Miracle-dz/UniFAR-FTUs huggingface dataset:
The structure of sc16 GaP obtained at 17.5 GPa and 1400 K

ABSTRACT Using a laser-heated diamond anvil cell, a powdered sample of gallium phosphide was compressed to 17.5 GPa and heated up to 1400 K. The material obtained was characterized at room temperature using synchrotron x-ray diffraction. Experimental results were compared with first-principles calculations. The polymorph observed assumes the simple cubic structure (sc16) initially reported for GaAs at high pressures. Microdiffraction mapping showed variable grain sizes of the synthesized phase, with the largest grains located in the middle of the heated spot. Structural refinements were performed on selected grains. The structure predicted based on first-principles calculations is in close agreement with the experiments. The two Ga–P bonds show similar lengths in sc16-GaP; however, the bond angles differ, resulting in a distorted tetrahedral coordination geometry.
The excitation of 12 micron emission from very small particles

The results of IRAS observations of visual reflection nebulae illuminated by stars with effective temperatures between 3000 and 33,000 K are presented. The ratio of the energy radiated in the IRAS 12-microns band to the total energy radiated at far-infrared wavelengths (60 and 100 microns) is about 0.2, with little or no dependence on the temperature of the illuminating star for stellar temperatures between about 5000 and 33,000 K. This is interpreted as requiring that visual photons as well as ultraviolet photons are capable of exciting 12-microns emission from very small particles. 39 refs.
Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools

Background: Identifying the health status of wind turbines becomes critical to reduce the impact of failures on generation costs (between 25–35%). This is a time-consuming task since a human expert has to explore turbines individually. Methods: To optimize this process, we present a strategy based on Self Organizing Maps, clustering and a further grouping of turbines based on the centroids of their SOM clusters, generating groups of turbines that have similar behavior for subsystem failure. The human expert can diagnose the wind farm health by the analysis of a small each group sample. By introducing post-processing tools like Class panel graphs and Traffic lights panels, the conceptualization of the clusters is enhanced, providing additional information of what kind of real scenarios the clusters point out contributing to a better diagnosis. Results: The proposed approach has been tested in real wind farms with different characteristics (number of wind turbines, manufacturers, power, type of sensors, .) and compared with classical clustering. Conclusions: Experimental results show that the states healthy, unhealthy and intermediate have been detected. Besides, the operational modes identified for each wind turbine overcome those obtained with classical clustering techniques capturing the intrinsic stationarity of the data.
Retrospective comparison of survival and responses to Fludarabine, Cytarabine, GCSF (FLAG) in combination with gemtuzumab ozogamicin (GO) or Idarubicin (IDA) in patients with newly diagnosed core binding factor (CBF) acute myelogenous leukemia: MD Anderson experience in 174 patients

Fludarabine, cytarabine, GCSF (FLAG)‐based induction/consolidation results in high remission rates in core binding factor (CBF) acute myelogenous leukemia. We treated 174 consecutive patients with newly diagnosed CBF‐AML in a prospective clinical trial of FLAG‐based induction/consolidation in combination with gemtuzumab ozogamicin (FLAG‐GO; N = 65) or in combination with idarubicin (FLAG‐IDA; N = 109). The 5 year RFS in the FLAG‐GO cohort was significantly better than the FLAG‐IDA cohort, 78% versus 59%, respectively (p‐value = .02). In multivariate analysis for RFS, age (p = .0001), FLAG‐GO regimen (p = .04), 4 log reduction in CBF‐related fusion transcript by quantitative polymerase chain reaction (qPCR) in bone marrow samples at end of consolidation therapy (p = .03), and additional cytogenetic abnormalities (p = .03) were significant variables. Lower age (p = .0001) and 3 log or more transcript reduction at end of induction (p = .04) were significant variables predicting for better overall survival (OS), while there was strong trend for better OS with FLAG‐GO (p = .06) regimen. FLAG‐GO regimen was superior in optimal disease specific fusion transcript reduction at end of induction (p = .002), mid‐consolidation (p < .01), and end of consolidation (p < .001) therapy. Induction/consolidation with FLAG‐GO regimen results in better clinical outcomes in newly diagnosed patients with CBF‐AML compared to FLAG‐IDA and achieves deeper molecular clearance by qPCR assessment of the fusion transcripts.
Nitrogen Deposition Drives Response and Recovery in the Context of Precipitation Change and Its Reversal in an Arid Ecosystem

