Miracle-dz/UniFAR-FTUs huggingface dataset:
Contact with Nature in Social Deprivation during COVID-19: The Positive Impact on Anxiety

The 2019 outbreak of the novel coronavirus disease (COVID-19) had a devastating impact on millions of people worldwide. Following the constantly changing course of the pandemic, the Italian government massively restricted public and private life to prevent the further spread of the virus. Unfortunately, lockdown policies negatively impacted many people’s mental and physical health. Numerous studies recognized an essential role of urban green areas in promoting human well-being. The present study aims to evaluate the effect of personal dispositions towards nature, measured using the connectedness to nature scale (CNS) and actual contact with green spaces (CwN) on human well-being (i.e., anxiety) and medicine intake during COVID-19 lockdowns. A total of 637 Italian residents answered a survey aimed at gathering information about the above variables. A series of path analyses were performed. The results showed that the CNS was positively associated with the CwN, and the latter, in turn, was negatively associated with anxiety. Finally, anxiety was positively related to medicine intake. In sum, these results identify the positive role of person–nature relationships for individual well-being during COVID-19 restrictions.
Numerical investigation of turbulent kinetic energy and heat transfer performance in axial water jacket of high-power-density permanent magnet synchronous motor

In the field of permanent magnet synchronous motors (PMSMs) cooling, turbulent kinetic energy (TKE) and turbulent dissipation (TD) are used for the first time to numerically evaluate the heat transfer performance of axial water jackets (WJs), in order to investigate the influence of the WJ's structural parameters on the water-cooled system of high-power-density permanent magnet synchronous motors (HPD-PMSMs). In the present work, the temperature distribution and fluid flow pattern of a 40kW HPD-PMSM were calculated using the finite element method, and temperature testing of the HPD-PMSM at different speeds using a built test platform. In addition, the effects of the bend shape of cooling channels (BSCC) on the axial WJ's performance for heat transfer are examined, and a detailed analysis of the mechanism underlying the development and evolution of high vorticity vortex is provided. The findings demonstrate that the TKE is attenuated by the positive vortex development, which is harmful to the TKE's normal function in axial WJ heat transfer. Furthermore, the maximum stator winding temperature was reduced to 93.49? C and the maximum flow velocity within the WJ could be increased by 14.34% owing to the BSCC improvement.
Screening Tools for Sarcopenia in Mild to Moderate Parkinson’s Disease: Assessing the Accuracy of SARC-F and Calf Circumference

Background: Parkinson’s disease (PD) and sarcopenia share similar pathophysiological mechanisms. Objective: Estimate the prevalence of sarcopenia in PD patients and describe clinical and demographic features associated with sarcopenia. Methods: A cross-sectional study was carried out at a tertiary public hospital in Brazil. A modified HY scale of stage 1 to 3, being at least 40 years old and having the ability to stand and walk unassisted were required for eligibility. We evaluated physical performance and muscle mass using DEXA. Results: The study population comprised 124 patients, of which 53 (42.7%) were women. The mean age and mean disease duration were 65.8±10.5 and 10.1±5.8 years, respectively. The mean handgrip strength of 20.4±6.9 in woman and 34.6±8.4 kg in men. Moreover, 50.8% patients had positive SARC-F, 20% patients had probable sarcopenia, 9.6% confirmed sarcopenia, and 16.8% patients showed low muscle mass quantity measured by DEXA. Lower Levodopa Equivalent Dosage (LED) and calf circumference (CC) were independently associated with confirmed sarcopenia. LLED, higher MDS-UPDRS Part III, and lower MMSE scores were independently associated with probable sarcopenia. The CC demonstrated accuracy to identify PD patients with confirmed sarcopenia with a cut-off of <31 cm in women and <34 cm in men. Conclusion: We found low prevalence of confirmed sarcopenia among PD patients. We propose that healthcare providers introduce measuring CC, which is a quick and inexpensive method to assess for sarcopenia in PD patients.
Multiscale self-calibrated pulmonary nodule detection network fusing dual attention mechanism

