Miracle-dz/UniFAR-FTUs huggingface dataset:
Mindfulness-based intervention in preterm young adolescents: benefits on neurobehavioural functioning and its association with white-matter microstructural changes

Very preterm (VPT) young adolescents are at high risk of executive, behavioural and socio-emotional difficulties. Previous research has shown significant evidence of the benefits of mindfulness-based intervention (MBI) on these abilities. This study aims to assess the association between the effects of MBI on neurobehavioral functioning and changes in white-matter microstructure in VPT young adolescents who completed an 8-week MBI program. Neurobehavioural assessments and multi-shell diffusion MRI were performed before and after MBI in 32 VPT young adolescents. Combined diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) measures were extracted on well-defined white matter tracts (TractSeg). A multivariate data-driven approach (partial least squares correlation) was used to explore associations between MBI-related changes on neurobehavioural measures and microstructural changes. Our finding showed an enhancement of global executive functioning after MBI that was associated with a general pattern of significant increase in fractional anisotropy (FA) and decrease in axonal dispersion (ODI) in white-matter tracts involved in executive processes. Young VPT adolescents with lower gestational age at birth showed the greatest gain in white-matter microstructural changes after MBI.
Uranium Exploration, Deposit and Resources: The Key of Nuclear Power Plant Development Program in Indonesia

Uranium deposit in Indonesia was found in almost all Indonesian Archipelago, mainly in Kalimantan, Sulawesi, Sumatera, Papua, Bangka Belitung and Riau islands. Uranium exploration activities started in the 1960s to recent, conducted in many exploration stages. The exploration in prospects area are completed with drilling activities to delineate the mineralization zone and continued to resources estimation. In Kalan Area, the research had been completed with underground/tunneling mining. The uranium resources are classified into discovered or undiscovered based on exploration stages, and conventional or unconventional based on sources of primary/secondary/by-product mineral production. The resources are calculated from Kalan Area and its surroundings (Kalimantan) with addition of Mamuju Area (West Sulawesi) and Sibolga Area (North Sumatera). Uranium identified resource in Indonesia is 13,503 tU while the undiscovered is 62,330 tU. Meanwhile, categorized by uranium source, the conventional and unconventional resources are 48,388 tU and 27,445 tU respectively. The uranium resources categories should be increased and completed with feasibility study to increase the resources to reserve classification. The exploration, deposit, and resources are the key to ensure the readiness of developing nuclear power plants in Indonesia, where one of them is Experimental Power Reactor (EPR) or Reaktor Daya Eksperimental (RDE) with domestic uranium fuel.
Solution-based, additive fabrication of flush metal conductors in plastic substrates by printing and plating in two-level capillary channels

A strategy to control the structure of electroless copper deposition in confined features was developed for applications in printed electronic devices and interconnects. This work builds on and refines a previous process of additively manufacturing metal conductors using a combined imprint, print, and plate strategy. A two-level imprinted channel geometry, consisting of inner, parallel capillary channels running along the base surface of a main channel, was designed to control electroless copper deposition. The inner capillary channels contain and precisely position the silver ink seed layer, and the main channel confines the electroless copper as it deposits from the seed layer, resulting in flat copper surfaces that are flush with the surface of the substrate or at a controlled depth. This process produces metal conductors with aspect ratios (height/width) up to ≈2 and low edge roughnesses as defined by the imprinting process. The resistance of the final conductors varies linearly with the length and inversely with the width of the main channel, suggesting high uniformity throughout. Conductive lines with a width of 40 μm and depth of 10 µm had low resistance per unit length (≈1Ω cm−1) and retained their performance with repeated mechanical flexing. The methods described here to confine the growth of electroless plated copper are adaptable to form conductors for numerous applications in flexible electronics.
A strategic review of recent progress, prospects and challenges of MoS2-based photodetectors

