Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Annealing and Etching Times on Anatase TiO2 Hollow Sphere

The development of high-efficiency photocatalysts plays an important role in the application of solar energy conversion. Titanium dioxide (TiO2) with an anatase crystalline phase is well-known as semiconductor thin layers for solar cells. This work has constructed a novel TiO2 hollow sphere (HST) in a 2-step synthesis. The first step is coating the SiO2 template with TiO2 to build a core-shell of SiO2@TiO2 (CSST). The second step is etching via sonication to elute the SiO2 from CSST and construct the HST. The annealing of CSST for 1 to 6 hours and etching for 1 to 7 hours has resulted in the HST with different crystallite sizes and microstrains. The HST of ~90 nm has been fabricated with crystallite size of 9.53 to 20.54 nm and microstrain from 0.34 to 3.42. It was found that the optimum crystallite size and microstrain of HST obtained via annealing the CSST for 2 hours and etching for 5 hours has the best photooxidation of I- under UV irradiation. The optimum crystallite size and microstrain of HST via annealing and etching times can be recommended for the future of solar cell fabrication and applications.
Exceptional Manifestation of Multiple Myeloma

Despite being a rare occurrence, multiple myeloma (MM) has been reported as an alternative cause of pleurisy, with approximately 50 documented cases in the literature so far. In this case report, we present the clinical scenario of a patient who sought medical attention due to symptoms of dyspnea, chest pain, and weight loss. Through a comprehensive diagnostic evaluation, it was determined that the patient's pleural involvement was attributable to MM, a hematological malignancy. This case highlights the importance of considering MM as a potential etiology in patients presenting with pleural manifestations, even in settings where tuberculosis is the prevailing cause.
Traces of Human-Mediated Selection in the Gene Pool of Red Deer Populations

Simple Summary Human activities have left indelible traces in the gene pool of livestock as well as recently domesticated wild animal species which are manifested by changes in their genome structure, often accompanied by the significant loss of biodiversity, especially in the case of small local populations. Despite the benefits of deer farming, intensive artificial selection associated with domestication can lead to several negative effects, including founder effects, inbreeding depression, or rapid decline in effective population size, which are also often present in traditional livestock. This study investigates the effect of human-mediated selection on the diversity of seven farmed red deer populations compared to two wild populations through the quantification of the level of genomic heterozygosity, inbreeding, admixture, and gene flow. These results will help to improve traditional breeding schemes and assist in a more sustainable utilisation of available animal genetic resources. Abstract In this study, we analysed the effect of human-mediated selection on the gene pool of wild and farmed red deer populations based on genotyping-by-sequencing data. The farmed red deer sample covered populations spread across seven countries and two continents (France, Germany, Hungary, Latvia, New Zealand, Poland, and Slovakia). The Slovak and Spain wild red deer populations (the latter one in a large game estate) were used as control outgroups. The gene flow intensity, relationship and admixture among populations were tested by the Bayesian approach and discriminant analysis of principal components (DAPC). The highest gene diversity (He = 0.19) and the lowest genomic inbreeding (FHOM = 0.04) found in Slovak wild population confirmed our hypothesis that artificial selection accompanied by bottlenecks has led to the increase in overall genomic homozygosity. The Bayesian approach and DAPC consistently identified three separate genetic groups. As expected, the farmed populations were clustered together, while the Slovak and Spanish populations formed two separate clusters. Identified traces of genetic admixture in the gene pool of farmed populations reflected a strong contemporary migration rate between them. This study suggests that even if the history of deer farming has been shorter than traditional livestock species, it may leave significant traces in the genome structure.
Sparse-view CT reconstruction based on gradient directional total variation

