Decay Function Research Articles

Assessing patients’ acceptable and realised distances to determine accessibility standards for the size of catchment areas in outpatient care

Healthcare planning aims to ensure availability of care in a needs-based, evenly distributed and locally available manner. However, many planning mechanisms lack accessibility standards. To determine standards, catchment areas must be derived from health-related travel assessments and a population's distance acceptance for different medical specialisation levels.We estimated distance acceptance using representative cross-sectional survey data (n = 1.598). Moreover, we used utilization data covering 88% of the German population (2014/15) to calculate realised travel distances for six medical specialties (n = 676.255.605 cases). We specified a gravity-based distance decay function and estimated regression-based distance thresholds from both samples.Realised distances were mostly below 30 min (90% of cases) indicating appropriate mean accessibility. The 5% observed distance threshold was between 23.7 min for GPs and 47.6 min for dermatologists. Depending on medical speciality, distance acceptance was mainly determined by distance, age, activity level and town size for GP visits and by health and income for specialist care. 5% acceptance thresholds varied between 27.9 min to GPs for elderly patients and 51.6 min to orthopaedists for younger patients. Acceptable distances for 90% of the population were 6 (8) minutes to GPs (specialists). The variation of thresholds, which depended on socio-demographic and health variables and the population share that is fully accepting, illustrates that healthcare planners should move beyond averages to realise equal access for equal need.

Heat Transfer Enhancement through Thermodynamical Activity of H2O/Clay Nanofluid Flow over an Infinite Upright Plate with Caputo Fractional-Order Derivative

This paper presents a modelling of nanofluid flow using Caputo fractional derivatives through conservative equations of mass and momentum, and provides an exact solution on un-steady convective flow over a vertical plate with the mass diffusion effect, in association with an energy equation. H2O is the base liquid with clay nanoparticles floating in it in a uniform way. Boussinessq’s approach is used in the momentum equation for pressure gradient. The non-dimensional fluid temperature, species concentration and fluid transport are derived together with Jacob Fourier sine and Laplace transform techniques in terms of exponential decay function, and the inverse is computed further in terms of the Mittag-Leffler function. The impact of various physical quantities is interpreted with the fractional order of the Caputo derivatives. The obtained temperature, transport and species concentration profiles show behaviors for 0 < α < 1, where α is the fractional parameter. The rate of heat and mass transfer coefficients for the significance of physical quantities of interest are also obtained and presented through graphs. The impact of the nanoparticle volume fraction on the flow field is observed. At larger values of the fractional parameter, the velocity, temperature, and concentration distributions grow more quickly. In addition to that, it is found the concentration profiles behave in the opposite way for the volume fraction of nanofluids.

