Methodology for Neural Network-Based Material Card Calibration Using LS-DYNA MAT_187_SAMP-1 Considering Failure with GISSMO

A neural network (NN)-based method is presented in this paper which allows the identification of parameters for material cards used in Finite Element simulations. Contrary to the conventionally used computationally intensive material parameter identification (MPI) by numerical optimization with internal or commercial software, a machine learning (ML)-based method is time saving when used repeatedly. Within this article, a self-developed ML-based Python framework is presented, which offers advantages, especially in the development of structural components in early development phases. In this procedure, different machine learning methods are used and adapted to the specific MPI problem considered herein. Using the developed NN-based and the common optimization-based method with LS-OPT, the material parameters of the LS-DYNA material card MAT_187_SAMP-1 and the failure model GISSMO were exemplarily calibrated for a virtually generated test dataset. Parameters for the description of elasticity, plasticity, tension–compression asymmetry, variable plastic Poisson’s ratio (VPPR), strain rate dependency and failure were taken into account. The focus of this paper is on performing a comparative study of the two different MPI methods with varying settings (algorithms, hyperparameters, etc.). Furthermore, the applicability of the NN-based procedure for the specific usage of both material cards was investigated. The studies reveal the general applicability for the calibration of a complex material card by the example of the used MAT_187_SAMP-1.

Evaluation of a parasite-density based pooled targeted amplicon deep sequencing (TADS) method for molecular surveillance of Plasmodium falciparum drug resistance genes in Haiti

Sequencing large numbers of individual samples is often needed for countrywide antimalarial drug resistance surveillance. Pooling DNA from several individual samples is an alternative cost and time saving approach for providing allele frequency (AF) estimates at a population level. Using 100 individual patient DNA samples of dried blood spots from a 2017 nationwide drug resistance surveillance study in Haiti, we compared codon coverage of drug resistance-conferring mutations in four Plasmodium falciparum genes (crt, dhps, dhfr, and mdr1), for the same deep sequenced samples run individually and pooled. Samples with similar real-time PCR cycle threshold (Ct) values (+/- 1.0 Ct value) were combined with ten samples per pool. The sequencing success for samples in pools were higher at a lower parasite density than the individual samples sequence method. The median codon coverage for drug resistance-associated mutations in all four genes were greater than 3-fold higher in the pooled samples than in individual samples. The overall codon coverage distribution for pooled samples was wider than the individual samples. The sample pools with < 40 parasites/μL blood showed more discordance in AF calls for dhfr and mdr1 between the individual and pooled samples. This discordance in AF estimation may be due to low amounts of parasite DNA, which could lead to variable PCR amplification efficiencies. Grouping samples with an estimated ≥ 40 parasites/μL blood prior to pooling and deep sequencing yielded the expected population level AF. Pooling DNA samples based on estimates of > 40 parasites/μL prior to deep sequencing can be used for rapid genotyping of a large number of samples for these four genes and possibly other drug resistant markers in population-based studies. As Haiti is a low malaria transmission country with very few mixed infections and continued chloroquine sensitivity, the pooled sequencing approach can be used for routine national molecular surveillance of resistant parasites.

Alleviating time poverty among the working poor: a pre-registered longitudinal field experiment

Poverty entails more than a scarcity of material resources—it also involves a shortage of time. To examine the causal benefits of reducing time poverty, we conducted a longitudinal field experiment over six consecutive weeks in an urban slum in Kenya with a sample of working mothers, a population who is especially likely to experience severe time poverty. Participants received vouchers for services designed to reduce their burden of unpaid labor. We compared the effect of these vouchers against equivalently valued unconditional cash transfers (UCTs) and a neutral control condition. In contrast to our pre-registered hypotheses, a pre-registered Bayesian ANCOVA indicated that the time-saving, UCT, and control conditions led to similar increases in subjective well-being, reductions in perceived stress, and decreases in relationship conflict (Cohen’s d’s ranged from 0.25 to 0.85 during the treatment weeks and from 0.21 to 0.36 at the endline). Exploratory analyses revealed that the time-saving vouchers and UCTs produced these benefits through distinct psychological pathways. We conclude by discussing the implications of these results for economic development initiatives. Protocol registration The Stage 1 protocol for this Registered Report was accepted in principle on 27/06/2019. The protocol, as accepted by Nature Human Behaviour, can be found at https://doi.org/10.6084/m9.figshare.c.4368455.

