Miracle-dz/UniFAR-FTUs huggingface dataset:
Event‐triggered control for a saturated nonlinear system with prescribed performance and finite‐time convergence

This paper presents an event‐triggered controller for a class of saturated uncertain nonlinear systems. We develop a performance constrained finite‐time controller to guarantee that the tracking error converges at a prescribed convergence rate and does not exceed the given maximum overshoot. A smooth function is designed to replace the absolute and signum operators in existing finite‐time controllers that lead to nondifferentiable virtual controls. Then, a novel backstepping design consisting of an adaptive law and an auxiliary system governed by a smooth switching function is developed to compensate for the uncertainty, the triggering event threshold, and the saturation constraint. Theoretical analysis demonstrates that under the proposed controller, all closed‐loop signals are bounded and the Zeno behavior is avoided. Furthermore, the tracking error will converge toward a residual set in finite time, and the prescribed transient and steady tracking performance bounds are never violated. Results from a comparative simulation study illustrate the effectiveness and advantages of the proposed method.
Positional Advantages of Higher Education Degrees and Occupational Upgrading in West Germany

Abstract This study examines how the association between higher education and the socioeconomic status is affected by positional educational advantages in West Germany from 1976 to 2015. Positional educational advantages account for the declining exclusiveness of higher education degrees in the course of educational expansion, which is accompanied by occupational upgrading. The empirical results indicate that the decreasing positional educational advantages affect higher education degrees differently than lower educational levels. Graduates from higher education enhance their advantage over degree holders below higher education with regard to socioeconomic status. Thereby, university degrees in particular gain in importance.
COMPUTATION OF ASYMMETRIC PERIODIC CONFIGURATIONS IN THE THREE-BODY PROBLEM

In this paper, we present the algorithm and the methodology used for locating periodic configurations for the planetary problem of three bodies. The repeated relative configurations of the planets can be associated with periodic orbits in an appropriate rotating frame of reference. In this frame, the general planar three-body problem, which is the basic model, is reduced to three degrees of freedom. Up to recent years, only symmetric periodic orbits have been presented in the literature. Recently, however, asymmetric periodic orbits have been computed. In the present work we describe the algorithm used for computing asymmetric configurations, and discuss the efficiency and the implementation of the method. An example of a 2/1 resonant family of asymmetric periodic orbits is given as an application of the algorithm.
Commissioning the ELENA Beam Diagnostics Systems at CERN

The Extra Low ENergy Antiproton ring (ELENA) at CERN entered the commissioning phase in November 2016 using Hions and antiprotons to setup the machine at the different energy plateaus. The low intensities and energy of the ELENA beam generate very weak signals making beam diagnostics very challenging. With a circulating beam current of less than 1 μA and an energy where the beam annihilates in less than a few microns of matter, special care was taken during the design phase to ensure an optimal performance of these measurement devices once installed on the ring and transfer lines. A year on we present the performance of the various devices that have been deployed to measure the beam parameters from the extraction point of the Antiproton Decelerator (AD), through the ELENA ring and in the experimental lines.
EFL TEACHERS' CPD EXPERIENCES: PERSPECTIVES FROM OMAN.

This article reports the findings of a study that explored Omani in-service TESOL teachers' continuous professional development (CPD) experiences. Data were collected using questionnaires, training room observations, semi-structured and focus group interviews with TESOL teachers in Oman. The findings indicated that participants have experienced different types of CPD activities which were mostly offered to them through the Ministry of Education in structured ways (e.g. INSET courses). However, the findings suggested conflicting views among participants as to the extent to which the different types of CPD activities/events available to teachers are seen to meet their needs. The article explores the implications of the findings for teacher education and the development of TESOL teachers in Oman. The qualitative data shows that participants experienced many CPD activities carried out at schools which can fit into teachers‟ daily work and timetables. These activities are usually organised by SETs through preparing a PD plan that includes a number of activities to be done throughout the school year. Laila clarifies why they do these PD plans and activities “look miss, we SETS each year do a professional development plan at school and we try to do many activities to help teachers with their professional development…. ” Examples of these activities as shown by the data are: peer observation, model lessons, discussion sessions, team teaching, English open days and workshops carried out by different staff members. activities/events slotted Amna the MOE in Oman for English teachers. However, the findings indicated that out of all these initiatives, structured formal in-service training (INSET) courses seem to be the most dominant in the CPD system
Fractionning Reaction Time to probe the validity of the Drift Diffusion Model parameters

