Search Results (348)

Lung cancer remains one of the most prevalent and lethal malignancies worldwide. Although conventional treatments such as surgery, chemotherapy, and radiotherapy have modestly improved patient survival, their overall efficacy remains limited, and the prognosis is generally poor. In recent years, immunotherapy, particularly immune checkpoint inhibitors, has revolutionized cancer treatment. Nevertheless, the immunosuppressive tumor microenvironment, tumor heterogeneity, and immune escape mechanisms significantly restrict the clinical benefit, which falls short of expectations. Within this context, cancer vaccines have emerged as a promising immunotherapeutic strategy. By activating the host immune system to eliminate tumor cells, cancer vaccines offer high specificity, low toxicity, and the potential to induce long-lasting immune memory. These advantages have positioned them as a focal point in cancer immunotherapy research. This paper provides a comprehensive overview of recent clinical advances in lung cancer vaccines, discusses the major challenges impeding their clinical application, and explores potential strategies to overcome these barriers. Full article

In order to achieve 0.1 m resolution and fully polarimetric observation capabilities for airborne SAR systems, the adoption of stepped-frequency modulation waveform combined with the polarization time-division transmit/receive (T/R) technique proves to be an effective technical approach. Considering the issue of range resolution degradation and paired echoes caused by multichannel amplitude–phase mismatch in fully polarimetric airborne SAR with 0.1 m resolution, an amplitude–phase error estimation algorithm based on echo data is proposed in this paper. Firstly, the subband amplitude spectrum correction curve is obtained by the statistical average of the subband amplitude spectrum. Secondly, the paired-echo broadening function is obtained by selecting high-quality sample points after single-band imaging and the nonlinear phase error within the subbands is estimated via Sinusoidal Frequency Modulation Fourier Transform (SMFT). Thirdly, based on the minimum entropy criterion of the synthesized compressed pulse image, residual linear phase errors between subbands are quickly acquired. Finally, two-dimensional cross-correlation of the image slice is utilized to estimate the positional deviation between polarization channels. This method only requires high-quality data samples from the echo data, then rapidly estimates both intra-band and inter-band amplitude/phase errors by using SMFT and the minimum entropy criterion, respectively, with the characteristics of low computational complexity and fast convergence speed. The effectiveness of this method is verified by the imaging results of the experimental data. Full article

As large language models (LLMs) increasingly enter specialized domains, inference without external resources often leads to knowledge gaps, opaque reasoning, and hallucinations. To address these challenges in power equipment defect grading, we propose a zero-shot question-answering framework that requires no task-specific examples. Our system performs two-stage retrieval—first using a Sentence-BERT model fine-tuned on power equipment maintenance texts for coarse filtering, then combining TF-IDF and semantic re-ranking for fine-grained selection of the most relevant knowledge snippets. We embed both the user query and the retrieved evidence into a Chain-of-Thought (CoT) prompt, guiding the pre-trained LLM through multi-step reasoning with self-validation and without any model fine-tuning. Experimental results show that on a held-out test set of 218 inspection records, our method achieves a grading accuracy of 54.2%, which is 6.0 percentage points higher than the fine-tuned BERT baseline at 48.2%; an Explanation Coherence Score (ECS) of 4.2 compared to 3.1 for the baseline; a mean retrieval latency of 28.3 ms; and an average LLM inference time of 5.46 s. Ablation and sensitivity analyses demonstrate that a fine-stage retrieval pool size of k = 30 offers the optimal trade-off between accuracy and latency; human expert evaluation by six senior engineers yields average Usefulness and Trustworthiness scores of 4.1 and 4.3, respectively. Case studies across representative defect scenarios further highlight the system’s robust zero-shot performance. Full article

High moisture content (MC) harms wheat storage quality and readily leads to mold growth. Accurate localization of abnormal/high-moisture regions enables early warning, ensuring proper storage and reducing economic losses. The present study introduces the 2D microwave scanning method and investigates a novel localization method for addressing such a challenge. Both static and scanning experiments were performed on a developed mobile and non-destructive microwave detection system to quantify the MC of wheat and then locate abnormal moisture regions. For quantifying the wheat’s MC, a dual-parameter wheat MC prediction model with the random forest (RF) algorithm was constructed, achieving a high accuracy (R² = 0.9846, MSE = 0.2768, MAE = 0.3986). MC scanning experiments were conducted by synchronized moving waveguides; the maximum absolute error of MC prediction was 0.565%, with a maximum relative error of 3.166%. Furthermore, both one- and two-dimensional localizing methods were proposed for localizing abnormal moisture regions. The one-dimensional method evaluated two approaches—attenuation value and absolute attenuation gradient—using computer simulation technology (CST) modeling and scanning experiments. The experimental results confirmed the superior performance of the absolute gradient method, with a center detection error of less than 12 mm in the anomalous wheat moisture region and a minimum width detection error of 1.4 mm. The study performed two-dimensional antenna scanning and effectively imaged the high-MC regions using phase delay analysis. The imaging results coincide with the actual locations of moisture anomaly regions. This study demonstrated a promising solution for accurately localizing the wheat’s abnormal/high-moisture regions with the use of an emerging microwave transmission method. Full article

