Search Results (171)

In this paper, a novel Tunneling-Drift-Diffusion Field-Effect Transistor (TDDFET) based on the combination of the quantum tunneling and conventional drift-diffusion mechanisms is proposed for the design of ternary logic circuits. The working principle of the TDDFET is analyzed in detail. Then, the device is packaged as a “black box” based on the table lookup method and further embedded into the HSPICE platform using the Verilog-A language. The basic unit circuits, such as the Standard Ternary Inverter (STI), Negative Ternary Inverter (NTI), Positive Ternary Inverter (PTI), Ternary NAND gate (T-NAND), and Ternary NOR gate (T-NOR), are designed. In addition, based on the designed unit circuits, the combinational logic circuits, such as the Ternary Encoder (T-Encoder), Ternary Decoder (T-Decoder), and Ternary Half Adder (T-HA), and the sequential logic circuits, such as the Ternary D-Latch and edge-triggered Ternary D Flip-Flop (T-DFF), are built, which has important significance for the subsequent investigation of ternary logic circuits. Full article

The increasing frequency and intensity of wildfires pose serious threats to ecosystems, property, and human safety worldwide. Accurate semantic segmentation of wildfire images is essential for real-time fire monitoring, spread prediction, and disaster response. However, existing deep learning methods heavily rely on large volumes of pixel-level annotated data, which are difficult and costly to obtain in real-world wildfire scenarios due to complex environments and urgent time constraints. To address this challenge, we propose a semi-supervised wildfire image segmentation framework that enhances segmentation performance under limited annotation conditions by integrating multi-scale structural information fusion and pixel-level contrastive consistency learning. Specifically, a Lagrange Interpolation Module (LIM) is designed to construct structured interpolation representations between multi-scale feature maps during the decoding stage, enabling effective fusion of spatial details and semantic information, and improving the model’s ability to capture flame boundaries and complex textures. Meanwhile, a Pixel Contrast Consistency (PCC) mechanism is introduced to establish pixel-level semantic constraints between CutMix and Flip augmented views, guiding the model to learn consistent intra-class and discriminative inter-class feature representations, thereby reducing the reliance on large labeled datasets. Extensive experiments on two public wildfire image datasets, Flame and D-Fire, demonstrate that our method consistently outperforms other approaches under various annotation ratios. For example, with only half of the labeled data, our model achieves 5.0% and 6.4% mIoU improvements on the Flame and D-Fire datasets, respectively, compared to the baseline. This work provides technical support for efficient wildfire perception and response in practical applications. Full article

This study addresses the complex challenges associated with flexible mattress (soft mattress) installation in the sharply curved and deep-water sections of the Yangtze River, particularly in the Yaozui revetment reconstruction project. Under extreme hydrodynamic conditions—water depths exceeding 30 m and velocities over 2.5 m/s—the risk of structural failures such as displacement, flipping, or tearing of the mattress becomes significant. To improve construction safety and stability, the study integrates numerical modeling and on-site strain monitoring to analyze the mechanical response of flexible mattresses during deployment. A three-dimensional finite element model based on the catenary theory was developed to simulate stress distributions under varying flow velocities and angles, revealing stress concentrations at the mattress’s upper edge and reinforcement junctions. Concurrently, a real-time monitoring system using high-precision strain sensors was deployed on critical shipboard components, with collected data analyzed through a remote IoT platform. The results demonstrate strong correlations between mattress strain, flow velocity, and water depth, enabling the identification of high-risk operational thresholds. The proposed monitoring and early-warning framework offers a practical solution for managing construction risks in extreme riverine environments and contributes to the advancement of intelligent construction management for underwater revetment works. Full article

