Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical Structuring of Black Silicon Wafers by Ion-Flow-Stimulated Roughening Transition: Fundamentals and Applications for Photovoltaics

Ion-flow-stimulated roughening transition is a phenomenon that may prove useful in the hierarchical structuring of nanostructures. In this work, we have investigated theoretically and experimentally the surface texturing of single-crystal and multi-crystalline silicon wafers irradiated using ion-beam flows. In contrast to previous studies, ions had relatively low energies, whereas flow densities were high enough to induce a quasi-liquid state in the upper silicon layers. The resulting surface modifications reduced the wafer light reflectance to values characteristic of black silicon, widely used in solar energetics. Features of nanostructures on different faces of silicon single crystals were studied numerically based on the mesoscopic Monte Carlo model. We established that the formation of nano-pyramids, ridges, and twisting dune-like structures is due to the stimulated roughening transition effect. The aforementioned variety of modified surface morphologies arises due to the fact that the effects of stimulated surface diffusion of atoms and re-deposition of free atoms on the wafer surface from the near-surface region are manifested to different degrees on different Si faces. It is these two factors that determine the selection of the allowable “trajectories” (evolution paths) of the thermodynamic system along which its Helmholtz free energy, F, decreases, concomitant with an increase in the surface area of the wafer and the corresponding changes in its internal energy, U (dU>0), and entropy, S (dS>0), so that dF=dU – TdS<0, where T is the absolute temperature. The basic theoretical concepts developed were confirmed in experimental studies, the results of which showed that our method could produce, abundantly, black silicon wafers in an environmentally friendly manner compared to traditional chemical etching.
INVESTIGATION THE MOST EFFICIENT EXTRACTION METHOD FOR OLEANOLIC ACID FROM OLIVE PLANT AND ITS CHARACTERIZATION

The effect of five solvents was tested, including water, ethyl alcohol 80%, methyl 80%, ethyl acetate and butanol, in order to reach the most efficient solvent for extraction oleanolic acid (Triterpen), depending on the extraction yield, which reached the in leaves 0.9, 1, 0.2, 0.2, and 0.5 g /100 g, respectively, less than in fruits and then Kernels. Five extraction methods were tested . Maceration in hot water, ethyl alcohol (80%), Soxhlet method, Reflux extraction, Microwave, and Defatting with petroleum ether (DPE) method. The latter method distinguished by the amount of yield by 4g/100g, the yield of other extraction methods ranged from 0.1-0.7g/100g. The results of the qualitative chemical detection of the active compounds from the three parts of the olive plant (leaves, fruits and Kernels) showed that the leaves were distinguished by containing a number of active compounds with the appearance of a precipitate, color or foam, as the contained terpenes. total terpenes another indicator to determine the plant part with the highest quantitative content. The results of the experiment indicated the superiority of olive leaves in their content of total terpenes for the five solvents with rates ranging from 40-90% in the leaves and then 39-70% in the fruits and the Kernels are lowest 20-50.Crystals of Oleanolic acid were appeared yellow and needle-shaped under microscope melting point was 299-205 C 0
Prediction Future Location for Multiple account users Across Social Network

The personal data of people over social networks bring many opportunities for developing a new application. Some users create multiple accounts on social networks. Correlate user information across social networks helps to design algorithms to predict the future location of multiple accounts of a single individual. Most social media site applications need to share their sites with friends and family and this feature has made many people need to predict the site. Prediction is one of the foremost essential issues that need to be investigated for mobility management In mobile computing systems. In this paper, a proposed algorithm for future location prediction of multiple account users based on using frequent pattern mining. First, apply the frequency algorithm. The proposed algorithm experimented using "Gowalla and foursquare datasets" were presented simple method can be used to infer the user location using publicly available attributes and also the geographic information associated with allocatable friends. We find that it is possible to infer the user city with high accuracy. The proposed algorithm prediction achieved accuracy 96% in the Gowalla dataset when applying the support and confidence for each region that visited the user in a specific time period through some years or many months.
Grupos moderados y tolerantes en la polarización política de las redes sociales

