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The reliable application of field deployable fluorescent dissolved organic matter (fDOM) probes is hindered by several influencing factors which need to be compensated. This manuscript describes the corrections of temperature, pH, turbidity and inner filter effect on fluorescence signal of a commercial fDOM probe (fDOM s). For this, Australian waters with wide ranging qualities were selected, e.g. dissolved organic carbon (DOC) ranging from ∼1 to ∼30 mg/L, specific UV absorbance at 254 nm from ∼1 to ∼6 L/m/mg and turbidity from ∼1 to ∼ 350 FNU. Laboratory-based model calibration experiments (MCEs) were performed. A model template was developed and used for the development of the correction models. For each factor, data generated through MCEs were used to determine model coefficient (α) values by fitting the generated model to the experimental data. Four discrete factor models were generated by determination of a factor-specific α value. The α values derived for each water of the MCEs subset were consistent for each factor model. This indicated generic nature of the four α values across wide-ranging water qualities. High correlation between fDOM s and DOC were achieved after applying the four-factor compensation models to new data (r , 0.96, p < 0.05). Also, average biases (and %) between DOC predicted through fDOM s and actual DOC were decreased by applying the four-factor compensation model (from 3.54 (60.9%) to 1.28 (16.7%) mg/L DOC). These correction models were incorporated into a Microsoft EXCEL-based software termed EXOf-Correct for ready-to-use applications. [ABSTRACT FROM AUTHOR]

ABSTRACT Analyzing the relationship between cell morphology, rheological characteristics, and production dynamics of cultivations with filamentous microorganisms is a challenging task. The complex interdependencies and the commonly low reproducibility of heterogeneous cultivations hinder the bioprocess development of commercially relevant production systems. The present study aims to characterize process parameters in Actinomadura namibiensis shake flask cultures to gain insights into relationships between culture behavior and rheological characteristics during salt‐enhanced labyrinthopeptin A1 production. Plate–plate (PP) and vane–cup rheometer measurements of viscous model fluids and culture broths are compared, revealing a more uniform distribution of broth when measured with the PP system. Additionally, rheological characteristics and culture performance of A. namibiensis cultures are evaluated using online data of the specific power input and the oxygen transfer rate. It is demonstrated that salt‐enhancement labyrinthopeptin A1 production by the addition of 50 mM (NH4)2SO4 increases the apparent viscosity of the A. namibiensis culture by four‐fold and significantly reduces the reproducibility of the culture resulting in a 46 h difference in lag‐phase duration. This approach demonstrates that the culture behavior of complex filamentous cell morphologies is challenging to decipher, but online monitoring of rheology and oxygen transfer can provide valuable insights into the cultivation dynamics of filamentous microbial cultures. [ABSTRACT FROM AUTHOR]

In the field of aerospace, real-time and accurate monitoring of the milling state is of great significance for improving processing quality and processing efficiency. Relying on milling force signals to monitor tool wear state has problems such as high cost and limited size of machined parts. Aiming at the above problem, a milling cutter wear condition monitoring method based on spindle current is proposed. Firstly, based on the linear correlation between the milling force and the current increment theory, the milling experiment was carried out. The characteristic variation trend and spectral characteristics of the milling force signal and spindle current signal under the change of spindle speed, feed rate, and radial cutting width are analyzed. The cosine similarity demonstrates that the similarity of the root mean square values of the two is 0.9865, 0.9943, and 0.9421, respectively, and the changing trend of the spectral amplitude is consistent. In the iSESOL platform, a self-learning feature boundary model is built from learning data, signal interception, root mean square feature extraction, drawing boundary, and threshold judgment to monitor the wear state of the milling cutter online. Finally, the data in each wear stage are selected to verify the model, and the validity of the model is proved by setting the threshold beyond the boundary and the alarm trigger condition, in real-time, and accurately monitoring the wear state of the milling cutter online. [ABSTRACT FROM AUTHOR]

Industrial robot machining systems are used extensively in processing and manufacturing sectors including aerospace, large ships, rail transit, engineering machinery, semiconductor processing, medical equipment, and new energy vehicles. However, chatter during the machining process can considerably diminish the machining accuracy and surface quality. In severe cases, it can cause damage to the tool and the machining robot. Robot machining chatter research is required to improve the stability of the machining process for high-efficiency, high-quality robotic machining. This review comprehensively summarizes research progress in three areas: robot machining chatter mechanisms, chatter stability prediction and online monitoring, and chatter suppression strategies. First, the generation mechanisms of regenerative chattering and modal-coupling chattering are clarified. Second, the methods of chattering stability prediction and online monitoring of robot machining are summarized. The differences between passive suppression, semi-active suppression, and active suppression strategies are explained. Existing problems and future directions in robot machining chattering research are summarized and prospected. [ABSTRACT FROM AUTHOR]