The article describes a simple and scalable preparation of 2-monothiacalix[4]arene 7, the simplest representative of the mixed-bridged (CH₂ and S) calix[4]arenes. The synthesis is based on the condensation of linear building blocks (bisphenols), which are relatively readily available, and allows, depending on the conditions, the use of two alternative reaction routes that provide macrocycle 7 in high yield. The dynamic behavior of the basic macrocyclic skeleton was investigated using NMR spectroscopy at variable temperatures. High-temperature measurements showed that compound 7 undergoes a cone–cone equilibrium with activation free energy ΔG^# of the inversion process of 63 kJ·mol⁻¹. Interestingly, the same barrier for the oxidized sulfone derivative 14 shows a value of 60 kJ·mol⁻¹, indicating weakened hydrogen bonds at the lower rim of the calixarene. The same was also confirmed at low temperatures, when barriers to changing the direction of the cyclic hydrogen bond arrays (flip-flop mechanism) were determined (compare ΔG^# = 44 kJ·mol⁻¹ for 7 vs. ΔG^# = 40 kJ·mol⁻¹ for 14). Full article

Chinese Optical Character Recognition (OCR) technology is essential for digital transformation in Chinese regions, enabling automated document processing across various applications. However, Chinese OCR systems struggle with visually similar characters, where subtle stroke differences lead to systematic recognition errors that limit practical deployment accuracy. This study develops FLIP (Feedback Learning-based Intelligent Plugin), a lightweight post-processing plugin designed to improve Chinese OCR accuracy across different systems without external dependencies. The plugin operates through three core components as follows: UTF-8 encoding-based output parsing that converts OCR results into mathematical representations, error correction using information entropy and weighted similarity measures to identify and fix character-level errors, and adaptive feedback learning that optimizes parameters through user interactions. The approach functions entirely through mathematical calculations at the character encoding level, ensuring universal compatibility with existing OCR systems while effectively handling complex Chinese character similarities. The plugin’s modular design enables seamless integration without requiring modifications to existing OCR algorithms, while its feedback mechanism adapts to domain-specific terminology and user preferences. Experimental evaluation on 10,000 Chinese document images using four state-of-the-art OCR models demonstrates consistent improvements across all tested systems, with precision gains ranging from 1.17% to 10.37% and overall Chinese character recognition accuracy exceeding 98%. The best performing model achieved 99.42% precision, with ablation studies confirming that feedback learning contributes additional improvements from 0.45% to 4.66% across different OCR architectures. Full article

Rafoxanide, originally developed as a veterinary anthelmintic for the treatment of parasitic infections in livestock, has recently emerged as a promising therapeutic prospect in oncology. This compound has demonstrated notable antineoplastic effects against a variety of cancers, including skin, gastric, colorectal, and lung cancers, as well as hematological malignancies such as multiple myeloma. Rafoxanide exerts its anticancer activity through multiple complementary mechanisms, including the induction of endoplasmic reticulum stress, cell cycle arrest, apoptosis, and immunogenic cell death. Furthermore, the drug has been reported to inhibit key oncogenic signaling pathways (e.g., STAT3, NF-κB, c-FLIP, survivin) that contribute to tumor growth and metastasis. Preclinical studies in murine models have demonstrated significant reductions in tumor volume of up to 50% and a tumor-free rate exceeding 80%, with effective doses ranging from 7.5 to 40 mg/kg. This multitargeted mode of action distinguishes rafoxanide from conventional therapies and may help overcome resistance mechanisms that often limit the efficacy of cancer treatments. In this review, we summarize and discuss the growing body of evidence supporting rafoxanide’s therapeutic potential in oncology, as well as its possible applications in cancer treatment. Full article

