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Abstract Nowadays the collection of operational risk data worldwide highly relies on human labor, leading to slow updates, data inconsistency, and limited quantity. There remains a substantial shortage of publicly accessible operational risk databases for risk analysis. This study proposes a new data collection framework by aggregating text mining methods to replace the exhausting manual collection process. The news about operational risk can be automatically collected from the web page, then its content is analyzed and the key information is extracted. Finally, the Public-Chinese Operational Loss Data (P-COLD) database for financial institutions is constructed and expanded. Each record contains 12 key information, such as occurrence time, loss amount, and business lines, offering a more thorough description of operational risk events. With 3,723 data records from 1986 to 2023, the P-COLD database has become one of the largest and most comprehensive external operational risk databases in China. We anticipate the P-COLD database will contribute to advancements in operational risk capital calculations, dependence analysis, and institutional internal controls.

Abstract Carbon emission reduction is an important means of achieving climate policy goals. However, the literature has neglected consideration of the carbon emission reduction mechanism from the perspective of technology risk spillovers in international trade. To explore this mechanism, this paper expands the classical linear model of foreign trade technology risk spillovers into a nonlinear spatial Durbin model. We construct a novel technology risk spillover index to forecast the magnitude of carbon emissions in agricultural trade across different regions. To validate our model, we consider the dual connotation of the spatial diffusion of disembodied technology communication (DTC) to map the relationships between different geographic and economic regions that are adjacent. The results suggest that disembodied technology risk spillover could promote carbon emission reductions. Specifically, technology risk spillovers from agricultural trade reduce agricultural carbon emissions by 0.79–1.16% in the local region. Disembodied technology risk spillovers also reduce agricultural carbon emissions by 2.85–11.57% in geographically adjacent regions and 4.9% in economically adjacent regions. Based on these conclusions, this paper proposes improving the closed and backward situation of rural areas in China’s central and western regions, promoting the transition toward the high end of the global agricultural industry chain and developing productive agricultural services and the rural cultural tourism industry to expand rural employment channels and stabilize farmers’ income.

The COVID-19 pandemic had an immense effect on the well-being of healthcare professionals. In this study, researchers utilized a quantitative cross-sectional study design to investigate the degree of compassion satisfaction and fatigue amongst respiratory therapists in the state of Mississippi as a result of providing care to patients during the COVID-19 pandemic. Quantitative data were collected using an anonymous online survey that assessed the well-being and satisfaction of licensed respiratory therapists in the state of Mississippi. More specially, survey responses (n = 326) were quantitatively evaluated to measure the association between demographic variables and compassion satisfaction (CS), burnout (BO), and secondary traumatic stress (STS). Ninety-seven percent of participants reported a medium to high CS level, while 74% indicated having a medium to high level of BO, and 69% reported a medium to high level of STS. Neither age nor gender had a significant difference in CS (p = 0.504; p = 0.405), BO (p = 0.161; p = 0.285), or STS (p = 0.145; p = 0.252). Those working for more than 10 years at their current employer had higher CS (M = 38.7) and lower BO (M = 24.9) and STS (M = 24.8) scores. The number of hours worked, specifically overtime, had a significant impact on BO (β = 0.09, p = 0.028) and STS (β = 0.0.11, p = 0.019), but not CS (β = 0.02, p = 0.655). These findings suggest that the number of years employed in the field impacts the level of compassion satisfaction and contributes to lower levels of burnout and secondary traumatic stress. The age of a patient may also affect levels of compassion and burnout. The results of this study highlight the importance of developing incentive plans in an effort to retain employees.

Underwater acoustic target recognition has always played a pivotal role in ocean remote sensing. By analyzing and processing ship-radiated signals, it is possible to determine the type and nature of a target. Historically, traditional signal processing techniques have been employed for target recognition in underwater environments, which often exhibit limitations in accuracy and efficiency. In response to these limitations, the integration of artificial intelligence (AI) methods, particularly those leveraging machine learning and deep learning, has attracted increasing attention in recent years. Compared to traditional methods, these intelligent recognition techniques can autonomously, efficiently, and accurately identify underwater targets. This paper comprehensively reviews the contributions of intelligent techniques in underwater acoustic target recognition and outlines potential future directions, offering a forward-looking perspective on how ongoing advancements in AI can further revolutionize underwater acoustic target recognition in ocean remote sensing.

Background Sudden cardiac death (SCD) is a significant global public health problem accounting for 15% to 20% of all deaths. A great majority of SCD is associated with coronary heart disease, which may first be detected at autopsy. The ankle–brachial index (ABI) is a simple, noninvasive measure of subclinical atherosclerosis. The purpose of this study was to examine the relationship between ABI and SCD in a middle‐aged biracial general population. Methods and Results Participants of the ARIC (Atherosclerosis Risk in Communities) study with an ABI measurement between 1987 and 1989 were included. ABI was categorized as low (≤0.90), borderline (0.90–1.00), normal (1.00–1.40), and noncompressible (>1.40). SCD was defined as a sudden pulseless condition presumed to be caused by a ventricular tachyarrhythmia in a previously stable individual and was adjudicated by a committee of cardiac electrophysiologists, cardiologists, and internists. Cox proportional hazards models were used to evaluate the associations between baseline ABI and incident SCD. Of the 15 081 participants followed for a median of 23.5 years, 556 (3.7%) developed SCD (1.96 cases per 1000 person‐years). Low and borderline ABIs were associated with an increased risk of SCD (demographically adjusted hazard ratios [HRs], 2.27 [95% CI, 1.64–3.14] and 1.52 [95% CI, 1.17–1.96], respectively) compared with normal ABI. The association between low ABI and SCD remained significant after adjustment for traditional cardiovascular risk factors (HR, 1.63 [95% CI, 1.15–2.32]). Conclusions Low ABI is independently associated with an increased risk of SCD in a middle‐aged biracial general population. ABI could be incorporated into future SCD risk prediction models.

