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As ancient China and Rome transformed into empires, both states showed an increasing interest in regulating family ethics and individuals’ sexuality. Using excavated documents and transmitted texts, this article compares legal statutes and practices against illicit consensual sex in early imperial China (221 BCE–220 CE) with those in the Roman empire. On the one hand, both legal systems aimed at consolidating social hierarchies based on gender, status, and generation. On the other, the Roman and Chinese statutes had different emphases due to their respective political, social, and cultural contexts, and the actual penalties for adultery and incest differed significantly from those prescribed in the statutes. In both empires, control over individuals’ sexuality facilitated state power’s penetration into the family during empire-building, giving rise to laws in areas that had been largely left to customs and individual will.

The timely and proper rehabilitation of damaged roads is essential for road maintenance, and an effective method to detect road surface distress with high efficiency and low cost is urgently needed. Meanwhile, unmanned aerial vehicles (UAVs), with the advantages of high flexibility, low cost, and easy maneuverability, are a new fascinating choice for road condition monitoring. In this paper, road images from UAV oblique photogrammetry are used to reconstruct road three-dimensional (3D) models, from which road pavement distress is automatically detected and the corresponding dimensions are extracted using the developed algorithm. Compared with a field survey, the detection result presents a high precision with an error of around 1 cm in the height dimension for most cases, demonstrating the potential of the proposed method for future engineering practice.

Van der Waals (vdW) metallic contacts have been demonstrated as a promising approach to reduce the contact resistance and minimize the Fermi level pinning at the interface of two-dimensional (2D) semiconductors. However, only a limited number of metals can be mechanically peeled and laminated to fabricate vdW contacts, and the required manual transfer process is not scalable. Here, we report a wafer-scale and universal vdW metal integration strategy readily applicable to a wide range of metals and semiconductors. By utilizing a thermally decomposable polymer as the buffer layer, different metals were directly deposited without damaging the underlying 2D semiconductor channels. The polymer buffer could be dry-removed through thermal annealing. With this technique, various metals could be vdW integrated as the contact of 2D transistors, including Ag, Al, Ti, Cr, Ni, Cu, Co, Au, Pd. Finally, we demonstrate that this vdW integration strategy can be extended to bulk semiconductors with reduced Fermi level pinning effect. Laminated van der Waals (vdW) metallic electrodes can improve the contact of 2D electronic devices, but their scalability is usually limited by the transfer process. Here, the authors report a strategy to deposit vdW contacts onto various 2D and 3D semiconductors at the wafer scale.

High-resolution remote sensing satellites have revolutionized remote sensing research, yet accurately segmenting specific targets from complex satellite imagery remains challenging. While the Segment Anything Model (SAM) has emerged as a promising universal segmentation model, its direct application to remote sensing imagery yields suboptimal results. To address these limitations, we propose RSAM-Seg, a novel deep learning model adapted from SAM specifically designed for remote sensing applications. Our model incorporates two key components: Adapter-Scale and Adapter-Feature modules. The Adapter-Scale modules, integrated within Vision Transformer (ViT) blocks, enhance model adaptability through learnable transformations, while the Adapter-Feature modules, positioned between ViT blocks, generate image-informed prompts by incorporating task-specific information. Extensive experiments across four binary and two multi-class segmentation scenarios demonstrate the superior performance of RSAM-Seg, achieving an F1 score of 0.815 in cloud detection, 0.834 in building segmentation, and 0.755 in road extraction, consistently outperforming established architectures like U-Net, DeepLabV3+, and Segformer. Moreover, RSAM-Seg shows significant improvements of up to 56.5% in F1 score compared to the original SAM. In addition, RSAM-Seg maintains robust performance in few-shot learning scenarios, achieving an F1 score of 0.656 with only 1% of the training data and increasing to 0.815 with full data availability. Furthermore, RSAM-Seg exhibits the capability to detect missing areas within the ground truth of certain datasets, highlighting its capability for completion.

