Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling the relation between CCN and the vertical evolution of cloud drop size distribution in convective clouds with parcel model

In this work we use values of effective radius of cloud drop size distribution and modal radius of liquid water content generated with a parcel model with detailed treatment of liquid phase microphysical process to evaluate the warm rain formation suppression over the Amazon due to the presence of aerosols generated by forest burning. Four cases based on observations are simulated: a clean marine environment as the one observed close to Fortaleza; a clean environment as the ones observed over the apparently unpolluted Amazon forest; a environment observed during the intense burning season in the polluted part of the Amazon; and the same polluted environment observed during the transition season from dry to wet. It is shown that the model reproduces quantitatively some of the observed features of clouds in different environments. The model indicated that in very polluted environment the warm rain formation process can be completely suppressed. Nevertheless, in certain situations the formation of warm rain can still be achieved and significant amount of rainwater can be formed.
Assessment of the chemical and genetic variability among accessions of Cicerbita alpina (L.) Wallr., an alpine plant with anthelmintic properties

Cicerbita alpina (L.) Wallr, is a perennial alpine plant and a member of the Asteraceae family, typically found at altitudes above 1000 meters in the Italian Alps. Although previously utilized primarily as a local delicacy, recent studies have revealed strong antiparasitic activity through in vitro experiments. In Europe, numerous chemical drugs employed to combat nematodes — helminths that infest the digestive tract of livestock — are banned due to their environmental harm or show only reduced efficiency because of the development of resistance. Consequently, there is a growing demand for new alternative anthelmintic treatments in agricultural practices. Specialized metabolites found in the extracts of C. alpina could offer a sustainable and biological alternative to chemical drugs, specifically for nematode control. For this purpose, a unique germplasm collection originating from eight distinct natural populations in the Italian Alps was analyzed for its chemical diversity using state-of-the-art targeted LC-MS/MS spectrometry, including quantification based on multiple reaction monitoring. The predominant metabolites identified from the species were the caffeic acid derivatives chicoric acid, chlorogenic acid, and 3. 5-dicaffeoylquinic acid, the sesquiterpene lactone derivative 8-O-acetyl-15-ß-D-glucopyranosyl lactucin and the flavone glycosides, apigenin-7-O-glucoside and luteolin-7-O-glucoside, alongside their precursors apigenin and luteolin, respectively. Additionally, the genetic diversity of eighty individual plants within the germplasm collection was evaluated using ten DNA molecular markers (Simple Sequence Repeats), successfully transferred from two closely related species (Cichorium intybus and Tanacetum parthenium). This investigation unveiled a significant range of genetic diversity within the examined populations, resulting in the establishment of three distinct genetic groups. The findings were further correlated with the original ecological environment and local climate conditions spanning a biennial period, indicating substantial variations among the different accessions and the intricate interplay between genetic background and environmental factors. These results could serve as a basis for future domestication of the species through plant breeding programs ensuring product quality, but also facilitating the cultivation of C. alpina in more diverse geographic regions.
Role of Lipid Rafts on LRP8 Signaling Triggered by Anti-β2-GPI Antibodies in Endothelial Cells

