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With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.

A translator’s style serves as a vital lens for examining the intricacies of the translation process. This is especially notable in translating technical classics, where a tension arises between the source text’s objective nature and the translator’s subjective stylistic choices. To address this tension, objective methods are required to complement subjective interpretation. A machine-learning approach is uniquely suited to this task, as it can impartially quantify stylistic features to reveal strategic decisions that might be obscured by subjective bias. Based on the self-constructed corpora and machine learning approach, this study investigates stylistic patterns in three complete English translations of the Chinese technical classic Tian Gong Kai Wu by applying the CVMI-RRMFT feature selection algorithm to identify the 15 most discriminative linguistic features across lexical, syntactic, and textual levels. These features delineate each translator’s unique style, revealing consistent preferences in linguistic expression. Specifically, Ren’s translation exhibits a scholarly “thick translation” style, marked by lexically rich and modified language, the longest average sentence length with nested syntactic patterns, and prolific use of square brackets for explanatory annotations. Li’s translation reflects a commitment to technical precision and cultural encoding, evident in numerically exact renderings and specialized terminology, a preference for complex phrasal construction, and the unique insertion of traditional Chinese characters within parentheses to assert cultural identity. By contrast, Wang’s translation pursues diplomatic clarity and reader-oriented accessibility through simplified vocabulary, shorter sentences, reduced syntactic complexity, and an overall streamlined use of punctuation to ensure fluent comprehension. Further analysis indicates that these stylistic features reflect not merely unconscious traces, but strategic choices influenced by the dynamic interplay between the translator’s individual agency and external factors. This study demonstrates the efficacy of computational methods in revealing stylistic variation, thereby advancing the empirical study of translation, and enriching the cross-cultural transmission of Chinese technical classics. Plain Language Summary How Machine Learning Reveal the Unique Styles of Translators: A Study of Three English Versions of a Chinese Technical Classic This study uses machine learning and computational analysis to objectively measure how different translators develop distinct styles, even when translating the same technical work. The researchers examined three complete English translations of Tian Gong Kai Wu, a 17th-century Chinese classic on technology and craftsmanship. By applying a specialized algorithm, they systematically identified the 15 most distinguishing linguistic features across thousands of vocabulary, sentence structure, and textual patterns. The analysis revealed clear stylistic differences: one translation used complex sentences and extensive scholarly annotations, targeting academic readers; another emphasized technical precision and incorporated traditional Chinese characters, reflecting the translator's scientific expertise and cultural identity; the third employed simpler vocabulary and shorter sentences to make the ancient text accessible to a general audience. These patterns show that a translator’s style is not accidental but results from a combination of personal expertise, intended readership, and historical context. By transforming subjective impressions into quantifiable data, this machine-learning approach uncovers what can be called the “stylistic fingerprints” of each translator, offering a new, evidence-based way to understand how technical classics are adapted across languages and cultures.

IκBζ (encoded by NFKBIZ) is the most recently identified IkappaB family protein. As an atypical member of the IkappaB protein family, NFKBIZ has been the focus of recent studies because of its role in inflammation. Specifically, it is a key gene in the regulation of a variety of inflammatory factors in the NF-KB pathway, thereby affecting the progression of related diseases. In recent years, investigations into NFKBIZ have led to greater understanding of this gene. In this review, we summarize the induction of NFKBIZ and then elucidate its transcription, translation, molecular mechanism and physiological function. Finally, the roles played by NFKBIZ in psoriasis, cancer, kidney injury, autoimmune diseases and other diseases are described. NFKBIZ functions are universal and bidirectional, and therefore, this gene may exert a great influence on the regulation of inflammation and inflammation-related diseases.

