Observation Network Research Articles

Updating predictions in a complex repertoire of actions and its neural representation

Even though actions we observe in everyday life seem to unfold in a continuous manner, they are automatically divided into meaningful chunks, that are single actions or segments, which provide information for the formation and updating of internal predictive models. Specifically, boundaries between actions constitute a hub for predictive processing since the prediction of the current action comes to an end and calls for updating of predictions for the next action. In the current study, we investigated neural processes which characterize such boundaries using a repertoire of complex action sequences with a predefined probabilistic structure. Action sequences consisted of actions that started with the hand touching an object (T) and ended with the hand releasing the object (U). These action boundaries were determined using an automatic computer vision algorithm. Participants trained all action sequences by imitating demo videos. Subsequently, they returned for an fMRI session during which the original action sequences were presented in addition to slightly modified versions thereof. Participants completed a post-fMRI memory test to assess the retention of original action sequences. The exchange of individual actions, and thus a violation of action prediction, resulted in increased activation of the action observation network and the anterior insula. At U events, marking the end of an action, increased brain activation in supplementary motor area, striatum, and lingual gyrus was indicative of the retrieval of the previously encoded action repertoire. As expected, brain activation at U events also reflected the predefined probabilistic branching structure of the action repertoire. At T events, marking the beginning of the next action, midline and hippocampal regions were recruited, reflecting the selected prediction of the unfolding action segment. In conclusion, our findings contribute to a better understanding of the various cerebral processes characterizing prediction during the observation of complex action repertoires.

Strong‐Motion Broadband Displacements From Collocated Ocean‐Bottom Pressure Gauges and Seismometers

AbstractDense and broad‐coverage ocean‐bottom observation networks enable us to obtain near‐fault displacement records associated with an offshore earthquake. However, simple integration of ocean‐bottom strong‐motion acceleration records leads to physically unrealistic displacement records. Here we propose a new method using a Kalman filter to estimate coseismic displacement waveforms using the collocated ocean‐bottom seismometers and pressure gauges. First, we evaluate our method using synthetic records and then apply it to an offshore Mw 6.0 event that generated a small tsunami. In both the synthetic and real cases, our method successfully estimates reasonable displacement waveforms. Additionally, we show that the computed waveforms improve the results of the finite fault modeling process. In other words, the proposed method will be useful for estimating the details of the rupture mechanism of offshore earthquakes as a complement to onshore observations.

Calibrating low-cost rain gauge sensors for their applications in Internet of Things (IoT) infrastructures to densify environmental monitoring networks

Abstract. Environmental observations are crucial for understanding the state of the environment. However, current observation networks are limited in their spatial and temporal resolution due to high costs. For many applications, data acquisition with a higher resolution would be desirable. Recently, Internet of Things (IoT)-enabled low-cost sensor systems have offered a solution to this problem. While low-cost sensors may have lower quality than sensors in official measuring networks, they can still provide valuable data. This study describes the requirements for such a low-cost sensor system, presents two implementations, and evaluates the quality of the factory calibration for a widely used low-cost precipitation sensor. Here, 20 sensors have been tested for an 8-month period against three reference instruments at the meteorological site of the TU Dresden (Dresden University of Technology). Furthermore, the factory calibration of 66 rain gauges has been evaluated in the lab. Results show that the used sensor falls short for the desired out-of-the-box use case. Nevertheless, it could be shown that the accuracy could be improved by further calibration.

