Continuous System Research Articles

Development of transparent soil grouting test system and its application in grouted gravel pile

To visualize the grouting process of grouted gravel piles, a transparent soil model test system was developed. This system allows for the study of soil movement around the piles during grouting reinforcement and enables load-bearing capacity testing after grout solidification into piles. The experimental system comprises a grouting system, a vertical load-bearing capacity testing system, a continuous physical field measurement system, and a model box. The grouting system facilitates sequential processes: pre-consolidation, constant-pressure grouting, and vertical loading. In transparent clay with a pre-consolidation pressure of 60 kPa, constant-pressure grouting and vertical load-bearing tests for grouted gravel piles were conducted. The results captured the disturbance deformation of the soil around the pile during both grouting and vertical loading. The study indicates that disturbance deformation of the soil surrounding the pile increases with prolonged grouting time. Soil beneath the pile end predominantly undergoes vertical compression, while soil at the pile sides experiences horizontal compression. The mid-section of the soil adjacent to the pile displays oblique upward bulging and vertical deformation. Under constant-pressure grouting, the filling rate of gravel voids in the pile hole by epoxy resin chemical grout with strong time-dependent viscosity shows a decreasing trend. During vertical loading, the displacement of the soil around the pile is significantly greater than that at the pile end. Consequently, this test system facilitates the visualization and simulation of grouting and vertical load-bearing tests for grouted gravel piles.

Next Generation Seismic Source Detection by Computer Vision: Untangling the Complexity of the 2016 Kaikōura Earthquake Sequence

AbstractSeismic source locations are fundamental to many fields of Earth and planetary sciences, such as seismology, volcanology and tectonics. However, seismic source detection and location are challenging when events cluster closely in space and time with signals tangling together at observing stations, such as they often do in major aftershock sequences. Though emerging algorithms and artificial intelligence (AI) models have made processing high volumes of seismic data easier, their performance is still limited, especially for complex aftershock sequences. In this study, we propose a novel approach that utilizes three‐dimensional image segmentation—a computer vision technique—to detect and locate seismic sources, and develop this into a complete workflow, Source Untangler Guided by Artificial intelligence image Recognition (SUGAR). In our synthetic and real data tests, SUGAR can handle complex, energetic earthquake sequences in near real time better than skillful analysts and other AI and non‐AI based algorithms. We apply SUGAR to the 2016 Kaikōura, New Zealand sequence and obtain five times more events than the analyst‐based GeoNet catalog. The improved aftershock distribution illuminates a continuous fault system with extensive fracture zones beneath the segmented, discontinuous surface ruptures. Our method has broader applicability to non‐earthquake sources and other time series image data sets.

A novel two-stage continuous capacitive deionization system with connected flow electrode and freestanding electrode

The desalination of seawater/wastewater utilizing flow-electrode capacitive deionization (FCDI) has received significant interest. However, challenges like the low electrical conductivity of flow electrode and excessive energy consumption have prevented the scaling up of desalination operations. To overcome these issues, this study introduces a new FCDI system termed the two-stage FCDI (TS-FCDI) system, in which desalination modules are equipped with freestanding and flow electrode. The TS-FCDI system offers a graded desalination module design that produces an average salt removal rate (ASRR) of 113 µg cm2·min−1. However, the TS-FCDI system requires substantially reduced infrastructure investment, device size, and energy expenses. Notably, the design allows for the simultaneous operation of freestanding and flow electrode and enables the transport of ions and charges in the electrode region. Experimental evidence from multiple device configurations (CDI, FCDI, and TS-FCDI) supports this claim. Notably, the TS-FCDI system shows about twice the desalination performance over FCDI, with approximately 50 h of single-cycle (SC) operation. This enhancement links the freestanding electrode with the flow electrode, enabling the device to be well-suited for practical applications. The continuous flow of electrode particles in contact with the freestanding electrode initiates secondary ion transport, merging two different types of electrodes to enhance the active space of the electrode, effectively improving ion adsorption performance. In summary, the results of this study indicate the potential of the TS-FCDI system as a suitable desalination technology for scaling up applications.