Antiphospholipid antibody syndrome is an autoimmune disease characterized by thrombosis and/or pregnancy morbidity in association with circulating antiphospholipid antibodies, mainly anti-β2 glycoprotein 1 antibodies (anti-β2-GPI antibodies). Previous studies demonstrated that the signaling pathway may involve lipid rafts, plasma membrane microdomains enriched in glycosphingolipid and cholesterol. In this study, we analyzed the signaling pathway of LRP8/ApoER2, a putative receptor of anti-β2-GPI antibodies, through lipid rafts in human endothelial cells. LRP8, Dab2 and endothelial nitric oxide synthase (e-NOS) phosphorylation were evaluated using Western blot, Nitric Oxide (NO) production with cytofluorimetric analysis, LRP8 enrichment in lipid rafts via sucrose gradient fractionation, and scanning confocal microscopy analysis of its association with ganglioside GM1 was also conducted. The analyses demonstrated that affinity-purified anti-β2-GPI antibodies induced LRP8 and Dab-2 phosphorylation, together with a significant decrease in e-NOS phosphorylation, with consequent decrease in NO intracellular production. These effects were almost completely prevented by Methyl-β-cyclodextrin (MβCD), indicating the involvement of lipid rafts. It was supported with the observation of LRP8 enrichment in lipid raft fractions and its association with ganglioside GM1, detected with scanning confocal microscopy. These findings demonstrate that LRP8 signaling triggered by anti-β2-GPI antibodies in endothelial cells occurs through lipid rafts. It represents a new task for valuable therapeutic approaches, such as raft-targeted therapy, including cyclodextrins and statins.
Empowering Future Entrepreneurs: Investigating University Students' Intentions in Entrepreneurship

Graduates continue to face challenges finding full-time employment, often resorting to part-time or day jobs, and sometimes taking positions that do not match their qualifications, often with lower salaries. In other words, they struggle to find full-time employment that matches their educational qualifications upon graduation. Recognizing this issue, the Malaysian government has taken proactive measures to encourage entrepreneurial mindset and equip graduates with the necessary entrepreneurial skills so that they can be more resourceful and self-reliant in creating their own opportunities. This paper investigates University of Malaysia Terengganu (UMT) students' intention to pursue entrepreneurship. The research sample includes 420 UMT students from various academic disciplines, all enrolled in the basic entrepreneurship course. Data will be analysed using descriptive, correlational and multiple regression analyses using Statistical Package for the Social Sciences (SPSS) software. The results show a positive correlation between students' intentions and multiple regression analysis and demonstrate a strong relationship with various factors that influence students' entrepreneurial intentions. Furthermore, these results suggest that universities can improve the employability of their graduates by increasing their students' engagement in entrepreneurial activities.
FFI: a software tool for ecological monitoring*

A new monitoring tool called FFI (FEAT/FIREMON Integrated) has been developed to assist managers with collection, storage and analysis of ecological information. The tool was developed through the complementary integration of two fire effects monitoring systems commonly used in the United States: FIREMON and the Fire Ecology Assessment Tool. FFI provides software components for: data entry, data storage, Geographic Information System, summary reports, analysis tools and Personal Digital Assistant use. In addition to a large set of standard FFI protocols, the Protocol Manager lets users define their own sampling protocol when custom data entry forms are needed. The standard FFI protocols and Protocol Manager allow FFI to be used for monitoring in a broad range of ecosystems. FFI is designed to help managers fulfil monitoring mandates set forth in land management policy. It supports scalable (project- to landscape-scale) moni- toring at the field and research level, and encourages cooperative, interagency data management and information sharing. Though developed for application in the USA, FFI can potentially be used to meet monitoring needs internationally.
Occlusion-Robust Autonomous Robotic Manipulation of Human Soft Tissues With 3-D Surface Feedback

Robotic manipulation of 3-D soft objects remains challenging in the industrial and medical fields. Various methods based on mechanical modeling, data-driven approaches or explicit feature tracking have been proposed. A unifying disadvantage of these methods is the high computational cost of simultaneous imaging processing, identification of mechanical properties, and motion planning, leading to a need for less computationally intensive methods. We propose a method for autonomous robotic manipulation with 3-D surface feedback to solve these issues. First, we produce a deformation model of the manipulated object, which estimates the robots' movements by monitoring the displacement of surface points surrounding the manipulators. Then, we develop a 6-degree-of-freedom velocity controller to manipulate the grasped object to achieve a desired shape. We validate our approach through comparative simulations with existing methods and experiments using phantom and cadaveric soft tissues with the da Vinci research kit. The results demonstrate the robustness of the technique to occlusions and various materials. Compared to state-of-the-art linear and data-driven methods, our approach is more precise by 46.5% and 15.9% and saves 55.2% and 25.7% manipulation time, respectively.
Evaluation of Antioxidant and Anti-anaemic Potential of Moringa oleifera Parts on Phenylhydrazine-induced Heamatotoxicity in Male Wistar Albino Rats

