Off-line Method Research Articles

A matrix-centered view of mass spectrometry platform innovation for volatilome research.

Volatile organic compounds (VOCs) are carbon-containing molecules with high vapor pressure and low water solubility that are released from biotic and abiotic matrices. Because they are in the gaseous phase, these compounds tend to remain undetected when using conventional metabolomic profiling methods. Despite this omission, efforts to profile VOCs can provide useful information related to metabolic status and identify potential signaling pathways or toxicological impacts in natural or engineered environments. Over the past several decades mass spectrometry (MS) platform innovation has instigated new opportunities for VOC detection from previously intractable matrices. In parallel, volatilome research linking VOC profiles to other forms of multi-omic information (DNA, RNA, protein, and other metabolites) has gained prominence in resolving genotype/phenotype relationships at different levels of biological organization. This review explores both on-line and off-line methods used in VOC profiling with MS from different matrices. On-line methods involve direct sample injection into the MS platform without any prior compound separation, while off-line methods involve chromatographic separation prior to sample injection and analyte detection. Attention is given to the technical evolution of platforms needed for increasingly resolved VOC profiles, tracing technical progress over time with particular emphasis on emerging microbiome and diagnostic applications.

Comparison of Four Rapid N-Glycan Analytical Methods and Great Application Potential in Cell Line Development

N-glycan, a critical quality attribute (CQA) of monoclonal antibodies, profoundly impacts potency, immunogenicity, safety, and clinical efficiency. Therefore, N-glycan should be monitored and controlled during development. The conventional 2-AB method is considered the gold standard for N-glycan analysis, which is labor intensive and time consuming. Moreover, its sample requirement is relatively high and cannot be met in early-stage development, including cell line development. In this study, we compared the performance of four rapid analytical methods in N-glycan analysis of mAbs, including the rapid 2-AB method, reduction method, off-line IdeS digestion method, and two-dimensional liquid chromatography-mass spectrometry (2D-LC-MS) method. Our results showed that these four rapid analytical methods could provide comparable N-glycan data. Moreover, these four rapid methods shortened the testing time for the conventional 2-AB method from days to just minutes. They also reduced the sample requirement for the conventional 2-AB method from milligrams to micrograms. Among these four rapid methods, the 2D-LC-MS method demonstrated great potential for applications in time-consuming cell line development because it required less testing time and a lower sample requirement.

Handwritten Signature Verification Method Using Convolutional Neural Network

Automatic signature verification methods play a significant role in providing a secure and authenticated handwritten signature in many applications, to prevent forgery problems, specifically institutions of finance, and transections of legal papers, etc. There are two types of handwritten signature verification methods: online verification (dynamic) and offline verification (static) methods. Besides, signature verification approaches can be categorized into two styles: writer dependent (WD), and writer independent (WI) styles. Offline signature verification methods demands a high representation features for the signature image. However, lots of studies have been proposed for WI offline signature verification. Yet, there is necessity to improve the overall accuracy measurements. Therefore, a proved solution in this paper is depended on deep learning via convolutional neural network (CNN) for signature verification and optimize the overall accuracy measurements. The introduced model is trained on English signature dataset. For model evaluation, the deployed model is utilized to make predictions on new data of Arabic signature dataset to classify whether the signature is real or forged. The overall obtained accuracy is 95.36\% based on validation dataset.

Development of an in-service NDT system for storage tanks

The large storage tank is one of the major infrastructures. Once a leak occurs, it often causes environmental pollution and even catastrophic accidents. In the early years, the method of emptying tanks and cutting plates for defects inspection was adopted to avoid leakage. However, there exist some deficiencies such as large blindness, long downtime and high cost. Since there are many problems in the traditional testing, inspection without emptying the tank and without stopping production is an indispensable technical means to ensure the safe operation of the tank. An in-service non-destructive testing (NDT) system for storage tanks without emptying is developed in this paper, which mainly includes sensors, the signal processing module, the testing crawler, the winding and releasing mechanism and control module et. al. The sensors, signal processing module and testing crawler need to be placed into the tank from the roof man-hole by the crane, while the control module is located outside the tank. The sensor can realize the combined inspection of magnetic field leakage (MFL) and electromagnetic acoustic transducer (EMAT). The laboratory experiments indicate that the system can realize in-service inspection of the corrosion defects on the bottom and works well in water. The new testing system will overcome the shortage of off-line service inspection methods that emptying and cleaning tanks are needed before inspection and environmental risks are involved. These research works guarantee the safe operation of storage tanks and provide an alternative NDT system for storages related industrial projects.

