Optimal Time Scale Research Articles

Crop water productivity assessment and planting structure optimization in typical arid irrigation district using dynamic Bayesian network

Enhancing crop water productivity is crucial for regional water resource management and agricultural sustainability, particularly in arid regions. However, evaluating the spatial heterogeneity and temporal dynamics of crop water productivity in face of data limitations poses a challenge. In this study, we propose a framework that integrates remote sensing data, time series generative adversarial network (TimeGAN), dynamic Bayesian network (DBN), and optimization model to assess crop water productivity and optimize crop planting structure under limited water resources allocation in the Qira oasis. The results demonstrate that the combination of TimeGAN and DBN better improves the accuracy of the model for the dynamic prediction, particularly for short-term predictions with 4 years as the optimal timescale (R2 > 0.8). Based on the spatial distribution of crop suitability analysis, wheat and corn are most suitable for cultivation in the central and eastern parts of Qira oasis while cotton is unsuitable for planting in the western region. The walnuts and Chinese dates are mainly unsuitable in the southeastern part of the oasis. Maximizing crop water productivity while ensuring food security has led to increased acreage for cotton, Chinese dates and walnuts. Under the combined action of the five optimization objectives, the average increase of crop water productivity is 14.97%, and the average increase of ecological benefit is 3.61%, which is much higher than the growth rate of irrigation water consumption of cultivated land. It will produce a planting structure that relatively reduced irrigation water requirement of cultivated land and improved crop water productivity. This proposed framework can serve as an effective reference tool for decision-makers when determining future cropping plans.

Predicting concentration changes via discrete receptor sampling

To successfully navigate chemical gradients, microorganisms need to predict how the ligand concentration changes in space. Due to their limited size, they do not take a spatial derivative over their body length but rather a temporal derivative, comparing the current signal with that in the recent past over the so-called adaptation time. This strategy is pervasive in biology, but it remains unclear what determines the accuracy of such measurements. Using a generalized version of the previously established sampling framework, we investigate how resource limitations and the statistics of the input signal set the optimal design of a well-characterized network that measures temporal concentration changes: the chemotaxis network. Our results show how an optimal adaptation time arises from the trade-off between the sampling error, caused by the stochastic nature of the network, and the dynamical error, caused by uninformative fluctuations in the input. A larger resource availability reduces the sampling error, which allows for a smaller adaptation time, thereby simultaneously decreasing the dynamical error. Similarly, we find that the optimal adaptation time scales inversely with the gradient steepness, because steeper gradients lift the signal above the noise and reduce the sampling error. These findings shed light on the principles that govern the optimal design of the chemotaxis network specifically, and any system measuring temporal changes more broadly. Published by the American Physical Society 2024

A smooth method for primary frequency of wind turbine based on variable power point tracking and energy storage cooperative control

AbstractThe conventional deloading control has certain problems, such as low power generation efficiency and a small speed adjustment range. To improve the system's frequency quality and enhance the power grid's stability, this study comprehensively considers the effect of random source‐load power fluctuations on the system frequency. Moreover, this study proposes a smooth primary frequency control strategy for wind turbine based on the coordinated control of the variable power point tracking and supercapacitor energy storage. The impact of wind power fluctuations on the system frequency at different timescales for wind turbine is studied based on the historical data of wind power fluctuations in a strong wind meteorological cycle of a wind farm. The method determines the capacity of the energy storage device required for frequency smoothing at the optimal timescale. In combination with the required capacity of the wind turbine to participate in the system's primary frequency regulation, the supercapacitor energy storage device is optimally configured, and a set of supercapacitor energy storage device with the lowest cost under the highest charging/discharging efficiency is designed. Simulation and experimental results show that the primary frequency adjustment capability of the control strategy proposed in this study is significantly improved compared with the conventional primary frequency modulation control.