Two-dimensional layered materials have emerged prominently in the past decade, largely being investigated fundamentally and practically. Their unique layered structure and atomic-scale thickness make them attractive with exclusive electrical and optical properties compared to their bulk counterparts. Molybdenum disulfide (MoS2) is the most widely studied material in the family of transition metal dichalcogenides. The direct and variable bandgap, high carrier mobility, thermal and chemical stability makes it an attractive choice for next-generation photodetector applications. MoS2 heterojunction-based photodetectors offer ultrafast charge transfer and broadband photoresponse, adding more functionality beyond their individual counterparts. Enormous efforts have been devoted to adopting a new strategy that can improve photodetector performance in terms of responsivity and response time. This review briefly discusses the photo-induced current mechanism and performance parameters along with some important aspects to realize better device performance. Here, we critically review the current status and progress made towards MoS2-based photodetectors, followed by a discussion on open challenges and opportunities in their future application.
X-ray differential phase-contrast imaging simulations with Geant4

The development of accurate simulation tools including x-ray phase-contrast effects is of great use in the design and optimization of x-ray phase-contrast imaging (XPCI) experimental setups. When the lateral coherence of the imaging system is limited, as in most compact laboratory-based XPCI applications, phase-contrast can be effectively described through refraction effects that can be simulated with a ray-tracing approach. This work presents a practical implementation of the x-ray phase-contrast in Geant4 and validates the simulation results against theoretical predictions for the propagation-based (PB) and edge-illumination (EI) imaging configurations. The simulation includes a realistic polychromatic tungsten anode spectrum and an extended x-ray source, a virtual phantom made of plastic wires with a diameter of 1 mm, as well as an accurate description of the absorbing masks and related movement during data acquisition. Results show a good agreement between theory and simulations, with maximum discrepancies below 2% in the intensity of PB images, below 0.3 µrad for EI refraction, and below 0.03 mm for EI projected thickness. Additionally, an often disregarded effect such as the refraction sensitivity inhomogeneity across the field of view (around 20%) was reproduced by the simulation and quantitatively explained through a theoretical model. The presented simulation platform will be extensively used for the design of a novel EI facility that is under construction at the INFN laboratories in Trieste (Italy) in the framework of the Photon-counting Edge-illumination Phase-contrast Imaging (PEPI) project.
Landform Features for Infrared Pedestrian Detection

Pedestrian detection has long been a research hot spot for its wide range of potential applications, e.g. public transportation, video surveillance. In this paper, we tackle the problem of infrared pedestrian detection as infrared images do not rely on sufficient illumination and have better environmental adaptability. To be specific, we propose a novel landform-based infrared feature extraction scheme, which richly exploits the heat source and temperature distribution information and therefore provides stronger infrared features. Following this feature extraction scheme, we build a LandForm Feature Network (LFF-Net) for infrared pedestrian detection. We show that the LFF-Net achieves a state-of-the-art performance of 70.75% on the widely-used FLIR dataset (improving 3.46% over its baseline), demonstrating the effectiveness of the proposed landform features for infrared images.
Deep Reinforcement Learning for Stock Recommendation

Recommending stocks is very important for investment companies and investors. However, without enough analysts, no stock selection strategy can capture the dynamics of all S&P 500 stocks. Nevertheless, most existing recommending strategies are based on predictive models to buy and hold stocks with high return potential. But these strategies fail to recommend stocks from different industrial sectors to reduce risks. In this article, we propose a novel solution that recommends a stock portfolio with reinforcement learning from the S&P 500 index. Our basic idea is to construct a stock relation graph (RG) which provide rich relations among stocks and industrial sectors, to generate diversified recommendation result. To this end, we design a new method to explore high-quality stocks from the constructed relation graph with reinforcement learning. Specifically, the reinforcement learning agent jumps from each industrial sector to select stock based on the feedback signals from the market. Finally, we apply portfolio allocation methods (i.e., mean-variance and minimum-variance) to test the validity of the recommendation. The empirical results show that the performance of portfolio allocation based on the selected stocks is better than the long-term strategy on the S&P 500 Index in terms of cumulative returns.
Increasing fire and logging disturbances in Siberian boreal forests: a case study of the Angara region

