Cycle Variability Research Articles

Transcranial direct current stimulation (tDCS) and cycling performance on the 3-minute aerobic test (3mAT): placebo and nocebo effects

Transcranial direct current stimulation (tDCS) has been used extensively but research on its efficacy within the sport and exercise science realm has been inconsistent. There may be placebo and nocebo effects present with its use. Our objective was to determine if subjects can be influenced to believe that tDCS will improve cycling performance. Subjects were separated into a belief group (B; 5 women, 6 men) and a disbelief group (DB; 9 women, 3 men). The B group was told that the stimulation would improve performance on a subsequent cycling test. In the DB group, subjects were told that it was not effective and would hinder performance. The cycling test was a 3-minute aerobic test (3mAT) where subjects maintained the highest power output possible for three minutes, after completing a full 20 min warmup. During the warmup, they were given either no stimulation (control) or 2 mA bilateral stimulation over the M1 region. There was a very slight increase in maximal minute power for the B group (0.22%) and a small decrease for the DB group (-1.00%); however, these differences were not significant. No significant differences were found for any of the cycling variables. In conclustion, tDCS was unable to improve performance on the 3mAT. These findings, in conjunction with others, suggest that the acute effect of tDCS is still questionable when aiming to enhance endurance performance.

Determining the optimum number of cycles for calculating joint coordination and its variability during running at different speeds: A timeseries analysis

Understanding the intricacies of human movement coordination and variability during running is crucial to unraveling the dynamics of locomotion, identifying potential injury mechanisms and understanding skill development. Identification of minimum number of cycles for calculation of reliable coordination and its variability could help with better test organization and efficient assessment time. By adopting a cross-sectional study design, this study investigated the minimum required cycles for calculating hip-knee, hip-ankle and knee-ankle coordination and their variability using a continuous relative phase (CRP) method. Twenty-nine healthy adults ran on a treadmill at speeds of 9, 12.5, and 16 km.h−1 while 3D kinematic data of their lower limbs were recorded using 6 optoelectronic cameras. Using Intraclass Correlation Coefficient (ICC) analysis, reliability between CRP and its variability (CRPv) in different gait cycles (3, 5, 10, 20, 30) was assessed for each speed. A minimum of 10 cycles was required for CRP calculation across all speeds, whereas CRPv necessitated a minimum of 30 cycles for moderate to good reliability. While increasing the number of cycles improved ICC values for inter-joint CRP, the same trend was not consistently observed for CRPv, emphasizing the importance of separately assessing CRP and its variability metrics.

Effects of freeze-thaw cycles on fatigue performance of asphalt mixture and a fatigue-freeze-thaw damage evolution model

To study the effect of freeze-thaw (FT) cycles on the fatigue performance of composite crumb rubber modified asphalt mixtures (CCRMA), a semicircular bending fatigue test based on the digital image correlation technique (DIC) was used to measure the fatigue life (Nf) of CCRMA and SBS modified asphalt mixtures (SBSMA) and the horizontal strain (Exx) of the DIC deformation field under repeated loading. The variation laws of Nf, Exx and the damage factor (D) defined based on Exx were analyzed under FT cycle conditions. In addition, considering the important influence of FT cycles on the Nf and damage evolution characteristics of asphalt mixtures, based on the classical Chaboche damage evolution model, the influence of FT cycle was considered, a fatigue-freeze-thaw damage evolution model that reflects the effects of FT cycling variables and stress levels on asphalt mixtures was constructed. The results indicate that the Exx peak of asphalt mixture shifts forward with increasing FT cycle number. After 20 FT cycles, the Nf of CCRMA decreased by 73.4 %, 62.1 %, and 75.1 %, while that of SBSMA decreased by 76.5 %, 58.6 %, and 74.6 % at stress ratios of 0.2, 0.3, and 0.4, respectively. The fatigue-freeze-thaw damage evolution model predictions were in agreement with experimental results, with the goodness of fit (R2) of all Nf prediction models being greater than 0.9. With the exception of a few outliers, the relative error between the measured and predicted Nf for the two asphalt mixtures was within 15 %. This indicates that the model can effectively predict the Nf and damage evolution laws of asphalt mixtures at different stress levels after an arbitrary number of FT cycles. The related research results provide a theoretical basis for predicting the Nf of new roads in cold regions under FT cycles.

