Miracle-dz/UniFAR-FTUs huggingface dataset:
Hypsometric relationship in Tectona grandis L. F. stands using quantile regression

Quantile regression is an alternative to the traditional methods of model fitting for problems caused by the presence of outliers or asymmetry in the data distribution. Our aim was to evaluate and propose hypsometric relationship models fitting Tectona grandis L. f. stands by means of the quantile regression and least squares method. The diameter was measured at 1.3 meters from the ground and the total height of 4,068 sampled trees were divided into two bases, one for adjustment and the other for validation. Later the non-linear logistic model was adjusted via quantile regression for which the first (0.25), second (0.50) and third quartiles (0.75) were tested. The least squares method (OLS) was also used to compare it with the quantile regression. To evaluate the precision and the adjustment of the evaluated methods, the statistics Akaike's Information Criterion (AIC), square root of the mean error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were used. In sequence, the analysis of variance was performed to verify the existence of significant difference between the estimate from the evaluated methods and the observed heights. The adjusted logistic model was accurate to estimate the total heights of Tectona grandis stands. Quantile regression allowed a more complete view of the relationship between response height and diameter at breast height since it was possible to build a model for each quantile of interest. Quantile regression showed good results and can be recommended for calibration of hypsometric models in Tectona grandis stands due to the ease of adjustment and implementation in forest inventories.
Climate vs. Human Impact: Quantitative and Qualitative Assessment of Streamflow Variation

This paper presents a novel framework comprising analytical, hydrological, and remote sensing techniques to separate the impacts of climate variation and regional human activities on streamflow changes in the Karkheh River basin (KRB) of western Iran. To investigate the type of streamflow changes, the recently developed DBEST algorithm was used to provide a better view of the underlying reasons. The Budyko method and the HBV model were used to investigate the decreasing streamflow, and DBEST detected a non-abrupt change in the streamflow trend, indicating the impacts of human activity in the region. Remote sensing analysis confirmed this finding by distinguishing land-use change in the region. The algorithm found an abrupt change in precipitation, reflecting the impacts of climate variation on streamflow. The final assessment showed that the observed streamflow reduction is associated with both climate variation and human influence. The combination of increased irrigated area (from 9 to 19% of the total basin area), reduction of forests (from 11 to 3%), and decreasing annual precipitation has substantially reduced the streamflow rate in the basin. The developed framework can be implemented in other regions to thoroughly investigate human vs. climate impacts on the hydrological cycle, particularly where data availability is a challenge.
Challenges Experienced by Subsidized Housing Residents with Dementia or Cognitive Impairment: A Scoping Review

Abstract Older adults with dementia or cognitive impairment face unique challenges to living safely and independently in subsidized housing. This scoping review summarizes previous studies to develop an understanding of the challenges faced by older populations with dementia or cognitive impairment. Three medical databases, five social science databases, and two gray literature databases were searched. Six themes emerged: tenants’ demographic profile, physical challenges, mental/social/psychological challenges, behavioral challenges, inadequate services/environment, and barriers posed by policy and practice. We conclude that housing providers and practitioners should collaborate in ensuring tenants with dementia or cognitive impairment in subsidized housing are well supported, and we suggest areas for future research.
A Novel Photonic Crystal BioNEMS Sensing Platform Based on Fano resonances

