Search Results (3,644)

Motor Imagery (MI) classification plays a crucial role in enhancing the performance of brain–computer interface (BCI) systems, thereby enabling advanced neurorehabilitation and the development of intuitive brain-controlled technologies. However, MI classification using electroencephalography (EEG) is hindered by spatiotemporal variability and the limited interpretability of deep learning (DL) models. To mitigate these challenges, dropout techniques are employed as regularization strategies. Nevertheless, the removal of critical EEG channels, particularly those from the sensorimotor cortex, can result in substantial spatial information loss, especially under limited training data conditions. This issue, compounded by high EEG variability in subjects with poor performance, hinders generalization and reduces the interpretability and clinical trust in MI-based BCI systems. This study proposes a novel framework integrating channel dropout—a variant of Monte Carlo dropout (MCD)—with class activation maps (CAMs) to enhance robustness and interpretability in MI classification. This integration represents a significant step forward by offering, for the first time, a dedicated solution to concurrently mitigate spatiotemporal uncertainty and provide fine-grained neurophysiologically relevant interpretability in motor imagery classification, particularly demonstrating refined spatial attention in challenging low-performing subjects. We evaluate three DL architectures (ShallowConvNet, EEGNet, TCNet Fusion) on a 52-subject MI-EEG dataset, applying channel dropout to simulate structural variability and LayerCAM to visualize spatiotemporal patterns. Results demonstrate that among the three evaluated deep learning models for MI-EEG classification, TCNet Fusion achieved the highest peak accuracy of 74.4% using 32 EEG channels. At the same time, ShallowConvNet recorded the lowest peak at 72.7%, indicating TCNet Fusion’s robustness in moderate-density montages. Incorporating MCD notably improved model consistency and classification accuracy, especially in low-performing subjects where baseline accuracies were below 70%; EEGNet and TCNet Fusion showed accuracy improvements of up to 10% compared to their non-MCD versions. Furthermore, LayerCAM visualizations enhanced with MCD transformed diffuse spatial activation patterns into more focused and interpretable topographies, aligning more closely with known motor-related brain regions and thereby boosting both interpretability and classification reliability across varying subject performance levels. Our approach offers a unified solution for uncertainty-aware, and interpretable MI classification. Full article

Turbulent diffusion plays a critical role in atmospheric pollutant dispersion, particularly in complex environments such as urban areas. This study proposes a novel theoretical approach to enhance the calculation of the turbulent diffusion coefficient in pollutant dispersion models. We propose a new expression for the turbulent diffusion coefficient (KC), which incorporates both hydrodynamic and turbulence-related time scales. This formulation links the turbulent diffusion coefficient to pollutant travel time and turbulence intensity, offering more accurate predictions of pollutant concentration distributions. By addressing the limitations of existing empirical models, this approach improves the parameterization of turbulence and reduces uncertainties in predicting maximum individual exposure under various atmospheric conditions. The study presents a theoretical model designed to advance the current understanding of atmospheric dispersion modeling. Experimental validation, while recommended, is beyond the scope of this work and is suggested as a direction for future empirical research to confirm the practical utility of the model. This theoretical formulation could be integrated into urban air quality management frameworks, providing improved estimations of pollutant peaks in complex environments. Full article

The cryopreservation of three-dimensional (3D) biofabricated constructs is a key enabler for their clinical application in regenerative medicine. Unlike two-dimensional (2D) cultures, 3D systems such as encapsulated cell spheroids, molded hydrogels, and bioprinted tissues present specific challenges related to cryoprotectant (CPA) diffusion, thermal gradients, and ice formation during freezing and thawing. This review examines the current strategies for preserving 3D constructs, focusing on the role of biomaterials as cryoprotective matrices. Natural polymers (e.g., hyaluronic acid, alginate, chitosan), protein-based scaffolds (e.g., silk fibroin, sericin), and synthetic polymers (e.g., polyethylene glycol (PEG), polyvinyl alcohol (PVA)) are evaluated for their ability to support cell viability, structural integrity, and CPA transport. Special attention is given to cryoprotectant systems that are free of dimethyl sulfoxide (DMSO), and to the influence of hydrogel architecture on freezing outcomes. We have compared the efficacy and limitations of slow freezing and vitrification protocols and review innovative approaches such as temperature-controlled cryoprinting, nano-warming, and hybrid scaffolds with improved cryocompatibility. Additionally, we address the regulatory and manufacturing challenges associated with developing Good Manufacturing Practice (GMP)-compliant cryopreservation workflows. Overall, this review provides an integrated perspective on material-based strategies for 3D cryopreservation and identifies future directions to enable the long-term storage and clinical translation of engineered tissues. Full article

