Search Results (3,645)

Background/Objectives: Recurrent meningiomas remain difficult to manage due to the absence of effective systemic therapies and comparatively high treatment failure rates, particularly in high-grade tumors. Stereotactic radiosurgery (SRS) offers a minimally-invasive and precise option, particularly for tumors in surgically complex locations. However, the risks associated with re-irradiation, and recent changes in the WHO classification of CNS tumors highlight the need for more personalized and strategic treatment approaches. This systematic review evaluates the safety, efficacy, and clinical considerations for use of SRS for recurrent meningiomas. Methods: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic literature search was conducted using the PubMed, Scopus, and Web of Science databases for studies reporting outcomes of SRS in recurrent, pathologically confirmed intracranial meningiomas. Studies were excluded if they were commentaries, reviews, case reports with fewer than three cases, or had inaccessible full text. The quality and risk of bias of the included studies were assessed using the modified Newcastle-Ottawa Scale. Data on patient and tumor characteristics, SRS treatment parameters, clinical outcomes, adverse effects, and statistical analysis results were extracted. Results: Sixteen studies were included. For WHO Grade I tumors, 3- to 5-year progression-free survival (PFS) ranged from 85% to 100%. Grade II meningiomas demonstrated more variable outcomes, with 3-year PFS ranging from 23% to 100%. Grade III tumors had consistently poorer outcomes, with reported 1-year and 2-year PFS rates as low as 0% and 46%, respectively. SRS performed after surgery alone was associated with superior outcomes, with local control rates of 79% to 100% and 5-year PFS ranging from 40.4% to 91%. In contrast, tumors previously treated with radiotherapy, with or without surgery, showed substantially poorer outcomes, with 3- to 5-year PFS ranging from 26% to 41% and local control rates as low as 31%. Among patients with prior radiotherapy, outcomes were particularly poor in Grade II and III recurrent tumors. Toxicity rates ranged from 3.7% to 37%, and were generally higher for patients with prior radiation. Predictors of worse PFS included prior radiation, older age, and Grade III histology. Conclusions: SRS may represent a reasonable salvage option for carefully selected patients with recurrent meningioma, particularly following surgery alone. Outcomes were notably worse in high-grade recurrent meningiomas following prior radiotherapy, emphasizing the prognostic significance of both histological grade and treatment history. Notably, the lack of molecular and genetic data in most existing studies represents a key limitation in the current literature. Future prospective studies incorporating molecular profiling may improve risk stratification and support more personalized treatment strategies. Full article

The solar angle of incidence is the angle between the sunlight and the normal on the impact surface. The lower the angle of incidence, the more sun radiation the surface can absorb. There are several methods for calculating of this angle. Determining the geographical location of the equivalent surface is one of the lesser-known options. The equivalent surface is a tangential plane to the Earth that is parallel to a reference inclined surface. The geographical coordinates of the point of tangency are clearly determined by the slope and aspect. Since the equivalent surface is horizontal, basic solar geometry equations apply. Unlike the conventional equations commonly used today, they provide easily interpretable results. The sunrise and sunset times for an inclined surface and the time of an extreme incidence angle can be calculated directly. Approximate calculations are not necessary. In addition, the geographical approach allows for the hour angle to be determined, as well as the tilt for a given azimuth of the solar panel that is perpendicular to direct sunlight. This new procedure sets the time for regular changes in the horizontal direction of the sun-tracker. The renaissance of the geographical approach for calculating the temporal characteristics, which allows for the use of simple equations and the interpretation of their results, can also benefit agriculture, forestry, land management, botany, architecture, and other sectors and sciences. Full article

