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Forest fire risk prediction is a crucial link in maintaining forest ecological security. Machine learning, due to its powerful non-linear modeling capabilities, has been widely applied in forest fire risk prediction research. However, existing studies often focus on the direct information provided by multiple environmental factor features when constructing the feature space, while overlooking the deeper information conveyed by feature cross-correlations. Additionally, fire risk prediction predominantly relies on single-model forecasting, exhibiting slightly insufficient generalization and stability in models. Model fusion algorithms (MFA) can combine the advantages of multiple models to compensate for this limitation. In this study, a machine learning framework, FC-StackGNB, combining feature crosses (FC) and model fusion, is proposed. This framework employs the FC method to analyze the temporal trends of various environmental factors influencing fire occurrence, constructing multiple seasonal cross features (SCFs) capable of effectively capturing the non-linear relationship between environmental factors and time. Moreover, the framework develops a Gaussian Naive Bayes (GNB) optimized stacking MFA to fully leverage the strengths of different ML algorithms. Results demonstrate that the introduction of SCFs effectively enhances the prediction performance of six machine learning models, with the mean values of five evaluation metrics (Accuracy, Precision, Recall, F1-score, and ROC_AUC) increasing by 1.58% to 6.30%. The fusion model constructed based on the StackGNB algorithm can effectively handle the multicollinearity issue of features, exhibiting significantly better prediction performance than single models, particularly in improving the Recall metric (increasing by around 3% and 5% compared to the top two ranked single models respectively), which signifies the model’s ability to predict positive samples (i.e., high-risk fire areas). The proposed modeling framework effectively enhances the robustness and prediction performance of the models, offering new modeling insights for subsequent research. This study holds significant importance for enhancing the level of forest fire risk warning.

Assessing the relationship between litter characteristics and soil microbial community traits across different forest types can enhance our understanding of the synergistic interactions among litter, soil, and microorganisms. This study focused on three representative forest types in the Funiu Mountains—Larix gmelinii (LG), Quercus aliena var. acutiserrata (QA), and Quercus aliena var. acutiserrata + Pinus armandii (QAPA). The findings indicated no significant differences in Chao1 among the three forests; however, the Shannon index exhibited an initial increase followed by a decline. NMDS and ANOSIM analyses revealed significant structural differences across these forest types. Network topological metrics (nodes, edges, average degree, and average path distance) for bacterial taxa were higher in LG and QA compared with QAPA. Additionally, LG and QA demonstrated significantly greater average niche breadth than QAPA. The results from the null models (the proportion occupied by dispersal limitation is 62.2%, 82.2%, and 64.4% in LG, QA, and QAPA), modified stochasticity ratio (LG: 0.708, QA: 0.664, and QAPA: 0.801), and neutral community models (LG: R2 = 0.665, QA: R2 = 0.630, and QAPA: R2 = 0.665) suggested that stochastic processes predominantly govern the assembly of soil bacterial communities. Random forest analysis alongside Mantel tests highlighted LTP (litter total phosphorus), STN (soil total nitrogen), MCP (carbon-to-phosphorus ratio of microbial biomass), and SCN (soil carbon-to-nitrogen ratio) as critical factors affecting bacterial niche width; conversely LCN (litter carbon-to-nitrogen ratio), RCP (ratio of dissolved carbon to phosphorus), MCP, and SCN emerged as key determinants influencing community assembly processes. Furthermore, the PLS-SEM results underscored how both litter characteristics along with soil properties—and their associated alpha diversity—impact variations in niche breadth while also shaping community assembly dynamics overall. This research provides vital insights into understanding synergistic relationships between litter quality, soil characteristics, and microbial community across diverse forest ecosystems.

As data science advances, automated machine learning (AutoML) gains attention for lowering barriers, saving time, and enhancing efficiency. However, with increasing data dimensionality, AutoML struggles with large-scale feature sets. Effective feature selection is crucial for efficient AutoML in multi-task applications. This study proposes an efficient modeling framework combining a multi-stage feature selection (MSFS) algorithm and AutoSklearn, a robust and efficient AutoML framework, to address high-dimensional data challenges. The MSFS algorithm includes three stages: mutual information gain (MIG), recursive feature elimination with cross-validation (RFECV), and a voting aggregation mechanism, ensuring comprehensive consideration of feature correlation, importance, and stability. Based on multi-source and time series remote sensing data, this study pioneers the application of AutoSklearn for forest fire risk prediction. Using this case study, we compare MSFS with five other feature selection (FS) algorithms, including three single FS algorithms and two hybrid FS algorithms. Results show that MSFS selects half of the original features (12/24), effectively handling collinearity (eliminating 11 out of 13 collinear feature groups) and increasing AutoSklearn’s success rate by 15%, outperforming two FS algorithms with the same number of features by 7% and 5%. Among the six FS algorithms and non-FS, MSFS demonstrates the highest prediction performance and stability with minimal variance (0.09%) across five evaluation metrics. MSFS efficiently filters redundant features, enhancing AutoSklearn’s operational efficiency and generalization ability in high-dimensional tasks. The MSFS–AutoSklearn framework significantly improves AutoML’s production efficiency and prediction accuracy, facilitating the efficient implementation of various real-world tasks and the wider application of AutoML.