The conceptual hydrological model represented by the ABCD model is one of the main methods to quantify and analyze the hydrological cycle processes. However, few studies have examined the physical basis of the variables and parameters of the ABCD model and quantified the relationship between parameters in the conceptual hydrological model and traditional soil hydraulic parameters. In this study, we select the Taigemiao mining area in the Ordos basin as the research site. The HYDRUS-1D software is used to construct the soil-plant-atmosphere continuum (SPAC) model at a site scale for obtaining the “real” actual evapotranspiration during the non-freezing period. The actual evapotranspiration obtained from the SPAC model is considered as the objective function of the ABCD model for calibrating the ABCD model. The soil water storage and soil water leakage simulated by the ABCD model are compared with the results of the SPAC model. The relationships between the parameters of a and b in the ABCD model and the traditional soil hydraulic parameters are analyzed through a large number of scenario simulations. The results show that the ABCD model can better simulate the site-scale monthly actual evapotranspiration and soil water leakage during the non-freezing period. It is indicated that parameter a in the ABCD model has a strong linear correlation with the saturated water content, residual water content and curve shape parameters, while it shows a negative logarithmic relation to the saturated coefficient of permeability. It is also found that parameter b has a strong linear correlation with the saturated water content and curve shape parameter n, while the logarithmic relationships is revealed between parameter b and the saturated coefficient of permeability and curve shape parameter α. This study can improve the understanding of the physical basis of variables and parameters in conceptual hydrological models, and expands the application of hydrological models to hydrogeology.

Abstract Interest in macrocycles as potential therapeutic agents has increased rapidly. Macrocyclization of bioactive acyclic molecules provides a potential avenue to yield novel chemical scaffolds, which can contribute to the improvement of the biological activity and physicochemical properties of these molecules. In this study, we propose a computational macrocyclization method based on Transformer architecture (which we name Macformer). Leveraging deep learning, Macformer explores the vast chemical space of macrocyclic analogues of a given acyclic molecule by adding diverse linkers compatible with the acyclic molecule. Macformer can efficiently learn the implicit relationships between acyclic and macrocyclic structures represented as SMILES strings and generate plenty of macrocycles with chemical diversity and structural novelty. In data augmentation scenarios using both internal ChEMBL and external ZINC test datasets, Macformer display excellent performance and generalisability. We showcase the utility of Macformer when combined with molecular docking simulations and wet lab based experimental validation, by applying it to the prospective design of macrocyclic JAK2 inhibitors.

Comprehending the spatiotemporal dynamics and driving factors of small and micro wetlands (SMWs) holds paramount significance in their conservation and sustainable development. This paper investigated the spatiotemporal evolution and driving mechanisms of SMWs in the Yellow River Basin, utilizing buffer zones, overlay analysis, and the Geodetector model based on Landsat satellite images and an open-surface water body dataset from 1990 to 2020. The results revealed that (1) from 1990 to 2020, SMWs in the Yellow River Basin exhibited an overall pattern of fluctuation reduction. The total area decreased by approximately 1.12 × 105 hm2, with the predominant decline occurring in the 0–1 hm2 and 1–3 hm2 size categories. In terms of spatial distribution, SMWs in Qinghai and Gansu decreased significantly, while the SMWs in Inner Mongolia, Henan, and Shandong gradually increased. (2) From 1990 to 2020, SMWs were mostly converted into grassland and cropland, with some transformed into impervious water surface and barren, and only a small percentage converted into other land types in the Yellow River basin. (3) The alterations in SMWs were influenced by factors, with their interplay exhibiting nonlinear or bilinear enhancement. Among these factors, annual precipitation, elevation, and potential evapotranspiration were the primary natural factors influencing the changes in the distribution of SMWs. On the other hand, land use cover type, gross domestic product (GDP), and road distance were the main anthropogenic factors.

Accurately understanding the distribution and changing trends of Center Pivot Irrigation (CPI) farmland in the Mu Us region and exploring the impact of CPI farmland construction on sandy land vegetation growth hold significant importance for local sustainable development. By using Landsat images to extract CPI farmland information and applying buffer zone analysis to explore the impact of CPI farmland construction on the surrounding vegetation growth, the results revealed the following key findings: (1) The number and area of CPI farmland units showed a continuous growth trend from 2008 to 2022. Spatially, Etoke Front Banner was the focal point of the CPI farmland unit construction, gradually expanding outward. In terms of scale, small-scale CPI farmland units (0–0.2 km2) dominated, while large-scale CPI farmland units (>0.4 km2) were primarily distributed in Yulin City (Mu Us). (2) The growth rate of CPI farmland units in Yulin City gradually slowed down, while that in Ordos City (Mu Us) continued to exhibit a high growth trend. Affected by water-resource pressure and policies, CPI farmland units in Ordos City may continue to increase in the future, while they may stop growing or even show a downward trend in Yulin City. (3) CPI farmland mainly came from the conversion of cultivated land, but over time, more and more grassland was reclaimed as CPI farmland. The absence of cover planting after crop harvesting and the lack of shelterbelt construction may exacerbate land desertification in the region. (4) Within the typical region, CPI farmland unit construction promoted vegetation growth within the CPI units and the 500 m buffer zone but had an inhibitory effect on vegetation growth within the 500–3000 m buffer zone and no significant effect on vegetation growth within the 3000–5000 m buffer zone. (5) The decrease in groundwater reserves caused by CPI farmland unit construction was the primary reason for inhibiting the vegetation growth within the 500–3000 m buffer zone of CPI farmland units in the Mu Us region. This study can provide a scientific basis for the sustainable development of CPI farmland in semi-arid areas.