Background: Potent androgen receptor pathway inhibitors induce small cell neuroendocrine prostate cancer (SCNC), a highly aggressive subtype of metastatic androgen deprivation-resistant prostate cancer (ARPC) with limited treatment options and poor survival rates. Patients with metastases in the liver have a poor prognosis relative to those with bone metastases alone. The mechanisms that underlie the different behavior of ARPC in bone vs. liver may involve factors intrinsic to the tumor cell, tumor microenvironment, and/or systemic factors, and identifying these factors is critical to improved diagnosis and treatment of SCNC. Metabolic reprogramming is a fundamental strategy of tumor cells to colonize and proliferate in microenvironments distinct from the primary site. Understanding the metabolic plasticity of cancer cells may reveal novel approaches to imaging and treating metastases more effectively. Methods: Using magnetic resonance (MR) imaging and spectroscopy, we interrogated the physiological and metabolic characteristics of SCNC patient-derived xenografts (PDXs) propagated in the bone and liver, and used correlative biochemical, immunohistochemical, and transcriptomic measures to understand the biological underpinnings of the observed imaging metrics. Results: We found that the influence of the microenvironment on physiologic measures using MRI was variable among PDXs. However, the MR measure of glycolytic capacity in the liver using hyperpolarized ¹³C pyruvic acid recapitulated the enzyme activity (lactate dehydrogenase), cofactor (nicotinamide adenine dinucleotide), and stable isotope measures of fractional enrichment of lactate. While in the bone, the congruence of the glycolytic components was lost and potentially weighted by the interaction of cancer cells with osteoclasts/osteoblasts. Conclusion: While there was little impact of microenvironmental factors on metabolism, the physiological measures (cellularity and perfusion) are highly variable and necessitate the use of combined hyperpolarized ¹³C MRI and multiparametric (anatomic, diffusion-, and perfusion- weighted) ¹H MRI to better characterize pre-treatment tumor characteristics, which will be crucial to evaluate treatment response. Full article

The analysis of anomalous diffusion characteristics within single-particle tracking data is a key problem in several applied-science domains, including biosignal processing, bioinformatics, and biotechnology. This task becomes particularly challenging in the presence of short trajectories, localization errors, and non-ergodicity, features that are common in real experimental data. To address these limitations, this work proposes an approach that improves the robustness and accuracy of estimating the anomalous diffusion exponent α, even for very short trajectories of up to 10 points. The approach includes an ensemble-based variance estimation of the exponent α, along with a bias correction based on time–ensemble averaged mean squared displacement, which reduces the systematic bias. These components integrate well into neural network architectures and are suitable for analyzing experimental trajectories in biotechnology and bioprocess engineering applications. Full article

Hydraulic tomography (HT) based on the successive linear estimator (SLE) offers the high-resolution characterization of aquifer heterogeneity but conventionally requires prolonged pumping to achieve steady-state conditions, limiting its applicability in contamination-sensitive or low-permeability settings. This study bridged theoretical and practical gaps (1) by identifying spatial periodicity (hole effect) as the mechanism underlying divergences in steady-state cross-correlation patterns between random finite element method (RFEM) and first-order analysis, modeled via an oscillatory covariance function, and (2) by validating a novel short-term sampling strategy for SLE-based HT using field experiments at the University of Göttingen test site. Utilizing early-time drawdown data, we reconstructed spatially congruent distributions of hydraulic conductivity, specific storage, and hydraulic diffusivity after rigorous wavelet denoising. The results demonstrate that the short-term sampling strategy achieves accuracy comparable to that of long-term sampling strategy in characterizing aquifer heterogeneity. Critically, by decoupling SLE from steady-state requirements, this approach minimizes groundwater disturbance and time costs, expanding HT’s feasibility to challenging environments. Full article