Optimizing Drought Assessment for Soil Moisture Deficits

AbstractAccurate drought assessments are critical for mitigating the deleterious impacts of water scarcity on communities across the world. In many regions, deficits in soil moisture represent a key driver of drought conditions. However, relationships between soil moisture and widely used drought indicators have not been thoroughly evaluated. In addition, there has not been an in‐depth assessment of the accuracy of operational soil moisture models used for drought monitoring. Here, we used 2,405 observed time series of soil moisture from 637 long‐term monitoring stations across the conterminous United States to test the ability of meteorological drought indices and soil moisture models to accurately characterize soil moisture drought. The optimal timescales for meteorological drought indices varied substantially by depth, but were ∼30 days for depth averaged conditions; progressively longer timescales (∼10–80 days) represent progressively deeper soil moisture (2–36 in.). However, soil moisture models (including Short‐term Prediction Research and Transition Center, Soil Moisture Active Passive L4, and Topofire) significantly outperformed the meteorological drought indices for predicting standardized soil moisture anomalies and drought conditions. Additionally, soil moisture models represent near instantaneous conditions, implicitly aggregating antecedent data thereby eliminating the need for timescales, providing a more effective and convenient method for soil moisture drought monitoring. We conclude that soil moisture models provide a straightforward and favorable alternative to meteorological drought indices that better characterize soil moisture drought.

Operational Risk Measurement: An Optimal Timescale Selection Method Based on Information Entropy

Operational risk capital is typically allocated on an annual timescale in extant research, while ignoring that the dependence between operational risk events may differ on different timescales, such as semi-annual and quarterly. This study examines whether operational risk dependence structures differ on multiple timescales and proposes an optimal timescale selection method based on information entropy theory to obtain more reasonable operational risk measurement results. Different dependence modeling methods, including linear correlation and copula functions, are employed to analyze the dependence structures on multiple timescales. The information changes between different timescales are calculated to select the optimal timescale. The empirical analysis is based on 2,024 operational risk events from 1994 to 2017 in the Chinese Operational Loss Database (COLD), which is one of the largest external operational risk datasets for the Chinese banking industry. The results reveal significant differences in operational risk dependence at different timescales which affect the operational risk measurement results. Crucially, the quarterly timescale should be considered for more reasonable measurements. The findings provide important insights for considering a more reasonable allocation of operational risk capital under multiple timescale dependence.

Twice-optimal control design with construction pruning for second order plus time delay systems

To effectively control a class of second-order plus time delay (SOPTD) systems, based on twice-optimal control (TOC) and construction pruning (CP) methods, an SOPTD-TOCCP controller is proposed, which can achieve strong robustness and excellent set-point tracking performance. The TOC controller of the SOPTD is designed based on a classical cascade controller and an extended state observer (ESO). A fast and accurate method is proposed to help engineers obtain the optimal time scale, which is the most critical parameter for regulating control performance. The influence of different parameter sensitivities on SOPTD is studied. In addition, a new robust enhancement method is proposed for SOPTD systems. A construction pruning method for SOPTD systems is proposed to further improve control performance, particularly robustness. Finally, a comparison with other control methods demonstrates that the SOPTD-TOCCP controller is simple, reliable, and versatile and can achieve better control performance.

Crucial roles of the optimal time-scale of water condition on grassland biomass estimation on Qinghai–Tibet Plateau

The effect of the time-scale of water conditions on vegetation productivity has been widely studied by the academic community. However, the relationship between the time-scale of water conditions and the vegetation growth rhythm and the effect of this relationship on vegetation biomass estimation have rarely been discussed. Here, we analyzed the occurrence times of major phenological events on alpine grasslands using the widely distributed “site-dominant species” dataset and set a series of time-scales for accumulated precipitation and standardized precipitation evapotranspiration index based on phenological information. Then, we combined large-scale aboveground/belowground biomass datasets to evaluate the role of the optimal time-scale for water conditions in aboveground/belowground biomass estimation. The results showed that (1) the optimal time-scale for water conditions with the greatest effects on aboveground biomass was on the month before the end of flowering or the onset of fruit maturity. The optimal time-scale for water condition effects on belowground biomass was earlier and longer than that for the aboveground biomass. The optimal time-scales for accumulated precipitation and standardized precipitation evapotranspiration index effects on belowground biomass were at five months before the end of flowering or the beginning of fruit ripening and the three months before the first flowering, respectively. (2) The aboveground and belowground biomass were underestimated by 11 % and 9 %, respectively, when the water conditions at the optimal time-scales were ignored. (3) The interannual variability in aboveground/belowground biomass was more effectively captured by considering the optimal time-scales of water conditions, especially in water-restricted areas. Overall, this study indicated that terrestrial carbon cycle models should incorporate information on the lag-effects of the water conditions in previous periods. In the future, increasing the number of belowground biomass observations and conducting monthly belowground biomass monitoring sooner will be key to revealing the mechanisms of the belowground biomass response to climate change.

