On the Adaptability Range, Self-Selection, and Economic Nature of Biological Evolution

This article employs a combined approach of biology and economics to reveal that biological evolution has an economic nature, evolving towards improved energy efficiency. The orthodox Darwinian theory of evolution describes evolution as the random variation of organisms and their survival through natural selection. In fact, the natural environment itself is a constantly changing context, and the strategy to adapt to this change is to enhance behavioral capabilities, thereby expanding the range and dimensions of behavior. Therefore, the improvement of behavioral capabilities is an important aspect of evolution. The enhancement of behavioral capabilities expands the range of adaptation to the natural environment and increases the space for behavioral choices. Within this space of behavioral choices, some options are more effective and superior to others;thus, the ability to select is necessary to make the improved behavioral capabilities more beneficial to the organism itself. The birth and development of the brain serve the purpose of selection. By using the brain to make selections, at least the “better” behavior will be chosen between two alternatives. Once the better behavior yields better results, and the organism can associate these results with the corresponding behavior, it will persist in this behavior. The persistent repetition of a behavior over generations will form a habit. Habits passed down through generations constitute a new environment, causing the organism’s genes to activate or deactivate certain functions, ultimately leading to genetic changes that are beneficial to that habit. Since the brain’s selection represents the organism’s self-selection, it differs from random variation;it is also a rational selection, choosing behaviors that either obtain more energy or reduce energy consumption. Thus, this evolution possesses an economic nature.

Hierarchical Human Action Recognition with Self-Selection Classifiers via Skeleton Data

Human action recognition has become one of the most active research topics in human-computer interaction and artificial intelligence, and has attracted much attention. Here, we employ a low-cost optical sensor Kinect to capture the action information of the human skeleton. We then propose a two-level hierarchical human action recognition model with self-selection classifiers via skeleton data. Especially different optimal classifiers are selected by probability voting mechanism and 10 times 10-fold cross validation at different coarse grained levels. Extensive simulations on a well-known open dataset and results demonstrate that our proposed method is efficient in human action recognition, achieving 94.19%the average recognition rate and 95.61% the best rate.

Walking and Aerobic Capacity in Old Adults after Concentric and Eccentric Endurance Exercise at Self-Selected Intensities

Self-selected exercise intensity can be a useful exercise prescription tool for older adults;however, it is not known if it can elicit improvements in walking and aerobic capacity. In older adults, effects of concentric or eccentric endurance exercise at self-selected walking speed were examined on 1-mile indoor walk performance, predicted maximum oxygen uptake and physiological parameters. Twenty-four older adults (67 ± 4 years) completed 3 × 30 min treadmill walks per week for 12-weeks on level (LTW, n = 11, 0%) or downhill (DTW, n = 13, ﹣10%) treadmill gradient at a self- selected speed, which progressed every 4 weeks. Maximal oxygen uptake was predicted using a 1-mile walk at 4-week intervals with physiological responses recorded using a portable metabolic system. One-mile walking speed increased from baseline following 8- and 12-weeks (12 weeks: LTW: 13% ± 6%, DTW: 14% ± 9%,

Comparison of sleep timing of people with different chronotypes affected by modern lifestyle

Circadian rhythm is an endogenous rhythmic behavior of organisms which can be entrained by daily light–dark cycles.The timing of human sleep-cycle is regulated by endogenous circadian rhythm and homeostatic processes. Light exposure affects both sleep timing and circadian rhythm. Now humans can extend lighting time by turning on artificial lights and wake up time is usually triggered by alarm clocks to meet social schedules. This modern lifestyle is believed to be related with a temporal mismatch between sleep and circadian rhythmicity(social jet-lag) and insufficient sleep, which lead to ill mental and physical health outcomes. At present, the impacts of self-selection of light exposure and social constrains on sleep timing is far from clear. According to preferred sleep-wake schedule, there are three different chronotypes. In this paper, we apply a mathematical model to get a quantitative comparison of sleep timing of people with different chronotypes with the effects of modern light consumption and social constrains. The results show that the prolonged day light and evening light exposure both delay preferred sleep timing with the sleep duration almost unchanged. People of evening-type or with longer intrinsic periods are most expected to be vulnerable to evening light. Increasing light exposure can offset the effect of evening light to some extent, but it is most difficult for evening-type people. Social constrains cause the largest social jet-lag in people of evening-type, which increases with evening light intensity or intrinsic periods. Morning-type people's sleep symptoms worsens, while that of evening-type people improves with age. This study provides a theoretical reference for preventing and treating sleep disorder and social jet-lag for individuals with different chronotypes.