Source contribution of black carbon aerosol during 2020–2022 at an urban site in Indo-Gangetic Plain

The extensive emissions of black carbon (BC) from the Indo-Gangetic Plain (IGP) region of India have been recognized to contribute significantly to global climatic warming and negatively affect human health. Particularly, biomass emissions from month-specific crop-residue burning (April, May, October, November) and heating activities (December–February) are considered substantial contributors to BC emissions in the IGP. However, their precise contribution to ambient BC aerosols has not been quantified yet and remains an issue of debate. Therefore, this study aims to fill this gap by quantifying the contribution of these month-specific biomass emissions to ambient BC at an urban site in IGP. This study presents the analysis of BC mass concentrations (MBC) measured for 3 years (2020−2022) in Delhi using an optical photometer i.e., continuous soot monitoring system (COSMOS)). A statistical analysis of monthly mean MBC and factors affecting the MBC (ventilation coefficients, air mass back trajectories, fire counts) is performed to derive month-wise contribution due to background concentration, conventional emission, regional transport, crop-residue burning, and heating activities. The yearly mean MBC (5.3 ± 4.7, 5.6 ± 5.0, and 5.3 ± 3.5 μg m−3 during 2020, 2021, and 2022, respectively) remained relatively consistent with repetitive monthly patterns in each year, where peak concentrations were observed from November to January and low concentrations from June to September. Anthropogenic activities contributed significantly to MBC over Delhi with background concentration contributing only 30 % of observed MBC. The percentage contribution of emissions from crop-residue burning varied from 15 % (May) to 37 % (November), while the contribution from heating activities ranged from 25 % (December) to 39 % (January). This source quantification study highlights the significant impact of month-specific biomass emissions in the IGP and can play a vital role in better management and control of these emissions in the region.

Periodic motions with impact chatters in an impact Duffing oscillator.

The periodic motions of discontinuous nonlinear dynamical systems are very difficult problems to solve in engineering and physics. Until now, except for numerical studies, one cannot find a better way to solve such problems. In fact, one still has difficulty obtaining periodic motions in continuous nonlinear dynamical systems. In this paper, a method is presented systematically for periodic motions in discontinuous nonlinear dynamical systems. The stability and grazing bifurcations of such periodic motions are studied. Such a method is presented through discussion on a periodically forced, impact Duffing oscillator. Thus, periodic motions with impact chatters in a periodically forced Duffing oscillator with one-sidewall constraint are studied. The analytical conditions for motion grazing at the boundary are developed from discontinuous dynamical systems. The impact Duffing oscillator is discretized to generate subimplicit mappings. With impact, the mapping structures are employed to construct specific impact periodic motions for an impact Duffing oscillator. The bifurcation trees of impact chatter periodic motions are achieved semi-analytically. The grazing and period-doubling bifurcations are obtained, and the grazing bifurcations are for the appearing and disappearance for an impact chatter periodic motion. The impact chatter periodic motions with and without grazing are presented for illustration of impact periodic motion complexity in the impact Duffing oscillator.

Strain accumulation in the Mentawai Forearc Sliver, Indonesia, inferred from continuous GNSS-derived strain rate

The Mentawai Forearc Sliver (MFS) is characterized by oblique deformation formed as slip partitioned between normal and parallel trench plate convergence. The surge of great earthquakes from 2004 to 2012 along the adjacent Sunda trench left a large unbroken segment known as the Mentawai Seismic Gap. Here, we adopted continuous Global Navigation Satellite System (GNSS) observation data to identify the present regional crustal deformation using geodetic strain rates. The principal strain rate, dilatation rate, and maximum shear strain rate are about 0.13 microstrain/yr, 0.2 microstrain/yr, and 0.1 microstrain/yr, respectively, with the range of its uncertainties between 0.01 and 0.04 microstrain/yr. It is found that dilatation and maximum shear strain rates reveal the spatial coverage of strike-slip duplex and backthrust tectonics along the Mentawai Forearc Sliver.

Classical Shadow Tomography for Continuous Variables Quantum Systems

Classical Shadow Tomography for Continuous Variables Quantum Systems

Neural network based adaptive robust synchronous control of double-lift overhead crane considering input saturation

This paper proposes a novel model-free controller, considering the double-lift overhead crane system cannot be accurately modeled and affected by external disturbances. Firstly, to improve the convergence speed of the system states, a time-varying sliding mode surface using a sigmoid-like function is proposed. Then, an adjustable weights radial basis function neural network (AWRBFNN) is introduced to estimate and compensate for the unknown dynamics of the system. This AWRBFNN introduces direct and effective model-free properties, but also brings approximation errors. To estimate the uncertain disturbances and the errors of the neural network, an adaptive uncertainty and disturbance estimator (AUDE) is designed. Furthermore, a fast hyperbolic power reaching law is developed to enhance the robustness while suppressing chattering. Considering that the driving capacity of the actual double-lift overhead crane system and the driving motor torque are limited, an innovative continuous auxiliary system is designed to mitigate the effects of the input saturation while reducing overshoot from synchronization errors. Finally, the stability of the control system is analyzed using the Lyapunov stability theory, and the effectiveness and robustness of the control scheme are verified by simulation.