Mongolian toponyms encode both natural and human information and serve as key carriers of the grassland cultural landscape. They help align the region’s natural scenery with its cultural value. This study builds a translational logic—symbol decoding—function interpretation—relational analysis—to convert cultural-landscape symbols and functions into spatial value. Grounded in the cultural-landscape framework of symbol–function–power, we recast symbolic “power” as spatial “value,” providing a theoretical basis for the translation path and clarifying the shared attributes of cultural meaning and spatial form. Using Zhengxiangbai Qi, Inner Mongolia as a case, we combine a toponym semantic network with landscape spatial analysis to decode cultural and spatial symbols in Mongolian place-names; we then interpret their functions through their referential, signifying, and simplifying roles. Next, we apply spatial autocorrelation and spatial clustering to resolve the element hierarchy of the cultural landscape, thereby achieving spatial translation, functional regeneration, and the shaping of “power” into “value.” Findings show that: (a) under the core theme of human–nature synergy, the cultural-landscape theory of value generation is supported; and (b) strong links exist between the toponym semantic network and landscape components. The common functional relations shared by cultural and landscape symbols can be turned into a pathway for value mining. The study verifies the applicability of cultural-landscape theory in grassland pastoral contexts and offers differentiated strategies for enhancing the spatial value of rural cultural-landscape resources. It advances a shift from interpretation to practice, supports the sustainable evolution of the human–environment relationship, and provides new ideas for uncovering local cultural value. Plain Language Summary How to Interpret the Cultural Landscape Characteristics of Grassland Herding Areas Using Place-Name Semantics Chinese grassland settlements offer rich natural scenery and cultural assets. As a key part of rural cultural landscapes, place names contain untapped cultural value. Landscape elements are dispersed and functionally isolated, so they have not been fully integrated or converted into useful resources. Drawing on cultural landscape theory, this study identifies the cultural symbols in place names and the matching landscape symbols in Inner Mongolia’s grassland herding areas, then examines their roles within the broader cultural landscape. By mapping where these symbols occur and assessing their relationships, we establish a localized translation pathway for cultural landscapes, which supports resource integration and uncovers spatial value. The results reveal a strong association between place-name semantics and nearby landscape elements, showing functional overlap between cultural and landscape symbols. This study also confirms the “value generation” concept of cultural landscape theory and provides practical guidance for the sustainable development of cultural landscape resources in China’s grassland regions.

Lysine methyltransferase KMT5C catalyzes deposition of trimethylation on histone H4 lysine 20 (H4K20me3), an epigenetic marker usually associated with gene repression and maintenance of heterochromatin. KMT5C is widely expressed in a variety of tissues, however, its functional role in liver has not been explored. Here, we show Kmt5c is a fasting- and glucagon-induced gene in liver which regulates hepatic gluconeogenesis. Loss of KMT5C in hepatocytes results in downregulated gluconeogenic gene expression and compromised glucose output during fasting. KMT5C fosters gluconeogenesis through decreasing ubiquitination-mediated PGC-1α degradation, which is unexpectedly independent of its methyltransferase activity. In fact, KMT5C impedes the E3 ligase RNF34 binding to the C-terminal of PGC-1α and subsequent ubiquitination-associated degradation. The diabetic mice models and patients show elevated KMT5C levels in the livers, and KMT5C knockdown beneficially reduces gluconeogenesis and fasting blood glucose levels. In conclusion, the present study identifies KMT5C as a hepatic gluconeogenesis regulator by affecting PGC-1α stability. Gluconeogenesis produces glucose from non-carbohydrate carbon substrates, particularly occurs during fasting. Here, the authors show lysine methyltransferase KMT5C promotes gluconeogenesis by decreasing PGC-1α degradation, which is independent of its methyltransferase activity.

Reduced imine cage (RCC3) was covalently bonded to the surface of silica spheres, and then the secondary amine group of the molecular cage was embedded in non-polar C10 for modification to prepare a novel RCC3-C10@silica HPLC stationary phase with multiple separation functions. Through infrared spectroscopy, thermogravimetric analysis and nitrogen adsorption–desorption characterization, it was confirmed that RCC3-C10 was successfully bonded to the surface of silica spheres. The resolution of RCC3-C10@silica in reversed-phase separation mode is as high as 2.95, 3.73, 3.27 and 4.09 for p-phenethyl alcohol, 1-phenyl-2-propanol, p-methylphenethyl alcohol and 1-phenyl-1-propanol, indicating that the stationary phase has excellent chiral resolution performance. In reversed-phase and hydrophilic separation modes, RCC3-C10@silica realized the separation and analysis of a total of 70 compounds in 8 classes of Tanaka mixtures, alkylbenzene rings, polyphenyl rings, phenols, anilines, sulfonamides, nucleosides and flavonoids, and the analysis of a variety of chiral and achiral complex mixtures have been completed at the same time. Compared with the traditional C18 commercial column, RCC3-C10@silica exhibits better chromatographic separation selectivity, aromatic selectivity and polar selectivity. The multifunctional separation mechanism exhibited by the stationary phase originates from various synergistic effects such as hydrophobic interaction, π-π interaction, hydrogen bonding and steric interaction provided by RCC3 and C10 groups. This work provides flexible selectivity and application prospects for novel multi-separation functional chromatographic columns.

