Continuous Online Research Articles

Development of ultrasound-assisted centrifugal extraction combined with two countercurrent chromatography systems for the simultaneous extraction and isolation of phytochemicals.

We proposed a method for the extraction of medicinal herbs, called ultrasound-assisted centrifugal extraction, and an online solvent concentration method. These techniques were coupled with two countercurrent chromatography systems and applied to the continuous extraction and online isolation of chemical constituents from Inonotus obliquus. Raw plants were extracted using a two-phase petroleum-ethanol-water (2.0:1.0:2.0, v/v/v) process, and then the aqueous and organic phases were concentrated using the proposed online solvent concentrator. The countercurrent chromatography preparation prior to separation includes pumping of the two-phase solution, rotating column, and equilibrium column. Following online concentration, the extracted solution was pumped into a second countercurrent chromatography process for separation. During separation, the extraction solution and concentrated extract were prepared automatically. Upon completion of the first cycle of ultrasound-assisted centrifugal extraction/two countercurrent chromatography, the second cycle experiment starts. This process can be indefinitely repeated. In this study, six target compounds with purities above 97.71% were successfully extracted and isolated online using a two-phase solvent system consisting of n-hexane-ethyl acetate-acetonitrile (4.5:1.5:5.5, v/v/v) and n-hexane-ethyl acetate-methanol-water (0.4:3.0:1.5:2.5, v/v/v/v). Compared to conventional extraction methods, the instrumental setup of the proposed method provides enhanced automation, efficiency, purity, and systematic extraction and isolation of natural products.

Online ultrasonic nebulizer assisted laser induced breakdown spectroscopy (OUN-LIBS): An online metal elements sensor for marine water analysis

Metal element detection technologies for seawater analysis are very important in the fields of marine scientific investigation and environmental monitoring. The current technologies cannot meet the requirements of detection capabilities, such as online, real-time, multi-element and highly sensitive while used in the above fields on-site. A newly developed sensor named online ultrasonic nebulizer assisted laser induced breakdown spectroscopy (OUN-LIBS) is introduced in this paper which is expected to be used for on-site seawater analysis. The core design idea of this sensor is to combine an online ultrasonic nebulizing auxiliary device with a traditional LIBS system to nebulize the liquid samples into aerosol for LIBS detection in order to realize a continuously, highly sensitive, stable and fast on-site detection. Based on this idea, a hardware system composed of an adapter module, a nebulizing module and an optical module and a software system with communication, control and display functions were designed. The laboratory experiments and marine field testing were carried out to assess the sensor. Under the status of continuous online detection, the LODs of the 6 target elements are lower than 1 ppm and the stability of spectral line intensity of Ca is lower than 0.38%. Besides the traditional calibration curve method, an online and on-site standard sample calibration method is adopted in the concentration quantitative analysis. The results of research show that the accuracy of online quantification analysis is very impressive even in the marine field testing.

Study on the measurement of isoprene by differential optical absorption spectroscopy

Abstract. In this paper, the continuous online measurements of isoprene in the atmosphere have been carried out by using differential optical absorption spectroscopy (DOAS) in the band of 202.71–227.72 nm for the first time. Under a zero optical path in the laboratory, different equivalent concentrations of isoprene were measured by the combination of known concentrations of gas and series calibration cells. The correlation between the measured concentrations and the equivalent concentrations was 0.9995, and the slope was 1.065. The correlation coefficient between DOAS and the online volatile organic compound (VOC) instrument observed from 23 d of field observations is 0.85 with a slope of 0.86. It was estimated that the detection limit of isoprene with DOAS is approximately 0.1 ppb at an optical path of 75 m, and it was verified that isoprene could be measured in the ultraviolet absorption band using the DOAS method with high temporal resolution and a low maintenance cost.

Edge Intelligence for Next Generation Wireless Networks

In the past decade we have witnessed the development of machine learning and artificial intelligence algorithms with cloud computing in providing intelligence for smart applications. By collecting raw data from a multitude of mobile and wireless devices, the cloud server executes machine learning algorithms to train the data and perform data prediction. However, the growing number of devices has generated a tremendous amount of data. Transmitting massive data to centralized machine learning in the cloud has significant communication overhead and computational complexity. With the support of edge gateways, the significant burdens of massive data flow and exchanged information from large groups of devices can be efficiently alleviated in terms of both communication and computational overhead. Meanwhile, the exchanged information aims to reach the remote online system, in which the continuous and robust online services should be always guaranteed and ensured. As a promising solution, cyberinfrastructure is designed and developed to boost network intelligence by offering powerful computing systems, flexible data storage repositories, and advanced virtualization environments at both the edge and the cloud. Edge intelligence has emerged as a new trend for next generation wireless networks.

