Explore the vast range of titles available.

Romantic music played a crucial role in the evolution of Western musical arts, bridging the classical traditions with modern expressions. Franz Liszt, in an era that emphasized individual emotion and intellect, developed his \"emotive music aesthetics,\" seamlessly integrating natural imagery with artistic expression through instrumental music. Through the use of program music and poetic atmospheres, Liszt invites listeners to engage in imaginative and creative interpretations, transforming music into a profound medium for emotional articulation. This study examines the natural imagery in Liszt's Première Année: Suisse from various analytical perspectives, including the composer's personal emotions and creative philosophy, historical and cultural contexts, and the musical aesthetic significance. Additionally, the study explores the lasting influence of this work on subsequent musical compositions, aesthetic development, and its contemporary artistic value.

Acidic CO 2 electroreduction (CO 2 R) using renewable electricity holds promise for high-efficiency generation of storable liquid chemicals with up to 100% CO 2 utilization. However, the strong parasitic hydrogen evolution reaction (HER) limits its selectivity and energy efficiency (EE), especially at ampere-level current densities. Here we present that enhancing CO 2 R intermediate coverage on catalysts promotes CO 2 R and concurrently suppresses HER. We identified and engineered robust Cu 6 Sn 5 catalysts with strong * OCHO affinity and weak * H binding, achieving 91% Faradaic efficiency (FE) for formic acid (FA) production at 1.2 A cm −2 and pH 1. Notably, the single-pass carbon efficiency reaches a new benchmark of 77.4% at 0.5 A cm −2 over 300 hours. In situ electrochemical Fourier-transform infrared spectroscopy revealed Cu 6 Sn 5 enhances * OCHO coverage ~2.8× compared to Sn at pH 1. Using a cation-free, solid-state-electrolyte-based membrane-electrode-assembly, we produce 0.36 M pure FA at 88% FE over 130 hours with a marked full-cell EE of 37%. The increased surface coverage of CO 2 electroreduction (CO 2 R) intermediates promotes CO 2 R at high operating current densities under strongly acidic conditions. Here the authors report a 77.4% single-pass carbon efficiency for conversion of acidic CO 2 R to pure formic acid.

Sentiment analysis has become a transformative technology in various contexts, particularly in Natural Language Processing (NLP), social media analytics, and literary analysis, as it can extract information from a wide range of texts. The advancements in deep learning, particularly with transformer models such as BERT and graph-based models like GATs, have enabled faster progress in analyzing complex language structures. However, the issue lies in incorporating these technologies into classical Chinese literature, which involves delicate syntax, semantics, and emotions that are difficult to harness using traditional methods. The existing methods, which rely on strictly labeled data or unsupervised learning methods that do not effectively manage contextual dependencies, are very limited in analyzing historical or philosophical texts that abound in metaphor and implicit sentiment. To minimize the limitations, this paper proposes an unsupervised deep learning framework that integrates BERT embeddings, sentiment lexicon enrichment, and graph attention networks (GATs) for sentiment analysis in classical Chinese literature. Firstly, the BERT-based model extracts contextualised embeddings from a raw text, providing a deep understanding of semantics. Secondly, embedding includes sentiment-specific data from the NTUSD lexicon, thus injecting it with emotional information. Thirdly, a graph-based formulation is developed, in which words are represented as nodes, and the relations between them are defined using GATs to modify the features of nodes based on their significance in the context. Finally, unsupervised sentiment labelling, or K-Means clustering, is used to classify sentiment. The experimental results demonstrate the proposed model’s efficiency – an accuracy of 0.95, precision of 0.97, recall of 0.96, and F1-score of 0.91 in several runs. These results surpass those of the traditional approach, which includes SentiCNN, MLT-ML4, and BERT-LLSTM-DL, which achieve an accuracy score of 0.90 to 0.95. Additionally, the comparison with large-scale foundation models (such as ChatGPT-4o and DeepSeek R1) in zero-shot prompt-based classification further validates the domain-adapted advantage of our model in the classical Chinese text processing. These results demonstrate that the proposed model significantly enhances the handling of the intricate linguistic features and cultural nuances in classical Chinese texts, providing a robust solution for sentiment analysis in low-resource domains.