Performance Assessment of Farm Machinery for Persimmon Fruit Cultivation in a Japanese Mountainous Area

Japanese agriculture is facing a decrease in agricultural workers. Mechanization, both to save time and reduce physical input, is essential to solving this issue. Recent worldwide progress in Internet-of-things technology has enabled the application of remote-controlled and unmanned machinery in agriculture. This study was conducted in the Gojo-Yoshino mountainous region in Nara, Japan, which is famous for its persimmon cultivation. The performance of newly introduced smart agricultural machinery was studied in the field by simulating cultivation work. The results showed that the remote-control weeder, speed sprayer, and remote-control mini crawler carrier saved 90%, 75%, and 5% of weeding, spraying, and harvesting times, respectively, when compared with conventional methods. Such time savings led to an 8% decrease in the total working time spent on persimmon cultivation. In addition, using the speed sprayer showed improvement in the fruit’s quality. Results of the power assist suits did not show a time-saving effect but showed a reduction of physical burden. These results suggest that the mechanization of persimmon cultivation is efficient and labor-saving, and satisfies the need for farmers. However, the high investment costs remain an issue in extending mechanization to the region.

Determination of dehydroepiandrosterone in dietary supplements by reversed-phase HPLC

Purpose: To develop a reversed phase high performance liquid chromatography (HPLC) method for the determination of dehydroepiandrosterone (DHEA) in dietary supplements. Methods: A reversed-phase high performance liquid chromatography (HPLC) method was developed for the determination of DHEA in dietary supplements. An isocratic system consisting of methanol and water (70:30 v/v) was run at a flow rate of 1 mL/min on a C18 HPLC column to achieve the separation. The method was validated with regard to linearity, intra-day and inter-day precision, and limits of both detection and quantification. Results: The method achieved a retention time of 10.8 min, a resolution of 4.12, a detection limit (LOD) of 50 ng/μL, a quantification limit (LOQ) of 166.7 ng/μL and a label claim of 108.6 % with a relative standard deviation (RSD) of 0.38 % over a range of 0.0625 – 0.50 mg/mL with a correlation coefficient of 0.9997. Conclusion: The method is simple, cost effective, time-saving and reliable for determining DHEA when compared to other reported methods in literature. Thus, it will be of benefit to manufacturers of this dietary supplement to adopt the method for quantitative laboratory analysis.

Machine–learning-enabled metasurface for direction of arrival estimation

Metasurfaces, interacted with artificial intelligence, have now been motivating many contemporary research studies to revisit established fields, e.g., direction of arrival (DOA) estimation. Conventional DOA estimation techniques typically necessitate bulky-sized beam-scanning equipment for signal acquisition or complicated reconstruction algorithms for data postprocessing, making them ineffective for in-situ detection. In this article, we propose a machine-learning-enabled metasurface for DOA estimation. For certain incident signals, a tunable metasurface is controlled in sequence, generating a series of field intensities at the single receiving probe. The perceived data are subsequently processed by a pretrained random forest model to access the incident angle. As an illustrative example, we experimentally demonstrate a high-accuracy intelligent DOA estimation approach for a wide range of incident angles and achieve more than 95% accuracy with an error of less than 0.5 ° $0.5{\degree}$ . The reported strategy opens a feasible route for intelligent DOA detection in full space and wide band. Moreover, it will provide breakthrough inspiration for traditional applications incorporating time-saving and equipment-simplified majorization.

Effect of Food Consumption Values of Vietnamese Consumers on the Reliability and Purchase Intention of Korean Home Meal Replacements

The Vietnamese home meal replacement (HMR) market is expected to face intensified competition due to economic development and urbanization. This study analyzes how the food consumption values of Vietnamese consumers affect the perceived reliability and intention to purchase Korean food in the form of HMRs and provides basic data to establish strategies for Korean HMRs to secure an advantage in an intensely competitive market. A survey was conducted with Vietnamese consumers who had used Korean HMRs before and are constantly using HMRs on a regular basis. To analyze the relationships between food consumption values, the reliability of Korean HMR, and the intention to purchase Korean HMRs, exploratory factor analysis, reliability analysis, confirmatory factor analysis, and covariance structural models were used. Among the factors of food consumption value, health, safety, time saving, and convenience have a positive effect on the reliability of Korean HMRs, while family and cost-effectiveness do not. Moreover, this reliability has a positive effect on the purchase intention of Korean HMRs. This study is significant in that while investigating Korean HMRs, it is pioneering research on Vietnamese consumers regarding HMRs. Therefore, the results of this study can be used to secure the competitiveness of Korean HMRs in the Vietnamese market.

