General Versatility Research Articles

Effect of Lewis and Brønsted acidity in Ni/ZSM-5 on catalytic reductive etherification of furfural and alcohols

Reductive etherification is an attractive and practical strategy to convert bio-platform compounds into valuable chemicals. In directed synthesis of furfuryl ethers, reductive etherification of bioderived furfurals is still challenging due to the complexity of catalytic reaction. Herein, reductive etherification of furfural with alcohol was investigated over Ni/ZSM-5 catalyst, which found that Si/Al ratios in ZSM-5 played an important role on the catalytic performance. For Ni/ZSM-5 with Si/Al ratio of 50, 80.9% yield of furfuryl ether at 90.8% conversion of furfural was achieved under the condition of 2 MPa H2 and 180 °C for 3 h in isopropanol. The catalyst exhibited superior stability and recyclability during the consecutive cycle run. Catalysts characterization demonstrated that both acidic properties and furfural adsorption were influenced by the Si/Al ratios in Ni/ZSM-5, which correlated to the reaction route and catalytic performance. Lewis acid sites enhanced furfural hydrogenation into furfuryl alcohol, while Brønsted acid sites on ZSM-5 effectively catalyzed sequential etherification of furfuryl alcohol to furfuryl ether. Under optimization conditions, various furfuryl ethers could be obtained with yields above 80% over Ni/ZSM-5 catalyst in different primary alcohols, demonstrating the general versatility of the approach. Our findings offered opportunities to develop an efficient furfuryl ethers synthesis method.

Shelf‐life estimation of packaged dried foods as affected by choice of moisture sorption isotherm models

The moisture sorption isotherms (MSIs) of Oswin, Halsey, GAB, and linear models were compared for their ability to predict the shelf life of typical permeable packages of four dried foods (onion flakes, rice crackers, cassava-flour-based baked product, and dried tea) whose isotherm data are given in the literature. Parameters of MSI equations were used to mathematically simulate aw change of the packaged foods. The simulation model was applied to predict the times to reach the critical aw values under storage at 30 or 37°C and 65% or 85% RH. The GAB equation was best able to fit the moisture sorption data with the highest coefficient of determination (>.99) and the lowest root mean square error (≤0.01). The linear model consistently estimated the shelf life in close agreement with the GAB equation. The Oswin and Halsey equations differed with each food in their ability to estimate the shelf life accurately. Practical applications Shelf-life determination is a critical issue for packaged dried foods where the moisture sorption isotherm selected may influence its accuracy. While a convenient and simple way of estimating shelf life is required, the validity and limitations of the applied method should be examined and determined. The general versatility of the GAB equation and the tolerable validity of the linear sorption model over the low aw range were confirmed, which is useful for practical shelf-life determination of packaged dried foods.

LiDAR DNN based self-attitude estimation with learning landscape regularities

This paper presents an EKF (extended Kalman filter) based self-attitude estimation method with a LiDAR DNN (deep neural network) learning landscape regularities. The proposed DNN infers the gravity direction from LiDAR data. The point cloud obtained with the LiDAR is transformed to a depth image to be input to the network. It is pre-trained with large synthetic datasets. They are collected in a flight simulator because various gravity vectors can be easily obtained, although this study focuses not only on UAVs. Fine-tuning with datasets collected with real sensors is done after the pre-training. Data augmentation is processed during the training in order to provide higher general versatility. The proposed method integrates angular rates from a gyroscope and the DNN outputs in an EKF. Static validations are performed to show the DNN can infer the gravity direction. Dynamic validations are performed to show the DNN can be used in real-time estimation. Some conventional methods are implemented for comparison.

