Miracle-dz/UniFAR-FTUs huggingface dataset:
Specific heat and thermal conductivity of nanomaterials

A model is proposed to study the size and shape effects on specific heat and thermal conductivity of nanomaterials. The formulation developed for specific heat is based on the basic concept of cohesive energy and melting temperature. The specific heat of Ag and Au nanoparticles is reported and the effect of size and shape has been studied. We observed that specific heat increases with the reduction of particle size having maximum shape effect for spherical nanoparticle. To provide a more critical test, we extended our model to study the thermal conductivity and used it for the study of Si, diamond, Cu, Ni, Ar, ZrO2, BaTiO3 and SrTiO3 nanomaterials. A significant reduction is found in the thermal conductivity for nanomaterials by decreasing the size. The model predictions are consistent with the available experimental and simulation results. This demonstrates the suitability of the model proposed in this paper.
Facial expression recognition based on spatio-temporal interest points for depth sequences

Of the numerous facial expression recognition methods previously proposed, most are based on texture frames or sequences. Recently, the development of depth sensors has raised new possibilities of dealing with 3D data. The proposed method extracts histograms of oriented gradient (HOG) and optical flow (HOF) for STIPs directly from depth sequences rather than involving registration/deformation techniques to find correspondence in 3D scan data. Mutual information score (MIS) and weighted matching score (WMS) are, respectively, calculated on the basis of naïve-Bayes mutual information maximization and constrained matching pairs. Finally, the MIS and WMS results are concatenated into feature vectors which are then fed into a support vector machine for facial expression classification. The proposed method is applied to the public BU-4DFE (Binghamton University 4D Facial Expression) database for six different facial expressions: anger, disgust, fear, happy, sad and surprise. Experimental results confirm that the proposed method is simple but effective.
Capturing the Kidney Transcriptome by Urinary Extracellular Vesicles—From Pre-Analytical Obstacles to Biomarker Research

Urinary extracellular vesicles (uEV) hold non-invasive RNA biomarkers for genitourinary tract diseases. However, missing knowledge about reference genes and effects of preanalytical choices hinder biomarker studies. We aimed to assess how preanalytical variables (urine storage temperature, isolation workflow) affect diabetic kidney disease (DKD)—linked miRNAs or kidney—linked miRNAs and mRNAs (kidney-RNAs) in uEV isolates and to discover stable reference mRNAs across diverse uEV datasets. We studied nine raw and normalized sequencing datasets including healthy controls and individuals with prostate cancer or type 1 diabetes with or without albuminuria. We focused on kidney-RNAs reviewing literature for DKD-linked miRNAs from kidney tissue, cell culture and uEV/urine experiments. RNAs were analyzed by expression heatmaps, hierarchical clustering and selecting stable mRNAs with normalized counts (>200) and minimal coefficient of variation. Kidney-RNAs were decreased after urine storage at −20 °C vs. −80 °C. Isolation workflows captured kidney-RNAs with different efficiencies. Ultracentrifugation captured DKD -linked miRNAs that separated healthy and diabetic macroalbuminuria groups. Eleven mRNAs were stably expressed across the datasets. Hence, pre-analytical choices had variable effects on kidney-RNAs—analyzing kidney-RNAs complemented global correlation, which could fade differences in some relevant RNAs. Replicating prior DKD-marker results and discovery of candidate reference mRNAs encourages further uEV biomarker studies.
Active control using moving bottom wall applied to open cavity self-sustained oscillation with mode switching

The self-sustained oscillations of open cavities with cavity aspect ratios of 2.0, 2.5, 3.0, 3.5, and 4.0 were studied with and without an active control using moving bottom wall. The studies were conducted by using 2D incompressible direct numerical simulations. Selfsustained oscillatory regimes of shear layer without control were classified into mode II, mode III and wake mode as the cavity aspect ratio increases. The results showed that the selfsustained oscillations were completely suppressed by our control method using suitable moderate moving velocities of the bottom wall for all oscillatory modes.
High order expansions for renewal functions and applications to ruin theory