Soil hydraulic parameters are influenced by various inherent soil properties, such as pore structure and organic matter content, which can vary with changes in the ecosystem. However, identifying the temporal variations of soil hydraulic parameters in a co-evolving soil-vegetation system remains a challenge. This study focused on a tropical forest with significant seasonal variations in vegetation attributes, evaporation, and carbon fluxes over a five-year monitoring period. The particle batch smoother algorithm was integrated with an unsaturated flow model to identify the seasonally varied soil hydraulic parameters through assimilation of in-situ measured soil moisture. As a benchmark, the Generalized Likelihood Uncertainty Estimation method was applied to optimize soil hydraulic parameters without considering temporal variation. The results indicated that the temporally varying soil hydraulic parameters exhibited regular seasonal patterns and outperformed the unvaried soil hydraulic parameters in terms of reducing the errors in modeling of soil moisture and evaporation. Moreover, the seasonal variations in soil hydraulic parameters were closely linked to changes in the litterfall and terrestrial carbon fluxes over time. Specifically, due to the hysteresis of the transformation from litterfall to soil organic matter, the accumulated litterfall in Hot-dry season can replenish the soil organic matter, resulting in an increase in field capacity and saturated hydraulic conductivity in the Hot-rainy season. However, the intense decomposition of soil organic matter under high temperature in Hot-dry season led to a decrease in field capacity and saturated hydraulic conductivity. This study emphasizes the value of the particle batch smoother algorithm in detecting temporal variations in soil hydraulic parameters within a coevolving soil-vegetation system, thereby contributing to a more comprehensive understanding of the intricate dynamics within the ecohydrological system under a changing environment.

In the era of internet-based data, recommendation systems are crucial for helping users access personalized content and facilitating business promotion and sales. Recommendation systems based on social relationships have gained popularity due to their ability to use social influence and enhance the impact and credibility of recommendations. However, building a successful recommendation system faces the challenge of protecting user data privacy, as it requires a significant amount of sensitive user data. For this challenge, we propose a privacy-preserving recommendation system based on social relationships in the dual-cloud model. To achieve the secure computation of this system, we also design and improve some underlying secure protocols such as secure equality protocol and secure comparison protocol. Our underlying protocol has the advantages of high efficiency and provable security. As a result, our recommendation system can provide excellent recommendations while ensuring privacy protection. Experiments show that our system can perform secure recommendation calculations on 128-bit data with semi-honest security in approximately 5.5 s.

Abstract Background The COVID‐19 pandemic has created challenges with respect to HIV care services. Remote online services might provide an effective method for health service delivery to people living with HIV (PLHIV). Few studies have focused on the efficacy of telemedical services for PLHIV and the effect of antiretroviral treatment via online services in China. Methods We developed a platform called the “No. 8 Health” for online antiretroviral drug collection and delivery services in Beijing from January 21 to June 30, 2022. We evaluated the online treatment service according to viral load suppression rates and compared differences in social characteristics between PLHIV who received antiretroviral drugs through online or offline treatment services. Results By June 2022, 9528 PLHIV had received outpatient treatment services, among which 44.6% (4031/9528) used the online treatment and drug delivery services for a total of 5590 person‐times. The satisfaction rate was 100%. Rates of viral load suppression among PLHIV who initiated antiretroviral therapy (ART) in 2020 and 2021 were 96.4% and 93.1%, respectively. Results showed that the viral load suppression rate was 97.9%. Regarding HIV rapid self‐testing, 4513 men who have sex with men used the online HIV rapid testing service. The number of users was approximately the same as in 2021, but both were slightly lower than those in 2020. Conclusion This study was the first to evaluate the effect of online drug collection and delivery services and virologic outcomes among PLHIV in China. The online service helped with maintenance of ART services, but the COVID‐19 pandemic still had some impacts on viral load suppression.

Abstract Introduction People living with HIV (PLHIV) relied on community-based organizations (CBOs) in accessing HIV care and support during the COVID-19 pandemic in China. However, little is known about the impact of, and challenges faced by Chinese CBOs supporting PLHIV during lockdowns. Methods A survey and interview study was conducted among 29 CBOs serving PLHIV in China between November 10 and November 23, 2020. Participants were asked to complete a 20-minute online survey on their routine operations, organizational capacity building, service provided, and challenges during the pandemic. A focus group interview was conducted with CBOs after the survey to gather CBOs’ policy recommendations. Survey data analysis was conducted using STATA 17.0 while qualitative data was examined using thematic analysis. Results HIV-focused CBOs in China serve diverse clients including PLHIV, HIV high-risk groups, and the public. The scope of services provided is broad, ranging from HIV testing to peer support. All CBOs surveyed maintained their services during the pandemic, many by switching to online or hybrid mode. Many CBOs reported adding new clients and services, such as mailing medications. The top challenges faced by CBOs included service reduction due to staff shortage, lack of PPE for staff, and lack of operational funding during COVID-19 lockdowns in 2020. CBOs considered the ability to better network with other CBOs and other sectors (e.g., clinics, governments), a standard emergency response guideline, and ready strategies to help PLHIV build resilience to be critical for future emergency preparation. Conclusion Chinese CBOs serving vulnerable populations affected by HIV/AIDS are instrumental in building resilience in their communities during the COVID-19 pandemic, and they can play significant roles in providing uninterrupted services during emergencies by mobilizing resources, creating new services and operation methods, and utilizing existing networks. Chinese CBOs’ experiences, challenges, and their policy recommendations can inform policy makers on how to support future CBO capacity building to bridge service gaps during crises and reduce health inequalities in China and globally.

With the increasing amount of real data, the challenges of large-scale model operations as well as poor generalization capacity, making selection of an appropriate feature set a significant concern. This study proposes ImprovedRFECV, an enhanced approach for cross-validated recursive feature elimination (RFECV). The algorithm first enhances the robustness of the optimal feature subset through random sampling of different data, building multiple models, and comparing their scores. Simultaneously, the L1 and L2 regularization terms are introduced to evaluate the value of each feature more comprehensively, thus reducing the impact on the anti-interference term and further improving the accuracy of the algorithm and its stability. Furthermore, a multi-model ensemble learning framework is employed to enhance generalization ability and effectively prevent overfitting. Lastly, a both-end expansion removal strategy is adopted to address the issue of strong covariance among features while enhancing the algorithm’s flexibility. The experimental results demonstrate that, compared to the RFECV algorithm, the ImprovedRFECV algorithm achieves fewer optimal average features and outperforms the optimal feature subset across five datasets spanning five different domains, demonstrating the algorithm’s high level of robustness and generalization ability.

