Internet dependency and psychosocial maturity among college students

 One salient impact of information technology on students' lives is the ever-increasing use of the Internet. Although there exist many reports in the media regarding the unhealthy Internet use among students, research is still limited and has mainly relied upon on-line self-selected reports on Internet dependency or “Internet addiction”. This paper attempts to look into the alleged Internet dependency within the Eriksonian psychosocial development framework. The results of a survey of the Internet use among 217 students in an Australian regional university are reported. The measures of patterns of the Internet use were correlated with that of psychosocial maturity and self-efficacy. The results showed that the Internet dependency seemed to be independent of the psychosocial maturity and the general perceived self-efficacy. A factor analysis extracted six factors from a set of 28 Internet experience-related questions and indicated that the Internet dependency could be of a multifaceted nature. The findings and their implications were discussed and a contextual perspective was proposed. </p

Diversity and Economic Performance in a Model with Progressive Taxation

Is a more heterogeneous population beneficial or harmful to long-term economic performance? This article addresses this and other questions in a dynamic general equilibrium model where consumers differ in their labour productivity and time preference. We show how differences in the cross-sectional distribution of these characteristics can affect the economy via two channels. The first one involves changing the composition of the labour force; and the second one involves changing the cross-sectional distribution of the marginal tax rate. We show how these channels are, respectively, determined by the shape of the labour supply function and the curvature of the marginal tax function

Covid-19 Detection by Wavelet Entropy and Self-adaptive PSO

The rapid global spread of COVID-19 disease poses a huge threat to human health and the global economy. The rapid increase in the number of patients diagnosed has strained already scarce healthcare resources to track and treat Covid-19 patients in a timely and effective manner. The search for a fast and accurate way to diagnose Covid-19 has attracted the attention of many researchers. In our study, a deep learning framework for the Covid-19 diagnosis task was constructed using wavelet entropy as a feature extraction method and a feedforward neural network classifier, which was trained using an adaptive particle swarm algorithm. The model achieved an average sensitivity of 85.14% ± 2.74%, specificity of 86.76% ± 1.75%, precision of 86.57% ± 1.36%, accuracy of 85.95% ± 1.14%, and F1 score of 85.82% ± 1.30%, Matthews correlation coefficient of 71.95 ± 2.26%, and Fowlkes-Mallows Index of 85.83% ± 1.30%. Our experiments validate the usability of wavelet entropy-based feature extraction methods in the medical image domain and show the non-negligible impact of different optimisation algorithms on the models by comparing them with other models

Modelling of the melt pool geometry in the laser deposition of nickel alloys using the anisotropic enhanced thermal conductivity approach

Use of appropriate modes of heat transfer in finite element modelling simulations of laser deposition is important for enhancing the reliability of the predicted results. An important contributory mode is melt pool convection, which is the focus of this work. Using the anisotropic enhanced thermal conductivity approach, this study examines the strategies relating to the choice of appropriate values for the thermal conductivity enhancement factors in the orthogonal axial directions x, y, and z. In order to investigate different combinations of values for these factors in the laser deposition of one track of Inconel 718 powder on an EN-43A mild steel substrate, finite element models were prepared and results from these were compared with the corresponding experimental results. The results of the study suggested that no thermal conductivity enhancement should be enforced in the direction of the depth of the sample. Thermal enhancement factors in the two orthogonal directions are required, but the factor in the direction parallel to the direction of beam scanning should be of greater magnitude. Analysis of the thermal gradients from the model also showed that failure to incorporate any allowance for the melt pool convection effect with appropriate choice of thermal conductivity enhancement factors in the finite element modelling of the laser deposition can result in overprediction of thermal stress, which can lead to undue threats of various forms of distortion during the deposition process

Defect induced cracking and modeling of fatigue strength for an additively manufactured Ti-6Al-4V alloy in very high cycle fatigue regime

Additively manufactured (AM) alloy usually inevitably contains defects during the manufacturing process or in service. Defect, as a harmful factor, could significantly reduce the fatigue performance of materials. This paper shows that the location and introduced form of defects play an important role in high cycle fatigue and very high cycle fatigue (VHCF) behavior of selective laser melting Ti-6Al-4V alloys. The S-N curve descends approximately linearly for internal defect induced failure. While for artificial surface defect induced failure, the S-N curve descends at first and then exhibits a plateau region feature. The competition of interior crack initiation with fine granular area feature is also observed in VHCF regime. The paper indicates that only the size or the stress intensity factor range of the defect is not an appropriate parameter for describing the effect of defect on fatigue crack initiation. Finally, the effect of artificial surface defect on high cycle fatigue and VHCF strength is modeled, i.e., the fatigue strength σ, fatigue life N and defect size area (square root of the projection area of defect perpendicular to principal stress direction) is expressed as σ=CNaarean,

H∞ output feedback control for descriptor networked systems with multiple packet dropouts