LncRNAs play a pivotal role in tumorigenesis and development. However, the potential involvement of lncRNAs in colon adenocarcinoma (COAD) needs to be further explored. All the data used in this study were obtained from The Cancer Genome Atlas database, and all analyses were conducted using R software. Basing on the seven prognosis-related lncRNAs finally selected, we developed a prognosis-predicting model with powerful effectiveness (training cohort, 1 year: AUC = 0.70, 95% Cl = 0.57–0.78; 3 years: AUC = 0.71, 95% Cl = 0.6–0.8; 5 years: AUC = 0.76, 95% Cl = 0.66–0.87; validation cohort, 1 year: AUC = 0.70, 95% Cl = 0.58–0.8; 3 years: AUC = 0.73, 95% Cl = 0.63–0.82; 5 years: AUC = 0.68, 95% Cl = 0.5–0.85). The VEGF and Notch pathway were analyzed through GSEA analysis, and low immune and stromal scores were found in high-risk patients (immune score, cor =  − 0.15, P  < 0.001; stromal score, cor =  − 0.18, P  < 0.001) , which may partially explain the poor prognosis of patients in the high-risk group. We screened lncRNAs that are significantly associated with the survival of patients with COAD and possibly participate in autophagy regulation. This study may provide direction for future research.

Noise-induced hearing loss (NIHL) is a common occupational condition. The aim of this study was to develop a classification model for NIHL on the basis of both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) by applying machine learning methods. fMRI indices such as the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and sMRI indices such as gray matter volume (GMV), white matter volume (WMV), and cortical thickness were extracted from each brain region. The least absolute shrinkage and selection operator was used to reduce and select the optimal features. The support vector machine (SVM), random forest (RF) and logistic regression (LR) algorithms, were used to establish the classification model for NIHL. Finally, the SVM model based on combined fMRI indices, achieved the best performance, with area under the receiver operating characteristic curve of 0.97 and an accuracy of 95%. The SVM classification model that integrates fMRI indicators has the greatest potential for identifying NIHL patients and healthy people, revealing the complementary role of fMRI indicators in classification and indicating that it is necessary to include multiple indicators of the brain when establishing a classification model.

Regional innovation cooperation focused upon either regions within one nation or transnational regions. Different from the discussion in the existing literature, the Guangdong- Hong Kong-Macao Greater Bay Area exists as an exceptional cross-border city-to-city cooperation under “one country, two systems”. Based on the social network analysis on co- patents in the Greater Bay Area, this paper aims to investigate the models and features of the innovation cooperation network of science and technology in the Greater Bay Area and the different characteristics of collaboration policy on cross-border knowledge flow before and after 2015. The results show that the innovation network of science and technology in the Greater Bay Area has a loosely structured pattern, with Hong Kong-Shenzhen-Guangzhou as a prominent hub. This structure portrays multiple centers that radiate out to its peripheries. The national policy launched in 2015 reduces the cooperation barriers and promotes the cross-border collaboration. The universities and research institutes possess significant intermediary roles and innovation autonomy in the scientific and technological innovation cooperation network of the Greater Bay Area.

Cloud detection is one of the primary challenges in preprocessing high-resolution remote sensing imagery, the accuracy of which is severely constrained by the multi-scale and complex morphological characteristics of clouds. Many approaches have been proposed to detect cloud. However, these methods still face significant challenges, particularly in handling the complexities of multi-scale cloud clusters and reliably distinguishing clouds from snow, ice and complex cloud shadows. To overcome these challenges, this paper proposes a novel cloud detection network based on the state space model (SSM), termed the Hierarchical Dilated Adaptive Mamba Network (HDAMNet). This network utilizes an encoder–decoder architecture, significantly expanding the receptive field and improving the capture of fine-grained cloud boundaries by introducing the Hierarchical Dilated Cross Scan (HDCS) mechanism in the encoder module. The multi-resolution adaptive feature extraction (MRAFE) integrates multi-scale semantic information to reduce channel confusion and emphasize essential features effectively. The Layer-wise Adaptive Attention (LAA) mechanism adaptively recalibrates features at skip connections, balancing fine-grained boundaries with global semantic information. On three public cloud detection datasets, HDAMNet achieves state-of-the-art performance across key evaluation metrics. Particularly noteworthy is its superior performance in identifying small-scale cloud clusters, delineating complex cloud–shadow boundaries, and mitigating interference from snow and ice.