The inversion factor is an important physical parameter for assessing and revealing the performance of semiconductor lasers, providing insights into the carrier-injected band-filling effect and radiation characteristics. In this paper, the carrier inversion factor (P_f) is measured to elucidate the luminescence mechanism of an InGaAs-based well-island composite (WIC) structure, formed by the self-assembly migration of indium atoms and exhibiting excellent spectral properties. P_f is obtained by collecting the amplified photoluminescence (PL) spectra from dual facets of the device, with carrier concentrations ranging from 9.0 × 10¹⁷ to 9.4 × 10¹⁷ cm⁻³. Compared with classical InGaAs/GaAs quantum well structures under the same operating conditions, the inversion level in the WIC structure can be as high as 2.2. Simulation results reveal enhanced quasi-Fermi-level separation and broadened spectral bandwidth. The research is of great significance in the development of new types of quantum-confined lasers with wide spectral output. Full article

The current–voltage (I-V) characteristic provides essential performance parameters of a solar cell, influenced by temperature and solar radiation. The efficiency of a solar cell is sensitive to variations in these conditions. This study electrically characterized a polycrystalline silicon solar cell in a solar simulator chamber at temperatures of 25–55 °C and irradiance levels of 600–1000 W/m². The acquired data were used to train and evaluate neural network models to predict the I-V characteristics of a polycrystalline silicon solar cell. Two recurrent neural network architectures were tested: LSTM and the GRU model. The performance of the model was assessed using MAE, RMSE, and R². The GRU model achieved the results, with MAE = $2.813\times {10}^{-3}$, RMSE = $5.790\times {10}^{-3}$, and R² = 0.9844, similar to LSTM (MAE = $2.6613\times {10}^{-3}$, RMSE = $5.858\times {10}^{-3}$, R² = 0.9840). These findings highlight the GRU network as the most efficient approach for modeling solar cell behavior under varying environmental conditions. Full article

This study investigates the underwater transient vibro-acoustic response of double-layered stiffened cylindrical shells through an integrated experimental-numerical approach. Initially, vibration and noise responses under transient impact loads were experimentally characterized in an anechoic water tank, establishing benchmark datasets. Subsequently, based on the theory of transient structural dynamics, a numerical framework was developed by extending the time-domain finite element/boundary element (FEM/BEM) method, enabling comprehensive analysis of the transient vibration and acoustic radiation characteristics of submerged structures. Validation through experimental-simulation comparisons confirmed the method’s accuracy and effectiveness. Key findings reveal broadband features with distinct discrete spectral peaks in both structural vibration and acoustic pressure responses under transient excitation. Systematic parametric investigations demonstrate that: (1) Reducing the load pulse width significantly amplifies vibration acceleration and sound pressure levels, while shifting acoustic energy spectra toward higher frequencies; (2) Loading position alters both vibration patterns and noise radiation characteristics. The established numerical methodology provides theoretical support for transient impact noise prediction and low-noise structural optimization in underwater vehicle design. Full article

The research objectives of engine plume radiation calculation primarily encompass two aspects: (1) addressing the additional heating induced by plume radiation on rocket thermal protection systems and (2) elucidating the variation patterns of spectral radiation intensity for infrared signature identification and tracking. Focusing on the thermal effects of radiation, this study first calculates the radiative properties of high-temperature combustion gases and particles separately. Subsequently, the radiative properties of mixed droplets with alumina caps are computed and analyzed. Building upon this and incorporating empirical formulas for aluminum droplet combustion, the engine’s radiative properties are calculated, accounting for the presence of mixed droplets. Ultimately, an integrated computational method for engine radiative properties (both internal and external flow fields) is established, which considers the non-equilibrium processes during droplet transformation. The radiative property parameters are then embedded into the fluid dynamics software via multidimensional interpolation. The radiation transfer equation is solved using the discrete ordinates method (DOM) to obtain the radiation intensity distribution within the plume flow field. This work provides technical support for investigating the radiative characteristics of solid rocket engine plumes. Full article

