Functionalized layered double hydroxide nanoparticles as an intelligent nanoplatform for synergistic photothermal therapy and chemotherapy of tumors

In this work, a novel layered double hydroxide (LDH)-based multifunctional nanoplatform was built for synergistic temperature photothermal therapy (PTT)/chemotherapy. The platform was modified using the peptide B3int to target cancer cells with overexpression of integrin αvβ3. Indocyanine green (ICG) and doxorubicin (DOX) were loaded into the nanocarrier (LDH-PEG-B3int NPs) to form a system having a high DOX drug loading (18.62%) and a remarkable photothermal conversion efficiency of 25.38%. It also showed pH-responsive and near-infrared (NIR)-triggered DOX release. In vitro and in vivo studies indicated that the anti-tumor activity of the combined delivery system was significantly higher than that of a single delivery system. This co-delivery nanosystem may be helpful for future application in the clinical treatment of cancer

Security Defense of Large Scale Networks Under False Data Injection Attacks: An Attack Detection Scheduling Approach

In large scale networks, communication links between nodes are easily injected with false data by adversaries, so this paper proposes a novel security defense strategy to ensure the security of the network from the perspective of attack detection scheduling. Compared with existing attack detection methods, the attack detection scheduling strategy in this paper only needs to detect half of the neighbor node information to ensure the security of the node local state estimation. We first formulate the problem of selecting the sensor to be detected as a combinatorial optimization problem, which is Nondeterminism Polynomial hard (NP-hard). To solve the above problem, we convert the objective function into a submodular function. Then, we propose an attack detection scheduling algorithm based on sequential submodular maximization, which incorporates expert problem to better cope with dynamic attack strategies. The proposed algorithm can run in polynomial time with a theoretical lower bound on the optimization rate. In addition, the proposed algorithm can guarantee the security of the whole network under two kinds of insecurity conditions from the perspective of the augmented estimation error. Finally, a numerical simulation of the industrial continuous stirred tank reactor verifies the effectiveness of the developed approach

Sensory Features in Affective Analysis: A Study Based on Neural Network Models

This study proposes an ensemble model to incorporate sensory features of lexical items in English from external resources into neural affective analysis frameworks. This allows the models to take the combined effects of bi-directional feeling between the sensory lexicon and the writer to infer human affective knowledge. We evaluate our model on two affective analysis tasks. The ensemble model exhibits the best accuracy and the results with 1% F1-score improvement over the baseline LSTM model in the sentiment analysis task. The performance shows that perceptual information can contribute to the performance of sentiment classification tasks significantly. This study also provides a support for the linguistic finding that correlations exist between sensory features and sentiments in the language

SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification

In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains. Yet, the significant influence of sensory experiences on emotional responses is undeniable. The natural language processing (NLP) community has often missed the opportunity to merge sensory knowledge with emotion classification. To address this gap, we propose SensoryT5, a neurocognitive approach that integrates sensory information into the T5 (Text-to-Text Transfer Transformer) model, designed specifically for fine-grained emotion classification. This methodology incorporates sensory cues into the T5’s attention mechanism, enabling a harmonious balance between contextual understanding and sensory awareness. The resulting model amplifies the richness of emotional representations. In rigorous tests across various detailed emotion classification datasets, SensoryT5 showcases improved performance, surpassing both the foundational T5 model and current state-of-the-art works. Notably, SensoryT5’s success signifies a pivotal change in the NLP domain, highlighting the potential influence of neurocognitive data in refining machine learning models’ emotional sensitivity

Multiband effects in thermoelectric and electrical transport properties of kagome superconductors AAV3_3Sb5_5 (AA = K, Rb, Cs)

We studied the effects of multiband electronic structure on the thermoelectric and electrical transport properties in the normal state of kagome superconductors $A$V$_3$Sb$_5$ ($A$ = K, Rb, Cs). In all three members, the multiband nature is manifested by sign changes in the temperature dependence of the Seebeck and Hall resistivity, together with sublinear response of the isothermal Nernst and Hall effects to external magnetic fields in the charge ordered state. Moreover, ambipolar transport effects appear ubiquitously in all three systems, giving rise to sizable Nernst signal. Finally, possible origins of the sign reversal in the temperature dependence of the Hall effect are discussed.Comment: 8 pages, 5 figures. To appear in New Journal of Physic

Leveraging Sensory Knowledge into Text-to-Text Transfer Transformer for Enhanced Emotion Analysis