A New Trajectory Reduction Method for Mobile Devices Operating Both Online and Offline

AbstractHighly accurate location data have become essential in nearly all contemporary global applications, including but not limited to route planning, processing traffic data, identifying common routes, map matching, and enhancing agricultural productivity. However, the abundance of unnecessary and redundant data leads to various challenges, especially concerning storage, processing, and transmission. Despite the existence of numerous studies aimed at addressing these GNSS data management challenges, the reduction problem is either partially resolved, or enhancements are made to existing solutions in nearly all of them. In this study, a novel reduction method is introduced, offering both a high reduction rate and accuracy, suitable for operation on mobile devices in both offline and online modes. The proposed method uses windowing with reference points during the decision phase to decrease the number of points. By utilizing the angle and its threshold between the decision point and reference points, we achieved a method characterized by low algorithmic complexity and a high reduction rate, suitable for online operation on mobile devices. Experiments and comparisons revealed that the proposed method had a 91.01% reduction in GNSS data which is 7.73% lower, a 5.8744e$$-$$ - 04 RMSE error which is 2e+7 times better, and a 14.54 ms running time which is 25% faster than RDP algorithm. The results indicate that incorporating the proposed method into current methodologies could be beneficial, particularly in scenarios where real-time, high-precision location data are essential.

방사선 의료기기 수출 활성화 방안에 관한 연구

Korea's trade development is accompanied by the government's policy to foster industries. Entering the ranks of advanced countries, Korea should seek directions for the development of technology-intensive and high-value-added industries such as medical devices and entry into overseas markets. In particular, the radiation medical device industry for the purpose of prevention and treatment due to the increase in cancer patients and aging population is expected to continue to grow. Therefore, this study set up five research questions to revitalize the export of radiation medical devices and conducted in-depth interviews with manufacturers. As a result of the interview analysis, the subjects are having difficulty preparing for exports due to the lack of time, cost, information, and manpower. In order to solve this problem, first, it is necessary to reorganize the trade consulting system and trade practical education, such as opening a Micro Degrees course for radiation medical devices and establishing Running Mate trade consulting. Second, it is necessary to attempt platform-oriented online trade marketing with different proportions of off-line trade marketing methods. Third, it is necessary to improve the linked government support system and conditional trade finance system.

An Ultra-Throughput Boost Method for Gamma-Ray Spectrometers

(1) Background: Generally, in nuclear medicine and nuclear power plants, energy spectrum measurements and radioactive nuclide identification are required for evaluation of strong radiation fields to ensure nuclear safety and security; thereby, damage is prevented to nuclear facilities caused by natural disasters or the criminal smuggling of nuclear materials. High count rates can lead to signal accumulation, negatively affecting the performance of gamma spectrometers, and in severe cases, even damaging the detectors. Higher pulse throughput with better energy resolution is the ultimate goal of a gamma-ray spectrometer. Traditionally, pileup pulses, which cause dead time and affect throughput, are rejected to maintain good energy resolution. (2) Method: In this paper, an ultra-throughput boost (UTB) off-line processing method was used to improve the throughput and reduce the pileup effect of the spectrometer. Firstly, by fitting the impulse signal of the detector, the response matrix was built by the functional model of a dual exponential tail convolved with the Gaussian kernel; then, a quadratic programming method based on a non-negative least squares (NNLS) algorithm was adopted to solve the constrained optimization problem for the inversion. (3) Results: Both the simulated and experimental results of the UTB method show that most of the impulses in the pulse sequence from the scintillator detector were restored to δ-like pulses, and the throughput of the UTB method for the NaI(Tl) spectrometer reached 207 kcps with a resolution of 7.71% @661.7 keV. A reduction was also seen in the high energy pileup phenomenon. (4) Conclusions: We conclude that the UTB method can restore individual and piled-up pulses to δ-like sequences, effectively boosting pulse throughput and suppressing high-energy tailing and sum peaks caused by the pileup effect at the cost of a slight loss in energy resolution.