In recent years, scientific interest in Moringa oleifera has surged due to its potential health-promoting properties in traditional healthcare services. Hence, the objective of this paper was to evaluate the antioxidant activities of ethanol extract of different parts of Moringa oleifera tree and the anti-anaemic potential of the parts in phenylhydrazine-induced heamatotoxicity in forty-eight (48) male Wistar albino rats using appropriate standard techniques. Qualitative phytochemical screening showed that Moringa oleifera parts contain flavonoids, saponins, and steroids and have antioxidant capabilities. The IC50 values for M. oleifera leaves and flowers DPPH were MOL 24.26 ± 2.92 µg/ml, MOF 43.69 ± 2.68 µg/ml, and gallic acid 16.71 ± 0.63 µg /ml. There was a significant increase in WBC, MCH, MCHC, and NEU in anaemic rats (14.07 ± 0.64 x 103, 22.6 ± 0.44 pg, 32.5 ± 0.85 g/dl, 33 ± 1.00 %, respectively) compared to the normal control (9.98 ± 0.92 x103, 18.98 ± 0.42 pg, 30.45 ± 0.5 g/dl, 29.05 ± 1.80 %, respectively) but lowered significantly with the treatment of different parts of Moringa oleifera. Histopathology showed moderate myeloblastic and lymphoblastic cellular traits in anaemic rats. The groups treated with hydroxyurea and Moringa oleifera parts showed varying frequencies of mild myeloblastic and lymphoblastic cellular traits, indicating improvement in the bone marrow. Findings from this study showed that Moringa oleifera parts have antioxidant capabilities and anti-anaemic potentials in a rat model of phenylhydrazine-induced heamatoxicity. However, the leaf is the most potent and efficient part based on the results of these findings.
Use of Deep Learning Techniques for Motor Events Detection in Polysomnographic Records

Sleep medicine deals with the diagnosis and treatment of sleep-related disorders. The diagnosis is carried out through the manual analysis and labeling of polysomnographic studies, which record various electrophysiological and pneumological signals of the patient throughout the night. This process involves the analysis of long duration signals, is complex, and demands considerable resources and time on the part of the clinical expert. The purpose of this proyect is the construction of automatic analysis algorithms that considerably reduce the analysis duration, reducing the manual workload, and minimizing possible human errors, providing repeatability and robustness. In particular, the objective is to use machine learning algorithms, based on Deep Learning techniques, for the identification and location of physiological events in these polysomnographic records. Specifically, the goal is to locate physiological events associated with involuntary motor movements that occur in the limbs, known as Limb Movements
An experimental study of mechanical properties and comfortability of knitted imitation woven shirt fabrics

Abstract The weft knitted shirt fabric has good elasticity and comfortability, but it is difficult to achieve the formability of woven shirts. In this study, 30D XLANCE® elastic fibers and 30D Lycra® elastic fibers were used, which paired with 40S cotton and 75D/36F moisture-absorbing and quick-drying polyester DTY to develop the woven shirt fabrics on a 32 needle/25.4 mm single-sided circular machine. The results indicate that the 30D XLANCE® knitted shirt fabric has undergone sufficient heat setting treatment, and its mechanical properties are close to those of the standard woven fabric, superior to 30D Lycra® and 30D XLANCE® without sufficient heat setting treatment fabrics. The ultraviolet resistance behavior of XLANCE® fabrics is significantly better than that of Lycra® fabrics. Meanwhile, the XLANCE® fabrics after heat setting still maintain the excellent elasticity and resilience, breathability, moisture absorption, and quick drying performance, and the dimensional stability after washing is also very good. In addition, compared with spandex, XLANCE® fabrics have the lower heat setting temperature, which is beneficial for reducing energy consumption and carbon emissions. It can be considered that XLANCE® elastic fibers are the ideal raw materials for the knitted woven products.
Capturing the transition from marine to land-terminating glacier from the 126-year retreat history of Nordenskiöldbreen, Svalbard