Assessment of water levels from 43 years of NOAA’s Coastal Ocean Reanalysis (CORA) for the Gulf of Mexico and East Coasts

Coastal water level information is crucial for understanding flood occurrences and changing risks. Here, we validate the preliminary version (0.9) of NOAA’s Coastal Ocean Reanalysis (CORA), which is a 43-year reanalysis (1979–2021) of hourly coastal water levels for the Gulf of Mexico and Atlantic Ocean (i.e., the Gulf and East Coast region, or GEC). CORA-GEC v0.9 was conducted by the Renaissance Computing Institute using the coupled ADCIRC+SWAN coastal circulation and wave model. The model uses an unstructured mesh of nodes with varying spatial resolution that averages 400 m near the coast and is much coarser in the open ocean. Water level variations associated with tides and meteorological forcing are explicitly modeled, while lower-frequency water level variations are included by dynamically assimilating observations from NOAA’s National Water Level Observation Network. We compare CORA to water level observations that were either assimilated or not, and find that the reanalysis generally performs better than a state-of-the-art global ocean reanalysis (GLORYS12) in capturing the variability on monthly, seasonal, and interannual timescales as well as the long-term trend. The variability of hourly non-tidal residuals is also shown to be well resolved in CORA when compared to water level observations. Lastly, we present a case study of extreme water levels and coastal inundations around Miami, Florida to demonstrate an application of CORA for studying flood risks. Our assessment suggests that NOAA’s CORA-GEC v0.9 provides valuable information on water levels and flooding occurrence from 1979–2021 in areas that are experiencing changes across multiple time scales. CORA potentially can enhance flood risk assessment along parts of the U.S. Coast that do not have historical water level observations.

Correlations reveal the hierarchical organization of biological networks with latent variables

Deciphering the functional organization of large biological networks is a major challenge for current mathematical methods. A common approach is to decompose networks into largely independent functional modules, but inferring these modules and their organization from network activity is difficult, given the uncertainties and incompleteness of measurements. Typically, some parts of the overall functional organization, such as intermediate processing steps, are latent. We show that the hidden structure can be determined from the statistical moments of observable network components alone, as long as the functional relevance of the network components lies in their mean values and the mean of each latent variable maps onto a scaled expectation of a binary variable. Whether the function of biological networks permits a hierarchical modularization can be falsified by a correlation-based statistical test that we derive. We apply the test to gene regulatory networks, dendrites of pyramidal neurons, and networks of spiking neurons.

Force/position tracking control of fracture reduction robot based on nonlinear disturbance observer and neural network

AbstractBackgroundFor the fracture reduction robot, the position tracking accuracy and compliance are affected by dynamic loads from muscle stretching, uncertainties in robot dynamics models, and various internal and external disturbances.MethodsA control method that integrates a Radial Basis Function Neural Network (RBFNN) with Nonlinear Disturbance Observer is proposed to enhance position tracking accuracy. Additionally, an admittance control is employed for force tracking to enhance the robot's compliance, thereby improving the safety.ResultsExperiments are conducted on a long bone fracture model with simulated muscle forces and the results demonstrate that the position tracking error is less than ±0.2 mm, the angular displacement error is less than ±0.3°, and the maximum force tracking error is 26.28 N. This result can meet surgery requirements.ConclusionsThe control method shows promising outcomes in enhancing the safety and accuracy of long bone fracture reduction with robotic assistance.

Fifteen Years of Integrated Terrestrial Environmental Observatories (TERENO) in Germany: Functions, Services, and Lessons Learned

AbstractThe need to develop and provide integrated observation systems to better understand and manage global and regional environmental change is one of the major challenges facing Earth system science today. In 2008, the German Helmholtz Association took up this challenge and launched the German research infrastructure TERrestrial ENvironmental Observatories (TERENO). The aim of TERENO is the establishment and maintenance of a network of observatories as a basis for an interdisciplinary and long‐term research program to investigate the effects of global environmental change on terrestrial ecosystems and their socio‐economic consequences. State‐of‐the‐art methods from the field of environmental monitoring, geophysics, remote sensing, and modeling are used to record and analyze states and fluxes in different environmental disciplines from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. Over the past 15 years we have collectively gained experience in operating a long‐term observing network, thereby overcoming unexpected operational and institutional challenges, exceeding expectations, and facilitating new research. Today, the TERENO network is a key pillar for environmental modeling and forecasting in Germany, an information hub for practitioners and policy stakeholders in agriculture, forestry, and water management at regional to national levels, a nucleus for international collaboration, academic training and scientific outreach, an important anchor for large‐scale experiments, and a trigger for methodological innovation and technological progress. This article describes TERENO's key services and functions, presents the main lessons learned from this 15‐year effort, and emphasizes the need to continue long‐term integrated environmental monitoring programmes in the future.

