System Input Research Articles

Efficiency scaling of inductively coupled plasmas for radio frequency propulsion systems

AbstractA generalized inductively coupled plasma (ICP) efficiency model is derived, which may be used to predict plasma generation efficiency with many types of radio frequency (RF) electric propulsion system. The analytical ICP discharge model is used to examine device scaling, and investigate trends in ICP performance as a function of geometry and system inputs. The ICP efficiency model is applied to an analytical RF cathode model to predict device behavior. Results from the updated RF cathode model show improvements in agreement with experimental data.

Adaptive third-order integral sliding mode control for the mobile petroleum crane-form pipelines system with input bounded

The mobile petroleum crane-form pipelines (MPCFP) system is one of the most important mechanical equipment in the petrochemical industry, consisting of rigid pipelines and rotating elbows, mainly used for loading and unloading petrochemical fluids. This paper proposes a novel neural network adaptive third-order integral sliding mode tracking control strategy with bounded inputs to achieve automatic alignment between the MPCFP system and the inlet of the tank truck. The proposed strategy replaces normal system inputs with the time derivative of the control torque. Although there are discontinuous terms in the derivative of the control torque, the integrated control torque is continuous. This unique feature ensures finite-time convergence of system state errors and eliminates sliding mode chattering. To address the issues of bounded input and modeling error, a state compensation method is introduced, and a neural network adaptive algorithm is utilized to estimate the modeling error. The stability proof process employs the Lyapunov function method, demonstrating that the system state error can converge to zero. Numerous simulation experiments validate the effectiveness of this control strategy.

Prediction of long-term future runoff under multi-source data assessment in a typical basin of the Yangtze River

Study regionThree Typical Basins of the Yangtze River (YRB), China Study focusMeteorological factors, such as precipitation, are key drivers of the hydrological system and critical inputs in hydrological modeling, and accurate meteorological data are essential to simulate hydrological processes. This study compares and evaluates the CN05.1, CMFD, and ERA5-L and meteorological forcing datasets in three typical basins of the Yangtze River and predicts the future runoff changes in the basin based on CMIP6 data. New hydrological insights for the regionThis study indicated that (1) the CN05.1 data exhibit the best applicability on the interannual and intra-annual scales in YRB, while ERA5-Land performs better in the upper reaches, and CMFD is more suitable for the middle and lower reaches. (2) Future runoff is projected to initially decrease and then increase, with the inflection and inflections points of runoff occurring earlier in the ssp585 scenario compared to the ssp245. The risk of spring flooding is increasing in CS and JZ river basin, and the risk of flooding is decreasing in the FH River Basin as the flood peaks are earlier. (3) Significant trend changes are anticipated in the future, with climate change contributing over 90 % of the runoff changes in the CS, while human factors will increasingly influence the JZ and FH basins.

Fixed-Time Control with an Improved Sparrow Search Algorithm for Robotic Arm Performance Optimization

This paper presents an innovative approach that integrates a fixed-time control (FTC) algorithm with an improved sparrow search algorithm (ISSA) to enhance the trajectory tracking accuracy of a two-degree-of-freedom (two-DOF) robotic arm. The FTC algorithm, which incorporates barrier Lyapunov function (BLF) and adaptive neural network strategies, ensures rapid convergence, effective vibration suppression, and the robust handling of system uncertainties and input saturation. The ISSA, inspired by the foraging behavior of sparrows, improves search efficiency through dynamic weight adjustments and chaotic mapping, balancing global and local search capabilities. By optimizing control parameters, ISSA minimizes tracking errors. Simulation results demonstrate that the combined FTC and ISSA approach significantly reduces tracking errors and improves response speed compared to the use of FTC alone, underscoring its potential for achieving high-precision control in robotic arms and offering a promising direction for precise robotic control applications.