30. Influence of toggling on pullout force and screw diameter increase in spine fixation

<h3>BACKGROUND CONTEXT</h3> Pedicle screw fixation provides spinal stabilization and maintains normal vertebral motions in degenerative spondylolisthesis and scoliosis. Pedicle screw fixation requires several parameters to be optimized at the screw-bone interface to minimize clinical and biomechanical failure, including screw trajectory, bone density and screw sizing. Although prior studies have demonstrated screw loosening as an effect of toggling, they ultimately focus on the magnitude of the pullout force after cyclic loading rather than screw-bone interface throughout cycling. <h3>PURPOSE</h3> A stiffness decay model may help to better predict stability and investigate the effects of pedicle screw diameter to pedicle width ratio on overall screw stability following cyclic loading in the thoracolumbar spine. <h3>OUTCOME MEASURES</h3> Pullout force, toggling forces, decay functions. <h3>METHODS</h3> Thoracolumbar vertebrae (T1-L5) were harvested from two cadaveric spines. Pedicle screws were placed using a standard surgical technique. Five-hundred cycles toggle testing was conducted with a uniaxial load frame followed by axial pullout tests. Exponential decay models with a stiffness coefficient were computed to examine toggling force over the number of toggle cycles for each screw. Linear regression modeling and Pearson's correlation tests were used to compare the decay coefficient with the ratio of the screw diameter to the pedicle diameter, the pullout strength, and pedicle loosening. Subgroup analysis was performed between under-sized (70% pedicle diameter) screws. <h3>RESULTS</h3> Sixty-eight pedicles were harvested. Of the 44 viable pedicles, 33 (75%) were from the thoracic spine and 11 (25%) were from the lumbar spine. There were 23 pedicle screws in the undersized group and 21 pedicle screws in the normal-sized group. The average screw diameter was 53% (SD 5%) of the pedicle diameter in the undersized group and 77% (SD 8%) of the pedicle diameter in the normal-sized group (p < 0.001). A significant association was found between the screw to pedicle diameter ratio and the natural log of the stiffness decay coefficient (p = 0.035). The average pullout force was 170 N (SD 75) in the under-sized pedicle group and 194 N (SD 109) in the normal-sized group (p=0.40). Higher pullout force was associated with a lower stiffness decay coefficient (p = 0.005). Both pullout force and the natural log of the stiffness decay coefficients were associated with the drop in maximum toggling force after 500 cycles (p = 0.026 and p < 0.001, respectively). Utilizing the stiffness decay coefficient provided a better fit model for the predicted drop in maximum toggling force with a lower Akaike information criterion. <h3>CONCLUSIONS</h3> Pedicle screws with a high stiffness decay coefficient during toggle testing had lower pullout strengths. While higher pedicle screw to bone diameter ratios were associated with increased stiffness decay coefficient, there was no relationship between pedicle screw size and pullout strength. In subgroup analysis, however, increasing pedicle screw to bone diameter ratios were associated with lower pullout strength in the normal-sized group. Clinically, selecting a screw size that maximizes the pedicle diameter-screw ratio may not always lead to better fixation in all spine levels or regions. <h3>FDA DEVICE/DRUG STATUS</h3> This abstract does not discuss or include any applicable devices or drugs.

The Hybrid Excess and Decay (HED) model: an automated approach to characterising changes in the photoplethysmography pulse waveform

Photoplethysmography offers a widely used, convenient and non-invasive approach to monitoring basic indices of cardiovascular function, such as heart rate and blood oxygenation. Systematic analysis of the shape of the waveform generated by photoplethysmography might be useful to extract estimates of several physiological and psychological factors influencing the waveform. Here, we developed a robust and automated method for such a systematic analysis across individuals and across different physiological and psychological contexts. We describe a psychophysiologically-relevant model, the Hybrid Excess and Decay (HED) model, which characterises pulse wave morphology in terms of three underlying pressure waves and a decay function. We present the theoretical and practical basis for the model and demonstrate its performance when applied to a pharmacological dataset of 105 participants receiving intravenous administrations of the sympathomimetic drug isoproterenol (isoprenaline). We show that these parameters capture photoplethysmography data with a high degree of precision and, moreover, are sensitive to experimentally-induced changes in interoceptive arousal within individuals. We conclude by discussing the possible value in using the HED model as a complement to standard measures of photoplethysmography signals.

F‐SWIR: Rumor Fick‐spreading model considering fusion information decay in social networks

SummaryThe spread of rumors has a major negative impact on social stability. Traditional rumor spreading models are mostly based on infectious disease models and do not consider the influence of individual differences and the network structure on rumor spreading. In this paper, we propose a rumor Fick‐spreading model that integrates information decay in social networks. The dissemination of rumors in social networks is random and uncertain and is affected by the dissemination capabilities of individuals and the network environment. The rumor Fick‐transition coefficient and Fick‐transition gradient are defined to determine the influence of the individual transition capacity and the network environment on rumor propagation, respectively. The Fick‐state transition probability is used to describe the probability of change of an individual's state. Moreover, an information decay function is defined to characterize the self‐healing probability of individuals. According to the different roles and reactions of users during rumor dissemination, the user state and the rumor dissemination rules among users are refined, and the influence of the network structure on the rumor dissemination is ascertained. The experimental results demonstrate that the proposed model outperforms other rumor spread models.

