Search Results (2,742)

Background/Objectives: This study aims to evaluate the quality of life (QoL) and oncological outcomes in premenopausal women diagnosed with hormone receptor-positive breast cancer who are receiving either bilateral oophorectomy (BO) or gonadotropin-releasing hormone agonist (GnRH) therapy. Both methods serve to inhibit ovarian function, which is essential for the management of estrogen-dependent tumors; however, their effects on QoL have yet to be fully clarified. Methods: Data were analyzed from the Mayo Clinic Breast Disease Registry, focusing on women under 55 diagnosed with estrogen receptor-positive breast cancer who received either BO or GnRH within one year of diagnosis. QoL was assessed using the Patient-Reported Outcomes Measurement Information System Global-10 (PROMIS-10) at baseline and annually for five years. Results: A total of 181 patients were enrolled in the study; 40 into the BO group and 141 to the GnRH group. Both groups exhibited similar levels of sexual dysfunction after a one-year period; however, the BO group stated a higher frequency of hot flashes. PROMIS-10 scores improved in both mental and physical health over time, with no significant differences between the groups. Within the BO group, one recurrence was observed, in contrast to the GnRH group, which had six events. Nonetheless, the difference in recurrence rates did not reach statistical significance. Conclusions: The long-term QoL and oncologic outcomes for premenopausal women with hormone receptor-positive breast cancer were similar for BO and GnRH therapy. These findings emphasize the need for individualized treatment decisions, considering patient preferences and side effects. Full article

Flash floods arise from the interaction of rugged topography, short-duration intense rainfall, and rapid flow concentration. Conventional risk mapping often builds empirical indices with expert-assigned weights or trains supervised models on historical event inventories—approaches that degrade in data-scarce regions. We propose a fully data-driven, unsupervised Geographic Information System (GIS) framework based on fractional order k-means, which clusters multi-dimensional geospatial features without labeled flood records. Five raster layers—elevation, slope, aspect, 24 h maximum rainfall, and distance to the nearest stream—are normalized into a feature vector for each 30 m × 30 m grid cell. In a province-scale case study of Zhejiang, China, the resulting risk map aligns strongly with the observations: 95% of 1643 documented flash flood sites over the past 60 years fall within the combined high- and medium-risk zones, and 65% lie inside the high-risk class. These outcomes indicate that the fractional order distance metric captures physically realistic hazard gradients while remaining label-free. Because the workflow uses commonly available GIS inputs and open-source tooling, it is computationally efficient, reproducible, and readily transferable to other mountainous, data-poor settings. Beyond reducing subjective weighting inherent in index methods and the data demands of supervised learning, the framework offers a pragmatic baseline for regional planning and early-stage screening. Full article

Bias Temperature Instability (BTI)-induced aging of transistors is a serious concern in modern electronic circuits, yet its effects on the operation of mixed-signal circuits have not been extensively studied. In this work, initially we analyze how BTI-induced aging degradation influences the analog front end of Flash analog-to-digital converters (ADCs). BTI-induced aging leads to substantial increments in the offset voltage of the ADC comparators, which in turn affect their trip point voltage, leading to the alteration of the ADC’s performance characteristics, such as gain, full-scale error and integral nonlinearity. Thus, erroneous responses are generated. Next, we propose a low-cost BTI-induced aging mitigation technique based on a circuit reconfiguration method which periodically alters the average voltage stress on the ADC comparators’ transistors. The proposed method limits the comparators’ offset voltage development, restricting the shift in their trip point voltage. Consequently, the impact of aging on the performance characteristics of the ADC is drastically reduced, and its reliability is improved. According to our simulations, after two years of operation, the gain error is reduced by 95.43%, the full-scale error is reduced by 63.31% and the integral nonlinearity is reduced by 63.00%, with respect to operation without applying the proposed aging mitigation technique. Full article

The upcycling of spent lithium-ion battery graphite constitutes an essential pathway for mitigating manufacturing expenditures and alleviating ecological burdens. This study proposes an integrated strategy to upcycle spent graphite into high-performance porous expanded graphite (PEG) anodes, leveraging flash Joule heating (FJH) as a core technique for efficient decontamination, interlayer expansion, and active etching. Results show that the binders and impurities are efficiently removed by FJH treatment, and the graphite interlayer spacing is expanded. The iron oxide, which acts as an etching reagent, can then be easily intercalated and laid into the decontaminated graphite for subsequent etching. A subsequent FJH treatment simultaneously releases oxidized intercalants and triggers in-situ metal oxide etching, yielding PEG with a rich porous architecture and enhanced specific surface area. This method successfully prepared high-performance porous expanded graphite anode material with a mesoporous structure. The resulting anode delivers a remarkable capacity retention of 419 mAh·g⁻¹ after 600 cycles at 2C, outperforming the performance of commercial graphite anodes. This innovative approach offers a promising route for sustainable graphite reclamation. Full article