H 2 O 2 photosynthesis represents an appealing approach for sustainable and decentralized H 2 O 2 production. Unfortunately, current reactions are mostly carried out in laboratory-scale single-phase batch reactors, which have a limited H 2 O 2 production rate (<100 μmol h −1 ) and cannot operate in an uninterrupted manner. Herein, we propose continuous H 2 O 2 photosynthesis and extraction in a biphasic fluid system. A superhydrophobic covalent organic framework photocatalyst with perfluoroalkyl functionalization is rationally designed and prepared via the Schiff-base reaction. When applied in a home-built biphasic fluid photo-reactor, the superhydrophobicity of our photocatalyst allows its selective dispersion in the oil phase, while formed H 2 O 2 is spontaneously extracted to the water phase. Through optimizing reaction parameters, we achieve continuous H 2 O 2 photosynthesis and extraction with an unprecedented production rate of up to 968 μmol h −1 and tunable H 2 O 2 concentrations from 2.2 to 38.1 mM. As-obtained H 2 O 2 solution could satisfactorily meet the general demands of household disinfection and wastewater treatments. Photocatalytic H 2 O 2 synthesis is often performed in lab-scale batch reactors with low efficiency. Here, the authors report a biphasic fluid system that enables continuous H 2 O 2 synthesis and automatic product extraction, addressing the limitations of traditional methods.

Pigeons, as the only altricial birds in poultry, are the primary Trichomonas gallinae ( T. gallinae ) host. To study the effects of T. gallinae infection on gut microbiota, we compared the microbiota diversity and composition in gastrointestinal (GI) tracts of pigeons at the age of 14 and 21 day with different degrees of T. gallinae infection. Thirty-six nestling pigeons were divided into three groups: the healthy group, low-grade and high-grade trichomonosis group. Then, the crop, small intestine and rectum contents were obtained for sequencing of the 16S rRNA gene V3–V4 hypervariable region. The results showed that the microbiota diversity was higher in crop than in small intestine and rectum, and the abundance of Lactobacillus genus was dominant in small intestine and rectum of healthy pigeons at 21 days. T. gallinae infection decreased the microbiota richness in crop at 14 days. The abundance of the Firmicutes phylum and Lactobacillus genus in small intestine of birds at 21 days were decreased by infection, however the abundances of Proteobacteria phylum and Enterococcus, Atopobium, Roseburia, Aeriscardovia and Peptostreptococcus genus increased. The above results indicated that crop had the highest microbiota diversity among GI tract of pigeons, and the gut microbiota diversity and composition of pigeon squabs were altered by T. gallinae infection.

Addressing the difficulty of intuitively identifying and effectively correcting induced heave error in multibeam measurements, this paper proposes a two-stage methodology comprising error identification and correction. This scheme includes an error discrimination method based on regression diagnostics and an error correction method based on Partial Least Squares Regression (PLSR). By establishing a mathematical model between bathymetric discrepancies and attitude parameters, statistical diagnosis and effective identification of the error are achieved. To further mitigate the impact of induced heave error on bathymetric data, an elimination model based on PLSR is developed, enabling high-precision prediction and compensation of the induced heave error. Validation using field survey data demonstrates that this method can effectively estimate the installation offset parameters of the attitude sensor. After correction, the root mean square of bathymetric discrepancies between adjacent survey lines is reduced by approximately 78.8%, periodic stripe-shaped distortions along the track direction are essentially eliminated, and the quality of terrain mosaicking is significantly improved. This provides an effective solution for controlling induced heave error under complex topographic conditions.