Saddle point sampling using scaled normal coordinates

The sampling of saddle points (SPs) on a potential energy surface (PES) is critical for describing the dynamics and transport properties of materials. Here, we propose a saddle point search (SPS) method that enables efficient sampling of practically meaningful SPs around a local minimum by converting the PES to the scaled normal coordinates (SNCs), together with the dimer method. We find that the pathway leading to a SP using SNCs is highly efficient and is independent of the system size. This results in the significantly increased SPS efficiency and the enhanced possibility of a complete catalog of the SPs. In addition, we perform SPSs using the SNCs for the diffusion of a vacancy and a dumbbell in body-centered cubic Fe and demonstrate (1) the required number of system force evaluations for a SPS decreases by at least an order of magnitude, (2) the effects of the system size on the number of force evaluations are greatly decreased, and (3) the effects of the number of atoms involved in the SPS on the probability of finding practically meaningful SPs is mostly eliminated. We also discuss the computational cost of introducing the SNCs. It should be highlighted that the SNCs could also be applied to other minimum-mode following methods, demonstrating the general versatility of the proposed method.

EKF-Based Real-Time Self-Attitude Estimation With Camera DNN Learning Landscape Regularities

This letter presents an EKF (extended Kalman filter) based real-time self-attitude estimation method with a camera DNN (deep neural network) learning landscape regularities. The proposed DNN infers the gravity direction from a single shot image. It outputs the gravity direction as a mean vector and a covariance matrix in order to express uncertainty of the inference. It is pre-trained with datasets collected in a simulator. Fine-tuning with datasets collected with real sensors is carried out after the pre-training. Data augmentation is processed during the training in order to provide higher general versatility. The proposed method integrates angular rates from a gyroscope and the DNN's outputs in an EKF. The covariance matrix output from the DNN is used as process noise of the EKF. Moreover, inferences with too large variance are filtered out before processing the integration in the EKF. Static validations are performed to show the DNN can infer the gravity direction with uncertainty expression. Dynamic validations are performed to show the DNN can be used in real-time estimation. Some conventional methods are implemented for comparison.

Framework to assess technology readiness of lithium-ion battery to fulfill the safety standard: IEC 62660-3:2016 (case study: smart lithium battery of UNS product)

Electric vehicles are one of the ways to solve the problem of air. Electric vehicles have a very vital component which is the battery as a source of power for other components. The type of battery that has bes performance for electric vehicles is lithium-ion battery. Reliability and safety present significant challenges in lithium-ion batteries. Thus, lithium product standards are important to guarantee the quality of lithium battery products. The International Electrotechnical Commission (IEC) published a lithium battery standard product regulation in document IEC 62660-3:2016, that provide a basic level of safety test methodology and criteria with general versatility, serves a function in common primary testing of lithium-ion cells to be used in variety of battery systems. This paper aims to identify the technological readiness of Universitas Sebelas Maret (UNS) lithium-ion enterprise to fulfill the lithium battery product standards by modifying the Quality Functional Deployment (QFD). Several production processes in technoware and humanware are not compatible with lithium battery standard, but all production processes in infoware and orgaware are compatible with lithium battery standard.

Serum interleukin-18 levels as a predictor for patients with genetic dysfunction of cytochrome P450 2C19 in dual antiplatelet therapy with clopidogrel

BackgroundP2Y12 reaction unit (PRU) is an index of platelet activity upon treatment with clopidogrel. In spite of suitable P2Y12 reactions in dual antiplatelet therapy (DAPT) with clopidogrel after percutaneous coronary intervention (PCI), cardiovascular events actually occur in some patients, possibly due to a genetic dysfunction of cytochrome P450 2C19 (CYP2C19), which is a major metabolic enzyme of clopidogrel. As testing the CYP2C19 phenotypes to predict such patients may lack general versatility in daily clinical practice, the aim of this study was to examine whether measuring the blood levels of some cytokines in patients showing desirable PRUs in DAPT with clopidogrel could be a substitute for testing the CYP2C19 phenotypes. MethodsWe analyzed relationships among PRU, serum levels of 51 cytokines, and CYP2C19 phenotypes in 22 patients receiving DAPT with aspirin and clopidogrel after PCI. ResultsSeventeen, 18, and 19 of 22 patients indicated PRU ≤ 208, PRU ≤ 230, and PRU ≤ 262, respectively. Approximately 60% of the patients had a genetically metabolic dysfunction of CYP2C19, and the serum levels of interleukin-18 were independently increased in those patients (p = 0.024 in patients with PRU ≤ 208, p = 0.021 with PRU ≤ 230, and p = 0.020 with PRU ≤ 262). The area under the curves in plot receiver operating characteristics curves for the serum levels of interleukin-18 were 0.94, 0.96, and 0.90 in the non-extensive metabolizer patients with PRU ≤ 208, PRU ≤ 230, and PRU ≤ 262, respectively. ConclusionsThe serum levels of interleukin-18 may be a predictor to diagnose patients who receive undesirable DAPT with clopidogrel, possibly due to the genetic dysfunction of CYP2C19 in spite of suitable P2Y12 reactions after PCI.

