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Cultural heritage (CH) relics are irreplaceable records of human civilization, encompassing diverse historical, technological, and artistic achievements. Extracting their structural and compositional information without affecting their physical integrity is a critical challenge. This review summarizes recent advances in non-destructive techniques (NDTs) for CH analysis and emphasizes the balance between the depth of analysis and conservation ethics. Techniques are broadly categorized into spectrum-based, X-ray-based, and digital-based methods. Spectroscopic techniques such as Fourier transform infrared (FTIR), Raman, and nuclear magnetic resonance (NMR) spectroscopy provide molecular-level insights into organic and inorganic components, often requiring minimal or no sampling. X-ray-based techniques, including conventional and spatially resolved XRD/XRF and total reflection XRF (TRXRF), provide powerful means for crystal and elemental analysis, including in situ pigment identification and trace material analysis. Digital-based methods include high-resolution imaging, three-dimensional modeling, data fusion, and AI-driven diagnosis to achieve the non-invasive visualization, monitoring, and virtual restoration of CH assets. This review highlights a methodology shift from traditional molecular-level detection to data-centric and AI-assisted diagnosis, reflecting the paradigm shift in heritage science.

With the rapid development of 3D scanning technologies, high-density point clouds of cultural heritage artifacts such as stone carvings, statues pose significant challenges in storage, processing, and accurate reconstruction. This paper proposes a point cloud simplification method tailored for cultural heritage applications, combining clustering and saliency analysis to preserve intricate surface details critical for archaeological studies. By segmenting point clouds into clusters with normal vector constraints and evaluating saliency through roughness and curvature metrics, our method adaptively retains primary features including engraved patterns weathered textures while simplifying non-feature regions. Experiments on stone carvings from the Northern Song Imperial Mausoleum, Terracotta Warriors, and Stanford datasets demonstrate that the algorithm effectively avoids mesh holes and maintains geometric fidelity, enabling efficient 3D reconstruction for heritage conservation. This work bridges advanced point cloud processing with practical archaeological needs, offering a robust tool for digitizing and analyzing cultural relics with minimal loss of historically significant details.

Archeological line drawings are essential in archeological research, providing visual representations of artifact morphology and precise geometric measurements. Traditionally, these drawings were meticulously created by hand, a process that is time-consuming and labor-intensive. To overcome these limitations, this paper presents an algorithm for extracting feature lines from sculpture point cloud, enabling the automated generation of archeological line drawings. The method introduces a weighted centroid-based geometric metric to identify surface feature points and classify them based on their concavity or convexity. An iterative refinement process is then applied to ensure that feature points align correctly along the feature lines, and boundary points are extracted using an angular criterion. Finally, an enhanced curve-growing algorithm connects the feature and boundary points, producing a 3D line drawing. Experiments conducted on various stone sculptures demonstrate the practicality and accuracy of the proposed approach, with results showing significant improvements over existing methods.

There have been selected 83 samples of terracotta warriors and horses of Emperor Qin Shi Huang’s Mausoleum, 20 samples of clays taken from around Qin’s Mausoleum and 2 samples of Yaozhou porcelain bodies. All these samples have been measured by instrument neutron activation analysis (INAA) and as many as 32 kinds of element contents of each sample are measured. The following conclusion has been reached when fuzzy cluster analysis is conducted to element contents of all these samples: (i) The samples are roughly classified into five categories: namely, samples from pits No. 1 and No. 2; samples from pit No. 3; loam layers; the mixture of loam and loess; and Yaozhou porcelain bodies. (ii) The terracotta warriors and horses in pits No. 1, No. 2 and No. 3 are relatively independent from one another. The clays from which they were made are not entirely identical. We have found that samples in pit No. 3 are very closely related and their clay sources are comparatively concentrated. Samples in pits No. 1 and No. 2 are less related and their clay sources are comparatively scattered. (iii) The clays from which the terracotta warriors and horses were made are closely related to the loam layer near Qin’s Mausoleum, particularly to the loam layer of Zaoyuan village and Gaoxing village, but they are not so related to loess layers there, nor to the loam layers of Anhoubao, even less related to Yaozhou porcelain bodies. A rational deduction thus drawn is that the raw material of clays from which the terracotta warriors and horses were made might probably be taken from loam layers around Zaoyuan and Gaoxing, or loam layers near Qin’s Mausoleum whose properties are identical with those of loam layers of Zaoyuan and Gaoxing, rather than loess layers around the above places. Since the raw material of the terracotta warriors and horses was taken from loam near Qin’s Mausoleum, it could be deducted that the kiln sites might be located in around Qin’s Mausoleum.

CUE domain containing protein 1 (CUEDC1) is implicated in tumor progression; however, its specific role in esophageal cancer (ESCA) remains unclear. In esophageal cancer, the expression of CUEDC1 is notably low, which correlates with reduced survival rates and adverse clinical outcomes. Overexpression of CUEDC1 results in decreased activity of the JAK1/STAT3 signaling pathway in cells, consequently diminishing their proliferation, migration, and invasion capabilities. This mechanism operates through the direct binding of CUEDC1 to STAT3, facilitating its ubiquitination and triggering the ubiquitin-proteasome degradation pathway, ultimately leading to a significant reduction in intracellular STAT3 levels. This study suggests that CUEDC1 can reduce intracellular STAT3 protein levels, thereby inhibiting JAK1/STAT3 signaling transduction and suppressing the progression of ESCA. This study aims to elucidate the regulatory mechanism of CUEDC1 on STAT3, which will enhance our understanding of the regulatory pathways involved in the treatment of esophageal cancer and potentially other tumors. Future breakthroughs and innovations may emerge from molecular research and development targeting this pathway.

