External Input Research Articles

An electrodiffusive network model with multicompartmental neurons and synaptic connections.

Most computational models of neurons assume constant ion concentrations, disregarding the effects of changing ion concentrations on neuronal activity. Among the models that do incorporate ion concentration dynamics, simplifications are often made that sacrifice biophysical consistency, such as neglecting the effects of ionic diffusion on electrical potentials or the effects of electric drift on ion concentrations. A subset of models with ion concentration dynamics, often referred to as electrodiffusive models, account for ion concentration dynamics in a way that ensures a biophysical consistent relationship between ion concentrations, electric charge, and electrical potentials. These models include compartmental single-cell models, geometrically explicit models, and domain-type models, but none that model neuronal network dynamics. To address this gap, we present an electrodiffusive network model with multicompartmental neurons and synaptic connections, which we believe is the first compartmentalized network model to account for intra- and extracellular ion concentration dynamics in a biophysically consistent way. The model comprises an arbitrary number of "units," each divided into three domains representing a neuron, glia, and extracellular space. Each domain is further subdivided into a somatic and dendritic layer. Unlike conventional models which focus primarily on neuronal spiking patterns, our model predicts intra- and extracellular ion concentrations (Na+, K+, Cl-, and Ca2+), electrical potentials, and volume fractions. A unique feature of the model is that it captures ephaptic effects, both electric and ionic. In this paper, we show how this leads to interesting behavior in the network. First, we demonstrate how changing ion concentrations can affect the synaptic strengths. Then, we show how ionic ephaptic coupling can lead to spontaneous firing in neurons that do not receive any synaptic or external input. Lastly, we explore the effects of having glia in the network and demonstrate how a strongly coupled glial syncytium can prevent neuronal depolarization blocks.

Efficient coding in biophysically realistic excitatory-inhibitory spiking networks.

The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically-plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.

Soil microbial carbon use efficiency and the constraints

Abstract Background Microbial contributions to soil organic carbon formation have received increasing attention, and microbial carbon use efficiency is positively correlated with soil organic carbon storage. Mainbody This work reviews the impact on microbial carbon use efficiency from six constraints, including plant community composition and diversity, soil pH, substrate quality, nutrient availability and stoichiometric ratios, soil texture and aggregates, water and thermal constraints, and external nutrient inputs. In general, the response of microbial carbon use efficiency showed large uncertainty to above constraints, including positive-, negative-, or non-correlation. However, some factors are biased, more likely to promote or inhibit carbon use efficiency. For example, external nutrient input (N, P, K, Ca) tended to promote carbon use efficiency, while climate warming showed more negative influence. Conclusion Further, overwhelming works focused on single constraint, we suggest the importance to consider the synergistic influence of multiple environmental variables on microbial carbon use efficiency, special for the regulation mechanism of biological-environmental interactions.

Manipulation in root-associated microbiome via carbon nanosol for plant growth improvements

BackgroundModulating the microbiome with nanomaterials has been proposed to improve plant growth, and reduce reliance on external inputs. Carbon Nanosol (CNS) was attracted for its potential to improve plant productivity. However, the mechanism between CNS and rhizosphere microorganisms remained largely elusive.ResultsHere, we tried to systematically explore the effects of CNS (600 and 1200 mg/L by concentration) on tobacco growth, soil physical properties, and root-associated microbiome. The influence of CNS on soil physicochemical properties and plant growth was significant and dose-dependent, leading to a 28.82% increase in biomass accumulation by 600 mg/L CNS. Comparison between the CNS-treated and control plants revealed significant differences in microbiome composition, including 1148 distinct ASVs (923 bacteria and 225 fungi), microbiome interactions, and metabolic function of root-associated microbiomes. Fungal and bacterial communities had different response patterns for CNS treatment, with phased and dose-dependent effects, with the most significant changes in microbial community structure observed at 1200 mg/L after 10 days of treatment. Microbial networks of CNS-treated plants had more nodes and edges, higher connectivity, and more hub microorganisms than those of control plants. Compared with control, CNS significantly elevated abundances of various bacterial biomarkers (such as Sphingomonas and Burkholderia) and fungi biomarkers (including Penicillium, Myceliophthora, and Talaromyces), which were potential plant-beneficial organisms. Functional prediction based on metagenomic data demonstrated pathways related to nutrient cycling being greatly enriched under CNS treatment. Furthermore, 391 culturable bacteria and 44 culturable fungi were isolated from soil and root samples. Among them, six bacteria and two fungi strains enriched upon CNS treatment were validated to have plant growth promotion effect, and two fungi (Cladosporium spp. and Talaromyces spp.) played their roles by mediating volatile organic compounds (VOCs). To some extent, the driving and shaping of the microbiome by CNS contributed to its impact on plant growth and development.ConclusionOur results revealed the key role of root-associated microbiota in mediating the interaction between CNS and plants, thus providing valuable insights and strategies for harnessing CNS to enhance plant growth.Graphical