<i>In silico</i> systems for predicting chemical-induced side effects using known and potential chemical protein interactions, enabling mechanism estimation

In silico models for predicting chemical-induced side effects have become increasingly important for the development of pharmaceuticals and functional food products. However, existing predictive models have difficulty in estimating the mechanisms of side effects in terms of molecular targets or they do not cover the wide range of pharmacological targets. In the present study, we constructed novel in silico models to predict chemical-induced side effects and estimate the underlying mechanisms with high general versatility by integrating the comprehensive prediction of potential chemical-protein interactions (CPIs) with machine learning. First, the potential CPIs were comprehensively estimated by chemometrics based on the known CPI data (1,179,848 interactions involving 3,905 proteins and 824,143 chemicals). Second, the predictive models for 61 side effects in the cardiovascular system (CVS), gastrointestinal system (GIS), and central nervous system (CNS) were constructed by sparsity-induced classifiers based on the known and potential CPI data. The cross validation experiments showed that the proposed CPI-based models had a higher or comparable performance than the traditional chemical structure-based models. Moreover, our enrichment analysis indicated that the highly weighted proteins derived from predictive models could be involved in the corresponding functions of the side effects. For example, in CVS, the carcinogenesis-related pathways (e.g., prostate cancer, PI3K-Akt signal pathway), which were recently reported to be involved in cardiovascular side effects, were enriched. Therefore, our predictive models are biologically valid and would be useful for predicting side effects and novel potential underlying mechanisms of chemical-induced side effects.

Automatic estimation of heading date of paddy rice using deep learning

BackgroundAccurate estimation of heading date of paddy rice greatly helps the breeders to understand the adaptability of different crop varieties in a given location. The heading date also plays a vital role in determining grain yield for research experiments. Visual examination of the crop is laborious and time consuming. Therefore, quick and precise estimation of heading date of paddy rice is highly essential.ResultsIn this work, we propose a simple pipeline to detect regions containing flowering panicles from ground level RGB images of paddy rice. Given a fixed region size for an image, the number of regions containing flowering panicles is directly proportional to the number of flowering panicles present. Consequently, we use the flowering panicle region counts to estimate the heading date of the crop. The method is based on image classification using Convolutional Neural Networks. We evaluated the performance of our algorithm on five time series image sequences of three different varieties of rice crops. When compared to the previous work on this dataset, the accuracy and general versatility of the method has been improved and heading date has been estimated with a mean absolute error of less than 1 day.ConclusionAn efficient heading date estimation method has been described for rice crops using time series RGB images of crop under natural field conditions. This study demonstrated that our method can reliably be used as a replacement of manual observation to detect the heading date of rice crops.