BrainQuake: An Open-Source Python Toolbox for the Stereoelectroencephalography Spatiotemporal Analysis

Intracranial stereoelectroencephalography (SEEG) is broadly used in the presurgical evaluation of intractable epilepsy, due to its high temporal resolution in neural activity recording and high spatial resolution within suspected epileptogenic zones. Neurosurgeons or technicians face the challenge of conducting a workflow of post-processing operations with the multimodal data (e.g., MRI, CT, and EEG) after the implantation surgery, such as brain surface reconstruction, electrode contact localization, and SEEG data analysis. Several software or toolboxes have been developed to take one or more steps in the workflow but without an end-to-end solution. In this study, we introduced BrainQuake, an open-source Python software for the SEEG spatiotemporal analysis, integrating modules and pipelines in surface reconstruction, electrode localization, seizure onset zone (SOZ) prediction based on ictal and interictal SEEG analysis, and final visualizations, each of which is highly automated with a user-friendly graphical user interface (GUI). BrainQuake also supports remote communications with a public server, which is facilitated with automated and standardized preprocessing pipelines, high-performance computing power, and data curation management to provide a time-saving and compatible platform for neurosurgeons and researchers.

Degradation of Azo Dyes with Different Functional Groups in Simulated Wastewater by Electrocoagulation

Increasing attention has been paid to the widespread contamination of azo dyes in water bodies globally. These chemicals can present high toxicity, possibly causing severe irritation of the respiratory tract and even carcinogenic effects. The present study focuses on the periodically reverse electrocoagulation (PREC) treatment of two typical azo dyes with different functional groups, involving methyl orange (MO) and alizarin yellow (AY), using Fe-Fe electrodes. Based upon the comparative analysis of three main parameters, including current intensity, pH, and electrolyte, the optimal color removal rates for MO and AY could be achieved at a rate of up to 98.7% and 98.6%, respectively, when the current intensity is set to 0.6 A, the pH is set at 6.0, and the electrolyte is selected as NaCl. An accurate predicted method of response surface methodology (RSM) was established to optimize the PREC process involving the three parameters above. The reaction time was the main influence for both azo dyes, while the condition of PREC treatment for AY simulated wastewater was time-saving and energy conserving. According to the further UV–Vis spectrophotometry analysis throughout the procedure of the PREC process, the removal efficiency for AY was better than that of MO, potentially because hydroxyl groups might donate electrons to iron flocs or electrolyze out hydroxyl free radicals. The present study revealed that the functional groups might pose a vital influence on the removal efficiencies of the PREC treatment for those two azo dyes.

Dispersive MUSIC algorithm for Lamb wave phased array

By controlling the excitation time delay on each element, the conventional phased array can physically focus signals transmitted by different elements on a desired point in turn. An alternative and time-saving strategy is that every element takes turns to transmit the excitation and the remaining elements receive the corresponding response signals, which is known as the full matrix capture (FMC) method for data acquisition, and then let the signals virtually focus on every desired point by post-processing technique. In this study, based on the FMC, a dispersive multiple signal classification (MUSIC) algorithm for Lamb wave phased array is developed to locate defects. The virtual time reversal is implemented to back propagate the wave packets corresponding to the desired focusing point and a window function is adopted to adaptively isolate the desired packets from the other components. Then those wave packets are forward propagated to the original focusing point at a constant velocity. For every potential focusing point and all receivers, the virtual array focuses the signals from all transmitters so as to obtain the focusing signals. The MUSIC algorithm with the obtained focusing signals is adopted to achieve Lamb wave imaging. Benefiting from the post-processing operations, the baseline subtraction as well as the estimation for the number of the scattering sources is no longer required in the proposed algorithm. Experiments on an aluminum plate with three artificial defects and a compact circular PZT array are implemented and the results demonstrate the efficacy of the proposed algorithm.