Abstract Accurate tide level prediction is crucial to human activities in coastal areas. Many practical applications show that compared with traditional harmonic analysis, long short-term memory (LSTM), gated recurrent units (GRUs) and other neural networks, along with ensemble learning models, such as light gradient boosting machine (LightGBM) and eXtreme gradient boosting (XGBoost), can achieve extremely high prediction accuracy in relatively stationary time series. Therefore, this paper proposes a variable weight combination model based on LightGBM and CNN-BiGRU with relevant research. It uses the variable weight combination method to weight and synthesize the prediction results of the two base models so that the combination model has a stronger ability to capture time series features and fits the data well. The experimental results show that in contrast to the base model LightGBM, the RMSE value and MAE value of the combination model are reduced by 43.2% and 44.7%, respectively; in contrast to the base model CNN-BiGRU, the RMSE value and MAE value of the combination model are reduced by 35.3% and 39.1%, respectively. This means that the variable weight combination model can greatly improve the accuracy of tide level prediction. In addition, we use tidal data from different geographical environments to further verify the good universality of the model. This study provides a new idea and method for tide prediction.

Both tunable perfect absorber and multifunctional logic device can be accomplished by a novel graphene metamaterial in this paper. The metamaterial utilizes gold as a substrate, covering with a graphene flake and a graphene double-strip-array on dielectric layers. First, the perfect absorption above 99.99% in the terahertz range is achieved depends on the coupling between the two graphene layers. In addition, the tunability is based on the correlation between the conductivity and the Fermi level of graphene. As the Fermi level changes, the absorption peak of the absorber will shift, thus the tunable perfect absorber is accomplished. On this basis, the input Boolean values of the logic function are defined by the Fermi levels of graphene. The output Booleans are distinguished by monitoring the reflections and comparing them with the thresholds. Finally, the multifunctional logic device have been successfully implemented in the terahertz domain. At different frequencies, the logic gates of OR, XNOR and NAND can be realized concurrently. And the NOT logic gate can be obtained synchronously at multiple frequencies. Furthermore, the operational frequencies of the aforementioned four logic functions can be tuned by varying the other Fermi level of graphene. The maximum extinction ratios of OR, XNOR, NAND, and NOT are 13.02 dB, 26.3 dB, 12.06 dB, and 26.3 dB, respectively. This multifunctional metamaterial has potential applications in terahertz optical communication due to its straightforward structure and multiple functions.

Cereals and cereal-based products are easily polluted by mycotoxins in all aspects of production, storage and transportation, and mycotoxins are difficult to prevent due to their variety, low concentration, strong toxicity andlarge difference in nature. This paper summarizes the new development of the pretreatment technologies （liquid-liquid extraction, solid-phase extraction, QuEChERS, immunoaffinity chromatography） and detection technologies （immunochromatography technology, optical spectrum technology, liquid chromatography and liquid chromatography-mass spectrometry） of mycotoxins in cereals and products. The development trend of mycotoxin detection technologies was also prospected.

Investing in the agricultural sector exposes producers to numerous risks and uncertainties. The COVID-19 pandemic exacerbated these challenges, and their impacts are still being felt globally. Our study aims to evaluate and describe the risk management strategies employed by small farmers in Missouri to mitigate and adapt to the impact of the COVID-19 pandemic on their farm operations. We analyzed 141 survey responses, and our findings indicate that most respondents employed multiple strategies, including using savings, diversification, reducing inputs, donating output, delaying investment, gaining off-farm employment, and utilizing government relief programs. However, apart from the COVID-19 stimulus checks, which were automatically disbursed to eligible households, few farmers were aware of or participated in other federal relief programs aimed at supporting small businesses and producers. We recommend that Extension specialists collaborate with other stakeholders and agencies responsible for federal relief programs to enhance small farmer awareness and participation in the future. Additionally, further research is necessary to understand the coping strategies employed by small farmers to remain resilient and maintain personal and mental health during the pandemic.

ObjectiveThe aim of this study is to evaluate the left atrial strain (LAS) in patients with hypertrophic cardiomyopathy (HCM) by treadmill exercise stress echocardiography, combined with three-dimensional speckle tracking technology, for predicting exercise tolerance.MethodsA total of 97 patients with HCM who underwent treadmill exercise stress echocardiography were recruited in Sichuan Provincial People's Hospital between January 2018 and January 2021, and 30 control subjects were selected to be included in the normal group. HCM patients with their metabolic equivalents (METS) ≤ 6.0 were included in the HCM-1 group, while those with METS > 6.0 were included in the HCM-2 group. The LAS and exercise tolerance were analyzed. The ultrasound parameters that could predict a decrease in exercise tolerance were screened, and a predictive model was constructed.ResultsIt was found that METS, Rest-LASr, Rest-LAScd, and Rest-LASct were significantly lower in HCM patients than those in normal controls. There was a significant difference in age, Target_HR, LVMI, LAVI, E/e'-Rest, E/e'-Peak, Rest-LASr, Rest-LAScd, and Rest-LASct between the HCM-1 and the HCM-2 groups. LASr is an independent resting echocardiographic predictor of METS ≤ 6.0. LASr remained significant for predicting different subtypes (AHCM, asymmetric HCM, and obstructive HCM). Rest-LASr (AUC 0.990) was better at predicting METS ≤ 6.0 than Peak-E/e' (AUC 0.753). A multivariate model (LASr + Age + Target_HR) was established for METS prediction.ConclusionLeft atrial reservoir strain (LASr) has the strongest association with METS ≤ 6.0. The LASr is an independent resting predictor of METS ≤ 6.0 and has a good performance record in predicting different subtypes of HCM. Compared with the traditional parameters, Peak-E/e' and Rest-E/e', Rest-LASr is the best predictor. Rest-LASr can serve as a reliable method for HCM patients who are unable to undergo exercise testing but require an urgent evaluation of their METS, which provides a basis for clinical treatment decision-making and treatment effect evaluation.