A high-sensitive and self-selective humanoid mechanoreceptor for spatiotemporal tactile stimuli cognition

The cognition of spatiotemporal tactile stimuli,including fine spatial stimuli and static/dynamic temporal stimuli,is paramount for intelligent robots to feel their surroundings and complete manipulation tasks.However,current tactile sensors have restrictions on simultaneously demonstrating high sensitivity and performing selective responses to static/dynamic stimuli,making it a challenge to effectively cognize spatiotemporal tactile stimuli.Here,we report a high-sensitive and self-selective humanoid mechanoreceptor(HMR)that can precisely respond to spatiotemporal tactile stimuli.The HMR with PDMS/chitosan@CNTs(PDMS:polydimethylsiloxane;CNT:carbon nanotube)graded microstructures and polyurethane hierarchical porous spacer exhibits high sensitivity of 3790.8 kPa^(-1).The HMR demonstrates self-selective responses to static and dynamic stimuli with mono signal through the hybrid of piezoresistive and triboelectric mechanisms.Consequently,it can respond to spatiotemporal tactile stimuli and generate distinguishable and multi-type characteristic signals.With the assistance of the convolutional neural network,multiple target objects can be easily identified with a high accuracy of 99.1%.This work shows great potential in object precise identification and dexterous manipulation,which is the basis of intelligent robots and natural human-machine interactions.

Differences in transportation and leisure physical activity by neighborhood design controlling for residential choice

Background:Cross-sectional studies provide useful insight about the associations between the built environment and physical activity(PA),particularly when reasons for neighborhood choice are considered.Our study analyzed the relationship between levels of weekly transportation and leisure PA among 3 neighborhood designs,statistically adjusting for sociodemographic characteristics and reasons for neighborhood choice.Methods:A stratified random sample of adults(age>20 years)living in Calgary(Canada)neighborhoods with different neighborhood designs(grid,warped-grid,and curvilinear)and socioeconomic status completed a self-administered questionnaire capturing PA,sociodemographic characteristics,and reasons for neighborhood choice(response rate=10.1%;n=1023).Generalized linear models estimated associations between neighborhood design and transportation and leisure PA outcomes(participation(any vs.none)and volume(metabolic equivalent:h/week)),adjusting for neighborhood socioeconomic status,sociodemographic characteristics(gender,age,ethnicity,education,household income,marital status,children,vehicle access,dog ownership,and injury),and reasons for neighborhood choice(e.g.,proximity and quality of recreational and utilitarian destinations,proximity to work,highway access,aesthetics,and sense of community).Results:Overall,854 participants had resided in their neighborhood for at least 12 months and provided complete data.Compared with those living in curvilinear neighborhoods,grid neighborhood participants had greater odds(p<0.05)of participating in any transportation walking(odds ratio(OR)=2.17),transportation and leisure cycling(OR=2.39 and OR=1.70),active transportation(OR=2.16),and high-intensity leisure PA(≥6 metabolic equivalent;OR=1.74),respectively.There were no neighborhood differences in the volume of any transportation or leisure PA undertaken.Adjustment for neighborhood selection had minimal impact on the statistical or practical importance of model estimates.Conclusion:Neighborhood design is associated with PA patterns in adults,independent of reasons for neighborhood choice and sociodemogranhic factors.

Pay-based Screening Mechanism: Personnel Selection in the View of Economics Theory

Based on economic theories, the paper studies the personnel selection at the asymmetric job market using signaling and screening model. The authors hold the opinion that an organization can screen the candidates' signaling based on the self-selection principle by providing an appropriate compensation choice. A pay-based screening mechanism is proposed to help the organization drive away the nonqualified applicants and retain the excellent applicants.

Determinants of Wages and Labour Supply in the UK

We explore characteristics of the UK labour market with special emphasis on explanation of the existing wage inequalities, determinants of participation, and variation in the magnitude of hours of labour supplied among individuals. We explain up to 92 percent of variance in the wage rates from the supply side. Accuracy of the model is accounted by a variety of factors relevant to the labour market such as gender gaps, marital status, on and off the job training, fluency in English, and regional characteristics. The study is quite distinct, since it not only incorporates variables pertinent from the economic point of view, but also some quantified qualitative regressors relating to individuals’ opinions and political preferences. Interestingly, we find that the psychological profile of an individual has a very big influence over his decision on whether to participate, but once he joined the labour force his personal beliefs and opinions have no further impact on the probability of finding a job. The chance of being employed once participating depends mainly on the local labour market conditions. We also report unbiased and reliable estimate of labour supply elasticity based on British Household Panel Survey （BHPS） sample.

The Ability of Nile Tilapia to Regulate Protein and Energy Intake Evaluated by Carbon Relative Enrichment （δ^13C）

The aim of this study was to assess the ability of Nile tilapia to balance its own diet, when two ingredient mixes were offered, using carbon stable isotopes. In order to accomplish that, 225 Nile tilapia juveniles （average initial weight 5.0 g ± 0.5 g） were distributed in five tanks, each containing a group of 45 fish. One group of fish were fed exclusively with a high protein mix （HPM; δ^13C = -22.62‰）, the second one fed only with a low protein mix （LPM; δ^13C = -14.34‰）. The other groups had free access to both mixes （free choice system）. The fish from all tanks were fed four times a day. Muscle, liver and blood samples were collected at each five days （from 2 fish/tank/collection） for 86 days, except for the fish fed with LPM （fed for a 120 days period）. The samples were analyzed in a mass spectrometer and proportions of the mixes consumed were estimated through its carbon isotope enrichment （δ^13C）. Energy intake slightly decreased after the 50th day and protein consumption increased after the same period. However, consumption did not present a clear pattern in relation to the individual weight, i.e., protein consumption patterns are mainly related to the age of the individuals and it is not clear if it is also correlated to their weight. Additionally, this technique allowed the observation of differences regarding consumption among the individuals from the experimental group.

