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Abstract Around one-third of the world’s most carbon-rich ecosystems, mangrove forests, have already been destroyed in Thailand owing to coastal development and aquaculture. Improving these degraded areas through mangrove plantations can restore various coastal ecosystem services, including CO2 absorption and protection against wave action. This study examines the biomass of three coastal mangrove plantations (Avicennia alba) of different ages in Samut Prakarn province, Central Thailand. Our aim was to understand the forest biomass recovery during the early stages of development, particularly fine root biomass expansion. In the chronosequence of the mangrove plantations, woody biomass increased by 40% over four years from 79.7 ± 11.2 Mg C ha-1 to 111.7 ± 12.3 Mg C ha−1. Fine root biomass up to a depth of 100 cm was 4.47 ± 0.33 Mg C ha−1, 4.24 ± 0.63 Mg C ha−1, and 6.92 ± 0.32 Mg C ha−1 at 10, 12, and 14 year-old sites, respectively. Remarkably, the fine root biomass of 14-year-old site was significantly higher than those of the younger sites due to increase of the biomass at 15–30 cm and 30–50 cm depths. Our findings reveal that the biomass recovery in developing mangrove plantations exhibit rapid expansion of fine roots in deeper soil layers.

This study deals with the control methods for automating the operation of a hydraulic excavator loading soil onto the bed of a dump truck. The loading operation requires moving the bucket to a target position without contacting the dump truck. In addition, there is a demand to minimize soil spillage during the loading process. We applied Model Predictive Control (MPC) using constraints of the hydraulic excavator and objective functions corresponding to the requirements of the loading problem. To achieve the desired behaviour using MPC, the weights of the objective function need to be tuned appropriately. Tuning these weights is not an easy task, even for control engineers. To solve this problem, we utilize a weight tuning method based on preference learning. Using this tuning method, the weights can be tuned by repeating the process of selecting the result that the user finds preferable from pairwise tuning results. To confirm the effectiveness of the proposed method, weight tuning experiments were conducted in a simulation environment with multiple users with different levels of skill and knowledge. Through these experiments, we confirmed that each user was able to tune the weights to achieve a desired loading behaviour that appropriately reflected their preferences, independent of their levels of skill and knowledge.

This study covers the model predictive control of linear discrete-time systems subject to stochastic additive disturbances and state chance constraints. The stochastic optimal control problem is reformulated in a dynamic programming fashion to obtain a closed-loop performance and is solved using the interior-point method combined with a Riccati-based approach. The proposed method eliminates active sets in conventional explicit model predictive control and does not suffer from the curse of dimensionality because it finds the value function and feedback policy only for a given initial state using the interior-point method. Moreover, the proposed method is proven to converge globally to the optimal solution Q-superlinearly. The numerical experiment shows that the proposed method achieves a less conservative performance with a low computational complexity compared to existing methods.

Abstract This study covers the output‐feedback model predictive control (MPC) of nonlinear systems subjected to stochastic disturbances and state chance constraints. The stochastic optimal control problem is solved in a stochastic dynamic programming fashion, and the output‐feedback control is performed with the extended Kalman filter. The information state is summarized as a dynamic Gaussian belief model. Thus, the stochastic Bellman equation is transformed into a deterministic equation using this model. The resulting constrained Bellman equation is solved using the proposed constrained, approximate dynamic programming algorithm. The algorithm is proved to have a Q‐superlinear local convergence rate. Numerical experiments show that the proposed algorithm can attain good control performance and reasonable chance‐constraint satisfaction and is computationally efficient owing to its dynamic programming structure.

The nitrogen (N) cycle, a major biogeochemical cycle in forest ecosystems, notably affects ecosystem multifunctionality. However, the magnitude and role of organic N and the snow season remain uncertain in this cycle. We assessed the N flux and pool data of a temperate deciduous broad-leaved forest to clarify N cycle processes. The results showed that the most important component of the N pool was the soil N pool. The N demand of the site amounted to 139.4 kg N ha−1 year−1 and was divided into tree production (83.8%) and bamboo production (16.2%). We clarified that retranslocation (37.4%), mineralization at a soil depth of 0–5 cm (15.3%), litter leachate (4.6%), throughfall (2.3%), and canopy uptake (0.5%) provided 60.1% of the N demand. In terms of soil at 0–5 cm in depth, the net mineralization rate during the snow season contributed to 30% of the annual mineralization. We concluded that the study site was not N-saturated as a result of a positive N input–output flux budget. More than half of the total N was accounted for by dissolved organic N flowing through several pathways, indicating that organic N plays a vital role in the cycle. The mineralization rate in the soil layer during the snow season is an important link in the N cycle.