An Efficient Data Analysis Framework for Online Security Processing

Industrial cloud security and internet of things security represent the most important research directions of cyberspace security. Most existing studies on traditional cloud data security analysis were focused on inspecting techniques for block storage data in the cloud. None of them consider the problem that multidimension online temp data analysis in the cloud may appear as continuous and rapid streams, and the scalable analysis rules are continuous online rules generated by deep learning models. To address this problem, in this paper we propose a new LCN-Index data security analysis framework for large scalable rules in the industrial cloud. LCN-Index uses the MapReduce computing paradigm to deploy large scale online data analysis rules: in the mapping stage, it divides each attribute into a batch of analysis predicate sets which are then deployed onto a mapping node using interval predicate index. In the reducing stage, it merges results from the mapping nodes using multiattribute hash index. By doing so, a stream tuple can be efficiently evaluated by going over the LCN-Index framework. Experiments demonstrate the utility of the proposed method.

Oxidation Studies on Mineral Insulating Oil Using an Accelerated Method with Continuous Online Monitoring

In this study, an alternative method was applied to evaluate the accelerated oxidation of mineral insulating oils using continuous real time monitoring, with smaller samples and lower experimental times than the standard ASTM D2440 method. Using this method, the influence of temperature and presence of a metal catalyst on the oxidation behavior was evaluated. The activities of commercial metal passivators and antioxidants were also studied at different conditions. The addition of the metal passivators (BTA: 1,2,3-benzotriazole, and Irgamet-39: n-(1,2,3-benzotriazol-1-methyl)-n-alkylamine) at low concentrations (8 mg/kg) increased the induction period (IP) of doped samples. Furthermore synergetic effects on retarding the oxidation reactions when using both metal passivators and two commercial antioxidants (DBPC, 2,6-di-tert-butyl-p-cresol, and Irganox L115, 2,2'-thiodiethylen-bis(3,5-di-tert-butyl-4 hydroxyphenyl-proprionate) were observed, increasing the IP values by up to 25 times when compared to a fresh oil sample. Experimental IP values from this newly proposed technique (up to 10 h) may be used to estimate the Total Acid Number values obtained when using the ASTM D2440 method (164 h).

On the Concept of Learning Transfer for Continuous and Online Training: A Literature Review

This work presents a bibliographic review of research related to the subject of learning transfer. Here, we seek to understand its concept, its dimensions, and the factors that influence its achievement. The review allowed us to verify empirical studies carried out that show which variables can facilitate or hinder transference, as well as the most suitable methodology to favor it and indicate considerations that should be taken into account in order to research learning transference in a continuous teacher training. The purpose of this review was to discuss the concept of learning transfer in order to propose a research on this subject in a context of continuous online training, once a lack of studies focused on this modality was detected. In order to analyze learning transfer in this context, we considered it necessary to confirm if the competences, skills, and knowledge acquired through e-learning could be applied in a pedagogical practice of participating teachers; this, in turn, would allow for the obtainment of guidelines for future educational strategies of continuous online training.

Development of ultrasound-assisted mixture extraction and online extraction solution concentration coupled with countercurrent chromatography for the preparation of pure phytochemicals from Phellinus vaninii

Herein, ultrasound-assisted mixture extraction (UAME) and online extraction solution concentration (OESC) were conducted to extract products from crops and plants. These techniques were coupled with parallel countercurrent chromatography (PCCC) and applied for continuous extraction and online isolation of chemical constituents from Phellinus vaninii. The UAME instrument comprises extraction and solution separation chambers. It provides higher extraction efficiency and fewer impurities and is suitable for processing various sample matrices. The OESC device comprises a spray nozzle, concentrating cylinder, and hot-blast air nozzle. The mechanical parameters for UAME and OESC were optimized, and the operation of online UAME and OESC coupled with PCCC was described. Raw plant materials were extracted using a two-phase extractant comprising petroleum–ethyl acetate–ethanol–water (0.5:2.0:0.5:2.0, v/v/v/v). The aqueous and organic phases were then concentrated using the OESC technique. Two CCC runs were conducted for preparatory work. After extraction and online concentration, the concentrate was pumped into the CCC for separation. During PCCC separation, continuous automated extraction and concentration were still conducted. When the first cycle of the UAME/OESC/PCCC was completed, followed by the initiation of the second cycle, and the process was continued. Six target compounds with purities exceeding 97.22% were successfully separated using the CCC solvent systems comprising n-hexane–ethyl acetate–acetonitrile–water (5.5:2.5:5.0:0.4, v/v/v/v) and n-butanol–ethanol–water (4.5:1.3:6.5, v/v/v). Compared with conventional extraction methods, the proposed UAME/OESC/PCCC method has higher efficiency, facilitates high-purity separation of analytes, and offers opportunity for automation and systematic preparation of natural products.