The sparse-view problem of image reconstruction encountered in computed tomography (CT) is an important research issue due to its considerable potential in decreasing radiation dose and improving detection efficiency. Among the current sparse-view CT reconstruction algorithms, iterative reconstruction algorithms that consider total variation (TV) regularization exhibit good performance. However, the gradient difference direction is singular or fixed in conventional TV algorithms, leading to undesired artefacts when minimizing TV in sparse-view CT. To effectively address this issue, based on TV minimization, the gradient difference directional information is introduced as additional prior information in the regularization term, and a new gradient-based directional total variation minimization algorithm is proposed, which adaptively chooses mutative gradient directions to calculate the directional difference operators, and calculates the sum of the direction difference operators. In addition, to solve the fault tolerance and computational load, considering redundant blocks of reconstructed image, we can estimate the gradient directional information of each subblock via the gradient approximation method. For simplicity, the proposed algorithm is termed the BDTV algorithm. To demonstrate the superiority of the proposed algorithm, the simulation data and actual CT data from different algorithms are compared, and the results indicate that the proposed algorithm is effective at preserving details that are lost in the TV minimization, artefact reduction and noise suppression in sparse-view CT.
Do the Rich Save More?

The question of whether higher–lifetime income households save a larger fraction of their income was the subject of much debate in the 1950s and 1960s, and while not resolved, it remains central to the evaluation of tax and macroeconomic policies. We resolve this long‐standing question using new empirical methods applied to the Panel Study of Income Dynamics, the Survey of Consumer Finances, and the Consumer Expenditure Survey. We find a strong positive relationship between saving rates and lifetime income and a weaker but still positive relationship between the marginal propensity to save and lifetime income. There is little support for theories that seek to explain these positive correlations by relying solely on time preference rates, nonhomothetic preferences, or variations in Social Security benefits. There is more support for models emphasizing uncertainty with respect to income and health expenses, bequest motives, and asset‐based means testing or behavioral factors causing minimal saving rates among low‐income households.
Study of microbial activity of synthetically prepared zinc- oxide (ZnO) nanoparticle utilizing Rivea -Hypocrateriformis extract against chosen micro- organisms

Due to its multifunctional efficiency and application in nano-cosmeceuticals and nano-pharmaceuticals, despite its fabrication manipulate, zinc oxide (ZnO) is currently employed superficially. Globally recognised for their ability to achieve stable, reliable, and toxic-free synthesis, bio-reducers. Consequently, the purpose of the present study is to create ZnO NPs in a more environmentally friendly method employing Rivea hypocrateriformis plant extract and to assess their antibacterial and antifungal activity. Hyperchromic shift were observed at 305nm. By using Fourier-transform Infrared spectroscopy, it was possible to gather proof that proteins serve as reducing and capping agents. The lattice planes and discovered peak positions from the X-Ray diffraction experiments supported the hexagonal close-packed crystalline structure of ZnO- NPs. The biosynthesized ZnO -NPs had star shapes with mean diameters of around 20–100 nm, according to surface topographical analyses. The synthesized ZnO-NPs have been characterized by various techniques and anti-microbial and anti-fungal activities are evaluated against Staphylococcus epidermidis and Candida vulgaris.
Difficult‐to‐treat patients with relapsed/refractory multiple myeloma: A review of clinical trial results

Overall outcomes for multiple myeloma have improved due to the availability of new therapies, but patients with relapsed/refractory multiple myeloma harbouring certain factors continue to pose a therapeutic challenge. These challenging features include high‐risk cytogenetics, renal impairment, patient characteristics such as age and frailty, and extramedullary disease. Prior refractory status and number of prior lines add further complexity to the treatment of these patients. While newer regimens are available and have suggested efficacy in these patient populations through subgroup analyses, differences in trial definitions and cut‐offs make meaningful comparisons difficult. This review aims to examine the available clinical trial data for patients with high‐risk cytogenetics, renal impairment, age and frailty and extramedullary disease.
MENGGABUNGKAN DUA WAJAH DENGAN METODA ENSEMBLE REGRESSION TREES MENGGUNAKAN PUSTAKA DLIB DAN OPENCV PYTHON

