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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

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.

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.

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. PLS1 models performed similarly despite modeling 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.

Background and purposeThe purpose of this study was to assess the dosimetric improvements achieved in prostate stereotactic body radiotherapy (SBRT) treatment within the PROMETHEUS and NINJA trials using an in-house real-time position monitoring system, SeedTracker.Methods and materialsThis study considered a total of 127 prostate SBRT patients treated in the PROMETHEUS (ACTRN12615000223538) and NINJA (ACTRN12618001806257) clinical trials. The SeedTracker position monitoring system was utilized for real-time position monitoring with a 3-mm position tolerance. The doses delivered to the clinical target volume (CTV), rectum, and bladder were assessed by incorporating the actual target position during treatment. The dose that would have been delivered without monitoring was also assessed by incorporating the observed position deviations.ResultsTreatment with position corrections resulted in a mean (range) CTV D99 difference of −0.3 (−1.0 to 0.0) Gy between the planned and delivered dose. Without corrections, this difference would have been −0.6 (−3.7 to 0.0) Gy. Not correcting for position deviations resulted in a statistically significant difference between the planned and delivered CTV D99 (p < 0.05). The mean (range) dose difference between the planned and delivered D2cc of the rectum and bladder for treatment with position corrections was −0.1 (−3.7 to 4.7) Gy and −0.1 (−1.7 to 0.5) Gy, respectively. Without corrections, these differences would have been −0.6 (−6.1 to 4.7) Gy and −0.2 (−2.5 to 0.9) Gy.ConclusionsSeedTracker improved clinical dose volume compliance in prostate SBRT. Without monitoring and corrections, delivered dose would significantly differ from the planned dose.

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.

The real-time transmission of online monitoring data for high-voltage transmission equipment in remote areas is a difficult problem in practical applications due to the lack of network. For this purpose, the article designs an online monitoring system for high-voltage transmission equipment based on ubiquitous IoT and LoRa technology. It constructs a low-power, long-distance LoRa wireless communication self-organizing network, transmits the transmission equipment status information and other data collected by front-end sensors to the LoRaWAN gateway, and then uploads the data to the back-end server through a 5G wireless network to achieve the overall communication link of the online monitoring system. The simulation experiment results show that compared with the existing technical solutions ZigBee, LoRa, and NB-IoT, using the scheme designed in the article, the system transmission speed (20-100 kbit/s) is the fastest, and the packet loss rate, transmission delay, and average time consumption (1.6%, 0.27s, and 60s, respectively) are the lowest. This indicates that the system not only has a fast transmission speed but also takes into account the characteristics of low power consumption and long distance, achieving stable data transmission, and is suitable for online monitoring of high-voltage transmission equipment in remote areas.

This article presents a novel scheme for condition monitoring of dc-link capacitors in modular multilevel converters (MMCs). The proposed solution uses estimated capacitance values of the dc-link capacitors for indicating their state-of-health (SoH). Moreover, a comparative approach is proposed, where the estimated capacitances of all submodule capacitors are used to separate parameter drifts caused by aging from parameter drifts caused by other factors such as temperature change. It is shown in simulation and experimental results that an equal drift in all capacitance estimates can be a result of factors other than aging. However, a drift in the capacitance of one capacitor compared to the average capacitance of all submodules may be attributed to aging of that specific unit. Using the proposed comparative technique, there is no need for additional temperature sensors to account for the effect of temperature variations on the online estimations. Simulation and experimental results demonstrate an overall estimation error of less than 1% when applying the proposed comparative technique.

The increase in ${R} _{DUT}$ , the on-state resistance of power semiconductor devices, is used to estimate the remaining useful life (RUL) of the devices. Conventional online measurement of $R_{DUT}$ involves on-state voltage measurement of the on-state device, which is easily polluted by switching noise to limit the measurement accuracy. This paper proposes a new noise-robust method to measure the increment of $R_{DUT}$ by inductor current and input/output DC voltage of DC-DC converters. The proposed method features higher accuracy in predicting RUL than the conventional one because the inductor current is immune to switching noise. The aging of MOSFET is emulated in this paper by adding a series resistor $R_{ext}$ . Experiments showed that the resistance increment measured by the proposed method is up to 99.2% accurate to value of $R_{ext}$ .

Along with the development of science and technology, chemical reactions exhibit an important role in various fileds including synthesis, chemical engineering, energy, environment, biology and medicine. The effective intermediates monitoring and mechanism exploration of chemical reactions can promote their development and contribute to the design of new chemical reactions. However, in the complex reaction matrix, a variety of intermediates with different molecular weights, life and dynamic changes will be generated at different reaction conditions, the effective capture and structure determinations of the short-lived and active reaction intermediates are hard to be achieved. In recent years, ambient mass spectrometry (AMS) has been widely used in the detection of chemical intermediates to explore reaction mechanisms, due to its advantages of high sensitivity, high selectivity, rapid analysis and in-situ monitoring. Herein, the research progress and development of AMS for the detection of reaction intermediates were reviewed, which involved the reactions in the fields of electrochemistry, photochemistry, plasma chemistry and microdroplet chemistry. For different types of reactions, the designs of different AMS detection technologies were introduced in details. Upon the different ionization strategies, different kinds of techniques have been reported, including desorption electrospray ionization mass spectrometry (DESI-MS), nano-electrospray ionization mass spectrometry (nESI-MS), extraction electrospray ionization mass spectrometry (EESI-MS), Venturi easy ambient sonic spray ionization mass spectrometry (VEASI-MS), spray-dependent plasma mass spectrometry (SDP-MS), accelerated plasma degradation coupled with electrospray ionization mass spectrometry (APD-MS), field-induced droplet ionization mass spectrometry (FIDI-MS) and so on. Subsequently, the progress and application of these mass spectrometry techniques in capturing intermediates of different kinds of reactions as well as the exploration of mechanisms were reviewed. Finally, the development of mass spectrometry for the detection of transient reaction intermediates in different chemical reactions, including electrochemical reactions, photochemical reactions, plasma reactions and microdroplet reactions were summarized and prospected. As reviewed, based on the characteristics of high throughput virtual screening and online monitoring, as well as the diversification of ion sources, AMS would be widely used in various fields including industrial production, environmental protection, energy and so on. In the future, the improved AMS techniques will be encouraged for examining more highly active intermediates with the increasing requirements for the examination of complicated reactions.

