Projections of health indicators for chronic disease under a semi-Markov assumption.

Chronic diseases are a growing public health problem due to the population aging. Their economic, social and demographic burden will worsen in years to come. Up to now, the method used to provide projections and assess the future disease burden makes a non-homogeneous Markov assumption in an illness-death model. Both age and calendar year have been taken into account in all parameter estimations, but the time spent with the disease was not considered. This work develops the method with a semi-Markov assumption to model mortality among the diseased and considering the time spent with the disease. The method is applied to estimate several health indicators for dementia in France in 2030. We find that mortality among the individuals with dementia depends on age, calendar year and disease duration, and it is greater for men than for women at all ages. The projections for 2030 suggest a 27% increase of the number of dementia cases. The model proposed in this work has flexible assumptions that make it adaptable to provide projections for various diseases.

An Optoelectronic Synapse Based on Two-Dimensional Violet Phosphorus Heterostructure.

Neuromorphic computing can efficiently handle data-intensive tasks and address the redundant interaction required by von Neumann architectures. Synaptic devices are essential components for neuromorphic computation. 2D phosphorene, such as violet phosphorene, show great potential in optoelectronics due to their strong light-matter interactions, while current research is mainly focused on synthesis and characterization, its application in photoelectric devices is vacant. Here, the authors combined violet phosphorene and molybdenum disulfide to demonstrate an optoelectronic synapse with a light-to-dark ratio of 106 , benefiting from a significant threshold shift due to charge transfer and trapping in the heterostructure. Remarkable synaptic properties are demonstrated, including a dynamic range (DR) of > 60 dB, 128 (7-bit) distinguishable conductance states, electro-optical dependent plasticity, short-term paired-pulse facilitation, and long-term potentiation/depression. Thanks to the excellent DR and multi-states, high-precision image classification with accuracies of 95.23% and 79.65% is achieved for the MNIST and complex Fashion-MNIST datasets, which is close to the ideal device (95.47%, 79.95%). This work opens the way for the use of emerging phosphorene in optoelectronics and provides a new strategy for building synaptic devices for high-precision neuromorphic computing.

Perfection behind the whistle: Perfectionism and perceived performance in soccer referees.

Purpose: The purpose of the study was to examine the relationship between self-oriented perfectionism, socially prescribed perfectionism, cognitive appraisals, psychobiosocial experiences, and self-evaluated performance in soccer referees, using the multi-states (MuSt) theory as the theoretical framework. Method: Participants were 67 soccer referees (57 men and 10 women, Mage = 23.03 years, SD = 2.71) with 3-15 years (M = 7.36, SD = 2.44) of refereeing experience in first-class, promotion, or excellence matches. They completed questionnaires assessing perfectionism, competitive appraisals, and psychobiosocial experiences two days before a game, and self-evaluated their performance one day after the event. Results: Results revealed significant positive correlations (r > 0.20) between self-oriented perfectionism and socially prescribed perfectionism with challenge appraisals, functional psychobiosocial experiences, and self-evaluated performance. Serial multiple mediation analyses showed positive indirect effects of both self-oriented and socially prescribed perfectionism on self-evaluated performance via challenge appraisals and psychobiosocial experiences (ß = 0.023, 95 % CI = 0.000, 0.097, and ß = 0.097, 95 % CI = 0.003, 0.253, respectively). Conclusion: The findings suggest that both dimensions of perfectionism may positively influence perceived performance when viewed as a positive challenge and associated with functional experiences. The study advances our understanding of the effects of perfectionism on perceived performance in the context of soccer refereeing. Practical implications for referee training programs are provided.

Biologically Plausible Artificial Synaptic Array: Replicating Ebbinghaus' Memory Curve with Selective Attention.

The nature of repetitive learning and oblivion of memory enables humans to effectively manage vast amounts of memory by prioritizing information for long-term storage. Inspired by the memorization process of the human brain, an artificial synaptic array is presented, which mimics the biological memorization process by replicating Ebbinghaus' forgetting curve. To construct the artificial synaptic array, signal-transmitting access transistors and artificial synaptic memory transistors are designed using indium-gallium-zinc-oxide and poly(3-hexylthiophene), respectively. To secure the desired performance of the access transistor in regulating the input signal to the synaptic transistor, the content of gallium in the access transistor is optimized. In addition, the operation voltage of the synaptic transistor is carefully selected to achieve memory-state efficiency. Repetitive learning characterizing Ebbinghaus' oblivion curves is realized using an artificial synaptic array with optimized conditions for both transistor components. This successfully demonstrates a biologically plausible memorization process. Furthermore, selective attention for information prioritization in the human brain is mimicked by selectively applying repetitive learning to a synaptic transistor with a high memory state. The demonstrated biologically plausible artificial synaptic array provides great scope for advancement in bioinspired electronics.

Design and performance analysis of a new optimization algorithm based on Finite Element Analysis.

Aiming at the problem that many algorithms could not effectively balance the global search ability and local search ability, a new optimization algorithm is proposed. Inspired by Finite Element Analysis (FEA) approach, a relationship of mapping between Finite Element Analysis approach and a population-based optimization algorithm is constructed through comparing the similarities and differences of FEA node and ideal particle. In algorithm framework, the stiffness coefficient corresponds to a user-defined function of the value of an objective function to be optimized, and the node forces among individuals are defined and an attraction-repulsion rule is established among them. The FEA approach that can simulate multi- states of matter is adopted to balance the global search ability and local search ability in the novel optimization algorithm. A theoretical analysis is made for algorithm parallelism. The conditions for convergence are deduced through analyzing the algorithm based on discrete-time linear system theory. In addition, the performance of the algorithm is compared with PSO for five states which include free state, diffusion state, solid state, entirely solid state, synthesis state. The simulation results of six benchmark functions show that the algorithm is effective. The algorithm supplies a new method to solve optimization problem.