Two-Level Scheme for Identification of the Relaxation Time Spectrum Using Stress Relaxation Test Data with the Optimal Choice of the Time-Scale Factor.

The viscoelastic relaxation spectrum is vital for constitutive models and for insight into the mechanical properties of materials, since, from the relaxation spectrum, other material functions used to describe rheological properties can be uniquely determined. The spectrum is not directly accessible via measurement and must be recovered from relaxation stress or oscillatory shear data. This paper deals with the problem of the recovery of the relaxation time spectrum of linear viscoelastic material from discrete-time noise-corrupted measurements of a relaxation modulus obtained in the stress relaxation test. A two-level identification scheme is proposed. In the lower level, the regularized least-square identification combined with generalized cross-validation is used to find the optimal model with an arbitrary time-scale factor. Next, in the upper level, the optimal time-scale factor is determined to provide the best fit of the relaxation modulus to experiment data. The relaxation time spectrum is approximated by a finite series of power-exponential basis functions. The related model of the relaxation modulus is proved to be given by compact analytical formulas as the products of power of time and the modified Bessel functions of the second kind. The proposed approach merges the technique of an expansion of a function into a series of independent basis functions with the least-squares regularized identification and the optimal choice of the time-scale factor. Optimality conditions, approximation error, convergence, noise robustness and model smoothness are studied analytically. Applicability ranges are numerically examined. These studies have proved that using a developed model and algorithm, it is possible to determine the relaxation spectrum model for a wide class of viscoelastic materials. The model is smoothed and noise robust; small model errors are obtained for the optimal time-scale factors. The complete scheme of the hierarchical computations is outlined, which can be easily implemented in available computing environments.

Phase-Space Geometry and Optimal State Preparation in Quantum Metrology with Collective Spins

We revisit well-known protocols in quantum metrology using collective spins and propose a unifying picture for optimal state preparation based on a semiclassical description in phase space. We show how this framework allows for quantitative predictions of the timescales required to prepare various metrologically useful states, and that these predictions remain accurate even for moderate system sizes, surprisingly far from the classical limit. Furthermore, this framework allows us to build a geometric picture that relates optimal (exponentially fast) entangled probe preparation to the existence of separatrices connecting saddle points in phase space. We illustrate our results with the paradigmatic examples of the two-axis countertwisting and twisting-and-turning Hamiltonians, where we provide analytical expressions for all the relevant optimal timescales. Finally, we propose a generalization of these models to include p-body collective interaction (or p-order twisting), beyond the usual case of p=2. Using our geometric framework, we prove a no-go theorem for the local optimality of these models for p>2.Received 8 November 2022Revised 1 February 2023Accepted 28 March 2023DOI:https://doi.org/10.1103/PRXQuantum.4.020314Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum metrologyQuantum sensingTechniquesPhase space dynamicsQuantum InformationNonlinear DynamicsAtomic, Molecular & Optical

The optimal time-scale of Standardized Precipitation Index for early identifying summer maize drought in the Huang-Huai-Hai region, China