Climatic change profoundly impacts terrestrial ecosystem function and structure. However, it remains unclear how an entire ecosystem responds to precipitation fluctuations in relation to nitrogen (N) deposition, especially in arid ecosystems. A field experiment was conducted in a desert steppe over the course of 4 years to determine plant community and belowground responses to precipitation alteration and a subsequent precipitation regime reversal treatment under increased N addition, with an initial 3‐year precipitation regime manipulation period followed by a fourth‐year reversal. The aboveground net primary production (ANPP) significantly increased with increased precipitation, particularly at high N levels. However, the precipitation regime reversal treatment in the final year reversed the ANPP effects induced by the previous precipitation alteration. Soil microbial carbon and N content significantly increased with increasing precipitation, whereas the precipitation reversal in the final year partly eliminated the soil microbial effects produced by this previous precipitation alteration. Plant community composition shifted under both the precipitation and N treatments, with a few critical species playing crucial roles. The response ratio (initial treatment vs. control) of ANPP increased with increased precipitation during the first 3 years. Both the resilience (precipitation regime reversal treatment vs. control) and the limitation or promotion index (LPI, precipitation reversal treatment vs. initial treatment) of NPP were relatively high under the initial low precipitation and the final high precipitation regimes. Structural equation modeling in particular illustrated that the precipitation alteration treatments made greater contributions to the response ratio, resilience and LPI of the NPP than did N deposition via either the soil microbial levels or the plant diversity index. We should therefore consider both vegetation productivity and its compositional shift, as well as the associations with soil microbes under contrasting environmental changes, to accurately assess and predict the responses of terrestrial ecosystems to climate change.
Ensemble Learning for Intrusion Detection in SDN-Based Zero Touch Smart Grid Systems

Software-defined network (SDN) is widely deployed on Smart Grid (SG) systems. It consists in decoupling control and data planes, to automate the monitoring and management of the communication network, and thus enabling zero touch management of SG systems. However, SDN-based SG is prone to several security threats and varios type of new attacks. To alleviate these issues, various Machine/Deep learning (ML/DL)-based intrusion detection systems (IDS) were designed to improve the detection accuracy of conventional IDS. However, they suffer from high variance and/or bias, which may lead to an inaccurate security threat detection. In this context, ensemble learning is an emerging ML technique that aims at combining several ML models; the objective is to generate less data-sensitive (i.e., less variance) and more flexible (i.e., less bias) machine learning models. In this paper, we design a novel framework, called BoostIDS, that leverages ensemble learning to efficiently detect and mitigate security threats in SDN-based SG system. BoostIDS comprises two main modules: (1) A data monitoring and feature selection module that makes use of an efficient Boosting Feature Selection Algorithm to select the best/relevant SG-based features; and (2) An ensemble learning-based threats detection moel that implements a Lightweight Boosting Algorithm (LBA) to timely and effectively detects SG-based attacks in a SDN environment. We conduct extensive experiments to validate BoostIDS on top of multiple real attacks; the obtained results using NSL-KDD and UNSW-NB15 datasets, confirm that BoostIDS can effectively detect/mitigate security threats in SDN-based SG systems, while optimizing training/test time complexity.
Analyzing Experiments with Correlated Multiple Responses