CdTe nanocrystals (NCs) have emerged as a promising active layer for efficient thin-film solar cells due to their outstanding optical properties and simple processing techniques. However, the low hole concentration and high resistance in the CdTe NC active layer lead to high carrier recombination in the back contact. Herein, we developed a novel 2-iodothiophene as a wet etching solution to treat the surface of CdTe NC. We found that surface treatment using 2-iodothiophene leads to reduced interface defects and improves carrier mobility simultaneously. The surface properties of CdTe NC thin films after iodide salt treatment are revealed through surface element analysis, space charge limited current (SCLC) studies, and energy level investigations. The CdTe NC solar cells with 2-iodothiophene treatment achieved power conversion efficiency (PCE) of 4.31% coupled with a higher voltage than in controlled devices (with NH₄I-treated ones, 3.08% PCE). Full article

Sign language recognition plays a crucial role in enabling communication for deaf individuals, yet current methods face limitations such as sensitivity to lighting conditions, occlusions, and lack of adaptability in diverse environments. This study presents a wearable multi-channel tactile sensing system based on smart textiles, designed to capture subtle wrist and finger motions for static sign language recognition. The system leverages triboelectric yarns sewn into gloves and sleeves to construct a skin-conformal tactile sensor array, capable of detecting biomechanical interactions through contact and deformation. Unlike vision-based approaches, the proposed sensor platform operates independently of environmental lighting or occlusions, offering reliable performance in diverse conditions. Experimental validation on American Sign Language letter gestures demonstrates that the proposed system achieves high signal clarity after customized filtering, leading to a classification accuracy of 94.66%. Experimental results show effective recognition of complex gestures, highlighting the system’s potential for broader applications in human-computer interaction. Full article

The cytokinin response regulator (ARR) gene is essential for cytokinin signal transduction, which plays a crucial role in plant growth and development. However, the functional mechanism of ARR genes in cotton leaf abscission remains incompletely understood. In this study, a total of 86 ARR genes were identified within the genome of Gossypium hirsutum. These genes were categorized into four distinct groups based on their phylogenetic characteristics, supported by analyses of gene structures and conserved protein motifs. The GhARR genes exhibited an uneven distribution across 25 chromosomes, with three pairs of tandem duplication events observed. Both segmental and tandem duplication events significantly contributed to the expansion of the ARR gene family. Furthermore, numerous putative cis-elements were identified in the promoter regions, with hormone and stress-related elements being common among all 86 GhARRs. Transcriptome expression profiling screening results demonstrated that GhARRs may play a mediating role in cotton’s response to TDZ (thidiazuron). The functional validation of GhARR16, GhARR43, and GhARR85 using virus-induced gene silencing (VIGS) technology demonstrated that the silencing of these genes led to pronounced leaf wilting and chlorosis in plants, accompanied by a substantial decrease in petiole fracture force. Overall, our study represents a comprehensive analysis of the G. hirsutum ARR gene family, revealing their potential roles in leaf abscission regulation. Full article

Accurate settlement prediction for high-speed railway (HSR) soft foundations remains challenging due to the irregular and dynamic nature of real-world monitoring data, often represented as non-equidistant and non-stationary time series (NENSTS). Existing empirical models lack clear applicability criteria under such conditions, resulting in subjective model selection. This study introduces a Monte Carlo-based evaluation framework that integrates data-driven simulation with geotechnical principles, embedding the concept of symmetry across both modeling and assessment stages. Equivalent permeability coefficients (EPCs) are used to normalize soil consolidation behavior, enabling the generation of a large, statistically robust dataset. Four empirical settlement prediction models—Hyperbolic, Exponential, Asaoka, and Hoshino—are systematically analyzed for sensitivity to temporal features and resistance to stochastic noise. A symmetry-aware comprehensive evaluation index (CEI), constructed via a robust entropy weight method (REWM), balances multiple performance metrics to ensure objective comparison. Results reveal that while settlement behavior evolves asymmetrically with respect to EPCs over time, a symmetrical structure emerges in model suitability across distinct EPC intervals: the Asaoka method performs best under low-permeability conditions (EPC ≤ 0.03 m/d), Hoshino excels in intermediate ranges (0.03 < EPC ≤ 0.7 m/d), and the Exponential model dominates in highly permeable soils (EPC > 0.7 m/d). This framework not only quantifies model robustness under complex data conditions but also formalizes the notion of symmetrical applicability, offering a structured path toward intelligent, adaptive settlement prediction in HSR subgrade engineering. Full article