A high order expansion of the renewal function is provided under the assumption that the inter-renewal time distribution is light tailed with finite moment generating function g on a neighborhood of 0. This expansion relies on complex analysis and is expressed in terms of the residues of the function 1/(1 − g). Under the assumption that g can be extended into a meromorphic function on the complex plane and some technical conditions , we obtain even an exact expansion of the renewal function. An application to risk theory is given where we consider high order expansion of the ruin probability for the standard compound Poisson risk model. This precises the well known Cramer-Lundberg approximation of the ruin probability when the initial reserve is large.
Learning environments in software testing education: An overview

Nowadays, we heavily depend on software, as we utilize it daily. Bugs in the software can cause significant damage, loss of private data and money, and even loss of human lives. Software testing and quality assurance discipline, which belongs to the software engineering domain, aims to ensure that the software is good enough before its release to the market. Therefore, it is vital to ensure that software engineers are highly trained in this domain. Unfortunately, this discipline is frequently neglected throughout software engineering courses. Students are mostly taught just the theoretical principles of software testing and gain little practical experience. Additionally, the software testing lectures are considered to be dull, especially if they are not followed by practical tasks where the students could apply learned techniques and gain hands‐on experience. Several tools, games, and collaborative learning environments have been proposed to make the lectures more interesting and increase student engagement. The main goal of this article is to provide a systematic survey of such environments, where each environment was evaluated in terms of major software testing topics covered and the approach used to engage the students.
Penalized Regression Methods in Time Series and Functional Data Analysis

In this thesis, we study penalized methods in time series and functional data analysis. In the first part, we introduce regularized periodograms for spectral analysis of unevenly spaced time series. The regularized periodograms, called regularized least squares periodogram and regularized quantile periodogram, are derived from trigonometric least squares and quantile regression with Group Lasso penalty. A simple model provides a theoretical justification for the use of regularized least squares periodogram as a tool for detecting hidden frequency in the time series. We give a data-dependent procedure for selection of the regularization parameter. An extensive simulation studies are conducted to examine whether our periodogram functions have the power to detect frequencies from the unevenly spaced time series with big gaps and outliers. In the second part, we propose a penalized likelihood approach for the estimation of the spectral density of a stationary time series. The approach involves L1 penalties, which were shown to be an attractive regularization device for nonparametric regression, image reconstruction, and model selection. We explore the use of penalties based on the total variation of the estimated derivatives of spectral density. An asymptotic analysis of the integrated absolute error between the estimator and the true spectral density is presented and gives a consistency result under certain regularity conditions. We also investigate the convergence of the total variation penalized Whittle likelihood estimator to the true spectral density via simulations. In the third part, we treat discrete time series data have as functional covariates in functional regression models with a scalar response. We develop an efficient
Improvement of Patients’ Knowledge, Attitude, and Practice on Tuberculosis Treatment Using Video and Leaflet

Background: The effectiveness of Tuberculosis (TB) treatment outcomes for patients is currently suboptimal, posing a significant challenge to comprehensive efforts aimed at eradicating the disease. To address this problem, several studies have proposed that the implementation of health education initiatives have the potential to enhance treatment adherence and behavior of patients. Therefore, this study aims to assess the efficacy of health education programs using video and leaflet modalities.Method: This was a quasi-experimental study with a non-randomized pretest-posttest control group design. The sample population consisted of 85 TB patients at the Kenanga Health Center, Tegal Sari Mandala II, Medan Denai District, Medan City in 2022. Furthermore, the samples size was obtained using a purposive sampling method, involving 64 patients, which were evenly divided into two groups, namely intervention and control. The controls and intervention groups were educated using leaflet and video media, respectively. Pre-test and post-test were given to all the participants using the same instrument. A post-test was administered on the twenty-fifth day, where the materials were the same as those used at the pre-test stage. The data collected in this study were analyzed using the Wilcoxon, Mann-Whitney, and N-Gain tests.Results: There were significant differences between knowledges, attitudes, and practices of the participants before and after being given the interventions. Furthermore, knowledges, attitudes, and practices scores increased after the interventions were administered. Based on the results, health education using video (N-Gain score of 76.82) improved behavior of TB patients compared to the use of leaflet (N-Gain score of 49.74). After being educated, people with TB were expected to exhibit higher treatment adherence and adoption of healthy lifestyles.
To Be, or Not to Be: That Is the Hamletic Question of Cryptic Evolution in the Eastern Atlantic and Mediterranean Raja miraletus Species Complex