A novel optical BioNEMS (Bio Nano-Electro-Mechanical-Systems) sensor based on a tunable 2D photonic crystal (PhC) cavity is proposed in this paper. The present device includes a functionalized BioNEMS cantilever and a tunable PhC structure. This optical detection system is a rod-type silicon photonic crystal cavity in which a designed defect allows the transmission of highly confined wave modes. While the immobilized capture probes are exposed to a solution containing the target biomarkers (i.e., DNA, mRNA or proteins), binding events cause a stress on the surface of the nanocantilever because of biological interactions. Therefore, the NEMS movable part is displaced as a result of an induced differential surface stress. This changes the position of the defect nanorods in the PhC cavity which leads to the transmitted wavelength variations. Finally, the concentration of biological quantities is measured by detecting the optical spectrum changes of the proposed biosensor. Furthermore, the presented BioNEMS sensor is analyzed by numerical and analytical approaches to obtain its functional characteristics as follows: optical sensitivity of 5140 nm/RIU, FOM of 10280 RIU-1, Q-factor of 3078, mechanical sensitivity of 1.05 µm/Nm-1 and resonant frequency of 23.45 kHz. Based on the obtained results, the proposed structure relied on Fano resonances provides a high-precision biosensor which has a great potential for highly sensitive detection of biomarkers in early diseases diagnosis as well as drug delivery test.
Enhanced Deep Blind Hyperspectral Image Fusion

The goal of hyperspectral image fusion (HIF) is to reconstruct high spatial resolution hyperspectral images (HR-HSI) via fusing low spatial resolution hyperspectral images (LR-HSI) and high spatial resolution multispectral images (HR-MSI) without loss of spatial and spectral information. Most existing HIF methods are designed based on the assumption that the observation models are known, which is unrealistic in many scenarios. To address this blind HIF problem, we propose a deep learning-based method that optimizes the observation model and fusion processes iteratively and alternatively during the reconstruction to enforce bidirectional data consistency, which leads to better spatial and spectral accuracy. However, general deep neural network inherently suffers from information loss, preventing us to achieve this bidirectional data consistency. To settle this problem, we enhance the blind HIF algorithm by making part of the deep neural network invertible via applying a slightly modified spectral normalization to the weights of the network. Furthermore, in order to reduce spatial distortion and feature redundancy, we introduce a Content-Aware ReAssembly of FEatures module and an SE-ResBlock model to our network. The former module helps to boost the fusion performance, while the latter make our model more compact. Experiments demonstrate that our model performs favorably against compared methods in terms of both nonblind HIF fusion and semiblind HIF fusion.
Bilingual advantage in L3 vocabulary acquisition: evidence of a generalized learning benefit among classroom-immersion children

Abstract The present study explored whether emergent bilingual children showed enhanced abilities to learn L3 vocabulary including written, spoken and conceptual forms compared to monolinguals, and the impact of L2/L3 cross-language similarities on such an effect. To this end, we contrasted the English word learning performance of French fifth-graders attending either a monolingual school program or a classroom-immersion program with German as an L2. Half of the items to be learned were German/English (L2/L3) cognate words while the other half were monolingual English (L3) words. Learning was assessed with a forced-choice recognition task, a go/no-go auditive recognition task and an orthographic judgment task. Results yielded a generalized bilingual advantage, with classroom-immersion children outperforming monolinguals on all tasks, irrespective of cognateness, except for the orthographic task. These findings advocate for a bilingual advantage in children that is globally not driven by the specific language properties of cognates, except for the written modality.
Effect of Wood Vinegar Substitutes on Acetic Acid for Coagulating Natural Para Rubber Sheets during the Drying Process

The coagulating properties of wood vinegar from para rubber wood, bamboo, and coconut shell used as a substitute for acetic acid in the production process of natural rubber (NR) sheets were investigated and considered. For the dirt and volatile content, the tensile strength at break, the percentage of elongation at break, and the 300% modulus, the results showed that the types of wood vinegar coagulants were not significantly different from acetic acid. However, the Mooney viscosity and plasticity retention index (PRI) properties were significantly different from those of acetic acid. The NR sheet temperature increased rapidly during the first hour after the drying process started due to heat transfer from the hot air. Afterward, the temperature of the NR sheet samples began to stabilize. When the drying process started, the drying temperature was increased, so the trend was reducing the drying time. For the yellowness index (YI) value, the increase in the YI value was related to the type of coagulating material, the increase in the airspeed, and the drying temperature. The dried sheet samples using para rubber wood vinegar as the coagulating material had a color value at the same level as acetic acid and the referent. However, the bamboo and coconut shell wood vinegars were at a lower level. In comparing the YI value data between the experimental results and prediction values, the second-degree model had a better fit in prediction than the zero-degree and first-degree models. This result was confirmed by the higher mean of the coefficient of determination. The dried sheet product coagulated by using wood vinegar had fungus growth prior to supplying it to the customer.
Evaluating the Signs of Suicide Program: Middle School Students at Risk and Staff Acceptability