We use Co doping to alter the magnetic relaxation dynamics in superparamagnetic nanofluids made of 18 nm average diameter Fe₃O₄ nanoparticles immersed in Isopar M. Ac-susceptibility data recorded at different frequencies and temperatures, χ″vs. T|_f, reveals a major (~100 K) increase in the superspin blocking temperature of the Co_0.2Fe_2.8O₄-based fluid (CFO) compared to its Fe₃O₄ counterpart (FO). We ascribe this behavior to the strengthening of the interparticle magnetic dipole interactions upon Co doping, as demonstrated by the relative χ″-peak temperature variation per frequency decade $\mathsf{\Phi }={\displaystyle \frac{∆\mathrm{T}}{\mathrm{T}·∆\mathrm{l}\mathrm{o}\mathrm{g}\left(\mathrm{f}\right)}}$, which decreases from Φ~0.15 in FO to Φ~0.025 in CFO. In addition, χ″vs. T|_f datasets from the CFO fluid reveal two magnetic events at temperatures T_p1 = 240 K and T_p2 = 275 K, both above the fluid’s freezing point (T_F = 197 K). We demonstrate that the physical rotation of the nanoparticles within the fluid, the Brown mechanism, is entirely responsible for the collective superspin relaxation observed at T_p1, whereas the Néel mechanism, the superspin flip across an energy barrier within the particle, is dominant at T_p2. We confirm this finding through fits of models that describe the temperature dependence of the relaxation time via the two mechanisms: ${\mathsf{\tau }}_{\mathrm{B}}(\mathrm{T})={\displaystyle \frac{3{\mathsf{\eta }}_0{\mathrm{V}}_{\mathrm{H}}}{{\mathrm{k}}_{\mathrm{B}}\mathrm{T}}}\exp \left[{\displaystyle \frac{{\mathrm{E}}^{\prime }}{{\mathrm{k}}_{\mathrm{B}}\left(\mathrm{T}-{{\mathrm{T}}_0}^{\prime }\right)}}\right]$ and ${\mathsf{\tau }}_{\mathrm{N}}\left(\mathrm{T}\right)={\mathsf{\tau }}_0\exp \left[{\displaystyle \frac{{\mathrm{E}}_{\mathrm{B}}}{{\mathrm{k}}_{\mathrm{B}}\left(\mathrm{T}-{\mathrm{T}}_0\right)}}\right]$. The best fits yield ${\mathsf{\gamma }}_0={\displaystyle \frac{3{\mathsf{\eta }}_0{\mathrm{V}}_{\mathrm{H}}}{{\mathrm{k}}_{\mathrm{B}}}}$= 1.5 × 10⁻⁸ s·K, E′/k_B = 7 03 K, and T₀′ = 201 K for the Brown relaxation, and E_B/k_B = 2818 K and T₀ = 143 K for the Néel relaxation. Full article

A prime role of biological membranes is to form barriers for material transport into and out of cells. Membranes consist of phospholipids with polar heads, which are presented to the aqueous solutions, and hydrophobic tails that form the membrane core. This construct prevents the permeation of hydrophilic, well-solvated molecules across the lipid hydrophobic barrier. The barrier is not absolute, and several approaches are available for efficient translocation. Channels and pumps enable selective and efficient transport across membranes. Another transport mechanism is passive permeation, in which permeants, without assistance, directly transport across membranes. Passive transport is coupled to transient defects in the membrane structure that make crossing the hydrophobic bilayer easier—for example, displacements of head groups from aqueous solution–membrane interface into the membrane core. The defects, in turn, are rare unless assisted by passively permeating molecules such as cell-penetrating peptides that distort the membrane structure. One possible defect is a phospholipid molecule with a head pointing to the hydrophobic core. This membrane distortion allows head group flipping from one layer to the other. We show computationally, using atomically detailed simulations and the Milestoning theory, that the presence of a cell-penetrating peptide in a membrane greatly increases phospholipid flip-flop rate and hence defect formation and the permeability of membranes. Full article

Over the past decade, deep neural networks (DNNs) have revolutionized the fields of computer vision (CV) and natural language processing (NLP), achieving unprecedented performance across a variety of tasks. The Vision Transformer (ViT) has emerged as a powerful alternative to convolutional neural networks (CNNs), leveraging self-attention mechanisms to capture long-range dependencies and global context. Owing to their flexible architecture and scalability, ViTs have been widely adopted in safety-critical applications such as autonomous driving, where system reliability is paramount. However, ViTs’ reliability issues induced by soft errors in large-scale digital integrated circuits have generally been overlooked. In this paper, we present a fine-grained fault sensitivity analysis of ViT variants under bit-flip fault injections, focusing on different ViT models, transformer encoder layers, weight matrix types, and attention-head dimensions. Experimental results demonstrate that the first transformer encoder layer is susceptible to soft errors due to its essential role in local and global feature extraction. Moreover, in the middle and later layers, the Multi-Layer Perceptron (MLP) sub-blocks dominate the computational workload and significantly influence representation learning, making them critical points of vulnerability. These insights highlight key reliability bottlenecks in ViT architectures when deployed in error-prone environments. Full article