The study of catalytic reaction mechanism is a critical and extremely challenging project. Online monitoring of reactants, intermediates and products during catalytic reactions can provide key evidence for the in-depth study of complex catalytic reaction networks, reaction pathways and reaction kinetics. As an online mass spectrometry based on soft ionization technique, vacuum ultraviolet photoionization mass spectrometry (VUV-PIMS) has proven itself as a versatile and powerful analytical technique for online and real-time process monitoring, which has gradually become a highly concerned characterization technique for catalytic reaction processes due to its advantages of high molecular ion yield, simple spectrum interpretation and high sensitivity. Compared with other commonly used synchrotron radiation and laser-based VUV light sources, the low pressure discharge lamps filled with various rare gases are compact, easy to operate, and low cost, such as krypton discharge lamps. However, the relatively low photon intensity of these lamps limits their applications. The efforts have been taken to develop stronger VUV light sources or lamp-based efficient combined ion sources to solve this issue. In this paper, the development of VUV-PIMS technology, especially the research progress of photoionization ion sources based on the VUV lamp, were reviewed, such as high-pressure photoionization (HPPI) source, single photon ionization-photoelectron ionization (SPI-PEI) combined ion source, and single photon ionization-chemical ionization (SPI-CI) combined ion source. By combining capillary sampling technology or molecular beam sampling technology, the applications of VUV-PIMS for real-time and online monitoring of stable products and active intermediates in catalytic processes were introduced, such as ammonia synthesis, methane/ethane catalytic conversion, methanol to olefins. The applications in online monitoring of these catalytic reaction processes exhibited an excellent performance and wide potential applications of VUV-PIMS in process monitoring and reaction mechanism research. Finally, the future development trend of VUV-PIMS was prospected for the application requirements of accurate characterization of catalytic reaction process.

BackgroundWith the rapid development of nuclear energy and the wide application of nuclear technology, the radioactivity level of bodies of water has become a highly concerning issue for the public and governments, especially after the Fukushima nuclear accident in Japan.PurposeThis study aims to develop an online γ radioactivitymonitoring system based on 4G Remote Terminal Unit (4G-RTU) to meet the needs of online and emergency monitoring of water radioactivity.MethodsFirst, the γ-ray monitoring device based on sodium iodide detector, 4G-RTU, an integrated power supply waterproof and compression resistant floating device and corresponding software were employed to compose an online monitoring. Second, Qt programming control software was used to realize the remote control of the system, real-time radioactivity monitoring, and data upload. Finally, the original data obtained by the system were used to test the performance indicators, applicability, accuracy, and software functions to verify the practicability of the system.ResultsWithin the coverage of the 4G network, the system realizes remote control of equipment, real-time online monitoring, and data upload throughout the day. The detectable energy range of the system is 30~3 000 keV, and the energy resolution of the system for 137Cs at 662 keV is 7.3% with minimum detectable activity of 0.75 Bq∙L-1. The spectral drift for 208Tl at 2 614 keV is 0.33%, and the linearity of the spectral energy is 0.999 970. The maximum value of energy spectrum stability is 2.28% for 7 h continuous operating, and the minimum value is -2.36%. The operating temperature range of the system is in the range of -5 ℃ to +50 ℃.ConclusionsThe on-line monitoring system meets the application demand and achieves the expected function. It has important popularization value and application prospects in the field of online radioactive monitoring of bodies of water such as oceans, lakes, and rivers.

An energy harvesting power supply based on AC electric field induction is introduced, which is used for power supply of online monitoring devices for transmission lines. However, the output power of this type of power supply is relatively low, which cannot meet the electricity demand of online monitoring devices. Therefore, based on theoretical analysis, a coaxial dual column inductive electrode structure is designed in the article to improve the output power of the power supply. A simulation model is established using COMSOL Multiphysics software and electromagnetic field simulation analysis is conducted. The simulation results show that the designed inductive electrode can generate an induced current of over 3.35 mA at a voltage level of 10 kV. To verify the accuracy of the simulation results, an experimental verification is conducted using a self built testing platform in the laboratory, and the experimental results are compared with the simulation results. The experimental results show that when the value of load resistance is higher than 5 kΩ, an energy harvesting power supply can output continuous power exceeding 630 mW, which is basically consistent with the simulation results. Through the research in the article, the problem of low output power of electric field induction energy harvesting power supply has been successfully solved, and a new design concept and technical solution of energy harvesting circuit has been proposed.

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.

This paper performs data fusion based on a multi-sensor fusion algorithm, which measures the sensors and calculates the distance between the data to obtain the measurement weights when participating in the fusion. The sensor observation model is created using an extended Kalman filter, and the nonlinear state transition function is linearized using the covariance matrix. Accurately estimating sensor inter-cluster data is necessary to achieve the scalability of online detection technology for charging piles. The results show that the disconnection time of the contactor of the charging pile transfer type equipment is 1.153s after the simulated charging pile output over-voltage in the disconnection time detection. The online detection efficiency can be improved by using multiple sensors, the method analysis can be intuitive, and the charging service capability of the electric vehicle charging pile can be effectively improved.

A Kalman filter fusion algorithm was proposed, and an online monitoring system was developed for real-time monitoring of the moisture content of materials in an air-impingement dryer. The Kalman filter algorithm was used to estimate the optimal state of the original detection values of the weighting sensor and air velocity sensor. A backpropagation (BP) neural network fusion model was established, where the weight detection value, elastic substrate temperature, air velocity, and impingement distance were considered inputs and the real weight of the material was the output. The optimal topology of the BP neural network was selected, and the initial weights and thresholds of the BP neural network were optimized using a genetic algorithm. The coefficient of determination (R2) and root mean square error (RMSE) of the optimized BP neural network fusion model were 0.9995 and 4.9, respectively. The Kalman filter fusion algorithm, which can realize online monitoring of moisture content, was established using the Kalman filter algorithm and fusion model. Moreover, an online monitoring system for material moisture content was developed, validation experiments were carried out, and the R2 and RMSE of the nine sets of validation experiments were 0.9963 and 0.78, respectively. The monitoring system satisfied the requirements of material moisture content detection accuracy in the drying process. The developed monitoring system is greatly important for improving the automation level of the drying equipment for fruits and vegetables. The proposed Kalman filter fusion algorithm also provides a reference for other multifactor fusion detection.