Abstract Extreme events have become more frequent in the Northern Hemisphere mid‐latitudes in recent decades, hugely impacting the ecosystem and society. The quasi‐resonance amplification (QRA) of planetary waves is considered one of the dynamic mechanisms leading to extreme weather. However, the specific impact of resonant waves caused by QRA on surface heat extremes is still an open issue. Here we show that the QRA of planetary waves leading to heat extremes is closely related to double jets, accompanied by the enhancement of atmospheric blocking and the weakening of planetary wave escaping into the stratosphere. Our results illustrate the important role of mid‐latitude planetary waves with zonal wavenumber m = 6–8 caused by QRA in the increasingly frequent heat waves in recent years, as it can increase the coverage area and occurrence frequency of heat extremes in mid‐latitudes and show a significant impact on preferred regions, especially in Eurasia.

Abstract The drivers of reputational risk are still far from explicit, making proactive risk management and quantitative research rather difficult. The Basel Committee on Banking Supervision encourages financial institutions to systematically identify reputational risk drivers; however, such drivers still represent an unsolved problem. Therefore, the objective of this paper is to systemically identify reputational risk drivers from textual risk disclosures in financial reports. We find that textual risk disclosures in financial reports contain abundant information about the causes of reputational risk, thus indicating the possibility of systematically identifying the reputational risk drivers. To accurately extract reputational risk drivers from massive and unstructured textual risk disclosure data, we modify a text mining method to make it more suitable for this type of textual data with noise words. Based on 352,326 risk headings extracted from 11,921 annual reports released by 1570 U.S. financial institutions from 2006 to 2019, a total of 13 reputational risk drivers are identified to extend upon existing studies. The importance of reputational risk drivers and their dynamic evolutions are also quantified to discover the drivers of greatest concern. This paper can clarify the sources of reputational risk to help companies realize proactive reputational risk management and provide a theoretical basis for further quantitative studies, especially the measurement of reputational risk.

Abstract Background Ovarian tissue cryopreservation and transplantation are novel therapeutic approaches for fertility preservation. However, follicle loss caused by ischemic and hypoxic damage is one of the issues after frozen-thawed ovarian tissue transplantation. Promoting angiogenesis in grafts is the key to restore cryopreserved ovarian function. Mesenchymal stem cells (MSCs) have been reported to facilitate angiogenesis in the cryopreserved ovarian tissue transplantation. However, the risk of embolization, immunogenic effect and tumorigenesis hinders the clinical application of MSCs to human organ transplantation. In this study, we established an in vitro ovarian culture system to restore frozen-thawed ovarian function before transplantation with the application of umbilical cord mesenchymal stem cells (UC-MSCs), and explored the effects of UC-MSCs on frozen-thawed ovaries in vitro ovarian culture system and the mechanisms of UC-MSCs on the angiogenesis of frozen-thawed ovaries. Methods A simple in vitro three dimensional (3D) ovarian culture system using Matrigel was established to support to an ideal niche, and ovary was alone cultured in the 24-well plate as a control. We also evaluated the effects of UC-MSCs treatment on ovarian function with or without Matrigel support. All thawed ovaries were randomly divided into control group (Matrigel−/UC-MSCs−), Matrigel group (Matrigel+/UC-MSCs−), UC-MSCs group (Matrigel−/UC-MSCs+) and UC-MSCs + Matrigel group (Matrigel+/UC-MSCs+). HE staining was used to detect the histological structure of follicles and TUNEL staining was used to detect cell apoptosis. The number of microvessels was counted to evaluate neovascularization. The mRNA expression of VEGFA, IGF1 and ANGPT2 were detected by RT-PCR. Western blotting was used to measure the expression of GSK-3β, β-catenin and p-β-catenin. Results In the absence of UC-MSCs, 3D culture system supported by Matrigel showed significantly improved follicular development and microvascular number. Additionally, UC-MSCs were also found to effectively improve follicular development and microvascular number regardless of the culture condition used. However, alleviated follicular apoptosis, increased mRNA expression of angiogenesis-related gene and activated Wnt/β-catenin pathway occurred only in the UC-MSCs + Matrigel group. Besides, with the application of IWP-2 in UC-MSCs + Matrigel group, Wnt//β-catenin pathway could be blocked by IWP-2 serving as one of Wnt/β-catenin pathway inhibitors. Conclusions This in vitro study showed the beneficial effects of UC-MSCs on thawed ovaries and explored a potential mechanism inducing angiogenesis. In particular, 3D ovarian culture system supported by Matrigel further improved UC-MSCs treatment. The in vitro culture system using Matrigel and UC-MSCs may provide a potential treatment strategy for improving the success rate of thawed ovaries transplantation.

