concrete system. We study the application of the compositional approach to two fundamental problems on transition systems: upper-bounding the topological properties (e.g. the largest distance between any two states, i.e. the diameter) of the state space, and computing reachability between states. We provide new compositional algorithms to solve both problems by exploiting different structures of the given system. In addition to the use of an existing abstraction (usually referred to as projection) based on removing state space variables, we develop two new abstractions for use within our compositional algorithms. One of the new abstractions is also based on state variables, while the other is based on assignments to state variables. We theoretically and experimentally show that our new compositional algorithms improve the state-of-the-art in solving both problems, upper-bounding state space topological parameters and reachability. We designed the algorithms as well as formally verified them with the aid of an interactive theorem prover. This is the first application that we are aware of, for such a theorem prover based methodology to the design of new algorithms in either AI planning or model checking.