Injection locking in DC-driven spintronic vortex oscillators via surface acoustic wave modulation

Control of the microwave signal generated by spin-transfer torque oscillators (STOs) is crucial for their applications in spin wave generation and neuromorphic computing. This study investigates injection locking of a DC-driven vortex STO using surface acoustic waves (SAWs) to enhance the STO’s signal and allow for its synchronization with external inputs. We employ a simplified model based on Thiele’s formalism and highlight the role of vortex deformations in achieving injection locking. Micromagnetic simulations are conducted to validate our theoretical predictions, revealing how the locking bandwidth depends on SAW amplitude, as well as on the amplitude and direction of an applied external field. Our findings are pivotal for advancing experimental research and developing efficient low-power synchronization methods for large-scale STO networks.

Research on combined damage characteristics of underwater armor-piercing and explosion based on supercavitating projectile

Recent studies of supercavitating projectiles primarily focus on the formation and evolution of the cavity, as well as its underwater ballistic characteristics, while neglecting the terminal damage effects. Little attention has been given to exploring the combined damage effects of armor-piercing-explosion supercavitating projectiles (APESP). Therefore, this study comparatively analyzes the response processes and failure modes of an underwater aluminum alloy cylindrical shell target under the action of three different types of loads: armor piercing, explosion, and combined armor-piercing and explosion. This study investigates the underwater combined damage mechanisms of the APESP, clarifies each damage phase under the combined effect, discusses the advantages of damage resulting from the combined armor-piercing and explosion effect based on the target responses and damage modes, and explores the reasons for dissipation of explosion energy. The results show that: the APESP combines localized point damage characteristics of armor piercing with overall surface damage features of underwater explosion. Depending on load stages and target responses, the target response process under the action of the APESP can be divided into the hydrodynamic ram phase, penetration phase, shock wave phase, stable vibration phase, and bubble pulsation phase. The entire physical system can be abstracted as a low-frequency series spring system (equivalent to bubble pulsation frequency) with high-frequency external energy input, based on the energy relationship of the medium and the structure. The concept of the 'blower effect' is proposed based on target behavior during the stable vibration phase. Following the application of different loads, the plastic deformation of the target in a stable state is ranked as: underwater explosion > combined armor-piercing and explosion > underwater armor piercing. Supercavity, shell casing and penetration hole will cause the dissipation of explosion energy.

An Algorithm for Affordable Vision-Based GNSS-Denied Strapdown Celestial Navigation

Celestial navigation is rarely seen in modern Uncrewed Aerial Vehicles (UAVs). The size and weight of a stabilized imaging system, and the lack of precision, tend to be at odds with the operational requirements of the aircraft. Nonetheless, celestial navigation is one of the few non-emissive modalities that enables global navigation over the ocean at night in Global Navigation Satellite System (GNSS) denied environments. This study demonstrates a modular, low cost, lightweight strapdown celestial navigation solution that is utilized in conjunction with Ardupilot running on a Cube Orange to produce position estimates to within 4 km. By performing an orbit through a full rotation of compass heading and averaging the position output, we demonstrate that the biases present in a strapdown imaging system can be nullified to drastically improve the position estimate. Furthermore, an iterative method is presented which enables the geometric alignment of the camera with the Attitude and Heading Reference System (AHRS) in-flight without an external position input. The algorithm is tested using real flight data captured from a fixed wing aircraft. The results from this study offer promise for the application of low cost celestial navigation as a redundant navigation modality in affordable, lightweight drones.