Detection and interpretation of the time-varying seasonal signals in China with multi-geodetic measurements

The time-varying periodic variations in Global Navigation Satellite System (GNSS) stations affect the reliable time series analysis and appropriate geophysical interpretation. In this study, we apply the singular spectrum analysis (SSA) method to characterize and interpret the periodic patterns of GNSS deformations in China using multiple geodetic datasets. These include 23-year observations from the Crustal Movement Observation Network of China (CMONOC), displacements inferred from the Gravity Recovery and Climate Experiment (GRACE), and loadings derived from Geophysical models (GM). The results reveal that all CMONOC time series exhibit seasonal signals characterized by amplitude and phase modulations, and the SSA method outperforms the traditional least squares fitting (LSF) method in extracting and interpreting the time-varying seasonal signals from the original time series. The decrease in the root mean square (RMS) correlates well with the annual cycle variance estimated by the SSA method, and the average reduction in noise amplitudes is nearly twice as much for SSA filtered results compared with those from the LSF method. With SSA analysis, the time-varying seasonal signals for all the selected stations can be identified in the reconstructed components corresponding to the first ten eigenvalues. Moreover, both RMS reduction and correlation analysis imply the advantages of GRACE solutions in explaining the GNSS periodic variations, and the geophysical effects can account for 71% of the GNSS annual amplitudes, and the average RMS reduction is 15%. The SSA method has proved to be useful for investigating the GNSS time-varying seasonal signals. It could be applicable as an auxiliary tool in the improvement of non-linear variations investigations.

A generalizable method for estimating meteor shower false positives

Context. The determination of meteor shower or parent body associations is inherently a statistical problem. Traditional methods, primarily the similarity discriminants, have limitations, particularly in handling the increasing volume and complexity of meteoroid orbit data. Aims. We introduce a new more statistically robust and generalizable method for estimating false positive detections in meteor shower identification, leveraging kernel density estimation (KDE). The method is applied to fireball data from the European Fireball Network, a comprehensive photographic fireball observation network established in 1963 for the detailed monitoring and analysis of fireballs across central Europe Methods. Utilizing a dataset of 824 fireballs observed by the European Fireball Network, we applied a multivariate Gaussian kernel within KDE and Z-score data normalization. Our method analyzes the parameter space of meteoroid orbits and geocentric impact characteristics, focusing on four different similarity discriminants: DSH, D′, DH, and DN. Results. The KDE methodology consistently converges toward a true established shower-associated fireball rate within the EFN dataset of 18–25% for all criteria. This indicates that the approach provides a more statistically robust estimate of the shower-associated component. Conclusions. Our findings highlight the potential of KDE combined with appropriate data normalization in enhancing the accuracy and reliability of meteor shower analysis. This method addresses the existing challenges posed by traditional similarity discriminants and offers a versatile solution adaptable to varying datasets and parameters.

Evaluation of the simulation performance of WRF-Solar for a summer month in China using ground observation network data