Phosphatization in the São Pedro and São Paulo Archipelago (SPSPA), Equatorial Atlantic, Brazil: Insights from guano leaching and chemical weathering

This research explores the phosphatization process in the São Pedro and São Paulo Archipelago (SPSPA), located in the Equatorial Atlantic, by analyzing the geochemical interactions between guano coatings and the underlying rocks. The study also examines the geochemical characteristics of the substrates, assessing the behavior of major, minor, trace, and rare earth elements, and determines the degree of weathering degradation of guano crusts, speleothems, peridotites and carbonate sedimentary rocks affected by environmental factors as rain, wind and halmyrolysis. The distribution of chemical elements in water-soluble, adsorbed, acid-extractable/exchangeable, readily acid and reducible, acid oxidizable, and residual phases were investigated. The principal findings of this method show that elevated Na levels are leached in water-soluble fractions, thereby highlighting the occurrence of rapid and active geochemical cycling, which is influenced by seawater. In contrast, in more acidic environments (pH close 4.8 to 2.0), considerable amounts of macroelements like P, Fe, Mg, Al, and Mn are found in association with remaining phases, indicating a slower and more incremental geochemical cycle of mature guano in the input and output systems of these nutrients in the archipelago. The mineralogy, as indicated by the presence of mafic minerals such as olivine, augite, and Cr-spinel, as well as phosphates including fluorapatite, collinsite, and spheniscidite, also supports this finding. The findings underscore the complex interplay between biological activities and geochemical processes, which influence the elemental dynamics in the phosphate-rich rocks of the SPSPA at depth. This study not only enhances our understanding of phosphatization processes but also offers important insights into the sustainable management of marine resources in environmentally sensitive areas. The research underscores the necessity of considering biological and environmental variables when examining geochemical cycles, particularly in regions exhibiting distinctive geological and biological interactions that challenge the prevailing models, such as those identified in the ASPSP.

Fixed-time prescribed performance control for nonlinear multi-agent systems with novel uncertainties

This paper addresses an approach to fixed-time prescribed performance control design for multi-agent systems with novel uncertainties, i.e., the uncertainties from system input powers, and a new neural backstepping control design is proposed such that the prescribed performance can be guaranteed within a fixed-time. Unlike existing adaptive results, the upper boundary of stability time is decided only by the design parameters and also is independent of the initial states of the system. By leveraging Lyapunov stability theory and graph theory, it is demonstrated that every follower agent can synchronize to the leader agent, and tracking errors converge to the origin’s neighborhood. Finally, the simulation results verify the effectiveness of the control strategy.

Fresh insights into the light‐induced pineal gland circadian rhythm transmission mechanism derived from mRNA and miRNA profiling

AbstractThe circadian clock significantly impacts animal health and productivity, with light playing a crucial role in regulating circadian rhythms. However, the mechanisms behind light‐induced circadian transmission remain unclear, particularly in light‐sensitive avian species. The pineal gland is a key component acting as the photosensitive master oscillator in the avian clock system. Using transcriptome sequencing and small RNA sequencing technologies, we identified circadian genes and miRNAs in the chick pineal gland under light–dark and sudden constant‐light conditions. We observed rhythmic oscillations in up to 1299 genes during the light–dark cycle, with 400 genes maintaining rhythms under constant light. Our findings highlight the light‐sensitive temporal organization in birds as the phase distribution of circadian genes in the pineal gland correlates with light exposure changes. A novel regulatory mechanism involving light, cyclic adenosine monophosphate, cyclic guanosine monophosphate, light‐sensitive miRNAs, such as gga‐miR‐34b‐5p, and light‐sensitive circadian genes, such as CRY2, was discovered to participate in the light input system of the chick pineal clock, through which light regulates the oscillators and outputs of the circadian clock system. Additionally, transcriptomic analysis, liquid chromatography–mass spectrometry, and Oil Red O staining revealed cyclic changes in lipid synthesis and metabolism throughout the circadian day, which may be a key mechanism through which the circadian clock influences pineal physiology. Our results enhance the understanding of light‐induced circadian transmission mechanisms and identify potential targets for optimizing the circadian clock through light.

