Explore the vast range of titles available.

The Jiangmen Underground Neutrino Observatory (JUNO) [1] in southern China has been designed to determine the neutrino mass ordering and precisely measure the oscillation parameters. JUNO plans to start datataking in 2025, with an expected event rate of approximately 1 kHz. This translates to around 60 MB of byte-stream raw data being produced every second, resulting in data volumes of 2PB per year. To address the challenges posed by this massive amount of data, JUNO is conducting data challenges on its distributed computing resources. The data challenges aim to achieve several objectives, including understanding the offline requirements, accurately estimating the necessary resources, identifying potential bottlenecks within the involved systems, and improving overall performance. The ultimate goal is to demonstrate the effectiveness of the JUNO computing model and ensure the smooth operation of the entire data processing chain, encompassing raw data transfer, simulation, reconstruction, and analysis. Furthermore, the data challenges seek to verify the availability and effectiveness of monitoring systems for each activity.

The Jiangmen Underground Neutrino Observatory (JUNO) [1] is a multipurpose neutrino experiment and the determination of the neutrino mass hierarchy is its primary physics goal. JUNO is going to start data taking in 2024 and plans to use distributed computing infrastructure for the data processing and analysis tasks. The JUNO distributed computing system has been designed and built based on DIRAC [2]. Since last year, the official Monte Carlo (MC) production has been running on the system, and petabytes of massive MC data have been shared among JUNO data centers through this system. In this paper, an overview of the JUNO distributed computing system will be presented, including workload management system, data management, and condition data access system. Moreover, the progress of adapting the system to support token-based AAI [3] and HTTP-TPC [4] will be reported. Finally, the paper will mention the preparations for the upcoming JUNO data-taking.

The present work aims to analyze the elements that affect corporate green technology innovation and investigate a method suitable for predicting and evaluating corporate performance. First, the elements of green technology innovation and their relationships are analyzed and explained. Then, the Complex Adaptive System (CAS) theory is introduced. On this basis, a computer model for the driving mechanism system of corporate green technology innovation is constructed on the Recursive Porus Agent Simulation (Repast) platform. Finally, the Backpropagation Neural Network (BPNN) model is optimized by Particle Swarm Optimization (PSO), constituting the PSO-BPNN algorithm to evaluate corporate performance. The results of network training and simulation demonstrate that compared with traditional BPNN, PSO-BPNN achieve a faster convergence speed and fewer errors. Besides, the actual output value has a tiny difference from the expected value, showing the application potential of this algorithm in corporate performance prediction. Moreover, the driving factors of green technology innovation greatly affect the profitability and performance of enterprises. Given insufficient corporate profit margin, continuous technological innovation activities can ensure the normal operation of enterprises. A smaller corporate tax rate can shorten the time for the system to reach equilibrium. When the corporate tax rate is above 0.2, the system takes longer to reach equilibrium. In addition, the public opinion coefficient directly affects the time needed for the system to attain equilibrium. When the public opinion coefficient is within 50,00 ~ 6,000 interval, the time that the system takes to reach equilibrium changes significantly. Furthermore, corporate internal and external driving factors have a direct effect on corporate green technology innovation and performance. The research findings indicate that the PSO-BPNN algorithm is of vital practical value to corporate performance evaluation.

Preserved ratio impaired spirometry (PRISm) is a prevalent yet under-researched state of diminished lung function, which has been proposed as a pre-clinical abnormal spirometry associated with chronic obstructive pulmonary disease (COPD) or early-stage COPD. PRISm is closely associated with cardiovascular disease. Preventing and improving quality of life in PRISm subjects is important. We aimed to examined the relationship between American Heart Association’s Life’s Essential 8 (LE8) and PRISm. This cross-sectional study utilized data of 2,869 adults aged ≥ 20 years from the National Health and Nutrition Examination Survey (NHANES) in 2007–2012. Multivariable logistic regression models were employed to examine the association between LE8 score, health behavior score, health factor score, each component of LE8 score, and PRISm. Moreover, the study explored this correlation in greater depth using restricted cubic spline curves and subgroup analyses. Of the 2,869 participants, the mean age was 44.09 ± 0.44 years, and 316 (11.01%) were defined as having PRISm. In fully adjusted models, higher LE8 scores were associated with a reduced odds ratio for PRISm (OR = 0.97; 95% CI, 0.96–0.98). A linear relationship between the LE8 score and PRISm was observed. Similar patterns emerged for health behavior and health factor subscores, with a particularly stronger correlation between health factors and PRISm. In the subgroup analysis, the inverse association between LE8 and PRISm was significantly more pronounced among those with high income. A higher LE8 score was associated with a lower likelihood of developing PRISm. Promoting optimal adherence to the LE8 metrics may improve PRISm and offers a meaningful approach for its prevention and management.