Globally, phosphogypsum (PG) is the primary by-product of the phosphorus industry. Aspergillus niger (A. niger), one of the most powerful types of phosphate-solubilizing fungi (PSF), can secrete organic acids to dissolve the phosphates in PG. This study investigated heavy metal (HM) remediation by PG and A. niger under the co-existence of Pb and Cd. It demonstrated that 1 mmol/L Pb²⁺ stimulated the bioactivity of A. niger during incubation, based on the CO₂ emission rate. PG successfully functioned as P source for the fungus, and promoted the growth of the fungal cells. Meanwhile, it also provided sulfates to immobilize Pb in the solution. The subsequently generated anglesite was confirmed using SEM imaging. The immobilization rate of Pb reached over 95%. Under co-existence, Pb²⁺ and 0.01 mmol/L Cd²⁺ maximized the stimulating effect of A. niger. However, the biotoxicity of Pb²⁺ and elevated Cd²⁺ (0.1 mmol/L) counterbalanced the stimulating effect. Finally, 1 mmol/L Cd²⁺ dramatically reduced the fungal activity. In addition, organic matters from the debris of A. niger could still bind Pb²⁺ and Cd²⁺ according to the significantly lowered water-soluble Pb and Cd concentrations. In all treatments with the addition of Cd²⁺, the relatively high biotoxicity of Cd²⁺ induced A. niger to absorb more Pb²⁺ to minimize the sorption of Cd²⁺ based on the XRD results. The functional group analysis of ATR-IR also confirmed the phenomenon. This pathway maintained the stability of Pb²⁺ immobilization using the fungus and PG. This study, hence, shed light on the application of A. niger and solid waste PG to remediate the pollution of Pb and Cd. Full article

The deep integration and application of artificial intelligence to organic chemistry are propelling the development of organic chemistry synthesis laboratories toward an intelligent automated laboratory model characterized by “hardware + software + AI”. This paper systematically explores the overall framework of AI-driven intelligent laboratories for organic chemistry synthesis, achieving automation and flexibility through standardized experimental integration workstations and intelligent scheduling and collaborative management of experimental resources. By leveraging multimodal databases, the integration of large models, machine learning, and other AI technologies enables AI-driven closed-loop intelligent chemical experiments, including product prediction, molecular retrosynthetic planning, and synthesis reaction optimization. The paper proposes a cloud-based shared operational model for chemical laboratories, aiming to achieve socialized sharing and intelligent matching of experimental resources, thereby facilitating the accumulation and sharing of chemical experimental data to promote the intelligent development of organic chemistry synthesis experiments. Practical cases of building intelligent chemical laboratories are shared, providing paths for technology implementation in constructing the next generation of automated and intelligent chemical laboratories. Full article

Spectrometers play a crucial role in photonic applications, but their design involves trade-offs related to miniaturization, spectral fidelity, and their measurement dynamic range. We demonstrated a high-resolution, low-stray-light spectrometer with a compact size comprising two symmetric multiple-diffraction monochromators. We analyzed the spectral resolution and stray light and built a platform with two double-diffraction monochromators. Multiple diffractions on one grating increased the spectral resolution without volumetric expansion, and the subtractive double-monochromator configuration suppressed stray light effectively. The simulation and experimental results show that compared with single diffraction, repeated diffractions improved the resolution by 5–7 times. The spectral resolution of the home-built setup was 18.8 pm at 1480 nm. The subtractive double monochromator significantly weakened the stray light. The optical signal-to-noise ratio was increased from 34.76 dB for the single monochromator to 69.17 dB for the subtractive double monochromator. This spectrometer design is promising for broadband high-resolution spectral analyses. Full article