Research on the Establishment of a Continuous Monitoring System for Shaft Behavior

Research on the Establishment of a Continuous Monitoring System for Shaft Behavior

Data-Driven Modeling for the Prediction of Stack Gas Concentration in a Coal-Fired Power Plant in Türkiye

In this research, deep learning and machine learning methods were employed to forecast the levels of stack gas concentrations in a coal-fired power plant situated in Türkiye. Real-time data collected from continuous emission monitoring systems (CEMS) serves as the basis for the predictions. The dataset includes measurements of carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxides (NOx), oxygen (O2), and dust levels, along with temperatures recorded. For this analysis, deep learning methods such as multi-layer perceptron network (MLP) and long short-term memory (LSTM) models were used, while machine learning techniques included light gradient boosted machine (LightGBM) and stochastic gradient descent (SGD) models were applied. The accuracy of the models was determined by analysing their performance using mean absolute error (MAE), root means square error (RMSE), and R-squared values. Based on the results, LightGBM achieved the highest R-squared (0.85) for O2 predictions, highlighting its variance-capturing ability. LSTM excelled in NOx (R-squared 0.87) and SO2 (R-squared 0.85) prediction, while showing the top R-squared (0.67) for CO. Both LSTM and LGBM achieved R-squared values of 0.78 for dust levels, indicating strong variance explanation. Conclusively, our findings highlight LSTM as the most effective approach for stack gas concentration forecasting, closely followed by the good performance of LightGBM. The importance of these results lies in their potential to effectively manage emissions in coal-fired power plants, thereby improving both environmental and operational aspects.Graphical

Investigation of Resonance Frequency and Stability of Solutions in a Continuous Rotor System

Investigation of Resonance Frequency and Stability of Solutions in a Continuous Rotor System

Ultrafast and efficient continuous flow organic synthesis with a modified extruder-grinder system

The study introduces a groundbreaking continuous system that combines an extruder and grinder to enable catalyst-free and solvent-free reactions under mild conditions. This temperature-controlled system facilitates the synthesis of highly functionalized chromenes, which have valuable applications in generating combinatorial libraries and complex target molecules. The newly developed mill extruder machine offers several advantages for industrial production on a large scale. It effectively reduces waste, saves energy, and enhances time efficiency. This system represents a significant advancement in the field, providing a new strategy for one-pot synthesis of various types of highly functionalized spirooxindoles and chromenes. Remarkably, these reactions can be accomplished within a short timeframe of 2–10 min, yielding impressive results of 75–98%. The results demonstrate superior performance compared to traditional reaction methods, making it an appealing tool and hotspot area of research in green chemistry.

Sustainable alternative to irrigated maize monoculture in a maize-dominated cropped area: Lessons learned from a system experiment

Maize is the most-produced food crop in the world and is grown in intensive rotations or in monoculture (continuous maize) systems. As maize production has expanded massively across the world, many concerns have emerged about its local environmental and other global impacts. Agronomists have designed innovative cropping systems and assessed them using system experiments to make arable systems more sustainable. However, knowledge is still lacking on the sustainability of innovative cropping systems compared to highly intensive systems such as irrigated maize-based monoculture. Here, we present the assessment results of a nine-year system experiment in Alsace, France, developed to compare an innovative system based on a diversified rotation and innovative management practices (three-year rotation of maize/soybean/winter wheat (plus a cover crop) combined with reduced tillage) with a continuous maize reference system. The results cover a six-year assessment period following an initial three-year design period. Classic criteria, such as profitability, workload, pesticide use, fossil energy consumption and nitrate leaching, were assessed along with other less studied criteria, such as pesticide leaching risk, soil structure, soil chemical quality and soil biological activity. Sustainability – which includes environmental, social and economic dimensions – was assessed with the MASC 2.0 method. Overall sustainability was substantially enhanced in the innovative system (5 out of 7 sustainability classes) in comparison with the low level of the reference system (2 out of 7). This was due to a clear improvement in the environmental performance (from 2 out of 5 to 5 out of 5) while social performance was high in both systems (4 out of 5) and economic performance was low (2 out of 5) due to very low contribution to economic development. Nevertheless, the innovative system had a major drawback: lower profitability, especially when scenarios included high maize prices. Furthermore, herbicide use on maize was higher in the innovative system than in the reference one. Avenues for progress, such as encouraging stakeholder participation at the assessment stage or additional innovations such as multiple cropping, are suggested.