The present study presents, for the first time, the real-time monitoring of an actual spiral-wound membrane unit used for CO 2 removal from natural gas in an actual industrial offshore platform, utilizing a detailed phenomenological model. An Object-Oriented Programming (OOP) paradigm was employed to simulate the offshore membrane separation unit, accounting for the diverse levels of the membrane separation setup. A parameter estimation procedure was implemented to fit the phenomenological model to the real industrial data in real-time, for the first time. In addition, estimated permeance parameters and calculated unmeasured variables (soft sensor) were used for monitoring Key Performance Indicators (KPIs), such as membrane selectivity, dew point temperature, and hydrocarbon loss. Finally, a reparametrization of the parameters was implemented to improve the robustness of the optimization procedure. Thus, the model variables presented good adjustments to the data, indicating the satisfactory performance of the estimation. Consequently, the good accuracy of the model provided reliable information to the soft sensors and KPIs. [ABSTRACT FROM AUTHOR]

Sequential change-point analysis, which identifies a change of probability distribution in an infinite sequence of random observations, has important applications in many fields. A good method should detect a change point as soon as possible, and keep a low amount of false alarms. As one of the most popular methods, Shiryaev-Roberts (SR) procedure holds many optimalities. However, its implementation requires the pre-change and post-change distributions to be known, which is not achievable in practice. In this paper, we construct a nonparametric version of the SR procedure by embedding different versions of empirical likelihood, assuming two training samples, before and after change, are available for parameter estimations. Simulations are conducted to compare the performance of the proposed method with existing methods. The results show that when the underlying distribution is unknown, and training sample sizes are small, the proposed modified procedure shows advantage by giving a smaller delay of detection. [ABSTRACT FROM AUTHOR]

Online monitoring of lanthanide fission product concentrations has been a great challenge in the pyrochemical reprocessing of spent nuclear fuels. Electrochemical techniques, with the advantages of accuracy and timeliness, provide a feasible strategy. In this work, the relationship between lanthanum ion (La(III)) concentration and reaction current was investigated by various electrochemical methods in LiCl–KCl eutectic salt. Tafel test results show that there is a linear relationship between the exchange current density (i0) and ion concentration (i0 = 0.06618x + 0.04409). In addition, according to the comparative analysis of electrochemical test results, it is found that anodic stripping voltammetry is a potential ion concentration detection technology. The peak current (ipc) and ion concentration satisfy the relationship: ipc = 0.08503x − 0.02245. According to the equation, the calculated La(III) concentration is almost consistent with that analyzed by inductively coupled plasma atomic spectrometer (ICP-AES), which provides a plausible way for ion concentration monitoring in high-temperature molten salt. [ABSTRACT FROM AUTHOR]

Copyright of Packaging & Food Machinery is the property of Packaging & Food Machinery Magazine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The operational status of geophones plays a pivotal role in ensuring the accuracy and reliability of microseismic monitoring systems. However, conventional techniques used to evaluate the operational status of geophones require human intervention or significant time delays. To address this issue, we propose a method for online monitoring of geophone status using observed data obtained from a microseismic system. First, the energy features of the preprocessed observation data are extracted via wavelet packet decomposition. Subsequently, the distribution parameters of energy features are obtained through log-logistic distribution fitting. These parameters are then applied to a change-point detection model, enabling the online monitoring of seismic geophones. In addition, we select a long short-term memory network to classify the operational status of the geophones, which is trained using the obtained energy distribution data and the time-frequency characteristics of the observed data. The experimental results indicate that the model achieves an accuracy of 98.33%, surpassing the 89.58% accuracy of the support vector machine. The proposed method not only contributes to online monitoring and precise determination of the operating status of detectors, but also has enormous application potential in other fields that require monitoring and evaluating the operating status of instruments. [ABSTRACT FROM AUTHOR]

The counting process has abundant applications in reality, and Poisson process monitoring actually has received extensive attention and research. However, conventional methods experience poor performance when shifts appears early and only small number of historical observations in Phase I can be used for estimation. To overcome it, we creatively propose a new online monitoring algorithm under the transfer learning framework, which utilizes the information from observations of additional data sources so that the target process can be described better. By making the utmost of the somewhat correlated data from other domains, which is measured by a bivariate Gamma distributed statistic presented by us, the explicit properties (e.g., posterior probability mass function, posterior expectation, and posterior variance) are also strictly proved. Furthermore, based on the above theoretical results, we design two computationally efficient control schemes in Phase II, that is a control chart based on the cumulative distribution function for large shifts and an exponentially weighted moving average control chart for small shifts. For a better understanding of the more practical applications and transferability matter, we provide some optimal values for parameter setting. Extensive numerical simulations and a case of skin cancer incidence in America verify the superiorities of our approach. [ABSTRACT FROM AUTHOR]