Deep learning models can produce unstable results by introducing imperceptible perturbations that are difficult for humans to recognize. This can have a significant impact on the accuracy and security of deep learning applications due to their poorly understood interpretability. As a field critical to security research, this problem clearly exists in underwater acoustic target recognition for ocean sensing. To address this issue, this article investigates the reliability of state-of-the-art deep learning models by exploring adversarial attack methods that add small, exquisite perturbations on acoustic Mel-spectrograms to generate adversarial spectrograms. Experimental results based on real-world datasets reveal that these models can be forced to learn unexpected features when subjected to adversarial spectrograms, resulting in significant accuracy drops. Specifically, when employing the iterative attack method, the overall accuracy of all models experiences a significant decrease of approximately 70% for two datasets under stronger perturbations.

BackgroundPublic sentiments arising from public opinion communication pose a serious psychological risk to public and interfere the communication of nonpharmacological intervention information during the COVID-19 pandemic. Problems caused by public sentiments need to be timely addressed and resolved to support public opinion management.ObjectiveThis study aims to investigate the quantified multidimensional public sentiments characteristics for helping solve the public sentiments issues and strengthen public opinion management.MethodsThis study collected the user interaction data from the Weibo platform, including 73,604 Weibo posts and 1,811,703 Weibo comments. Deep learning based on pretraining model, topics clustering and correlation analysis were used to conduct quantitative analysis on time series characteristics, content-based characteristics and audience response characteristics of public sentiments in public opinion during the pandemic.ResultsThe research findings were as follows: first, public sentiments erupted after priming, and the time series of public sentiments had window periods. Second, public sentiments were related to public discussion topics. The more negative the audience sentiments were, the more deeply the public participated in public discussions. Third, audience sentiments were independent of Weibo posts and user attributes, the steering role of opinion leaders was invalid in changing audience sentiments.DiscussionSince the COVID-19 pandemic, there has been an increasing demand for public opinion management on social media. Our study on the quantified multidimensional public sentiments characteristics is one of the methodological contributions to reinforce public opinion management from a practical perspective.

In this work, the wear and friction behavior of nanoscale SiCp (1 vol.%) reinforced Mg composite were investigated. Experiments were carried out by rubbing a GCr15 steel ball on the surface of pin specimens with the different sliding velocities of 0.1, 0.3 and 0.5 m/s at distinctive normal loads of 10, 20, and 30 N, respectively (without the aid of lubricant). Detailed analysis demonstrated that SiCp efficiently block the grain growth and improve the mechanical properties of the composite due to grain refining and dislocation strengthening. As a result, the wear resistance of composite was more prominent s compared with the initial alloy under all test condition. With the increase of sliding speed or load, the wear rate of composite increases gradually. The wear rate is reduced by 12% compared to the alloy, particularly in high load condition. Under low speeds, abrasive wear and slight oxidative wear were prevalent. Subsequently, the wear mechanism transitions to the initial delamination wear as the sliding speed increases. Severe plastic deformation occurs on the surface of the friction sample under high load, and the surface damage is serious.

Olfactory function has significant implications for human health, but few risk factors for olfactory decline have been identified. We examined the factors associated with olfactory status and decline over five years in the Atherosclerosis Risk in Communities (ARIC) Neurocognitive Study. A 12-item odor identification test was used to assess olfaction in 6053 participants in 2011–2013 (ARIC visit 5, mean age: 75.6, 41% male, 23% Black race) and in 3235 participants in 2016–2017 (visit 6). We used Poisson regression models to examine cross-sectional associations of a range of potential factors with the total odor identification errors (mean errors: 2.8 ± 2.4) in visit 5 participants. We used mixed-effect Poisson regression to examine associations with olfactory decline between visits 5 and 6. We also examined associations with visit 5 anosmia prevalence (847 cases, 14%) and incident anosmia between the two visits (510 cases, 16%) using Poisson models. Older age, male sex, lower education, Black race, APOE ε4 alleles, and diabetes were associated with higher odor identification errors and higher anosmia prevalence, and greater physical activity and hypertension with better olfaction. Age, male sex, lower education, Black race, APOE ε4 allele, and vitamin B12 levels were associated with incident anosmia over 5 years. Older age was associated with faster olfactory decline. Future studies with longer follow-ups are warranted.

The accurate estimation of a regional ecosystem’s carbon storage and the exploration of its spatial distribution and influencing factors are of great significance for ecosystem carbon sink function enhancements and management. Using the Yellow River Basin as the study area, we assessed the changes in regional terrestrial ecosystem carbon storage through geographically weighted regression modeling based on a large number of measured sample sites, explored the main influencing factors through geographic probe analysis, and predicted the carbon sequestration potentials under different scenarios from 2030 to 2050. The results showed that (1) the total carbon storage in the Yellow River Basin in 2020 was about 8.84 × 109 t. Above-ground biological carbon storage, below-ground biological carbon storage, and soil carbon storage accounted for 6.39%, 5.07%, and 89.70% of the total ecosystem carbon storage, respectively. From 2000 to 2020, the carbon storage in the basin showed a trend in decreasing and then increasing, and the carbon storage in the west was larger than in the east and larger in the south than in the north. (2) Forest ecosystem was the main contributor to the increase in carbon storage in the Yellow River Basin. Elevation, temperature, and precipitation were the main factors influencing the spatial pattern of carbon storage. (3) The ecological conservation scenario had the best carbon gain effect among the four future development scenarios, and appropriate ecological conservation policies could be formulated based on this scenario in the future to help achieve the goals of carbon sequestration and sink increase.

