Miracle-dz/UniFAR-FTUs huggingface dataset:
EVALUATION OF PATTERN OF PRESCRIPTION FOR ASTHMA : A MULTICENTER PRO- SPECTIVE OBSERVATIONAL STUDY FROM PAKISTAN

Asthma is a common allergic reaction and a number of drugs are available. This study was conducted to evaluate the prescribing pattern and usage of anti-asthma medications at different medical setups in Hyderabad. The study was conducted from August 2017 to July 2018. A total of 1000 patients from different medical setups diagnosed with asthma were recruited. A pre-designed, detailed questionnaire was used for their data collection. Their prescriptions were collected to evaluate and compare them with the standard guidelines of GINA. The prescribing pattern of antiasthma was assessed regarding the type of therapy, route of administration, single or multiple treatments, class of drugs, doses, and frequency. Demographically, more asthmatics were found to be males (i.e 73.5%), 46.5% were adults, 58.3% from urban areas and 51.68% of the sample had outdoor jobs, while 41.5% of illiterate people were found to be asthmatic. A major type of asthma was persistent at 65.3%. A maximum prescription of 85.5% for asthma management was combined therapy. From long-term use, the monotherapy ICS group was reported in a considerable number (44.13%) (i.e. Beclomethasone in 70% and Budesonide in 7%). Prescriptions of multiple routes of administration were comparatively higher (79.5%). The study concluded that male, young, urban, illiterate and people with outdoor jobs were more likely to get asthma. Preventer drugs were prescribed more in combined therapy, in which Fluticasone + Salmeterol were preferred. The oral inhalational route was selected more. The treatment pattern was consistent with standard guidelines.
Properties of linear entropy of the atom in a tripartite cavity-optomechanical system

We investigate the dynamics of linear entropy of an atom in a tripartite cavity-optomechanical system consisting of a two-level atom in a high-finesse optical cavity with a vibrating mirror at one end. The influence of atomic coherence on the time evolution of linear entropy is examined. It is shown that a Greenberger–Horne–Zeilinger like state can be generated. Moreover, it is found that the entanglement between the atom and the subsystem of field and mirror can be controlled by atomic coherence and the parameters of optomechanical coupling coefficient and atom-field coupling strength.
Mg-Ti-H thin films for smart solar collectors

Mg–Ti–H thin films are found to have very attractive optical properties: they absorb 87% of the solar radiation in the hydrogenated state and only 32% in the metallic state. Furthermore, in the absorbing state Mg–Ti–H has a low emissivity; at 400K only 10% of blackbody radiation is emitted. The transition between both optical states is fast, robust, and reversible. The sum of these properties highlights the applicability of such materials as switchable smart coatings in solar collectors.
Motor Imagery Classification Based on EEG Sensing with Visual and Vibrotactile Guidance

Motor imagery (MI) is a technique of imagining the performance of a motor task without actually using the muscles. When employed in a brain–computer interface (BCI) supported by electroencephalographic (EEG) sensors, it can be used as a successful method of human–computer interaction. In this paper, the performance of six different classifiers, namely linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and three classifiers from the family of convolutional neural networks (CNN), is evaluated using EEG MI datasets. The study investigates the effectiveness of these classifiers on MI, guided by a static visual cue, dynamic visual guidance, and a combination of dynamic visual and vibrotactile (somatosensory) guidance. The effect of filtering passband during data preprocessing was also investigated. The results show that the ResNet-based CNN significantly outperforms the competing classifiers on both vibrotactile and visually guided data when detecting different directions of MI. Preprocessing the data using low-frequency signal features proves to be a better solution to achieve higher classification accuracy. It has also been shown that vibrotactile guidance has a significant impact on classification accuracy, with the associated improvement particularly evident for architecturally simpler classifiers. These findings have important implications for the development of EEG-based BCIs, as they provide valuable insight into the suitability of different classifiers for different contexts of use.
Identification of AP2/ERF gene family of Salicaceae and their response to salt stress, abscisic acid, and gibberellic acid in Populus euphratica seeds