Toward a Live Homogeneous Database of Solar Active Regions Based on SOHO/MDI and SDO/HMI Synoptic Magnetograms. II. Parameters for Solar Cycle Variability

Solar active regions (ARs) determine solar polar fields and cause solar cycle variability within the framework of the Babcock–Leighton dynamo. The contribution of an AR to the polar field is measured by its dipole field, which results from flux emergence and subsequent flux transport over the solar surface. The dipole fields contributed by an AR before and after the flux transport are referred to as the initial and final dipole fields, respectively. For a better understanding and prediction of solar cycles, in this paper, we provide a database including AR's initial and final dipole fields and the corresponding results of their bipolar magnetic region (BMR) approximation from 1996 onward. We also identify the repeated ARs and provide the optimized transport parameters. Based on our database, we find that although the commonly used BMR approximation performs well for the initial dipole field, it exhibits a significant deviation for the final dipole field. To accurately assess an AR’s contribution to the polar field, the final dipole field with its real configuration should be applied. Despite the notable contributions of a few rogue ARs, approximately the top 500 ARs ordered by their final dipole fields are necessary to derive the polar field at the cycle minimum. While flux transport may increase or decrease the dipole field for an individual AR, its collective impact over all ARs in a cycle is a reduction in their total dipole field.

Climatic characteristics and meteorology-sensitivity of surface solar radiation in reanalysis products compared to observations and satellite data over China

Due to the significant role of the surface solar radiation (SSR) in climate, hydrological, and biogeochemical cycles, this study evaluates the climatology, annual cycle, and interannual variability and meteorological sensitivity (total cloud cover (TCC), aerosol optical depth (AOD), and precipitable water (PW)) of six reanalysis SSR datasets (GLDAS, ERA5, NCEP1, NCEP2, JRA55, and MERRA2) using a satellite dataset (CERES SYN), and 130 radiation stations in China during 2001–2020.The results show that the climatology (R = 0.88–0.93) and annual cycle (R = 0.92–0.98) of the SSR datasets exhibit better spatial characteristics in these products compared to interannual variability (EOF1: R = 0.03–0.88; EOF2: R = 0.27–0.83). The GLDAS has relatively small estimation SSR error in the climatology but is limited to reproduce interannual variability (EOF1: R = 0.08). As the concentration of meteorological elements increases, the sensitivity of most SSRs errors to TCC increases, while the trend of sensitivity to AOD and PW decreases due to the mitigating effect of the corresponding tendency of TCC error reduction. The importance of PW for estimating SSRs increased significantly in high altitude, ranging from 29.5 to 53%. Overall, ERA5 is the most consistent in quality of the three time-scales and high-meteorological variability.

FAQ: How do I estimate the output gap?

Abstract I investigate the properties of output gaps in New Keynesian DSGE models and study the relationship between theory-based quantities and the estimates obtained with standard approaches. Theoretical gaps display low frequency variations, have similar frequency domain representation as potentials and are generally correlated with them. Potentials have important business cycle variability. Existing statistical approaches fail to recognize these features and generate distorted estimates. Gaps are best estimated with a Polynomial filter. Explanations for the outcomes are given. I propose a statistical procedure reducing estimation biases.