espanolEste articulo pretende investigar el tamano minimo que debe tener un grupo de individuos moderados y tolerantes para llevar a una red social altamente polarizada e intolerante al consenso politico, asi como analizar el posible efecto de diferentes propiedades estructurales de las redes (densidad y desigualdad en la distribucion de vinculos), y de ciertas propiedades individuales (tolerancia y arraigo de las opiniones politicas) sobre dicho tamano minimo. Para ello, se combinan la simulacion basada en agentes y el analisis de redes sociales con objeto de realizar experimentos virtuales con redes artifi ciales. Los resultados revelan la existencia de un tamano minimo critico por debajo del cual el consenso politico es practicamente imposible, y ponen de manifi esto que dicho tamano depende acusadamente de diversas propiedades estructurales e individuales EnglishThis paper attempts to research the minimum size which a moderate and tolerant group of individuals should have in order to lead an extremely intolerant and polarised social network to achieve political consensus. It also analyses the potential effect of different structural properties of networks (density and inequality of link distribution), and certain individual properties (tolerance and entrenchment of political opinions) on that minimum size. To do so, it combines agent-based modelling and social network analysis in order to carry out virtual experiments with artifi cial networks. The results reveal the existence of a minimum, critical size under which political consensus is hardly ever achieved, as well as showing that such size depends heavily on various structural and individual properties.
Emerging Opportunities for 2D Materials in Neuromorphic Computing

Recently, two-dimensional (2D) materials and their heterostructures have been recognized as the foundation for future brain-like neuromorphic computing devices. Two-dimensional materials possess unique characteristics such as near-atomic thickness, dangling-bond-free surfaces, and excellent mechanical properties. These features, which traditional electronic materials cannot achieve, hold great promise for high-performance neuromorphic computing devices with the advantages of high energy efficiency and integration density. This article provides a comprehensive overview of various 2D materials, including graphene, transition metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), and black phosphorus (BP), for neuromorphic computing applications. The potential of these materials in neuromorphic computing is discussed from the perspectives of material properties, growth methods, and device operation principles.
A Computational Tool for Recoloring Based on User Emotions

This work describes a system to recolor a user’s painting based on the perceived emotional state of the viewer. An automatic palette selection algorithm is used to generate color palettes for a set of emotions. A user can create a painting using one of the generated palettes. To notify the end of the painting, the user clicks on the DONE button. Once the button is pressed, the colors of the user's painting change as the facial expression of the user changes. Facial emotion recognition is used in this process to classify the emotional status of the user’s face.
Integrative profiling of metabolome and transcriptome of skeletal muscle after acute exercise intervention in mice

This study aims to explore the molecular regulatory mechanisms of acute exercise in the skeletal muscle of mice. Male C57BL/6 mice were randomly assigned to the control group, and the exercise group, which were sacrificed immediately after an acute bout of exercise. The study was conducted to investigate the metabolic and transcriptional profiling in the quadriceps muscles of mice. The results demonstrated the identification of 34 differentially expressed metabolites (DEMs), with 28 upregulated and 6 downregulated, between the two groups. Metabolic pathway analysis revealed that these DEMs were primarily enriched in several, including the citrate cycle, propanoate metabolism, and lysine degradation pathways. In addition, the results showed a total of 245 differentially expressed genes (DEGs), with 155 genes upregulated and 90 genes downregulated. KEGG analysis indicated that these DEGs were mainly enriched in various pathways such as ubiquitin mediated proteolysis and FoxO signaling pathway. Furthermore, the analysis revealed significant enrichment of DEMs and DEGs in signaling pathways such as protein digestion and absorption, ferroptosis signaling pathway. In summary, the identified multiple metabolic pathways and signaling pathways were involved in the exercise-induced physiological regulation of skeletal muscle, such as the TCA cycle, oxidative phosphorylation, protein digestion and absorption, the FoxO signaling pathway, ubiquitin mediated proteolysis, ferroptosis signaling pathway, and the upregulation of KLF-15, FoxO1, MAFbx, and MuRF1 expression could play a critical role in enhancing skeletal muscle proteolysis.
One IOTA of Countless Legions: A Next-Generation Botnet Premises Design Substrated on Blockchain and Internet of Things

