Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Reported Sleeping Time Effects on Physical Performance and Body Composition Among Spanish Older Adults: EXERNET-Elder 3.0 study

Objective: To determine the link between physical fitness and body composition with nocturnal and nap time in Spanish older adults. Methods: Eight hundred thirty older adults underwent several tests. Sleep was measured using Jenkins Sleep Scale. Nocturnal sleep was categorized (<7, 7–9, and >9 hours), and nap time (no nap, 1–30 minutes, and >30 minutes). Physical fitness was evaluated using validated tests, and body composition by electric bioimpedance. Results: 75.1% of participants were female, mean age 77.7 ± 5.1 years. Mean nocturnal sleep and nap time was 6.7 hours and 23.3 minutes, respectively. Models showed nocturnal sleep >9 hours was significant and positively associated with body shape index (Odds ratio[OR] = 4.07 (p = .011)) and waist circumference OR = 1.04 (p = .024) in females. Males’ waist and hip circumference were positively significantly related to nap time between 1 and 30 minutes, OR = 1.08, p = . 009 and OR = 1.08, p = . 048, respectively. In females, nap time >30 minutes was associated with greater fat mass and body shape index OR = 1.22, p = . 032 and OR = 3.95, p = . 027, respectively. Physical fitness showed no associations with sleep outcomes. Conclusions: Sleep patterns do not influence physical fitness but body composition, being more related to female body composition as nocturnal and nap sleep were associated with higher fat mass, waist circumference and body shape index, while only short nap times were related to higher waist and hip circumference in males.
Realizable and Context-Free Hyperlanguages

Hyperproperties lift conventional trace-based languages from a set of execution traces to a set of sets of executions. From a formal-language perspective, these are sets of sets of words, namely hyperlanguages. Hyperautomata are based on classical automata models that are lifted to handle hyperlanguages. Finite hyperautomata (NFH) have been suggested to express regular hyperproperties. We study the realizability problem for regular hyperlanguages: given a set of languages, can it be precisely described by an NFH? We show that the problem is complex already for singleton hyperlanguages. We then go beyond regular hyperlanguages, and study context-free hyperlanguages. We show that the natural extension to context-free hypergrammars is highly undecidable. We then suggest a refined model, namely synchronous hypergrammars, which enables describing interesting non-regular hyperproperties, while retaining many decidable properties of context-free grammars.
E-Commerce Sales Revenues Forecasting by Means of Dynamically Designing, Developing and Validating a Directed Acyclic Graph (DAG) Network for Deep Learning

As the digitalization process has become more and more important in our daily lives, during recent decades e-commerce has greatly increased in popularity, becoming increasingly used, therefore representing an extremely convenient alternative to traditional stores. In order to develop and maintain profitable businesses, traders need accurate forecasts concerning their future sales, a very difficult task considering that these are influenced by a wide variety of factors. This paper proposes a novel e-commerce sales forecasting method that dynamically builds a Directed Acyclic Graph Neural Network (DAGNN) for Deep Learning architecture. This will allow for long-term, fine-grained forecasts of daily sales revenue, refined up to the level of product categories. The developed forecasting method provides the e-commerce store owner an accurate forecasting tool for predicting the sales of each category of products for up to three months ahead. The method offers a high degree of scalability and generalization capability due to the dynamically incremental way in which the constituent elements of the DAGNN’s architecture are obtained. In addition, the proposed method achieves an efficient use of data by combining the numerous advantages of its constituent layers, registering very good performance metrics and processing times. The proposed method can be generalized and applied to forecast the sales for up to three months ahead in the case of other e-commerce stores, including large e-commerce businesses.
Speech-based Depression Detection Using Unsupervised Autoencoder