User-Centric Companion Screen Architecture for Improving Accessibility of Broadcast Services

Bridging broadcast and internet services can be difficult because the system architectures are typically different. Although integrated broadcast-broadband systems that can provide companion screen (CS) services have been standardized, the existing systems are television-centric. Therefore, it can be difficult for users to easily access broadcast services through mobile applications. We propose a CS architecture (CSA) that can be used to tune to specific TV channels and launch broadcast–related applications from a CS such as a smartphone, smart speaker, or smartwatch. The general versatility and feasibility of the proposed CSA was verified through prototyping of multiple use cases. Moreover, based on user tests, we confirmed that our proposed CSA has the potential to improve broadcast accessibility from mobile applications and increase the number of times the users watch broadcast programs. Thus, our proposed CSA can help bridge broadcast and mobile or Internet-of-Things services.

Solitary density waves in the strongly coupled one component Coulomb particle structures as experimental support of the general versatility of the caustic theory

In this paper we demonstrate that generation of the solitary density wave in the strongly coupled one component Coulomb system of particles confined in a long quadrupole electrodynamic Paul trap is possible, when the energy losses due to air viscosity can be compensated by the energy contribution of the altering electric fields of the trap. Results of this paper allow to identify observed solitary waves as the caustics (according to definition of the book “Catastrophe Theory” by V. I. Arnold) and can be considered as the new experimental support of the general versatility of the caustic theory in describing different physical phenomena not only in collisionless systems of particles but also when interparticle interaction and interaction with external fields in viscous media are strong.

HoTPiG: a novel graph-based 3-D image feature set and its applications to computer-assisted detection of cerebral aneurysms and lung nodules.

A novel image feature set named histogram of triangular paths in graph (HoTPiG) is presented. The purpose of this study is to evaluate the feasibility of the proposed HoTPiG feature set through two clinical computer-aided detection tasks: nodule detection in lung CT images and aneurysm detection in head MR angiography images. The HoTPiG feature set is calculated from an undirected graph structure derived from a binarized volume. The features are derived from a 3-D histogram in which each bin represents a triplet of shortest path distances between the target node and all possible node pairs near the target node. First, the vessel structure is extracted from CT/MR volumes. Then, a graph structure is extracted using an 18-neighbor rule. Using this graph, a HoTPiG feature vector is calculated at every foreground voxel. After explicit feature mapping with an exponential-χ2 kernel, each voxel is judged by a linear support vector machine classifier. The proposed method was evaluated using 300 CT and 300 MR datasets. The proposed method successfully detected lung nodules and cerebral aneurysms. The sensitivity was about 80% when the number of false positives was three per case for both applications. The HoTPiG image feature set was presented, and its high general versatility was shown through two medical lesion detection applications.

Holographic description of total hadronic cross sections at high energies

We investigate the hadron-hadron total cross sections at high energies in the framework of holographic QCD. In our model setup, the involved strong interaction is described by the Brower-Polchinski-Strassler-Tan Pomeron exchange kernel, and the two form factors are obtained from the bottom-up AdS/QCD models. We show that the resulting nucleon-nucleon total cross section is consistent with the experimental data recently measured by the TOTEM collaboration at the LHC. As examples to see the general versatility of the model, we also present our analysis on the pion-nucleon and pion-pion cases, which can be predicted because all the adjustable parameters are fixed by the nucleon-nucleon data.

The Latest Developments of Functional Molecular Imaging in the Diagnosis of Primary Aldosteronism.

Differentiation of unilateral from bilateral aldosterone hypersecretion is the essential step in the clinical practice of primary aldosteronism (PA). Although adrenal venous sampling (AVS) has been established as the most standard test recommended by the guideline, its invasive and technically difficult nature has facilitated the approach to develop non-invasive functioning imaging as an alternative test. Compared to the conventional adrenocortical scintigraphy with cholesterol derivatives as tracer, the first-generation imaging, both of 11C-MTO/PET and 123I-IMTO/SPECT/CT, the second-generation imaging, bind with high specificity and affinity to CYP11B enzymes and have advantages in shortening the time for obtaining specific images, reducing the radiation exposure to the patient, and resolution of the images. Because of very short half-life of 11C-MTO, 123I-IMTO has a potential for a wider application than 11C-MTO. Sensitivity of identifying an adenoma smaller than 1 cm in diameter is still a common limitation of these new functional imaging methods. The new functional imaging could be supplementary to AVS in lateralization of PA when the results of AVS are not conclusive. To be a substitute for AVS, however, it should fulfill various conditions including high selectivity and binding affinity to CYP11B2, high sensitivity in detecting small adenoma, high resolution image, reduction of radiation exposure, and general versatility. Considering the potential number of patients, cost-effectiveness of the subtype testing in the clinical practice of PA could be one of the issues of the medical expenses. Thus, development of a new non-invasive functional imaging will have a significant impact on the clinical practice of PA and hypertension.