Objective. In this study, we propose a model called DEPMSCNet (a multiscale self-calibration network) that has a high sensitivity and low false positive rate for detecting pulmonary nodules. Approach. First, at the feature extraction stage, we propose to use the REPSA-MSC module instead of the traditional convolutional neural network. The module extracts multiscale information from the feature map based on the image pyramid strategy while introducing adaptive convolutional branches to detect contextual information at each position of the multiscale, thereby expanding the receptive field and improving sensitivity. At the same time, multiple branches are adaptively weighted by channel attention, and the weights of different branches are adjusted to better generate pixel-level attention. Secondly, the proposed DSAM (dual-path spatial attention module) operates at the information fusion stage. This module fully exploits the rich spatial information of CT scans, obtains receptive field information from two branches, combines low-level feature map information with high-level semantic information, and enhances location-related information to effectively improve specificity. Thirdly, the focal loss function is used to solve the problem of positive and negative sample imbalance. Main results. The proposed model has been evaluated on the public lung nodule analysis (LUNA16) challenge dataset. The technique outperforms the most recent state-of-the-art detection algorithms in terms of sensitivity and specificity, obtaining a sensitivity of 0.988 and a competitive performance metric (CPM) of 0.963. Significance. Ablation experiments show that the two modules proposed in this paper effectively reduce false positives and improve sensitivity. This model effectively reduces the number of false positive nodules that doctors see on CT scans.
Electrical constant envelope signals for nonlinearity mitigation in coherent-detection orthogonal frequency-division multiplexing systems

Abstract. A theoretical analysis and a numerical investigation of 3-dB peak-to-average power ratio electrical constant envelope over orthogonal frequency-division multiplexing (OFDM) signals in coherent-detection optical (CO) systems are proposed. A 100-Gb/s optical system is studied to increase the nonlinear tolerance (NLT) to both Mach–Zehnder modulation and optical propagation impairments. Simulation results show that, with 16- and 64-QAM subcarrier modulations, the proposed system outperforms a conventional CO-OFDM system, if an optical modulation index OMI=2.5 and an electrical phase modulation index 2πh=3 are simultaneously adopted. The achieved NLT denoted by a performance gain of 24 dB is attractive after propagation through 1200 km of dispersion-uncompensated standard single-mode fiber, especially for 10-dBm fiber input power and despite the intrinsic bandwidth enlargement of PMs.
Evaluasi Kesuksesan Sistem Informasi Terpadu Layanan Prodi (SIPLO) Menggunakan Model Delone Dan Mclean Pada Fakultas Teknik Universitas Mulawarman

Sistem Informasi Terpadu Layanan Prodi (SIPLO) merupakan sistem informasi yang berisi akses informasi resmi dan layanan akademik dari Program Studi Sistem Informasi Fakultas Teknik Universitas Mulawarman. Tujuan penelitian ini mengevaluasi kesuksesan penggunaan SIPLO menggunakan model DeLone dan McLean dengan variabel kualitas sistem (system quality), kualitas informasi (information quality), kualitas layanan (service quality), kepuasan pemakai (user satisfaction), penggunaan (use) dan manfaat bersih (net benefit) sebagai dimensi kesuksesan. Populasi penelitiaan ini adalah mahasiswa aktif angkatan tahun 2019 sampai 2021 Program Studi Sistem Informasi. Data diperoleh dari hasil kuesioner dengan total 200 responden. Analisis data menggunakan metode Structural Equation Modeling (SEM) berbasis Partial Least Square (PLS) dengan bantuan perangkat lunak SmartPLS versi 4.0. 8. Hasil penelitian menjelaskan bahwa 9 hipotesis penelitian dapat diterima karena hubungan antara variabel saling berpengaruh secara positif dan signifikan satu sama lain. Secara keseluruhan menyatakan bahwa SIPLO telah terbukti sukses atau berhasil dengan kualitas sistem, kualitas informasi, kualitas layanan, penggunaan, kepuasan pengguna dan manfaat bersih terhadap SIPLO yang baik. Kesuksesan SIPLO menunjukkan bahwa pengguna memperoleh manfaat dari penggunaan SIPLO yang dapat memenuhi kebutuhannya terhadap informasi dan layanan akademik dengan lebih baik.
Multiscale Atmospheric Overturning of the Indian Summer Monsoon as Seen through Isentropic Analysis