Inovasi Madrasah: Strategi Pembelajaran Daring di Masa Pandemi

The influence of the Covid-19 pandemic, which robs people of their rights and movements in carrying out activities outside the home. Until now, economics, social, and even education have not found a common ground in direct learning, even some schools in the corners of the city are desperate to do face-to-face learning. Complaints of parents, students, and educators as one unit found the reason that continuous learning online at home is not effective. This paper tries to analyze how effective online learning was during a pandemic and how enthusiastic students were when learning online. The discussion of this article proves that society is starting to adapt to technological developments that focus on learning. Furthermore, madrasas make good progress through their teaching staff and educational staff. The discussion of this article proves that society is starting to adapt to technological developments that focus on learning. Furthermore, madrasas make good progress through their teaching staff and educational staff. Madrasah innovation followed by support from the community made the goal of distance learning an alternative in carrying out the teaching and learning process. The results of this study will also be used as a reference in developing and finding new theories related to the effectiveness of distance learning. The results of this study will also be used as a reference in developing and finding new theories related to the effectiveness of distance learning.

Application of machine learning in orbital correction of storage ring

Synchrotron light source is one of the most powerful tools in modern science and technology. Shanghai Synchrotron Radiation Facility (SSRF), located in Shanghai, China, is an advanced 3.5 GeV 3rd-generation medium energy light source. The 3rd-generation synchrotron radiation light source will provide high brilliance and high stability synchrotron radiation to fulfill the advanced experimental conditions in frontier researches. To achieve highly stable radiation, it is important to have highly stable beam orbit. Thus we adopted machine learning method to control and feedback the orbit. Using this neural network-based orbit correction method, which doesn’t rely on the response matrix, we can establish a nonlinear mapping relationship between correctors and the orbit distortions and perform continuous online retraining. This new method can significantly improve the orbit stability of SSRF.

Pendampingan Pengembangan Sistem Informasi Antrian Disabilitas Rumah Sakit Panti Waluyo Purworejo

Every patient has the right to receive healthcare service. However, not everyone has equal access to these services. This problem is especially apparent in patients with disabilities. Duta Wacana Christian University’s Faculty of Information Technology is working alongside Panti Waluyo Hospital in Purworejo to build a priority queueing system for disabled patients. This system is expected to improve patient admission and queueing process. The proposed system consists of a website and a mobile application which has undergo 6 months of continuous development and online mentorship, starting from January 2020 up until June 2020.

Continuous On-Line Tar Measurements for Gasification Process Monitoring using Fluorescence Excitation-Emission Matrices at Elevated Temperature

Biomass has been extensively investigated, because of its presence as clean energy source. Tars and particulates formation problems are still the major challenges in development especially in the implementation of gasification technologies into nowadays energy supply systems. Laser Induced Fluorescence spectroscopy (LIF) method is incorporated for determining aromatic and Polycyclic Aromatic Hydrocarbons (PAH) produced at high temperature gasification technology. The effect of tars deposition when the gases are cooled has been highly reduced by introducing a new concept of measurement cell. The samples of PAH components have been prepared with the standard constrictions of measured PAHs by using gas chromatograph (GC). OPO laser with tuning rang of (200 to 2400) nm and peak energy of 2.2 mJ were used to excite the tar compounds, which have fluorescence properties in the range of ultraviolet| blue spectral range. The measurements have been evaluated by incorporating the Excitation-Emission Matrices (EEM), presented as time consuming method for tar monitoring.