Promoting local food systems is crucial to providing a more viable economy, eco-friendly production, and equal opportunities for producers, consum­ers, and communities. Meat processors are critical to local meat producers and the meat supply chain. However, various barriers have restricted small-scale meat processors and challenged the local meat supply chain. Although local food systems have gained enormous scholarly attention, little attention has been devoted to specifically exploring the meat processing sector. This study investigated the characteristics and challenges of small-scale (

IntroductionThe decomposition of plant litter mass is responsible for substantial carbon fluxes and remains a key process regulating nutrient cycling in natural and managed ecosystems. Litter decomposition has been addressed in agricultural monoculture systems, but not in intercropping systems, which produce species-diverse litter mass mixtures. The aim here is to quantify how straw type, the soil environment and their combined effects may influence straw decomposition in widely practiced maize/legume intercropping systems.MethodsThree decomposition experiments were conducted over 341 days within a long-term intercropping field experiment which included two nitrogen (N) addition levels (i.e. no-N and N-addition) and five cropping systems (maize, soybean and peanut monocultures and maize/soybean and maize/peanut intercropping). Experiment I was used to quantify litter quality effects on decomposition; five types of straw (maize, soybean, peanut, maize-soybean and maize-peanut) from two N treatments decomposed in the same maize plot. Experiment II addressed soil environment effects on root decomposition; soybean straw decomposed in different plots (five cropping systems and two N levels). Experiment III addressed ‘home’ decomposition effects whereby litter mass (straw) was remained to decompose in the plot of origin. The contribution of litter and soil effects to the home-field advantages was compared between experiment III (‘home’ plot) and I-II (‘away’ plot).Results and discussionsStraw type affected litter mass loss in the same soil environment (experiment I) and the mass loss values of maize, soybean, peanut, maize-soybean, and maize-peanut straw were 59, 77, 87, 76, and 78%, respectively. Straw type also affected decomposition in the ‘home’ plot environment (experiment III), with mass loss values of maize, soybean, peanut, maize-soybean and maize-peanut straw of 66, 74, 80, 72, and 76%, respectively. Cropping system did not affect the mass loss of soybean straw (experiment II). Nitrogen-addition significantly increased straw mass loss in experiment III. Decomposition of maize-peanut straw mixtures was enhanced more by ‘home-field advantage’ effects than that of maize-soybean straw mixtures. There was a synergistic mixing effect of maize-peanut and maize-soybean straw mixture decomposition in both 'home' (experiment III) and ‘away’ plots (experiment I). Maize-peanut showed greater synergistic effects than maize-soybean in straw mixture decomposition in their 'home' plot (experiment III). These findings are discussed in terms of their important implications for the management of species-diverse straw in food-production intercropping systems.

As the promising next generation power system, smart grid can collect and analyze the grid information in real time, which greatly improves the reliability and efficiency of the grid. However, as smart grid coverage expands, more and more data is being collected. To store and manage the massive amount of smart grid data, the data owners choose to upload the grid data to the cloud for storage and regularly check the integrity of their data. However, traditional public auditing schemes are mostly based on Public Key Infrastructure (PKI) or Identity Based Cryptography (IBC) system, which will lead to complicated certificate management and inherent key escrow problems. We propose a certificateless public auditing scheme for cloud-based smart grid data, which can avoid the above two problems. In order to prevent the disclosure of the private data collected by the smart grid during the phase of auditing, we use the random masking technology to protect data privacy. The security analysis and the performance evaluation show that the proposed scheme is secure and efficient.

Low Earth Orbit (LEO) satellite navigation signal can be used as an opportunity signal in the case of a Global Navigation Satellite System (GNSS) outage, or as an enhancement by means of traditional GNSS positioning algorithms. No matter which service mode is used, signal acquisition is a prerequisite for providing enhanced LEO navigation services. Compared with the medium orbit satellite, the transit time of the LEO satellite is shorter. Thus, it is of great significance to expand the successful acquisition time range of the LEO signal. Previous studies on LEO signal acquisition are based on simulation data. However, signal acquisition research based on real data is crucial. In this work, the signal characteristics of LEO satellites: power space density in free space and the Doppler shift of LEO satellites are individually studied. The unified symbolic definitions of several integration algorithms based on the parallel search signal acquisition algorithm are given. To verify these algorithms for LEO signal acquisition, a Software Defined Receiver (SDR) is developed. The performance of these integration algorithms on expanding the successful acquisition time range is verified by the real data collected from the Luojia-1A satellite. The experimental results show that the integration strategy can expand the successful acquisition time range, and it will not expand indefinitely with the integration duration. The performance of the coherent integration and differential integration algorithms is better than the other two integration algorithms, so the two algorithms are recommended for LEO signal acquisition and a 20 ms integration duration is preferred. The detection threshold of 2.5 is not suitable for all integration algorithms and various integration durations, especially for the Maximum-to-Mean Ratio indicator.