Smartphone usage during walking decreases the positive persistency in gait cycle variability

Gait cycle variability during steady walking, described by the stride interval time series, has been used as a gait-stability-related measure. In particular, the positive persistency in the stride intervals with 1/f-like fluctuation and reduction of the persistency are the well-documented metrics that can characterize gait patterns of healthy young adults and elderly including patients with neurological diseases, respectively. Here, we examined effects of a dual task on gait cycle variability in healthy young adults, based on the mean and standard deviation statistics as well as the positive persistency of the stride intervals during steady walking on a treadmill. Specifically, three gait conditions were examined: control condition, non-cognitive task with holding a smartphone in front of the chest using their dominant hand and looking fixedly at a blank screen of the smartphone, and cognitive motor task with holding a smartphone as in the non-cognitive task and playing a puzzle game displayed on the smartphone by one-thumb operation. We showed that only the positive persistency, not the mean and standard deviation statistics, was affected by the cognitive and motor load of smartphone usage in the cognitive condition. More specifically, the positive persistency exhibited in the control and the non-cognitive conditions was significantly reduced in the cognitive condition. Our results suggest that the decrease in the positive persistency during the cognitive task, which might represent the deterioration of healthy gait pattern, is caused endogenously by the cognitive and motor load, not necessarily by the reduction of visual field as often hypothesized.

Clutchesof the Cat-eye snakes genus Leptodeiraspp. (SERPENTES: Dipsadidae), with a bibliographic review and new reports for Panama

The reproduction of Neotropical snakes, such as those of the genus Leptodeira, varies widely influenced by climatic factors, with species ranging from strictly seasonal to continuous reproductive patterns. Recent research reveals significant variability in reproductive cycles, determined by local environmental conditions such as temperature and precipitation. Nesting behaviors remain poorly understood, and snakes use diverse environments, from natural shelters to communal nests of other species. Within the family Dipsadidae, species such as L. maculataand L. punctatademonstrate correlations between female body size (SVL) and clutch size, reflecting adaptive strategies to maximize reproductive success in diverse ecological contexts. Field work in Panama provided new insights, documenting egg-laying and gravid individuals of species such as L. rhombiferaand L. ornata, highlighting further research to elucidate the reproductive dynamics and ecological adaptations particular to each. species. Analytical approaches including Pearson and Spearman correlationsandcluster analysis reveal distinct patterns in clutch size relative to female size, underscoring species adaptations and variability within the genus

Drivers of seasonal to decadal mixed layer carbon cycle variability in subantarctic water in the Munida Time Series

Drivers of seasonal to decadal mixed layer carbon cycle variability in subantarctic water in the Munida Time Series

High‐Resolution Neural Network Demonstrates Strong CO2 Source‐Sink Juxtaposition in the Coastal Zone

AbstractThe role of coastal oceans in regulating atmospheric carbon dioxide remains poorly quantified and understood. Here, we use a two‐step neural network approach to generate estimates from sparse observational data in the coastal Northeast Pacific Ocean at an unprecedented spatial resolution of 1/12° with coverage in the nearshore (0–25 km offshore). We compiled partial pressure of carbon dioxide (pCO2) observations as well as a range of predictor variables including satellite‐based and physical oceanographic reanalysis products. With the predictor variables representing processes affecting pCO2, we created non‐linear relationships to interpolate observations from 1998 to 2019. Compared to in situ shipboard and mooring observations, our coastal pCO2 product captures broad spatial patterns and seasonal cycle variability well. A sensitivity analysis identifies that the parameters responsible for the neural network's ability to capture regional pCO2 variability are associated with mechanistic processes, including mixed layer deepening, mesoscale eddies, and gyre upwelling. Using wind speed and atmospheric CO2, we calculated air‐sea CO2 fluxes. We report an anticorrelation between annual air‐sea CO2 flux and its seasonal amplitude with the relationship driven by circulation, opposing seasonal upwelling/relaxation versus downwelling, and the effects of winter mixing and primary productivity. We show that the inclusion of nearshore net outgassing fluxes lowers the overall regional net flux. Overall, our results suggest that the region is a net sink (−0.7 mol m−2 yr−1) for atmospheric CO2 with trends indicating increasing oceanic uptake due to strong connectivity to subsurface waters.