In this work, a new time series prediction network is proposed in the framework of CasualLSTM with physical constraints and an adjusted Fourier neural operator (FNO) for the solution of the time-dependent partial differential equation. The framework of CasualLSTM is employed to learn the time evolution of spatial features which strengthens the extrapolation capability. With the help of adjusted Fourier layers (AFLs), residual connection, and the adaptive time-marching strategy, the network can quickly converge and extrapolate without labeled data by encoding PDE constraints into loss functions. Two examples, namely, Burger’s equation and two-dimensional Navier–Stokes (N-S) equation are used to evaluate the proposed method. Numerical results show that the proposed method has a good performance in solution accuracy and extrapolability.

Physics-informed convolutional recurrent network (PhyCRNet) can solve partial differential equations without labeled data by encoding physics constraints into the loss function. However, the finite-difference filter makes the solution of 2D incompressible flow challenging. Hence, this paper proposes a Fourier filter-based physics-informed convolution recurrent network (named Fourier filter-based PhyCRNet), which replaces the finite-difference filter in PhyCRNet with the Fourier filter to solve the 2D incompressible flow problem. The suggested network improves the accuracy of the partial derivatives, solves the inverse Laplacian operator, and has similar generalization ability due to inheriting the framework of PhyCRNet. Four examples, including the 2D viscous Burger, FitzHugh–Nagumo RD, vorticity and the two-dimensional Navier- Stokes (N-S) equations, validate the correctness and reliability of the proposed Fourier filter-based PhyCRNet.

Geoacoustic inversion is a challenging task in marine research due to the complex environment and acoustic propagation mechanisms. With the rapid development of deep learning, various designs of neural networks have been proposed to solve this issue with satisfactory results. As a data-driven method, deep learning networks aim to approximate the inverse function of acoustic propagation by extracting knowledge from multiple replicas, outperforming conventional inversion methods. However, existing deep learning networks, mainly incorporating stacked convolution and fully connected neural networks, are simple and may neglect some meaningful information. To extend the network backbone for geoacoustic inversion, this paper proposes a transformer-based geoacoustic inversion model with additional frequency and sensor 2-D positional embedding to perceive more information from the acoustic input. The simulation experimental results indicate that our proposed model achieves comparable inversion results with the existing inversion networks, demonstrating its effectiveness in marine research.

Objective: This study aimed to investigate the outcomes of patients who had preimplantation genetic testing for chromosomal structural rearrangement (PGT-SR) or for aneuploidy screening (PGT-A) with different indications. Methods: This was a retrospective study at a single center. Pregnancy outcomes of all couples who had PGT from 2014 to 2018 were retrospectively analyzed, and the cumulative pregnancy rates (CPR) and the cumulative live birth/ongoing pregnancy rate (CLB/OPR) per patient with at least one transfer cycle were calculated. Results: A total of 313 PGT-SR cycles of 255 patients, 22 PGT-sexing cycles of 20 patients, and 190 PGT-A cycles of 168 patients were performed during the period. In PGT-SR, the overall CPR and the CLB/OPR were 68.04% and 59.79%, respectively. In PGT-A, the CPR and CLB/OPR were 67.52% and 58.12%, respectively. We also found that the CPR (93.75%) and CLB/OPR (87.50%) were highest in patients for PGT-sexing with a diagnosis of Y chromosomal microdeletion. In addition, we discovered a significant trend that aneuploidy rate significantly increased with maternal age (p = 0.000) in PGT-A population. No significant difference was found in the mosaicism rate or clinical outcomes among the age groups. Similarly, the significance was absent in the PGT-SR population. Conclusion: We reviewed the CPR and CLB/OPR for different indications since the 24-chromosome technique has been applied in the clinical setting for 4 years in our center. We hope that our results will provide some pointed guidance and a new perspective on outcomes for PGT in certain patients and clinicians.

Gravity waves (GWs) are important for the vertical coupling of the Martian atmosphere. The middle atmosphere is the key region where GWs propagate to the upper thermosphere and generate momentum and energy exchange, but the knowledge of middle-atmosphere GWs is incomplete, due to the lack of observations with the kilometer-scale resolution. We have analyzed the climatology of GW activity in the middle and upper atmosphere of Mars using 20–180 km temperature profiles measured by the Atmospheric Chemistry Suite instrument on board the Trace Gas Orbiter. The results show that the amplitudes of GWs extracted in this study are generally less than 15% and that the centers of the strongest GW activity vary significantly with the seasons. Second, the strongest GW activity in the mesosphere indicates the strong dissipation effects of the mesopause, and the mid-atmospheric GWs show a seasonal pattern that is stronger in the winter hemisphere. During the global dust event of MY34, the enhancement of GWs in the middle atmosphere is most pronounced at low and middle latitudes where the dust storms are active. It is possible that changes in the temperature structure of the middle atmosphere adjust the atmospheric circulation and thus improve the propagation of GWs. Furthermore, GW activity is stronger on the dayside than on the nightside, and there is no significant correlation between amplitudes and background temperature. This suggests a limited role of convective instability in limiting the growth of GWs in the middle atmosphere, with nonlinear damping competing with that of molecular diffusion at different harmonics.