Driven by the rising demand for digitalization and intelligent development of infrared payloads, next-generation systems must be developed within compressed timelines. High-precision digital modeling and simulation techniques offer essential data sources but often falter in complex space-based scenarios due to the limited availability of infrared characteristic data, hindering evaluation of the payload effectiveness. To address this, we propose a digital imaging simulation and verification (DISV) model for high-fidelity infrared image generation and closed-loop validation in the context of cloud–sea target detection. Based on on-orbit infrared imagery, we construct a cloud cluster database via morphological operations and generate physically consistent backgrounds through iterative optimization. The DISV model subsequently calculates scene infrared radiation, integrating radiance computations with an electron-count-based imaging model for radiance-to-grayscale conversion. Closed-loop verification via blackbody radiance inversion is performed to confirm the model’s accuracy. The mid-wave infrared (MWIR, 3–5 µm) system achieves mean square errors (RSMEs) < 0.004, peak signal-to-noise ratios (PSNRs) > 49 dB, and a structural similarity index measure (SSIM) > 0.997. The long-wave infrared (LWIR, 8–12 µm) system yields RMSEs < 0.255, PSNRs > 47 dB, and an SSIM > 0.994. Under 20–40% cloud coverage, the target radiance inversion errors remain below 4.81% and 7.30% for the MWIR and LWIR, respectively. The DISV model enables infrared image simulation across multi-domain scenarios, offering vital support for optimizing on-orbit payload performance. Full article

Background/Objectives: MR-guided radiation therapy (MRgRT) has rapidly evolved into an important treatment modality, with the Elekta Unity and ViewRay MRIdian systems being two major MR-linac platforms. Despite the shared concept of MRgRT, the two platforms elected different system designs that could potentially impact the dosimetric characteristics and quality of a treatment. In this study, we aim to perform a comparative dosimetric investigation between these two MR-linac systems in prostate and pancreas cancers. Methods: Dosimetric characteristics were evaluated by retrospectively re-creating 20 clinical prostate and pancreas cases originally treated on MRIdian using the Unity system, adhering to MIRAGE and SMART clinical trial constraints. Treatment plans were re-created with matching planning images, structures, beam geometry, and dose parameters. To ensure comparison consistency, all Unity treatment plans were normalized to match the target coverage of the MRIdian counterparts, and the organ-at-risk (OAR) dose was investigated. Results: Most OARs’ dose-volume metrics showed no statistically significant differences. For prostate patients, Unity demonstrated lower rectum V36Gy (p = 0.0095), V38Gy (p = 0.0043), V40Gy (p = 0.0469), and lower left (p = 0.0137) and right femur V20Gy (p = 0.0020). For pancreas patients, Unity plans had a lower mean liver dose (p = 0.0371). All Unity plans had a Gamma passing rate > 90%, confirming the clinical deliverability. Mean delivery times were 12.78 ± 1.68 and 13.53 ± 1.88 min for MRIdian and Unity prostate plans, respectively, and 14.58 ± 2.78 and 17.40 ± 3.77 min for MRIdian and Unity pancreas plans, respectively. Conclusions: Overall, comparable treatment quality and delivery times were observed between the two platforms. Full article

Emerging lighting technology aims to enhance indoor light quality while conserving energy through control systems that integrate with natural light. In related technologies, it is crucial to identify quickly and accurately indoor light environments that are constantly changing due to natural light. Consequently, a large number of sensors must be installed, but installing multiple sensors would cause an increasing data processing load and inconvenience to users’ activities. Some have attempted to calculate natural light characteristics, such as solar radiation and color temperature cycles, and implement natural light lighting technology by applying deep learning technology. However, there are only a few cases of using deep learning to analyze indoor illuminance, which is essential for commercializing natural light lighting technology. Research on minimizing the number of sensors is also lacking. This paper proposes a method for generating a detailed indoor illuminance map using deep learning, which calculates the illuminance values of the entire indoor area with a single illuminance sensor. A dataset was constructed by collecting dynamically changing indoor illuminance and the position of the sun, and a single sensor was selected through analysis. Then, a DNN model was built to calculate the illuminance of every region of an indoor space by inputting the illuminance measured by a single sensor and the position of the sun, and it was applied to generate a detailed indoor illuminance map. Research has demonstrated that calculating the illuminance levels across an entire indoor area is feasible. Specifically, on clear days with a color temperature anomaly of about 1%, a detailed illuminance map of the indoor space was created, achieving an average MAE of 2.0 Lux or an MAPE of 2.5%. Full article