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

We examine whether equity incentive can encourage exploratory innovation from the perspective of dynamic innovation decision-making process. Using the data of equity incentives in China’s listed companies from 2006 to 2017, we construct exploratory intensity of innovation strategy and analyze the impact of equity incentive on corporation exploratory innovation strategy from both the cross-sectional and time-series perspectives. We find a positive relationship between the vesting period and explorative innovation strategy in the cross-sectional dimension. However, the time-series analyses show that the innovation strategy becomes less explorative and more exploitative after the third period during equity incentive. The effect of vesting period is stronger in smaller firms and during the non-financial crisis period. Further analysis reveals that followed by the changes in innovation strategy, the growth rates of innovation output and firm performance also decline.

Stroke patients with dysphagia and family caregivers will experience multiple transitions during the whole process of the disease and various nursing needs will be generated. There is a lack of knowledge about their experiences at different transition stages. Thus, we aimed to explore the transition experiences of patients with post stroke dysphagia and family caregivers from admission to discharge home. A semi-structured interview based on Meleis's transition theory was used during hospitalization and telephone follow-up interviews were conducted in the first, third, and sixth month after the diagnosis of dysphagia. Interview transcripts were analyzed using the conventional content analysis method. A total of 17 participants enrolled in the first face-to-face interview, 16 participants took part in the first month's telephone follow-up interview, 14 participants in the third month, and 12 participants in the sixth month. The transition experiences of patients with post stroke dysphagia and family caregivers could be summarized into three themes: (1)transition from onset to admission; (2)transition from discharge to other rehabilitation institutions; and (3)transition from discharge to home. Each theme had identified interrelated subthemes. The experiences of patients with post stroke dysphagia and family caregivers during transition are a dynamic process with enormous challenges in each phase. Collaboration with health care professionals, follow-up support after discharge, and available community and social support should be integrated into transitional nursing to help patients facilitate their transition.

Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.

Ultra-wideband (UWB) technology is used for indoor positioning, but its positioning accuracy is usually degenerated by various obstacles in the indoor environment because of non-line-of-sight (NLOS). Facing the complex and changeable indoor environment, an indoor positioning system with UWB based on a digital twin is presented in this paper. The indoor positioning accuracy is improved with a perception–prediction feedback of cyber-physics space in this indoor positioning system. In addition, an anchor layout method with virtuality–reality interaction and an error mitigation method based on neural networks is put forward in this system. Finally, a case study is presented to validate this indoor positioning system with a significant improvement in positioning accuracy.

RTKN2 is a new effector protein of Rho GTPase, and has been indicated to be a tumor inhibitor in colon cancer. In this article, we explored the function of RTKN2 in BC cell development. RTKN2 expression in BC tissues and BC cell lines was evaluated by RT-qPCR and Western blot assay. CCK-8, Wound-healing and Transwell assays were carried out to examine the role of RTKN2 knockdown on proliferation, the migratory ability and the invasive ability of BC cells. FCM and Western blot assay were performed to measure the function of RTKN2 silencing on BC cell apoptosis. In addition, the regulatory effect of RTKN2 on Wnt/β-catenin pathway was studied via Western blot assay. RTKN2 expression was elevated in BC tissues and BC cells. Down-regulation of RTKN2 restrained BC cell progression by suppressing cell proliferation, migratory ability, invasive ability, and inducing apoptosis. In addition, reduced of RTKN2 sharply reduced the expressing levels of Wnt3A, β-catenin, C-Myc, and Cyclin D1, suggesting that RTKN2 silencing blocked the motivation of Wnt/β-catenin pathway in BC development. The in vivo experiment also confirmed the inhibitory effect of RTKN2 on BC tumors. Our study confirmed that RTKN2 was highly expressed in BC. Moreover, RTKN2 knockdown suppressed the development of BC through affecting the Wnt/β-catenin pathway. Hence, we deduced that RTKN2 was a possible treatment target for BC.