Search

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]

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]

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]

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]

Copyright of Advances in New & Renewable Energy is the property of Editorial Office of Advances in New & Renewable Energy 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.)

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]

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 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]

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]

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING 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.)

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

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]

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]

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]

ObjectiveThe weak deformation signal generated by propeller thrust on a shaft system is easily overshadowed by interfering noise, making it challenging to accurately measure the thrust. Therefore, this study proposes a new method for accurately measuring the thrust of propellers under low signal-to-noise ratio conditions. MethodsBy taking into consideration the influence of measurement data errors and the underlying mechanical mechanisms, a thrust identification model is established based on a combination of Kalman filtering, and the thrust-displacement state equation. Thrust measurement research is conducted under three different operating conditions: constant rotational speed, variable rotational speed and low-frequency fluctuating rotational speed. The precision and robustness of the proposed method are then analyzed under different signal-to-noise ratios.ResultsThe results demonstrate that even at a signal-to-noise ratio of only 20 dB, the maxi-mum relative error in thrust identification is only 3.56%. Thus, the proposed method exhibits highly accurate and robust thrust identification performance under low signal-to-noise ratio conditions. Conclusion The findings of this study can provide valuable insights for the real-time and high-precision monitoring of propeller thrust and shaft system status in actual ship environments.

With the continuous advancement of agricultural Internet of Things (IoT) technologies, real-time monitoring of agricultural environments has become increasingly significant. This monitoring provides valuable information on pest and disease occurrences and corresponding ecological conditions. However, as the agricultural environments have extensive coverage, the number of monitoring IoT devices and the volume of information will rapidly increase. This leads to a surge in network traffic and computing demands. To address this issue, this article proposes an agricultural environmental monitoring IoT system that utilizes edge computing and deep learning technologies. It combines the Long-Range Wide Area Network (LoRaWAN) for long-range transmission with pest recognition and counting modules. By offloading workloads traditionally processed in the cloud to edge nodes, the proposed system effectively reduces transmission and cloud computing pressures for the agricultural monitoring IoT. Simulation experiments demonstrate stable LoRaWAN protocol-based data transmission at the edge, with an overall packet loss rate of less than 5%, meeting the transmission quality requirements. Moreover, this article investigates a pest recognition and counting method based on deep learning technology. Pest images, captured by monitoring nodes, are recognized and counted online using the TensorFlow framework. Experimental results indicate an accuracy of 89% in pest recognition. By digitally transmitting pest image recognition results to the cloud, the proposed system significantly alleviates transmission and cloud computing pressures for the monitoring IoT.

In the process of exoskeleton design, the evaluation of assistance performance directly impacts the overall structural safety and efficiency. Addressing the current issue of predominantly utilizing single metrics for performance evaluation, a method based on multi‑source physiological signals (surface electromyography, photopretismography, and respiration) for LSTM prediction of motion energy consumption was proposed. This method involves preprocessing and feature extraction of physiological signals, followed by prediction using a 6‑layer LSTM model and validation through K-fold cross‑validation. Comparative experiments with DT and SVM were conducted. Finally, an online energy consumption monitoring system was established, serving as a basis for evaluating exoskeleton assistance performance. Results indicate the feasibility of utilizing multi‑source physiological signals for fusion prediction, with an RMSE of 0. 073 kJ for the three‑source signal. The LSTM model achieves a 39. 53% and 15. 68% reduction in RMSE compared to DT and SVM, respectively. The total energy consumption error on the test set is 23. 98 kJ, demonstrating the superior performance of the LSTM model for total energy consumption prediction and its suitability for exoskeleton assistance performance evaluation. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive instrumental analysis experiment featuring online monitoring of 3,5- diamino-1,2,4-triazole (DAT) synthesis via Raman spectroscopy and the exploration of the reaction mechanism. Highlighting a synthesis that integrates instrumental analysis with scientific research, the experiment encompasses DAT synthesis, online reaction monitoring, and mechanism analysis through Raman spectroscopy. By employing chemometric techniques for the analysis of Raman spectral data, the study offers profound insights into the synthesis process, deepening the understanding of the reaction mechanism. This experiment reinforces students’ chemical knowledge, improves their practical laboratory skills, ignites their passion for scientific investigation, and develops their capabilities for research and inquiry. [ABSTRACT FROM AUTHOR]

