Miracle-dz/UniFAR-FTUs huggingface dataset:
Undersampled Ionospheric Irregularity Threat Parameterization Using a Three-Dimensional Model for Satellite-Based Augmentation Systems

Single-frequency Satellite-based Augmentation Systems (SBASs) compute and broadcast estimates of vertical ionospheric delays and integrity bounds on the estimates called the Grid Ionospheric Vertical Errors (GIVEs) at Ionospheric Grid Points (IGPs). The dominant contribution of the GIVE comes from the undersampled ionospheric irregularity threat model. This article presents a methodology for the undersampled ionospheric threat parameterization to reduce the magnitude of the GIVE. A threat model metric that measures the uniformity of angular separation of measurements was designed and incorporated into the current metric set, the fit radius, and the relative centroid metric (RCM), such that threat geometries were parameterized rigorously based on the extended metric set. An undersampled threat model was constructed with the proposed three-dimensional metric set and historical ionospheric storm data from the Global Navigation Satellite System stations in South Korea. We also simulated SBAS availability in the Korean region to demonstrate the benefit of the presented threat model methodology. In our preliminary assessment, the implementation of the proposed method was found to improve the 99.9% availability of the approach procedure with vertical guidance I by up to 12% when the approach was applied to the GIVE monitor algorithm.
Generalized linear models with random e ects: a Gibbs sampling approach

Abstract Generalized linear models have unified the approach to regression for a wide variety of discrete, continuous, and censored response variables that can be assumed to be independent across experimental units. In applications such as longitudinal studies, genetic studies of families, and survey sampling, observations may be obtained in clusters. Responses from the same cluster cannot be assumed to be independent. With linear models, correlation has been effectively modeled by assuming there are cluster-specific random effects that derive from an underlying mixing distribution. Extensions of generalized linear models to include random effects has, thus far, been hampered by the need for numerical integration to evaluate likelihoods. In this article, we cast the generalized linear random effects model in a Bayesian framework and use a Monte Carlo method, the Gibbs sampler, to overcome the current computational limitations. The resulting algorithm is flexible to easily accommodate changes in the number.
An Integrative Human-Centered Architecture for Interactive Programming Assistants

—Programming has become a collaboration between human programmers, who drive intent, and interactive assistants that suggest contextually relevant editor actions. There has been considerable work on suggestion synthesis strategies—from semantic autocomplete to modern program synthesis, repair, and machine learning research. This diversity of contextually viable strategies creates a need for an integrative, human-centered perspective on the problem of programming assistant design that (1) confronts the problem of integrating a variety of synthesis strategies, fed by shared semantic analyses capable of operating on program sketches, and (2) centers the needs of the human programmer: comprehending, comparing, ranking, and filtering suggestions generated by various synthesizers, and in some cases participating in a synthesizer’s search by supplying additional expressions of intent. This paper contributes a conceptual architecture and API to guide programming assistant designers as they confront these integration and human-centered design challenges. We then instantiate this architecture with two prototype end-to-end assistant designs, both developed for the Hazel programming environment, that emphasize understudied design aspects, namely continuity, explainability, human-in-the-loop synthesis, and the integration of multiple analyses with multiple synthesis strategies.
Low-Power And Area-Efficient Shift Register Utilizing Beat Latches

This paper proposes a low-power and zone effective move enlist using beat locks. The range and power utilization are lessened by superseding flip-flops with beat locks. This technique settles the planning bind between beat hooks through the use of different non-cover postponed beat check motions instead of the regular single beat clock flag. The move enroll uses a modest number of the beat clock motions by gathering the locks to a few sub shifter enlists and using incidental fleeting stockpiling hooks. A 256-piece move enlists using beat hooks was outlined using Verilog HDL. The proposed move enroll jam region and power contrasted with the customary move enlists with flip-flops.
Chaldene: Towards Visual Programming Image Processing in Jupyter Notebooks