Penggabungan dua wajah telah menjadi masalah menarik di seluruh dunia dan menjadi perhatian banyak orang. Hal ini dikarenakan informasi tentang wajah seseorang dapat diekstraksi dari citra yang kemudian direpresentasikan sebagai identias diri seseorang untuk media pengenalan wajah. Tulisan ini membahas masalah Face Alignment untuk sebuah gambar tunggal. Kami menunjukkan bagaimana ensemble regression trees dapat digunakan untuk memperkirakan posisi tengara wajah langsung dari subset tipis intensitas piksel, pencapaian performa super realtime dengan prediksi kualitas tinggi. Kami menyajikan kerangka kerja umum berdasarkan peningkatan gradien untuk mempelajari Ensemble Regression Trees yang mengoptimalkan jumlah kesalahan kesalahan persegi dan secara alami menangani hilang atau sebagian data berlabel. Kami menunjukkan bagaimana menggunakan yang sesuai orang yang memanfaatkan struktur data citra membantu dengan pilihan fitur yang memuaskan strategi regularisasi yang berbeda dan kepentingannya untuk memerangi lebih dari penyesuaian juga diinvestigasi terjaga keamanannya. Selain itu, kami menganalisis pengaruh kuantitas data pelatihan tentang keakuratan prediksi dan penjelajahan efek augmentasi data menggunakan data yang disintesis.
Ensemble Deep Learning (EDL) for Cyber-bullying on Social Media

—Cyber-bullying is a growing problem in the digital age, affecting millions of people worldwide. Deep learning algorithms have the potential to assist in identifying and combating Cyber-bullying by detecting and classifying harmful messages. This paper uses two Ensemble deep learning (EDL) models to detect Cyber-bullying on text data, images and videos—and an overview of Cyber-bullying and its harmful effects on individuals and society. The advantages of using deep learning algorithms in the fight against Cyber-bullying include their ability to process large amounts of data and learn and adapt to new patterns of Cyber-bullying behaviour. For text data, firstly, a pre-trained model BERT (Bidirectional Encoder Representations from Transformers) is used to train on cyber-bullying text data. The next step describes the data pre-processing and feature extraction techniques required to prepare data for deep learning algorithms. We also discuss the different types of deep learning algorithms that can be used for Cyber-bullying detection, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs). This paper combines the sentiment analysis model, such as Aspect-based Sentiment Analysis (ABSA), for classifying bullying messages. Deep Neural network (DNN) used the classification of Cyber-bullying images and videos. Experiments were conducted on three datasets such as Twitter (Kaggle), Images (Online), and Videos (Online). Datasets are collected from various online sources. The results demonstrate the effectiveness of EDL and DNN in detecting Cyber-bullying in terms of detecting bullying data from relevant datasets. The EDL and DNN obtained an accuracy of 0.987, precision of 0.976, F1-score of 0.975, and recall of 0.971 for the Twitter dataset. The performance of Ensemble CNN brought an accuracy of 0.887, precision of 0.88, F1-score of 0.88, and recall of 0.887 for the Image dataset. For the video dataset, the performance of Ensemble CNN is an accuracy of 0.807, precision of 0.81, F1-score of 0.82, and recall of 0.81. Future research should focus on developing more accurate and efficient deep learning algorithms for Cyber-bullying detection and investigating the ethical implications of using such algorithms in practice.
Market Participation of Small-Scale Rice Farmers in Eastern Bolivia

Using a double-hurdle approach, we assess factors associated with the extent of participation in the rice market with data for small-scale farmers drawn from a nationally representative dataset. The results suggest that larger endowments and assets, animal farming and commercialization, and alternative off-farm income make farmers less likely to participate. Conversely, having access to credit, larger farm sizes, and being part of a farmers’ association all increase the likelihood of participation. Farms with better technological resources are also those with higher sales volumes. Further understanding market participation dynamics should prove useful for deriving evidence-based policy recommendations to strengthen this Bolivian sector.
Interventions to Minimize Medication Error by Nurses in Intensive Care: A Scoping Review Protocol