The Anderson localization of phonons in disordered superlattices has been proposed as a route to suppress thermal conductivity beyond the limits imposed by conventional scattering mechanisms. A commonly used signature of phonon localization is the emergence of the nonmonotonic dependence of thermal conductivity $\kappa$ on system length L, i.e., a $\kappa$-L maximum. However, such behavior has rarely been observed. In this work, we conduct extensive non-equilibrium molecular dynamics (NEMD) simulations, using the LAMMPS package, on both periodic superlattices (SLs) and aperiodic random multilayers (RMLs) constructed from Si/Ge and Lennard-Jones materials. By systematically varying acoustic contrast, interatomic bond strength, and average layer thickness, we examine the interplay between coherent and incoherent phonon transport in these systems. Our two-phonon model decomposition reveals that coherent phonons alone consistently exhibit a strong nonmonotonic $\kappa$-L. This localization signature is often masked by the diffusive, monotonically increasing contribution from incoherent phonons. We further extract the ballistic-limit mean free paths for both phonon types, and demonstrate that incoherent transport often dominates, thereby concealing localization effects. Our findings highlight the importance of decoupling coherent and incoherent phonon contributions in both simulations and experiments. This work provides new insights and design principles for achieving phonon Anderson localization in superlattice structures. Full article

Guaranteeing food safety in the European Union (EU) is a continuing issue affected by diverse national traditions, regulatory power, and consumer culture. Despite the presence of a harmonized regulatory context, there continues to be variability in performance among the 27 member states. This study gives an extensive comparative evaluation of EU food safety based on three indicators: Rapid Alert System for Food and Feed (RASFF) alerts, pesticide maximum-residue-limit (MRL) violation, and per capita food loss. Fuzzy TOPSIS, K-means clustering, and scenario-based sensitivity tests are used to give an extensive appraisal of the performance of member states. Alarming differences are quoted as findings of significance. The highest number of RASFF notifications (212) and percentage of pesticide MRL non-compliance (1.5%) were reported in 2022 by Bulgaria, whereas the lowest values were reported by Estonia and Lithuania—15–20 RASFF notifications and less than 0.6% MRL violation rates. A statistically significant correlation (r = 0.72, p < 0.001) between pesticide MRL violation and food safety warnings was confirmed in favor of pesticide regulation as the optimal predictor of food safety warnings. On the other hand, food loss did not significantly affect safety measures but indicated very high variation (from 76 kg/capita per year in Croatia to 142 kg/capita per year in Greece). These findings suggest that while food loss remains an environmental problem, pesticide control is more central to the protection of food safety. Targeted policy is what the research necessitates: intervention and stricter enforcement in low-income countries, and diffusion of best practice from successful states. The composite approach adds to EU food safety policy discourse through the combination of performance indicators and targeted regulatory emphasis. Full article

Amorphous boron powder, as a high-energy fuel, is widely used in the energy sector. However, its ignition and combustion difficulties have long limited its performance in propellants, explosives, and pyrotechnics. In this study, Mg/Al-coated boron powder with enhanced combustion properties was synthesized using the electrical explosion method. To investigate the effect of Mg/Al coating on the ignition and combustion behavior of boron powder, four samples with different Mg/Al coating contents (4 wt.%, 6 wt.%, 8 wt.%, and 10 wt.%) were prepared. Compared with raw B95 boron powder, the coated powders showed a significant reduction in particle size (from 2.9 μm to 0.2–0.3 μm) and a marked increase in specific surface area (from 10.37 m²/g to over 20 m²/g). The Mg/Al coating formed a uniform layer on the boron surface, which reduced the ignition delay time from 143 ms to 40–50 ms and significantly improved the combustion rate, combustion pressure, and combustion calorific value. These results demonstrate that Mg/Al coating effectively promotes rapid ignition and sustained combustion of boron particles. Furthermore, with the increasing Mg/Al content, the ignition delay time decreased progressively, while the combustion rate, combustion pressure, and heat release increased accordingly, reaching optimal values at 8 wt.% Mg/Al. An analysis of the combustion residues revealed that both Mg and Al reacted with boron oxide to form new multicomponent compounds, which reduced the barrier effect of the oxide layer on oxygen diffusion into the boron core, thereby facilitating continuous combustion and high heat release. This work innovatively employs the electrical explosion method to prepare dual-metal-coated boron powders and, for the first time, reveals the synergistic promotion effect of Mg and Al coatings on the ignition and combustion performance of boron. The results provide both experimental data and theoretical support for the high-energy release and practical application of boron-based fuels. Full article