Statistically designed experiments have been employed extensively to improve product or process quality and to make products and processes robust. In this paper, we consider experiments with correlated multiple responses whose means, variances, and correlations depend on experimental factors. Analysis of these experiments consists of modeling distributional parameters in terms of the experimental factors and finding factor settings which maximize the probability of being in a specification region, i.e., all responses are simultaneously meeting their respective specifications. The proposed procedure is illustrated with three experiments from the literature.
Valence band energy in confined Si1−xGex (0.28

Using internal photoemission of electrons we monitored the composition dependent shift of the valence band in Si1−xGex (0.28<x<0.93) films confined between two layers of SiO2, as prepared by the condensation technique. As referenced to the bottom of the oxide conduction band, the increase in Ge content results in upshift in the valence band top with respect to that of pure silicon. This shift occurs nearly linearly with Ge concentration x and can be approximated as δEv(eV)=(0.55±0.05)x.
Boron Tolerance of Squash, Melon, Cucumber, and Corn1

Squash (Cucurbita pepo), cucumber (Cucumis sativus), muskmelon (Cucumis melo) and corn (Zea mays.) plants were grown in sand culture with B as the variable. The plants were harvested when they showed a definite gradation of B toxicity symptoms from severe to none. Mature blades were analyzed for total B. A significant decrease in top growth took place in corn and cucumber for culture solutions with B concentrations greater than 2 ppm; in squash and muskmelon, greater than 4 ppm. A 50% decrease in top growth took place in solutions with 6, 12, 12 and 16 ppm of B for cucumber, squash, muskmelon and corn, respectively, with cucumber the most sensitive and corn the least sensitive to B supply. The critical concentrations for B toxicity in mature blades at the onset of decreased top growth are 100, 400, 800 and 1,000 ppm, dry basis, for corn, cucumber, muskmelon and squash, respectively.
Rank-GCN for Robust Action Recognition

We present a robust skeleton-based action recognition method with graph convolutional network (GCN) that uses the new adjacency matrix, called Rank-GCN. In Rank-GCN, the biggest change from previous approaches is how the adjacency matrix is generated to accumulate features from neighboring nodes by re-defining “adjacency. ” The new adjacency matrix, which we call the rank adjacency matrix, is generated by ranking all the nodes according to metrics including the Euclidean distance from the nodes of interest, whereas the previous GCNs methods used only 1-hop neighboring nodes to construct adjacency. By adopting the rank adjacency matrix, we find not only performance improvements but also robustness against swapping, location shifting and dropping of certain nodes. The fact that the human-made rank adjacency matrix wins against the deep-learning-based matrix, implies that there are still some parts that need touch of humans. We expect our Rank-GCN can make performance improvements especially when the predicted human joints are less accurate and unstable.
Optical Redox Imaging of Ex Vivo Hippocampal Tissue Reveals Age-Dependent Alterations in the 5XFAD Mouse Model of Alzheimer’s Disease

A substantial decline in nicotinamide adenine dinucleotide (NAD) has been reported in brain tissue homogenates or neurons isolated from Alzheimer’s disease (AD) models. NAD, together with flavin adenine dinucleotide (FAD), critically supports energy metabolism and maintains mitochondrial redox homeostasis. Optical redox imaging (ORI) of the intrinsic fluorescence of reduced NAD (NADH) and oxidized FAD yields cellular redox and metabolic information and provides biomarkers for a variety of pathological conditions. However, its utility in AD has not been characterized at the tissue level. We performed ex vivo ORI of freshly dissected hippocampi from a well-characterized AD mouse model with five familial Alzheimer’s disease mutations (5XFAD) and wild type (WT) control littermates at various ages. We found (1) a significant increase in the redox ratio with age in the hippocampi of both the WT control and the 5XFAD model, with a more prominent redox shift in the AD hippocampi; (2) a higher NADH in the 5XFAD versus WT hippocampi at the pre-symptomatic age of 2 months; and (3) a negative correlation between NADH and Aβ42 level, a positive correlation between Fp and Aβ42 level, and a positive correlation between redox ratio and Aβ42 level in the AD hippocampi. These findings suggest that the ORI can be further optimized to conveniently study the metabolism of freshly dissected brain tissues in animal models and identify early AD biomarkers.
Demonstrate and evaluate lab activity about antimicrobial sensitivity