Solar energy is a pivotal clean energy source in the transition to carbon neutrality from fossil fuels. However, the intermittent and stochastic characteristics of solar radiation pose challenges for accurate simulation and prediction. Accurately simulating and predicting solar radiation and its variability are crucial for optimizing solar energy utilization. This study conducted simulation experiments using the WRF-Solar model from 25 June to 25 July 2022, to evaluate the accuracy and performance of the simulated solar radiation across China. The simulations covered the whole country with a grid spacing of 27 km and were compared with ground observation network data from the Chinese Ecosystem Research Network. The results indicated that WRF-Solar can accurately capture the spatiotemporal patterns of global horizontal irradiance over China, but there is still an overestimation of solar radiation, and the model underestimates the total cloud cover. The root-mean-square error ranged from 92.83 to 188.13 Wm−2 and the mean bias (MB) ranged from 21.05 to 56.22 Wm−2. The simulation showed the smallest MB at Lhasa on the Qinghai–Tibet Plateau, while the largest MB was observed in Southeast China. To enhance the accuracy of solar radiation simulation, the authors compared the Fast All-sky Radiation Model for Solar with the Rapid Radiative Transfer Model for General Circulation Models and found that the former provides better simulation.摘要准确模拟和预测太阳辐射及其变化对于优化太阳能利用至关重要. 本研究使用WRF-Solar模式对中国2022 年 6 月 25 日至 7 月 25 日的太阳辐射情况进行了深入模拟, 模式网格水平分辨率为27 km, 通过与中国生态系统研究网络 (CERN) 的地面观测网络数据的37个观测站点进行对比, 以评估模式性能. 结果表明: WRF-Solar模式能较好地捕捉到中国上空的辐照度GHI (Global Horizontal Irradiance) 的时空分布特征, 但存在高估太阳辐射量, 以及低估总云量的情况. 全国的总辐射模拟均方根误差范围为92.83–188.13 W m−2, 平均偏差范围为21.05–56.22 W m−2. 青藏高原拉萨站的偏差最小, 在中国东南部的偏差最大. 为了进一步提升太阳辐射模拟的精确度, 本研究还对比了FARMS与RRTMG辐射方案, 发现FARMS方案的模拟精度更高.

A Flexible, Multi-Instrument Optimal Estimation Retrieval for Wind Profiles

Abstract Instruments such as Doppler lidars, radar wind profilers, and uncrewed aircraft systems could be used in observation networks to fill in the temporal and spatial gap that exists for low-level wind observations. These instruments, however, do not directly observe the wind and require a retrieval to be used to obtain wind estimates from their observations. Also, the depth and uncertainty of observations collected by these instruments vary depending on the environment that they are sampling. Optimal estimation is a variational retrieval method that combines information from a prior dataset and observations to retrieve an atmospheric state. This technique can be beneficial to use when observations have large uncertainties or provide insufficient information to obtain the atmospheric state by themselves. A new optimal estimation retrieval for obtaining wind profiles from typical lower atmospheric wind profiling instrumentation has been developed. This retrieval allows for more observations from wind profiling instrumentation to be used when retrieving wind profiles, increases the depth of retrieved profiles, and eliminates vertical data gaps. This retrieval can also be used to easily combine observations from different instruments or even with model data to create combined data wind retrievals that leverage the strengths of the different data sources to retrieve a wind profile that is superior to those obtained by the individual observations or data sources. It is envisioned that this retrieval will be continued to be developed and maintained as community software as lower atmospheric wind observing capabilities further develop and expand.

Temporal Variations in Frequency‐Dependent Shear‐Wave Anisotropy Above a Plate Interface Following Episodic Slow‐Slip Events