Populus euphratica belongs to Salicaceae family and grows in extreme desert environments. At present, the identification of the AP2/ERF gene family of transcription factors in Salicaceae is rare, and the role of the AP2/ERF gene family in P. euphratica under salt stress and exogenous hormones has not been reported. In this study, 197, 210, 231, 192, and 147 AP2/ERF genes were identified in P. euphratica , Populus trichocarpa , Populus deltoides , Salix sinopurpurea , and Arabidopsis thaliana , respectively. The 197 AP2/ERF gene family members of P. euphratica were divided into five subfamilies, namely, AP2 (35), RAV (5), ERF (96), DREB (65), and Soloist (1), by sequence alignment and phylogenetic analysis. In addition, these genes were scattered across 19 chromosomes. The detection of 10 motifs in the P. euphratica AP2/ERF gene family revealed that motif-8 and motif-9 only appeared in the ERF subfamily and DREB subfamily, respectively. Transcriptome data showed that PeAP2/ERF genes had different expression patterns under salt stress, abscisic acid (ABA) and gibberellic acid (GA 3 ) treatments, suggesting that the genes PeERF002 , PeERF037 , PeERF082 , PeERF090 , and PeAP2-14 may play important roles under salt stress and exogenous hormone treatments. This study provides a reference for the functional study of the PeAP2/ERF gene, and it also lays a foundation for the breeding strategy to improve the salt tolerance of P. euphratica
Curriculum Management’s Implementation of the E-Sistem Penilaian Purata Nilaian Gred from The Perspective of Teachers of Selected MRSM in Selangor

The objective of this study is to examine teachers’ perception of the e -SPPNG system in terms of perceived usefulness, ease of use, and usage of the actual system for strategic curriculum management in the selected Maktab Rendah Sains Mara (MRSM) in Selangor. This study was designed quantitatively where the data was collected from a survey involving one-hundred-seventy-six (176) MRSM teachers. The respondents were chosen randomly and conveniently from two MRSMs in Selangor and have experience in using the SPPNG system. Based on descriptive analysis, it was found that most of the teachers had a positive perception toward the implementation of the e-SPPNG system in curriculum management. This shows that they believed the e-SPPNG system is useful and easy to use. Overall, the results of this research were significant in illustrating the usage of the e-SPPNG among MRSM teachers.
Error estimation and hp–adaptive mesh refinement for discontinuous Galerkin methods

We present adjoint-based techniques to estimate the error of a numerical flow solution with respect to a given target quantity like an aerodynamic force coefficient. This estimate can be used to judge the overall accuracy of a computation, to enhance the computed value of the target quantity and to drive a solution-adaptive mesh refinement process. The error estimation procedure is extended to multiple target quantities. The discontinuous ansatz spaces of the DG discretization allow for both element subdivision as well as a local increase of polynomial degrees for increasing the flow resolution. Targeting optimal rates of convergence, a smoothness estimation based on a truncated Legendre series expansion of the solution is employed to locally select the more promising strategy. Numerical examples for inviscid, laminar viscous and turbulent viscous flows demonstrate the efficiency of the proposed algorithms.
A Novel Oncogenic Role of FDX1 in Human Melanoma Related to PD-L1 Immune Checkpoint