Deception of Heart Rate is Unable to Improve Functional Threshold Power in Cyclists

Introduction: Studies investigating the effects of deception on endurance performance have been somewhat inconclusive. The purpose of this study was to explore the use of biofeedback deception of heart rate (HR) in an attempt to improve athletic performance. Methods: Thirteen individuals (10 men, mean±SD age 29.4±10.2 years) completed two 20-minute functional threshold power (FTP) cycling tests. In the control (CON) condition, their correct HR was displayed, while in the deception (DEC) condition an incorrect HR was displayed (correct HR – 10 bpm). Paired samples t-tests were used to compare cycling variables such as power output, speed and work between conditions. Actual HR was analyzed using 2 (condition) x 10 (time; every two minutes) repeated-measures ANOVAs. Results: Mean power output in the CON condition was 226.7±69.3 W and 222.6±65.4 W in the DEC condition (p>.05). The total amount of work performed was nearly identical between conditions (CON: 709.2± J, DEC: 709.3± J; p>.05). There were no differences in actual HR between conditions (all p>.05). Conclusions: Biofeedback deception of HR was unable to improve performance. Verbal encouragement may be a moderating variable that allows successful deception to occur. Possible future studies are presented that will allow further testing of this hypothesis.

The spatial variation and drivers of soil δ13C in an alpine-gorge region on the eastern Qinghai-Tibetan Plateau

Soil stable carbon isotope (soil δ13C) can reflect soil carbon metabolic processes and record environmental and vegetation change information, so mapping the spatial distribution of soil δ13C (isoscapes of soil δ13C) can help us better understand the spatial variability of ecosystem carbon cycles. However, efficient approaches for obtaining the isoscapes of soil δ13C remain challenging, especially in mountainous areas with complex terrain. A total of 150 soil samples were collected and their soil δ13C was measured to investigate the spatial variation of soil δ13C in an alpine-gorge region on the eastern Qinghai-Tibetan Plateau which is characterized by complex terrain. Four prediction methods included ordinary kriging interpolation, multiple linear regression, random forest regression, and geographically weighted regression (GWR) were selected and compared to find the best prediction model for mapping the isoscapes of soil δ13C. The pathways of vegetation, topography, soil, and spatial factors influencing the spatial variability of soil δ13C were explored using variance partitioning analysis and structural equation model. The soil δ13C was significantly different among vegetation types, and ranged from −27.155‰ to −9.647‰. The spatial heterogeneity of soil δ13C showed moderate variation, and was dominated by spatial structural factors. The GWR model had higher prediction accuracy in the modeling soil δ13C in comparison to other models. Soil carbon to nitrogen ratio and normalized difference vegetation index were the main factors determining the spatial variability of soil δ13C, and other topographic and soil factors indirectly regulated the spatial distribution of soil δ13C by influencing these two factors. Our results provided a useful tool (GWR model) for mapping the isoscapes of soil δ13C and explored the factors controlling the spatial variability of soil δ13C in the alpine-gorge region on the eastern Qinghai-Tibetan Plateau, which are important for an in-depth understanding of soil carbon cycling in mountain ecosystems.

A Stochastic Statistical Model for U.S. Outbreak-Level Tornado Occurrence Based on the Large-Scale Environment