BackgroundThe aim of this study was to evaluate exercise tolerance in non-obstructive hypertrophic cardiomyopathy (HCM) by investigating the value of myocardial work (MW) combined with strain peak dispersion.MethodsA total of 65 patients with non-obstructive HCM and normal left ventricular ejection fraction were enrolled and 60 healthy subjects were selected as controls. The automated function imaging (AFI)-two-dimensional ultrasonic speckle-tracking technology was used to obtain the values for peak global longitudinal strain (GLS), longitudinal strain peak time dispersion (PSD), 18-segment systolic longitudinal peak strain (LPS), 18-segment longitudinal strain peak time (TTPLS), global waste work (GWW), global constructive work (GCW), global work index (GWI), global work efficiency (GWE), and exercise metabolic equivalents (METS).Results(1) Values for LV-GLS (−17.77 ± 0.20 vs. −21.66 ± 0.42%) were lower and PSD (95.10 ± 8.15 vs. 28.97 ± 1.50 ms) was prolonged in patients with HCM (p < 0.01). (2) An increasing trend was shown in the basal segment < intermediate segment < apical segment for both patients with HCM and controls, although each segment had lower values in the HCM group. (3) TTPLS was prolonged in the HCM group (p < 0.01). (4) GWE, GWI, and GCW were all lower (p < 0.01) and GWW was higher in patients with HCM (p < 0.01). (5) Values of GWE were less than 92.5%, GWI less than 1,200 mmHg, GCW less than 1,399 mmHg, these abnormal values are helpful for the diagnosis of impaired exercise tolerance and poor prognosis (6) The METS and LV-GLS of HCM in the asymmetric group were significantly lower than that in AHCM group, but the PSD was significantly greater than that in the AHCM group. Values of LPS-BL (−13.13% ± 2.51% vs −10.17% ± 2.20%) in the apical HCM group were better than in the asymmetric HCM group (p < 0.05).ConclusionGCW, GWI, and GWE can be safely measured by resting echocardiography to evaluate exercise tolerance in patients with HCM who cannot perform an exercise-based examination. Such measurements provide a basis for clinical decisions regarding exercise and drug prescription.

Background: The aim of this study was to evaluate the reservoir, conduit, and contraction function of the left atrium and to evaluate the predictive value of left atrial strain (LAS) on exercise tolerance in hypertrophic cardiomyopathy (HCM) patients with an E/e’ between 8 and 14 by two-dimensional speckle tracking using treadmill stress echocardiography. Methods: This was a retrospective study in which we analyzed a total of 70 patients with HCM between 2016 and 2017. According to the resting state E/e’, patients were either assigned to an HCM-1 group (E/e’ >14) or an HCM-2 group (E/e’ of 8 to 14). Thirty age-matched healthy controls were included in the normal group. Analysis involved the left atrial reservoir, conduit, contraction strain and reserve function. Results: The normal group had a higher left atrial reservoir and conduit strain than the HCM-2 group; the lowest values were in the HCM-1 group. The LAS reserve capacity of the HCM-1 and HCM-2 groups was lower than those of the normal group. The left atrial contraction strain reserve (ΔLASct%) and global longitudinal strain reserve (ΔGLS%) were lower in the HCM-2 and HCM-1 groups than in the normal group. We also found that the ΔLASct% and ΔGLS% in the HCM-2 group were higher than in the HCM-1 group. Furthermore, the metabolic equivalents (METS) in the HCM-2 group was greater than that in the HCM-1 group. Finally, the Rest-LASr indicated the highest differential diagnostic performance for METS

To investigate the persistence time and the effectiveness of exercise preconditioning (EP) on myocardial protection in exhausted rats from myocardial enzymes, electrocardiogram (ECG), cardiac function, and mitochondrial respiratory function after cessation of exercise training. One hundred and twelve healthy male Sprague–Dawley rats were randomly divided into seven groups (n = 16): control group (CON), exhaustive exercise (EE) group, EP group, and EE after EP (EP + EE); furthermore, EP + EE group was randomly divided into 1D, 3D, 9D, and 18D groups (1D, 3D, 9D, and 18D) and performed exhaustive treadmill exercise at a speed of 30 m/min on the 1st, 3rd, 9th, and 18th days separately after EP exercise stopped. We detected the serum contents of N-terminal pro B type natriuretic peptide (NT-proBNP) and cardiac troponin I (cTnI) by the enzyme-linked immunosorbent assays method, recorded ECG, detected heart function by pressure volume catheter, measured the respiratory rates of rat myocardial mitochondria state 3 and 4 of complex I, complex II, and IV by high-resolution breathing apparatus. EP could decrease the serum content of NT-proBNP and cTnI, improved the electrical derangement and the left ventricular function in exhausted rats. Moreover, the protective effect was more obvious in the 9th day after EP stopped, whereas it would disappear when EP stopped for more than 18 days. Compared with EE group, the respiratory rate value of myocardial mitochondrial complex increased in 1D, 3D, and 9D groups. Therefore, the protective effect of EP on the heart of exhausted rats decreased with the prolongation of stopping training time, and the effect was significant within 3 days of discontinuing training, then decreased gradually, and completely disappeared in the 18th day. EP enhanced the cardiac function in exhausted rats through raising the nicotinamide adenine diphosphate hydride (NADH) electron transport chain and increased the respiration rates of mitochondrial respiratory complex I and IV state 3, thereby improved myocardial mitochondrial respiratory function and energy metabolism.

The cloud storage auditing with deduplication is able to verify the integrity of data stored in the cloud while the cloud needs to keep only a single copy of duplicated file. To the best of our knowledge, all of the existing cloud storage auditing schemes with deduplication are vulnerable to brute-force dictionary attacks, which incurs the leakage of user privacy. In this paper, we focus on a new aspect of being against brute-force dictionary attacks on cloud storage auditing. We propose a cloud storage auditing scheme with deduplication supporting strong privacy protection, in which the privacy of user's file would not be disclosed to the cloud and other parties when this user's file is predictable or from a small space. In the proposed scheme, we design a novel method to generate the file index for duplicate check, and use a new strategy to generate the key for file encryption. In addition, the user only needs to perform lightweight computation to generate data authenticators, verify cloud data integrity, and retrieve the file from the cloud. The security proof and the performance evaluation demonstrate that the proposed scheme achieves desirable security and efficiency.