The aim of this study was to evaluate the association between Ferredoxin 1 (FDX1) expression and the prognostic survival of tumor patients and predict the efficacy of immunotherapy response to antitumor drug sensitivity. FDX1 plays an oncogenic role in thirty-three types of tumors, based on TCGA and GEO databases, and further experimental validation in vitro was provided through multiple cell lines. FDX1 was expressed highly in multiple types of cancer and differently linked to the survival prognosis of tumorous patients. A high phosphorylation level was correlated with the FDX1 site of S177 in lung cancer. FDX1 exhibited a significant association with infiltrated cancer-associated fibroblasts and CD8+ T cells. Moreover, FDX1 demonstrated correlations with immune and molecular subtypes, as well as functional enrichments in GO/KEGG pathways. Additionally, FDX1 displayed relationships with the tumor mutational burden (TMB), microsatellite instability (MSI), DNA methylation, and RNA and DNA synthesis (RNAss/DNAss) within the tumor microenvironment. Notably, FDX1 exhibited a strong connection with immune checkpoint genes in the co-expression network. The validity of these findings was further confirmed through Western blotting, RT-qPCR, and flow cytometry experiments conducted on WM115 and A375 tumor cells. Elevated FDX1 expression has been linked to the enhanced effectiveness of PD-L1 blockade immunotherapy in melanoma, as observed in the GSE22155 and GSE172320 cohorts. Autodocking simulations have suggested that FDX1 may influence drug resistance by affecting the binding sites of antitumor drugs. Collectively, these findings propose that FDX1 could serve as a novel and valuable biomarker and represent an immunotherapeutic target for augmenting immune responses in various human cancers when used in combination with immune checkpoint inhibitors.
Reconstructed Graph Constrained Auto-Encoders for Multi-View Representation Learning

The application of Auto-Encoder (AE) to multi-view representation learning has gained traction due to advancements in deep learning. While some current AE-based multi-view representation learning algorithms incorporate the geometric structure of the input data into their feature representation learning process, their use of a shallow structured graph regularization term can be restrictive when used in conjunction with deep models. Furthermore, current multi-view representation learning algorithms do not fully utilize the diversity and consistency presented in different views, leading to a reduction in the efficacy of feature learning. This paper introduces a novel approach, reconstructed graph constrained auto-encoders (RGCAE), for multi-view representation learning. Unlike existing methods, our approach incorporates deep adaptive graph regularization based on multi-layer perceptron to ensure the preservation of the geometric similarity graph, which is constructed based on the local invariance principle. By decoupling the feature representation learning from the preservation of the geometric structure among different views, our approach can better leverage the diversity presented in multi-view data. We obtain view-specific representations that preserve the geometric structure and then combine them by averaging to obtain a common representation. To ensure the consistency of the multi-view data, we minimize the loss between the view-specific and common representations. Consequently, our RGCAE approach can maintain the geometric structure of multi-view data and is better suited for integration with deep models. Extensive experiments on six datasets demonstrate that RGCAE obtained promising performance, compared with the state-of-the-art methods.
Experimental Study on the Cutting Process of Single Triticale Straws

This paper presents experimental research on cutting a single stalk of triticale straw for the production of biofuel in the process of its compaction using the piston technique. In the first stage of the experimental study of cutting single triticale straws, the variable parameters were the moisture contents of the stem equal to 10% and 40%, the offset between the blade and the counter-blade g, and the linear velocity of the knife blade V. The blade angle and rake angle were equal to α = 0° and β = 0°. In the second stage, the variables, including the blade angle values α = 0°, 15°, 30°, and 45° and the rake angle values β = 5°, 15°, and 30°, were introduced. Taking into account the analysis of the distribution of forces on the knife edge leading to the determination of the force quotients Fc″/Fc and Fw/Fc, and on the basis of the optimization performed and the adopted optimization criteria, the optimal knife edge angle α can be determined (at values g = 0.1 mm and V = 8 mm/s) at α ≅ 0° and the angle of attack β within the range of 5–26°. What the value will be in this range depends on the value of the weight adopted in the optimization. The choice of their values may be decided by the constructor of the cutting device.
Security Attack on Remote Sensing Equipment: PoIs Recognition Based on HW with Bi-LSTM Attention