An Ensemble Learning–Online Semi-Supervised Approach for Vehicle Behavior Recognition

In autonomous vehicles, recognizing different maneuvering behaviors of surrounding vehicles is crucial to reduce traffic risks and achieve safe path planning. Conventional vehicle behavior recognition methods adopt mainly supervised learning methods and assume that many sample labels are available. However, manual sample labeling is often time-consuming and laborious. Also, onboard sensors collecting surrounding vehicle movement information in data streams often cannot process information in real-time. To tackle these problems, we propose a semi-supervised approach using K-nearest neighbor- ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-NN</i> )-based ensemble learning to classify the maneuvering behaviors of surrounding vehicles. The framework is divided into three parts: initial model training, online classification, and online model updating. First, k-means clustering of the maneuvering behavior is performed, cluster features are calculated, and a set of micro-clusters is obtained to establish the initial model. Second, the ensemble <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-NN</i> -based learning method is used to classify the incoming instances. Finally, the model is updated online using error-driven representative learning and an exponential decay function. Typical lane-changing and turning maneuvers are used as representatives to verify the performance of the proposed method. The data are provided by a next-generation simulation project. The results show that the proposed model achieves highest average recognition accuracy compared with other benchmark methods for the lane-changing and turning maneuvers shortly after the maneuver begins, even for a small sample size.

Neural network for multi-exponential sound energy decay analysis.

An established model for sound energy decay functions (EDFs) is the superposition of multiple exponentials and a noise term. This work proposes a neural-network-based approach for estimating the model parameters from EDFs. The network is trained on synthetic EDFs and evaluated on two large datasets of over 20 000 EDF measurements conducted in various acoustic environments. The evaluation shows that the proposed neural network architecture robustly estimates the model parameters from large datasets of measured EDFs while being lightweight and computationally efficient. An implementation of the proposed neural network is publicly available.

Correlation Decay and Partition Function Zeros: Algorithms and Phase Transitions

We explore connections between the phenomenon of correlation decay and the location of Lee-Yang and Fisher zeros for various spin systems. In particular we show that, in many instances, proofs showing that weak spatial mixing on the Bethe lattice (infinite $\Delta$-regular tree) implies strong spatial mixing on all graphs of maximum degree $\Delta$ can be lifted to the complex plane, establishing the absence of zeros of the associated partition function in a complex neighborhood of the region in parameter space corresponding to strong spatial mixing. This allows us to give unified proofs of several recent results of this kind, including the resolution by Peters and Regts of the Sokal conjecture for the partition function of the hard core lattice gas. It also allows us to prove new results on the location of Lee-Yang zeros of the anti-ferromagnetic Ising model. We show further that our methods extend to the case when weak spatial mixing on the Bethe lattice is not known to be equivalent to strong spatial mixing on all graphs. In particular, we show that results on strong spatial mixing in the anti-ferromagnetic Potts model can be lifted to the complex plane to give new zero-freeness results for the associated partition function. This extension allows us to give the first deterministic FPTAS for counting the number of $q$-colorings of a graph of maximum degree $\Delta$ provided only that $q\ge 2\Delta$. This matches the natural bound for randomized algorithms obtained by a straightforward application of Markov chain Monte Carlo. We also give an improved version of this result for triangle-free graphs.

Analytical methods for assessing retinal cell coupling using cut-loading

Electrical coupling between retinal neurons contributes to the functional complexity of visual circuits. "Cut-loading" methods allow simultaneous assessment of cell-coupling between multiple retinal cell-types, but existing analysis methods impede direct comparison with gold standard direct dye injection techniques. In the current study, we both improved an existing method and developed two new approaches to address observed limitations. Each method of analysis was applied to cut-loaded dark-adapted Guinea pig retinae (n = 29) to assess coupling strength in the axonless horizontal cell type ('a-type', aHCs). Method 1 was an improved version of the standard protocol and described the distance of dye-diffusion (space constant). Method 2 adjusted for the geometric path of dye-transfer through cut-loaded cells and extracted the rate of dye-transfer across gap-junctions in terms of the coupling coefficient (kj). Method 3 measured the diffusion coefficient (De) perpendicular to the cut-axis. Dye transfer was measured after one of five diffusion times (1-20 mins), or with a coupling inhibitor, meclofenamic acid (MFA) (50-500μM after 20 mins diffusion). The standard protocol fits an exponential decay function to the fluorescence profile of a specified retina layer but includes non-specific background fluorescence. This was improved by measuring the fluorescence of individual cell soma and excluding from the fit non-horizontal cells located at the cut-edge (p<0.001) (Method 1). The space constant (Method 1) increased with diffusion time (p<0.01), whereas Methods 2 (p = 0.54) and 3 (p = 0.63) produced consistent results across all diffusion times. Adjusting distance by the mean cell-cell spacing within each tissue reduced the incidence of outliers across all three methods. Method 1 was less sensitive to detecting changes induced by MFA than Methods 2 (p<0.01) and 3 (p<0.01). Although the standard protocol was easily improved (Method 1), Methods 2 and 3 proved more sensitive and generalisable; allowing for detailed assessment of the tracer kinetics between different populations of gap-junction linked cell networks and direct comparison to dye-injection techniques.

