Subsidence monitoring of offshore platforms

AbstractThe normal subsidence monitoring technologies, used in civil engineering, are hard to apply in ocean engineering. Because it is hard to find a fixed reference for subsidence monitoring. A new method, which is suitable for subsidence monitoring of offshore platforms, is proposed in this paper. Firstly, the compression characteristic of the soil was analyzed and the harms of subsidence are discussed. Based on the analysis, the subsidence monitoring method was given. Finally, an real application is shown. Some advanced measurement technologies, such as the FBG strain measurement techniques and so on, were used in this application. The real application indicates that the new method is suitable for the subsidence monitoring of offshore platforms

Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach

Graph Neural Networks (GNNs) are popular machine learning methods for modeling graph data. A lot of GNNs perform well on homophily graphs while having unsatisfactory performance on heterophily graphs. Recently, some researchers turn their attention to designing GNNs for heterophily graphs by adjusting the message passing mechanism or enlarging the receptive field of the message passing. Different from existing works that mitigate the issues of heterophily from model design perspective, we propose to study heterophily graphs from an orthogonal perspective by rewiring the graph structure to reduce heterophily and making the traditional GNNs perform better. Through comprehensive empirical studies and analysis, we verify the potential of the rewiring methods. To fully exploit its potential, we propose a method named Deep Heterophily Graph Rewiring (DHGR) to rewire graphs by adding homophilic edges and pruning heterophilic edges. The detailed way of rewiring is determined by comparing the similarity of label/feature-distribution of node neighbors. Besides, we design a scalable implementation for DHGR to guarantee high efficiency. DHRG can be easily used as a plug-in module, i.e., a graph pre-processing step, for any GNNs, including both GNN for homophily and heterophily, to boost their performance on the node classification task. To the best of our knowledge, it is the first work studying graph rewiring for heterophily graphs. Extensive experiments on 11 public graph datasets demonstrate the superiority of our proposed methods.Comment: 11 page

A model linking digital media dependence, exercise empowerment, and social physique anxiety among emerging adulthood college students

Background/objectiveSocial physique anxiety (SPA) is a prevalent psychological issue among emerging adults, regardless of gender. Many studies have shown that high levels of SPA are associated with various negative consequences on both physical and mental well-being. Considering the potential severity of SPA’s consequences and its high prevalence among emerging adults, it is imperative to investigate the factors and mechanisms that contribute to SPA in this population. Although prior studies have identified associations between emerging adulthood, digital media use, and SPA in young individuals, the underlying mechanisms remain unclear. The objective of this study is to examine the associations between SPA, emerging adulthood characteristics, digital media dependency, and exercise empowerment.MethodsIn this cross-sectional study, Chinese college students were recruited using snowball sampling. The study utilized an online survey to assess SPA, emerging adulthood characteristics, digital media dependency, and exercise empowerment. The collected data was analyzed using path analysis.ResultsA total of 1,661 Chinese college students (mean age = 19.63 ± 0.32 years, 44.97% male) were included in this study. The results showed that SPA exhibited positive correlations with responsibility and instability in emerging adulthood characteristics, digital media dependency, and exercise empowerment. Additionally, digital media dependency showed positive correlations with responsibility and instability, as well as with exercise empowerment. Furthermore, exercise empowerment demonstrated positive correlations with self-exploration, responsibility, instability, and possibilities in emerging adulthood characteristics. SPA can be directly influenced by digital media dependency, self-exploration, and instability. Furthermore, digital media dependency has a positive indirect impact on SPA through exercise empowerment. Similarly, self-exploration also has a positive indirect impact on SPA through exercise empowerment. On the other hand, instability has a negative indirect impact on SPA through exercise empowerment.ConclusionThis study provides new insights into the complex correlations with emerging adulthood characteristics, digital media dependency, exercise empowerment, and SPA. Instability, self-exploration in emerging adulthood characteristics, as well as digital media dependency, have the potential to influence SPA among college students through exercise empowerment Interventions and strategies aimed at addressing these psychological factors may prove beneficial in reducing SPA among emerging adults, especially college students

A sub-convex similarity-based model updating method considering multivariate uncertainties