We designed a two-alternative forced choice experiment in which the by-trial reaction times could be fraction-ated into pre-motor (PMT) and motor times (MT), based on the onset of muscular activity from the electromyo-graphic (EMG) recordings (Figure 2c). We then compared this empirical decomposition to the decomposition performed by the Drift Diffusion Model (DDM, Ratcliff, 1978). Using these two decompositions, we show that the non-decision time parameter of the DDM is highly correlated with the Motor Time that was recorded when the participants stressed Accuracy over Speed. Furthermore, we show that fitting the by-trial Pre-Motor Time with the DDM mainly modulated the non-decision time parameters. The relative contribution of decision time and motor time components in the overall Reaction Time (based on speed versus accuracy instructions) was observed. Correlation analyses between speed instructions on empirical data suggest that their could be a change in the architecture of cognitive processes rather than a quantitative change between Speed-Accuracy tradeoff (SAT) levels.
GeNetClassifier: Classify diseases and build associated gene networks using gene expression profiles

3 Main function of the package: geNetClassifier() 5 3.1 Loading the package and data . 6 3.2 Run geNetClassifier() . 7 3.3 Overview of the data returned by geNetClassifier() . 9 3.4 Return I: Genes ranking . 10 3.4. 1 Significant genes . 14 3.5 Return II: Classifier . 15 3.5. 1 Gene selection procedure . 16 3.5. 2 Estimation of performance and generalization error procedure . 18 3.6 Return III: Gene networks . 21
Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study

Background Patients with non-small cell lung cancer (NSCLC) and patients with NSCLC combined with chronic obstructive pulmonary disease (COPD) have similar physiological conditions in early stages, and the latter have shorter survival times and higher mortality rates. The purpose of this study was to develop and compare machine learning models to identify future diagnoses of COPD combined with NSCLC patients based on the patient’s disease and routine clinical data. Methods Data were obtained from 237 patients with COPD combined with NSCLC as well as NSCLC admitted to Ningxia Hui Autonomous Region People’s Hospital from October 2013 to July 2022. Six machine learning algorithms (K-nearest neighbor, logistic regression, eXtreme gradient boosting, support vector machine, naïve Bayes, and artificial neural network) were used to develop prediction models for NSCLC combined with COPD. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, Mathews correlation coefficient (MCC), Kappa, area under the receiver operating characteristic curve (AUROC)and area under the precision-recall curve (AUPRC) were used as performance indicators to evaluate the performance of the models. Results 135 patients with NSCLC combined with COPD, 102 patients with NSCLC were included in the study. The results showed that pulmonary function and emphysema were important risk factors and that the support vector machine-based identification model showed optimal performance with accuracy:0.946, recall:0.940, specificity:0.955, precision:0.972, npv:0.920, F1 score:0.954, MCC:0.893, Kappa:0.888, AUROC:0.975, AUPRC:0.987. Conclusion The use of machine learning tools combining clinical symptoms and routine examination data features is suitable for identifying the risk of concurrent NSCLC in COPD patients.
Review: Kirkwood–Riseman Model in Non-Dilute Polymeric Fluids

In two prior articles, I demonstrated from extensive simulational studies by myself and others that the Rouse model of polymer dynamics is invalid in polymer melts and in dilute solution. However, the Rouse model is the foundational basis for most modern theories of polymeric fluid dynamics, such as reptation/scaling models. One therefore rationally asks whether there is a replacement. There is, namely by extending the Kirkwood–Riseman model. Here, I present a comprehensive review of one such set of extensions, namely the hydrodynamic scaling model. This model assumes that polymer dynamics in dilute and concentrated solution is dominated by solvent-mediated hydrodynamic interactions; chain crossing constraints are taken to create only secondary corrections. Many other models assume, contrariwise, that in concentrated solutions, the chain crossing constraints dominate the dynamics. An extended Kirkwood–Riseman model incorporating interchain hydrodynamic interactions is developed. It yields pseudovirial series for the concentration and molecular weight dependencies of the self-diffusion coefficient Ds and the low-shear viscosity η. To extrapolate to large concentrations, rationales based on self-similarity and on the Altenberger–Dahler positive-function renormalization group are presented. The rationales correctly predict how Ds and η depend on polymer concentration and molecular weight. The renormalization group approach leads to a two-parameter ansatz that correctly predicts the functional forms of the frequency dependencies of the storage and loss moduli. A short description is given of each of the papers that led to the hydrodynamic scaling model. Experiments supporting the aspects of the model are noted.
Graphene-based positron charge sensor

