Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots can be leveraged for surgery, rehabilitation, biomimetics, unstructured environment ...exploring, and industrial grippers. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data-driven methods are utilized in pairs or separately. This review first briefly introduces two foundations for data-driven approaches, which are physical models and the Jacobian matrix, then summarizes three kinds of data-driven approaches, which are statistical method, neural network, and reinforcement learning. This review compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. Finally, we summarize the features of each method. A discussion about the advantages and limitations of the existing modeling and control approaches is presented, and we forecast the future of data-driven approaches in soft robots. A website (https://sites.google.com/view/23zcb) is built for this review and will be updated frequently. Note to Practitioners -This work is motivated by the need for a review introducing soft robot modeling and control methods in parallel. Modeling and control play significant roles in robot research, and they are challenging especially for soft robots. The nonlinear and complex deformation of such robots necessitates specific modeling and control approaches. We introduce the state-of-the-art data-driven methods and survey three approaches widely utilized. This review also compares the performance of these methods, considering some important features like data amount requirement, control frequency, and target task. The features of each approach are summarized, and we discuss the possible future of this area.
Fumonisins are mycotoxins produced primarily by Fusarium verticillioides and F. proliferatum on maize. These mycotoxins are secondary, carcinogenic metabolites with a worldwide distribution. This ...study was performed to evaluate the relationship between weather variables, the colonization of grain by F. verticillioides and resultant fumonisin contamination. Grain colonization by Fusarium spp. was determined using quantitative real-time PCR (qPCR) and contamination with fumonisins using HPLC analysis. Results indicated high natural infection by fumonisin-producing Fusarium spp. and fumonisin concentrations in warmer production areas such as Northern Cape, North West and Free State Provinces. Site-specific weather data, including temperature, radiation, humidity, rainfall and evapo-transpiration were provided by the ARC-Institute for Soil Water and Climate's meteorology office. Stepwise multiple regression analysis selected the variables mean maximum temperature and mean minimum humidity (days 1–14 post-silking) as having significant relationships with colonization of maize kernels by fumonisin-producing Fusarium spp.. Further relationships were calculated using the non-linear, 3-dimensional Lorentzian equation (Sigmaplot 10.0). The optimum temperature for colonization of maize by Fusarium spp. were 28.97, 32.14 and 30.40 °C and the optimum minimum humidities were calculated at 27.29, 31.86 and 29.74% respectively over the three recorded seasons. The application of correlation analysis and stepdown multiple regression analysis on sequential means of weather data and the inclusion of observed fungal biomass as a variable, suggested two phases in the development of fumonisins in maize kernels i.e. colonization of maize tissues during the early post-silking stage, followed by fumonisin production during the dough stage of grain fill. Based on this statistical model it would appear that fumonisin production by a specific biomass of fumonisin producing Fusarium spp. on grain was influenced largely by temperature and less so by rainfall. Optimum fumonisin concentrations were obtained with fungal biomasses of 39.94 pg during 2007, 47.65 pg during 2008 and 90.69 pg during 2009. Optimum temperatures for fumonisin production were 30.33, 31.12 and 29.80 °C for 2007–2009, respectively.
Although these statistical models were not consistent over seasons with regard to actual prediction values, they were consistent regarding time of fungal infection and fumonisin production. It is evident that additional driving variables need to be identified.
•The effect of prevailing weather conditions on F. verticillioides colonization and fumonisin contamination was evaluated.•High natural fungal colonization and corresponding fumonisin contamination occurred in warmer maize production areas.•Mean maximum temperature and mean minimum humidity had significant relationships with fungal colonization of maize grain.•The statistical model suggests fumonisin production in maize kernels early post-silking and at dough stage of grain fill.
Joint species movement modeling Ovaskainen, Otso; Ramos, Danielle Leal; Slade, Eleanor M. ...
Ecology (Durham),
April 2019, Letnik:
100, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Joint species distribution modeling has enabled researchers to move from species-level to community-level analyses, leading to statistically more efficient and ecologically more informative use of ...data. Here, we propose joint species movement modeling (JSMM) as an analogous approach that enables inferring both species- and community-level movement parameters from multispecies movement data. The species-level movement parameters are modeled as a function of species traits and phylogenetic relationships, allowing one to ask how species traits influence movements, and whether phylogenetically related species are similar in their movement behavior. We illustrate the modeling framework with two contrasting case studies: a stochastic redistribution model for direct observations of bird movements and a spatially structured diffusion model for capture–recapture data on moth movements. In both cases, the JSMM identified several traits that explain differences in movement behavior among species, such as movement rate increasing with body size in both birds and moths. We show with simulations that the JSMM approach increases precision of species-specific parameter estimates by borrowing information from other species that are closely related or have similar traits. The JSMM framework is applicable for many kinds of data, and it facilitates a mechanistic understanding of the causes and consequences of interspecific variation in movement behavior.