Study regionThe Huang-Huai-Hai region (HHH). Study focusThe optimal time-scales of Standardized Precipitation Index (SPI) for early drought identification was determined by using meteorological data and agrometeorological disaster observation records of summer maize in HHH in this study. Two performance measures were used, one is accuracy, defined as the proportion of the drought samples identified by SPI in the total samples, and the other is sensitivity, defined as the ratio of the number of early recognized samples to the number of identified samples. New hydrological insights for the regionThe SPI can be calculated with only precipitation at any time-scales, mainly including 3-, 6-, 12-, or 24-month periods in previous studies. However, the growth period of maize is only 3–4 months, and the SPI with more than 3-month time-scales may not be applicable for maize drought identification. Additionally, meteorological drought is considered to be the origin for agricultural drought. It is important to identify meteorological drought signals in advance by using applicable time-scale SPI to avoid crop yield reduction. Results showed that 10-day SPI (SPI10) was the optimal index for drought identification with the highest accuracy (95.8 %) and sensitivity (77.72 %). Additionally, most of the drought samples identified by SPI10 were 0–16 days ahead of the observed onset date. The findings can provide theoretical guidance for early warning and near real-time monitoring for summer maize drought disasters in HHH.

Insights into the Effects of Pore Structure, Time Scale, and Injection Scenarios on Pore-Filling Sequence and Oil Recovery by Low-Salinity Waterflooding Using a Mechanistic DLVO-Based Pore-Scale Model

Summary Despite the proven advantage of the low-salinity waterflooding (LSWF) technique, mechanistic understanding of the underlying phenomena at pore-scale remains uncertain. Hence, the corresponding models have limited predictability. In this study, wettability alteration via electrical double-layer (EDL) expansion is captured in a pore-scale model using a multispecies, multiphase computational fluid dynamics simulator. A combination of a pore-doublet and snap-off model is used to evaluate the low-salinity effect (LSE) in two geometries with different pore-throat size distributions. Contact angle is calculated intrinsically within the model using the concept of disjoining pressure through the implementation of the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory and augmented Young-Laplace equation. The results illustrate that even in a simple pore structure, various pore-filling sequences and recoveries are obtained based on the pore geometrical factors, time effects, backward mixing, and injection scenarios. Secondary LSWF results in higher ultimate oil recovery since both small and large pores are accessible to flow and breakthrough is delayed, giving more time for more efficient displacement. Regarding the pore-throat geometry, the case with larger pores connected via larger throats generally exhibits higher ultimate recoveries. However, the geometry with larger pores connected by small throats results in higher incremental recovery via tertiary LSWF. Moreover, an optimal time scale exists in secondary LSWF due to the snap-off phenomenon, while faster LSE results in higher recovery in tertiary mode. The proposed model is capable of mechanistically capturing and predicting LSE and its subsequent flow dynamics, which exhibits a higher recovery factor by LSWF compared to the commonly used linear wettability model. Thus, this approach improves the predictive capability of the previous models as it does not require contact angle data and arbitrary interpolation schemes.

Evaluation of Three Long-Term Remotely Sensed Precipitation Estimates for Meteorological Drought Monitoring over China

Remotely sensed precipitation estimates (RSPEs) play an essential role in monitoring drought, especially in ungauged or sparsely gauged areas. In this study, we evaluated the ability of three popular long-term RSPEs (PERSIANN, CHIRPS, and MSWEP) in capturing the meteorological drought variations over the 10 first-level water resource basins of China, based on the standardized precipitation index (SPI). Drought events were identified by run theory, and the drought characteristics (i.e., duration, severity, and intensity) were also evaluated and compared with a gridded in situ observational precipitation dataset (CMA). The results showed that the three RSPEs could generally capture the spatial patterns and trends of the CMA and showed better performance in the wetter basins. MSWEP had the best performance for the categorical skill of POD, followed by CHIRPS and PERSIANN for the four timescales. SPI6 was the optimal timescale for identifying meteorological drought events. There were large skill divergences in the 10 first-level basins for capturing the drought characteristics. CHIRPS can efficiently reproduce the spatial distribution of drought characteristics, with similar metrics of MDS, MDI, and MDP, followed by MSWEP and PERSIANN. Overall, no single product always outperformed the other products in capturing drought characteristics, underscoring the necessity of multiproduct ensemble applications. Our study’s findings may provide useful information for drought monitoring in areas with complex terrain and sparse rain-gauge networks.