With the rapid development of society, over three hundred million people worldwide suffer from depression, which has become one of the most serious health problems in the world. As we know, depression detection is of great importance for its timely treatment. In this paper, a speech-based depression detection method using unsupervised autoencoder is proposed. Most previous methods encode the frame-level speech features into sentence-level features with statistical functions which lead to the loss of the temporal information between frames. To solve this, we propose an unsupervised network based on transformer. The unsupervised network is adopted to obtain the audio embedding vector of an audio segment from depressed or non-depressed people. Then the embedding audio vector is used for depression detection. The experimental results show that the proposed method achieves superior performance on both the English database DAIC and our self-built Chinese database CMDC.
Low-Ammonium Environment Increases the Nutrient Exchange between Diatom–Diazotroph Association Cells and Facilitates Photosynthesis and N2 Fixation—a Mechanistic Modeling Analysis

Diatom–diazotroph associations (DDAs) are one of the most important symbiotic dinitrogen (N2) fixing groups in the oligotrophic ocean. Despite their capability to fix N2, ammonium (NH4+) remains a key nitrogen (N) source for DDAs, and the effect of NH4+ on their metabolism remains elusive. Here, we developed a coarse-grained, cellular model of the DDA with NH4+ uptake and quantified how the level of extracellular NH4+ influences metabolism and nutrient exchange within the symbiosis. The model shows that, under a fixed growth rate, an increased NH4+ concentration may lower the required level of N2 fixation and photosynthesis, and decrease carbon (C) and N exchange. A low-NH4+ environment leads to more C and N in nutrient exchange and more fixed N2 to support a higher growth rate. With higher growth rates, nutrient exchange and metabolism increased. Our study shows a strong effect of NH4+ on metabolic processes within DDAs, and thus highlights the importance of in situ measurement of NH4+ concentrations.
Height Estimation from a Single SAR Image by Depth-Aware Proxyless Neural Architecture Search

Height estimation can be estimated from a single synthetic aperture radar (SAR) Image via an advanced U-shaped convolution neural network (CNN)—Unet. Manually designed architectures are applied to achieve high reconstruction accuracy. Neural Architecture Search (NAS) is efficient for designing the architecture of convolution neural network (CNN) automatically for a specific task, but it is difficult to be deployed directly due to the limitations of computational resources. Then, proxy tasks are developed for traditional NAS methods. However, the trained model for proxy tasks is not ptimal for the target task. In this paper, we propose a depth-aware penalty proxyless NAS method for Unet (DPNAS-Unet) to estimate the height from a single SAR Image. Symmetric residual blocks are proposed to improve the representation capability of the model. A depth-aware architecture loss is proposed for optimal architecture learning. Supplementary sparse height map is included as inputs of our model to improve the performance. In the end, comparison experiments are performed to show the superiority of PDNAS-Unet.
SENSITIVITY OF TRANSIENT TEMPERATURE FIELD IN THE CASTING DOMAIN WITH RESPECT TO LATENT HEAT PERTURBATIONS

The mathematical model of thermal processes in the casting domain can be formulated using the fixed domain approach. The energy equation corresponding to this model contains the parameter called a substitute thermal capacity (STC), in which the alloy latent heat appears. The aim of considerations is the application of sensitivity analysis methods for estimation of temperature field changes due to the perturbations of alloy latent heat. In this the analytical form of STC results from the consideration concerning the lever arm model and the Scheil model. The equations determining the sensitivity function are found using the so-called direct approach. At the stage of numerical modeling the finite difference method is used. In the final part of the paper the example of computations is shown.
Factorial analysis and gender invariance of GPIUS2 scale and evaluation of Caplan’s cognitive-behavioral model of problematic Internet use in adolescents