Abstract Tornado outbreaks—when multiple tornadoes occur within a short period of time—are rare yet impactful events. Here we developed a two-part stochastic tornado outbreak index for the contiguous United States (CONUS). The first component produces a probability map for outbreak tornado occurrence based on spatially resolved values of convective precipitation, storm relative helicity (SRH), and convective available potential energy. The second part of the index provides a probability distribution for the total number of tornadoes given the outbreak tornado probability map. Together these two components allow stochastic simulation of location and number of tornadoes that is consistent with environmental conditions. Storm report data from the Storm Prediction Center for the 1979–2021 period are used to train the model and evaluate its performance. In the first component, the probability of an outbreak-level tornado is most sensitive to SRH changes. In the second component, the total number of CONUS tornadoes depends on the sum and gridpoint maximum of the probability map. Overall, the tornado outbreak index represents the climatology, seasonal cycle, and interannual variability of tornado outbreak activity well, particularly over regions and seasons when tornado outbreaks occur most often. We found that El Niño–Southern Oscillation (ENSO) modulates the tornado outbreak index such that La Niña is associated with enhanced U.S. tornado outbreak activity over the Ohio River Valley and Tennessee River Valley regions during January–March, similar to the behavior seen in storm report data. We also found an upward trend in U.S. tornado outbreak activity during winter and spring for the 1979–2021 period using both observations and the index. Significance Statement Tornado outbreaks are when multiple tornadoes happen in a short time span. Because of the rare, sporadic nature of tornadoes, it can be challenging to use observational tornado reports directly to assess how climate affects tornado and tornado outbreak activity. Here, we developed a statistical model that produces a U.S. map of the likelihood that an outbreak-level tornado would occur based on environmental conditions. In addition, using that likelihood map, the model predicts a range of how many tornadoes could occur in these events. We found that “storm relative helicity” (a proxy for potential rotation in a storm’s updraft) is especially important for predicting outbreak tornado likelihood, and the sum and maximum value of the likelihood map is important for predicting total numbers for an event. Overall, this model can represent the typical behavior and fluctuations in tornado outbreak activity well. Both the tornado outbreak model and observations agree that the state of sea surface temperature in the tropical Pacific (El Niño–Southern Oscillation) is linked to tornado outbreak activity over the Ohio River Valley and Tennessee River Valley in winter through early spring and that there are upward trends in tornado outbreak activity.

Characterizing the Solar Cycle Variability Using Nonlinear Time Series Analysis at Different Amounts of Dynamo Supercriticality: Solar Dynamo is Not Highly Supercritical

The solar dynamo is essentially a cyclic process in which the toroidal component of the magnetic field is converted into the poloidal one and vice versa. This cyclic loop is disturbed by some nonlinear and stochastic processes mainly operating in the toroidal to poloidal part. Hence, the memory of the polar field decreases in every cycle. On the other hand, the dynamo efficiency and, thus, the supercriticality of the dynamo decreases with the Sun’s age. Previous studies have shown that the memory of the polar magnetic field decreases with the increase of supercriticality of the dynamo. In this study, we employ popular techniques of time series analysis, namely, compute Higuchi’s fractal dimension, Hurst exponent, and Multi-Fractal Detrended Fluctuation Analysis to the amplitude of the solar magnetic cycle obtained from dynamo models operating at near-critical and supercritical regimes. We show that the magnetic field in the near-critical regime is governed by strong memory, less stochasticity, intermittency, and breakdown of self-similarity. On the contrary, the magnetic field in the supercritical region has less memory, strong stochasticity, and shows a good amount of self-similarity. Finally, applying the same time series analysis techniques in the reconstructed sunspot data of 85 cycles and comparing their results with that from models, we conclude that the solar dynamo is possibly operating near the critical regime and not too much supercritical regime. Thus the Sun may not be too far from the critical dynamo transition.

Gastric myoelectrical associations with autonomic and central nervous system activity during state negative emotionality and perseverative negative thinking: A two-study investigation