AbstractRecent observations beneath Kanto, Japan have shown that seismic activity and seismic attenuation within the overlying continental plate change with time due to drainage caused by slow‐slip events (SSEs) along the upper boundary of the Philippine Sea plate. However, associated changes in rock properties have not been investigated. In this study, we estimate frequency‐dependent shear‐wave anisotropy to provide a detailed insight into the structural change associated with drainage. We perform shear‐wave splitting analysis in frequency ranges of 1–4, 2–6, and 4–8 Hz for 306 earthquakes that occur during September 2009–August 2021 and recorded at the Metropolitan Seismic Observation network. Obtained time differences between fast and slow S waves (delay time) range from almost zero to 0.16–0.18 s, exhibiting spatio‐temporal variation and frequency dependence. The fast S‐wave polarization directions are generally consistent with the direction of the maximum horizontal compressional axis in the study region, which suggests that the observed anisotropy is probably caused by the NE–SW‐oriented fractures developed under the regional stress field. The temporal variation in delay times is correlated with SSEs activity with a lag time of 0.0–0.1 year. Furthermore, comparisons between observed frequency‐dependent delay times and numerical calculation of fracture‐induced anisotropy suggest that the average fracture radius is almost constant (0.30–0.35 m) over time but fracture density temporally varies from 0.025 to 0.035. We infer that the fracture density is probably enhanced by opening of the NE–SW‐oriented fractures during the upward migration of fluids that are expelled from the plate interface.

NeonPlantEcology: An R package for preparing NEON plant data for use in ecological research

The National Ecological Observatory Network (NEON) is a continental-scale endeavor of ecological data collection for 30 years. We created a software package, neonPlantEcology that automatically arranges the raw data from the plant presence and percent cover (DP1.10058.001) data product from NEON into tables familiar to plant ecologists. Because of the broad scale of the observatory, it is necessary to tailor the data collection to the idiosyncrasies of each of 47 different ecosystems. Furthermore, data collection practices are occasionally modified for various reasons. These complexities, along with the volume and multiscalar nature of the data, need to be understood and accounted for in order to correctly process the data. This is particularly true for the plant diversity data product. We present three case studies using the package, centered around the three primary functions of neonPlantEcology. By automating the process of preparing NEON's plant diversity data, neonPlantEcology makes it more accessible to a wide range of users.

Broadscale spatial synchrony in a West Nile virus mosquito vector across multiple timescales

Insects often exhibit irruptive population dynamics determined by environmental conditions. We examine if populations of the Culex tarsalis mosquito, a West Nile virus (WNV) vector, fluctuate synchronously over broad spatial extents and multiple timescales and whether climate drives synchrony in Cx. tarsalis, especially at annual timescales, due to the synchronous influence of temperature, precipitation, and/or humidity. We leveraged mosquito collections across 9 National Ecological Observatory Network (NEON) sites distributed in the interior West and Great Plains region USA over a 45-month period, and associated gridMET climate data. We utilized wavelet phasor mean fields and wavelet linear models to quantify spatial synchrony for mosquitoes and climate and to calculate the importance of climate in explaining Cx. tarsalis synchrony. We also tested whether the strength of spatial synchrony may vary directionally across years. We found significant annual synchrony in Cx. tarsalis, and short-term synchrony during a single period in 2018. Mean minimum temperature was a significant predictor of annual Cx. tarsalis spatial synchrony, and we found a marginally significant decrease in annual Cx. tarsalis synchrony. Significant Cx. tarsalis synchrony during 2018 coincided with an anomalous increase in precipitation. This work provides a valuable step toward understanding broadscale synchrony in a WNV vector.