Correction of stopping power and LET quenching for radiophotoluminescent glass dosimetry in a therapeutic proton beam

To measure the absorbed dose to water Dw in proton beams using a radiophotoluminescent glass dosimeter (RGD), a method with the correction for the change of the mass stopping power ratio (SPR) and the linear energy transfer (LET) dependence of radiophotoluminescent efficiency is proposed.The calibration coefficient in terms of Dw for RGDs (GD-302M, Asahi Techno Glass) was obtained using a 60Co γ-ray. The SPR of water to the RGD was calculated by Monte Carlo simulation, and was investigated experimentally using a 70 MeV proton beam. For clinical usage, the residual range Rres was used as a quality index to determine the correction factor for the beam quality and the LET quenching effect of the RGD . The proposed method was evaluated by measuring Dw at different depths in a 200 MeV proton beam.For both non-modulated and modulated proton beams, decreases rapidly where Rres is less than 4 cm. The difference in between a non-modulated and a modulated proton beam is less than 0.5% for the Rres range from 0 cm to 22 cm. decreases rapidly at a LET range from 1 to 2 keV µm−1. In the evaluation experiments, Dw using RGDs, showed good agreement with that obtained using an ionization chamber and the relative difference was within 3% where Rres was larger than 1 cm. The uncertainty budget for in a proton beam was estimated to investigate the potential of RGD postal dosimetry in proton therapy.These results demonstrate the feasibility of RGD dosimetry in a therapeutic proton beam and the general versatility of the proposed method. In conclusion, the proposed methodology for RGDs in proton dosimetry is applicable where Rres > 1 cm and the RGD is feasible as a postal audit dosimeter for proton therapy.

Imaging of Left Ventricular Hypertrophy: a Practical Utility for Differential Diagnosis and Assessment of Disease Severity.

Left ventricular hypertrophy (LVH) is often encountered in clinical practice, and it is a risk factor for cardiac mortality and morbidity. Determination of the etiology and disease severity is important for the management of patients with LVH. The aim of this review is to show the remarkable progress in cardiac imaging and its importance in clinical practice. This review focuses on clinical features and characteristic cardiac imaging in LVH caused by various diseases including hypertension, aortic valve stenosis, hypertrophic cardiomyopathy, and secondary cardiomyopathies. The usefulness of echocardiography as a tool of general versatility including hemodynamic evaluation and the usefulness of cardiac magnetic resonance imaging for assessment of cardiac morphology and myocardial tissue characteristics of relevance for LVH are described. Imaging modalities now have central roles in the differentiation and prognostic assessment of LVH.

Texture-Guided Multisensor Superresolution for Remotely Sensed Images

This paper presents a novel technique, namely texture-guided multisensor superresolution (TGMS), for fusing a pair of multisensor multiresolution images to enhance the spatial resolution of a lower-resolution data source. TGMS is based on multiresolution analysis, taking object structures and image textures in the higher-resolution image into consideration. TGMS is designed to be robust against misregistration and the resolution ratio and applicable to a wide variety of multisensor superresolution problems in remote sensing. The proposed methodology is applied to six different types of multisensor superresolution, which fuse the following image pairs: multispectral and panchromatic images, hyperspectral and panchromatic images, hyperspectral and multispectral images, optical and synthetic aperture radar images, thermal-hyperspectral and RGB images, and digital elevation model and multispectral images. The experimental results demonstrate the effectiveness and high general versatility of TGMS.