Time-Dependent Model for Brittle Rocks Considering the Long-Term Strength Determined from Lab Data

The excavation of tunnels in brittle rocks with high in-situ strengths under large deviatoric stresses has been shown to exhibit brittle failure at the periphery of tunnels parallel to the maximum in-situ stress. This failure can either occur instantaneously or after several hours due to the strength degradation that is implicitly and indirectly considered in typical brittle constitutive models. While these models are powerful tools for engineering analyses, they cannot predict the time at which brittle rupture occurs, but rather, they show a possible failure pattern occurring instantaneously. In this paper, a model referred to as the long-term strength (LTS) model is introduced and implemented into FLAC2D. The model is built as a modified version of the CVISC model, introduced by Itasca, by adding a strength decay function. This function is developed from lab-scale time-to-failure (TTF) data. The LTS model is verified against its corresponding analytical solution using a constant stress creep lab test and implemented into a tunnel-scale model using the geometry, stress, and geologic conditions from the Atomic Energy of Canada Limited Underground Research Laboratory (AECL URL). The results of the LTS tunnel model are then compared to an identical model using the Cohesion Weakening Friction Strengthening (CWFS) approach.

Time-Weighted Community Search Based on Interest

Community search aims to provide users with personalized community query services. It is a prerequisite for various recommendation systems and has received widespread attention from academia and industry. The existing literature has established various community search models and algorithms from different dimensions of social networks. Unfortunately, they only judge the representative attributes of users according to the frequency of attribute keywords, completely ignoring the temporal characteristics of keywords. It is clear that a user’s interest changes over time, so it is essential to select users’ representative attributes in combination with time. Therefore, we propose a time-weighted community search model (TWC) based on user interests which fully considers the impact of time on user interests. TWC reduces the number of query parameters as much as possible and improves the usability of the model. We design the time-weighted decay function of the attribute. We then extract the user’s time-weighted representative attributes to express the user’s short-term interests more clearly in the query window. In addition, we propose a new attribute similarity scoring function and a community scoring function. To solve the TWC problem, we design and implement the Local Extend algorithm and the Shrink algorithm. Finally, we conduct extensive experiments on a real dataset to verify the superiority of the TWC model and the efficiency of the proposed algorithm.

Bi-objective approach for designing a regional waste management system: A case study of Vojvodina (Serbia).

This article presents a bi-objective mixed-integer linear model for the waste management system design. Two types of facilities, landfills and transfer stations (TSs), are identified based on two objectives: minimising the total system's costs and the total negative impact of located facilities on end users as waste generators through a defined pollution decay function. Landfills and TSs are categorised as undesirable facilities due to associated environmental risks. In this article undesirability of a located facility is defined as a two-threshold linear decreasing function of the distance between the end users and the located facility. Also, we considered additional restrictions that no two selected landfills or TSs are within a pre-specified distance from each other, limiting the superimposed pollution generated in these facilities. The model performances are tested on a real-scale example for Vojvodina (Serbia). Results showed that the proposed model has potentials and can be beneficial for government organisations, local authorities and other organisations related to waste management issues.

The Role of Soil Salinization in Shaping the Spatio-Temporal Patterns of Soil Organic Carbon Stock