We utilize a graphene field-effect transistor to measure back-gate charging by positrons. The device consists of an exfoliated graphene flake transferred onto hexagonal Boron Nitride, placed on a 1 cm2 substrate of 500 μm thick conducting p-Si capped by 285 nm-thick SiO2. It is placed at close proximity to a 25 μCi 22Na positron source emitting a constant flux of positrons, which during the measurement annihilate within the back-gate. We demonstrate that when the back-gate is allowed to float, the charging current of ≈20 fA causes the buildup of positive charge which capacitively couples to the graphene device and is detected as a variation in the two-terminal conductance. Furthermore, a prolonged exposure to positrons causes a shift in the graphene transport characteristics, associated with local charges at the immediate environment of the graphene flake. Our results demonstrate the utility of two-dimensional layered materials as probes for charging dynamics of positrons in solids.We utilize a graphene field-effect transistor to measure back-gate charging by positrons. The device consists of an exfoliated graphene flake transferred onto hexagonal Boron Nitride, placed on a 1 cm2 substrate of 500 μm thick conducting p-Si capped by 285 nm-thick SiO2. It is placed at close proximity to a 25 μCi 22Na positron source emitting a constant flux of positrons, which during the measurement annihilate within the back-gate. We demonstrate that when the back-gate is allowed to float, the charging current of ≈20 fA causes the buildup of positive charge which capacitively couples to the graphene device and is detected as a variation in the two-terminal conductance. Furthermore, a prolonged exposure to positrons causes a shift in the graphene transport characteristics, associated with local charges at the immediate environment of the graphene flake. Our results demonstrate the utility of two-dimensional layered materials as probes for charging dynamics of positrons in solids.
Direct Integration of 3D Printing and Cryogel Scaffolds for Bone Tissue Engineering

Cryogels, known for their biocompatibility and porous structure, lack mechanical strength, while 3D-printed scaffolds have excellent mechanical properties but limited porosity resolution. By combining a 3D-printed plastic gyroid lattice scaffold with a chitosan–gelatin cryogel scaffold, a scaffold can be created that balances the advantages of both fabrication methods. This study compared the pore diameter, swelling potential, mechanical characteristics, and cellular infiltration capability of combined scaffolds and control cryogels. The incorporation of the 3D-printed lattice demonstrated patient-specific geometry capabilities and significantly improved mechanical strength compared to the control cryogel. The combined scaffolds exhibited similar porosity and relative swelling ratio to the control cryogels. However, they had reduced elasticity, reduced absolute swelling capacity, and are potentially cytotoxic, which may affect their performance. This paper presents a novel approach to combine two scaffold types to retain the advantages of each scaffold type while mitigating their shortcomings.
Heat transfer enhancement in self-sustained oscillatory flow in a staggered baffled vertical channel under the buoyancy effect

ABSTRACT Unsteady laminar heat transfer enhancement in asymmetrically heated vertical baffled channel under buoyancy effect is investigated numerically. The baffles are installed on the two walls in an offset manner with constant spacing. The governing equations are solved by the finite volume formulation using openFoam© open-source code. Air (Pr = 0.71) is used as working fluid. The effects of Reynolds number (100–1400) and Grashof number (2.5 × 104 to 2 × 105) in addition to the baffle height (0.1–0.5) on heat transfer and friction factor are studied. The results are given in the form of dimensionless isotherm contours and streamlines in addition to the Nusselt number and friction factor. The results obtained revealed that the flow bifurcates to self-sustained oscillatory flow at moderate Reynolds number (below 600 for a blockage ratio of 0.25). The unsteady self-sustained flow leads to heat transfer enhancement up to 2.8 times for baffle height hb = 0.25 and up to 3.7 when compared to the smooth channel. Unfortunately, this heat transfer is accompanied by an important increase in pumping power.
Charge-plasma-based inverted T-shaped source-metal dual-line tunneling FET with improved performance at 0.5 V operation

In this paper, a charge plasma-based inverted T-shaped source-metal dual line-tunneling field-effect transistor (CP-ITSM-DLTFET) has been proposed to improve the ON current (ION) by increasing the line-tunneling area. In the proposed structure, the charge plasma technique is used to induce the dopants in the source and drain regions. Due to its doping-less structure, the proposed CP-ITSM-DLTFET is immune to random dopant fluctuations and does not require an expensive thermal annealing technique. This makes the proposed device’s fabrication easier and more efficient. The proposed CP-ITSM-DLTFET comprises an inverted T-shaped source metal (sandwiched between the Si-channel) and creates gate-to-source overlap and increases the tunneling area vertically on both sides of the Si-channel. The vertical line-tunneling area in the proposed structure makes the device able to be aggressively scaled compared to conventional TFETs for future technology. The proposed CP-ITSM-DLTFET outperforms almost all pre-existing dopingless TFETs in terms of DC and RF parameters. The switching performance (like high ION = 31.88 uA um−1, steeper AVSS = 23.42 mV dec−1 (over 12-order of drain current), and high ION/IOFF ratio of 1.6 × 1013) and the RF performance (like transconductance (gm) = 0.37 mS, Cut-off frequency (fT) = 90.18 GHz, and Gain Bandwidth product (GBW) = 32.3 GHz) of the proposed CP-ITSM-DLTFET are superior to almost all pre-existing Si, SiGe, and Ge based doping-less TFETs. Moreover, the proposed CP-ITSM-DLTFET-based CMOS inverter has also been comprehensively studied in the paper, showing a good noise margin NMH = 0.198 V (39.8% of VDD) and NML = 0.206 V (41.2% of VDD) with a high voltage gain of 30.25 at VDD = 0.5 V, suggesting great potential for future low power applications.
“Diagnostic and Prognostic Biomarkers of Luminal Breast Cancer: Where are We Now?”