The Human Capital Return and Social Integration of Migrants in China

From the perspective of migrants ＇self-selection and skill transferability theories, this paper compares human capital return rates of different migrant groups in urban China and discuss the possibility of economic status identified by migrants as local labors. Results suggest that positive self-selection works for all types of migrant labors. Migrant workers with non-agricultural household registration or feinong hukou have no trouble to be integrated into the labor market of destination. They even have higher income and human capital return rates than local urban workers. Newly-arrived migrant peasants earn less than local labors. However, after a certain period, the skilled migrant workers catch up with local labors and are economically integrated into the local labor market while the low-skilled migrant group still earns less. Therefore the integration of migrant labors in urban labor market is yet a kind of segregated integration.

Self-selective memristor-enabled in-memory search for highly efficient data mining

Similarity search,that is,finding similar items in massive data,is a fundamental computing problem in many fields such as data mining and information retrieval.However,for large-scale and high-dimension data,it suffers from high computational complexity,requiring tremendous computation resources.Here,based on the low-power self-selective memristors,for the first time,we propose an in-memory search(IMS)system with two innovative designs.First,by exploiting the natural distribution law of the devices resistance,a hardware locality sensitive hashing encoder has been designed to transform the realvalued vectors into more efficient binary codes.Second,a compact memristive ternary content addressable memory is developed to calculate the Hamming distances between the binary codes in parallel.Our IMS system demonstrated a 168energy efficiency improvement over all-transistors counterparts in clustering and classification tasks,while achieving a software-comparable accuracy,thus providing a low-complexity and low-power solution for in-memory data mining applications.

Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

Image forgery detection remains a challenging problem.For the most common copy-move forgery detection,the robustness and accuracy of existing methods can still be further improved.To the best of our knowledge,we are the first to propose an image copy-move forgery passive detection method by combining the improved pulse coupled neural network(PCNN)and the self-selected sub-images.Our method has the following steps:First,contour detection is performed on the input color image,and bounding boxes are drawn to frame the contours to form suspected forgery sub-images.Second,by improving PCNN to perform feature extraction of sub-images,the feature invariance of rotation,scaling,noise adding,and so on can be achieved.Finally,the dual feature matching is used to match the features and locate the forgery regions.What’s more,the self-selected sub-images can quickly obtain suspected forgery sub-images and lessen the workload of feature extraction,and the improved PCNN can extract image features with high robustness.Through experiments on the standard image forgery datasets CoMoFoD and CASIA,it is effectively verified that the robustness score and accuracy of proposed method are much higher than the current best method,which is a more efficient image copy-move forgery passive detection method.

Productivity and Export Performance: Micro-Level Evidence from the Manufacturing Sector in Nigeria

This paper investigates how productivity influences firms' exports.Various finn characteristics are employed to test the self-selection hypothesis alongside the effects of firms' productivity on their share of exports in total sales.Using a pseudo-panel data set constructed from the firm-level data for the manufacturing sector in Nigeria,we find no evidence that higher productivity influences the decision on whether or not a firm would participate in exports.However,it is established that higher productivity increases the share of exports in total sales of firms that are already participating in foreign markets.A policy implication of our result is that Nigeria can realize a larger share of manufactured exports in total merchandise exports by directing efforts towards improving primarily the productivity of firms that are already involved in exports.

Do exports firms outperform non-export ones?An empirical study on different classification of manufacturing industries in China

Using Chinese Industrial Enterprise Database between 2005 and 2009,this paper constructs indicators,from aspects of efficiency,size and financing condition to explore comprehensively the new entry exporters’performance advantage over non-exporters by the method of PSM.The manufacturing industries are then classified into four categories,namely high-technology,medium-high-technology,medium-low-technology and low-technology,for detailed study.The results show that neither self-selection effect nor learning-by-exporting effect exists in Chinese exporters on the whole.Firms with lower productivity show higher export preference.Export is helpful for employment expansion and income increase;however,it does not enhance productivity and return on asset improvement.Firms in medium-high-technology industry learn by exporting significantly with obviously better performance than those in other categories,while low-technology export firms’performance is the worst.The policy implications is as follows:first,foreign trade structure needs to be upgraded steadily;second,medium-high-technology industry is suggested to be the key area to cultivate competitive advantage for export;third,increasing support to high-technology industry should be considered.