Abstract Students at risk of suicide can be identified effectively through school-based prevention programs such as Signs of Suicide (SOS), which incorporate both psychoeducation and screening components. Prior research has focused on high school students; however, there is limited research in middle schools, and even less is known of school staff’s responsiveness to these programs. The current study evaluated the implementation of SOS with students at two U.S. middle schools (N = 2,537; 49% female) and through a treatment evaluation survey completed by school implementation staff (N = 26; 89% female). Nearly 20% of students emerged as at risk for suicide; risk was significantly higher among females, sixth graders, students with lower socioeconomic status, and students eligible for special education or accommodation plans. Most staff rated SOS as an acceptable program, and qualitative feedback regarding strengths and barriers is described. These findings advance practical knowledge for effectively implementing school-based suicide prevention programs and may guide school psychologists in advocating for and implementing such interventions. Impact Statement Suicide is the second leading cause of death among youth in the U.S., and prevention programs delivered in school settings have been effective at identifying and supporting these students. Nearly 20% of students participating in a suicide prevention program emerged as at risk for suicide, with several groups of students at significantly higher risk. Staff implementing this program generally reported positive perceptions of its acceptability, while also identifying several barriers and suggestions for improving future implementations.
Temperature and spatial connectivity drive patterns in freshwater macroinvertebrate diversity across the Arctic

1. Warming in the Arctic is predicted to change freshwater biodiversity through loss of unique taxa and northward range expansion of lower latitude taxa. Detecting such changes requires establishing circumpolar baselines for diversity, and under -standing the primary drivers of diversity. 2. We examined benthic macroinvertebrate diversity using a circumpolar dataset of > 1,500 Arctic lake and river sites. Rarefied α diversity within catchments was as -sessed along latitude and temperature gradients. Community composition was
Pengaruh Variasi Bio Based Adhesive terhadap Kekuatan Peeling dan Shearing Kayu Pinus pada Adhesive Joint

The methods of joining engineering materials using adhesives is mostly implemented by using synthetic adhesives from non-renewable sources. Even though it has good joining performance, its poisonous nature and difficult to decompose naturally makes the adhesive needs to seek alternatives that are more environmentally friendly. Another reason is to support FAO campaigns for the use of environmentally friendly and biodegradable materials. One that has the potential to replace synthetic one is adhesives from natural sources such as gum rubber, jackfruit, and breadfruit. Because each natural sap has a different composition, the adhesive strength will also be different. Therefore it is necessary to examine the adhesive strength of each of these natural gums. In this research, natural adhesives (gums) and synthetic adhesives were used to joint pine wood as a test specimen. After joining, peeling and shearing tests on the specimens were carried out so that the mechanical strength of the joints for each adhesive variation was recognized. Fracture analysis was then performed to analyze the mechanism of joint failure for each adhesive variation. From this research, the highest peeling strength was obtained from rubber tree sap of 0.15 MPa, while the highest shearing strength was also obtained for rubber sap of 0.68 MPa.
Online Learning: The Role Of Academic Self-Efficacy In Creating Academic Flow

The spread of Covid-19 encourages educational institution to maximize online learning. Online learning can work well if students are able to focus on learning. The ability to focus on learning is known as academic flow. This study aims to explain effect of academic self-efficacy on flow academic in online learning. This research involve 296 participants who were selected by incidental sampling. The instruments used in this research are self-efficacy scale and flow short scale which were analyzed by using linear regression techniques. The results shows that academic self-efficacy have a significant effect on academic flow (β=0,609; p=<0,001). This means that any increase in academic self-efficacy will have an impact on an increase in academic flow.
Process Optimization of Bio -Ethanol Production through Cashew Apple Juice Fermentation