Medication errors represent a concern for healthcare organizations due to their negative consequences. In the nursing context, these errors represent a threat to the quality of care and patient safety. Many factors have been identified as potential causes for these errors in intensive care units. A scoping review will be developed to identify interventions/strategies to minimize the occurrence of medication errors by nurses, considering the Joanna Briggs Institute (JBI) methodology. A search will be conducted in the EbscoHost (CINAHL Complete and MEDLINE), Embase and PubMed databases. Data analysis, extraction and synthesis will be carried out by two reviewers independently. This review will attempt to map which interventions are more specific to minimizing medication error by nurses in intensive care and to recognize which factors influence this type of error to mitigate practices that may lead to error. This protocol acts as the framework for a scoping review in the strategy to map the interventions and which factors contribute to the medication error by intensive care nurses. This study was prospectively registered with the Open Science Framework on 21 April 2023 with registration number DOI 10.17605/OSF.IO/94KH3.
Intermittent Exposure to a Single Bottle of Ethanol Modulates Stress Sensitivity: Impact of Age at Exposure Initiation

Alcohol use during adolescence is a serious public health problem, with binge drinking and high-intensity drinking being particularly harmful to the developing adolescent brain. To investigate the adverse consequences of binge drinking and high-intensity adolescent drinking, adolescent rodents were intermittently exposed to ethanol through intragastric gavage, intraperitoneal injection, or vapor inhalation. These models revealed the long-lasting behavioral and neural consequences of adolescent intermittent ethanol (AIE) exposure. The present study was designed to characterize a different AIE model, namely, intermittent exposure to a single bottle of 10% ethanol as the only source of fluids on a 2 days on/2 days off (water days) schedule, and to determine whether this AIE exposure model would produce changes in hormonal and neuroimmune responsiveness to challenges of differing modalities. Assessments of ethanol intake as well as blood and brain ethanol concentrations (BECs and BrECs, respectively) in adult male and female rats (Experiment 1) revealed that BECs and BrECs peaked following access to ethanol for a 2 h period when assessed 1 h into the dark cycle. Experiment 2 revealed age differences in ethanol intake, BECs, and BrECs following a 2 h access to ethanol (1 h into the dark cycle), with adolescents ingesting more ethanol and reaching higher BECs as well as BrECs than adults. In Experiment 3, intermittent exposure to a single bottle of 10% ethanol for 10 cycles of 2 days on/2 days off was initiated either in early or late adolescence, followed by an acute systemic immune challenge with lipopolysaccharide (LPS) in adulthood. LPS increased corticosterone and progesterone levels regardless of sex and prior ethanol history, whereas an LPS-induced increase in cytokine gene expression in the hippocampus was evident only in ethanol-exposed males and females, with females who underwent early exposure to ethanol being more affected than their later-exposed counterparts. In Experiment 4, intermittent ethanol exposure in females was initiated either in adolescence or adulthood and lasted for 12 ethanol exposure cycles. Then, behavioral (freezing behavior), hormonal (corticosterone and progesterone levels), and neuroimmune (cytokine gene expression in the PVN, amygdala, and hippocampus) responses to novel environments (mild stressors) and shock (intense stressors) were assessed. More pronounced behavioral and hormonal changes, as well as changes in cytokine gene expression, were evident in the shock condition than following placement in the novel environment, with prior history of ethanol exposure not playing a substantial role. Interleukin (IL)-1β gene expression was enhanced by shock in the PVN, whereas shock-induced increases in IL-6 gene expression were evident in the hippocampus. Together, these findings demonstrate that our intermittent adolescent exposure model enhances responsiveness to immune but not stress challenges, with females being more vulnerable to this AIE effect than males.
SYNTHESIS, CHARACTERISTICS AND APPLICATION OF DOPED CARBON STRUCTURES