The clinical electroretinogram (ERG) is a non-invasive diagnostic test used to assess the functional state of the retina by recording changes in the bioelectric potential following brief flashes of light. The recorded ERG waveform offers ways for diagnosing both retinal dystrophies and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and Parkinson’s disease. In this study, different time-series-based machine learning methods were used to classify ERG signals from ASD and typically developing individuals with the aim of interpreting the decisions made by the models to understand the classification process made by the models. Among the time-series classification (TSC) algorithms, the Random Convolutional Kernel Transform (ROCKET) algorithm showed the most accurate results with the fewest number of predictive errors. For the interpretation analysis of the model predictions, the SHapley Additive exPlanations (SHAP) algorithm was applied to each of the models’ predictions, with the ROCKET and KNeighborsTimeSeriesClassifier (TS-KNN) algorithms showing more suitability for ASD classification as they provided better-defined explanations by discarding the uninformative non-physiological part of the ERG waveform baseline signal and focused on the time regions incorporating the clinically significant a- and b-waves of the ERG. With the potential broadening scope of practice for visual electrophysiology within neurological disorders, TSC may support the identification of important regions in the ERG time series to support the classification of neurological disorders and potential retinal diseases. Full article

Using lipid nanocarriers to deliver the mRNA of a specific antigen to immune cells is a powerful innovative approach to rapidly develop new safe and effective vaccines. Understanding and optimizing the mixing process necessary for mRNA lipid nanoparticles (LNPs) is the focus of this review. The first objective is to review the fundamentals of microfluidic and turbulent fluid-mixing basics needed to understand the mixing process. The mRNA LNP self-assembly flash nanoprecipitation/self-assembly process will be discussed. Then, some important experimental nanoparticle studies which are the basis for the current understanding of microfluidic and turbulent mRNA LNP mixing process will be reviewed. Finally, the current commercially available LNP mixing technology will be summarized. There appears to be no universally “best” mixing process for formulating nanoparticles or mRNA LNPs. Both chaotic advection and turbulent flow microfluidic mixing devices, using the proper parameters for each device, will formulate similar mRNA LNP vaccines during development research. However, the low fluid output of microfluidic devices may not be practicable at higher fluid flow rates. Larger-scale turbulent mixing devices are more suitable for clinical-scale mRNA LNP production. Full article

River spring and flash floods are highly dependent on variations in meteorological conditions. In the Baltic States, substantial changes in air temperature and precipitation have been observed between the two most recent climate normal periods (1961–1990 and 1991–2020). Therefore, changes in the magnitude of spring and flash floods across different hydrological regions between these periods were analyzed to better understand shifting hydrological patterns. Daily flow data from 1961 to 2020 were obtained from 68 water gauging stations on 55 rivers. The Pettitt and Mann–Kendall tests, as well as Sen’s slope estimator, were applied to analyze the time series of flood maximum discharges. The most pronounced negative trends in spring and flash floods were observed in Lithuanian rivers, with the magnitude of these trends gradually weakening toward Latvia and Estonia. The maximum flood heights (h_MAX) generally declined during 1961–2020, particularly in Lithuania and western Latvia. Spring flood data showed the most significant decrease, particularly during 1991–2020, when h_MAX declined on average by 0.14 mm/year in Lithuania and 0.05 mm/year in Latvia. Flash floods exhibited smaller declines, also concentrated in 1991–2020. In the major rivers (Nemunas, Neris, and Daugava), peak discharges of both floods declined consistently throughout the study period. Full article

The increasing demand for renewable energy has intensified research on lignocellulosic biomass pyrolysis as a versatile route for sustainable energy and resource recovery. This study provides a comparative overview of main pyrolysis regimes (slow, intermediate, fast, and flash), emphasizing operational parameters, typical product yields, and technological readiness levels (TRLs). Reactor configurations, including fixed-bed, fluidized-bed, rotary kiln, auger, and microwave-assisted systems, are analyzed in terms of design, advantages, limitations, and TRL status. Key process parameters, such as temperature, heating rate, vapor residence time, reaction atmosphere, and catalyst type, critically influence the yields and properties of biochar, bio-oil, and syngas. Increased temperatures and fast heating rates favor liquid and gas production, whereas lower temperatures and longer residence times enhance biochar yield and carbon content. CO₂ and H₂O atmospheres modify product distribution, with CO₂ increasing gas formation and biochar surface area and steam enhancing bio-oil yield at the expense of solid carbon. Catalytic pyrolysis improves selectivity toward target products, though trade-offs exist between char and oil yields depending on feedstock and catalyst choice. These insights underscore the interdependent effects of process parameters and reactor design, highlighting opportunities for optimizing pyrolysis pathways for energy recovery, material valorization, and sustainable bioeconomy applications. Full article