Time scale selection and periodicity analysis of grassland drought monitoring index in Inner Mongolia

Drought monitoring index is the basis for drought monitoring and early warning. SPEI index is characterized by multiple time scales, which time scale SPEI is more applicable to grassland drought monitoring continues to be a hot topic of research. To find the optimal time scale of SPEI for drought monitoring in Inner Mongolia, we randomly selected 11 stations in the lively area of Inner Mongolia grasslands and calculated the correlation coefficients between EVI and SPEI at different time scales for these stations. We founded that the SPEI6 is more applicability and practicability for drought monitoring in Inner Mongolia. Moreover, we applied wavelets to discuss drought periodic fluctuations in Inner Mongolia during 1960–2018. The drought evolution of Inner Mongolia grasslands was characterized by multiple time scales with small cycles nested within larger cycles during the study period. There were 4 types of cycles for the drought evolution of Inner Mongolia grasslands: 3–8 years, 4–32 years, 24–32 years and 6–24 years. Besides, we also found that the 17-year time scale was the primary period for the drought evolution of Inner Mongolia grasslands. Overall, the drought in Inner Mongolia grasslands has eased since 2017. The results will provide some theoretical basis for grassland drought monitoring and early warning. • SPEI6 has better applicability and practicability in the monitoring and analysis of drought in Inner Mongolia grassland. • The drought evolution of Inner Mongolia grasslands was characterized by multiple time scales. • The 17-year time scale was the primary period for the drought evolution of Inner Mongolia grassland. • The drought in Inner Mongolia grassland has eased since 2017.

A new approach to estimating oxygen off-gas fraction and dynamic alpha factor in aeration systems using hybrid machine learning and mechanistic models

The aeration process is the largest energy consumer in wastewater treatment plants (WWTP), and the optimization of the process based on computational models can offer significant savings for the plant. Recent theoretical developments have revealed that many of the parameters commonly assumed as constants in aeration modelling have a dynamic nature; however, there still lacks a universal way to model these factors accurately. This work proposed a new framework and optimal time scale for building hybrid machine learning-mechanistic oxygen transfer rate models. The data used for the modelling were from existing sensors in a full-scale WWTP supplemented by a two-month field sampling campaign for collecting 15 min of dynamic data, including off-gas air fraction. Two machine learning (ML) models were built: the first set was a novel hybrid ML-mechanistic model developed to estimate the off-gas aeration fraction from wastewater parameters and calculate dynamic alpha per the off-gas method. The second ML model directly estimated dynamic alpha from wastewater process and operation parameters. The performance of the ML models was compared with recalibrated published regression-based models. Results showed that the hybrid ML-mechanistic alpha model (NSE = 0.96 and RMSE = 0.03) were more accurate than the direct ML alpha (NSE = 0.67 and RMSE = 0.07) and recalibrated regression (NSE < 0 and RMSE = 0.18 to 0.28) models, and therefore could be efficiently applied to predict dynamic alpha factor of an activated sludge plant. In addition to typical surfactant indicators such as MLSS and COD, other parameters like temperature and aeration ammonia concentration were substantial in dynamic off-gas and alpha estimation.