Using an abandoned underground goaf in coal mines as water reservoirs has been successfully applied for protecting mine water resources in western China. The water storage media are composed of broken rock masses and the voids between the rock masses. It is critical for reservoir capacity calculation that the deformation characteristics and seepage evolution of the water storage media under triaxial stress are theoretically described. In this study, broken rock masses and the space among the rock masses are simplified as two springs in a series. The mechanical behavior of the broken rock mass is described by Hooke’s law of linear elasticity, and the deformation characteristics of the space among the rock masses are represented by a nonlinear elastic constitutive model. The nonlinear stress-strain constitutive model of the water storage media is established by combining Hooke’s law and the fractional derivative stress-strain model. Similarly, a non-Darcy seepage model of the water storage media is obtained. The nonlinear stress-strain model is verified by mechanical experiments, physical simulation tests, and field measured data, and parameter sensitivity analysis is performed. The non-Darcy seepage equation is fitted and analyzed by using the seepage experimental data of the broken rock mass under triaxial compression conditions. The fractional non-Darcy model rather than the Forchheimer equation can more accurately describe the nonlinear seepage process in water storage media.

The great losses caused by financial fraud have attracted continuous attention from academia, industry, and regulatory agencies. More concerning, the ongoing coronavirus pandemic (COVID-19) unexpectedly shocks the global financial system and accelerates the use of digital financial services, which brings new challenges in effective financial fraud detection. This paper provides a comprehensive overview of intelligent financial fraud detection practices. We analyze the new features of fraud risk caused by the pandemic and review the development of data types used in fraud detection practices from quantitative tabular data to various unstructured data. The evolution of methods in financial fraud detection is summarized, and the emerging Graph Neural Network methods in the post-pandemic era are discussed in particular. Finally, some of the key challenges and potential directions are proposed to provide inspiring information on intelligent financial fraud detection in the future.

In this study, the effective exospheric temperature, derived from CHAMP and GRACE density measurements during 2002–2010, was utilized to develop a new exospheric temperature model (ETM) with the aid of the NRLMSIS 2.0 empirical model. We characterized the dominant modes of global exospheric temperature using the principal component analysis (PCA) method, and the first five derived empirical orthogonal functions (EOFs) captured 98.2% of the total variability. The obtained mean field, first five EOFs and the corresponding amplitudes were applied to build ETM using the polynomial method. The ETM and NRLMSIS 2.0 models were independently validated by the SWARM-C and GRACE Follow-On (GRACE-FO) density measurements. ETM can reproduce thermospheric density much better than the NRLMSIS 2.0 model, and the Root Mean Square Errors (RMSE) of ETM predictions were approximately 26.45% and 26.17% for the SWARM-C and GRACE-FO tests, respectively, while they were 39.52% and 44.41% for the NRLMSIS 2.0 model. In addition, ETM can accurately capture the equatorial thermospheric anomaly feature, seasonal variation and hemispheric asymmetry in the thermosphere.

There is growing concern that chemotherapy drugs can damage Leydig cells and inhibit the production of testosterone. Increasing evidence shows that melatonin benefits the reproductive process. This study mainly explores the protective effect and possible molecular mechanism of melatonin regarding cisplatin-induced oxidative stress in testicular tissue and Leydig cells. We found that there were only Leydig and Sertoli cells in the testes of gastrointestinal tumor patients with azoospermia caused by platinum chemotherapeutic drugs. Melatonin (Mel) receptor 1/melatonin receptor 2 (MT1/MT2) was mainly expressed in human and mouse Leydig cells of the testes. We also observed that the melatonin level in the peripheral blood decreased and oxidative stress occurred in mice treated with cisplatin or gastrointestinal tumor patients treated with platinum-based chemotherapeutic drugs. iTRAQ proteomics showed that SIRT1/Nrf2 signaling and MT1 proteins were downregulated in cisplatin-treated mouse testes. The STRING database predicted that MT1 might be able to regulate the SIRT1/Nrf2 signaling pathway. Melatonin reduced oxidative stress and upregulated SIRT1/Nrf2 signaling in cisplatin-treated mouse testes and Leydig cells. Most importantly, after inhibiting MT1/MT2, melatonin could not upregulate SIRT1/Nrf2 signaling in cisplatin-treated Leydig cells. The MT1/MT2 inhibitor aggravated the cisplatin-induced downregulation of SIRT1/Nrf2 signaling and increased the apoptosis of Leydig cells. We believe that melatonin stimulates SIRT1/Nrf2 signaling by activating MT1/MT2 to prevent the cisplatin-induced apoptosis of Leydig cells.

Objective: Oxidative stress is increasingly recognized as a risk factor associated with the development and progression of osteoporosis. Fufang Lurong Jiangu Capsule (FLJC) has a known anti-osteoporotic effect, but its pharmacological effect on osteoblasts is not clearly understood. This study was designed to investigate FLJC effects/mechanisms on in vitro hydrogen peroxide (H2O2)-induced oxidative damage of osteoblasts and on in vivo lipopolysaccharide (LPS)-induced mice bone loss. FLJC alleviates osteoporosis via unknown pharmacological mechanisms. Methods: Chemical compositions of FLJC preparations were analyzed using high-performance liquid chromatographic fingerprinting. After rat bone marrow mesenchymal stem cell differentiation induction, resulting osteoblasts received various 48 h FLJC pretreatments before H2O2-based (200 μM) oxidative stress exposure. FLJC effects were measured on osteoblast cell viability, morphological changes, levels of intracellular reactive oxygen species (ROS), localization of mitochondria, activity of antioxidant enzymes, alkaline phosphatase (ALP) and mineralization, the secretion of Col I and expression of osteogenic markers. The percentages of apoptosis were determined by flow cytometric analysis; apoptosis-related protein levels, including nuclear factor (erythroid-derived 2)-like 2 (Nrf2) and heme oxygenase-1 (HO-1) with or without Nrf2 inhibitor were analyzed via western blot. Hematoxylin and eosin (H&E) and ALP staining revealed in vivo FLJC effect on mice LPS-induced bone loss. Results: Five chemical components in FLJC were identified, and fingerprint analysis showed good reproducibility. FLJC pretreatment significantly reduced H2O2-induced ROS levels in osteoblasts and increased antioxidant enzyme activities to reduce oxidative damage. With regard to osteoblast differentiation, FLJC pretreatment increased ALP expression, as well as levels of mineralization and osteoblast markers. Additionally, FLJC protected against H2O2-induced apoptosis by inhibiting changes in expression of major Bcl-2 family effector proteins of the mitochondrial apoptosis pathway. Furthermore, FLJC protected cells from H2O2-induced oxidative damage by up-regulating Nrf2 and HO-1 protein levels. Finally, we confirmed that FLJC administration could reverse the bone loss in LPS-induced mice. Conclusion: These results indicate that FLJC may significantly attenuate oxidative damage of osteoblasts induced by H2O2 via the Nrf2/HO-1 signaling pathway, providing new insights to guide development of treatments for osteoporosis induced by oxidative injury.