Changes in soil nutrient dynamics after biochar application may affect indirect carbon sequestration through changes in plant productivity in forest ecosystems. In the present study, we examined the effects of woody biochar application on soil nitrogen (N) cycling over 8 months in a warm-temperate deciduous broad-leaved forest. Mineral soil samples were collected from the plots treated with different biochar applications (0, 5, and 10 Mg ha−1), and the soil inorganic N concentration was measured. Net mineralization and nitrification rates were determined in each plot using the resin–core method. Soil temperature and water content did not change significantly, but the pH increased significantly following biochar application. Soil inorganic N concentrations (NH4+ and NO3−) and net N transformation rates (mineralization and nitrification rates) were significantly reduced. Microbial biomass and the nitrification ratio (the ratio of nitrification rate to mineralization rate) were unchanged, indicating that the decrease in soil inorganic N concentration was due to the reduced mineralization rate. Adsorption of substrates (from organic matter) by the applied biochar is the most likely reason for the reduction in the N mineralization rate. The results indicate that biochar application does not necessarily stimulate N transformation, which will affect indirect carbon sequestration.

Seasonal variabilities in hydrological fluxes of dissolved organic carbon (DOC) and their driving factors in the evergreen broad-leaved forest are inadequately understood. To aid this understanding, we conducted a three-year study to examine seasonal changes in DOC concentration and flux in throughfall, stemflow, and litter leachate in an evergreen broad-leaved subtropical forest in central Japan. We specifically addressed (1) how DOC in different hydrological fluxes vary on a monthly to seasonal basis, and (2) how canopy phenology and meteorology shape the DOC concentration and flux of throughfall, stemflow, and litter leachate trends in this evergreen forest. Clear seasonal changes were found in throughfall and stemflow DOC concentration but not in litter leachate DOC concentration; the highest throughfall DOC concentrations were observed in spring (10.03 mg L−1 in 2017 and 9.59 mg L−1 in 2018, respectively) and the highest stemflow DOC concentrations were observed in summer (13.95 mg L−1 in 2017 and 16.50 mg L−1 in 2018, respectively). Correlation analysis revealed the monthly throughfall DOC concentration to be positively related to the dry weight of fallen leaves (r = 0.72, p < 0.05) and flowers (r = 0.91, p < 0.05). In addition, Random Forest models predicted that the dry weight of flowers was a primary driver of throughfall DOC concentration and that the DOC concentrations of stemflow and litter leachate were constrained by the throughfall DOC concentration. DOC fluxes in different hydrological flux were significantly positive related to bulk precipitation amounts and temperature. Moreover, the throughfall DOC concentration had a considerable effect on throughfall and litter leachate DOC fluxes. Over 75% of annual net tree-DOC (throughfall + stemflow) fluxes and more than 70% of the annual litter leachate DOC fluxes were produced in the flowering season. Thus, we speculated that the seasonal phenological canopy changes (leaf emergence, fallen leaves, flowering, and pollen) and the sufficient rainfall had great impacts on the amount and quality of DOC concentrations in the evergreen forest; and, furthermore, that the DOC from different forest hydrological fluxes was a significant fraction of the carbon that accumulates in soils.

Lucidophyllous (evergreen broad-leaved) forests are the dominant forests in human-dominated subtropical/warm-temperate regions in East Asia. Biometric-based estimates of net primary production (NPP) were conducted in a secondary lucidophyllous forest on Mt. Kinka (35°26′ N, 136°47′ E) near the northern limit of their distribution in central Japan for three years, including the masting event. The forest stand mainly consists of Castanopsis cuspidata (Thunb.) Schottky and Cleyera japonica Thunb. in the canopy and subtree layers, respectively. In 2018, the total NPP of the masting year was 14.53 ± 2.03 ton ha−1 yr−1, including woody NPP (above: 2.63 ± 0.35 ton ha−1 yr−1; below: 0.57 ± 0.08 ton ha−1 yr−1), foliage NPP (4.07 ± 0.23 ton ha−1 yr−1), reproductive NPP (4.81 ± 0.77 ton ha−1 yr−1), and fine root production (Pfr) (2.46 ± 1.84 ton ha−1 yr−1). Pfr and belowground production comprised 16.9% and 20.9%, respectively, of the total NPP. The nut production of C. cuspidata in 2018 (4.31 ± 0.75 ton ha−1 yr−1) was significantly higher than that in 2017 (0.77 ± 0.13 ton ha−1 yr−1) and 2019 (0.23 ± 0.06 ton ha−1 yr−1). No significant change was observed for the three years of foliage NPP and total NPP without Pfr. However, the woody NPP in 2018 (3.20 ± 0.43) was lower than in 2017 (5.37 ± 0.33 ton ha−1 yr−1) and 2019 (4.71 ± 0.38 ton ha−1 yr−1). This suggests that nut production in the masting years compensated by decreasing woody production in the Castanopsis forest.