Although botnet had been at the top of the list of main threats to the cyber world for an extended period of time, its harmfulness has been constrained nowadays due to the development of kaleidoscopic network security enforcing tools and people’s increasing awareness. And the underlying technology of the botnet has been stagnant ascribing to many drawbacks such as inadequate protection of the identity of the Botmaster and weak resilience of the botnet’s infrastructure. In this article, we first introduce a new classification of the botnet based on botnets’ underlying network, then briefly analyze the main flaws of the traditional botnet and some looming Blockchain-based botnets, with pros and cons of leveraging Blockchain to construct botnets. Furthermore, we propose one IOTA of countless legions (OICL), a newfangled versatile botnet infrastructure that overcomes the bottlenecks that other contemporaries cannot eliminate. It leverages Blockchain, also known as distributed ledger technology (DLT), to be its premises and uses many advantages of it without paying too many tradeoffs. Also, we invent a whole set of communication protocols for OICL and a novel scheme called Proof of Honest (PoH) to identify the espionage infiltrated into the botnet to further promote the robustness. In addition, we discover and propose a mechanism called collateral damage binding (CDB), which proves that the botnet has it such as OICL is far more robust than those who do not. Performance evaluations show that OICL is effective, more cost-saving, and fast-responding compared with the Bitcoin-based botnets as baselines.
Molecular Characteristics of Barley Yellow Dwarf Virus—PAS—The Main Causal Agent of Barley Yellow Dwarf Disease in Poland

Barley yellow dwarf is a threat to cereal crops worldwide. Barley yellow dwarf virus—PAS (BYDV-PAS) was detected for the first time in Poland in 2015, then in 2019. In the spring of 2021, in several locations in Poland, winter wheat and barley plants with dwarfism and leaf yellowing were collected. Reverse transcription—polymerase chain reaction results revealed BYDV presence in 47 samples and excluded wheat streak mosaic virus infections. Next, immuno-captured polymerase chain reactions confirmed only one case of co-infection caused by BYDV and wheat dwarf virus. Moreover, restriction fragment length polymorphism analysis showed that BYDV-PAS was predominant. The preliminary results were confirmed using sequencing. Infected cereal plants originated mainly from northwestern Poland. The complete coding sequence of coat protein (CP) and a fragment of RNA-dependent RNA polymerase (RdRp) genes of 14 Polish isolates were determined and deposited in the GenBank database. The nucleotide and deduced amino acid sequences of local isolates were compared with others reported to date, indicating their high similarity, from 75.4% to 99.5% and from 81.1% to 100% nucleotide sequence identity, in RdRp and CP, respectively. Phylogenetic analysis, based on the CP gene, revealed the presence of 3 main groups. The Polish isolates clustered together within the Ia group.
SEDIMENT CONCENTRATIONS RESULTING FROM FLOW DEPTH/DROP SIZE INTERACTIONS IN SHALLOW OVERLAND FLOW

Flow depth influences the erosive stress applied by raindrop impact to the surface under shallow flow. An analysis of data from laboratory experiments shows the sediment concentration associated with drops of a given size to decrease exponentially with flow depth within two regimes when the susceptibility of the surface to erosion remains constant. In the first regime, raindrop size has a non-significant effect because the height of the water surface above the bed constrains the height to which particles are lifted in the flow. In the second regime, which occurs when flows are deeper than a critical depth that varies with the energy of the impacting drops, drop size and velocity influence sediment concentration from the sediment transport perspective. These effects need to be considered in experiments designed to evaluate the susceptibility of soil to erosion when raindrop impact is a dominant force in the erosion process. They also need to be considered in models designed to predict raindrop-driven erosion.
The Development of Teacher Performance Assessment Instruments in Boarding School