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Grinding process monitoring is key to assessing the intelligence of grinding processing and ensuring manufacturing quality of the intended product. The grinding force in the grinding process is an important characteristic, and the grinding force signal has a close relationship with the degree of wheel wear, grinding heat, grinding wheel and workpiece contact state, and other entities. Therefore, the analysis of the grinding force signal using the time domain and frequency domain analysis methods can effectively reveal the grinding characteristics and wheel wear evolution mechanism in the gear grinding process. In this study, the microcrystalline corundum grinding wheel was used to conduct the profile grinding test on 20CrMnTi steel gears installed in vertical machining centers. A high-performance general-purpose dynamometer was used to collect the discrete time series data of the grinding force in real time; a confocal laser microscope and scanning electron microscope were used to observe the wear characteristics of the microcrystalline wheel surface; finally, a micrometer was used to measure the thickness of the wheel’s end-top surface to assess the degree of wheel wear in a quick and simple way. To improve the precision, automation, and intelligence level of the gear grinding process considering the microcrystalline corundum grinding wheel, online monitoring of the grinding process and wear mechanism of the grinding wheel was performed. The intrinsic correlation between the time-frequency domain signals of the grinding force, the contact status of the grinding wheel and workpiece, and the wear degree of the grinding wheel was established. Thus, in this study, a new online monitoring method for monitoring the grinding process of the grinding wheel was proposed based on real-time force signals. Meanwhile, the wear mechanism underlying the morphology of microcrystalline corundum grinding wheels was characterized and analyzed using laser confocal microscopy and scanning electron microscopy. The results showed that the distribution pattern of waveform characteristic indicators such as kurtosis, waveform index, peak index, and pulse index in the time domain spectrum of the grinding force showed a distinct trend of varying cutting depth grinding state ≥ steady-state grinding state of a significantly worn grinding wheel ≥ steady-state grinding state of a lightly worn grinding wheel > noncontact state. However, the variation of the two waveform characteristic values of the grinding force, amplitude spectrum and pulse indicator under different grinding states is completely opposite to that of the time domain signals. After assessing the three-dimensional roughness of the working surface of the grinding wheel, it is observed that there is a slight decrease and a subsequent rapid increase in the roughness of the working surface of the wheel with the evolution of wheel wear. The polycrystalline structure of the microcrystalline corundum abrasive particles gives them the ability to update and sharpen along the microcrystalline interface, showing layer-by-layer cleavage peeling.

Objective With the rapid development and expansion of urban rail transit, the problem of noise pollution is increasingly prominent due to the continuous extension of train operation lifespan. This not only causes severe harm to passenger riding experience and health, but also directly affects the brand image and service quality of rail transit enterprises. Therefore, it is necessary to collect and monitor noise levels during train operation to implement targeted noise control measures, maximizing the effectiveness of noise mitigation efforts. Method With a combination of hardware and software resources such as noise sensors, STM32 microcontrollers, 4G transmission modules, Keil μVision5, Python, and OneNET cloud platform, an online noise monitoring system based on IoT (Internet of Things) is designed and developed specifically for urban rail transit trains. Result & Conclusion The system is successfully tested and validated on Kunming Metro Line 4, achieving real-time monitoring of train interior noise with advantages such as short sampling cycles, high data accuracy, and low data loss and latency rates. The system can issue timely alerts when noise levels exceed national standards, effectively meeting the design objectives. Additionally, the system can be applied to other environmental noise monitoring scenarios, providing reference for environmental noise treatment, enhancing the quality of life for residents, and promoting sustainable urban development.

Abstract A key aspect of sustainable bioeconomy is the recirculation of renewable, agricultural waste streams as substrates for microbial production of high-value compounds. One approach is the bioconversion of corn stover, an abundant maize crop byproduct, using the fungal maize pathogen Ustilago maydis. U. maydis is already used as a unicellular biocatalyst in the production of several industrially-relevant compounds using plant biomass hydrolysates. In this study, we demonstrate that U. maydis can grow using untreated corn stover as its sole carbon source. We developed a small-scale bioreactor platform to investigate U. maydis processing of corn stover, combining online monitoring of fungal growth and metabolic activity profiles with biochemical analyses of the pre- and post-fermentation residues. Our results reveal that U. maydis primarily utilizes soluble sugars i.e., glucose, sucrose and fructose present in corn stover, with only limited exploitation of the abundant lignocellulosic carbohydrates. Thus, we further explored the biotechnological potential of enhancing U. maydis´ lignocellulosic utilization. Additive performance improvements of up to 120 % were achieved when using a maize mutant with increased biomass digestibility, co-fermentation with a commercial cellulolytic enzyme cocktail, and exploiting engineered fungal strains expressing diverse lignocellulose-degrading enzymes. This work represents a key step towards scaling up the production of sustainable compounds from corn stover using U. maydis and provides a tool for the detailed monitoring of the fungal processing of plant biomass substrates. Graphical abstract