The power generation side control is the foundation of stable operation of power grid.However,the existing control methods cannot meet the growing demands of energy,environment,and human society to the power generation process.Therefore,the theory of ACP based parallel power generation control system was presented for the safety,high efficiency as well as environmental friendliness of power generation process.At first,the artificial power generation control system which is equivalent to the physical power generation control system was constructed.Secondly,the optimal model of power plant and its corresponding control strategy were chosen through the computational experiments of artificial control system to guide the operation of physical control system.Meanwhile,the behaviors of physical system were assessed for the approaching of artificial system.Then,the stable operation,maximum efficiency,and minimum energy consumption were achieved with the parallel execution of artificial control system and physical control system.Finally,the parallel control of gas turbine system in combined cycle power plant was taken as an example to demonstrate the feasibility and remarkable advantages of the adopted method.

Summary: Both caloric restriction (CR) and mitochondrial proteostasis are linked to longevity, but how CR maintains mitochondrial proteostasis in mammals remains elusive. MicroRNAs (miRNAs) are well known for gene silencing in cytoplasm and have recently been identified in mitochondria, but knowledge regarding their influence on mitochondrial function is limited. Here, we report that CR increases miRNAs, which are required for the CR-induced activation of mitochondrial translation, in mouse liver. The ablation of miR-122, the most abundant miRNA induced by CR, or the retardation of miRNA biogenesis via Drosha knockdown significantly reduces the CR-induced activation of mitochondrial translation. Importantly, CR-induced miRNAs cause the overproduction of mtDNA-encoded proteins, which induces the mitochondrial unfolded protein response (UPRmt), and consequently improves mitochondrial proteostasis and function. These findings establish a physiological role of miRNA-enhanced mitochondrial function during CR and reveal miRNAs as critical mediators of CR in inducing UPRmt to improve mitochondrial proteostasis. : Biological Sciences; Molecular Biology; Cell Biology; Functional Aspects of Cell Biology Subject Areas: Biological Sciences, Molecular Biology, Cell Biology, Functional Aspects of Cell Biology

In this paper, a plasmonic logic device with the novel manipulation method is proposed. It consists of a hexagonal resonator in the center of the structure and dual rectangular resonators in which two sliders are embedded. The input states of the logic gate are represented by the positions of the slider. The temporal coupled-mode theory demonstrates that different positions of the slider lead to changes in the transmission spectra, which is the main idea in achieving logic operation. Ultimately, three logic operations (AND, XOR, and NOR) can be implemented simultaneously by moving the sliders and the finite-difference time-domain method is used for numerical validation. In addition, the influence of structural parameters on transmission characteristics and contrast ratio is also investigated. This paper is of great significance to the design of optical logic device on-chip and provides a new method for designing other integrated photonic devices.

Global interests in organic foods are of importance to researchers and the food industry. Traditional questionnaire-based methods do not provide a broad picture. To meet this need, worldwide interests in organic foods were studied by integrating query data from the Google search engine and deep learning methods. The results show that organic oil, organic milk, organic chicken, and organic apples are the most interested organic foods; people from Singapore, US, New Zealand, Australia, United Kingdom and Canada care about organic foods the most; consumers' interest in organic foods has no correlation with GDP and life expectancy but has significant correlations with other dimensions of culture such as individualism, uncertainty avoidance, and long-term orientation. A recurrent neural network (RNN) model structure is useful in predicting people's interests in major organic foods over time.

Early risk identification of an unexpected sudden cardiac death (SCD) in a person who is suffering malignant ventricular arrhythmias is highly significant for timely intervention and increasing the survival rate. For this purpose, we have presented an automated strategy for prediction of SCD with a high-level accuracy by using measurable arrhythmic markers in this paper. The set of arrhythmic parameters includes three repolarization interval ratios, such as TpTe/QT, JTp/JTe, and TpTe/JTp and two conduction-repolarization markers, such as TpTe/QRS and TpTe/(QT×QRS). Each of them is calculated directly from the detected QRS complex waves and T-wave of electrocradiogram (ECG) signals. Then, all calculated markers are used for the automatical classification of normal and SCD risk groups by employing machine learning classifiers, such as k-nearest neighbor (KNN), decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and random forest (RF). The effectiveness and usefulness of the proposed method is evaluated using a database of measured ECG data acquired from 28 SCD and 18 normal patients. For the automated strategy, the set of five arrhythmic risk markers can predict SCD in less than one second with an average accuracy of 98.91% (KNN), 98.70% (SVM), 98.99% (DT), 97.46% (NB), and 99.49% (RF) for 30 minutes before the occurrence of SCD. Moreover, a practical and straightforward SCD index (SCDI) through a judicious integration of these markers is also proposed by using the Student's t-test. The obtained SCDIs are 1.2058 ± 0.0795 and 1.7619 ± 0.1902 for normal and SCD patients, respectively, which provide a sufficient discrimination degree with a p-value of 6.5061e-35. The present results show that both the automated classifier and the integrated SCDI can predict the SCD up to 30 minutes earlier, and that these predictions could be more practical and efficient if applied in portable smart devices with real-time requirements in hospital settings or at home.

A lightweight convolutional neural network (CNN) is presented in this study to automatically indentify atrial fibrillation (AF) from single-lead ECG recording. In contrast to existing methods employing a deeper architecture or complex feature-engineered inputs, this work presents an attempt to employ a lightweight CNN to confront current drawbacks such as higher computational requirement and inadequate training dataset, by using representative rhythms features of AF rather than raw ECG signal or hand-crafted features without any electrophysiological considerations. The experimental results suggested that this method presents the following significant advantages: (1) higher performances for indentifying AF in terms of accuracy, sensitivity, and specificity that are 97.5%, 97.8%, and 97.2%, respectively; (2) It is capable of automatically extracting the shared features of AF episodes of different patients and would be much robust and reliable; (3) with the cardiac rhythm features as input dataset, rather than complex transforming and classifying the raw data, thus requiring a lower computational resource. In conclusion, this automated method could analyze large amounts of data in a short time while assuring a relative high accuracy, and thus would potentially serve to provide a comfortable single-lead monitoring for patients and a clinical useful tool for doctors.