X-ray imaging is the most widely used modality for fracture diagnosis in clinical practice due to its efficiency and accessibility. However, automated X-ray fracture detection faces two major challenges. First, fracture regions often contain subtle and low-contrast crack patterns, making it difficult for models to capture essential fine details. Second, fractures exhibit strong directional variability, while conventional detection frameworks have limited capacity to model rotation changes. To address these issues, we propose FDEA-Net, an enhanced detection framework tailored for fracture analysis. It integrates two lightweight improvement modules. The Fracture Detail Enhancer (FDE) strengthens high-frequency textures and fine-grained structural cues that are closely associated with fracture lines. The Rotation Aware Encoder (RAE) encodes rotation-sensitive representations, improving recognition under diverse fracture orientations. Experiments on a large-scale X-ray fracture dataset show clear performance gains, achieving an mAP50 of 0.742 and an F1-score of 0.738. These findings verify the effectiveness of combining detail enhancement with rotation-aware feature modeling. FDEA-Net provides an efficient and generalizable solution for reliable detection of subtle fractures in medical imaging.

The turbocharger, a pivotal technology for energy conservation and emission reduction, offers substantial academic significance, particularly in the in-depth study of its core component: the centrifugal compressor impeller. This research aims to enhance the centrifugal compressor’s overall efficiency by optimizing its impeller meridional profile. By modifying the impeller meridional profile of a certain centrifugal compressor impeller to address the issue of discontinuous curvature, this paper aims to optimize the comprehensive average efficiency under variable speed and variable flow conditions. The optimization goal is to improve the overall average efficiency while maintaining the high pressure ratio based on the prototype scheme. The NSGA-III optimization algorithm is employed for multi-condition optimization, aiming to achieve comprehensive efficiency improvements under multiple operating speeds. The following conclusions are drawn: under multi-speed conditions, the optimized scheme exhibits comprehensive efficiency improvements over the prototype scheme, with an expanded stable operating flow range and maintained high-pressure ratio based on efficiency improvement, without sacrificing the operating flow range. Comparisons of internal flow conditions indicate that the optimized impeller features a smoother meridional passage and a reduced high-entropy region at the outlet, leading to lower entropy values. Additionally, pressure increases on both sides of the impeller blades while pressure differentials diminish, signifying enhanced internal flow conditions.

As a key convergence zone between the Circum-Bohai Sea cultural circle and the land–sea interface of Northeast Asia, the Liaoning coastal area has been shaped by multicultural integration, endowing its dwellings with distinctive cultural hybridity and geographic adaptability. This study takes 160 traditional dwellings as samples and integrates field surveys, historical documents, and multi-source geographic data to construct a multi-dimensional feature identification system. Quantitative classification is conducted using principal component analysis and systematic clustering, and external validity is verified through historical document comparison and spatial overlay analysis. The results indicate that five dwelling pedigrees are identified: the Coastal Quadrangle Courtyard Type, the Coastal Flat-Roofed Middle Courtyard Type, the Coastal Gabled-Roof Small Courtyard Type, the Mountainous Gabled-Roof Small Courtyard Type, and the Plain Flat-Roofed Long Courtyard Type. Regarding the formation mechanism, geographic detectors reveal that the coupling effect of migration culture and topographical conditions is the dominant mechanism shaping pedigree differentiation. This study verifies the applicability of integrating quantitative and qualitative methods in dwelling research within multicultural convergence zones, constructs a pedigree framework for traditional dwellings in coastal Liaoning, and provides a theoretical basis for the systematic understanding and sustainable conservation of vernacular architectural heritage in the Circum-Bohai Sea region.

Direct air capture (DAC) is an emerging technology to aid decarbonization. Exploring metal−organic frameworks (MOFs) for DAC needs to encompass vast numbers of materials in the presence of humid CO2. We present a data set with over 38 million quantum chemistry calculations on thousands of MOFs containing CO2 and/or H2O, enabling machine learning models to accelerate development of MOFs for DAC.