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Landslides are frequent and hazardous geological disasters, posing significant risks to human safety and infrastructure. Accurate assessments of landslide susceptibility are crucial for risk management and mitigation. However, geological surveys of landslide areas are typically conducted at the township level, have lowsample sizes, and rely on experience. This study proposes a framework for assessing landslide susceptibility in Taiping Township, Zhejiang Province, China, using data balancing, machine learning, and data from 1,325 slope units with nine slope characteristics. The dataset was balanced using the Synthetic Minority Oversampling Technique and the Tomek link undersampling method (SMOTE-Tomek). A comparative analysis of six machine learning models was performed, and the SHapley Additive exPlanation (SHAP) method was used to assess the influencing factors. The results indicate that the machine learning algorithms provide high accuracy, and the random forest (RF) algorithm achieves the optimum model accuracy (0.791, F1 = 0.723). The very low, low, medium, and high sensitivity zones account for 92.27%, 5.12%, 1.78%, and 0.83% of the area, respectively. The height of cut slopes has the most significant impact on landslide sensitivity, whereas the altitude has a minor impact. The proposed model accurately assesses landslide susceptibility at the township scale, providing valuable insights for risk management and mitigation.

The Terrain Observation with Progressive Scans (TOPS) acquisition mode of Sentinel-1A provides a wide coverage per acquisition and features a repeat cycle of 12 days, making this acquisition mode attractive for surface subsidence monitoring. A few studies have analyzed wide-coverage surface subsidence of Wuhan based on Sentinel-1A data. In this study, we investigated wide-area surface subsidence characteristics in Wuhan using 15 Sentinel-1A TOPS Synthetic Aperture Radar (SAR) images acquired from 11 April 2015 to 29 April 2016 with the Small Baseline Subset Interferometric SAR (SBAS InSAR) technique. The Sentinel-1A SBAS InSAR results were validated by 110 leveling points at an accuracy of 6 mm/year. Based on the verified SBAS InSAR results, prominent uneven subsidence patterns were identified in Wuhan. Specifically, annual average subsidence rates ranged from −82 mm/year to 18 mm/year in Wuhan, and maximum subsidence rate was detected in Houhu areas. Surface subsidence time series presented nonlinear subsidence with pronounced seasonal variations. Comparative analysis of surface subsidence and influencing factors (i.e., urban construction, precipitation, industrial development, carbonate karstification and water level changes in Yangtze River) indicated a relatively high spatial correlation between locations of subsidence bowl and those of engineering construction and industrial areas. Seasonal variations in subsidence were correlated with water level changes and precipitation. Surface subsidence in Wuhan was mainly attributed to anthropogenic activities, compressibility of soil layer, carbonate karstification, and groundwater overexploitation. Finally, the spatial-temporal characteristics of wide-area surface subsidence and the relationship between surface subsidence and influencing factors in Wuhan were determined.

The liver is the central metabolic organ and plays a pivotal role in regulating homeostasis of glucose and lipid metabolism. Aberrant liver metabolism promotes insulin resistance, which is reported to be a common characteristic of metabolic diseases such as non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM). There is a complex and bidirectional relationship between NAFLD and T2DM. NAFLD patients with hepatic insulin resistance generally share a high risk of impaired fasting glucose associated with early diabetes; most patients with T2DM experience non-alcoholic fatty liver (NAFL), non-alcoholic steatohepatitis (NASH), and other more severe liver complications such as cirrhosis and hepatocellular carcinoma (HCC). Additionally, hepatic insulin resistance, which is caused by diacylglycerol-mediated activation of protein kinase C epsilon (PKC𝜀), may be the critical pathological link between NAFLD and T2DM. Therefore, this review aims to illuminate current insights regarding the complex and strong association between NAFLD and T2DM and summarize novel and emerging targets for the treatment of hepatic insulin resistance based on established mechanistic knowledge.