Overview and Analysis of Ground Subsidence along China’s Urban Subway Network Based on Synthetic Aperture Radar Interferometry

Deformation along a subway rail network is related to the safe operation of the subway and the stability of construction facilities on the surface, making long-term deformation monitoring imperative. Long-term monitoring of surface deformation along the subway network and statistical analysis of the overall deformation situation are lacking in China. Therefore, targeting 35 Chinese cities whose subway mileage exceeds 50 km, we extracted their surface deformation along subway networks between 2018 and 2022, using spaceborne synthetic aperture radar (SAR) interferometry (InSAR) technology and Sentinel-1 satellite data. We verified the results with the continuous global navigation satellite system (GNSS) stations’ data and found that the root mean square error (RMSE) of the InSAR results was 3.75 mm/year. Statistical analysis showed that ground subsidence along the subways was more prominent in Beijing, Tianjin, and other areas in the North China Plain, namely Kunming (which is dominated by karst landforms), as well as Shanghai, Guangzhou, Qingdao, and other coastal cities. In addition, an analysis revealed that the severity of surface subsidence correlated positively with a city’s gross domestic product (GDP) with a Pearson correlation of 0.787, since the higher the GDP, the more frequent the construction and maintenance of subway, and the more commuters there are, which in turn exacerbates the disturbance to the surface. Additionally, the type of land cover also affects the ground deformation. Our findings provide a reference for constructing, operating, and maintaining the urban subway systems in China.

Technology for the Management of Type 1 Diabetes Mellitus in Saudi Arabia and MENA Region: A Systematic Review.

Diabetes technology in the form of digital health or medical devices holds a promise for improving the quality of life and glycemic outcomes. A comprehensive insight into diabetes technology and its impact in Saudi Arabia and the MENA region may improve type 1 diabetes mellitus (T1DM) management. This study aimed to assess the impact of different DM-specific technologies: insulin pump therapy, continuous glucose monitoring (CGM), and automated insulin delivery system in terms of glycemic control and QoL among T1DM patients in Saudi Arabia and the MENA region. A systematic literature search was performed in PubMed and Scopus from 2005 until August 2023. The search was based on the PICO strategy, focusing on T1DM patients, diabetes technology, and QoL. The inclusion criteria were studies illustrating the effect of diabetes technologies on glycemic control or quality of life or both among T1DM patients. Systematic reviews, books, letters, or studies, including type 2 diabetes mellitus, were excluded. From 101 articles, eighteen studies were duplicated, and thirty-three studies were excluded after reading the title and abstract. Of the 50 articles analyzed, twenty-five articles did not meet the inclusion criteria. Therefore, 25 articles involving a total of 3088 participants were enrolled in the study. It was shown that a continuous glucose monitoring system and continuous subcutaneous insulin infusion improved the glycemic control and the QoL of T1DM patients. There was a positive impact of insulin pumps, continuous glucose monitoring (CGM) systems, and telemedicine in achieving optimal glucose control and better QoL. Further studies are recommended to clarify the significant role of advanced diabetes technologies.

Enhancing hydrogen generation from sodium borohydride hydrolysis and the role of a Co/CuFe2O4 nanocatalyst in a continuous flow system

In this study, a series of cobalt-based spinel ferrites catalysts, including nickel, cobalt, zinc, and copper ferrites, were synthesized using the sol–gel auto-combustion method followed by a chemical reduction process. These catalysts were employed for accelerating hydrogen generation via the sodium borohydride hydrolysis process. A continuous stirred tank reactor was used to perform catalytic reactor tests. All samples were subjected to analysis using XRD, FESEM, EDX, FTIR, and nitrogen adsorption–desorption techniques. The results revealed that the cobalt-based copper ferrite sample, Co/Cu-Ferrite, exhibited superior particle distribution, and porosity characteristics, as it achieved a high hydrogen generation rate of 2937 mL/min.gcat. In addition, the higher electrical donating property of Cu-Ferrite which leads to the increase in the electron density of the cobalt active sites can account for its superior performance towards hydrolysis of NaBH4. Using the Arrhenius equation and the zero-order reaction calculation, activation energy for the sodium borohydride hydrolysis reaction on the Co/Cu-Ferrite catalyst was determined to be 18.12 kJ/mol. This low activation energy compared to other cobalt-based spinel ferrite catalysts confirms the catalyst's superior performance as well. Additionally, the outcomes from the recycling experiments revealed a gradual decline in the catalyst's performance after each cycle during 4 repetitive cycles. The aforementioned properties render the Co/Cu-Ferrite catalyst an efficient catalyst for hydrogen generation through NaBH4 hydrolysis.