This paper proposes a recursive elimination method for optimal filtering problems of a class of discrete-time nonlinear systems with non-Gaussian noise. By this method, most of the computations to solve an optimal filtering problem can be carried out off-line by using symbolic computation based on the results from algebraic geometry. This property is suitable for moving horizon estimation, where a certain optimal filtering problem must be solved for different measurement sequences in each sampling interval. A numerical example is provided to compare the proposed method with other state estimation methods including the particle filter, and the efficiency of the proposed method is shown.

We developed a load frequency control system with stochastic model predictive control (SMPC) for power systems where the market penetration of wind power generation is high. The controller adjusts the electricity price for heat pump water heaters while at the same time controlling thermal power plants and batteries in order to maintain the frequency in the designated range. We propose an approach for solving SMPC problems on Hammerstein models including affine disturbance feedback parametrization. Simulation results show that SMPC with affine disturbance feedback parametrization outperforms both SMPC without parametrization and deterministic model predictive control in terms of the stage-cost and constraint violation.

This work applies real-time nonlinear model predictive control (NMPC) to fault-tolerant control problems of an unmanned aerial vehicle (UAV) with failed rotors. In the control problem, a hexacopter with up to three failed rotors out of the six available rotors is considered. The NMPC approach includes a quaternion-based nonlinear model of the hexacopter as well as constraints in the thrusts to consider the inherent nonlinearities of UAVs. The proposed method aims to achieve real-time optimization of the NMPC in the on-board computers without any linearization. We explore all possible scenarios in up to three rotor failures and demonstrate control designs in the NMPC for these scenarios. The simulation results indicate that by using the quaternion model, the position and attitude of a hexacopter can be controlled from a large inclined initial state with a non-zero angular velocity and falling velocity. Moreover, the results reveal that the quaternion model is superior to the Euler angle model in terms of the computation time. We also conduct hardware experiments using an actual hexacopter with a failed rotor to demonstrate the real-time NMPC optimization. The results of the simulations and hardware experiments demonstrate that the NMPC can deal with various operation conditions of a hexacopter in a unified manner, with only minor modifications in the performance index.

This paper studies nonlinear finite-horizon optimal control problems with terminal constraints, where all nonlinear functions are rational or algebraic functions. We first extend a recursive elimination method, which decouples the Euler-Lagrange equations into sets of algebraic equations, where each set contains only the variables at the same time instant. Therefore, a candidate of an optimal feedback control law at each time instant is obtained by solving each set of algebraic equations. Next, we provide a sufficient condition such that each set of algebraic equations gives a unique local optimal feedback control law at each time instant. Illustrative and practical examples are provided to illustrate the proposed method and sufficient condition.

Few studies have evaluated the application of biochar to forest ecosystems and their responses under field conditions. We manually spread grounded biochar on the forest floor, at rates of 0 (control), 5, and 10 Mg ha−1 (C0, C5 and C10, respectively), of an oak forest in central Japan to test the effects of biochar on tree growth and productivity. The relative growth rate of the diameter at breast height (dbh) of canopy oak trees (dbh > 20 cm) significantly increased in C10 compared with that of the control (C0), but not in C5, in the second to third years after application. Despite the increasing growth rate of canopy trees, foliage production (NPPF) and woody production (NPPW) did not respond to biochar application. Conversely, the production of reproductive organs (NPPR, mainly oak acorns) increased in line with the biochar application rate gradients (1.04 ± 0.09 Mg ha−1 yr−1 in C0, 1.30 ± 0.08 Mg ha−1 yr−1 in C5, and 1.47 ± 0.13 Mg ha−1 yr−1 in C10). Since the contribution of NPPR to total NPP was fairly small, there were no significant differences in total NPP (=NPPW + NPPF + NPPR) for C5 (14.57 ± 0.20 Mg ha−1 yr−1) or C10 (16.11 ± 0.73 Mg ha−1 yr−1) compared with the control (15.07 ± 0.48 Mg ha−1 yr−1).