Background & Objective: Recent studies have showed a correlation between hyperglycemia and insulin resistance with adverse outcomes in multiple critical diseases, including sepsis. The triglyceride-glucose index (TyGi) is now recognized as a proxy indicator of insulin therapy resistance. We aimed to ascertain the connection between TyGi and the sepsis prevalence and clinical outcomes in patient’s post-abdominal surgery. Method: Data for this retrospective cohort study was acquired from the Medical Information Mart for Intensive Care IV database from 2008 to 2019. Patients (≥18 years) who had elective major abdominal surgery were included. The primary outcome was the occurrence of sepsis following abdominal surgery. The connection between TyGi and sepsis incidence was investigated with multivariable Cox regression analysis. Results: One thousand eight hundred eighty-four patients were included in this study. The cumulative incidence of sepsis during hospitalization was 12.3%. The adjusted Cox regression model showed that raised TyGi levels were linked to a greater probability of sepsis incidence (Hazard’s ratio, 1.907; 95% CI, 1.327-2.739; p<0.001). Restricted Cubic Spline analysis demonstrated that TyGi possessed a strong and almost linear connection with the likelihood of postoperative sepsis. Subgroup analysis showed interaction effects in the subgroup with low high-density lipoprotein cholesterol (p for interaction=0.018). Furthermore, the incorporation of TyGi into the existing prediction model shows an enhancement in outcome prediction. The C-statistic elevated from 0.696 to 0.722, p<0.007. The continuous net reclassification improvement (NRI) was 0.203, p=0.005, and the integrated discrimination improvement (IDI) was 0.007, p<0.001. Conclusion: Patients at increased risk of developing sepsis following abdominal surgery may be identified in clinical practice with TyGi. doi: https://doi.org/10.12669/pjms.41.6.12187 How to cite this: Xia Q, Wang Y, Wu D, Lv Z. Association of triglyceride glucose index with sepsis risk after major abdominal surgery: A retrospective cohort study. Pak J Med Sci. 2025;41(6):1734-1742. doi: https://doi.org/10.12669/pjms.41.6.12187 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Compared with traditional powder metallurgy, pulse current sintering is an advanced powder-forming technology, but its bonding mechanism is still an open topic for debate. In this paper, pulse current sintering is used as the connection technology and millimeter-sized Al particles are used as the research object. In the whole sintering process, no pressure was loaded; the function of the pulse current was the only source of heat with which to achieve the bonding of Al particles. The bonding mechanism of pulse current sintering was investigated from the perspective of material connection behavior. The results show that the pulse current density of the particle surface reaches 3.48 × 105 A/m2 instantly, while the current density of the particle center is only 8187 A/m2 at the initial stage, which is the main difference between pulse current sintering and traditional powder metallurgy sintering. With the densification process, the current density and temperature distribution in the contact region as well as the center of Al particles contact region tend to be more consistent. Finally, dense interfacial bonding was obtained, and the contact region of Al particles also demonstrated a high hardness value of 0.6385 GPa and yield strength value of 212.83 MPa. The whole process can be considered as a comprehensive action of melting (evaporation), diffusion, and plastic deformation. Based on the above results, a new technology, named high-frequency pulse current sintering, was proposed.

Landslide is a global environmental geological hazard caused by natural or human activities. Since the impoundment of the Three Gorges reservoir area, the geological disasters such as collapses, landslides and other kinds of geological disasters increased obviously due to the periodic fluctuation of the water level in the Yangtze River. Wanzhou District is located in the center of the Three Gorges Reservoir Area, which plays an important role in the prevention and control of geological hazards in the whole Three Gorges Reservoir Area. This is because a large number of deep bedrocks old landslides are distributed among this region, such as Taibaiyan ancient landslide, Caojiezi ancient landslide, Anlesi ancient landslide, Pipaping ancient landslide, and Diaoyanping ancient landslide. In this study, Quasi-Persistent Scatterers InSAR (QPS-InSAR) time-series method is proposed to identify and monitor the ancient landslides in Wanzhou. In this method, the High-coherent test is applied to Quasi-Persistent Scatterers (PSC) selection, and PSC and persistent scatterer are combined to improve the density of measurement points in vegetation area. The QPS-InSAR method is also characterized by the appropriate combination of differential interferograms produced by a Minimum Spanning Tree and the employment of the phase triangulation algorithm to estimate the optimal phase. This technique was performed on 8 scenes of L-band ALOS PALSAR ascending data acquired during 2015–2018, then deformation rate maps and time series for ancient landslide were generated, which were applied to retrieve time series displacement for the large-scale landslide in Wanzhou District. The experiment results show that there are obvious landslide deformation patterns detected in this region with displacement velocity larger than − 21 mm/yr during the observation period. Finally, the influencing factors such as geological conditions, distribution of rainfall and reservoir water level change in the Three Gorges Reservoir area, and deformation mechanism of Wanzhou landslide are analyzed. The monitoring results will help the local government to carry out regular landslide inspection and strengthen landslide disaster early warning.

Sleep disorders frequently result in poor memory, attention deficits, as well as a worse prognosis for neurodegenerative changes, such as Alzheimer's disease. The purpose of this study is to investigate the impact of sleep disorders on cognition. We screened four databases for all meta-analyses and systematic reviews from the establishment through March 2022. We have carried out quality evaluation and review the eligible systematic reviews. Evidence grading and quality assessment were performed on 22 eligible articles. Sleep deprivation primarily affects simple attention, complex attention, and working memory in cognition and alertness. The moderate-to-high-quality evidence proves optimal sleep time as 7–8 h. Sleep time outside this range increases the risk of impaired executive function, non-verbal memory, and working memory. Sleep-related breathing disorders is more likely to cause mild cognitive impairment and affects several cognitive domains. In older adults, insomnia primarily affects working memory, episodic memory, inhibitory control, cognitive flexibility, problem-solving, operational ability, perceptual function, alertness, and complex attention, and maintaining sensitivity. Sleep disturbances significantly impair cognitive function, and early detection and intervention may be critical steps in reducing poor prognosis. A simple neuropsychological memory test could be used to screen people with sleep disorders for cognitive impairment.