The Neuroaesthetics of Art and Design Education

AbstractTeaching is increasingly defined through the syntax of cognitive science, by retrieval practice, spaced learning, and interleaving, generating a computational rhythm for learning as a system of inputs and outputs that builds up an individual's memory over time. This, I argue, is at odds with the choreography of art and design education as an aesthetic, social, and material practice. An alternative mapping is required to fully understand the chronology of learning that takes place in and through the subject of art and design with human and nonhuman others. Drawing from a review of research in the field of Neuroaesthetics, I will seek to defend the unique temporality of art and design education and imagine different visualisations of learning in the subject beyond the computational.

Georgia’s potentials for sustainable intensification, increasing food security and rural incomes.

Abstract Increasing global demand for agricultural commodities spurs conversions of natural ecosystems. Sustainable intensification in areas of high yield gaps has been proposed a pathway to achieve food security, support rural livelihoods, while also reducing the impact of commodity production by narrowing yield gaps on existing agricultural lands, while improving resource efficiency. Following the dissolution of the Union of Soviet Socialist Republics (USSR), Georgia experienced one of the highest losses of agricultural productivity among all former USSR countries and is now highly dependent on food imports. Closing yield gaps in Georgia through sustainable intensification has the potential to increase food self-sufficiency, support rural livelihoods, and strengthen food security and sovereignty. We estimated its potential for sustainable intensification in current agricultural areas to achieve self-sufficiency for wheat, maize, and barley. We found that crop yields can be doubled to tripled under high-input production systems, using high-yielding varieties, optimized inputs, fertilizers, and pest control. Yet, self-sufficiency in wheat can only be reached if at least 80% of the potentially attainable yields are achieved and if land is strategically allocated between crops. To achieve such increases, farmers need access to and training for using different crop varieties, fertilizers, and pest and disease control practices and products. Intensification increases the risks to ecosystem services’ health and livelihoods, particularly raising equity concerns. Yet, intensifying very low input systems is often found to be more sustainable, with high yield increases compared to limited impacts on the environment. The high employment rate in the agricultural sector in Georgia particularly provides opportunities to reduce poverty and increase livelihoods through increasing incomes and food security.

Dynamic Self‐Triggered Parallel Control of Guidance Intercept Systems With Constrained Inputs via Adaptive Dynamic Programming

ABSTRACTThis paper introduces an anti‐saturation optimization algorithm based on dynamic self‐triggered adaptive dynamic programming (ADP) for bounded acceleration guidance interception. By establishing a nonlinear input‐constrained guidance intercept and control system, the smooth bounded function is used to constrain the system input to ensure that the system operates in a controllable range. Subsequently, the appropriate performance function that can accurately reflect the system is designed. In alignment with the advantages of parallel control and ADP, a group of parallel systems are constructed by modeling the derivation of control input. A self‐learning control framework is explored, facilitating virtual‐actual interaction and mutual reinforcement between multiple controllers, optimizing the management of the interception system. Furthermore, event‐triggered control (ETC) policies are devised, which can be updated only when needed by setting trigger conditions, thus saving data resources. The stability proof of the closed‐loop system is given to ensure that the system can keep stable operation when the trigger condition is satisfied. On this basis, a dynamic self‐triggered control (DSTC) with soft computing is put forward, enabling trigger instant calculation without real‐time system monitoring and further extending the trigger interval. Simulation results evidence that the devised guidance scheme can intercept the maneuvering target at a reduced expenditure of communication resources.

Determinants of profit among maize farmers in Osogbo ADP zone of Osun State, Nigeria