One of the keys to medical microwave tomography is understanding the sensitivity of transmit–receive signals to changes in the electromagnetic properties to be reconstructed. This information is embedded in the Jacobian matrix for traditional inverse problem approaches and is a function of transmitter–receiver design characteristics and associated signal radiation/detection patterns. Previous efforts focused primarily on the 2D imaging problem for which sensitivity maps were generated in a single plane. In this paper, we describe sensitivity maps for the full 3D problem for monopole transceivers and their implications for associated antenna array configurations, including imaging zone coverage and computational efficiency. Full article

Developing structural materials capable of maintaining integrity under extreme irradiation conditions is a cornerstone challenge for advancing sustainable nuclear energy technologies. The complexity and severity of radiation-induced microstructural changes—spanning multiple length and timescales—pose significant hurdles for purely experimental approaches. This review critically evaluates recent advancements in atomistic modeling, emphasizing its transformative potential to decipher fundamental mechanisms driving microstructural evolution in irradiated alloys. Atomistic simulations, such as molecular dynamics (MD), have successfully unveiled initial defect formation processes at picosecond scales. However, the inherent temporal limitations of conventional MD necessitate advanced methodologies capable of exploring slower, thermally activated defect kinetics. We specifically traced the development of powerful potential energy landscape (PEL) exploration algorithms, which enable the simulation of high-barrier, rare events of defect evolution processes that govern long-term material degradation. The review systematically examines point defect behaviors in various crystal structures—BCC, FCC, and HCP metals—and elucidates their characteristic defect dynamics, respectively. Additionally, it highlights the pronounced effects of chemical complexity in concentrated solid-solution alloys and high-entropy alloys, notably their sluggish diffusion and enhanced defect recombination, underpinning their superior radiation tolerance. Further, the interaction of extended defects with mechanical stresses and their mechanistic implications for material properties are discussed, highlighting the critical interplay between thermal activation and strain rate in defect evolution. Special attention is dedicated to the diverse mechanisms of dislocation–obstacle interactions, as well as the behaviors of metastable grain boundaries under far-from-equilibrium environments. The integration of data-driven methods and machine learning with atomistic modeling is also explored, showcasing their roles in developing quantum-accurate potentials, automating defect analysis, and enabling efficient surrogate models for predictive design. This comprehensive review also outlines future research directions and fundamental questions, paving the way toward autonomous materials’ discovery in extreme environments. Full article

This article comprehensively analyzes the new developments and challenges faced by several typical prediction models in the field of radiation effects in recent years. The models discussed include the RPP model, the extended RPP (rectangular parallelepiped) model, and the IRPP (integral rectangular parallelepiped) model. The article conducts a comprehensive analysis of the limitations of the assumption that uses the linear energy transfer (LET) of incident particles and the SEU (single-particle upset) cross-section (without considering the energy and type of ions) to predict the rate of single-particle effects (SEUs). Additionally, the article points out that with the continuous progress of integrated circuit technology, the geometric shape of the target circuit, the energy of the incident particles, the type of particles, and more precise physical models corresponding to the interaction between radiation and matter have become increasingly important in evaluating the sensitivity to single-particle effects (SEEs). Subsequently, based on the probability characteristics of SEE, a series of general estimation equations for the SEE rate are derived, considering particle energy, particle type, and the probability of influence at a specific moment. Then, by introducing the concept of interaction volume, the concept of sensitive volume is further expanded, and using these general equations, the relationship between the SEE rate cross-section and the SEE projected area is derived, simplifying the SEU rate prediction equation to a form that can be directly used in engineering applications. Finally, the article emphasizes a complete method of applying the general prediction equation to engineering to estimate the radiation disturbance performance of two typical verification circuits, and provides the corresponding prediction results. Full article