Forest disturbances are a critical environmental issue globally and within the boreal biome, yet detailed attribution and trends in disturbances are lacking for many Siberian regions. The Angara region located in the southern taiga of Central Siberia has experienced significant disturbances during the past several decades and is a hotspot of change in Eurasia. Here we estimated fire and logging disturbances using MODIS and Landsat data for the period 2002–2020 across the Angara region and analyzed the resulting trends. Average annual burned and logged area was about 220 and 31 thousand ha or 2 and 0.3% of the study area, respectively. In total, about 4.1 million ha (38% of the region) and 0.6 million ha (6% of the region) were disturbed by fires and logging, respectively. Spatial analysis showed that almost 50% of fires were ignited within 2 km of anthropogenic features such as settlements, roads and logged areas. Almost 5% of the Angara region was burned two or more times during the 19 years of observations. Improved and strictly-enforced conservation and management policies are required to halt continued forest degradation in the Angara region and similarly-affected boreal forests in Siberia.
Investigation of Orbital Decay and Global Modeling of the Planet WASP-43 b

WASP-43 b is one of the most important candidates for detecting an orbital decay. We investigate pieces of evidence for this expectation as variations in its transit timings, based on the ground and space observations. The data set includes the transit observations at the TÜBİTAK National Observatory of Turkey and Transiting Exoplanet Survey Satellite (TESS). We present a global model of the system, based on the most precise photometry from space, ground, and archival radial velocity data. Using the homogenized data set and modeled light curves, we measure the mid-transit times for WASP-43 b. Our analysis agrees with a linear ephemeris for which we refine the light elements for future observations of the system. However, there is a negative difference between the transit timings derived from TESS data in two sectors (9 and 35) and a hint of an orbital period decrease in the entire data set. Both findings are statistically insignificant due to the short baseline of observations, which prevents us from drawing firm conclusions about the orbital decay of this ultra-short-period planet. However, assuming the effect of this decrease of the period in the planet's orbit, we derive a lower limit for the reduced tidal quality factor as Q*′>(4.01±1.15)×105 from the best-fitting quadratic function. Finally, we calculate a probable rotational period for this system as 7.52 days from the out-of-transit flux variation in the TESS light curves due to spot modulation.
Latent Topic Analysis of the Post Property for Sales to Predict a Selling Price of Second-Hand Condominiums

This research objective is to study the latent topics analysis in selling post of real estate of second-hand condominium by using Latent Dirichlet Allocation (LDA) and build a price prediction model of second-hand condominium using multiple linear regression and artificial neural networks by measuring and comparing the performance of the second hand condominium price prediction model with root mean square error (RMSE). This experiment included four variables are room size, number of bathroom, number of bedroom and latent topics from LDA. The result of LDA indicated that selling post of real estate can be separated into 4 topics, in which finding the factors that affect the price use the regression analysis method to get five variables are room size, number of bathroom, floors, topic 2 and topic 4. The RMSE based on the multiple linear regression analysis was 1.349, while the RMSE based on artificial neural network was 1.156. Thus, it can be concluded that the predictive model using the artificial neural networks is superior to multiple linear regression.
Does Election Impact on Stock Market Returns in Bangladesh?

This paper investigates the impact of parliamentary general election on the stock market returns by considering the previous fifteen days and the after fifteen days of each of six elections in Bangladesh held between 1991 and 2018. The study analyzed the election effect on stock returns through considering both abnormal returns by choosing 20 stocks as a proxy of portfolio motive of the investors and the broad index returns as a measurement of whole market scenario. The study employed descriptive statistics, t-tests, and F-tests to understand the impact of election by gauging the changes in return series. Descriptive statistics showed very high differences in means, standard deviations, and volatilities. Paired t-tests showed significant differences between the means and F-tests showed significant differences between the variances of the returns during before and after days of these elections. The results were the same for abnormal returns and broad index returns. The impacts of individual election on the returns were also found as the same in most cases. The study has found some very useful insights part of which can benefit the policymakers to reform the policies. The common investors and the financial market participants can also make better investment plan.
A Mechanism and Matching in a Social Dilemma

