Future orientation and perceived employability of chinese undergraduates: a moderated mediation model.

Although scholars and practitioners have highlighted the significance of students' attitudes for their future employment, few empirical examinations have attempted to determine the potential association between students' future orientation and their perceived employability. Thus, drawing on career construction theory, we test the positive effect of students' future orientation on their perceived employability by exploring the mediator of problem-based learning and the moderators of job market knowledge and proactive personality. Collecting our data via a time-lagged design (N = 368), we have found that the positive association between future orientation and employability is mediated by problem-based learning. Our moderation analyses further revealed that job market knowledge positively moderates the relationship between future orientation and problem-based learning and that students' proactive personality positively moderates the relationship between problem-based learning and perceived employability.

COVID-19 diagnosis using chest CT scans and deep convolutional neural networks evolved by IP-based sine-cosine algorithm.

The prevalence of the COVID-19 virus and its variants has influenced all aspects of our life, and therefore, the precise diagnosis of this disease is vital. If a polymerase chain reaction test for a subject is negative, but he/she cannot easily breathe, taking a computed tomography (CT) image from his/her lung is urgently recommended. This study aims to optimize a deep convolution neural network (DCNN) structure to increase the COVID-19 diagnosis accuracy in lung CT images. This paper employs the sine-cosine algorithm (SCA) to optimize the structure of DCNN to take raw CT images and determine their status. Three improvements based on regular SCA are proposed to enhance both the accuracy and speed of the results. First, a new encoding approach is proposed based on the internet protocol (IP) address. Then, an enfeebled layer is proposed to generate a variable-length DCNN. The suggested model is examined over the COVID-CT and SARS-CoV-2 datasets. The proposed method is compared to a standard DCNN and seven variable-length models in terms of five known metrics, including sensitivity, accuracy, specificity, F1-score, precision, and receiver operative curve (ROC) and precision-recall curves. The results demonstrate that the proposed DCNN-IPSCA surpasses other benchmarks, achieving final accuracy of (98.32% and 98.01%), the sensitivity of (97.22% and 96.23%), and specificity of (96.77% and 96.44%) on the SARS-CoV-2 and COVID-CT datasets, respectively. Also, the proposed DCNN-IPSCA performs much better than the standard DCNN, with GPU and CPU training times, which are 387.69 and 63.10 times faster, respectively.

A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis.

Understanding long-term trends in marine ecosystems requires accurate and repeatable counts of fishes and other aquatic organisms on spatial and temporal scales that are difficult or impossible to achieve with diver-based surveys. Long-term, spatially distributed cameras, like those used in terrestrial camera trapping, have not been successfully applied in marine systems due to limitations of the aquatic environment. Here, we develop methodology for a system of low-cost, long-term camera traps (Dispersed Environment Aquatic Cameras), deployable over large spatial scales in remote marine environments. We use machine learning to classify the large volume of images collected by the cameras. We present a case study of these combined techniques' use by addressing fish movement and feeding behavior related to halos, a well-documented benthic pattern in shallow tropical reefscapes. Cameras proved able to function continuously underwater at deployed depths (up to 7 m, with later versions deployed to 40 m) with no maintenance or monitoring for over five months and collected a total of over 100,000 images in time-lapse mode (by 15 minutes) during daylight hours. Our ResNet-50-based deep learning model achieved 92.5% overall accuracy in sorting images with and without fishes, and diver surveys revealed that the camera images accurately represented local fish communities. The cameras and machine learning classification represent the first successful method for broad-scale underwater camera trap deployment, and our case study demonstrates the cameras' potential for addressing questions of marine animal behavior, distributions, and large-scale spatial patterns.

How Can Hotel Employees Produce Workplace Environmentally Friendly Behavior? The Role of Leader, Corporate and Coworkers.

Although previous studies have acknowledged that leaders' such environmental behaviors and environmental issues are becoming critical for long-term development, little research has focused on why, how and when perceived environmentally specific servant leadership contributes to employees' workplace environmentally friendly behavior in the hotel industry. This paper aims to fill this research gap by using social identity theory to test employees' green role identity as a mediator and their perceived corporate environmental responsibility and perceived coworkers' work group green advocacy as moderators in the relationship between perceived environmentally-specific servant leadership and workplace environmentally friendly behavior. Using a sample of 527 leader-follower dyads from six hotels in mainland China at two points in time, we found that employees' green role identity mediates the positive relationship between perceived environmentally specific servant leadership and employees' workplace environmentally friendly behavior. Moreover, employees' perceived corporate environmental responsibility and perceived coworkers' work group green advocacy were found to positively moderate the relationship between perceived environmentally-specific servant leadership and green role identity and between green role identity and workplace environmentally friendly behavior, respectively. Theoretical and practical implications are discussed.

How Environmental Leadership Boosts Employees' Green Innovation Behavior? A Moderated Mediation Model.

We examined employees' green organizational identity as a mediator and green organizational climate as a moderator in the relationship between environmental leadership and follower green innovation behavior. Through collecting data (N = 313) from public organizations in China at different times, we found that environmental leadership is positively related to employees' green innovation behavior through increasing their green organizational identity. Meanwhile, the mediating relationship is conditional on the moderator of green organizational climate. The current study aims to clarify the mechanism and boundary condition in the relationship between environmental leadership and employees' green innovation behaviors.

Improved Generalized Predictive Control Algorithm for Blood Glucose Control of Type 1 Diabetes.

Artificial pancreas (AP) is an important treatment for patients with Type 1 diabetes (T1D). The control algorithm adopted in an AP system determines its reliability and accuracy. The generalized predictive control (GPC) is a representative adaptive control algorithm and has been widely applied to AP systems. However, we found that the traditional GPC controller does not work well for adolescents with T1D because of their high-fluctuating blood glucose and high insulin resistance. Here, we propose an improved GPC algorithm with an adaptive reference glucose trajectory and an adaptive softening factor. The slopes of the reference trajectory and the value of softening factor are calculated real-time on the basis of the blood glucose concentration (BGC) variations. In silico testing was done using the US Food and Drug Administration (FDA) approved virtual patient software T1D mellitus. The BGC trace and density of 20 patient-subjects (10 adults and 10 adolescents) were recorded. Results showed that the average BGC percentage within the target regions (70-180 mg/dL) of the tests with adaptive reference glucose trajectory and softening factor for adolescents (0.93 ± 0.07) was significantly higher than that of the traditional GPC algorithm tests (0.88 ± 0.11), suggesting that the control quality of the blood glucose of adolescents is significantly improved with our GPC algorithm. Therefore, our improved GPC controller is effective and should have a good applicability in AP systems.

[A modified fast independent component analysis and its application to ERP extraction].

Feature extraction of event-related potentials (ERP) plays an important part in both basic and clinical researches for cerebral neurophysiology. ICA is a method for separating blind signals based on signal statistic characteristics. In this paper, the fundamental principle, the discrimination condition and the practical algorithm of Independent Component Analysis are discussed. Then, a fast Independent Component Analysis algorithm (Fast ICA) is introduced. But like Fast ICA, its convergence is dependent on initial weight. We bring in a revision factor into the algorithm; thus the new algorithm could implement convergence on a largescale. In this paper, the revision factor is calculated by gradient. By modifying kernel iterate course, several iterations of Fast ICA are merged into one iteration of Modified Fast ICA, so the convergence of ICA will be accelerated. Finally, Modified ICA is applied to ERP extraction. The simulation shows that the convergence speed can be increased by using the improved algorithm.