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Gliosarcoma (GS) is an uncommon central nervous system tumor with several characteristics of a malignant neoplasm and poor prognosis. The majority of GS reports describe a predilection for the cerebral hemispheres, and cases of intraventricular GS are extremely rare, with only a few reported. In addition, intraventricular GS has not been associated with any unique radiographic or clinical features, which can result in misdiagnosis as other intraventricular tumor types. In this report, we present the case of a 32-year-old woman with GS in the trigone of the lateral ventricle and provide a retrospective review of similar, previously reported cases.

Chordoid gliomas are uncommon neoplasms located within the anterior portion of the third ventricle. In this article, we aimed to describe the clinical presentation, magnetic resonance imaging characteristics, histological findings, and surgical treatment applied to a case of chordoid glioma. Chordoid gliomas are typically observed as solid masses within the anterior segment of the third ventricle, characterized by homogenous and vivid enhancement. Despite being classified as a low-grade neoplasm, the outcome of choroid glioma is often uncertain.

Argumentative agents in AI are inspired by how humans reason by exchange of arguments. Given the same set of arguments possibly attacking one another (Dung's AA framework) these agents are bound to accept the same subset of those arguments (aka extension) unless they reason by different argumentation semantics. However humans may not be so predictable, and in this paper we assume that this is because any real agent's reasoning is inevitably influenced by her own preferences over the arguments. Though such preferences are usually unobservable, their effects on the agent's reasoning cannot be washed out. Hence by reconstructing her reasoning process, we might uncover her hidden preferences, which then allow us to predict what else the agent must accept. Concretely we formalize and develop algorithms for such problems as uncovering the hidden argument preference relation of an agent from her expressed opinion, by which we mean a subset of arguments or attacks she accepted from a given AA framework; and uncovering the collective preferences of a group from a dataset of individual opinions. A major challenge we addressed in this endeavor is to deal with “answer sets” of argument preference relations which are generally exponential or even infinite. So we start by developing a compact representation for such answer sets called preference states. Preference revelation tasks are then structured as derivations of preference states from data, and reasoning prediction tasks are reduced to manipulations of derived preference states without enumerating the underlying (possibly infinite) answer sets. We also apply the presented results to two non-trivial problems: learning preferences over rules in structured argumentation with priorities – an open problem so far; and analyzing public polls in apparently deeper ways than existing social argumentation frameworks allow.

Gd2O3:Er3+ nanophosphors were fabricated by the combustion method in presence of Na2 ethylene diamine tetra acetic acid (EDTA-Na2) as fuel at not high temperature (≤350 oC) within a very short time of 5 min. The added concentration of Er3+ ions in Gd2O3 matrix was changed from 0.5 mol% to 5.0 mol%. The X-ray diffraction pattern of samples indicates the monoclinic structure of Gd2O3:Er3+. The morphology and chemical composition analysis of the Gd2O3:Er3+ samples are characterized by a field emission scanning electron microscope (FESEM) and a Fourier-transform infrared spectrometer (FTIR). The photoluminescence (PL), photoluminescence excitation (PLE) and upconversion (UC) at room temperature of the prepared materials with different concentrations of Er3+ were investigated. The PL of Gd2O3:Er3+ nanomaterials are shown in visible at 545, 594, 623, 648, 688 nm under excitation at 275 nm. The emission bands from transitions of Er3+ from 2P3/2 to 4F9/2 are observed. UC luminescent spectra of the Gd2O3:Er3+/silica nanocomposites under 976 nm excitation show the bands at 548 and 670 nm. The influence of excitation power at 980 nm for transitions were measured and calculated. The results indicate that the upconversion process of Gd2O3:Er3+/silica is two photons absorption mechanism. The low temperature dependence of UC luminescent intensities of the main bands of Gd2O3:Er3+ was investigated towards development of a nanotemperature sensor in the range of 10–300 K.

This paper investigates the variability of dynamic responses of a beam resting on an elastic foundation, which is subjected to a vehicle with uncertain parameters, such as random mass, stiffness, damping of the vehicle and random fields of mass density, and the elastic modulus of the beam and stiffness of elastic foundation. The vehicle is modeled as a two-degree-of-freedom spring-damper-mass system. The equations of motion of the beam was constructed using a finite element method. The mass and elastic properties of the beam, and the stiffness of foundation are assumed to be Gaussian random fields and were simulated by the spectral represent method. Masses, stiffness of the spring, and the damping coefficient of the vehicle are assumed as Gaussian random variables. The numerical analyses were performed using the finite element method (FEM) in conjunction with the Monte Carlo simulation (MCS). The variability of dynamic responses of the beam were investigated with various cases of random parameters. For each sample, the equations of motions were solved with the Wilson-q integral method to find dynamic responses. The influence of random system parameters and their correlation on the response variability is discussed in detail.

The development of emission-tunable structures opens an optimistic future for white light-emitting diodes (WLED) utilizing a UV pump. Herein, we report emission-tunable Mn-doped ZnS/ZnO heterostructure nanobelts synthesized by the thermal evaporation method, following by an appropriate oxidation process. Under the UV excitation of 330 nm, the as-received nanobelts show an orange-yellow light with CCT of 3056 K, the 500 and 700 °C-oxidized samples strongly exhibit a warm white light emission with CCT of 3393 K and 3551 K, respectively. Surprisingly, the Mn-doped ZnS/ZnO heterostructures oxidized at 600 °C show a white light emission with CIE chromaticity coordinate of (0.31, 0.39) and CCT value of 6290 K, which are nearly very close to that of the commercial YAG:Ce3+ phosphor. A prototype WLED with the color coordinates of (0.33, 0.45), the CCT of 5491 K, and the QE ∼ 61% was successfully fabricated using Mn-doped ZnS/ZnO heterostructure nanobelts coated on the surface of UV (370 nm) chip. The obtained Mn-doped ZnS/ZnO heterostructures are promising to be applied in UV-pumped WLED fabrication.