The objective of this study is to create a tool for evaluating teachers’ effectiveness in boarding schools. Planning and preparation stages, instrument testing, and measurements were used in this study to implement research on the creation of the Mardapi model instrument. In order to generate instrument items, the planning and preparation stage tasks included a literature assessment of teacher performance appraisal manuals. Data were gathered utilizing documentation approaches, and descriptive and qualitative analysis was performed. Six specialists validated the built instruments, which were subsequently put through limited testing at two boarding schools and extensive trials at nine boarding schools in Surakarta residency area. The Aiken formula was used to examine the expert’s evaluation data, and confirmatory factor analysis was used to analyze the test results. The results of this study indicate that the instrument for assessing teacher performance in boarding schools meets the validity criteria. This is indicated by the existence of loading factor values ranging from . 51 to . 72 (>.4) and t-sign values ranging from 4.75 to 9.25 (>1.96) and meeting the requirements of a fit model since Chi-square = 1307.95 < 2524 (2*df), p-value = . 17956 (>.05), and RSMEA value = . 014 (<.08). The instrument items are reliable assessment packages, and this is shown by the reliability value of omega . 967 > . 70. Because there is a guarantee of validity and reliability, the test can be used to further assess teacher performance in boarding schools.
The application of forest and grassland fires monitoring based on HJ-1 CCD/IRS images

Wild fire of forest and grassland is one of the major natural disasters in the world. The fire of forest and grassland occurs frequently due to global changing and uneven precipitation distribution, which may affect large areas and cause great economic loss. With the advantages of wide-coverage, high spatio-temporal resolution, remote sensing technology has been used for the wild fire monitoring activities. In this paper, the fire point detection model and burned area monitoring model, for HJ-1A/B data, have been developed and applied for the southeast Australian wild fire monitoring in 2009. The application shows that the models and the data are efficient for the wild fire monitoring.
Prevalence and Clinical Predictors of Psoriatic Arthritis in Saudi Patients With Psoriasis: A Single-Center Retrospective Cohort Study

Background Among psoriatic patients, psoriatic arthritis is the most common and most impactful comorbidity. In most cases, it occurs after the onset of psoriasis. Detecting and treating it early is crucial for rheumatologists and dermatologists. Objectives The study aimed to determine the prevalence of psoriatic arthritis among recognized cases of psoriasis, as well as to determine the clinical features of psoriasis that are linked to a greater prevalence of psoriatic arthritis at King Abdulaziz Medical City (KAMC), Riyadh. Methods A retrospective cohort study of 487 psoriatic patients diagnosed between 2015 and 2023 was conducted at KAMC, Riyadh. The study included subjects aged 18 years or older with a psoriasis diagnosis documented by a dermatologist and a psoriatic arthritis diagnosis documented by a rheumatologist based on the Classification Criteria for Psoriatic Arthritis (CASPAR). Patients younger than 18 years, diagnosed with psoriatic arthritis concurrently with psoriasis, or within 90 days of psoriasis diagnosis, or who lack a documented diagnosis of psoriasis by a dermatologist were excluded. The study evaluated demographic data and medical variables concerning psoriasis (age at onset, type of psoriasis, site of psoriasis, and nail dystrophy) and psoriatic arthritis. SPSS Statistics version 25 (IBM Corp., Armonk, NY) was used to conduct statistical analysis. The p-value of 0.05 was used to evaluate statistical significance. Results Overall, 487 patients had psoriasis in this study. Of these, 49 (10%) were diagnosed with psoriatic arthritis. The mean ± standard deviation of the age of the psoriasis group was 41.7 ± 15.6 years, with 264 (54.2%) females and 223 (44.8%) males. The clinical features of psoriasis that were linked to a greater frequency of psoriatic arthritis in our study included female gender (71.4%), plaque psoriasis (95.9%), psoriatic lesions involving the extremities (75%), scalp (42.9%), and trunk (36.7%), nail dystrophy (28.6%), as well as the involvement of three or more sites (40.8%) at the time of their initial diagnosis of psoriasis. Conclusion Our study indicated that 10% of Saudi patients with psoriasis had psoriatic arthritis. Moreover, the present study shows that patients with greater psoriatic lesions at initial presentation are more likely to develop psoriatic arthritis.
A comparison of methods for estimating u * from given u z and air‐sea temperature differences