Evaluation of Gridded Rainfall Products in Three West African Basins

In recent years, accessing rainfall data from ground observation networks maintained by national meteorological services in West Africa has become increasingly challenging. This is primarily due to high acquisition costs and the often sparse distribution of rainfall gauges across the region, which limits their use in hydrological studies and related research. At the same time, the rising availability of precipitation products derived from satellite/earth observations, reanalysis datasets, and in situ measurements presents exciting prospects for hydrological applications. Nonetheless, these datasets constitute indirect measurements, necessitating rigorous validation against ground-based rainfall data. This study comprehensively assesses twenty-three gridded rainfall products, including sixteen from satellites, six from reanalysis data, and one from in situ measurements, across the Senegal, Gambia, and Casamance River basins. Performance evaluation is conducted across distinct climatic zones, both pre- and post-resampling against observed rainfall data gathered from forty-nine rainfall stations over a six-year period (2003–2008). Evaluation criteria include the Kling–Gupta Efficiency (KGE) and Percentage of Bias (PBIAS) metrics, assessed at daily, monthly, and seasonal time steps. The results reveal distinct performance levels among the evaluated rainfall products. RFE, ARC2, and CPC notably yield the highest KGE scores at the daily time step, while GPCP, CHIRP, CHIRPS, RFE, MSWEP, ARC2, CPC, TAMSAT, and CMORPHCRT demonstrate superior performance at the monthly time step. During the rainy season, these products generally exhibit robustness. However, rainfall estimates derived from reanalysis datasets (ERA5, EWEMBI, MERRA2, PGF, WFDEICRU, and WFDEIGPCC) perform poorly in the studied basins. Based on the PBIAS metric, most products tend to underestimate precipitation values, while only PERSIANN and PERSIANNCCS lead to significant overestimations. Spatially, optimal performance of the products is observed in the Casamance basin and the Sudanian and Sahelian climatic zones within the Gambia and Senegal basins. Conversely, in the Guinean zone of the Gambia and Senegal Rivers, the rainfall products displayed the poorest performance.

Scientific perspective on rukyatul hilal: A case study of assalam kartasura sukoharjo observatory

In essence, rukyat is looking deeply to see whether the moon has undergone changes to indicate the change of the moon or not. With the advancement of the times, observations no longer only use manual binoculars but are supported by various adequate equipment, making it very easy to carry out observations. . Therefore, it is important for humanity to understand what an observatory is and its uses. This research uses qualitative methods supported by documentation and direct field research at the Assalam observatory in Sukoharjo, Central Java. The results of this research are the contribution of the Assalam Observatory in the implementation of rukyatul hilal in Indonesia. The Assalam Observatory is also the Secretary General of JOPI (Indonesian Planetarium Observatory Network) so its involvement in rukyatul hilal is beyond doubt. So far, the results of observations at the Assalam observatory are at 7-8 degrees every month.

Low-cost UAV coordinated carbon observation network: Carbon dioxide measurement with multiple UAVs

Improving the mitigation of global greenhouse effects requires drastic reductions in carbon emissions. Effective carbon management at different spatial scales is therefore crucial to a more sustainable path of economic development. Regional and local carbon management currently rely on the traditional bottom-up method of emission reports, which are known for their opaque and complex procedures. Novel top-down carbon monitoring methods including space-based, aerial and ground-based observations are tantalising alternatives to more reliable data sources of carbon emissions as well as sinks. Unmanned Aerial Vehicles (UAVs) based carbon monitoring is an upcoming field because of its lower costs, higher spatial-temporal resolution and operability at a wider range of weather conditions compared to satellite observations. UAVs equipped with Non-dispersive Infrared (NDIR) carbon dioxide (CO2) sensors can measure in the air with faster response and can intelligently navigate themselves within areas of greater interest. With the help of lighter NDIR payloads and multiple UAVs flying in synchrony, larger cross sections or areas for maximising information acquisition of emission plumes can be covered. In this article, we first introduce the design of a miniaturised NDIR payload, which is compatible with a lightweight UAV to achieve a single system weighing less than 4 kg. Then, we test our design via comparisons against another NDIR payload to determine its accuracy (−0.82 ppm and −0.44 ppm) and precision (0.12 ppm and −0.03 ppm) in measurements of two vertical concentration profiles. Finally, we deployed four UAVs on two major anthropogenic emission source monitoring campaigns with coordinated flight patterns. To fully understand the benefit of multi-UAV carbon monitoring, we attempted two flight patterns and computed power plant emission rates via the cross-sectional flux method. While all flights successfully captured the concentration enhancements downwind of the power plants, the single-sectional flight pattern measured an emission rate of 10ktCO2/day which is 44% less than that reported monthly and the multi-sectional flight pattern yielded relatively varied results (8.5ktCO2/day on average, which is 54% less) for the respective sections due to wind underestimations, plume section undersampling and wind fluctuations.