Measuring magnetic flux density in actual motor systems and understanding the influence of power harmonics on iron loss are urgent challenges under variable frequency drive (VFD) power supply conditions, and this research is studied based on the classical Bertotti's three constant coefficient model. An iron loss model incorporating various harmonic voltages is established by substituting the amplitude and frequency of voltage for magnetic density amplitude and considering the effects of output harmonic voltage by the VFD. The voltage waveform was measured under a VFD power supply and decomposed via Fourier analysis to determine the amplitude of each harmonic voltage. The iron loss coefficients are solved using the least squares method. The iron loss and harmonic iron loss are calculated and analyzed with and without filtering under different frequencies and voltage conditions. Experimental results indicated that iron loss increases with the increase of voltage, frequency, and magnetic flux. The harmonic iron loss increased with the voltage and magnetic flux at low frequencies. While the harmonic iron loss decreased as voltage and magnetic flux increased at high frequencies. The iron loss is significantly higher than in sine-driven ones in pulse width modulation VFD-driven spindle motors, and harmonic iron loss took up a proportion of at least 20% of the total iron loss. This research not only offers a precise method to predict the iron loss of high-speed spindle motors but also reveals the pattern of iron loss variation, providing a theoretical basis for motor design and optimization.

The study is focused on the ecological-fragile and water-shortage region in Northwest China (NWC). A drought-heatwave cluster projection (DHCP) approach is developed based on Stepwise Cluster Analysis (SCA) and multi-level factor analysis (MFA). A multi-model ensemble consisting of 5 global climate models (GCMs) under two shared socioeconomic pathways (i.e., SSPs) is used in the approach. It could investigate the spatiotemporal characteristics of heatwaves, drought, and compound drought-heatwave events (CDHEs) in NWC. A precise projection is given by SCA in 48 stations and four indicators of CDHEs (i.e., CEN, CEDU, CEI, and CEM) are thus calculated. Finally, the individual and interactive effects of uncertainties (i.e., period, SSP, and GCM) are analyzed by MFA. Results show the SCA method can effectively reproduce precise projections for temperature and precipitation for NWC. The frequency, intensity, and duration of heatwaves increase significantly under SSP5-8.5. Drought will ease and then intensify projected by all SSPs. The long-term intensification trend of drought is more pronounced under SSP5-8.5. CDHEs will increase, especially under SSP5-8.5 by the 2090s. These parameters will increase to 4.59 events, 2.86 days per event, 16.7°C per event, and 4.69 days, respectively. Higher increases are found in southeastern Qinghai, northwestern Gansu, and northern Xinjiang. Period affects CDHEs projection mostly. The GCM selection and its interaction with period also affect the projection significantly. DHCP provides a comprehensive analysis of heatwaves and droughts. It is equally applicable to other water shortage areas and is expected to help better manage water resources.

Multiple rivers flowing into the same bay can be correlated in water quality management and together determine the environmental status of the bay. Nonpoint source pollution management for multi-watershed aiming to alleviate environmental contamination as well as yield considerable economic and environmental benefits can be under additional challenges. In this study, a Bayesian simulation-based multi-watershed effluent trading designing model (BS-METM) is established for multi-watershed nonpoint source pollution management through incorporating techniques of water quality simulation, uncertainty analysis with Bayesian inference, optimal design for effluent trading, as well as mechanism analysis. BS-METM is capable of reflecting parameter uncertainties in nutrient simulation, disclosing the detailed optimal trading schemes under the impact of uncertainties and vital factors, and identifying optimal effluent trading mechanisms through revealing interaction among trading processes of multiple watersheds. BS-METM is applied to a real case of adjacent coastal watersheds (i.e. Daguhe and Moshuihe watersheds), which are identified as major sources of total phosphorus and ammonia nitrogen loadings to Jiaozhou Bay, China. Effluent trading optimization under multiple mechanisms, including intra-watershed trading, cross-watershed trading and non-trading, are conducted. The optimized industry scales and trading processes are obtained. The effects of vital factors on the trading process (i.e. environmental allowance-violation risk level and water availability level) are investigated. The interactions between water availability level and trading mechanism are also analyzed. It is proved that non-trading mechanism would be recommended under low water availability level and cross-watershed trading mechanism would be recommended under medium and high water availability level. The results provide a solid scientific basis for nonpoint source pollution management as well as effective sustainable development for multi-watershed region.

Assessing flood mitigation strategies is crucial on a global scale, where Large-scale Hydraulic Projects (LHPs) are essential in enhancing socio-economic resilience and mitigating flood impacts. However, the failure to recognize the spatial distribution patterns of these direct and indirect benefits can lead to a substantial underestimation of the positive effects that LHPs can have on various socio-economic activities and regions susceptible to flooding. This study develops a Spatial Footprint Impact Assessment framework (SFIA), which is applied to the Three Gorges Project (TGP), evaluating the spatial heterogeneity of indirect flood-retention benefits induced by the TGP across 31 provinces in China. Our findings demonstrate that while Hubei and Hunan gain the largest direct reductions in GDP and welfare losses, more distant provinces benefit indirectly through stabilized supply chains supported by the TGP. Quantitatively, the TGP’s flood-retention capacity helped reduce China’s flood-induced GDP losses by 28–37 billion Chinese Yuan. By introducing the concept of “hydraulic project’s footprints,” this study demonstrates that overlooking such indirect benefits substantially underestimates the value of LHPs. Our findings can offer detailed, region-specific insights into formulating flood management strategies and provide a transferable framework for assessing other large-scale hydraulic projects worldwide.