Quantifying the Emergence of Basic Research Capabilities in Cluster Enterprises: An Analytical Framework Based on Information Entropy

This study looks at how basic research capabilities develop within enterprise clusters, focusing on the complex and adaptive nature of these systems. It builds a conceptual model using systems theory and applies information entropy to measure how much these capabilities have emerged. This study introduces an innovative application of information entropy to model and quantify the emergence of research capabilities within enterprise clusters, offering a novel framework for assessing research development. To dive deeper, China Pingmei Shenma Group (Henan, China) was used as a case study. A case study approach was used to gather empirical data. This case—focused on a state-owned enterprise cluster in China’s coal-based energy and chemical industries—highlights the key factors that influence research capability growth. These factors include support from external systems, how internal resources are used, and their renewal over time. From 2017 to 2022, the study tracked how the organization of research capabilities evolved over time by tracking changes in entropy, revealing the process of research development driven by both internal and external forces. The methodology involves measuring system entropy to evaluate the degree of orderliness and innovation performance, incorporating entropy generation and exchange metrics, which allows for a more precise understanding of system emergence and complexity. The interactions within the system, such as knowledge exchange, research collaboration, and external input from government subsidies or tax incentives, are modeled to track how they influence the system’s overall entropy. This study finds that the ability of an enterprise cluster to bring in external resources and reduce internal inefficiencies is critical for enhancing research capabilities. This model can help policymakers and enterprises in strategic decision-making, particularly in industries undergoing technological transformation. This framework also provides practical insights for improving research collaboration and innovation in enterprise clusters, especially in rapidly evolving industries like energy and chemicals.

Challenges to ethical public engagement in research funding: a perspective from practice.

European research funding organizations (RFOs) are increasingly experimenting with public engagement in their funding activities. This case study draws attention to the challenges they face in preparing, implementing, and evaluating ethical public engagement in the context of setting funding priorities, formulating calls for proposals, and evaluating project proposals. We discuss challenges related to seven themes: (1) recruiting participants; (2) commitments and expectations; (3) meaningful dialogue and equal engagement; (4) accommodating vulnerability; (5) funding call formulations; (6) lack of expertise in engagement ethics; and (7) uncertainty, resource constraints, and external factors. To address these challenges, we propose the following seven interventions: (1) developing comprehensive recruitment strategies with experienced recruiters and community organizations; (2) establishing clear communication of roles, expectations, and outcomes through codes of conduct; (3) training mediators to address power imbalances; (4) designing flexible engagement methods and providing tailored support; (5) implementing collaborative feedback loops for inclusive funding call formulation; (6) enhancing ethical standards through internal expertise and external advisory inputs; and (7) developing adaptive strategies for flexible and ethical public engagement. These recommendations emphasize the need for context-adaptive insights to support funding organizations to implement ethical public engagement activities, even when faced with organizational constraints and a lack of ethical expertise.

Exploring the predictive power of artificial neural networks in linking global Islamic indices with a local Islamic index

This study aims to analyze the correlations and information flow between a local Sharia-compliant index and global Sharia-compliant indices using Artificial Neural Network (ANN) models, shedding light on the integration of international and national Sharia-compliant markets. Given recent market conditions and uncertainties at both local and global levels, the research investigates the predictive power of ANNs and examines the relationship between the Dubai Financial Market Sharia Index (DFMSI) and three well-known global Islamic indices: the Dow Jones Islamic Market World Index, the Standard and Poor’s Global Sharia Index, and the Financial Times Stock Exchange Sharia All World Index. By incorporating data from multiple global Sharia indices, the study expands data sources beyond the local market, providing a comprehensive analysis of factors influencing the DFMSI’s price. This approach reduces reliance on a single dataset and strengthens the robustness of the research findings. Additionally, the inclusion of the three global Sharia indices offers benefits such as access to global market insights, risk management, and practical applications for investors. The study demonstrates that ANNs, particularly the Nonlinear Autoregressive with External Input Neural Network (NARX), outperform classical models in accurately predicting the DFMSI. This research contributes significantly to the understanding of Sharia-compliant investments by providing a comprehensive analysis of how multiple global indices affect a local market index. It also offers valuable insights for investors and portfolio managers, suggesting that the integration of advanced machine learning models can enhance decision-making processes and improve risk management strategies.

