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The accident-tolerant fuel concept involves replacing the conventional cladding system (zirconium) with a new material or coating that has specific thermomechanical properties. The aim of this study is to evaluate the neutronics performance of a chromium coating concept and design solutions. A Zircaloy–uranium fuel system (Zr–U) is currently used as a standard fuel system in pressurized water reactors around the world. This investigation presents the benefits of utilizing an alternative cladding material such as chromium coating and the effects on the thermal neutron parameters of the way in which the chromium coating is introduced in the reactor fuel. Among these significant benefits is an increase in the reactor fuel’s thermal conductivity, which improves reactor safety. Two types of fuel-cladding systems were investigated: Zircaloy–uranium (Zr–U) and Zircaloy–chromium (Zr–Cr–U) coating fuel systems. Neutronics analysis evaluations were performed for the selected fuel assemblies and a two-dimensional full core based on an APR-1400 reactor design. Neutronics analyses were performed for the application of the new fuel-cladding material systems using the reactor-physics Monte Carlo code Serpent 2.31.

Early detection of Parkinson’s disease (PD) is very important in clinical diagnosis for preventing disease development. In this study, we present efficient discrete wavelet transform (DWT)-based methods for detecting PD from health control (HC) in two cases, namely, off-and on-medication. First, the EEG signals are preprocessed to remove major artifacts before being decomposed into several EEG sub-bands (approximate and details) using DWT. The features are then extracted from the wavelet packet-derived reconstructed signals using different entropy measures, namely, log energy entropy, Shannon entropy, threshold entropy, sure entropy, and norm entropy. Several machine learning techniques are investigated to classify the resulting PD/HC features. The effects of DWT coefficients and brain regions on classification accuracy are being investigated as well. Two public datasets are used to verify the proposed methods: the SanDiego dataset (31 subjects, 93 min) and the UNM dataset (54 subjects, 54 min). The results are promising and show that four entropy measures: log energy entropy, threshold entropy, sure entropy, and modified-Shannon entropy (TShEn) lead to high classification accuracy, indicating they are good biomarkers for PD detection. With the SanDiego dataset, the classification results of off-medication PD versus HC are 99.89, 99.87, and 99.91 for accuracy, sensitivity, and specificity, respectively, using the combination of DWT + TShEn and KNN classifier. Using the same combination, the results of on-medication PD versus HC are 94.21, 93.33, and 95%. With the UNM dataset, the obtained classification accuracy is around 99.5% in both cases of off-and on-medication PD using DWT + TShEn + SVM and DWT + ThEn + KNN, respectively. The results also demonstrate the importance of all DWT coefficients and that selecting a suitable small number of EEG channels from several brain regions could improve the classification accuracy.

The aim of this study is to investigate the potential improvement of accident-tolerant fuels in pressurized water reactors for replacing existing reference zircaloy (Zr) fuel-cladding systems. Three main strategies for improving accident-tolerant fuels are investigated: enhancement of the present state-of-the-art zirconium fuel-cladding system to improve oxidation resistance, replacement of the current referenced fuel-cladding system material with an alternative high-performance oxidation-resistant cladding, and replacement of the current fuel with alternative fuel forms. This study focuses on a preliminary analysis of the neutronic behavior and properties of silicon carbide (SiC)-fuel and FeCrAl cladding systems, which provide a better safety margin as accident-tolerant fuel systems for pressurized water reactors. The typical physical behavior of both cladding systems is investigated to determine their general neutronic performance. The multiplication factor, thermal neutron flux spectrum, 239Pu inventory, pin power distribution, and radial power are analyzed and compared with those of a reference Zr fuel-cladding system. Furthermore, the effects of a burnable poison rod (Gd2O3) in different fuel assemblies are investigated. SiC cladding assemblies present a softer neutron spectrum and a lower linear power distribution compared with the conventional Zr-fuel-cladding system. Additionally, the SiC fuel-cladding system exhibits behaviors that are consistent with the neutronic behavior of conventional Zr fuel-cladding systems, thereby affording greater economic and safety improvements.

The current study emphasizes the synthesis of iron oxide nanoparticles (IONPs) and impact of hydrophilic polymer polyvinyl alcohol (PVA) coating concentration as well as anticancer drug doxorubicin (DOX) loading on saturation magnetization for target drug delivery applications. Iron oxide nanoparticles particles were synthesized by a reformed version of the co-precipitation method. The coating of polyvinyl alcohol along with doxorubicin loading was carried out by the physical immobilization method. X-ray diffraction confirmed the magnetite (Fe.sub.3 O.sub.4) structure of particles that remained unchanged before and after polyvinyl alcohol coating and drug loading. Microstructure and morphological analysis was carried out by transmission electron microscopy revealing the formation of nanoparticles with an average size of 10 nm with slight variation after coating and drug loading. Transmission electron microscopy, energy dispersive, and Fourier transform infrared spectra further confirmed the conjugation of polymer and doxorubicin with iron oxide nanoparticles. The room temperature superparamagnetic behavior of polymer-coated and drug-loaded magnetite nanoparticles were studied by vibrating sample magnetometer. The variation in saturation magnetization after coating evaluated that a sufficient amount of polyvinyl alcohol would be 3 wt. % regarding the externally controlled movement of IONPs in blood under the influence of applied magnetic field for in-vivo target drug delivery.