Soil salinization is closely related to land degradation, and it is supposed to exert a significant negative effect on soil organic carbon (SOC) stock dynamics. This effect and its mechanism have been examined at site and transect scales in previous studies while over a large spatial extent, the salinity-induced changes in SOC stock over space and time have been less quantified, especially by machine learning and remote sensing techniques. The main focus of this study is to answer the following question: to what extent can soil salinity exert an additional effect on SOC stock over time at a larger spatial scale? Thus, we employed the extreme gradient boosting models (XGBoost) combined with field site-level measurements from 433 sites and 41 static and time-varying environmental covariates to construct methods capable of quantifying the salinity-induced SOC changes in a typical inland river basin of China between the 1990s and 2020s. Results showed that the XGBoost models performed well in predicting the soil electrical conductivity (EC) and SOC stock at 0–20 cm, with the R2 value reaching 0.85 and 0.81, respectively. SOC stock was found to vary significantly with increasing soil salinity following an exponential decay function (R2 = 0.27), and salinity sensitivity analysis showed that soils in oasis were expected to experience the largest carbon loss (−137.78 g m−2), which was about 4.84, 14.37, and 25.95 times higher than that in the saline, bare, and sandy land, respectively, if the soil salinity increased by 100%. In addition, the decrease in the soil salinity (−0.32 dS m−1) from the 1990s to the 2020s was estimated to enhance the SOC stock by 0.015 kg m−2, which contributed an additional 10% increase to the total SOC stock enhancement. Overall, the proposed methods can be applied for quantification of the direction and size of the salinity effect on SOC stock changes in other salt-affected regions. Our results also suggest that the role of soil salinization should not be neglected in SOC changes projection, and soil salinization control measures should be further taken into practice to enhance soil carbon sequestration in arid inland river basins.

Organic matter pools in sediments of the tidal Elbe river

Anaerobic sediment organic matter decay generates methane, delays sediment consolidation, reduces sediment density, viscosity and shear strength, all impacting the sediment rheological parameters and the navigable depth. This study quantifies the share of anaerobically and aerobically degradable sediment organic matter (SOM) in a depth profile and along a transect through the tidal river Elbe in the section of the Port of Hamburg. From exponential organic matter decay functions, organic matter decay rates (mg C gTOC−1 d−1) were derived and clustered with a k-Means Cluster Analysis. The reactivity of different (kinetic) organic matter pools along the river transect were characterized based on their biodegradation rates. A fast, medium, slowly and non-degradable pool (pools 1–4) were identified based on the measured organic matter lability. SOM lability decreased from upstream to downstream, evidenced by the decreasing amount of the easily degradable pool 1 material from upstream to downstream. The size of the slowly degradable pool 3, assumed to be associated with SOM bound to the mineral particles, did not show any spatial gradient and is therefore suggested to represent a baseline share of hardly accessible SOM in the investigation area (about 12%−16% of TOC). Total degradability thus appears to be governed by the amount of SOM present in addition to this basis (pool 3), which in turn follows a source gradient and an age gradient from upstream to downstream. The recalcitrant pool 4 was the largest at any part of the harbour, for any depth, and for both, anaerobic and aerobic conditions (about 75%−85% of TOC). This indicates that the sediment in the investigation area, including the uppermost fluidic and freshly settled layers, mostly comprises stabilised organic matter and contributes largely to storage of organic carbon. Differently sized anaerobic SOM pools with depth were observed as well as seasonal changes of the easily degradable SOM pool 1. The degradability was larger in upper sediment layers, it was also larger under aerobic conditions (by about 10% of TOC) but the differences between aerobic and anaerobic decay decreased from upstream to downstream.

Interpretable machine learning for real estate market analysis

AbstractMachine Learning (ML) excels at most predictive tasks but its complex nonparametric structure renders it less useful for inference and out‐of sample predictions. This article aims to elucidate and enhance the analytical capabilities of ML in real estate through Interpretable ML (IML). Specifically, we compare a hedonic ML approach to a set of model‐agnostic interpretation methods. Our results suggest that IML methods permit a peek into the black box of algorithmic decision making by showing the web of associative relationships between variables in greater resolution. In our empirical applications, we confirm that size and age are the most important rent drivers. Further analysis reveals that certain bundles of hedonic characteristics, such as large apartments in historic buildings with balconies located in affluent neighborhoods, attract higher rents than adding up the contributions of each hedonic characteristic. Building age is shown to exhibit a U‐shaped pattern in that both the youngest and oldest buildings attract the highest rents. Besides revealing valuable distance decay functions for spatial variables, IML methods are also able to visualise how the strength and interactions of hedonic characteristics change over time, which investors could use to determine the types of assets that perform best at any given stage of the real estate investment cycle.