Svalbard has experienced a dramatic increase in air temperature and glacier retreat since the end of the Little Ice Age. In many cases, this retreat has resulted in glaciers transitioning from being marine-terminating to land-terminating. Nordenskiöldbreen is an excellent contemporary example of this transition. A set of historical observations of glacier front positions was used to assess Nordenskiöldbreen's retreat rate and we found that the southern portion of the glacier front retreated by ~3500 m, since records began in 1896. The general retreat rate corresponds well with the air temperature trend during most of the 20th century. However, the average retreat rate has slowed since the 1990s despite increasing air temperatures. We show that this discrepancy between air temperature and retreat rate marks the transition from marine-terminating towards a land-terminating glacier, as the glacier's bedrock topography started to play an essential role in the glacier margin geometry, ice flow and retreat dynamics.
Proteome Dynamics with Heavy Water — Instrumentations, Data Analysis, and Biological Applications

The quantitative assessment of the synthesis of individual proteins has been greatly hin‐ dered by the lack of a high-throughput nonradioactive method. We recently developed a method that we call “proteome dynamics” and software that enables high-throughput ki‐ netic analyses of peptides on a proteome-wide scale. Previous studies established that or‐ al administration of heavy water ( 2 H 2 O or deuterium oxide, D 2 O) is safe and well tolerated in humans. Briefly, a loading dose of 2 H 2 O, a nonradioactive isotope, is adminis‐ tered in drinking water. 2 H 2 O rapidly labels body water and transfers 2 H from 2 H 2 O to 2 H-labeled amino acids, which incorporates into proteins dependent upon the rate of syn‐ thesis of the specific protein. Proteins are analyzed by high-resolution mass spectrometry and protein synthesis is calculated using specialized software. We have established the effectiveness of this method for plasma and mitochondrial proteins. We demonstrated that fasting has a differential effect on the synthesis rates of proteins. We also applied this method to assess the effect of heart failure on the stability of mitochondrial proteins. In this review, we describe the study design, instrumentation, data analysis, and biological application of heavy water-based proteome turnover studies. We summarize this chapter with the challenges in the field and future directions. The effects of dietary factors on tissue protein synthesis were investigated in acute fasting (20 h) vs. chronic food restriction (7 days), and feeding (a single meal) conditions in rats. Both acute and chronic fasting significantly reduced mixed tissue protein synthesis in the liver and gastrocnemius muscle, while it did not affect protein synthesis in the left ventricle of the heart [32], indicating that cardiac protein synthesis is preserved in conditions of nutritional pertur‐ bations. The follow-up studies demonstrated that diet-induced obesity in mice did not affect the skeletal muscle protein synthesis; however, it did impair the response of muscle protein synthesis to nutrient supply [34].
Investigation of Low and High-Speed Fluid Dynamics Problems Using Physics-Informed Neural Network

In this work, we have employed physics-informed neural networks (PINNs) to solve a few fluid dynamics problems at low and high speeds, with a focus on the latter. For high-speed fluid dynamics problems, we deal with the 1D compressible Euler equation, which is used to solve shock-tube problem, viz., Sod shock-tube, with weighted physics-informed neural networks (W-PINNs). This paper also demonstrates how domain extension (W-PINNs-DE) can improve the accuracy of the W-PINNs method. For high-speed flows, dispersion and dissipation errors are present near discontinuities. The W-PINNs-DE method is shown to mitigate this effect and is proven to have advantage over other approximations. Finally, we have solved the same high-speed problem with low-fidelity solution data to generate high-fidelity solutions. We have demonstrated that we can obtain accurate solutions using low-fidelity data in a few seconds of inference time. We have used relative L2 error for validation with exact or high-fidelity solutions.
A new sparse Bayesian learning method for inverse synthetic aperture radar imaging via exploiting cluster patterns