Novel hybrid laminates consisting of carbon fiber-reinforced polymers and steel plate cold commercial (CFRP-SPCC) have been successfully manufactured and tested in the present study. Four different tests have been made i.e., tensile test, flexural test, double cantilever beam (DCB) or Mode I test, and end notched flexure (ENF) or Mode II test. After being tested, these samples need to be evaluated. Hence, an inappropriate evaluation step while identifying the cause of failure of the failed specimens can create a false conclusion. The present study aims to systematically evaluate the fractured specimens of CFRP-SPCC in different tests (tensile, flexural, DCB, and ENF test) using different tools, i.e., pocket camera, digital microscope, light microscope, and Scanning Electron Microscopy (SEM). The results showed, in terms of tensile, the highest strength occurred with 1475.4 MPa. In comparison, the highest flexural strength occurred with 1516.1 MPa, while DCB and ENF showed the highest values with 116.4 and 330 J/m2, respectively. Furthermore, the results showed that digital and light microscopes are satisfied to evaluate the specimen in tensile test. However, for the flexural test, ENF, and DCB test, besides the light microscope, further analysis using SEM-EDX is recommended to evaluate the specimen after failure.

To solve the problem of nondestructive diagnosis of coating aging of steel pipe piles and jackets in offshore wind power booster stations，a set of coating aging monitoring and defect damage automatic identification system was designed.By arranging electrochemical impedance monitoring sensors and cameras on the surface of the tower coating in the exposed area，splash area，tidal range area，and fully immersed area of the offshore booster station，monitoring the in-situ aging status of the coating and automatic identification of coating defects and damages was achieved.With the aid of data wireless transmission and cloud service network，the changes in aging status of coatings at different zones can be online monitored.Through the image recognition system and optimized algorithm，the damage defects on coatings in different areas were automatically inspected，and the evaluation indicators of coating defects degree were quantified.Moreover，machine learning was applied to improve the accuracy of damage identification.For unmanned offshore wind power piles，the combination of electrochemical non-destructive monitoring and visual damage identification was used to establish an coating aging online monitoring network，which could achieve graded management of coating aging status in key areas，provide early warning for areas with severe coating failure，remind personnel to repair in a timely manner，and assist in the efficient and safe operation and maintenance of offshore wind power.

In order to solve the problem of measurement distortion of resistive current caused by space interference in the live test of MOA （metal oxide arresters）， and improve the measurement accuracy of resistive current for MOA， the law of spatial interference for MOA in the production site is analyzed and summarized， and different compensation strategies are proposed for two different spatial interference models of MOA. The parameter estimation method based on K-nearest neighbor （KNN） is adopted to reduce the influence of humidity change on spatial interference compensation strategy. Circuit simulation based on Simulink shows that this method can effectively improve the calculation accuracy of resistive circuits of MOA. Finally， through further field measurement results and comparison with power outage test data， it is shown that the "automatic edge compensation" algorithm used by existing instruments is not applicable in the case of spatial imbalance interference. At the same time， it is proved that the proposed algorithm can effectively improve the calculation accuracy of resistive current for MOA.

Abstract Background One critical parameter in microbial cultivations is the composition of the cultivation medium. Nowadays, the application of chemically defined media increases, due to a more defined and reproducible fermentation performance than in complex media. In order, to improve cost-effectiveness of fermentation processes using chemically defined media, the media should not contain nutrients in large excess. Additionally, to obtain high product yields, the nutrient concentrations should not be limiting. Therefore, efficient medium optimization techniques are required which adapt medium compositions to the specific nutrient requirements of microorganisms. Results Since most Paenibacillus cultivation protocols so far described in literature are based on complex ingredients, in this study, a chemically defined medium for an industrially relevant Paenibacillus polymyxa strain was developed. A recently reported method, which combines a systematic experimental procedure in combination with online monitoring of the respiration activity, was applied and extended to identify growth limitations for Paenibacillus polymyxa. All cultivations were performed in microtiter plates. By systematically increasing the concentrations of different nutrient groups, nicotinic acid was identified as a growth-limiting component. Additionally, an insufficient buffer capacity was observed. After optimizing the growth in the chemically defined medium, the medium components were systematically reduced to contain only nutrients relevant for growth. Vitamins were reduced to nicotinic acid and biotin, and amino acids to methionine, histidine, proline, arginine, and glutamate. Nucleobases/-sides could be completely left out of the medium. Finally, the cultivation in the reduced medium was reproduced in a laboratory fermenter. Conclusion In this study, a reliable and time-efficient high-throughput methodology was extended to investigate limitations in chemically defined media. The interpretation of online measured respiration activities agreed well with the growth performance of samples measured in parallel via offline analyses. Furthermore, the cultivation in microtiter plates was validated in a laboratory fermenter. The results underline the benefits of online monitoring of the respiration activity already in the early stages of process development, to avoid limitations of medium components, oxygen limitation and pH inhibition during the scale-up.

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 Background In research and production, reproducibility is a key factor, to meet high quality and safety standards and maintain productivity. For microbial fermentations, complex substrates and media components are often used. The complex media components can vary in composition, depending on the lot and manufacturing process. These variations can have an immense impact on the results of biological cultivations. The aim of this work was to investigate and characterize the influence of the complex media component yeast extract on cultivations of Azotobacter vinelandii under microaerobic conditions. Under these conditions, the organism produces the biopolymer alginate. The focus of the investigation was on the respiration activity, cell growth and alginate production. Results Yeast extracts from 6 different manufacturers and 2 different lots from one manufacturer were evaluated. Significant differences on respiratory activity, growth and production were observed. Concentration variations of three different yeast extracts showed that the performance of poorly performing yeast extracts can be improved by simply increasing their concentration. On the other hand, the results with well-performing yeast extracts seem to reach a saturation, when their concentration is increased. Cultivations with poorly performing yeast extract were supplemented with grouped amino acids, single amino acids and micro elements. Beneficial results were obtained with the supplementation of copper sulphate, cysteine or a combination of both. Furthermore, a correlation between the accumulated oxygen transfer and the final viscosity (as a key performance indicator), was established. Conclusion The choice of yeast extract is crucial for A. vinelandii cultivations, to maintain reproducibility and comparability between cultivations. The proper use of specific yeast extracts allows the cultivation results to be specifically optimised. In addition, supplements can be applied to modify and improve the properties of the alginate. The results only scratch the surface of the underlying mechanisms, as they are not providing explanations on a molecular level. However, the findings show the potential of optimising media containing yeast extract for alginate production with A. vinelandii, as well as the potential of targeted supplementation of the media.