Effective separation of maslinic acid and oleanolic acid from olive pomace using high-speed shear off-line coupled with high-speed countercurrent chromatography and their antibacterial activity test

In this work, a high-speed shear extraction off-line coupling high-speed countercurrent chromatography method was developed to separate maslinic acid and oleanolic acid from olive pomace. To improve extraction efficiency, the polar disparity between maslinic acid and oleanolic acid necessitated the concurrent utilization of both polar and non-polar solvents during high-speed shear extraction. Then, the high-speed shear extraction was directly feed to high-speed countercurrent chromatography for subsequently separation. A total of 250 min were needed to complete the extraction and separation process. This yielded two molecules from 3.3 g of defatted olive pomace: 7.2 mg of 93.8 % pure maslinic acid and 2.3 mg of 90.1 % pure oleanolic acid, both determined by HPLC at 210 nm. Furthermore, the compounds exhibited inhibitory activity against Escherichia coli and Staphylococcus aureus. At a concentration of 100 μg/mL, its efficacy in inhibiting hyaluronidase was comparable to that of the standard drug indomethacin. Compared with the conventional separation method, this coupled technique reduced the whole time due to the direct injection of sample extraction solution. This technique provides a useful approach for the separation of natural products with significant polarity differences.

Error state prediction method for electronic voltage transformer based on empirical mode decomposition and auto-regressive moving average

Electronic voltage transformers have been used in the power system in conjunction with the development of power grid intelligence due to their good insulation structure, economy, volume, and other advantages. However, in its long-term operation, the phenomena of error overshooting and unstable operation will inevitably occur, and the existing periodic off-line testing methods require a high calibration cycle, which is difficult to achieve. To solve these problems, an error state prediction method based on empirical mode decomposition and autoregressive moving average model is proposed. Firstly, according to the physical correlation between the secondary output signal and the error, the output signal is quantified as a statistic Q by the method of principal component analysis, and then the statistic is used as the prediction object to establish a prediction model for predicting the operating state of the electronic voltage transformer. The simulation results show that the model can accurately predict the trend of the transformer error, which meets the prediction requirements.

Online Trajectory Planning Control for a Class of Underactuated Mechanical Systems

Efficient control of underactuated systems has always been a great challenge, since one has to stabilize both actuated and unactuated states with fewer control inputs. During the past decades, great efforts have been devoted to improving the transient performance and robustness of the system, which are very crucial in plenty of application scenarios. To motivate corresponding research, an online trajectory planning method is proposed for a class of underactuated mechanical systems in this paper. Specifically, reference trajectories are first generated for actuated states. Based on that, appropriate online adjustments are made to these reference trajectories, so as to realize efficient stabilization of the unactuated states. Different from traditional off-line planning methods, the proposed one utilizes real-time feedback to counteract various unfavorable conditions, which endows it stronger robustness and better transient performance. Rigorous Lyapunov-based analysis is performed to show that the closed-loop system is asymptotically stable around the desired equilibrium point. Finally, the proposed method is applied to two underactuated mechanical systems, with simulation/experiment results provided to demonstrate its superior performance.

COPSRO: An Offline Empirical Game Theoretic Method With Conservative Critic.