Lab activities in primary and secondary education are essential to promote students' scientific skills. In this article, we propose a lab activity where different antimicrobial agents are used to the microorganisms' susceptibility to them. Moreover, we produce a rubric, a scoring tool, to quantify students' replies and evaluate the whole activity. We concluded that using the scoring rubric, we have been able to evaluate students' replies and students' benefit.
Factors Affecting Learners’ Adoption of an Educational Metaverse Platform: An Empirical Study Based on an Extended UTAUT Model

This study examined the factors affecting learners’ adoption of an educational metaverse platform using an extended UTAUT (unified theory of acceptance and use of technology) model and incorporating perceived risk. Data were derived from a survey of 495 respondents from China and analyzed using structural equation modeling. The results revealed that (i) performance expectancy, effort expectancy, social influence, and facilitating conditions had significantly positive effects on learners’ satisfaction with the Eduverse; (ii) learners’ satisfaction had a positive effect on their continued usage intention; (iii) learners’ intention to use the Eduverse was reduced after they perceived risks. Our study provided empirical evidence of the validity of the UTAUT model in explaining learners’ adoption of the Eduverse. Our findings have significant practical implications for enterprises, educational institutions, and governments.
Analysis of Elite Sports Development Patterns through Sports Education

Elite sports are one of the keys to developing sports as enshrined in the Sports Law. In this law, sport is positioned as an instrument for national development. However, it is more dominated by the sports development paradigm, even though Indonesia's sports achievements have significantly developed in several international sports competitions. It is contributed through the coaching factor through sports training education from an early age under the auspices of sports education at the school level. However, the impact has not been able to influence other aspects of national development more broadly. It has become a gap between what is stated in the Sports Law and practice in the field. Therefore, this research aims to examine the implementation of elite sports policies as part of the Sports Law to realize development through sports. To achieve the objectives of this study, we designed eleven semi-structured interview indicators based on the essence of the elite sports section of the sports law. This study found that the implementation of applied elite sports policies was close to conformity with the sports law. However, on the other hand, there are still shortcomings and inequities in several aspects. Inequality in several aspects occurs due to a lack of understanding of the importance of the role of sport as an instrument of national development. Therefore, the government as a stakeholder must immediately act decisively to deal with the problems. The implications of this research can be considered for evaluating the implementation of policies that will be implemented in the future/
Identification of Gender- and Subtype-Specific Gene Expression Associated with Patient Survival in Low-Grade and Anaplastic Glioma in Connection with Steroid Signaling

Simple Summary Gliomas are primary brain tumors that are initially slow growing but progress to be more aggressive and, ultimately, fatal within a few years. They are more common in men than in women, suggesting a protective role for female hormones. By analyzing patient data collected in the public TGCA-LGG database, we have demonstrated a link between the expression level of key steroid biosynthesis enzymes or hormone receptors with patient survival, in ways that are dependent on gender and molecular subtype. We also determined the genes which expression associated with these actors of steroid signaling and the functions they perform, to decipher the mechanisms underlying gender-dependent differences. Together, these results establish, for the first time, the involvement of hormones in low-grade and anaplastic gliomas and provide clues for refining their classification and, thus, facilitating more personalized management of patients. Abstract Low-grade gliomas are rare primary brain tumors, which fatally evolve to anaplastic gliomas. The current treatment combines surgery, chemotherapy, and radiotherapy. If gender differences in the natural history of the disease were widely described, their underlying mechanisms remain to be determined for the identification of reliable markers of disease progression. We mined the transcriptomic and clinical data from the TCGA-LGG and CGGA databases to identify male-over-female differentially expressed genes and selected those associated with patient survival using univariate analysis, depending on molecular characteristics (IDH wild-type/mutated; 1p/19q codeleted/not) and grade. Then, the link between the expression levels (low or high) of the steroid biosynthesis enzyme or receptors of interest and survival was studied using the log-rank test. Finally, a functional analysis of gender-specific correlated genes was performed. HOX-related genes appeared to be differentially expressed between males and females in both grades, suggesting that a glioma could originate in perturbation of developmental signals. Moreover, aromatase, androgen, and estrogen receptor expressions were associated with patient survival and were mainly related to angiogenesis or immune response. Therefore, consideration of the tight control of steroid hormone production and signaling seems crucial for the understanding of glioma pathogenesis and emergence of future targeted therapies.
Biodegradable Polymers-Based Smart Nanocrystals for Loxoprofen Delivery with Enhanced Solubility: Design, Fabrication and Physical Characterizations

