Systematic Effects Research Articles

Identifying frequency de-correlated dust residuals in B-mode maps by exploiting the spectral capability of bolometric interferometry

Context. Astrophysical polarized foregrounds represent the most critical challenge in cosmic microwave background (CMB) B-mode experiments, requiring multifrequency observations to constrain astrophysical foregrounds and isolate the CMB signal. However, recent observations indicate that foreground emission may be more complex than anticipated. Not properly accounting for these complexities during component separation can lead to a bias in the recovered tensor-to-scalar ratio. Aims. In this paper we investigate how the increased spectral resolution provided by band-splitting in bolometric interferometry (BI) through a technique called spectral imaging can help control the foreground contamination in the case of an unaccounted-for Galactic dust frequency de-correlation along the line of sight (LOS). Methods. We focused on the next-generation ground-based CMB experiment CMB-S4 and compared its anticipated sensitivity, frequency, and sky coverage with a hypothetical version of the same experiment based on BI (CMB-S4/BI). We performed a Monte Carlo analysis based on parametric component separation methods (FGBuster and Commander) and computed the likelihood of the recovered tensor-to-scalar ratio, r. Results. The main result is that spectral imaging allows us to detect systematic uncertainties on r from frequency de-correlation when this effect is not accounted for in the component separation. Conversely, an imager such as CMB-S4 would detect a biased value of r and would be unable to spot the presence of a systematic effect. We find a similar result in the reconstruction of the dust spectral index, and we show that with BI we can more precisely measure the dust spectral index when frequency de-correlation is present and not accounted for in the component separation. Conclusions. The in-band frequency resolution provided by BI allows us to identify dust LOS frequency de-correlation residuals where an imager with a similar level of performance would fail. This creates the possibility of exploiting this potential in the context of future CMB polarization experiments that will be challenged by complex foregrounds in their quest for B-mode detection.

An iterative interval estimation approach to nonlinear discrete-time systems

This paper is devoted to the iterative interval estimation for nonlinear discrete-time systems. To reconstruct the system state, a sequence of iterative observers is established based on the iterative disturbance estimation and measured output. By means of the Lipschitz condition and [Formula: see text] technique, sufficient conditions are built by the Lyapunov function method to make observation errors convergent. Resorting to the zonotope-based reachability analysis, the reachable set of nonlinear terms and observation errors are analyzed such that the state interval can be supplied. The presented approach is validated by a simulation comparison.

Mengekstrak Parameter Fungsi Skala Kanopi di Kawasan Konservasi Lembah Danum, Sabah Menggunakan Data Pesawat Udara LiDAR

Forest restoration is an effort to restore the growth and balance degraded forest ecosystems, through various approaches such as reforestation, land restoration, endangered species conservation, and forest fire prevention. Conventional field data collection challenges, such as limited spatial coverage, tall tree measurement difficulties, and human observation errors, hinder canopy-scale functional traits monitoring and extraction across forest treatments. The study addresses the importance of forest restoration and the challenges associated with conventional field measurements in extracting canopy scale functional traits by utilizing Airborne light detection and ranging (LiDAR) technology for more accurate information involving the old growth, natural regeneration, and active restored forests in Danum Valley Conservation Area and INFAPRO Sabah. The study utilized diverse processing algorithms, allometric equations, and correlative modeling methods. At the grid level, various LiDAR functional traits were calculated, leaf area index, gap fraction, and canopy density, alongside assessment of multiple correlative modeling strategies. Linear regression and analysis of variance analyzed relationships between LiDAR-derived and field-derived canopy scale functional traits between different forest treatments. The results showed high R-squared values ranging from 0.73 to 0.91, Anova F-statistics and probability values (p) showed that there is a strong relationship between the field data and predicted LiDAR values for three-canopy scale functional traits in different forest treatment types. These results indicate that LiDAR technology is effective in predicting canopy scale functional traits and has the potential to provide accurate and detailed information on forest restoration areas for future conservation and management efforts.