—Jupyter Notebook [1] is an open source, interactive computing platform widely used in the scientific computing and artificial intelligence community [2], [3], [4], [5]. The popularity of the platform is a consequence of the generated single notebook document combining source code, markdown, and visualizations (Fig.1). This makes the platform ideal for tasks such as data analysis and scientific image processing, where repeatability and transparency of analysis tasks are just as important as functionality and performance. However, the obligatory use of code is an obstacle to acceptance of the platform in scientific communities where programming is not generally taught in the curriculum. Consequently, many experimental communities rely on manual image processing using graphical user interfaces [6], [7], [8]. The obvious disadvantages are the lack of re-peatability, transparency, and precision in image processing and data analysis tasks. To solve these issues, we propose to extend Jupyter Notebook with visual programming cells. In each visual programming cell, users can create the program by assembling graphical nodes that represent computational instructions, and the textual program is automatically generated and executed by the environment. Cells will support version control aware serialization and deserialization. The core innovation of our proposed work lies in a change of workflow and the adaption of a jupyter-based workflow in experimental communities that have no culture of working with source code. The system can be adapted to multiple applications and domains by integrating new node types. We hereby present an early version of the system and provide one use case from microscopy image processing to demonstrate the integration of existing non-Python software.
Common Practice Elements in Treatment Programs for Adolescents with Externalizing and Internalizing Problems: A Meta-Analysis

There is a pressing need for effective interventions targeting mental disorders for service-involved youth across child welfare, correc- tional and mental health residential settings. Identifying effective common practice elements (CPEs) is a promising direction toward developing effective, flexible, and feasible therapeutic interventions. The aims of this study were: 1) to identify CPEs in treatment programs for adolescents with internalizing and/or externalizing disorders across residential settings, 2) to identify which CPEs are present in effective versus all trials, and 3) to estimate whether the presence of each CPE is associated with treatment effects. A systematic search identified 24 trials of programs targeting adolescent internalizing and/or externalizing symptoms, yielding 148 effect estimates. Discrete practices were coded into CPE categories across effective trials and the full sample. Eleven CPEs were identi- fied and employed as moderators in three-level meta-analyses. We found large significant moderator effects of two elements on externalizing symptoms: Emotion recognition and differentiation, and Psychoeducation. No CPE was significantly related to effects on internalizing outcomes. The results illustrate the difference between CPE frequency and association with effect, favoring a combination of the two approaches. Emotion recognition and differentiation, and Psychoeducation should be prioritized in novel interventions targeting adolescent externalizing behavior in residential settings.
Prevalence of Orthorexia Nervosa among Medical Students in Chennai

Background: Orthorexia nervosa (ON) is characterised by an obsession with healthy eating, which may lead to severe physical, psychological and social disorders. It is particularly important to study this problem in order to improve early detection and treatment. Aim and Objective: The aim of this study was to estimate the prevalence of Orthorexia Nervosa in among medical students in Chennai. Materials and Methods: Cross-sectional study done at a medical college, among undergraduate medical students. After institutional ethical committee approval, the study was conducted on 306 students. Sociodemographic details were collected and ORTHO-15 questionnaire was used to estimate the prevalence of Orthorexia Nervosa. Data was entered in excel and analysed using SPSS version 22. Chi-square test and Multiple logistic regression were used for data analysis. p- value < 0.05 was considered as statistically significant. Results: The prevalence of Orthorexia Nervosa among undergraduate medical students was 69.93% (95% CI 64.46 to 75.02%). No significant association was found between age group, gender, BMI category, place of stay and Orthorexia Nervosa. Conclusion: Orthorexia Nervosa tendency is common among medical students. Better awareness of healthy eating will help prevention of the problem.
Bayesian Periodic Cramér-Rao Bound