Controlled-potential in situ thin-layer spectropotentiometry was leveraged to generate visible/near-infrared (VIS/NIR) absorption spectral data sets for the development of chemometric models to quantify Np(III/IV/V/VI) oxidation states in HNO3. This technology would be valuable in laboratory studies and when monitoring process solutions to guide feed adjustments for radiochemical separations—the performance of which depends on oxidation state. This approach successfully isolated and stabilized Np species in pure (∼99%) oxidation states without compromising solution optical properties. Multivariate curve resolution–alternating least squares models were evaluated to resolve spectral and component concentrations from a scan that sequentially produced Np(VI), Np(V), Np(IV), and Np(III) spectra with mixtures of two valences at a time. Although it provided a useful approximation, the method was not able to quantitively resolve each component likely because of rotational ambiguity. Additionally, partial least squares regression models were built from artificial and electrochemically generated VIS/NIR spectral training sets to study the effect of interionic interactions on spectral characteristics. Models built with true Bi-chemical mixtures of coexisting Np oxidation states and spectra generated from additive combinations of pure end points had similar prediction performance. This methodology can be used to directly quantify Np concentration and the ratio of Np oxidation states and other actinides in remote settings such as hot cells.

In recent times, the rapid advancements in technology have led to a digital revolution in urban areas, and new computing frameworks are emerging to address the current issues in monitoring and fault detection, particularly in the context of the growing renewable decentralized energy systems. This research proposes a novel framework for monitoring the condition of decentralized photovoltaic systems within a smart city infrastructure. The approach uses edge computing to overcome the challenges associated with costly processing through remote cloud servers. By processing data at the edge of the network, this concept allows for significant gains in speed and bandwidth consumption, making it suitable for a sustainable city environment. In the proposed edge-learning scheme, several machine learning models are compared to find the best suitable model achieving both high accuracy and low latency in detecting photovoltaic faults. Four light and rapid machine learning models, namely, CBLOF, LOF, KNN, ANN, are selected as best performers and trained locally in decentralized edge nodes. The overall approach is deployed in a smart solar campus with multiple distributed PV units located in the R&D platform Green & Smart Building Park. Several experiments were conducted on different anomaly scenarios, and the models were evaluated based on their supervision method, f1-score, inference time, RAM usage, and model size. The paper also investigates the impact of the type of supervision and the class of the model on the anomaly detection performance. The findings indicated that the supervised artificial neural network (ANN) had superior performance compared to other models, obtaining an f1-score of 80 % even in the most unfavorable conditions. The findings also showed that KNN was the most suitable unsupervised model for the investigated experiments achieving good f1-scores (100 %, 95 % and 92 %) in 3 out of 4 scenarios making it a good candidate for similar anomaly detection tasks.

Sweeping frequency ultrasound (SFU) was used to assist extraction of amur grape (Vitis amurensis) seed (AGS) oil. Extraction conditions and physicochemical properties were optimized and analyzed under different extraction methods. Meanwhile, frequency and time domains were online monitored during SFU assisted extraction of AGS oil. PVDF piezoelectric sensor was used in time domain, and the hydrophone in frequency domain, so as to obtain the time–voltage waveform, signal power, spectrum distribution and other visual models. Physical models of the spatial peak acoustic intensity, charge quantity and work done by electric field force under different ultrasonic conditions were derived. The mathematical model between the work done by electric field force and the spatial peak acoustic intensity under the working state of PVDF piezoelectric sensor was constructed. Results show that the content of AGS oil by SFU assisted extraction was higher than that by organic extraction. Furthermore, the optimal single-frequency was 40 kHz and dual-frequency was 28/33 kHz, and SFU extraction time of 30 min was suitable with higher oil yield of 16.70 % and 16.94 %, respectively. In addition, the selection and combination of SFU also affected the oil oxidation degree. The peak voltage, spatial peak acoustic intensity, signal power and work of electric field force at 28/33 kHz were all higher than those at 40 kHz.

Abstract Background In industrial microbial biotechnology, fed-batch processes are frequently used to avoid undesirable biological phenomena, such as substrate inhibition or overflow metabolism. For targeted process development, fed-batch options for small scale and high throughput are needed. One commercially available fed-batch fermentation system is the FeedPlate®, a microtiter plate (MTP) with a polymer-based controlled release system. Despite standardisation and easy incorporation into existing MTP handling systems, FeedPlates® cannot be used with online monitoring systems that measure optically through the transparent bottom of the plate. One such system that is broadly used in biotechnological laboratories, is the commercial BioLector. To allow for BioLector measurements, while applying the polymer-based feeding technology, positioning of polymer rings instead of polymer disks at the bottom of the well has been proposed. This strategy has a drawback: measurement requires an adjustment of the software settings of the BioLector device. This adjustment modifies the measuring position relative to the wells, so that the light path is no longer blocked by the polymer ring, but, traverses through the inner hole of the ring. This study aimed at overcoming that obstacle and allowing for measurement of fed-batch cultivations using a commercial BioLector without adjustment of the relative measurement position within each well. Results Different polymer ring heights, colours and positions in the wells were investigated for their influence on maximum oxygen transfer capacity, mixing time and scattered light measurement. Several configurations of black polymer rings were identified that allow measurement in an unmodified, commercial BioLector, comparable to wells without rings. Fed-batch experiments with black polymer rings with two model organisms, E. coli and H. polymorpha, were conducted. The identified ring configurations allowed for successful cultivations, measuring the oxygen transfer rate and dissolved oxygen tension, pH, scattered light and fluorescence. Using the obtained online data, glucose release rates of 0.36 to 0.44 mg/h could be determined. They are comparable to formerly published data of the polymer matrix. Conclusion The final ring configurations allow for measurements of microbial fed-batch cultivations using a commercial BioLector without requiring adjustments of the instrumental measurement setup. Different ring configurations achieve similar glucose release rates. Measurements from above and below the plate are possible and comparable to measurements of wells without polymer rings. This technology enables the generation of a comprehensive process understanding and target-oriented process development for industrial fed-batch processes.