[Please see PDF for full article text] Objectives: To identify the factors associated with post-stroke depression (PSD) and develop a machine learning predictive model using a large dataset, considering sociodemographic, lifestyle, and clinical factors. Methods: Our 2025 study used data from the 2023 Behavioral Risk Factor Surveillance System, released in September 2024. Data processing was carried out using Google Colab and Python. We carried out descriptive statistics, logistic regression, and feature importance analyses (mutual information and adjusted mutual information). A total of 4 machine-learning models were trained and evaluated: random forest, decision tree, gradient boosting, and logistic regression. Model performance was assessed using the accuracy, precision, recall, harmonic mean of precision and recall (F1-score), and area under the curve - receiver operating characteristic (AUC-ROC). The best-performing model was fine-tuned using GridSearchCV with 5-fold cross-validation. Results: Increasing age, male gender, being married, higher income, and physical activity were associated with lower odds of PSD. Obesity, smoking, diabetes, and high cholesterol are associated with increased odds of PSD. Age and gender were the most informative features for predicting the PSD. Random forest demonstrated the best performance for predicting PSD (accuracy=0.73, precision=0.71, recall=0.77, F1-score=0.74, and AUC-ROC=0.81), which was further improved by hyperparameter optimization. Conclusion: Post-stroke depression's complex etiology involves sociodemographic, lifestyle, and clinical factors, notably age and gender. A random forest model effectively predicts PSD, highlighting the need for comprehensive assessment, early intervention, and management of modifiable risks (obesity, smoking, and inactivity) to improve stroke survivors' outcomes. Keywords: post-stroke depression, risk factors, machine learning, mutual information, logistic regression

Efficient quantum state measurement is important for maximizing the extracted information from a quantum system. For multiqubit quantum processors, in particular, the development of a scalable architecture for rapid and high-fidelity readout remains a critical unresolved problem. Here we propose reservoir computing as a resource-efficient solution to quantum measurement of superconducting multiqubit systems. We consider a small network of Josephson parametric oscillators, which can be implemented with minimal device overhead and in the same platform as the measured quantum system. We theoretically analyze the operation of such a device as a reservoir computer to classify stochastic time-dependent signals subject to quantum statistical features. We apply this reservoir computer to the task of multinomial classification of measurement trajectories from joint multiqubit readout. For a 2-qubit dispersive measurement under realistic conditions we demonstrate a classification fidelity reliably exceeding that of an optimal linear filter using only 2–5 reservoir nodes, while simultaneously requiring far less calibration data—as little as a few shots per state. We understand this remarkable performance through an analysis of the network dynamics and develop an intuitive picture of reservoir processing generally. Finally, we demonstrate how to operate this device to perform 2-qubit state tomography and continuous parity monitoring with equal effectiveness and ease of calibration. This reservoir processor avoids computationally intensive training common to other machine learning frameworks and can be implemented as an integrated cryogenic superconducting device for low-latency processing of quantum signals on the computational edge.

Geriatric suicide is a complex phenomenon that often goes overlooked and under-addressed. This article examines the factors surrounding suicide in older adults, particularly in the context of health challenges. While mental health disorders are a significant factor, physical health issues, cognitive decline, social isolation, financial stress, and environmental conditions also play a role. Additionally, the stigma surrounding mental health and ageism can prevent older individuals from seeking help, exacerbating their isolation and despair. A coordinated effort is needed among healthcare professionals, policymakers, and communities to create supportive environments and address this alarming trend. [Psychiatr Ann. 2025;55(6):e130–e134.]

This paper presents an improvement to the fault ride-through (FRT) capabilities for a wind turbine that employs a switched reluctance generator (SRG) using a dynamic voltage restorer (DVR). The wind turbine may be disconnected from the grid by voltage sag, swell, and faulty line voltage in the grid. To improve the stator voltage of an SRG during grid failures, the DVR is applied to inject voltage at the point of common coupling (PCC) into the grid voltage. A control strategy for the DVR based on fuzzy logic controller (FLC) is proposed in this study to improve the FRT capability and meet the grid codes while avoiding the disconnection of the turbine from the grid. MATLAB/SIMULINK simulation validated the effectiveness and performance of this approach under three test cases: balanced sag, unbalanced sag, and a single line-to-ground fault. In addition, the total harmonic distortions utilizing different controllers were compared in sag mode. Furthermore, the simulation results exhibited significant improvement in transient and steady-state response, thus verifying the effectiveness of the control strategy compared to traditional methods.

This review aims to familiarize the reader with research efforts on the cultivation media of lactic acid bacteria (LAB). We have also included a brief discussion on standard ingredients used in LAB media and chemically defined media as related to bacterial growth requirements. Recent research has focused on modifying standard media for the enumeration, differentiation, isolation, and identification of starter cultures and probiotics. Even though large numbers of these media have been developed to serve dairy microbial control, they have failed to provide consistent results. The research consequently points to the need to develop a reliable lactobacilli growth medium for the dairy industry.

For the last few years, the world has been going through a difficult time, and the reason behind this is severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), one of the significant members of the Coronaviridae family. The major research groups have shifted their focus towards finding a vaccine and drugs against SARS-CoV-2 to reduce the infection rate and save the life of human beings. Even the WHO has permitted using certain vaccines for an emergency attempt to cut the infection curve down. However, the virus has a great sense of mutation, and the vaccine’s effectiveness remains questionable. No natural medicine is available at the community level to cure the patients for now. In this study, we have screened the vast library of experimental drugs of Drug Bank with Schrodinger’s maestro by using three algorithms: high-throughput virtual screening (HTVS), standard precision, and extra precise docking followed by Molecular Mechanics/Generalized Born Surface Area (MMGBSA). We have identified 3-(7-diaminomethyl-naphthalen-2-YL)-propionic acid ethyl ester and Thymidine-5′-thiophosphate as potent inhibitors against the SARS-CoV-2, and both drugs performed impeccably and showed stability during the 100 ns molecular dynamics simulation. Both of the drugs are among the category of small molecules and have an acceptable range of ADME properties. They can be used after their validation in in-vitro and in-vivo conditions.