In this paper, the control problem with both multiple measurement and control packets dropouts for descriptor systems is investigated. Firstly, the descriptor systems are transferred to the normal systems. Then two independent Bernoulli distributed white sequence are used to describe the multiple measurement and control packet dropouts. An H∞ output controller is designed to make the systems stable and achieve the prescribed H∞ disturbance attenuation level. A sufficient condition for the existence of the dynamic output feedback controller is presented via matrix inequalities. The extended CCLM algorithm is applied to solve the parameters of the controller. Finally, an example is provided to illustrate the usefulness of the design method

Observer-based H∞ control for networked systems with consecutive packet delays and losses

This paper considers the control problem for networked control systems (NCSs) with unreliable data communication. The unreliable data communication simultaneously exists in both control channel (from controller to actuator) and measurement channel (from sensor to controller) and may cause consecutive packet delays and losses. A new model is established based on all possible consecutive packet delays and losses. The observer-based controller is designed to exponentially stabilize the networked system in the sense of mean square, and also achieve the prescribed H∞ disturbance attenuation level. An iterative algorithm is developed to compute the optimal H∞ disturbance attenuation and the controller parameters by solving the semi-definite programming problem via interior-point approach. An illustrative example is provided to show the applicability of the proposed method

Analysis SimCO algorithms for sparse analysis model based dictionary learning

In this paper, we consider the dictionary learning problem for the sparse analysis model. A novel algorithm is proposed by adapting the simultaneous codeword optimization (SimCO) algorithm, based on the sparse synthesis model, to the sparse analysis model. This algorithm assumes that the analysis dictionary contains unit l2-norm atoms and learns the dictionary by optimization on manifolds. This framework allows multiple dictionary atoms to be updated simultaneously in each iteration. However, similar to several existing analysis dictionary learning algorithms, dictionaries learned by the proposed algorithm may contain similar atoms, leading to a degenerate (coherent) dictionary. To address this problem, we also consider restricting the coherence of the learned dictionary and propose Incoherent Analysis SimCO by introducing an atom decorrelation step following the update of the dictionary. We demonstrate the competitive performance of the proposed algorithms using experiments with synthetic data and image denoising as compared with existing algorithms

MEEDNets: Medical Image Classification via Ensemble Bio-inspired Evolutionary DenseNets

Inspired by the biological evolution, this paper proposes an evolutionary synthesis mechanism to automatically evolve DenseNet towards high sparsity and efficiency for medical image classification. Unlike traditional automatic design methods, this mechanism generates a sparser offspring in each generation based on its previous trained ancestor. Concretely, we use a synaptic model to mimic biological evolution in the asexual reproduction. Each generation's knowledge is passed down to its descendant, and an environmental constraint limits the size of the descendant evolutionary DenseNet, moving the evolution process towards high sparsity. Additionally, to address the limitation of ensemble learning that requires multiple base networks to make decisions, we propose an evolution-based ensemble learning mechanism. It utilises the evolutionary synthesis scheme to generate highly sparse descendant networks, which can be used as base networks to perform ensemble learning in inference. This is specially useful in the extreme case when there is only a single network. Finally, we propose the MEEDNets (Medical Image Classification via Ensemble Bio-inspired Evolutionary DenseNets) model which consists of multiple evolutionary DenseNet-121s synthesised in the evolution process. Experimental results show that our bio-inspired evolutionary DenseNets are able to drop less important structures and compensate for the increasingly sparse architecture. In addition, our proposed MEEDNets model outperforms the state-of-the-art methods on two publicly accessible medical image datasets. All source code of this study is available at https://github.com/hengdezhu/MEEDNets.</p

Evidence for the transportation of aggregated microplastics in the symplast pathway of oilseed rape roots and their impact on plant growth

As an emerging contaminant, microplastics are absorbed by crops, causing diverse impacts on plants. Plants may have different physiological responses to different uptake modes of microplastics various stage of growth. In this study, the distribution of polystyrene (PS) microspheres in the roots of oilseed rape and the physiological responses at different growth stages were investigated by confocal laser scanning microscope, scanning electron microscopy, and biochemical analysis. This study, conducted via scanning electron microscopy, discovered that agglomerates of microspheres, rather than individual plastic pellets, were taken up by plant roots in solution for the first time. The agglomerates subsequently migrate into the vascular bundles of the root system. Moreover, this study provided the proof for the first time that PS is transported in plants via the symplast system. On the physiological and biochemical function, the exposure of PS at the flowering and bolting stages caused oxidative stress on oilseed rape. That is, the addition of PS with different particle sizes significantly increased peroxidase (POD), malondialdehyde (MDA), photosynthetic rate, chlorophyll content and inhibited superoxide dismutase (SOD) content in oilseed rape at different developmental stages. These changes regulated the chloroplast structure and chlorophyll synthesis, maintained a high photosynthetic rate, and mitigated the toxicity of PS. In addition, correlation analysis showed that MDA and citric acid contents were significantly positively correlated with chlorophyll contents (p < 0.05), which suggested that the 80 nm PS treatment stimulated organic acid secretion in oilseed rape at the bolting stage to maintain a higher chlorophyll content. This study expands the current understanding of the effects of microplastics on crop growth, and the results holding significant implications for exploring the impact of microplastics on vegetables during various developmental stages and for future risk assessment