BackgroundDistress is known to set off a variety of physiological dysfunctions. There have been previous investigations of central and peripheral (e.g., cardiac) correlates of distress and a burgeoning field examining immunological and inflammatory relationships to distress. However, despite a connection between gastrointestinal symptoms and distress at the clinical level, little research has connected this activity to other biological correlates of distress. Heightened negative emotions and dysregulation of emotions in the form of perseverative negative thinking (PNT, e.g., worry, rumination) are two components that underlie distress. Investigating the effect that these components have on gastric and related biological measures is crucial to better understanding the relationship between distress and health outcomes. We present results from two studies, which examined the impact of experimentally induced negative emotionality and PNT on gastric, autonomic nervous system, central nervous system, and inflammatory processes. MethodIn Study 1 (N = 95), we examined changes in electrogastrogram (EGG), respiratory sinus arrythmia (RSA), and alpha- and beta-band electroencephalogram (EEG) power at baseline and during experimental inductions of fear and sadness in a diverse sample of undergraduate students. In Study 2 (N = 30), we examined changes in EGG, RSA, and EEG at baseline and during worry and rumination inductions. We also collected inflammatory markers post-inductions in Study 2. ResultsIn Study 1, normal gastric activity, vagal tone, and alpha- and beta-band power decreased, whereas EGG cycle length and variability increased during fear and sadness compared to baseline. We also observed significant relationships among EGG, RSA, and EEG during fear and sadness. In Study 2, normal gastric activity and RSA decreased, whereas EGG cycle length and variability increased during rumination compared to baseline. We also observed increases in frontal alpha-band power during worry and rumination compared to baseline. Regarding relationships among EGG, RSA, EEG, and inflammation, greater EGG cycle variability was associated with higher vagal tone during worry; additionally, EGG cycle length and variability during rumination were positively related to CRP and TNF-α inflammation composite scores post-inductions. ConclusionsThese findings offer insight into how negative emotionality and PNT might relate to gastric, ANS, CNS, and inflammatory processes.

An Assessment of CO2 Storage and Sea‐Air Fluxes for the Atlantic Ocean and Mediterranean Sea Between 1985 and 2018

AbstractAs part of the second phase of the Regional Carbon Cycle Assessment and Processes project (RECCAP2), we present an assessment of the carbon cycle of the Atlantic Ocean, including the Mediterranean Sea, between 1985 and 2018 using global ocean biogeochemical models (GOBMs) and estimates based on surface ocean carbon dioxide (CO2) partial pressure (pCO2 products) and ocean interior dissolved inorganic carbon observations. Estimates of the basin‐wide long‐term mean net annual CO2 uptake based on GOBMs and pCO2 products are in reasonable agreement (−0.47 ± 0.15 PgC yr−1 and −0.36 ± 0.06 PgC yr−1, respectively), with the higher uptake in the GOBM‐based estimates likely being a consequence of a deficit in the representation of natural outgassing of land derived carbon. In the GOBMs, the CO2 uptake increases with time at rates close to what one would expect from the atmospheric CO2 increase, but pCO2 products estimate a rate twice as fast. The largest disagreement in the CO2 flux between GOBMs and pCO2 products is found north of 50°N, coinciding with the largest disagreement in the seasonal cycle and interannual variability. The mean accumulation rate of anthropogenic CO2 (Cant) over 1994–2007 in the Atlantic Ocean is 0.52 ± 0.11 PgC yr−1 according to the GOBMs, 28% ± 20% lower than that derived from observations. Around 70% of this Cant is taken up from the atmosphere, while the remainder is imported from the Southern Ocean through lateral transport.

CyTRACK: An open-source and user-friendly python toolbox for detecting and tracking cyclones

This work introduces CyTRACK (Cyclone TRACKing framework), a new open-source, comprehensive and user-friendly Python toolbox for detecting and tracking cyclones in model and reanalysis datasets. The kernel of CyTRACK is based on detecting critical cyclone centres in the mean sea level pressure field at a single time slice, which are then filtered following several threshold parameters. This paper also compares ten years of CyTRACK outputs forced with the ERA5 reanalysis against best-track archives and available cyclones track datasets. The results reveal that CyTRACK can capture the inter-annual (year to year) and intra annual (seasonal cycle) variability of cyclone frequency, life cycle characteristics and spatial distribution of track densities. Largest differences were observed in the annual and seasonal frequency. In summary, CyTRACK provides a user-friendly framework for sensitivity analysis of several free parameters used to perform the tracking, and it is useful for case or climatological studies of cyclone features.

Comparative transcriptomic analysis reveals differences in gene expression and regulatory pathways between nonacral and acral melanoma in Asian individuals.