The Cramér-Rao bound (CRB) has been extensively used as a benchmark for estimation performance in both Bayesian and non-Bayesian frameworks. In many practical periodic parameter estimation problems, such as phase, frequency, and direction-of-arrival estimation, the observation model is periodic with respect to the unknown parameters and thus, the appropriate criterion is periodic in the parameter space. Consequently, Bayesian lower bounds on the mean-squared-error (MSE) are not valid bounds for periodic estimation problems. In addition, many Bayesian MSE lower bounds cannot be derived in the periodic case due to their restrictive regularity conditions. For example, the regularity conditions of the Bayesian CRB (BCRB) are not satisfied for parameters with uniform prior distribution. In this letter, we derive a Bayesian Cramér-Rao-type lower bound on the mean-squared-periodic-error (MSPE). The proposed periodic BCRB (PBCRB) is a lower bound on the MSPE of any estimator and has less restrictive regularity conditions than the BCRB. The PBCRB is compared with the MSPE of the minimum MSPE estimator for phase estimation in Gaussian noise and it is shown that the PBCRB is a valid and tight lower bound for this problem.
Investigating the Effect of Stock Market Variables on GDP in Iran Using Autoregressive Distributed Lag (ARDL) Model

Paying attention to the development of macroeconomic variables, including GDP, has been an important part of the planners’ concerns in different communities, because it is not only affected by the economic and political conditions of the communities, but also by financial and capital markets. This study aimed to investigate the effect of stock market variables on GDP in Iran. In this regard, this has been studied using quarterly statistical data from 2009 to 2020 and an autoregressive distributed lag (ARDL) model. To do this, three variables of total index, industrial index and trade value as the stock market activity and GDP variable have been used. The results showed that the variables of total index and industry index have no effect on GDP in the short run and in the long run, but the value of stock trading affects GDP, and this effect is reversed. The number of vector error correction (VEC) factor was -0.49 that showed the equilibrium in almost two time periods.
A New Ultra-Fast-Response Low-Transient-Voltage Boost Converter Suitable for Low-Voltage Solar Cells in Wireless Sensor Networks

In this article, a new ultra-fast-response low-transient-voltage boost converter suitable for low-voltage solar cells in wireless sensor networks is presented. The proposed converter not only achieves a faster transient response but also higher power conversion efficiency. With the current-feedback adaptive-controlled technique, the system can operate in the more fixed switching frequency without extra phase-locked-loop circuits. The proposed new rail-to-rail current sensing circuit, which produces the inductor current information to the controlled loop, not only increases the stability of the system but also improves the dynamic response. The addition of a zero-current detector (ZCD) prevents the reverse current as the converter operates at light load, which enhances the conversion efficiency, and ensures that the converter works stably in the discontinuous conduction mode (DCM). The proposed converter has been implemented with TSMC 0.18- $ {\mu }\text{m}$ 1P6M CMOS processes and occupies an area of 1.19 mm $\times1.2$ mm. The measured results show that the transient response is $2 ~{\mu }\text{s}$ when the load current changes between 1 and 200 mA. The peak conversion efficiency is 92.3% under 140-mA load current. The maximum efficiency improvement is 14.8% at 5-mA load current. The major contributions of this article are listed in the following: 1) we proposed a new architecture of boost converters with improved current-feedback and adaptive-controlled techniques; 2) the proposed architecture has one current loop and one voltage loop to balance the output voltage and the output current; 3) the proposed boost converter has the characteristics of fast response time and low transient voltage; 4) we have designed, implemented, and measured the proposed converters; and 5) from the figure of merit (FOM) formula, the performance of the proposed boost converter is better than other recent works.
Stem Cells from Healthy and Tendinopathic Human Tendons: Morphology, Collagen and Cytokines Expression and Their Response to T3 Thyroid Hormone