An attempt has been made to develop a non-intrusive and robust technique for online detection of roping in hydrocyclones using commercially available vibration-based sensors. A 50.8 mm diameter hydrocyclone was operated with two different silica slurry size distributions till roping was achieved in the underflow. Vibration signals were captured simultaneously at the spigot region for various operating conditions. Empirical Mode Decomposition (EMD) was applied to the raw vibration signal for extracting Intrinsic Mode Function (IMF) within the frequency range of the natural frequency of the hydrocyclone. Subsequently, Power Spectral Density (PSD) and frequency spectrum were computed along the axial direction. The Grms (square root of the area under PSD) and peak frequency values were found to be different for the spray and rope discharge which were used for detecting roping conditions. The differences were considered to be associated with the changes in the axial velocity fluctuations of the air core at the spigot region. Further, a flowchart has been proposed based on the methodology suggested in this work, which has the potential for online detection of roping in the hydrocyclone. [ABSTRACT FROM AUTHOR]

This study considers the online monitoring problem for detecting the parameter change in time series of counts. For this task, we construct a monitoring process based on the residuals obtained from integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) models. We consider this problem within a more general framework using martingale difference sequences as the monitoring problem on GARCH-type processes based on the residuals or score vectors can be viewed as a special case of the monitoring problems on martingale differences. The limiting behavior of the stopping rule is investigated in this general set-up and is applied to the INGARCH processes. To assess the performance of our method, we conduct Monte Carlo simulations. A real data analysis is also provided for illustration. Our findings in this empirical study demonstrate the validity of the proposed monitoring process. [ABSTRACT FROM AUTHOR]

The statistical anionic copolymerization of 4‐trimethylsilylstyrene (TMSS) with isoprene (I) in cyclohexane was investigated using in situ near‐infrared (NIR) spectroscopy in the presence of various amounts of the polar modifier tetrahydrofuran (THF). Polymers with narrow molecular weight distribution of 85–138 kg/mol and dispersities of 1.09–1.22 were obtained. By increasing modifier content, the reactivity ratios can be adjusted over a wide range from rTMSS < rI to rTMSS >> rI. Compared to the system styrene/isoprene (S/I) only a minute amount of modifier (0.5 eq THF relative to lithium) is sufficient to alter the reactivity ratios, resulting in an ideally random copolymerization, which validates the higher reactivity of TMSS compared to styrene. Using these reactivity ratios, molar and volume composition gradients were calculated. Additionally, the glass transition temperature and microstructure of the polyisoprene units were investigated via differential scanning calorimetry and proton nuclear magnetic resonance. The results are encouraging for the use of these materials in high‐end applications like membranes. [ABSTRACT FROM AUTHOR]

Sudden pollution incidents in surface water have occurred frequently and caused serious environmental and social impacts, which raised new requirements and challenges towards to environmental supervision departments. Online monitoring data of surface water quality have features of high-frequency and high time-efficiency. The recent research status and progress of water-quality prediction models with online time-series monitoring data were summarized, including data soft measurement, preprocessing methods, and water-quality prediction models. The problems in the practical applications of the relevant water-quality prediction models were analyzed. Also, the future research directions with respect to water-quality prediction are proposed. The aim of this work is to provide reference and technology support for water-quality prediction and warning, and environmental supervision. [ABSTRACT FROM AUTHOR]

Dry-type air-core shunt reactors are used for reactive power compensation and suppression of power frequency overvoltage. The number and capacity in the power grid are increasing day by day, but the accident rate is high. Among them, the ratio of interturn insulation faults is the highest, and effective online monitoring means of winding insulation state are urgently needed. The model of dry-type air-core shunt reactor was carried out by using finite element software. By analyzing the characteristics of electrical, magnetic and force changes of short circuit between different turns and the same turn of the winding, the development process and formation mechanism of short circuit between turns of the winding were studied, and the sensitive state variables convenient for fault monitoring were explored, and an on-line monitoring method of winding insulation state based on the change rate of active power loss was proposed. The results show that the effect of the short-circuit between the different turns of the winding on the physical parameters such as electricity, magnetism and force of the reactor is greater than that of the short-circuit condition at the same position with the same turns, and the effect of the short-circuit between the different turns of the winding occurs at the end of the reactor winding is the greatest. Active power loss is sensitive to the process of reactor winding insulation fault development, and it is easy to monitor. By selecting appropriate insulation fault early warning value and insulation failure value, the state can be judged based on the change rate of active power loss, which can find reactor winding insulation fault in real time, facilitate timely measures and prevent the fault from spreading. [ABSTRACT FROM AUTHOR]