In this paper, we propose the threshold successive cancellation flip (Th-SCF) decoding algorithm for polar codes, which enhances the performance of the SC decoder while maintaining low complexity. Theoretical analysis reveals that Th-SCF asymptotically delays the first error position (FEP, the first part where the SC decoder fails) with probability 1, ensuring high decoding performance. Simulation results show that the Th-SCF algorithm achieves performance comparable to the dynamic SC flip (D-SCF) algorithm, but with a reduction in complexity by eliminating the need for sorting operations. A key contribution of this work is the rigorous theoretical framework supporting the Th-SCF algorithm, distinguishing it from existing SC flip (SCF) decoding methods. This theoretical foundation not only explains the performance improvements but also provides insights into the underlying mechanisms of flipping. The proposed Th-SCF algorithm demonstrates strong performance across a wide range of code lengths and rates, and its performance remains stable within a certain threshold range, indicating its practical applicability in real-world communication systems. These results offer valuable perspectives for the design of efficient flip decoding strategies in 5G and future networks. Full article

Cholesterol plays an essential role in biological membranes and is crucial for maintaining their stability and functionality. In addition to biological membranes, cholesterol is also used in various synthetic lipid-based structures such as liposomes, proteoliposomes, and nanodiscs. Cholesterol regulates membrane properties by influencing the density of lipids, phase separation into liquid-ordered (Lo) and liquid-disordered (Ld) areas, and stability of protein–membrane interactions. For planar bilayers, cholesterol thickens the membrane, decreases permeability, and brings lipids into well-ordered domains, thereby increasing membrane rigidity by condensing lipid packing, while maintaining lateral lipid mobility in disordered regions to preserve overall membrane fluidity. It modulates membrane curvature in curved bilayers and vesicles, and stabilizes low-curvature regions, which are important for structural integrity. In liposomes, cholesterol facilitates drug encapsulation and release by controlling bilayer flexibility and stability. In nanodiscs, cholesterol enhances structural integrity and protein compatibility, which enables the investigation of protein–lipid interactions under physiological conditions. In proteoliposomes, cholesterol regulates the conformational stability of embedded proteins that have implications for protein–lipid interaction. Developments in molecular dynamics (MD) techniques, from coarse-grained to all-atom simulations, have shown how cholesterol modulates lipid tail ordering, membrane curvature, and flip-flop behavior in response to concentration. Such simulations provide insights into the mechanisms underlying membrane-associated diseases, aiding in the design of efficient drug delivery systems. In this review, we combine results from MD simulations to provide a synoptic explanation of cholesterol’s complex function in regulating membrane behavior. This synthesis combines fundamental biophysical information with practical membrane engineering, underscoring cholesterol’s important role in membrane structure, dynamics, and performance, and paving the way for rational design of stable and functional lipid-based systems to be used in medicine. In this review, we gather evidence from MD simulations to provide an overview of cholesterol’s complex function regulating membrane behavior. This synthesis connects the fundamental biophysical science with practical membrane engineering, which highlights cholesterol’s important role in membrane structure, dynamics, and function and helps us rationally design stable and functional lipid-based systems for therapeutic purposes. Full article

Norovirus, a major cause of acute gastroenteritis, possesses a single-stranded positive-sense RNA genome. The viral 3C-like cysteine protease (3CL^pro) plays a critical role in processing the viral polyprotein into mature non-structural proteins, a step essential for viral replication. Targeting 3CL^pro has emerged as a promising strategy for developing small-molecule inhibitors against Norovirus. In this study, we employed a combination of virtual screening and the FlipGFP assay to identify potential inhibitors targeting the 3CL^pro of Norovirus genotype GII.4. A library of approximately 58,800 compounds was screened using AutoDock Vina tool, yielding 20 candidate compounds based on their Max Affinity scores. These compounds were subsequently evaluated using a cell-based FlipGFP assay. Among them, eight compounds demonstrated significant inhibitory activity against 3CL^pro, with Gedatolisib showing the most potent effect (IC₅₀ = 0.06 ± 0.01 μM). Molecular docking and molecular dynamics simulations were conducted to explore the binding mechanisms and structural stability of the inhibitor–3CL^pro complexes. Our findings provide valuable insights into the development of antiviral drugs targeting Norovirus 3CL^pro, offering potential therapeutic strategies to combat Norovirus infections. Full article