Vertical field effect transistor (VFET), in which the semiconductor is sandwiched between source/drain electrodes and the channel length is simply determined by the semiconductor thickness, has demonstrated promising potential for short channel devices. However, despite extensive efforts over the past decade, scalable methods to fabricate ultra-short channel VFETs remain challenging. Here, we demonstrate a layer-by-layer transfer process of large-scale indium gallium zinc oxide (IGZO) semiconductor arrays and metal electrodes, and realize large-scale VFETs with ultra-short channel length and high device performance. Within this process, the oxide semiconductor could be pre-deposited on a sacrificial wafer, and then physically released and sandwiched between metals, maintaining the intrinsic properties of ultra-scaled vertical channel. Based on this lamination process, we realize 2 inch-scale VFETs with channel length down to 4 nm, on-current over 800 A/cm 2 , and highest on-off ratio up to 2 × 10 5 , which is over two orders of magnitude higher compared to control samples without laminating process. Our study not only represents the optimization of VFETs performance and scalability at the same time, but also offers a method of transfer large-scale oxide arrays, providing interesting implication for ultra-thin vertical devices. Vertical field-effect transistors (VFETs) have potential for the realization of ultra-scaled devices, but their fabrication is usually limited by trade-offs between scalability and channel length. Here, the authors report a large-scale transfer method to realize indium gallium zinc oxide/graphene VFETs with van der Waals metallic contacts and reduced channel length.

Background Metabolic dysfunction-associated steatotic liver disease (MASLD) is closely associated with insulin resistance (IR). However, the prognostic value of different alternative IR surrogates in patients with MASLD remains unclear. This study aimed to evaluate the association between various IR indices and all-cause mortality and cardiovascular mortality in MASLD patients. Methods A total of 8,753 adults aged ≥ 20 years with MASLD from the National Health and Nutrition Examination Survey (NHANES, 2003–2018) were included, and their mortality data were obtained from the National Death Index (NDI). Insulin resistance surrogates [including the triglyceride-glucose (TyG) index, TyG-body mass index (TyG-BMI), TyG-waist circumference index, TyG-waist-to-height ratio index, and Homeostatic Model Assessment for IR] were stratified into quartiles. Cox proportional hazards models, receiver operating characteristic (ROC) curve analysis, restricted cubic spline (RCS), mediation analyses, and subgroup analyses were used to explore the associations between these indices and all-cause mortality as well as cardiovascular mortality in MASLD patients. Results During a median follow-up of 98 months, 1,234 deaths were observed, including 409 cardiovascular disease (CVD)-related deaths. In the fully adjusted model, higher quartiles of TyG-related indices were significantly associated with an increased risk of all-cause mortality in MASLD patients. Furthermore, the TyG-BMI index was associated with both all-cause mortality and CVD mortality [all-cause mortality: HR (95% CI) 2.84 (1.73–4.67), P  < 0.001; CVD mortality: HR (95% CI) 5.32 (2.26–12.49), P  < 0.001]. The RCS analyses indicated a U-shaped relationship between TyG-BMI and mortality, with a threshold value of 270.49. Subgroup analyses demonstrated that TyG-related indices had stronger associations with mortality in elderly MASLD patients. Conclusions Our findings highlight the prognostic value of IR indices, particularly TyG-BMI index, in predicting all-cause mortality and CVD mortality in MASLD patients. Graphical abstract This study highlights the prognostic value of IR indices, particularly TyG-BMI index, in predicting all-cause mortality and CVD mortality in MASLD patients.