This article studies how to learn approximate Nash equilibrium (NE) from static historical datasets by empirical game-theoretic analysis (EGTA), which provides a simulation-based framework to model complex multiagent interactions. Generally, EGTA requires plentiful interactions with the environment or simulator to estimate a cogent and tractable game model approximating the underlying game. However, these exploratory interactions often suffer from low data utilization efficiency and may not be feasible in risk-sensitive applications. To address these problems, this article investigates a new EGTA paradigm for offline settings and introduces a novel algorithm called conservative offline policy space response oracle (COPSRO) to identify NE from fixed datasets without active data collection. COPSRO initiates by extracting a set of strategies from the offline dataset to construct an overcomplete strategy population, achieving an approximation to the policy space of the original game. Then, COPSRO integrates the conservative critic (CC) to tackle the challenge of overestimation inherent in offline learning scenarios. Additionally, it devises the offline NE solver to iteratively compute approximate NE. Consequently, COPSRO can ascertain equilibrium strategies without real-world interaction, markedly enhancing its utility in risk-averse settings. This article provides both theoretical analysis and empirical evaluation to demonstrate the effectiveness and superiority of COPSRO across various real-world tasks in the offline setting. Our method surpasses existing approaches in terms of convergence and exploitability, especially when the coverage ration of dataset is low (20% or 10%).

Research on up and down line control method of 500kV live working robot

It is a developing trend for transmission line operation robots to replace manual operations in line operation and maintenance. However, the existing on-line methods of robots are complex and inefficient, which seriously affects the progress of the entire operation and maintenance. In order to solve this problem, this paper studies the automatic on-line and off-line control method of live working robot for 500kV transmission lines. Firstly, the on-line and off-line structure of the robot and its operating mechanism are analyzed, and on this basis, the relevant mathematical model is established. Through the analysis of the model, cascade fuzzy PID is used to control the robot’s attitude leveling. Finally, through MATLAB software simulation, the cascade fuzzy PID control effect is the best.

Optimalisasi Hasil Belajar Peserta Didik Melalui Pembelajaran Covid-19 Tema Pekerjaan di Kelas B1 Tk Negeri Percontohan Kota Payakumbuh Semester Genap Tahun Pelajaran 2020/2021

This study aims to see and analyze the improvement of student Learning Outcomes through Covid 19 Learning on the theme of work in Class B1 of the Pilot State Kindergarten of Payakumbuh City Even Semester TP 2020/2021, This research was conducted using the Classroom Action Research method which consists of two cycles, where each cycle consists of planning, action, reflection, and evaluation. It was carried out from January to March 2021. The data is processed in each cycle and then analyzed using percentages. The results obtained from this study are able to improve student learning outcomes. This is evidenced by the increase in student learning outcomes in the field of motoric development for 5 activity points. In the implementation of the pre-cycle the learning outcomes of students ranged from 30% to 50%, cycle I began to increase ranging from 50%, to 60% and in the implementation of cycle II the learning outcomes of students have experienced a better increase, ranging from 70% to 80% with the value category Developing As Expected (BSH). The conclusion obtained from this research is that through Covid Learning 19 Offline Methods can improve student learning outcomes. Keywords: Covid 19 learning, student learning outcomes, offline methods.

Online diagnosis of MVB termination fault based on expert system

As a train communication technology, multifunction vehicle bus (MVB) plays a vital role in the transmission of control and monitoring data. However, the termination fault on MVB bus equipment often occurs, which leads to the increase of bit error rate and packet loss rate, and seriously affects the communication quality and stable operation of the train. The traditional off-line diagnosis method has poor timeliness. Therefore, this paper proposes and implements an online diagnosis method of MVB termination fault based on expert system. Firstly, the obtained physical waveform data is preprocessed, and then the waveform characteristics are extracted. Finally, the expert system algorithm is used for diagnosis, so as to quickly and accurately detect the MVB termination fault. The experimental platform was built for online fault diagnosis test. The experimental results show that the diagnosis method has high accuracy and reliability, which is conducive to improving the diagnosis efficiency.