The cashew apple (or cashew fruit) is the fleshy part of the cashew fruit that is attached to the cashew nut. Cashew apples are extremely soft and easily bruised in shipment. For this reason, cashew apple are converted into cashew juice and cashew juice concentrate. Cashew apple contains higher concentration of sugar content which ranges from 162.7 to 168.1 g/l. Hence the presence of sugar contents offers a wide potential for its conversion to ethanol. The aim of the present study is to optimize the process variables for the fermentation of cashew apple using microorganisms. The influencing process parameters that effect the percentage conversion of ethanol are pH, temperature of fermentation, time and agitation speed. The experiments are conducted with pH ranging from of 4 to 8, temperature is varied in the range of 30-45°C and fermentation time is varied in the range of 24 to 36 hours. The experiment is carried at static and agitated conditions. The process of conversion of cashew apple into ethanol by fermentation is optimized.
Can Deep Learning Predict Complete Ruptures in Numerical Megathrust Faults?

We propose a binary classification model rooted in state‐of‐the‐art deep learning techniques to predict whether or not complete‐interface rupture is imminent along a numerical megathrust fault. The models are trained on labeled 2D space‐time input features taken from the synthetic fault system. We contrast the performance of two neural networks trained on three types of data, to determine the relative predictive power of each. The neural networks are able to discriminate imminent complete rupture precursors from everything else, thus providing a relative size and time forecast. Vertical displacements along the fault demonstrate relatively good predictive power. The results confirm previous qualitative observations that precursory deformation scales with upcoming event size, consistent with the preslip model for earthquake nucleation. The methods we propose are adaptable and can be modified to use 3D data in the future.
Prediction of soil erosion risk using earth observation data under recent emission scenarios of CMIP6

Abstract The earth observation data and CMIP6 models were used to predict plausible soil loss from the Ghaghara river basin. The decadal prediction of soil loss (28.64 ton/ha/year) was found high for SSP585 of CanESM5 during 2015–2025. However, the lower value was reported as 21.71 ton/ha/year for SSP245 of MRI-ESM2-0 during 2035–2045. The century level future rainfall erosivity factor was found lowest for SSP245, however highest for SSP585 of Access-ESM1-5, CanESM5, and IPSL-CM6A-LR. The SSP585 (Access-ESM1-5, CanESM5, and IPSL-CM6A-LR) have maximum soil erosion rate as 29.07, 28.03, and 28.0 ton/ha/year, respectively. For the SSP585, increments were observed as 35.93%, 31.04%, and 30%, respectively, compared to the baseline year (2014). Whereas, lowest was reported as 21.7 and 24.9 ton/ha/year and consequently the low increment as 1.31% and 16.55% for both scenarios of MRI-ESM2-0 compared to baseline. We observed that the soil erosion rate is aligned with the predicted rainfall erosivity factor.
The DKU-Duke-Lenovo System Description for the Fearless Steps Challenge Phase III

This paper describes the systems developed by the DKU-Duke-Lenovo team for the Fearless Steps Challenge Phase III. For the speech activity detection (SAD) task, we employ the U-Net-based model which has not been used for SAD before, observing a DCF of 1.915% on the eval set. For the speaker identiﬁcation (SID) task, we adopt the ResNet-SE and ECAPA-TDNN model, and we obtain a Top-5 accuracy of 86.21%. For the speaker diarization (SD) task, we employ several different clustering methods. Besides, domain adaptation, system fusion, and Target-Speaker Voice Activity Detection (TS-VAD) signiﬁcantly improve the SD performance. We obtain a DER of 12.32% on track 2, and the major contribution is from our ResNet-based TS-VAD model. We ﬁnally achieve a ﬁrst-place ranking for SD and SID and a second-place for SAD in the challenge.
Childhood Trauma in Bipolar Disorder: A North-African Study