•An epidemiological dataset of coronavirus disease 2019 (COVID-19) in Japan was analyzed.•The ascertainment rate of non-severe cases was estimated at 0.44 (95% confidence interval 0.37–0.50).•Severe ...cases are twice as likely to be diagnosed and reported when compared to other cases.•Mild cases of COVID-19 are under-ascertained.
To estimate the ascertainment rate of novel coronavirus disease (COVID-19).
The epidemiological dataset of confirmed cases with COVID-19 in Japan as of February 28, 2020 was analyzed. A statistical model was constructed to describe the heterogeneity of the reporting rate by age and severity. We estimated the number of severe and non-severe cases, accounting for under-ascertainment.
The ascertainment rate of non-severe cases was estimated at 0.44 (95% confidence interval 0.37–0.50), indicating that the unbiased number of non-severe cases would be more than twice the reported count.
Severe cases are twice as likely to be diagnosed and reported when compared to other cases. Considering that reported cases are usually dominated by non-severe cases, the adjusted total number of cases is also approximately double the observed count. This finding is critical in interpreting the reported data, and it is advised that the mild case data for COVID-19 should always be interpreted as under-ascertained Au?1.
In probabilistic inversion of geophysical data, one must describe the expected noise in the system, and any prior information. In a geoscience context, prior information can provide a quantitative ...description of the expected spatial variability and correlations of the geology. But in practical inversion cases, driven by difficulty in quantifying geological information and computational complexity, an analytical mathematical smooth prior model is often chosen to describe the spatial variability. This is one of the primary reasons that realistic geological structures are difficult to resolve in geophysical models. Thus, there is currently a need for investigating and proposing practical ways of capturing complex (and often qualitative) geological information in statistical prior models that can be used in probabilistic inversion, which satisfies both the geologist, geophysicist, engineer and the geostatistician. In this research we show how a 1D statistical prior model can be designed that emulates the spatial distribution found in 188 boreholes with Miocene and Quaternary deposits from a study area (approx. 177,5 km2) near Horsens, Denmark. The prior model is built in two major steps 1) a multidimensional distribution describing the sub-division of major geological elements (here represented by lithologies from defined geological periods) and 2) a truncated pluri-Gaussian distribution describing the internal structure of lithologies within each element. The presented prior model can both be used to generate independent realizations, which can be used as part of the extended rejection sampler, as well as allowing the possibility of doing a “random walk” in the model space, as required by the extended Metropolis algorithm. We demonstrate, as an example, how the developed prior model can be used in a probabilistic inversion of airborne transient electromagnetic (AEM) data and discuss the implications the use of such informed prior models can have.
•A method is proposed for quantification of complex geology from boreholes in a statistical prior model.•The method allows the incorporation of background geological knowledge.•The prior model is represented by realizations and can be utilized in sampling methods.•The prior model is validated on its ability to reproduce the boreholes and its spatial representativeness.
In Japan, vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was initiated on 17 February 2021, mainly using messenger RNA vaccines and prioritizing health care ...professionals. Whereas nationwide vaccination alleviated the coronavirus disease 2019 (COVID-19)-related burden, the population impact has yet to be quantified in Japan. We aimed to estimate the numbers of COVID-19 cases and deaths prevented that were attributable to the reduced risk among vaccinated individuals via a statistical modeling framework.
We analyzed confirmed cases registered in the Health Center Real-time Information-sharing System on COVID-19 (3 March–30 November 2021) and publicly reported COVID-19-related deaths (24 March–30 November 2021). The vaccination coverage over this time course, classified by age and sex, was extracted from vaccine registration systems. The total numbers of prevented cases and deaths were calculated by multiplying the daily risk differences between unvaccinated and vaccinated individuals by the population size of vaccinated individuals.
For both cases and deaths, the averted numbers were estimated to be the highest among individuals aged 65 years and older. In total, we estimated that 564,596 (95% confidence interval: 477,020–657,525) COVID-19 cases and 18,622 (95% confidence interval: 6522–33,762) deaths associated with SARS-CoV-2 infection were prevented owing to vaccination during the analysis period (i.e., fifth epidemic wave, caused mainly by the Delta variant). Female individuals were more likely to be protected from infection following vaccination than male individuals whereas more deaths were prevented in male than in female individuals.
The vaccination program in Japan led to substantial reductions in the numbers of COVID-19 cases and deaths (33% and 67%, respectively). The preventive effect will be further amplified during future pandemic waves caused by variants with shared antigenicity.