This study investigates multiscale atmospheric overturning during the 2009 Indian summer monsoon (ISM) using a cloud-permitting numerical model. The isentropic analysis technique adopted here sorts vertical mass fluxes in terms of the equivalent potential temperature of air parcels, which is capable of delineating the atmospheric overturning between ascending air parcels with high entropy and subsiding air parcels with low entropy. The monsoonal overturning is further decomposed into contributions from three characteristic scales: the basinwide ascent over the Indian monsoon domain, the regional-scale overturning associated with synoptic and mesoscale systems, and the convective-scale overturning. Results show that the convective-scale component dominates the upward mass transport in the lower troposphere while the region-scale component plays an important role by deepening the monsoonal overturning. The spatial variability of the convective-scale overturning is analyzed, showing intense convection over the Western Ghats and the Bay of Bengal while the deepest overturning is localized over northern India and the Himalayan foothills. The equivalent potential temperature in convective updrafts is higher over land than over the ocean or coastal regions. There is also substantial variability in the atmospheric overturning associated with the intraseasonal variability. The upward mass and energy transport increase considerably during the active phases of the ISM. A clear northeastward propagation in the peak isentropic vertical mass and energy transport over different characteristic regions can be found during the ISM, which corresponds to the intraseasonal oscillations of the ISM. Altogether, this study further demonstrates the utility of the isentropic analysis technique to characterize the spatiotemporal variations of convective activities in complex atmospheric flows.
Non-destructive method for strain imaging in an individual GaN nanorod by confocal Raman technique

GaN based layer structures on highly lattice mismatched substrates are widely used for electronic and optoelectronic devices. Top down etched, GaN based nanorod structures are mainly studied due to their more effective strain relaxation. The previous measurements on the strain state of these structures have been performed either on single detached nanorods or on ensembles of nanorods still on the substrate. Here we demonstrate a technique based on confocal Raman scattering spectroscopy to probe the strain state of a single GaN nanorod still on the original substrate non-destructively. Both lateral and depth resolved imaging is achieved close to the diffraction limit of light. We observe that a GaN nanorod on the substrate is compressively strained throughout. The strain decreases from the base of the nanorod towards the top surface, but the top surface is still compressively strained. The detached GaN nanorod is less compressively strained overall, and the strain relaxes from the center towards all the edges.
Automatic Ventriculomegaly Detection in Fetal Brain MRI: A Step-by-Step Deep Learning Model for Novel 2D-3D Linear Measurements

In this study, we developed an automated workflow using a deep learning model (DL) to measure the lateral ventricle linearly in fetal brain MRI, which are subsequently classified into normal or ventriculomegaly, defined as a diameter wider than 10 mm at the level of the thalamus and choroid plexus. To accomplish this, we first trained a UNet-based deep learning model to segment the brain of a fetus into seven different tissue categories using a public dataset (FeTA 2022) consisting of fetal T2-weighted images. Then, an automatic workflow was developed to perform lateral ventricle measurement at the level of the thalamus and choroid plexus. The test dataset included 22 cases of normal and abnormal T2-weighted fetal brain MRIs. Measurements performed by our AI model were compared with manual measurements performed by a general radiologist and a neuroradiologist. The AI model correctly classified 95% of fetal brain MRI cases into normal or ventriculomegaly. It could measure the lateral ventricle diameter in 95% of cases with less than a 1.7 mm error. The average difference between measurements was 0.90 mm in AI vs. general radiologists and 0.82 mm in AI vs. neuroradiologists, which are comparable to the difference between the two radiologists, 0.51 mm. In addition, the AI model also enabled the researchers to create 3D-reconstructed images, which better represent real anatomy than 2D images. When a manual measurement is performed, it could also provide both the right and left ventricles in just one cut, instead of two. The measurement difference between the general radiologist and the algorithm (p = 0.9827), and between the neuroradiologist and the algorithm (p = 0.2378), was not statistically significant. In contrast, the difference between general radiologists vs. neuroradiologists was statistically significant (p = 0.0043). To the best of our knowledge, this is the first study that performs 2D linear measurement of ventriculomegaly with a 3D model based on an artificial intelligence approach. The paper presents a step-by-step approach for designing an AI model based on several radiological criteria. Overall, this study showed that AI can automatically calculate the lateral ventricle in fetal brain MRIs and accurately classify them as abnormal or normal.
Arbitrary-order exponential energy-preserving collocation methods for solving conservative or dissipative systems