The aim of this study was to investigate the relationship between surface subsidence and groundwater changes. To investigate this relationship, we first analyzed surface subsidence. This paper presents the results of a case study of surface subsidence in Beijing from 1 August 2007 to 29 September 2010. The Multi-temporal Interferometric Synthetic Aperture Radar (multi-temporal InSAR) technique, which can simultaneously detect point-like stable reflectors (PSs) and distributed scatterers (DSs), was used to retrieve the subsidence magnitude and distribution in Beijing using 18 ENVISAT ASAR images. The multi-temporal InSAR-derived subsidence was verified by leveling at an accuracy better than 5 mm/year. Based on the verified multi-temporal InSAR results, a prominent uneven subsidence was identified in Beijing. Specifically, most of the subsidence velocities in the downtown area were within 10 mm/year, and the largest subsidence was detected in Tongzhou, with velocities exceeding 140 mm/year. Furthermore, Gravity Recovery and Climate Experiment (GRACE) data were used to derive the groundwater change series and trend. By comparison with the multi-temporal InSAR-derived subsidence results, the long-term decreasing trend between groundwater changes and surface subsidence showed a relatively high consistency, and a significant impact of groundwater changes on the surface subsidence was identified. Additionally, the spatial distribution of the subsidence funnel was partially consistent with that of groundwater depression, i.e., the former possessed a wider range than the latter. Finally, the relationship between surface subsidence and groundwater changes was determined.

Accurate individual tree detection by unmanned aerial vehicles (UAVs) is a critical technique for smart forest management and serves as the foundation for evaluating ecological functions. Existing object detection and segmentation methods, on the other hand, have reduced accuracy when detecting and segmenting individual trees in complicated urban forest landscapes, as well as poor mask segmentation quality. This study proposes a novel Mask-CSP-attention-coupled network (MCAN) based on the Mask R-CNN algorithm. MCAN uses the Cross Stage Partial Net (CSPNet) framework with the Sigmoid Linear Unit (SiLU) activation function in the backbone network to form a new Cross Stage Partial Residual Net (CSPResNet) and employs a convolutional block attention module (CBAM) mechanism to the feature pyramid network (FPN) for feature fusion and multiscale segmentation to further improve the feature extraction ability of the model, enhance its detail information detection ability, and improve its individual tree detection accuracy. In this study, aerial photography of the study area was conducted by UAVs, and the acquired images were used to produce a dataset for training and validation. The method was compared with the Mask Region-based Convolutional Neural Network (Mask R-CNN), Faster Region-based Convolutional Neural Network (Faster R-CNN), and You Only Look Once v5 (YOLOv5) on the test set. In addition, four scenes—namely, a dense forest distribution, building forest intersection, street trees, and active plaza vegetation—were set up, and the improved segmentation network was used to perform individual tree segmentation on these scenes to test the large-scale segmentation ability of the model. MCAN’s average precision (AP) value for individual tree identification is 92.40%, which is 3.7%, 3.84%, and 12.53% better than that of Mask R-CNN, Faster R-CNN, and YOLOv5, respectively. In comparison to Mask R-CNN, the segmentation AP value is 97.70%, an increase of 8.9%. The segmentation network’s precision for the four scenes in multi-scene segmentation ranges from 95.55% to 92.33%, showing that the proposed network performs high-precision segmentation in many contexts.

The query model (or black-box model) has attracted much attention from the communities of both classical and quantum computing. Usually, quantum advantages are revealed by presenting a quantum algorithm that has a better query complexity than its classical counterpart. In the history of quantum algorithms, the Deutsch algorithm and the Deutsch-Jozsa algorithm play a fundamental role and both are exact one-query quantum algorithms. This leads us to consider the problem: what functions can be computed by exact one-query quantum algorithms? This problem has been addressed in the literature for total Boolean functions and symmetric partial Boolean functions, but is still open for general partial Boolean functions. Thus, in this paper, we continue to characterize the computational power of exact one-query quantum algorithms for general partial Boolean functions. First, we present several necessary and sufficient conditions for a partial Boolean function to be computed by exact one-query quantum algorithms. Second, inspired by these conditions, we discover some new representative functions that can be computed by exact one-query quantum algorithms but have an essential difference from the already known ones. Specially, it is worth pointing out that before our work, the known functions that can be computed by exact one-query quantum algorithms are all symmetric functions and the quantum algorithm used is essentially the Deutsch-Jozsa algorithm, whereas the functions discovered in this paper are generally asymmetric and new algorithms to compute these functions are required. Thus, this expands the class of functions that can be computed by exact one-query quantum algorithms.