Maize output in Nigeria is declining and worsening economic situations continue to afflict rural households increasing the level of food insecurity. This study examined the determinants of profit among maize farmers in Osogbo ADP zone of Osun State, Nigeria. Primary data were sourced using a well-structured questionnaire from a total of 120 maize farmers. A two-stage sampling procedure was employed to select the farmers. The analytical tools used were descriptive statistics, farm budgetary techniques and regression analysis. The results showed that maize cultivation was dominated by male farmers with an average age of 50 ± 9.4 years. Additionally, less than 30.8% of the maize farmers had formal education above secondary school. The farming experience of the maize farmers was 14.7 ± 5.3 years and the average farm size was estimated at 7.85 ± 2.15 acres. The average farm income was N207 314.40 ± 23 290.95 with the Benefit-Cost Ratio (BCR) of 2.24. The major constraints of maize production, ranked based on their higher values of WMS, were the high labour costs, expenditures for pest and disease control, and the high costs of fertilizers The determinants of farm income that had positive relationship and were significant at 1% level of significance include years of formal education, expenditure on fertilizer while age of the framers negatively influenced the farm income. Also, the farm size and years of farming experience were positive and statistically significant to farm income at 5%. The study suggests a policy focus of training farmers in modern farming system and implement sustainable farm inputs subsidy programs. Keywords: determinants, profit, maize, farmers, Nigeria

The ketone body β-hydroxybutyrate ameliorates neurodevelopmental deficits in the GABAergic system of daf-18/PTEN Caenorhabditis elegans mutants.

A finely tuned balance between excitation and inhibition (E/I) is essential for proper brain function. Disruptions in the GABAergic system, which alter this equilibrium, are a common feature in various types of neurological disorders, including autism spectrum disorders (ASDs). Mutations in Phosphatase and Tensin Homolog (PTEN), the main negative regulator of the phosphatidylinositol 3-phosphate kinase/Akt pathway, are strongly associated with ASD. However, it is unclear whether PTEN deficiencies can differentially affect inhibitory and excitatory signaling. Using the Caenorhabditis elegans neuromuscular system, where both excitatory (cholinergic) and inhibitory (GABAergic) inputs regulate muscle activity, we found that daf-18/PTEN mutations impact GABAergic (but not cholinergic) neurodevelopment and function. This selective impact results in a deficiency in inhibitory signaling. The defects observed in the GABAergic system in daf-18/PTEN mutants are due to reduced activity of DAF-16/FOXO during development. Ketogenic diets (KGDs) have proven effective for disorders associated with E/I imbalances. However, the mechanisms underlying their action remain largely elusive. We found that a diet enriched with the ketone body β-hydroxybutyrate during early development induces DAF-16/FOXO activity, therefore improving GABAergic neurodevelopment and function in daf-18/PTEN mutants. Our study provides valuable insights into the link between PTEN mutations and neurodevelopmental defects and delves into the mechanisms underlying the potential therapeutic effects of KGDs.

The ketone body β-hydroxybutyrate ameliorates neurodevelopmental deficits in the GABAergic system of daf-18/PTEN Caenorhabditis elegans mutants

A finely tuned balance between excitation and inhibition (E/I) is essential for proper brain function. Disruptions in the GABAergic system, which alter this equilibrium, are a common feature in various types of neurological disorders, including autism spectrum disorders (ASDs). Mutations in Phosphatase and Tensin Homolog (PTEN), the main negative regulator of the phosphatidylinositol 3-phosphate kinase/Akt pathway, are strongly associated with ASD. However, it is unclear whether PTEN deficiencies can differentially affect inhibitory and excitatory signaling. Using the Caenorhabditis elegans neuromuscular system, where both excitatory (cholinergic) and inhibitory (GABAergic) inputs regulate muscle activity, we found that daf-18/PTEN mutations impact GABAergic (but not cholinergic) neurodevelopment and function. This selective impact results in a deficiency in inhibitory signaling. The defects observed in the GABAergic system in daf-18/PTEN mutants are due to reduced activity of DAF-16/FOXO during development. Ketogenic diets (KGDs) have proven effective for disorders associated with E/I imbalances. However, the mechanisms underlying their action remain largely elusive. We found that a diet enriched with the ketone body β-hydroxybutyrate during early development induces DAF-16/FOXO activity, therefore improving GABAergic neurodevelopment and function in daf-18/PTEN mutants. Our study provides valuable insights into the link between PTEN mutations and neurodevelopmental defects and delves into the mechanisms underlying the potential therapeutic effects of KGDs.

Characterizing the Areal Extent of PFAS Contamination in Fish Species Downgradient of AFFF Source Zones.