Exploiting residual errors in nonlinear online prediction

We introduce a novel online (or sequential) nonlinear prediction approach that incorporates the residuals, i.e., prediction errors in the past observations, as additional features for the current data. Including the past error terms in an online prediction algorithm naturally improves prediction performance significantly since this information is essential for an algorithm to adjust itself based on its past errors. These terms are well exploited in many linear statistical models such as ARMA, SES, and Holts-Winters models. However, the past error terms are rarely or in a certain sense not optimally exploited in nonlinear prediction models since training them requires complex nonlinear state-space modeling. To this end, for the first time in the literature, we introduce a nonlinear prediction framework that utilizes not only the current features but also the past error terms as additional features, thereby exploiting the residual state information in the error terms, i.e., the model’s performance on the past samples. Since the new feature vectors contain error terms that change with every update, our algorithm jointly optimizes the model parameters and the feature vectors simultaneously. We achieve this by introducing new update equations that handle the effects resulting from the changes in the feature vectors in an online manner. We use soft decision trees and neural networks as the nonlinear prediction algorithms since these are the most widely used methods in highly publicized competitions. However, as we show, our methods are generic and any algorithm supporting gradient calculations can be straightforwardly used. We show through our experiments on the well-known real-life competition datasets that our method significantly outperforms the state-of-the-art. We also provide the implementation of our approach including the source code to facilitate reproducibility (https://github.com/ahmetberkerkoc/SDT-ARMA).

Serum vitamin D concentrations in rabbits (Oryctolagus cuniculus) are more affected by UVB irradiation of food than irradiation of animals

Rabbits kept under ultraviolet B (UVB)-irradiation respond with increasing serum vitamin D (25(OH)D) concentrations, but it is unknown whether irradiation of the animals or their feed contributes more. Twenty-four New Zealand White rabbits were divided into three groups for a four-week period: the control group (C) received no UVB-exposure and non-irradiated hay (ergocalciferol (vitamin D2) concentration 2.22 µg/100 g dry matter). The direct exposure group (D) was provided with 12 h of UVB-irradiation daily and fed the same hay as group C in shaded areas to prevent UVB-irradiation thereof. The indirect exposure group (I) did not receive direct UVB-irradiation but was fed hay of the same batch that was exposed to 12 h of UVB-irradiation (vitamin D2 6.06 µg/100 g dry matter). Serum 25(OH)D2, 25(OH)D3, ionised calcium, total calcium, phosphorus, and magnesium concentrations were measured weekly. There was no systematic effect on serum mineral concentrations. The serum 25(OH)D2 concentrations were significantly higher in group I compared to groups C and D from the second week onwards. 25(OH)D3 concentrations increased only in group D, with significant differences to both other groups from the third week onwards, yet at lower magnitudes than the noted increase of 25(OH)D2 in group I. Total 25(OH)D concentrations were highest in group I, intermediate in group D and lowest in group C. Serum total 25(OH)D concentration was more affected by UVB-irradiation of rabbits’ feed than by direct irradiation of the animals themselves. If rabbit serum total 25(OH)D concentrations should be managed, diet manipulation rather than animal UVB-exposure appears to be more effective.

Multiscale model diagnostics

I consider the problem of model diagnostics, that is, the problem of criticizing a model prior to history matching by comparing data to an ensemble of simulated data based on the prior model (prior predictions). If the data are not deemed as a credible prior prediction by the model diagnostics, some settings of the model should be changed before history matching is attempted. I particularly target methodologies that are computationally feasible for large models with large amounts of data. A multiscale methodology, that can be applied to analyze differences between data and prior predictions in a scale-by-scale fashion, is proposed for this purpose. The methodology is computationally inexpensive, straightforward to apply, and can handle correlated observation errors without making approximations. The multiscale methodology is tested on a set of toy models, on two simplistic reservoir models with synthetic data, and on real data and prior predictions from the Norne field. The tests include comparisons with a previously published method (termed the Mahalanobis methodology in this paper). For the Norne case, both methodologies led to the same decisions regarding whether to accept or discard the data as a credible prior prediction. The multiscale methodology led to correct decisions for the toy models and the simplistic reservoir models. For these models, the Mahalanobis methodology either led to incorrect decisions, and/or was unstable with respect to selection of the ensemble of prior predictions.