Nanoporous Transparent MOF Glasses with Accessible Internal Surface.

While glassy materials can be made from virtually every class of liquid (metallic, molecular, covalent, and ionic), to date, formation of glasses in which structural units impart porosity on the nanoscopic level remains undeveloped. In view of the well-established porosity of metal-organic frameworks (MOFs) and the flexibility of their design, we have sought to combine their formation principles with the general versatility of glassy materials. Although the preparation of glassy MOFs can be achieved by amorphization of crystalline frameworks, transparent glassy MOFs exhibiting permanent porosity accessible to gases are yet to be reported. Here, we present a generalizable chemical strategy for making such MOF glasses by assembly from viscous solutions of metal node and organic strut and subsequent evaporation of a plasticizer-modulator solvent. This process yields glasses with 300 m(2)/g internal surface area (obtained from N2 adsorption isotherms) and a 2 nm pore-pore separation. On a volumetric basis, this porosity (0.33 cm(3)/cm(3)) is 3 times that of the early MOFs (0.11 cm(3)/cm(3) for MOF-2) and within range of the most porous MOFs known (0.60 cm(3)/cm(3) for MOF-5). We believe the porosity originates from a 3D covalent network as evidenced by the disappearance of the glass transition signature as the solvent is removed and the highly cross-linked nanostructure builds up. Our work represents an important step forward in translating the versatility and porosity of MOFs to glassy materials.

A new 500-m resolution map of canopy height for Amazon forest using spaceborne LiDAR and cloud-free MODIS imagery

In the present study, we aimed to map canopy heights in the Brazilian Amazon mainly on the basis of spaceborne LiDAR and cloud-free MODIS imagery with a new method (the Self-Organizing Relationships method) for spatial modeling of the LiDAR footprint. To evaluate the general versatility, we compared the created canopy height map with two different canopy height estimates on the basis of our original field study plots (799 plots located in eight study sites) and a previously developed canopy height map. The compared canopy height estimates were obtained by: (1) a stem diameter at breast height (D) – tree height (H) relationship specific to each site on the basis of our original field study, (2) a previously developed D–H model involving environmental and structural factors as explanatory variables (Feldpausch et al., 2011), and (3) a previously developed canopy height map derived from the spaceborne LiDAR data with different spatial modeling method and explanatory variables (Simard et al., 2011). As a result, our canopy height map successfully detected a spatial distribution pattern in canopy height estimates based on our original field study data (r=0.845, p=8.31×10−3) though our canopy height map showed a poor correlation (r=0.563, p=0.146) with the canopy height estimate based on a previously developed model by Feldpausch et al. (2011). We also confirmed that the created canopy height map showed a similar pattern with the previously developed canopy height map by Simard et al. (2011). It was concluded that the use of the spaceborne LiDAR data provides a sufficient accuracy in estimating the canopy height at regional scale.

View-based teaching/playback for robotic manipulation

In this paper, we study a new method for robot programming: view-based teaching/playback. The motivation of its development is to achieve more robustness against changes of task conditions than conventional teaching/playback without losing its general versatility. For proof of concept, the method was implemented and tested on a virtual environment. The method is composed of two parts: teaching phase and playback phase. In the teaching phase, a human operator commands a robot to achieve a manipulation task. All the movements of the robot are recorded. All the images of the teaching scenes are also recorded by a camera. Then, a mapping from the recorded images to the movements is obtained as an artificial neural network. In the playback phase, the motion of the robot is determined by the output of the neural network calculated from scene images. We applied this view-based teaching/playback to pick-and-place and pushing by a robot hand with eight degrees of freedom in the virtual environment. Human demonstrated manipulation was successfully reproduced by the robot hand with our proposed method. Moreover, manipulation of the object from some initial positions that are not identical to those in the demonstrations was also successfully achieved with our method.