The significance of aquatic lateral carbon (C) export in mangrove ecosystems highlights the extensive contribution of aquatic pathways to the net ecosystem carbon budget. However, few studies have investigated lateral fluxes of dissolved organic carbon (DOC) and inorganic carbon (DIC), partly due to methodological difficulty. Therefore, we evaluated area-based lateral C fluxes in a small mangrove estuary that only had one exit for water exchange to the coast. We sampled water from the mouth of the creek and integrated discharge and consecutive concentration of mangrove-derived C (ΔC). Then, we estimated the area-normalized C fluxes based on the inundated mangrove area. DIC and DOC concentrations at the river mouth increased during ebb tide during both summer and winter. We quantified the ΔC in the estuary using a two-component conservative mixing model of freshwater and seawater. DIC and DOC proportions of ΔC concentrations at the river mouth during ebb tide was between 34% and 56% in the winter and 26% and 42% in the summer, respectively. DIC and DOC fluxes from the estuary were estimated to be 1.36 g C m−2 d−1 and 0.20 g C m−2 d−1 in the winter and 3.35 g C m−2 d−1 and 0.86 g C m−2 d−1 in the summer, respectively. Based on our method, daily fluxes are mangrove area-based DIC and DOC lateral exports that can be directly incorporated into the mangrove carbon budget.

Few studies have reported the estimation of nitrogen (N) deposition, including dissolved organic N (DON) fluxes, through water flows and the contribution of snowfall in Asia. In this study, the concentrations and fluxes of DON and dissolved inorganic N (DIN) in bulk precipitation (BP), the throughfall (TF) of trees and understory dwarf bamboo, and stemflow (SF) were evaluated in a cool-temperate forest over three years to clarify N fluxes via precipitation and responses of trees and understory canopies to N deposition. The input of N to the study site in BP was 11.1 ± 1.71 kg N ha−1 year−1, with a significant contribution from DON (78%). Snowfall fluxes contributed up to 46% of the total N input, with variations related to the amount of snowfall (2.08−5.52 kg N ha−1 year−1). The forest canopy enriched DON (2.11 ± 0.42 kg N ha−1 year−1) but consumed NO3 + NO2−N (−0.73 ± 0.19 kg N ha−1 year−1). In contrast, through the understory bamboo canopy, DON (−1.02 ± 0.55 kg N ha−1 year−1) decreased while DIN (0.35 ± 0.44 kg N ha−1 year−1) increased. This study indicates that DON and snowfall should not be neglected when evaluating total N deposition into forest ecosystems, especially in remote regions. The canopy processes related to the dissolved N in the presence of understory plants might have significant implications for the internal N cycle in forest ecosystems.

Periodic somite segmentation is controlled by the cyclic gene Hes7, whose oscillatory expression depends upon negative feedback with a delayed timing. The mechanism that regulates the pace of segmentation remains to be determined, but mathematical modeling has predicted that negative feedback with shorter delays would give rise to dampened but more rapid oscillations. Here, we show that reducing the number of introns within the Hes7 gene shortens the delay and results in a more rapid tempo of both Hes7 oscillation and somite segmentation, increasing the number of somites and vertebrae in the cervical and upper thoracic region. These results suggest that the number of introns is important for the appropriate tempo of oscillatory expression and that Hes7 is a key regulator of the pace of the segmentation clock.

Receding horizon control is a type of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial time and terminal time. Spatiotemporal dynamic systems characterized by both spatial and temporal variables often occur in many research fields. In this study, we develop a novel design method of receding horizon control for a generalized class of spatiotemporal dynamic systems. Using the variational principle, we first derive the exact stationary conditions that must be satisfied for a performance index to be optimized. Next, we provide a numerical algorithm to solve the stationary conditions via a finite-dimensional approximation. Finally, the effectiveness of the proposed method is verified by numerical simulations.