Transport parameters from AMS-02 F/Si data and fluorine source abundance

The AMS-02 collaboration recently released cosmic ray F/Si data with an unprecedented accuracy. Cosmic ray fluorine is predominantly produced by fragmentation of heavier progenitors, while silicon is mostly accelerated at source. This ratio is thus maximally sensitive to cosmic ray propagation. We study the compatibility of the transport parameters derived from the F/Si ratio with those obtained from the lighter Li/C, Be/C, and B/C ratios. We also inspect the cosmic ray source abundance of F, which is one of the few elements that has a high first ionisation potential but is only moderately volatile and is a potentially key element to study the acceleration mechanism of cosmic rays. We used the 1D diffusion model implemented in the code and performed $ analyses accounting for several systematic effects (energy correlations in data, nuclear cross sections, and solar modulation uncertainties). We also took advantage of the nuclear database to update the F production cross sections for its most important progenitors (identified to be 56Fe, 32S, 28Si, 27Al, 24Mg, 22Ne, and 20Ne). The transport parameters obtained from AMS-02 F/Si data are compatible with those obtained from AMS-02 (Li,Be,B)/C data. The combined fit of all of these ratios leads to a $ 1.1$, with $ 10<!PCT!>$ adjustments of the B and F production cross sections (which are based on very few nuclear data points and would strongly benefit from new measurements). The F/Si ratio is compatible with a pure secondary origin of F, with a best-fit relative source abundance F/^ Si) CRS $ and an upper limit of $ $. Unfortunately, this limit is not sufficient to test global acceleration models of cosmic ray nuclei, for which values at the level of $ $ are required. Such levels could be attained with F/Si data with a few percent of accuracy at a few tens of TV, which is possibly within reach for the next generation of cosmic ray experiments.

An Innovative Correction–Fusion Approach for Multi-Satellite Precipitation Products Conditioned by Gauge Background Fields over the Lancang River Basin

Satellite precipitation products can help improve precipitation estimates where ground-based observations are lacking; however, their relative accuracy and applicability in data-scarce areas remain unclear. Here, we evaluated the accuracy of different satellite precipitation datasets for the Lancang River Basin, Western China, including the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, the Global Precipitation Measurement Integrated Multi-satellitE Retrievals (GPM IMERG), and Fengyun 2G (FY-2G) datasets. The results showed that GPM IMERG and FY-2G are superior to TRMM 3B42RT for meeting local research needs. A subsequent bias correction on these two datasets significantly increased the correlation coefficient and probability of detection of the products and reduced error indices such as the root mean square error and mean absolute error. To further improve data quality, we proposed a novel correction–fusion method based on window sliding data correction and Bayesian data fusion. Specifically, the corrected FY-2G dataset was merged with GPM IMERG Early, Late, and Final Runs. The resulting FY-Early, FY-Late, and FY-Final fusion datasets showed high correlation coefficients, strong detection performances, and few observation errors, thereby effectively extending local precipitation data sources. The results of this study provide a scientific basis for the rational use of satellite precipitation products in data-scarce areas, as well as reliable data support for precipitation forecasting and water resource management in the Lancang River Basin.

Sampling and misspecification errors in the estimation of observation‐error covariance matrices using observation‐minus‐background and observation‐minus‐analysis statistics

AbstractSpecification of the observation‐error covariance matrix for data assimilation systems affects the observation information content retained by the analysis, particularly for observations known to have correlated observation errors (e.g., geostationary satellite and Doppler radar data). A widely adopted approach for estimating observation‐error covariance matrices uses observation‐minus‐background and observation‐minus‐analysis residuals, which are routinely produced by most data assimilation systems. Although this approach is known to produce biased and noisy estimates, due to sampling and misspecification errors, there has been no systematic study of sampling errors with this approach to date. Furthermore, the eigenspectrum of the estimated observation‐error covariance matrix is known to influence the analysis information content and numerical convergence of variational assimilation schemes. In this work, we provide new theorems for the sampling error and eigenvalues of the estimated observation‐error covariance matrices with this approach. We also conduct numerical experiments to illustrate our theoretical results. We find that this method produces large sampling errors if the true observation‐error standard deviation is large, while the other error characteristics, including the true background‐error standard deviation and observation‐ and background‐error correlation length‐scales, have a relatively small effect. We also find that the smallest eigenvalues of the estimated matrices may be smaller or larger than the true eigenvalues, depending on the assumed and true observation‐ and background‐error statistics. These results may provide insights for practical applications: for example, in deciding on appropriate sample sizes and choosing parameters for matrix reconditioning techniques.