As the CMOS technology approaches its physical and economic limits, further advancement of Moore’s Law for enhanced computing performance can no longer rely solely on smaller transistors and higher integration density. Instead, the computing landscape is poised for a fundamental transformation that transcends hardware scaling to embrace innovations in architecture, software, application-specific algorithms, and cross-disciplinary integration. Among the most promising enablers of this transition is non-volatile memory (NVM), which provides new technological pathways for restructuring the future of computing systems. Recent advancements in non-volatile memory (NVM) technologies, such as flash memory, Resistive Random-Access Memory (RRAM), and magneto-resistive RAM (MRAM), have significantly narrowed longstanding performance gaps while introducing transformative capabilities, including instant-on functionality, ultra-low standby power, and persistent data retention. These characteristics pave the way for developing more energy-efficient computing systems, heterogeneous memory hierarchies, and novel computational paradigms, such as in-memory and neuromorphic computing. Beyond isolated hardware improvements, integrating NVM at both the architectural and algorithmic levels would foster the emergence of intelligent computing platforms that transcend the limitations of traditional von Neumann architectures and device scaling. Driven by these advances, next-generation computing platforms powered by NVM are expected to deliver substantial gains in computational performance, energy efficiency, and scalability of the emerging data-centric architectures. These improvements align with the broader vision of both “More Moore” and “More than Moore”—extending beyond MOS device miniaturization to encompass architectural and functional innovation that redefines how performance is achieved at the end of CMOS device downsizing. Full article

Streak cameras, essential for ultrahigh temporal resolution diagnostics in laser-driven inertial confinement fusion, underpin the streak tube imaging LiDAR (STIL) system—a flash LiDAR technology offering high spatiotemporal resolution, precise ranging, enhanced sensitivity, and wide field of view. This study establishes a theoretical model of the STIL system, with numerical simulations predicting limits of temporal and spatial resolutions of ~6 ps and 22.8 lp/mm, respectively. Dynamic simulations of laser backscatter signals from targets at varying depths demonstrate an optimal distance reconstruction accuracy of 98%. An experimental STIL platform was developed, with the key parameters calibrated as follows: scanning speed (16.78 ps/pixel), temporal resolution (14.47 ps), and central cathode spatial resolution (20 lp/mm). The system achieved target imaging through streak camera detection of azimuth-resolved intensity profiles, generating raw streak images. Feature extraction and neural network-based three-dimensional (3D) reconstruction algorithms enabled target reconstruction from the time-of-flight data of short laser pulses, achieving a minimum distance reconstruction error of 3.57%. Experimental results validate the capability of the system to detect fast, low-intensity optical signals while acquiring target range information, ultimately achieving high-frame-rate, high-resolution 3D imaging. These advancements position STIL technology as a promising solution for applications that require micron-scale depth discrimination under dynamic conditions. Full article

In recent years, many electronic device industries have shown interest in using artificial intelligence (AI) to quickly estimate package warpage. Machine learning is one of the AI techniques which will give an express prediction on package warpage with the help of several attributes of the data and different algorithms. This study uses a deep learning (DL) model which combines with a deep neural network (DNN) technique and finite element analysis (FEA) to estimate the package warpage of a mobile universal flash storage (UFS) package. Developing a DL model requires a training database from finite element simulation results and a DNN algorithm. The developed DL model accuracy for package warpage is calculated by validating FEA simulation results and experiment data. The error between the DL model prediction and FEA simulation result is less than 7%. This proposed approach can help effectively and efficiently assess package warpage for new product introduction (NPI) with less FEA simulation work and less test vehicle of a real package for warpage measurement and assessment. Full article

Urban flooding represents a growing global concern, especially in areas with rapid urbanization, unregulated urban sprawl and climate change conditions. Conventional flood modeling approaches do not effectively capture the complex dynamics between natural watershed behavior and urban infrastructure; they typically simulate these domains in isolation. This study introduces the Watershed-BIM methodology, a three-dimensional simulation framework that integrates Building and City Information Modeling (BIM/CIM), Geographic Information Systems (GIS), Flood Risk Assessment (FRA), and Flood Risk Management (FRM) into a single framework. Autodesk InfraWorks 2024, Civil 3D 2024, and RiverFlow2D v8.14 software are incorporated in the development. The methodology enhances interoperability and prediction accuracy by bridging hydrological processes with detailed urban-scale data. The framework was tested on a real-world flash flood event in Mandra, Greece, an area frequently exposed to extreme rainfall and runoff events. A specific comparison with observed flood characteristics indicates improved accuracy in comparison to other hydrological analyses (e.g., by HEC-RAS simulation). Beyond flood depth, the model offers additional insights into flow direction, duration, and localized water accumulation around buildings and infrastructure. In this context, integrated tools such as Watershed-BIM stand out as essential instruments for translating complex flood dynamics into actionable, city-scale resilience planning. Full article