Simple Summary The Raja miraletus species complex exhibits high levels of morphological and ecological stasis along with the antipodean distribution in the Eastern Atlantic and Indian Oceans. We investigated genetic variability and differentiation between taxa and geographical populations by integrating mitochondrial and nuclear DNA markers. The extraordinary occurrence of at least five different sibling taxa in the Northeastern Atlantic Ocean and Mediterranean Sea is documented, supporting cryptic speciation and stabilising selection. Abstract Despite a high species diversity, skates (Rajiformes) exhibit remarkably conservative morphology and ecology. Limited trait variations occur within and between species, and cryptic species have been reported among sister and non-sister taxa, suggesting that species complexes may be subject to stabilising selection. Three sibling species are currently recognised in the Raja miraletus complex: (i) R. miraletus occurring along the Portuguese and Mediterranean coasts, (ii) R. parva in the Central-Eastern Atlantic off West Africa and (iii) R. ocellifera in the Western Indian Ocean off South Africa. In the present study, the genetic variation at mitochondrial and nuclear markers was estimated in the species complex by analysing 323 individuals sampled across most of its geographical distribution area to test the hypothesis that restricted gene flow and genetic divergence within species reflect known climate and bio-oceanographic discontinuities. Our results support previous morphological studies and confirm the known taxonomic boundaries of the three recognised species. In addition, we identified multiple weakly differentiated clades in the Northeastern Atlantic Ocean and Mediterranean, at least two additional cryptic taxa off Senegal and Angola, a pronounced differentiation of ancient South African clades. The hidden genetic structure presented here may represent a valuable support to species’ conservation action plans.
MAVEN observations of dayside peak electron densities in the ionosphere of Mars

The peak electron density in the dayside Martian ionosphere is a valuable diagnostic of the state of the ionosphere. Its dependence on factors like the solar zenith angle, ionizing solar irradiance, neutral scale height, and electron temperature has been well studied. The Mars Atmosphere and Volatile EvolutioN spacecraft's September 2015 “deep dip” orbits, in which the orbital periapsis was lowered to ~125 km, provided the first opportunity since Viking to sample in situ a complete dayside electron density profile including the main peak. Here we present peak electron density measurements from 37 deep dip orbits and describe conditions at the altitude of the main peak, including the electron temperature and composition of the ionosphere and neutral atmosphere. We find that the dependence of the peak electron density and the altitude of the main peak on solar zenith angle are well described by analytical photochemical theory. Additionally, we find that the electron temperatures at the main peak display a dependence on solar zenith angle that is consistent with the observed variability in the peak electron density. Several peak density measurements were made in regions of large crustal magnetic field, but there is no clear evidence that the crustal magnetic field strength influences the peak electron density, peak altitude, or electron temperature. Finally, we find that the fractional abundance of O2+ and CO2+ at the peak altitude is variable but that the two species together consistently represent ~95% of the total ion density.
Demystifying the roles of organisational smart technology, artificial intelligence, robotics and algorithms capability: A strategy for green human resource management and environmental sustainability

With growing climate change concerns, and constant advancements in smart technology, artificial intelligence, robotics, and algorithms (STARA), organisations in emerging economies are becoming more compelled to go green, develop and deploy their STARA capability to boost profits more effectively, and their environmental sustainability (ES). Likewise, with governments increasingly calling for ES, organisations' human resource management (HRM) is further pressured to ensure their programmes aid realisation of environmental objectives without compromising profit maximisation. However, it remains unclear how complementary Green HRM (GHRM) programmes can be supported by organisational STARA capability (OSC) to bolster ES. Accordingly, we investigate how OSC and GHRM programmes predict ES through a time lagged survey design with data from 461 managers of 177 manufacturing organisations in Nigeria. Results indicate that OSC positively predicts all GHRM programmes and ES but dampens the positive relationship between green training, involvement and development (GTID), and ES. Apart from green performance and compensation (GPC), which is a negative predictor, other GHRM programmes positively predict ES. While green recruitment and selection (GRS) and GTID are complementary mediators, GPC plays a competitive mediating role. Policy implications are subsequently discussed
First Report of Olive Branch Dieback in Croatia Caused by Cytospora pruinosa Défago