Caplan's (2010) revised cognitive-behavioral model of Problematic Internet Use (PIU) has become one of the most promising theoretical frameworks on such behavior in the last decade. The Generalized Problematic Internet Use Scale (GPIUS2) (Caplan, 2010) has been adapted and validated for use with Spanish adolescents, but gender invariance has not been tested yet in this country. The present study focuses on three objectives: to confirm the factorial structure of GPIUS2, to analyze gender invariance, and to test Caplan’s theoretical model, in a sample of Spanish adolescents. A cross-sectional study was carried out. The sample was composed of 909 participants from Spain, aged between 12 and 18 years (Mean age = 14.2, SD = 1.6). The results indicated a good fit of the GPIUS2 factor structure and gender factorial invariance. Moreover, the cognitive-behavioral model fit the data. GPIUS2 has a very good structure and presents gender factorial invariance, which favors its application in Spanish adolescent population. This study supports the cognitive-behavioral model of PIU. El modelo cognitivo-conductual revisado de Caplan (2010) sobre el Uso Problemático de Internet (UPI) es uno de los marcos teóricos más prometedores sobre dicho comportamiento en la última década. La Escala Uso Problemático de Internet Generalizado (GPIUS2) (Caplan, 2010) ha sido adaptada y validada para su uso con adolescentes españoles, pero aún no se ha comprobado su invarianza de género en España. El presente estudio se centra en tres objetivos: confirmar la estructura factorial de la escala GPIUS2, analizar la invarianza de género de dicha escala y poner a prueba el modelo teórico de Caplan en una muestra de adolescentes españoles. Se realizó un estudio transversal. La muestra estuvo compuesta por 909 participantes de España, con edades comprendidas entre los 12 y los 18 años (Edad media = 14.2, DT = 1.6). Los resultados indicaron un buen ajuste de la estructura factorial del GPIUS2 e invarianza factorial en función del género. Además, el modelo cognitivo-conductual mostró un buen ajuste de los datos. La GPIUS2 tiene una muy buena estructura y presenta invarianza factorial para el género, lo que favorece su aplicación en la población adolescente española. Este estudio apoya el modelo cognitivo-conductual en el UPI.
Early Detection of Parkinson’s Disease using Machine Learning and Convolutional Neural Networks from Drawing Movements

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes uncontrollable movements and difficulty with balance and coordination. It is highly important for early detection of Parkinson’s disease in order for patients to receive proper treatment. This paper aims to aid in the early detection of Parkinson’s disease by using a convolutional neural network for PD detection from drawing movements. This CNN consists of 2 convolutional layers, 2 max-pooling layers, 2 dropout layers, 2 dense layers, and a flattened layer. Additionally, our approach explores multiple types of drawings, specifically spiral, meander, and wave datasets hand-drawn by patients and healthy controls to find the most effective one in the discrimination process. The models can be continuously trained in which the test data can be inputted to differentiate between healthy controls and PD patients. By analyzing the training and validation accuracy and loss, we were able to find the most appropriate model and dataset combination, which was the spiral drawing with an accuracy of 85%. With a proper model and a larger dataset for increased accuracy, this approach has the potential to be implemented in a clinical setting.
New complex variable meshless method for advection—diffusion problems

In this paper, an improved complex variable meshless method (ICVMM) for two-dimensional advection—diffusion problems is developed based on improved complex variable moving least-square (ICVMLS) approximation. The equivalent functional of two-dimensional advection—diffusion problems is formed, the variation method is used to obtain the equation system, and the penalty method is employed to impose the essential boundary conditions. The difference method for two-point boundary value problems is used to obtain the discrete equations. Then the corresponding formulas of the ICVMM for advection—diffusion problems are presented. Two numerical examples with different node distributions are used to validate and inestigate the accuracy and efficiency of the new method in this paper. It is shown that ICVMM is very effective for advection—diffusion problems, and has a good convergent character, accuracy, and computational efficiency.
The Volatile Profile of Brussels Sprouts (Brassica oleracea Var. gemmifera) as Affected by Pulsed Electric Fields in Comparison to Other Pretreatments, Selected to Steer (Bio)Chemical Reactions