Drivers, changes, and impacts of hydrological extremes in India: A review

AbstractThe frequency of hydrological extremes (droughts and floods) has increased in recent years in India. A dense population, intensive agriculture, and rapidly developing physical infrastructure put India under the risk of hydrological extremes. However, a comprehensive overview of the drivers, changes, and impacts associated with hydrological extremes in India has been lacking. We examine the key processes and the role of climate and human interventions on hydrological extremes in India. We discuss the observed and projected changes in hydrological extremes along with their impacts. While the Indian summer monsoon is the primary driver of the hydrological extremes in India, human interventions (irrigation, reservoir storage, and groundwater pumping) play a crucial role in the changes and variability of hydrological extremes. The relative role of climate change and direct human interventions on changing the intensity and impacts of hydrological extremes need to be considered for future adaptation planning under climate change. The observed increase in the frequency and intensity of hydrological extremes will continue in the future with compound and cascading impacts due to the warming climate. We need to strengthen the observational network, improve hydrological modeling, reduce climate uncertainty, and develop robust early warning systems to adapt and mitigate hydrological extremes in India.This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Extremes Science of Water > Water and Environmental Change

The Impact of Music Therapy in a Pediatric Oncology Setting: An Italian Observational Network Study.

Music Therapy (MT) is a non-pharmacological, art-based intervention that employs music experiences within a therapeutic alliance to attend to clients' physical, emotional, cognitive, and social requirements. This is the first study aiming at investigating the impact of MT on the psychological facets of children suffering from cancer. The study, combining the AQR and m-YPAS assessment tools, evaluated behavioral, sound-musical, and interactive parameters in pediatric oncology patients undergoing MT sessions during hospitalization. Fifty patients admitted to the Paediatric Oncology and Haematology Unit at Policlinico S. Orsola Hospital in Bologna, Italy, were enrolled, irrespective of their treatment regimen. Data collection occurred on the first day of the MT session between 3 p.m. and 5 p.m., with observations conducted by independent observers. In addition to traditional statistical analysis, network analysis was used to explore the combined interactions of all parameters, effectively discerning the distinctive roles played by each one during therapy sessions and their influence on all others. Network analysis highlighted distinct patterns of interactions among parameters during the various sessions, emphasizing the role of positive emotions and a calm setting, the child's ability to take the initiative in sessions, their sense of agency, and the parent's role in guiding them. Significant differences were recorded at each time point between all variables considered. The results of this innovative study may pave the way for future multicenter studies aimed at further exploring the role of MT in children undergoing both curative and palliative treatments for cancer.

Neural adaptive dynamic surface control for position tracking of electrohydraulic servo systems based on extended state observer

High-performance motion tracking of electro-hydraulic servo systems plays an important role in developing industrial technology. However, the system itself has parameter uncertainty, uncertain nonlinearity, and measurement noise, significantly decreasing the performance of conventional controllers designed based on nominal models. To improve the tracking performance of electro-hydraulic servo systems, an adaptive dynamic surface control method combining extended state observer and radial basis function neural network is proposed. Firstly, an extended state observer combining neural network output and parameter adaptation technology is designed without the need for speed and pressure feedback to observe the system’s unknown state variables and nonlinear matched disturbance. The design of neural networks is used to approximate mismatched disturbance with significant nonlinearity online. Then, the proposed extended state observer is introduced into the neural adaptive backstepping design, effectively compensating for simultaneous matched and mismatched disturbances, thereby suppressing most of the uncertain nonlinearity of the system. Meanwhile, online update of uncertain parameters further improves the tracking performance of the controller. In addition, the introduction of dynamic surface overcomes the “explosion of complexity” issue in backstepping design. The rigorous stability of the closed-loop system is proved by the Lyapunov stability theory. Finally, the effectiveness of the proposed controller is validated through comparative experiments on valve-controlled hydraulic cylinder.