In atmospheric pressure chemical ionization mass spectrometry (APCI-MS), [M−3H+H2O]+ ions can deliver analyte-specific signals that enable direct analysis of volatile n-alkane mixtures. The underlying ionization mechanisms have been the subject of open debate, and in particular the role of water is insufficiently clarified to allow for reliable process analytics when the humidity level changes over time. This can be a problem, particularly in online monitoring, where analyte accumulation in the ion source can also occur. Here, we investigated the role of water during APCI-MS of volatile n-alkanes by changing the carrier gas for sample injection from a dry to a wetted state as well as by using 18O-labeled water. This allowed for a distinction between gaseous and surface-adsorbed water molecules. While adsorbed water seems to be responsible for the desired [M−3H+H2O]+ signals through surface reactions with the analyte molecules, gaseous water was found to promote the formation of CnH2n+1O+ of different (and analyte-independent) hydrocarbons, revealing a reaction with hydrocarbon species which accumulated in the ion source during continuous operation. At the same time, gaseous water competed with analyte molecules for ionization and thus suppressed the formation of alkyl (CnH2n+1+) and alkenyl (CnH2n−1+) ions. The results reveal a memory effect due to hydrocarbon adsorption, which may cause severe interpretation difficulties when the ionization chamber undergoes sudden humidity changes. The use of [M−3H+H2O]+ for n-alkane analysis in alkane/water mixtures can be facilitated by constantly maintaining high humidity and hence stabilizing the ionization conditions. [ABSTRACT FROM AUTHOR]

Battery pack provides the backup power supply for DC system of power substations. In the event of an AC power outage or other accidents, it is an important guarantee for the reliable operation of power substation. To prevent possible failures, batteries usually require careful maintenance. Common methods are online monitoring, condition assessments, and health management. Among these, model-based techniques are widely used for battery monitoring and prognostics optimization. Data-driven methods are a good alternative solution when no mathematical models are available. As substations develop towards intelligent and unmanned modes, this paper proposes an online battery monitoring and management system based on the "cloud-network-edge-end" Internet of Things (IoT) architecture. Firstly, advanced battery monitoring system based on IoT architecture is reviewed in depth. It provides basis for later designing. Secondly, the battery online monitoring and management system is designed considering functional requirements and data link. Designing functions include ledger management, basic battery information display, real-time display of battery monitoring data, and the visualization of battery alarm information. It can implement online monitoring and intelligent maintenance management for battery operating status. Finally, the designed and developed system is applied in a 110 kV offshore substation, mainly providing battery maintenance suggestions and fault alarm prompts. Typical results of ledger information management, key parameter monitoring and alarm prompt are presented. This verifies the effectiveness and convenience of IoT-based system for the monitoring and management of batteries. [ABSTRACT FROM AUTHOR]

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert–Huang transform (HHT) was applied to each source signal to obtain a joint time–frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%. [ABSTRACT FROM AUTHOR]

A key aspect of sustainable bioeconomy is the recirculation of renewable, agricultural waste streams as substrates for microbial production of high-value compounds. One approach is the bioconversion of corn stover, an abundant maize crop byproduct, using the fungal maize pathogen Ustilago maydis. U. maydis is already used as a unicellular biocatalyst in the production of several industrially-relevant compounds using plant biomass hydrolysates. In this study, we demonstrate that U. maydis can grow using untreated corn stover as its sole carbon source. We developed a small-scale bioreactor platform to investigate U. maydis processing of corn stover, combining online monitoring of fungal growth and metabolic activity profiles with biochemical analyses of the pre- and post-fermentation residues. Our results reveal that U. maydis primarily utilizes soluble sugars i.e., glucose, sucrose and fructose present in corn stover, with only limited exploitation of the abundant lignocellulosic carbohydrates. Thus, we further explored the biotechnological potential of enhancing U. maydis´ lignocellulosic utilization. Additive performance improvements of up to 120 % were achieved when using a maize mutant with increased biomass digestibility, co-fermentation with a commercial cellulolytic enzyme cocktail, and exploiting engineered fungal strains expressing diverse lignocellulose-degrading enzymes. This work represents a key step towards scaling up the production of sustainable compounds from corn stover using U. maydis and provides a tool for the detailed monitoring of the fungal processing of plant biomass substrates. [ABSTRACT FROM AUTHOR]

Online monitoring of public places could prevent the occurrence of violent actions, while the efficiency of utilized methods plays a key role in detecting abnormal events in a short time. Even though some methods have been dedicated to violence detection, all exploit raw video information. Since information is usually transmitted in a compressed format, utilizing available compressed domain data, which obviates the need for further feature extraction, is advantageous. This paper presents a compressed domain method through which residual information obtained by partial decoding is utilized as spatiotemporal features instead of other raw-domain time-consuming approaches. Using residual data enables us to save time in the laborious feature extraction step, which is one of the imperative factors in real-time applications. Furthermore, a new approach for accumulating similar adjacent residuals has been presented, which can considerably drop the number of processed frames for detection. Comparing the proposed methods with the latest state-of-the-art methods reveals their high effectiveness. The entire implementation of this work can be found at https://github.com/hnarges91/violence-detection. [ABSTRACT FROM AUTHOR]