Deep learning is an influencer in hardware security applications, which grows up to be an essential tool in hardware security, threats the confidentiality, integrity, and availability of remote sensing equipment. Comparing to traditional physical attack, not only it can greatly reduce the workload of manual selection of POIs (Points of Interests) in security attack and Trojan backdoor, but also replenishes the toolbox for attacking. On account of minute changes between network structure model and hyperparameters constantly affecting the training and attacking effect, literally, deep learning serves as a tool but not key role in hardware security attack, which means it cannot completely replace template attack and other traditional energy attack methods. In this study, we present a method using Bi-LSTM Attention mechanism to focus on the POIs related to Hamming Weight at the last round s-box output. Firstly, it can increase attacking effect and decrease guessing entropy, where attacking FPGA data demonstrates the efficiency of attacking. Secondly, it is different from the traditional template attack and deep learning attack without preprocessing subjecting to raw traces but provides attentional POIs which is the same with artificial selection. Finally, it provides a solution for attacking encrypting equipment running in parallel.
An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application

Abstract Epilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30–70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone. This retrospective study aims to improve surgical outcome through in silico testing of surgical hypotheses through a personalized computational neurosurgery model created from individualized patient’s magnetoencephalography recording and MRI. The framework assesses the extent of the epileptic network and evaluates underlying spike dynamics, resulting in identification of one single brain volume as a candidate for resection. Dynamic-locked networks were utilized for virtual cortical resection. This in silico protocol was tested in a cohort of 24 paediatric patients with focal drug-resistant epilepsy who underwent epilepsy surgery. Of 24 patients who were included in the analysis, 79% (19 of 24) of the models agreed with the patient's clinical surgery outcome and 21% (5 of 24) were considered as model failures (accuracy 0.79, sensitivity 0.77, specificity 0.82). Patients with unsuccessful surgery outcome typically showed a model cluster outside of the resected cavity, while those with successful surgery showed the cluster model within the cavity. Two of the model failures showed the cluster in the vicinity of the resected tissue and either a functional disconnection or lack of precision of the magnetoencephalography–MRI overlapping could explain the results. Two other cases were seizure free for 1 year but developed late recurrence. This is the first study that provides in silico personalized protocol for epilepsy surgery planning using magnetoencephalography spike network analysis. This model could provide complementary information to the traditional pre-surgical assessment methods and increase the proportion of patients achieving seizure-free outcome from surgery.
Eye Movement Traces of Linguistic Knowledge in Native and Non-Native Reading

Abstract The detailed study of eye movements in reading has shed considerable light into how language processing unfolds in real time. Yet eye movements in reading remain inadequately studied in non-native (L2) readers, even though much of the world’s population is multilingual. Here we present a detailed analysis of the quantitative functional influences of word length, frequency, and predictability on eye movement measures in reading in a large, linguistically diverse sample of non-native English readers. We find many similar qualitative effects as in L1 readers, but crucially also a proficiency-sensitive “lexicon-context tradeoff”. The most proficient L2 readers’ eye movements approach an L1 pattern, but as L2 proficiency diminishes, readers’ eye movements become less sensitive to a word’s predictability in context and more sensitive to word frequency, which is context-invariant. This tradeoff supports a rational, experience-dependent account of how context-driven expectations are deployed in L2 language processing.
A Study on the Coherence of the Precipitation Simulated by the WRF Model during a Changma Period in 2005

The present study uses the GOES IR brightness temperature to examine the temporal and spatial variability of cloud activity over the region , and analyzes the coherence of eastern Asian summer season rainfall in Weather Research and Forecast (WRF) model. Time-longitude diagram of the time period from June to July 2005 shows a signal of eastward propagation in the WRF model and convective index derived from GOES IR data. The rain streaks in time-latitude diagram reveal coherence during the experiment period. Diurnal and synoptic scales are evident in the power spectrum of the time series of convective index and WRF rainfall. The diurnal cycle of early morning rainfall in the WRF model agrees with GOES IR data in the Korean Peninsula, but the afternoon convection observed by satellite observation in China is not consistent with the WRF rainfall which is represented at the dawn. Although there are errors in strength and timing of convection, the model predicts a coherent tendency of rainfall occurrence during summer season.
Valorization of Peanut Skin as Agricultural Waste Using Various Extraction Methods: A Review