This paper proposes an innovative model updating technique that thoroughly considers the interrelations among multivariate output features. The approach involves developing a novel uncertainty quantification metric, termed Sub-Convex Similarity. A specialised data preprocessing operator is proposed to reveal the inherent distributional attributes of multivariate datasets through a sequencing pre-processing. To manage the inherent randomness associated with sample location dispersion, we propose a binning algorithm based on the equal-bin-datapoints principle. This method allows for the quantification of multidimensional stochastic data without the need to calculate the joint probability distribution function. Utilising convex hull theory, sub-regional boundaries are established within each bin to reveal multivariate dataset characteristics. Sub-Convex Similarity serves as a metric for quantifying both interval-based and stochastic uncertainties, measuring discrepancies between simulated and experimental datasets in the context of both interval and stochastic model updating. The proposed model updating framework employs the sparrow search algorithm, a swarm intelligence-based optimization mechanism. The effectiveness and broad applicability of this approach are demonstrated through case studies involving a three-degree-of-freedom mass-spring system and a finite element model of a satellite, addressing multivariate uncertainties

When ChatGPT Meets Smart Contract Vulnerability Detection: How Far Are We?

With the development of blockchain technology, smart contracts have become an important component of blockchain applications. Despite their crucial role, the development of smart contracts may introduce vulnerabilities and potentially lead to severe consequences, such as financial losses. Meanwhile, large language models, represented by ChatGPT, have gained great attentions, showcasing great capabilities in code analysis tasks. In this paper, we presented an empirical study to investigate the performance of ChatGPT in identifying smart contract vulnerabilities. Initially, we evaluated ChatGPT's effectiveness using a publicly available smart contract dataset. Our findings discover that while ChatGPT achieves a high recall rate, its precision in pinpointing smart contract vulnerabilities is limited. Furthermore, ChatGPT's performance varies when detecting different vulnerability types. We delved into the root causes for the false positives generated by ChatGPT, and categorized them into four groups. Second, by comparing ChatGPT with other state-of-the-art smart contract vulnerability detection tools, we found that ChatGPT's F-score is lower than others for 3 out of the 7 vulnerabilities. In the case of the remaining 4 vulnerabilities, ChatGPT exhibits a slight advantage over these tools. Finally, we analyzed the limitation of ChatGPT in smart contract vulnerability detection, revealing that the robustness of ChatGPT in this field needs to be improved from two aspects: its uncertainty in answering questions; and the limited length of the detected code. In general, our research provides insights into the strengths and weaknesses of employing large language models, specifically ChatGPT, for the detection of smart contract vulnerabilities

Subjective Cognitive Decline May Be Associated With Post-operative Delirium in Patients Undergoing Total Hip Replacement: The PNDABLE Study

Objective: Subjective cognitive decline (SCD) is associated with an increased risk of clinical cognitive disorders. Post-operative delirium (POD) is a common complication after total hip replacement. We aimed to investigate the relationship between SCD and POD in patients undergoing total hip replacement.Methods: Our study recruited 214 cognitively intact individuals from the Perioperative Neurocognitive Disorder And Biomarker Lifestyle (PNDABLE) study in the final analysis. SCD was diagnosed with Subjective Cognitive Decline Scale (SCDS), Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA). The incidence of POD was evaluated by using Confusion Assessment Method (CAM), and POD severity was measured by using the Memorial Delirium Assessment Scale (MDAS). Preoperative cerebrospinal fluid (CSF) Aβ40, Aβ42, T-tau, and P-tau levels were measured by enzyme-linked immune-sorbent assay (ELISA).Results: Overall, the incidence of POD was 26.64% (57/214), including 32.43% (36/111) in the SCD group and 20.39% (21/103) in the NC group. With the increase of age, the incidence of POD in all age groups increased (P < 0.05). Logistic regression analysis showed that after adjusting for SCD, Aβ42, Aβ40, P-tau, and T-tau, SCD (OR 2.32, CI 1.18–4.55, P = 0.01) and the increased CSF level of P-tau (OR 1.04, CI 1.01–1.06, P < 0.001) were risk factors for POD, while the level of aβ42 (OR 0.99, CI 0.99–1.00, P < 0.001) was a protective factor for POD.Conclusion: SCD is one of the preoperative risk factors for POD.Clinical Trial Registration: This study was registered at China Clinical Trial Registry (Chictr200033439)