Melanoma predominantly occurs in White individuals, which is associated with factors such as exposure to UV radiation and skin pigmentation. Despite its low incidence, melanoma is the primary cause of skin cancer-related death in Asia, typically in areas with low sun exposure. In our previous whole-exome sequencing study, we identified mutational signature 12 as the most prevalent variant in Asian patients, differing from the common UV-associated mutational signature 7 observed in White individuals. We also observed major differences between acral melanoma (AM) and nonacral melanoma (NAM) in terms of signatures 7, 21, and 22. Notably, few studies have investigated the genomic differences between AM and NAM in Asian individuals. Therefore, in this study, we conducted transcriptomic sequencing to examine the disparities in RNA expression between AM and NAM. Ribosomal RNA depletion was performed to enhance the detection of functionally relevant coding and noncoding transcripts. Ingenuity pathway analysis revealed significant differences in gene expression and regulatory pathways between AM and NAM. The results also indicate that the genes involved in cell cycle signaling or immune modulation and programmed cell death protein 1/programmed cell death 1 ligand 1 signaling were differentially expressed in NAM and AM. In addition, high CDK4 expression and cell cycle variability were observed in AM, with high immunogenicity in NAM. Overall, these findings provide further insights into the pathogenesis of melanoma and serve as a reference for future research on this major malignant disease.

3D Stackable Vertical‐Sensing Electrochemical Random‐Access Memory Using Ion‐Permeable WS2 Electrode for High‐Density Neuromorphic Systems

AbstractIon‐based electrochemical random‐access memory (ECRAM) is proposed for synaptic applications owing to its promising characteristics that have the potential to accelerate data processing through neuromorphic systems. However, attaining ideal synaptic functionalities and constructing high‐density vertical synapse arrays are challenging due to issues related to uncontrolled ion migration and constraints in 3D multi‐stacking. Here, a breakthrough using 3D stackable Li ion‐based vertical‐sensing ECRAM (VS‐ECRAM) is presented with an ion‐permeable ultrathin WS2 electrode synthesized through low‐temperature (200 °C) atmospheric‐pressure plasma‐enhanced chemical vapor deposition (AP‐PECVD). The direct AP‐PECVD of the WOx channel layer induces WS2 formation in the surface region, which exhibits sufficient electrical conductivity to function as an electrode. By utilizing the WS2 electrode as an ion‐barrier layer in the VS‐ECRAM synapse, excellent weight update linearity and cycling variability are achieved due to the finely controlled ion migration. Furthermore, a two‐layer stacked 3D VS‐ECRAM is successfully fabricated through the vertical WS2 formation, and independent weight updates without any disturbance are confirmed. Finally, a high pattern recognition accuracy of 95.22% is obtained using a multi‐layer perceptron‐based neural network. Therefore, the proposed 3D stackable WS2‐based VS‐ECRAM exhibits a strong potential for application in high‐density neuromorphic devices with excellent synaptic performances.

How to assess water quality change in temperate headwater catchments of western Europe under climate change: examples and perspectives

Climate change effects on water quality are related directly and indirectly to the water cycle and human activities. We present examples of these effects using a retrospective analysis across European catchments according to three objectives: (i) identification of extreme or anomalous values in climatic and chemical variables at multiple time scales, (ii) assessment of variability in seasonal and inter-annual chemical cycles, and (iii) identification of a general water chemistry response to the North Atlantic Oscillation. From these examples, we highlight four challenges for research on the relationships between climate and water quality: (i) developing functional typologies of chemical elements, (ii) performing multi-temporal and multi-spatial analysis by aggregating data into water-cycle periods, (iii) decoupling effects of climate conditions and human activities by testing hypotheses using parsimonious models, and (iv) incorporating water quality and aquatic ecosystem health into integrated models. Water quality integrates current and past (legacy) conditions, flow pathways, and biogeochemical reactivity, which themselves depend on the climate. As our results highlight, water quantity and quality need to be studied together.