The processing of used nuclear fuels and related materials is often complex and variable. The ability to quickly optimize conditions to the material being processed can aid in increasing efficiency and safety, but requires very quick determination of the conditions present in the feedstock, the process, and the product. Furthermore, accurate quantification of materials such as enriched uranium and plutonium aids in maintaining material accountancy and avoiding nuclear proliferation risks. Traditional analytical methods require process samples to be collected and analyzed in a laboratory, which often takes days to weeks. Online monitoring is suitable for collecting this information nearly instantaneously, enabling much faster optimization of the process or detection of material diversion. Online monitoring is also beneficial as it is typically based on robust and nondestructive analytical methods, so no material is removed as samples. This review examines online monitoring relevant to used nuclear fuel processing for the determination of both chemical and physical parameters. The chemical parameters include quantities such as concentration, isotopic composition, and speciation. These values are often well suited to spectroscopic or spectrometric measurements as they are fast, nondestructive, and easily implemented in an online manner. Physical quantities are often more varied and include temperature, pressure, tank fill levels, and others. Due to the specificity of these quantities, specialized instrumentation is often used. However, this instrumentation is often amendable to online monitoring. [ABSTRACT FROM AUTHOR]

The increasing use of collaborative robots in smart manufacturing, owing to their flexibility and safety benefits, underscores a critical need for robust predictive maintenance strategies to prevent unexpected faults/failures of the machine. This paper focuses on fault detection and employs multivariate operational data from a universal robot to detect anomalies or early-stage faults using test data from designed anomalous conditions and artificial-intelligence-based anomaly detection techniques called autoencoders. The performance of three autoencoders, namely, a multi-layer-perceptron-based autoencoder, convolutional-neural-network-based autoencoder, and sparse autoencoder, was compared in detecting anomalies. The results indicate that the autoencoders effectively detected anomalies in the examined complex and noisy datasets with more than 93% overall accuracy and an F1 score exceeding 96% for the considered anomalous cases. Moreover, the integration of trajectory change detection and anomaly detection algorithms (i.e., the dynamic time warping algorithm and sparse autoencoder, respectively) was proposed for the local implementation of online condition monitoring. This integrated approach to anomaly detection and trajectory change provides a practical, adaptive, and economical solution for enhancing the reliability and safety of collaborative robots in smart manufacturing environments. [ABSTRACT FROM AUTHOR]

Copyright of Metal Working (1674-165X) is the property of Metal Working Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rise of wire arc additive manufacturing (WAAM) is attributed to the various field applications of additive manufacturing (AM), especially in producing large metal structures. The wire arc additive manufacturing is a process that can produce near-net-shape structures with high deposition rates and relatively lower costs. Due to the nature of the WAAM process, which is based on layer-by-layer fabrication and repeated heating and cooling cycles, surface defects can occur on the deposited layer, such as pores, cracks, oxidation, and geometric deviation. In understanding the factors that can affect the stability and mechanism of the WAAM process, online monitoring systems has been studied by researchers. This paper aims to critically review the different monitoring techniques as well as distinct sensing methods that can be applied in the WAAM process to enhance the quality of the 3D object. In conducting this review paper, the systematic literature review (SLR) method is chosen to enhance a better understanding of the current research works of monitoring systems in the WAAM process. The high heat accumulation and cooling rate process are found to be the most crucial factors that could influence the geometrical accuracy, microstructure, and mechanical properties of the deposited layer. [ABSTRACT FROM AUTHOR]

This study delves into the scale-down optimization of the in situ cell detachment process for MA 104 cells cultivated on Cytodex 1 microcarriers (MCs). Through a systematic exploration, critical operational parameters—the agitation speed, incubation time, Trypsin–EDTA volume and corresponding activity, and washing steps—were identified as key factors influencing the efficiency and scalability of in situ cell detachment in microcarrier-based cell culture. Maintaining an appropriate agitation speed (1.25 × Njs, minimum agitation speed at which no microcarriers remain stationary for the signification period of 5 s), optimizing the Trypsinization incubation time (up to 60 min), and implementing multiple washing steps (two times) post-medium removal were found to be crucial for efficient cell detachment and subsequent growth. Our study demonstrates the feasibility of reducing the final Trypsin volume to 50 mL per gram of microcarrier while maintaining a Trypsin activity above 380 USP/mL. These conditions ensure complete cell dissociation and improve the cost effectiveness in large-scale productions. Additionally, we introduced real-time monitoring using a capacitance sensor during in situ cell detachment. This method has proven to be an effective process analytical technology (PAT) tool for tracking the cell detachment progress and efficiency. It allows for the prediction of cell detachment based on signals recorded between 3 and 7 min of Trypsinization, enabling rapid process decisions without the need for offline sampling, thereby enhancing the overall process control. This systematic approach not only optimizes in situ cell detachment processes but also has significant implications for the scalability and efficiency of microcarrier-based cell culture systems. [ABSTRACT FROM AUTHOR]