Sintering Temperature Induced Evolution of Microstructures and Enhanced Electrochemical Performances: Sol-Gel Derived LiFe(MoO4)2 Microcrystals as a Promising Anode Material for Lithium-Ion Batteries

A facile sol-gel process was used for synthesis of LiFe(MoO4)2 microcrystals. The effects of sintering temperature on the microstructures and electrochemical performances of the as-synthesized samples were systematically investigated through XRD, SEM and electrochemical performance characterization. When sintered at 650°C, the obtained LiFe(MoO4)2 microcrystals show regular shape and uniform size distribution with mean size of 1–2 μm. At the lower temperature (600°C), the obtained LiFe(MoO4)2 microcrystals possess relative inferior crystallinity, irregular morphology and vague grain boundary. At the higher temperatures (680 and 700°C), the obtained LiFe(MoO4)2 microcrystals are larger and thicker particles. The electrochemical results demonstrate that the optimized LiFe(MoO4)2 microcrystals (650°C) can deliver a high discharge specific capacity of 925 mAh g−1 even at a current rate of 1 C (1,050 mA g−1) after 500 cycles. Our work can provide a good guidance for the controllable synthesis of other transition metal NASICON-type electrode materials

Alterations in brain structure and function associated with pediatric growth hormone deficiency: A multi-modal magnetic resonance imaging study

IntroductionPediatric growth hormone deficiency (GHD) is a disease resulting from impaired growth hormone/insulin-like growth factor-1 (IGF-1) axis but the effects of GHD on children’s cognitive function, brain structure and brain function were not yet fully illustrated.MethodsFull Wechsler Intelligence Scales for Children, structural imaging, diffusion tensor imaging, and resting-state functional magnetic resonance imaging were assessed in 11 children with GHD and 10 matched healthy controls.Results(1) The GHD group showed moderate cognitive impairment, and a positive correlation existed between IGF-1 levels and cognitive indices. (2) Mean diffusivity was significantly increased in both corticospinal tracts in GHD group. (3) There were significant positive correlations between IGF-1 levels and volume metrics of left thalamus, left pallidum and right putamen but a negative correlation between IGF-1 levels and cortical thickness of the occipital lobe. And IGF-1 levels negatively correlated with fractional anisotropy in the superior longitudinal fasciculus and right corticospinal tract. (4) Regional homogeneity (ReHo) in the left hippocampus/parahippocampal gyrus was negatively correlated with IGF-1 levels; the amplitude of low-frequency fluctuation (ALFF) and ReHo in the paracentral lobe, postcentral gyrus and precentral gyrus were also negatively correlated with IGF-1 levels, in which region ALFF fully mediates the effect of IGF-1 on working memory index.ConclusionMultiple subcortical, cortical structures, and regional neural activities might be influenced by serum IGF-1 levels. Thereinto, ALFF in the paracentral lobe, postcentral gyrus and precentral gyrus fully mediates the effect of IGF-1 on the working memory index

Theoretical Analysis on the Short-Circuit Current of Inverter-Interfaced Renewable Energy Generators with Fault-Ride-Through Capability

Renewable energy generators (REGs) usually employ power electronic devices for connecting with the grid, which makes their fault characteristics completely different from those of conventional synchronous generators. In the existing studies, the simulation methods are mainly adopted to analyze fault current contribution from REG. As a result, the explanations on the fault current show diversity and cannot reach a recognized standard. The REGs’ mathematical model in relay-setting calculations is unknown. Thus, this paper theoretically analyses the fault current characteristics of inverter-interfaced REGs (IIREGs) with fault-ride-through (FRT) ability. In order to understand the fault current characteristics, the FRT control strategy for IIREGs is firstly studied. Then the characteristics of high-frequency and fundamental-frequency fault currents from IIREGs are theoretically analyzed after and during the faults. The affecting factors and duration time of different frequency fault currents are, respectively, revealed. Further, the mathematical expression of fundamental fault currents from IIREGs are derived and verified based on the experimental test bench. The results can be used in estimating the IIREGs’ fault contributions and developing the fault calculation model