Cooperation can be achieved via incentives from future interactions, specifically in the case of public monitoring. But, today, our social and professional spheres keep shifting rapidly and we interact often with strangers. We are interested in such sporadic interactions which can be modeled as a continuous Prisoner’s Dilemma in an environment of the symmetric market where the whole population is competing among themselves to interact with other agents who will contribute the most. The interaction is private, only the agents involved know how much they have contributed to each other’s well-being, and partners may change in the next period. In such an environment if the reputation of agents is not available, then there is no incentive to cooperate. In this paper, we show that if an experience reporting mechanism facilitates assortative matching, then cooperation and honest reporting is evolutionarily (neutral) stable.
Regulation of Phosphorylated State of NMDA Receptor by STEP61 Phosphatase after Mild-Traumatic Brain Injury: Role of Oxidative Stress

Traumatic Brain Injury (TBI) mediates neuronal death through several events involving many molecular pathways, including the glutamate-mediated excitotoxicity for excessive stimulation of N-methyl-D-aspartate receptors (NMDARs), producing activation of death signaling pathways. However, the contribution of NMDARs (distribution and signaling-associated to the distribution) remains incompletely understood. We propose a critical role of STEP61 (Striatal-Enriched protein tyrosine phosphatase) in TBI; this phosphatase regulates the dephosphorylated state of the GluN2B subunit through two pathways: by direct dephosphorylation of tyrosine-1472 and indirectly via dephosphorylation and inactivation of Fyn kinase. We previously demonstrated oxidative stress’s contribution to NMDAR signaling and distribution using SOD2+/− mice such a model. We performed TBI protocol using a controlled frontal impact device using C57BL/6 mice and SOD2+/− animals. After TBI, we found alterations in cognitive performance, NMDAR-dependent synaptic function (decreased synaptic form of NMDARs and decreased synaptic current NMDAR-dependent), and increased STEP61 activity. These changes are reduced partially with the STEP61-inhibitor TC-2153 treatment in mice subjected to TBI protocol. This study contributes with evidence about the role of STEP61 in the neuropathological progression after TBI and also the alteration in their activity, such as an early biomarker of synaptic damage in traumatic lesions.
Associations between SNPs in Intestinal Cholesterol Absorption and Endogenous Cholesterol Synthesis Genes with Cholesterol Metabolism

Single nucleotide polymorphisms (SNPs) have been associated with cholesterol metabolism and may partly explain large inter-individual variability in intestinal cholesterol absorption and endogenous cholesterol synthesis rates. This cross-sectional study therefore examined whether SNPs in genes encoding for proteins involved in intestinal cholesterol absorption (ABCG5, ABCG8, and NPC1L1) and endogenous cholesterol synthesis (CYP51A1, DHCR7, DHCR24, HMGCR, HSD17B7, LBR, and MSMO1) were associated with intestinal cholesterol absorption markers (total cholesterol (TC) standardized campesterol and sitosterol levels), an endogenous cholesterol synthesis marker (TC-standardized lathosterol levels), and serum low-density lipoprotein cholesterol (LDL-C) concentrations in a European cohort. ABCG5 (rs4245786) and the tag SNP ABCG8 (rs4245791) were significantly associated with serum campesterol and/or sitosterol levels. In contrast, NPC1L1 (rs217429 and rs217416) were significantly associated with serum lathosterol levels. The tag SNP in HMGCR (rs12916) and a SNP in LBR (rs12141732) were significantly associated with serum LDL-C concentrations. SNPs in the cholesterol absorption genes were not associated with serum LDL-C concentrations. SNPs in CYP51A1, DHCR24, HSD17B7, and MSMO1 were not associated with the serum non-cholesterol sterols and LDL-C concentrations. Given the variable efficiency of cholesterol-lowering interventions, the identification of SNPs associated with cholesterol metabolism could be a step forward towards personalized approaches.
In Vitro Effects of Particulate Matter Associated with a Wildland Fire in the North-West of Italy