Net Power Optimization Based on Extremum Search and Model-Free Adaptive Control of PEMFC Power Generation System for High Altitude

The operating characteristics of proton exchange membrane fuel cell (PEMFC) generation systems will change in high altitude. In order to improve the system output performance, this paper proposes a net power optimization (NPO) based on extremum search and model-free adaptive control (MFAC) of PEMFC power generation system. A PEMFC power generation system model for high-altitude environment is established, the off-line analysis shows that with the increase of altitude, the performance of the air compressor decreases, the optimal oxygen excess ratio (OER) of the system decreases, and surge phenomenon is more likely to occur. Considering the non-minimum phase characteristic of the net power to OER, the OER is optimized online by extremum search strategy and the optimization step and cycle ensure the convergence of the strategy. Combined with a model-free adaptive control strategy based on compact form dynamic linearization (CFDL), the optimal OER is tracked real time, the data model identified online ensures the adaptability of the system. Finally, a hardware-in-the-loop platform is built and comparative experiment is carried out. The results show that: compared with the off-line optimization method (OOM), (1) NPO can improve the net power of the system at different altitudes through the adaptive adjustment of OER and no off-line data is required inappropriate static operating points are avoided. (2) in high altitude areas, NPO has higher operational stability and can effectively avoid surge phenomenon.

Online multimethod platform for comprehensive characterization of monoclonal antibodies in cell culture fluid from a single sample injection - Intact protein workflow

BackgroundTherapeutic monoclonal antibodies (mAbs) comprise a large structural variability with respect to charge, size and post-translational modifications. These critical quality attributes (CQAs) need to be assessed during and after the production of mAbs. This normally requires off-line purification and sample preparation as well as several chromatographic selectivities, which makes the whole process time-consuming and error-prone. To improve on this, we developed an integrated and automated multi-dimensional analytical platform for the simultaneous assessment of multiple CQAs of mAbs in cell culture fluid (CCF) from upstream processes. ResultsThe on-line system allows mAb characterization at the intact level, combining protein A affinity chromatography (ProtA) with size-exclusion, ion-exchange, and reversed-phase liquid chromatographic modes with UV and mass spectrometric detection. Multiple heart cuts of a single mAb elution band from ProtA are stored in 20-μL loops and successively sent to the multimethod options in the second dimension. ProtA loading and elution conditions and their compatibility with second-dimension LC modes were studied and optimized. Subsequently, heart-cutting and valve-switching schemes were investigated to achieve effective and reproducible analyses. The applicability of the developed workflow was demonstrated by the direct analysis (i.e. not requiring off-line sample preparation) of a therapeutic mAb in CCF, obtaining useful information on accurate molecular mass, glycosylation, and charge and size variants of the mAb product at the same time and in just over 1 h. SignificanceThe developed multidimensional platform is the first system that allows for multiple fractions from a single ProtA band to be characterized using different chromatographic selectivities in a single run allowing direct correlation between CQAs. The performance of the system is comparable to established off-line methods, fully compatible with upstream process samples, and provides a significant time-reduction of the characterization procedure.

Micro simulated moving bed chromatography-mass spectrometry as a continuous on-line process analytical tool.

Continuous manufacturing is becoming increasingly important in the (bio-)pharmaceutical industry, as more product can be produced in less time and at lower costs. In this context, there is a need for powerful continuous analytical tools. Many established off-line analytical methods, such as mass spectrometry (MS), are hardly considered for process analytical technology (PAT) applications in biopharmaceutical processes, as they are limited to at-line analysis due to the required sample preparation and the associated complexity, although they would provide a suitable technique for the assessment of a wide range of quality attributes. In this study, we investigated the applicability of a recently developed micro simulated moving bed chromatography system (µSMB) for continuous on-line sample preparation for MS. As a test case, we demonstrate the continuous on-line MS measurement of a protein solution (myoglobin) containing Tris buffer, which interferes with ESI-MS measurements, by continuously exchanging this buffer with a volatile ammonium acetate buffer suitable for MS measurements. The integration of the µSMB significantly increases MS sensitivity by removing over 98% of the buffer substances. Thus, this study demonstrates the feasibility of on-line µSMB-MS, providing a versatile PAT tool by combining the detection power of MS for various product attributes with all the advantages of continuous on-line analytics.

Prediction of systemic free and total valproic acid by off-line analysis of exhaled breath in epileptic children and adolescents.