Peanuts (Arachis hypogea) can be made into various products, from oil to butter to roasted snack peanuts and candies, all from the kernels. However, the skin is usually thrown away, used as cheap animal feed, or as one of the ingredients in plant fertilizer due to its little value on the market. For the past ten years, studies have been conducted to determine the full extent of the skin’s bioactive substance repertoire and its powerful antioxidant potential. Alternatively, researchers reported that peanut skin could be used and be profitable in a less-intensive extraction technique. Therefore, this review explores the conventional and green extraction of peanut oil, peanut production, peanut physicochemical characteristics, antioxidant activity, and the prospects of valorization of peanut skin. The significance of the valorization of peanut skin is that it contains high antioxidant capacity, catechin, epicatechin resveratrol, and procyanidins, which are also advantageous. It could be exploited in sustainable extraction, notably in the pharmaceutical industries.
Cardioprotective Properties of Kaempferol: A Review

Cardiac diseases, such as myocardial infarction and heart failure, have become a major clinical problem globally. The accumulating data demonstrate that bioactive compounds with antioxidant and anti-inflammatory properties have favorable effects on clinical problems. Kaempferol is a flavonoid found in various plants; it has demonstrated cardioprotective properties in numerous cardiac injury models. This review aims to collate updated information regarding the effects of kaempferol on cardiac injury. Kaempferol improves cardiac function by alleviating myocardial apoptosis, fibrosis, oxidative stress, and inflammation while preserving mitochondrial function and calcium homeostasis. However, the mechanisms of action of its cardioprotective properties remain unclear; therefore, elucidating its action could provide insight into directions for future studies.
A New Experimental Method for the Nonlinear Modal Parameter Identification of a Pressurized Water Reactor Fuel Assembly

Establishing a dynamic model that accurately describes a realistic pressurized water reactor (PWR) fuel assembly is crucial to precisely evaluate the mechanical properties of the fuel assembly in seismic or loss of coolant accidents (LOCAs). The pluck test combined with the logarithmic decrement method has been widely applied in previous studies to extract fundamental modal parameters to calibrate dynamic models. However, most previous investigations focused on the first cycle of free vibration, which is strongly affected by stiction, baseline shift, drop conditions, and high-order mode interference, leading to inaccurate results. Moreover, these traditional methods cannot be used to extract high-order modal parameters. In this work, a novel experimental method for identifying the nonlinear modal parameters of a PWR fuel assembly is proposed. First, two algorithms are adopted to decompose the free vibration. Second, the local linearized modal parameters are extracted by a single-degree-of-freedom fitting method with a sliding window. Finally, these local linearized modal parameters are summed to obtain the nonlinear relationships between the modal parameters and amplitude. The new method makes more effective use of experimental data, obtains more accurate modal parameters than the logarithmic decrement method, and is capable of extracting high-order modal parameters. In the end, the test results are fitted by a fractional polynomial, which is of great value for numerical simulations.
Advances in Computational Techniques for Bio-Inspired Cellular Materials in the Field of Biomechanics: Current Trends and Prospects

Cellular materials have a wide range of applications, including structural optimization and biomedical applications. Due to their porous topology, which promotes cell adhesion and proliferation, cellular materials are particularly suited for tissue engineering and the development of new structural solutions for biomechanical applications. Furthermore, cellular materials can be effective in adjusting mechanical properties, which is especially important in the design of implants where low stiffness and high strength are required to avoid stress shielding and promote bone growth. The mechanical response of such scaffolds can be improved further by employing functional gradients of the scaffold’s porosity and other approaches, including traditional structural optimization frameworks; modified algorithms; bio-inspired phenomena; and artificial intelligence via machine learning (or deep learning). Multiscale tools are also useful in the topological design of said materials. This paper provides a state-of-the-art review of the aforementioned techniques, aiming to identify current and future trends in orthopedic biomechanics research, specifically implant and scaffold design.