Wildland fires, increasing in recent decades in the Mediterranean region due to climate change, can contribute to PM levels and composition. This study aimed to investigate biological effects of PM2.5 (Ø < 2.5 µm) and PM10 (Ø < 10 µm) collected near a fire occurred in the North-West of Italy in 2017 and in three other areas (urban and rural areas). Organic extracts were assessed for mutagenicity using Ames test (TA98 and TA100 strains), cell viability (WST-1 and LDH assays) and genotoxicity (Comet assay) with human bronchial cells (BEAS-2B) and estrogenic activity using a gene reporter assay (MELN cells). In all sites, high levels of PM10 and PM2.5 were measured during the fire suggesting that near and distant sites were influenced by fire pollutants. The PM10 and PM2.5 extracts induced a significant mutagenicity in all sites and the mutagenic effect was increased with respect to historical data. All extracts induced a slight increase of the estrogenic activity but a possible antagonistic activity of PM samples collected near fire was observed. No cytotoxicity or DNA damage was detected. Results confirm that fires could be relevant for human health, since they can worsen the air quality increasing PM concentrations, mutagenic and estrogenic effects.
Identification of Bioactive Phytochemicals from Six Plants: Mechanistic Insights into the Inhibition of Rumen Protozoa, Ammoniagenesis, and α-Glucosidase

Simple Summary Rumen protozoa have some contribution to feed digestibility in the rumen, but Entodinium, the most predominant genus, is the main culprit of inefficient nitrogen utilization in ruminants. Using chemical drugs, many studies have attempted to inhibit the rumen protozoa, but few of the approaches are either effective or practical. In this study, we investigated the nutritional and functional properties of Adansonia digitata (baobab), Flemingia macrophylla (waras tree), Kalimeris indica (Indian aster), Brassica rapa subsp. chinensis (bok choy), Portulaca oleracea (common purslane), and Calotropis gigantea (giant milkweed) for their potential as feed additives in animal husbandry. The plants were also analyzed for their major phytochemicals using reversed phase-high performance liquid chromatography (HPLC) and then evaluated for their ability to inhibit rumen protozoa, ammoniagenesis, and microbial α-glucosidase activity in vitro. C. gigantea inhibited the rumen protozoa and reduced the wasteful ammoniagenesis, thereby indicating improved nitrogen utilization. A. digitata also reduced the microbial α-glucosidase activity that can potentially contribute to rumen acidosis. The tested plants, especially C. gigantea and A. digitata, could be used as potential alternatives to chemicals or antibiotics to ensure sustainable and green animal husbandry. Abstract Rumen protozoa prey on feed-degrading bacteria synthesizing microbial protein, lowering nitrogen utilization efficiency in ruminants. In this in vitro study, we evaluated six plants (Adansonia digitata, Flemingia macrophylla, Kalimeris indica, Brassica rapa subsp. chinensis, Portulaca oleracea, and Calotropis gigantea) for their potential to inhibit rumen protozoa and identified the phytochemicals potentially responsible for protozoa inhibition. Rumen protozoa were anaerobically cultured in vitro in the presence of each plant at four doses. All of the tested plants reduced total rumen protozoa (p ≤ 0.05), but C. gigantea and B. rapa were the most inhibitory, inhibiting rumen protozoa by 45.6 and 65.7%, respectively, at the dose of 1.1 mg/mL. Scanning electron microscopy revealed a disruption of the extracellular structure of protozoa cells. Only C. gigantea also decreased the wasteful ammoniagenesis (p ≤ 0.05). Moreover, the A. digitata extract inhibited α-glucosidase activity by about 70% at 100 µg/mL. Reversed-phase high-performance liquid chromatography analysis detected quercetin, anthraquinone, 3-hydroxybenzoic acid, astragaloside, and myricetin in the tested plant leaves. These plants may hold potential as feed additives to reduce rumen protozoa and α- glucosidase activity. Future research is needed to identify the specific anti-protozoal compound(s), the effects on the rumen microbiome, and its fermentation characteristics.