In this paper, we present and analyse a novel class of exponential energy-preserving collocation methods for solving conservative or dissipative systems based on exponential integrators and finite element methods. It is shown that these novel methods can be of arbitrarily high order and preserve first integrals or Lyapunov functions. We also consider order estimates and effective implementations of the new methods. Furthermore, we explore and discuss the application of our methods in important stiff gradient systems, and it turns out that our methods are unconditionally energy-diminishing and strongly damped even for very stiff gradient systems. Practical examples of the new methods are derived and the efficiency and superiority are confirmed and demonstrated by three numerical experiments including a nonlinear Schr\"{o}dinger equation. As a byproduct of this paper, arbitrary-order trigonometric/RKN energy-preserving collocation methods are presented and analysed for second-order highly oscillatory/general systems.
The association between adherence to diet quality index and cardiometabolic risk factors in overweight and obese women: a cross-sectional study

Background Obesity and overweight status increase the risk of cardiovascular disease. Diet quality can also predict the risk of cardiovascular diseases in obese and overweight patients. Therefore, in this study, we sought to examine the relationship between diet quality index (DQI) and cardiometabolic risk factors in obese and overweight women. Method A cross-sectional study was conducted on 197 Iranian women with a Body Mass Index (BMI) > 25, 18–48 years, and recruited from 20 Tehran Health Centers. Nutrition intake and DQI were assessed using a 147-item semi-quantitative food frequency questionnaire (FFQ). Additionally, anthropometric measurements, body composition, biochemical evaluations, and cardiometabolic risk factors were evaluated. Results There was an association between DQI and waist-to-hip ratio (WHR), atherogenic index of plasma (AIP), and CHOLINDEX in obese women, after adjusting for potential confounders. Whereas, there were no significant associations of the tertiles of DQI compared with the first tertile in other cardiometabolic risk factors, before and after adjustment. Conclusion This study provides evidence that dietary intake and DQI are associated with cardiometabolic risk factors and that dietary modification may be a predictor for reducing WHR, AIP, and CHOLINDEX. However, more research is needed to develop a DQI that reflects changes in cardiometabolic risk factors by considering women's eating habits and patterns.
Effects of Inﬂammatory Conditions on Liver Activity in Puerperium Period and Consequences for Performance in Dairy Cows

The relationship between inﬂammation and general health conditions in dairy cows and the link between inﬂammation, liver function, and fertility are poorly understood. To clarify these relationships, 120 multiparous dairy cows were followed throughout an entire lactation. Blood samples were collected during the ﬁrst month of lactation for a metabolic proﬁle, and milk yield, disease occurrence, and fertility parameters were monitored during the entire lactation. Twenty-four cows were culled, and another 19 were excluded because they had serious problems after 30 d in milk (DIM) and before the ﬁrst insemination. The remaining 77 cows were pregnant at the end of lactation and were retrospectively grouped into quartiles based on liver activity index (LAI), which is based on plasma negative acute phase proteins. Cows in the lower (LO) and intermediate lower (INLO) quartiles of LAI had more severe inﬂammations with high concentrations of haptoglobin (0.77 and 0.61 g/L) and globulin (42.5 and 39.0 g/L), respectively, during the ﬁrst week of lactation compared with cows in the upper (UP) and intermediate upper (INUP) quartiles of LAI (haptoglobin: 0.28 and 0.45 g/L, and globulin: 34.2 and 36.9 g/L, respectively). At 7 DIM, the cows in LO and INLO had greater bilirubine-mia (8.7 and 10.5 vs. 6.3 μ M /L in UP) and lower blood urea (3.5 and 3.7 vs. 4.1 m M in UP). The INLO group exhibited more days open (139 vs. 93) and services per pregnancy (2.68 vs. 1.65), but lower milk yield (38.3 vs. 40.8 kg/d at 28 DIM) compared with UP. The LO group did not have a signiﬁcantly lower fertility status, but presented the lowest milk yield (34.1 kg/d at 28 DIM). Our data suggest that cows with lower LAI scores had a more pronounced inﬂammatory status during the ﬁrst month of lactation, an impairment of usual hepatic functions (e.g., bilirubin clearance), and a larger negative energy balance. The same cows had poorer performance (lower milk yield and fertility) than cows with higher LAI scores. Overall data suggest that any effort to avoid the acute phase response in the transition period would be useful for optimizing the productive and reproductive performance of high-yielding dairy cows.
Arroz irrigado por gotejamento com água residuária da piscicultura