The aim of this study was to investigate the effect of triiodothyronine (T3) on tendon specific markers and cytokines expression of stem cells extracted from human tendons. Indeed, thyroid hormones have been reported to be protective factors, maintaining tendons’ homeostasis, whereas tendinopathy is believed to be related to a failed healing response. Healthy and tendinopathic human tendons were harvested to isolate tendon stem/progenitor cells (TSPCs). TSPCs obtained from pathological samples showed gene expression and morphological modifications at baseline in comparison with cells harvested from healthy tissues. When cells were maintained in a medium supplemented with T3 (10−6 M), only pathological populations showed a significant upregulation of tenogenic markers (DCN, TNC, COL1A1, COL3A1). Immunostaining revealed that healthy cells constantly released type I collagen, typical of tendon matrix, whereas pathological ones overexpressed and secreted type III collagen, typical of scarred and impaired tissue. Pathological cells also overexpressed pro- and anti-inflammatory cytokines, suggesting an impaired balance in the presence of T3, without STAT3 activation. Moreover, DKK-1 was significantly high in the culture medium of pathological cell cultures and was reversed by T3. This study opens perspectives on the complex biochemical alteration of cells from pathological tendons, which may lead to the chronic disease context with an impaired extracellular matrix.
What contribution of agroecology to job creation in sub-Saharan Africa? The case of horticulture in the Niayes, Senegal

ABSTRACT In the context of Sub-Saharan Africa’s demographic boom, the issue of youth employment has become a major concern. Many debates are ongoing regarding agriculture’s role in the structural transformation process and providing jobs. In this regard, we explore the opportunity of an agroecological intensification of family farming. We analyze data from agricultural households in the Niayes area of Senegal collected in 2019 and use a clustering method to group farms and rank them according to agroecological practices. Diversity and livestock integration are the most differentiating factors across the identified farming systems. Considering labor allocation complexity within family farms, we compare employment indicators between farming systems to look for agroecology’s effect on agricultural work. We observe diversity in the intensity of labor requirements across the different systems but no overall increase for the most agroecological. However, women working hours appear significantly increased for two groups suggesting a substitution with wage workers for the most agroecological systems.
Características morfométricas, reproductivas y germinativas del germoplasma de Swietenia humilis Zucc. en Guerrero

Swietenia humilis es una especie tropical de madera preciosa amenazada por la fragmentación de su hábitat debido a deforestación y cambios en el uso del suelo. Por lo tanto, es esencial caracterizar el germoplasma de las poblaciones naturales para definir estrategias de conservación, propagación y reforestación. Se analizaron características morfométricas y reproductivas de frutos, así como la viabilidad y capacidad germinativa de semillas de S. humilis de seis sitios en el estado de Guerrero, México: Zacapalco, Buenavista, Santa Fe Tepetlapa, Cieneguillas, Tuxpan y Arcelia. En cada sitio, se caracterizó el tamaño y peso de frutos y semillas, el número y proporción de semillas desarrolladas y no desarrolladas, la viabilidad y la capacidad germinativa. Para cada variable se hizo un análisis de varianza no paramétrico Kruskal-Wallis y una comparación de medias. Las características de los frutos y semillas variaron entre los sitios evaluados. Cieneguillas presentó menor tamaño de frutos, peso y número de semillas. Tuxpan registró la mayor cantidad de semillas, pero una proporción alta fueron no desarrolladas. Para Santa Fe se obtuvo la mayor cantidad de semillas desarrolladas, aunque las más pesadas, viables, vigorosas y con mayor capacidad germinativa fueron las de Zacapalco. Los árboles de S. humilis de los rodales naturales analizados en el estado de Guerrero producen frutos y semillas que difieren en sus características morfométricas y reproductivas, así como en su capacidad germinativa.
Advances in Preclinical In Vitro Models for the Translation of Precision Medicine for Cystic Fibrosis