Cell line generation of mammalian cells is a time‐consuming and labor‐intensive process, especially because of challenges in clone selection after transfection. Antibiotics are common selection agents for mammalian cells due to their simplicity of use. However, the optimal antibiotic concentration must be determined with a kill curve experiment before clone selection starts. The traditional kill curve experiments are resource‐intensive and time‐consuming due to necessary sampling and offline analysis effort. This study, thus, explores the potential of online monitoring the oxygen transfer rate (OTR), as a non‐invasive and efficient alternative for kill curve experiments. The OTR is monitored using the Transfer‐rate Online Measurement (TOM) system and the micro(μ)‐scale Transfer‐rate Online Measurement (μTOM) device, which was used for mammalian cells first. It could be shown that the OTR curves for both devices align perfectly, affirming consistent cultivation conditions. The μTOM device proves effective in performing kill curve experiments in 96‐deep‐well plates without the need for sampling and offline analysis. The streamlined approach reduces medium consumption by 95%, offering a cost‐effective and time‐efficient solution for kill curve experiments. The study validates the generalizability of the method by applying it to two different CHO cell lines (CHO‐K1 and sciCHO) with two antibiotics (puromycin and hygromycin B) each. In conclusion, the broad application of OTR online monitoring for CHO cell cultures in 96‐deep‐well plates is highlighted. The μTOM device proves as a valuable tool for high‐throughput experiments, paving the way for diverse applications, such as media and clone screening, cytotoxicity tests, and scale‐up experiments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems of grinding wheel wear during high-speed cylindrical grinding, communication delays, and slow response during data acquisition, processing, and system operation, an intelligent online monitoring technology frame for CNC manufacturing units is proposed, incorporating a real-time-perception grinding mechanism and a cloud-edge device. Based on the grinding data and grinding wheel wear mechanism, a monitoring model using multi-sensor information fusion is constructed to assess the grinding wheel wear state. In addition, edge data acquisition and online monitoring software have been developed to improve the speed of data transmission and processing. Finally, based on the proposed framework, a cloud-edge device collaborative intelligent monitoring system for assessing grinding wheel wear during high-speed cylindrical grinding of bearing rings is constructed. It improves the grinding quality and efficiency, reduces the grinding cost, and incorporates remote control functionality. [ABSTRACT FROM AUTHOR]

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10-320 g L-1) and HNO3 (0.6-6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO3, and NO3 - of 3.7%, 3.6%, and 2.9%, respectively. PLS1models performed similarly despitemodeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO3). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments. [ABSTRACT FROM AUTHOR]

Automated fiber placement (AFP) in situ consolidation (ISC) of thermoplastic composite possess the potential to reduce manufacturing costs and improve manufacturing efficiency. The properties of composite manufactured by the ISC are affected by several mechanisms including polymer degradation, crystallization, intimate contact, polymer healing and void dynamics. All these mechanisms are directly affected by the temperature history. Consequently, the control and accurate measurement of temperature history during ISC are particularly important for improving the properties of composite. In this study, a simplified three-dimensional transient heat transfer model was established. The effect of tool temperature and placement speed on the temperature history and peak temperature were predicted. Simultaneously, an online temperature monitoring system was built and the optical Fiber Bragg Grating sensors (FBGS) was used to measure the temperature history. The results indicated that the predicted results of the model were consistent with the measured results, the error was below 8%. In addition, the temperature history of layers was significantly affected by the tool temperature and placement speed. The temperature of the layers decreased to near the tool temperature after cooling, and a higher tool temperature increasing its peak temperature because of the reduce of the cooling rate. On the contrary, an increase in placement speed will reduce the peak temperature of the layers. [ABSTRACT FROM AUTHOR]

With the rapid development of data acquisition and analysis technology, the online monitoring of data streams is particularly important in modern industries. This study proposes an efficient online monitoring scheme for multiple high-dimensional data streams. We first construct a point-wise process monitoring control chart based on statistical extreme value theory. Then we use the clustering method to roughly obtain reliable data streams and suspected abnormal data streams, within the sliding window range, further identify abnormal data streams one by one over time points, and estimate the potential change points of these abnormal streams. Finally, we conduct simulations and case studies on this method, and compare it with existing competitive methods to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Unmanned sensing vehicles (USVs) have been widely used for real-time anomaly detection in various applications, including environmental monitoring, precision agriculture, and military surveillance. The USVs collecting data can only provide partial information about the space being monitored. Thus, it is critical to decide where to deploy the USVs at each point in time to maximize the change detection capability, while minimizing deployment costs. This article proposes an adaptive sampling strategy for real-time anomaly detection with USVs. First, a novel spatio-temporal sequential tensor decomposition algorithm is developed to decompose the high-dimensional data collected by the USVs into three components, a spatial component, a temporal component, and a sparse component, that captures the locations suspicious of change. The spatial and temporal components are used for one-step prediction to guide the adaptive sampling strategy. The strategy is designed to maximize the detection power and control the deployment costs. The main idea is to balance exploration and exploitation by designing a sampling distribution function to decide where to collect data at each acquisition time. The movement of the USVs is controlled by using Voronoi tessellations on the sampling distribution function. The performance of the proposed framework is demonstrated through simulations and case studies. [ABSTRACT FROM AUTHOR]