A young adult patient presented to the gastrointestinal outpatient department with a suspected hepatic tumor. The patient was in a traffic accident ten years ago and underwent splenectomy and distal pancreatectomy at another medical institution. The physical examination was unremarkable. The liver function tests and tumor markers were within normal limits, with the alpha-fetoprotein level at 1.38 ng/mL. Both hepatitis B surface antigen and anti-HCV were negative. Based on the clinical history, intrahepatic splenosis was suspected first. Dynamic computed tomography revealed a 2.3 cm lesion exhibiting suspicious early wash-in and early wash-out enhancement patterns. As previous studies have reported, this finding makes hepatocellular carcinoma and metastatic lesions the major differential diagnoses. For further evaluation, dynamic magnetic resonance imaging was performed, and similar enhancing features were observed, along with restricted diffusion. As hepatocellular carcinoma still could not be confidently ruled out, the patient underwent an ultrasound-guided biopsy. The diagnosis of intrahepatic splenosis was confirmed by the pathologic examination. Intrahepatic splenosis is a rare condition defined as an acquired autoimplantation of splenic tissue within the hepatic parenchyma. Diagnosis can be challenging due to its ability to mimic liver tumors in imaging studies. Therefore, in patients with a history of splenic trauma and/or splenectomy, a high index of suspicion and awareness is crucial for accurate diagnosis and for prevention of unnecessary surgeries or interventions. Full article

Alzheimer’s disease (AD) is a progressive, non-curable neurodegenerative disorder that poses persistent challenges for early diagnosis due to its gradual onset and the difficulty in distinguishing pathological changes from normal aging. Neuroimaging, particularly MRI and PET, plays a key role in detection; however, limitations in data availability and the complexity of early structural biomarkers constrain traditional diagnostic approaches. This review investigates the use of generative models, specifically Generative Adversarial Networks (GANs) and Diffusion Models, as emerging tools to address these challenges. These models are capable of generating high-fidelity synthetic brain images, augmenting datasets, and enhancing machine learning performance in classification tasks. The review synthesizes findings across multiple studies, revealing that GAN-based models achieved diagnostic accuracies up to 99.70%, with image quality metrics such as SSIM reaching 0.943 and PSNR up to 33.35 dB. Diffusion Models, though relatively new, demonstrated strong performance with up to 92.3% accuracy and FID scores as low as 11.43. Integrating generative models with convolutional neural networks (CNNs) and multimodal inputs further improved diagnostic reliability. Despite these advancements, challenges remain, including high computational demands, limited interpretability, and ethical concerns regarding synthetic data. This review offers a comprehensive perspective to inform future AI-driven research in early AD detection. Full article

MXenes, a family of two-dimensional carbide and nitride nanomaterials, have demonstrated significant promise across various technological domains, particularly in energy storage applications. This review critically examines scalable synthesis techniques for MXenes and their potential integration into next-generation rechargeable battery systems. We highlight both top-down and emerging bottom-up approaches, exploring their respective efficiencies, environmental impacts, and industrial feasibility. The paper further discusses the electrochemical behavior of MXenes in lithium-ion, sodium-ion, and aluminum-ion batteries, as well as their multifunctional roles in solid-state batteries—including as electrodes, additives, and solid electrolytes. Special emphasis is placed on surface functionalization, interlayer engineering, and ion transport properties. We also compare MXenes with conventional graphite anodes, analyzing their gravimetric and volumetric performance potential. Finally, challenges such as diffusion kinetics, power density limitations, and scalability are addressed, providing a comprehensive outlook on the future of MXenes in sustainable energy storage technologies. Full article