Abstract Background Non-invasive online fluorescence monitoring in high-throughput microbioreactors is a well-established method to accelerate early-stage bioprocess development. Recently, single-wavelength fluorescence monitoring in microtiter plates was extended to measurements of highly resolved 2D fluorescence spectra, by introducing charge-coupled device (CCD) detectors. Although introductory experiments demonstrated a high potential of the new monitoring technology, an assessment of the capabilities and limits for practical applications is yet to be provided. Results In this study, three experimental sets introducing secondary substrate limitations of magnesium, potassium, and phosphate to cultivations of a GFP-expressing H. polymorpha strain were conducted. This increased the complexity of the spectral dynamics, which were determined by 2D fluorescence measurements. The metabolic responses upon growth limiting conditions were assessed by monitoring of the oxygen transfer rate and extensive offline sampling. Using only the spectral data, subsequently, partial least-square (PLS) regression models for the key parameters of glycerol, cell dry weight, and pH value were generated. For model calibration, spectral data of only two cultivation conditions were combined with sparse offline sampling data. Applying the models to spectral data of six cultures not used for calibration, resulted in an average relative root-mean-square error (RMSE) of prediction between 6.8 and 6.0%. Thus, while demanding only sparse offline data, the models allowed the estimation of biomass accumulation and glycerol consumption, even in the presence of more or less pronounced secondary substrate limitation. Conclusion For the secondary substrate limitation experiments of this study, the generation of data-driven models allowed a considerable reduction in sampling efforts while also providing process information for unsampled cultures. Therefore, the practical experiments of this study strongly affirm the previously claimed advantages of 2D fluorescence spectroscopy in microtiter plates.

This letter proposes an on-state drain-source voltage (${V}_{{\rm{ds,on}}}$) measurement circuit for SiC MOSFETs. The proposed circuit effectively solves the problem of inconsistent diode volt-ampere characteristics through an additional current source. The voltage difference between the two diodes is compensated by adjusting the auxiliary current under the condition of a fixed first current. This circuit does not require diode pre-selection and significantly improves measurement accuracy. The circuit is implemented in a double-pulse test (DPT) of a SiC MOSFET (900 V/23 A, 25 °C). The results show that the circuit has a fast response speed with a delay time of 0.4 μs. Even at the same current, the forward voltage difference between the two diodes reaches 3.32 mV. Compared to measurement results from a six-bit semi-digital multimeter, the measurement error of the proposed circuit is less than 1.5% at different currents and temperatures.

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.

With ultrasonic fatigue testing (UFT), it is possible to investigate the damage initiation and accumulation from the weakest link of the composite material in the very high cycle fatigue (VHCF) regime in a shorter time frame than conventional fatigue testing. However, the thermal influence on the mechanical fatigue of composites and the scatter in fatigue data for composites under ultrasonic cyclic three-point bending loading still need to be investigated. In this study, we conducted interrupted constant-amplitude fatigue experiments on a carbon-fiber satin-fabric reinforced in poly-ether-ketone-ketone (CF-PEKK) composite material. These experiments were carried out using a UFT system, which operates at a cyclic frequency of 20 kHz with a pulse–pause sequence. Various parameters, such as the CF-PEKK specimen’s surface temperature, acoustic activity, and the ultrasonic generator’s input resonance parameters, were measured during cyclic loading. During experiment interruption, stiffness measurement and volumetric damage characterization in the CF-PEKK specimens using 3D X-ray microscopy (XRM) were performed. The locations of damage initiation and accumulation and their influence on the changes in in situ parameters were characterized. Under fixed loading conditions, damage accumulation occurred at different locations, leading to scattering in fatigue life data. Further, the damage population decreased from the surface to the bulk of the composite material.

The European Union Water Framework Directive (2000/60/EC; WFD) aims to achieve a good ecological and chemical status of all bodies of surface water by 2027. The development of integrated guidance on surface water chemical monitoring (e.g., WFD Guidance Document No. 7/19) has been transferred into national German law (Ordinance for the Protection of Surface Waters, OGewV). For the majority of compounds, this act requires monthly sampling to assess the chemical quality status of a body of surface water. To evaluate the representativeness of the sampling strategy under the OGewV, high-frequency online monitoring data are investigated under different sampling scenarios and compared with current, monthly grab sampling data. About 23 million data points were analyzed for this study. Three chemical parameters (dissolved oxygen, nitrate-nitrogen, and chloride concentration) and discharge data were selected from four catchments of different sizes, ranging from 51,391 km2 to 84 km2 (Elbe, Vereinigte Mulde, Neiße and two stations at Lockwitzbach). In this paper, we propose short-term online-monitoring (STOM) as a sampling alternative. STOM considers the placement of online sensors over a limited duration and return interval. In general, we: (I) compare the results of conventional grab sampling with STOM, (II) investigate the different performance of STOM and grab sampling using discharge data as a proxy for analyzing event-mobilized pollutants, and (III) investigate the related uncertainties and costs of both sampling methods. Results show that STOM outperforms grab sampling for parameters where minimum/maximum concentrations are required by law, as the probability of catching a single extreme value is higher with STOM. Furthermore, parameters showing a pronounced diurnal pattern, such as dissolved oxygen, are also captured considerably better. The performance of STOM showed no substantial improvements for parameters with small concentration variability, such as nitrogen-nitrate or chloride. The analysis of discharge events as a proxy parameter for event-mobilized pollutants proves that the probability of capturing samples during events is significantly increased by STOM.

The health care cost of cardiovascular diseases (CVD) in the EU is estimated to be today over 282 billion euros. It is well documented today that exercise training is one of the main strategies for secondary disease prevention and the follow-up integration of these patients. This study aimed to examine patients’ attitudes towards online exercise with mobile monitoring of their vital signs. More specifically, the research objectives were as follows: (a) to examine patients’ attitudes and expectations of online exercise, (b) cluster patients in high- and low-attitude groups and examine their intention to participate in online exercise, and (c) to examine age and gender differences in terms of their intention to exercise online. The final goal of this project was to develop a real application that could be of use to patients and professionals. Data were collected from fifty patients in the city of Thessaloniki, Greece. The results revealed that most patients were positive about exercising online if the programs were perceived as fun and, especially, safe. The use of an online monitoring application with the distant supervision of health professionals could both motivate them and strengthen their feeling of safety.