This study introduces a memristive Hopfield neural network (M-HNN) model to investigate electromagnetic radiation impacts on neural dynamics in complex electromagnetic environments. The proposed framework integrates a magnetic flux-controlled memristor into a three-neuron Hopfield architecture, revealing significant alterations in network dynamics through comprehensive nonlinear analysis. Numerical investigations demonstrate that memristor-induced electromagnetic effects induce distinctive phenomena, including coexisting attractors, transient chaotic states, symmetric bifurcation diagrams and attractor structures, and constant chaos. The proposed system can generate more than 12 different attractors and extends the chaotic region. Compared with the chaotic range of the baseline Hopfield neural network (HNN), the expansion amplitude reaches 933%. Dynamic characteristics are systematically examined using phase trajectory analysis, bifurcation mapping, and Lyapunov exponent quantification. Experimental validation via a DSP-based hardware implementation confirms the model’s operational feasibility and consistency with numerical predictions, establishing a reliable platform for electromagnetic–neural interaction studies. Full article

The main goal of this study is to create a computational solver that analyzes the effects of magnetohydrodynamics (MHD) on heat radiation in Cu–water-based Prandtl nanofluid flow using artificial neural networks. Copper nanoparticles are utilized to boost the water-based fluid’s thermal effect. This study primarily focuses on heat transfer over a horizontal sheet, exploring different scenarios by varying key factors such as the magnetic field and thermal radiation properties. The mathematical model is formulated using partial differential equations (PDEs), which are then transformed into a corresponding set of ordinary differential equations (ODEs) through appropriate similarity transformations. The bvp4c solver is then used to simulate the numerical behavior. The effects of relevant parameters on the temperature, velocity, skin friction, and local Nusselt number profiles are examined. It is discovered that the parameters of the Prandtl fluid have a considerable impact. The local skin friction and the local Nusselt number are improved by increasing these parameters. The dataset is split into 70% training, 15% validation, and 15% testing. The ANN model successfully predicts skin friction and Nusselt number profiles, showing good agreement with numerical simulations. This hybrid framework offers a robust predictive approach for heat management systems in industrial applications. This study provides important insights for researchers and engineers aiming to comprehend flow characteristics and their behavior and to develop accurate predictive models. Full article

This study proposes a dual-element wideband circularly polarized (CP) slot-integrated multiple-input multiple-output (MIMO) antenna with an X-notch square-shaped artificial magnetic conductor (AMC) for dedicated short-range communications (DSRC) applications. The proposed antenna design consists of two substrate layers separated by an air gap. The upper layer features a dual-element coplanar waveguide-fed slot antenna and a defected ground structure decoupling isolator, while the lower layer comprises an 8 × 8 array of X-notch square-shaped elemental units, functioning as an AMC reflector. Characteristic mode analysis shows that circular polarization is produced by the dominant orthogonal mode pair (modes J₅ and J₆), whose modal significance exceeds 0.92 and whose characteristic angle separation is 82° around the 5.9 GHz DSRC band. An I-shaped slot embedded in the ground plane of the upper layer serves as a defected ground structure isolator to suppress mutual coupling between antenna elements. Meanwhile, the X-notch square AMC reflector enhances radiation characteristics and antenna gain. The measured return loss bandwidth and axial ratio bandwidth are 32% (4.72–6.61 GHz) and 21.18% (5.2–6.45 GHz), respectively. The dual-element antenna scheme achieves high isolation exceeding 19 dB, with a maximum gain of 8.6 dBic at 5.9 GHz. The envelop correlation coefficient remains below 0.003, while the diversity gain exceeds 9.98 dB. Full article