This Doctoral thesis is on the synthesis and application of carbon-based materials. The emerging environmental and energy issues demand low cost and efficient ways to produce materials with improved properties for application in energy storage, sensors and degradation of organic pollutants. The work elucidates the synthesis of doped carbon materials: gold nanoparticle/carbon nanoflower (AuNP/CNF) composite, Si@C, SiC@graphite, and Fe/graphene via induction annealing. This work provides insight into the material formation (e.g., crystal phase, size, shape, and agglomeration state) of silicon-doped carbon structures and their applicability in energy storage. Furthermore, iron-doped carbon structures were synthesized and characterized, and packed bed experiments were carried out to test their capability for tetracycline removal from aqueous media. Additionally, the CNF structures doped with AuNPs were tested for their stability in organic solvents and deposited as thin films using an aerosol filtration method. This study explores the formation of carbon structures (amorphous carbon, graphene, and graphite) synthesized using barley straw-derived carbon mixed with silicon nanoparticles, via induction annealing. The formation of a new SiC phase from amorphous carbon and its re-nucleation to form graphene and graphite were studied in detail. It was observed that the characteristics and yield of the synthesized materials can be controlled by varying the temperature from 1000 °C for amorphous carbon to 2400 °C for graphitic carbon. Likewise, barley husk in combination with glucose was also found to be a viable material for the growth of graphene-like carbon. The carbon doping studies resulted in the formation of an amorphous carbon-coated Si@C composite. The amorphous carbon was present atop the silicon particles as an approximately 5 nm-thick coating and as interstitial carbon between the silicon nanoparticles. The synthesized Si@C was tested as an anode material for Li-ion coin cells. Owing to the carbon coating, it demonstrated an improved cycling stability with an initial specific capacity of 1200 mAh g-1 at 0.1 A g-1. The iron doping studies resulted in the formation of approximately 1 μm-sized paramagnetic α-Fe and non-magnetic γ-Fe particles embedded in graphene sheets (Fe/graphene). The synthesized composite displayed an initial tetracycline adsorption of 442 mg g-1, which further increased to 660 mg g-1 owing to the self-regeneration capability of the composite. The stabilities of the CNF suspensions were tested in a series of organic solvents. The dispersion of CNFs in poorly dispersing solvents, such as hexane and toluene, was improved by applying the Hansen solubility parameter theory. It was found that the addition of ethanol to hexane and toluene improved the short-term dispersion quality of CNFs. Furthermore, the gold doping of these dispersions resulted in gold nanoparticle surface-decorated carbon
Effect of Using Basal Fertilizer 15-15-15 on Leaf Chlorophyll a Fluorescence, Plant Growth and Fruit Yield of Table Grapes Grown under the Mediterranean Climate Conditions of the Northeast of Morocco

Plant nutrition presents one of the main concerns of table growers in Morocco. Since the increase of the prices of fertilizers, the optimization of the amount of nutrients elements is important. Crop deficiency in terms of Nitrogen (N), Phosphorus (P) and Potassium (K) was demonstrated to decrease growth and productivity of plants. The ob-jective of this research was to investigate the effect of adding Basal N-P-K Fertilizer (15-15-15) to soil on some physiological parameters of table grapes, such as chlorophyll fluorescence, plant growth and fruit yield. Trials were conducted northeast of Morocco and under Mediterranean climate conditions on a production of 8-year-old table grapes (v. Regal). The planting density was 2000 plants/ha. In a field of 10 ha of commercial production, a plot of twenty-four trees were selected for each treatment (control (C) and treated (Tr) plants with basal fertilizer). A basal fertilizer (15-15-15) was applied in the beginning of the vegetative growth stage, at 5 cm above to the root system. An amount of 150 g/tree was applied. A conventional fertilization program was used by the grower in both control and treated plots, except for the prototype treatment plots where the basal fertilizer was only applied. No significant effect of the treatment on plant growth and fruit yield was noted. Moreover, no significant difference was recorded on leaves relative water content (RWC), chlorophyll content (LCC), and chlorophyll fluorescence parameters such as: F 0 , Fm, F v /F m , V i V j , ABS/RC, DI 0 /RC, TR 0 /RC, ET 0 /RC and RE 0 /RC.
Modeling Soil Erosion within Small Moutainous Watershed in Central Vietnam Using GIS and SWAT

Soil erosion has been considered the primary cause of soil degradation because soil erosion leads to the loss of topsoil and soil organic matter, which are essential for the growing of plants. The purpose of this study is to integrate Geographic Information System (GIS) and Soil and Water Assessment Tools (SWAT) for simulating soil erosion within small mountainous watershed that is an upstream of Bo River watershed in Central Vietnam. The results of this study found that the largest amount of soil erosion was 92.33 t ha -1 in 2007, followed by 2010 (85.41 tha -1 ) and 2005 (76.79 t ha -1 ). The