Relaxing Method in the Evaluation of MPPT of Photovoltaic Cells Based on MIT Modeling

A relaxation method is proposed to determine the maximum power point tracking of a small to medium powered photovoltaic module, based on the non referenced MIT model, with analytical evaluation of the maximum power of the generated voltage. In implementing this algorithm, there is no need for continuous online measurements of temperature or irradiance, which are the most used variables used in the evaluation of the I-V relationship of PV cells. The method has the advantage of adequate accuracy without any oscillations, compared with certain conventional methods. The method can track accurately the maximum power point under changes in atmospheric conditions and load variations, by referring it with the MIT model.

A Model Predictive Control with Preview-Follower Theory Algorithm for Trajectory Tracking Control in Autonomous Vehicles

Research on trajectory tracking is crucial for the development of autonomous vehicles. This paper presents a trajectory tracking scheme by utilizing model predictive control (MPC) and preview-follower theory (PFT), which includes a reference generation module and a MPC controller. The reference generation module could calculate reference lateral acceleration at the preview point by PFT to update state variables, and generate a reference yaw rate in each prediction point. Since the preview range is increased, PFT makes the calculation of yaw rate more accurate. Through physical constraints, the MPC controller can achieve the best tracking of the reference path. The MPC problem is formulated as a linear time-varying (LTV) MPC controller to achieve a predictive model from nonlinear vehicle dynamics to continuous online linearization. The MPC-PFT controller method performs well by increasing the effective length of the reference path. Compared with MPC and PFT controllers, the effectiveness and robustness of the proposed method are proved by simulations of two typical working conditions.

An autonomous real-time detector system for radionuclide monitoring in drinking water systems

For monitoring radioactive contamination of water systems due to nuclear accidents or terrorist attacks, there is need of an in situ online measurement to assess alpha-, beta- and gamma-emitting radionuclide contaminants quickly and accurately. However, there is no well-established online monitoring system for permanent surveillance of drinking water systems. Therefore, a real-time measurement system was developed based on the readout of plastic scintillator sheets by a photomultiplier. Direct contact between scintillator and streaming water allows for the detection of both long- and short-ranged particles. Using a passive cooling system, detection limits of 141 Bq/L for ^{241}Am, 20 Bq/L for ^{60}Co and 17 Bq/L for ^{137}Cs were achieved with measurements of 60 s counting time. The system has been designed to be operated safely without deep knowledge of radiation measurement technology. The detector can be connected as a bypass to a water system and be operated in a continuous online survey mode, making it applicable as an early warning system.

Continuous online monitoring of ice-nucleating particles: development of the automated Horizontal Ice Nucleation Chamber (HINC-Auto)

Abstract. The incomplete understanding of aerosol–cloud interactions introduces large uncertainties when simulating the cloud radiative forcing in climate models. The physical and optical properties of a cloud, as well as the evolution of precipitation, are strong functions of the cloud hydrometeor phase. Aerosol particles support the phase transition of water in the atmosphere from a meta-stable to a thermodynamically preferred stable phase. In the troposphere, the transition of liquid droplets to ice crystals in clouds, via ice-nucleating particles (INPs) which make up only a tiny fraction of all tropospheric aerosol, is of particular relevance. For accurate cloud modeling in climate projections, the parameterization of cloud processes and information such as the concentrations of atmospheric INPs are needed. Presently, only few continuous real-time INP counters are available and the data acquisition often still requires a human operator. To address this restriction, we developed HINC-Auto, a fully automated online INP counter, by adapting an existing custom-built instrument, the Horizontal Ice Nucleation Chamber. HINC-Auto was able to autonomously sample INPs in the immersion mode at a temperature of 243 K and a water saturation ratio of 1.04 for 97 % of the time for 90 consecutive days. Here, we present the technical setup used to acquire automation, discuss improvements to the experimental precision and sampling time, and validate the instrument performance. In the future, the chamber will allow a detailed temporal analysis (including seasonal and annual variability) of ambient INP concentrations observing repeated meteorological phenomena compared to previous episodic events detected during campaigns. In addition, by deploying multiple chambers at different locations, a spatiotemporal variability of INPs at any sampling site used for monitoring INP analysis can be achieved for temperatures ≤ 243 K.

Silver Enhances Hematite Nanoparticles Based Ethanol Sensor Response and Selectivity at Room Temperature.