The application of sparse representation to SAR/ISAR imaging has attracted much attention over the past few years. This new class of sparse representation based imaging methods present a number of unique advantages over conventional range-Doppler methods, the basic idea behind these works is to formulate SAR/ISAR imaging as a sparse signal recovery problem. In this paper, we propose a new two-dimensional pattern-coupled sparse Bayesian learning(SBL) method to capture the underlying cluster patterns of the ISAR target images. Based on this model, an expectation-maximization (EM) algorithm is developed to infer the maximum a posterior (MAP) estimate of the hyperparameters, along with the posterior distribution of the sparse signal. Experimental results demonstrate that the proposed method is able to achieve a substantial performance improvement over existing algorithms, including the conventional SBL method.
Chebyshev Polynomial Fitting Model for GPS Orbit Calculation

It is important to get satellite coordinates continually in GPS positioning. Real time satellite coordinates can be gained according to broadcast ephemeris received. A great deal of memory is required in order to get satellite coordinates. The Chebyshev polynomial was applied to fit satellite coordinates of the broadcast ephemeris to realize satellite orbit standardization. The precision in fitting satellite coordinates of broadcast ephemeris by the Chebyshev polynomial and the rational choice of polynomial orders of and time intervals are analyzed. Some meaningful conclusions are made.
Basalt: A Rock-Solid Byzantine-Tolerant Peer Sampling for Very Large Decentralized Networks

Recent large-scale Byzantine-Fault-Tolerant (BFT) algorithms provide scalability at a low cost by exploiting a secure Random Peer Sampling (RPS) service: a service that provides a stream of random network nodes where no attacking entity can become over-represented. Unfortunately, producing good peer samples untainted by Byzantine behavior in a large-scale network is particularly difficult, with existing solutions unable to withstand aggressive attacks. In this paper, we propose a novel RPS algorithm, BASALT, that implements what we have termed a stubborn chaotic search over node IDs to counter attackers' attempts at becoming over-represented. Our evaluation based on a theoretical analysis, Monte Carlo simulations, and experiments on a live cryptocurrency network shows that BASALT delivers close-to-optimal protection against malicious behaviors and outperforms state-of-the-art solutions by a wide margin.
Problematic Smartphone Usage in Singaporean University Students: An Analysis of Self-Reported Versus Objectively Measured Smartphone Usage Patterns

Introduction: Problematic smartphone usage is the excessive usage of the smartphone, leading to addiction symptoms that impair one’s functional status. Self-administered surveys developed to describe the symptoms and measure the risk of problematic smartphone usage have been associated with depressive symptoms, symptoms of anxiety disorder, and perceived stress. However, self-reported smartphone usage can be unreliable, and previous studies have identified a better association between objectively measured smartphone usage and problematic smartphone usage. Methodology: A self-administered survey was used to investigate the relationships between the risk of problematic smartphone usage (SAS–SV) with depressive symptoms (PHQ–9), anxiety disorder symptoms (GAD–7), and perceived stress (PSS) in Singaporean full-time university students. Self-reported screentime and objectively measured screentime were collected to determine if there is any difference between perceived smartphone usage and objective smartphone usage. Results: There was no statistical difference between self-reported and app-measured screentime in the study population. However, there were significant positive correlations between SAS–SV with PHQ–9, GAD–7, and PSS. In the logistic regression model, PHQ–9 was found to be the sole predictor for variances in SAS–SV score in the study population. Conclusion: This study suggests that problematic smartphone usage may potentially related to depressive symptoms, symptoms of anxiety disorder, and greater perceived stress in university students.