Monitoring Forest Dynamics and Conducting Restoration Assessment Using Multi-Source Earth Observation Data in Northern Andes, Colombia

Examining the efficacy of current assessment methodologies for forest conservation and restoration initiatives to align with global and national agendas to combat deforestation and facilitate restoration efforts is necessary to identify efficient and robust approaches. The objective of this study is to understand forest dynamics (1996–2021) and assess restoration implementations at the Urra’s supplying basin hydroelectric reservoir in Colombia. The processing approach integrates optical and radar Earth Observation (EO) data from Sentinel-2 and Landsat for forest mapping and multi-temporal forest change assessment (1996–2021), and a Sentinel-1 backscatter time-series analysis is conducted to assess the state of forest restoration implementations. The processing chain was scaled in a cloud-based environment using the Nebari and SEPPO software and the Python language. The results demonstrate an overall substantial decrease in forested areas in the 1996–2000 period (37,763 ha). An accuracy assessment of multi-temporal forest change maps showed a high precision in detecting deforestation events, while improvements are necessary for accurately representing non-forested areas. The forest restoration assessment suggests that the majority of the 270 evaluated plots are in the intermediate growth state (82.96%) compared to the reference data. This study underscores the need for robust and continuous monitoring systems that integrate ground truth data with EO techniques for enhanced accuracy and effectiveness in forest restoration and conservation endeavors.

Efficient Synthesis of (S)-Nornicotine using Co-Immobilized IRED and GDH in Batch and Continuous Flow Reaction Systems

Efficient Synthesis of (<i>S</i>)-Nornicotine using Co-Immobilized IRED and GDH in Batch and Continuous Flow Reaction Systems

Impact of diversified grazing systems on milk production, nutrient use and enteric methane emissions in dual-purpose cows.

This study evaluated three continuous grazing systems: Brachiaria Brizantha, Clitoria ternatea and naturalized pastures, complemented with commercial concentrate and C. ternatea silage on milk yield, nutrient use and enteric methane (CH4) emissions. Nine multiparous cows of local Zebu breeds, with an average weight of 448 ± 87kg, were used. The chemical composition of the food was determined. Live weight, milk production, and quality were assessed. Furthermore, serum urea, urea nitrogen, creatinine and glucose in blood were monitored, and nitrogen use efficiency were calculated. Enteric methane (CH4) emissions were estimated using Tier-2 methodology. A 3 × 3 latin square experimental design was applied. The grazing systems of B. brizantha and C. ternatea had the greater live weights of 465.8 and 453.3kg/cow, although the latter is similar to naturalized pasture. Milk production and quality were not affected by grazing system, with the exception of the non-fat solids, where the C. ternatea system was lower (102.2g/kg) than the other grazing systems. The crude protein and N intake, and N excretion in feces and urine were lower in naturalized pasture systems (1139.0g/day). N outputs in milk was high in the C. ternatea system (56.3g/cow/day). The naturalized pastures systems showed the better feed use efficiency (25.7%) compared to others. Serum urea and blood urea nitrogen were greater in B. brizantha followed by C. ternatea. Enteric CH4 emissions were indifferent among grazing systems when expressed as a percentage of greenhouse gases (7.1%). In conclusion, the grazing C. ternatea supplemented with commercial concentrate and C. ternatea silage maintains milk production and quality, reduced cow/day emissions (by 2.5%) and lowered energy losses as methane.

Polytopic energy-to-peak filter design for uncertain continuous systems over finite frequency ranges

A new approach for the synthesis of polytopic L 2 − L ∞ filters is proposed for uncertain systems over Finite Frequency (FF) ranges. The novelty derives from the use of FF specifications combined with the L 2 − L ∞ performance index: First, conditions are provided for the asymptotic stability of the polytopic augmented systems, by trace calculation of matrices and the use of Finsler's Lemma. Then, synthesis conditions are proposed as LMIs for the polytopic filter parameters over several FF intervals. Furthermore, the use of slack matrices through Finsler's Lemma provides design conditions with less conservative outcomes. A numerical example is solved and simulated to demonstrate the effectiveness of the ongoing approach when compared with previous results in the literature.