Water quality and pollution source apportionment responses to rainfall in steppe lake estuaries: A case study of Hulun Lake in northern China

Hulun Lake, the largest inland steppe lake in China, is encountering severe water quality degradation. Estuaries play important roles in material and energetic exchange between rivers and lakes. The water quality at the estuaries of Hulun Lake directly reflects the impact of both human activities and natural factors on the lake’s overall water quality, especially during rainfall events. From July 28, 2021, to August 4, 2021, water samples from 62 sites were collected in the three estuaries of Hulun Lake before and after a moderate rainfall event. 13 water parameters, including dissolved oxygen (DO), Turbidity (Tur), Total Nitrogen (TN), Total Phosphorus (TP), Total Organic Nitrogen (TON), and Total Organic Phosphorus (TOP) were measured. The spatio-temporal distribution of water quality in the estuaries was assessed based on water quality index (WQI). Besides, an improved approach integrating stepwise linear regression (SLR) and principal component analysis (PCA) was utilized to construct a WQImin model for an effective assessment of water quality in these estuaries. Furthermore, the absolute principal component scores-multiple linear regression (APCS-MLR) model was employed to identify and quantify the environmental drivers underlying the water quality in the estuaries. The results of WQI indicated that the water quality of the sites in the estuaries of Hulun Lake was “medium” or “poor”, both before and after the rainfall, with a general deterioration in water quality in response to the rainfall. The simplified WQImin model consisted of 5 crucial parameters (i.e., TN, TP, ammonium (NH4+-N), Tur, and permanganate index (CODMn)), and it performed well without parameter weights. Spatial differences in some water parameters among the estuaries were detected, which were attributed to the natural factors and human activities upstream. The principal environmental factors affecting the water quality in the estuaries consisted of hydrodynamic processes, internal phosphorus release, external phosphorus input, external nitrogen input, nitrification in the estuaries, and external organic input and internal organic release. Therefore, we propose basin management strategies such as limiting grazing pressure, adopting enclosed pasture, wetland restoration, optimizing water renewal cycle in Hulun Lake, and transboundary water quality management to tackle water contamination in Hulun Lake.

Start-up investigation and heat transfer enhancement analysis of a loop thermosyphon with biomimetic honeycomb-channel evaporator

Loop thermosyphon has the ability of heat transfer without external energy input, which has good application potential in many areas. Biomimetic flow channel is an effective way for heat transfer enhancement and resistance optimization, therefore it is a promising method to improve the performance of loop thermosyphon. While currently few studies have been conducted in this field. In this paper, the start-up stages, flow patterns and heat transfer performance of a loop thermosyphon with biomimetic honeycomb-channel evaporator are experimentally investigated, and compared with a loop thermosyphon with parallel-flow evaporator. The results show that the start-up of can be divided into three stages: stage dominated by heat conduction, stage dominated by boiling and stage transited to stable operation; For the steady-state performance, the heating power of the optimal point with the lowest thermal resistance increases from 90 W to 150 W with the increase of the filling ratio from 50% to 70%; Compared to loop thermosyphon with parallel-flow evaporator, loop thermosyphon with biomimetic honeycomb evaporator has lower thermal resistance. The decline of thermal resistance is 4.1%-21.6%, and is more significant under small heating power. This paper provides a simple and affordable method for heat transfer improvement of loop thermosyphon.

Uphill directional passive transport of water droplets on axisymmetric surfaces

Spontaneous directional transport of droplets by a surface curvature gradient, adopted by many biological species such as cactus and sand moss, is particularly suitable for applications including anti-icing, self-cleaning, and water harvesting, which eliminates the need for external energy input. However, this directional droplet transport is limited to short transport distance and no maneuverability, i.e., droplets can only migrate toward a flatter region and gradually stop. Fixed structures that can regulate droplet movement, if they could be created, would significantly advance their applications in a variety of areas. In this work, we propose a method to regulate the spontaneous motion of droplets on solid surfaces using surface curvature gradients. Molecular dynamics simulations show that droplets on general bowl-shaped axisymmetric surfaces can travel in the uphill direction (from the base to the apex) and move continuously to the apex with almost a constant speed. The mechanisms governing opposite directional transport of droplets are explained, and the conditions required to guarantee the transport in the reversed direction are discussed.