Terrestrial forcing of marine biodiversification

The diversification of the three major marine faunas during the Phanerozoic was intimately coupled to the evolution of the biogeochemical cycles of carbon and nutrients via nutrient runoff from land and the diversification of phosphorus-rich phytoplankton. Nutrient input to the oceans has previously been demonstrated to have occurred in response to orogeny and fueling marine diversification. Although volcanism has typically been associated with extinction, the eruption of continental Large Igneous Provinces (LIPs) is also a very significant, but previously overlooked, source of phosphorus involved in the diversification of the marine biosphere. We demonstrate that phosphorus input to the oceans peaked repeatedly following the eruption and weathering of LIPs, stimulating the diversification of nutrient-rich calcareous and siliceous phytoplankton at the base of marine food webs that in turn helped fuel diversification at higher levels. These developments were likely furthered by the evolution of terrestrial floras. Results for the Meso-Cenozoic hold implications for the Paleozoic Era. Early-to-middle Paleozoic diversity was, in contrast to the Meso-Cenozoic, limited by nutrient-poor phytoplankton resulting from less frequent tectonism and poorly-developed terrestrial floras. Nutrient runoff and primary productivity during the Permo-Carboniferous likely increased, based on widespread orogeny, the spread of deeper-rooting forests, the fossil record of phytoplankton, and biogeochemical indices. Our results suggest that marine biodiversity on geologic time scales is unbounded (unlimited), provided sufficient habitat, nutrients, and nutrient-rich phytoplankton are also available in optimal amounts and on optimal timescales.

A copula model to identify the risk of river water temperature stress for meteorological drought

Drought is a natural phenomenon that can occur in all climatic zones, and is persistent and regionally widespread. Extreme drought caused by climate change can have serious consequences for freshwater ecosystems, which can have significant social and economic impacts. In this study, the effect of meteorological drought on river water temperature was analyzed probabilistically in order to identify the risk of river water temperature stress experienced by the aquatic ecosystem when a meteorological drought occurs. Meteorological drought is divided into a situation in which moisture is insufficiently supplied from the atmosphere and a situation in which the atmosphere requires excessive moisture from the earth's surface. Using the copula theory, a joint probabilistic model between the river water temperature and each meteorological drought caused by two causes is proposed. In order to consider the propagation time from meteorological drought to river water temperature, the optimal time-scale meteorological drought index is adopted through correlation analysis between the meteorological drought index calculated at various time-scales and the river water temperature. The optimal copula function of the drought index and river water temperature is determined using AIC analysis. Using the proposed model, a risk map is drawn for the river water temperature stress experienced by the aquatic ecosystem under the user-defined meteorological drought severity. The risk map identifies the stream sections where the river water temperature is relatively more sensitive to meteorological drought. The identified stream sections appear differently depending on the cause of the meteorological drought, the region, and the season.

Composite Search of Active Particles in Three‐dimensional Space Based on Non‐directional Cues

AbstractWe theoretically address minimal search strategies of self‐propelled particles towards hidden targets in three‐dimensional space. The particles can sense if targets are close, e. g., by detecting released signaling molecules, but they cannot deduce directional cues. We investigate composite search strategies, where particles switch between extensive outer search and intensive inner search; inner search is started when proximity of a target is detected and ends when a certain inner search time has elapsed. In the simplest strategy, active particles move ballistically during outer search, and transiently reduce their directional persistence during inner search. In a second, adaptive strategy, particles exploit a dynamic scattering effect by reducing directional persistence only outside a well‐defined target zone. These two search strategies require only minimal information processing capabilities, yet increase target encounter rates substantially. The optimal inner search time scales as power‐law with exponent −2/3 with target density, reflecting a trade‐off between exploration and exploitation.

Uncertainties in eddy covariance air–sea CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; flux measurements and implications for gas transfer velocity parameterisations