This study proposes an innovative model (i.e., SensoryT5), which integrates sensory knowledge into the T5 (Text-to-Text Transfer Transformer) framework for emotion classification tasks. By embedding sensory knowledge within the T5 model's attention mechanism, SensoryT5 not only enhances the model's contextual understanding but also elevates its sensitivity to the nuanced interplay between sensory information and emotional states. Experiments on four emotion classification datasets, three sarcasm classification datasets one subjectivity analysis dataset, and one opinion classification dataset (ranging from binary to 32-class tasks) demonstrate that our model outperforms state-of-the-art baseline models (including the baseline T5 model) significantly. Specifically, SensoryT5 achieves a maximal improvement of 3.0% in both the accuracy and the F1 score for emotion classification. In sarcasm classification tasks, the model surpasses the baseline models by the maximal increase of 1.2% in accuracy and 1.1% in the F1 score. Furthermore, SensoryT5 continues to demonstrate its superior performances for both subjectivity analysis and opinion classification, with increases in ACC and the F1 score by 0.6% for the subjectivity analysis task and increases in ACC by 0.4% and the F1 score by 0.6% for the opinion classification task, when compared to the second-best models.} These improvements underscore the significant potential of leveraging cognitive resources to deepen NLP models' comprehension of emotional nuances and suggest an interdisciplinary research between the areas of NLP and neuro-cognitive science

Charge fluctuations above TCDWT_\mathrm{CDW} revealed by glasslike thermal transport in kagome metals AAV3_3Sb5_5 (AA = K, Rb, Cs)

We present heat capacity, electrical and thermal transport measurements of kagome metals $A$V$_3$Sb$_5$ ($A$ = K, Rb, Cs). In all three compounds, development of short-range charge fluctuations above the charge density wave (CDW) transition temperature $T_\mathrm{CDW}$ strongly scatters phonons via electron-phonon coupling, leading to glasslike phonon heat transport, i.e., phonon thermal conductivity decreases weakly upon cooling. Once the long-range charge order sets in below $T_\mathrm{CDW}$, short-range charge fluctuations are quenched, and the typical Umklapp scattering dominated phonon heat transport is recovered. The charge-fluctuations-induced glasslike phonon thermal conductivity implies sizable electron-phonon coupling in $A$V$_3$Sb$_5$.Comment: 8 pages, 5 figure

Measuring the Optical Absorption Cross Sections of Au−Ag Nanocages and Au Nanorods by Photoacoustic Imaging

This paper presents a method for measuring the optical absorption cross sections (σ_a) of Au−Ag nanocages and Au nanorods. The method is based on photoacoustic (PA) imaging, where the detected signal is directly proportional to the absorption coefficient (μ_a) of the nanostructure. For each type of nanostructure, we first obtained μ_a from the PA signal by benchmarking against a linear calibration curve (PA signal versus μ_a) derived from a set of methylene blue solutions with different concentrations. We then calculated σ_a by dividing the μ_a by the corresponding concentration of the Au nanostructure. Additionally, we obtained the extinction cross section (σ_e, sum of absorption and scattering) from the extinction spectrum recorded using a conventional UV−vis−NIR spectrometer. From the measurements of σ_a and σ_e, we were able to easily derive both the absorption and scattering cross sections for each type of gold nanostructure. The ratios of absorption to extinction obtained from experimental and theoretical approaches agreed well, demonstrating the potential use of this method in determining the optical absorption and scattering properties of gold nanostructures and other types of nanomaterials

Photoacoustic quantification of the optical absorption cross-sections of gold nanostructures

This study demonstrates a method for measuring the optical absorption cross-sections (σ_a) of Au-Ag nanocages and Au nanorods using photoacoustic (PA) sensing. PA signals are directly proportional to the absorption coefficient (μ_a) of the nanostructure. For each type of nanostructure, we first obtained μa from the PA signal by benchmarking against a linear calibration curve (PA signal vs. μ_a) derived from a set of methylene blue solutions with different concentrations. We then calculated σ_a by dividing the μ_a by the corresponding concentration of the Au nanostructure. Additionally, we obtained the extinction cross-section (σ_e, sum of absorption and scattering cross-sections) from the extinction spectrum recorded using a conventional UV-vis-NIR spectrometer. From the measurements of σ_a and σ_e, we were able to easily derive both the absorption and scattering cross-sections for each type of gold nanostructure. This method can potentially provide the optical absorption and scattering properties of gold nanostructures and other types of nanomaterials