Speed Sensor-less Control of Induction Motor based on Super-helical Sliding Mold

Background: With the continuous progress of technology and the development of shield enterprises, higher requirements are put forward for speed estimation and system stability in the sensorless control system of induction motors in shield machines. Method: In order to solve the problems of velocity observation error and sliding mode vibration of the induction motor velocity observation method based on sliding mold observer, an improved superhelical sliding mold observer is proposed by combining the currently known invention patents and the information based on the induction motor control. Result: The open square of the current error value is introduced into the continuous function of the sliding variable of the observer, and two different functions are used in the boundary layer to replace the sign function, which reduces the sliding mode noise and vibration problems, and the stability of the observer is proved by the Lyapunov stability theory. After that, a phase-locked loop alternative model-referenced adaptive algorithm is used as the rotational speed observation link, which solves the problem that the conventional model-referenced adaptive algorithm relies on the current-modeled magnetic chain observer. Conclusion: The improved super-helical sliding mode observer and phase-locked loop control method effectively reduce the system vibration and speed estimation error of the motor under different speeds and loading conditions, and the observed currents are smoother, which improves the stability of the system. Simulation and experimental results under different working conditions show the effectiveness of the proposed method.

Do Sexual Harassment Claimants’ Gender Identity and Race Influence Third-Party Observers’ Assumptions About the Harassment Incident?

Sexual harassment research typically focuses on White cisgender women. Are harassment targets who are not White cisgender women perceived similarly? In three preregistered studies, participants (Study 1: N = 442; Study 2: N = 2,088, nationally representative U.S. sample; Study 3: N = 577) read about a formal (Studies 1 and 2) or informal (Study 3) sexual harassment claim made by a woman whose gender identity (cisgender vs. transgender) and race (Black vs. White) varied. Details about the incident were redacted, and participants wrote about what they thought the harasser had done. These free-response data indicated that cisgender (vs. transgender) women were seen as more likely to have experienced unwanted references to appearance, unwanted sexual attention (in Studies 2 and 3), or unwanted romantic attention (especially if they were White). Transgender (vs. cisgender) women, regardless of race, were seen as more likely to have experienced gender harassment. The claimant's race had little systematic effect, suggesting that perceivers are less attentive to the potential impact of race (vs. gender identity) on sexual harassment. People's inferences about the types of sexual harassment that women are most likely to face can impact the support claimants may receive when they come forward with a sexual harassment claim. Additional online materials for this article are available on PWQ's website at http://journals.sagepub.com/doi/suppl/10.1177/03616843241252658

Proficiency testing for event-specific quantification of genetically modified maize MON87427: Performance assessment based on the metrologically traceable reference values as assigned values

The Asia Pacific Metrology Program and the Accreditation Cooperation joint Proficiency Testing (PT) program for the quantification of genetically modified maize MON87427 was organized by the National Institute of Metrology, China, to enhance the measurement accuracy and metrological traceability in the region. Certified reference materials were employed as test samples; metrologically traceable certified reference values served as PT reference values (PTRVs) for evaluating the participants results. The consensus values obtained from the participants were higher than the assigned values, potentially due to the systematic effects of DNA extraction process. The participants' relatively poor overall performance by the ζ-score compared with z-score demonstrates their need to thoroughly investigate quantification bias to elevate the measurement capability of genetically modified (GM) content and deepen their understanding of uncertainty estimation. This program confirmed the importance of using metrologically traceable reference values instead of consensus values as PTRV for reliable performance assessment.

Secure state estimation with disturbance rejection against switched sparse sensor attacks

This paper studies the issue of secure state estimation for cyber-physical systems vulnerable to sparse sensor attacks, where attackers might change their attack targets at any time to deteriorate the estimation performance. To address this issue, a Luenberger-like observer is designed to estimate the system state under the attack interference, which employs an improved projection operator and the least square method to handle the switched attacks and introduces a disturbance estimator to enhance the anti-disturbance capability. It is proven that the observation error system can achieve asymptotic stability and dissipative performance, even under the influence of switched sensor attacks and disturbances. Different from the existing results, this method can not only defend against more erratic attacks but also effectively reject disturbances. The conclusion presented is validated through simulation experiments.

Dibble’s Reduction Thesis: Implications for Global Lithic Analysis

Harold Dibble demonstrated the systematic effects of reduction by retouch upon the size and shape of Middle Paleolithic tools. The result was the reduction thesis, with its far-reaching implications for the understanding of Middle Paleolithic assemblage variation that even now are incompletely assimilated. But Dibble’s influence extended beyond the European Paleolithic. Others identified additional reduction methods and measures that complement Dibble’s reduction thesis, and applied analytical concepts and methods consistent with it to industries and assemblages around the world. These developments facilitated comprehensive reduction analysis of archaeological tools and assemblages and their comparison in the abstract despite the great diversity of their time–space contexts. Dibble argued that many assemblages are time-averaged accumulations. In cases from New Zealand to North America, methods he pioneered and that others extended reveal the complex processes by which behavior, tool use, curation, and time interacted to yield those accumulations. We are coming to understand that the record is no mere collection of ethnographic vignettes, instead a body of data that requires macroarchaeological approaches. Archaeology’s pending conceptual revolution in part is a legacy of Dibble’s thought.