Gas sensors are fundamental for continuous online monitoring of volatile organic compounds. Gas sensors based on semiconductor materials have demonstrated to be highly competitive, but are generally made of expensive materials and operate at high temperatures, which are drawbacks of these technologies. Herein is described a novel ethanol sensor for room temperature (25 °C) measurements based on hematite (α‑Fe2O3)/silver nanoparticles. The AgNPs were shown to increase the oxide semiconductor charge carrier density, but especially to enhance the ethanol adsorption rate boosting the selectivity and sensitivity, thus allowing quantification of ethanol vapor in 2–35 mg L−1 range with an excellent linear relationship. In addition, the α-Fe2O3/Ag 3.0 wt% nanocomposite is cheap, and easy to make and process, imparting high perspectives for real applications in breath analyzers and/or sensors in food and beverage industries. This work contributes to the advance of gas sensing at ambient temperature as a competitive alternative for quantification of conventional volatile organic compounds.

Monocular guidance of reaches-to-grasp using visible support surface texture: data and model.

We investigated monocular information for the continuous online guidance of reaches-to-grasp and present a dynamical control model thereof. We defined an information variable using optical texture projected from a support surface (i.e. a table) over which the participants reached-to-grasp target objects sitting on the table surface at different distances. Using either binocular or monocular vision in the dark, participants rapidly reached-to-grasp a phosphorescent square target object with visibly phosphorescent thumb and index finger. Targets were one of three sizes. The target either sat flat on the support surface or was suspended a few centimeters above the surface at a slant. The later condition perturbed the visible relation of the target to the support surface. The support surface was either invisible in the dark or covered with a visible phosphorescent checkerboard texture. Reach-to-grasp trajectories were recorded and Maximum Grasp Apertures (MGA), Movement Times (MT), Time of MGA (TMGA), and Time of Peak Velocities (TPV) were analyzed. These measures were selected as most indicative of the participant's certainty about the relation of hand to target object during the reaches. The findings were that, in general, especially monocular reaches were less certain (slower, earlier TMGA and TPV) than binocular reaches except with the target flat on the visible support surface where performance with monocular and binocular vision was equivalent. The hypothesized information was the difference in image width of optical texture (equivalent to density of optical texture) at the hand versus the target. A control dynamic equation was formulated representing proportional rate control of the reaches-to-grasp (akin to the model using binocular disparity formulated by Anderson and Bingham (Exp Brain Res 205: 291-306, 2010). Simulations were performed and presented using this model. Simulated performance was compared to actual performance and found to replicate it. To our knowledge, this is the first study of monocular information used for continuous online guidance of reaches-to-grasp, complete with a control dynamic model.

Uri’s Art and Frozen Sunset

The Methodist DeBakey Cardiovascular Journal provides timely updates on important topics in cardiovascular medicine with quarterly, thematic review-article issues. Each issue is curated by subject-matter-expert guest editors and includes invited reviews along with case reports, interesting multimodality images, and humanities submissions. Additional case reports, images, CME quizzes, and editorials are published on a continuous basis online. MDCVJ is geared towards cardiologists, cardiovascular surgeons, general practitioners, and CV residents and fellows and is indexed in PubMed and MEDLINE.MDCVJ is published by Houston Methodist DeBakey Heart & Vascular Center as part of DeBakey CV Education, an epicenter for cardiovascular academic and clinical education programs located in the heart of the Texas Medical Center. Along with the Journal, DeBakey CV Education offers online and hands-on training for cardiovascular healthcare professionals at all stages in their careers. DeBakey CV Education’s vast YouTube library features hundreds of training videos and live webcasts, including Journal features. Subscribe to the YouTube channel today and sign up for newsletter updates about training opportunities.

Reinforcement Learning-Based Variable Speed Limits Control to Reduce Crash Risks Near Traffic Oscillations on Freeways

Traffic oscillation on freeways results in abrupt and frequent vehicle deceleration, which remarkably increases crash risks. Artificial Intelligence based traffic control provides a new opportunity for addressing the safety issues. This study aims at proposing a Reinforcement Learning (RL)-based variable speed limits (VSL) control algorithm to reduce crash risks associated with oscillations. The state, action and reward, which are the critical components in the RL, were designed carefully for safety improvement. The RL was trained to learn the optimal speed limit for various traffic states to achieve a goal of safety optimization. A rear-end crash risk model was applied to assess crash risks associated with oscillations near freeway bottlenecks. The cell transmission model was modified as the simulation platform for evaluating the control effects. The results showed that after the training process, the proposed RL-based VSL control successfully reduced the crash risks by 19.4%. A continuous online learning function was developed in RL to enhance the robustness of our strategy. The results showed that with continuous learning, the RL-based VSL control performed reasonably well under lower driver compliance situations.