Reduced model of MIMO systems using Meixner-like functions

This article investigates the use of orthogonal basis functions, particularly Meixner-like functions, to streamline and optimize the parameter complexity in multi-input multi-output (MIMO) system modeling. We introduce a new optimal model for MIMO systems, developed by decomposing the auto-regressive MIMO model with external inputs (ARX) using orthonormal Meixner-like bases. This model, referred to as the MIMO ARX Meixner-like model, enables filtering of the system’s inputs and outputs with Meixner-like functions. Parameter reduction is achieved through the optimization of Meixner-like poles using an iterative technique based on the Newton-Raphson method. The proposed model’s efficacy is demonstrated through simulations on two experimental systems: a two-degrees-of-freedom (DOF) helicopter and a two-tank process. Results reveal that the model effectively reduces approximation errors with fewer parameters. Comparative analysis with the traditional MIMO ARX model confirms the superior performance of the MIMO ARX Meixner-like model.

Organic cropping systems balance environmental impacts and agricultural production

Agriculture provides food to a still growing population but is a major driver of the acceleration of global nutrient flows, climate change, and biodiversity loss. Policies such as the European Farm2Fork strategy aim to mitigate the environmental impact of land-use by fostering organic farming. To assess long-term environmental impact of organic food production we synthesized more than four decades of research on agronomic and environmental performance of the oldest system comparison experiment on organic and conventional cropping systems (DOK experiment). Two organic systems (bioorganic and biodynamic) are compared with two conventional (manure-based integrated and mineral-based) systems all with the same arable crop rotation including grass-clover, and manure from livestock integrated in all except the mineral-based system. Organic systems used 92% less pesticides and 76% less mineral nitrogen than conventional systems. Nitrogen use efficiency, that also considers biological nitrogen fixation, was above 85% for all systems. Organic fertilization with farmyard manure maintained or increased soil carbon and nitrogen stocks in the long term, especially in the biodynamic system with manure compost. Conventional mineral-based cropping reduced soil organic carbon and nitrogen stocks. Higher soil organic carbon stocks in organic cropping did not translate to increased N2O emissions, which were the main driver for 56% lower soil-based, area-scaled climate impact compared to the integrated conventional system with manure. Organic cropping systems, especially compost-based biodynamic, showed enhanced soil health, richness of micro- and macrofauna and weed species. Highest yields were achieved in integrated conventional system, with highest total nitrogen inputs and enhanced soil health compared to pure mineral fertilization. Yet, these benefits come at the cost of lower nitrogen use efficiency and higher N2O emissions. Despite a rigorous reduction of inputs yields of the organic systems achieved 85% of the conventional systems. We demonstrate at field level that organic cropping systems with reduced external nutrient inputs have less climate impact and a larger in-situ biodiversity, while providing a fertile ground for the future development of sustainable agricultural production systems.

Distribution and Preservation of Total Organic Carbon and Total Inorganic Carbon in Pipahai Lake over the Past Century

Carbon burial patterns in lakes and their dynamic changes significantly impact terrestrial carbon sink fluxes and global carbon budgets. In this study, multi-indicator analysis of sediment core samples (P1, P2, and P3) from Pipahai Lake was conducted. Integrating the chronological sequences of 210Pb and 137Cs, we identified the historical changes and spatial characteristics of total organic carbon (TOC) and inorganic carbon (TIC) burial in Pipahai Lake since 1884. The results show that the TOC content was higher than that of the TIC. They exhibited an increasing trend with decreasing depth. Linear regression results indicated that the variation of TOC is less directly affected by precipitation (R = 0.39) and temperature (R = 0.58), while temperature may have a greater impact on TOC. From 1884 to 1995, nutrients were not the primary factor influencing changes in TOC. The synchronous variation in TIC and TOC contents reflects a higher contribution of external inputs to carbon burial in the Pipahai Lake basin. After 1996, nutrients may have begun to affect variations in TOC. The TOC primarily originates from distal aeolian transport or autochthonous sources, though human activity has played a role in its evolution. The TIC content is controlled by the TOC content and autochthonous sources. This study will contribute to the understanding of the carbon cycling dynamics and their influencing mechanisms in a high-altitude lake ecosystem.