7 LISTA DE FIGURAS . 8 LISTA DE TABELAS . 9 1. INTRODUÇÃO . 11 2. REVISÃO DE LITERATURA . 13 2.1. Arroz (Oryza sativa) . 13 2.2. Arroz irrigado . 15 2.3. Irrigação com água residuária da piscicultura . 17 3. OBJETIVOS . 21 3.1. Objetivos específicos . 21 4. MATERIAL E MÉTODOS . 23 4.1. Local da pesquisa. 23 4.2. Peixes e condições de criação . 24 4.3. Caracterização da água residuária da piscicultura . 26 4.4. Delineamento experimental . 27 4.5. Solo e adubação . 29 4.6. Irrigação . 30 4.7. Cultivares de arroz e avaliação do desempenho agronômico . 32 4.8. Análise dos dados . 34 5. RESULTADOS E DISCUSSÃO . 35 5.1. Dados meteorológicos . 35 5.2. Água residuária da piscicultura . 39 5.3. Irrigação e aporte de nutrientes . 45 5.4. Produção de peixes . 49 5.5. Solo . 50 5.6. Componentes de produção e produtividade da água . 54 5.7. Análise foliar . 60 6. CONCLUSÕES . 65 REFERÊNCIAS . 67
Improving document ranking with genetic and optimization algorithms

There are many applications to ranking including page searching, question answering, recommender systems, sentiment analysis, and collaborative filtering, to name a few. In the past several years, machine learning and information retrieval techniques have been used to develop ranking algorithms, and several listwise approaches to learning to rank have been developed. We propose a new method, which we call GeneticListMLE++ and GeneticListNet++, which builds on the original ListMLE and ListNet algorithms. Our method substantially improves on the original ListMLE and ListNet ranking approaches by incorporating genetic optimization of hyperparameters, a nonlinear neural network ranking model, and a regularization technique.
Conducting Sentiment Analysis on Twitter Tweets to Predict the Outcomes of the Upcoming Karnataka State Elections

- This research paper aims to predict how Twitter tweets from a specific politician correlate to their winning a seat in a state election. To understand this effect, sentiment analysis has been conducted on tweets by politicians in Karnataka to help predict who will win in the upcoming 2023 Karnataka Legislative Assembly election. Though previous research has already been done in this area, most studies have only focussed on the sentiment analysis of tweets. This paper goes further as it also looks at other factors, including the number of retweets and comments a tweet garners, which measures the tweet's engagement. A model has been created that weighs each factor to help predict who will win an election for a particular constituency. Through this model, a 72.7% accuracy has been achieved. However, the Twitter API severely limited the quantity and quality of data collected. These results can be expanded to help predict elections for other states. They could potentially help understand the effect of positive and negative sentiment on the winnability of a political candidate.
Coupling Analysis of Compound Continuum Robots for Surgery: Another Line of Thought

The compound continuum robot employs both concentric tube components and cable-driven continuum components to achieve its complex motions. Nevertheless, the interaction between these components causes coupling, which inevitably leads to reduced accuracy. Consequently, researchers have been striving to mitigate and compensate for this coupling-induced error in order to enhance the overall performance of the robot. This paper leverages the coupling between the components of the compound continuum robot to accomplish specific surgical procedures. Specifically, the internal concentric tube component is utilized to induce motion in the cable-driven external component, which generates coupled motion under the constraints of the cable. This approach enables the realization of high-precision surgical operations. Specifically, a kinematic model for the proposed robot is established, and an inverse kinematic algorithm is developed. In this inverse kinematic algorithm, the solution of a highly nonlinear system of equations is simplified into the solution of a single nonlinear equation. To demonstrate the effectiveness of the proposed approach, simulations are conducted to evaluate the efficiency of the algorithm. The simulations conducted in this study indicate that the proposed inverse kinematic (IK) algorithm improves computational speed by a significant margin. Specifically, it achieves a speedup of 2.8 × 103 over the Levenberg–Marquardt (LM) method. In addition, experimental results demonstrate that the coupled-motion system achieves high levels of accuracy. Specifically, the repetitive positioning accuracy is measured to be 0.9 mm, and the tracking accuracy is 1.5 mm. This paper is significant for dealing with the coupling of the compound continuum robot.