Herein, we report a USB-powered VR-HMD prototype integrated with our 33 mm aperture varifocal liquid lenses and electronic drive components, all assembled in a conventional VR-HMD form-factor. In this volumetric-display-based VR system, a sequence of virtual images are rapidly flash-projected at different plane depths in front of the observer and are synchronized with the correct accommodations provided by the varifocal lenses for depth-matched focusing at chosen sweep frequency. This projection mechanism aids in resolving the VAC that is present in conventional fixed-depth VR. Additionally, this system can address refractive error corrections like myopia and hyperopia for prescription users and do not require any eye-tracking systems. We experimentally demonstrate these lenses can vibrate up to frequencies approaching 100 Hz and report the frequency response of the varifocal lenses and their focal characteristics in real time as a function of the drive frequency. When integrated with the prototype’s 120 fps VR display system, these lenses produce a net diopter change of 2.3 D at a sweep frequency of 45 Hz while operating at ~70% of its maximum actuation voltage. The components add a total weight of around 50 g to the off-the-shelf VR set, making it a cost-effective but lightweight minimal solution. Full article

Despite the continued growth of the gluten-free food market, there is a dearth of sensory and consumer knowledge on commercial products. The existing research is mostly limited to hedonic measurements and ingredient effects instead of analytical methods for a better understanding of product characteristics of gluten-free crackers specifically. In this work, a semi-trained consumer panel used projective mapping to choose objectively different plain/original crackers from a pool of sixteen commercial gluten-free cracker varieties. The cracker samples represented a widespread sensory space originating from different key ingredients such as brown rice, white rice, flaxseed, cassava flour, nut flour blend, millet blend, and tapioca/potato starch blend. Based on projective mapping results, the crackers that mostly represented the sensory space were selected for characterization by a modified flash profiling method. The consumer panel developed 74 descriptors: 30 aromas, 28 flavors, 15 texture terms, and a mouthfeel attribute. The samples were monadically rated for intensity on a 4-point scale (0 = none, 1 = low, 2 = medium, and 3 = high). Rice, toasted, salt, grain, burnt, flaxseed, bitter, earthy, nutty, seeds, and grass were the prevalent aromas and flavors. Others were specific to cracker type. Some of these attributes can be traced back to the ingredients list. Results suggest that ingredients used in small portions are defining the flavor properties over the major grains/flour blends. All samples had some degree of crunchiness, crispness, and pasty mouthfeel; rice crackers were particularly firm, hard, and chewy; brown rice crackers were gritty; crackers with tuber starches/flours were more airy, soft, smooth, and flaky. Overall, the samples shared more aroma and flavor notes than texture attributes. In comparison to trained panel results, consumers generated a greater number of terms and were successful in finding subtle differences primarily in texture but had many overlapped flavors. The developed consumer terminology will facilitate the gluten-free industry to tailor communication that better resonates with consumer experiences, needs, and product values. Full article

Ultrahigh dose rate (UHDR) radiotherapy, also known as FLASH radiotherapy (FLASH-RT), has shown potential for increasing tumor control while sparing normal tissue. In parallel, gold nanoparticles (GNPs) have been extensively explored as radiosensitizers due to their high atomic number and ability to enhance the generation of reactive oxygen species (ROS) through water radiolysis. In this study, we investigate the synergistic effects of UHDR electron beams and GNP-mediated radiosensitization using Monte Carlo (MC) simulations based on the Geant4-DNA code. A spherical water phantom with embedded GNPs of varying sizes (5–100 nm) was irradiated using pulsed electron beams (100 keV and 1 MeV) at dose rates of 60, 100, and 150 Gy/s. The chemical yield of ROS near the GNPs was quantified and compared to an equivalent water nanoparticle model, and the yield enhancement factor (YEF) was used to evaluate radiosensitization. Results demonstrated that YEF increased with smaller GNP sizes and at lower UHDR, particularly for 1 MeV electrons. A maximum YEF of 1.25 was observed at 30 nm from the GNP surface for 5 nm particles at 60 Gy/s. The elevated ROS concentration near GNPs under FLASH conditions is expected to intensify DNA damage, especially double-strand breaks, due to increased hydroxyl radical interactions within nanometric distances of critical biomolecular targets. These findings highlight the significance of nanoparticle size and beam parameters in optimizing ROS production for FLASH-RT. The results provide a computational basis for future experimental investigations into the combined use of GNPs and UHDR beams in nanoparticle-enhanced radiotherapy. Full article