The accuracy and efficiency of sound field calculations highly concern issues of hydroacoustics. Recently, one-dimensional spectral methods have shown high-precision characteristics when solving the sound field but can solve only simplified models of underwater acoustic propagation, thus their application range is small. Therefore, it is necessary to directly calculate the two-dimensional Helmholtz equation of ocean acoustic propagation. Here, we use the Chebyshev–Galerkin and Chebyshev collocation methods to solve the two-dimensional Helmholtz model equation. Then, the Chebyshev collocation method is used to model ocean acoustic propagation because, unlike the Galerkin method, the collocation method does not need stringent boundary conditions. Compared with the mature Kraken program, the Chebyshev collocation method exhibits a higher numerical accuracy. However, the shortcoming of the collocation method is that the computational efficiency cannot satisfy the requirements of real-time applications due to the large number of calculations. Then, we implemented the parallel code of the collocation method, which could effectively improve calculation effectiveness.

The accurate calculation of the sound field is one of the most concerning issues in hydroacoustics. The one-dimensional spectral method has been used to correctly solve simplified underwater acoustic propagation models, but it is difficult to solve actual ocean acoustic fields using this model due to its application conditions and approximation error. Therefore, it is necessary to develop a direct solution method for the two-dimensional Helmholtz equation of ocean acoustic propagation without using simplified models. Here, two commonly used spectral methods, Chebyshev–Galerkin and Chebyshev–collocation, are used to correctly solve the two-dimensional Helmholtz model equation. Since Chebyshev–collocation does not require harsh boundary conditions for the equation, it is then used to solve ocean acoustic propagation. The numerical calculation results are compared with analytical solutions to verify the correctness of the method. Compared with the mature Kraken program, the Chebyshev–collocation method exhibits higher numerical calculation accuracy. Therefore, the Chebyshev–collocation method can be used to directly solve the representative two-dimensional ocean acoustic propagation equation. Because there are no model constraints, the Chebyshev–collocation method has a wide range of applications and provides results with high accuracy, which is of great significance in the calculation of realistic ocean sound fields.

To further explore the long-term stability of nano-dielectrics, experiments were carried out to investigate the moisture absorption characteristics and electrical properties of silicone rubber (SiR) doped with different inorganic nanoparticles. Thermogravimetric analysis (TGA) is utilized to research the moisture absorption characteristics including mass fraction and binding forms. The trap depth and electron orbitals are calculated by density functional theory to explain the influence mechanism of water molecules on SiR. It is found that the addition of nanoparticles will increase the moisture content of SiR. Additionally, the nano-TiO2-doped SiR absorbs more water and binds with water relatively more loosely than nano-Al2O3. There is a degradation of space charge inhibition capability and breakdown strength after moisture absorption, which might be explained by shallow traps brought by water molecules and the narrowed forbidden bandwidth of SiR.

In this paper, a novel signal detector based on matrix information geometric dimensionality reduction (DR) is proposed, which is inspired from spectrogram processing. By short time Fourier transform (STFT), the received data are represented as a 2-D high-precision spectrogram, from which we can well judge whether the signal exists. Previous similar studies extracted insufficient information from these spectrograms, resulting in unsatisfactory detection performance especially for complex signal detection task at low signal-noise-ratio (SNR). To this end, we use a global descriptor to extract abundant features, then exploit the advantages of matrix information geometry technique by constructing the high-dimensional features as symmetric positive definite (SPD) matrices. In this case, our task for signal detection becomes a binary classification problem lying on an SPD manifold. Promoting the discrimination of heterogeneous samples through information geometric DR technique that is dedicated to SPD manifold, our proposed detector achieves satisfactory signal detection performance in low SNR cases using the K distribution simulation and the real-life sea clutter data, which can be widely used in the field of signal detection.

Laws of rumor makers’ behaviors are the root of curbing rumor and effective way to block rumor occurrence. Therefore, based on system dynamics model, this paper proposed the rumors behavior evolution model of rumor makers, aimed at discovering the laws of rumor makers’ behaviors to achieve rumors blocking. First, by refining the driving factors in disinformation behavior, we constructed causal diagram of disinformation behavior evolution; secondly, by means of causal diagram, we constructed stock-flow diagram for quantitative analysis; finally, simulation experiment was carried out by using Vensim Personal Learning Edition software (Vensim PLE). The results showed that negative attitude is a major factor in the occurrence of disinformation behavior; personal factors are more pronounced than the factors of social and government on the impact of disinformation propensity score.