Trolling behavior is not simply a result of ‘bad actors’, an individual trait, or a linguistic phenomenon, but emerges from complex contagious social dynamics. This study uses formal concepts from information theory and complexity science to study it as such. The data comprised over 13 million Reddit comments, which were classified as troll or non-troll messages using the BERT model, fine-tuned with a human coding set. We derive the unique, minimally complex, and maximally predictive model from statistical mechanics, i.e., ε-machines and transducers, and can distinguish which aspects of trolling behaviors are both self-motivated and socially induced. While the vast majority of self-driven dynamics are like flipping a coin (86.3%), when social contagion is considered, most users (95.6%) show complex hidden multiple-state patterns. Within this complexity, trolling follows predictable transitions, with, for example, a 76% probability of remaining in a trolling state once it is reached. We find that replying to a trolling comment significantly increases the likelihood of switching to a trolling state or staying in it (72%). Besides being a showcase for the use of information-theoretic measures from dynamic systems theory to conceptualize human dynamics, our findings suggest that users and platform designers should go beyond calling out and removing trolls, but foster and design environments that discourage the dynamics leading to the emergence of trolling behavior. Full article

Artificial retinal devices require a high-density electrode array and mechanical flexibility to effectively stimulate retinal cells. However, designing such devices presents significant challenges, including the need to conform to the curvature of the eyeball and cover a large area using a single platform. To address these issues, we developed a parylene-based multi-module retinal device (MMRD) integrating a complementary metal-oxide semiconductor (CMOS) system. The proposed device is designed for suprachoroidal transretinal stimulation, with each module comprising a parylene-C thin-film substrate, a CMOS chip, and a ceramic substrate housing seven platinum electrodes. The smart CMOS system significantly reduces wiring complexity, enhancing the device’s practicality. To improve fabrication reliability, we optimized the encapsulation process, introduced multiple silane coupling modifications, and utilized polyvinyl alcohol (PVA) for easier detachment in flip-chip bonding. This study demonstrates the fabrication and evaluation of the MMRD through in vitro and in vivo experiments. The device successfully generated the expected current stimulation waveforms in both settings, highlighting its potential as a promising candidate for future artificial vision applications. Full article

Aiming at the challenges of multiple influencing factors, complex data characteristics, and limited data in streaming movie prediction, a feature fusion long- and short-term memory enhanced attention network (FFLSTMEA) was developed to achieve the short-term prediction of key indicators such as streaming movie revenue and to support business decisions. To address issues such as single data dimensions, difficulty in focusing on key information, limited data scale, and lack of diversity, several improvements were introduced. First, a feature fusion strategy was designed to integrate multi-dimensional features, including holiday factors, movie characteristics, principal component analysis (PCA) for time series dimensionality reduction, and platform exclusivity. These features were combined with a long- and short-term memory network to explore their internal correlations. Second, an attention mechanism was applied to dynamically assign importance to different time steps and features, enabling the model to focus on the most critical information based on time periods and movie types. Finally, the model’s capacity to capture data structures and variations was improved by using data augmentation techniques, such as flipping and scaling operations, to increase the dataset’s size and diversity. The experimental results show that the proposed algorithm FFLSTMEA achieves better prediction results with an average absolute error (MAE) of 3.50, a root mean square error (RMSE) of 5.28, and a coefficient of determination (R-squared) of 0.87 in the evaluation index. And compared with convolutional networks (CNN) class, long short-term memory (LSTM) class and Transformer class prediction methods, it performs better in terms of accuracy and stability, providing a more reliable basis for the operation and promotion of online movies. Full article