Estimating the impact of the 1991 Pinatubo eruption on mesospheric temperature by analyzing HALOE (UARS) temperature data

Abstract. The Mt. Pinatubo eruption in 1991 had a severe impact on the Earth system, with a well-documented warming of the tropical lower stratosphere and a general cooling of the surface. This study focuses on the impact of this event on the mesosphere by analyzing solar occultation temperature data from the Halogen Occultation Experiment (HALOE) instrument on the Upper Atmosphere Research Satellite (UARS). Previous analyses of lidar temperature data found positive temperature anomalies of up to 12.9 K in the upper mesosphere that peaked in 1993 and were attributed to the Pinatubo eruption. Fitting the HALOE data according to a previously published method indicates a maximum warming of the mesosphere region of 4.1 ± 1.4 K and does not confirm significantly higher values reported for that lidar time series. An alternative fit is proposed that assumes a more rapid response of the mesosphere to the volcanic event and approximates the signature of the Pinatubo with an exponential decay function having an e-folding time of 6 months. It suggests a maximum warming of 5.4 ± 3.0 K, if the mesospheric perturbation is assumed to reach its peak 4 months after the eruption. We conclude that the HALOE time series probably captures the decay of a Pinatubo-induced mesospheric warming at the beginning of its measurement period.

Rural-Urban Disparities in Realized Spatial Access to General Practitioners, Orthopedic Surgeons, and Physiotherapists among People with Osteoarthritis in Alberta, Canada.

Rural Canadians have high health care needs due to high prevalence of osteoarthritis (OA) but lack access to care. Examining realized access to three types of providers (general practitioners (GPs), orthopedic surgeons (Ortho), and physiotherapists (PTs)) simultaneously helps identify gaps in access to needed OA care, inform accessibility assessment, and support health care resource allocation. Travel time from a patient’s postal code to the physician’s postal code was calculated using origin–destination network analysis. We applied descriptive statistics to summarize differences in travel time, hotspot analysis to explore geospatial patterns, and distance decay function to examine the travel pattern of health care utilization by urbanicity. The median travel time in Alberta was 11.6 min (IQR = 4.3–25.7) to GPs, 28.9 (IQR = 14.8–65.0) to Ortho, and 33.7 (IQR = 23.1–47.3) to PTs. We observed significant rural–urban disparities in realized access to GPs (2.9 and IQR = 0.0–92.1 in rural remote areas vs. 12.6 and IQR = 6.4–21.0 in metropolitan areas), Ortho (233.3 and IQR = 171.3–363.7 in rural remote areas vs. 21.3 and IQR = 14.0–29.3 in metropolitan areas), and PTs (62.4 and IQR = 0.0–232.1 in rural remote areas vs. 32.1 and IQR = 25.2–39.9 in metropolitan areas). We identified hotspots of realized access to all three types of providers in rural remote areas, where patients with OA tend to travel longer for health care. This study may provide insight on the choice of catchment size and the distance decay pattern of health care utilization for further studies on spatial accessibility.

TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System

In recent years, commercial platforms have embraced recommendation algorithms to provide customers with personalized recommendations. Collaborative Filtering is the most widely used technique of recommendation systems, whose accuracy is primarily reliant on the computed similarity by a similarity measure. Data sparsity is one problem that affects the performance of the similarity measures. In addition, most recommendation algorithms do not remove noisy data from datasets while recommending the items, reducing the accuracy of the recommendation. Furthermore, existing recommendation algorithms only consider historical ratings when recommending the items to users, but users’ tastes may change over time. To address these issues, this research presents a Deep Neural Network based on Time Decay (TD-DNN). In the data preprocessing phase of the model, noisy ratings are detected from the dataset and corrected using the Matrix Factorization approach. A power decay function is applied to the preprocessed input to provide more weightage to the recent ratings. This non-noisy weighted matrix is fed into the Deep Learning model, consisting of an input layer, a Multi-Layer Perceptron, and an output layer to generate predicted ratings. The model’s performance is tested on three benchmark datasets, and experimental results confirm that TD-DNN outperforms other existing approaches.

Guest Editorial: Situational awareness of integrated energy systems

Guest Editorial: Situational awareness of integrated energy systems

Dual-Resistance of Ion Migration and Moisture Erosion via Hydrolytic Crosslinking of Siloxane Functionalized Poly(Ionic Liquids) for Efficient and Stable Perovskite Solar Cells

Dual-Resistance of Ion Migration and Moisture Erosion via Hydrolytic Crosslinking of Siloxane Functionalized Poly(Ionic Liquids) for Efficient and Stable Perovskite Solar Cells