Therapeutic drug monitoring (TDM) of medications with a narrow therapeutic window is a common clinical practice to minimize toxic effects and maximize clinical outcomes. Routine analyses rely on the quantification of systemic blood concentrations of drugs. Alternative matrices such as exhaled breath are appealing because of their inherent non-invasive nature. This is especially the case for pediatric patients. We have recently showcased the possibility of predicting systemic concentrations of valproic acid (VPA), an anti-seizure medication by real-time breath analysis in two real clinical settings. This approach, however, comes with the limitation of the patients having to physically exhale into the mass spectrometer. This restricts the possibility of sampling from patients not capable or available to exhale into the mass spectrometer located on the hospital premises. In this work, we developed an alternative method to overcome this limitation by collecting the breath samples in customized bags and subsequently analyzing them by secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS). A total ofn= 40 patients (mean ± SD, 11.5 ± 3.5 y.o.) diagnosed with epilepsy and taking VPA were included in this study. The patients underwent three measurements: (i) serum concentrations of total and free VPA, (ii) real-time breath analysis and (iii) off-line analysis of exhaled breath collected in bags. The agreement between the real-time and the off-line breath analysis methods was evaluated using Lin's concordance correlation coefficient (CCC). CCC was computed for ten mass spectral predictors of VPA concentrations. Lin's CCC was >0.6 for all VPA-associated features, except for two low-signal intensity isotopic peaks. Finally, free and total serum VPA concentrations were predicted by cross validating the off-line data set. Support vector machine algorithms provided the most accurate predictions with a root mean square error of cross validation of 29.0 ± 7.4 mg l-1and 3.9 ± 1.4 mg l-1for total and free VPA (mean ± SD), respectively. As a secondary analysis, we explored whether exhaled metabolites previously associated with side-effects and response to medication could be rendered by the off-line analysis method. We found that five features associated with side effects showed a CCC > 0.6, whereas none of the drug response-associated peaks reached this cut-off. We conclude that the clinically relevant free fraction of VPA can be predicted by this combination of off-line breath collection with rapid SESI-HRMS analysis. This opens new possibilities for breath based TDM in clinical settings.

Towards simultaneous determination of tablet porosity and height by terahertz time-domain reflection spectroscopy

The quality control of pharmaceutical tablets is still based on testing small sample numbers using at- and off-line testing methods. Traditional in-process controls, such as tablet mass, height, mechanical strength, and disintegration time are time- and resource-consuming and poorly suited to support an effective transition towards continuous manufacturing. Another suitable parameter to monitor during production would be tablet porosity. Porosity can be linked to mechanical strength and disintegration but typically requires knowledge of tablet dimensions and mass. Tablet porosity measurements based on terahertz time-domain spectroscopy (THz-TDS) offer a fast and non-destructive approach to in-process control testing for physical tablet properties. This study presents THz-TDS reflection measurements as an alternative to the previously reported transmission setup. It is shown that the proposed method can determine porosity based on the reflected amplitude from the tablet surface, but also allows for precise determination of tablet height in the same measurement. The tablet mass can be estimated by combining the height and porosity measurements. This opens up for the opportunity to determine the tablet's mechanical strength by using the possible correlation to the determined porosity.

An Off-Line Error Compensation Method for Absolute Positioning Accuracy of Industrial Robots Based on Differential Evolution and Deep Belief Networks

Industrial robots have been increasingly used in the field of intelligent manufacturing. The low absolute positioning accuracy of industrial robots is one of the difficulties in their application. In this paper, an accuracy compensation algorithm for the absolute positioning of industrial robots is proposed based on deep belief networks using an off-line compensation method. A differential evolution algorithm is presented to optimize the networks. Combined with the evidence theory, a position error mapping model is proposed to realize the absolute positioning accuracy compensation of industrial robots. Experiments were conducted using a laser tracker AT901-B on an industrial robot KR6_R700 sixx_CR. The absolute position error of the end of the robot was reduced from 0.469 mm to 0.084 mm, improving the accuracy by 82.14% after the compensation. Experimental results demonstrated that the proposed compensation algorithm could improve the absolute positioning accuracy of industrial robots, as well as its potential uses for precise operational tasks.