The probability of chromatin to be at the nuclear lamina has no systematic effect on its transcription level in fruit flies

BackgroundMultiple studies have demonstrated a negative correlation between gene expression and positioning of genes at the nuclear envelope (NE) lined by nuclear lamina, but the exact relationship remains unclear, especially in light of the highly stochastic, transient nature of the gene association with the NE.ResultsIn this paper, we ask whether there is a causal, systematic, genome-wide relationship between the expression levels of the groups of genes in topologically associating domains (TADs) of Drosophila nuclei and the probabilities of TADs to be found at the NE. To investigate the nature of this possible relationship, we combine a coarse-grained dynamic model of the entire Drosophila nucleus with genome-wide gene expression data; we analyze the TAD averaged transcription levels of genes against the probabilities of individual TADs to be in contact with the NE in the control and lamins-depleted nuclei. Our findings demonstrate that, within the statistical error margin, the stochastic positioning of Drosophila melanogaster TADs at the NE does not, by itself, systematically affect the mean level of gene expression in these TADs, while the expected negative correlation is confirmed. The correlation is weak and disappears completely for TADs not containing lamina-associated domains (LADs) or TADs containing LADs, considered separately. Verifiable hypotheses regarding the underlying mechanism for the presence of the correlation without causality are discussed. These include the possibility that the epigenetic marks and affinity to the NE of a TAD are determined by various non-mutually exclusive mechanisms and remain relatively stable during interphase.ConclusionsAt the level of TADs, the probability of chromatin being in contact with the nuclear envelope has no systematic, causal effect on the transcription level in Drosophila. The conclusion is reached by combining model-derived time-evolution of TAD locations within the nucleus with their experimental gene expression levels.

Affordance-based control of braking in cycling: Experience reveals differences in the style of control

We investigated whether in an in-situ collision avoidance experiment cyclists regulate braking by adopting an affordance-based control strategy. Within an affordance-based control strategy for braking, deceleration is controlled relative to the maximum achievable deceleration rather than by nulling out deviations from ideal deceleration, and potentially allowing for different braking styles. Twenty active- and eighteen inactive-cyclists were asked to cycle on a straight path in an indoor gym and to stop as close as possible in front of a stationary obstacle. Maximum achievable deceleration was manipulated by loading the bike: no-load, load-5 kg, and load-10 kg. Two approach distances were used to vary cycling speed. Participants in both groups stopped farther from the obstacle when approaching with long- than short-initial distance conditions. No systematic effects of loading on braking performance and control were found across the two groups. However, both groups did increase the magnitude of brake adjustments as ideal deceleration increased and got closer to the action boundary, even when current deceleration approached the ideal deceleration. This indicates that participants adopted an affordance-based control strategy for braking. Two braking styles were identified: an aggressive style, characterized by a late braking onset and a high, steep peak in ideal deceleration, and a conservative style, characterized by an early braking onset and gradual, linear increase in ideal deceleration. The aggressive braking style was more prevalent among the active-cyclists. We suggest that the braking styles emerge from differences in calibration between information and action. The novelty of our work lies in confirming that cyclists adopt an affordance-based control strategy in an in-situ experiment and in demonstrating and explicating how affordance-based control can incorporate the emergence of different styles of braking.

Firm systematic risk after the Russia–Ukraine invasion

We explore the systematic risk effects of the Russia–Ukraine war on European non-financial firms during the period 2021Q1–2022Q4. Using quarterly firm-level data, we find that, after the Russia–Ukraine conflict, European non-financial firms experienced a statistically significant increase of systematic risk (Beta). Moreover, we also find that firms with higher foreign sales reduced their exposure to such unprecedented geopolitical event, showing a statistically significant lower Beta in the aftermath of the announcement of the Russian invasion of Ukraine. Results are robust also using alternative measures of firm risk as well as after running alternative econometric approaches.