Olive (Olea europaea L.) is a very important crop grown in the Mediterranean part of Croatia. Olive branch and fruit dieback symptoms were observed in two olive orchards in Istria, Croatia. The samples from symptomatic trees were collected and brought to the laboratory for analysis. Based on their morphological characterization, isolated fungi were identified as Cytospora sp. Two representative isolates (one per orchard) were taken for molecular analysis, and based on DNA sequence data of the ITS and TUB gene regions, and phylogenetic analysis of the sequences, the isolates were identified as Cytospora pruinosa Défago. To determine pathogenicity, pathogenicity tests were conducted on detached olive branches and two-year-old olive trees in the greenhouse. This is the first report of C. pruinosa causing olive branch and fruit dieback in Croatia.
Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals

The magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient observational constraints; satellite column‐average dry‐air mole fraction carbon dioxide (XCO2) retrievals may fill some of this gap. Here, we estimate China's carbon sink using atmospheric inversions of the Orbiting Carbon Observatory 2 (OCO‐2) XCO2 retrievals within different platforms, including the Global Carbon Assimilation System (GCAS) v2, the Copernicus Atmosphere Monitoring Service, and the OCO‐2 Model Inter‐comparison Project (MIP). We find that they consistently place the largest net biome production (NBP) in the south on an annual basis compared to the northeast and other main agricultural areas during peak growing season, coinciding well with the distribution of forests and crops, respectively. Moreover, the mean seasonal cycle amplitude of NBP in OCO‐2 inversions is obviously larger than that of biosphere model simulations and slightly greater than surface CO2 inversions. More importantly, the mean seasonal cycle of the OCO‐2 inversions is well constrained in the temperate, tropical, and subtropical monsoon climate zones, with better inter‐model consistency at a sub‐regional scale compared to in situ inversions and biosphere model simulations. In addition, the OCO‐2 inversions estimate the mean annual NBP in China for 2015–2019 to be between 0.34 (GCASv2) and 0.47 ± 0.16 PgC/yr (median ± std; OCO‐2 v10 MIP), and indicate the impacts of climate extremes (e.g., the 2019 drought) on the interannual variations of NBP. Our results suggest that assimilating OCO‐2 XCO2 retrievals is crucial for improving our understanding of China's terrestrial carbon sink regime.
What Is the Significance of Indeterminate Pulmonary Nodules in High-Grade Soft Tissue Sarcomas? A Retrospective Cohort Study