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Anaerobic digestion (AD) relies on the cooperation of specific microbial communities, making it susceptible to process disruptions that could impact biogas production. In this regard, this study presents a technological solution based on the Arduino platform, in the form of a simple online monitoring system that can track the produced biogas profile, named as biogas analyzer module (BAM). The applicability of the BAM focused on monitoring the biogas produced from sugarcane vinasse inoculated with sewage sludge biodigestion processed in mesophilic conditions (38 oC), in a pH range of 6.5–7.5, and following a three-stage operational model: (i) an adaptation (168 h), (ii) complete mixing (168 h), and (iii) bio-stimulation with glycerol (192 h). Then, the lab-made BAM was used to trace the produced biogas profile, which registered a total biogas volume of 8,719.86 cm3 and biomethane concentration of 95.79% (vol.), removing 90.8% (vol) of carbon dioxide (CO2) and 65.2% (vol) of hydrogen sulfide (H2S). In conclusion, the results ensured good accuracy and efficiency to the device created by comparisons with established standards (chromatographic and colorimetric methods), as well as the cost reduction. The developed device would likely be six times cheaper than what is available in the market. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: An important step in replacing petrochemical products with sustainable, cost-effective alternatives is the use of feedstocks other than, e.g., pure glucose in the fermentative production of platform chemicals. Ustilaginaceae offer the advantages of a wide substrate spectrum and naturally produce a versatile range of value-added compounds under nitrogen limitation. A promising candidate is the dicarboxylic acid malic acid, which may be applied as an acidulant in the food industry, a chelating agent in pharmaceuticals, or in biobased polymer production. However, fermentable residue streams from the food and agricultural industry with high nitrogen content, e.g., sugar beet molasses, are unsuited for processes with Ustilaginaceae, as they result in low product yields due to high biomass and low product formation. Results: This study uncovers challenges in evaluating complex feedstock applicability for microbial production processes, highlighting the role of secondary substrate limitations, internal storage molecules, and incomplete assimilation of these substrates. A microliter-scale screening method with online monitoring of microbial respiration was developed using malic acid production with Ustilago trichophora on molasses as an application example. Investigation into nitrogen, phosphate, sulphate, and magnesium limitations on a defined minimal medium demonstrated successful malic acid production under nitrogen and phosphate limitation. Furthermore, a reduction of nitrogen and phosphate in the elemental composition of U. trichophora was revealed under the respective secondary substrate limitation. These adaptive changes in combination with the intricate metabolic response hinder mathematical prediction of product formation and make the presented screening methodology for complex feedstocks imperative. In the next step, the screening was transferred to a molasses-based complex medium. It was determined that the organism assimilated only 25% and 50% of the elemental nitrogen and phosphorus present in molasses, respectively. Due to the overall low content of bioavailable phosphorus in molasses, the replacement of the state-of-the-art nitrogen limitation was shown to increase malic acid production by 65%. Conclusion: The identification of phosphate as a superior secondary substrate limitation for enhanced malic acid production opens up new opportunities for the effective utilization of molasses as a more sustainable and cost-effective substrate than, e.g., pure glucose for biobased platform chemical production. [ABSTRACT FROM AUTHOR]

This paper aims to detect anomalous changes in social network structure in real time and to offer early warnings by phase II monitoring social networks. First, the exponential random graph model is used to model social networks. Then, a test and online monitoring technique of the exponential random graph model is developed based on the split likelihood-ratio test after determining the model and its parameters for a specific data set. This proposed approach uses pseudo-maximum likelihood estimation and likelihood ratio to construct the test statistics, avoiding the several steps of discovering Monte Carlo Markov Chain maximum likelihood estimation through an iterative method. A bisection algorithm for the control limit is given. Simulations on three data sets Flobusiness, Kapferer and Faux.mesa.high are presented to study the performance of the procedure. Different change points and shift sizes are compared to see how they affect the average run length. A real application example on the MIT reality mining social proximity network is used to illustrate the proposed modelling and online monitoring methods. [ABSTRACT FROM AUTHOR]

In this study, we introduce an online monitoring procedure designed to sequentially detect change points in the conditional quantiles of location‐scale time series models. This statistical process control issue holds great significance in risk management, particularly in measuring the value‐at‐risk or expected shortfall of financial assets. Our approach employs suitable detectors, including cumulative sum statistics. We then define a stopping rule and determine control limits based on asymptotic theorems to signal an anomaly. To further evaluate the proposed methods, we conduct a comprehensive empirical study analyzing various aspects of our monitoring procedures when applied to location‐scale time series models. Additionally, we perform a real data analysis using the daily returns of the Korea Composite Stock Price Index (KOSPI) and EuroStoxx 50 indices to affirm the adequacy of the proposed monitoring procedures in real‐world applications. [ABSTRACT FROM AUTHOR]