This project was supported by the Japan Science and Technology Agency; the Japan Agency for Medical Research and Development; the Japan Society for the Promotion of Science; and the Ministry of Health, Labour and Welfare.
NMF-Based Speech Enhancement Using Bases Update Kwon, Kisoo; Shin, Jong Won; Kim, Nam Soo
IEEE signal processing letters,
2015-April, 2015-4-00, 20150401, Letnik:
22, Številka:
4
Journal Article
Recenzirano
This letter presents a speech enhancement technique combining statistical models and non-negative matrix factorization (NMF) with on-line update of speech and noise bases. The statistical model-based ...enhancement methods have been known to be less effective to non-stationary noises while the template-based enhancement techniques can deal with them quite well. However, the template-based enhancement techniques usually rely on a priori information. To overcome the shortcomings of both approaches, we propose a novel speech enhancement method that combines the statistical model-based enhancement scheme with the NMF-based gain function. For a better performance in time-varying noise environments, both the speech and noise bases of NMF are adapted simultaneously with the help of the estimated speech presence probability. Experimental results showed that the proposed method outperformed not only the statistical model-based and NMF approaches, but also their combination in various noise environments.
The liquid water movement is commonly considered in the seepage analysis related to the unsaturated soil (e.g, residual soil above the ground water table). The hydraulic conductivity of unsaturated ...soil is commonly considered to decrease with the increase in soil suction (or decrease in the degree of saturation). As a result, the estimated hydraulic conductivity of unsaturated soil from the conventional method is very low in the high suction range (i.e., beyond the residual suction). However, the experimental results from recent studies indicate that in the high suction zone, the hydraulic conductivity of unsaturated soil may increase with an increase in soil suction. In addition, recent studies also indicate that in the high suction zone (i.e., greater than 3100 kPa), water in soil moves mainly in the vapour form. As a result, the estimation of the hydraulic conductivity of unsaturated soil due to vapour flow in soil is crucial for engineers in the estimation of water transmission through a relative dry soil. In this note, the theoretical method is proposed for the estimation of vapour conductivity from the soil-water characteristic curve. The proposed method is rational and it has the theoretical basis without any empirical parameter. It is observed that the estimated results from the proposed method agree well with the results reported in the published literature.
•Water flow in soil consists of capillary water flow, film flow and vapour flow.•Calculation of hydraulic conductivity due to vapour flow•Vapour flow dominated in the high suction.•Conventional method underestimate the hydraulic conductivity in high suction range.
In this paper, in the scope of a non-extensive statistical model for the nucleon’s structure function, the volume of the gluons in the nucleons and the relations among the temperature, T, the ...parameter “q” of Tsallis statistics, and the scattering energies, Q2, are studied. A system of equations with the usual sum rules are solved for the valence quarks, the experimental results for the polarized structure function, and the estimated carried moments for gluons and quarks. Each state of T and q leads to a set of chemical potentials and different radii for gluons and quarks. We conclude that gluons must occupy a larger volume than the quarks to fit the fraction of the total momentum. A linear function of the temperature with Q2 is obtained as an approach. The obtained range of temperatures is different from the previous models.
•Adoption of carpooling for educational trips in university is studied in Thailand.•Policy for participants to be COVID-19 vaccinated will increase carpooling adoption.•Providing time credits as a ...payment system will encourage carpooling adoption.•Easy trip arrangements and payment indirectly influence carpooling adoption.•Encouraging social connections and enjoyment is key to carpooling success.
Carpooling is emerging as a more appealing “sharing economy” form with promising benefits in reducing carbon emissions, traveling costs, and traffic congestion. However, a thorough understanding of carpooling adoption is lacking for policymakers and transport planners in developing countries due to limited scientific research, specifically in Southeast Asia. Therefore, the present study aimed to understand the behavioral influences of carpool adoption in Thailand by conducting a multivariate analysis on a dataset of 307 observations gathered at Thammasat University, Pathum Thani, Thailand. First, a conceptual model was developed to assess the influence of effort expectancy, perceived safety, hedonic motivation, and social influence on carpool behavior intention. Additionally, two constructs related to COVID-19 and time credits were added to assess their impacts. Then, the sample data were analyzed using Structural Equation Modelling (SEM). It was found that hedonic motivation, social influence, and time credits as payment method factors play statistically significant direct roles in the carpool behavior intention, whereas effort expectancy, perceived safety, and perception towards compliance with COVID-19 guidelines for carpooling did not. However, significant indirect impacts of effort expectancy and social influence through hedonic motivation were discovered. Upon analysis of the findings, policy implications are presented.
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