This review emphasizes the significance of formulating control strategies for biological and advanced oxidation process (AOP)-based wastewater treatment systems. The aim is to guarantee that the effluent quality continuously aligns with environmental regulations while operating costs are minimized. It highlights the significance of understanding the dynamic behaviour of the process in developing effective control schemes. The most common process control strategies in wastewater treatment plants (WWTPs) are explained and listed. It is emphasized that the proper control scheme should be selected based on the process dynamic behaviour and control goal. This study further discusses the challenges associated with the control of wastewater treatment processes, including inadequacies in developed models, the limitations of most control strategies to the simulation stage, the imperative requirement for real-time data, and the financial and technical intricacies associated with implementing advanced controller hardware. It is discussed that the necessity of the availability of real-time data to achieve reliable control can be achieved by implementing proper, accurate hardware sensors in suitable locations of the process or by developing and implementing soft sensors. This study recommends further investigation on available actuators and the criteria for choosing the most appropriate one to achieve robust and reliable control in WWTPs, especially for biological and AOP-based treatment approaches.

Micro direct methanol fuel cells (μDMFCs) are a promising power source for microelectronic devices and systems. As the operating state and performance of a μDMFC is generally determined by both electrochemical polarization and methanol crossover, it is important to monitor the methanol concentration in μDMFCs. Here, we design and fabricate a microwave sensor and integrate it with a μDMFC for the online detection of methanol concentration in a nonintrusive way. The sensing area is set at the bottom of the anode chamber of a μDMFC which exhibits a maximum output power density of 28.8 mW cm−2 at 30 °C. With a square ring structure, the dual-mode microwave sensor shows a sensitivity of 9.5 MHz mol−1 L. Furthermore, the importance of methanol concentration monitoring is demonstrated in the long term. A relatively smooth methanol decline curve was obtained, which indicated a normal and stable operating status of the μDMFC. Derived from real-time recording data, fuel utilization was additionally calculated as 28.5%.

Abstract In the field of bioprocess development miniaturization, parallelization and flexibility play a key role reducing costs and time. To precisely meet these requirements, additive manufacturing (3D‐printing) is an ideal technology. 3D‐printing enables rapid prototyping and cost‐effective fabrication of individually designed devices with complex geometries on demand. For successful bioprocess development, monitoring of process‐relevant parameters, such as pH, dissolved oxygen (DO), and biomass, is crucial. Online monitoring is preferred as offline sampling is time‐consuming and leads to loss of information. In this study, 3D‐printed cultivation vessels with optical prisms are evaluated for the use in upstream processes of different industrially relevant microorganisms and cell lines. It was shown, that the 3D‐printed optically modified well (OMW) is of benefit for a wide range of biotechnologically relevant microorganisms and even for mammalian suspension cells. Evaluation tests with Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae, and Chinese hamster ovary (CHO) cells were performed, providing highly reproducible results. Growth behavior of OMW cultures was comparable to behavior of shake flask (SF) cultivations and the signal to noise ratio in online biomass measurement was shown to be reduced up to 95.8% by using the OMW. Especially the cultivation phases with low turbidity respective optical densities below 1.0 rel.AU could be monitored accurately for the first time. Furthermore, it was demonstrated that the 3D‐printed optics are transferable to different well geometries and sizes, enabling efficient biomass monitoring for individual requirements with tailor‐made 3D‐printed cultivation vessels in small scale.

Online monitoring of the spatial-temporal distribution of harmful gases has always been a complex problem in the environmental field. This paper proposes a novel mathematical method for online monitoring of the spatial-temporal distribution of reactants by machine learning, which can help to remove harmful gases efficiently. In this model, we take the advanced oxidation of NO as an example to evaluate the model performance. The spatial features were extracted by CNN, and GRU extracted the temporal features in the sequence of spatial features. Five physical field variables (mass fraction of ozone, velocity, temperature, the wind direction of the horizontal plane, and the wind direction of the vertical plane) were put into the network to predict NO's spatial-temporal mass fraction distribution. Furthermore, the impact of sampling time interval on monitoring performance was also evaluated. The results show that both the instantaneous and continuous CFD (Computational Fluid Mechanics) and predicted values show high consistency, which indicates that the model can online monitor the spatial-temporal distribution of reactants successfully. In addition, the most suitable sampling time interval is 0.5 s, with low training error (RMSE=0.06 and nRMSE=0.3) and high relation coefficient (r=0.99), which shows the model has great perceived and predicted performance under this condition.

Endotoxin (lipopolysaccharide: LPS), a bacterial toxin, is currently quantified by batch analysis using a reagent made from Limulus amebocyte lysate (LAL). Although the LAL method is sensitive, a long measurement time and gelation have hindered its application to online water quality monitoring. In this paper, we report a novel high-throughput analytical technique that combines flow injection analysis (FIA) and a dipicolylamine-type fluorescent probe, which was previously reported to exhibit strong fluorescence emission in response to LPS. A comparison of normal and reverse FIA systems revealed that the normal FIA offers more rapid detection with throughput of 30 samples per hour, which is the highest among known LAL methods and alternative LPS detection methods that have been recently reported. On the other hand, the reverse FIA requires much less reagent (480 μL per sample). Continuous online monitoring of water quality and analysis of real samples spiked with LPS were realized by this technique.

Accurate data is the basis for all marine scientific research. Different from the traditional sampling monitoring process, it is fully automated for marine buoy monitoring from sample collection to data results, and the focus of data quality control is no longer focused on the "sample collection-analysis and testing" . In this study, the quality control system for marine buoy monitoring data through analysis of the buoy data in the past two years was designed, including six items: floating body stability, instrument equipment calibration, system operation and maintenance, data transmission and reception, contrast monitoring, abnormal data determination, and abnormal data determination and contrast monitoring were focused on. This research established a quality control method library, and proposed three levels of abnormal data determination rules for buoy data quality control: valid, credible, and relatively accurate, which was innovative. The first layer is the valid data set obtained after removing the obvious fault values, outliers, and other invalid data; the second layer is the credible data set obtained after screening by various quality control methods from the method library; the third layer is relatively accurate data set verified against field contrast monitoring results. Based on the marine buoy monitoring data in Zhejiang Province, the design ideas and methods of the quality control system for buoy monitoring data were proposed, which would help to improve the quality of the online monitoring data of buoys in the coastal waters of Zhejiang. This design would provide a reference for the national marine buoy monitoring data quality control.

