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Increasing landslide activities in cold regions have been attributed to rising temperatures and consequent permafrost degradation. While previous studies have linked permafrost degradation to slope instability, the elevation-dependent effects of this degradation on landslide occurrences in the high-mountain regions of the Qinghai–Tibet Plateau (QTP) remain poorly understood, particularly concerning their spatial distribution and timing. This study addresses this gap by investigating the distribution and timing of landslides in the Babao River catchment, located in the southeastern Qilian Mountains of the northeastern QTP. Our results reveal a substantial increase in landslide events during the study period of 2009–2018: only 14 occurrences were recorded before and in 2009, 22 between 2010 and 2015, and 105 during 2016–2018. Notably, we observed an upward shift in the elevation of landslide occurrences, with an average increase of approximately 130 m over the ten-year period. Analysis of annual permafrost distribution maps indicates that this shift coincides with the rising lower altitudinal limit of mountain permafrost in the study area, likely driven by increased temperatures and precipitation. These findings highlight the critical role of elevation-dependent processes in influencing landslide dynamics under changing climatic conditions, particularly the transition from undisturbed permafrost to seasonally frozen ground at higher elevations. This study provides valuable insights for disaster prevention and mitigation in high-altitude regions, emphasizing the heightened risks posed by permafrost degradation under ongoing warmer and wetter climatic conditions.

Rainfall is a common trigger for landslide reactivation, as it raises groundwater levels and reduces bedrock or soil shear resistance. This study focuses on the Kualiangzi landslide in the southern region of Sichuan Province, China. Real-time monitoring of groundwater levels and rainfall from July 2013 to September 2016 is analyzed. Groundwater table increments, considering groundwater drainage rate, were calculated using the water-table fluctuation and master recession curve method and the response time of the groundwater table to rainfall events was estimated using the cross-correlation function. Results reveal that groundwater level declines from tension troughs to landslide fronts in the rainy season, with a significant positive correlation between the groundwater level in the tension trough and landslide surface displacement. Evaluated spring elevations for groundwater discharge range from 410 m to 440 m, which is in agreement with the actual spring elevations (390–423 m). Lag times of groundwater response to rainfall decreases with cumulative rainfall of the rainy periods. In the middle part of the landslide, two responses between rainfall and groundwater levels indicate two water movement pathways: Vertical cracks or fractures resulting from the slow landslide movement, and matrix pore space in unconsolidated sediment. Variations in peak values of the cross-correlation function suggest early dominance of the uniform matrix flow and later dominance of preferential flow during the rainy period.

By using the weight coefficients and the techniques of real analysis, a new extended Mulholland’s inequality with multi-parameters involving one partial sum is given. The equivalent statements of the best value related to several parameters are provided. The equivalent forms, some inequalities in particular parameters, and the operator expressions are obtained.

The study aims to establish the essence and structure of a psychologically safe educational environment. The authors determine the state of psychological safety as a key characteristic of the safe educational environment in higher education institutions and identify problems in its diagnostics. The paper discloses the essence of the concepts of “educational environment”, “safe educational environment”, and “psychological safety of the educational environment”. The research reveals that while the level of psychological safety among students and teachers is generally high, there are significant differences in assessments based on psycho-emotional states and interpersonal relationships. The findings highlight the need for targeted interventions, including psychological and pedagogical training of personnel and implementation of innovative technologies to enhance safety. This study emphasizes the importance of monitoring and creating a sustainable environment to support mental health, personal growth, and professional development in higher education.