What is the impact of a bank failure on the whole banking industry? To resolve this issue, the paper develops a transfer entropy-based method to determine the interbank exposure matrix between banks. This method constructs the interbank market structure by calculating the transfer entropy matrix using bank stock price sequences. This paper also evaluates the stability of Chinese banking system by simulating the risk contagion process. This paper contributes to the literature on interbank contagion mainly in two ways: it establishes a convincing connection between interbank market and transfer entropy, and exploits the market information (stock price) rather than presumptions to determine the interbank exposure matrix. Second, the empirical analysis provides an in depth understanding of the stability of the current Chinese banking system.

The dependencies between different business lines of banks have serious effects on the accuracy of operational risk estimation. Furthermore, the dependencies are far more complicated than simple linear correlation. While Pearson correlation coefficient is constructed based on the hypothesis of a linear association, the mutual information that measures all the information of a random variable contained in another random variable is a powerful alternative. Based on mutual information, the generalized correlation coefficient which can capture both linear and nonlinear correlation can be derived. This paper models the correlation between business lines by mutual information and normal copula. The experiment on a real-world Chinese bank operational risk data set shows that using mutual information to model the dependencies between business lines is more reasonable than linear correlation.

It is generally accepted that the choice of severity distribution in loss distribution approach has a significant effect on the operational risk capital estimation. However, the usually used parametric approaches with predefined distribution assumption might be not able to fit the severity distribution accurately. The objective of this paper is to propose a nonparametric operational risk modeling approach based on Cornish-Fisher expansion. In this approach, the samples of severity are generated by Cornish-Fisher expansion and then used in the Monte Carlo simulation to sketch the annual operational loss distribution. In the experiment, the proposed approach is employed to calculate the operational risk capital charge for the overall Chinese banking. The experiment dataset is the most comprehensive operational risk dataset in China as far as we know. The results show that the proposed approach is able to use the information of high order moments and might be more effective and stable than the usually used parametric approach.

The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.

How to develop a trust management model and then to efficiently control and manage nodes is an important issue in the scope of social network security. In this paper, a trust management model based on a cloud model is proposed. The cloud model uses a specific computation operator to achieve the transformation from qualitative concepts to quantitative computation. Additionally, this can also be used to effectively express the fuzziness, randomness and the relationship between them of the subjective trust. The node trust is divided into reputation trust and transaction trust. In addition, evaluation methods are designed, respectively. Firstly, the two-dimension trust cloud evaluation model is designed based on node’s comprehensive and trading experience to determine the reputation trust. The expected value reflects the average trust status of nodes. Then, entropy and hyper-entropy are used to describe the uncertainty of trust. Secondly, the calculation methods of the proposed direct transaction trust and the recommendation transaction trust involve comprehensively computation of the transaction trust of each node. Then, the choosing strategies were designed for node to trade based on trust cloud. Finally, the results of a simulation experiment in P2P network file sharing on an experimental platform directly reflect the objectivity, accuracy and robustness of the proposed model, and could also effectively identify the malicious or unreliable service nodes in the system. In addition, this can be used to promote the service reliability of the nodes with high credibility, by which the stability of the whole network is improved.

To tackle the exponentially increasing throughput of Next-Generation Sequencing (NGS), most of the existing short-read aligners can be configured to favor speed in trade of accuracy and sensitivity. SOAP3-dp, through leveraging the computational power of both CPU and GPU with optimized algorithms, delivers high speed and sensitivity simultaneously. Compared with widely adopted aligners including BWA, Bowtie2, SeqAlto, CUSHAW2, GEM and GPU-based aligners BarraCUDA and CUSHAW, SOAP3-dp was found to be two to tens of times faster, while maintaining the highest sensitivity and lowest false discovery rate (FDR) on Illumina reads with different lengths. Transcending its predecessor SOAP3, which does not allow gapped alignment, SOAP3-dp by default tolerates alignment similarity as low as 60%. Real data evaluation using human genome demonstrates SOAP3-dp's power to enable more authentic variants and longer Indels to be discovered. Fosmid sequencing shows a 9.1% FDR on newly discovered deletions. SOAP3-dp natively supports BAM file format and provides the same scoring scheme as BWA, which enables it to be integrated into existing analysis pipelines. SOAP3-dp has been deployed on Amazon-EC2, NIH-Biowulf and Tianhe-1A.

Abstract Background There is a rapidly increasing amount of de novo genome assembly using next-generation sequencing (NGS) short reads; however, several big challenges remain to be overcome in order for this to be efficient and accurate. SOAPdenovo has been successfully applied to assemble many published genomes, but it still needs improvement in continuity, accuracy and coverage, especially in repeat regions. Findings To overcome these challenges, we have developed its successor, SOAPdenovo2, which has the advantage of a new algorithm design that reduces memory consumption in graph construction, resolves more repeat regions in contig assembly, increases coverage and length in scaffold construction, improves gap closing, and optimizes for large genome. Conclusions Benchmark using the Assemblathon1 and GAGE datasets showed that SOAPdenovo2 greatly surpasses its predecessor SOAPdenovo and is competitive to other assemblers on both assembly length and accuracy. We also provide an updated assembly version of the 2008 Asian (YH) genome using SOAPdenovo2. Here, the contig and scaffold N50 of the YH genome were ~20.9 kbp and ~22 Mbp, respectively, which is 3-fold and 50-fold longer than the first published version. The genome coverage increased from 81.16% to 93.91%, and memory consumption was ~2/3 lower during the point of largest memory consumption.