The development of preclinical in vitro models has provided significant progress to the studies of cystic fibrosis (CF), a frequently fatal monogenic disease caused by mutations in the gene encoding the CF transmembrane conductance regulator (CFTR) protein. Numerous cell lines were generated over the last 30 years and they have been instrumental not only in enhancing the understanding of CF pathological mechanisms but also in developing therapies targeting the underlying defects in CFTR mutations with further validation in patient-derived samples. Furthermore, recent advances toward precision medicine in CF have been made possible by optimizing protocols and establishing novel assays using human bronchial, nasal and rectal tissues, and by progressing from two-dimensional monocultures to more complex three-dimensional culture platforms. These models also enable to potentially predict clinical efficacy and responsiveness to CFTR modulator therapies at an individual level. In parallel, advanced systems, such as induced pluripotent stem cells and organ-on-a-chip, continue to be developed in order to more closely recapitulate human physiology for disease modeling and drug testing. In this review, we have highlighted novel and optimized cell models that are being used in CF research to develop novel CFTR-directed therapies (or alternative therapeutic interventions) and to expand the usage of existing modulator drugs to common and rare CF-causing mutations.
SINR Meta Distribution for mmWave Heterogeneous Networks Under Varying Queue Status: A Spatio-Temporal Analysis

A fine-grained analysis of network performance is crucial for system design. In this paper, we focus on the meta distribution of the signal-to-interference-plus-noise-ratio (SINR) in the mmWave heterogeneous networks where the base stations (BS) in each tier are modeled as a Poisson point process (PPP). By utilizing stochastic geometry and queueing theory, we characterize the spatial and temporal randomness while the special characteristics of mmWave communications, including different path loss laws for line-of-sight and non-line-of-sight links and directional beamforming, are incorporated into the analysis. We derive the moments of the conditional successful transmission probability (STP). By taking the temporal random arrival of traffic into consideration, an equation on the meta distribution is formulated and its solution is obtained in a recursive manner. The critical arrival rate for a fraction of the queues at the BSs to be stable is provided. In addition, the mean delay is obtained. The numerical results reveal the impact of the key network parameters, such as the blockage parameter, the bias factor, the number of antenna elements and the BS density, on the network performance. Specifically, offloading the users from the macro BS tier to the small BS tier by biasing can reduce the performance fluctuation while only causing a slight degradation of the overall network performance.
Transcriptomic Profiling Analysis of Castration-Resistant Prostate Cancer Cell Lines Treated with Chronic Intermittent Hypoxia

Simple Summary Prostate cancer is the second most frequently diagnosed cancer and the fifth cause of cancer mortality among men. Although localized and confined tumors are relatively curable, patients with advanced metastatic prostate cancer are still problematic. Hypoxia, which is a marked characteristic of advanced solid tumors, has been suggested to induce the progression of prostate cancer. This study aimed to evaluate the impact of chronic intermittent hypoxia on a castration-resistant prostate cancer cell line in inducing cancer progression using RNA sequencing analysis. Through RNA sequencing analysis, we prove that COL13A1, which is a key factor for the progression of metastasis, is closely related to metastatic prostate cancer. These results suggest that our findings indicate a novel strategy for the clinical management of mCRPC. Abstract Castration-resistant prostate cancer (CRPC) is still a major concern in men’s health, with 375,000 cancer deaths annually. Hypoxia, which is a marked characteristic of advanced solid tumors, has been suggested to induce prostate cancer towards CRPC, metastasis and treatment resistance. To evaluate the effect of hypoxia on prostate cancer, two and five cycles of hypoxia and reoxygenation were administered using 22Rv1 cell lines and denominated as 22Rv1-CI and 22Rv1-PCI, respectively. Cancer cell migration was promoted in 22Rv1-CI compared to controls, and the expression of COL13A1 was significantly up-regulated in 22Rv1-CI according to differentially expressed gene analysis of RNA sequencing among groups. Cancer cell migration was impeded in a wound healing assay after transfecting si-COL13A1. Moreover, the expression of COL13A1 was also higher in the cell line originating from bone metastatic prostate cancer compared to other cell lines. Using the open database GEO, we also confirmed that the expression of COL13A1 was higher in bone metastatic prostate cancer tissue than in localized prostate cancer tissue in patients. Therefore, COL13A1 may be closely related to the bony metastasis of prostate cancer, and our findings may provide valuable information on the pathophysiology of the metastatic niche induced by hypoxia in patients with CRPC.