Performance support team effectiveness in elite sport: a narrative review

ABSTRACT In pursuit of competitive advantage, elite sport organizations are increasingly relying on the support of diverse sport medicine and sport science staff, who are collectively referred to as the performance support team. Whilst it has been suggested that the accumulative input from diverse multiteam systems has the potential to contribute to a resultant whole that is greater than the sum of its parts, team effectiveness is reliant on more than the mere aggregate of diverse experts. The aim of this narrative review was to appraise, summarize, and apply pertinent performance support team literature to a conceptual framework for teamwork and team effectiveness in sport. It specifically explores team effectiveness, with reference to its inputs (i.e. characteristics of individual, team, and environment) and mediators (i.e. team processes and emergent states). This review provides an insight into the individual (i.e. disciplinary knowledge, technical competency, and interpersonal qualities), team (i.e. team composition and leadership), and external (i.e. hierarchical arrangement and environmental factors) inputs that are necessary for team effectiveness, as well as the mediators (i.e. behavioral processes and emergent states) that translate such inputs into desired outcomes.

Intrinsic dynamic shapes responses to external stimulation in the human brain.

Sensory stimulation of the brain reverberates in its recurrent neuronal networks. However, current computational models of brain activity do not separate immediate sensory responses from intrinsic recurrent dynamics. We apply a vector-autoregressive model with external input (VARX), combining the concepts of "functional connectivity" and "encoding models", to intracranial recordings in humans. We find that the recurrent connectivity during rest is largely unaltered during movie watching. The intrinsic recurrent dynamic enhances and prolongs the neural responses to scene cuts, eye movements, and sounds. Failing to account for these exogenous inputs, leads to spurious connections in the intrinsic "connectivity". The model shows that an external stimulus can reduce intrinsic noise. It also shows that sensory areas have mostly outward, whereas higher-order brain areas mostly incoming connections. We conclude that the response to an external audiovisual stimulus can largely be attributed to the intrinsic dynamic of the brain, already observed during rest.

Synchrony dynamics underlie irregular neocortical spiking.

Cortical neurons are characterized by their variable spiking patterns. We challenge prevalent theories for the origin of spiking variability. We examine the specific hypothesis that cortical synchrony drives spiking variability in vivo . Using dynamic clamp, we demonstrate that intrinsic neuronal properties do not contribute substantially to spiking variability, but rather spiking variability emerges from weakly synchronous network drive. With large-scale electrophysiology we quantify the degree of synchrony and its time scale in cortical networks in vivo . We demonstrate that physiological levels of synchrony are sufficient to generate irregular responses found in vivo . Further, this synchrony shifts over timescales ranging from 25 to 200 ms, depending on the presence of external sensory input. Such shifts occur when the network moves from spontaneous to driven modes, leading naturally to a decline in response variability as observed across cortical areas. Finally, while individual neurons exhibit reliable responses to physiological drive, different neurons respond in a distinct fashion according to their intrinsic properties, contributing to stable synchrony across the neural network.

A Composite Control Method Based on Model Predictive Control and a Disturbance Observer for the Acquisition, Tracking, and Pointing System

Nonlinear disturbances, such as friction, backlash, and base vibration, are the main factors restricting the further improvement of the dynamic performance of the Acquisition, Tracking and Pointing (ATP) system. However, the modeling and compensation of the time-varying nonlinearities are still challenging. In this paper, a compound control algorithm based on model predictive control (MPC) and a Proportional Multiple-Integral State-Augmented Kalman Filter (PMISAKF) disturbance observer is proposed to improve the disturbance rejection performance. Specifically, a PMISAKF is used to estimate the system’s state and external disturbances in real-time. These observed disturbances are then treated as known external inputs at the current or future time points and incorporated into the predictive model of MPC. This allows the optimization process to take these external factors into consideration, enabling the calculation of an optimal control strategy and achieving high pointing and tracking accuracy for nonlinear time-varying disturbances. Experimental results show that the proposed method can effectively improve the system’s anti-interference ability and tracking accuracy.