ABSTRAK Danau Toba merupakan danau terbesar di Indonesia yang memiliki peran penting sebagai sumberdaya untuk mendukung potensi ekologis dan ekonomis masyarakat setempat. Akan tetapi, tekanan pencemaran yang diakibatkan oleh aktivitas masyarakat di sekitar danau terus bertambah. Penelitian ini memanfaatkan teknologi ONLIMO yang dikembangkan oleh Pusat Teknologi Lingkungan, Badan Pengkajian dan Penerapan Teknologi untuk meningkatkan kegiatan pemantauan kualitas air di Danau Toba. Penelitian ini bertujuan untuk monitoring kualitas air di Danau Toba dengan menggunakan sistem ONLIMO yang memanfaatkan teknologi telemetri dalam pengaplikasiannya. Data diambil pada bulan Desember 2017, di 2 lokasi stasiun pemantauan yaitu stasiun 1 (STO11 di Toba Marom) dan stasiun 2 (STO12 di Toba Ajibata). Sensor yang digunakan berupa multiprobe sensor yang terdiri dari sensor suhu, DHL, TDS, kekeruhan, pH, DO, Nitrat dan Amonia. Data berasal dari database ONLIMO dan dianalisis dengan menggunakan metode storet. Hasil menunjukkan bahwa di kedua stasiun secara umum tergolong ke dalam perairan yang tercemar ringan, dengan parameter yang berada di atas baku mutu terdiri dari DO, kekeruhan, dan amonia. Penggunaan sistem teknologi ONLIMO dapat mempermudah kegiatan monitoring kualitas air di Danau Toba secara kontinyu. Kata kunci: analisis storet, danau toba, kualitas air, pemantauan online, status mutu air ABSTRACT Lake Toba is the largest lake in Indonesia which has an important role as a resource to support the ecological and economic potential of the local community. However, the pressure caused by community activities around the lake continues to grow. This research utilizes the ONLIMO technology developed by the Center for Environmental Technology, Agency for the Assessment and Application of Technology to improve water quality monitoring in Lake Toba. This study aims to monitor air quality in Lake Toba using the ONLIMO system that utilizes telemetry technology in its application. Data was collected in December 2017, at 2 monitoring locations, namely station 1 (STO11 in Toba Marom) and station 2 (STO12 in Toba Ajibata). The sensor used is a multiprobe sensor consisting of a temperature sensor, DHL, TDS, turbidity, pH, DO, Nitrate and Ammonia. Data were derived from the ONLIMO database and analyzed using the storet method. The results show that both stations are generally classified as lightly polluted waters, with parameters above the quality standards consisting of DO, turbidity, and ammonia. The use of the ONLIMO technology system can facilitate continuous monitoring of water quality in Lake Toba. Keywords: storet analysis, toba lake, water quality, online monitoring, water quality status

The characteristics of tunable diode laser absorption spectroscopy (TDLAS) gas detection technology are high resolution, low detection limit and fast response speed, it has been widely used in on-line monitoring of ambient gases. The gas composition is complex in coal mines. Traditional gas sensors, chromatographic analysis and spectroscopic analysis techniques are limited by the method itself or the environmental conditions on site, and there are different degrees of limitations. Firstly, we introduced the working principles and characters of TDLAS gas detection technology. Secondly, the development process and research status of TDLAS technology in the fields of fire protection, petrochemical, environmental protection and coal mine gas detection are focused on analyzed. Finally, the application effect of this technology in downhole methane gas detection and online monitoring of multiple mixed gases is analyzed.

The estuary is the transition zone between the river and the marine ecosystem. The terrestrial ecological sensitive factors entering the sea will greatly affect the ecological status of the coastal waters. Therefore, monitoring of ecologically sensitive factors entering the sea is an important basis for coastal ecological protection and ecological disaster prevention and mitigation, and is also a key part of promoting the sustainable development of the marine economy. However, the establishment of the ecological early warning and monitoring system for the estuary in China is still in its infancy, and there is still a big gap between it and developed countries. This paper started with the impact of the ecological status of the estuary on the health of the marine ecosystem, summarized the development process, construction and application direction of the marine ecological early warning and monitoring technology in the estuary at home and abroad, and deeply discussed the ecological significance of building ecological early warning monitoring stations in the estuary and system development direction.

Portions of dynamic traffic volumes consisting of multiple vehicle classes are accurately monitored without vehicle detectors using vehicle-to-infrastructure (V2I) communication systems. This offers the feasibility of online monitoring of the total traffic volumes with multi-vehicle classes without any advanced vehicle detectors. To evaluate this prospect, this article presents a method of monitoring dynamic multi-class vehicular traffic volumes in a road location where road-side equipment (RSE) for V2I communication is in operation. The proposed method aims to estimate dynamic total traffic volume data for multiple vehicle classes using the V2I sensing probe volume (i.e. partial vehicular traffic volumes) collected through the RSE. An experimental study was conducted using real-world V2I sensing probe volume data. The results showed that traffic volumes for vehicle types I and II (i.e. cars and heavy vehicles, respectively) can be effectively monitored with average errors of 6.69% and 10.89%, respectively, when the penetration rates of the in-vehicle V2I device for the two vehicle types average 0.384 and 0.537, respectively. The performance of the method in terms of detection error is comparable to those of widely used vehicle detectors. Therefore, V2I sensing probe data for multi-vehicle classes can complement the functions of vehicle detectors because the penetration rate of in-vehicle V2I devices is currently high.

To evaluate the online presence of wildlife crime and the public’s reaction to these crimes, we collected data on species, locations, animal status, and motives of wildlife crime depicted on nine Pakistani Facebook pages and eight groups from January 2016 to May 2021. We categorized reactions to images depicting birds, mammals, and reptiles and applied generalized linear models to the total number of reactions on posts. In 594 relevant posts, we identified 10,644 animals of 138 species. Common Krait (Bungarus caeruleus) had the highest number of posts, followed by Grey Francolin (Ortygornis pondicerianus) and Black Francolin (Francolinus francolinus). The number of posts has generally increased, particularly after the Covid-19 lockdown in March 2020. Almost 66 % of the posts showed animals being killed, 27 % were captured with no visible signs of torture, and the rest showed visible signs of torture in captivity. The most common reason for wildlife crimes was illegal hunting, followed by fear and illegal trade. Most victims of illegal hunting were birds, while reptiles were predominantly killed for fear. However, illegal trade was affecting all three classes of animals. Killed or tortured animals received more reactions than non-tortured animals, as well as more comments both against and in favor of the crime. Crimes against reptiles received more pro-crime comments than birds and mammals. Our results reflect the importance of social media to monitor wildlife crimes. Urgent actions are necessary by the relevant authorities to improve management and strengthen the response of law enforcement to wildlife crime.