Most monitoring programs next to large per- and polyfluoroalkyl substances (PFAS) sources focus on drinking water contamination near source zones. However, less is understood about how these sources affect downgradient hydrological systems and food webs. Here, we report paired PFAS measurements in water, sediment, and aquatic biota along a hydrological gradient away from source zones contaminated by the use of legacy aqueous film-forming foam (AFFF) manufactured using electrochemical fluorination. Clustering analysis indicates that the PFAS composition characteristic of AFFF is detectable in water and fishes >8 km from the source. Concentrations of 38 targeted PFAS and extractable organofluorine (EOF) decreased in fishes downgradient of the AFFF-contaminated source zones. However, PFAS concentrations remained above consumption limits at all locations within the affected watershed. Perfluoroalkyl sulfonamide precursors accounted for approximately half of targeted PFAS in fish tissues, which explain >90% of EOF across all sampling locations. Suspect screening analyses revealed the presence of a polyfluoroketone pharmaceutical in fish species, and a fluorinated agrochemical in water that likely does not accumulate in biological tissues, suggesting the presence of diffuse sources such as septic system and agrochemical inputs throughout the watershed in addition to AFFF contamination. Based on these results, monitoring programs that consider all hydrologically connected regions within watersheds affected by large PFAS sources would help ensure public health protection.

Decoupled Model-Free Adaptive Control with Prediction Features Experimentally Applied to a Three-Tank System Following Time-Varying Trajectories

In this paper, the performance of three model-free control approaches on a multi-input, multi-output (MIMO) nonlinear system with constant and time-varying references is compared. The first control algorithm is model-free adaptive control (MFAC). The second is a modified version of MFAC (MMFAC) designed to handle delays in the system by incorporating the output error difference (over two sample time steps) in the control input. The third approach, model-free adaptive predictive control (MFAPC) with a one-step-ahead forecast of the system input, is obtained by using predictions of the outputs based on the data-based linear model. The experimental device used is an MIMO three-tank system (3TS) assumed to be an interconnected system with multiple coupled single-input, single-output (SISO) subsystems with unmeasurable couplings. The novelty of this contribution is that each coupled SISO partition is assumed to be controlled independently using a decoupled control algorithm, leading to fewer control parameters compared to a centralized MIMO controller. Additionally, both parameter tuning for each controller and performance evaluation are conducted using an evaluation criterion considering energy consumption and accumulated tracking error. The results demonstrate that almost all the proposed model-free controllers effectively control an MIMO system by controlling its SISO subsystems individually. Moreover, the predictive features in the decoupled MFAPC contribute to more accurate tracking of time-varying references. The utilization of tracking error differences helps in reducing energy consumption.

Nonlinear normal modes and response to random inputs of systems with bilinear stiffness

This work seeks to provide a comprehensive review of the effects that stiffness bilinearity can have on the nonlinear modes of a system, its response in a random vibration environment, and the connection between the two. Stiffness bilinearity here refers to a continuous piecewise linear force vs displacement function that is composed of two linear regions. This work focuses on a bilinear stiffness function that is regularized so there is a smooth transition at the point where the two linear regions meet. A single-degree-of-freedom system (SDOF) and a two-degree-of-freedom (2DOF) system are explored and several interesting behaviors are shown. The SDOF bilinear spring model is characterized by four parameters: the low amplitude frequency, the ratio of the linear stiffnesses on either side of the transition, the displacement at which the transition occurs, and the rate or sharpness of the transition. The effect of each parameter on the shape of the NNM is described. In a 2DOF system, these parameters have similar effects, but modal coupling is found to play a significant role. When a bilinear system is subjected to random excitation, many harmonics appear in the response for both the SDOF and 2DOF cases. The root-mean-square (RMS) response of the bilinear system can be larger or smaller than the corresponding linear case depending on the values of the parameters and the type of forcing (broadband, bandlimited, in the shape of a vibration mode, etc.). However, many cases were observed in which the RMS response of the bilinear system was almost the same as that of a linear system, and hence the response could be predicted well using linear analysis. It is hoped that the results presented herein can assist engineers in helping to determine when linear analysis would be adequate to predict the failure of a system, when a rigorous nonlinear analysis is required, and what phenomena are likely to be observed in the latter case.