Pulsed electric fields (PEF) at low field strength is considered a non-thermal technique allowing membrane permeabilization in plant-based tissue, hence possibly impacting biochemical conversions and the concomitant volatile profile. Detailed studies on the impact of PEF at low field strength on biochemical conversions in plant-based matrices are scarce but urgently needed to provide the necessary scientific basis allowing to open a potential promising field of applications. As a first objective, the effect of PEF and other treatments that aim to steer biochemical conversions on the volatile profile of Brussels sprouts was compared in this study. As a second objective, the effect of varying PEF conditions on the volatile profile of Brussels sprouts was elucidated. Volatile fingerprinting was used to deduce whether and which (bio)chemical reactions had occurred. Surprisingly, PEF at 1.01 kV/cm and 2.7 kJ/kg prior to heating was assumed not to have caused significant membrane permeabilization since similar volatiles were observed in the case of only heating, as opposed to mixing. A PEF treatment with an electrical field strength of 3.00 kV/cm led to a significantly higher formation of certain enzymatic reaction products, being more pronounced when combined with an energy input of 27.7 kJ/kg, implying that these PEF conditions could induce substantial membrane permeabilization. The results of this study can be utilized to steer enzymatic conversions towards an intended volatile profile of Brussels sprouts by applying PEF.
The Essential Guide to Effect Sizes: Introduction to effect sizes

The primary product of a research inquiry is one or more measures of effect size, not p values. ~ Jacob Cohen (1990: 1310) The dreaded question “So what? ” It was the question every scholar dreads. In this case it came at the end of a PhD proposal presentation. The student had done a decent job outlining his planned project and the early questions from the panel had established his familiarity with the literature. Then one old professor asked the dreaded question. “So what? Why do this study? What does it mean for the man on the street? You are asking for a three-year holiday from the real world to conduct an academic study. Why should the taxpayer fund this? ” The student was clearly unprepared for these sorts of questions. He referred to the gap in the literature and the need for more research, but the old professor wasn't satisfied. An awkward moment of silence followed. The student shuffled his notes to buy another moment of time. In desperation he speculated about some likely implications for practitioners and policy-makers. It was not a good answer but the old professor backed off. The point had been made. While the student had outlined his methodology and data analysis plan, he had given no thought to the practical significance of his study. The panel approved his proposal with one condition. If he wanted to pass his exam in three years' time he would need to come up with a good answer to the “so what? ”
CRISPR/Cas9-Mediated Genome Editing of Soluble Starch Synthesis Enzyme in Rice for Low Glycemic Index

In the present study, the selected rice (Oryza sativa cv. indica) was genetically modified to increase the amylose content in the seeds. The ‘CRISPR-Cas9’ genome-editing tool was used to knock out three isoforms of soluble starch synthase (SSS) viz. SSSII-1, SSSII-2 and SSSII-3. A genetic transformation vector designed with appropriate gRNAs, Cas9, and antibiotic resistance was used to create SSS knockout mutants to enrich the content of amylose. Putative rice mutants were developed with high amylose content in the seeds of up to 63% as compared to 23% in the wild types (control). Rice with a low Glycemic Index (GI) value and high amylose content rice is preferred to avoid a sudden rise in glucose in the bloodstream. The frequencies of bi-allelic or homozygous transgenic lines of SSSII-1, SSSII-2, and SSSII-3 in the first generation were tested via the Mendelian fashion of segregated bi-allelic lines in the T1 generation of the putative rice mutants. The T1 generation segregation showed a frame-shift mutation. A molecular characterization of the putative mutants successfully demonstrated the development of a Cas9-free rice mutant with a higher amount of amylose in the rice.
An Exploration of Restorative Artmaking During COVID-19

This research explores a curriculum, delivered on Zoom, that blended art education with art therapy to support educators’ well-being during the COVID-19 pandemic. To understand the restorative aspects of collective artmaking and reflection. I established a series of artmaking workshops to educators via Zoom. As an artist/researcher/teacher, I made collages as an arts-based inquiry method. I found that participants needed a safe place to express, create, and share in a community of others who have similar needs, desires, and experiences, a respite from the early, terrifying days of the pandemic. Meditation, blended artmaking experiences, and reflection encouraged everyone to feel less alone. Participants and I had space to acknowledge our emotional exhaustion, feel supported, and regain the energy to survive. This research explores a curriculum that with art to support during the COVID-19 To understand the restorative aspects of collective artmaking and reflection, I established a series of six artmaking workshops for educators via Zoom. I used methods, including collage as inquiry, to analyze participants’ experiences.
Death Anxiety and Related Factors among Nurses during the COVID-19 Pandemic: A Systematic Review