Nanocrystals are carrier-free, submicron-sized, colloidal drug delivery systems with particle sizes in the mean nanometer range. Nanocrystals have high bioavailability and fast absorption because of their high dissolution velocity and enhanced adhesiveness to cell membranes. Loxoprofen, a nonsteroidal anti-inflammatory drug belonging to the Biopharmaceutical Classification System (BCS) II drug class, was selected as the model drug. The aim of this study was to formulate nanocrystals of loxoprofen. A total of 12 formulations (F1 to F12) were prepared. An antisolvent technique was used to determine the effects of various stabilizers and processing conditions on the optimization of formulations. The various stabilizers used were hydroxypropyl methylcellulose (0.5%), polyvinylpyrrolidone (0.5%), and sodium lauryl sulfate (0.1%). The various characterizations conducted for this research included stability studies at 25 °C and 4 °C, scanning electron microscopy, transmission electron microscopy (TEM), X-ray powder diffraction (XRPD), differential scanning calorimetry (DSC), zeta potentials, polydispersity indexes, and dissolution studies. F10 was the optimized formulation that showed stability at room temperature, as well as at a refrigerated temperature, for 30 days. A high dissolution rate (100% within the first 10 min) was shown by comparative dissolution studies of nano-suspensions with the micro-suspension and raw loxoprofen. F10 formulation had a non-porous and crystalline morphology on evaluation by TEM and XRPD, respectively, and the average particle size was 300 ± 0.3 nm as confirmed by TEM. DSC recorded a reduction in the melting point (180 °C processed and 200 °C unprocessed melting points). The dissolution rate and solubility of the formulated loxoprofen nanocrystals were significantly enhanced. It can be concluded that selecting suitable stabilizers (i.e., polymers and surfactants) can produce stable nanocrystals, and this can potentially lead to a scaling up of the process for commercialization.
Distributed Framework Matching

This article studies the problem of distributed framework matching (FM), which originates from the assignment task in multirobot coordination and the matching task in pattern recognition. The objective of distributed FM is to distributively seek a correspondence which minimizes some metrics describing the disagreement between two frameworks (i.e., graphs and their embeddings). In view of the type of the underlying graph in the framework, two formulations, undirected framework matching (UFM) and directed framework matching (DFM), and their convex relaxations, relaxed UFM (RUFM), and relaxed DFM (RDFM), are presented. UFM is converted into a graph matching (GM) problem with the adjacency matrix being replaced by a matrix constructed from the undirected framework under certain graphical conditions, and can be solved distributively. Sufficient conditions for the equivalence between UFM and RUFM, and the perturbation admitting exact recovery of correspondence are established. On the other hand, DFM embeds the configuration of the directed framework via another type of matrix, whose computation is distributed, and can deal with the case of two frameworks with different sizes of node sets. A distributed optimization algorithm for solving RDFM is proposed and its convergence results are established which allows DFM to be solved in a fully distributed manner. Simulation examples on both synthetic data and real world datasets demonstrate the applicability and efficacy of our theoretical results in formation control and object matching problems.