Simple Summary Sarcomas are rare cancers; they can arise anywhere in the body and most often spread to the lungs. When patients are diagnosed, they have a scan of the chest to look for this. The scan often finds small nodules whereby we cannot be certain whether they are cancer or not; these are called indeterminate pulmonary nodules or IPNs. We do not yet understand what the presence of IPNs means for patients with high-grade sarcomas in their soft tissues, although we know that some of these reveal themselves later on as being a spreading of the cancer. Currently, patients with IPNs normally have repeat scans a number of months down the line to see whether they have changed in size, suggesting that they may be cancer. This study has identified a number of different characteristics that make these IPNs more likely to be cancer. Abstract Background: Sarcomas are rare, aggressive cancers which frequently metastasise to the lungs. Following diagnosis, patients typically undergo staging by means of a CT scan of their chest. This often identifies indeterminate pulmonary nodules (IPNs), but the significance of these in high-grade soft tissue sarcoma (STS) is unclear. Identifying whether these are benign or malignant is important for clinical decision making. This study analyses the clinical relevance of IPNs in high-grade STS. Methods: All patients treated at our centre for high-grade soft tissue sarcoma between 2010 and 2020 were identified from a prospective database. CT scans and their reports were reviewed, and survival data were collected from patient records. Results: 389 suitable patients were identified; 34.4% had IPNs on their CT staging scan and 20.1% progressed into lung metastases. Progression was more likely with IPNs ≥ 5 mm in diameter (p = 0.006), multiple IPNs (p = 0.013) or bilateral IPNs (p = 0.022), as well as in patients with primaries ≥ 5 cm (p = 0.014), grade 3 primaries (p = 0.009) or primaries arising deep to the fascia (p = 0.041). The median time to progression was 143 days. IPNs at diagnosis were associated with an increased risk of developing lung metastases and decreased OS in patients with grade 3 STS (p = 0.0019 and p = 0.0016, respectively); this was not observed in grade 2 patients. Conclusions: IPNs at diagnosis are associated with significantly worse OS in patients with grade 3 STS. It is crucial to consider the primary tumour as well as the IPNs when considering the risk of progression. Surveillance CT scans should be carried out within 6 months.
The Defocusing Energy-critical Klein-Gordon-Hartree Equation.

In this paper, we study the scattering theory for the defocusing energy-critical Klein-Gordon equation with a cubic convolution $u_{tt}-\Delta u+u+(|x|^{-4}\ast|u|^2)u=0$ in the spatial dimension $d \geq 5$. We utilize the strategy in [S. Ibrahim, N. Masmoudi and K. Nakanishi, Scattering threshold for the focusing nonlinear Klein-Gordon equation. Analysis and PDE. , 4 (2011), 405-460. ] derived from concentration compactness ideas to show that the proof of the global well-posedness and scattering is reduced to disprove the existence of the soliton-like solution. Employing technique from [B. Pausader, Scattering for the Beam Equation in Low Dimensions. Indiana Univ. Math. J., 59 (2010), 791-822. ], we consider a virial-type identity in the direction orthogonal to the momentum vector so as to exclude such solution.
The Junta Method for Hypergraphs and Chv\'atal's Simplex Conjecture

Numerous problems in extremal hypergraph theory ask to determine the maximal size of a $k$-uniform hypergraph on $n$ vertices that does not contain an `enlarged' copy $H^+$ of a fixed hypergraph $H$. These include well-known open problems such as the Erd\H{o}s matching conjecture, the Erd\H{o}s-S\'os `forbidding one intersection' problem, the Frankl-F\"uredi `special simplex' problem, etc. We present a general approach to such problems, using a `junta approximation method' that originates from analysis of Boolean functions. We prove that any $H^+$-free hypergraph is essentially contained in a `junta' -- a hypergraph determined by a small number of vertices -- that is also $H^+$-free, which effectively reduces the extremal problem to an easier problem on juntas. Using this approach, we obtain, for all $C<k<n/C$, a complete solution of the extremal problem for a large class of $H$'s, which includes all the aforementioned problems. We apply our method to the 1974 Chv\'atal's simplex conjecture, which asserts that for any $d n_0(d)$.
Solar wind ion charge state distributions and compound cross sections for solar wind charge exchange X-ray emission

Solar Wind Charge eXchange X-ray (SWCX) emission in the heliosphere and Earth's exosphere is a hard to avoid signal in soft X-ray observations of astrophysical targets. On the other hand, the X-ray imaging possibilities offered by the SWCX process has led to an increasing number of future dedicated space missions for investigating the solar wind-terrestrial interactions and magnetospheric interfaces. In both cases, accurate modelling of the SWCX emission is key to correctly interpret its signal, and remove it from observations, when needed. In this paper, we compile solar wind abundance measurements from ACE for different solar wind types, and atomic data from literature, including charge exchange cross-sections and emission probabilities, used for calculating the compound cross-section $\alpha$ for the SWCX X-ray emission. We calculate $\alpha$ values for charge-exchange with H and He, relevant to soft X-ray energy bands (0.1 - 2.0 keV) for various solar wind types and solar cycle conditions.