In large-scale dairy farming, the use of high-temperature-fermented dairy manure bedding instead of rice husk-based bedding and other commercial types of bedding is widely favored. Strip-stacking aerobic fermentation is the main production method of dairy manure bedding, but it has problems including unstable fermentation and the secondary breeding of pathogens. In this work, a multi-probe, integrated, online monitoring system for temperature and relative humidity was used for fermentation process optimization. The effects of the temporal and spatial distribution of fermentation temperature and relative humidity on the nutrient content curve and the moisture and ash content of manure bedding materials were systematically studied. The effect of the fermentation process on the retention rate of effective bedding materials (cellulose, hemicellulose, and lignin) was analyzed. The experiments proved that high-quality bedding material can be obtained through reasonable stacking fermentation. The fabricated bedding material has a total dry base content consisting of cellulose, hemicellulose, and lignin of 78%, an ash content of 6%, and a nutrient content of 17%. The obtained bedding material was produced to increase the bed rest rate and continuously inhibit the bedding bacteria content, keeping it at a low level for 5 days. This study proves that temperature and humidity monitoring can guide the optimization of the strip-stacking fermentation process of dairy manure and that it can be applied to large-scale farms to improve fermentation parameters. [ABSTRACT FROM AUTHOR]

Plasma fluctuations, the uncertainty of laser ablation, and bremsstrahlung limit the identification of online element analysis during laser processing and cause difficulty in achieving concentration results with sufficient accuracy and repeatability. A laser‐induced breakdown spectroscopy (LIBS) online monitoring system with plasma spatial filtering and spectral screening is proposed to solve this problem. In this system, the high‐frequency ablation noise component of the plasma is eliminated using a specially designed optical Fourier filtering structure, and a spectral screening system based on plasma time waveform monitoring is used to suppress the influence of plasma fluctuations. Without noise filters or algorithm optimizations and based only on the basic internal standard method, the calibration curves of all nine elements in the alloy sample exhibits a Pearson's R2 value ranged from 0.91 to 0.99, with a mean of 0.94. The relative standard deviations are all in the range of 3.5%–8.4% with a mean of 5.4%. The accuracy and repeatability are comparable to those of typical LIBS systems. [ABSTRACT FROM AUTHOR]

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Abstract This work presents a customized and configurable solution for the online monitoring and diagnosis of power transformers that is already installed in the field and provides the main diagnostics of the asset, such as, aging acceleration factor, hotspot, apparent power, gas generation rate in the oil, temperature for the formation of free water in the oil, for blistering and hot spot formation, water content in the paper. The focus of this article is to describe the embedded system, which covers the hardware part, the information from the sensors installed for asset monitoring and communication with the server, as well as the development of the Web application of the monitoring system, based on the microservices architecture. The solution consists of the analog signal acquisition modules, RS-485 communication module, and digital inputs and outputs module, controlled by the NI central processing unit, CompactRIO through the NI LabVIEW development environment. The embedded system program is based on the MQTT protocol and the "Actor Framework" architecture, where "actors" responsible for the operations within the implemented logic are defined. The access or sending of information is done through messages between the actors, one of whom is responsible for managing the exchange of data between the others. The main focus of this structure is to receive the information from the sensors and link it to a "topic", where the algorithm allows directing this information to the registered senders. This work is part of the ANEEL R&D project to Campolarguense Energy Company - COCEL.

Background The recycling of spent fuel can increase the longevity and decrease the waste footprint associated with the nuclear fuel cycle. However, nuclear fuel reprocessing introduces the risk of proliferation of fissile material. For example, the recycling of uranium (U) and plutonium (Pu) is generally accomplished via a solvent extraction process. The separation efficiency of the process is dependent on a variety of parameters such as acid concentration, organic to aqueous (O/A) volume ratio, number of separation stages, etc. A change in any of these parameters could result in different extraction conditions of fissile material and lead to a process upset with potential safeguards and proliferation concerns. Thus, the ability to monitor the solvent extraction process in real-time would enable the rapid identification and diagnosis of process upsets. Methods In this work, we discuss the development of a digital twin of a surrogate small-scale nuclear fuel reprocessing solvent extraction process utilizing online optical spectroscopic monitoring, machine learning (ML), and chemical modeling. A cascade of centrifugal contactors was employed with the TRUEX (TransUranic Extraction) solvent and surrogate minor actinides. Spectroscopic sensors were placed at strategic locations within the contactor bank and were used to predict TRUEX surrogate analyte concentrations at those locations. Results A solvent extraction digital twin consisting of the data acquisition, chemical model, and anomaly detection, provided expected concentrations at each contactor during normal operations. The digital twin demonstrated the ability to accurately simulate the extraction behavior under ideal conditions. Furthermore, the digital twin’s ML anomaly detection algorithm was shown to be successful in identifying several non-ideal process upset conditions.