Assimilating ASCAT normalized backscatter and slope into the land surface model ISBA-A-gs using a Deep Neural Network as the observation operator: Case studies at ISMN stations in western Europe

ASCAT normalized backscatter (σ40o) and slope (σ′) contain valuable information about soil moisture and vegetation. While σ40o has been assimilated to constrain soil moisture, sometimes together with Leaf Area Index (LAI), this study is the first to assimilate σ′ directly to constrain vegetation states. Here, we assimilate σ40o and slope σ′ into the ISBA-A-gs LSM using the Simplified Extended Kalman Filter (SEKF) using a Deep Neural Network (DNN) as the observation operator. The performances of the data assimilation (DA) and open loop (OL) are evaluated against in-situ soil moisture observations from the International Soil Moisture Network (ISMN), and LAI observations from the Copernicus Global Land Service (CGLS). Given an accurate and physically plausible observation operator, along with well-defined model and observation errors, the data assimilation system should yield improved estimates of the model states. However, results show that the DA performance is neutral compared to the OL in terms of the median unbiased root mean square error (ubRMSE) and Pearson correlation coefficient (ρ) across all validation sites. In addition, an analysis of the residuals and innovations confirms that DA had limited or no impact. This poor performance is perplexing. Furthermore, given the growing interest in the use of machine-learning-based observation operators, it is essential to understand the role that the use of the DNN may be playing in this poor performance. While representativeness errors and error specification play some part, it is demonstrated that the key factor constraining the efficacy of the SEKF is the correct estimation of the Jacobians that control the degree to which the observations update the states in the SEKF. It is argued that the DNN relating model states to satellite observations must have physically-plausible and robust Jacobians for the DNN to be effective in a data assimilation framework.

Heterogeneous unmanned swarm formation containment control based on reinforcement learning

In response to the problem of time-varying spherical formation control for a heterogeneous unmanned aerial vehicle (UAV) swarm system with dynamic uncertainty in the system model, this paper proposes an optimal distributed formation containment control method based on reinforcement learning. By combining the time-varying formation containment vector design with the distributed predefined time observer and constructing the augmented system of a multi-quadrotor UAV system and an observer, the problem of time-varying formation containment control in heterogeneous swarm systems is transformed into a stabilization problem. By introducing a value function with a discount factor, the stabilization problem of a heterogeneous UAV swarm system is transformed into an optimal control problem. Using the “actor-critic” neural network, combined with an off-policy reinforcement learning algorithm and distributed control methods, the solution to the formation containment controller is achieved in a data-driven manner. The stability of distributed observer and formation containment controller as well as the convergence of reinforcement learning algorithms are demonstrated using Lyapunov and related theory. The numerical simulation results demonstrate that the observation error of the designed distributed observer converges within a predefined time of 0.1 s, while the overall formation tracking error of the heterogeneous swarm converges within 2.74 s, thereby further validating the effectiveness and superiority of the designed control scheme.

Stimuli-Responsive Delivery Systems for Intervertebral Disc Degeneration.

As a major cause of low back pain, intervertebral disc degeneration is an increasingly prevalent chronic disease worldwide that leads to huge annual financial losses. The intervertebral disc consists of the inner nucleus pulposus, outer annulus fibrosus, and sandwiched cartilage endplates. All these factors collectively participate in maintaining the structure and physiological functions of the disc. During the unavoidable degeneration stage, the degenerated discs are surrounded by a harsh microenvironment characterized by acidic, oxidative, inflammatory, and chaotic cytokine expression. Loss of stem cell markers, imbalance of the extracellular matrix, increase in inflammation, sensory hyperinnervation, and vascularization have been considered as the reasons for the progression of intervertebral disc degeneration. The current treatment approaches include conservative therapy and surgery, both of which have drawbacks. Novel stimuli-responsive delivery systems are more promising future therapeutic options than traditional treatments. By combining bioactive agents with specially designed hydrogels, scaffolds, microspheres, and nanoparticles, novel stimuli-responsive delivery systems can realize the targeted and sustained release of drugs, which can both reduce systematic adverse effects and maximize therapeutic efficacy. Trigger factors are categorized into internal (pH, reactive oxygen species, enzymes, etc.) and external stimuli (photo, ultrasound, magnetic, etc.) based on their intrinsic properties. This review systematically summarizes novel stimuli-responsive delivery systems for intervertebral disc degeneration, shedding new light on intervertebral disc therapy.