Abstract. Air–sea carbon dioxide (CO2) flux is often indirectly estimated by the bulk method using the air–sea difference in CO2 fugacity (ΔfCO2) and a parameterisation of the gas transfer velocity (K). Direct flux measurements by eddy covariance (EC) provide an independent reference for bulk flux estimates and are often used to study processes that drive K. However, inherent uncertainties in EC air–sea CO2 flux measurements from ships have not been well quantified and may confound analyses of K. This paper evaluates the uncertainties in EC CO2 fluxes from four cruises. Fluxes were measured with two state-of-the-art closed-path CO2 analysers on two ships. The mean bias in the EC CO2 flux is low, but the random error is relatively large over short timescales. The uncertainty (1 standard deviation) in hourly averaged EC air–sea CO2 fluxes (cruise mean) ranges from 1.4 to 3.2 mmolm-2d-1. This corresponds to a relative uncertainty of ∼ 20 % during two Arctic cruises that observed large CO2 flux magnitude. The relative uncertainty was greater (∼ 50 %) when the CO2 flux magnitude was small during two Atlantic cruises. Random uncertainty in the EC CO2 flux is mostly caused by sampling error. Instrument noise is relatively unimportant. Random uncertainty in EC CO2 fluxes can be reduced by averaging for longer. However, averaging for too long will result in the inclusion of more natural variability. Auto-covariance analysis of CO2 fluxes suggests that the optimal timescale for averaging EC CO2 flux measurements ranges from 1 to 3 h, which increases the mean signal-to-noise ratio of the four cruises to higher than 3. Applying an appropriate averaging timescale and suitable ΔfCO2 threshold (20 µatm) to EC flux data enables an optimal analysis of K.

Robust optimization control for turbidity process with large inertia and time-delay in waterworks

To address the difficulty of optimizing turbidity process with large inertia and long time delay, a twice-optimal control and construction pruning (TOCCP) strategy is proposed, which can be used to improve the fast stability and strong robustness of industrial process. The new approach owns the advantages of twice optimal control (TOC) and construction pruning (CP) method. The TOC constructs an infinite-dimensional state observer of the system, which can eliminate the influence of time delay on the system. The CP is adopted to improve the mid-band damping of the open-loop frequency characteristics to improve the robustness of the system. Moreover, an exhaustive algorithm is designed to find the optimal time scale, the most important parameter for adjusting the system response speed. In addition, the influences of different parameter changes on the robustness of the system are analyzed, and a practical parameter tuning formula is provided. Simulation-based comparisons of TOCCP and the other two algorithms were made. The results show that the system optimized by TOCCP has strong robustness, especially in disturbances rejection and set-point tracking. Moreover, TOCCP is practical and easily implemented to set parameters, providing guidelines for industrial application of large inertia and time-delay systems.

Decision support for pest management: Using field data for optimizing temperature-dependent population dynamics models

Insect physiology is highly dependent on the environmental temperature, and the relationship can be mathematically defined. Thus, many models that aim to predict insect-pest population dynamics, use meteorological data as input to descriptive functions that predict the development rate, survival and reproduction of pest populations. In most cases, however, these functions/models are laboratory-driven and are based on data from constant-temperature experiments. Therefore, they lack an important optimization and validation steps that test their accuracy under field conditions. Here, we developed a realistic and robust regional framework for modeling the field population dynamics of the global insect pest Bemisia tabaci. First, two non-linear functions, development rate (DR) and female reproduction (EN) were fitted to data collected in constant temperature experiments. Next, nine one-generation field experiments were conducted in order to establish a field-derived database of insect performance, representing a variety of growing conditions (different seasons, regions and host plants). Then, sensitivity analyses were performed for identifying the optimal time-scale for which the running-averaged temperatures should be fed to the model. Setting the time to 6 h (i.e., each of the 24-time steps per day represents the last 6 h average) produced the best fit (RMSD score of 1.59 days, 5.7% of the mean) between the field observations and the model simulations. We hypothesize that the 6 h ‘relevant biological time-scale’ captures the insect's physiological memory of daily cycling temperature events. Lastly, we evaluated the potential of the developed modeling framework to serve as a decision support tool in pest-management programs by correlating the model predictions with field-observations of three pest control inspectors during 2019. The model successfully predicted the first notable appearance of the insect in the field (completion of the third generation in May). Also, the model correctly identified the sharp rise in abundance (outbreak point) in mid-July (completion of the fifth generation), and the persistent rise in abundance through August and September. Comparing the simulations of the 2018 and 2019 seasons indicated that the model can also serve as a tool for retrospective systematic assessment of major decisions. Taken together, these data demonstrate the model robustness and its potential to provide an excellent decision-making support platform in regional control of pest species.