Harmful algal blooms (HABs) have been deteriorating global water bodies, and the accurate prediction of algal dynamics using the modelling method is a challenging research area. High-frequency monitoring and deep learning technology have opened up new horizons for HAB forecasting. However, the non-stationary and stochastic process behind algal dynamics monitoring largely limits the prediction performance and the early warning of algal booms. Through an analysis of the published literature, we found that decomposition methods are widely used in time-series analysis for hydrological processes. Predictions of ecological indicators have received less attention due to their inherent fluctuations. This study explores and demonstrates the predictive enhancement for chlorophyll fluorescence data based on the coupling of three decomposition algorithms with conventional deep learning models: the convolutional neural network (CNN) and long short-term memory (LSTM). We found that the decomposition algorithms can successfully capture the time-series patterns of chlorophyll fluorescence concentrations. The results indicate that decomposition-based models can enhance the accuracy of single models in predicting chlorophyll concentrations in terms of the improvement percentages in RMSE (with increases ranging from 25.7% to 71.3%), MAE (ranging from 28.3% to 75.7%), and R2 values (increasing ranging from 14.8% to 34.8%). In addition, the comparison experiment for different decomposition methods might suggest the superiority of singular spectral analysis in hourly predictive tasks of chlorophyll fluorescence over the wavelet transform and empirical mode decomposition models. Overall, while decomposition methods come with their respective strengths and weaknesses, they are undeniably efficient in combination with deep learning models in dealing with the high-frequency monitoring of chlorophyll fluorescence data. We also suggest that model developers pay more attention to online data preprocessing and conduct comparative analyses to determine the best model combinations for forecasting algal blooms and water management.

The crossbeam is frequently subjected to alternating loads during work as an essential load-bearing part of the crane. However, due to the large volume and the limitations of detection technology, it is impossible to realize online monitoring of the mechanical state. The ongoing advancement of ROMing and digital twin technology plays a pivotal role in facilitating the resolution of this particular issue. In this paper, we take the crane beam as the physical entity and combine the Twin Builder reduced-order technology and Deployer digital twin deployment technology to establish a digital twin of the beam. The load recognition model within the twin system exhibits a prediction error rate of ±5%. Furthermore, the accuracy of the ROM surpasses that of conventional machine learning models by a factor of 25. Upon deployment on the web platform, the results are delivered within 0.5 s, representing a substantial improvement as it is merely 1/15 of the time required for traditional 3D displays. The digital twin online monitoring system has the advantages of high accuracy and low requirements for monitoring equipment, which can be widely used in engineering practice to solve the problem that the mechanical state of large parts cannot be accurately monitored online.

Aiming at the phenomenon of partial discharge caused by the insulation fault of switchgears, an ultra-high frequency (UHF) partial discharge online monitoring system based on the Internet of Thing (IoT) is designed. The hardware of the system mainly consists of UHF sensors, signal conditioning circuits, and data acquisition circuits to realize the monitoring of the discharge signal. The monitoring data is uploaded to the cloud platform through the 4G DTU module, and the host computer software based on B/S architecture is designed on the server side, which can monitor the operation status of the switchgear anytime and anywhere. The experimental results show that the detection system is stable and reliable and can meet practical needs.

Coal spontaneous combustion (CSC) in gob not only leads to wasted resources and casualties, but also produces a lot of waste gas that pollutes the underground environment. Mastering the degree of CSC helps ensure that timely and effective control measures are taken. The real-time and accurate monitoring of temperature, as the primary indicator of the extent of CSC, is difficult due to the harsh and hidden environment of gob, resulting in a reduced ability to anticipate and prevent CSC. In this work, a complete distributed optical fiber temperature sensing system (DTSS) set with strong anti-damage ability was developed. The optical cable is protected by internal parallel steel cables and external protective pipes, which greatly improve the system’s reliability and integrity when used in gob. During its application in the Wangyun Coal Mine, an abnormal temperature rise in gob was discovered in time, and the effect of inhibitor spraying was monitored and evaluated. The degree of CSC in the gob was accurately identified, which shows that the work of coal mining can be targeted. This work is expected to improve early warning capability to prevent the risk of CSC in gob, and has promising applications.

In order to realize the lubricant fluid condition monitoring of ships and offshore engineering equipment, a multi-channel, dual-mode oil multi-pollution detection sensor is proposed and fabricated. The sensor has three detection channels connected via tee tubes, as well as two different detection modes, inductive and capacitive, respectively. In comparison to the traditional sensor, this sensor not only has the ability to distinguish and identify a diverse range of pollutants, but it also experiences an 11-fold increase in its volume of flow, resulting in a significant enhancement in detection efficiency. The mechanism of the inductive and capacitive modes for the differentiated detection of multiple pollutants is elucidated through theoretical analysis. The performance of the sensor is investigated using the constructed experiment platform. The experimental results show that the sensor can realize the simultaneous detection of metallic and non-metallic contaminants in lubricating oil fluids. It can detect the smallest iron particle size of 54 μm, the smallest copper particle size of 90 μm, the smallest water droplet size of 116 μm, and the smallest air bubble size of 130 μm. A novel approach for achieving ship and marine engineering equipment health monitoring and fault diagnosis is presented in this study.

A water quality monitoring buoy installed in the center of the eutrophic shallow lake Ulansuhai was used to explore the dissolved oxygen (DO) balance. A revised DO model for shallow eutrophic lakes was applied to identify trends in the lake's DO content during the non-frozen period and determine the equilibrium relationship of DO in the water body. The coefficient of determination and the Nash efficiency of the model proved the feasibility of the model. The main drivers affecting the DO balance of the lake were photosynthesis, aeration and the lateral movements of oxygen-rich water, which accounted for 49.28, 14.72 and 36%, respectively, whereas respiration and sediment oxygen consumption, on the other hand, accounted for 1.56 and 98.44%, respectively. These findings suggest that photosynthesis and sediment oxygen consumption dominate the DO balance in eutrophic shallow lakes. A trend analysis of the average oxygen production and consumption rates indicated a maximum of 0.22 mg/L·h for photosynthesis and 0.20 mg/L·h for sediment oxygen consumption. A correlation analysis showed that water temperature was involved in changing the DO level of the lake mainly by affecting the oxygen consumption process. HIGHLIGHTS Continuous DO data were obtained by an on-line monitoring instrument with high frequency, and the data were simulated.; A model of DO in a shallow water eutrophication lake is improved and successfully applied to typical lakes in the study area.; The main driving factors of DO were found out and their variation range was determined.;