Abstract Luminal breast cancers are hormone receptor (estrogen and/or progesterone) positive that are further divided into HER2-negative luminal A and HER2-positive luminal B subtypes. According to currently accepted convention, they represent the most common subtypes of breast cancer, accounting for approximately 70% of cases. Biomarkers play a critical role in the functional characterization, prognostication, and therapeutic prediction, rendering them indispensable for the clinical management of invasive breast cancer. Traditional biomarkers include clinicopathological parameters, which are increasingly extended by genetic and other molecular markers, enabling the comprehensive characterization of patients with luminal breast cancer. Liquid biopsies capturing and analyzing circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) are emerging technologies that envision personalized management through precision oncology. This article reviews key biomarkers in luminal breast cancer and ongoing developments.
Influence of EGF and pro-NGF on EGFR/SORTILIN interaction and clinical impact in head and neck squamous cell carcinoma

Head and Neck Squamous Cell Carcinoma (HNSCC) remains a cancer with a poor prognosis, with a 5-year survival rate of less than 50%. Although epidermal growth factor receptor (EGFR) is almost always overexpressed, targeted anti-EGFR therapies have modest efficacy and are mainly used in palliative care. Growth factors such as Nerve Growth Factor (NGF) and its precursor proNGF have been shown in our laboratory to play a role in tumor growth and aggressiveness. Interestingly, an interaction between Sortilin, a proNGF receptor, and EGFR has been observed. This interaction appears to interfere with the pro-oncogenic signaling of EGF and modulate the membrane expression of EGFR. The aim of this study was to characterize this interaction biologically, to assess its impact on clinical prognosis and to analyze its role in the cellular trafficking of EGFR. Using immunohistochemical staining on tumor sections from patients treated at our university center and PLA (Proximity Ligation Assay) labeling, we showed that Sortilin expression is significantly associated with reduced 5-year survival. However, when Sortilin was associated with EGFR, this association was not found. Using the Cal-27 and Cal-33 cancer cell lines, we observed that proNGF reduces the effects of EGF on cell growth by inducing the internalization of its receptor. These results therefore suggest a regulatory role for Sortilin in the degradation or renewal of EGFR on the membrane. It would be interesting in future work to show the intracellular fate of EGFR and the role of (pro)neurotrophins in these mechanisms.
An approach of filtering simulated magnetospheric X-ray images based on self-supervised network and random forest

Imaging is an important method for observing the Earth’s space environment. Future missions, such as the Solar wind Magnetosphere Ionosphere Link Explorer (SMILE), aim to explore the interaction between the solar wind and the Earth’s magnetosphere via soft x-ray imaging. With the advent of these missions, a large number of magnetospheric images may be acquired. However, as the viewing geometry and solar wind conditions change, satellites sometimes fail to capture the magnetopause inside the field of view. We propose an approach that blends machine learning and deep learning to filter the simulated x-ray images for the SMILE mission, aiming to achieve automatic classification of the detected images. First, we performed magnetohydrodynamic simulations to derive the predicted SMILE x-ray images. Then, we used a self-supervised contrast feature extraction network to study the features of the images. Using this network, the random forest classifier can distinguish whether the subsolar point at the magnetopause has been detected. Finally, we designed the magnetopause filter to obtain the subsolar magnetopause images with observation positions outside the magnetosphere. As a result, the prediction accuracy of the classifier is up to 93%. And the F1 score is up to 95.5%. The stratified predictions allow an automatic screening of whether satellite magnetospheric images cover the subsolar magnetopause. These images, which have observation positions outside the magnetosphere, can be used to invert the three-dimensional magnetopause.
Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning

In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input–multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The method applies the reciprocity of the uplink and downlink, uses uplink channel state information in the base station to help users give feedback on unknown downlink information, and compresses and restores the channel state information. First, an encoder–decoder structure is established. The encoder reduces the network depth and uses multi-resolution convolution to increase the accuracy of channel state information extraction while reducing the number of computations relating to user equipment. Afterward, the channel state information is compressed to reduce feedback overhead in the channel. At the decoder, with the help of the reciprocity of the uplink and downlink, the feature extraction of the uplink’s magnitudes is carried out, and the downlink channel state information is integrated into a channel state information feature matrix, which is restored to its original size. The simulation results show that compared with CSINet, CRNet, CLNet, and DCRNet, indoor reconstruction precision was improved by an average of 16.4%, and outside reconstruction accuracy was improved by an average of 21.2% under all compressions.