Background/Objectives: Anorectal and retroperitoneal abscesses, although differing in frequency and presentation, present significant diagnostic and therapeutic challenges, especially when interconnected through complex fascial planes. Rare cases such as horseshoe ischiorectal phlegmons with extraperitoneal spread are particularly difficult to manage due to limited literature and the absence of standardized protocols. This article presents a rare case alongside a narrative review of similar cases, aiming to highlight key diagnostic pitfalls and therapeutic strategies. Methods: We conducted a narrative literature review using PubMed, Embase, Scopus, and Web of Science to identify reports on horseshoe ischiorectal phlegmons with extraperitoneal or retroperitoneal extension. Relevant studies were compared with the present case. Results: We describe a 59-year-old male who presented with severe sepsis, diffuse abdominal pain, and hemodynamic instability. Imaging and surgery revealed extensive necrotizing spread to the anterior abdominal wall, peritoneum, and retroperitoneal space, despite absent local perianal signs. Emergency midline laparotomy, wide debridement, and drainage were performed. Despite intensive care, the patient suffered rapid clinical deterioration and died within six hours postoperatively. Conclusions: This case and literature review highlight how a clinically silent ischiorectal phlegmon can progress to extensive extraperitoneal involvement and fatal sepsis. This underscores the need for early recognition, advanced imaging, and aggressive multidisciplinary management. Further studies are needed to develop evidence-based guidelines for complex anorectal abscesses with deep fascial extension. Full article

Iron-rich bauxite residue (red mud) is a hazardous alkaline solid waste produced during the production of alumina from high-iron bauxite, which poses severe environmental challenges due to its massive stockpiling and limited utilization. In this study, metallic iron was recovered from high-iron red mud using the smelting reduction process. Thermodynamic analysis results show that an increase in temperature and sodium oxide content, along with an appropriate mass ratio of Al₂O₃ to SiO₂ (A/S) and mass ratio of CaO to SiO₂ (C/S), contribute to the enhancement of the liquid phase mass fraction of the slag. During the smelting reduction process of high-iron red mud, iron recoveries for low-alkali high-iron red mud and high-alkali high-iron red mud under optimal conditions were 98.14% and 98.36%, respectively. The metal obtained through reduction meets the industrial standard for steel-making pig iron, which is also confirmed in the pilot-scale experiment. The smelting reduction process of high-iron red mud can be divided into two stages, where the reaction is predominantly governed by interfacial chemical reaction and diffusion control, respectively. The apparent activation energy of high-alkali high-iron red mud is lower than that observed for low-alkali high-iron red mud. The reduced slag can be used as a roadside stone material or cement clinker. This proposed method represents a sustainable process for the comprehensive utilization of high-iron red mud, which also promotes the minimization of red mud. Full article

Optical tactile sensing is gaining traction as a foundational technology in collaborative and human-interactive robotics, where reliable touch and pressure feedback are critical. Traditional systems based on total internal reflection (TIR) and frustrated TIR (FTIR) often require complex infrared setups and lack adaptability to curved or flexible surfaces. To overcome these limitations, we developed OptoSkin—a novel tactile platform leveraging direct time-of-flight (ToF) LiDAR principles for robust contact and pressure detection. In this extended study, we systematically evaluate how key optical properties of waveguide materials affect ToF signal behavior and sensing fidelity. We examine a diverse set of materials, characterized by varying light transmission (82–92)%, scattering coefficients (0.02–1.1) cm⁻¹, diffuse reflectance (0.17–7.40)%, and refractive indices 1.398–1.537 at the ToF emitter wavelength of 940 nm. Through systematic evaluation, we demonstrate that controlled light scattering within the material significantly enhances ToF signal quality for both direct touch and near-proximity sensing. These findings underscore the critical role of material selection in designing efficient, low-cost, and geometry-independent optical tactile systems. Full article