Bipolar disorder is a multifactorial psychiatric disease with a gene–environment interaction model. In this context, Childhood trauma (CT) has been considered as a promising environmental determinant in the pathogenesis of bipolar disorder (BD). Studies examining CT in bipolar patients have mainly been conducted in Western countries. To our knowledge, no surveys have been reported to date in North-Africa. The present work aimed to assess CT in patients with BD and to examine the relationship between the subtypes of trauma and the clinical features of the disease. We conducted a case-control study including 60 euthymic bipolar patients, presenting to the maintenance treatment consultation at Razi hospital, Tunisia in North-Africa, and 60 healthy controls, matched to the bipolar group for age, gender, and educational level. We compared cases and controls by CT history, using Childhood Trauma Questionnaire (CTQ). The CTQ contains 5 subscales: emotional neglect, physical neglect, emotional abuse, physical abuse, sexual abuse. We explored childhood trauma within the group of cases according to clinical characteristics of BD. Bipolar patients showed significantly higher scores of CT at total CTQ score (p< 0.001), emotional neglect (p1⁄4 0.02), emotional abuse (p1⁄4 0.006), physical abuse (p< 0.001), and sexual abuse (p1⁄4 0.012). There were no significant differences between groups regarding physical neglect score (p1⁄4 0.054). We highlighted significant associations between significant positive correlations between sexual abuse and psychotic features in mood episodes (p1⁄4 0.01), and between physical neglect and mixed episodes number (p1⁄4 0.03). Otherwise, we did not establish significant relationships between different scores at CTQ and other clinical characteristics of the disease including the age of onset of BP, duration of BP, the number of depressive and mania or hypomania episodes, lifetime suicide attempts, and substance use disorders. This study demonstrates significant associations between CT and BD being consistent with several previous reports (Garno et al., 2005; Watson et al., 2014). In our study, we tried to explore mechanisms underlying the association between BD and CT. Exposition to early stress triggers a cascade of neuroendocrine changes mostly involving hypothalamo–pituitary–adrenal axis activity (Schreuder et al., 2016). Previous studies pointed toward neurobiological disturbances involving neurotrophic factors depletion (Machado-Vieira et al., 2007). Chronic stress has been suggested to have long-term neurotoxic effects, causing structural and functional impairments of the medial prefrontal cortex and the hippocampus, and resulting in emotional dysregulation (Nemeroff, 2004). Literature data suggest that these consequences occur preferentially in subjects with genetic susceptibility according to the gene–environment interaction model (Azorin et al., 2010). Furthermore, the association between childhood maltreatment and bipolar disorder can be explained by familial factors. Children’s behavioral disorders related to the early
Socio-educational action in self-managed youth meeting spaces in the Basque country

ABSTRACT This study focuses on the emerging phenomenon of youth hangouts as a self-managed sphere for leisure and free time in the context of the Basque Country. The aim of this research was to analyse the following: the characterisation of these spaces and the profile of their participants; possible problems of coexistence; and youth participation and development in their context and in relation to their community. Employing a quantitative methodology, questionnaires were administeried to a sample of 2,014 young people from seven municipalities of Bizkaia (Basque Country). Some of the most significant findings refer to the continuous growth of this phenomenon and the consideration of youth spaces as a context for growth and social participation. These are important aspects to be considered when developing educational and youth policies.
Development of Integrated Thermoelectric Sensors for Power Components

Power components such as High Electron Mobility Transistors (HEMTs) are used for high power and high frequency applications. These tend to overheat and destroy their packaging and connections to the wire bondings. This paper presents planar micro-thermoelectric sensors ( $\mu $ TESs) developed to measure partial heat flow dissipated by the HEMTs to avoid the HEMTs from reaching critical temperatures. These sensors use the same active materials as well as the same fabrication process as the HEMTs, enabling them to be fabricated simultaneously for a simple integration. The HEMTs’ active layers consist in AlGaN/GaN heterostructures resulting in the formation of a 2D Electron Gas (2DEG) at the interface. Tension factors of 47 mV/(K.mm2) were obtained with these sensors and heat flows of 0.2 W to 7 W were measured.