Probabilistic Argumentation (PA) is a recent line of research in AI aiming to combine the strengths of argumentation and probabilistic reasoning. Though several models of PA have been proposed, the development of practical applications is still hindered by the lack of inference procedures and reasoning engines. In this paper, we present a reduction method to compute a recently proposed model of PA called PABA. Using the method we design inference procedures to compute the credulous semantics, the ideal semantics and the grounded semantics for a general class of PABA frameworks, that we refer to as Bayesian PABA frameworks. We also show that, though restricting to Bayesian PABA frameworks, the inference procedures can be used to compute other PA models thanks to simple translations. Finally, we implement the inference procedures to obtain a multi-semantics engine for probabilistic argumentation and demonstrate its usage.

Hydrological models play a crucial role in water planning and decision making. Machine Learning-based models showed several drawbacks for frequent high and a wide range of streamflow records. These models also experience problems during the training process such as over-fitting or trapping in searching for global optima To overcome these limitations, the current study attempts to hybridize the recently developed physics-inspired metaheuristic algorithms (MHAs) such as Equilibrium Optimization (EO), Henry Gases Solubility Optimization (HGSO), and Nuclear Reaction Optimization(NRO) with Multi-layer Perceptron (MLP). These models’ accuracy will be inspected to solve the streamflow forecasting problem where the streamflow dataset was collected through 130 years from a station located on the High Aswan Dam (HAD). The performance of proposed models then will be compared with two traditional neural network models(MLP and RNN), and nine well-known hybrid MLP-based models belong to the different branches of the metaheuristic field (evolutionary group, swarm group, and physics group). The internal parameters of the proposed models will be initialized and optimized. Different performance metrics will be used to examine the performance of the proposed models. The stability of the proposed models and the convergence speed will be evaluated. Finally, ranking these models based on different performance evaluations will be carried out. The results show that the models in the group of Physics-MLP are more reliable in capturing the streamflow patterns, followed by the Swarm-MLP group and then by the Evolutionary-MLP group. Finally, among the all employed methods, the NRO has the best accuracy with the lowest RMSE(2.35), MAE(1.356), MAPE(16.747), and the highest WI(0.957), R(0.924), and confidence in forecasting the streamflow of Aswan High Dam. It can be concluded that augmenting the NRO algorithm with MLP can be a reliable tool in forecasting the monthly streamflow with a high level of precision, speed convergence, and high constancy level.

Ge nanocrystals (GeNCs) embedded in SiO2 matrix were prepared by co-sputtering method and heat treatment. The formation of the GeNCs were confirmed by X-ray diffraction and Raman spectroscopy. Photon absorbance recordedly increased with the increased Ge content, being in consistent with the results shown in the transient induced absorption measurements. The direct transitions between Γ and L points in the Brillouin zone, assigned as E1, were established at approximately 2 eV and relatively independent from the GeNC sizes. Raman scattering peaks characterised for crystalline Ge at approximately 300 cm−1 were observed to shift with the increasing laser excitation flux. While the displacement of the Raman peak (ΔRs) increased with the Ge content, the ratios of the displacement rates and optical coefficients (a), ΔRs/a, were found to be the same for all the samples, suggesting an intrinsic thermal characteristic of the GeNCs.

In this study, we present a simple and versatile way to growth and modify photoemission of high quality ZnS microrods by thermal evaporation method in combination with post oxidation in oxygen environment. The as-grown ZnS microrods show strong near edge luminescence doublets at room temperature indicating the high crystalline quality. Using ultrahigh-resolution scanning electron microscope integrated with energy microanalysis and cathodoluminescence capacity we elucidate the effect of oxidation temperature on microstructure surface, chemical composition and emission spectra of ZnS microrods. Under appropriate oxidation condition, the initial high quality ZnS microrods can be converted into ZnS/ZnO microrod heterostructures or optically active porous ZnO microrods. More particularly, we demonstrate that the emission wavelength of an oxygen-related defect could be tuned in between optical band-gap of ZnS and ZnO upon increasing the oxidation temperature. This research introduces a simple approach to synthesize and tune optical property of high quality ZnS crystals.

Nanohybrid materials based on silver nanoparticles (Ag-NPs) and graphene oxide (GO) are attracting considerable research interest because of their potential many applications including surface-enhanced Raman scattering, catalysis, sensors, biomedicine and antimicrobials. In this study, we established a simple and effective method of preparing a finely dispersed Ag-GO aqueous solution using modified Hummer and photochemical technique. The Ag-NPs formation on GO nanosheets was analyzed by X-ray diffraction, transmission electron microscopy, Raman spectroscopy, and Fourier-transform infrared spectroscopy. The average size of Ag-NPs on the GO nanosheets was approximately 6–7 nm with nearly uniform size distribution. The Ag-GO nanohybrid also exhibits an adsorption band at 435 nm because of the presence of Ag-NPs on the GO nanosheets. Photoluminescence emission of the Ag-GO nanohybrid was found at 400 and 530 nm, which can be attributed to the interaction between the luminescence of exploited GO nanosheets and localized surface plasmon resonance from metallic Ag-NPs. The observed excellent optical properties of the as-prepared Ag-GO nanohybrid showed a significant potential for optoelectronics applications.