While immersive media services represented by virtual reality (VR) are booming, They are facing fundamental challenges, i.e., soaring multimedia applications, large operation costs and scarce spectrum resources. It is difficult to simultaneously address these service challenges in a conventional radio access network (RAN) system. These problems motivated us to explore a quality-of-service (QoS)-driven resource allocation framework from VR service perspective based on the fog radio access network (F-RAN) architecture. We elaborated details of deployment on the caching allocation, dynamic base station (BS) clustering, statistical beamforming and cost strategy under the QoS constraints in the F-RAN architecture. The key solutions aimed to break through the bottleneck of the network design and to deep integrate the network-computing resources from different perspectives of cloud, network, edge, terminal and use of collaboration and integration. Accordingly, we provided a tailored algorithm to solve the corresponding formulation problem. This is the first design of VR services based on caching and statistical beamforming under the F-RAN. A case study provided to demonstrate the advantage of our proposed framework compared with existing schemes. Finally, we concluded the article and discussed possible open research problems.

Polyetheretherketone (PEEK) has been considered as an excellent orthopedic implant material due to its similar elastic modulus to that of human bones. However, many obstacles including the bio-inertness nature and inferior osteoconduction of PEEK limit its wide applications in clinics. Nano hydroxyapatite (HA) and zirconium dioxide (ZrO2) were incorporated into PEEK to fabricate HA/ZrO2/PEEK biocomposites. The result of wettability of these biocomposites showed that the introduction of the ZrO2 and HA nanoparticles in PEEK could enhance its hydrophilic property at 1–2% HA and 1–5% ZrO2 concentrations. The biotribological properties and bioactivity of PEEK and HA/ZrO2/PEEK biocomposites were investigated in detail. The friction coefficient of these biocomposites increased with the increase of frequency and their wear width increased obviously with the increase of load. Biological and tribological experiments indicated that bovine serum possessed the best lubrication performance, and the 1%HA–1%ZrO2 and 2%HA–5%ZrO2 samples exhibited the lowest friction coefficient and wear rate. Then, the in vitro tests demonstrated that the HA/ZrO2/PEEK biocomposites exhibited improved cell activity and alkaline phosphatase activity than pure PEEK, and both 1%HA/1%ZrO2/PEEK and 2%HA/5%ZrO2/PEEK biocomposite samples could better promote bone calcification. The PEEK biocomposite with 2 wt% HA and 5 wt% ZrO2 nanoparticles exhibited the best tribological properties and bioactivity, which could be expected to develop a kind of potential medical material for practical clinical applications.Graphic abstract

I examined the relationship between corporate social responsibility (CSR) and students' ecological behavior within the Chinese higher education sector, whilst controlling for the mediating variables of commitment to environmental sustainability at the educational institution and students' environmental knowledge, as well as the moderating variable of the students' environmental concern. The data were obtained from 348 students at universities in Xi???an, Chengdu, and Hangzhou. The findings, analyzed through SMART-PLS, point toward a direct relationship between CSR and students' ecological behavior and mediating effects of environmental sustainability commitment at the institution and students' environmental knowledge. The findings also support a significant moderation role of students' environmental concern. Such insights might be used for designing educational approaches toward CSR, focused on establishing positive attitudes toward environmental sustainability and improving the level of environmental knowledge within the interface of academic and practical contexts.

Traditional unsupervised speech enhancement models often have problems such as non-aggregation of input feature information, which will introduce additional noise during training, thereby reducing the quality of the speech signal. In order to solve the above problems, this paper analyzed the impact of problems such as non-aggregation of input speech feature information on its performance. Moreover, this article introduced a temporal convolutional neural network and proposed a SASEGAN-TCN speech enhancement model, which captured local features information and aggregated global feature information to improve model effect and training stability. The simulation experiment results showed that the model can achieve 2.1636 and 92.78% in perceptual evaluation of speech quality (PESQ) score and short-time objective intelligibility (STOI) on the Valentini dataset, and can accordingly reach 1.8077 and 83.54% on the THCHS30 dataset. In addition, this article used the enhanced speech data for the acoustic model to verify the recognition accuracy. The speech recognition error rate was reduced by 17.4%, which was a significant improvement compared to the baseline model experimental results.