Secondary lucidophyllous forest is one of the dominant forests in human-dominated subtropical/warm-temperate regions in East Asia. There were few direct monitoring techniques to elucidate the following hypotheses: (a) self-thinning may govern the stand development process and (b) wood production decline can be observed during secondary succession in a lucidophyllous forest. We conducted a long-term study at a permanent plot in central Japan, since 1989. The forest consists mainly of Castanopsis cuspidata in a canopy layer, Cleyera japonica, and Eurya japonica in a subtree layer. During the 28-year period, the basal area of the stand significantly increased due to the growth of C. cuspidata, from 29.18 ± 1.84 (87.8% of total) to 38.71 ± 2.22 m2 ha−1 (91.9%), while the stem density of C. cuspidata significantly decreased from 666 ± 13 to 404 ± 10 stems ha−1 in proportion to accumulating biomass (117.8 to 166.6 ton ha−1). The annual woody net primary production ranged from 2.40 ± 0.13 to 3.93 ± 0.33 ton ha−1 year−1 as a nearly 70-year-old forest. There was no age-related decline of woody net primary production (NPP) was found during secondary succession, and the growth of individual tree still increased when the self-thinning process governed the stand.

Leucine-rich repeat kinase 2 (LRRK2) is a key molecule in the pathogenesis of familial and idiopathic Parkinson's disease (PD). We have identified two novel LRRK2-associated proteins, a HECT-type ubiquitin ligase, HERC2, and an adaptor-like protein with six repeated Neuralized domains, NEURL4. LRRK2 binds to NEURL4 and HERC2 via the LRRK2 Ras of complex proteins (ROC) domain and NEURL4, respectively. HERC2 and NEURL4 link LRRK2 to the cellular vesicle transport pathway and Notch signaling, through which the LRRK2 complex promotes the recycling of the Notch ligand Delta-like 1 (Dll1)/Delta (Dl) through the modulation of endosomal trafficking. This process negatively regulates Notch signaling through cis-inhibition by stabilizing Dll1/Dl, which accelerates neural stem cell differentiation and modulates the function and survival of differentiated dopaminergic neurons. These effects are strengthened by the R1441G ROC domain-mutant of LRRK2. These findings suggest that the alteration of Notch signaling in mature neurons is a component of PD etiology linked to LRRK2.

Carbon dioxide (CO2) efflux from the soil surface, which is a major source of CO2 from terrestrial ecosystems, represents the total CO2 production at all soil depths. Although many studies have estimated the vertical profile of the CO2 production rate, one of the difficulties in estimating the vertical profile is measuring diffusion coefficients of CO2 at all soil depths in a nondestructive manner. In this study, we estimated the temporal variation in the vertical profile of the CO2 production rate using a data assimilation method, the particle filtering method, in which the diffusion coefficients of CO2 were simultaneously estimated. The CO2 concentrations at several soil depths and CO2 efflux from the soil surface (only during the snow-free period) were measured at two points in a broadleaf forest in Japan, and the data were assimilated into a simple model including a diffusion equation. We found that there were large variations in the pattern of the vertical profile of the CO2 production rate between experiment sites: the peak CO2 production rate was at soil depths around 10 cm during the snow-free period at one site, but the peak was at the soil surface at the other site. Using this method to estimate the CO2 production rate during snow-cover periods allowed us to estimate CO2 efflux during that period as well. We estimated that the CO2 efflux during the snow-cover period (about half the year) accounted for around 13% of the annual CO2 efflux at this site. Although the method proposed in this study does not ensure the validity of the estimated diffusion coefficients and CO2 production rates, the method enables us to more closely approach the "actual" values by decreasing the variance of the posterior distribution of the values.

The structural complexity, especially canopy and gap structure, of old‐growth forests affects the spatial variation of soil respiration (Rs). Without considering this variation, the upscaling of Rs from field measurements to the forest site will be biased. The present study examined responses of Rs to soil temperature (Ts) and water content (W) in canopy and gap areas, developed the best fit modelof Rs and used the unique spatial patterns of Rs and crown closure to upscale chamber measurements to the site scale in an old‐growth beech‐oak forest. Rs increased with an increase in Ts in both gap and canopy areas, but the effect of W on Rs was different between the two areas. The generalized linear model (GLM) analysis identified that an empirical model of Rs with thecoupling of Ts and W was better than an exponential model of Rs with only Ts. Moreover, because of different responses of Rs to W between canopy and gap areas, it was necessary to estimate Rs in these areas separately. Consequently, combining the spatial patterns of Rs and the crown closure could allow upscaling of Rs from chamber‐based measurements to the whole site in the present study.