This case reports a novel hemizygous frameshift EMD mutation (c.487delA, p.Ser163fs) in twins of an Emery–Dreifuss muscular dystrophy family with severe cardiac involvement and mild muscle weakness. Their mother carried the same heterozygous mutation.

Motivated by the recent successful synthesis of Janus monolayer of transition metal (TM) dichalcogenides, MXenes with Janus structures are worthy of further study, concerning its electronic structure and magnetic properties. Here, we study the effect of different transition metal atoms on the structure stability and magnetic and electronic properties of M’MCO2 (M’ and M = V, Cr and Mn). The result shows the output magnetic moment is contributed mainly by the d orbitals of the V, Cr, and Mn atoms. The total magnetic moments of ferromagnetic (FM) configuration and antiferromagnetic (AFM) configuration are affected by coupling types. FM has a large magnetic moment output, while the total magnetic moments of AFM2’s (intralayer AFM/interlayer FM) configuration and AFM3’s (interlayer AFM/intralayer AFM) configuration are close to 0. The band gap widths of VCrCO2, VMnCO2, CrMnCO2, V2CO2, and Cr2CO2 are no more than 0.02 eV, showing metallic properties, while Mn2CO2 is a semiconductor with a 0.7071 eV band gap width. Janus MXenes can regulate the size of band gap, magnetic ground state, and output net magnetic moment. This work achieves the control of the magnetic properties of the available 2D materials, and provides theoretical guidance for the extensive design of novel Janus MXene materials.

Optical network-on-chip (ONoC) is based on optical interconnects and optical routers (ORs), which have obvious advantages in bandwidth and power consumption. Transmission capacity is a significant performance in ONoC architecture, which has to be fully considered during the design process. Relying on mode-division multiplexing (MDM) technology, the system capacity of optical interconnection is greatly improved compared to the traditional multiplexing technology. With the explosion in MDM technology, the optical router supporting MDM came into being. In this paper, we design a multimode optical router (MDM-OR) model and analyze its indicators. Above all, we propose a novel multimode switching element and design an N-port universal multimode optical router (MDM-OR) model. Secondly, we analyze the insertion loss model of different optical devices and the crosstalk noise model of N-port MDM-OR. On this basis, a multimode router structure of a single-mode five-port optical router is proposed. At the same time, we analyze the transmission loss, crosstalk noise, signal-to-noise radio (OSNR), and bit error rate (BER) of different input–output pairs by inputting the 1550 nm TE0, TE1, and TE2 modes to the router.

2D MXenes have been found to be one of the most promising catalysts for hydrogen evolution reaction (HER) due to their excellent electronic conductivity, hydrophilic nature, porosity and stability. Nonmetallic (NM) element doping is an effective approach to enhance the HER catalytic performance. By using the density functional theory (DFT) method, we researched the effect of nonmetallic doping (different element types, variable doping concentrations) and optimal hydrogen absorption concentration on the surface of NM-Ti3C2O2 for HER catalytic activity and stability. The calculation results show that doping nonmetallic elements can improve their HER catalytic properties; the P element dopants catalyst especially exhibits remarkable HER performance (∆GH = 0.008 eV when the P element doping concentration is 100% and the hydrogen absorption is 75%). The origin mechanism of the regulation of doping on stability and catalytic activity was analyzed by electronic structures. The results of this work proved that by controlling the doping elements and their concentrations we can tune the catalytic activity, which will accelerate the further research of HER catalysts.

The dynamic characteristics of long-span suspension bridges are complex. The natural vibration frequency is changed with different structural parameters, and the sensitivity to different parameters is different. In order to solve this problem, the spatial model of a long-span suspension bridge was established by using finite element software, and the first 20 natural vibration periods, natural vibration frequencies and vibration modes were analyzed and calculated. The accuracy of the obtained natural vibration frequency data was verified through field tests. Finally, based on the model, the stiffness of structural components is studied by one -factor-at-one-time, and the influence of various variables on the frequency and mode of a certain mode is studied by one-factor-at-one-time method. The results show that different structural parameters have different effects on the vibration frequency. When the stiffness of stiffening girder and main tower is changed, with the increase of stiffness, the variation of frequency mostly presents an upward trend, and the range is large. With the change of the secondary dead load, most of the frequencies decrease first and then tend to be stable. It can be seen from the field test results that the vibration shapes and frequencies measured by numerical simulation and test are close to each other, which can meet the requirements of engineering precision. The stiffness of the main cable and the main tower has a great influence on the modes and periods corresponding to them. The increase of the secondary dead load can reduce the natural vibration frequency of the suspension bridge, but it is not unlimited to increase the secondary dead load to reduce the frequency. The stiffness of the stiffening girder has a great influence on the frequency of the suspension bridge. When the bending stiffness of the stiffening girder increases to 3 times of the original one, the order of vibration modes of the structure will change. The research results can provide references for structural design and dynamic parameter adjustment of long-span suspension bridge.