Online monitoring and real-time feedback on inclusions in molten metal are essential for metal quality control. However, existing methods for detecting aluminum melt inclusions face challenges, including interference, prolonged processing times, and latency. This paper presents the design and development of an online monitoring system for molten metal inclusions. Initially, the system facilitates real-time adjustment of signal acquisition parameters through a multiplexer. Subsequently, it employs a detection algorithm capable of swiftly extracting pulse peaks, with this task integrated into our proprietary host computer software to ensure timely detection and data visualization. Ultimately, we developed a monitoring device integrated with this online monitoring system, enabling the online monitoring of the aluminum alloy filtration process. Our findings indicate that the system can accurately measure the size and concentration of inclusions during the filtration process in real time, offering enhanced detection speed and stability compared to the industrial LiMCA CM (liquid metal cleanliness analyzer continuous monitoring) standard. Furthermore, our evaluation of the filtration process demonstrates that the effectiveness of filtration significantly improves with the increase in inclusion sizes, and the synergistic effect of combining CFF (ceramic foam filter) and MCF (metallics cartridge filter) filtration methods exceeds the performance of the CFF method alone. This system thus provides valuable technical support for optimizing filtration processes and controlling inclusion quality. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design 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.)

Improving the reliability of the transmission gear module in the gearbox of a nuclear power plant is of considerable and far-reaching significance to ensure energy security. As a key transmission part of this specific type of power plant, this gear module often runs with heavy load in complex environments and without shutting down for a long time. In such severe working conditions, the failure of this transmission gear module is primarily caused by the deterioration of lubricating oils and the aggravated wear of gears. Once the gear module is damaged owing to the poor lubrication or serious wear of the gear, it will not only cause huge power loss, but also nuclear power accidents, seriously threatening the life of the staff. It is essential that the transmission gear module used in a nuclear power plant is not arbitrarily shut down, which makes testing and monitoring difficult. Moreover, it is very important to monitor the lubricating state of oils in the gearbox in real time by measurements with various suitable high-tech sensors. However, there were few studies concerning this issue. To solve this problem, we first designed and built an experimental test platform to simulate the real working conditions of the transmission gear module in the gearbox of a nuclear power plant. Second, an innovative online oil monitoring method was proposed on the basis of the concept of marginal values of the normal distribution theory in statistics. Finally, a real-time prediction model based on the gray system theory was established to predict the variation of the dielectric constant of lubricating oils. [ABSTRACT FROM AUTHOR]

The accurate online detection of laser welding penetration depth has been a critical problem to which the industry has paid the most attention. Aiming at the laser welding process of TC4 titanium alloy, a multi-sensor monitoring system that obtained the keyhole/molten pool images and laser-induced plasma spectrum was built. The influences of laser power on the keyhole/molten pool morphologies and plasma thermo-mechanical characteristics were investigated. The results showed that there were significant correlations among the variations of the keyhole–molten pool, plasma spectrum, and penetration depth. The image features and spectral features were extracted by image processing and dimension-reduction methods, respectively. Moreover, several penetration depth prediction models based on single-sensor features and multi-sensor features were established. The mean square error of the neural network model built by multi-sensor features was 0.0162, which was smaller than that of the model built by single-sensor features. The established high-precision model provided a theoretical basis for real-time feedback control of the penetration depth in the laser welding process. [ABSTRACT FROM AUTHOR]

This paper presents a digital twin-assisted online monitoring and control system for machining operations. Process information including Cutter-Workpiece Engagement (CWE) maps are extracted from the virtual model of the process and fused with the CNC internal data as well as the measured external sensor data. Several applications are illustrated to demonstrate the system. Adaptive control of cutting load based on directly measured forces or reconstructed forces from an accelerometer-integrated tool holder is demonstrated. The spindle current data integrated with CWE information from the virtual model is utilized for the estimation of tool wear progression and tool breakage detection. Digital twin-assisted chatter detection and avoidance are presented by avoiding false alarms at the cutter's entry into and exit from work material which triggers transient vibrations. It is shown that by utilization of the digital twin data and information fusion, robust process monitoring and control can be achieved while anomalies and false alarms are avoided. [ABSTRACT FROM AUTHOR]

The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers. Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. The model is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) µm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity. [ABSTRACT FROM AUTHOR]