Yellow passion fruit (Passiflora edulis f. flavicarpa) is valuable edible specie with economic significance in China. Passion fruit faces yield losses due to viral diseases. This study used tissue culture of nodal shoot segments to propagate virus-free plants. Surface-sterilized nodal shoot segments were used and shoots were initiated on Murashige and Skoog (MS) medium supplemented with various concentrations of 6-Benzyladenine (6-BA) (0.0, 0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 mg L−1). Multiple shoots were initiated (5.2 ± 1.09 shoots/explant) with a 6-BA concentration of 2.0 mg L−1. Sub-culturing of these multiple shoots on MS medium augmented with 2.0 mg L−1 6-BA and 0.2 mg L−1 IAA was successful in vitro with maximum numbers of shoots (6.6 ± 1.17) within four weeks of incubation. These regenerated shoots were assayed for presence of passion fruit woodiness virus (PWV) using enzyme-linked immunosorbent assay (ELISA); only virus-free shoots were used for further propagation. Root formation was very good in each indole-3-butyric acid (IBA) and (IAA) at a concentration of 0.6 mg L−1 with a maximum number of roots (7.2 ±1.30). Rooted plantlets were gradually acclimatized in a greenhouse and successfully established in soil. Thus, virus-free plants can be successfully regenerated directly from nodal shoot segments.

Combining unmanned aerial vehicles (UAVs) with deep learning algorithms offers an efficient, safe and inexpensive alternative to maritime search and rescue (mSAR) missions. Maritime UAV images present unique challenges for object detection due to their complex nature, including dense distribution, multi-scale objects and occlusion. Aiming to address this problem, we propose a novel lightweight model specifically designed for maritime small object detection, named AB2D-YOLO. Firstly, the attention based intra-scale feature interaction (AIFI) module is used to replace the spatial pyramid pooling fast (SPPF) module on the backbone, enhancing the detection precision of occluded and densely small targets by integrating global and contextual feature information. Secondly, the dilation-wise residual (DWR) module is integrated into the network. The module employs three sets of dilated convolution with different sampling rates to obtain multi-scale receptive fields, which effectively improves the capacity for detecting multi-scale objects. Then, we propose an improved network fusion model based on weighted bi-directional feature pyramid network (BiFPN) to reconstruct the neck, which can enhance the features of small targets through weighted fusion of feature information of different scales and bidirectional cross-scale connection. Finally, we add a new detection layer in the neck to capture more object location information in images. When compared to the benchmark model YOLOv8s, AB2D-YOLO achieves an 8.96% increase in mean average precision (mAP) on the SeaDroneSee dataset, while maintaining a low model complexity with only 6.95 MB of parameters. When compared to state-of-the-art models, AB2D-YOLO model is conducive to the deployment of maritime UAV.

Abstract Spatiotemporal traffic data usually suffers from missing entries in the data acquisition and transmission process. Existing imputation methods only consider the global/local structure of spatiotemporal traffic data, resulting in insufficient estimation performance. Fortunately, it is found that traffic data admits the nonlocal self‐similarity (NSS) prior. This paper incorporates the global and nonlocal low‐rank priors of traffic data and proposes a tensor completion model for spatiotemporal traffic data imputation. To be specific, the proposed method uses tensor ring (TR) decomposition with an enhanced representation capability to characterize the global low‐TR‐rank prior of traffic data, e.g. the correlation of sensor and time modes of the tensor (i.e. traffic data). An implicit plug‐and‐play (PnP)‐based regularization is further utilized to exploit the NSS prior, which depicts the nonlocal similar traffic data patterns. Furthermore, the proximal alternating minimization algorithm under the PnP framework is derived to solve this model. The experiment results on various datasets and missing scenarios show the superiority of the proposed model.

Mycoplasma pneumoniae causes respiratory tract infections, affecting both children and adults, with varying degrees of severity ranging from mild to life-threatening. In recent years, a new class of regulatory RNAs called long non-coding RNAs (lncRNAs) has been discovered to play crucial roles in regulating gene expression in the host. Research on lncRNAs has greatly expanded our understanding of cellular functions involving RNAs, and it has significantly increased the range of functions of lncRNAs. In lung cancer, transcripts associated with lncRNAs have been identified as regulators of airway and lung inflammation in a process involving protein complexes. An excessive immune response and antibacterial immunity are closely linked to the pathogenesis of M. pneumoniae. The relationship between lncRNAs and M. pneumoniae infection largely involves lncRNAs that participate in antibacterial immunity. This comprehensive review aimed to examine the dysregulation of lncRNAs during M. pneumoniae infection, highlighting the latest advancements in our understanding of the biological functions and molecular mechanisms of lncRNAs in the context of M. pneumoniae infection and indicating avenues for investigating lncRNAs-related therapeutic targets.