Celerity and velocity of subsurface flow in soil porous medium are intimately linked with tracer transport, while only few current studies focused on their relations. This study conducted theoretical analyses based on a pore bundle model, under which celerity in unsaturated flow is equivalent to maximum velocity. Furthermore, under 3 models Brooks-Corey model and unmodified and modified Mualem-van Genuchten models, we used a celerity function to derive breakthrough curves aiming to quantify the advective tracer transport for 5 typical soil textures. The results showed that the celerity under near-saturated conditions can be 5 up to 100 times larger than the saturated hydraulic conductivity, and a small volumetric fraction (

In forested regions, transpiration as a main component of evaporation fluxes is important for evaporation partitioning. Physiological behaviours among various vegetation species are quite different. Thus, an accurate estimation of the transpiration rate from a certain tree species needs specific parameterization of stomatal response to multiple environmental conditions. In this study, we chose a 300-m2 beech forest plot located in Vydra basin, the Czech Republic, to investigate the transpiration of beech (Fagus sylvatica) from the middle of the vegetative period to the beginning of the deciduous period, covering 100 days. The sap flow equipment was installed in six trees with varying ages among 32 trees in the plot, and the measurements were used to infer the stomatal conductance. The diurnal pattern of stomatal conductance and the response of stomatal conductance under the multiple environmental conditions were analysed. The results show that the stomatal conductance inferred from sap flow reached the highest at midday but, on some days, there was a significant drop at midday, which might be attributed to the limits of the hydraulic potential of leaves (trees). The response of stomatal conductance showed no pattern with solar radiation and soil moisture, but it did show a clear correlation with the vapour deficit, in particular when explaining the midday drop. The relation to temperature was rather scattered as the measured period was in the moderate climate. The findings highlighted that the parametrization of stress functions based on the typical deciduous forest does not perfectly represent the measured stomatal response of beech. Therefore, measurements of sap flow can assist in better understanding transpiration in newly formed beech stands after bark beetle outbreaks in Central Europe.

Stem cell factor (SCF) and granulocyte colony-stimulating factor (G-CSF) are initially discovered as the essential hematopoietic growth factors regulating bone marrow stem cell proliferation and differentiation, and SCF in combination with G-CSF (SCF+G-CSF) has synergistic effects on bone marrow stem cell mobilization. In this study we have determined the effect of SCF and G-CSF on neurite outgrowth in rat cortical neurons. Using molecular and cellular biology and live cell imaging approaches, we have revealed that receptors for SCF and G-CSF are expressed on the growth core of cortical neurons, and that SCF+G-CSF synergistically enhances neurite extension through PI3K/AKT and NFκB signaling pathways. Moreover, SCF+G-CSF induces much greater NFκB activation, NFκB transcriptional binding and brain-derived neurotrophic factor (BDNF) production than SCF or G-CSF alone. In addition, we have also observed that BDNF, the target gene of NFκB, is required for SCF+G-CSF-induced neurite outgrowth. These data suggest that SCF+G-CSF has synergistic effects to promote neurite growth. This study provides new insights into the contribution of hematopoietic growth factors in neuronal plasticity.

Multidrug resistance mediated by P-glycoprotein in cancer cells has been a major issue that cripples the efficacy of chemotherapy agents. Aimed for improved efficacy against resistant cancer cells, we designed and synthesized 25 oxindole derivatives based on indirubin by structure-activity relationship analysis. The most potent one was named PH II-7, which was effective against 18 cancer cell lines and 5 resistant cell lines in MTT assay. It also significantly inhibited the resistant xenograft tumor growth in mouse model. In cell cycle assay and apoptosis assay conducted with flow cytometry, PH II-7 induced S phase cell cycle arrest and apoptosis even in resistant cells. Consistently revealed by real-time PCR, it modulates the expression of genes related to the cell cycle and apoptosis in these cells, which may contributes to its efficacy against them. By side-chain modification and FITC-labeling of PH II-7, we were able to show with confocal microscopy that not only it was not pumped by P-glycoprotein, it also attenuated the efflux of Adriamycin by P-glycoprotein in MDR tumor cells. Real-time PCR and western blot analysis showed that PH II-7 down-regulated MDR1 gene via protein kinase C alpha (PKCA) pathway, with c-FOS and c-JUN as possible mediators. Taken together, PH II-7 is a dual-functional compound that features both the cytotoxicity against cancer cells and the inhibitory effect on P-gp mediated drug efflux.

Mapping the characteristics of Africa's smallholder-dominated croplands, including the sizes and numbers of fields, can provide critical insights into food security and a range of other socioeconomic and environmental concerns. However, accurately mapping these systems is difficult because there is 1) a spatial and temporal mismatch between satellite sensors and smallholder fields, and 2) a lack of high-quality labels needed to train and assess machine learning classifiers. We developed an approach designed to address these two problems, and used it to map Ghana's croplands. To overcome the spatio-temporal mismatch, we converted daily, high resolution imagery into two cloud-free composites (the primary growing season and subsequent dry season) covering the 2018 agricultural year, providing a seasonal contrast that helps to improve classification accuracy. To address the problem of label availability, we created a platform that rigorously assesses and minimizes label error, and used it to iteratively train a Random Forests classifier with active learning, which identifies the most informative training sample based on prediction uncertainty. Minimizing label errors improved model F1 scores by up to 25%. Active learning increased F1 scores by an average of 9.1% between first and last training iterations, and 2.3% more than models trained with randomly selected labels. We used the resulting 3.7 m map of cropland probabilities within a segmentation algorithm to delineate crop field boundaries. Using an independent map reference sample (n = 1,207), we found that the cropland probability and field boundary maps had respective overall accuracies of 88 and 86.7%, user's accuracies for the cropland class of 61.2 and 78.9%, and producer's accuracies of 67.3 and 58.2%. An unbiased area estimate calculated from the map reference sample indicates that cropland covers 17.1% (15.4-18.9%) of Ghana. Using the most accurate validation labels to correct for biases in the segmented field boundaries map, we estimated that the average size and total number of field in Ghana are 1.73 ha and 1,662,281, respectively. Our results demonstrate an adaptable and transferable approach for developing annual, country-scale maps of crop field boundaries, with several features that effectively mitigate the errors inherent in remote sensing of smallholder-dominated agriculture.