This paper presents an objective assessment of four methods for estimating sea surface friction velocity u* from wind speed at height z, uz, and air-sea temperature difference. The methods are compared by using the computed friction velocity as the normalization factor in parametric correlations of wind-wave parameters with the wind and wave measurements made by NOMAD buoys in the Great Lakes. The results show that (1) wind profile parameters obtained from the four methods are generally comparable, (2) parametric correlations with parameters normalized by u* lead to significantly decreased percentage deviations over correlations normalized by uz, and (3) correlations based on u* derived from the four methods show nearly identical percentage deviations. The conclusions are that (1) u* normalization acts to eliminate the effect of atmospheric stability and (2) any of the four methods can be used effectively in practical applications.
In Vitro Induction of Polyploidy by Colchicine in the Protocorm of the Orchid Dendrobium wardianum Warner

Dendrobium wardianum is a key ornamental plant and a valuable traditional Chinese medicine. This research aimed to find the optimal protocol for in vitro inducement of polyploidy in D. wardianum by treating protocorms with colchicine (an antimitotic agent). The experiment consisted of two series of treatments. For the first treatment, the protocorms were subjected to colchicine concentrations of 25, 75, 125, 250, and 500 μM (weight/volume) for 6, 12, and 24 hours. For the second treatment, protocorms were cultivated in culture medium with colchicine (25, 75, 125, and 250 μM) for 30 days. A total of 18 polyploids were confirmed by chromosome counts and anatomical parameters. Polyploids had broad, dark green leaves with increased stem lengths compared with those of diploids. The optimal protocol for these two methods consisted of soaking in 250 μM of colchicine solution for 12 hours, resulting in inductivity of 26%, and cultivating in 75 μM for 30 days, resulting in a mutation rate of 34%. A comparison of these two protocols showed that the latter one induced more stable polyploids, but that the survival rate was slightly lower. The survival and induced mutation rates of these plants were significantly influenced by the colchicine concentration and exposure time. Higher concentrations for longer periods of time resulted in greater mortality rates and longer-lasting side effects. The protocol involving a solid medium and colchicine is worth considering. It will be intriguing to examine this methodology for the induction of stable polyploids of other orchid species.
Textural and Volumetric Changes of the Temporal Lobes in Semantic Variant Primary Progressive Aphasia and Alzheimer’s Disease

Background Texture analysis may capture subtle changes in the gray matter more sensitively than volumetric analysis. We aimed to investigate the patterns of neurodegeneration in semantic variant primary progressive aphasia (svPPA) and Alzheimer’s disease (AD) by comparing the temporal gray matter texture and volume between cognitively normal controls and older adults with svPPA and AD. Methods We enrolled all participants from three university hospitals in Korea. We obtained T1-weighted magnetic resonance images and compared the gray matter texture and volume of regions of interest (ROIs) between the groups using analysis of variance with Bonferroni posthoc comparisons. We also developed models for classifying svPPA, AD and control groups using logistic regression analyses, and validated the models using receiver operator characteristics analysis. Results Compared to the AD group, the svPPA group showed lower volumes in five ROIs (bilateral temporal poles, and the left inferior, middle, and superior temporal cortices) and higher texture in these five ROIs and two additional ROIs (right inferior temporal and left entorhinal cortices). The performances of both texture- and volume-based models were good and comparable in classifying svPPA from normal cognition (mean area under the curve [AUC] = 0.914 for texture; mean AUC = 0.894 for volume). However, only the texture-based model achieved a good level of performance in classifying svPPA and AD (mean AUC = 0.775 for texture; mean AUC = 0.658 for volume). Conclusion Texture may be a useful neuroimaging marker for early detection of svPPA in older adults and its differentiation from AD.