Background and Objectives: High workload, insufficient resources, and many stressors in the workplace have led to the imposition of physical and psychological pressures on nurses, which exposes them to Death Anxiety (DA). This systematic review aimed to assess the DA and factors associated with its in nurses during the COVID-19 pandemic. Material and Methods: An extensive search was conducted on Scopus, PubMed, Web of Science, Iranmedex, and Scientific Information Database (SID) databases via keywords including "Death", "Death Anxiety", "Nurses", and "COVID-19", from December 2019 to November 10, 2021. Results: 818 nurses were enrolled in four papers. Nurses’ mean age and work experiences were 31.21 (SD=5.43) and 7.60 (SD=6.73) years, respectively. The mean DA of nurses during the COVID-19 pandemic was 7.30 (SD=2.23). Also, 31.05% of nurses had a high DA level during the COVID-19 pandemic. Age, sex, work experience, working hours per week, childbearing, several patients needing end ‑ of ‑ life care, direct participation in resuscitation operations, patient death, depression, mental health status, and life satisfaction were influential factors in DA nurses during the COVID-19 pandemic. Conclusion: Thus, nursing policymakers should pay special attention to these factors related to nurses' health maintenance and promotion programs to increase the quality of nursing care for COVID-19 patients. Also, it is recommended that psychological and communication support be provided to nurses during the COVID-19 pandemic.
Submodular Dispatching

Motivated by applications in e-commerce logistics where orders or items arrive at different times and must be dispatched or processed in batches, we propose the submodular dispatching problem (SMD), a strongly NP-hard model deﬁned by a set of orders with release times and a non-decreasing submodular dispatch time function. A single uncapacitated vehicle must dispatch orders in batches to minimize the makespan, the time at which all orders have been dispatched. We analyze FIFO solutions, discuss cases when they are optimal and provide a dynamic program to obtain such solutions. Furthermore, we give a polynomial-time 1.5-approximation algorithm based on the linear relaxation of an MILP formulation; we show that this approximation guarantee is best possible among a certain class of heuristics, and give additional results on the problem’s approximability. We computationally test our methods on applications in same-day delivery and warehousing.
Any-Shot GIN: Generalizing Implicit Networks for Reconstructing Novel Classes

We address the task of estimating the 3D shapes of novel shape classes from a single RGB image. Prior works are either limited to reconstructing known training classes or are unable to reconstruct high-quality shapes. To solve those issues, we propose Generalizing Implicit Networks (GIN) which decomposes 3D reconstruction into 1. ) front-back depth estimation followed by differentiable depth voxelization, and 2. ) implicit shape completion with 3D features. The key insight is that the depth estimation network learns local class-agnostic shape priors, allowing us to generalize to novel classes, while our implicit shape completion network is able to predict accurate shapes with rich details by learning implicit surfaces in 3D voxel space. We conduct extensive experiments on a large-scale benchmark using 55 classes of ShapeNet and real images of Pix3D. We qualitatively and quantitatively show that the proposed GIN significantly outperforms the state of the art on both seen and novel shape classes for single-image 3D reconstruction. We also illustrate that our GIN can be further improved by using only few-shot depth supervision from novel classes.
A PE MODEL OF NEON EXPORTS FROM UKRAINE

We have built an industry-specific partial equilibrium model of Ukrainian neon exports as an extension of the analysis of supply shocks and the international supply chain in DeCarlo and Goodman (2022). The value of adding the simple yet formal economic model is that it quantifies the impact of the 2015 and 2022 conflicts on industry prices and on the diversity and flexibility of the industry’s international supply chain.