While crumb rubber powder has demonstrated effectiveness in enhancing the mechanical properties of asphalt binders, its viscoelastic behavior under freeze–thaw conditions in clean water and de-icing salt, typically urban road conditions in winter, remains insufficiently explored. This study systematically investigated the microstructural evolution, compositional changes, and mechanical behavior of asphalt modified with rubber under the influence of freeze–thaw conditions in clean water and de-icing salt. The findings revealed that rubber powder incorporation accelerates the precipitation of oil, enhancing material stability in both aqueous and saline environments. Notably, asphalt containing 10% crumb rubber powder (Asphalt-10% RP) and 20% crumb rubber powder (Asphalt-20% RP) exhibit creep recovery rates 50.53% and 28.94% higher, respectively, under de-icing salt freeze–thaw cycles than under clean water freeze–thaw cycles. Therefore, in regions with extremely low temperatures and frequent snowfall, rubber powder exhibits significant research potential, providing theoretical support for the design of asphalt pavements in cold climates. Full article

This paper addresses the challenge of ineffective coordination between terminal maneuvering and precision strike capabilities in hypersonic unmanned aerial vehicles (UAVs). To resolve this issue, an intelligent spiral maneuver trajectory generation method utilizing a virtual trajectory framework is proposed. Initially, a relative motion model between the UAV and the virtual center of mass (VCM) is established based on the geometric principles of the Archimedean spiral. Subsequently, the interaction dynamics between the VCM and the target are formulated as a Markov decision process (MDP). A deep reinforcement learning (DRL) approach, employing the proximal policy optimization (PPO) algorithm, is implemented to train a policy network capable of end-to-end virtual trajectory generation. Ultimately, the relative spiral motion is superimposed onto the generated virtual trajectory to synthesize a composite spiral maneuvering trajectory. The simulation results demonstrate that the proposed method achieves expansive spiral maneuvering ranges while ensuring precise target strikes. Full article

Satellite cloud images exhibit complex multidimensional characteristics, including spectral, textural, and spatiotemporal dynamics. The temporal evolution of cloud systems plays a crucial role in accurate classification, particularly under the coexistence of multiple weather systems. However, most existing models—such as those based on convolutional neural networks (CNNs), Transformer architectures, and their variants like Swin Transformer—primarily focus on spatial modeling of static images and do not explicitly incorporate temporal information, thereby limiting their ability to effectively integrate spatiotemporal features. To address this limitation, we propose SIG-ShapeFormer, a novel classification model specifically designed for satellite cloud images with temporal continuity. To the best of our knowledge, this work is the first to transform satellite cloud data into multivariate time series and introduce a unified framework for multi-scale and multimodal feature fusion. SIG-Shapeformer consists of three core components: (1) a Shapelet-based module that captures discriminative and interpretable local temporal patterns; (2) a multi-scale Inception module combining 1D convolutions and Transformer encoders to extract temporal features across different scales; and (3) a differentially enhanced Gramian Angular Summation Field (GASF) module that converts time series into 2D texture representations, significantly improving the recognition of cloud internal structures. Experimental results demonstrate that SIG-ShapeFormer achieves a classification accuracy of 99.36% on the LSCIDMR-S dataset, outperforming the original ShapeFormer by 2.2% and outperforming other CNN- or Transformer-based models. Moreover, the model exhibits strong generalization performance on the UCM remote sensing dataset and several benchmark tasks from the UEA time-series archive. SIG-Shapeformer is particularly suitable for remote sensing applications involving continuous temporal sequences, such as extreme weather warnings and dynamic cloud system monitoring. However, it relies on temporally coherent input data and may perform suboptimally when applied to datasets with limited or irregular temporal resolution. Full article

The honey badger algorithm (HBA) has gained significant attention as a metaheuristic optimization method; however, despite these design strengths, it still faces challenges such as premature convergence, suboptimal exploration–exploitation balance, and low population diversity. To address these limitations, we integrate a power-law degree distribution (PDD) topology into the HBA population structure. Three improved versions of the HBA are proposed, with each employing different population update strategies: PDDHBA-R, PDDHBA-B, and PDDHBA-H. In the PDDHBA-R strategy, individuals randomly select neighbours as references, promoting diversity and randomness. The PDDHBA-B strategy allows individuals to select the best neighbouring individual, speeding up convergence. The PDDHBA-H strategy combines both approaches, using random selection for elite individuals and best selection for non-elite individuals. These algorithms were tested on 30 benchmark functions from CEC2017, 21 real-world problems from CEC2011, and four constrained engineering problems. The experimental results show that all three improvements significantly improve the performance of the HBA, with PDDHBA-H delivering the best results across various tests. Further analysis of the parameter sensitivity, computational complexity, population diversity, and exploration–exploitation balance confirms the superiority of PDDHBA-H, highlighting its potential for use in complex optimization problems. Full article