Control of continuous digester kappa number using generalized model predictive control

Kappa number variability at the digester impacts pulp yield, physical strength properties, and lignin content for downstream delignification processing. Regulation of the digester kappa number is therefore of great importance to the pulp and paper industry. In this work, an industrial application of model-based predictive control (MPC), based on generalized prediction control, was developed for kappa number feedback control and applied to a dual vessel continuous digester located in Western Canada. The problem was complicated by the need to apply heat at multiple locations in the cook. In this study, the problem was reduced from a multiple to a single input system by identifying three potential single variable permutations for temperature adjustment. In the end, a coordinated approach to the heaters was adopted. The process was perturbed and modeled as a simple first order plus dead time model and implemented in generalized predictive control (GPC). The GPC was then configured to be equivalent to Dahlin’s controller, which reduced tuning parameterization to a single closed loop time constant. The controller was then tuned based on robustness towards a worst-case dead time mismatch of 50%. The control held the mean value of the kappa number close to the setpoint, and a 40% reduction in the kappa number’s standard deviation was achieved. Different kappa number trials were run, and the average fiberline yield for each period was evaluated. Trial results suggested yield gains of 0.3%–0.5% were possible for each 1 kappa number target increase.

Neural relational and dynamics inference for complex systems

Many complex processes in the real world can be viewed as complex systems and their evolution is governed by underlying nonlinear dynamics. However, one can only access the trajectories of the system without knowing the underlying system structure and dynamics in most cases. To address this challenge, this paper proposes a model called Neural Relational and Dynamics Inference (NRDI) that combines graph neural networks (GNNs) and ordinary differential equation systems (ODEs) to handle both continuous-time dynamics prediction and network topology inference for complex systems. Our model contains two modules: (1) the network inference module, which infers system structure from input system trajectories using GNNs, and (2) the dynamics learning module, which employs GNNs to fit the differential equation system for predicting future trajectories. We tested NRDI’s performance on system trajectory prediction and graph reconstruction separately. Experimental results show that the proposed NRDI outperforms many baseline models on continuous-time complex network dynamics prediction, and can explicitly infer network structures with high accuracy.

Development of exhaust gas insulation system for quarry diesel locomotives

This study focuses on developing an innovative device for isolating and managing exhaust gases from diesel locomotive engines, specifically aiming to reduce environmental impact and enhance operational safety. The study used experimental analysis, validated results using Cochrane and Fisher criteria, and conducted empirical data analysis. Study utilized an experimental stand with a 1.9-liter Volkswagen diesel engine, ZIL-130 compressor, custom exhaust gas storage system with tanks, thermometers, pressure gauges, and control valves. The study developed a mathematical model analysing input parameters and system outputs, achieving 95% accuracy, proving its reliability in representing exhaust gas dynamics of TEM2 diesel locomotives.

Designing a practical physical framework for AI-enhanced sound detection and localization in vehicles

This paper presents the practical framework of an AI-implemented system currently in development for the detection and localization of external sound events to the vehicle. We delve into the initial block of the automotive project, analyzing sounds of interest and the noise we encounter, addressing the strategic arrangement of the four microphones on the sides of the vehicle, conceived as the sound input system. This configuration not only optimizes signal capture but also acts as sensitive ears, allowing the system to effectively analyze the acoustic environment. To mitigate wind interference, we have designed a microphone placement system that considers the vehicle's aerodynamics. The strategic location and implementation of aerodynamic protectors significantly minimize the impact of wind on the microphones, ensuring a sharper capture of acoustic signals. Complementing this arrangement, we apply advanced noise elimination processes, further enhancing the quality of the captured signals. Although in its early stages, this project aspires to provide effective solutions for vehicular acoustic perception, marking the beginning of a journey towards innovation in the detection and localization of sounds in mobile environments.